phd thesis a.y.m. atiquil islam submission...

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ABSTRACT

In this age of exponential knowledge growth, where Information and Communication

Technology (ICT) has been playing a dominant role, the authorities of Higher Education

concerned have to ensure that the ICT facilities remain within the reach of the lecturers

for their teaching and research purposes. However, despite a decade of existence, the

ICT facilities were found to be underutilized by lecturers. Constructs such as computer

self-efficacy, perceived ease of use, perceived usefulness, use and gratification were

hypothesized to be major barriers. In doing so, the prime objective of this study was to

develop and validate the Technology Adoption and Gratification (TAG) Model to assess

the lecturers’ adoption and gratification in using ICT facilities in higher education. The

second purpose of this study was to evaluate the cross-cultural validation of the causal

structure of the TAG model. The respondents were collected from two public

universities, namely, University of Malaya in Malaysia and Jiaxing University in China.

A total of 396 lecturers were selected using a stratified random sampling procedure. A

questionnaire consisting of items validated from prior studies were put together and

modified to suit the current study. A seven-point Likert scale asking the respondents of

the extent of their agreement/disagreement to the items constituting the construct in the

questionnaire was used. The questionnaire’s validity and reliability were established

through a Rasch model using Winsteps version 3.94. The sample size was judged

adequate for the application of Structural Equation Modeling (SEM) to develop and

validate the Technology Adoption and Gratification (TAG) Model as well as assess all

the research hypotheses. The data was analyzed using AMOS version 18. The findings

of the TAG model discovered that computer self-efficacy had a statistically significant

direct influence on perceived usefulness and perceived ease of use. Subsequently,

lecturers’ perceived ease of use and perceived usefulness had also statistically

significant direct influence on their gratification in using ICT facilities in higher

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education. Moreover, perceived usefulness and perceived ease of use revealed

significant direct influence on lecturers’ intention to use ICT facilities for their teaching

and research. Similarly, lecturers’ intention to use had statistically significant direct

effect on their gratification as well as actual use of ICT facilities, respectively. On the

other hand, computer self-efficacy had a significant indirect influence on gratification

mediated by perceived ease of use and perceived usefulness, respectively. Meanwhile,

computer self-efficacy had also significant indirect influence on lecturers’ intention to

use mediated by perceived ease of use and perceived usefulness, respectively.

Eventually, lecturers’ perceived ease of use and perceived usefulness had statistically

significant indirect influence on their gratification in using ICT facilities in higher

education mediated by intention to use. The results of the invariance analysis of the

TAG model demonstrated that the model was valid for measuring lecturers’ adoption

and gratification in using ICT facilities. However, the TAG model works differently in

the cross-cultural settings. The findings contribute to the body of knowledge by

developing and validating the TAG Model as proposed in this study. The results will

also help the Universities’ higher authority in taking and providing necessary steps

towards training to the personnel regarding the application of the ICT facilities in higher

education.

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ABSTRAK

Dalam zaman pertumbuhan eksponen pengetahuan, di mana Teknologi Komunikasi

Maklumat (ICT) dan telah memainkan peranan yang dominan, pihak berkuasa

Pengajian Tinggi berkenaan perlu memastikan bahawa kemudahan ICT berada di dalam

jangkauan pensyarah untuk tujuan pengajaran dan penyelidikan mereka. Walau

bagaimanapun, walaupun satu dekad kewujudan, kemudahan ICT didapati kurang

digunakan oleh pensyarah. Konstruk seperti komputer sendiri keberkesanan, mudah

dilihat penggunaan, tanggapan kegunaan, penggunaan dan kepuasan hipotesis telah

menjadi halangan utama. Dengan berbuat demikian, objektif utama kajian ini adalah

untuk membangunkan dan mengesahkan Penggunaan Teknologi dan Wang Suapan

(TAG) Model untuk menilai penerimaan pensyarah dan kepuasan dalam menggunakan

kemudahan ICT dalam pendidikan tinggi. Tujuan kedua kajian ini adalah untuk menilai

pengesahan silang budaya struktur yang menyebabkan model TAG. Responden telah

dikumpulkan dari dua universiti awam, iaitu Universiti Malaya di Malaysia dan

Universiti Jiaxing di China; pensyarah yang bekerja di fakulti yang berbeza. Seramai

396 pensyarah telah dipilih menggunakan persampelan berstrata prosedur rawak. Satu

soal selidik yang terdiri daripada item disahkan daripada kajian sebelum telah bersama-

sama dan diubah suai untuk disesuaikan dengan kajian semasa. A tujuh mata skala

Likert meminta responden setakat perjanjian mereka / tidak bersetuju dengan perkara-

perkara yang menjadi binaan di dalam soal selidik yang telah digunakan. Kesahan soal

selidik dan kebolehpercayaan telah ditubuhkan melalui model Rasch menggunakan

Winsteps versi 3.94. Saiz sampel dihakimi mencukupi untuk aplikasi pemodelan

persamaan berstruktur (SEM) untuk membangunkan dan mengesahkan Penggunaan

Teknologi dan Wang Suapan (TAG) Model serta menilai semua hipotesis penyelidikan.

Data dianalisis menggunakan Amos versi 18. Dapatan model TAG mendapati bahawa

komputer self-efficacy mempunyai pengaruh langsung yang signifikan secara statistik

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pada manfaat dan tahap kemudahan dilihat penggunaan. Selepas itu, pensyarah dilihat

kemudahan penggunaan dan kegunaan dilihat mempunyai pengaruh langsung juga

statistik yang signifikan pada kepuasan mereka dalam menggunakan kemudahan ICT

dalam pendidikan tinggi. Selain itu, manfaat dan tahap kemudahan dilihat penggunaan

mendedahkan pengaruh langsung yang signifikan terhadap niat pensyarah menggunakan

kemudahan ICT untuk pengajaran dan penyelidikan mereka. Begitu juga dengan niat

pensyarah untuk penggunaan mempunyai kesan yang signifikan secara statistik

langsung pada suapan mereka serta penggunaan sebenar kemudahan ICT, masing-

masing. Sebaliknya, komputer self-efficacy mempunyai pengaruh yang ketara tidak

langsung pada suapan pengantara dengan mudah dilihat penggunaan dan tanggapan

kegunaan masing-masing. Sementara itu, komputer self-efficacy juga mempunyai

pengaruh yang ketara tidak langsung kepada niat pensyarah untuk menggunakan

pengantara dengan mudah dilihat penggunaan dan tanggapan kegunaan masing-masing.

Akhirnya, pensyarah dilihat kemudahan penggunaan dan kegunaan dilihat mempunyai

pengaruh langsung statistik yang signifikan pada kepuasan mereka dalam menggunakan

kemudahan ICT dalam pendidikan tinggi pengantara dengan niat untuk digunakan.

Keputusan analisis invariance yang model TAG menunjukkan bahawa model itu adalah

sah untuk mengukur penggunaan pensyarah dan kepuasan dalam menggunakan

kemudahan ICT. Walau bagaimanapun, model TAG bekerja secara berbeza dalam

tetapan silang budaya. Penemuan menyumbang kepada badan pengetahuan dengan

mengembangkan dan mengesahkan Model TAG seperti yang dicadangkan dalam kajian

ini. Keputusan juga akan membantu pihak berkuasa Universiti 'yang lebih tinggi dalam

mengambil dan menyediakan langkah-langkah yang perlu ke arah latihan kepada

kakitangan mengenai permohonan daripada kemudahan ICT dalam pendidikan tinggi.

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DEDICATION

This research is dedicated to my parents, A.Y.M. Nazrul Islam and Noor Aptap Pear

Zahan Chowdhury, as well as my wife, Qian Xiuxiu, for their encouragement, sacrifice,

endeavor and ideas when I was conducting this research. Moreover, this work is also

devoted to human beings as my contribution to the development of human capital which

is of utmost significance in Islam.

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ACKNOWLEDGEMENT

The greatest thank to Allah, for his consents, strengths and opportunities that enable me

to complete this research.

I would like to express my sincere appreciation and profound gratitude to my supervisor,

Dr. Chin Hai Leng, for all her guidance, motivation and patience in supervising this

research endeavor. Most of all I am obliged to her as She always helped me with wise

suggestions and corrections. Thus, I have benefited a lot and she has contributed greatly

towards my intellectual aspirations and academic development. I will be forever grateful

to her for taking the time to read, correct and verify my TAG model. My gratitude also

goes to Associate Professor Dr. Diljit Diljit Singh A/L Balwant Singh for his knowledge,

skills, and ideas on the application “modeling”, which I would not have learnt otherwise.

He taught me to be a creative and critical thinker in teaching, learning and research.

I would like to thank the lecturers, and administrative staff for their help and support. A

special vote of thanks goes to authorities of University of Malaya and Jiaxing

University in China, for giving me the chance to collect the appropriate data.

Lastly, I would like to extend my utmost gratitude to my beloved wife Qian Xiuxiu and

parents A.Y.M. Nazrul Islam and Noor Aptap Pear Zahan Chowdhury for their love,

sympathy, inspiration, and support throughout my study.

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TABLE OF CONTENTS

Abstract…………………………………………………………………………….. iii

Abstrak…………………………………………………………………………….. v

Dedication………………………………………………………………………...... vii

Acknowledgement…………………………………………………………………. viii

List of Figures..……………………………………………………………………. xi

List of Tables ……………………………………………………………………… xiv

List of Appendices…………………………………………………………………. xvi

CHAPTER 1: INTRODUCTION

1.1 Background of the Study ……………………………………………... 1

1.2 Conceptual Model……… …………………………………………….. 8

1.3 Statement of the Problem………………………………………………. 11

1.4 Objectives of the Study ………………………………………………. 13

1.5 Research Hypotheses ………………………………………………… 13

1.6 Significance of the Study ...…………………………………………… 17

1.7 Limitation of the Study ………………………………………………. 18

1.8 Operational Definition of Terms..……………………………………... 18

CHAPTER 2: LITERATURE REVIEW

2.1 Introduction …………………………………………………………… 21

2.2 Information and Communication Technology Usage ….……………… 21

2.3 Technology Acceptance Model (TAM)….…………………………… 33

2.4 Technology Satisfaction Model (TSM)…..…………………………… 54

2.5 Online Database Adoption and Satisfaction (ODAS) Model…………. 57

2.6 Construction of Hypotheses………………………..…………………. 60

2.6.1 Cross-cultural Studies ………………………………………… 71

CHAPTER 3: METHODOLOGY

3.1 Introduction ………………………………………………………….. 77

3.2 Population and Sample ………………………………………………. 77

3.2.1 Lecturers’ Demographic Information in University of Malaya.. 78

3.2.2 Lecturers’ Demographic Information in Jiaxing University ….. 83

3.3 Sampling Procedure…………………………………………………… 87

3.4 Research Instruments…..……………………………………………… 89

3.4.1 Use of ICT facilities…………………………………………... 90

3.4.2 Perceived Ease of Use……..………………………………….. 91

3.4.3 Perceived Usefulness………………………………………….. 92

3.4.4 Computer Self-Efficacy……………………………………….. 93

3.4.5 Intention to Use………………………………………………... 94

3.4.6 Gratification…………………………………………………… 95

3.4.7 Pilot Test………………………………………………………. 96

3.4.8 Reliability and Validity of Instrument………………………… 104

3.5 Data Analysis Procedures……………………………………………... 113

3.6 Structural Equation Modeling (SEM)…………………………………. 114

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3.7 Model Evaluation ……………………………………………………. 115

CHAPTER 4: RESULTS

4.1 Introduction …………………………………………………………… 117

4.2 One-Factor Measurement Model of Perceived Ease of Use (PEU)..….. 117

4.3 One-Factor Measurement Model of Perceived Usefulness (PU)..…….. 121

4.4 One-Factor Measurement Model of Computer Self-efficacy (CSE)....... 124

4.5 One-Factor Measurement Model of Intention to Use (INT)…………… 127

4.6 One-Factor Measurement Model of Use (USE)……………………….. 130

4.7 One-Factor Measurement Model of Gratification (GRAT)..………….. 134

4.8 The Hypothesized Technology Adoption and Gratification (TAG)

Model…………………………………………………………………. 137

4.8.1 Hypotheses Testing ………………………………………….. 141

4.9 The Revised Technology Adoption and Gratification (TAG) Model..... 147

4.10 Cross-cultural Validation of TAG Model…………………………… 154

4.10.1 Configural and Metric Invariance Analyses of the TAG

Model…………………………………………………………. 166

CHAPTER 5: DISCUSSION AND CONCLUSION

5.1 Introduction …………………………………………………………... 170

5.2 Discussion of the Findings……………………………………………. 171

5.3 Conclusion ……………………………………….................................. 184

REFERENCES......………………………………………………………………. 193

APPENDICES …………………………………………………………………… 211

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LIST OF FIGURES

Figure No. Page No.

1.1 Technology Acceptance Model 9

1.2 Conceptual Model 10

2.1 Technology Acceptance Model 33

2.2 Technology Satisfaction Model (TSM) 54

2.3 Online Database Adoption and Satisfaction (ODAS)

Model 58

2.4 The Hypothesized Technology Adoption and

Gratification (TAG) Model 59

3.1 Sample of Study According to Gender 79

3.2 Sample of Study According to Faculties 79

3.3 Sample of Study According to Educational Background 80

3.4 Sample of Study According to Age Groups 81

3.5 Sample of Study According to Nationality 81

3.6 Sample of Study According to Work Experience 82

3.7 Sample of Study According to Designation 82

3.8 Sample of Study According to Gender 83

3.9 Sample of Study According to Colleges 84

3.10 Sample of Study According to Educational Background 84

3.11 Sample of Study According to Age Groups 85

3.12 Sample of Study According to Nationality 85

3.13 Sample of Study According to Work Experience 86

3.14 Sample of Study According to Designation 86

3.15 Summary of Statistics 98

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3.16 Item Polarity Map 99

3.17 Item Map 101

3.18 Item Fit order 102

3.19 Principal Components 103

3.20 Summary of Statistics 104

3.21 Item Polarity Map 105

3.22 Item Map 107

3.23 Item Fit order 108

3.24 Principal Components 113



4.1 One-Factor Measurement Model for

Perceived Ease of Use 119

4.2 One-Factor Measurement Model for Perceived

Usefulness 122

4.3 One-Factor Measurement Model for Computer

Self-efficacy 125

4.4 One-Factor Measurement Model for Intention to Use 128

4.5 One-Factor Measurement Model for Use 132

4.6 One-Factor Measurement Model for Gratification 135



4.8 The Revised Technology Adoption and


4.9 The Revised TAG Model for Malaysian Participants 155

4.10 The Revised TAG Model for Chinese Participants 161

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4.11 The Constrained TAG Model 167

4.12 The Unconstrained TAG Model 168

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LIST OF TABLES

Table No. Page No.

1.1 The list of Specific Objectives and Hypotheses 15

2.1 The Summary of TAM Literature Matrix 40

3.1 Stratified Random Sampling Procedure 89

3.2 The number of Components Measured 90

3.3 Use items 91

3.4 Perceived Ease of Use Items 92

3.5 Perceived Usefulness Items 93

3.6 Computer Self-efficacy Items 94

3.7` Intention to Use Items 95

3.8 Gratification items 96

3.9 The Valid Items Measuring Perceived Ease of Use (PEU),

Perceived Usefulness (PU), Computer Self-Efficacy (CSE),

Intention to Use (INT), Actual Use (USE) and

Gratification (GRAT) 109

3.10 The Recommended Goodness-of-Fit Indices 116

4.1 The List of Removed Items 118

4.2 The Loadings for Perceived Ease of Use 120

4.3 The Squared Multiple Correlations 120


4.5 The Loadings for Perceived-Usefulness Items 123



4.8 The Loadings for Computer Self-efficacy 126

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4.11 The Loadings for Intention-to-Use Items 129



4.14 The Loadings for Use Items 133



4.17 The Loadings for Gratification Items 136


4.19 The Summary of the Hypotheses Testing 146


4.21 Valid Items of the Revised TAG Model and Their

Corresponding Loadings, Means, Standard

Deviations and Alpha Values 151

4.22 Valid Items of the Revised TAG Model for Malaysian

Participants and Their Corresponding Loadings,

Means, Standard Deviations and Alpha Values 158

4.23 Valid Items of the Revised TAG Model for Chinese

Participants and Their Corresponding Loadings,

Means, Standard Deviations and Alpha Values 164

4.24 Results of Critical Value of Chi-squared 169

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LIST OF APPENDICES

Appendix No. Page No.

A The Original Model of Computer Self-efficacy (CSE) and

Outputs 211

B The original Model of Perceived Usefulness (PU) and

Outputs 220

C The original Model of Perceived Ease of Use (PEU) and

Outputs 232

D The original Model of Intention to Use (INT) and

Outputs 242

E The original Model of Actual Use (USE) and Outputs 246

F The original Model of Gratification (GRAT) and Outputs 257

G The Hypothesized TAG Model’s Outputs 264

H The Revised TAG Model’s Outputs 282

I The Outputs of Invariance Analyses 300

J The Survey Questionnaire 329

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CHAPTER 1

INTRODUCTION

1.1 BACKGROUND OF THE STUDY

Information and Communication Technology (ICT) constitutes a key dimension in the

process of the wider development of a country. The estimation of the ICT evolution

involves use of an appropriate metric for assessing the information society in a

country on the sub-indices of access, use and skills (Kyriakidou, Michalakelis &

Sphicopoulos, 2013). Their study of the influential components of the ICT maturity

level revealed considerable discrepancies in their influence in developed and

developing countries. The use factor was found to be considerably more important in

developed countries than in developing countries. Notwithstanding the fact that,

during previous years ICT witnessed an escalating dissemination. The distinctions in

the level of use, access and skills of ICT can be identified both within and between

countries. The policy and decision makers have stated that these dissimilarities cause

an ICT gap and thus strategies aiming at the expansion of ICT have been applied in

many countries. Hence, assessing and analyzing the digital split among countries is of

overriding consequence for researchers as well as managers.

As information and communication technology (ICT) is increasingly used in

education, its integration to teaching, learning and research have had immense

significance in fostering technology-based education among university lecturers,

students and staff. Hong and Songan (2011) contend that in Southeast Asia, ICT is

being utilized more to deal with the challenges that are faced by higher education

systems. In this regard, they have discussed a series of likely applications of ICT. For

instance, what and how students learn, when and where students learn, how new

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students and lecturers interact, and how to reduce the cost of education. However,

there is very little research on effective utilization of ICT in tertiary education

programs in Southeast Asia. Thus, it is immensely significant for related and advanced

staff of higher education to learn from success stories, experiences and lessons from

the application of ICT in countries within the region. Among those, particular stress

should be laid on the various practical and research-based experiences with respect to

teaching and learning, which include e-learning and blended learning, pedagogic

innovation in using ICT for increased usefulness, accessibility and efficiency of higher

education, applying ICT for online approaches for professional improvement in higher

education, and implementing ICT projects in higher education to enhance access and

quality of learning. Furthermore, they affirmed that the higher education systems need

to keep up with the rapid progress in knowledge and skills that followed the

introduction of new advanced technologies rather than the requirements on relevancy

of employees. Moreover, to be competitive in the gradually challenging global

economy circumstance, it is very essential for university students in this region to be

armed with the appropriate knowledge, skills and aptitudes.

Malaysia is a developing country that is progressing towards development. It

follows the models implemented and practiced in developed countries to maintain

right and steady pace towards development. Malaysia extracts its ideology for the

development process, especially for the education sector, from developed countries

such as Australia, where about 80% of academics use technology in regular ways as

part of the teaching and learning program (Oliver and Towers, 2000). In Malaysia, the

implementation of technology in teaching and learning activity has attracted great

interest from the practitioners in higher education institutions (HEIs), which have

started to adopt and implement information and communication technology (ICT)

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solutions as a source of a flexible teaching and learning process (Azizan, 2010).

With rapid expansion of information technology, information access is

expected to become central to life in the 21st century (Huang, Liu, Chang, Sung,

Huang, Chen, Shen, Huang, Liao, Hu, Luo, & Chang, 2010). The incorporation of

information and communication technology into classroom practices can integrate a

wide range of activities. From those designed to inspire learners to devour knowledge

(e.g. teachers’ use of presentation software, DVDs or podcasting) to those designed to

increase students’ capabilities to generate their own knowledge (e.g. development of a

reflective blog, collaborative wiki site or e-portfolio). Thus, ICT offers the prospect of

improving both teaching and learning and it is for the educator to decide when and

how it can be applied. Nevertheless, there is extensive agreement in the literature

demonstrating that teachers are not willing to acquire complete benefit of these

opportunities (Groff & Mouza, 2008; Sutherland, Robertson & John, 2009; Levin &

Wadmany, 2008).

In a study, Bate (2010) evaluated how new teachers applied ICT in the first

three years of their teaching and understood the role that ICT played in developing

pedagogical practices of the educators engaged. The findings demonstrated that fresh

teachers expressed pedagogical beliefs that entail their learners in dynamic meaning

making. These teachers were capable of using a basic suite of ICT software.

Nevertheless, pedagogical beliefs that resonate with modern learning theory and

operational ICT capability did not translate into practices that synergised pedagogical

content and technological knowledge. Moreover, educators did not use ICT in ways

that are consistent with their affirmed pedagogical beliefs. Liu (2012) asserted that

pre-service teachers must be prepared with the aptitude to assimilate technology to

facilitate their prospective students’ learning. Furthermore, Liu (2012) concluded that

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teacher education courses did not facilitate teachers’ use of technology when teaching

students in practicum environments.

According to Blackwell, Lauricella, Wartella, Robb and Schomburg (2013),

the enhanced access to technology with sustained under-utilization in education

necessitates comprehending the obstacles faced by teachers in incorporating

technology into their classrooms. It is remarkable that many models assume that

educators have a moderate R2 value when using technology and anticipate a

reasonable proportion of the variance in how frequently teachers use a certain

technology. Their findings showed that models for the teachers’ use of computers,

iPod touch devices, and tablet computers accounted for around 27–35% of the

variance. The inclusion of attitudes and beliefs constructs as additional dimensions

altered these models from insignificant to significant and enhanced the assumption of

teachers’ actual use of technology. They also suggested that the reliability and

significance of the affordances and obstacles scales discovered in their study makes

their application practical in future quantitative studies of teachers’ use of technology.

Zhou, Zhang and Li (2011) stated that over the last two decades, the

incorporation of ICT into teaching and learning has become an indispensable topic in

education. Studies have revealed that ICT can improve teaching and learning

outcomes. As such, they investigated how well secondary pre-service teachers were

equipped to use technology for teaching in China and evaluated teachers’ experiences

with views of and anticipations about the use of technology. Their findings showed an

overall low level of capability to use technology and few mutual concerns shared by

technology training teachers observed in the teacher education programs. The results

also discovered that universities fall behind in terms of ICT facilities. More than forty

percent of teachers desired the university to update its hardware, and over fifty percent

5

recommended that the university expand its quantity of computers and labs. Over half

of the respondents wished the software were updated and roughly seventy-six percent

demanded a higher-speed campus network. The insufficient access to technology was

also demonstrated by the disappointments voiced by teachers having restricted access

to the computer labs.

According to Soffer, Nachmias and Ram (2010), in the recent decade, the

information technologies have penetrated each field of our daily life, including

education. The incorporation of instructional technology is no longer an extravagance

in higher education but a necessity for all universities. The main contribution of this

study is highlighting the necessity of acquiring better comprehension of the

procedures of the diffusion of web-based learning among lecturers in higher education

and the reasons that influence adoption rate.

Lai (2011) asserted that the use of digital technologies might assist a shift of

cultural practices in teaching and learning, to serve the needs of the 21st century

higher education learners. As a result, higher education institutions require improved

efficiency, additional transparent accountability and better performance in both

teaching and research. However, from the studies that have been undertaken to assess

the overall impact of ICT on teaching and learning in higher education in the last two

decades, one can conclude that higher education institutions have been slow in

acquiring the complete benefit of the prospective advantages offered by the use of ICT.

In this regards, several obstacles have been identified, as to why such a little impact

has been made on the use of ICT in teaching and learning in higher education. The

lack of understanding of why and how technology should be included in pedagogy by

university educators has been recognized as the key cause, and the lack of professional

expansion opportunities is typically quoted as the reason for this lack of understanding.

6

According to Lau and Yuen (2013), social revolution is inevitable with the

emergence of ICT. Further, technology also leads to numerous momentous alterations

in a number of school practices; and in this process, teachers are the main players in

the introduction and continuity of those changes in daily classroom practices. Thus, in

order to assist their use of technology in teaching, educational technology training

becomes indispensable. However, this leads to another paramount problem of

identifying the factors that influence technology adoption and the formulation of

workable solutions to sustainably integrate technology into teaching practices. In fact,

teachers’ perception towards technology is a significant element that affects the

decision on technology adoption despite that training was found to influence

perceptions, especially in terms of teachers’ self-efficacy, attitudes and beliefs.

Furthermore, the actual gap between research and practice hedges the application of

educational research in daily routines which causes criticism. Although a lot of effort

has been put into identifying both contributing and inhibiting factors of technology

use, teachers are still reluctant and opposed to adopting technology in their classrooms.

Nevertheless, teachers are the most important factor in the series of educational

changes. And, teachers’ professional development is the most valuable solution to

stimulate their technological adoption. Moreover, they claimed that effective

integration of technology in education is a complex and multi-faceted issue. They

conducted their study among in-service mathematics teachers and identified a variety

of technology adoption parameters. Meanwhile, their study demonstrated that senior

teachers’ attitudes towards educational benefits of utilizing technology are not

consistent with their perceived efficacy. Thus, it not only reveals that improvement on

educational technology training for in-service teachers is needed, but also there is a

gap between practice and research in educational research which is a serious issue.

7

Despite the benefits that increased technological options can provide, there are

still many barriers to the successful integration and usage of emerging educational

technology such as Internet within educational environments (Roblyer, 2006; Wozney,

Venkatesh & Abrami, 2006). According to Zejno and Islam (2012), determinants such

as perceived ease of use, benefits, and ability to use the ICT facilities were assumed to

be major barriers by postgraduate students in higher education. Islam (2011a) claimed

that one of the ICT facilities, namely, the research database was discovered to be

underutilized, especially by postgraduate students. Factors such as perceived benefits,

computer self-efficacy and satisfaction were hypothesized to be major barriers.

Despite a decade of existence, literatures have demonstrated that the majority of

studies were concerned with the development of integration of information and

communication technology (ICT) in teaching and learning and the acceptance or

adoption of the use of ICT applications from the perspective of preschool teachers and

students. Little research has been conducted on discovering lecturers’ adoption and

gratification in using ICT facilities for research and teaching in higher education.

Moreover, in its endeavor to foster the use of technology in higher education, the

University of Malaya (UM) in Malaysia and Jiaxing University (JU) in China provide

extensive ICT facilities to cater to the needs of the lecturers. However, since its

execution, no research has been conducted to assess the lecturers’ adoption and

gratification of using this emerging educational technology within their environments

for teaching, learning and research purposes. Therefore, there exists a gap for further

investigation.

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1.2 CONCEPTUAL MODEL

Extant literatures that examined relationships among variables related to technology

acceptance model abound extensively (Kaba & Osei-Bryson, 2013; Kreijns,

Vermeulen, Kirschner, Buuren and Acker, 2013; Moses, Wong, Bakar and Rosnaini,

2013; Islam, 2011a; Zejno & Islam, 2012; Saleh, 2009; Usluel, Askar & Bas, 2008;

Ferdousi, 2009; Shroff, Deneen & Ng, 2011; Shittu, Basha, Rahman & Ahmad, 2011;

Gibson, Harris & Colaric, 2008; Yuen & Ma, 2008; Martínez-Torres, Marín, García,

Vázquez, Oliva & Torres, 2008; Lee, Hsieh & Hsu, 2011; Teo & van Schalk, 2009;

Teo, 2009; Liu, Liao & Pratt, 2009; Agarwal & Prasad, 1998; Dillon & Morris, 1996;

Taylor & Todd, 1995). As such, numerous theoretical models have been propounded

to provide detailed explanations on users’ intention to use technology, and actual

technology use (Venkatesh, Morris, Davis, & Davis, 2003). Following the

introduction of the Technology Acceptance Model (TAM) by Davis (1989), the model

has been widely used to explain computer-usage behavior and factors associated with

acceptance of technology.

According to the model, the technology adoption is influenced by the user’s

intention to use which consequently determined his or her attitudes towards

technology. Perhaps, user’s perceptions— perceived ease of use (PEU) and perceived

usefulness (PU) determine the variability in these attitudinal and behavioural

dimensions (Ahmad, Basha, Marzuki, Hisham and Sahari, 2010). While PEU reveals

the degree to which technology might be free of efforts in its application, PU on the

other hand indicates the extent to which technology usage can help in improving

users’ performance in order to enhance one’s task (Davis, Bagozzi, & Warshaw,

1989). The suggested Technology Acceptance Model (TAM) indicates that,

behavioral intention mediates the influence of the two extrinsic motivation

9

components, namely; PEU and PU as presented in Figure 1.1.

Figure 1.1: Technology Acceptance Model (Davis et al., 1989)

According to Ahmad et al. (2010), TAM has attracted the interest of decision

makers, researchers and practitioners due to the fact that, it is simple, robust and

reasonable. Though, it has unanimously gained empirical support in terms of its

strength through applications and validations in assuming the use of information

systems (Davis, 1993; Taylor & Todd, 1995; Venkatesh & Morris, 2000), current

meta-analyses demonstrates that, if various overriding issues are considered, it could

be resolved through proper understanding (Ma & Liu, 2004; Schepers & Wetzels,

2007; Yousafzai, Foxall and Pallister, 2007).

The Online Database Adoption and Satisfaction Model, proposed by Islam

(2011a) is developed and validated by incorporating two intrinsic motivation attributes,

namely, computer self-efficacy and satisfaction into the original TAM. The findings

of the model affirm a better understanding of the TAM (p. 50). Additional studies on

factors related to technology acceptance and refinement of acceptance models that can

facilitate its generalizability has been suggested (Sun & Zhang, 2006; Thompson,

Compeau, & Higgins, 2006). Considering the evolving of new technologies, perceived

ease of use and perceived usefulness are not the only suitable constructs that

determine technology acceptance (Thompson et al., 2006). Many researchers have

shown that, including more dimensions with other IT acceptance models in order to

10

enhance its specificity and explanatory utility would perform better for a particular

context and focus (Agarwal & Prasad, 1998; Mathieson, 1991). Moreover, there is a

suggestion by previous researchers (Legris, Ingham & Collerette, 2003) that, TAM

deserves to be extended by integrating additional factors that could facilitate the

explanation of more than 40 percent of technology acceptance and usage. Against this

backdrop, the proposed conceptual model was analyzed to develop and validate the

Technology Adoption and Gratification (TAG) model of ICT usage as shown in

Figure 1.2, two other intrinsic motivation attributes – gratification and self-efficacy –

were incorporated into the original TAM based on prior studies (Islam, 2011b &

2011b; Islam, 2014; Wu, Chang & Guo, 2008; Ahmad et al., 2010; Huang, 2008;

Shamdasani, Mukherjee & Malhotra, 2008).

Figure 1.2: Conceptual Model

Note: PEU= “perceived ease of use”, PU= “perceived usefulness”, CSE= “computer

self-efficacy”, INT= “Intention to Use”, USE= “ICT Usage”, GRAT= “Gratification”.

11

1.3 STATEMENT OF THE PROBLEM

Several studies have been and are being conducted on TAM in the attempt to measure

the usage of Information and Communication Technology (ICT) among teachers and

students (Islam, 2011b; Zejno & Islam, 2012; Saleh, 2009; Wozney et al., 2006;

Usluel et al., 2008; Ferdousi, 2009; Kaba & Osei-Bryson, 2013; Kreijns et al., 2013;

Moses et al., 2013). However, despite a decade of existence, the usage of ICT was

found to be underutilized, especially by postgraduate students (Islam, Ahmad,

Madziah & Sahari, 2011; Islam, Leng & Singh, 2015; Islam, 2011a). Besides this,

Goktas, Gedik and Baydas (2013) discovered that the major obstacles that primary

school teachers confronted in the assimilation of ICT as of 2011 emerged to be the

lack of hardware and appropriate software materials, restrictions of hardware, lack of

in-service training and technical support. Kim, Choi, Han and So (2012) asserted that

the appearance and adoption of cutting-edge technologies generate demands for

developing roles and competencies of teachers in the new knowledge society.

However, it is ambiguous how well teachers have been prepared to face the challenges

and difficulties of teaching and learning in the 21st century; and what directions must

be acquired to better practice the new generation of teachers. In reality, the issue of

human use and acceptance of innovative technologies is much more difficult and

demanding (Spector, 2013). Moreover, the effective combination of ICT into

education is still occasionally complex and challenging (Zhao, Paugh, Sheldon, &

Byers, 2002; Keengwe, Onchwari, & Wachira, 2008).

According to Bringula and Basa (2011), even though teachers were skilled in

executing diverse computer and Internet-associated activities, have a high sense of

dedication to the use of the faculty web portal, and have internet access at home. The

faculty web portal which comprised all online research materials, namely, Electronic

12

Journals, Books, Articles and Case Studies were still not used frequently. Buabeng-

Andoh (2012) stated that the worldwide investment in ICT to enhance teaching and

learning in schools have been instigated by many governments. Despite all these

investments on ICT infrastructure, equipments and professional development to

extend education in many countries, ICT adoption and incorporation in teaching and

learning have been limited. If educators do not utilize technology the way it was

planned to serve, the affordances of technology cannot be maximised for effectual

teaching and learning to take place. As such, numerous studies on technology

acceptance have been conducted over the years. Literature revealed that several

acceptance studies had focused on the exploration of factors that affected technology

acceptance among teachers and students (Teo, 2011). However, most studies have

been conducted to assess the students (Stone & Baker-Eveleth, 2013; Alenezi, Karim

& Veloo, 2010; Martínez-Torres et al., 2008; Shroff et al., 2011; Shipps & Phillips,

2013; Terzis, Moridis, Economides & Rebolledo-Mendez, 2013; Chang & Tung, 2008;

Guo & Stevens, 2011; Zejno & Islam, 2012; Islam, 2011a; Rasimah, Ahmad & Zaman,

2011) and teachers (Al-Ruz & Khasawneh, 2011; Tezci, 2011; Yuena, & Ma, 2008;

Wong, Teo & Russo, 2012) acceptance or adoption in using ICT facilities for their

teaching and learning, little is done to investigate on the lecturers’ adoption and

gratification in using ICT facilities in higher education. In so doing, determinants such

as perceived ease of use, perceived usefulness, computer self-efficacy, intention to use,

use and gratification were postulated to be major barriers. To examine the effect of

these factors on lecturers’ ICT usage, the technology adoption and gratification (TAG)

model was developed and validated. Finally, there is a lack of adequate facilities for

the adoption and gratification of the infrastructure considered for this study to

determine its implementation and use as experienced by the lecturers.

13

1.4 OBJECTIVES OF THE STUDY

This study was conducted to explore and understand factors that influenced lecturers’

adoption and gratification of the comprehensive Public Universities’ (University of

Malaya in Malaysia and Jiaxing University in China) ICT usage, the relationships

among perceived ease of use, perceived usefulness, computer self-efficacy, intention

to use, use and gratification and how these constructs interacted to influence adoption

and gratification framed within the TAG Model. Specifically, the objectives were to

(1) develop and validate the Technology Adoption and Gratification (TAG) Model

consisting of the constructs, namely, computer self-efficacy and gratification into the

original TAM, (2) measure the lecturers’ views on the ease of use of ICT facilities, (3)

evaluate the lecturers’ perception regarding the usefulness of ICT facilities, (4)

examine the lecturers’ computer self-efficacy to access ICT facilities, (5) examine the

lecturer’s intention to use of ICT facilities, (6) determine the lecturers’ actual use of

ICT facilities, (7) assess the lecturers’ gratification in using ICT services at the public

universities in Malaysia and China, (8) examine the cross-cultural validation of the

TAG model in higher education.

1.5 RESEARCH HYPOTHESES

Based on existing literature review, the following research hypotheses were generated:

1. Computer self-efficacy (CSE) will have a positive direct influence on

lecturers’ perceived ease of use (PEU) and perceived usefulness (PU) of ICT

facilities in higher education.

2. Perceived ease of use (PEU) will have a positive direct influence on lecturers’

intention to use (INT), actual use (USE) and gratification (GRAT) in using

ICT facilities in higher education.

14

3. Perceived usefulness (PU) will have a positive direct influence on lecturers’

intention to use (INT), actual use (USE) and gratification (GRAT) in using

ICT facilities in higher education.

4. Intention to use (INT) will have a positive direct influence on lecturers’

gratification (GRAT) and actual use (USE) of ICT facilities in higher

education.

5. Actual use (USE) will have a positive direct influence on gratification (GRAT)

in using ICT services in higher education.

6. Computer self-efficacy (CSE) will have a positive indirect influence on

lecturers’ intention to use (INT) of ICT facilities mediated by perceived ease

of use (PEU) and perceived usefulness (PU), respectively.


lecturers’ gratification (GRAT) in using ICT facilities mediated by perceived

ease of use (PEU) and perceived usefulness (PU), respectively.


lecturers’ use (USE) of ICT facilities mediated by perceived ease of use (PEU)

and perceived usefulness (PU), respectively.

9. Perceived ease of use (PEU) and Perceived usefulness (PU) will have a

positive indirect influence on lecturers’ gratification (GRAT) in using ICT

facilities mediated by intention to use (INT), respectively.

10. Intention to use (INT) will have a positive indirect influence on lecturers’

gratification (GRAT) mediated by actual use (USE) of ICT facilities in higher

education.

11. There will be a cross-cultural invariant of the causal structure of the TAG

model.

15

The broad objective of this study was to develop and validate the Technology

Adoption and Gratification (TAG) Model by incorporating two intrinsic motivation

attributes, namely, computer self-efficacy and gratification into the original

Technology Acceptance Model (TAM). The broad objective was achieved for this

study through the specific objectives. To address the specific objectives, the present

study generated eleven hypotheses as shown in Table 1.1.

Table 1.1: The list of Specific Objectives and Hypotheses

List of Specific Research Objectives related to Hypotheses

The specific objectives were to

measure the lecturers’ views on the

ease of use of ICT facilities and

evaluate the lecturers’ perception

regarding the usefulness of ICT

facilities.

To address the objectives 2 and 3, the

present study hypothesized that:

H2: Perceived ease of use (PEU) will

have a positive direct influence on

lecturers’ intention to use (INT), actual

use (USE) and gratification (GRAT) in

using ICT facilities in higher

education.

H3: Perceived usefulness (PU) will


lecturers’ intention to use (INT), actual

use (USE) and gratification (GRAT) in

using ICT facilities in higher

education.

H9: Perceived ease of use (PEU) and

Perceived usefulness (PU) will have a

positive indirect influence on lecturers’

gratification (GRAT) in using ICT

facilities mediated by intention to use

(INT), respectively.

16

Table 1.1, continued

The specific objective was to examine

the lecturers’ computer self-efficacy to

access ICT facilities.

To address the objective 4, the present

study hypothesized that:

H1: Computer self-efficacy (CSE) will


lecturers’ perceived ease of use (PEU)

and perceived usefulness (PU) of ICT



have a positive indirect influence on

lecturers’ intention to use (INT) of ICT

facilities mediated by perceived ease of

use (PEU) and perceived usefulness

(PU), respectively.



lecturers’ gratification (GRAT) in

using ICT facilities mediated by

perceived ease of use (PEU) and

perceived usefulness (PU),

respectively.



lecturers’ use (USE) of ICT facilities

mediated by perceived ease of use

(PEU) and perceived usefulness (PU),

respectively.


the lecturer’s intention to use of ICT

facilities.



H4: Intention to use (INT) will have a

positive direct influence on lecturers’

gratification (GRAT) and actual use

(USE) of ICT facilities in higher

education.

H10: Intention to use (INT) will have a

positive indirect influence on lecturers’

gratification (GRAT) mediated by

actual use (USE) of ICT facilities in

higher education.

17


The specific objective was to

determine the lecturers’ actual use of

ICT facilities.



H5: Actual use (USE) will have a

positive direct influence on

gratification (GRAT) in using ICT

services in higher education.

The specific objective was to assess the

lecturers’ gratification in using ICT

services at the public universities in

Malaysia and China.


study may not necessarily generate the

hypothesis, which is an endogenous

variable tested using Confirmatory

Factor Analyses as well as Structural

Equation Modeling like other

objectives.


the cross-cultural validation of the

TAG model in higher education.



H8: There will be a cross-cultural

invariant of the causal structure of the

TAG model.

1.6 SIGNIFICANCE OF THE STUDY

This study has provided useful information on the lecturers of the public universities

in Malaysia and China adoption of and gratification in using the universities’ ICT

facilities. This information can give insight to the universities authority and

administration to better gauge the utility of the ICT services provided. This study has

contributed significantly in getting the lecturers’ perspectives on their adoption of and

gratification in using the ICT facilities. This research exercise has comprised lecturers

of all faculties of the University of Malaya in Malaysia and Jiaxing University in

China; it is, therefore, comprehensive and representative in nature. The results will

help the Universities’ higher authority in taking and providing necessary steps

towards training personnel regarding the application of this ICT service in higher

education. Besides, this study will also contribute to the body of the knowledge by

18

developing and validating the technology adoption and gratification (TAG) model in

higher education.

1.7 LIMITATION OF THE STUDY

The study was confined and limited to two public universities, namely, University of

Malaya in Malaysia and Jiaxing University in China. Lecturers who work in the

different faculties/colleges, namely, Arts and Social Sciences, Built Environment,

Business and Accountancy, Computer Science and Information Technology, Dentistry,

Economics and Administration, Education, Engineering, Language and Linguistics,

Law, Medicine, Sciences, Academy of Islamic Studies, Foreign Language Studies,

Mathematics and Engineering, Biology and Chemistry, Material Engineering,

Education, Civil Engineering & Architecture and Jiaxing University Nanhu were

sampled to evaluate their adoption of and gratification in using the ICT facilities for

their teaching and research. However, this study did not include all lecturers due to the

restriction of time.

1.8 OPERATIONAL DEFINITION OF TERMS

The following operational definitions were adopted in the study:

1. Perceived Ease of Use

According to Davis (1989), perceived ease of use refers to the degree to which

the user believes that using the technology would be free of effort. In this

study, perceived ease of use (PEU) refers to the lecturers’ perception of how

easy it is to use the ICT facilities measured through items in the questionnaire.

2. Perceived Usefulness

Perceived usefulness refers to the degree to which a person believes that using

19

the technology would enhance his or her performance (Davis, 1989). In this

study, perceived usefulness (PU) refers to the lecturers’ perception of the

benefits derived from using the ICT services measured through items in the

questionnaire.

3. Computer self-efficacy

In this study, computer self-efficacy (CSE) refers to the lecturers’ beliefs in

their computer ability to use the ICT facilities measured through items in the

questionnaire.

4. Intention to Use

In this study, intention to use (INT) refers to the lecturers’ perception of how

the ICT facilities are examined in terms of the likelihood of lecturers’ use of

such facilities for enhancing their teaching, learning and researching ability as

measured through items in the questionnaire.

5. Gratification

In this study, the extent to which the usage of ICT is consistent with existing

values, needs and the experience of the lecturers’ which will be measured

through items in the questionnaire.

6. Actual Use

In present study, actual use (USE) refers to the lecturers’ view of how

frequently they use ICT facilities for their research and academic-related

activities in higher education as measured through items in the questionnaire.

7. ICT facilities

The ICT facilities have been provided by the authorities of University of

Malaya in Malaysia and Jiaxing University in China, respectively. ICT

20

facilities include computers, web browser (WWW), search engine (e.g. Google,

Yahoo, etc.), Microsoft office applications, document processing, editing and

composing, email and wireless internet, research related software, computer

graphics, computer labs, multimedia facilities (CD-ROM, VCD, DVD), and

university research databases. The research databases comprise volumes of

research articles and reports published in journals. It also includes thesis and

dissertations that would be helpful for lecturers and staff to conduct thorough

literature reviews. In addition, it also helps lecturers to get access to their

subject related research materials which give them extra assistance in their

teaching, learning and research.

8. University Lecturers

This refers to the lecturers of the University of Malaya and Jiaxing University

who are currently working as professors, associate professors, assistant

professors or senior lecturers and lecturers at the different faculties or colleges

which were measured through items in the questionnaire.

21

CHAPTER 2

LITERATURE REVIEW

2.1 INTRODUCTION

This chapter provides an overview of the information and communication technology

usage, Technology Acceptance Model (TAM), and its constructs. It then briefly

defines the Technology Satisfaction Model (TSM) as well as Online Database

Adoption and Satisfaction (ODAS) Model. It is followed by a detailed description of

the conceptual model as proposed in this study, and its constructs to develop and

validate the Technology Adoption and Gratification (TAG). This section is capped

with an overview of the construction of hypotheses.

2.2 INFORMATION AND COMMUNICATION TECHNOLOGY USAGE

In a study by Elen, Clarebout, Sarfo, Louw, Pöysä-Tarhonen, and Stassens (2010), the

definition of information communication technology (ICT) was cited from Wikipedia

(retrieved May 25 2009) and stated that “Information and communication

technologies (ICT) is an umbrella term that includes all technologies for the

manipulation and communication of information. The term is sometimes used in

preference to Information Technology (IT), particularly in two communities:

education and government. In the common usage it is often assumed that ICT is

synonymous with IT; ICT in fact encompasses any medium to record information

(magnetic disk/tape, optical disks (CD/DVD), flash memory etc. and arguably also

paper records); technology for broadcasting information - radio, television; and

technology for communicating through voice and sound or images - microphone,

22

camera, loudspeaker, telephone to cellular phones. It includes the wide variety of

computing hardware (PCs, servers, mainframes, networked storage), the rapidly

developing personal hardware market comprising mobile phones, personal devices,

MP3 players, and much more; the gamut of application software from the smallest

home-developed spreadsheet to the largest enterprise packages and online software

services; and the hardware and software needed to operate networks for transmission

of information, again ranging from a home network to the largest global private

networks operated by major commercial enterprises and, of course, the Internet. Thus,

"ICT" makes more explicit that technologies such as broadcasting and wireless mobile

telecommunications are included.

According to Zhou et al., (2011), like many other countries, the Chinese

government also granted enormous attention and significance to educational

technology. In order to accelerate their post-secondary institutions’ technology

popularizing rate, the China Educational Technology in Higher Education Committee

was established by its Ministry of Education in December 1999. One year later, a

program of Connecting Every School which devoted to connect 90% of their schools

onto the Internet was launched for school level. So, high quality online educational

resources can be obtained by their students and teachers within the next five to ten

years. Further, in 2002, a series of educational technology norms were formulated by

the Ministry for the purpose of guiding the practical technology application for school

teachers, facilitators, as well as administrators, which was followed by the publication

of National Educational Technology Standards put forward firstly by the International

Society for Technology in Education (ISTE) for teachers and students in 2000. The

research they conducted revealed some positive viewpoints as well as problems of

technology and its use among school teachers. In detail, the positive perspectives can

23

be reflected from the following findings: nearly 90% of participators confirmed the

useful role of technology in teaching process; more than 80% agreed teachers need to

learn how to integrate technology into teaching practices, and 77% identified that it

was significant for school students to learn technology. On the contrary, only 11%

stated that they are proficient in using technology and lower than 17% were self-

confident with the use of technology. In addition, compared with the fact that most of

the participators knew technology could be a helpful teaching tool, only 9% felt

skillful to adopt it in actual teaching activities. Thus, strong confidence and ability in

using technology did not assist in direct proportion to the optimistic opinions.

Moreover, the insufficient technological preparation for teaching was frequently

pointed out by focus group participants. They also uncovered that the participants’

education program did not work well in helping them to apply technology in teaching.

However, concerning the use of technology in teaching, they shared similar

viewpoints, and their suggestions on how to integrate technology in teacher education

was not very different to what had been discussed in other countries. Consequently,

the technology course should be re-constructed by the teacher education program to

tackle the participants’ concerns. In order to acquire technology skills more effectively,

it is necessary for teaching candidates to seek better ways of compounding the lecture

and lab components.

Du Plessis and Webb (2012) claimed that there is a growing number of schools

that have been established with ICT infrastructure, and there is an increasing necessity

for the teachers’ professional development in ICT. As a result information on ways to

assist teachers, especially those who are digital immigrants, is needed to help them

deal with the demands of the curriculum and the 21st century skills implied therein.

Nevertheless, they highlighted the caveat that the execution of ICT associated

24

techniques may not be a one-off event, and that the participants require dissimilar

levels of support and training (professional ICT teacher development), generally in a

planned manner, to attain the confidence and competence obligatory to use innovative

ICT strategies effectively. Against this backdrop, they examined whether revelation to

an Internet based extended Cyberhunt strategy allows teachers to achieve a set of

outcomes related to Prensky’s ‘Essential 21st Century Skills’ and the ‘Critical

Outcomes of the South African National Curriculum Statement (NCS)’. The outcomes

comprised effective planning, designing, decision making and goal setting; improved

computer and data searching skills; enhanced confidence, interest, reflective ability,

collaboration, judgment and creative and critical thinking; as well as effective problem

solving and the ability to communicate and interact with individuals and groups. The

quantitative and qualitative data were collected from 26 school teachers, even though

the sample size was not adequate. The findings of their study demonstrated

statistically significant gains in increasing each teacher's decision making, searching

skills, ability to compose questions on dissimilar cognitive levels, planning ability,

notion of audience, computer skills, confidence, reflective ability, interest, and

collaboration skills. The results also show that learning by the design context prepared

the respondents conscious of the inconsistencies that are linked with the traditional

teaching environment and were made aware of the affirmative likelihoods that a

diverse ICT related approach might provide for learning.

Eyal (2012) observed teacher’s skills, abilities, and perceptions required in the

digital environment through assessment, and showed the significance of adapting the

different technologies to the dissimilar estimation purposes. In the study, Eyal (2012)

concluded the significances of teachers in the digital learning environment are: a) the

role of teachers who appreciate the digital learning environments is primary and

25

significant; b) wise use of technological tools to assess learners is essential for the

students, teachers, and for other students participating in educational processes; c)

teachers in the 21st century prefer to use technologies that advance the assessment

methods which emphasize the learning process, enable peer assessment and develop

reflective abilities. Eyal also articulated that teachers are obliged to have some

abilities and skills in basic, advance and intermediate digital assessment literacy,

although there was no empirical assessment.

Afshari, Bakar, Luan and Siraj (2012) affirmed that despite the significance of

computer use in education and the role of the principals of the schools in assisting

technology assimilation, there has been little study on the use of ICT by the principal

themselves, their computer competencies as well as their transformational leadership

role in implementing ICT in schools. As such, they conducted an empirical research to

observe whether the transformational leadership role of principals in implementing

ICT in secondary schools was affected by the computer competence, level of

computer use, and the professional development activities of the principals. The data

for their study were gathered from 320 secondary school principals in Iran. To

develop and validate the research model, a structural equation modeling was applied

to test five hypotheses. Their findings exhibited that computer competence had a

significant effect on secondary school principals’ computer use and it indirectly

influenced the transformational leadership role of principals in implementing ICT in

schools, while it did not show any significant direct effect on transformational

leadership as hypothesized. Thus, It recommended that continuing professional

development opportunities on the subject of leadership and technology must be

provided for principals to enhance their levels of ability in computer use which might

assist future study comprehending the significance of the use of technology in

26

education which may further lead to the discovery of model that include the

transformational leadership dimensions of charisma, inspirational motivation,

intellectual stimulation and individualized contemplation in their schools.

Kregor, Breslin and Fountain (2012) conducted a study with regards to e-

learning practice, demand and capacity which aims to improve planning, decision

making and the quality of the online experience. A total of 2,300 students and 250

staff members participated in the study conducted at the University of Tasmania

(UTAS) in Australia. The findings of their study on technology use presented

information about the overall familiarity on various regular and irregular technology

usages from students and staff. An indication can be perceived from the usage records

and trail on how easily adopted a technology might be if it is known as an innovative

way of teaching and learning, or on the contrary, how much endeavor would be

required to increase usage and acceptance. Most of the staff and students were web

users on a daily or at least regular basis for information searching and general

reference, online transactions like shopping, banking and email. On the other hand, a

large number of students showed satisfaction on the availability of computers and the

wireless network at UTAS. A slightly greater minority (30%) found that applying

technology in courses sometimes challenged their IT skills. Staff were obviously

unsatisfied with their experience because 50% of the staff believed that the workload

associated with e-learning did not involve adequate recognition and planning;

compared to only 18% who did and 42% did not consider learning spaces to be

physically appropriate for “innovative use of learning technology”, while 18% held

the opposite opinion. They also found teaching staff were somewhat less convinced

that the integration of ICT and the online environment in teaching and learning is

beneficial to learners. Therefore, in order to advance more effective and broader use

27

of technology at the university, the support of teaching staff is crucially significant.

Jang and Tsai (2013) studied the technological pedagogical and content

knowledge (TPACK) of science teachers in secondary schools employing a new

contextualized TPACK model. The research model consisted of four factors: content

knowledge (CK), pedagogical content knowledge in context (PCKCx), technology

knowledge (TK) and technological pedagogical content knowledge in context

(TPCKCx). The contextualized TPACK model was tested using a total of 1292

science teachers from Taiwan. Their findings discovered that the four underlying

factors were consistent with prior study. The results also showed that (the percentage

of use of computer technology) computer technology was employed by 94.6% of

secondary school science teachers in Taiwan. However, the technology knowledge

and technological content knowledge in context rated by science teachers with less

teaching experience tended to be considerably higher than that by teachers with more

teaching experience.

According to Kreijns et al., (2013), the introduction of novel pedagogical

practices which complied with 21st knowledge society’s educational demands can be

enabled, supported and reinforced by Information and communication technology

(ICT). Thus, teachers are expected to employ ICT pedagogically. On the contrary,

they feel more often reluctant rather than willing to use ICT, even though the level of

ICT infrastructure increases and this potential skills-based professional development

is delivering. The Technology Acceptance Model (TAM) and its descendants TAM2

and TAM3 are frequently applied to expand insight into the dimensions effecting

acceptance of a specific technology. TAM’s fundamental constructs are perceived

ease of use and perceived usefulness. TAM is primarily a technology-oriented model

which overlooks various factors that might explicate why teachers are not willing to

28

use ICT. In doing so, they alternatively proposed the application of Fishbein’s (2000)

Integrative Model of Behaviour Prediction (IMBP). The IMBP consisted of attitude,

self-efficacy and subjective norm that are the direct and indirect predecessors of

intention to use of ICT and actual ICT usage. Nevertheless, there was no empirical

validation for their proposed research model in the domain of teachers’ intention to

pedagogically use an ICT object. They suggested that if teachers are short of

knowledge and skills, then it would hint that the most significant concerns for

interfering are self-efficacy and its antecedent factors.

According to Fu (2013), Information and Communication Technology (ICT) is

broadly employed in the current educational fields, including computers, the Internet,

and electronic delivery systems like televisions, radios, projectors, and more. As such,

Fu’s study made a summarization of the pertinent researches on the application of

information and communication technology (ICT) in educational fields. Particularly,

the research looked at studies that mentioned the advantages of integrating ICT into

schools, the factors affecting successful ICT integration, the challenges and

difficulties faced in using ICT, the significance of school culture in using ICT as well

as pre-service and in-service teachers’ perceptions, attitudes, and confidence in the use

of ICT. Based on the information gathered, Fu (2013) concluded that it is obvious that

ICT integration is not one-off task, but is adjustable and it is an evolving process.

Moreover, only few studies have focused on challenges of ICT integration faced by

teachers, students and administrative when they participated in specific teaching

activities. Thus, further investigation is required in this area by future researchers.

Buabeng-Andoh (2012) examined teachers’ adoption and integration of

information and communication technology in teaching and learning processes. The

author explained several factors that prevent teachers from using ICT such as: the lack

29

of ICT skills, confidence, pedagogical teacher training, suitable educational software,

access, rigid structure of traditional education systems and restrictive curricula.

However, there was no empirical assessment to generalize this research. The study

concluded that the adoption and integration of technologies by classroom teachers has

been complicated because of its fast progress. Teachers are challenged by the efficient

integration of technology into classroom practices which required more than just

connecting computers to a network. In addition, teachers’ intentions to apply

technology in their teaching practice or attitude toward technology is the key factor in

the study. Providing teachers who have negative attitudes toward technology with

excellent ICT facilities may not have an influence on their use of it in their teaching.

Thus, it is necessary to assure teachers that their teaching can be made interesting,

easier, more fun, more motivating and more enjoyable through technology.

Lim and Pannen (2012) carried out a research to find out how the Indonesian

teacher education institutions (TEIs) applied strategic planning to improve their

capability in developing pre-service teachers’ educational competence in ICT usage.

A holistic method was adopted towards strategic planning by these TEIs through

utilizing the six dimensions of the Capacity Building Toolkit for TEIs in the Asia

Pacific. Among them, the pre-service teacher education program (practicum,

curriculum and assessment) is the key dimension of the strategic planning which is

motivated by the philosophy and vision of the TEI. ICT plan, professional learning,

research and evaluation, and communication and partnerships are four other

dimensions supporting the program. The development of pre-service teacher education

programs were focused on by three of the four TEIs’ strategic planning, while

research and evaluation was focused on by one. During this process, support from

management was essential to the TEIs, but they also faced challenges from senior staff

30

because they were reluctant to change, lack of funding, and less qualified.

On the basis of Gibson, Stringer, Cotton, Simoni, O’Neal, and Howell-

Moroney (2014), helping students to use technology as a learning tool is a significant

aim of incorporating computer usage into daily classroom tutoring. Except access to

computers, teachers’ implementation of computing into learning can also lead to

fruitful classroom integration. The teachers’ belief on classroom computing affects

successful implementation greatly. Therefore, they determined the impacts of a

teacher-focused technology intervention on the students’ computer usage, anxiety and

attitudes towards learning tools, while Teachers’ classroom computer usage, attitudes

and anxiety were investigated as mediators of this relationship. They predicted that

increased teacher participation will have a statistically significant influence on

students’ educational use of computers and attitudes towards classroom computing,

and reduce student anxiety towards computing. On the other hand, the relationship

between the student outcomes and interventions were mediated by teachers’ computer

usage, anxiety and attitudes towards classroom computing. To test the hypotheses, a

total of 696 students and 60 teachers were collected using survey questionnaire. The

findings of their study found that the technology intervention had a significant

influence on the students’ computer usage and attitudes for educational purposes.

Nonetheless, there was no confirmation that teachers’ attitudes or use had any

mediating impact on this relationship. The findings also suggest that it is possible to

enhance learners’ attitudes toward computer use through intense interventions aimed

at their educators.

Kim, Choi, Han and So (2012) stated that since the initiation of the first IT

Master Plan of Korea in 1996, teachers there are commonly deemed well trained to

incorporate ICT into their teaching practice. Therefore, they investigated novel

31

educational measures to improve teachers’ ICT capacities in the contemporary

learning atmosphere. The literature showed that innovative and efficient adaptive

expertise and novel media literacy skills are the new requirements for teachers. Under

this consideration, three cases were observed: (1) learning Scratch for computational

and creative thinking, (2) learning robotics as emerging technology requires

convergent and divergent thinking, and (3) learning to design with ICT systems

thinking. Thus, some new methods helped teachers to fostering adaptive expertise, for

example, offering a variety of contexts unlike usual classroom lessons, and

emphasizing on thinking skills instead of technical skills. However, difficulties were

also encountered by participants in the above three cases, such as problems in

handling different course activities, incorporating new ideas, and comprehending new

design contexts in their comprehensive projects. They suggested that in order to

validate the challenges and potentials of those methods, it is compulsory to look into

teachers’ learning processes and their outcomes with a wider variety of cases with

more depth and with more sources of data.

Bringula and Basa (2011) studied the determinants that may significantly

influence faculty web portal usability. The data for their study was collected from 82

faculty members of the University of the East-Manila in Philippine. Descriptive

analysis demonstrated that the majority of the faculty members were moderately

young, were holders of Master degree, were experts in using the internet and

computers, were dedicated in usage of the web portal, and had internet access at home.

The outcome of their multiple regression analysis discovered the only way to predict

web portal usability was information content, as a web portal design-related factor.

Therefore, the null hypothesis, defining that web portal design-related and faculty-

related dimensions do not have significant impact on faculty web portal usability, is

32

well-received. They recommended that the search for accounts of low usability of

faculty web portals by the internet-connected, dedicated and expert faculty staff was

worth further investigation.

Goktas, Gedik and Baydas (2013) conducted a research to explore the

difficulties faced in the integration of ICT by Turkish primary school teachers, to

make a comparison between the present (in 2011) condition of ICT integration and

that in 2005, and to recommend possible enablers to solve those difficulties. A total of

1373 teachers were collected from 52 schools in 39 provinces in Turkey. The findings

of their study exhibited that the lack of hardware, appropriate software materials,

limitations of hardware, in-service training, and technical support were the most

significant difficulties. Besides this, the maximum ordered enablers were allocation of

more budgets, specific units for peer support, support offices and personnel for

teachers and higher quality pre-service training for ICT. Additional leading enablers

were supporting teachers to enable effective ICT use, having technology plans,

offering higher quality and more quantity of in-service training and designing

appropriate course content/instructional programs. The results also showed that the

majority of barriers indicated statistically significant differences and most enablers

demonstrated moderate or low differences between teachers’ views of their situation

in 2005 and in 2011. Their study recommended that in order to help teachers

incorporate ICT into their teaching practice more efficiently, technical support should

be provided to them. Moreover, online system supports, special ICT units, and

personnel allocation should be focused to facilitate them (besides tutor teachers). And,

to improve the quality of in-service training programs, further approaches should be

taken. Additionally, it should be mandatory for both teachers and administrators to

attend the in-service training programs for classroom ICT application.

33

2.3 TECHNOLOGY ACCEPTANCE MODEL (TAM)

The technology acceptance model (TAM) was rooted in the theory of reasoned action

(TRA), a model concerned with determinants of consciously intended behaviors

(Fishbein & Ajzen, 1975). TRA states that beliefs influence attitudes, which in turn

lead to intentions and then generate behaviors. TAM assumes that beliefs about

usefulness and ease of use are always the primary determinants of Information

Technology/Information System adoption in organizations. According to TAM, these

two determinants serve as the basis for attitudes towards a particular system, which in

turn determines the intention to use, and then generates the actual usage behavior. The

model is shown in Figure 2.1. However, the Technology Acceptance Model (TAM)

developed by Davis (1989) ignored the issue of computer self-efficacy and

satisfaction (Islam, 2014). Moreover, the findings of the Online Database Adoption

and Satisfaction (ODAS) Model (Islam, 2011a) contributed to a better understanding

of the TAM and technology adoption among postgraduate students by incorporating

two intrinsic motivation attributes, namely, computer self-efficacy and satisfaction.

Figure 2.1: Technology Acceptance Model (Davis et al. 1989)

Furthermore, many researchers have extended the TAM by incorporating

additional constructs for measuring e-Learning systems (Lee et al., 2011; Alenezi et

al., 2010; Martínez-Torres et al., 2008; Yuena & Ma, 2008), ICT usage (Tezci, 2011;

Ahmad et al., 2010; Terzis et al., 2013; Zejno & Islam, 2012; Usluel et al., 2008;

34

Wong et al., 2012; Al-Ruz & Khasawneh, 2011), Social Networks (Shipps & Phillips,

2013; Shittu, Basha, Rahman & Ahmad, 2013; Shittu et al., 2011), mobile learning

(Chang, Yan & Tseng, 2012; Wang, Lin & Luarn, 2006; Aljuaid, Alzahrani & Islam,

2014), online learning (Chang & Tung, 2008; Guo & Stevens, 2011; Rasimah et al.,

2011), Internet banking (Amin, 2007), e-Commerce (Devaraj, Fan & Kohli, 2002;

Lallmahamood, 2007; Lee & Park, 2008), wireless internet (Islam, 2011b; Islam,

2014), online shopping (Barkhi, Belanger & Hicks, 2008; Stone & Baker-Eveleth,

2013), mobile banking (Kumar & Ravindran, 2012; Chitungo & Munongo, 2013;

Hanafizadeh, Behboudi, Koshksaray & Tabar, 2014), online research databases (Islam,

2011a; Islam et al., 2015) and YouTube (Lee & Lehto, 2013). The summary of these

studies are shown in Table 2.1. However, most of these studies are concerned with the

acceptance or adoption of technological applications from the perception of school

teachers, students as well as consumers, while, very few studies examined the

lecturers’ adoption and gratification in using ICT facilities for their teaching and

research purposes in higher education.

Teo (2011) extended the technology acceptance model (TAM) by including

two additional constructs, namely, subjective norm and facilitative conditions to

measure school teachers’ intention to use technology. The research model for this

study incorporated six factors, namely, perceived usefulness, perceived ease of use,

subjective norm, facilitative conditions, attitude and behavioural intention to test all

nine hypotheses. The sample used in Teo’s study consisted of 592 teachers from

schools in Singapore. The research model was estimated using the structural equation

modeling. The findings of this study discovered that perceived usefulness, attitude

towards use, and facilitating conditions had direct influences on teachers’ behavioural

intention to use technology, while subjective norm and perceived ease of use had

35

significant indirect effect on behavioural intention to use technology mediated by

attitude towards use and perceived usefulness, respectively. From the direct effects, it

was obvious that when teachers perceive technology to be useful and that applying

technology would enhance their productivity, similarly, their intention to use will be

drastically improved. A positive attitude also showed a positive and direct effect on

teachers’ behavioural intention to use technology, while subjective norm did not exert

any significant influence on behavioural intention. Teo (2011) also suggested that with

the use of technology in education becoming pervasive internationally, proportional

studies across countries or cultures could be conducted to discover the culture-

invariant variables that effect teachers’ intention to use technology.

Lai and Chen (2011) asserted that there has been recently a remarkable

propagation in the number of teaching blogs; nonetheless, little has been discovered

about what encourages teachers to adopt teaching blogs. As a result, they conducted a

study to assess secondary school teachers’ adoption of teaching blogs. To examine

teachers’ adoption, they extended Rogers’ (1995) Innovation Diffusion Theory (IDT)

to develop and validate their research model which integrated four main dimensions,

namely, individual (codification effort, loss of knowledge power, reputation,

enjoyment in helping others, knowledge self-efficacy and personal innovativeness),

technological (perceived usefulness, perceived ease of use, compatibility and

perceived enjoyment), school (school support and school incentives) and

environmental (supervisor influences and peer influence) characteristics to test the

hypotheses. To estimate the research model, survey data were collected from a total

325 secondary school teaching blog adopters and secondary school teachers and

analyzed using discriminant analysis. The findings of their study demonstrated that the

secondary school teachers’ decisions to adopt teaching blogs were strongly related to

36

eight constructs, namely, perceived enjoyment, codification effort, compatibility,

perceived ease of use, personal innovativeness, enjoyment in helping others, school

support and perceived usefulness. It was found that self-efficacy did not significantly

influence teaching blog adoption among others. The results also showed that

perceived ease of use and usefulness were important factors for teachers to adopt

teaching blogs. If teachers observe teaching blogs easy to use and useful, they are

much more likely to use and update their blogs, effecting decisions regarding teaching

blog adoption. However, their study indicated that still very few teachers were used

teaching blogs.

Lee and Lehto (2013) modified the TAM to examine users’ behavioral

intention to use YouTube. Their conceptual model integrated user satisfaction, task-

technology fit, vividness, YouTube self-efficacy, as well as one second-order factor of

content richness into the TAM. A total of 432 participants were collected in their

study. The data for their study were analyzed using structural equation modeling to

validate the extended TAM. The findings of their study showed that users’ perceived

usefulness of YouTube was significantly influenced by task-technology fit, vividness

and content richness, while behavioral intention to use was affected by user

satisfaction and perceived usefulness. Users’ perceived usefulness had a significant

effect on satisfaction whereas YouTube self-efficacy showed a moderate effect on

usefulness. However, perceived ease of use did not have a significant influence on

either perceived usefulness or intention to use of YouTube.

Mac Callum and Jeffrey (2013) investigated how ICT skills influenced

university students' adoption of mobile technology in the tertiary learning

environment. An extended TAM was applied as a theoretical framework of their study.

The hypothesized model included ICT self-efficacy (basic ICT skill, advanced ICT

37

skill & advanced mobile skill) into the TAM. A total of 446 students were collected

using stratified convenience sampling procedure from three tertiary institutions in

New Zealand. The questionnaire was distributed via electronic methods. The

hypothesized model was validated using structural equation modeling to test twelve

hypotheses and their causal relationships. The findings of their study confirmed that

students' behavioral intention to adopt mobile learning was influenced by three factors

which were: perceived ease of use, perceived usefulness and basic ICT skill. However,

perceived ease of use and basic ICT skill had a moderate influence on intention to

adopt. Besides this, basic ICT skill and advanced mobile skill were moderately

significant factors of mobile learning adoption.

In a recent study, Suki, Ramayah, Nee and Suki (2014) studied consumer

intention to use anti-spyware software. Their theoretical framework was developed

based on the innovation diffusion theory and the theory of planned behavior, which

consisted of relative advantage, moral compatibility, ease of use, subjective norms,

image, computing capacity, perceived cost, trialability and intention to use the anti-

spyware software. The theoretical framework was tested using data collected from the

survey questionnaire that engaged 200 students studying in a comprehensive public

university in Malaysia. Data were analyzed using structural equation modeling to

validate eight hypotheses of the model. The findings of their study revealed that

students’ perceived cost, image, moral compatibility and ease of use were significant

determinants of students’ intention to use anti-spyware software, while relative

advantage and subjective norms negatively influenced students’ intention to use anti-

spyware software. However, computing capacity and trialability did not have an

influence on consumer intention to use anti-spyware software in higher education.

38

Niu (2014) examined adolescents’ shopping in cyberspace. One of the

purposes of this study was to identify the influence extent of the perceived use of

technology applied by shopping websites on buyers’ buying behavior. In doing so, the

TAM was used as a moderating variable. She hypothesized that perceived ease of use

and perceived usefulness moderate the relationship between teenage consumers’

decision-making styles and purchasing behavior. The results of her study confirmed

the moderating influences of perceived usefulness and ease of use. However,

perceived ease of use and usefulness moderately influenced adolescents’ purchasing

behavior.

Lee, Hsiao and Purnomo (2014) extended the TAM by including additional

factors of internet self-efficacy, computer self-efficacy, instructor attitude toward

students, learning content and technology accessibility to investigate students’

acceptance of e-learning systems. Their proposed research model was validated using

Structural Equation Modeling to test altogether eleven hypotheses. The findings of

their model confirmed that students’ perceived usefulness and ease of use had

significant effect on intention to use e-learning systems, while perceived usefulness

and ease of use were positively influenced by learning content. On the other hand,

computer self-efficacy had a significant effect on perceived ease of use but it had a

negative insignificant effect on perceived usefulness. Subsequently, perceived ease of

use and usefulness were moderately influenced by internet self-efficacy. However,

instructor attitude towards students showed an insignificant impact on perceived

usefulness. Eventually, perceived ease of use was moderately influenced by

technology accessibility, while it did significantly influence perceived usefulness.

Their suggestions for researchers demonstrated that the individual characteristics of

computer self-efficacy and internet self-efficacy play crucial roles in influencing user

39

beliefs about ease of use.

In a related study, Pynoo, Tondeur, Braak, Duyck, Sijnave and Duyck (2012)

developed and validated their theoretical framework based on a combination of

Theory of Planned Behavior (TPB) and Technology Acceptance Model (TAM) to

examine teachers’ acceptance and use of an educational portal. Their theoretical

framework was consisted of perceived usefulness, perceived ease of use, subjective

norms, perceived behavioral control, attitude, behavioral intention and self-reported

frequency of use. A total of 919 teachers were collected for their study. The research

model was estimated using path analyses. The results of their study indicated that

perceived usefulness, perceived ease of use, subjective norms, perceived behavioral

control, attitude and behavioral intention to use influenced teachers’ acceptance of a

portal. Teachers’ perceived usefulness and attitude were the most significant

predictors of intention to use the portal. The findings also showed that rather than

uploading or sharing information, the portal was used to search for and download

information by the teachers. While, few teachers indicated to browse through the

portal for fun, or without a precise goal.

40

Tab

le 2

.1:

The

Sum

mar

y o

f T

AM

Lit

erat

ure

Mat

rix

Auth

ors

T

itle

s S

ample

C

onst

ruct

s M

easu

red

M

ajor

Fin

din

gs

Dav

is,

Bag

ozz

i an

d

War

shaw

(1989)

Use

r ac

cepta

nce

of

com

pute

r-

tech

nolo

gy:

A

com

par

ison o

f tw

o

theo

reti

cal

model

s.

107 M

BA

studen

ts

Ex

tern

al v

aria

ble

s,

Per

ceiv

ed u

sefu

lnes

s,

Per

ceiv

ed e

ase

of

use

,

Att

itude

tow

ard u

sing,

Beh

avio

ral

inte

nti

on t

o

use

, A

ctual

syst

em u

se

Per

ceiv

ed

use

fuln

ess

stro

ngly

in

fluen

ced

studen

ts’

inte

nti

ons,

ex

pla

inin

g m

ore

than

hal

f of

the

var

iance

in

inte

nti

ons.

P

erce

ived

ea

se

of

use

had

a

smal

l but

signif

ican

t ef

fect

on i

nte

nti

ons

as w

ell,

whil

e th

is e

ffec

t

subsi

ded

over

ti

me.

A

ttit

udes

only

m

od

erat

ely

med

iate

d

the

effe

cts

of

thes

e b

elie

fs

on

in

ten

tions.

Subje

ctiv

e norm

s had

no e

ffec

t on i

nte

nti

ons.

Lee

and P

ark

(2008)

Mobil

e te

chnolo

gy

usa

ge

and B

2B

mar

ket

per

form

ance

under

man

dat

ory

adopti

on

86 u

sefu

l

resp

onse

s

from

alco

holi

c

bev

erag

e

whole

sale

r

Per

ceiv

ed u

sefu

lnes

s,

Per

ceiv

ed e

ase

of

use

,

Per

ceiv

ed l

oss

of

contr

ol,

Use

r sa

tisf

acti

on,

Per

ceiv

ed m

arket

per

form

ance

Com

bin

ing

per

ceiv

ed

loss

of

contr

ol

wit

h

use

r

sati

sfac

tion a

nd t

he

TA

M i

n a

sin

gle

model

can

bet

ter

expla

in

the

Busi

nes

s-to

-Busi

nes

s (B

2B

) tr

ansa

ctio

n

mar

ket

per

form

ance

m

odel

. T

he

findin

gs

sugges

ted

that

per

ceiv

ed l

oss

of

contr

ol

has

a n

egat

ive

effe

ct o

n

use

r sa

tisf

acti

on a

nd p

erce

ived

mar

ket

per

form

ance

is

infl

uen

ced

by

use

r sa

tisf

acti

on

and

per

ceiv

ed

use

fuln

ess.

Lee

and

Leh

to (

2013)

Use

r ac

cepta

nce

of

YouT

ube

for

pro

cedura

l le

arnin

g:

An e

xte

nsi

on

of

the

Tec

hnolo

gy

Acc

epta

nce

Mod

el

432 Y

ouT

ub

use

rs

Per

ceiv

ed e

ase

of

use

,

task

-tec

hnolo

gy f

it,

conte

nt

rich

nes

s,

viv

idnes

s, a

nd Y

ouT

ube

spec

ific

sel

f-ef

fica

cy,

beh

avio

ral

inte

nti

on,

per

ceiv

ed u

sefu

lnes

s,

and u

ser

sati

sfac

tion

Beh

avio

ral

inte

nti

on

was

si

gnif

ican

tly

infl

uen

ced

by

both

per

ceiv

ed u

sefu

lnes

s an

d u

ser

sati

sfac

tion.

Tas

k-

tech

nolo

gy

fit,

co

nte

nt

rich

nes

s,

viv

idnes

s,

and

YouT

ube

self

-eff

icac

y

wer

e dis

cover

ed

to

be

signif

ican

t p

redic

tors

of

per

ceiv

ed

use

fuln

ess.

Nev

erth

eles

s,

per

ceiv

ed

ease

of

use

w

as

not

signif

ican

tly p

redic

tive

of

eith

er p

erce

ived

use

fuln

ess

or

beh

avio

ral

inte

nti

on.

41

Tab

le 2

.1, co

nti

nued

Auth

ors

T

itle

s S

ample

C

onst

ruct

s M

easu

red

M

ajor

Fin

din

gs

Han

afiz

adeh

,

Beh

boudi,

Kosh

ksa

ray

and T

abar

(2014)

Mobil

e-ban

kin

g

adopti

on

by

Iran

ian

ban

k c

lien

ts

361 b

ank

clie

nts

Per

ceiv

ed u

sefu

lnes

s,

per

ceiv

ed e

ase

of

use

,

nee

d f

or

inte

ract

ion,

per

ceiv

ed r

isk, per

ceiv

ed

cost

, co

mpat

ibil

ity

wit

h l

ife

style

, per

ceiv

ed

cred

ibil

ity, tr

ust

and

inte

nti

on t

o u

se m

obil

e

Per

ceiv

ed u

sefu

lnes

s, p

erce

ived

eas

e of

use

, nee

d f

or

inte

ract

ion,

per

ceiv

ed

risk

, per

ceiv

ed

cost

,

com

pat

ibil

ity w

ith l

ife

style

, p

erce

ived

cre

dib

ilit

y a

nd

trust

su

cces

sfull

y

expla

in

the

adopti

on

of

mobil

e

ban

kin

g

among

Iran

ian

cl

ients

. A

dap

tati

on

wit

h

life

style

and t

rust

wer

e re

vea

led t

o b

e th

e m

ost

sig

nif

ican

t

ante

ced

ents

ex

pla

inin

g t

he

adopti

on o

f m

obil

e ban

kin

g.

Gult

ekin

(2011)

Tec

hnolo

gy

Acc

epta

nce

and t

he

Eff

ect

of

Gen

der

in

the

Turk

ish N

atio

nal

Poli

ce:

The

Cas

e of

the

poln

et s

yst

em

407 p

oli

ce

off

icer

s

Per

ceiv

ed u

sefu

lnes

s,

per

ceiv

ed e

ase

of

use

,

subje

ctiv

e norm

and

inte

nti

on t

o u

se

The

findin

gs

dem

onst

rate

d

that

gen

der

has

no

signif

ican

t ef

fect

s on

beh

avio

ral

inte

nti

on

to

use

PO

LN

ET

. B

esid

es,

the

effe

cts

of

per

ceiv

ed u

sefu

lnes

s,

per

ceiv

ed e

ase

of

use

an

d s

ubje

ctiv

e norm

do

not

dif

fer

bet

wee

n m

ale

and f

emal

e poli

ce o

ffic

ers.

Al-

Ruz

and

Khas

awn

eh

(2011)

Jord

ania

n P

re-

Ser

vic

e T

each

ers'

and T

echnolo

gy

Inte

gra

tion:

A

Hum

an R

esourc

e

Dev

elopm

ent

Appro

ach

1,0

08

pre

-ser

vic

e

teac

her

s

Model

ing o

f te

chnolo

gy

in T

each

er e

du

cati

on

cours

es, te

chn

olo

gy s

elf-

effi

cacy, te

chnolo

gy

pro

fici

ency, use

fuln

ess

of

tech

nolo

gy, over

all

support

, te

chnolo

gy

inte

gra

tion a

nd

tech

nolo

gy

avai

labil

ity

The

resu

lts

indic

ated

that

the

model

ing o

f te

chnolo

gy

was

a h

ighly

infl

uen

tial

const

ruct

eff

ecti

ng p

re-s

ervic

e

teac

her

s’

tech

nolo

gy

self

-eff

icac

y,

tech

nolo

gy

pro

fici

ency,

and u

sefu

lnes

s of

tech

nolo

gy.

Tec

hn

olo

gy

self

-eff

icac

y w

as th

e m

ost

im

port

ant

fact

or

wit

h th

e

hig

hes

t dir

ect

effe

ct

on

tech

nolo

gy

inte

gra

tion.

More

over

, th

e su

pport

st

ruct

ure

w

as

the

most

infl

uen

tial

fa

cto

r w

ith

the

hig

hes

t dir

ect

effe

ct

on

tech

nolo

gy i

nte

gra

tion.

42

Tab

le 2

.1, co

nti

nued

Auth

ors

T

itle

s S

ample

C

onst

ruct

s M

easu

red

M

ajor

Fin

din

gs

Sto

ne

and

Bak

er-

Evel

eth

(2013)

Stu

den

ts’

inte

nti

ons

to p

urc

has

e

elec

tronic

tex

tbooks

1,3

82

studen

ts f

rom

a m

id-s

ized

univ

ersi

ty

Eas

e of

use

, at

titu

de

tow

ard e

-tex

tbooks,

beh

avio

ral

inte

nti

ons

to

purc

has

e e-

tex

tbooks,

outc

om

e

expec

tancy/u

sefu

lnes

s,

ver

bal

per

suas

ion/s

oci

al

norm

s, s

elf-

effi

cacy a

nd

pre

vio

us

com

pute

r

exper

ience

The

findin

gs

dem

onst

rate

d t

hat

both

eas

e of

e-te

xtb

ook

use

an

d

ver

bal

per

suas

ion/s

oci

al

norm

posi

tivel

y

infl

uen

ced

beh

avio

ral

inte

nti

ons

on

purc

has

ing

e-

tex

tbooks

thro

ugh

both

se

lf-e

ffic

acy

and

outc

om

e

expec

tancy/u

sefu

lnes

s. P

revio

us

com

pute

r ex

per

ience

posi

tivel

y i

nfl

uen

ces

beh

avio

ral

inte

nti

ons

to p

urc

has

e

an e

-tex

tbook o

nly

thro

ugh s

elf-

effi

cacy

Lee

, H

sieh

and H

su

(2011)

Addin

g I

nnov

atio

n

Dif

fusi

on T

heo

ry t

o

the

Tec

hnolo

gy

Acc

epta

nce

Mod

el:

Support

ing

Em

plo

yee

s'

Inte

nti

ons

to u

se E

-

Lea

rnin

g S

yst

ems

552 b

usi

nes

s

emplo

yee

s

Per

ceiv

ed u

sefu

lnes

s,

per

ceiv

ed e

ase

of

use

,

inte

nti

on t

o u

se,

com

pat

ibil

ity,

com

ple

xit

y, re

lati

ve

advan

tages

, obse

rvab

ilit

y

and t

rial

abil

ity

The

findin

gs

rev

eale

d th

at fi

ve

per

cepti

ons,

nam

ely,

com

pat

ibil

ity,

com

ple

xit

y,

rela

tive

adv

anta

ges

,

obse

rvab

ilit

y

and

tria

labil

ity

of

innovat

ion

char

acte

rist

ics

signif

ican

tly in

fluen

ced em

plo

yee

s’ e-

lear

nin

g s

yst

em b

ehav

iora

l in

tenti

on.

The

effe

cts

of

the

com

pat

ibil

ity,

com

ple

xit

y,

rela

tive

adv

anta

ge,

an

d

tria

labil

ity o

n t

he

per

ceiv

ed u

sefu

lnes

s ar

e si

gnif

ican

t.

More

over

, th

e co

mp

lex

ity,

rela

tive

advan

tage,

tria

labil

ity,

and co

mple

xit

y on th

e per

ceiv

ed ea

se of

use

hav

e a

signif

ican

t in

fluen

ce.

Chit

ungo

and

Munongo

(2013)

Ex

tendin

g t

he

Tec

hnolo

gy

Acc

epta

nce

Mod

el

to M

obil

e B

ankin

g

Adopti

on i

n R

ura

l

Zim

bab

we

275 r

ura

l

consu

mer

s

Per

ceiv

ed e

ase

of

use

,

per

ceiv

ed u

sefu

lnes

s,

rela

tive

adv

anta

ges

,

per

ceiv

ed r

isk, per

son

al

innovat

iven

ess,

soci

al

norm

s an

d p

erce

ived

cost

The

resu

lts

show

ed

that

per

ceiv

ed

use

fuln

ess,

per

ceiv

ed

ease

of

use

, re

lati

ve

advan

tages

, per

sonal

innovat

iven

ess

and

soci

al

norm

s hav

e si

gnif

ican

t

infl

uen

ce on use

rs’

atti

tude

thus

effe

ct th

e in

tenti

on

tow

ard

mobil

e ban

kin

g,

whil

st

per

ceiv

ed

risk

s an

d

cost

s det

erre

d t

he

adopti

on o

f th

e se

rvic

e.

43

Tab

le 2

.1, co

nti

nued

Auth

ors

T

itle

s S

ample

C

onst

ruct

s M

easu

red

M

ajor

Fin

din

gs

Ale

nez

i,

Kar

im a

nd

Vel

oo

(2010)

An E

mpir

ical

Inv

esti

gat

ion i

nto

the

Role

of

Enjo

ym

ent,

Com

pute

r A

nx

iety

,

Com

pute

r S

elf-

Eff

icac

y a

nd I

nte

rnet

Ex

per

ience

in

Infl

uen

cin

g t

he

Stu

den

ts' I

nte

nti

on t

o

Use

E-L

earn

ing:

A

Cas

e S

tud

y f

rom

Sau

di

Ara

bia

n G

ov

ernm

enta

l

Univ

ersi

ties

402

gover

nm

enta

l

univ

ersi

ties

'

studen

ts

Enjo

ym

ent,

com

pute

r an

xie

ty,

per

ceiv

ed u

sefu

lnes

s,

per

ceiv

ed e

ase

of

use

, co

mpute

r se

lf

effi

cacy, in

tern

et

exper

ience

, at

titu

de

tow

ard u

sing E

-

lear

nin

g a

nd

beh

avio

ral

inte

nti

on

to u

se E

-lea

rnin

g

The

findin

gs

show

ed t

hat

com

pute

r an

xie

ty,

com

pute

r

self

-eff

icac

y

and

Enjo

ym

ent

signif

ican

tly

infl

uen

ced

the

studen

ts'

inte

nti

on

to

use

E

-lea

rnin

g,

whil

e th

e

Inte

rnet

ex

per

ience

was

of

insi

gnif

ican

tly i

nfl

uen

ce.

In

addit

ion,

the

signif

ican

ce o

f A

ttit

ude

in m

edia

ting t

he

rela

tionsh

ip bet

wee

n per

ceiv

ed use

fuln

ess,

p

erce

ived

ease

of

use

and t

he

stud

ents

' b

ehav

iora

l in

tenti

on w

as

confi

rmed

.

Dav

is (

1993)

Use

r ac

cepta

nce

of

info

rmat

ion

tech

nolo

gy:

syst

em

char

acte

rist

ics,

use

r

per

cepti

ons

and

beh

avio

ral

impac

ts

122

pro

fess

ional

and m

anag

eria

l

emplo

yee

s

Syst

em d

esig

n

feat

ure

s, p

erce

ived

use

fuln

ess,

per

ceiv

ed

ease

of

use

, at

titu

de

tow

ard u

sing a

nd

actu

al s

yst

em u

se

The

resu

lts

show

ed t

hat

TA

M c

om

ple

tely

med

iate

d t

he

effe

cts

of

syst

em

char

acte

rist

ics

on

usa

ge

beh

avio

r.

Poss

ibly

the

most

str

ikin

g f

indin

g w

as t

hat

per

ceiv

ed

use

fuln

ess

was

50

% m

ore

sig

nif

ican

t th

an e

ase

of

use

in d

eter

min

ing u

sage.

Tez

ci (

2011)

Fac

tors

th

at i

nfl

uen

ce

pre

-ser

vic

e te

ach

ers’

ICT

usa

ge

in

educa

tion

1898

pre

-ser

vic

e

teac

her

s

Com

pute

r at

titu

de,

inte

rnet

att

itude,

sel

f-

confi

den

ce, sc

hool

clim

ate/

support

, IC

T

usa

ge,

IC

T

know

ledge

The

findin

gs

show

ed

that

m

ost

pre

-ser

vic

e te

acher

s

report

ed t

hat

they u

se o

nly

bas

ic I

CT

appli

cati

ons

for

educa

tional

purp

ose

s.

Inte

rnal

an

d

exte

rnal

fa

ctors

wer

e fo

und t

o b

e re

late

d t

o e

ach o

ther

and t

o I

CT

usa

ge

level

s.

More

over

, m

ale

pre

-ser

vic

e te

acher

s’

know

ledge

and

usa

ge

level

s of

ICT

wer

e hig

her

than

fem

ale

teac

her

s.

44

Tab

le 2

.1, co

nti

nued

Auth

ors

T

itle

s S

ample

C

onst

ruct

s M

easu

red

M

ajor

Fin

din

gs

Shin

(2012)

3D

TV

as

a so

cial

pla

tform

for

com

munic

atio

n a

nd

inte

ract

ion

229 u

sers

of

3D

TV

Soci

al p

rese

nce

,

per

ceiv

ed e

njo

ym

ent,

per

ceiv

ed q

ual

ity,

flow

, per

ceiv

ed

use

fuln

ess,

att

itude,

inte

nti

on, usa

ge

The

resu

lts

indic

ated

that

the

signif

ican

t ro

les

for

soci

al

pre

sence

and f

low

, both

of

whic

h i

nfl

uen

ced a

ttit

ude

as

wel

l as

per

ceiv

ed u

sefu

lnes

s an

d p

erce

ived

enjo

ym

ent.

This

se

t of

fact

ors

is

k

ey

to

use

rs’

expec

tati

ons

of

3D

TV

.

Mar

tínez

-

Torr

es,

Mar

ín,

Gar

cía,

Váz

quez

,

Oli

va

and

Torr

es

(2008)

A t

echnolo

gic

al

acce

pta

nce

of

e-

lear

nin

g t

ools

use

d i

n

pra

ctic

al a

nd

labora

tory

tea

chin

g,

acco

rdin

g t

o t

he

Euro

pea

n h

igher

educa

tion a

rea

220 s

tuden

ts

Use

, In

tenti

on o

f use

,

Per

ceiv

ed u

sefu

lnes

s,

Eas

e of

use

,

Met

hodolo

gy,

Acc

essi

bil

ity,

Rel

iabil

ity,

Enjo

ym

ent,

Use

r

adap

tati

on,

Com

munic

ativ

enes

s,

Fee

db

ack,

Form

at,

Inte

ract

ivit

y a

nd

contr

ol,

Dif

fusi

on

and U

ser

tools

The

findin

gs

stro

ngly

su

pport

th

e ex

tended

T

AM

in

pre

dic

ting a

stu

den

t’s

inte

nti

on t

o u

se e

-lea

rnin

g a

nd

def

ine

a se

t of

exte

rnal

var

iable

s w

ith

a si

gnif

ican

t

infl

uen

ce

in

the

ori

gin

al

TA

M

var

iable

s.

Ho

wev

er,

per

ceiv

ed e

ase

of

use

did

not

posi

t a

signif

ican

t im

pac

t

on s

tuden

t at

titu

de

or

inte

nti

on t

ow

ards

e-le

arnin

g t

ool

usa

ge.

Ahm

ad,

Bas

ha,

Mar

zuki,

His

ham

and

Sah

ari

(2010)

Fac

ult

y’s

acc

epta

nce

of

com

pute

r-b

ased

tech

nolo

gy:

cross

-

val

idat

ion o

f an

exte

nded

model

731 f

acult

y

mem

ber

s

Sel

f-ef

fica

cy,

per

ceiv

ed u

sefu

lnes

s,

inte

nti

on, use

The

resu

lts

support

ed th

e ad

equac

y o

f th

e ex

tended

tech

nolo

gy a

ccep

tance

model

(T

AM

E).

Alt

hough t

he

TA

ME

’s c

ausa

l st

ruct

ure

was

val

id t

o b

oth

mal

e an

d

fem

ale

staf

f, ag

e gro

up

s sh

ow

ed m

oder

ate

stru

ctura

l

rela

tionsh

ips

among t

he

const

ruct

s of

inte

rest

.

45

Tab

le 2

.1, co

nti

nued

Auth

ors

T

itle

s S

ample

C

onst

ruct

s M

easu

red

M

ajor

Fin

din

gs

Shro

ff,

Den

een a

nd

Ng (

2011)

Anal

ysi

s of

the

tech

nolo

gy a

ccep

tance

model

in e

xam

inin

g

studen

ts’

beh

avio

ura

l

inte

nti

on t

o u

se a

n e

-

port

foli

o s

yst

em

72 s

tuden

ts

Per

ceiv

ed u

sefu

lnes

s,

per

ceiv

ed e

ase

of

use

, at

titu

des

tow

ards

usa

ge

and

beh

avio

ura

l in

tenti

on

to u

se

The

resu

lts

confi

rmed

that

stu

den

ts’

per

ceiv

ed e

ase

of

use

had

a

signif

ican

t in

fluen

ce

on

atti

tude

tow

ards

usa

ge.

C

onse

qu

entl

y,

per

ceiv

ed ea

se o

f use

had

th

e

stro

nges

t si

gnif

ican

t in

flu

ence

on p

erce

ived

use

fuln

ess.

Ship

ps

and

Phil

lips

(2013)

Soci

al N

etw

ork

s,

Inte

ract

ivit

y a

nd

Sat

isfa

ctio

n:

Ass

essi

ng

Soci

o-T

echnic

al

Beh

avio

ral

Fac

tors

as

an E

xte

nsi

on t

o

Tec

hnolo

gy

Acc

epta

nce

164 s

tuden

ts

Per

ceiv

ed u

sefu

lnes

s,

per

ceiv

ed e

ase

of

use

, at

titu

des

,

sati

sfac

tion,

conce

ntr

atio

n f

ocu

s,

per

sonal

involv

emen

t,

per

ceiv

ed

inte

ract

ivit

y

com

munic

atio

n a

nd

per

ceiv

ed

inte

ract

ivit

y c

ontr

ol

The

resu

lts

show

ed

that

per

ceiv

ed

inte

ract

ivit

y

and

level

of

focu

s/co

nce

ntr

atio

n do af

fect

an

en

d

use

r’s

sati

sfac

tion

wit

h

a so

cial

net

work

al

on

g

wit

h

ante

ced

ents

fr

om

th

e te

chnolo

gy

acce

pta

nce

m

odel

.

Thes

e fi

ndin

gs

sugges

ted

that

both

T

AM

re

late

d

fact

ors

and m

arket

ing r

elat

ed f

acto

rs b

oth

im

pac

t th

e

use

r ex

per

ience

on a

soci

al n

etw

ork

ing s

ite.

Chan

g, Y

an

and T

sen

g

(2012)

Per

ceiv

ed c

onven

ien

ce

in a

n e

xte

nded

tech

nolo

gy a

ccep

tance

model

: M

obil

e

tech

nolo

gy a

nd

Engli

sh l

earn

ing f

or

coll

ege

studen

ts

158

coll

ege

studen

ts

Per

ceiv

ed

conven

ience

,

per

ceiv

ed e

ase

of

use

, per

ceiv

ed

use

fuln

ess,

att

itude

and c

onti

nuan

ce

inte

nti

on t

o u

se

The

findin

gs

show

ed

that

per

ceiv

ed

conven

ience

,

per

ceiv

ed e

ase

of

use

and p

erce

ived

use

fuln

ess

wer

e

ante

ced

ent

fact

ors

th

at

affe

cted

th

e ac

cepta

nce

o

f

Engli

sh

mobil

e le

arnin

g.

Per

ceiv

ed

conven

ience

,

per

ceiv

ed e

ase

of

use

and p

erce

ived

use

fuln

ess

had

a

signif

ican

t posi

tive

effe

ct o

n t

hei

r at

titu

de.

Per

ceiv

ed

use

fuln

ess

and

att

itude

tow

ards

use

had

a s

ignif

ican

t

posi

tive

effe

ct o

n c

onti

nuan

ce o

f in

tenti

on t

o u

se.

46

Tab

le 2

.1, co

nti

nued

Auth

ors

T

itle

s S

ample

C

onst

ruct

s M

easu

red

M

ajor

Fin

din

gs

Ter

zis,

Mori

dis

,

Eco

nom

ides

and

Reb

oll

edo-

Men

dez

(2013)

Com

pute

r B

ased

Ass

essm

ent

Acc

epta

nce

: A

Cro

ss-

cult

ura

l S

tud

y i

n

Gre

ece

and M

exic

o

168 s

tuden

ts

from

Gre

ece

and M

exic

o

Per

ceiv

ed

pla

yfu

lnes

s,

Per

ceiv

ed u

sefu

lnes

s,

Per

ceiv

ed e

ase

of

use

, C

om

pute

r se

lf

effi

cacy, G

oal

expec

tancy, C

onte

nt,

faci

lita

ting

condit

ions,

beh

avio

ral

inte

nti

on

and s

oci

al i

nfl

uen

ce

The

resu

lts

indic

ated

th

at

the

Com

pute

r B

ased

Ass

essm

ent

Acc

epta

nce

M

odel

(C

BA

AM

) w

as

val

id

for

both

countr

ies.

How

ever

, th

ere

are

som

e cu

ltura

l

dif

fere

nce

s.

Gre

ek

stud

ents

’ beh

avio

ral

inte

nti

on

is

trig

ger

ed

mai

nly

b

y

Per

ceiv

ed

Pla

yfu

lnes

s an

d

Per

ceiv

ed

Eas

e o

f U

se,

whil

e M

exic

an

stu

den

ts’

beh

avio

ral

inte

nti

on i

s ca

use

d b

y P

erce

ived

Pla

yfu

lnes

s

and P

erce

ived

Use

fuln

ess.

Chan

g a

nd

Tung (

2008

)

An e

mpir

ical

inves

tigat

ion o

f

studen

ts’

beh

avio

ura

l

inte

nti

ons

to u

se t

he

onli

ne

lear

nin

g c

ours

e

web

site

s

212

under

gra

du

ate

studen

ts

Com

pute

r se

lf-

effi

cacy, P

erce

ived

syst

em q

ual

ity,

Per

ceiv

ed e

ase

of

use

, P

erce

ived

use

fuln

ess,

Com

pat

ibil

ity a

nd

inte

nti

on t

o u

se

The

findin

gs

show

ed

that

co

mpat

ibil

ity,

per

ceiv

ed

use

fuln

ess,

per

ceiv

ed

ease

of

use

, p

erce

ived

sy

stem

qual

ity

and

com

pute

r se

lf-e

ffic

acy

wer

e im

port

ant

fact

ors

for

studen

ts’

beh

avio

ura

l in

tenti

ons

to u

se t

he

onli

ne

lear

nin

g c

ours

e w

ebsi

tes.

Yuen

a an

d

Ma

(2008)

Ex

plo

ring t

each

er

acce

pta

nce

of

e-

lear

nin

g t

echnolo

gy

152 i

n-s

ervic

e

teac

her

s

Subje

ctiv

e norm

,

com

pute

r se

lf-

effi

cacy, per

ceiv

ed

use

fuln

ess,

per

ceiv

ed

ease

of

use

, in

tenti

on

The

resu

lts

found t

hat

subje

ctiv

e norm

and c

om

pute

r

self

-eff

icac

y

serv

e as

th

e tw

o

signif

ican

t per

cepti

on

anch

ors

of

the

fundam

enta

l co

nst

ruct

s in

T

AM

.

How

ever

, co

ntr

ary

to

pre

vio

us

lite

ratu

re,

per

ceiv

ed

ease

o

f use

bec

ame

the

sole

d

eter

min

ant

for

the

pre

dic

tion

of

inte

nti

on

to

use

, w

hil

e per

ceiv

ed

use

fuln

ess

was

not

signif

ican

t in

th

e pre

dic

tion

of

inte

nti

on t

o u

se.

47

Tab

le 2

.1, co

nti

nued

Auth

ors

T

itle

s S

ample

C

onst

ruct

s M

easu

red

M

ajor

Fin

din

gs

Guo a

nd

Ste

ven

s

(2011)

Fac

tors

in

fluen

cin

g

per

ceiv

ed u

sefu

lnes

s

of

wik

is f

or

gro

up

coll

abora

tive

lear

nin

g

by f

irst

yea

r st

uden

ts

205 s

tuden

ts

Att

itude

tow

ards

wik

is, W

iki

use

fuln

ess,

Wik

i use

,

studen

t’s

pri

or

exper

tise

, ea

se o

f

acce

ss,

The

findin

gs

dem

onst

rate

d t

hat

wik

i use

infl

uen

ced b

y

the

studen

t’s

pri

or

exper

tise

. T

hei

r per

ceiv

ed

use

fuln

ess

of

wik

is w

ere

stro

ngly

infl

uen

ced

by t

hei

r

teac

her

s’ a

ttit

udes

tow

ards

the

tech

nolo

gy a

s w

ell

as

the

ease

of

acce

ss t

o t

he

wik

is.

Att

itude

tow

ards

wik

is

was

lar

gel

y i

nfl

uen

ced b

y t

he

exte

nt

to w

hic

h s

tuden

ts

saw

wik

is a

s a

sourc

e of

hel

p w

ith t

hei

r as

signm

ent

work

, an

d t

hei

r in

tenti

on t

o u

se w

ikis

in t

he

futu

re w

as

dri

ven

by t

hei

r per

cepti

on o

f w

iki’

s use

fuln

ess.

Am

in (

2007)

In

tern

et B

ankin

g

Adopti

on a

mong

Young I

nte

llec

tual

s

250 s

tuden

ts

Per

ceiv

ed u

sefu

lnes

s,

per

ceiv

ed e

ase

of

use

, per

ceiv

ed

cred

ibil

ity a

nd

com

pute

r se

lf-

effi

cacy a

nd

beh

avio

ral

inte

nti

on

The

resu

lts

show

ed t

hat

per

ceiv

ed u

sefu

lnes

s, e

ase

of

use

an

d

per

ceiv

ed

cred

ibil

ity

had

a

signif

ican

t

rela

tionsh

ip

wit

h

beh

avio

ral

inte

nti

on.

More

over

,

per

ceiv

ed u

sefu

lnes

s an

d e

ase

of

use

had

a s

ignif

ican

t

rela

tionsh

ip

wit

h

com

pute

r se

lf-e

ffic

acy.

How

ever

,

com

pute

r se

lf-e

ffic

acy d

id n

ot

asso

ciat

e w

ith p

erce

ived

cred

ibil

ity.

Ev

entu

ally

, per

ceiv

ed

ease

of

use

had

a

signif

ican

t re

lati

onsh

ip w

ith p

erce

ived

use

fuln

ess

and

cred

ibil

ity w

hic

h s

how

s th

at t

hes

e sc

ales

are

rel

ated

to

per

ceiv

ed

ease

of

use

in

ex

pla

inin

g

under

gra

duat

e

pre

fere

nce

.

Zej

no a

nd

Isla

m (

20

12)

Dev

elopm

ent

and

val

idat

ion o

f li

bra

ry

ICT

usa

ge

scal

e fo

r th

e

IIU

M p

ost

gra

du

ate

studen

ts

285

Post

gra

duat

e

studen

ts

Per

ceiv

ed u

sefu

lnes

s,

per

ceiv

ed e

ase

of

use

, co

mpute

r se

lf-

effi

cacy a

nd u

se

The

findin

gs

of

the

stud

y s

how

ed t

hat

per

ceiv

ed e

ase

of

use

, per

ceiv

ed u

sefu

lnes

s an

d c

om

pute

r se

lf-e

ffic

acy

are

signif

ican

t p

redic

tors

of

post

gra

duat

e st

uden

ts’

ICT

usa

ge.

48

Tab

le 2

.1, co

nti

nued

Auth

ors

T

itle

s S

ample

C

onst

ruct

s M

easu

red

M

ajor

Fin

din

gs

Usl

uel

,

Aşk

ar a

nd

Baş

(2008

)

A S

truct

ura

l E

quat

ion

Model

for

ICT

Usa

ge

in H

igher

Educa

tion

814 f

acult

y

mem

ber

s

ICT

fac

ilit

ies

(cla

ssro

om

, la

b a

nd

off

ice)

, p

erce

ived

attr

ibute

s (r

elat

ive

advan

tages

,

com

pat

ibil

ity,

ease

of

use

, obse

rvab

ilit

y,

trai

abil

ity),

IC

T

usa

ge

(inst

ruct

ional

and m

anag

eria

l

purp

ose

s)

The

resu

lts

rev

eale

d th

at th

e p

erce

ived

at

trib

ute

s o

f

ICT

and I

CT

fac

ilit

ies

in t

he

univ

ersi

ties

pre

dic

t th

e

ICT

use

. T

he

facu

lty m

ember

s use

IC

T m

ost

ly as

a

mea

ns

of

sear

chin

g f

or

info

rmat

ion a

bout

the

cours

e

thro

ugh t

he

Inte

rnet

, an

d a

s a

mea

ns

of

com

munic

atio

n

such

as

: publi

shin

g

thei

r le

cture

note

s,

annou

nci

ng

cours

e as

signm

ents

, an

d p

roje

cts-

on W

WW

.

Dev

araj

, F

an

and K

ohli

(2002)

Ante

ced

ents

of

B2C

Chan

nel

Sat

isfa

ctio

n

and P

refe

rence

:

Val

idat

ing e

-

Com

mer

ce M

etri

cs

134 c

onsu

mer

s

Ass

et s

pec

ific

ity,

unce

rtai

nty

,

use

fuln

ess,

eas

e of

use

, ti

me,

pri

ce

savin

gs,

em

pat

hy,

reli

abil

ity,

resp

onsi

ven

ess,

assu

rance

,

sati

sfac

tion w

ith

elec

tronic

com

mer

ce

chan

nel

, ch

annel

pre

fere

nce

The

findin

gs

show

ed th

at per

ceiv

ed ea

se of

use

an

d

use

fuln

ess

are

import

ant

in f

orm

ing c

onsu

mer

att

itudes

and

sati

sfac

tion

wit

h

the

elec

tronic

co

mm

erce

(E

C)

chan

nel

. E

ase

of

use

was

als

o f

ound

to b

e a

signif

ican

t

det

erm

inan

t of

sati

sfac

tion.

The

stud

y a

lso d

isco

ver

ed

empir

ical

su

pport

fo

r th

e as

sura

nce

dim

ensi

on

of

SE

RV

QU

AL

as

a

det

erm

inan

t in

E

C

chan

nel

sati

sfac

tion.

More

over

, th

e re

sult

s al

so

indic

ated

gen

eral

su

pport

fo

r co

nsu

mer

sa

tisf

acti

on

as

a

det

erm

inan

t of

chan

nel

pre

fere

nce

.

49

Tab

le 2

.1, co

nti

nued

Auth

ors

T

itle

s S

ample

C

onst

ruct

s M

easu

red

M

ajor

Fin

din

gs

Wan

g,

Lin

and L

uar

n

(2006)

Pre

dic

ting c

onsu

mer

inte

nti

on t

o u

se m

obil

e

serv

ice

258 c

onsu

mer

s

Sel

f-ef

fica

cy,

Per

ceiv

ed f

inan

cial

reso

urc

e, p

erce

ived

use

fuln

ess,

eas

e of

use

, per

ceiv

ed

cred

ibil

ity a

nd

inte

nti

on

The

resu

lts

exhib

ited

th

at

per

ceiv

ed

use

fuln

ess,

per

ceiv

ed

ease

of

use

, per

ceiv

ed

cred

ibil

ity,

self

-

effi

cacy

and

per

ceiv

ed

finan

cial

re

sourc

es

all

had

a

signif

ican

t in

fluen

ce

on

beh

avio

ura

l in

tenti

on.

Furt

her

mo

re,

self

-eff

icac

y

was

fo

und

to

hav

e a

signif

ican

t ef

fect

on p

erce

ived

eas

e of

use

, w

hic

h,

in

turn

, had

a p

osi

tive

effe

ct o

n b

oth

per

ceiv

ed u

sefu

lnes

s

and

per

ceiv

ed

cred

ibil

ity.

Fin

ally

, both

per

ceiv

ed

finan

cial

re

sourc

e an

d

per

ceiv

ed

cred

ibil

ity

had

a

signif

ican

t ef

fect

on p

erce

ived

use

fuln

ess.

Isla

m

(2011b)

Via

bil

ity o

f th

e

exte

nded

tec

hnolo

gy

acce

pta

nce

model

: an

empir

ical

stu

dy

285 s

tuden

ts

Per

ceiv

ed e

ase

of

use

, per

ceiv

ed

use

fuln

ess,

com

pute

r

self

-eff

icac

y a

nd

Sat

isfa

ctio

n

The

resu

lts

indic

ated

th

at s

tuden

ts’

per

ceiv

ed e

ase

of

use

had

a

stat

isti

call

y

signif

ican

t ef

fect

on

th

eir

sati

sfac

tion.

The

stud

y a

lso d

isco

ver

ed t

hat

gen

der

did

not

exer

t an

y

infl

uen

ce

as

a m

oder

atin

g

var

iable

tow

ards

studen

ts’

sati

sfac

tion

in

usi

ng

Wir

eles

s

Inte

rnet

.

Bar

khi,

Bel

anger

and

Hic

ks

(2008)

A m

odel

of

the

det

erm

inan

ts o

f

purc

has

ing F

rom

vir

tual

sto

res

277 s

tuden

ts

Act

ual

purc

has

e,

Per

ceiv

ed s

ecuri

ty,

Att

itude

tow

ard

onli

ne

shoppin

g,

Per

ceiv

ed u

sefu

lnes

s,

Per

ceiv

ed p

eer

infl

uen

ce a

nd

per

ceiv

ed b

ehav

iora

l

contr

ol

The

findin

gs

show

ed

that

per

ceiv

ed

use

fuln

ess,

per

ceiv

ed

beh

avio

ral

contr

ol,

an

d

per

ceiv

ed

pee

r

infl

uen

ce at

titu

de

tow

ards

purc

has

ing fr

om

a

vir

tual

store

. O

n

the

oth

er

han

d,

atti

tude

infl

uen

ced

actu

al

purc

has

ing f

rom

a v

irtu

al s

tore

.

50

Tab

le 2

.1, co

nti

nued

Auth

ors

T

itle

s S

ample

C

onst

ruct

s M

easu

red

M

ajor

Fin

din

gs

Lal

lmah

amo

od (

2007)

An e

xam

inat

ion o

f

indiv

idual

’s p

erce

ived

secu

rity

and p

rivac

y o

f

the

inte

rnet

in

Mal

aysi

a an

d t

he

infl

uen

ce o

f th

is o

n

thei

r in

tenti

on t

o u

se e

-

com

mer

ce:

Usi

ng a

n

exte

nsi

on o

f th

e

tech

nolo

gy a

ccep

tance

model

187 s

tuden

ts

Per

ceiv

ed u

sefu

lnes

s,

per

ceiv

ed e

ase

of

use

, per

ceiv

ed

secu

rity

& p

rivac

y

and i

nte

nti

on t

o u

se

inte

rnet

ban

kin

g

The

resu

lts

dem

onst

rate

d t

hat

per

ceiv

ed s

ecu

rity

an

d

pri

vac

y

had

a

signif

ican

t im

pac

t on

per

ceiv

ed

use

fuln

ess,

per

ceiv

ed e

ase

of

use

and i

nte

nti

on t

o u

se

inte

rnet

ban

kin

g,

whil

e per

ceiv

ed

ease

o

f use

infl

uen

ces

on

per

ceiv

ed

use

fuln

ess

and

inte

nti

on

to

use

. M

ore

ov

er,

per

ceiv

ed

use

fuln

ess

show

ed

a

signif

ican

t im

pac

t on

inte

nti

on

to

use

, w

hic

h is

th

e

most

im

port

ant

pre

dic

tor

among o

ther

s.

Ras

imah

,

Ahm

ad a

nd

Zam

an

(2011)

Eval

uat

ion o

f use

r

acce

pta

nce

of

mix

ed

real

ity t

echnolo

gy

63 s

tuden

ts

Per

sonal

innovat

iven

ess,

per

ceiv

ed e

njo

ym

ent,

per

ceiv

ed e

ase

of

use

, per

ceiv

ed

use

fuln

ess

and

inte

nti

on t

o u

se

Res

ult

s in

dic

ated

posi

tive

corr

elat

ions

bet

wee

n

per

sonal

in

novat

iven

ess,

per

ceiv

ed

enjo

ym

ent,

per

ceiv

ed

ease

of

use

, per

ceiv

ed

use

fuln

ess

and

inte

nti

on t

o a

ccep

t m

ixed

rea

lity

tec

hnolo

gy.

Ho

wev

er,

findin

gs

from

re

gre

ssio

n

anal

ysi

s su

gges

ted

th

at

per

ceiv

ed u

sefu

lnes

s w

as t

he

most

im

port

ant

pre

dic

tor

in d

eter

min

ing u

sers

’ in

tenti

on t

o u

se t

his

tec

hn

olo

gy

in t

he

futu

re.

Kum

ar a

nd

Rav

indra

n

(2012)

An e

mpir

ical

stu

dy o

n

serv

ice

qu

alit

y

per

cepti

ons

and

conti

nuan

ce i

nte

nti

on

in m

obil

e ban

kin

g

conte

xt

in I

ndia

184 c

ust

om

ers

Per

ceiv

ed s

ervic

e

qual

ity, per

ceiv

ed

use

fuln

ess,

eas

e of

use

, per

ceiv

ed

cred

ibil

ity,

sati

sfac

tion,

per

ceiv

ed r

isk a

nd

inte

nti

on

The

resu

lts

dis

cover

ed th

at per

ceiv

ed se

rvic

e q

ual

ity

had

a s

ignif

ican

t ef

fect

on s

atis

fact

ion.

Per

ceiv

ed r

isk

had

an

ad

ver

se

impac

t on

serv

ice

qual

ity

and

sati

sfac

tion.

Inte

nti

on t

o u

se o

f m

-ban

kin

g w

as f

ound

to

be

sole

ly

dep

end

ent

on

sati

sfac

tion.

How

ever

,

per

ceiv

ed e

ase

of

use

an

d p

erce

ived

ris

k d

id n

ot

exer

t

any s

ignif

ican

t in

fluen

ce o

n s

atis

fact

ion.

51

Tab

le 2

.1, co

nti

nued

Auth

ors

T

itle

s S

ample

C

onst

ruct

s M

easu

red

M

ajor

Fin

din

gs

Gib

son,

Har

ris

and

Cola

ric

(2008)

Tec

hnolo

gy

Acc

epta

nce

in a

n

Aca

dem

ic C

onte

xt:

Fac

ult

y A

ccep

tance

of

Onli

ne

Educa

tion

110 f

acult

y

mem

ber

s

Per

ceiv

ed e

ase

of

use

and p

erce

ived

use

fuln

ess

The

resu

lts

show

ed t

hat

TA

M w

as a

ppli

cable

to f

ost

er

a re

ason

able

ex

pla

nat

ion o

f th

e in

tenti

ons

of

ph

ysi

cian

s

in u

sing t

elem

edic

ine

tech

nolo

gy.

Per

ceiv

ed u

sefu

lnes

s

was

found t

o h

ave

a si

gn

ific

ant

and s

tron

g i

nfl

uen

ce o

n

the

ph

ysi

cian

s’

inte

nti

on

to

use

te

lem

edic

ine

tech

nolo

gy;

how

ever

, per

ceiv

ed e

ase

of

use

pro

vid

es

litt

le

addit

ional

pre

dic

tive

pow

er

beyond

th

at

contr

ibute

d

by

per

ceiv

ed

use

fuln

ess

of

onli

ne

educa

tion t

echnolo

gy.

Shit

tu,

Bas

ha,

Rah

man

and

Ahm

ad

(2013)

Det

erm

inan

ts o

f so

cial

net

work

ing s

oft

war

e

acce

pta

nce

: A

mult

i-

theo

reti

cal

app

roac

h

500

under

gra

du

ate

studen

ts

Per

ceiv

ed u

sefu

lnes

s,

soci

al i

den

tity

,

com

pat

ibil

ity,

gro

up

norm

, obse

rvab

ilit

y

and a

ccep

tan

ce t

o

use

soci

al

net

work

ing

The

resu

lts

indic

ated

th

at co

mpat

ibil

ity,

gro

up norm

and

obse

rvab

ilit

y

po

siti

vel

y

infl

uen

ced

soci

al

net

work

ing a

ccep

tan

ce.

How

ever

, per

ceiv

ed u

sefu

lnes

s

and s

oci

al i

den

tity

did

not

hav

e a

signif

ican

t ef

fect

on

the

acce

pta

nce

in u

sing s

oci

al n

etw

ork

ing.

Alj

uai

d,

Alz

ahra

ni

and I

slam

(2014)

Ass

essi

ng m

obil

e

lear

nin

g r

eadin

ess

in

Sau

di

Ara

bia

hig

her

educa

tion:

An

empir

ical

stu

dy.

140 l

ectu

rers

P

erce

ived

use

fuln

ess,

per

ceiv

ed e

ase

of

use

and r

eadin

ess

of

mobil

e le

arnin

g

The

resu

lts

show

ed

that

per

ceiv

ed

use

fuln

ess

and

per

ceiv

ed e

ase

of

use

wer

e si

gnif

ican

t v

alid

pre

dic

tors

of

lect

ure

rs’

read

ines

s of

mobil

e le

arnin

g

in

Sau

di

Ara

bia

n h

igh

er e

duca

tion

. M

ore

over

, per

ceiv

ed e

ase

of

use

an

d

use

fuln

ess

had

a

signif

ican

t im

pac

t on

read

ines

s of

mobil

e le

arn

ing.

52

Tab

le 2

.1, co

nti

nued

Auth

ors

T

itle

s S

ample

C

onst

ruct

s M

easu

red

M

ajor

Fin

din

gs

Isla

m (

20

14)

V

alid

atio

n o

f th

e

tech

nolo

gy s

atis

fact

ion

model

(T

SM

)

dev

eloped

in H

igher

Educa

tion:

The

Appli

cati

on o

f

Str

uct

ura

l E

quat

ion

Model

ing

285 s

tuden

ts

Com

pute

r se

lf-

effi

cacy, per

ceiv

ed

use

fuln

ess,

per

ceiv

ed

ease

of

use

and

sati

sfac

tion

The

resu

lts

dis

cover

ed t

hat

per

ceiv

ed e

ase

of

use

and

per

ceiv

ed

use

fuln

ess

had

a

stat

isti

call

y

signif

ican

t

posi

tive

dir

ect

infl

uen

ce

on

sa

tisf

acti

on.

Bes

ides

,

com

pute

r se

lf-e

ffic

acy

show

ed

a si

gnif

ican

t posi

tive

dir

ect

infl

uen

ce o

n p

erce

ived

use

fuln

ess

and

per

ceiv

ed

ease

of

use

. In

addit

ion,

the

findin

gs

also

indic

ated

that

com

pute

r se

lf-e

ffic

acy

had

a

signif

ican

t in

dir

ect

infl

uen

ce

on

sa

tisf

acti

on

med

iate

d

by

per

ceiv

ed

use

fuln

ess.

F

inal

ly,

com

pute

r se

lf-e

ffic

acy

also

exhib

ited

a s

tati

stic

ally

sig

nif

ican

t in

dir

ect

infl

uen

ce o

n

sati

sfac

tion m

edia

ted b

y t

he

per

ceiv

ed e

ase

of

use

of

wir

eles

s in

tern

et.

Wong, T

eo

and R

uss

o

(2012)

Infl

uen

ce o

f gen

der

and c

om

pute

r te

achin

g

effi

cacy o

n c

om

pute

r

acce

pta

nce

am

on

g

Mal

aysi

an s

tuden

t

teac

her

s: A

n e

xte

nded

tech

nolo

gy a

ccep

tance

model

302 s

tuden

t-

teac

her

s

Com

pute

r te

achin

g

effi

cacy, per

ceiv

ed

use

fuln

ess,

per

ceiv

ed

ease

of

use

, at

titu

de

tow

ard c

om

pute

r use

,

and b

ehav

ioura

l

inte

nti

on

Com

pute

r te

achin

g e

ffic

acy s

how

ed s

ignif

ican

t ef

fect

on

per

ceiv

ed

use

fuln

ess,

per

ceiv

ed

ease

o

f use

an

d

atti

tude.

On t

he

oth

er h

and,

per

ceiv

ed u

sefu

lnes

s had

a

signif

ican

t in

fluen

ce

on

atti

tude

and

beh

avio

ura

l

inte

nti

on,

whil

e at

titu

de

infl

uen

ced

beh

avio

ura

l

inte

nti

on.

Fin

ally

, per

ceiv

ed e

ase

of

use

was

fou

nd t

o

hav

e a

signif

ican

t ef

fect

on p

erce

ived

use

fuln

ess.

Shit

tu,

Bas

ha,

Rah

man

and

Ahm

ad

(2011)

Inv

esti

gat

ing s

tuden

ts’

atti

tude

and i

nte

nti

on

to u

se s

oci

al s

oft

war

e

in h

igher

inst

ituti

on o

f

lear

nin

g i

n M

alaysi

a

151 s

tuden

ts

Att

itude,

Subje

ctiv

e

norm

, B

ehav

iora

l

inte

nti

on, P

erce

ived

use

fuln

ess

and

Per

ceiv

ed e

ase

of

use

The

resu

lts

show

ed

that

per

ceiv

ed

use

fuln

ess,

subje

ctiv

e norm

, an

d

per

ceiv

ed

ease

of

use

had

signif

ican

t ef

fect

on th

e at

titu

de

of

studen

ts to

war

ds

soci

al s

oft

war

e ad

opti

on

; w

hil

e at

titu

de

was

fou

nd t

o

be

the

stro

nger

fa

ctor

of

studen

ts’

inte

nti

on

to

use

soci

al s

oft

war

e.

53

Tab

le 2

.1, co

nti

nued

Auth

ors

T

itle

s S

ample

C

onst

ruct

s M

easu

red

M

ajor

Fin

din

gs

Isla

m,

Len

g

and S

ingh

(2015)

Eff

icac

y o

f th

e

tech

nolo

gy s

atis

fact

ion

model

(T

SM

): A

n

empir

ical

stu

dy

180

post

gra

du

ate

studen

ts

Com

pute

r se

lf-

effi

cacy, per

ceiv

ed

use

fuln

ess,

per

ceiv

ed

ease

of

use

and

sati

sfac

tion

The

findin

gs

show

ed t

hat

com

pute

r se

lf-e

ffic

acy h

ad a

stat

isti

call

y

signif

ican

t dir

ect

infl

uen

ce

on

per

ceiv

ed

ease

of

use

an

d

per

ceiv

ed

use

fuln

ess.

O

n

the

oth

er

han

d,

studen

ts’

per

ceiv

ed ea

se of

use

an

d per

ceiv

ed

use

fuln

ess

had

a s

tati

stic

ally

sig

nif

ican

t posi

tive

dir

ect

infl

uen

ce o

n t

hei

r sa

tisf

acti

on i

n u

sing o

nli

ne

rese

arch

dat

abas

es.

Mo

reover

, co

mpute

r se

lf-e

ffic

acy

had

a

signif

ican

t in

dir

ect

infl

uen

ce o

n s

atis

fact

ion m

edia

ted

by

per

ceiv

ed

ease

of

use

. F

inal

ly,

com

pute

r se

lf-

effi

cacy

was

al

so

rev

eale

d

to

hav

e a

stat

isti

call

y

signif

ican

t in

dir

ect

infl

uen

ce o

n s

atis

fact

ion m

edia

ted

by p

erce

ived

use

fuln

ess

of

dat

abas

es.

54

2.4 TECHNOLOGY SATISFACTION MODEL (TSM)

The gratification of ICT usage by lecturers, like many cases of technology

gratification, may be understood from the perspective of the Technology Satisfaction

Model (TSM). The TSM, proposed by Islam (2014) is developed and validated by

incorporating two additional intrinsic motivation attributes, namely, computer self-

efficacy and satisfaction into the original Technology Acceptance Model (Davis et al.,

1989). The TSM posits that three factors influence technology satisfaction, the first

being users’ perception of the benefits derived from using the technology (Perceived

Usefulness or PU), the second, how easy they perceive the use of technology

(Perceived Ease of Use or PEU) and the third, technology requires a considerable

degree of computer self-efficacy (CSE) to perform effective searches. These factors

then determine users’ satisfaction in using the technology in higher education. The

TSM contains one exogenous variable (CSE), two mediated variables (PEU, PU) and

one endogenous variable (SAT), which are shown in Figure 2.2.

Figure 2.2: Technology Satisfaction Model (TSM) (Islam, 2014)

The initial TSM was applied to assess the satisfaction on wireless internet

usage with a particular focus to the students studying in Higher Education. However,

TSM ignored the issues of two extrinsic motivation components, namely, intention to

use and actual use, even though they are important constructs in measuring users’

technology acceptance or adoption. The findings of the study have in many ways

55

contributed to the body of knowledge on the Technology Satisfaction Model (TSM).

In addition, the findings of TSM indicated that perceived ease of use and perceived

usefulness had a statistically significant positive direct influence on satisfaction.

Subsequently, computer self-efficacy was found to have a significant positive direct

influence on perceived usefulness and perceived ease of use. Moreover, the results

also demonstrated that computer self-efficacy had a significant indirect influence on

satisfaction mediated by perceived usefulness. Eventually, computer self-efficacy was

also revealed to have a statistically significant indirect influence on satisfaction

mediated by perceived ease of use of wireless internet. However, Islam (2014)

recommended that the usage of internet and the users hold a global pattern; thus this

model has a great potential to work globally regardless of geographical, economical,

social and cultural patterns in a particular country or region. As such, this study

included TSM as one of the theoretical frameworks to develop and validate the

Technology Adoption and Gratification (TAG) model to assess lecturers’ ICT usage

in higher education.

On the other hand, Lee and Park (2008) examined the relationship between the

mandatory adoption of mobile technology and market performance in the business-to-

business (B2B) setting. They presented and validated the B2B technology satisfaction

model, adding perceived loss of control with satisfaction and two extrinsic motivation

components of TAM, namely, perceived usefulness and perceived ease of use (Davis,

1989). However, they ignored the issue of computer self-efficacy even though this

was demonstrated to be the most significant predictor of TSM developed and

validated by Islam (2014). The proposed B2B technology satisfaction model showed

that the construct of user satisfaction was a mediator variable, while perceived market

performance was an endogenous variable. In this case, it would be better to have user

56

satisfaction as an endogenous variable to claim or generalize the B2B TSM. The

findings demonstrated that perceived usefulness had a significant direct effect on user

satisfaction and perceived market performance. Subsequently, perceived ease of use

has a significant direct effect on user satisfaction. However, Lee and Park (2008) did

not assess the indirect relationships between the latent variables. User satisfaction was

represented by only two valid items which was not sufficient for measuring or

validating the B2B TSM. In other related studies, Adamson and Shine (2003)

regarded perceived ease of use as a marginal influencer of the end user satisfaction in

a mandatory environment of a Bank treasury. Their findings suggested that computer

self-efficacy and user satisfaction played an important role in new technology

acceptance, thus the two constructs require more thought when designing information

systems in mandatory environments. Along this line, Al-Jabri and Roztocki (2010)

proposed a conceptual mandatory information technology implementation framework.

It included the TAM with user satisfaction and explained based on literature review,

the complexity of issues of technology adoption and use, especially in mandatory

settings. However, there is no validation to their proposed framework with empirical

data to examine the causal relationships among the constructs. Shipps and Phillips

(2013) found that concentration and perceived interactivity influenced an end user’s

satisfaction with a social network along with antecedents from the TAM. The

structural model indicated that perceived usefulness poorly influenced satisfaction (β

= .16).

57

2.5 ONLINE DATABASE ADOPTION AND SATISFACTION (ODAS)

MODEL

Islam (2011a) developed and validated the Online Database Adoption and Satisfaction

(ODAS) Model by consisting of the constructs computer self-efficacy and satisfaction

into the TAM (Davis, 1989). The ODAS model containing three exogenous variables

(perceived ease of use, perceived usefulness and computer self-efficacy), one

mediated variable (intention to use) and one endogenous variable (satisfaction) as

shown in Figure 2.3. The findings revealed significant influences of perceived

usefulness and computer self-efficacy on postgraduate students’ intention to use the

database and their satisfaction in using it. In addition, perceived ease of use and

intention to use were found to have a significant direct influence on satisfaction. The

findings contributed to a better understanding of the TAM and technology adoption

among postgraduate students. Moreover, the ODAS Model (Islam, 2011a) also

demonstrated that perceived ease of use found to be most significant predictor

compared to the behavioural intention influence on the students’ satisfaction in using

the online database. Besides this, the results of ODAS model discovered that the two

exogenous variables (PU and CSE) explained almost 53% of the variance intention to

use, and the three exogenous variables (PEU, PU and CSE) and one mediated (INT)

variable together explained approximately 61% of the variability of the students’

satisfaction and adoption in using the online database. However, ODAS model also

ignored the issue of an extrinsic motivation attribute, namely, actual use, which is a

vital factor in assessing users’ technology adoption.

58

Figure 2.3: Online Database Adoption and Satisfaction (ODAS) Model (Islam, 2011a)

In a recent study, Ahmad et al., (2010) demonstrate that computer self-efficacy

influences technology use. In fact, the influence of this construct is evident in many

cases involving the employment of computer technology (Ball & Levy, 2008;

Charalambous & Papaioannou, 2010; Kenzie, Delecourt & Power, 1994; Moos &

Azevedo, 2009). Eastin and LaRose (2000) studied computer self-efficacy in the

context of Internet use among experienced and novice users, and concluded that it is a

determining factor in closing the digital divide between the two groups of users.

Moreover, TSM (Islam, 2014) and ODAS model (Islam, 2011a) also explored that

computer self-efficacy was the most important construct in measuring students’

technology satisfaction and adoption in higher education. Based on this observation

and taking into account that the universities ICT facilities entail a considerable degree

of computer self-efficacy to perform effective usage, therefore this study proposed the

inclusion of computer self-efficacy (CSE) as an important predictor of lecturers’

adoption and gratification of ICT facilities for their teaching and research.

Lecturers’ intention to use and actual use of the ICT are more likely to persist

if they are satisfied with the resources and facilities provided therein, and if their

educational or research needs are met. Togia and Tsigilis (2009) found satisfaction to

be one of the barriers hampering graduate students’ utilization of the electronic

resources made available to them. While TSM discovered that students’ satisfaction

59

was influenced computer self-efficacy, perceived usefulness and perceived ease of use

of wireless internet in higher education (Islam, 2014). Along this line, ODAS model

also found that satisfaction was determined by intention to use, computer self-efficacy,

perceived usefulness and perceived ease of use (Islam, 2011a). Based on this

observation, the present study included gratification (GRAT) as an endogenous

variable explaining ICT facilities gratification among the universities lecturers. This

study predicted that gratification would also directly and indirectly be affected by the

ICT’s ease of use, usefulness, computer self-efficacy, intention to use and actual use.

Therefore, in the study’s hypothesized model as shown in Figure 2.4, two other

intrinsic motivation attributes – gratification and computer self-efficacy – were taken

from TSM and ODAS model and incorporated into the original TAM to develop and

validate the Technology Adoption and Gratification (TAG) model to examine

lecturers’ adoption of and gratification in using the universities ICT facilities.

Figure 2.4: The Hypothesized Technology Adoption and Gratification (TAG) Model

60

2.6 CONSTRUCTION OF HYPOTHESES

In this section, this study provided a concise but relevant literature review pertaining

to the constructs impacting lecturers’ utilization of ICT facilities, as well as the use of

the TAM, TSM and ODAS model in predicting technology adoption and gratification.

Based on the review, the study proposed eleven hypotheses to develop and validate

the TAG model and elucidated the dimensions influencing lecturers’ adoption and

gratification of ICT facilities provided by the universities. These hypotheses clarified

the causal relationships among all the constructs under study: computer self-efficacy,

perceived usefulness, perceived ease of use, intention to use, actual use and

gratification in using ICT facilities in higher education.

According to Bandura and Schunk (1981), self-efficacy is defined as one's

judgments about how well one can perform various courses of actions in different

prospective situations fraught with many unpredictable and stressful elements. This

definition was echoed by Aiken (1993), who opined that self-efficacy stands for a

person's expectation of his/her capability in learning or performing certain behaviours

that will translate into desirable outcomes in a particular situation. In the learning

process, it conveys students’ judgments about their cognitive capabilities to

accomplish a specific academic task or particular goals (Schunk, 1991). In the context

of Internet use, it denotes a user's belief in his/her capabilities to organize and execute

courses of Internet actions required to produce given attainments. This, Eastin and

LaRose (2000) asserts, is a determining factor in efforts to close the digital divide

between the experienced Internet users and the novices.

Research confirms propositions that self-efficacy influences the choice as to

whether or not to engage in a task, the effort made in performing it, and the level of

persistence required in accomplishing it (Bandura, 1977; Bandura & Schunk, 1981;

61

Delcourt & Kinzie, 1993). It influences how people feel, think, and behave (Bandura,

1986: 393). According to Ahmad et al., (2010, p. 271), the inclusion of self-efficacy as

an intrinsic motivation construct offers ‘deeper and richer understanding of why and

how the technology is used’.

In their study, Compeau and Higgins (1995) empirically tested a 10-item

measure of computer self-efficacy (CSE) and found a significant relationship between

CSE and outcome expectations and use. In a further empirical validation of the CSE

instrument developed in this study, Compeau, Higgins, and Huff (1999) reaffirmed the

evidence that CSE influences an individual’s behavioral orientations to adopt an

information system.

The findings of TSM (Islam, 2014) discovered that computer self-efficacy is

the most important predictor in measuring the students’ satisfaction in using wireless

internet for their learning purposes. Moreover, TSM also showed that computer self-

efficacy revealed a significant positive direct influence on perceived usefulness in

using wireless internet. On the other hand, Ahmad et al., (2010) indicated that

computer self-efficacy indirectly influences faculty’s use of computer mediated

technology through perceived usefulness and intention to use. Subsequently, Islam

(2011a) revealed the presence of significant indirect relationship between computer

self-efficacy and satisfaction through the mediating influence of intention to use

online research database. Along this line, in his book entitled “Online Database

Adoption and Satisfaction Model”, Islam, (2011a) also discovered the significant

interrelationships among the exogenous variables were measured in the revised

structural model, and it was found that the interrelationships between computer self-

efficacy, perceived ease of use and perceived usefulness were statistically significant.

Similarly, Islam (2011b) also demonstrated significant positive interrelationships

62

between computer self-efficacy, perceived ease of use and perceived usefulness.

YouTube users’ self-efficacy showed a significant effect on perceived usefulness (Lee

& Lehto, 2013). Stone and Baker-Eveleth (2013) hypothesized that a mid-sized

university students’ self-efficacy regarding the use of an e-textbook significantly

influences outcome expectancy/usefulness regarding e-textbooks. However, their

findings showed that it did not exert statistically significant effect on outcome

expectancy/usefulness in purchasing electronic textbooks. In a previous study, pre-

service teachers’ self-efficacy levels regarding ICT usage in education were found to

be moderate. Thus, teachers’ self-efficacy or self-confidence concerning ICT usage

must be high in order for them to be highly encouraged users of ICT, which in turn

would lead to flourishing ICT technology integration (Tezci, 2011). Amin (2007)

confirmed that university students’ computer self-efficacy of internet banking had a

significant effect on their perceived usefulness, while it was not related with perceived

credibility of internet banking system. Zejno and Islam (2012) demonstrated that

computer self-efficacy is one of the significant predictors of postgraduate students’

ICT usage in higher education. In a related study, Wong et al., (2012) revealed that

computer teaching efficacy had a significant effect on perceived usefulness (β=.22,

p<.00), perceived ease of use (β=.48, p<.00) and attitude toward computer use (β=.38,

p<.00). However, the findings also showed that computer teaching efficacy

moderately effects on usefulness compare to other constructs.

On the other hand, according to Islam (2014), computer self-efficacy had a

significant positive direct influence on perceived ease of use of wireless internet.

Islam (2011a & 2011b) also exhibited significant interrelationships between computer

self-efficacy and perceived ease of use. Terzis et al., (2013) predicted that Greek and

Mexican university students’ computer self-efficacy will have a positive influence on

63

their Perceived Ease of Use. Their findings were consistent with the prediction. The

significant influence of Greek and Mexican university students’ computer self-

efficacy shows that a student who recognizes how to use computers, perhaps he/she

will find easy to use a Computer Based Assessment that needs fundamental

information technology skills. Amin (2007) found that university students’ computer

self-efficacy had a positive effect on their perceived ease of use of the Internet

banking systems. In a study involving university students’ use of an information

system, Agarwal and Karahanna (2000) discovered CSE to be a key antecedent of

PEU, while Wu et al., (2008) found a statistically significant relationship between

teachers’ CSE and their intention to use ICT in teaching. In light of such findings, it is

important to assess lecturers’ beliefs about their computer ability and the extent to

which it impacts their decision to adopt technology. Besides this, Islam et al., (2015)

revealed that postgraduate students’ computer self-efficacy had a statistically

significant direct influence on perceived ease of use and perceived usefulness in using

online research databases in higher education. These observations led to hypothesize

that in the study:

H1: Computer self-efficacy (CSE) will have a positive direct influence on lecturers’

perceived ease of use (PEU) and perceived usefulness (PU) of ICT facilities in higher

education.

In addition to perceived benefits, another important influencer is how easy

lecturers find the ICT facilities to use. In the original TAM, perceived ease of use

means the extent to which the user believes the use of technology will be free of effort

(Davis et al., 1989). Prior studies have found a significant influence of PEU on users’

intention to use computer-based systems (Chau, 1996; Davis et al. 1989; Karahanna &

Straub, 1999; Venkatesh & Davis, 1996, 2000; Venkatesh & Morris, 2000). However,

64

PEU has been found to be less influential and reliable (Ma & Liu, 2004; Wu et al.,

2008; Huang, 2008). Tenopir (2003), specifically, discovered that ease of use was a

major factor influencing her respondents’ adoption of the online database. Outside the

educational context, Lee, Fiore and Kim (2006) demonstrated that PEU significantly

enhanced consumer attitude and behavioral intention towards an online retailer, while

Ramayah and Lo (2007) revealed a significant effect of PEU on intention to use an

electronic resource planning system. Moreover, Lee and Lehto (2013) hypothesized

that perceived ease of use will have a significant effect on YouTube users’ behavioral

intention. Nevertheless, the results of their study showed that perceived ease of use

did not indicate significant effect on either behavioral intention or perceived

usefulness of YouTube. Similarly, Islam (2011a) also predicted that perceived ease of

use will have a positive direct influence on postgraduate students’ intention to use the

online research databases. However, the finding of his study revealed that perceived

ease of use had a significant direct effect on postgraduate students’ intention to use of

online research databases, but in an adverse direction (β = -.31, p < .005).

In addition, Hanafizadeh et al., (2014) found out that bank clients’ perceived

ease of use had significant influence on intention to use mobile banking. Subsequently,

Gultekin (2011) discovered that the effects of perceived ease of use on police officers’

intention to use the POLNET system were statistically significant. Meanwhile, Stone

and Baker-Eveleth (2013) confirmed that perceived ease of e-textbook use positively

influences behavioral intentions to purchase an e-textbook, while Martínez-Torres et

al., (2008) discovered that perceived ease of use, despite indicating a slightly weaker

influence compared to perceived usefulness on behavioural intention to use e-learning

tools. In other study, Shroff et al., (2011) confirmed that students’ perceived ease of

use had a significant influence on attitude towards usage of portfolio system.

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Consequently, perceived ease of use had the strongest significant influence on

perceived usefulness. Along this line, Chang et al., (2012) demonstrated that

perceived ease of use was antecedent factor that affected intention to accept of English

mobile learning. Terzis et al., (2013) found that the students’ perceived ease of use

had a significant effect on intention to use computer based assessment in higher

education.

On the other hand, the Online Database Adoption and Satisfaction Model

(Islam, 2011) revealed that perceived ease of use found to be more significant

predictor compared to the behavioural intention influence on the students’ satisfaction

in using the online research databases. According to Langeard, Bateson, Lovelock,

and Eiglier (1981), the study discovered that, customers consider effort expended in

utilizing service delivery to be more relevant in choosing between available service

delivery options, the greater the service delivery, the more the consumer satisfaction

(Churchill & Suprenant, 1982). Szymanski & Hise (2000) and Dabholkar & Bagozzi

(2002) respectively revealed that, convenience and ease of use are important

antecedents of e-satisfaction. In a related study, Shamdasani et al. (2008) regarded

ease of use as one of the factors of service quality and investigated its impact on

consumer satisfaction in using self-service internet technologies. Along this line,

Huang (2008) indicates that, the impact of e-consumers’ perceived ease of use

mediated by their behavioral attitude on their satisfaction are statistically significant.

The TSM confirmed that perceived ease of use demonstrated a significant direct effect

on students’ satisfaction in using wireless internet (Islam, 2014). Meanwhile, Islam

(2011b) also represented that perceived ease of use had a statistically significant effect

on students’ satisfaction in using wireless internet technology in higher education.

Furthermore, Lee and Park (2008) discovered that perceived ease of use had a

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significant effect on user satisfaction in using mobile technology. On the other hand,

the significance of perceived ease of use in predicting technology adoption is highly

emphasized in the TAM (Davis et al., 1989). In addition, Devaraj et al. (2002)

discovered that perceived ease of use was found to be a significant determinant of

satisfaction in transaction cost analysis as well as important component of TAM in

forming consumer attitude and satisfaction with the electronic commerce channel,

while Islam et al., (2015) confirmed that perceived ease of use had a significant effect

on students’ satisfaction in using online research database in higher education. These

observations led to next hypothesis, which reads as follows:

H2: Perceived ease of use (PEU) will have a positive direct influence on lecturers’

intention to use (INT), actual use (USE) and gratification (GRAT) in using ICT


In the TAM, perceived usefulness is defined as the extent to which an

individual believes that using the technology will enhance his or her performance

(Davis 1989). PU has also been conceived as the benefits and advantages derived from

using a technology. The impact of PU on users’ intention to use (INT) various types of

computer-based and Internet-based systems has been comprehensively documented in

numerous studies (Ahmad et al. 2010; Davis, 1993; Guriting & Ndubisi, 2006;

Mathwick, Rigdon & Malhotra, 2001; Ramayah & Lo, 2007). Convenience of access,

in particular, has been frequently cited as a major advantage by users of electronic

journals in multiple studies (Maughan, 1999; Tenner & Yang, 1999; Hiller, 2002). In

Liew, Li, Foo and Chennupati (2000), the benefits of electronic resources were found

to be the facility to link to additional information, the ability to browse, and the

currency of materials. Other benefits and advantages include robust search capabilities,

hyperlinks to outside content, efficient acquisition of information (Dillon & Hahn,

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2002; Ray & Day, 1998), time saving factor, and print and save capabilities (Togia &

Tsigilis, 2009). These benefits and advantages were realized all the more by students

when their learning and academic pursuits became greatly enhanced. Wu et al., (2008)

corroborated these findings on the impact of PU by noting that it is a significant

determinant of science teachers’ intention to integrate technology into their instruction.

In recent studies have also explored a significant effect of perceived usefulness on

users’ intention to use mobile learning (Hanafizadeh et al., 2014; Chang et al., 2012),

POLNET system (Gultekin, 2011), e-learning tools (Martínez-Torres et al., 2008) and

e-portfolio system (Shroff et al., 2011), while Terzis et al., (2013) discovered that

perceived usefulness showed a significant influence on students’ intention to use

computer based assessment in higher education.

The TSM (Islam, 2014) showed that perceived usefulness had a statistically

significant positive direct influence on satisfaction in using wireless internet in higher

education. Regarding the Online Database Adoption and Satisfaction Model, Islam,

(2011a) discovered that, perceived usefulness has a statistically significant indirect

influence on satisfaction mediated by intention to use online database, while Islam et

al., (2015) found that perceived usefulness was the most significant factor of TSM in

effecting postgraduate students’ satisfaction in using online research database in

higher education. Besides this, Lee and Park (2008) found that perceived usefulness

had a significant effect on user satisfaction, a surrogate of information system success.

Similarly, Islam (2011b) hypothesized that perceived usefulness positively influences

students’ satisfaction in their acceptance of wireless technology. Even though, the

results indicated the low path coefficients of 0.19 between perceived usefulness and

satisfaction. In addition to this, in the extant literature, numerous studies exist vis-à-

vis the effect of perceived usefulness on consumer satisfaction (Anderson, Fornell, &

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Lehmann, 1994). According to Huang (2008), perceived usefulness was found to have

an impact on consumer satisfaction mediated by their behavioral attitudes. On the

other hand, Clemons and Woodruff (1992) reported that, perceived value might

directly lead to the formation of overall feelings of satisfaction. This argument is

corroborated by McDougall and Levesque (2000) who indicated that, perceived value

is a significant driver of customer satisfaction. Devaraj et al., (2002) revealed that

perceived usefulness was an essential factor in forming consumer attitude and

satisfaction with the electronic commerce channel. Against this backdrop, It is thus

hypothesized that:

H3: Perceived usefulness (PU) will have a positive direct influence on lecturers’



H9: Perceived ease of use (PEU) and Perceived usefulness (PU) will have a positive

indirect influence on lecturers’ gratification (GRAT) in using ICT facilities mediated

by intention to use (INT), respectively.

The result of ODAS model (Islam, 2011a) depicted that intention to use had a

positive direct effect on students’ satisfaction in using online research databases in

higher education. From various prior studies performed on the relationship between

CSE and behavioural intention to use technology or infusion (Wu et al., 2008; Ahmad

et al., 2010) and the latter with individual satisfaction (Huang, 2008; Shamdasani et

al., 2008), it is inferred that there is a relationship between intention to use and

satisfaction. Against this backdrop, one purpose of this study was to develop and

validate the TAG model by incorporating computer self-efficacy and gratification as

two attributes to examine lecturers’ adoption and gratification in using ICT facilities.

The presented study assessed the direct effects of intention to use on lecturers’ actual

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use and gratification in using the ICT facilities for their teaching and research

purposes, and hypothesized that:

H4: Intention to use (INT) will have a positive direct influence on lecturers’

gratification (GRAT) and actual use (USE) of ICT facilities in higher education.

H5: Use (USE) will have a positive direct influence on gratification (GRAT) in using

ICT facilities.

H10: Intention to use (INT) will have a positive indirect influence on lecturers’

gratification (GRAT) mediated by actual use (USE) of ICT facilities in higher

education.

Havelka’s (2003) study perceived significant differences in CSE among

students in different areas of academic discipline. In light of such findings, it is

imperative on the part of the researchers to assess students' self-beliefs about their

academic capabilities as well as the acknowledgement of this aspect as being

instrumental on their motivation and academic achievement (Pajares, 2002). Besides,

Wu et al. (2008) found a statistically significant relationship between CSE and

intention to infuse Information Technology, although Stone and Baker-Eveleth (2013)

indicated that computer self-efficacy positively influenced students’ attitudes

regarding purchasing e-textbooks. Alenezi et al., (2010) confirmed that computer self-

efficacy was significantly influence the students' intention to use e-learning, while

Ahmad et al., (2010) hypothesized that computer self efficacy indirectly effects

faculty’s use of the computer mediated technology medicated by perceived usefulness

and intention to use. Their findings concluded that computer self efficacy was

moderately more influential than did the behavioural intention in influencing the

faculty’s use of computer technology. According to Chang and Tung (2008), self-

efficacy was an important factor for university students’ behavioural intention to use

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the online learning course websites. Similarly, Zejno and Islam (2012) claimed that

computer self-efficacy is one of the significant predictors of postgraduate students’

ICT usage.

In addition, Islam (2014) demonstrated that computer self-efficacy is the most

significant construct of the TSM and it also exerted indirect significant influence on

satisfaction mediated by perceived ease of use and perceived usefulness, respectively.

The effect of computer self-efficacy on perceived usefulness, ease of use and

satisfaction were limited to their capabilities and requisite skills in using wireless

internet facility and online research databases, saving and printing journals or articles,

and accessing the database from the university website. However, students described

obstacles related to accessing journals from the online database. In other studies,

computer self-efficacy showed a significant indirect effect on students’ satisfaction

mediated by intention to use the online database (Islam, 2011a, p. 50). On the other

hand, Islam (2011b) hypothesized that computer self-efficacy positively influences

students’ satisfaction in accepting wireless technology. However, it did not depict the

direct impact on satisfaction; while it might be reflected through the significant

interrelationships among the exogenous variables, namely, perceived ease of use and

perceived usefulness. At the same time, Islam et al., (2015) discovered that students’

computer self-efficacy had a significant indirect influence on satisfaction mediated by

perceived ease of use and perceived usefulness of research databases. In other studies,

Ahmad et al., (2010), Gong, Xu and Yu (2004), Hu, Clark and Ma (2003) and Ellen,

Bearden and Sharma (1991) demonstrate that computer self-efficacy influences

technology acceptance and use. In fact, the influence of this construct is evident in

many cases involving the employment of computer technology (Ball & Levy, 2008;

Charalambous & Papaioannou, 2010; Kenzie, Delecourt & Power, 1994; Moos &

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Azevedo, 2009). Eastin and LaRose (2000) studied computer self-efficacy in the

context of Internet use among experienced and novice users, and concluded that it is a

determining factor in closing the digital divide between the two groups of users. It is

therefore hypothesized that:

H6: Computer self-efficacy (CSE) will have a positive indirect influence on lecturers’

intention to use (INT) of ICT facilities mediated by perceived ease of use (PEU) and

perceived usefulness (PU), respectively.


gratification (GRAT) in using ICT facilities mediated by perceived ease of use (PEU)



use (USE) of ICT facilities mediated by perceived ease of use (PEU) and perceived

usefulness (PU), respectively.

2.6.1 CROSS-CULTURAL STUDIES

Terzis et al., (2013) stated that the necessity to comprehend the individual acceptance

drives numerous researchers to a cross cultural analysis concerning acceptance. Many

prior studies included a cross-cultural factor by comparing the efficacy of an

acceptance model such as TAM or by comprising constructs that were distinguishing

the cultures. However, earlier cross-cultural researches exhibited that acceptance or

adoption models such as TAM were affected by culture. As such, they examined

university students’ acceptance of Computer Based Assessment (CBA) Systems by

applying Computer Based Assessment Acceptance Model (CBAAM) in the two

diverse cultures of Greece and Mexico. The CBAAM was integrated by nine factors,

namely, perceived playfulness, perceived usefulness, perceived ease of use, computer

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self-efficacy, social influence, facilitating conditions, goal expectancy, content and

behavioral intention. To validate the model, a total of 168 students were selected from

two universities in Greece and Mexico. They consisted of 117 first-year Greek and 51

first-year Mexican students. The CBAAM was estimated using partial least squares

(PLS) to test fifteen hypotheses in a cross cultural setting. The findings of their study

demonstrated that the CBAAM was valid for both countries in general. Nevertheless,

there were some distinctions due to the culture. Greek students’ behavioral intention

was primarily influenced by perceived ease of use and perceived playfulness, although

Mexican students’ behavioral intention was affected by perceived usefulness and

perceived playfulness. However, these results may not be generalized due to the small

sample size. Besides this, this study was limited between two universities in Greece

and Mexico. They suggested that the findings of this study could be useful for

researchers, developers and educators and they should take them into contemplation

for future, especially researches interest in cultural dimensions and their influences in

education.

Teo, Luan and Sing (2008) conducted a cross-cultural study to investigate the

pre-service teachers’ intention to use technology in Singapore and Malaysia applying

the Technology Acceptance Model (TAM). However, so far, there was no cross-

cultural research to assess pre-service teachers’ acceptance by applying TAM in

Singapore and Malaysia so that it would be of interest to the research community to

observe if the TAM could be validated in the diverse culture. The data for their study

were gathered through an online survey questionnaire. The pre-service teachers for

their study consisted of 250 and 245 in Singapore and Malaysia, respectively. They

proposed research model based on TAM for this study which included the four factors,

(a) perceived ease of use, (b) perceived usefulness, (c) computer attitude, and (c)

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behavioral intention control, influence the future intentions to use computer in the

different culture. The Structural Equation Modeling (SEM) was used to validate the

model. The results of their study revealed that there were significant dissimilarities

between Malaysian and Singaporean pre-service teachers in terms of their computer

attitude, perceived ease of use and perceived usefulness of technology. However, no

differences were identified in behavioral intention among these two countries with

regards to technology acceptance. They recommended that the SEM could be applied

to assess structural invariance of the TAM to examine of its validity in the cross-

culture. In addition, future study also can extend TAM by including additional

variables of interest to the education community.

According to Singh, Fassott, Chao and Hoffmann (2006), the technology

acceptance model (TAM) has been one of the most influential models in the

information technology literature. However, TAM has not been applied in the

marketing literature to comprehend user acceptance of international web sites. In so

doing, they extended the TAM by incorporating cultural adaptation factor to measure

international students’ acceptance and usage of multinational company web sites in

the different cultures in German, Brazil and Taiwan. They also asserted that one of the

causes they preferred to collect data from Brazil, Germany, and Taiwan that the

findings were more comparable and meaningful in the different culture. Data were

collected from 40 Brazilian, 110 German, and 100 Taiwanese online consumers.

However, the total sample size seems to be inadequate in order to generalize the

findings of their study. To evaluate the extended TAM and its causal relationships

between the constructs, the partial least squares (PLS) were applied. The result of their

study discovered that the causal relationships between the extended TAM factors,

namely, perceived ease of use, perceived usefulness, cultural adaptation, attitude and

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behavioral intention to use international websites were presented strong evidence for

the viability of TAM in elucidating web site usage in Brazil, Germany, and Taiwan.

The findings also showed that the total influence of consumers’ perceived usefulness

was greater than that of perceived ease of use PEOU on intention to purchase, while

perceived usefulness had a greater effect on students’ attitude toward the website in

comparison to perceived ease of use. Furthermore, cultural adaptation was found to be

an important exogenous variable for the three diverse cultures in Brazil, Germany, and

Taiwan in using international web site. These three countries students’ attitude toward

using the web site was demonstrated to have a strong influence on their intention to

purchase from the international web sites. They suggested based on their findings that

future researchers may apply TAM to examine numerous forms of technology usage

and in diverse contexts to discover its validity and frontier surroundings.

On the one hand, Al-Gahtani, Hubona and Wang (2007) investigated a study

of Culture and the acceptance and use of information technology (IT) in Saudi Arabia.

Using a survey of 722 knowledge workers those who were utilizing desktop computer

applications on a voluntary basis in Saudi Arabia, they applied a modified unified

theory of acceptance and use of technology (UTAUT) to measure users’ acceptance

and use of IT. Their findings confirmed that the UTAUT model explained 39.1% of

intention to use, and 42.1% of usage of the total variance. Besides this, applying on

the theory of cultural dimensions, they examined the similarities and dissimilarities

between the Saudi Arabia and North American validations of UTAUT with regards of

cultural diversities that influenced the users’ acceptance of IT in these two societies.

Straub, Keil and Brenner (1997) estimated the technology acceptance model

(TAM) through comparison among three different countries: United States; Japan; and

Switzerland. The data for their study were collected from 99 American, 142 Japanese,

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and 152 Swiss employees of three different airlines. Their results indicated that TAM

was influenced by the different cultures. Additionally, TAM model was significant

and explained around 10% of the variance in usage and adoption observed in both the

U.S. and Swiss respondents, it was not significant for the Japanese. However, the total

sample size may not be adequate in order to generalize their findings.

Huang, Ming-te, and Wong (2003) asserted that the culture of People’s

Republic of China (PRC) typically varies from that of North America, and is a typical

transitional economy at the accepting end of information technology transfer. Thus,

the cross-cultural validity of technology acceptance models may not be justified

without an assessment on the applicability of technology acceptance in the context of

the PRC, whereas acceptance models were explored in North America. As a result,

they conducted a study of assessing of the cross-cultural applicability of technology

acceptance model (TAM) as evident from China. The data for their study were

collected from 121 employees in Guangzhou, China. The results confirmed that the

TAM was applicable across cultures. The finding provides evidence on the

applicability of Technology Acceptance Model (TAM) across cultures. Therefore, the

validity of TAM was expanded by assessing technology acceptance beliefs in a

developing country with Chinese culture. They believed that with a broad

comprehending of cross-cultural technology adoption process and outcome,

organizations would be in a better position to utilize the usefulness of the new

communication media, particularly in culturally different non-western economies that

are at the accepting end of technology transfer. They recommended that many studies

need to be conducted in diverse countries or cultures in order to generalize the validity

and applicability of TAM.

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In a recent cross-cultural study, Chong, Chan and Ooi (2012) extended the

technology acceptance model (TAM) and diffusion of innovation (DOI) theory by

adding more constructs, namely, trust, cost, social influence, variety of services, and

control variables such as age, educational level, and gender of consumers to develop

their conceptual model for this study. The model was validated to predict consumers’

intention to adopt mobile commerce in China and Malaysia. To develop and validate

their model, data obtained through a survey conducted with 172 Malaysian and 222

Chinese consumers, and the model was evaluated using hierarchical regression

analysis to test fourteen hypotheses. The findings of their research model

demonstrated that the factors in TAM and DOI models were not competent to predict

consumer decisions to adopt mobile commerce. However, constructs such as trust,

cost, social influence, and variety of services had a significant influence on

consumers’ intention to adopt mobile commerce. The results also showed that

consumers’ decision to adopt mobile commerce was affected by the different cultures.

They suggested that future studies can incorporate additional adoption dimensions,

namely, self efficacy and perceived enjoyment into the research models, although

their study ignored them. These observations led this study to hypothesize that:

H11: There will be a cross-cultural invariant of the causal structure of the TAG model

between Malaysian and Chinese lecturers.

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CHAPTER 3

METHODOLOGY

3.1 INTRODUCTION

The broad objective of this study was to develop and validate the technology adoption

and gratification (TAG) model to evaluate lecturers’ ICT usage in higher education.

The factors that influenced lecturers’ adoption and gratification of the ICT facilities

were hypothesized to be the ICT’s ease of use, usefulness, computer self-efficacy,

actual use and gratification from the usage of this service.

This study also applied the method of Rasch model to develop and validate the

psychometric properties of lecturers’ ICT usage and Structural Equation Modeling

(SEM) to examine the causal relationships among the various exogenous, endogenous

and mediated constructs of TAG model. Subsequently, this chapter provides detailed

descriptions of the population and sample, sampling procedure, research instrument,

and data analysis procedures, structural equation modeling, and model evaluation.

3.2 POPULATION AND SAMPLE

The present study was conducted at two public universities, namely, University of

Malaya in Malaysia and Jiaxing University in China. As such, the target population of

this study was different from each other and the total sample size of this study was

400 academic staff, which was equally divided for both universities. The total

population of this study comprised all the academic staff of the University of Malaya

and Jiaxing University. The total sample size was recommended by Hair, Black,

Babin, and Anderson (2010). According to Hair et al., (2010), “the smallest sample

size is to have at least five times as many observations (respondents) as the number of

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variables (items) to be analyzed”. The meaning is that each item in the questionnaire

should have minimum five respondents. In this case, the total number of items has to

be multiplied by five to compute the total sample size whereas the questionnaire of the

present study was containing almost eighty items to develop and validate the

technology adoption and gratification (TAG) Model to assess lecturers’ ICT usage.

Approximately 1965 lecturers were taken as the sampling frame; out of this, a total of

200 lecturers from thirteen faculties of the University of Malaya (Arts and Social

Sciences, Built Environment, Business and Accountancy, Computer Science and

Information Technology, Dentistry, Economics and Administration, Education,

Engineering, Language and Linguistics, Law, Medicine, Sciences and Academy of

Islamic Studies) were selected using stratified random sampling procedure.

Meanwhile, the target population of Jiaxing University was approximately 811

lecturers included as the sampling frame; out of this, a total of 200 lecturers from

seven colleges (Foreign Language Studies, Mathematics and Engineering, Biology

and Chemistry, Material Engineering, Education, Civil Engineering & Architecture

and Jiaxing University Nanhu) were collected applying stratified random sampling

procedure.

3.2.1 LECTURERS’ DEMOGRAPHIC INFORMATION IN THE

UNIVERSITY OF MALAYA

The 200 participants were lecturers from all the faculties of the University of Malaya.

They consisted of 47% male and 53% female. The gender breakdown is shown in

Figure 3.1.

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Figure 3.1: Sample of Study According to Gender

The 200 participants were lecturers from thirteen faculties of the University of

Malaya, namely, Arts and Social Sciences (FASS), Built Environment (FBE),

Business and Accountancy (FBA), Computer Science and Information Technology

(FCSIT), Dentistry (FD), Economics and Administration (FEA), Education (FEDU),

Engineering (FE), Language and Linguistics (FLL), Law (FL), Medicine (FM),

Sciences (FS) and Academy of Islamic Studies (AIS). The sample breakdown

according to faculties is shown in Figure 3.2.

Figure 3.2: Sample of Study According to Faculties

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The sample consisted of lecturer’s educational background. They were holding

the different levels of educational background such as undergraduate (UG), Master

and PhD and working in various faculties of the University of Malaya. The majority

of the lecturers were holding PhD degree (74%) whereas only 25% lecturers were

Master degree holders and the remaining 1% was undergraduate holders. The

breakdown of which is shown in Figure 3.3.

Figure 3.3: Sample of Study According to Educational Background

The sample consisted of different age groups. Most of the lecturers (33%)

were above 45 years of age, followed by 23%, 19% and 18% were between 35 to 39,

30 to 34 and 40 to 44 years of age groups, respectively. Lastly, only 7% of the

lecturers were between 25 to 29 years of age. The age group breakdown is shown in

Figure 3.4.

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Figure 3.4: Sample of Study According to Age Groups

This study was conducted among local (88%) and international (12%)

academic staffs. The nationality breakdown is shown in Figure 3.5.

Figure 3.5: Sample of Study According to Nationality

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Most of the lecturers (29%) had between 6 to 10 years of working experiences,

followed by 26% which had between 1 to 5 years, and the remaining 14%, 15% and

16% of the lecturers had between 16 to 20, 11 to 15 and 21 above years of experience,

respectively. The working experience breakdown is shown in Figure 3.6.

Figure 3.6: Sample of Study According to Working Experience

With regards to designations, majority of the academic staff surveyed were

assistant professors/senior lecturers (44%), followed by lecturers (30%), associate

professors (16%) and professors (10%), respectively. The designation breakdown is

shown in Figure 3.7.

Figure 3.7: Sample of Study According to Designation

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3.2.2 LECTURERS’ DEMOGRAPHIC INFORMATION IN JIAXING

UNIVERSITY

The 200 participants were lecturers from seven colleges of Jiaxing University.

However, 4 participants were removed from the final study due to incomplete

responses. They consisted of 48% male and 52% female. The gender breakdown is

shown in Figure 3.8.

Figure 3.8: Sample of Study According to Gender

The 196 participants were lecturers from seven colleges of Jiaxing University,

namely, Foreign Language Studies (CFLS), Mathematics and Engineering (CME),

Biology and Chemistry (CBC), Material Engineering (CME), Education (CE), Civil

Engineering & Architecture (CCEA) and Jiaxing University Nanhu (CJUN). The

sample breakdown according to colleges is shown in Figure 3.9.

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Figure 3.9: Sample of Study According to Colleges

The sample consisted of lecturer’s educational background. They had different

levels of educational background such as undergraduate (UG), Master and PhD and

worked in various colleges in Jiaxing University. The majority of the lecturers held

Master degree (48%) whereas only 19% of the lecturers were undergraduate holders

and the remaining 33% were PhD degree holders. The breakdown of which is shown

in Figure 3.10.

Figure 3.10: Sample of Study According to Educational Background

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The sample consisted of different age groups. Most of the lecturers (29%)

were between 30 to 34 years of age, followed by 25%, 21%, and 17% who were

between 45 years and above, 35 to 39 and 40 to 44 years of age, respectively, whereas

only 8% of the lecturers were between 25 to 29 years of age. The age group

breakdown is shown in Figure 3.11

Figure 3.11: Sample of Study According to Age Groups

This study was conducted among local (100%) academic staff. The nationality

breakdown is shown in Figure 3.12.

Figure 3.12: Sample of Study According to Nationality

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Most of the lecturers (32%) had between 6 to 10 years of working experience,

followed by 22% who had between 1 to 5 years, and the remaining 18%, 10% and

18% of the lecturers had between 11 to 150, 16 to 20 and 21 above years of

experience, respectively. The working experience breakdown is shown in Figure 3.13.

Figure 3.13: Sample of Study According to Working Experience

With regards to designation, majority of the surveyed academic staff were

lecturers (53%), followed by associate professors (38%), professors (7%) and assistant

professors/Senior lecturer (2%). The designation breakdown is shown in Figure 3.14.

Figure 3.14: Sample of Study According to Designation

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3.3 SAMPLING PROCEDURE

The respondents involved in this study were from two comprehensive public

universities, namely, the University of Malaya and Jiaxing University and they are

currently working there as professors, associate professors, assistant professors or

senior lecturers and lecturers in the different faculties of the universities. The sample

size of this study was based on previous studies For instance, Terzis et al., (2013)

conducted a research on “computer based assessment acceptance: a cross-cultural

study in Greece and Mexico”. Their study was conducted at two universities in Greece

and Mexico. The data for their study was collected from 117 Greek and 51 Mexican

students. Similarly, Chong et al., (2012) investigated a cross-cultural study between

China and Malaysia to examine consumers’ decision to adopt mobile commerce. The

data was collected from 172 Malaysian and 222 Chinese consumers. Teo et al., (2008)

conducted a study on “a cross-cultural examination of the intention to use technology

between Singaporean and Malaysian pre-service teachers: An application of the

Technology Acceptance Model (TAM)”. The respondents for their study comprised of

250 and 245 pre-service teachers in Singapore and Malaysia respectively. As such, in

the current study, a total of 400 academic staffs were separated into two equal parts.

Data was collected through a survey questionnaire using stratified random sampling

procedure. The questionnaire was distributed to all the faculties (Arts and Social

Sciences, Built Environment, Business and Accountancy, Computer Science and

Information Technology, Dentistry, Economics and Administration, Education,

Engineering, Language and Linguistics, Law, Medicine, Sciences and Academy of

Islamic Studies) academic staff based on the total number of faculty members as well

as the specific respondents of each faculty. Half of lecturers of the sample were from

the University of Malaya, so the researcher was able to hand it to the lecturers of the

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faculties: Arts and Social Sciences (12), Built Environment (8), Business and

Accountancy (7), Computer Science and Information Technology (8), Dentistry (10),

Economics and Administration (6), Education (9), Engineering (19), Language and

Linguistics (14), Law (4), Medicine (61), Sciences (32) and Academy of Islamic

Studies (10). On the other hand, the remaining 200 lecturers were collected from

seven colleges (Foreign Language Studies, Mathematics and Engineering, Biology

and Chemistry, Material Engineering, Education, Civil Engineering & Architecture

and Jiaxing University Nanhu) of Jiaxing University using stratified random sampling

procedure. To collect the data from Jiaxing University, the researcher traveled to

China and was able to hand it to the lecturers in Foreign Language Studies (20),

Mathematics and Engineering (28), Biology and Chemistry (23), Material Engineering

(16), Education (5), Civil Engineering & Architecture (17) and Jiaxing University

Nanhu (91). The formula of stratified random sampling procedure is as follows:

(expected sample size/total population)*each faculty population = each faculty sample

size. The sum of all the faculties sample size represents the total sample size of the

study. The detail of stratified random sampling procedure is shown in Table 3.1.

89

Table 3.1: Stratified Random Sampling Procedure

Universities Faculties/Colleges Each Faculty

Population

Expected Sample

Size for Each

Faculty

University of Malaya

in Malaysia

(Expected Sample

size/Total

Population)*Each

faculty population

e.g. (200/1965)*121

=Sample size for each

faculty

Arts and Social Sciences 121 12.315

Built Environment 75 7.633

Business and

Accountancy

72 7.328

Computer Science and

Information Technology

76 7.735

Dentistry 97 9.872

Economics and

Administration

60 6.106

Education 90 9.160

Engineering 186 18.931

Language and

Linguistics

140 14.249

Law 39 3.969

Medicine 598 60.865

Sciences 313 31.857

Academy of Islamic

Studies

98 9.978

Total Population of University of Malaya 1965

Total sample size for University of Malaya 199.998

Jiaxing University in

China

Foreign Language

Studies

83 20.468

Mathematics and

Engineering

113 27.866

Biology and Chemistry 95 23.427

Material Engineering 63 15.536

Education 19 4.685

Civil Engineering &

Architecture

68 16.769

Jiaxing University Nanhu

College

370 91.245

Total Population of Jiaxing University 811

Total sample size for Jiaxing University 199.996

3.4 RESEARCH INSTRUMENTS

This study used a questionnaire as the primary instrument to collect data. The

questionnaire consisted of seven sections. The first section consisted of 7 items about

the participants’ demographic information that included their gender, age, working

experience, educational background, designation, faculty/college/school and

90

nationality. The second section consisted of 16 items intended to measure the

mediator variable (use), on a 7-point Likert-type agreement scale with 1 being “never”

and 7 being “always”. The third, fourth and fifth sections comprised of 46 items

intended to measure the other two mediator variables (perceived ease of use and

perceived usefulness) and one exogenous variable (computer self-efficacy), on a 7-

point Likert-type agreement scale with 1 being “strongly disagree” and 7 being

“strongly agree”. The sixth section involved 10 questions about the participants’

intention to use of ICT facilities (mediator variable), on a 7-point Likert-type

agreement scale with 1 being “very unlikely” and 7 being “very likely”. The last

section consisted of 10 questions that addressed the participants’ perceptions of the

gratification (endogenous variable) of ICT usage. To measure the endogenous

(gratification), the study used a 7-point Likert scale survey questionnaire, with 1 being

“very unsatisfied” and 7 being “very satisfied”. The number of components is shown

in Table 3.2.

Table 3.2: The number of Components Measured

Section Components Measured No of Items

I Demographic Information

II Use 16

III Perceived Ease of Use 15

IV Perceived Usefulness 16

V Computer self-efficacy 15

VI Intention to Use 10

VII Gratification 10

Total 82

3.4.1 Use of ICT facilities

The items for actual use of ICT were adapted from previous studies (Ahmad et al.,

2010; Usluel et al., 2008; Islam, 2011a; Zejno & Islam, 2012) and modified

accordingly to suit the needs of the present study. Use was measured by sixteen items.

91

The items are shown in Table 3.3.

Table 3.3: Use Items

Items

How often do you use the following ICT facilities for research

and academic-related activities?

USE1 The University online research databases/Online

library research databases.

USE2 Laptop or Desktop computers.

USE3 Wireless Internet.

USE4 Web browser (www).

USE5 Search engine (e.g. Google, Yahoo, etc.).

USE6 Microsoft Office applications.

USE7 Research related software to analyze the data.

USE8 Document processing (e.g. PDF).

USE9 Computer graphics.

USE10 Email

USE11 Document editing/composing.

USE12 Computer labs.

USE13 Multimedia facilities (e.g. CD-ROM, VCD, DVD

etc.).

USE14 I use ICT for preparing the course and lecture notes.

USE15 I use ICT facilities for making presentations in the

course.

USE16 I use ICT facilities for carrying out studies in

laboratories or workshops, and making experiments.

3.4.2 Perceived Ease of Use

The Items for perceived ease of use were adapted from previous studies (Islam, 2011a

and 2011b; Islam 2014) and modified accordingly to address the needs of the present

study. Perceived ease of use was measured by fifteen items. The items are shown in

Table 3.4.

92

Table 3.4: Perceived Ease of Use Items

Items

PEU1 I find the university ICT facilities easy to use.

PEU2 I find it easy to access the university research databases.

PEU3 It is easy for me to become skilful at using university

databases for conducting research.

PEU4 Interacting with the research databases system requires

minimal effort on my part.

PEU5 I find it easy to get the research databases to help

facilitate my research.

PEU6 Interacting with the university research databases

system is very stimulating for me.

PEU7 I find it easy to select articles/journals of different

categories using research databases

(Education/Engineering/Business, etc.).

PEU8 With Wireless Internet, I find it easy to access

university databases to do research.

PEU9 Wireless Internet allows me to access research and

learning materials from Web Browser (WWW).

PEU10 Wireless Internet is easy to use for teaching and

research.

PEU11 I find it easy to use computers provided by the

university.

PEU12 My interaction with university ICT services available is

clear and understandable.

PEU13 I find it easy to download teaching, learning and

research materials using Wireless Internet in terms of

speed.

PEU14 It is easy for me to use multimedia facilities at the

university.

PEU15 I find it easy to use Microsoft office for teaching and

research purposes.

3.4.3 Perceived Usefulness

The Items for perceived usefulness were adapted from previous studies (Islam, 2011a

and 2011b; Islam 2014; Ahmad et al., 2010; Usluel et al., 2008) and modified

accordingly to suit the needs of the present study. Perceived usefulness was measured

by sixteen items. The items are shown in Table 3.5.

93

Table 3.5: Perceived Usefulness Items

Items

PU1 Using the ICT facilities at the university enables me to

accomplish tasks more quickly.

PU2 Using the ICT services available at the university

increases my research productivity.

PU3 The current university ICT system makes work more

interesting.

PU4 ICT facilities at the university improve the quality of

the work I do.

PU5 Using the university ICT facilities enhances my

research skills.

PU6 Using the university ICT facilities would make it easier

for me to find information.

PU7 ICT services at the university make information always

available to users.

PU8 Using the ICT facilities helps me write my journal

articles.

PU9 My job would be difficult to perform without the ICT

facilities.

PU10 Using the ICT facilities saves my time.

PU11 Using the university ICT facilities provides me with the

latest information on specific areas of research.

PU12 I can use ICT facilities from anywhere, anytime at the

campus.

PU13 I believe that the use of ICT in the classroom enhances

student learning in my discipline.

PU14 I believe that email and other forms of electronic

communication are important tools in faculty/student

communication.

PU15 I believe that web-based instructional materials enhance

student learning.

PU16 Using the ICT services enables me to download

teaching, learning and research materials from the

internet.

3.4.4 Computer Self-Efficacy

The Items for computer self-efficacy were adapted from previous studies (Islam,

2011a and 2011b; Islam 2014; Ahmad et al., 2010) and modified accordingly to suit

the needs of the present study. Computer self-efficacy was measured by fifteen items.

94

The items are shown in Table 3.6.

Table 3.6: Computer Self-Efficacy Items

Items

CSE1 I have the skills required to use computer applications

for writing my research papers.

CSE2 I have the skills and knowledge required to use

computer applications for demonstrating specific

concepts in class.

CSE3 I have the skills required to use computer applications

for presenting lectures.

CSE4 I have the skills required to communicate

electronically with my colleagues and students.

CSE5 I have the ability to e-mail, chat, download teaching,

learning and research materials, and search different

websites.

CSE6 I have the ability to use the Wireless Internet service

provided by the university.

CSE7 I have the skills required to use ICT facilities to

enhance the effectiveness of my teaching, learning

and research.

CSE8 I feel capable of using the university research

databases for writing journal papers.

CSE9 I have the ability to navigate my way through the ICT

facilities.

CSE10 I have the skills required to use the ICT facilities to

enhance the quality of my research works.

CSE11 I have the ability to save and print journals/articles

from the research databases.

CSE12 I have the knowledge and skills required to benefit

from using the university ICT facilities.

CSE13 I am capable of accessing the research databases from

the university website.

CSE14 I am capable to download and install research related

software using ICT facilities at the university.

CSE15 I am capable of using multimedia facilities for

delivering lecturers.

3.4.5 Intention to Use

The Items for intention to use were adapted from previous studies (Islam, 2011a;

Ahmad et al., 2010) and modified accordingly to suit the needs of the present study.

Intention to use was measured by ten items. The items are indicated in Table 3.7.

95

Table 3.7: Intention to Use Items

Items

INT1 I will use the ICT facilities to carry out my research

activities.

INT2 I will use computer applications to present lecture

materials in class.

INT3 I will use Microsoft office applications to write my

research papers.

INT4 I will teach a course that will be delivered totally on

the world wide web.

INT5 I intend to use ICT facilities for teaching, learning

and research purposes frequently.

INT6 I intend to download research materials from the

university databases frequently.

INT7 I will use the university research databases to update

myself on the research areas I am pursuing.

INT8 I intend to use the ICT facilities to upgrade my

research performance.

INT9 I will use the university research databases to write

outstanding journal articles.

INT10 I intend to use research related software to analyze

the data.

3.4.6 Gratification

The Items for gratification were adapted from previous studies (Islam, 2011a & 2011b;

Islam 2014) and modified accordingly to suit the needs of the present study.

Gratification was measured by ten items. The items are shown in Table 3.8.

96

Table 3.8: Gratification Items

Items

GRAT1 Overall I am satisfied with the ease of completing my

task using ICT facilities.

GRAT2 I am satisfied with the ICT facilities provided by the

university.

GRAT3 I am satisfied with the ease of use of the ICT

facilities.

GRAT4 The university ICT facilities have greatly affected the

way I search for information and conduct my

research.

GRAT5 Providing the ICT is an indispensable services

provided by the university.

GRAT6 Overall I am satisfied with the amount of time it takes

to complete my task.

GRAT7 I am satisfied with the structure of accessible

information (available as categories of research

domain, or by date of issue—of journals in particular,

or as full-texts or abstracts of theses and

dissertations) of the university research databases.

GRAT8 I am satisfied with the support information provided

by the university’s ICT facilities.

GRAT9 I am satisfied in using ICT facilities for teaching,

learning and research.

GRAT10 Overall I am satisfied with the Wireless Internet

service provided at the university.

3.4.7 Pilot Test

The validity of an instrument is concerned with the soundness and the effectiveness of

the measurement. According to Babbie (2001), validity explains a measure that

precisely reflects the concept which is intended to be measured. The questionnaire

items used in this study were adopted and modified from Islam (2011a and 2011b),

Islam (2014), Ahmad et al., (2010), Usluel et al., (2008) and Zejno and Islam (2012).

Their instruments were validated using well established statistical tools. Specially,

Islam (2011a and 2011b; 2014) developed and validated previous instruments by

applying a Rasch Model and most of the items in the present study were adopted and

97

modified from these instrument. Moreover, the Technology Acceptance Model (Davis,

1989), Technology Satisfaction Model (Islam, 2014) and Online Database Adoption

and Satisfaction (ODAS) model were applied as a theoretical framework for this study.

According to Mathieson, Peacock, and Chin (2001), the TAM has been examined and

broadly accepted among researchers as a theoretically-based model with good

predictive validity and confirmed reliability in the information technology field. The

instrument of Technology Adoption and Gratification (TAG) model was translated

from English to Chinese in order to collect data from Jiaxing University in China. The

questionnaire was translated by a young researcher Qian Xiuxiu from Jiaxing

University who was my former research partner. The translated questionnaire was

verified by Professor Dr. Zhang Quan and Professor Tao Danyu from the college of

foreign language studies, Jiaxing University.

To establish the validity and reliability of the questionnaire items, a Rasch

analysis was used. Using Winsteps version 3.49, a pilot test was conducted with 82

items. A total of 60 lecturers were equally collected from the University of Malaya

and Jiaxing University to conduct pilot tests. The findings of the Rasch model showed

that the summary statistics of the 82 items obtained from the Rasch analysis,

indicating the items measured reliability (r = .90), and persons measured reliability (r

= .95). In addition, items separation (3.05) and persons separation (4.35) are

statistically significant as shown in Figure 3.15.

98

TABLE 3.1 TAG MODEL ZOU999ws.txt Nov 16 21:43 2014

INPUT: 60 PERSONS, 82 ITEMS MEASURED: 60 PERSONS, 82 ITEMS, 7 CATS 3.49

--------------------------------------------------------------------------------

SUMMARY OF 60 MEASURED PERSONS

+-----------------------------------------------------------------------------+

| RAW MODEL INFIT OUTFIT |

| SCORE COUNT MEASURE ERROR MNSQ ZSTD MNSQ ZSTD |

|-----------------------------------------------------------------------------|

| MEAN 459.8 82.0 .97 .11 1.17 .3 1.12 .1 |

| S.D. 47.3 .0 .65 .04 .70 2.9 .64 2.9 |

| MAX. 563.0 82.0 3.38 .30 4.80 7.7 4.38 6.7 |

| MIN. 368.0 82.0 .09 .08 .27 -6.4 .24 -6.8 |

|-----------------------------------------------------------------------------|

| REAL RMSE .15 ADJ.SD .64 SEPARATION 4.35 PERSON RELIABILITY .95 |

|MODEL RMSE .12 ADJ.SD .64 SEPARATION 5.37 PERSON RELIABILITY .97 |

| S.E. OF PERSON MEAN = .09 |

+-----------------------------------------------------------------------------+

PERSON RAW SCORE-TO-MEASURE CORRELATION = .95

CRONBACH ALPHA (KR-20) PERSON RAW SCORE RELIABILITY = .96

SUMMARY OF 82 MEASURED ITEMS

+-----------------------------------------------------------------------------+



|-----------------------------------------------------------------------------|

| MEAN 336.4 60.0 .00 .13 1.02 -.1 1.12 .1 |

| S.D. 32.1 .0 .45 .02 .53 2.3 .78 2.7 |

| MAX. 390.0 60.0 1.44 .19 2.72 6.7 5.06 9.7 |

| MIN. 195.0 60.0 -1.11 .09 .40 -3.5 .42 -3.4 |

|-----------------------------------------------------------------------------|

| REAL RMSE .14 ADJ.SD .43 SEPARATION 3.05 ITEM RELIABILITY .90 |

|MODEL RMSE .13 ADJ.SD .43 SEPARATION 3.38 ITEM RELIABILITY .92 |

| S.E. OF ITEM MEAN = .05 |

+-----------------------------------------------------------------------------+

UMEAN=.000 USCALE=1.000

ITEM RAW SCORE-TO-MEASURE CORRELATION = -.98

Figure 3.15: Summary Statistics

The item polarity map exhibits most of the items measured in the same

direction (ptmea. Corr. > 0.30), which are greater than the recommended value (.30).

However, few items discovered low point measure correlations due to the small

sample size as shown in Figure 3.16.

99

Figure 3.16: Item Polarity Map

100

The item map exhibits that the map of the valid items is located in their

calibrations on a single continuum. As shown on the right hand side of Figure 3.17,

the items on this single continuum are structured from the “less difficult to be rated as

technology adoption and gratification” to the “more difficult to be rated as technology

adoption and gratification” The left hand side of this figure also matches the level of a

person’s ability in a single line, whereby the respondents are ordered from the “higher

level of agreement to endorse lecturers’ adoption and gratification with the ICT

usage” to the “lower level agreement to endorse lecturers’ adoption and gratification

with the ICT usage”. Therefore, this study demonstrated that the most difficult item

was use12 and the least difficult use2, although the majority of the lecturers elected to

use the ICT facilities in higher education as shown in Figure 3.17.

102

slightly revised in order to validate the final questionnaire as shown in Figure 3.18.

Statistically, INFIT and OUTFIT Mean square (MNSQ) statistics for items should be

greater than 0.5 and less than 1.5 for rating scale application. Similarly, mean square

(MNSQ) statistics for Pearson measures should also be less than 1.8.

Figure 3.18: Item Fit Order

103

This study discovered that the variance explained by the measures was 48%

which indicated that the items were able to endorse the lecturers’ adoption and

gratification in using ICT facilities as shown in Figure 3.19.



--------------------------------------------------------------------------------

PRINCIPAL COMPONENTS (STANDARDIZED RESIDUAL) FACTOR PLOT

Factor 1 extracts 9.8 units out of 82 units of ITEM residual variance noise.

Yardstick (variance explained by measures)-to-This Factor ratio: 7.7:1

Yardstick-to-Total Noise ratio (total variance of residuals): .9:1

Table of STANDARDIZED RESIDUAL variance (in Eigenvalue units)

Empirical Modeled

Total variance in observations = 157.6 100.0% 100.0%

Variance explained by measures = 75.6 48.0% 50.6%

Unexplained variance (total) = 82.0 52.0% 49.4%

Unexpl var explained by 1st factor = 9.8 6.2%

-2 -1 0 1 2

++---------------+---------------+---------------+---------------++

.7 + A | +

| | |

.6 + FEC BD | +

| I GHJ | |

.5 + L MK | +

| N O | |

F .4 + Q SRPT +

A | V |UW |

C .3 + Y X Z | +

T | 1 | |

O .2 + 1 1 | +

R | 1 1 3 1 3 21 |

.1 + 1 1| +

1 | |1 1 |

.0 +---------------------1----------|1--1----------------------------+

L | 1 1 1 | 1 1 |

O -.1 + | 2 +

A | | 1z |

D -.2 + y | p x +

I | r | uvs twq |

N -.3 + | mn o +

G | | k j l |

-.4 + | g i hf +

| | d e |

-.5 + | +

| | cb |

-.6 + | a +

| | |

++---------------+---------------+---------------+---------------++

-2 -1 0 1 2

ITEM MEASURE

Figure 3.19: Principal Component

104

3.4.8 Reliability and Validity of Instrument

The questionnaire’s reliability and validity were established through a RASCH Model

applying Winsteps version 3.49. A total of 396 lecturers were collected from the

different faculties of the University of Malaya and Jiaxing University for the final

validation. The findings of the Rasch analyses discovered in the summary statistics

were that: (i) the items reliability was found to be 0.98 (SD=195.5), while the persons

reliability was 0.96 (SD=57.8); (ii) the items and persons separation were 7.99 and

5.00, respectively as shown in Figure 3.20.



--------------------------------------------------------------------------------

SUMMARY OF 396 MEASURED PERSONS

+-----------------------------------------------------------------------------+



|-----------------------------------------------------------------------------|

| MEAN 455.8 82.0 1.02 .12 1.14 .1 1.10 .0 |

| S.D. 57.8 .1 .78 .04 .68 3.1 .64 3.0 |

| MAX. 568.0 82.0 4.14 .41 4.99 9.3 5.52 9.3 |

| MIN. 238.0 81.0 -.70 .08 .17 -7.3 .17 -7.5 |

|-----------------------------------------------------------------------------|

| REAL RMSE .15 ADJ.SD .76 SEPARATION 5.00 PERSON RELIABILITY .96 |

|MODEL RMSE .13 ADJ.SD .77 SEPARATION 6.12 PERSON RELIABILITY .97 |

| S.E. OF PERSON MEAN = .04 |

+-----------------------------------------------------------------------------+

VALID RESPONSES: 99.9%

SUMMARY OF 82 MEASURED ITEMS

+-----------------------------------------------------------------------------+



|-----------------------------------------------------------------------------|

| MEAN 2201.2 396.0 .00 .05 1.03 -.3 1.10 .0 |

| S.D. 195.5 .1 .44 .01 .44 4.6 .56 4.9 |

| MAX. 2533.0 396.0 1.39 .07 2.53 9.9 3.06 9.9 |

| MIN. 1372.0 395.0 -1.00 .04 .56 -6.4 .57 -5.9 |

|-----------------------------------------------------------------------------|

| REAL RMSE .05 ADJ.SD .43 SEPARATION 7.99 ITEM RELIABILITY .98 |

|MODEL RMSE .05 ADJ.SD .43 SEPARATION 8.71 ITEM RELIABILITY .99 |

| S.E. OF ITEM MEAN = .05 |

+-----------------------------------------------------------------------------+

UMEAN=.000 USCALE=1.000

Figure 3.20: Summary of Statistics

The item polarity map exhibits the majority of the items measured in the same

direction (ptmea. Corr. > 0.31), which are greater than the recommended value (.30).

However, only three items (use13, use, 12 and use3) were slightly smaller than the

recommended value as shown in Figure 3.21.

105

Figure 3.21: Item Polarity Map

106

According to Bond and Fox (2001), the Rasch model is based on the idea that

all persons are more likely to respond correctly to easy items than difficult ones, and

all items are more likely to be answered by persons who have a higher ability than a

lower one. Figure 3.22 reveals that the map of the valid items is located in their

calibrations on a single continuum. As shown on the right hand side of Figure 3.22,

the items on this single continuum are structured from the “less difficult to be rated as

technology adoption and gratification” to the “more difficult to be rated as technology

adoption and gratification” The left hand side of this figure also matches the level of a

person’s ability in a single line, whereby the respondents are ordered from the “higher

level of agreement to endorse lecturers’ adoption and gratification with the ICT

facilities” to the “lower level agreement to endorse lecturers’ adoption and

gratification with the ICT facilities”. Thus, this study discovered that the most difficult

item was use12 and the least difficult were use2 and use5. Majority of the lecturers

elected to use the ICT facilities in higher education as shown in Figure 3.22.

107



--------------------------------------------------------------------------------

PERSONS MAP OF ITEMS

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79grat7 82grat10

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28peu12 30peu14 34pu3 36pu5 38pu7

74grat2 75grat3 78grat6 80grat8

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49cse2 56cse9 57cse10 60cse13 68int6

71int9 72int10 77grat5

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Figure 3.22: Item Map

Item fit order demonstrated that most items showed good item fit and were

constructed on a continuum of increasing intensity. However, eight items (use12,

108

use13, use16, use9, use3, use7, use1 and use14) showing a misfit to the Rasch model

had to be removed in order to validate the final questionnaire as shown in Figure 3.23.

Figure 3.23: Item Fit Order

109

Table 3.9: The Valid Items Measuring Perceived Ease of Use (PEU), Perceived

Usefulness (PU), Computer Self-Efficacy (CSE), Intention to Use (INT), Actual Use

(USE) and Gratification (GRAT)

Constructs The List of the Valid Items

PEU

PEU1 I find the university ICT facilities easy to use.

PEU2 I find it easy to access the university research databases.

PEU3 It is easy for me to become skilful at using university

databases for conducting research.

PEU4 Interacting with the research databases system requires


PEU5 I find it easy to get the research databases to help facilitate

my research.

PEU6 Interacting with the university research databases system is

very stimulating for me.

PEU7 I find it easy to select articles/journals of different categories

using research databases (Education/Engineering/Business,

etc.).

PEU8 With Wireless Internet, I find it easy to access university

databases to do research.

PEU9 Wireless Internet allows me to access research and learning

materials from Web Browser (WWW).

PEU10 Wireless Internet is easy to use for teaching and research.

PEU11 I find it easy to use computers provided by the university.

PEU12 My interaction with university ICT services available is clear

and understandable.

PEU13 I find it easy to download teaching, learning and research

materials using Wireless Internet in terms of speed.

PEU14 It is easy for me to use multimedia facilities at the university.

PEU15 I find it easy to use Microsoft office for teaching and research

purposes.

PU

PU1 Using the ICT facilities at the university enables me to

accomplish tasks more quickly.

PU2 Using the ICT services available at the university increases

my research productivity.

PU3 The current university ICT system makes work more

interesting.

PU4 ICT facilities at the university improve the quality of the

work I do.

PU5 Using the university ICT facilities enhances my research

skills.

PU6 Using the university ICT facilities would make it easier for

me to find information.

PU7 ICT services at the university make information always

available to users.

PU8 Using the ICT facilities helps me write my journal articles. PU9 My job would be difficult to perform without the ICT

facilities.

110


PU

PU10 Using the ICT facilities saves my time.

PU11 Using the university ICT facilities provides me with the latest

information on specific areas of research.

PU12 I can use ICT facilities from anywhere, anytime at the

campus.

PU13 I believe that the use of ICT in the classroom enhances

student learning in my discipline.

PU14 I believe that email and other forms of electronic

communication are important tools in faculty/student

communication.

PU15 I believe that web-based instructional materials enhance

student learning.

PU16 Using the ICT services enables me to download teaching,

learning and research materials from the internet.

CSE

CSE1 I have the skills required to use computer applications for

writing my research papers.

CSE2 I have the skills and knowledge required to use computer

applications for demonstrating specific concepts in class.

CSE3 I have the skills required to use computer applications for

presenting lectures.

CSE4 I have the skills required to communicate electronically with

my colleagues and students.

CSE5 I have the ability to e-mail, chat, download teaching, learning

and research materials, and search different websites.

CSE6 I have the ability to use the Wireless Internet service provided

by the university.

CSE7 I have the skills required to use ICT facilities to enhance the

effectiveness of my teaching, learning and research.

CSE8 I feel capable of using the university research databases for

writing journal papers.

CSE9 I have the ability to navigate my way through the ICT

facilities.

CSE10 I have the skills required to use the ICT facilities to enhance

the quality of my research works.

CSE11 I have the ability to save and print journals/articles from the

research databases.

CSE12 I have the knowledge and skills required to benefit from

using the university ICT facilities.

CSE13 I am capable of accessing the research databases from the

university website.

CSE14 I am capable to download and install research related

software using ICT facilities at the university.

CSE15 I am capable of using multimedia facilities for delivering lecturers.

111


INT

INT1 I will use the ICT facilities to carry out my research activities.

INT2 I will use computer applications to present lecture materials

in class.

INT3 I will use Microsoft office applications to write my research

papers.

INT4 I will teach a course that will be delivered totally on the

world wide web.

INT5 I intend to use ICT facilities for teaching, learning and

research purposes frequently.

INT6 I intend to download research materials from the university

databases frequently.

INT7 I will use the university research databases to update myself

on the research areas I am pursuing.

INT8 I intend to use the ICT facilities to upgrade my research

performance.

INT9 I will use the university research databases to write

outstanding journal articles.

INT10 I intend to use research related software to analyze the data.

USE

USE2 How often do you use the following ICT facilities for

research and academic-related activities? - Laptop or Desktop

computers


research and academic-related activities? - Web browser

(www)


research and academic-related activities? - Search engine

(e.g. Google, Yahoo, etc.).


research and academic-related activities? - Microsoft Office

applications.


research and academic-related activities? - Document

processing (e.g. PDF).


research and academic-related activities? - Email.


research and academic-related activities? - Document

editing/composing.

USE15 I use ICT facilities for making presentations in the course GRAT1 Overall I am satisfied with the ease of completing my task

using ICT facilities. GRAT2 I am satisfied with the ICT facilities provided by the

university.

112


GRAT3 I am satisfied with the ease of use of the ICT facilities. GRAT4 The university ICT facilities have greatly affected the way I

search for information and conduct my research.

GRAT5 Providing the ICT is an indispensable services provided by

the university. GRAT6 Overall I am satisfied with the amount of time it takes to

complete my task. GRAT7 I am satisfied with the structure of accessible information

(available as categories of research domain, or by date of

issue—of journals in particular, or as full-texts or abstracts of

theses and dissertations) of the university research databases. GRAT8 I am satisfied with the support information provided by the

university’s ICT facilities. GRAT9 I am satisfied in using ICT facilities for teaching, learning

and research. GRAT10 Overall I am satisfied with the Wireless Internet service

provided at the university.

This study revealed that the variance explained by the measures was 55.3%

which demonstrated that the items were able to endorse the University of Malaya and

Jiaxing University lecturers’ adoption of and gratification in using ICT facilities in

higher education for their teaching and research purposes as shown in Figure 3.24.

113



--------------------------------------------------------------------------------

PRINCIPAL COMPONENTS (STANDARDIZED RESIDUAL) FACTOR PLOT

Factor 1 extracts 9.5 units out of 82 units of ITEM residual variance noise.

Yardstick (variance explained by measures)-to-This Factor ratio: 10.7:1

Yardstick-to-Total Noise ratio (total variance of residuals): 1.2:1

Table of STANDARDIZED RESIDUAL variance (in Eigenvalue units)

Empirical Modeled

Total variance in observations = 183.4 100.0% 100.0%

Variance explained by measures = 101.4 55.3% 57.3%

Unexplained variance (total) = 82.0 44.7% 42.7%

Unexpl var explained by 1st factor = 9.5 5.2%

-2 -1 0 1 2

++---------------+---------------+---------------+---------------++

.7 + | +

| A D BC| |

.6 + E GF | +

| JI H | |

F .5 + K | +

A | L| |

C .4 + M | +

T | P O QN | |

O .3 + RT S | +

R | V ZY XW U |

.2 + 1 1 11 2 | +

1 | 1 21 |1 |

.1 + | +

L | 1 1111 1 |

O .0 +-----------------------------1--|---------11---------------------+

A | | 1 1 |

D -.1 + 11 +

I | 1| 1 1 |

N -.2 + |z 1 1 1 +

G | | xy w |

-.3 + |suqprvt +

| | m oln |

-.4 + | gkji h +

| | de f |

-.5 + | acb +

++---------------+---------------+---------------+---------------++

-2 -1 0 1 2

ITEM MEASURE

Figure 3.24: Principal Components

3.5 DATA ANALYSIS PROCEDURES

The data analysis procedures involved in the study included the use of descriptive

statistics using SPSS version 22. The specific purpose of RASCH analysis was to

develop and validate the psychometric properties of TAG model. As such, the

questionnaire’s reliability and validity were established through a RASCH analysis

applying Winsteps version 3.49. This present study also applied the three-stage of the

structural equation modeling (SEM), namely, confirmatory factor analysis (CFA),

114

full-fledged structural model and invariance analysis (metric and configural invariance

analyses) to develop and validate the Technology Adoption and Gratification (TAG)

model in a cross-culture that addressed the objectives of the study. In order to analyse

the causal relationships among the various constructs, AMOS software version 18.0

was applied. Furthermore, the structural aspects of the validity are specially addressed

in the validation of the TAG model. Finally, a Sobel test was also conducted to

examine the indirect relationships between the constructs of the TAG model.

3.6 STRUCTURAL EQUATION MODELING (SEM)

Structural Equation Modeling (SEM) is one of the statistical models that seek to

validate, extend and propose new theories. Primarily, this study used the Structural

Equation Modeling (SEM) techniques to validate the measurement models and

estimate the fit of the hypothesized and revised models to the survey data. Specifically,

the three-step Structural Equation Modeling, involving Confirmatory Factor Analysis

(CFA), a Full-fledged Structural model and invariance analysis, were performed to

develop and validate the Technology Adoption and Gratification (TAG) model, and to

explain the cross-cultural validation in higher education. The hypothesized Structural

Equation Model (SEM) for the study is shown in Figure 3.25.


115

3.7 MODEL EVALUATION

In order to analyze the structural relationships among the various constructs, namely,

computer self-efficacy, perceived ease of use, perceived usefulness, intention to use,

use and gratification, the analysis used the greatest probability estimation in

generating estimates of the six Confirmatory Factor Analysis (CFA) models and Full-

fledged structural equation model. In other words, the models were estimated by

applying a set of measures to assess the goodness-of-fit of each model. The goodness-

of-fit measures, guided by the universally accepted criteria for deciding good fit,

assessed the (i) reliability of the hypothesized model with the observed data, (ii)

justifications of variability, (iii) fairness of the estimates, and (iv) simplicity of the

measured models. The adequacy of the model was determined using five measures of

good fit. The first Chi-square (χ2) measured the compatibility of the data with the

hypothesized model. The chi-square value divided (χ2/df) by the degree of freedom

value should be less than or equal to 3.0. Second, the Root Mean Square Error of

Approximation (RMSEA) measures the intimacy of model fit, and should be less

than .08. Third, the Comparative Fit Index (CFI) evaluates the fit of the existing

model with a null model that predicts the latent variables to be uncorrelated in the

model. The value of the CFI should be greater than or equal to 0.90. Fourth, the

Goodness of Fit Index (GFI), which deals with error in imitating the variance-

covariance matrix, must be greater than .90 to indicate good fit. Finally, the Tucker-

Lewis Index (TLI) measures the estimated model against the null-model, and should

display a value of .90 or more to show good fit of the model. The summary of the

goodness-of-fit indices is shown in Table 310.

116

Table 3.10: The Recommended Goodness-of-Fit Indices

Fit Indices Recommended

Chi-square (χ2) Non-significant

p value ≥ .05

RMSEA ≤ .08

CFI ≥ .90

TLI ≥ .90

117

CHAPTER 4

RESULTS

4.1 INTRODUCTION

This chapter presents the findings of this study. The data were examined to discover

the causal relationships of the Technology Adoption and Gratification (TAG) model

between the University of Malaya (UM) and Jiaxing University (JU) lecturers’

adoption and gratification in using ICT facilities and the constructs of: computer self-

efficacy, perceived ease of use, perceived usefulness, intention to use, actual use and

gratification. Thus, A 3-step Structural Equation Modeling (SEM) approach was

adopted. Section 1 demonstrates the results of validating the Confirmatory Factor

Analysis (CFA) for 6 measurement models, namely, computer self-efficacy, perceived

ease of use, perceived usefulness, intention to use, actual use and gratification.

Section 2 displays the estimation of the full-fledged structural model of TAG. Section

3 shows the cross-cultural validation of the TAG model using invariance analyses.

Moreover, the measurement models were evaluated, followed by an explanation of the

full-fledged Structural Equation Modeling (SEM) as well as cross-cultural-invariant.

SECTION 1: VALIDATING THE CONFIRMATORY FACTOR ANALYSIS

MODELS

4.2 ONE-FACTOR MEASUREMENT MODEL OF PERCEIVED EASE OF

USE (PEU)

The specific objective was to measure the lecturers’ views on the ease of use of ICT

facilities for their teaching and research purposes in higher education. To address the

objective 2, the present study conducted a Confirmatory Factor Analysis (CFA) to

118

confirm lecturers’ perceptions on the ease of use and to test the impact of perceived

ease of use in the hypothesized TAG model; hypotheses 2 and 9 were evaluated. The

preliminary CFA model was performed on 15 items that explain the factor of

perceived ease of use. The majority of the standardized factor loadings were greater

than .70, with only few items demonstrating loadings ranging from .57 to .69.

Nevertheless, the findings of one-factor measurement model of perceived ease of use

discovered that the overall fit indices did not fulfill the statistical requirements as

indicated by the following fit indices: χ2 (df = 90) = 1152.997; p = 0.000; RMSEA =

0.173; CFI = 0.723; TLI = 0.677; SRMR = 0.185 (see Appendix C). As a result, the

model required revision due to the violations and estimation. After deleting these

items (PEU1 PEU3, PEU5, PEU8, PEU10, PEU11, PEU12, PEU13 and PEU14), the

analysis demonstrated an exceptional goodness-of-fit of the model. The items were

removed one at a time according to their modification indices, with the largest being

removed first, which showed the presence of multiple collinearity as shown in Table

4.1.

Table 4.1: The List of Removed Items

Item

Deleted

Chi-square

(χ2)

df p value RMSEA CFI TLI SRMR

PEU1 1060.716 77 .000 .180 .724 .674 .189

PEU3 932.028 65 .000 .184 .729 .675 .181

PEU5 746.723 54 .000 .180 .749 .694 .175

PEU8 513.111 44 .000 .164 .791 .739 .159

PEU10 267.821 35 .000 .130 .872 .835 .119

PEU11 216.366 27 .000 .133 .882 .842 .119

PEU12 204.390 20 .000 .153 .862 .806 .132

PEU13 66.497 14 .000 .097 .950 .924 .071

PEU14 19.151 9 .024 .053 .987 .979 .043

The following 6 items remained in the original pool of 15 items: PEU2, PEU4,

PEU6, PEU7, PEU9 and PEU15. These items were used as the indicators for the one-

119

factor measurement model of perceived ease of use. The Figure 4.1 shows the error

variances in the model represented by e2, e4, e6, e7, e9 and e15.

Figure 4.1: One-Factor Measurement Model for Perceived Ease of Use

The results of the model’s overall goodness-of-fit statistics showed an

exceptional fit; the model chi-square, χ2 (df=9) =19.151, p=.024 and the root mean

square error of approximation (RMSEA) represented a goodness-of-fit of the model

with a satisfactory value of .053; the value of the comparative fit index (CFI) was

.987; the Tucker-Lewis index (TLI) value was .979 and the value of the standardized

root mean square residual (SRMR) was .043. The CFA model fit indices were

supported by prior studies (Hu & Bentler, 1999; Marsh, Hau & Wen, 2004). The

results in Figure 4.1 indicate that the parameters were free from offending estimates.

The standardized factor demonstrated loadings ranging from .55 to .78, representing

statistically significant indicators. The item loadings are shown in Table 4.2.

120

Table 4.2: The Loadings for Perceived Ease of Use

Item

No Item label Loadings M SD α

PEU2 I find it easy to access the university

research databases.

.72 5.300 1.313

.833

PEU4 Interacting with the research

databases system requires minimal

effort on my part.

.72 4.931 1.366

PEU6 Interacting with the university

research databases system is very

stimulating for me.

.78 4.846 1.388

PEU7 I find it easy to select articles/journals

of different categories using research

databases

(Education/Engineering/Business,

etc.).

.75 5.346 1.246

PEU9 Wireless Internet allows me to access

research and learning materials from

Web Browser (WWW).

.55 5.287 1.486

PEU15 I find it easy to use Microsoft office

for teaching and research purposes.

.55 5.828 1.123

The findings of the one-factor measurement model of perceived ease of use

also revealed that the lecturers’ views on the ease of use of ICT facilities were

indicated by six valid predictors. They explained approximately 46.7% of the

variability of lecturers’ perceived ease of use of ICT facilities for their teaching and

research purposes in higher education as represented in Table 4.3.

Table 4.3: The Squared Multiple Correlations

Items Measured

PEU2 .524

PEU4 .515

PEU6 .609

PEU7 .558

PEU9 .301

PEU15 .299

The average total variance 0.467

121

4.3 ONE-FACTOR MEASUREMENT MODEL OF PERCEIVED

USEFULNESS (PU)

The specific objective was to evaluate the lecturers’ perception regarding the

usefulness of ICT facilities in higher education. To address the objective 3, this study

performed a Confirmatory Factor Analysis (CFA) to confirm lecturers’ perceptions on

the usefulness of ICT facilities for their teaching and research purposes and to

examine the influence of perceived usefulness in the hypothesized TAG model,

hypotheses 3 and 9 were tested. The initial CFA model was estimated with 16

indicators of perceived usefulness. All items exhibited a loading greater than .70 on

their respective factor. However, the results of the one-factor measurement model of

perceived usefulness exhibited that the overall fit indices were not adequate as

indicated by the following fit indices: χ2 (df = 104) = 1217.231; p = 0.000; RMSEA =

0.165; CFI = 0.783; TLI = 0.750; SRMR = 0.137. Thus, 9 items were dropped due to

violations and estimation (see Appendix B). After deleting these items (PU1, PU8,

PU9, PU10, PU12, PU13, PU14, PU15 and PU16), the overall fit indices showed an

adequate fit. The items were removed one at a time according to their modification

indices, with the largest being removed first, which showed the presence of multiple

collinearity as indicated in Table 4.4.


Item

Deleted

Chi-square

(χ2)


PU1 1090.810 90 .000 .168 .784 .748 .141

PU8 1005.069 77 .000 .175 .784 .745 .147

PU9 783.672 65 .000 .167 .819 .783 .137

PU10 653.472 54 .000 .168 .837 .801 .136

PU12 573.215 44 .000 .174 .848 .810 .136

PU13 444.767 35 .000 .172 .870 .833 .124

PU14 216.395 27 .000 .133 .933 .910 .084

PU15 91.114 20 .000 .095 .973 .962 .049

PU16 44.929 14 .000 .075 .987 .981 .032

122

Eventually, only seven items were selected for this model; they were: PU2,

PU3, PU4, PU5, PU6, PU7 and PU11. These items were used as the indicators for the

one-factor measurement model of perceived usefulness with e2, e3, e4, e5, e6, e7 and

e11 representing error variances. This is shown in Figure 4.2.

Figure 4.2: One-Factor Measurement Model for Perceived Usefulness

The model’s overall goodness-of-fit statistics showed a satisfactory fit; the

model chi-square, χ2 (df=14) =44.929, p=.000 and the root mean square error of

approximation (RMSEA) signified an adequacy of the model with a value of .075; the

value of the comparative fit index (CFI) was .987; the Tucker-Lewis index (TLI)

value was .981 and the value of the standardized root mean square residual (SRMR)

was .032. The overall CFA model fit indices were supported by previous studies (Hu

& Bentler, 1999; Marsh, Hau & Wen, 2004). The results in Figure 4.6 exhibit that the

items were free from offending estimates. The standardized factor established

loadings ranging from .70 to .89, indicating statistically significant indicators. The

123

item loadings are shown in Table 4.5.

Table 4.5: The Loadings for Perceived-Usefulness Items

Item


PU2 Using the ICT services available at the

university increases my research

productivity.

.83 5.555 1.270

.944

PU3 The current university ICT system

makes work more interesting.

.89 5.217 1.324

PU4 ICT facilities at the university improve

the quality of the work I do.

.88 5.401 1.289

PU5 Using the university ICT facilities

enhances my research skills.

.89 5.335 1.285


would make it easier for me to find

information.

.86 5.558 1.304

PU7 ICT services at the university make

information always available to users.

.83 5.247 1.379


provides me with the latest information

on specific areas of research.

.70 5.628 1.226

The results of the one-factor measurement model of perceived usefulness also

discovered that the lecturers’ perceptions on the usefulness of ICT facilities for their

teaching and research purposes in higher education were demonstrated by seven valid

predictors. They explained almost 71.1% of the variability of lecturers’ perceived

usefulness of ICT facilities as shown in Table 4.6.


Items Measured

PU2 .696

PU3 .799

PU4 .774

PU5 .796

PU6 .736

PU7 .688

PU11 .489


124

4.4 ONE-FACTOR MEASUREMENT MODEL OF COMPUTER SELF-

EFFICACY (CSE)

The specific objective was to examine the lecturers’ computer self-efficacy to access

ICT facilities in higher education for their teaching and research purposes. To address

the objective 4, the current study conducted Confirmatory Factor Analysis (CFA) to

assess the lecturers’ perceptions on their computer self-efficacy of ICT use and to

evaluate the effect of computer self-efficacy in the hypothesized TAG model,

hypotheses 1, 6, 7 and 8 were estimated. The original CFA model was estimated with

15 items of computer self-efficacy. All items demonstrated a loading greater than .59

on their respective factor. However, the findings of the one-factor measurement model

of computer self-efficacy revealed that the overall fit indices did not execute the

statistical requirements as indicated by the following fit indices: χ2 (df = 90) =

834.732; p = 0.000; RMSEA = 0.145; CFI = 0.861; TLI = 0.838; SRMR = 0.067. As

such, 8 items were removed due to violations and estimation (see Appendix A). After

deleting these items (CSE1, CSE2, CSE3, CSE5, CSE6, CSE8, CSE13 and CSE14),

the analysis showed a goodness-of-fit of the model. The items were removed one at a

time according to their modification indices, with the largest being removed first,

which showed the presence of multiple collinearity as shown in Table 4.7.


Item

Deleted

Chi-square

(χ2)


CSE1 663.238 77 .000 .139 .880 .858 .062

CSE2 568.268 65 .000 .140 .885 .863 .062

CSE3 427.488 54 .000 .132 .904 .883 .060

CSE5 266.433 44 .000 .113 .934 .918 .057

CSE6 217.543 35 .000 .115 .942 .925 .059

CSE8 170.934 27 .000 .116 .947 .930 .061

CSE13 48.627 20 .000 .060 .988 .983 .028

CSE14 20.893 14 .104 .035 .997 .995 .012

125

The items that remained were: CSE4, CSE7, CSE9, CSE10, CSE11, CSE12

and CSE15. These items were used as the indicators for the one-factor measurement

model of computer self-efficacy. Figure 4.3 shows the error variances in the model

represented by e4, e7, e9, e10, e11, e12 and e15.

Figure 4.3: One-Factor Measurement Model for Computer Self-efficacy

The model’s overall goodness-of-fit statistics showed excellent fit; the model

chi-square was statistically non-significant, χ2 (df=14) =20.893, p=.104 and the root

mean square error of approximation (RMSEA) indicated the model’s adequacy at an

acceptable value of .035. The value of the comparative fit index (CFI) was .997; the

Tucker-Lewis index (TLI) value was .995 and the value of the standardized root mean

126

square residual (SRMR) was .012. The overall CFA model fit indices were

recommended by earlier researchers (Hu & Bentler, 1999; Marsh, Hau & Wen, 2004).

The results in Figure 4.3 show that the parameters were free from offending estimates.

The standardized factor demonstrated loadings ranging from .72 to .88, indicating

statistically significant indicators. The item loadings are shown in Table 4.8.

Table 4.8: The Loadings for Computer Self-efficacy

Item


CSE4 I have the skills required to

communicate electronically with my

colleagues and students.

.72 5.974 .988

.938

CSE7 I have the skills required to use ICT

facilities to enhance the effectiveness

of my teaching, learning and research.

.85 5.931 .984

CSE9 I have the ability to navigate my way

through the ICT facilities.

.87 5.714 1.051

CSE10 I have the skills required to use the

ICT facilities to enhance the quality of

my research works.

.88 5.785 .009

CSE11 I have the ability to save and print

journals/articles from the research

databases.

.84 5.989 1.016

CSE12 I have the knowledge and skills

required to benefit from using the

university ICT facilities.

.87 5.891 .955

CSE15 I am capable of using multimedia

facilities for delivering lecturers.

.77 5.939 1.029

The findings of the one-factor measurement model of computer self-efficacy

also exhibited that the lecturers’ views on the computer self-efficacy of ICT usage

were indicated by seven valid predictors. They explained approximately 68.8% of the

variability of lecturers’ computer self-efficacy of ICT usage for their teaching and

research purposes in higher education as shown in Table 4.9.

127


Items Measured

CSE4 .517

CSE7 .721

CSE9 .760

CSE10 .771

CSE11 .699

CSE12 .763

CSE15 .586


4.5 ONE-FACTOR MEASUREMENT MODEL OF INTENTION TO USE

(INT)

The specific objective was to examine the lecturers’ intention to use of ICT facilities

for their teaching and research purposes in higher education. To address the objective

5, the present study performed a Confirmatory Factor Analysis (CFA) to measure the

lecturers’ perceptions on their intention to use of ICT facilities and to determine the

impact of intention to use in the hypothesized TAG model, hypotheses 4 and 10 were

tested. The primary CFA model was estimated with 10 items of intention to use. The

majority of the items exhibited a loading greater than .63 on their respective factor.

The majority of the standardized factor loadings were greater than .63, with only two

items demonstrating loadings ranging from .32 to .40 (INT4 and INT8). However, the

results of the one-factor measurement model of intention to use showed that the

overall fit indices did not confirm a satisfactory fit to the data as indicated by the

following fit indices: χ2 (df = 35) = 269.468; p = 0.000; RMSEA = 0.130; CFI = 0.892;

TLI = 0.861; SRMR = 0.083. Thus, the researcher dropped items: INT4, INT6 and

INT9 due to violations and estimation (see Appendix D). After deleting these items,

the analysis showed a goodness-of-fit of the model. The items were removed one at a

time according to their modification indices, with the largest being removed first,

128

which showed the presence of multiple collinearity as shown in Table 4.10.


Item

Deleted

Chi-square

(χ2)


INT4 235.920 27 .000 .140 .900 .866 .070 INT6 162.597 20 .000 .134 .912 .876 .070 INT9 55.478 14 .000 .087 .966 .949 .069

Eventually, only seven items were taken, namely: INT1, INT2, INT3, INT5,

INT7, INT8 and INT10. These items were used as the indicators for the one-factor

measurement model of Intention to Use. Figure 4.14 shows the error variances in the

model represented by e1, e2, e3, e5, e7, e8, and e10.

Figure 4.4: One-Factor Measurement Model for Intention to Use

129

The model’s overall goodness-of-fit statistics demonstrated an adequate fit; the

model chi-square, χ2 (df=14) =55.478, p=.000 and the root mean square error of

approximation (RMSEA) showed the model’s adequacy with an acceptable value of

.087; the value of the comparative fit index (CFI) was .966; the Tucker-Lewis index

(TLI) value was .949 and the value of the standardized root mean square residual

(SRMR) was .069. The CFA model fit indices were supported by prior studies (Hu &

Bentler, 1999; Marsh, Hau & Wen, 2004). The majority of the items standardized

factor demonstrated loadings ranging from .69 to .83, indicating statistically

significant indicators. The only exception was the sixth item (INT8) which had a

loading of .30. This loading was supported by Hair et al., (2010). The results in Figure

4.4 show that the parameters were free from offending estimates. The item loadings

are shown in Table 4.11.

Table 4.11: The Loadings for Intention-to-Use Items

Item


INT1 I will use the ICT facilities to carry

out my research activities.

.83 6.070 1.038

.700

INT2 I will use computer applications to

present lecture materials in class.

.72 6.280 1.001

INT3 I will use Microsoft office

applications to write my research

papers.

.71 6.189 1.157

INT5 I intend to use ICT facilities for

teaching, learning and research

purposes frequently.

.80 5.916 1.104

INT7 I will use the university research

databases to update myself on the

research areas I am pursuing.

.75 5.861 1.092

INT8 I intend to use the ICT facilities to

upgrade my research performance.

.30 6.108 3.724

INT10 I intend to use research related

software to analyze the data.

.69 5.755 1.221

130

The results of the one-factor measurement model of intention to use also

confirmed that the lecturers’ perceptions on the intention to use of ICT facilities were

demonstrated by seven valid predictors. They explained almost 50% of the variability

of lecturers’ intention to use of ICT facilities for their teaching and research purposes

in higher education as shown in Table 4.12.


Items Measured

INT1 .691

INT2 .524

INT3 .506

INT5 .638

INT7 .570

INT8 .093

INT10 .478


4.6 ONE-FACTOR MEASUREMENT MODEL OF USE (USE)

The specific objective was to determine the lecturers’ actual use of ICT facilities for

their teaching and research purposes in higher education. To address the objective 6,

this study analyzed Confirmatory Factor Analysis (CFA) to examine the lecturers’

views on their actual use of ICT facilities and to measure the effect of use in the

hypothesized TAG model, hypotheses 5 was estimated. The initial CFA model was

tested for the factor of use by 16 items. Most items demonstrated a loading greater

than .56 on their particular factor. However, few items indicated a loading smaller

than the recommended value (see Appendix E). Subsequently, the findings of the one-

factor measurement model of use exhibited that the overall fit indices did not show a

satisfactory fit to the data as indicated by the following fit indices: χ2 (df = 104) =

875.902; p = 0.000; RMSEA = 0.137; CFI = 0.644; TLI = 0.589; SRMR = 0.293. As a

131

result, the initial model required revision due to violations and estimation. After

removing 11 items (USE1, USE3, USE7, USE8, USE9, USE10, USE12, USE13,

USE14, USE15 AND USE16), the measurement model executed the statistical

requirements. The items were removed one at a time according to their modification

indices, with the largest being removed first, which showed the presence of multiple

collinearity as shown in Table 4.13.


Item

Deleted

Chi-square

(χ2)


USE1 833.444 90 .000 .145 .642 .582 .305

USE3 792.271 77 .000 .153 .642 .577 .319

USE7 647.096 65 .000 .151 .680 .616 .291

USE8 562.157 54 .000 .154 .689 .620 .303

USE9 480.960 44 .000 .159 .715 .644 .258

USE10 436.691 35 .000 .170 .692 .604 .281

USE12 359.946 27 .000 .177 .730 .640 .214

USE13 319.156 20 .000 .195 .749 .648 .205

USE14 107.751 14 .000 .130 .893 .840 .174

USE15 18.893 9 .026 .053 .986 .977 .078

USE16 8.475 5 .132 .042 .995 .990 .024

The following 5 items (USE2, USE4, USE5, USE6 and USE11) were used as

the indicator for the one-factor measurement model of use. Figure 4.5 shows the error

variances in the model represented by e2, e4, e5, e6 and e11.

132

Figure 4.5: One-Factor Measurement Model for Use

The model’s overall goodness-of-fit statistics indicated a satisfactory fit; the

model chi-square was statistically non-significant, χ2 (df=5) =8.475, p=.132 and the

root mean square error of approximation (RMSEA) showed an adequate fit with a

value of .042. The value of the comparative fit index (CFI) was .995; the Tucker-

Lewis index (TLI) value was .990 and the value of the standardized root mean square

residual (SRMR) was .024. The CFA model fit indices were supported by previous

researchers (Hu & Bentler, 1999; Marsh, Hau & Wen, 2004). The results in Figure 4.5

exhibit that the items were free from offending estimates. The standardized factor

established loadings ranging from .47 to .83, representing statistically significant

indicators. The item loadings are shown in Table 4.14.

133

Table 4.14: The Loadings for Use Items

Item


USE2 How often do you use the

following ICT facilities for

research and academic-related

activities? - Laptop or Desktop

computers.

.69 6.396 .892 .793



research and academic? - Web

browser (www).

.75 6.239 1.098



research and academic? - Search

engine (e.g. Google, Yahoo,

etc.).

.83 6.383 .902



research and academic? -

Microsoft Office applications.

.73 6.308 933



research and academic-related

activities? - Document

editing/composing.

.47 5.785 1.438

The findings of the one-factor measurement model of use also revealed that the

lecturers’ views on the actual use of ICT facilities were indicated by five valid

predictors. They explained approximately 49.5% of the variability of lecturers’ use of

ICT facilities for their teaching and research purposes in higher education as shown in

Table 4.15.


Items Measured

USE2 .479

USE4 .560

USE5 .692

USE6 .529

USE11 .219


134

4.7 ONE-FACTOR MEASUREMENT MODEL OF GRATIFICATION

(GRAT)

The specific objective was to assess the lecturers’ gratification in using ICT services

for their teaching and research purposes in higher education. To address the objective

7, this study conducted a Confirmatory Factor Analysis (CFA) to confirm the

lecturers’ perceptions on their gratification in using ICT facilities and to examine the

lecturers’ overall gratification in the hypothesized TAG model; the Structural

Equation Modeling (SEM) was applied. The original CFA model was performed on

10 items that explained the factor of gratification. The majority of the standardized

factor loadings were greater than .78, with only two items that demonstrated loadings

ranging from .61 to .65 (GRAT5 and GRAT10). However, the value of RMSEA and

TLI were not as expected (see Appendix F). As such, the one-factor measurement

model of gratification was revised due to violations and estimation. After deleting

these items (GRAT1, GRAT2, GRAT5 and GRAT8), the analysis demonstrated an

exceptional goodness-of-fit of the model. The items were removed one at a time

according to their modification indices, with the largest being removed first, which

showed the presence of multiple collinearity as shown in Table 4.16.


Item

Deleted

Chi-square

(χ2)


GRAT1 205.309 27 .000 .129 .938 .918 .063

GRAT2 125.762 20 .000 .116 .955 .937 .056

GRAT5 47.803 14 .000 .078 .984 .976 .038

GRAT8 13.301 9 .149 .035 .997 .995 .030

135

These are the 6 items that remained after the corrections: GRAT3, GRAT4,

GRAT6, GRAT7, GRAT9 and GRAT10. These items were used as the indicators for

the one-factor measurement model of gratification. Figure 4.6 depicts the error

variances in the model represented by e3, e4, e6, e7, e9 and e10.

Figure 4.6: One-Factor Measurement Model for Gratification

The results of the model’s overall goodness-of-fit statistics showed an

exceptional fit; the model chi-square was statistically non-significant, χ2 (df=9)

=13.301, p=.149 and the root mean square error of approximation (RMSEA)

represented a goodness-of-fit of the model with a satisfactory value of .035; the value

of the comparative fit index (CFI) was .997; the Tucker-Lewis index (TLI) value was

.995 and the value of the standardized root mean square residual (SRMR) was .030.

The CFA model fit indices were recommended by earlier studies (Hu & Bentler, 1999;

Marsh, Hau & Wen, 2004). The results in Figure 4.6 indicate that the parameters were

free from offending estimates. The standardized factor demonstrated loadings ranging

136

from .65 to .86, representing statistically significant indicators. The item loadings are

shown in Table 4.17.

Table 4.17: The Loadings for Gratification Items

Item No Item label Loadings M SD α

GRAT3 I am satisfied with the ease of use

of the ICT facilities.

.82 5.346 1.294 .908

GRAT4 The university ICT facilities have

greatly affected the way I search for

information and conduct my

research.

.79 5.457 1.251

GRAT6 Overall I am satisfied with the

amount of time it takes to complete

my task.

.86 5.237 1.361

GRAT7 I am satisfied with the structure of

accessible information (available as

categories of research domain, or by

date of issue—of journals in

particular, or as full-texts or

abstracts of theses and dissertations)

of the university research databases.

.82 5.149 1.335

GRAT9 I am satisfied in using ICT facilities

for teaching, learning and research.

.85 5.467 1.239


Wireless Internet service provided

at the university.

.65 4.997 1.596

The findings of the one-factor measurement model of gratification showed that

the lecturers’ perceptions on the gratification of ICT facilities were represented by six

valid predictors. They explained almost 64.1% of the variability of lecturers’

gratification in using ICT facilities for their teaching and research purposes in higher

education as shown in Table 4.18.

137


Items Measured

GRAT3 .666

GRAT4 .623

GRAT6 .748

GRAT7 .667

GRAT9 .724

GRAT10 .422


SECTION 2: ESTIMATING THE FULL-FLEDGED STRUCTURAL

EQUATION MODELING

4.8 THE HYPOTHESIZED TECHNOLOGY ADOPTION AND

GRATIFICATION (TAG) MODEL

The structural equation modeling was adopted to evaluate the hypothesized

Technology Adoption and Gratification (TAG) model. The estimation of parameters

used the utmost likelihood technique provided by AMOS 18 to assess the model fit

statistics, structural model, and measurement model (Byrne, 2000). The hypothesized

TAG model was integrated by the six measurement models of the latent constructs,

namely, computer self-efficacy (CSE), perceived usefulness (PU), perceived ease of

use (PEU), intention to use (INT), use (USE) and gratification (GRAT) as tested

individually by the Confirmatory Factor Analysis (CFA) for their overall goodness-of-

fit of the models. The TAG model contained one exogenous variable (CSE), four

mediator variables (PU, PEU, INT and USE), one endogenous variable (GRAT),

thirty eight observed variables and forty three error variances as shown in Figure 4.7.

Perceived usefulness and perceived ease of use were determined by computer self-

efficacy. Furthermore, intention to use was determined directly by perceived ease of

use and perceived usefulness and indirectly by computer self-efficacy. Besides this,

the actual use was directly and indirectly influenced by intention to use, perceived

138

usefulness and perceived ease of use as well as computer self-efficacy. Similarly,

gratification was directly and indirectly influenced by four mediator variables (PEU,

PU, INT and USE), whereas computer self-efficacy was an indirect determinant of

gratification.

In the hypothesized TAG model, the majority of the items exhibited a loading

greater than .70. However, few items produced multiple large residuals with a low

factor loading, and furthermore, they had large modification indices suggesting the

presence of multiple collinearity (see Appendix G). Subsequently, the overall

statistical analyses demonstrated inadequate fit statistics of the hypothesized TAG

model to the empirical data: the chi-square, χ2 (df=654) =1781.099, p=.000; the value

of the comparative fit index (CFI) was .893 and the Tucker-Lewis index (TLI) value

was .885. The only exception was the root mean square error of approximation

(RMSEA) value of .066, which was smaller than the recommended value. The results

in Figure 4.7 exhibit that the parameter estimates of the hypothesized TAG model

were free from offending values. Most path coefficients of the hypothesized TAG

model’s critical ratio (CR >1.96) were statistically significant. Significant paths were

from: computer self-efficacy (CSE) to perceived usefulness (PU) and perceived ease

of use (PEU), perceived usefulness to intention to use (INT) and gratification (GRAT)

and perceived ease of use (PEU) to intention to use (INT) and gratification (GRAT).

Similarly, the paths from intention to use (INT) to use (USE) and gratification (GRAT)

were also significant. However, the paths from perceived usefulness (PU) and

perceived ease of use (PEU) to use (USE) were not significant as well as the path from

use (USE) to gratification (GRAT).

139


140

The data demonstrated that computer self-efficacy was the most influential

factor by a moderate amount in the TAG model in affecting the lecturers’ perceived

usefulness and perceived ease of use of ICT facilities in higher education. Likewise,

perceived usefulness was slightly more influential than perceived ease of use in

affecting lecturers’ gratification in using ICT facilities. On the other hand, perceived

ease of use was a more influential predictor than perceived usefulness in affecting

lecturers’ intention to use ICT facilities. The total standardized effect sizes of (i)

computer self-efficacy → perceived ease of use and perceived usefulness were .566

and .528 respectively, (ii) perceived usefulness → intention to use, use and

gratification were .278, .148 and .525 respectively, (iii) perceived ease of use →

intention to use, use and gratification were .394, .250 and .378 respectively, (iv)

intention to use → use and gratification were .373 and .147 respectively, (v) use →

gratification was -.078, and (vi) computer self-efficacy → intention to use, use and

gratification were .370, .220 and .491 respectively. In addition, the analysis revealed

that the exogenous variable (CSE) explained almost 32% and 28% of the variance in

perceived ease of use and perceived usefulness respectively, while the exogenous

variable (CSE) and the two mediator variables (PU and PEU) collectively explained

approximately 30% of the variability of lecturers’ intention to use of ICT facilities in

higher education. The exogenous variable (CSE) and the three mediator variables (PU,

PEU and INT) explained almost 20% of the variance of lecturers’ actual use of ICT

facilities. Finally, the exogenous variable (CSE) and the four mediator variables (PU,

PEU, INT and USE) collectively explained approximately 56% of the variability of

lecturers’ gratification in using ICT facilities in higher education. The TAG model,

however, had to be revised due to a few items that produced multiple large residuals

and had large modification indices suggesting the presence of multi-collinearity.

141

Furthermore, the presence of a negative path coefficient that contradicted the

hypothesis as well as the TAG model also exhibited inadequate convergent and

discriminant validity. Thus, indicating that the parameters were not estimated or

revealed in detail.

4.8.1 Hypotheses Testing

Testing the hypothesized TAG model, using the structural equation modeling, gave

results that demonstrated the findings of the overall causal model, and specified the

direct and indirect effect of exogenous variables, namely, computer self-efficacy on

the mediated (perceived ease of use, perceived usefulness, intention to use and use)

and endogenous (gratification) variables. The findings of the hypotheses test are as

followed:

Hypothesis 1

Computer self-efficacy (CSE) will have a positive direct influence on lecturers’

perceived ease of use (PEU) and perceived usefulness (PU) of ICT facilities in higher

education.

The findings revealed that computer self-efficacy had a significant direct

influence on perceived ease of use (β = .57, p < .001) and perceived usefulness (β =

.53, p < .001) of ICT facilities in higher education.

Hypothesis 2

Perceived ease of use (PEU) will have a positive direct influence on lecturers’



142

The results demonstrated that perceived ease of use had a significant direct

influence on lecturers’ intention to use (β = .39, p < .001) and gratification (β = .33, p

< .001) in using ICT facilities in higher education. However, it did not exert a

statistically significant influence on use (β = .10, p < .005).

Hypothesis 3

Perceived usefulness (PU) will have a positive direct influence on lecturers’ intention

to use (INT), actual use (USE) and gratification (GRAT) in using ICT facilities in

higher education.

The results of the hypothesized TAG model showed that perceived usefulness

had a significant positive direct effect on intention to use (β = .28, p < .001) and

gratification (β = .49, p < .001) in using ICT facilities in higher education.

Nevertheless, perceived usefulness did not have a statistically significant influence on

use (β = .04, p > .005).

Hypothesis 4

Intention to use (INT) will have a positive direct influence on lecturers’ gratification

(GRAT) and actual use (USE) of ICT facilities in higher education.

The findings of the TAG Model exhibited that intention to use had a positive

direct influence on use (β = .37, p < .001) and gratification (β = .18, p < .001) in using

ICT facilities.

Hypothesis 5

Actual use (USE) will have a positive direct influence on gratification (GRAT) in

using ICT services in higher education.

143

The finding depicted that use had a direct effect on gratification, but in an

adverse direction (β = -.08, p > .005).

Hypothesis 6

Computer self-efficacy (CSE) will have a positive indirect influence on lecturers’

intention to use (INT) of ICT facilities mediated by perceived ease of use (PEU) and


The results indicated that computer self-efficacy had a statistically significant

and practically important indirect influence on intention to use mediated by perceived

usefulness (Chi-square, χ2 = 3.510; p = 0.000). Subsequently, computer self-efficacy

also had a statistically significant and practically important indirect influence on

intention to use mediated by perceived ease of use (Chi-square, χ2 = 3.599; p = 0.000)

as conducted by the Sobel test (Sobel, 1982).

Hypothesis 7

Computer self-efficacy (CSE) will have a positive indirect influence on lecturers’

gratification (GRAT) in using ICT facilities mediated by perceived ease of use (PEU)


The findings showed that computer self-efficacy had a statistically significant

and practically important indirect influence on lecturers’ gratification in using ICT

facilities mediated by perceived ease of use (Chi-square, χ2 = 2.978; p = 0.001).

Similarly, computer self-efficacy also had a statistically significant and practically

important indirect influence on gratification mediated by perceived usefulness (Chi-

square, χ2 = 5.098; p = 0.000) as conducted by the Sobel test (Sobel, 1982).

144

Hypothesis 8

Computer self-efficacy (CSE) will have a positive indirect influence on lecturers’ use

(USE) of ICT facilities mediated by perceived ease of use (PEU) and perceived

usefulness (PU), respectively.

The results discovered that computer self-efficacy did not have a significant

indirect influence on lecturers’ use of ICT facilities mediated by perceived ease of use

(χ2 = 1.254; p = 0.104) and perceived usefulness (χ2 = 0.724; p = 0.234), respectively

as conducted by the Sobel test (Sobel, 1982).

Hypothesis 9

Perceived ease of use (PEU) and Perceived usefulness (PU) will have a positive

indirect influence on lecturers’ gratification (GRAT) in using ICT facilities mediated

by intention to use (INT), respectively.

The findings confirmed that perceived ease of use had a statistically significant

indirect influence on gratification in using ICT facilities mediated by intention to use

(Chi-square, χ2 = 2.257; p = 0.011), while perceived usefulness showed a significant

indirect influence on lecturers’ gratification mediated by intention to use of ICT

facilities (Chi-square, χ2 = 2.255; p = 0.012) as conducted by the Sobel test (Sobel,

1982).

Hypothesis 10

Intention to use (INT) will have a positive indirect influence on lecturers’ gratification

(GRAT) mediated by actual use (USE) of ICT facilities in higher education.

The results showed that intention to use did not have a significant indirect

influence on lecturers’ gratification mediated by actual use of ICT facilities due to the

145

negative path coefficient (Chi-square, χ2 = -1.015; p = 0.154) as conducted by the

Sobel test (Sobel, 1982).

Hypothesis 11

There will be a cross-cultural invariant of the causal structure of the TAG model.

The findings of the invariance analysis of the TAG model demonstrated that

the revised TAG model is valid for measuring lecturers’ adoption and gratification in

using ICT facilities in higher education for their teaching and research purposes.

However, the invariance analysis revealed that the TAG model works differently in

cross-cultures. This means that there was a cross-cultural invariant in lecturers’

adoption and gratification in using ICT facilities.

The results of the Hypotheses testing are summarized in Table 4.19.

146

Table 4.19: The Summary of the Hypotheses Testing

Hypotheses Description Results

H1 Computer self-efficacy (CSE) will have a positive direct

influence on lecturers’ perceived ease of use (PEU) and

perceived usefulness (PU) of ICT facilities in higher

education.

Supported

H2 Perceived ease of use (PEU) will have a positive direct

influence on lecturers’ intention to use (INT), actual use

(USE) and gratification (GRAT) in using ICT facilities


Supported

H3 Perceived usefulness (PU) will have a positive direct

influence on lecturers’ intention to use (INT), actual use

(USE) and gratification (GRAT) in using ICT facilities


Supported

H4 Intention to use (INT) will have a positive direct

influence on lecturers’ gratification (GRAT) and actual

use (USE) of ICT facilities in higher education.

Supported

H5 Actual use (USE) will have a positive direct influence

on gratification (GRAT) in using ICT services in higher

education.

Not

Supported

H6 Computer self-efficacy (CSE) will have a positive

indirect influence on lecturers’ intention to use (INT) of

ICT facilities mediated by perceived ease of use (PEU)


Supported


indirect influence on lecturers’ gratification (GRAT) in

using ICT facilities mediated by perceived ease of use

(PEU) and perceived usefulness (PU), respectively

Supported


indirect influence on lecturers’ use (USE) of ICT

facilities mediated by perceived ease of use (PEU) and


Not

Supported

H9 Perceived ease of use (PEU), Perceived usefulness (PU)

will have a positive indirect influence on lecturers’

gratification (GRAT) in using ICT facilities mediated by

intention to use (INT), respectively.

Supported

H10 Intention to use (INT) will have a positive indirect

influence on lecturers’ gratification (GRAT) mediated

by actual use (USE) of ICT facilities in higher

education.

Not

Supported

H11 There will be a cross-cultural invariant of the causal structure of the TAG model.

Supported

147

4.9 THE REVISED TECHNOLOGY ADOPTION AND GRATIFICATION

(TAG) MODEL

The hypothesized TAG model was revised and re-estimated in order to examine its

overall adequacy. In the re-specified TAG model, the items were removed one at a

time (CSE15, PEU9, PEU15, GRAT7, GRAT10, USE2, USE11, INT8 and INT10)

which produced multiple large residuals and associated with the largest modification

indices with relatively low factor loadings, and that were found to be cross-loaded as

shown in Table 4.20.


Item

Deleted

Chi-square

(χ2)

df p value RMSEA CFI TLI

CSE15 1701.441 621 .000 .066 .894 .886

PEU9 1571.680 586 .000 .065 .901 .894

PEU15 1431.166 552 .000 .063 .909 .902

GRAT7 1361.367 519 .000 .064 .909 .902

GRAT10 1279.240 487 .000 .064 .913 .905

USE2 1226.239 456 .000 .065 .913 .905

USE11 1165.950 426 .000 .066 .915 .907

INT8 1154.941 397 .000 .070 .913 .905

INT10 1095.131 369 .000 .071 .914 .906

After removing these items, the revised TAG model exhibited adequate

convergent and discriminant validity (see Appendix H), with all loadings showing a

value greater than 0.70 as shown in Table 4.21. Thus, only 29 items now remained

from the initial TAG model set of 38 items. Figure 4.8 presents the parameter

estimates of the revised TAG, which were free from offending values. The magnitude

of the factor loadings and path coefficients were statistically significant.

148

Figure 4.8: The Revised Technology Adoption and Gratification (TAG) Model

149

With the revised TAG model, the direct paths from perceived usefulness (PU)

and perceived ease of use (PEU) to use (USE) were dropped due to low coefficients of

0.04 (PU > USE) and 0.10 (PEU > USE), respectively. The direct path from use (USE)

to gratification (GRAT) was also dropped due to a negative path coefficient of -0.08

(USE > GRAT) which indicated that the respective hypotheses were not supported.

The revised model depicted a goodness-of-fit to the empirical data as indicated by the

following fit indices: χ2 (df = 369) = 1095.131; p < 0.000; RMSEA = 0.071; CFI =

0.914; TLI = 0.906 as supported by Byrne (2000). The Figure 4.8 shows the error

variances in the revised TAG model represented by e1, e2, e3, e5, e7, e8, e9, e10, e11,

e15, e16, e17, e18, e19, e20, e21, e22, e23, e24, e25, e26, e27, e30, e31, e32, e33, e35,

e36, e37, e39, e40, e41, e42 and e43.

In addition, the data of the revised TAG model revealed standardized path

coefficients as shown in Figure 4.8. As anticipated, hypotheses H1 and H2 were

supported, the findings revealed that computer self-efficacy had a significant positive

direct influence on perceived usefulness (β = 0.54, p<0.001) and perceived ease of use

(β = 0.53, p < .001). On the other hand, perceived ease of use had a significant direct

influence on gratification (β = 0.26, p<0.001) and intention to use (β = 0.25, p<0.001).

Subsequently, hypotheses H3 and H4 were supported, the results of the revised TAG

model discovered that perceived usefulness had a significant direct influence on

intention to use (β = 0.37, p<0.001) and gratification (β = 0.49, p<0.001). Besides this,

intention to use had also a significant direct influence on use (β = 0.45, p<0.001) and

gratification (β = 0.21, p<0.001). Moreover, H6 was also supported by the findings of

the revised TAG model which indicated that computer self-efficacy had a statistically

significant and practically important indirect influence on intention to use mediated by

perceived usefulness (Chi-square, χ2 = 4.451; p = 0.000). Subsequently, computer

150

self-efficacy also had a statistically significant and practically important indirect

influence on intention to use mediated by perceived ease of use (Chi-square, χ2 =

3.224; p = 0.000) as conducted by the Sobel test (Sobel, 1982). Furthermore, The

results of the revised TAG model demonstrated that computer self-efficacy had a

statistically significant and practically important indirect influence on lecturers’

gratification in using ICT facilities mediated by perceived ease of use (Chi-square, χ2

= 3.411; p = 0.000). Similarly, computer self-efficacy also had a statistically

significant and practically important indirect influence on gratification mediated by

perceived usefulness (Chi-square, χ2 = 5.253; p = 0.000) as conducted by the Sobel

test and validated H7 (Sobel, 1982). Eventually, H9 was supported as the results of the

revised TAG model discovered that perceived ease of use had a statistically significant

indirect influence on gratification in using ICT facilities mediated by intention to use

(Chi-square, χ2 = 2.503; p = 0.006), while perceived usefulness showed a significant

indirect influence on lecturers’ gratification mediated by intention to use of ICT

facilities (Chi-square, χ2 = 2.912; p = 0.001) as conducted by the Sobel test (Sobel,

1982). Furthermore, all path coefficients of the causal structure were practically

important and statistically significant, and the strongest value of the standardized path

coefficient of computer self-efficacy on perceived usefulness was 0.54. The data also

demonstrated that computer self-efficacy was a moderately more influential factor in

the revised TAG model in directly affecting the lecturers’ perceived usefulness and

perceived ease of use as well as indirectly influencing the lecturers’ intention to use,

use and gratification in using ICT facilities in higher education for their research and

teaching purposes. Moreover, the total standardized effect sizes of (i) computer self-

efficacy → perceived ease of use and perceived usefulness were .525 and .536

respectively, (ii) perceived usefulness → intention to use, use and gratification

151

were .375, .168 and .574 respectively, (iii) perceived ease of use → intention to use,

use and gratification were .247, .111 and .310 respectively, (iv) intention to use → use

and gratification were .447 and .231 respectively, and (v) computer self-efficacy →

intention to use, use and gratification were .331, .148 and .471 respectively. In

addition, the analysis revealed that the exogenous variable (CSE) explained almost

28% and 29% of the variance in perceived ease of use and perceived usefulness

respectively, while the exogenous variable (CSE) and the two mediator variables (PU

and PEU) collectively explained approximately 25% of the variability of lecturers’

intention to use of ICT facilities in higher education. Similarly, the exogenous variable

(CSE) and the three mediator variables (PU, PEU and INT) explained almost 20% of

the variance of lecturers’ actual use of ICT facilities. Finally, the exogenous variable

(CSE) and the three mediator variables (PU, PEU and INT) collectively explained

approximately 56% of the variability of lecturers’ gratification in using ICT facilities


Table 4.21: Valid Items of the Revised TAG Model and Their Corresponding

Loadings, Means, Standard Deviations and Alpha Values

Constructs Items Item Measure Loadings M SD Alpha

Computer

Self-

efficacy

(CSE)


communicate electronically with

my colleagues and students.

.72 5.974 .988

CSE7 I have the skills required to use

ICT facilities to enhance the

effectiveness of my teaching,


.84 5.931 .984

CSE9 I have the ability to navigate my

way through the ICT facilities. .88 5.714 1.051 .933


the ICT facilities to enhance the

quality of my research works.

.88 5.785 1.009

CSE11 I have the ability to save and

print journals/articles from the

research databases.

.83 5.989 1.016

152




required to benefit from using

the university ICT facilities.

.87 5.891 .955

Perceived

Usefulness

(PU)

PU2 Using the ICT services available

at the university increases my

research productivity.

.84 5.555 1.270

PU3 The current university ICT

system makes work more

interesting.

.89 5.217 1.324 .944

PU4 ICT facilities at the university

improve the quality of the work I

do.

.88 5.401 1.289

PU5 Using the university ICT

facilities enhances my research

skills.

.89 5.335 1.285


facilities would make it easier


.86 5.558 1.304

PU7 ICT services at the university

make information always

available to users.

.83 5.247 1.379


facilities provides me with the

latest information on specific

areas of research.

.70 5.628 1.226

Perceived

Ease of Use

(PEU)

PEU2 I find it easy to access the

university research databases. .70 5.300 1.313


databases system requires


.74 4.931 1.366 .830


research databases system is


.77 4.846 1.388

PEU7 I find it easy to select

articles/journals of different

categories using research

databases

(Education/Engineering/Busines

s, etc.).

.75 5.346 1.246

153



Intention to

Use (INT)

INT1 I will use the ICT facilities to

carry out my research activities. .83 6.070 1.038

INT2 I will use computer applications

to present lecture materials in

class.

.72 6.280 1.001



papers.

.70 6.189 1.157 .874




.79 5.916 1.104


databases to update myself on

the research areas I am pursuing.

.72 5.861 1.092

Actual Use

(USE)



research and academic? – Web

browser (www).

.72 6.239 1.098



research and academic? – Search

engine (e.g. Google, Yahoo,

etc.).

.85 6.383 .902 .802



research and academic? –


.72 6.308 .933

Gratificatio

n (GRAT)

GRAT3 I am satisfied with the ease of

use of the ICT facilities. .80 5.346 1.294

GRAT4 The university ICT facilities

have greatly affected the way I

search for information and

conduct my research.

.80 5.457 1.251 .899


amount of time it takes to

complete my task.

.84 5.237 1.361

GRAT9 I am satisfied in using ICT

facilities for teaching, learning

and research.

.82 5.467 1.239

154

4.10 CROSS-CULTURAL VALIDATION OF TAG MODEL

One of the objectives of this study was to evaluate the structural invariance of the

TAG model regarding the moderating effect of the cross-cultural dimension of the

respondents. In order to test cross-cultural-invariant, a two-stage analysis i.e.,

configural and metric invariance, was conducted on both the respondents of the

University of Malaya (n1 = 200) and Jiaxing University (n2 = 196). The revised TAG

model was estimated using a total of 396 lecturers who were collected from two

comprehensive public universities, namely, the University of Malaya in Malaysia and

Jiaxing University in China. However, to perform invariance analyses between the

groups of Malaysian and Chinese participants, the present study separated the original

pool of data into two groups. The overall fit indices of the revised TAG model for

Malaysian respondents discovered a satisfactory fit to the empirical data as

demonstrated by the following fit indices: χ2 (df = 369) = 897.527; p < 0.000;

RMSEA = 0.085; CFI = 0.882; TLI = 0.870 as shown in Figure 4.9. Moreover, the

data of the revised TAG model for Malaysian respondents confirmed the standardized

path coefficients as indicated in Figure 4.30. As anticipated, hypotheses H1 and H2

were accepted, the results exhibited that Malaysian lecturers’ computer self-efficacy

had a significant positive direct effect on their perceived usefulness (β = 0.63, p<0.001)

and perceived ease of use (β = 0.57, p < .001). On the other hand, Malaysian lecturers’

perceived ease of use had a significant direct influence on their gratification (β = 0.26,

p<0.001) but it did not exert any significant direct effect on intention to use (β = 0.06,

p≥0.05). Additionally, hypotheses H3 and H4 were supported, the findings of revised

TAG model for Malaysian participants revealed that lecturers’ perceived usefulness

had a significant direct influence on their intention to use (β = 0.56, p<0.001) and

gratification (β = 0.40, p<0.001), while Malaysian lecturers’ intention to use had a

155

significant direct effect on their actual use (β = 0.32, p<0.001) and gratification (β =

0.30, p<0.001) in using ICT facilities for their teaching and research purposes in

higher education.

Figure 4.9: The Revised TAG Model for Malaysian Participants

156

Furthermore, H6 was also supported, the results of the revised TAG model for

Malaysian respondents demonstrated that Malaysiann lecturers’ computer self-

efficacy had a statistically significant and practically important indirect effect on their

intention to use mediated by perceived usefulness (Chi-square, χ2 = 4.952; p = 0.000).

However, Malaysian lecturers’ computer self-efficacy did not show a statistically

significant and practically important indirect effect on their intention to use mediated

by perceived ease of use due to the low path coefficient. In addition, The findings of

the revised TAG model for Malaysian participants showed that lecturers’ computer

self-efficacy had a statistically significant and practically important indirect influence

on their gratification in using ICT facilities mediated by perceived ease of use (Chi-

square, χ2 = 2.956; p = 0.001). Meanwhile, Malaysian lecturers’ computer self-

efficacy also had a statistically significant and practically important indirect effect on

their gratification mediated by perceived usefulness (Chi-square, χ2 = 3.273; p =

0.000) as conducted by the Sobel test and validated H7 (Sobel, 1982). Lastly, H9 was

supported as the findings of the revised TAG model for Malaysian participants

indicated that perceived usefulness had a statistically significant indirect effect on


education mediated by intention to use (Chi-square, χ2 = 2.702; p = 0.003) as

conducted by the Sobel test (Sobel, 1982), while lecturers’ perceived ease of use did

not exert any significant indirect influence on their gratification mediated by intention

to use of ICT facilities due to the insignificant path coefficient. Moreover, all path

coefficients of the causal structure were practically important and statistically

significant, while the strongest value of the standardized path coefficient of Malaysian

lecturers’ computer self-efficacy on their perceived usefulness was 0.63. The data also

confirmed that Malaysian lecturers’ computer self-efficacy was the most influential

157

factor in the revised TAG model for Malaysian participants in directly influencing the

lecturers’ perceived usefulness and perceived ease of use as well as indirectly

affecting the lecturers’ intention to use, use and gratification in using ICT facilities for

their teaching and research purposes in higher education.

Subsequently, the total standardized effect sizes of (i) computer self-efficacy

→ perceived ease of use and perceived usefulness were .570 and .629 respectively, (ii)

perceived usefulness → intention to use, use and gratification were .563, .179

and .573 respectively, (iii) perceived ease of use → intention to use, use and

gratification were .056, .018 and .277 respectively, (iv) intention to use → use and

gratification were .318 and .301 respectively, and (v) computer self-efficacy →

intention to use, use and gratification were .386, .123 and .518 respectively.

Consequently, the analysis of the revised TAG model for Malaysian participants

discovered that the exogenous variable (CSE) explained almost 32% and 40% of the

variance in perceived ease of use and perceived usefulness respectively, while the

exogenous variable (CSE) and the two mediator variables (PU and PEU) collectively

explained almost 34% of the variability of Malaysian lecturers’ intention to use of ICT

facilities for their teaching and research purposes in higher education. Similarly, the

exogenous variable (CSE) and the three mediator variables (PU, PEU and INT)

explained almost 10% of the variance of lecturers’ actual use of ICT facilities. Finally,

the exogenous variable (CSE) and the three mediator variables (PU, PEU and INT)

collectively explained approximately 58% of the variability of lecturers’ gratification

in using ICT facilities in higher education. The revised TAG model for Malaysian

participants depicted adequate convergent and discriminant validity (see Appendix I),

with the majority of the items’ loadings showing a value greater than 0.70 as shown in

Table 4.22.

158

Table 4.22: Valid Items of the Revised TAG Model for Malaysian Participants and

Their Corresponding Loadings, Means, Standard Deviations and Alpha Values


Computer

Self-

efficacy

(CSE)


communicate electronically

with my colleagues and

students.

.69 5.974 .988

.933





.82 5.931 .984

CSE9 I have the ability to navigate my

way through the ICT facilities. .87 5.714 1.051


the ICT facilities to enhance the

quality of my research works.

.90 5.785 1.009



research databases.

.77 5.989 1.016




.86 5.891 .955

Perceived

Usefulness

(PU)

PU2 Using the ICT services available

at the university increases my

research productivity.

.81

5.555 1.270

.944



interesting.

.87

5.217 1.324


improve the quality of the work

I do.

.87

5.401 1.289



skills.

.88

5.335 1.285




.82 5.558 1.304



available to users.

.81 5.247 1.379




areas of research.

.74 5.628 1.226

159



Perceived

Ease of Use

(PEU)



.830




.78 4.931 1.366




.81 4.846 1.388




databases

(Education/Engineering/Busines

s, etc.).

.80 5.346 1.246

Intention to

Use (INT)


carry out my research activities. .86 6.070 1.038

.874

INT2 I will use computer applications

to present lecture materials in

class.

.81 6.280 1.001


applications to write my

research papers.

.72 6.189 1.157




.78 5.916 1.104


databases to update myself on

the research areas I am

pursuing.

.65 5.861 1.092

Actual Use

(USE)




browser (www).

.82 6.239 1.098

.802




Search engine (e.g. Google,

Yahoo, etc.).

.85 6.383 .902





.68 6.308 .933

160



Gratificatio

n (GRAT)



.899





.75 5.457 1.251



complete my task.

.89 5.237 1.361



and research.

.83 5.467 1.239

On the other hand, the overall fit statistics of the revised TAG model for

Chinese participants showed an adequate fit to the empirical data as indicated by the

following fit indices: χ2 (df = 369) = 840.593; p < 0.000; RMSEA = 0.081; CFI =

0.882; TLI = 0.870 as depicted Figure 4.10. In addition, the data of the revised TAG

model for Chinese participants revealed the standardized path coefficients as shown in

Figure 4.10. As anticipated, hypotheses H1 and H2 were supported, the findings

discovered that Chinese lecturers’ computer self-efficacy had a significant positive

direct influence on their perceived usefulness (β = 0.39, p<0.001) and perceived ease

of use (β = 0.44, p < .001). On the other hand, Chinese lecturers’ perceived ease of use

had a significant direct influence on their gratification (β = 0.29, p<0.001) and

intention to use (β = 0.46, p<0.001). In addition, hypotheses H3 and H4 were accepted,

the results of the revised TAG model for Chinese respondents exhibited that lecturers’

perceived usefulness had a significant direct influence on their gratification (β = 0.50,

p<0.001) but it did not exert any significant influence on intention to use (β = 0.05,

p≥0.05), while Chinese lecturers’ intention to use had a significant direct effect on

their actual use (β = 0.42, p<0.001) and gratification (β = 0.15, p<0.05) in using ICT

facilities for their teaching and research purposes in higher education.

161

Figure 4.10: The Revised TAG Model for Chinese Participants

Moreover, hypothesis H6 was also supported as the results of the revised TAG

model for Chinese participants indicated that Chinese lecturers’ computer self-

162

efficacy had a statistically significant and practically important indirect effect on their

intention to use mediated by perceived ease of use (Chi-square, χ2 = 3.767; p = 0.000).

However, Chinese lecturers’ computer self-efficacy did not confirm a statistically

significant and practically important indirect effect on their intention to use mediated

by perceived usefulness due to the insignificant path coefficient. Subsequently, The

results of the revised TAG model for Chinese respondents demonstrated that lecturers’

computer self-efficacy had a statistically significant and practically important indirect

effect on their gratification in using ICT facilities mediated by perceived ease of use

(Chi-square, χ2 = 2.920; p = 0.001). Similarly, Chinese lecturers’ computer self-

efficacy also had a statistically significant and practically important indirect influence

on their gratification mediated by perceived usefulness (Chi-square, χ2 = 3.349; p =

0.000) as conducted by the Sobel test and validated H7 (Sobel, 1982). Finally,

hypothesis H9 was supported as the results of the revised TAG model for Chinese

lecturers’ revealed that perceived ease of use had a statistically significant indirect

effect on gratification in using ICT facilities for their teaching and research purposes

in higher education mediated by intention to use (Chi-square, χ2 = 1.464; p = 0.071)

as conducted by the Sobel test (Sobel, 1982), while lecturers’ perceived usefulness did

not exert any significant indirect effect on their gratification mediated by intention to

use of ICT facilities due to the low path coefficient. Additionally, all the path

coefficients of the causal structure were practically important and statistically

significant, while the strongest value of the standardized path coefficient of Chinese

lecturers’ perceived usefulness on their gratification was 0.50. The data also explored

that Chinese lecturers’ perceived usefulness of ICT facilities was the most dominant

determinant in the revised TAG model for Chinese participants in directly affecting

the lecturers’ perceived usefulness of ICT facilities for their teaching and research

163

purposes in higher education.

Furthermore, the total standardized effect sizes of (i) computer self-efficacy →

perceived ease of use and perceived usefulness were .432 and .370 respectively, (ii)

perceived usefulness → intention to use, use and gratification were .061, .024

and .597 respectively, (iii) perceived ease of use → intention to use, use and

gratification were .500, .201 and .407 respectively, (iv) intention to use → use and

gratification were .402 and .161 respectively, and (v) computer self-efficacy →

intention to use, use and gratification were .238, .096 and .397 respectively.

Subsequently, the analysis of the revised TAG model for Chinese participants

exhibited that the exogenous variable (CSE) explained almost 19% and 15% of the

variance in perceived ease of use and perceived usefulness respectively, while the

exogenous variable (CSE) and the two mediator variables (PU and PEU) collectively

explained approximately 23% of the variability of Chinese lecturers’ intention to use

of ICT facilities for their teaching and research purposes in higher education.

Meanwhile, the exogenous variable (CSE) and the three mediator variables (PU, PEU

and INT) explained almost 18% of the variance of lecturers’ actual use of ICT

facilities. Finally, the exogenous variable (CSE) and the three mediator variables (PU,

PEU and INT) collectively explained around 47% of the variability of lecturers’


education. The revised TAG model for Chinese participants confirmed adequate

convergent and discriminant validity (see Appendix I), with most of the indicators’

loadings showing a value greater than 0.70 as demonstrated in Table 4.23.

164

Table 4.23: Valid Items of the Revised TAG Model for Chinese Participants and

Their Corresponding Loadings, Means, Standard Deviations and Alpha Values


Computer

Self-

efficacy

(CSE)


communicate electronically

with my colleagues and

students.

.69 5.974 .988

.933





.82 5.931 .984

CSE9 I have the ability to navigate

my way through the ICT

facilities.

.87 5.714 1.051


the ICT facilities to enhance

the quality of my research

works.

.90 5.785 1.009



research databases.

.77 5.989 1.016




.86 5.891 .955

Perceived

Usefulness

(PU)

PU2 Using the ICT services

available at the university

increases my research

productivity.

.81

5.555 1.270

.944



interesting.

.87

5.217 1.324


improve the quality of the work

I do.

.87

5.401 1.289



skills.

.88

5.335 1.285




.82 5.558 1.304



available to users.

.81 5.247 1.379




areas of research.

.74 5.628 1.226

165



Perceived

Ease of Use

(PEU)



.830




.78 4.931 1.366




.81 4.846 1.388




databases

(Education/Engineering/Busine

ss, etc.).

.80 5.346 1.246

Intention to

Use (INT)


carry out my research

activities.

.86 6.070 1.038

.874

INT2 I will use computer

applications to present lecture

materials in class.

.81 6.280 1.001


applications to write my

research papers.

.72 6.189 1.157




.78 5.916 1.104

INT7 I will use the university

research databases to update

myself on the research areas I

am pursuing.

.65 5.861 1.092

Actual Use

(USE)




browser (www).

.82 6.239 1.098

.802




Search engine (e.g. Google,

Yahoo, etc.).

.85 6.383 .902





.68 6.308 .933

166



Gratificatio

n (GRAT)



.899





.75 5.457 1.251



complete my task.

.89 5.237 1.361



and research.

.83 5.467 1.239

4.10.1 Configural and Metric Invariance Analyses of the TAG Model

First, without constraining the structural paths, the results derived a baseline Chi-

square value. Next, the structural paths were constrained to be equal for the University

of Malaya and Jiaxing University. The results for both constrained and unconstrained

models were found to be consistent with the data as shown in Figure 4.11 and 4.12.

The analysis of this constrained TAG model produced another Chi-square value

(1768.175) and df = 746, which was then tested against the unconstrained Chi-square

value (1738.118) and df = 738 for statistically significant differences.

167

Figure 4.11: The Constrained TAG Model

168

Figure 4.12: The Unconstrained TAG Model

169

The invariance test between the University of Malaya in Malaysia and Jiaxing

University in China resulted in a statistically significant change in the Chi-square

value, Chi-square (df = 8) = 30.057, p > 0.05, while the critical value was at 15.507 as

shown in Table 4.24. This meant that cross-culture did interact with the exogenous

variables to influence the lecturers’ adoption and gratification in using ICT facilities in

higher education in Malaysia and China. The results of the invariance analysis of the

TAG model also demonstrated that the model was valid for measuring lecturers’

adoption and gratification in using ICT facilities in the diverse context of higher

education. It is, therefore, reasonable to conclude that cross-culture was a moderating

variable, whereas the TAG model works differently in cross-cultural settings.

Table 4.24: Results of Critical Value of Chi-squared

Models Chi squared df Critical

value

Chi squared

change

A Cross-cultural

validation between

University of

Malaya (Malaysia)

and Jiaxing

University (China)

Unconstrained 1738.118 738 15.507

(p > 0.05)

30.057

Constrained 1768.175 746

170

CHAPTER 5

DISCUSSION AND CONCLUSION

5.1 INTRODUCTION

The main objective of the study was twofold. The first was to develop and validate the

Technology Adoption and Gratification (TAG) Model to assess the lecturers’ adoption

and gratification in using ICT facilities for their teaching and research in higher

education which was achieved through the specific objectives 2 to 7. The second

purpose of the study was to evaluate the cross-cultural validation of the causal

structure of the TAG model. To achieve these objectives two versatile statistical tools

were applied. Firstly, the questionnaire’s reliability and validity were established

through a Rasch analysis. Secondly, the data were analysed using a three-step

Structural Equation Modeling (SEM) involving Confirmatory Factor Analysis (CFA)

to validate the measurement models, full-fledged structural model to estimate the

hypothesized and revised models of the lecturers’ adoption and gratification in using

ICT facilities and invariance analysis (metric and configural invariance analyses) to

examine the cross-cultural-invariant of the TAG model. The data were collected

through a questionnaire, and was administered to 396 lecturers who were selected

using stratified random sampling from two comprehensive public universities, namely,

the University of Malaya in Malaysia and Jiaxing University in China. This chapter

highlights the main findings of the study, discusses them in relation to theory and

prior studies, and concludes with recommendations and limitations for future research.

171

5.2 DISCUSSION OF THE FINDINGS

The results have theoretically contributed to the existing body of knowledge and

current understanding on the Technology Adoption and Gratification (TAG) model in

numerous ways. The model was developed and validated by incorporating three

models, namely, Technology Acceptance Model (Davis et al., 1989), Online Database

Adoption and Satisfaction Model (Islam, 2011a) and Technology Satisfaction Model

(Islam, 2014), to assess the lecturers’ adoption and gratification in using ICT facilities

for their teaching and research in higher education. The broad objective of this study

was achieved through specific objectives. To address the specific objectives 2 and 3,

the present study generated and tested three hypotheses (H2, H3 and H9) as discussed

below.

First, perceived ease of use had a statistically significant direct influence on

lecturers’ intention to use and gratification but not actual use, thereby validating the

second hypothesis (H2); a finding also consistent with earlier studies which

demonstrated the influential effect of perceived ease of use on users’ intention to use

computer-based systems (Chau, 1996; Karahanna & Straub, 1999; Venkatesh & Davis,

1996, 2000; Venkatesh & Morris, 2000). However, perceived ease of use was found to

be less influential and reliable (Ma & Liu, 2004; Wu et al., 2008; Huang, 2008).

Several researches prior to this also observed no significant relationship between the

perceived ease of use and behavioral intention (Jackson, Chow, & Leitch, 1997; Hu et.

al., 1999). In other studies, Tenopir (2003) found that ease of use was a major factor

influencing her respondents’ adoption of the online database. Outside the educational

context, Lee et al., (2006) showed that perceived ease of use significantly enhanced

consumer attitude and behavioral intention towards an online retailer, while Ramayah

and Lo (2007) demonstrated a significant effect of perceived ease of use on intention

172

to use an electronic resource planning system. Along this line, Lee and Lehto (2013)

and Islam (2011a) hypothesized that perceived ease of use will have a significant

effect on intention to use. Nevertheless, the findings of their study indicated that

perceived ease of use did not exert statistically significant effect on behavioral

intention to use.

On the one hand, Hanafizadeh et al., (2014) found that bank clients’ perceived

ease of use had a significant influence on intention to use mobile banking, while

Gultekin (2011) discovered that the effects of perceived ease of use on police officers’

intention to use the POLNET system were also statistically significant. Subsequently,

Stone and Baker-Eveleth (2013) confirmed that perceived ease of e-textbook use

positively influences behavioral intentions to purchase an e-textbook, however,

Martínez-Torres et al., (2008) discovered that perceived ease of use had a slightly

weaker influence compared to perceived usefulness on behavioural intention to use e-

learning tools. Many previous studies also demonstrated that perceived ease of use

was an antecedent factor that affected intention to accept English mobile learning

(Chang et al., 2012) as well as computer based assessment in higher education (Terzis

et al., 2013). Even though, Shroff et al., (2011) confirmed that students’ perceived

ease of use had significant influence on perceived usefulness and attitude towards

usage of portfolio system.

On the other hand, the present study inferred the possible relationship between

these two variables from several studies that investigated the relationship between

perceived ease of use and satisfaction (Lee & Park, 2008; Islam, 2014; Islam, 2011a;

Islam, 2011b; Huang, 2008; Shamdasani et al., 2008). The results of Szymanski and

Hise (2000) and Dabholkar and Bagozzi (2002) found convenience (a construct very

similar in concept to ease of use) to be an important factor in e-satisfaction.

173

Furthermore, Devaraj et al., (2002) exhibited that perceived ease of use was revealed

to be a significant determinant of satisfaction in the transaction cost analysis as well as

an important component of TAM in forming consumer attitude and satisfaction with

the electronic commerce channel, while Islam et al., (2015) confirmed that perceived

ease of use had a significant effect on students’ satisfaction in using online research

database in higher education. Thus, this study concluded that lecturers are more likely

to adopt ICT facilities for its ease of use coupled with their gratification in using it,

rather than its actual use.

Second, the results of the revised TAG model confirmed the significant direct

influence of perceived usefulness on lecturers’ intention to use and gratification but

not actual use of ICT facilities, hence validating the third hypothesis (H3). This was

congruent with the findings of prior studies (Wu et al., 2008; Davis, 1989; Guriting &

Ndubisi, 2006; 2004; Ramayah et al., 2005; Ramayah & Lo, 2007; Venkatesh, 2000;

Agarwal et al., 2003; Davis, 1993; Mathwick et al., 2001; Ahmad et al., 2010), which

found the significant influence of perceived usefulness on technology adoption.

Similarly, the impact of perceived usefulness on users’ intention to use different types

of computer-based and Internet-based systems has been comprehensively documented

in numerous studies (Ahmad et al., 2010; Davis, 1993; Guriting & Ndubisi, 2006;

Mathwick et al., 2001; Ramayah & Lo, 2007). Wu et al., (2008) corroborated these

findings on the impact of perceived usefulness by noting that it is a significant

determinant of science teachers’ intention to assimilate technology into their

instruction. Recent studies have also explored a significant effect of perceived

usefulness on users’ intention to use mobile learning (Hanafizadeh et al., 2014; Chang

et al., 2012), the POLNET system (Gultekin, 2011), e-learning tools (Martínez-Torres

et al., 2008) and e-portfolio systems (Shroff et al., 2011). In line with this, Terzis et al.,

174

(2013) showed that perceived usefulness demonstrated a significant influence on

students’ intention to use computer based assessment in higher education. According

to Davis et al., (1989) perceived usefulness was an important predictor, which

strongly influenced users’ intentions, while TAM (Davis, 1993) showed that perceived

usefulness had a significant direct effect on the actual use of information technology.

Additionally, the TSM (Islam, 2014) discovered that perceived usefulness had

a significant positive direct effect on students’ satisfaction in using wireless internet in

higher education. Concerning the Online Database Adoption and Satisfaction (ODAS)

Model, Islam, (2011a) confirmed that, perceived usefulness had a statistically

significant indirect influence on postgraduate students’ satisfaction mediated by

intention to use online research databases, while Islam et al., (2015) exhibited that

perceived usefulness was the most important construct of TSM in affecting

postgraduate students’ satisfaction in using online research databases in higher

education. Subsequently, Lee and Park (2008) indicated that perceived usefulness had

a significant effect on user satisfaction. Meanwhile, Islam (2011b) found that

perceived usefulness moderately influenced students’ satisfaction in using wireless

technology. However, its impact on satisfaction might be reflected through the

significant interrelationships with other latent constructs, namely, perceived ease of

use and computer self-efficacy. In addition to this, in extant literature, numerous

studies exist vis-à-vis the effect of perceived usefulness on consumer satisfaction

(Anderson et al., 1994). According to Huang (2008), perceived usefulness was found

to have an impact on consumer satisfaction mediated by their behavioral attitudes. On

the other hand, Clemons and Woodruff (1992) reported that, perceived value might

directly lead to the formation of overall feelings of satisfaction. This argument is

corroborated by McDougall and Levesque (2000) who indicated that, perceived value

175

is a significant driver of customer satisfaction, while Devaraj et al., (2002) revealed

that perceived usefulness was an important dimension in forming consumer attitude

and satisfaction with the electronic commerce channel. The discovery was that when

lecturers understand the benefits of ICT facilities, they are more likely to use the

service provided by the universities because they believe ICT facilities can enhance

their research and teaching ability and performance. Moreover, the data exhibited that

perceived usefulness was one of the important factors of the TAG model in affecting

lecturers’ adoption and gratification in using ICT facilities in higher education.

Third, the results of the TAG model depicted that perceived ease of use and

perceived usefulness had positive indirect influences on lecturers’ gratification in

using ICT facilities mediated by intention to use separately validated the ninth

hypothesis (H9). This concurred with an earlier study (Islam, 2011a), which found

perceived usefulness indirectly influenced students’ satisfaction in using online

research databases mediated by intention to use, while perceived ease of use had a

direct effect on satisfaction. However, in their study, Shamdasani et al., (2008)

observed that ease of use mediated by service quality, did not have a big influence on

satisfaction. In recent studies, perceived usefulness and perceived ease of use showed

a significant direct effect on students’ satisfaction in using wireless internet (Islam,

2014) and online research databases in higher education (Islam et al., 2015).

Therefore, it was found that when lecturers understand the ease of use and benefits of

ICT facilities, they are more likely to use the service provided by the universities

because they believe ICT facilities can increase their research and teaching

performance.

176

To address the specific objective 4, this study generated and tested four

hypotheses (H1, H6, H7 and H8) as discussed below.

Firstly, the hypothesis (H1) that computer self-efficacy would exert a positive

direct influence on lecturers’ perceived ease of use and perceived usefulness of the

ICT facilities in higher education stands validated as the findings were congruent with

prior studies which discovered the significant influence of computer self-efficacy on

perceived usefulness and perceived ease of use, respectively (Islam, 2014; Islam et al.,

2015). Moreover, the results also revealed that computer self-efficacy was the most

significant construct of the TAG model in directly affecting lecturers’ perceived

usefulness and perceived ease of use of the ICT facilities, while the technology

satisfaction model (TSM) confirmed that computer self-efficacy was the most

influential factor in measuring students’ satisfaction in using wireless internet (Islam,

2014) and the online research databases (Islam et al., 2015) in higher education.

Previous studies confirm propositions that self-efficacy influences the choice as to

whether or not one engages in a task, the effort made in performing it, and the level of

persistence required in accomplishing it (Bandura, 1977; Bandura & Schunk, 1981;

Delcourt & Kinzie, 1993). It influences how people feel, think, and behave (Bandura,

1986: 393). According to Ahmad et al., (2010), the inclusion of self-efficacy as an

intrinsic motivation construct offers ‘deeper and richer understanding of why and how

the technology is used’. However, they discovered that computer self-efficacy

indirectly influences faculty’s use of computer mediated technology through

perceived usefulness and intention to use. Islam (2011a & 2011b) demonstrated the

significant interrelationships among the exogenous variables which were measured in

the revised structural model, and it was found that the interrelationships between

computer self-efficacy, perceived ease of use and perceived usefulness were

177

statistically significant. YouTube users’ self-efficacy indicated a significant effect on

perceived usefulness (Lee & Lehto, 2013), whereas Stone and Baker-Eveleth (2013)

hypothesized that a mid-sized university students’ self-efficacy regarding the use of an

e-textbook significantly influences outcome expectancy/usefulness regarding e-

textbooks but it did not exert statistically significant effect on outcome

expectancy/usefulness in purchasing electronic textbooks. In a related study, Tezci

(2011) asserted that pre-service teachers’ self-efficacy levels regarding ICT usage in

education were found to be moderate. Thus, teachers’ self-efficacy or self-confidence

concerning ICT usage must be high in order for them to be highly encouraged in the

use of ICT, which in turn would lead to flourishing ICT integration. Amin (2007)

confirmed that university students’ computer self-efficacy of internet banking had a

significant effect on their perceived usefulness and perceived ease of use. Similarly,

Wong et al., (2012) revealed that computer teaching efficacy had a significant effect

on perceived usefulness, perceived ease of use and attitude towards computer use.

Nevertheless, the findings of their studies showed that computer teaching efficacy

only moderately effects perceived usefulness when compared to other constructs.

According to Zejno and Islam (2012), computer self-efficacy is one of the significant

predictors of postgraduate students’ ICT usage in higher education.

On the other hand, Terzis et al., (2013) explored that Greek and Mexican

university students’ computer self-efficacy had a positive influence on their perceived

ease of use, which shows that if a student knows how to use computers, perhaps

he/she will find it easy to use a computer based assessment that needs fundamental

information technology skills. In a study involving university students’ use of an

information system, Agarwal and Karahanna (2000) discovered that computer self-

efficacy was a key antecedent of perceived ease of use, while Wu et al., (2008) found

178

a statistically significant relationship between teachers’ CSE and their intention to use

ICT in teaching. More importantly, the present study discovered that lecturers’

adoption and gratification of ICT facilities does not depend only on perceived ease of

use and perceived usefulness, but also on their beliefs in their own ability to use it.

Thus, computer self-efficacy is acknowledged as a distinct antecedent of perceived

usefulness and perceived ease of use, which is an important intrinsic motivation factor

of technology adoption and gratification.

Secondly, the findings of the TAG model discovered that computer self-

efficacy had a significant indirect effect on lecturers’ intention to use of ICT facilities

mediated by perceived ease of use and perceived usefulness separately, thereby

validating the sixth hypothesis (H6). However, Ahmad et al., (2010) found that

computer self-efficacy indirectly influences faculty’s use of computer mediated

technology through perceived usefulness and intention to use. Prior studies also

demonstrated the influential effect of computer self-efficacy on technology adoption

and usage (Wu et al., 2008; Gong et al., 2004; Hu et al., 2003; Agarwal & Karahanna,

2000; Compeau et al., 1999; Ahmad et al., 2010; Compeau & Higgins, 1995;

Dabholkar & Bogazzi, 2002; Ellen et al., 1991). Stone and Baker-Eveleth (2013)

showed that computer self-efficacy positively influenced students’ attitudes regarding

the purchase of e-textbooks. Alenezi et al., (2010) confirmed that computer self-

efficacy significantly influenced students' intention to use e-learning. According to

Chang and Tung (2008), self-efficacy was an important factor for university students’

behavioural intention to use the online learning course websites, while Zejno and

Islam (2012) claimed that computer self-efficacy is one of the significant predictors of

postgraduate students’ ICT usage. In a recent study, Islam (2011a) demonstrated that

computer self-efficacy had a direct influence on postgraduate students’ intention to

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use online research databases. In fact, the influence of this construct is evident in

many cases involving the employment of computer technology (Ball & Levy, 2008;

Charalambous & Papaioannou, 2010; Kenzie et al., 1994; Moos & Azevedo, 2009).

More importantly, the present study discovered that lecturers’ adoption and

gratification of ICT usage does not only depend on perceived ease of use and

perceived usefulness, but also on their beliefs in their own ability to use it. Thus,

computer self-efficacy is acknowledged as a distinct antecedent of intention to use,

which is an important intrinsic motivation factor of technology adoption and

gratification. As such, computer self-efficacy can indirectly influence lecturers’

intention to use ICT facilities in higher education.

Thirdly, the results of the TAG model demonstrated that computer self-

efficacy had a statistically significant indirect influence on lecturers’ gratification in

using ICT facilities mediated by perceived usefulness and perceived ease of use

separately, which validated the seventh hypothesis (H7). The results were consistent

with prior studies (Islam, 2014; Islam et al., 2015) which exhibited that computer self-

efficacy was the most dominant factor of the TSM and it also had an indirect

significant influence on satisfaction mediated by perceived ease of use and perceived

usefulness, respectively. The ODAS model (Islam, 2011a) revealed the significant

interrelationships among the exogenous variables, namely, computer self-efficacy,

perceived ease of use and perceived usefulness in measuring postgraduate students’

adoption and satisfaction in using online research databases. Similarly, Islam (2011b)

also found significant positive interrelationships among the three exogenous variables,

namely, computer self-efficacy, perceived ease of use and perceived usefulness in

assessing students’ satisfaction in using wireless internet in higher education.

However, computer self-efficacy had a significant indirect effect on students’

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satisfaction mediated by intention to use (Islam, 2011a, p. 50). Thus, the findings

suggested that lecturers the ICT usage for its self-efficacy because of their beliefs in

their computer ability.

Finally, in regards to the indirect effect of computer self-efficacy on lecturers’

use of ICT facilities mediated by perceived ease of use and perceived usefulness

separately, the findings had an insignificant path co-efficient, thereby invalidating the

eighth hypothesis (H8). However, several studies explored the direct relationships

between computer self-efficacy, perceived ease of use as well as perceived usefulness

(Islam, 2014; Islam et al., 2015). As a result, this hypothesis was dropped in the

revised model.

To address the specific objective 5, the present study generated and tested two

hypotheses (H4 and H10) as discussed below.

First, as revealed by the significant direct path coefficient, intention to use

significantly influenced lecturers’ gratification and actual use of ICT facilities in

higher education, thereby validating the fourth hypothesis (H4). This finding

corroborated the results of the ODAS model (Islam, 2011a), which demonstrated that

intention to use had a positive direct effect on students’ satisfaction in using online

research databases in higher education. This study had inferred the possible

relationship between intention to use and gratification from several studies

investigating the relationship between computer self-efficacy and intention to use (Wu

et al., 2008; Ahmad et al., 2010) and intention to use with individual satisfaction

(Huang, 2008; Shamdasani et al., 2008). On the other hand, Davis et al., (1989)

hypothesized and confirmed that behavioral intention was the key determinant of

actual use of computer technology. Their findings also stated that behavior should be

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predictable from measures of users’ behavioral intention and that if there were any

additional constructs that effected user behavior, they do so indirectly by influencing

behavioral intention. However, Davis (1993) ignored the behavioral intention, which

was the most important factor of the TAM, whereas attitude toward using revealed a

significant direct effect on actual system use of information technology. Furthermore,

the data for this study explored that lecturers’ behavioral intention to use was one of

the dominant factors in affecting their adoption and gratification in using ICT facilities

in teaching and research.

Second, the indirect influence of intention to use on lecturers’ gratification

mediated by actual use of ICT facilities in higher education was found to be a

statistically insignificant path coefficient, thereby invalidating the tenth hypothesis

(H10). However, Islam (2011a) investigated the direct relationship between intention

to use and satisfaction and intention to use with individual satisfaction (Huang, 2008;

Shamdasani et al., 2008). In addition, the findings of the TAG model also confirmed

that lecturers’ intention to use of ICT facilities was one of the vital factors in

evaluating technology adoption and gratification in higher education.

To address the specific objective 6, this study generated and tested one

hypothesis (H5) as discussed below.

In regards to the effect of actual use on lecturers’ gratification in using ICT

facilities, it had an insignificant path co-efficient, thereby invalidating the fifth

hypothesis (H5). However, user satisfaction was found to be positively related to

perceived market performance (Lee & Park, 2008). According to Shipps and Phillips

(2013), attitude toward the social networking tool showed a significant effect on user

satisfaction, while Devaraj et al., (2002) indicated general support for consumer

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satisfaction as a determinant of channel preference. Thus, the finding of this study

suggested that lecturers adopted and gratified the ICT facilities not only for its actual

usage, but rather for its ease of use, usefulness, intention to use and because of their

beliefs in their computer self-efficacy.

To address the specific objective 7, it was not necessary to generate a

hypothesis, as the endogenous variable was assessed applying Confirmatory Factor

Analyses (CFA) as well as Structural Equation Modeling (SEM) like other objectives.

For instance, the objectives 2 to 7 were firstly evaluated using CFA to confirm

lecturers’ perceptions in using ICT facilities for their teaching and research purposes

in higher education in terms of perceived ease of use, perceived usefulness, computer

self-efficacy, intention to use, actual use and gratification. Later on, to examine the

causal relationships and the effect of perceived ease of use, perceived usefulness,

computer self-efficacy, intention to use and actual use on gratification, the present

study tested nine hypotheses as discussed above. However, lecturers’ gratification did

not have any influence on other latent constructs, so the hypothesis was not required

to test for this objective. Besides this, the results of the one-factor measurement model

of gratification were analyzed using CFA and confirmed that lecturers’ views on the

gratification in using ICT facilities. The one-factor measurement model of

gratification explained almost 64.1% of the variability of lecturers’ gratification in

using ICT facilities for their teaching and research purposes in higher education.

To address the specific objective 8, the present study generated and tested one

hypothesis (H8) as discussed below.

This study exhibited that there was a cross-cultural invariant of the causal

structure of the TAG model, thereby validating the eighth hypothesis (H8). This

183

meant that cross-culture did interact with the exogenous and mediating variables to

influence lecturers’ adoption and gratification in using ICT facilities in higher

education in both Malaysia and China. The findings of this study confirmed that there

were significant differences between Malaysian and Chinese lecturers in using ICT

facilities for their teaching and research purposes in higher education. This was

consistent with a prior study (Teo et al., 2008), which found that there were

significant dissimilarities between Malaysian and Singaporean pre-service teachers in

terms of their computer attitude, perceived ease of use and perceived usefulness of

technology. Chong et al., (2012) explored that consumers’ intention to adopt mobile

commerce was affected by the different cultures, namely, Malaysian and Chinese. On

the other hand, Terzis et al., (2013) discovered that the Computer Based Assessment

Acceptance Model (CBAAM) was valid for both countries (Greece & Mexico) in

general; however, there were some distinctions due to the culture. Moreover, Singh et

al., (2006) showed that the causal relationships between the extended TAM factors,

namely, perceived ease of use, perceived usefulness, cultural adaptation, attitude and

behavioral intention to use international websites presented strong evidence for the

viability of TAM in elucidating website usage in Brazil, Germany, and Taiwan.

Along this line, Al-Gahtani et al., (2007) examined the similarities and dissimilarities

between Saudi Arabian and North American validations of UTAUT with regards to

cultural diversities that influenced the users’ acceptance of IT in these two societies.

Similarly, Keil and Brenner (1997) demonstrated that TAM was influenced by the

three different cultures in the United States, Japan, and Switzerland. According to

Huang et al., (2003), the validity of TAM was expanded by assessing technology

acceptance beliefs in a developing country with Chinese culture.

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5.3 CONCLUSION

As information technology is increasingly used in education, its integration in

teaching, learning and research has had an immense significance in fostering

technology-based education among the lecturers of universities. The findings

contributed to a better understanding of the Technology Adoption and Gratification

(TAG) model, which was developed and validated for assessing lecturers’ adoption

and gratification in using ICT facilities for their teaching and research purposes in

higher education among the two comprehensive public universities, namely, the

University of Malaya in Malaysia and Jiaxing University in China. To develop and

validate the TAG model, data were gained through a survey questionnaire. The

psychometric properties of the TAG models’ reliability and validity were performed

by a Rasch model using Winsteps version 3.49. The advantage of using a Rasch model

was that it measured not only items’ reliability and validity but also persons’

reliability and validity, while most of the studies focused only on the items’ reliability

and validity. This study discovered that the TAG model was integrated by six valid

measurement models of the latent constructs, namely, computer self-efficacy (CSE),

perceived usefulness (PU), perceived ease of use (PEU), intention to use (INT), actual

use (USE) and gratification (GRAT) as tested individually by the Confirmatory Factor

Analysis (CFA) to obtain specific objectives. To test all the hypotheses, the structural

equation modeling (SEM) was applied to develop and validate the TAG model as well

as its causal relationships among the constructs. The findings of the TAG model

revealed that lecturers’ computer self-efficacy had a statistically significant direct

influence on perceived ease of use and usefulness of ICT facilities in higher education

for their teaching and research. On the other hand, lecturers’ perceived ease of use and

perceived usefulness had a significant direct influence on their gratification in using

185

ICT facilities in higher education. Subsequently, perceived usefulness and perceived

ease of use were found to have a significant direct influence on lecturers’ intention to

use ICT facilities for their teaching and research. Similarly, lecturers’ intention to use

had a statistically significant direct effect on their gratification as well as actual use of

ICT facilities. Moreover, computer self-efficacy had a significant indirect influence on

gratification mediated by perceived ease of use and perceived usefulness, respectively.

Meanwhile, computer self-efficacy had also a significant indirect influence on

lecturers’ intention to use mediated by perceived ease of use and perceived usefulness,

respectively. Lastly, lecturers’ perceived ease of use and perceived usefulness had a

significant indirect effect on their gratification in using ICT facilities in higher

education mediated by intention to use. The data also exhibited that computer self-

efficacy moderately more influential factor in the revised TAG model in directly

affecting the lecturers’ perceived usefulness and perceived ease of use as well as

indirectly influencing the lecturers’ intention to use and gratification in using ICT

facilities in higher education for their research and teaching purposes. In addition, the

analysis demonstrated that the exogenous variable (CSE) explained almost 28% and

29% of the variance in perceived ease of use and perceived usefulness respectively,

while the exogenous variable (CSE) and the two mediator variables (PU and PEU)

collectively explained approximately 25% of the variability of lecturers’ intention to

use of ICT facilities in higher education. Similarly, the exogenous variable (CSE) and

the three mediator variables (PU, PEU and INT) explained almost 20% of the variance

of lecturers’ actual use of ICT facilities. This implies that lecturers’ use of ICT

facilities are found to be underutilized. Finally, the exogenous variable (CSE) and the

three mediator variables (PU, PEU and INT) collectively explained approximately

56% of the variability of lecturers’ gratification in using ICT facilities in higher

186

education. The revised TSM model exhibited adequate convergent and discriminant

validity, with all loadings indicating a value greater than 0.70 as shown in Figure 4.15.

Several findings of the TAG model were consistent with the Technology

Satisfaction Model (TSM), Technology Acceptance Model (TAM) and Online

Database Adoption and Satisfaction (ODAS) model and contributed that the TAG is

able to evaluate lecturers’ adoption and gratification in using ICT facilities in teaching

and research. However, TSM focused only on students’ satisfaction in using wireless

internet (Islam, 2014) and online research databases (Islam et al., 2015) in higher

education but ignored its adoption. Similarly, TAM, developed by Davis (1989),

ignored the issues of computer self-efficacy and satisfaction. In general, TAM is used

for measuring the acceptance of technology (Islam, 2014). On the other hand, Islam

(2011a) developed and validated the ODAS model by incorporating two intrinsic

motivation attributes, namely, computer self-efficacy and satisfaction into the original

TAM. However, the ODAS model overlooked an extrinsic motivation component,

namely, actual use which is an important factor for assessing technology acceptance

or adoption as suggested by prior studies. As such, this study combined TSM, TAM

and the ODAS model to develop and validate the TAG model which would be able to

examine any kind of technology adoption and gratification simultaneously.

The findings brought forth some noteworthy information about lecturers’

adoption and gratification of the universities’ ICT facilities. First, they used the ICT

facilities largely because they found it beneficial and easy to use for their teaching and

research purposes. Moreover, they also had strong self-efficacy in using ICT facilities

in higher education. The ability (computer self-efficacy) appeared to be a stronger

factor than perceived usefulness and perceived ease of use in influencing their

adoption and gratification of ICT. As the universities provide a library information on

187

the use of ICT facilities, lecturers’ lack of ICT skills and efficacy is most likely not a

real threat to its adoption and gratification. Lecturers will persist in the use of ICT

facilities if the benefits are much greater than the difficulties encountered. To increase

the usefulness of ICT facilities, the university authorities should reexamine the utility

of resources because teaching, learning and research are facilitated only if lecturers

have access to the facilities.

Second, ease of use is a determinant for lecturers’ gratification, intention to use

and actual use of ICT, which directly and indirectly influences their adoption and

gratification in using it. In other words, the university authorities can increase

lecturers’ gratification by enhancing the user-friendliness of their ICT facilities in

terms of the user interface, hyperlinks and search facilities. Gratification may also be

directly influenced by the users’ ability to retrieve desired information from electronic

resources. To address difficulties in retrieving information, authorities should focus

their training on database-specific search skills, rather than just increasing the

awareness of their existence and utilities. In fact, the ability to retrieve information

from different databases could be a stronger predictor of adoption and gratification,

compared to computer self-efficacy because a discrepancy usually exists between

users’ beliefs in their ability to retrieve information from electronic systems and their

actual ability to do it.

Evidenced from the revised TAG model showed that the direct and indirect

effect of lecturers’ computer self-efficacy on perceived ease of use, perceived

usefulness, intention to use and gratification in using ICT facilities was limited to their

requisite skills to communicate electronically with colleagues and students, use ICT

facilities to enhance the effectiveness of teaching, learning and research, improve the

quality of research works in using ICT facilities, navigate their way through ICT

188

facilities, save and print journals or articles from the research databases. In addition,

they also had the knowledge and skills to benefit from using the university ICT

facilities. However, Islam (2011a & 2014) discovered that postgraduate students

articulated obstacles related to accessing journals from the online research databases.

The influence of lecturers’ perceived usefulness on intention to use,

gratification and actual use of ICT facilities in teaching and research manifested in

lecturers increasing research productivity, making work more interesting, improving

the quality of the work they do, enhancing research skills, making information easier

to find, making information readily available, and providing the latest information on

specific areas of research. Notwithstanding, Islam (2011a) revealed that postgraduate

students articulated their limitations on retrieving current information on a particular

area of research and enhancing their knowledge and research skills in using the online

research databases. In a related study, Islam (2014) found that students expressed their

constraints vis-à-vis slow wireless internet speeds compared to the LAN as well as

difficulty in getting wireless internet access from anywhere and anytime in the campus.

Moreover, the convenience of access, in particular, has been frequently cited as a

major advantage by users of electronic journals in multiple studies (Maughan, 1999;

Tenner & Yang, 1999; Hiller, 2002). In Liew et al., (2000), the benefits of electronic

resources were found to be the facility to link to additional information, the ability to

browse, and the currency of materials. Other benefits and advantages included robust

search capabilities, hyperlinks to outside content, efficient acquisition of information

(Dillon & Hahn, 2002; Ray & Day, 1998), time saving factors, and print and save

capabilities (Togia & Tsigilis, 2009). These benefits and advantages were realized all

the more by students when their learning and academic pursuits became greatly

enhanced.

189

The influence of lecturers’ perceived ease of use on intention to use, actual use

and gratification in using ICT facilities was exhibited in terms of their easy access to

the university research databases, the minimal effort required in interacting with the

research databases system, and it being a stimulating interaction. It was also evident

that lecturers found it easy to select articles/journals of different categories using

university research databases. However, Islam (2011a) demonstrated that some

barriers still persisted. These were manifested by the postgraduate students’

inadequacy in choosing articles or journals from the online research databases,

requiring a lot of mental effort to download research materials, and the difficulty in

searching for research materials from the online research databases. In addition, it was

not easy to become skillful in using online research databases according to the

students. Subsequently, the findings of the TSM (Islam, 2014) indicated that students

showed that some obstacles still lingered; these were noticeable in the inadequate

speed experienced by the students in downloading the materials from the wireless

internet. Moreover, it was not easy to connect and register for wireless facilities as

perceived by the students.

The significant effect of intention to use on actual use and gratification in

using ICT facilities was captured in terms of their intention to carry out research

activities, use computer applications to present lecture materials in class, use

Microsoft office applications to write research papers, use ICT facilities for teaching,

learning and research purposes frequently, and use the university research databases to

update themselves on the research areas they are pursuing. Nonetheless, prior study

indicated that postgraduate students’ intention to use online research databases was

limited to their wiliness to carry out research activities and write theses and journals

(Islam, 2011a). Thus, the results of TAG model confirmed lecturers’ adoption and

190

overall gratification in using ICT facilities for their teaching, learning and research

purposes in higher education. However, the actual use was found to be underutilized,

especially by the lecturers of the University of Malaya in Malaysia and Jiaxing

University in China.

Additionally, the findings of this study also revealed that the TAG model was

able to measure lecturers’ adoption and gratification in using ICT facilities at two

comprehensive public universities in Malaysia and China. However, the TAG model

works differently in cross-cultural settings. This meant that cross-culture did interact

with the exogenous and mediating variables to influence the lecturers’ adoption and

gratification in using ICT facilities in higher education. It is, therefore, reasonable to

conclude that cross-culture was a moderating variable for the TAG model. This is a

research area worth looking into by the university authorities, researchers,

academicians and administrations in higher education.

Finally, the findings have several important implications for ICT providers in

universities worldwide. The rapid uptake of the technology has made ICT facilities

indispensable tools for research, teaching and learning, and considering their

paramount importance in the contemporary context of university education, it would

do the providers and universities well to address issues of ICT usefulness, ease of use,

efficacy, use and gratification. The benefits gained from ICT use would certainly

increase lecturers’ gratification, therefore resulting in their continued adoption of the

facility. The present study applies systematic research approaches of model

development and validation by using a Rasch model and a structural equation

modeling so that researchers from other countries might benefit from applying these

techniques in their future studies. Researchers could also benefit from this study by

knowing the constructs of the TAG model, namely, computer self-efficacy, perceived

191

usefulness, perceived ease of use, intention to use, actual use and gratification of ICT

facilities, which could be applied to their further studies. The findings of this study

confirmed the cross-cultural validation of the TAG model in assessing technology

adoption and gratification in higher education, whereas existing models such as TAM

and Diffusion and Innovation theory are only allowing researchers to measure either

technology acceptance or adoption. As a result, the TAG model could be a unique

contribution to researchers from other countries.

Albeit the research methodology appears valid, reliable and eliciting, only two

higher educational institutions with a small sample size are used. It is therefore, the

research may lack in a variation of respondents. A wider research using more

organizations with more samples may help to validate the (TAG) model with a greater

acceptability. Critics may generate the questions on its validity showing less number

of samples as the excuse. However, the usage of ICT and its users hold a global

pattern; thus, this model has a great potential to work globally regardless of

geographical, economical, social and cultural patterns in a particular country or region.

Further research in this area should examine the true utility of all the services provided

in a university before money is invested in their purchase and installation.

Furthermore, future research could investigate additional constructs, namely, teaching

effectiveness, and knowledge and skill acquisition in the classroom to included into

the TAG model which may foster a better understanding of it. Additional studies

could also be done upon this study by applying the TAG model in cross-cultures to

measure new areas in detail such as digital library services, online education, online

shopping, mobile learning, social networks, and any other innovative technological

services.

192

However, further research on cross-validation of the TAG model could be

done to explain the moderating effects of various demographic attributes on lecturers’

or students’ adoption and gratification. The gender, faculties, and nationalities of the

teachers and students might be explored in this regard.

193

REFERENCES

Adamson, I., & Shine, J. (2003). Extending the new technology acceptance model to

measure the end user information systems satisfaction in a mandatory

environment: A bank's treasury. Technology Analysis & Strategic Management,

15(4): 441-455.

Afshari, M., Bakar, K.A., Luan, W.S., & Siraj, S. (2012). Factors affecting the

transformational leadership role of principals in implementing ict in schools.

The Turkish Online Journal of Educational Technology, 11 (4): 164-176.

Agarwal, R., & Karahanna, E. (2000). Time flies when you’re having fun: cognitive

absorption and beliefs about information technology uses. MIS Quarterly,

24(4): 665-694.

Agarwal, R., & Prasad, J. (1998). A conceptual and operational definition of personal

innovativeness in the domain of information technology. Information Systems

Research, 9(2): 204-215.

Agarwal, S., Erramilli, M., & Chekitan, S. (2003). Market orientation and

performance in service firms: role of innovation. Journal of Service Marketing,

17(1): 68-82.

Ahmad, T.B.T., Basha, K.M., Marzuki, A.M., Hisham, N.A., & Sahari, M. (2010).

Faculty’s acceptance of computer-based technology: cross-validation of an

extended model. Australasian Journal of Educational Technology, 26(2): 268-

279.

Al-Jabri, I.M., & Roztocki, N. (2010). Adoption and Use of Information Technology

in Mandatory Settings: Preliminary Insights from Saudi Arabia. Proceedings of

the Sixteenth Americas Conference on Information Systems, Lima, Peru,

August 12th

-15th

. Available at:

http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1965269

Aiken, R.L. (1993). Personality, theories, research, and applications. NJ: Prentice-

Hall Inc.

194

Alenezi, A.R., Karim, A.M.A., & Veloo, A. (2010). An empirical investigation into

the role of enjoyment, computer anxiety, computer self-efficacy and internet

experience in influencing the students' intention to use e-Learning: A case study

from Saudi Arabian governmental universities. The Turkish Online Journal of

Educational Technology, 9(4): 22-34.

Al-Gahtani, S.S., Hubona G.S., & Wang J. (2007). Information technology (IT) in

Saudi Arabia: Culture and the acceptance and use of IT. Information and

Management, 44(8): 681–691.

Al-Ruz, J.A., & Khasawneh, S. (2011). Jordanian pre-service teachers' and technology

integration: A human resource development approach. Educational Technology

& Society, 14(4): 77–87.

Aljuaid, N.M.F., Alzahrani, M.A.R., & Islam, A.Y.M.A. (2014). Assessing mobile

learning readiness in Saudi Arabia higher education: An empirical study. The

Malaysian Online Journal of Educational Technology, 2(2): 1-14.

Amin, H. (2007). Internet banking adoption among young intellectuals. Journal of

Internet Banking and Commerce, 12(3): 1-13.

Anderson E.W., Fornell, C., & Lehmann, D.R. (1994). Customer satisfaction, market

share and profitability. Journal of Marketing, 58(3): 53-66.

Azizan, F.Z. (2010). Blended Learning in Higher Education Institute in Malaysia.

Paper presented at the Regional Conference on Knowledge Integration in ICT,

Selangor, Malaysia. Available at:

http://library.oum.edu.my/oumlib/sites/default/files/file_attachments/odl-

resources/4334/blended-learning.pdf

Babbie, E. (2001). The practice of social research, Belmont, CA: Wadsworth

Thomson Learning.

Ball, D., & Levy, Y. (2008). Emerging educational technology: assessing the factors

that influence instructors’ acceptance in information systems and other

classrooms. Journal of Information Systems Education, 19(4): 431-443.

Bandura, A. (1977). Self-efficacy: Toward a unifying theory of behavioral change.

Psychological Review, 84: 191-125.

195

Bandura, A. (1986). Social foundations of thought and action: A social cognitive

theory. Englewood Cliffs, New Jersey: Prentice Hall.

Bandura, A., & Schunk, D.H. (1981). Cultivating competence, self-efficacy and

intrinsic interest through self-motivation. Journal of Personality and Social

Psychology, 41: 586-598.

Barkhi, R., Belanger, F., & Hicks, J. (2008). A model of the determinants of

purchasing from virtual stores. Journal of Organizational Computing and

Electronic Commerce, 18: 177–196. DOI: 10.1080/10919390802198840

Bate, F. (2010). A bridge too far? Explaining beginning teachers’ use of ICT in

Australian schools. Australasian Journal of Educational Technology, 26(7):

1042-1061.

Blackwell, C.K., Lauricella, A.R., Wartella, E., Robb. M., & Schomburg, R. (2013).

Adoption and use of technology in early education the interplay of extrinsic

barriers and teacher attitudes. Computers & Education, 69: 310-319.

Bond, T.G., & Fox, C.M. (2001). Applying the Rasch model: Fundamental

measurement in the human science. Lawrence Erlbaum, Mahwah, NJ.

Bringula, R.P., & Basa, R.S. (2011). Factors affecting faculty web portal usability.

Educational Technology & Society, 14 (4): 253–265.

Buabeng-Andoh, C. (2012). Factors influencing teachers’ adoption and integration of

information and communication technology into teaching: A review of the

literature. International Journal of Education and Development using

Information and Communication Technology, 8(1): 136-155.

Byrne, B.M. (2000). Structural equation modeling with Amos: Basic concepts,

applications and programming. Mahwah, NJ: Lawrence Erlbaum.

Clemons, D.S., & Woodruff, R.B. (1992). Broadening the view of consumers (dis)

satisfaction: A proposed means–end disconfirmation model. In Marketing theory

and applications, Allen, C. T., Madden, T. J., Shimp, T. A., Howell, R. D.,

Zinkhan, G. M., Heisley, D. D., 1992. Ed. Chicago: American Marketing

Association (pp 209-216).

196

Chang, C.C., Yan, C.F., & Tseng, J.S. (2012). Perceived convenience in an extended

technology acceptance model: Mobile technology and english learning for

college students. Australasian Journal of Educational Technology, 28(5): 809-

826. http://www.ascilite.org.au/ajet/ajet28/chang-cc.html

Chang, S., & Tung, F.C. (2008). An empirical investigation of students’ behavioural

intentions to use the online learning course websites. British Journal of

Educational Technology, 39(1): 71-83. doi:10.1111/j.1467-8535.2007.00742.x

Chau, P.Y.K. (1996). An empirical assessment of a modified technology acceptance

model. Journal of Management Information Systems, 13(2): 185-204.

Charalambous, K., & Papaioannou, F. (2010). The public primary school principals’

self-perceived competence and use of ICT for personal, teaching and

administrative purposes. In A. Jimoyiannis (ed.), Proceedings of the 7th Pan-

Hellenic Conference with International Participation: ICT in Education, 1, 177-

184, University of Peloponnese, Korinthos, Greece, 23-26 September. Available

at: http://www.etpe.gr/custom/pdf/etpe1582.pdf

Chitungo, S.K., & Munongo, S. (2013). Extending the technology acceptance model

to mobile banking adoption in rural Zimbabwe. Journal of Business

Administration and Education, 3(1): 51-79.

Chong, A.Y., Chan, F.T.S., & Ooi, K. (2012). Predicting consumer decisions to adopt

mobile commerce: Cross country empirical examination between China and

Malaysia. Decision Support Systems, 53(1): 34–43.

doi:10.1016/j.dss.2011.12.001

Churchill, G. A., & Suprenant, C. (1982). An investigation into the determinants of

customer satisfaction. Journal of Marketing Research, 19: 491-504.

Compeau, R.D., & Higgins, A.C. (1995). Computer Self-efficacy: Development of a

Measure and Initial Test. MIS Quarterly, 19(2): 189-211.

Compeau, R.D., Higgins, A.C., & Huff, S. (1999). Social Cognitive Theory and

Individual Reactions to Computing Technology: A longitudinal Study. MIS

Quarterly, 23(2): 145-158.

197

Dabholkar, P.A., & Bogazzi, R. (2002). An attitudinal model of technology-based

self-service: Moderating effect of consumer traits and situational factors.

Journal of Academy of Marketing Science, 30(3): 184–202.

Davis, F.D. (1989). Perceived usefulness, perceived ease of use and user acceptance

of information technology. MIS Quarterly, 13(3): 319-340.

Davis, F.D. (1993). User acceptance of information technology: system characteristics,

user perceptions and behavioral impacts. International Journal of Man-

Machine Studies, 38(3): 475-487.

Davis, F. Bagozzi, R.P., & Warshaw, P.R. (1989). User acceptance of computer-

technology: a comparison of two theoretical models. Management Science,

38(8): 982-1003.

Delcourt, M.A.B., & Kinzie, M.B. (1993). Computer technologies in teacher

education: the measurement of attitude and self-efficacy. Journal of Research

and Development in Education, 27(1): 34-41.

Devaraj, S., Fan, M., & Kohli, R. (2002). Antecedents of b2c channel satisfaction and

preference: Validating e-commerce metrics. Information Systems Research

13(3):316-333. http://dx.doi.org/10.1287/isre.13.3.316.77

Dillon, A., & Morris, G.M. (1996). User acceptance of information technology

theories and models. In M. Williams (ed) Annual Review of Information

Science and Technology, 31, Medford, NJ: Information Today, pp. 3-32.

Dillon, I.F., & Hahn, K. (2002). Are researchers ready for the electronic only journal

collection? Libraries and the Academy, 2: 375-390.

Du Plessis, A., & Webb, P. (2012). Digital immigrant teacher perceptions of an

extended Cyberhunt strategy. Australasian Journal of Educational Technology,

28(2): 341-363. http://www.ascilite.org.au/ajet/ajet28/duplessis.html

Eastin, M.S., & LaRose, R. (2000). Internet self-efficacy and the psychology of the

digital divide. Journal of Computer-Mediated Communication, 6(1).

doi: 10.1111/j.1083-6101.2000.tb00110.x

198

Elen, J., Clarebout, G., Sarfo, F.K., Louw, L.P.F., Pöysä-Tarhonen, J., & Stassens, N.

(2010). Computer and information and communication technology: Students'

culture specific interpretations. Educational Technology & Society, 13 (4): 227–

239.

Ellen, P.S., Bearden, W.O., & Sharma, S. (1991). Resistance for technological

innovations: An examination of the role of self-efficacy and performance

satisfaction. Journal of Academic of Marketing Science, 19(4): 297-307.

Eyal, L. (2012). Digital assessment literacy—the core role of the teacher in a digital

environment. Educational Technology &Society, 15 (2): 37–49.

Ferdousi, B. J. (2009). A Study of Factors that Affect Instructors’ Intention to Use E-

Learning Systems in Two-Year Colleges. PhD Dessertation. Graduate School of

Computer and Information Sciences, Nova Southeastern University.

Fishbein, M. (2000). The role of theory in HIV prevention. AIDS Care, 12(3), 273–

278.

Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention and behavior: an

introduction to theory and research. MA: Addison-Wesley.

Fu, J.S. (2013). ICT in education: A critical literature review and its implications.

International Journal of Education and Development using Information and

Communication Technology, 9(1): 112-125.

Gibson, S.G., Harris, M.L., & Colaric, S.M. (2008). Technology acceptance in an

academic context: Faculty acceptance of online education, Journal of Education

for Business, 83(6): 355-359. http://dx.doi.org/10.3200/JOEB.83.6.355-359

Gibson, P.A., Stringer, K., Cotton, S.R., Simoni, Z., O’Neal, L.J., & Howell-Moroney,

M. (2014). Changing teachers, changing students? The impact of a teacher-

focused intervention on students’ computer usage, attitudes, and anxiety.

Computers & Education, 71: 165-174.

Goktas, Y., Gedik, N., & Baydas, O. (2013). Enablers and barriers to the use of ICT in

primary schools in Turkey: A comparative study of 2005–2011. Computers &

Education, 68: 211-222. http://dx.doi.org/10.1016/j.compedu.2013.05.002

199

Gong, M., Xu, Y., & Yu, Y. (2004). An enhanced technology acceptance model for

web-based learning. Journal of Information Systems Education, 15(4): 365-373.

Groff, J., & Mouza, C. (2008). A framework for addressing challenges to classroom

technology use. Association for the Advancement of Computing in Education

Journal, 16(1): 21-46.

Gultekin, K. (2011). Technology acceptance and the effect of gender in the Turkish

national police: The Case of the polnet system. Turkish Journal of Police

Studies, 13(3): 61-80.

Guo, Z., & Stevens, K.J. (2011). Factors influencing perceived usefulness of wikis for

group collaborative learning by first year students. Australasian Journal of


http://www.ascilite.org.au/ajet/ajet27/guo.html

Guriting, P., & Ndubisi, N. (2006). Borneo online banking: evaluating customer

perceptions and behavioral intention. Management Research News, 29(1/2): 6-

15.

Hanafizadeh, P., Behboudi, M., Koshksaray, A.A., & Tabar, M.J.S. (2014). Mobile-

banking adoption by Iranian bank clients. Telematics and Informatics, 31: 62–78.

http://dx.doi.org/10.1016/j.tele.2012.11.001

Havelka, D. (2003). Predicting software self-efficacy among business students: A

preliminary assessment. Journal of Information Systems Education, 14(2): 145-

152.

Hair, Jr. J.F., Black, W.C., Babin, B.J., & Anderson, R.E. (2010). Multivariate data

analysis a global perception: Pearson Education, Inc., Upper Saddle River,

New Jersey.

Hiller, S. (2002). How different are they? A comparison by academic area of library

use, priorities, and information needs at the University of Washington. Issues in

Science and Technology Librarianship. Available at: http://istl.org/02-

winter/article1.html

200

Hong, K.S., & Songan, P. (2011). ICT in the changing landscape of higher education

in Southeast Asia. In Hong, K. S. & Lai, K. W. (Eds), ICT for accessible,

effective and efficient higher education: Experiences of Southeast Asia.

Australasian Journal of Educational Technology, 27(Special issue, 8): 1276-

1290. http://www.ascilite.org.au/ajet/ajet27/hong.html

Hu, L.T., & Bentler, P.M. (1999). Cutoff criteria for fit indexes in covariance structure

analysis: Conventional criteria versus new alternatives. Structural Equation

Modeling, 6: 1-55.

Hu, P.J., Clark, T.H.K., & Ma, W.W. (2003). Examining technology acceptance by

school teachers: A longitudinal study. Information and Management, 41(2):

227-241.

Hu, P.J., Chau, P.Y.K., Sheng, O.R.L., & Tam, K.Y. (1999). Examining the

technology acceptance model using physician acceptance of telemedicine

technology. Journal of Management Information Systems, 16(2): 91-112.

Huang, E. (2008). Use and gratification in e-consumers. Internet Research, 18(4):

405-426.

Huang, C.J., Liu, M.C., Chang, K.E., Sung, Y.T., Huang, T.H., Chen, C.H., Shen,

H.Y., Huang, K.L., Liao, J.J., Hu, K.W., Luo, Y.C., & Chang, T.Y. (2010). A

learning assistance tool for enhancing ICT literacy of elementary school

students. Educational Technology & Society, 13 (3): 126–138.

Huang, L., Ming-te, L., & Wong, B.K. (2003). Testing of the cross-cultural

applicability of technology acceptance model: Evidence from the PRC,

information technology and organizations. Hershey, New Jersey, USA, Idea

Group Publishing. DOI: 10.4018/978-1-59140-066-0.ch094

Islam, A.Y.M.A. (2011a). Online database adoption and satisfaction model. Germany:

Lambert Academic Publishing.

Islam, A.Y.M.A. (2011b). Viability of the extended technology acceptance model: an

empirical study. Journal of Information and Communication Technology, 10:

85-98.

201

Islam, A.Y.M.A., Ahmad, T.B.T., Madziah, A.Z., & Sahari, M. (2011). Determinants

of Postgraduate Students’ Adoption of an Online Database and Their

Satisfaction in Using it, The New Intellectual Horizon in Education. Malaysia,

KL: International Islamic University Malaysia Press.

Islam, A.Y.M.A. (2014). Validation of the technology satisfaction model (TSM)

developed in Higher Education: The Application of Structural Equation

Modeling. International Journal of Technology and Human Interaction, 10(3):

44-57. doi:10.4018/ijthi.2014070104

Islam, A.Y.M.A., Leng, C.H., & Singh, D. (2015). Efficacy of the technology

satisfaction model (TSM): An empirical study. International Journal of

Technology and Human Interaction, 11(2): 45-60. DOI:

10.4018/ijthi.2014070104

Jang, S.J., & Tsai, M.F. (2013). Exploring the TPACK of Taiwanese secondary school

science teachers using a new contextualized TPACK model. Australasian

Journal of Educational Technology, 29(4): 566-580.

Jackson, C.M., Chow, R.A., & Leitch, R.A. (1997). Toward an understanding of the

behavioral intention to use an information system. Decision Science, 28(2):

357-389.

Kaba, B, & Osei-Bryson, K.M. (2013). Examining influence of national culture on

individuals’ attitude and use of information and communication technology:

Assessment of moderating effect of culture through cross countries study.

International Journal of Information Management, 33(3): 441–452

Karahanna, E., & Straub, D. (1999). The psychological origins of perceived

usefulness and ease of use. Information and Management, 35: 237-250.

Keengwe, J., Onchwari, G., & Wachira, P. (2008). Computer technology integration

and student learning: barriers and promise. Journal of Science of Education

Technology, 17: 560–565.

Kenzie, M., Delecourt, M., & Powers, S. (1994). Computer technologies; Attitudes

and self-efficacy across undergraduates disciplines. Research in Higher

Education, 35(6): 745-768.

202

Kim, H., Choi, H., Han, J. & So, H.J. (2012). Enhancing teachers’ ICT capacity for

the 21st century learning environment: Three cases of teacher education in

Korea. In C. P. Lim & C. S. Chai (Eds), Building the ICT capacity of the next

generation of teachers in Asia. Australasian Journal of Educational Technology,

28(Special issue, 6): 965-982. http://www.ascilite.org.au/ajet/ajet28/kim.html

Kregor, G., Breslin, M., & Fountain, W. (2012). Experience and beliefs of technology

users at an Australian university: Keys to maximising e-learning potential.

Australasian Journal of Educational Technology, 28(8): 1382-1404.

http://www.ascilite.org.au/ajet/ajet28/kregor.html

Kreijns, K., Vermeulen, M., Kirschner, P.A., Buuren, H.V., & Acker, F.V. (2013).

Adopting the integrative model of behaviour prediction to explain teachers’

willingness to use ICT: A perspective for research on teachers’ ict usage in

pedagogical practices. Technology, Pedagogy and Education, 22(1): 55-71. DOI:

10.1080/1475939X.2012.754371

Kumar, R.G., & Ravindran, S. (2012). An empirical study on service quality

perceptions and continuance intention in mobile banking context in India.

Journal of Internet Banking and Commerce, 17(1): 1-22.

Kyriakidou, V., Michalakelis, C., & Sphicopoulos, T. (2013). Assessment of

information and communications technology maturity level.

Telecommunications Policy, 37(1): 48–62. doi:10.1016/j.telpol.2012.08.001

Lallmahamood, M. (2007). An examination of individual’s perceived security and

privacy of the internet in Malaysia and the influence of this on their intention to

use e-commerce: Using an extension of the technology acceptance model.

Journal of Internet Banking and Commerce, 12(3): 1-26.

Lai, K.W. (2011). Digital technology and the culture of teaching and learning in

higher education. In Hong, K. S. & Lai, K. W. (Eds), ICT for accessible,

effective and efficient higher education: Experiences of Southeast Asia.

Australasian Journal of Educational Technology, 27(Special issue, 8): 1263-

1275. http://www.ascilite.org.au/ajet/ajet27/lai.html

Lai, H.M., & Chen, C.P. (2011). Factors influencing secondary school teachers’

adoption of teaching blogs. Computers & Education, 56: 948-960.

doi:10.1016/j.compedu.2010.11.010

203

Langeard, E., Bateson, J.E.G., Lovelock, C.H., & Eiglier, P. (1981). Marketing of

service: new insights from customers and managers. Cambridge, MA:

Marketing Science Institute.

Lau, W.W.F., & Yuen, A.H.K. (2013). Educational technology training workshops for

mathematics teachers: An exploration of perception changes. Australasian

Journal of Educational Technology, 29(4): 595-611.

Legris, P., Ingham, J., & Collerette, P. (2003). Why do people use information

technology? A critical review of the technology acceptance model. Information

& Management, 40: 191-204.

Lee, D.Y., & Lehto, M.R. (2013). User acceptance of youtube for procedural learning:

An extension of the technology acceptance model. Computers & Education, 61:

193-208. http://dx.doi.org/10.1016/j.compedu.2012.10.001

Lee, T.M., & Park, C. (2008). Mobile technology usage and b2b market performance

under mandatory adoption. Industrial Marketing Management, 37(7): 833-840.

Lee, H., Fiore, A. M., & Kim, J. (2006). The role of the technology acceptance model

in explaining effects of image interactivity technology on consumer responses.

International Journal of Retail & Distribution Management, 34(8): 621-644.

Lee, Y.H., Hsieh, Y.C., & Hsu, C.N. (2011). Adding innovation diffusion theory to

the technology acceptance model: Supporting employees' intentions to use e-

learning systems. Educational Technology & Society, 14 (4): 124–137.

Lee, Y-H., Hsiao, C., & Purnomo, S.H. (2014). An empirical examination of

individual and system characteristics on enhancing e-learning acceptance.

Australasian Journal of Educational Technology, 30(5), 561-579.

Levin, T., & Wadmany, R. (2008). Teachers' views on factors affecting effective

integration of information technology in the classroom: Developmental scenery.

Journal of Technology and Teacher Education, 16(2): 233-263.

Liew, C.L., Foo, S., & Chennupati, K.R. (2000). A study of graduate student end-

users’ use and perception of electronic journals. Online Information Review,

24(4): 302-315.

204

Lim, C.P., & Pannen, P. (2012). Building the capacity of Indonesian education

universities for ICT in pre-service teacher education: A case study of a strategic

planning exercise. In C. P. Lim & C. S. Chai (Eds), Building the ICT capacity of

the next generation of teachers in Asia. Australasian Journal of Educational

Technology, 28(Special issue, 6): 1061-1067.

http://www.ascilite.org.au/ajet/ajet28/lim-cp.html

Liu, S.H. (2012). A multivariate model of factors influencing technology use by pre-

service teachers during practice teaching. Educational Technology & Society, 15

(4): 137–149.

Liu, S., Liao, H., & Pratt, J. (2009). Impact of media richness and flow on e-learning

technology acceptance. Computers & Education, 52(3): 599-607.

Ma, Q., & Liu, L. (2004). Technology acceptance model: a meta-analysis of empirical

findings. Journal of Organizational End User Computing, 60: 59-72.

Mac Callum, K., & Jeffrey, L. (2013). The influence of students' ICT skills and their

adoption of mobile learning. Australasian Journal of Educational Technology,

29(3): 303-314.

Marsh, H.W., Hau, K.T., & Wen, Z. (2004). In search of golden rules: Comment on

hypothesis testing approaches to setting cutoff values for fit indices and dangers

in overgeneralizing findings. Structural Equation Modeling, 11: 320-341.

Martínez-Torres, M.R., Marín, S.L.T., García, F.B., Vázquez, G., Oliva, M.A., &

Torres, T. (2008). A technological acceptance of e-learning tools used in

practical and laboratory teaching, according to the European higher education

area. Behaviour & Information Technology, 27(6): 495–505. DOI:

10.1080/01449290600958965

Mathieson, K. (1991). Predicting user intention: comparing the TAM with the theory

of planned behavior. Information Systems research, 2: 173-191.

Mathieson, M., Peacock, E., & Chin, W.W. (2001). Extending the technology

acceptance model: the influence of perceived user resources. Data Base for

Advance in Information Systems, 32(3): 86-112.

205

Mathwick, C., Rigdon, E., & Malhotra, N. (2001). Experiential value

conceptualization measurement and application in the catalog and internet

shopping environment. Journal of Retailing, 77(1): 39-53.

Maughan, P.D. (1999). Library resources and services: a cross-disciplinary survey of

faculty and graduate student use and satisfaction. Journal of Academic

Librarianship, 25(5): 354-366.

McDougall, G.H.G., & Levesque, T. (2000). Customer satisfaction with services:

putting perceived value into the equation. Journal of Service Marketing, 14(5):

392-401.

Moses, P., Wong, S.L., Bakar, K.A. & Rosnaini, M. (2013). Perceived usefulness and

perceived ease of use: Antecedents of attitude towards laptop use among

Science and Mathematics teachers in Malaysia. The Asia-Pacific Education

Researcher, 22(3), 293-299.

Moos, D.C., & Azevedo, R. (2009). Learning with computer-based learning

environments: a literature review of computer self-efficacy. Review of

Educational Research, 79(2): 576-600.

Niu, H. (2014). Shopping in cyberspace: Adolescent technology acceptance attitude

with decision-making styles. International Journal of Technology and Human

Interaction, 10(3), 1-18. doi:10.4018/ijthi.2014070101

Oliver, R., & Towers, S. (2000). Up time: Information Communication Technology:

Literacy and Access for Tertiary students in Australia. Canberra: Department of

Education, Training and Youth Affairs.

Pajares, F. (2002). Self-efficacy beliefs in academic contexts: an outline, Retreived

December 1, 2011 from http://www.uky.edu/~eushe2/Pajares/efftalk.html

Pynoo, B., Tondeur, J., Braak, J.V., Duyck, W., Sijnave, B., & Duyck, P. (2012).

Teachers’ acceptance and use of an educational portal. Computers & Education,

58, 1308–1317. doi:10.1016/j.compedu.2011.12.026

Ramayah, T., Ignatius, J., & Aafaqi, B. (2005). PC usage among students in a private

institution of higher learning: The moderating role of prior experience. Journal

of Educators and Education, 20: 131-152.

206

Ramayah, T., & Lo, M.C. (2007). Impact of shared beliefs on perceived usefulness

and ease of use in the implementation of an entrerprise resource planning

system. Management Research News, 30(6): 420-431.

Rasimah, C.M.Y., Ahmad, A., & Zaman, H.B. (2011). Evaluation of user acceptance

of mixed reality technology. In Hong, K. S. & Lai, K. W. (Eds), ICT for

accessible, effective and efficient higher education: Experiences of Southeast

Asia. Australasian Journal of Educational Technology, 27(Special issue, 8):

1369-1387. http://www.ascilite.org.au/ajet/ajet27/rasimah.html

Ray, K., & Day, J. (1998). Student attitudes towards electronic information resources.

Information Research, 4(2). Available at http://informationr.net/ir/4-

2/paper54.html

Roblyer, D.M. (2006). Integrating Educational Technology into Teaching (4th ed.).

NJ: Pearson Prentice Hall.

Rogers, E. M. (1995). Diffusion of innovations (4th ed.). New York: The Free Press.

Saleh, A.S. (2009). Information Technology (IT) in the Sultan Qaboos University;

Sultanate of Oman: A Study on the IT Culture and the Acceptance and the Use

of IT. Unpublished Doctoral Dissertation, International Islamic University

Malaysia.

Schepers, J., & Wetzels, M. (2007). A meta-analysis of the technology acceptance

model: investigating subjective norm and moderation effects. Information &

Management, 44: 90-103.

Schunk, D.H. (1991). Learning Theories: an Educational Perspective. New York:

McMillan Publishing Company.

Shamdasani, P., Mukherjee, A., & Malhotra, N. (2008). Antecedents and

consequences of service quality in consumer evaluation of selt-service internet

technology. The Service Industry Journal, 28(1): 117-138.

Shin, D.H. (2012). Factors that influence pre-service teachers’ ICT usage in education.

Information Technology & People, 25 (1): 55-80. DOI

10.1108/09593841211204344

207

Shittu, A.T., Basha, K.M., Rahman, N.S.N.A., & Ahmad, T.B.T. (2011). Investigating

students’ attitude and intention to use social software in higher institution of

learning in Malaysia. Multicultural Education & Technology Journal, 5(3): 194-

208. http://dx.doi.org/10.1108/17504971111166929

Shittu, A.T., Basha, K.M., Rahman, N.S.N.A., & Ahmad, T.B.T. (2013).

Determinants of social networking software acceptance: A multi-theoretical

approach. The Malaysian Online Journal of Educational Technology, 1(1): 27-

43.

Shroff, R.H., Deneen, C.D., & Ng, E.M.W. (2011). Analysis of the technology

acceptance model in examining students’ behavioural intention to use an e-

portfolio system. Australasian Journal of Educational Technology, 27(4): 600-

618. http://www.ascilite.org.au/ajet/ajet27/shroff.html

Shipps, B., & Phillips, B. (2013). Social networks, interactivity and satisfaction:

Assessing socio-technical behavioral factors as an extension to technology

acceptance. Journal of Theoretical and Applied Electronic Commerce Research,

8(1): 35-52. DOI: 10.4067/S0718-18762013000100004

Singh, N., Fassott, G., Chao, M.C.H., and Hoffmann, J.A. (2006). Understanding

international web site usage: A cross-national study of German, Brazilian, and

Taiwanese online consumers. International Marketing Review, 23(1): 83-97.

DOI 10.1108/02651330610646304

Sobel, M.E. (1982). Asymptotic confidence intervals for indirect effects in structural

equation models. Sociological methodology, 13: 290-312.

Soffer, T., Nachmias, R., & Ram, J. (2010). Diffusion of web supported instruction in

higher education - The case of Tel-Aviv University. Educational Technology &

Society, 13 (3): 212–223.

Spector, J.M. (2013). Emerging educational technologies and research directions.

Educational Technology & Society, 16 (2): 21–30.

Stone, R. W., & Baker-Eveleth, L.J. (2013). Students’ intentions to purchase

electronic textbooks. Journal of Computing in Higher Education, 25(1): 27-47.

doi: 10.1007/s12528-013-9065-7.

208

Straub, D., Keil, M., & Brenner, W. (1997). Testing the technology acceptance model

across cultures: A three country study. Information and Management, 33(1): 1-

11.

Suki, N.M., Ramayah, T., Nee, A.S., & Suki, N.M. (2014). Consumer intention to use

anti-spyware software: An application of structural equation modeling.

International Journal of Technology and Human Interaction, 10(3), 19-31.

doi:10.4018/ijthi.2014070102

Sun, H., & Zhang, P. (2006). The role of moderating factors in user technology

acceptance. International Journal of human Computer Studies, 64(2): 53-78.

Sutherland, R., Robertson, S., & John, P. (2009). Improving classroom learning with

ICT. London: Routledge.

Szymanski, D.M., & Hise, R.T. (2000). E-satisfation: an initial examination. Journal

of Retailing, 76(3): 309-322.

Taylor, S., & Todd, P. (1995). Understanding information technology usage: a test of

competing models. Information Systems Research, 6(2): 144-176.

Tenopir, C. (2003). Use and users of electronic library resources: an overview and

analysis of recent research studies. Washington, DC: Council on Library and

Information Resources. Available at:

http://www.clir.org/pubs/reports/pub120/pub120.pdf.

Tenner, E., & Yang, Z.Y. (1999). End-user acceptance of electronic journals: a case

study from a major academic research library. Technical Services Quarterly,

17(2): 1-11.

Teo, T. (2011). Factors influencing teachers’ intention to use technology: Model

development and test. Computers & Education, 57: 2432–2440.

doi:10.1016/j.compedu.2011.06.008

Teo, T., Luan, W.S., & Sing, C.C. (2008). A cross-cultural examination of the

intention to use technology between Singaporean and Malaysian pre-service

teachers: An application of the Technology Acceptance Model (TAM).

Educational Technology and Society, 11(4): 265–280.

209

Terzis, V., Moridis, C.N., Economides, A.A., & Rebolledo-Mendez, G. (2013).

Computer based assessment acceptance: A cross-cultural study in Greece and

Mexico. Educational Technology & Society, 16 (3): 411–424.

Tezci, E. (2011). Factors that influence pre-service teachers’ ICT usage in education.

European Journal of Teacher Education, 34(4): 483-499.

http://dx.doi.org/10.1080/02619768.2011.587116

Thompson, R., Compeau, R.D., & Higgins, C. (2006). Intentions to use information

technologies: an integrative model. Journal of organizational and End User

Computing, 18(3): 25-47.

Togia, A., & Tsigilis, N. (2009). Awareness and use of electronic information

resources by education graduate students: Preliminary results from the

Aristotle University of Thessaloniki. In Proceedings of QQML2009:

Qualitative and Quantitative Methods in Libraries, International Conference,

Chania Crete Greece, 26-29 May.

Usluel, Y.K., Askar, P., & Bas, T. (2008). A structural equation model for ICT usage

in higher education. Educational Technology & Society, 11 (2): 262-273.

Venkatesh, V., & Davis, F.D. (1996). A model of the antecedents of perceived ease of

use: development and test. Decision Science, 27(3): 451-482.

Venkatesh, V. (2000). Determinants of perceived ease of use: integrating control,

intrinsic motivation, and emotion into the technology acceptance model.

Information Systems Research, 4(4): 342-365.

Venkatesh, V., and Morris, G.M. (2000). Why don’t men ever stop to ask for

directions? Gender, social influence and their role in technology acceptance

and usage behavior. MIS Quarterly, 24(1) : 115-139.

Venkatesh, V., Morris, G.M., Davis, B.G., & Davis, D.F. (2003). User acceptance of

information technology: toward a unified view. MIS Quarterly, 27(3): 425-478.

Wang, Y.S., Lin, H.H., & Luarn, P. (2006). Predicting consumer intention to use

mobile service. Information Systems Journal, 16: 157–179.

210

Wong, K.T., Teo, T., & Russo, S. (2012). Influence of gender and computer teaching

efficacy on computer acceptance among Malaysian student teachers: An

extended technology acceptance model. Australasian Journal of Educational

Technology, 28(7): 1190-1207. http://www.ascilite.org.au/ajet/ajet28/wong-

kt.html

Wozney, L., Venkatesh, V., & Abrami, C.P. (2006). Implementing computer

technologies: teachers' perceptions and practices. Journal of Technology and

Teacher Education, 14(1): 173- 207.

Wu, W., Chang, H., & Guo, C. (2008). An empirical assessment of science teacher’s

intention towards technology integration. Journal of Computers in

Mathematics and Science Teaching, 27(4): 499-520.

Yousafzai, S. Y., Foxall, G.R., & Pallister, J. G. (2007). Technology acceptance: a

meta-analysis of TAM (Part I). Journal of Modeling in Management, 2(3): 251-

280.

Yuena, A.H.K., & Ma, W.W.K. (2008). Exploring teacher acceptance of e-learning

technology. Asia-Pacific Journal of Teacher Education, 36(3): 229–243. DOI:

10.1080/13598660802232779

Zejno, B., & Islam, A. (2012). Development and validation of library ICT sage scale

for the IIUM postgraduate students. OIDA International Journal of Sustainable

Development, 3(10): 11-18.

Zhao, Y., Pugh, K., Sheldon, S., & Byers, J.L. (2002). Conditions for classroom

technology innovations. Teachers College Record, 104(3): 482–515.

Zhou, G., Zhang, Z. & Li, Y. (2011). Are secondary preservice teachers well prepared

to teach with technology? A case study from China. Australasian Journal of


http://www.ascilite.org.au/ajet/ajet27/zhou.html

211

APPENDIX A The original Model of Computer Self-efficacy (CSE) and Outputs

212

Assessment of normality (Group number 1)

Variable min max skew c.r. kurtosis c.r.

cse15 1.000 7.000 -1.064 -8.644 1.448 5.883

cse14 1.000 7.000 -.898 -7.298 .876 3.557

cse13 1.000 7.000 -.927 -7.534 1.094 4.445

cse12 2.000 7.000 -.882 -7.164 .964 3.917

cse11 2.000 7.000 -.980 -7.959 .587 2.383

cse10 2.000 7.000 -.758 -6.155 .302 1.227

cse9 2.000 7.000 -.759 -6.162 .363 1.474

cse8 2.000 7.000 -.708 -5.749 .111 .450

cse7 2.000 7.000 -1.025 -8.331 .865 3.514

cse6 1.000 7.000 -1.545 -12.552 3.292 13.374

cse5 2.000 7.000 -1.154 -9.378 1.290 5.239

cse4 1.000 7.000 -1.021 -8.296 1.496 6.075

cse3 2.000 7.000 -.815 -6.624 .462 1.877

cse2 2.000 7.000 -.698 -5.669 .189 .768

cse1 1.000 7.000 -1.029 -8.358 1.306 5.305

Multivariate 248.386 109.436

Regression Weights: (Group number 1 - Default model)

Estimate S.E. C.R. P Label

cse1 <--- CSE 1.000

cse2 <--- CSE 1.001 .055 18.189 *** par_1

cse3 <--- CSE .975 .053 18.261 *** par_2

cse4 <--- CSE .868 .053 16.219 *** par_3

cse5 <--- CSE .922 .054 17.110 *** par_4

cse6 <--- CSE .835 .063 13.223 *** par_5

cse7 <--- CSE .978 .052 18.796 *** par_6

cse8 <--- CSE .974 .059 16.380 *** par_7

cse9 <--- CSE 1.039 .056 18.599 *** par_8

cse10 <--- CSE 1.021 .053 19.283 *** par_9

cse11 <--- CSE .980 .054 18.032 *** par_10

cse12 <--- CSE .947 .050 18.805 *** par_11

cse13 <--- CSE .867 .061 14.226 *** par_12

cse14 <--- CSE .844 .070 12.104 *** par_13

cse15 <--- CSE .908 .056 16.206 *** par_14

Standardized Regression Weights: (Group number 1 - Default model)

Estimate

cse1 <--- CSE .765

cse2 <--- CSE .827

cse3 <--- CSE .833

cse4 <--- CSE .759

213

Estimate

cse5 <--- CSE .797

cse6 <--- CSE .640

cse7 <--- CSE .859

cse8 <--- CSE .771

cse9 <--- CSE .854

cse10 <--- CSE .874

cse11 <--- CSE .834

cse12 <--- CSE .857

cse13 <--- CSE .682

cse14 <--- CSE .591

cse15 <--- CSE .763

Variances: (Group number 1 - Default model)


CSE .746 .084 8.918 *** par_15

e1 .529 .040 13.229 *** par_16

e2 .345 .027 12.805 *** par_17

e3 .314 .025 12.732 *** par_18

e4 .412 .031 13.305 *** par_19

e5 .364 .028 13.077 *** par_20

e6 .749 .055 13.678 *** par_21

e7 .253 .020 12.578 *** par_22

e8 .483 .036 13.233 *** par_23

e9 .298 .024 12.578 *** par_24

e10 .239 .019 12.331 *** par_25

e11 .314 .025 12.825 *** par_26

e12 .241 .019 12.598 *** par_27

e13 .645 .047 13.579 *** par_28

e14 .990 .072 13.760 *** par_29

e15 .442 .033 13.334 *** par_30

Squared Multiple Correlations: (Group number 1 - Default model)

Estimate

cse15 .581

cse14 .350

cse13 .465

cse12 .735

cse11 .695

cse10 .765

cse9 .730

cse8 .594

cse7 .738

214

Estimate

cse6 .410

cse5 .636

cse4 .577

cse3 .693

cse2 .684

cse1 .585

Total Effects (Group number 1 - Default model)

CSE

cse15 .908

cse14 .844

cse13 .867

cse12 .947

cse11 .980

cse10 1.021

cse9 1.039

cse8 .974

cse7 .978

cse6 .835

cse5 .922

cse4 .868

cse3 .975

cse2 1.001

cse1 1.000

Standardized Total Effects (Group number 1 - Default model)

CSE

cse15 .763

cse14 .591

cse13 .682

cse12 .857

cse11 .834

cse10 .874

cse9 .854

cse8 .771

cse7 .859

cse6 .640

cse5 .797

cse4 .759

cse3 .833

cse2 .827

cse1 .765

215

Direct Effects (Group number 1 - Default model)

CSE

cse15 .908

cse14 .844

cse13 .867

cse12 .947

cse11 .980

cse10 1.021

cse9 1.039

cse8 .974

cse7 .978

cse6 .835

cse5 .922

cse4 .868

cse3 .975

cse2 1.001

cse1 1.000

Standardized Direct Effects (Group number 1 - Default model)

CSE

cse15 .763

cse14 .591

cse13 .682

cse12 .857

cse11 .834

cse10 .874

cse9 .854

cse8 .771

cse7 .859

cse6 .640

cse5 .797

cse4 .759

cse3 .833

cse2 .827

cse1 .765

Modification Indices (Group number 1 - Default model)

Covariances: (Group number 1 - Default model)

M.I. Par Change

e14 <--> e15 18.019 .146

e13 <--> e15 5.832 .068

e13 <--> e14 109.539 .431

216

M.I. Par Change

e12 <--> e14 6.215 .065

e11 <--> e13 9.395 .073

e10 <--> e12 8.036 .038

e9 <--> e10 14.658 .057

e8 <--> e11 16.854 .086

e8 <--> e10 9.674 .058

e8 <--> e9 44.292 .137

e7 <--> e14 7.503 -.073

e7 <--> e13 7.850 -.061

e7 <--> e10 6.090 -.034

e6 <--> e12 24.981 -.114

e6 <--> e10 4.856 -.050

e6 <--> e7 11.624 .080

e5 <--> e12 23.425 -.078

e5 <--> e11 17.730 .077

e5 <--> e10 15.511 -.064

e5 <--> e8 11.478 -.076

e5 <--> e7 23.725 .081

e5 <--> e6 36.863 .166

e4 <--> e14 6.105 -.082

e4 <--> e11 4.453 -.041

e4 <--> e10 13.067 -.062

e4 <--> e8 4.910 -.052

e4 <--> e5 23.677 .100

e3 <--> e14 12.370 -.104

e3 <--> e13 16.722 -.098

e3 <--> e11 19.933 -.076

e3 <--> e9 23.173 -.081

e3 <--> e8 26.268 -.107

e3 <--> e4 56.250 .145

e2 <--> e14 4.138 -.063

e2 <--> e13 13.164 -.091

e2 <--> e11 15.185 -.070

e2 <--> e9 13.621 -.065

e2 <--> e8 10.050 -.069

e2 <--> e5 7.979 -.054

e2 <--> e3 56.002 .134

e1 <--> e15 10.752 -.084

e1 <--> e14 8.571 -.110

e1 <--> e11 11.842 -.075

e1 <--> e9 11.853 -.074

e1 <--> e8 14.635 -.102

e1 <--> e6 15.802 -.131

217

M.I. Par Change

e1 <--> e3 44.476 .146

e1 <--> e2 111.191 .241


M.I. Par Change

cse15 <--- cse14 11.510 .094

cse15 <--- cse1 4.254 -.063

cse14 <--- cse15 7.184 .132

cse14 <--- cse13 56.822 .347

cse13 <--- cse14 69.943 .278

cse13 <--- cse3 4.742 -.088

cse12 <--- cse6 14.418 -.088

cse12 <--- cse5 8.074 -.074

cse11 <--- cse13 4.884 .059

cse11 <--- cse8 6.516 .069

cse11 <--- cse5 6.103 .073

cse11 <--- cse3 5.678 -.069

cse11 <--- cse2 4.465 -.059

cse11 <--- cse1 4.694 -.056

cse10 <--- cse5 5.353 -.061

cse10 <--- cse4 5.304 -.061

cse9 <--- cse8 17.140 .109

cse9 <--- cse3 6.611 -.073

cse9 <--- cse2 4.011 -.055

cse9 <--- cse1 4.703 -.055

cse8 <--- cse11 4.757 .077

cse8 <--- cse9 10.888 .113

cse8 <--- cse3 7.463 -.097

cse8 <--- cse1 5.791 -.076

cse7 <--- cse14 4.798 -.047

cse7 <--- cse13 4.083 -.049

cse7 <--- cse6 6.709 .061

cse7 <--- cse5 8.178 .076

cse6 <--- cse12 6.004 -.113

cse6 <--- cse5 12.638 .157

cse6 <--- cse1 6.244 -.098

cse5 <--- cse12 5.654 -.078

cse5 <--- cse11 5.009 .069

cse5 <--- cse8 4.432 -.060

cse5 <--- cse7 5.644 .076

cse5 <--- cse6 21.254 .128

cse5 <--- cse4 9.580 .098

cse4 <--- cse5 8.131 .094

218

M.I. Par Change

cse4 <--- cse3 15.975 .131

cse3 <--- cse14 7.907 -.067

cse3 <--- cse13 8.692 -.079

cse3 <--- cse11 5.641 -.069

cse3 <--- cse9 5.715 -.067

cse3 <--- cse8 10.155 -.086

cse3 <--- cse4 22.783 .142

cse3 <--- cse2 16.466 .114

cse3 <--- cse1 17.629 .109

cse2 <--- cse13 6.842 -.073

cse2 <--- cse11 4.296 -.063

cse2 <--- cse3 15.947 .121

cse2 <--- cse1 44.064 .181

cse1 <--- cse15 4.294 -.076

cse1 <--- cse14 5.475 -.071

cse1 <--- cse8 5.647 -.082

cse1 <--- cse6 9.108 -.101

cse1 <--- cse3 12.633 .132

cse1 <--- cse2 32.603 .205

Model Fit Summary

CMIN

Model NPAR CMIN DF P CMIN/DF

Default model 30 834.732 90 .000 9.275

Saturated model 120 .000 0

Independence model 15 5453.298 105 .000 51.936

RMR, GFI

Model RMR GFI AGFI PGFI

Default model .067 .759 .679 .569

Saturated model .000 1.000

Independence model .629 .157 .037 .138

Baseline Comparisons

Model NFI

Delta1

RFI

rho1

IFI

Delta2

TLI

rho2 CFI

Default model .847 .821 .861 .838 .861

Saturated model 1.000 1.000 1.000

Independence model .000 .000 .000 .000 .000

219

Parsimony-Adjusted Measures

Model PRATIO PNFI PCFI

Default model .857 .726 .738

Saturated model .000 .000 .000

Independence model 1.000 .000 .000

NCP

Model NCP LO 90 HI 90

Default model 744.732 655.974 840.944


Independence model 5348.298 5109.707 5593.206

FMIN

Model FMIN F0 LO 90 HI 90

Default model 2.113 1.885 1.661 2.129

Saturated model .000 .000 .000 .000

Independence model 13.806 13.540 12.936 14.160

RMSEA

Model RMSEA LO 90 HI 90 PCLOSE

Default model .145 .136 .154 .000


AIC

Model AIC BCC BIC CAIC

Default model 894.732 897.265 1014.175 1044.175

Saturated model 240.000 250.132 717.770 837.770


ECVI

Model ECVI LO 90 HI 90 MECVI

Default model 2.265 2.040 2.509 2.272



HOELTER

Model HOELTER

.05

HOELTER

.01

Default model 54 59

Independence model 10 11

220

APPENDIX B The original Model of Perceived Usefulness (PU) and Outputs

221



pu16 1.000 7.000 -1.216 -9.881 2.085 8.470

pu15 1.000 7.000 -.910 -7.390 .666 2.704

pu14 1.000 7.000 -1.216 -9.877 1.330 5.402

pu13 1.000 7.000 -.814 -6.614 .505 2.050

pu12 1.000 7.000 -.694 -5.636 -.207 -.842

pu11 1.000 7.000 -.872 -7.081 .671 2.725

pu10 2.000 7.000 -.943 -7.658 .445 1.806

pu9 1.000 7.000 -1.064 -8.643 .743 3.017

pu8 1.000 7.000 -.859 -6.981 .531 2.155

pu7 1.000 7.000 -.664 -5.397 .055 .223

pu6 1.000 7.000 -.844 -6.857 .421 1.712

pu5 1.000 7.000 -.494 -4.010 -.274 -1.112

pu4 1.000 7.000 -.636 -5.165 .040 .163

pu3 1.000 7.000 -.527 -4.284 -.084 -.343

pu2 1.000 7.000 -.814 -6.613 .222 .901

pu1 1.000 7.000 -.883 -7.176 .417 1.692




pu1 <--- PU 1.000

pu2 <--- PU 1.077 .051 20.921 *** par_1

pu3 <--- PU 1.119 .055 20.495 *** par_2

pu4 <--- PU 1.091 .053 20.556 *** par_3

pu5 <--- PU 1.083 .053 20.345 *** par_4

pu6 <--- PU 1.102 .053 20.634 *** par_5

pu7 <--- PU 1.095 .059 18.630 *** par_6

pu8 <--- PU .820 .052 15.843 *** par_7

pu9 <--- PU .659 .055 11.927 *** par_8

pu10 <--- PU .707 .050 14.189 *** par_9

pu11 <--- PU .921 .053 17.257 *** par_10

pu12 <--- PU .881 .077 11.389 *** par_11

pu13 <--- PU .852 .055 15.448 *** par_12

pu14 <--- PU .685 .053 12.931 *** par_13

pu15 <--- PU .658 .055 11.968 *** par_14

pu16 <--- PU .723 .050 14.469 *** par_15


Estimate

pu1 <--- PU .817

pu2 <--- PU .852

222

Estimate

pu3 <--- PU .850

pu4 <--- PU .850

pu5 <--- PU .847

pu6 <--- PU .849

pu7 <--- PU .798

pu8 <--- PU .709

pu9 <--- PU .565

pu10 <--- PU .651

pu11 <--- PU .755

pu12 <--- PU .544

pu13 <--- PU .696

pu14 <--- PU .605

pu15 <--- PU .568

pu16 <--- PU .663



PU 1.009 .103 9.813 *** par_16

e1 .503 .039 12.748 *** par_17

e2 .441 .036 12.356 *** par_18

e3 .487 .040 12.210 *** par_19

e4 .460 .037 12.270 *** par_20

e5 .465 .038 12.263 *** par_21

e6 .472 .038 12.403 *** par_22

e7 .689 .053 12.875 *** par_23

e8 .672 .050 13.359 *** par_24

e9 .936 .068 13.680 *** par_25

e10 .687 .051 13.476 *** par_26

e11 .645 .049 13.192 *** par_27

e12 1.861 .135 13.785 *** par_28

e13 .780 .058 13.386 *** par_29

e14 .820 .060 13.592 *** par_30

e15 .915 .067 13.695 *** par_31

e16 .674 .050 13.455 *** par_32


Estimate

pu16 .439

pu15 .323

pu14 .366

pu13 .484

pu12 .296

223

Estimate

pu11 .571

pu10 .424

pu9 .319

pu8 .503

pu7 .637

pu6 .722

pu5 .718

pu4 .723

pu3 .722

pu2 .726

pu1 .667


PU

pu16 .723

pu15 .658

pu14 .685

pu13 .852

pu12 .881

pu11 .921

pu10 .707

pu9 .659

pu8 .820

pu7 1.095

pu6 1.102

pu5 1.083

pu4 1.091

pu3 1.119

pu2 1.077

pu1 1.000


PU

pu16 .663

pu15 .568

pu14 .605

pu13 .696

pu12 .544

pu11 .755

pu10 .651

pu9 .565

pu8 .709

224

PU

pu7 .798

pu6 .849

pu5 .847

pu4 .850

pu3 .850

pu2 .852

pu1 .817


PU

pu16 .723

pu15 .658

pu14 .685

pu13 .852

pu12 .881

pu11 .921

pu10 .707

pu9 .659

pu8 .820

pu7 1.095

pu6 1.102

pu5 1.083

pu4 1.091

pu3 1.119

pu2 1.077

pu1 1.000


PU

pu16 .663

pu15 .568

pu14 .605

pu13 .696

pu12 .544

pu11 .755

pu10 .651

pu9 .565

pu8 .709

pu7 .798

pu6 .849

pu5 .847

pu4 .850

225

PU

pu3 .850

pu2 .852

pu1 .817



M.I. Par Change

e15 <--> e16 75.620 .352

e14 <--> e16 80.538 .345

e14 <--> e15 106.810 .460

e13 <--> e16 30.720 .209

e13 <--> e15 70.903 .368

e13 <--> e14 52.205 .300

e12 <--> e13 22.088 .293

e11 <--> e16 8.070 .098

e11 <--> e14 6.551 .097

e11 <--> e12 15.655 .226

e10 <--> e16 48.446 .246

e10 <--> e15 13.105 .148

e10 <--> e14 32.229 .220

e10 <--> e13 20.453 .172

e10 <--> e11 20.549 .158

e9 <--> e16 31.894 .232

e9 <--> e15 17.520 .199

e9 <--> e14 67.782 .371

e9 <--> e13 13.246 .161

e9 <--> e11 9.809 .127

e9 <--> e10 123.618 .460

e8 <--> e16 20.822 .160

e8 <--> e15 14.373 .154

e8 <--> e14 9.092 .116

e8 <--> e13 5.514 .089

e8 <--> e11 6.200 .086

e8 <--> e10 41.578 .228

e8 <--> e9 46.613 .281

e7 <--> e16 8.160 -.103

e7 <--> e15 6.625 -.108

e7 <--> e14 14.917 -.153

e7 <--> e13 7.924 -.110

e7 <--> e12 15.831 .237

e7 <--> e10 30.420 -.201

e7 <--> e9 32.239 -.240

226

M.I. Par Change

e7 <--> e8 6.588 -.093

e6 <--> e16 5.589 -.072

e6 <--> e15 23.824 -.172

e6 <--> e14 4.446 -.071

e6 <--> e13 18.444 -.141

e6 <--> e12 8.189 -.144

e6 <--> e10 14.800 -.118

e6 <--> e9 9.737 -.111

e6 <--> e8 4.402 -.064

e6 <--> e7 20.834 .143

e5 <--> e16 32.329 -.172

e5 <--> e15 7.484 -.096

e5 <--> e14 34.761 -.195

e5 <--> e13 13.661 -.120

e5 <--> e10 29.641 -.166

e5 <--> e9 33.304 -.204

e5 <--> e7 6.546 .079

e4 <--> e16 9.624 -.093

e4 <--> e15 8.576 -.102

e4 <--> e14 24.924 -.165

e4 <--> e11 15.466 -.117

e4 <--> e10 9.445 -.093

e4 <--> e9 28.315 -.187

e4 <--> e8 16.656 -.123

e4 <--> e7 8.339 .089

e4 <--> e5 50.429 .183

e3 <--> e16 21.953 -.145

e3 <--> e15 13.264 -.130

e3 <--> e14 42.345 -.221

e3 <--> e13 12.532 -.118

e3 <--> e11 8.520 -.089

e3 <--> e10 38.488 -.194

e3 <--> e9 29.551 -.197

e3 <--> e8 19.927 -.138

e3 <--> e7 31.300 .178

e3 <--> e6 6.255 .067

e3 <--> e5 42.953 .174

e3 <--> e4 31.849 .149

e2 <--> e16 17.065 -.122

e2 <--> e15 18.347 -.146

e2 <--> e14 4.885 -.072

e2 <--> e13 6.319 -.080

e2 <--> e12 8.232 -.139

227

M.I. Par Change

e2 <--> e5 5.094 .057

e1 <--> e15 9.125 -.109

e1 <--> e12 20.662 -.233

e1 <--> e11 4.143 -.062

e1 <--> e7 7.498 -.088

e1 <--> e6 7.647 .074

e1 <--> e5 10.546 -.087

e1 <--> e2 69.127 .217


M.I. Par Change

pu16 <--- pu15 50.216 .256

pu16 <--- pu14 49.900 .261

pu16 <--- pu13 15.250 .133

pu16 <--- pu10 27.113 .200

pu16 <--- pu9 21.317 .165

pu16 <--- pu8 9.943 .114

pu16 <--- pu5 8.202 -.094

pu16 <--- pu3 5.481 -.074

pu16 <--- pu2 4.180 -.068

pu15 <--- pu16 41.073 .284

pu15 <--- pu14 66.156 .348

pu15 <--- pu13 35.180 .234

pu15 <--- pu10 7.331 .121

pu15 <--- pu9 11.707 .142

pu15 <--- pu8 6.860 .110

pu15 <--- pu6 5.946 -.091

pu15 <--- pu2 4.487 -.081

pu14 <--- pu16 43.750 .278

pu14 <--- pu15 70.915 .334

pu14 <--- pu13 25.907 .191

pu14 <--- pu10 18.032 .179

pu14 <--- pu9 45.296 .265

pu14 <--- pu8 4.340 .083

pu14 <--- pu7 5.032 -.075

pu14 <--- pu5 8.810 -.107

pu14 <--- pu4 6.191 -.089

pu14 <--- pu3 10.562 -.113

pu13 <--- pu16 16.696 .169

pu13 <--- pu15 47.090 .267

pu13 <--- pu14 32.350 .227

pu13 <--- pu12 15.288 .109

pu13 <--- pu10 11.449 .140

228

M.I. Par Change

pu13 <--- pu9 8.855 .115

pu13 <--- pu6 4.614 -.075

pu12 <--- pu13 10.958 .186

pu12 <--- pu11 6.364 .143

pu12 <--- pu7 5.338 .116

pu12 <--- pu1 6.323 -.142

pu11 <--- pu16 4.389 .079

pu11 <--- pu14 4.061 .074

pu11 <--- pu12 10.838 .084

pu11 <--- pu10 11.509 .129

pu11 <--- pu9 6.559 .091

pu10 <--- pu16 26.323 .198

pu10 <--- pu15 8.702 .107

pu10 <--- pu14 19.968 .166

pu10 <--- pu13 10.153 .110

pu10 <--- pu11 8.361 .100

pu10 <--- pu9 82.620 .328

pu10 <--- pu8 19.853 .162

pu10 <--- pu7 10.268 -.098

pu10 <--- pu5 7.518 -.090

pu10 <--- pu3 9.608 -.099

pu9 <--- pu16 17.323 .187

pu9 <--- pu15 11.631 .144

pu9 <--- pu14 41.983 .280

pu9 <--- pu13 6.572 .102

pu9 <--- pu10 69.154 .374

pu9 <--- pu8 22.246 .200

pu9 <--- pu7 10.872 -.118

pu9 <--- pu5 8.436 -.111

pu9 <--- pu4 7.029 -.101

pu9 <--- pu3 7.367 -.101

pu8 <--- pu16 11.318 .129

pu8 <--- pu15 9.546 .112

pu8 <--- pu14 5.634 .088

pu8 <--- pu10 23.276 .186

pu8 <--- pu9 31.161 .200

pu8 <--- pu4 4.146 -.067

pu8 <--- pu3 4.981 -.071

pu7 <--- pu16 4.440 -.083

pu7 <--- pu15 4.403 -.078

pu7 <--- pu14 9.252 -.116

pu7 <--- pu12 10.965 .088

pu7 <--- pu10 17.048 -.164

229

M.I. Par Change

pu7 <--- pu9 21.566 -.172

pu7 <--- pu6 5.235 .076

pu7 <--- pu3 7.856 .092

pu6 <--- pu15 15.847 -.125

pu6 <--- pu13 9.187 -.090

pu6 <--- pu12 5.676 -.054

pu6 <--- pu10 8.305 -.097

pu6 <--- pu9 6.519 -.080

pu6 <--- pu7 7.080 .071

pu5 <--- pu16 17.615 -.139

pu5 <--- pu15 4.978 -.070

pu5 <--- pu14 21.580 -.148

pu5 <--- pu13 6.804 -.077

pu5 <--- pu10 16.631 -.135

pu5 <--- pu9 22.295 -.146

pu5 <--- pu4 12.663 .100

pu5 <--- pu3 10.830 .090

pu4 <--- pu16 5.245 -.075

pu4 <--- pu15 5.704 -.074

pu4 <--- pu14 15.475 -.125

pu4 <--- pu11 6.324 -.074

pu4 <--- pu10 5.300 -.076

pu4 <--- pu9 18.957 -.134

pu4 <--- pu8 7.983 -.088

pu4 <--- pu5 12.921 .101

pu4 <--- pu3 8.033 .077

pu3 <--- pu16 11.962 -.117

pu3 <--- pu15 8.823 -.095

pu3 <--- pu14 26.290 -.167

pu3 <--- pu13 6.243 -.075

pu3 <--- pu10 21.596 -.158

pu3 <--- pu9 19.784 -.141

pu3 <--- pu8 9.551 -.099

pu3 <--- pu7 10.636 .088

pu3 <--- pu5 11.005 .096

pu3 <--- pu4 8.000 .082

pu2 <--- pu16 9.300 -.098

pu2 <--- pu15 12.205 -.106

pu2 <--- pu12 5.705 -.052

pu2 <--- pu1 21.350 .133

pu1 <--- pu15 6.066 -.079

pu1 <--- pu12 14.313 -.087

pu1 <--- pu2 17.049 .121

230

Model Fit Summary

CMIN


Default model 32 1217.231 104 .000 11.704



RMR, GFI


Default model .137 .623 .507 .476




Model NFI

Delta1

RFI

rho1

IFI

Delta2

TLI

rho2 CFI

Default model .769 .733 .784 .750 .783








NCP


Default model 1113.231 1004.610 1229.278



FMIN


Default model 3.082 2.818 2.543 3.112



231

RMSEA


Default model .165 .156 .173 .000


AIC


Default model 1281.231 1284.109 1408.636 1440.636

Saturated model 272.000 284.233 813.472 949.472


ECVI


Default model 3.244 2.969 3.537 3.251



HOELTER

Model HOELTER

.05

HOELTER

.01

Default model 42 46


232

Appendix C The original Model of Perceived Ease of Use (PEU) and Outputs

233



peu15 2.000 7.000 -.850 -6.907 .309 1.255

peu14 1.000 7.000 -.753 -6.120 .602 2.445

peu13 1.000 7.000 -.864 -7.019 .327 1.328

peu12 1.000 7.000 -.437 -3.553 .207 .843

peu11 1.000 7.000 -.898 -7.296 .466 1.893

peu10 1.000 7.000 -.838 -6.808 .377 1.533

peu9 1.000 7.000 -.880 -7.153 .394 1.602

peu8 1.000 7.000 -.709 -5.757 .093 .378

peu7 1.000 7.000 -.574 -4.661 -.003 -.014

peu6 1.000 7.000 -.371 -3.012 -.189 -.769

peu5 1.000 7.000 -.610 -4.957 .123 .498

peu4 1.000 7.000 -.545 -4.427 .056 .226

peu3 1.000 7.000 -.649 -5.269 .050 .202

peu2 1.000 7.000 -.808 -6.564 .308 1.250

peu1 1.000 7.000 -.952 -7.730 .769 3.122




peu1 <--- PEU 1.000

peu2 <--- PEU 1.176 .089 13.265 *** par_1

peu3 <--- PEU 1.095 .085 12.950 *** par_2

peu4 <--- PEU 1.132 .093 12.156 *** par_3

peu5 <--- PEU 1.195 .091 13.190 *** par_4

peu6 <--- PEU 1.257 .096 13.116 *** par_5

peu7 <--- PEU 1.065 .085 12.511 *** par_6

peu8 <--- PEU 1.286 .105 12.191 *** par_7

peu9 <--- PEU 1.198 .102 11.725 *** par_8

peu10 <--- PEU 1.175 .102 11.482 *** par_9

peu11 <--- PEU 1.001 .094 10.649 *** par_10

peu12 <--- PEU 1.031 .082 12.594 *** par_11

peu13 <--- PEU 1.184 .104 11.366 *** par_12

peu14 <--- PEU 1.162 .092 12.686 *** par_13

peu15 <--- PEU .777 .074 10.428 *** par_14


Estimate

peu1 <--- PEU .656

peu2 <--- PEU .743

peu3 <--- PEU .732

peu4 <--- PEU .687

234

Estimate

peu5 <--- PEU .762

peu6 <--- PEU .751

peu7 <--- PEU .709

peu8 <--- PEU .700

peu9 <--- PEU .668

peu10 <--- PEU .653

peu11 <--- PEU .584

peu12 <--- PEU .710

peu13 <--- PEU .645

peu14 <--- PEU .724

peu15 <--- PEU .574



PEU .687 .096 7.134 *** par_15

e1 .910 .069 13.223 *** par_16

e2 .770 .061 12.672 *** par_17

e3 .714 .056 12.641 *** par_18

e4 .982 .075 13.015 *** par_19

e5 .708 .057 12.428 *** par_20

e6 .839 .066 12.633 *** par_21

e7 .770 .059 12.957 *** par_22

e8 1.180 .093 12.735 *** par_23

e9 1.220 .095 12.881 *** par_24

e10 1.279 .099 12.976 *** par_25

e11 1.331 .099 13.488 *** par_26

e12 .718 .055 12.950 *** par_27

e13 1.353 .103 13.128 *** par_28

e14 .841 .065 12.849 *** par_29

e15 .844 .062 13.537 *** par_30


Estimate

peu15 .329

peu14 .524

peu13 .416

peu12 .504

peu11 .341

peu10 .426

peu9 .447

peu8 .490

peu7 .503

235

Estimate

peu6 .564

peu5 .581

peu4 .472

peu3 .536

peu2 .552

peu1 .430

PEU

peu15 .777

peu14 1.162

peu13 1.184

peu12 1.031

peu11 1.001

peu10 1.175

peu9 1.198

peu8 1.286

peu7 1.065

peu6 1.257

peu5 1.195

peu4 1.132

peu3 1.095

peu2 1.176

peu1 1.000


PEU

peu15 .574

peu14 .724

peu13 .645

peu12 .710

peu11 .584

peu10 .653

peu9 .668

peu8 .700

peu7 .709

peu6 .751

peu5 .762

peu4 .687

peu3 .732

peu2 .743

peu1 .656

236


PEU

peu15 .777

peu14 1.162

peu13 1.184

peu12 1.031

peu11 1.001

peu10 1.175

peu9 1.198

peu8 1.286

peu7 1.065

peu6 1.257

peu5 1.195

peu4 1.132

peu3 1.095

peu2 1.176

peu1 1.000


PEU

peu15 .574

peu14 .724

peu13 .645

peu12 .710

peu11 .584

peu10 .653

peu9 .668

peu8 .700

peu7 .709

peu6 .751

peu5 .762

peu4 .687

peu3 .732

peu2 .743

peu1 .656



M.I. Par Change

e14 <--> e15 17.239 .186

e13 <--> e14 54.686 .422

e12 <--> e15 10.367 .133

237

M.I. Par Change

e12 <--> e14 19.688 .186

e11 <--> e15 9.040 .166

e11 <--> e14 4.495 .119

e11 <--> e12 52.356 .375

e10 <--> e13 59.690 .537

e9 <--> e15 5.964 -.130

e9 <--> e13 34.826 .401

e9 <--> e10 172.831 .870

e8 <--> e15 14.611 -.202

e8 <--> e13 40.201 .426

e8 <--> e12 17.601 -.207

e8 <--> e11 4.684 -.143

e8 <--> e10 132.798 .754

e8 <--> e9 179.338 .857

e7 <--> e14 13.342 -.159

e7 <--> e13 21.557 -.252

e7 <--> e10 8.174 -.151

e6 <--> e15 7.001 -.119

e6 <--> e13 11.630 -.195

e6 <--> e11 4.100 -.114

e6 <--> e10 8.718 -.165

e6 <--> e9 10.412 -.176

e6 <--> e7 4.107 .088

e5 <--> e12 18.259 -.166

e5 <--> e11 14.163 -.196

e5 <--> e10 18.363 -.220

e5 <--> e9 19.848 -.224

e5 <--> e8 7.290 -.134

e5 <--> e7 32.773 .230

e5 <--> e6 23.850 .207

e4 <--> e14 6.059 -.120

e4 <--> e13 9.901 -.192

e4 <--> e11 12.868 -.216

e4 <--> e10 13.295 -.217

e4 <--> e9 19.790 -.259

e4 <--> e8 8.977 -.173

e4 <--> e6 16.924 .202

e4 <--> e5 50.626 .322

e3 <--> e14 5.247 -.096

e3 <--> e13 34.399 -.309

e3 <--> e10 35.765 -.306

e3 <--> e9 25.610 -.254

e3 <--> e8 24.438 -.245

238

M.I. Par Change

e3 <--> e6 14.093 .158

e3 <--> e5 14.955 .150

e3 <--> e4 20.269 .203

e2 <--> e13 25.768 -.278

e2 <--> e10 12.880 -.191

e2 <--> e9 12.307 -.183

e2 <--> e3 40.798 .258

e1 <--> e13 8.909 -.175

e1 <--> e11 11.359 .195

e1 <--> e8 10.827 -.182

e1 <--> e5 10.242 -.139

e1 <--> e3 9.645 .134

e1 <--> e2 57.356 .341


M.I. Par Change

peu15 <--- peu14 7.623 .098

peu15 <--- peu12 4.808 .086

peu15 <--- peu11 5.761 .079

peu15 <--- peu8 6.987 -.082

peu14 <--- peu15 11.220 .143

peu14 <--- peu13 30.570 .174

peu14 <--- peu12 9.161 .121

peu14 <--- peu7 6.221 -.096

peu13 <--- peu14 24.212 .222

peu13 <--- peu10 32.673 .230

peu13 <--- peu9 18.289 .173

peu13 <--- peu8 19.246 .173

peu13 <--- peu7 10.030 -.153

peu13 <--- peu6 4.660 -.093

peu13 <--- peu4 4.930 -.098

peu13 <--- peu3 14.815 -.186

peu13 <--- peu2 10.640 -.149

peu13 <--- peu1 4.837 -.104

peu12 <--- peu15 6.746 .102

peu12 <--- peu14 8.733 .098

peu12 <--- peu11 33.415 .180

peu12 <--- peu8 8.439 -.084

peu12 <--- peu5 6.999 -.090

peu11 <--- peu15 5.875 .128

peu11 <--- peu12 24.286 .242

peu11 <--- peu5 5.409 -.106

peu11 <--- peu4 6.402 -.110

239

M.I. Par Change

peu11 <--- peu1 6.163 .116

peu10 <--- peu13 33.323 .221

peu10 <--- peu9 90.773 .374

peu10 <--- peu8 63.588 .306

peu10 <--- peu5 7.025 -.119

peu10 <--- peu4 6.621 -.110

peu10 <--- peu3 15.406 -.185

peu10 <--- peu2 5.319 -.103

peu9 <--- peu13 19.447 .166

peu9 <--- peu10 94.638 .372

peu9 <--- peu8 85.901 .348

peu9 <--- peu6 4.175 -.084

peu9 <--- peu5 7.596 -.121

peu9 <--- peu4 9.859 -.131

peu9 <--- peu3 11.036 -.153

peu9 <--- peu2 5.085 -.098

peu8 <--- peu15 9.506 -.155

peu8 <--- peu13 22.461 .176

peu8 <--- peu12 8.183 -.134

peu8 <--- peu10 72.760 .323

peu8 <--- peu9 94.278 .369

peu8 <--- peu4 4.475 -.088

peu8 <--- peu3 10.541 -.148

peu8 <--- peu1 5.885 -.108

peu7 <--- peu14 5.918 -.084

peu7 <--- peu13 12.046 -.104

peu7 <--- peu10 4.479 -.065

peu7 <--- peu5 12.563 .125

peu6 <--- peu15 4.559 -.092

peu6 <--- peu13 6.506 -.081

peu6 <--- peu10 4.783 -.071

peu6 <--- peu9 5.481 -.076

peu6 <--- peu5 9.162 .112

peu6 <--- peu4 8.451 .103

peu6 <--- peu3 6.092 .096

peu5 <--- peu12 8.510 -.108

peu5 <--- peu11 9.048 -.094

peu5 <--- peu10 10.078 -.094

peu5 <--- peu9 10.454 -.097

peu5 <--- peu7 15.306 .140

peu5 <--- peu6 9.602 .099

peu5 <--- peu4 25.291 .164

peu5 <--- peu3 6.468 .091

240

M.I. Par Change

peu5 <--- peu1 5.576 -.083

peu4 <--- peu13 5.530 -.079

peu4 <--- peu11 8.210 -.104

peu4 <--- peu10 7.282 -.093

peu4 <--- peu9 10.401 -.112

peu4 <--- peu8 4.302 -.070

peu4 <--- peu6 6.790 .097

peu4 <--- peu5 19.389 .174

peu4 <--- peu3 8.740 .123

peu3 <--- peu13 19.233 -.128

peu3 <--- peu10 19.610 -.131

peu3 <--- peu9 13.474 -.109

peu3 <--- peu8 11.726 -.100

peu3 <--- peu6 5.664 .076

peu3 <--- peu5 5.739 .082

peu3 <--- peu4 10.114 .103

peu3 <--- peu2 16.896 .139

peu3 <--- peu1 5.246 .080

peu2 <--- peu13 14.412 -.115

peu2 <--- peu10 7.064 -.082

peu2 <--- peu9 6.478 -.079

peu2 <--- peu3 17.629 .156

peu2 <--- peu1 31.208 .204

peu1 <--- peu13 4.974 -.072

peu1 <--- peu11 7.245 .093

peu1 <--- peu8 5.185 -.074

peu1 <--- peu3 4.155 .081

peu1 <--- peu2 23.689 .183

Model Fit Summary

CMIN


Default model 30 1152.997 90 .000 12.811



RMR, GFI


Default model .185 .674 .565 .506



241


Model NFI

Delta1

RFI

rho1

IFI

Delta2

TLI

rho2 CFI

Default model .708 .659 .724 .677 .723








NCP


Default model 1062.997 957.164 1176.250



FMIN


Default model 2.919 2.691 2.423 2.978



RMSEA


Default model .173 .164 .182 .000


AIC


Default model 1212.997 1215.530 1332.439 1362.439

Saturated model 240.000 250.132 717.770 837.770


242

Appendix D The original Model of Intention to Use (INT) and Outputs

243



int10 1.000 7.000 -1.194 -9.700 1.697 6.895

int9 1.000 7.000 -1.102 -8.955 1.431 5.812

int8 2.000 77.000 17.451 141.776 330.280 1341.605

int7 2.000 7.000 -.843 -6.849 .407 1.653

int6 2.000 7.000 -.750 -6.095 -.174 -.707

int5 2.000 7.000 -.962 -7.819 .566 2.298

int4 1.000 7.000 -.573 -4.659 -.241 -.979

int3 1.000 7.000 -1.866 -15.158 3.929 15.958

int2 1.000 7.000 -1.644 -13.353 3.022 12.276

int1 2.000 7.000 -1.065 -8.650 .605 2.458




int1 <--- INT 1.000

int2 <--- INT .803 .062 12.897 *** par_1

int3 <--- INT .949 .072 13.178 *** par_2

int4 <--- INT .839 .107 7.829 *** par_3

int5 <--- INT 1.074 .068 15.914 *** par_4

int6 <--- INT 1.195 .070 17.187 *** par_5

int7 <--- INT 1.206 .068 17.651 *** par_6

int8 <--- INT 1.491 .244 6.107 *** par_7

int9 <--- INT 1.232 .075 16.518 *** par_8

int10 <--- INT 1.144 .076 15.049 *** par_9


Estimate

int1 <--- INT .758

int2 <--- INT .632

int3 <--- INT .646

int4 <--- INT .401

int5 <--- INT .765

int6 <--- INT .845

int7 <--- INT .869

int8 <--- INT .315

int9 <--- INT .810

int10 <--- INT .738

244



INT .618 .072 8.624 *** par_10

e1 .458 .037 12.233 *** par_11

e2 .601 .046 13.156 *** par_12

e3 .779 .059 13.140 *** par_13

e4 2.270 .164 13.837 *** par_14

e5 .504 .041 12.365 *** par_15

e6 .352 .032 10.952 *** par_16

e7 .292 .029 10.183 *** par_17

e8 12.459 .894 13.935 *** par_18

e9 .494 .042 11.855 *** par_19

e10 .678 .053 12.684 *** par_20


Estimate

int10 .544

int9 .655

int8 .099

int7 .755

int6 .715

int5 .586

int4 .161

int3 .417

int2 .399

int1 .575


INT

int10 1.144

int9 1.232

int8 1.491

int7 1.206

int6 1.195

int5 1.074

int4 .839

int3 .949

int2 .803

int1 1.000

245


INT

int10 .738

int9 .810

int8 .315

int7 .869

int6 .845

int5 .765

int4 .401

int3 .646

int2 .632

int1 .758


INT

int10 1.144

int9 1.232

int8 1.491

int7 1.206

int6 1.195

int5 1.074

int4 .839

int3 .949

int2 .803

int1 1.000


INT

int10 .738

int9 .810

int8 .315

int7 .869

int6 .845

int5 .765

int4 .401

int3 .646

int2 .632

int1 .758

246

Appendix E The original Model of Actual Use (USE) and Outputs

247



use16 1.000 7.000 -.908 -7.378 -.236 -.960

use15 1.000 7.000 -1.681 -13.658 3.123 12.685

use14 1.000 7.000 -1.670 -13.565 2.957 12.013

use13 1.000 7.000 -.691 -5.613 -.382 -1.550

use12 1.000 7.000 .015 .119 -1.459 -5.926

use11 1.000 7.000 -1.457 -11.838 1.968 7.994

use10 1.000 7.000 -2.093 -17.001 5.058 20.545

use9 1.000 7.000 -.510 -4.147 -.850 -3.453

use8 1.000 7.000 -1.077 -8.752 .854 3.469

use7 1.000 7.000 -.746 -6.059 -.187 -.759

use6 2.000 7.000 -1.880 -15.274 4.500 18.278

use5 2.000 7.000 -1.990 -16.164 4.948 20.100

use4 1.000 7.000 -2.024 -16.447 4.592 18.653

use3 1.000 7.000 -1.967 -15.979 4.347 17.656

use2 1.000 7.000 -2.338 -18.993 7.340 29.814

use1 1.000 7.000 -.287 -2.334 -.956 -3.881




use1 <--- USE 1.000

use2 <--- USE .799 .107 7.462 *** par_1

use3 <--- USE .619 .115 5.389 *** par_2

use4 <--- USE 1.010 .134 7.540 *** par_3

use5 <--- USE .942 .120 7.857 *** par_4

use6 <--- USE .837 .112 7.476 *** par_5

use7 <--- USE .905 .156 5.796 *** par_6

use8 <--- USE 1.114 .157 7.100 *** par_7

use9 <--- USE .911 .168 5.413 *** par_8

use10 <--- USE .947 .127 7.477 *** par_9

use11 <--- USE 1.096 .155 7.060 *** par_10

use12 <--- USE .336 .150 2.244 .025 par_11

use13 <--- USE .515 .136 3.787 *** par_12

use14 <--- USE 1.043 .148 7.031 *** par_13

use15 <--- USE 1.005 .144 6.976 *** par_14

use16 <--- USE 1.014 .175 5.784 *** par_15


Estimate

use1 <--- USE .415

use2 <--- USE .663

248

Estimate

use3 <--- USE .355

use4 <--- USE .681

use5 <--- USE .773

use6 <--- USE .664

use7 <--- USE .386

use8 <--- USE .560

use9 <--- USE .353

use10 <--- USE .672

use11 <--- USE .564

use12 <--- USE .125

use13 <--- USE .222

use14 <--- USE .582

use15 <--- USE .573

use16 <--- USE .398



USE .547 .135 4.054 *** par_16

e1 2.629 .193 13.645 *** par_17

e2 .446 .036 12.407 *** par_18

e3 1.452 .105 13.766 *** par_19

e4 .645 .053 12.148 *** par_20

e5 .327 .031 10.703 *** par_21

e6 .486 .039 12.397 *** par_22

e7 2.560 .188 13.632 *** par_23

e8 1.488 .114 13.052 *** par_24

e9 3.187 .232 13.708 *** par_25

e10 .595 .048 12.459 *** par_26

e11 1.406 .107 13.108 *** par_27

e12 3.899 .278 14.014 *** par_28

e13 2.807 .201 13.946 *** par_29

e14 1.163 .091 12.782 *** par_30

e15 1.129 .088 12.865 *** par_31

e16 2.990 .220 13.574 *** par_32


Estimate

use16 .158

use15 .328

use14 .338

use13 .049

use12 .016

249

Estimate

use11 .318

use10 .452

use9 .125

use8 .313

use7 .149

use6 .441

use5 .598

use4 .463

use3 .126

use2 .439

use1 .172


USE

use16 1.014

use15 1.005

use14 1.043

use13 .515

use12 .336

use11 1.096

use10 .947

use9 .911

use8 1.114

use7 .905

use6 .837

use5 .942

use4 1.010

use3 .619

use2 .799

use1 1.000


USE

use16 .398

use15 .573

use14 .582

use13 .222

use12 .125

use11 .564

use10 .672

use9 .353

use8 .560

250

USE

use7 .386

use6 .664

use5 .773

use4 .681

use3 .355

use2 .663

use1 .415


USE

use16 1.014

use15 1.005

use14 1.043

use13 .515

use12 .336

use11 1.096

use10 .947

use9 .911

use8 1.114

use7 .905

use6 .837

use5 .942

use4 1.010

use3 .619

use2 .799

use1 1.000


USE

use16 .398

use15 .573

use14 .582

use13 .222

use12 .125

use11 .564

use10 .672

use9 .353

use8 .560

use7 .386

use6 .664

use5 .773

use4 .681

251

USE

use3 .355

use2 .663

use1 .415



M.I. Par Change

e15 <--> e16 47.450 .663

e14 <--> e16 20.360 .441

e14 <--> e15 153.629 .757

e13 <--> e16 8.970 .443

e12 <--> e16 36.930 1.057

e12 <--> e13 18.864 .726

e11 <--> e13 19.534 .456

e11 <--> e12 9.775 .379

e10 <--> e14 6.852 .118

e10 <--> e12 11.093 -.269

e9 <--> e16 23.303 .764

e9 <--> e13 8.648 .448

e9 <--> e12 49.139 1.255

e8 <--> e12 29.408 .676

e8 <--> e11 6.801 .200

e8 <--> e9 27.205 .592

e7 <--> e16 35.802 .851

e7 <--> e15 5.291 -.205

e7 <--> e13 8.583 .400

e7 <--> e12 35.372 .956

e7 <--> e9 67.692 1.204

e7 <--> e8 40.874 .651

e6 <--> e16 11.980 -.223

e6 <--> e15 6.978 -.106

e6 <--> e14 8.801 -.121

e6 <--> e13 4.328 -.129

e6 <--> e10 6.631 -.076

e6 <--> e7 4.354 -.124

e5 <--> e16 24.816 -.275

e5 <--> e15 13.220 -.125

e5 <--> e14 14.502 -.133

e5 <--> e13 4.511 -.113

e5 <--> e12 21.728 -.291

e5 <--> e11 5.418 -.089

e5 <--> e10 12.219 .089

252

M.I. Par Change

e5 <--> e9 16.287 -.229

e5 <--> e7 12.978 -.184

e5 <--> e6 22.937 .110

e4 <--> e16 7.007 -.197

e4 <--> e15 6.062 -.114

e4 <--> e14 24.295 -.233

e4 <--> e13 8.682 -.211

e4 <--> e12 7.987 -.238

e4 <--> e9 18.431 -.329

e4 <--> e7 7.785 -.192

e4 <--> e6 5.459 .072

e4 <--> e5 25.782 .134

e3 <--> e14 5.719 -.163

e3 <--> e8 10.936 -.253

e2 <--> e16 9.429 -.189

e2 <--> e12 17.335 -.290

e2 <--> e11 6.005 -.105

e2 <--> e9 6.792 -.165

e2 <--> e8 26.622 -.227

e2 <--> e7 6.725 -.148

e2 <--> e6 6.578 .066

e2 <--> e5 4.828 .048

e2 <--> e4 14.998 .115

e2 <--> e3 22.284 .202

e1 <--> e15 5.405 -.210

e1 <--> e12 11.910 .563

e1 <--> e10 5.820 -.161

e1 <--> e8 12.728 .369

e1 <--> e7 18.798 .579


M.I. Par Change

use16 <--- use15 30.093 .372

use16 <--- use14 12.687 .236

use16 <--- use13 8.479 .149

use16 <--- use12 36.288 .266

use16 <--- use9 20.070 .207

use16 <--- use7 29.865 .277

use16 <--- use6 6.099 -.233

use16 <--- use5 8.291 -.281

use16 <--- use2 4.814 -.216

use15 <--- use16 39.124 .183

use15 <--- use14 95.960 .407

253

M.I. Par Change

use15 <--- use7 4.417 -.067

use15 <--- use5 4.452 -.129

use15 <--- use1 4.372 -.065

use14 <--- use16 16.788 .122

use14 <--- use15 97.673 .426

use14 <--- use6 4.499 -.127

use14 <--- use5 4.886 -.137

use14 <--- use4 11.810 -.175

use14 <--- use3 4.921 -.096

use13 <--- use16 7.387 .122

use13 <--- use12 18.535 .183

use13 <--- use11 12.598 .209

use13 <--- use9 7.446 .121

use13 <--- use7 7.157 .130

use13 <--- use4 4.194 -.158

use12 <--- use16 30.410 .291

use12 <--- use13 17.830 .244

use12 <--- use11 6.303 .174

use12 <--- use10 5.500 -.224

use12 <--- use9 42.307 .339

use12 <--- use8 19.126 .295

use12 <--- use7 29.493 .311

use12 <--- use5 7.228 -.297

use12 <--- use2 8.832 -.331

use12 <--- use1 9.621 .173

use11 <--- use13 18.469 .153

use11 <--- use12 9.606 .096

use11 <--- use8 4.437 .088

use10 <--- use14 4.292 .064

use10 <--- use12 10.902 -.068

use10 <--- use5 4.153 .092

use10 <--- use1 4.714 -.050

use9 <--- use16 19.195 .211

use9 <--- use13 8.174 .151

use9 <--- use12 48.284 .316

use9 <--- use8 17.708 .259

use9 <--- use7 56.460 .392

use9 <--- use5 5.435 -.234

use9 <--- use4 8.913 -.247

use8 <--- use12 28.899 .170

use8 <--- use11 4.399 .092

use8 <--- use9 23.445 .160

use8 <--- use7 34.120 .212

254

M.I. Par Change

use8 <--- use3 9.408 -.150

use8 <--- use2 13.637 -.261

use8 <--- use1 10.296 .114

use7 <--- use16 29.494 .234

use7 <--- use13 8.113 .135

use7 <--- use12 34.757 .241

use7 <--- use9 58.300 .325

use7 <--- use8 26.611 .285

use7 <--- use5 4.334 -.188

use7 <--- use1 15.192 .178

use6 <--- use16 9.887 -.061

use6 <--- use15 4.447 -.060

use6 <--- use14 5.511 -.065

use6 <--- use13 4.093 -.043

use6 <--- use5 7.788 .114

use5 <--- use16 20.530 -.076

use5 <--- use15 8.479 -.071

use5 <--- use14 9.143 -.072

use5 <--- use13 4.269 -.038

use5 <--- use12 21.358 -.073

use5 <--- use10 6.191 .076

use5 <--- use9 14.072 -.062

use5 <--- use7 10.868 -.060

use5 <--- use6 11.897 .117

use5 <--- use4 12.736 .103

use4 <--- use16 5.784 -.054

use4 <--- use14 15.225 -.125

use4 <--- use13 8.212 -.071

use4 <--- use12 7.849 -.060

use4 <--- use9 15.898 -.089

use4 <--- use7 6.506 -.063

use4 <--- use5 8.774 .140

use4 <--- use2 7.725 .133

use3 <--- use8 7.118 -.111

use3 <--- use2 11.370 .231

use2 <--- use16 7.781 -.052

use2 <--- use12 17.037 -.073

use2 <--- use9 5.858 -.045

use2 <--- use8 17.410 -.100

use2 <--- use7 5.619 -.048

use2 <--- use4 7.321 .087

use2 <--- use3 19.184 .120

use1 <--- use12 11.703 .142

255

M.I. Par Change

use1 <--- use8 8.288 .162

use1 <--- use7 15.681 .189

Model Fit Summary

CMIN


Default model 32 875.902 104 .000 8.422



RMR, GFI


Default model .293 .738 .658 .565




Model NFI

Delta1

RFI

rho1

IFI

Delta2

TLI

rho2 CFI

Default model .617 .558 .646 .589 .644








NCP


Default model 771.902 681.164 870.095



FMIN


Default model 2.217 1.954 1.724 2.203


256



RMSEA


Default model .137 .129 .146 .000


AIC


Default model 939.902 942.780 1067.307 1099.307

Saturated model 272.000 284.233 813.472 949.472


ECVI


Default model 2.379 2.150 2.628 2.387



HOELTER

Model HOELTER

.05

HOELTER

.01

Default model 59 64


257

Appendix F The original Model of Gratification (GRAT) and Outputs

258



grat10 1.000 7.000 -.866 -7.039 .204 .829

grat9 1.000 7.000 -.900 -7.310 .825 3.352

grat8 1.000 7.000 -.784 -6.365 .458 1.860

grat7 1.000 7.000 -.862 -6.999 .716 2.907

grat6 1.000 7.000 -.917 -7.452 .642 2.608

grat5 1.000 7.000 -1.105 -8.977 1.426 5.794

grat4 1.000 7.000 -.951 -7.725 .945 3.837

grat3 1.000 7.000 -.893 -7.259 .649 2.637

grat2 1.000 7.000 -.993 -8.067 .932 3.786

grat1 1.000 7.000 -.922 -7.487 .684 2.779




grat1 <--- GRAT 1.000

grat2 <--- GRAT 1.249 .064 19.414 *** par_1

grat3 <--- GRAT 1.179 .062 19.173 *** par_2

grat4 <--- GRAT 1.075 .061 17.619 *** par_3

grat5 <--- GRAT .769 .060 12.837 *** par_4

grat6 <--- GRAT 1.260 .065 19.281 *** par_5

grat7 <--- GRAT 1.202 .065 18.442 *** par_6

grat8 <--- GRAT 1.270 .063 20.218 *** par_7

grat9 <--- GRAT 1.122 .060 18.744 *** par_8

grat10 <--- GRAT 1.125 .082 13.686 *** par_9


Estimate

grat1 <--- GRAT .783









grat10 <--- GRAT .652

259



GRAT .854 .093 9.194 *** par_10

e1 .537 .042 12.840 *** par_11

e2 .483 .040 12.104 *** par_12

e3 .483 .040 12.216 *** par_13

e4 .575 .045 12.866 *** par_14

e5 .842 .062 13.607 *** par_15

e6 .491 .040 12.193 *** par_16

e7 .544 .044 12.378 *** par_17

e8 .352 .031 11.186 *** par_18

e9 .457 .037 12.412 *** par_19

e10 1.462 .108 13.552 *** par_20


Estimate

grat10 .425

grat9 .701

grat8 .797

grat7 .694

grat6 .734

grat5 .375

grat4 .632

grat3 .711

grat2 .734

grat1 .614


GRAT

grat10 1.125

grat9 1.122

grat8 1.270

grat7 1.202

grat6 1.260

grat5 .769

grat4 1.075

grat3 1.179

grat2 1.249

grat1 1.000

260


GRAT

grat10 .652

grat9 .838

grat8 .892

grat7 .833

grat6 .857

grat5 .612

grat4 .795

grat3 .843

grat2 .857

grat1 .783


GRAT

grat10 1.125

grat9 1.122

grat8 1.270

grat7 1.202

grat6 1.260

grat5 .769

grat4 1.075

grat3 1.179

grat2 1.249

grat1 1.000


GRAT

grat10 .652

grat9 .838

grat8 .892

grat7 .833

grat6 .857

grat5 .612

grat4 .795

grat3 .843

grat2 .857

grat1 .783

261



M.I. Par Change

e9 <--> e10 4.652 .095

e7 <--> e8 53.929 .186

e5 <--> e9 13.050 .121

e5 <--> e8 19.791 -.136

e5 <--> e6 4.392 -.073

e4 <--> e5 34.429 .216

e3 <--> e9 5.297 -.061

e3 <--> e8 12.315 -.084

e3 <--> e7 12.806 -.103

e2 <--> e10 8.809 .136

e2 <--> e9 21.996 -.124

e2 <--> e5 23.175 -.167

e2 <--> e3 20.615 .124

e1 <--> e10 8.520 -.137

e1 <--> e8 21.892 -.116

e1 <--> e7 15.806 -.118

e1 <--> e5 14.906 .137

e1 <--> e4 6.984 .080

e1 <--> e3 47.652 .194


M.I. Par Change

grat9 <--- grat5 7.959 .087

grat9 <--- grat2 5.207 -.061

grat8 <--- grat7 15.171 .096

grat8 <--- grat5 12.104 -.099

grat8 <--- grat1 7.978 -.079

grat7 <--- grat8 9.257 .091

grat7 <--- grat1 5.716 -.079

grat5 <--- grat4 11.715 .128

grat5 <--- grat2 5.416 -.081

grat5 <--- grat1 5.355 .092

grat4 <--- grat5 20.979 .157

grat3 <--- grat2 4.884 .061

grat3 <--- grat1 17.247 .131

grat2 <--- grat10 4.920 .052

grat2 <--- grat9 5.969 -.074

grat2 <--- grat5 14.144 -.121

grat2 <--- grat3 5.393 .067

262

M.I. Par Change

grat1 <--- grat10 4.748 -.052

grat1 <--- grat7 4.379 -.060

grat1 <--- grat5 9.081 .099

grat1 <--- grat3 12.365 .104

Model Fit Summary

CMIN


Default model 20 297.460 35 .000 8.499



RMR, GFI


Default model .069 .863 .784 .549




Model NFI

Delta1

RFI

rho1

IFI

Delta2

TLI

rho2 CFI

Default model .911 .886 .921 .898 .921








NCP


Default model 262.460 211.105 321.291



263

FMIN


Default model .753 .664 .534 .813



RMSEA


Default model .138 .124 .152 .000


AIC


Default model 337.460 338.606 417.089 437.089

Saturated model 110.000 113.151 328.978 383.978


ECVI


Default model .854 .724 1.003 .857



HOELTER

Model HOELTER

.05

HOELTER

.01

Default model 67 77


264

Appendix G The Hypothesized TAG Model’s Outputs



use11 1.000 7.000 -1.457 -11.838 1.968 7.994

use6 2.000 7.000 -1.880 -15.274 4.500 18.278

use5 2.000 7.000 -1.990 -16.164 4.948 20.100

use4 1.000 7.000 -2.024 -16.447 4.592 18.653

use2 1.000 7.000 -2.338 -18.993 7.340 29.814

peu2 1.000 7.000 -.808 -6.564 .308 1.250

peu4 1.000 7.000 -.545 -4.427 .056 .226

peu6 1.000 7.000 -.371 -3.012 -.189 -.769

peu7 1.000 7.000 -.574 -4.661 -.003 -.014

peu9 1.000 7.000 -.880 -7.153 .394 1.602

peu15 2.000 7.000 -.850 -6.907 .309 1.255

pu2 1.000 7.000 -.814 -6.613 .222 .901

pu3 1.000 7.000 -.527 -4.284 -.084 -.343

pu4 1.000 7.000 -.636 -5.165 .040 .163

pu5 1.000 7.000 -.494 -4.010 -.274 -1.112

pu6 1.000 7.000 -.844 -6.857 .421 1.712

pu7 1.000 7.000 -.664 -5.397 .055 .223

pu11 1.000 7.000 -.872 -7.081 .671 2.725

cse4 1.000 7.000 -1.021 -8.296 1.496 6.075

cse7 2.000 7.000 -1.025 -8.331 .865 3.514

cse9 2.000 7.000 -.759 -6.162 .363 1.474

cse10 2.000 7.000 -.758 -6.155 .302 1.227

cse11 2.000 7.000 -.980 -7.959 .587 2.383

cse12 2.000 7.000 -.882 -7.164 .964 3.917

cse15 1.000 7.000 -1.064 -8.644 1.448 5.883

int10 1.000 7.000 -1.194 -9.700 1.697 6.895

int8 2.000 77.000 17.451 141.776 330.280 1341.605

int7 2.000 7.000 -.843 -6.849 .407 1.653

int5 2.000 7.000 -.962 -7.819 .566 2.298

int3 1.000 7.000 -1.866 -15.158 3.929 15.958

int2 1.000 7.000 -1.644 -13.353 3.022 12.276

int1 2.000 7.000 -1.065 -8.650 .605 2.458

grat10 1.000 7.000 -.866 -7.039 .204 .829

grat9 1.000 7.000 -.900 -7.310 .825 3.352

grat7 1.000 7.000 -.862 -6.999 .716 2.907

grat6 1.000 7.000 -.917 -7.452 .642 2.608

grat4 1.000 7.000 -.951 -7.725 .945 3.837

grat3 1.000 7.000 -.893 -7.259 .649 2.637

265





PU <--- CSE .583 .065 9.039 *** par_33

PEU <--- CSE .487 .061 7.980 *** par_34

INT <--- PU .267 .072 3.702 *** par_35

INT <--- PEU .487 .100 4.853 *** par_38

USE <--- PU .031 .055 .569 .569 par_36

USE <--- PEU .094 .079 1.181 .238 par_39

USE <--- INT .273 .051 5.307 *** par_41

GRAT <--- PU .555 .075 7.359 *** par_37

GRAT <--- PEU .479 .105 4.578 *** par_40

GRAT <--- USE -.126 .078 -1.615 .106 par_42

GRAT <--- INT .208 .065 3.188 .001 par_43

grat3 <--- GRAT 1.000

grat4 <--- GRAT .951 .052 18.290 *** par_1

grat6 <--- GRAT 1.111 .055 20.283 *** par_2

grat7 <--- GRAT 1.042 .055 18.874 *** par_3

grat9 <--- GRAT .999 .051 19.762 *** par_4

grat10 <--- GRAT .979 .071 13.791 *** par_5

int1 <--- INT 1.000

int2 <--- INT .827 .052 15.766 *** par_6

int3 <--- INT .941 .061 15.352 *** par_7

int5 <--- INT 1.023 .057 17.955 *** par_8

int7 <--- INT .964 .058 16.563 *** par_9

int8 <--- INT 1.327 .224 5.935 *** par_10

int10 <--- INT .986 .066 14.890 *** par_11

cse15 <--- CSE 1.000

cse12 <--- CSE 1.065 .057 18.813 *** par_12

cse11 <--- CSE 1.086 .061 17.852 *** par_13

cse10 <--- CSE 1.135 .060 18.921 *** par_14

cse9 <--- CSE 1.174 .063 18.730 *** par_15

cse7 <--- CSE 1.065 .059 18.081 *** par_16

cse4 <--- CSE .910 .061 14.964 *** par_17

pu11 <--- PU 1.000

pu7 <--- PU 1.328 .083 15.998 *** par_18

pu6 <--- PU 1.293 .078 16.510 *** par_19

pu5 <--- PU 1.323 .078 17.052 *** par_20

pu4 <--- PU 1.313 .078 16.807 *** par_21

pu3 <--- PU 1.368 .080 17.051 *** par_22

pu2 <--- PU 1.226 .076 16.075 *** par_23

peu15 <--- PEU 1.000

266


peu9 <--- PEU 1.221 .137 8.942 *** par_24

peu7 <--- PEU 1.397 .125 11.148 *** par_25

peu6 <--- PEU 1.523 .143 10.680 *** par_26

peu4 <--- PEU 1.446 .136 10.653 *** par_27

peu2 <--- PEU 1.383 .130 10.673 *** par_28

use2 <--- USE 1.000

use4 <--- USE 1.324 .102 13.020 *** par_29

use5 <--- USE 1.206 .088 13.746 *** par_30

use6 <--- USE 1.110 .088 12.675 *** par_31

use11 <--- USE 1.118 .130 8.572 *** par_32


Estimate

PU <--- CSE .528

PEU <--- CSE .566

INT <--- PU .278

INT <--- PEU .394

USE <--- PU .044

USE <--- PEU .103

USE <--- INT .373

GRAT <--- PU .488

GRAT <--- PEU .328

GRAT <--- USE -.078

GRAT <--- INT .176






grat10 <--- GRAT .620

int1 <--- INT .822

int2 <--- INT .700

int3 <--- INT .689

int5 <--- INT .788

int7 <--- INT .750

int8 <--- INT .297

int10 <--- INT .684

cse15 <--- CSE .760

cse12 <--- CSE .873

cse11 <--- CSE .836

cse10 <--- CSE .879

cse9 <--- CSE .873

cse7 <--- CSE .845

267

Estimate

cse4 <--- CSE .721

pu11 <--- PU .705

pu7 <--- PU .832

pu6 <--- PU .857

pu5 <--- PU .890

pu4 <--- PU .880

pu3 <--- PU .893

pu2 <--- PU .834

peu15 <--- PEU .599

peu9 <--- PEU .552

peu7 <--- PEU .754

peu6 <--- PEU .738

peu4 <--- PEU .712

peu2 <--- PEU .708

use2 <--- USE .687

use4 <--- USE .740

use5 <--- USE .822

use6 <--- USE .729

use11 <--- USE .475



CSE .610 .069 8.786 *** par_44

e39 .538 .068 7.859 *** par_45

e40 .307 .050 6.107 *** par_46

e43 .484 .053 9.093 *** par_47

e42 .294 .041 7.143 *** par_48

e41 .427 .050 8.601 *** par_49

e1 .566 .047 11.943 *** par_50

e2 .562 .046 12.116 *** par_51

e3 .484 .044 11.020 *** par_52

e4 .578 .049 11.800 *** par_53

e5 .429 .038 11.252 *** par_54

e6 1.483 .112 13.278 *** par_55

e7 .331 .032 10.377 *** par_56

e8 .490 .040 12.325 *** par_57

e9 .676 .054 12.491 *** par_58

e10 .439 .039 11.234 *** par_59

e11 .498 .043 11.666 *** par_60

e12 12.523 .901 13.892 *** par_61

e13 .764 .061 12.500 *** par_62

e14 .447 .034 12.959 *** par_63

e15 .217 .019 11.470 *** par_64

268


e16 .311 .025 12.186 *** par_65

e17 .231 .020 11.292 *** par_66

e18 .262 .023 11.450 *** par_67

e19 .276 .023 12.021 *** par_68

e20 .468 .036 13.188 *** par_69

e21 .756 .057 13.338 *** par_70

e22 .584 .047 12.404 *** par_71

e23 .450 .037 12.050 *** par_72

e24 .344 .030 11.331 *** par_73

e25 .376 .032 11.625 *** par_74

e26 .356 .032 11.257 *** par_75

e27 .490 .039 12.412 *** par_76

e28 .807 .064 12.597 *** par_77

e29 1.532 .117 13.058 *** par_78

e30 .669 .060 11.081 *** par_79

e31 .876 .079 11.081 *** par_80

e32 .918 .079 11.629 *** par_81

e33 .857 .073 11.714 *** par_82

e34 .415 .035 11.804 *** par_83

e35 .536 .049 11.017 *** par_84

e36 .259 .029 8.887 *** par_85

e37 .401 .036 11.164 *** par_86

e38 1.587 .119 13.328 *** par_87


Estimate

PEU .321

PU .279

INT .298

USE .204

GRAT .557

use11 .226

use6 .532

use5 .675

use4 .547

use2 .472

peu2 .502

peu4 .507

peu6 .544

peu7 .568

peu9 .305

peu15 .359

pu2 .696

269

Estimate

pu3 .797

pu4 .774

pu5 .791

pu6 .735

pu7 .692

pu11 .496

cse4 .519

cse7 .715

cse9 .762

cse10 .773

cse11 .698

cse12 .761

cse15 .577

int10 .467

int8 .088

int7 .562

int5 .622

int3 .474

int2 .490

int1 .675

grat10 .384

grat9 .692

grat7 .644

grat6 .711

grat4 .608

grat3 .630


CSE PEU PU INT USE GRAT

PEU .487 .000 .000 .000 .000 .000

PU .583 .000 .000 .000 .000 .000

INT .393 .487 .267 .000 .000 .000

USE .171 .227 .104 .273 .000 .000

GRAT .617 .552 .597 .174 -.126 .000

use11 .191 .254 .117 .305 1.118 .000

use6 .190 .252 .116 .303 1.110 .000

use5 .207 .273 .126 .329 1.206 .000

use4 .227 .300 .138 .362 1.324 .000

use2 .171 .227 .104 .273 1.000 .000

peu2 .674 1.383 .000 .000 .000 .000

peu4 .704 1.446 .000 .000 .000 .000

peu6 .742 1.523 .000 .000 .000 .000

peu7 .680 1.397 .000 .000 .000 .000

270


peu9 .595 1.221 .000 .000 .000 .000

peu15 .487 1.000 .000 .000 .000 .000

pu2 .715 .000 1.226 .000 .000 .000

pu3 .798 .000 1.368 .000 .000 .000

pu4 .766 .000 1.313 .000 .000 .000

pu5 .771 .000 1.323 .000 .000 .000

pu6 .754 .000 1.293 .000 .000 .000

pu7 .775 .000 1.328 .000 .000 .000

pu11 .583 .000 1.000 .000 .000 .000

cse4 .910 .000 .000 .000 .000 .000

cse7 1.065 .000 .000 .000 .000 .000

cse9 1.174 .000 .000 .000 .000 .000

cse10 1.135 .000 .000 .000 .000 .000

cse11 1.086 .000 .000 .000 .000 .000

cse12 1.065 .000 .000 .000 .000 .000

cse15 1.000 .000 .000 .000 .000 .000

int10 .387 .480 .263 .986 .000 .000

int8 .521 .647 .354 1.327 .000 .000

int7 .379 .470 .257 .964 .000 .000

int5 .402 .498 .273 1.023 .000 .000

int3 .370 .459 .251 .941 .000 .000

int2 .325 .403 .221 .827 .000 .000

int1 .393 .487 .267 1.000 .000 .000

grat10 .605 .541 .585 .170 -.123 .979

grat9 .617 .552 .597 .174 -.126 .999

grat7 .643 .575 .623 .181 -.131 1.042

grat6 .686 .613 .664 .193 -.140 1.111

grat4 .587 .525 .568 .165 -.120 .951

grat3 .617 .552 .597 .174 -.126 1.000



PEU .566 .000 .000 .000 .000 .000

PU .528 .000 .000 .000 .000 .000

INT .370 .394 .278 .000 .000 .000

USE .220 .250 .148 .373 .000 .000

GRAT .491 .378 .525 .147 -.078 .000

use11 .104 .119 .070 .177 .475 .000

use6 .160 .183 .108 .272 .729 .000

use5 .181 .206 .122 .306 .822 .000

use4 .163 .185 .109 .276 .740 .000

use2 .151 .172 .102 .256 .687 .000

peu2 .401 .708 .000 .000 .000 .000

271


peu4 .403 .712 .000 .000 .000 .000

peu6 .418 .738 .000 .000 .000 .000

peu7 .427 .754 .000 .000 .000 .000

peu9 .313 .552 .000 .000 .000 .000

peu15 .339 .599 .000 .000 .000 .000

pu2 .440 .000 .834 .000 .000 .000

pu3 .471 .000 .893 .000 .000 .000

pu4 .464 .000 .880 .000 .000 .000

pu5 .469 .000 .890 .000 .000 .000

pu6 .452 .000 .857 .000 .000 .000

pu7 .439 .000 .832 .000 .000 .000

pu11 .372 .000 .705 .000 .000 .000

cse4 .721 .000 .000 .000 .000 .000

cse7 .845 .000 .000 .000 .000 .000

cse9 .873 .000 .000 .000 .000 .000

cse10 .879 .000 .000 .000 .000 .000

cse11 .836 .000 .000 .000 .000 .000

cse12 .873 .000 .000 .000 .000 .000

cse15 .760 .000 .000 .000 .000 .000

int10 .253 .270 .190 .684 .000 .000

int8 .110 .117 .082 .297 .000 .000

int7 .277 .296 .208 .750 .000 .000

int5 .292 .311 .219 .788 .000 .000

int3 .255 .272 .191 .689 .000 .000

int2 .259 .276 .194 .700 .000 .000

int1 .304 .324 .228 .822 .000 .000

grat10 .304 .234 .326 .091 -.048 .620

grat9 .408 .314 .437 .122 -.065 .832

grat7 .394 .303 .422 .118 -.063 .803

grat6 .414 .319 .443 .124 -.066 .843

grat4 .383 .295 .409 .114 -.061 .780

grat3 .390 .300 .417 .117 -.062 .794



PEU .487 .000 .000 .000 .000 .000

PU .583 .000 .000 .000 .000 .000

INT .000 .487 .267 .000 .000 .000

USE .000 .094 .031 .273 .000 .000

GRAT .000 .479 .555 .208 -.126 .000

use11 .000 .000 .000 .000 1.118 .000

use6 .000 .000 .000 .000 1.110 .000

use5 .000 .000 .000 .000 1.206 .000

272


use4 .000 .000 .000 .000 1.324 .000

use2 .000 .000 .000 .000 1.000 .000

peu2 .000 1.383 .000 .000 .000 .000

peu4 .000 1.446 .000 .000 .000 .000

peu6 .000 1.523 .000 .000 .000 .000

peu7 .000 1.397 .000 .000 .000 .000

peu9 .000 1.221 .000 .000 .000 .000

peu15 .000 1.000 .000 .000 .000 .000

pu2 .000 .000 1.226 .000 .000 .000

pu3 .000 .000 1.368 .000 .000 .000

pu4 .000 .000 1.313 .000 .000 .000

pu5 .000 .000 1.323 .000 .000 .000

pu6 .000 .000 1.293 .000 .000 .000

pu7 .000 .000 1.328 .000 .000 .000

pu11 .000 .000 1.000 .000 .000 .000

cse4 .910 .000 .000 .000 .000 .000

cse7 1.065 .000 .000 .000 .000 .000

cse9 1.174 .000 .000 .000 .000 .000

cse10 1.135 .000 .000 .000 .000 .000

cse11 1.086 .000 .000 .000 .000 .000

cse12 1.065 .000 .000 .000 .000 .000

cse15 1.000 .000 .000 .000 .000 .000

int10 .000 .000 .000 .986 .000 .000

int8 .000 .000 .000 1.327 .000 .000

int7 .000 .000 .000 .964 .000 .000

int5 .000 .000 .000 1.023 .000 .000

int3 .000 .000 .000 .941 .000 .000

int2 .000 .000 .000 .827 .000 .000

int1 .000 .000 .000 1.000 .000 .000

grat10 .000 .000 .000 .000 .000 .979

grat9 .000 .000 .000 .000 .000 .999

grat7 .000 .000 .000 .000 .000 1.042

grat6 .000 .000 .000 .000 .000 1.111

grat4 .000 .000 .000 .000 .000 .951

grat3 .000 .000 .000 .000 .000 1.000



PEU .566 .000 .000 .000 .000 .000

PU .528 .000 .000 .000 .000 .000

INT .000 .394 .278 .000 .000 .000

273


USE .000 .103 .044 .373 .000 .000

GRAT .000 .328 .488 .176 -.078 .000

use11 .000 .000 .000 .000 .475 .000

use6 .000 .000 .000 .000 .729 .000

use5 .000 .000 .000 .000 .822 .000

use4 .000 .000 .000 .000 .740 .000

use2 .000 .000 .000 .000 .687 .000

peu2 .000 .708 .000 .000 .000 .000

peu4 .000 .712 .000 .000 .000 .000

peu6 .000 .738 .000 .000 .000 .000

peu7 .000 .754 .000 .000 .000 .000

peu9 .000 .552 .000 .000 .000 .000

peu15 .000 .599 .000 .000 .000 .000

pu2 .000 .000 .834 .000 .000 .000

pu3 .000 .000 .893 .000 .000 .000

pu4 .000 .000 .880 .000 .000 .000

pu5 .000 .000 .890 .000 .000 .000

pu6 .000 .000 .857 .000 .000 .000

pu7 .000 .000 .832 .000 .000 .000

pu11 .000 .000 .705 .000 .000 .000

cse4 .721 .000 .000 .000 .000 .000

cse7 .845 .000 .000 .000 .000 .000

cse9 .873 .000 .000 .000 .000 .000

cse10 .879 .000 .000 .000 .000 .000

cse11 .836 .000 .000 .000 .000 .000

cse12 .873 .000 .000 .000 .000 .000

cse15 .760 .000 .000 .000 .000 .000

int10 .000 .000 .000 .684 .000 .000

int8 .000 .000 .000 .297 .000 .000

int7 .000 .000 .000 .750 .000 .000

int5 .000 .000 .000 .788 .000 .000

int3 .000 .000 .000 .689 .000 .000

int2 .000 .000 .000 .700 .000 .000

int1 .000 .000 .000 .822 .000 .000

grat10 .000 .000 .000 .000 .000 .620

grat9 .000 .000 .000 .000 .000 .832

grat7 .000 .000 .000 .000 .000 .803

grat6 .000 .000 .000 .000 .000 .843

grat4 .000 .000 .000 .000 .000 .780

grat3 .000 .000 .000 .000 .000 .794

274

Indirect Effects (Group number 1 - Default model)


PEU .000 .000 .000 .000 .000 .000

PU .000 .000 .000 .000 .000 .000

INT .393 .000 .000 .000 .000 .000

USE .171 .133 .073 .000 .000 .000

GRAT .617 .073 .042 -.034 .000 .000

use11 .191 .254 .117 .305 .000 .000

use6 .190 .252 .116 .303 .000 .000

use5 .207 .273 .126 .329 .000 .000

use4 .227 .300 .138 .362 .000 .000

use2 .171 .227 .104 .273 .000 .000

peu2 .674 .000 .000 .000 .000 .000

peu4 .704 .000 .000 .000 .000 .000

peu6 .742 .000 .000 .000 .000 .000

peu7 .680 .000 .000 .000 .000 .000

peu9 .595 .000 .000 .000 .000 .000

peu15 .487 .000 .000 .000 .000 .000

pu2 .715 .000 .000 .000 .000 .000

pu3 .798 .000 .000 .000 .000 .000

pu4 .766 .000 .000 .000 .000 .000

pu5 .771 .000 .000 .000 .000 .000

pu6 .754 .000 .000 .000 .000 .000

pu7 .775 .000 .000 .000 .000 .000

pu11 .583 .000 .000 .000 .000 .000

cse4 .000 .000 .000 .000 .000 .000

cse7 .000 .000 .000 .000 .000 .000

cse9 .000 .000 .000 .000 .000 .000

cse10 .000 .000 .000 .000 .000 .000

cse11 .000 .000 .000 .000 .000 .000

cse12 .000 .000 .000 .000 .000 .000

cse15 .000 .000 .000 .000 .000 .000

int10 .387 .480 .263 .000 .000 .000

int8 .521 .647 .354 .000 .000 .000

int7 .379 .470 .257 .000 .000 .000

int5 .402 .498 .273 .000 .000 .000

int3 .370 .459 .251 .000 .000 .000

int2 .325 .403 .221 .000 .000 .000

int1 .393 .487 .267 .000 .000 .000

grat10 .605 .541 .585 .170 -.123 .000

grat9 .617 .552 .597 .174 -.126 .000

grat7 .643 .575 .623 .181 -.131 .000

grat6 .686 .613 .664 .193 -.140 .000

grat4 .587 .525 .568 .165 -.120 .000

275


grat3 .617 .552 .597 .174 -.126 .000

Standardized Indirect Effects (Group number 1 - Default model)


PEU .000 .000 .000 .000 .000 .000

PU .000 .000 .000 .000 .000 .000

INT .370 .000 .000 .000 .000 .000

USE .220 .147 .103 .000 .000 .000

GRAT .491 .050 .037 -.029 .000 .000

use11 .104 .119 .070 .177 .000 .000

use6 .160 .183 .108 .272 .000 .000

use5 .181 .206 .122 .306 .000 .000

use4 .163 .185 .109 .276 .000 .000

use2 .151 .172 .102 .256 .000 .000

peu2 .401 .000 .000 .000 .000 .000

peu4 .403 .000 .000 .000 .000 .000

peu6 .418 .000 .000 .000 .000 .000

peu7 .427 .000 .000 .000 .000 .000

peu9 .313 .000 .000 .000 .000 .000

peu15 .339 .000 .000 .000 .000 .000

pu2 .440 .000 .000 .000 .000 .000

pu3 .471 .000 .000 .000 .000 .000

pu4 .464 .000 .000 .000 .000 .000

pu5 .469 .000 .000 .000 .000 .000

pu6 .452 .000 .000 .000 .000 .000

pu7 .439 .000 .000 .000 .000 .000

pu11 .372 .000 .000 .000 .000 .000

cse4 .000 .000 .000 .000 .000 .000

cse7 .000 .000 .000 .000 .000 .000

cse9 .000 .000 .000 .000 .000 .000

cse10 .000 .000 .000 .000 .000 .000

cse11 .000 .000 .000 .000 .000 .000

cse12 .000 .000 .000 .000 .000 .000

cse15 .000 .000 .000 .000 .000 .000

int10 .253 .270 .190 .000 .000 .000

int8 .110 .117 .082 .000 .000 .000

int7 .277 .296 .208 .000 .000 .000

int5 .292 .311 .219 .000 .000 .000

int3 .255 .272 .191 .000 .000 .000

int2 .259 .276 .194 .000 .000 .000

int1 .304 .324 .228 .000 .000 .000

grat10 .304 .234 .326 .091 -.048 .000

grat9 .408 .314 .437 .122 -.065 .000

276


grat7 .394 .303 .422 .118 -.063 .000

grat6 .414 .319 .443 .124 -.066 .000

grat4 .383 .295 .409 .114 -.061 .000

grat3 .390 .300 .417 .117 -.062 .000


M.I. Par Change

e39 <--> e40 152.910 .298

e43 <--> CSE 56.460 .233

e43 <--> e40 31.918 -.136

e43 <--> e39 22.504 -.140

e42 <--> CSE 7.039 .065

e42 <--> e40 4.004 -.038

e38 <--> CSE 8.679 .153

e37 <--> e43 4.127 .054

e36 <--> e39 4.433 -.049

e33 <--> CSE 9.699 -.125

e33 <--> e40 5.317 .071

e33 <--> e39 24.481 .190

e33 <--> e43 5.832 -.093

e33 <--> e41 4.442 .078

e32 <--> e36 4.430 .067

e31 <--> CSE 6.344 -.104

e31 <--> e39 35.597 .235

e31 <--> e43 22.890 -.188

e31 <--> e37 5.472 -.083

e31 <--> e34 4.346 -.074

e31 <--> e32 16.343 .212

e30 <--> e34 7.561 .086

e29 <--> e39 7.104 .131

e29 <--> e41 6.213 .119

e29 <--> e35 5.230 .119

e28 <--> CSE 16.321 .152

e28 <--> e40 9.108 -.088

e28 <--> e43 26.731 .186

e28 <--> e37 17.553 .137

e28 <--> e36 4.353 -.060

e28 <--> e31 10.718 -.156

e27 <--> CSE 4.029 .060

e27 <--> e40 6.950 .061

e27 <--> e43 9.632 .089

277

M.I. Par Change

e27 <--> e41 15.681 -.109

e27 <--> e31 5.307 -.088

e27 <--> e30 19.439 .148

e26 <--> CSE 7.247 -.072

e26 <--> e43 6.763 -.066

e26 <--> e42 5.285 -.046

e26 <--> e41 8.294 .071

e26 <--> e31 10.110 .108

e26 <--> e30 8.264 -.086

e25 <--> e34 4.511 .049

e25 <--> e28 6.242 .079

e24 <--> e42 5.298 -.045

e24 <--> e34 6.708 -.058

e24 <--> e33 5.359 -.075

e24 <--> e32 7.365 .091

e24 <--> e31 15.146 .129

e24 <--> e25 9.070 .064

e23 <--> e42 5.899 .053

e23 <--> e34 4.520 .053

e23 <--> e33 6.709 .093

e23 <--> e28 6.830 .088

e22 <--> e40 14.691 .097

e22 <--> e43 5.540 -.073

e22 <--> e42 5.347 -.057

e22 <--> e41 28.171 .160

e22 <--> e24 5.573 -.061

e22 <--> e23 6.142 .072

e21 <--> CSE 16.407 .146

e21 <--> e39 6.519 -.087

e21 <--> e42 8.952 .081

e21 <--> e36 4.166 .056

e21 <--> e26 5.669 -.070

e21 <--> e25 7.033 -.079

e20 <--> e43 4.753 .059

e20 <--> e29 4.382 .095

e20 <--> e28 6.924 .087

e20 <--> e23 9.464 .078

e19 <--> e39 5.310 -.050

e19 <--> e27 4.077 -.042

e19 <--> e26 12.329 -.066

e19 <--> e25 4.447 .040

e18 <--> e25 7.235 -.051

e17 <--> e33 4.725 -.058

278

M.I. Par Change

e17 <--> e32 11.474 .093

e17 <--> e27 7.779 .055

e17 <--> e23 6.276 -.048

e17 <--> e18 5.594 .035

e16 <--> e43 8.441 .067

e16 <--> e36 5.405 .042

e16 <--> e32 7.987 -.087

e16 <--> e23 4.378 .045

e16 <--> e22 4.314 -.050

e15 <--> e30 4.686 -.050

e14 <--> e40 5.586 -.052

e14 <--> e39 6.910 -.070

e14 <--> e38 4.065 .091

e14 <--> e37 5.263 .056

e14 <--> e35 9.214 -.086

e14 <--> e32 5.361 -.084

e14 <--> e31 4.987 -.080

e14 <--> e28 6.968 .086

e14 <--> e23 5.217 -.057

e13 <--> e38 5.235 .136

e13 <--> e34 8.122 -.091

e13 <--> e32 8.252 .137

e13 <--> e20 5.765 -.078

e13 <--> e19 4.571 -.056

e11 <--> e40 6.325 .060

e11 <--> e43 5.436 -.067

e11 <--> e42 9.056 -.070

e11 <--> e38 6.585 .126

e11 <--> e34 6.462 -.067

e11 <--> e31 5.481 .091

e11 <--> e29 6.037 -.120

e11 <--> e25 5.683 -.061

e11 <--> e24 8.076 .070

e11 <--> e16 5.958 .056

e11 <--> e13 16.220 .140

e10 <--> CSE 4.114 .060

e10 <--> e40 4.822 -.050

e10 <--> e39 4.386 -.059

e10 <--> e29 4.388 -.098

e10 <--> e14 4.692 .055

e10 <--> e11 4.844 .060

e9 <--> e40 5.574 -.064

e9 <--> e37 8.153 .086

279

M.I. Par Change

e9 <--> e33 4.336 -.090

e9 <--> e32 8.090 -.128

e9 <--> e31 4.794 -.097

e9 <--> e28 20.939 .186

e9 <--> e20 7.626 .085

e8 <--> e40 12.138 -.081

e8 <--> e38 10.361 -.154

e8 <--> e34 4.574 .055

e8 <--> e32 6.031 -.094

e8 <--> e31 16.132 -.153

e8 <--> e25 7.439 .068

e8 <--> e20 5.619 .062

e8 <--> e19 8.045 .059

e8 <--> e14 4.386 .054

e8 <--> e13 7.491 -.093

e8 <--> e11 9.318 -.085

e8 <--> e9 11.629 .109

e7 <--> e34 4.204 .046

e7 <--> e29 11.280 .141

e7 <--> e23 5.189 .054

e7 <--> e21 4.122 -.059

e7 <--> e20 5.062 .052

e7 <--> e11 6.611 -.063

e7 <--> e8 10.629 .078

e6 <--> e43 10.993 -.160

e6 <--> e32 6.722 -.168

e6 <--> e31 11.890 .222

e6 <--> e29 56.114 .601

e6 <--> e28 4.610 -.126

e6 <--> e27 9.977 -.147

e6 <--> e24 4.484 .086

e6 <--> e23 9.160 -.137

e6 <--> e16 11.577 -.127

e6 <--> e7 4.947 -.091

e5 <--> e40 6.498 -.058

e5 <--> e43 6.155 .069

e5 <--> e31 15.580 -.147

e5 <--> e25 4.659 -.053

e5 <--> e24 4.304 -.049

e5 <--> e10 8.571 .078

e5 <--> e8 4.588 .058

e5 <--> e6 4.901 .100

e4 <--> e40 8.478 .075

280

M.I. Par Change

e4 <--> e42 4.605 -.054

e4 <--> e33 17.675 .173

e4 <--> e27 6.018 -.075

e4 <--> e22 11.648 .114

e4 <--> e19 5.017 -.052

e4 <--> e11 7.875 .088

e4 <--> e8 4.383 -.064

e3 <--> e38 5.145 -.114

e3 <--> e36 6.289 .060

e3 <--> e32 5.537 .095

e3 <--> e28 8.880 -.109

e3 <--> e27 5.092 -.065

e2 <--> e43 7.029 .082

e2 <--> e41 4.632 -.063

e2 <--> e27 13.606 .110

e2 <--> e20 4.105 .058

e1 <--> e30 4.839 -.081

e1 <--> e11 6.291 -.078

Model Fit Summary

CMIN


Default model 87 1781.099 654 .000 2.723



RMR, GFI


Default model .188 .800 .773 .706




Model NFI

Delta1

RFI

rho1

IFI

Delta2

TLI

rho2 CFI

Default model .841 .829 .893 .885 .893



281






NCP


Default model 1127.099 1005.428 1256.393



FMIN


Default model 4.509 2.853 2.545 3.181



RMSEA


Default model .066 .062 .070 .000


AIC


Default model 1955.099 1974.161 2301.482 2388.482

Saturated model 1482.000 1644.354 4432.228 5173.228


ECVI


Default model 4.950 4.642 5.277 4.998

Saturated model 3.752 3.752 3.752 4.163


HOELTER

Model HOELTER

.05

HOELTER

.01

Default model 159 165


282

Appendix H The Revised TAG Model’s Outputs



use6 2.000 7.000 -1.880 -15.274 4.500 18.278

use5 2.000 7.000 -1.990 -16.164 4.948 20.100

use4 1.000 7.000 -2.024 -16.447 4.592 18.653

peu2 1.000 7.000 -.808 -6.564 .308 1.250

peu4 1.000 7.000 -.545 -4.427 .056 .226

peu6 1.000 7.000 -.371 -3.012 -.189 -.769

peu7 1.000 7.000 -.574 -4.661 -.003 -.014

pu2 1.000 7.000 -.814 -6.613 .222 .901

pu3 1.000 7.000 -.527 -4.284 -.084 -.343

pu4 1.000 7.000 -.636 -5.165 .040 .163

pu5 1.000 7.000 -.494 -4.010 -.274 -1.112

pu6 1.000 7.000 -.844 -6.857 .421 1.712

pu7 1.000 7.000 -.664 -5.397 .055 .223

pu11 1.000 7.000 -.872 -7.081 .671 2.725

cse4 1.000 7.000 -1.021 -8.296 1.496 6.075

cse7 2.000 7.000 -1.025 -8.331 .865 3.514

cse9 2.000 7.000 -.759 -6.162 .363 1.474

cse10 2.000 7.000 -.758 -6.155 .302 1.227

cse11 2.000 7.000 -.980 -7.959 .587 2.383

cse12 2.000 7.000 -.882 -7.164 .964 3.917

int7 2.000 7.000 -.843 -6.849 .407 1.653

int5 2.000 7.000 -.962 -7.819 .566 2.298

int3 1.000 7.000 -1.866 -15.158 3.929 15.958

int2 1.000 7.000 -1.644 -13.353 3.022 12.276

int1 2.000 7.000 -1.065 -8.650 .605 2.458

grat9 1.000 7.000 -.900 -7.310 .825 3.352

grat6 1.000 7.000 -.917 -7.452 .642 2.608

grat4 1.000 7.000 -.951 -7.725 .945 3.837

grat3 1.000 7.000 -.893 -7.259 .649 2.637




PU <--- CSE .557 .058 9.602 *** par_24

PEU <--- CSE .588 .066 8.864 *** par_25

INT <--- PU .367 .073 5.009 *** par_26

INT <--- PEU .225 .071 3.154 .002 par_28

GRAT <--- PU .568 .077 7.407 *** par_27

283


GRAT <--- PEU .275 .069 3.994 *** par_29

USE <--- INT .419 .055 7.562 *** par_30

GRAT <--- INT .250 .059 4.206 *** par_31

grat3 <--- GRAT 1.000

grat4 <--- GRAT .970 .053 18.442 *** par_1

grat6 <--- GRAT 1.107 .056 19.835 *** par_2

grat9 <--- GRAT .985 .052 19.036 *** par_3

int1 <--- INT 1.000

int2 <--- INT .841 .052 16.289 *** par_4

int3 <--- INT .941 .061 15.528 *** par_5

int5 <--- INT 1.008 .057 17.667 *** par_6

int7 <--- INT .916 .058 15.763 *** par_7

cse12 <--- CSE 1.000

cse11 <--- CSE 1.018 .047 21.772 *** par_8

cse10 <--- CSE 1.071 .044 24.308 *** par_9

cse9 <--- CSE 1.110 .047 23.835 *** par_10

cse7 <--- CSE 1.000 .045 22.272 *** par_11

cse4 <--- CSE .855 .050 17.073 *** par_12

pu11 <--- PU 1.000

pu7 <--- PU 1.328 .083 15.975 *** par_13

pu6 <--- PU 1.294 .078 16.495 *** par_14

pu5 <--- PU 1.324 .078 17.038 *** par_15

pu4 <--- PU 1.313 .078 16.789 *** par_16

pu3 <--- PU 1.368 .080 17.026 *** par_17

pu2 <--- PU 1.229 .076 16.088 *** par_18

peu7 <--- PEU 1.000

peu6 <--- PEU 1.150 .083 13.812 *** par_19

peu4 <--- PEU 1.086 .083 13.104 *** par_20

peu2 <--- PEU .991 .077 12.923 *** par_21

use4 <--- USE 1.000

use5 <--- USE .968 .071 13.704 *** par_22

use6 <--- USE .841 .067 12.621 *** par_23


Estimate

PU <--- CSE .536

PEU <--- CSE .525

INT <--- PU .375

INT <--- PEU .247

GRAT <--- PU .494

GRAT <--- PEU .258

USE <--- INT .447

GRAT <--- INT .213

284

Estimate





int1 <--- INT .834

int2 <--- INT .723

int3 <--- INT .700

int5 <--- INT .788

int7 <--- INT .723

cse12 <--- CSE .870

cse11 <--- CSE .832

cse10 <--- CSE .881

cse9 <--- CSE .877

cse7 <--- CSE .843

cse4 <--- CSE .719

pu11 <--- PU .704

pu7 <--- PU .831

pu6 <--- PU .857

pu5 <--- PU .890

pu4 <--- PU .879

pu3 <--- PU .892

pu2 <--- PU .836

peu7 <--- PEU .746

peu6 <--- PEU .771

peu4 <--- PEU .740

peu2 <--- PEU .702

use4 <--- USE .725

use5 <--- USE .855

use6 <--- USE .717



CSE .688 .064 10.773 *** par_32

e39 .530 .068 7.843 *** par_33

e40 .625 .081 7.758 *** par_34

e43 .534 .057 9.387 *** par_35

e41 .432 .052 8.368 *** par_36

e42 .501 .068 7.415 *** par_37

e1 .562 .050 11.343 *** par_38

e2 .519 .046 11.210 *** par_39

e3 .489 .048 10.174 *** par_40

e5 .457 .042 10.797 *** par_41

e7 .313 .032 9.655 *** par_42

285


e8 .461 .039 11.940 *** par_43

e9 .659 .054 12.234 *** par_44

e10 .443 .041 10.814 *** par_45

e11 .550 .046 11.842 *** par_46

e15 .222 .020 11.298 *** par_47

e16 .317 .026 12.070 *** par_48

e17 .228 .021 10.994 *** par_49

e18 .255 .023 11.097 *** par_50

e19 .279 .024 11.863 *** par_51

e20 .471 .036 13.119 *** par_52

e21 .757 .057 13.346 *** par_53

e22 .586 .047 12.426 *** par_54

e23 .450 .037 12.061 *** par_55

e24 .344 .030 11.349 *** par_56

e25 .376 .032 11.637 *** par_57

e26 .358 .032 11.291 *** par_58

e27 .486 .039 12.399 *** par_59

e30 .687 .065 10.565 *** par_60

e31 .781 .078 9.986 *** par_61

e32 .843 .079 10.718 *** par_62

e33 .873 .076 11.442 *** par_63

e35 .567 .053 10.608 *** par_64

e36 .217 .036 6.031 *** par_65

e37 .420 .040 10.575 *** par_66


Estimate

PEU .276

PU .287

INT .253

USE .200

GRAT .560

use6 .514

use5 .730

use4 .525

peu2 .493

peu4 .547

peu6 .594

peu7 .557

pu2 .698

pu3 .796

pu4 .773

pu5 .791

286

Estimate

pu6 .735

pu7 .691

pu11 .496

cse4 .516

cse7 .711

cse9 .769

cse10 .776

cse11 .692

cse12 .756

int7 .522

int5 .622

int3 .490

int2 .523

int1 .696

grat9 .676

grat6 .711

grat4 .641

grat3 .636



PEU .588 .000 .000 .000 .000 .000

PU .557 .000 .000 .000 .000 .000

INT .337 .225 .367 .000 .000 .000

USE .141 .094 .154 .419 .000 .000

GRAT .563 .331 .660 .250 .000 .000

use6 .119 .079 .129 .352 .841 .000

use5 .137 .091 .149 .405 .968 .000

use4 .141 .094 .154 .419 1.000 .000

peu2 .583 .991 .000 .000 .000 .000

peu4 .639 1.086 .000 .000 .000 .000

peu6 .677 1.150 .000 .000 .000 .000

peu7 .588 1.000 .000 .000 .000 .000

pu2 .685 .000 1.229 .000 .000 .000

pu3 .762 .000 1.368 .000 .000 .000

pu4 .732 .000 1.313 .000 .000 .000

pu5 .738 .000 1.324 .000 .000 .000

pu6 .721 .000 1.294 .000 .000 .000

pu7 .740 .000 1.328 .000 .000 .000

pu11 .557 .000 1.000 .000 .000 .000

cse4 .855 .000 .000 .000 .000 .000

cse7 1.000 .000 .000 .000 .000 .000

cse9 1.110 .000 .000 .000 .000 .000

287


cse10 1.071 .000 .000 .000 .000 .000

cse11 1.018 .000 .000 .000 .000 .000

cse12 1.000 .000 .000 .000 .000 .000

int7 .309 .206 .337 .916 .000 .000

int5 .340 .227 .370 1.008 .000 .000

int3 .317 .212 .346 .941 .000 .000

int2 .283 .189 .309 .841 .000 .000

int1 .337 .225 .367 1.000 .000 .000

grat9 .554 .326 .650 .246 .000 .985

grat6 .623 .367 .731 .277 .000 1.107

grat4 .546 .321 .640 .242 .000 .970

grat3 .563 .331 .660 .250 .000 1.000



PEU .525 .000 .000 .000 .000 .000

PU .536 .000 .000 .000 .000 .000

INT .331 .247 .375 .000 .000 .000

USE .148 .111 .168 .447 .000 .000

GRAT .471 .310 .574 .213 .000 .000

use6 .106 .079 .120 .321 .717 .000

use5 .126 .094 .143 .382 .855 .000

use4 .107 .080 .121 .324 .725 .000

peu2 .369 .702 .000 .000 .000 .000

peu4 .389 .740 .000 .000 .000 .000

peu6 .405 .771 .000 .000 .000 .000

peu7 .392 .746 .000 .000 .000 .000

pu2 .448 .000 .836 .000 .000 .000

pu3 .478 .000 .892 .000 .000 .000

pu4 .471 .000 .879 .000 .000 .000

pu5 .477 .000 .890 .000 .000 .000

pu6 .459 .000 .857 .000 .000 .000

pu7 .446 .000 .831 .000 .000 .000

pu11 .377 .000 .704 .000 .000 .000

cse4 .719 .000 .000 .000 .000 .000

cse7 .843 .000 .000 .000 .000 .000

cse9 .877 .000 .000 .000 .000 .000

cse10 .881 .000 .000 .000 .000 .000

cse11 .832 .000 .000 .000 .000 .000

cse12 .870 .000 .000 .000 .000 .000

int7 .239 .179 .271 .723 .000 .000

int5 .261 .195 .295 .788 .000 .000

int3 .231 .173 .262 .700 .000 .000

288


int2 .239 .179 .271 .723 .000 .000

int1 .276 .206 .312 .834 .000 .000

grat9 .387 .255 .472 .175 .000 .822

grat6 .397 .262 .484 .180 .000 .843

grat4 .377 .248 .460 .171 .000 .800

grat3 .375 .248 .458 .170 .000 .798



PEU .588 .000 .000 .000 .000 .000

PU .557 .000 .000 .000 .000 .000

INT .000 .225 .367 .000 .000 .000

USE .000 .000 .000 .419 .000 .000

GRAT .000 .275 .568 .250 .000 .000

use6 .000 .000 .000 .000 .841 .000

use5 .000 .000 .000 .000 .968 .000

use4 .000 .000 .000 .000 1.000 .000

peu2 .000 .991 .000 .000 .000 .000

peu4 .000 1.086 .000 .000 .000 .000

peu6 .000 1.150 .000 .000 .000 .000

peu7 .000 1.000 .000 .000 .000 .000

pu2 .000 .000 1.229 .000 .000 .000

pu3 .000 .000 1.368 .000 .000 .000

pu4 .000 .000 1.313 .000 .000 .000

pu5 .000 .000 1.324 .000 .000 .000

pu6 .000 .000 1.294 .000 .000 .000

pu7 .000 .000 1.328 .000 .000 .000

pu11 .000 .000 1.000 .000 .000 .000

cse4 .855 .000 .000 .000 .000 .000

cse7 1.000 .000 .000 .000 .000 .000

cse9 1.110 .000 .000 .000 .000 .000

cse10 1.071 .000 .000 .000 .000 .000

cse11 1.018 .000 .000 .000 .000 .000

cse12 1.000 .000 .000 .000 .000 .000

int7 .000 .000 .000 .916 .000 .000

int5 .000 .000 .000 1.008 .000 .000

int3 .000 .000 .000 .941 .000 .000

int2 .000 .000 .000 .841 .000 .000

int1 .000 .000 .000 1.000 .000 .000

grat9 .000 .000 .000 .000 .000 .985

grat6 .000 .000 .000 .000 .000 1.107

grat4 .000 .000 .000 .000 .000 .970

grat3 .000 .000 .000 .000 .000 1.000

289



PEU .525 .000 .000 .000 .000 .000

PU .536 .000 .000 .000 .000 .000

INT .000 .247 .375 .000 .000 .000

USE .000 .000 .000 .447 .000 .000

GRAT .000 .258 .494 .213 .000 .000

use6 .000 .000 .000 .000 .717 .000

use5 .000 .000 .000 .000 .855 .000

use4 .000 .000 .000 .000 .725 .000

peu2 .000 .702 .000 .000 .000 .000

peu4 .000 .740 .000 .000 .000 .000

peu6 .000 .771 .000 .000 .000 .000

peu7 .000 .746 .000 .000 .000 .000

pu2 .000 .000 .836 .000 .000 .000

pu3 .000 .000 .892 .000 .000 .000

pu4 .000 .000 .879 .000 .000 .000

pu5 .000 .000 .890 .000 .000 .000

pu6 .000 .000 .857 .000 .000 .000

pu7 .000 .000 .831 .000 .000 .000

pu11 .000 .000 .704 .000 .000 .000

cse4 .719 .000 .000 .000 .000 .000

cse7 .843 .000 .000 .000 .000 .000

cse9 .877 .000 .000 .000 .000 .000

cse10 .881 .000 .000 .000 .000 .000

cse11 .832 .000 .000 .000 .000 .000

cse12 .870 .000 .000 .000 .000 .000

int7 .000 .000 .000 .723 .000 .000

int5 .000 .000 .000 .788 .000 .000

int3 .000 .000 .000 .700 .000 .000

int2 .000 .000 .000 .723 .000 .000

int1 .000 .000 .000 .834 .000 .000

grat9 .000 .000 .000 .000 .000 .822

grat6 .000 .000 .000 .000 .000 .843

grat4 .000 .000 .000 .000 .000 .800

grat3 .000 .000 .000 .000 .000 .798



PEU .000 .000 .000 .000 .000 .000

PU .000 .000 .000 .000 .000 .000

INT .337 .000 .000 .000 .000 .000

USE .141 .094 .154 .000 .000 .000

290


GRAT .563 .056 .092 .000 .000 .000

use6 .119 .079 .129 .352 .000 .000

use5 .137 .091 .149 .405 .000 .000

use4 .141 .094 .154 .419 .000 .000

peu2 .583 .000 .000 .000 .000 .000

peu4 .639 .000 .000 .000 .000 .000

peu6 .677 .000 .000 .000 .000 .000

peu7 .588 .000 .000 .000 .000 .000

pu2 .685 .000 .000 .000 .000 .000

pu3 .762 .000 .000 .000 .000 .000

pu4 .732 .000 .000 .000 .000 .000

pu5 .738 .000 .000 .000 .000 .000

pu6 .721 .000 .000 .000 .000 .000

pu7 .740 .000 .000 .000 .000 .000

pu11 .557 .000 .000 .000 .000 .000

cse4 .000 .000 .000 .000 .000 .000

cse7 .000 .000 .000 .000 .000 .000

cse9 .000 .000 .000 .000 .000 .000

cse10 .000 .000 .000 .000 .000 .000

cse11 .000 .000 .000 .000 .000 .000

cse12 .000 .000 .000 .000 .000 .000

int7 .309 .206 .337 .000 .000 .000

int5 .340 .227 .370 .000 .000 .000

int3 .317 .212 .346 .000 .000 .000

int2 .283 .189 .309 .000 .000 .000

int1 .337 .225 .367 .000 .000 .000

grat9 .554 .326 .650 .246 .000 .000

grat6 .623 .367 .731 .277 .000 .000

grat4 .546 .321 .640 .242 .000 .000

grat3 .563 .331 .660 .250 .000 .000



PEU .000 .000 .000 .000 .000 .000

PU .000 .000 .000 .000 .000 .000

INT .331 .000 .000 .000 .000 .000

USE .148 .111 .168 .000 .000 .000

GRAT .471 .053 .080 .000 .000 .000

use6 .106 .079 .120 .321 .000 .000

use5 .126 .094 .143 .382 .000 .000

use4 .107 .080 .121 .324 .000 .000

peu2 .369 .000 .000 .000 .000 .000

peu4 .389 .000 .000 .000 .000 .000

291


peu6 .405 .000 .000 .000 .000 .000

peu7 .392 .000 .000 .000 .000 .000

pu2 .448 .000 .000 .000 .000 .000

pu3 .478 .000 .000 .000 .000 .000

pu4 .471 .000 .000 .000 .000 .000

pu5 .477 .000 .000 .000 .000 .000

pu6 .459 .000 .000 .000 .000 .000

pu7 .446 .000 .000 .000 .000 .000

pu11 .377 .000 .000 .000 .000 .000

cse4 .000 .000 .000 .000 .000 .000

cse7 .000 .000 .000 .000 .000 .000

cse9 .000 .000 .000 .000 .000 .000

cse10 .000 .000 .000 .000 .000 .000

cse11 .000 .000 .000 .000 .000 .000

cse12 .000 .000 .000 .000 .000 .000

int7 .239 .179 .271 .000 .000 .000

int5 .261 .195 .295 .000 .000 .000

int3 .231 .173 .262 .000 .000 .000

int2 .239 .179 .271 .000 .000 .000

int1 .276 .206 .312 .000 .000 .000

grat9 .387 .255 .472 .175 .000 .000

grat6 .397 .262 .484 .180 .000 .000

grat4 .377 .248 .460 .171 .000 .000

grat3 .375 .248 .458 .170 .000 .000



M.I. Par Change

e39 <--> e40 138.247 .405

e43 <--> CSE 59.435 .266

e43 <--> e40 27.557 -.190

e43 <--> e39 24.673 -.153

e42 <--> CSE 11.188 .115

e33 <--> e39 27.440 .204

e31 <--> CSE 4.103 -.088

e31 <--> e39 38.408 .239

e31 <--> e43 18.723 -.175

e31 <--> e32 4.734 .109

e30 <--> e32 4.219 -.095

e27 <--> e40 6.720 .087

e27 <--> e43 11.115 .099

e27 <--> e41 10.739 -.093

292

M.I. Par Change

e27 <--> e31 6.742 -.097

e27 <--> e30 20.072 .154

e26 <--> CSE 7.121 -.076

e26 <--> e40 4.559 .064

e26 <--> e43 9.081 -.080

e26 <--> e41 7.428 .069

e26 <--> e31 9.890 .105

e26 <--> e30 11.014 -.102

e24 <--> e33 7.350 -.089

e24 <--> e32 5.391 .077

e24 <--> e31 14.227 .123

e24 <--> e25 9.175 .065

e23 <--> e42 4.175 .059

e23 <--> e33 9.432 .113

e22 <--> e40 14.242 .138

e22 <--> e43 4.167 -.067

e22 <--> e42 4.235 -.067

e22 <--> e41 23.213 .149

e22 <--> e27 4.144 -.061

e22 <--> e24 5.276 -.060

e22 <--> e23 6.315 .073

e21 <--> CSE 16.488 .156

e21 <--> e39 6.825 -.089

e21 <--> e42 11.634 .123

e21 <--> e36 4.064 .057

e21 <--> e26 5.311 -.068

e21 <--> e25 6.807 -.078

e20 <--> e43 7.838 .080

e20 <--> e26 4.640 -.051

e20 <--> e23 8.341 .074

e19 <--> e39 6.085 -.054

e19 <--> e41 4.479 .046

e19 <--> e27 4.321 -.044

e19 <--> e26 14.087 -.071

e19 <--> e25 5.249 .044

e18 <--> e25 6.206 -.047

e17 <--> e33 5.645 -.065

e17 <--> e32 10.280 .088

e17 <--> e31 6.073 -.067

e17 <--> e27 7.835 .056

e17 <--> e23 7.802 -.054

e16 <--> e43 8.489 .071

e16 <--> e36 4.250 .039

293

M.I. Par Change

e16 <--> e33 4.817 .068

e16 <--> e32 8.629 -.091

e16 <--> e22 4.462 -.052

e15 <--> e30 4.947 -.054

e11 <--> CSE 7.263 .092

e11 <--> e40 14.229 .135

e11 <--> e43 7.113 -.084

e11 <--> e42 4.645 -.069

e11 <--> e25 4.369 -.056

e11 <--> e24 7.167 .069

e11 <--> e19 4.004 -.046

e11 <--> e16 7.396 .065

e10 <--> CSE 5.808 .077

e10 <--> e11 12.174 .101

e9 <--> e40 8.579 -.114

e9 <--> e37 7.960 .087

e9 <--> e36 4.857 -.060

e9 <--> e20 5.681 .073

e8 <--> e40 14.499 -.125

e8 <--> e31 12.703 -.131

e8 <--> e25 8.432 .071

e8 <--> e24 4.254 -.049

e8 <--> e20 4.848 .057

e8 <--> e19 8.363 .060

e8 <--> e11 7.545 -.079

e8 <--> e9 7.027 .083

e7 <--> e23 4.898 .052

e7 <--> e21 4.368 -.061

e5 <--> e31 8.752 -.113

e5 <--> e25 4.247 -.053

e5 <--> e10 8.195 .081

e3 <--> CSE 5.332 -.080

e3 <--> e43 5.025 -.073

e3 <--> e27 11.615 -.102

e3 <--> e22 8.883 .097

e2 <--> e27 9.149 .089

e1 <--> e30 4.702 -.083

e1 <--> e11 4.345 -.068


M.I. Par Change

PEU <--- PU 94.519 .522

PU <--- PEU 91.573 .430

294

M.I. Par Change

INT <--- CSE 59.435 .387

USE <--- CSE 11.188 .168

use6 <--- int3 8.396 .091

use5 <--- cse11 4.302 .064

use5 <--- int3 4.162 -.056

use4 <--- pu6 4.561 .069

peu2 <--- PU 10.711 .198

peu2 <--- GRAT 7.821 .151

peu2 <--- pu2 10.560 .131

peu2 <--- pu3 11.211 .129

peu2 <--- pu4 5.221 .090

peu2 <--- pu6 17.764 .165

peu2 <--- pu7 13.426 .136

peu2 <--- pu11 7.955 .117

peu2 <--- cse7 5.263 -.119

peu2 <--- cse9 4.184 -.099

peu2 <--- cse10 7.318 -.137

peu2 <--- cse12 4.523 -.114

peu2 <--- grat9 8.441 .125

peu2 <--- grat4 4.935 .094

peu2 <--- grat3 7.648 .113

peu6 <--- CSE 4.103 -.127

peu6 <--- PU 16.359 .243

peu6 <--- INT 4.216 -.131

peu6 <--- pu3 23.117 .184

peu6 <--- pu4 13.890 .146

peu6 <--- pu5 25.763 .200

peu6 <--- pu6 7.725 .108

peu6 <--- pu7 15.572 .145

peu6 <--- pu11 10.194 .132

peu6 <--- cse7 5.415 -.120

peu6 <--- cse10 7.710 -.139

peu6 <--- cse11 6.906 -.131

peu6 <--- int5 4.206 -.096

peu6 <--- int3 4.804 -.097

peu6 <--- int2 13.670 -.190

peu7 <--- PU 4.486 .117

peu7 <--- pu2 16.422 .148

peu7 <--- pu4 5.045 .081

peu7 <--- pu6 4.248 .074

peu7 <--- pu7 5.645 .080

peu7 <--- cse11 4.413 .096

pu2 <--- PEU 10.086 .138

295

M.I. Par Change

pu2 <--- INT 12.650 .166

pu2 <--- USE 8.905 .153

pu2 <--- use6 7.863 .112

pu2 <--- use5 6.129 .103

pu2 <--- peu2 7.740 .079

pu2 <--- peu4 5.059 .061

pu2 <--- peu7 23.555 .145

pu2 <--- cse10 8.003 .104

pu2 <--- int7 7.175 .093

pu2 <--- int5 5.486 .080

pu2 <--- int3 11.423 .110

pu2 <--- int2 10.862 .125

pu2 <--- int1 10.436 .118

pu3 <--- CSE 7.121 -.110

pu3 <--- INT 6.056 -.103

pu3 <--- USE 7.038 -.122

pu3 <--- use5 6.211 -.092

pu3 <--- use4 5.628 -.072

pu3 <--- cse4 10.974 -.112

pu3 <--- cse7 16.424 -.137

pu3 <--- cse11 4.255 -.068

pu3 <--- cse12 6.760 -.091

pu3 <--- int3 8.442 -.085

pu3 <--- int2 7.247 -.091

pu3 <--- int1 6.805 -.086

pu4 <--- int2 4.056 .069

pu5 <--- peu4 4.813 .052

pu5 <--- peu6 8.596 .069

pu5 <--- int2 4.740 -.072

pu6 <--- USE 4.334 .104

pu6 <--- use5 4.318 .084

pu6 <--- use4 6.415 .084

pu6 <--- cse10 4.239 -.074

pu7 <--- PEU 4.261 .098

pu7 <--- USE 5.440 -.131

pu7 <--- GRAT 9.993 .137

pu7 <--- use6 6.739 -.114

pu7 <--- use5 4.649 -.098

pu7 <--- peu2 6.705 .080

pu7 <--- peu6 4.701 .064

pu7 <--- peu7 4.672 .071

pu7 <--- cse11 6.546 -.103

pu7 <--- int2 4.290 -.086

296

M.I. Par Change

pu7 <--- grat9 8.931 .102

pu7 <--- grat6 15.425 .123

pu7 <--- grat3 11.140 .109

pu11 <--- CSE 16.488 .226

pu11 <--- USE 7.778 .173

pu11 <--- use5 8.753 .148

pu11 <--- use4 5.388 .095

pu11 <--- cse4 5.591 .107

pu11 <--- cse7 17.611 .191

pu11 <--- cse9 15.610 .169

pu11 <--- cse10 10.724 .146

pu11 <--- cse11 16.042 .177

pu11 <--- cse12 14.522 .179

cse4 <--- INT 5.518 .105

cse4 <--- int3 9.935 .099

cse4 <--- int2 9.117 .110

cse4 <--- int1 6.955 .093

cse7 <--- PU 4.174 -.070

cse7 <--- peu4 4.591 -.045

cse7 <--- pu2 7.537 -.062

cse7 <--- pu3 10.981 -.072

cse7 <--- pu5 5.025 -.050

cse7 <--- int2 4.703 .063

cse11 <--- INT 4.226 .078

cse11 <--- USE 5.407 .098

cse11 <--- use5 6.833 .088

cse11 <--- peu4 4.494 -.047

cse11 <--- int7 9.954 .089

int7 <--- CSE 7.263 .134

int7 <--- PEU 20.842 .213

int7 <--- PU 8.866 .141

int7 <--- peu2 13.089 .110

int7 <--- peu4 16.157 .118

int7 <--- peu6 17.520 .120

int7 <--- peu7 13.327 .117

int7 <--- pu2 6.960 .083

int7 <--- pu3 7.912 .085

int7 <--- pu5 13.655 .115

int7 <--- pu6 4.692 .066

int7 <--- pu7 10.317 .093

int7 <--- pu11 5.239 .075

int7 <--- cse9 6.649 .098

int7 <--- cse10 4.164 .081

297

M.I. Par Change

int7 <--- cse11 12.844 .141

int7 <--- cse12 5.222 .096

int7 <--- grat4 4.193 .068

int5 <--- CSE 5.808 .111

int5 <--- cse7 5.554 .089

int5 <--- cse9 7.835 .099

int5 <--- cse10 5.993 .090

int5 <--- cse11 4.137 .074

int5 <--- cse12 4.190 .080

int5 <--- int7 5.255 .079

int5 <--- grat9 4.518 .067

int3 <--- PEU 5.380 -.118

int3 <--- peu2 5.853 -.080

int3 <--- peu4 7.335 -.086

int3 <--- peu6 5.481 -.073

int2 <--- PEU 11.988 -.148

int2 <--- GRAT 4.029 -.078

int2 <--- peu4 11.278 -.090

int2 <--- peu6 19.795 -.117

int2 <--- peu7 4.072 -.059

int2 <--- pu3 4.207 -.057

int2 <--- pu5 5.096 -.064

int2 <--- pu7 5.419 -.062

int2 <--- grat6 5.076 -.063

int2 <--- grat3 4.592 -.063

int1 <--- cse4 4.498 .070

grat9 <--- peu6 6.144 -.068

grat9 <--- int5 5.275 .081

grat6 <--- CSE 5.332 -.116

grat6 <--- INT 4.225 -.104

grat6 <--- pu2 4.987 -.071

grat6 <--- cse4 7.546 -.112

grat6 <--- cse9 4.231 -.079

grat6 <--- cse10 4.992 -.089

grat6 <--- cse11 7.795 -.111

grat6 <--- int5 4.943 -.083

grat6 <--- int2 5.236 -.094

grat4 <--- INT 4.580 .107

grat4 <--- pu2 5.759 .075

grat4 <--- int7 5.365 .086

298

Model Fit Summary

CMIN


Default model 66 1095.131 369 .000 2.968



RMR, GFI


Default model .186 .838 .809 .711




Model NFI

Delta1

RFI

rho1

IFI

Delta2

TLI

rho2 CFI

Default model .876 .864 .914 .906 .914








NCP


Default model 726.131 630.633 829.246



FMIN


Default model 2.772 1.838 1.597 2.099



299

RMSEA


Default model .071 .066 .075 .000


AIC


Default model 1227.131 1237.980 1489.904 1555.904

Saturated model 870.000 941.507 2601.915 3036.915


ECVI


Default model 3.107 2.865 3.368 3.134

Saturated model 2.203 2.203 2.203 2.384


HOELTER

Model HOELTER

.05

HOELTER

.01

Default model 150 157


300

Appendix I The Outputs of Invariance Analyses

“The Outputs of the Revised TAG Model for Malaysian Participants”

Estimates (Group number 1 - Default model)

Scalar Estimates (Group number 1 - Default model)

Maximum Likelihood Estimates



PU <--- CSE .625 .077 8.109 *** par_24

PEU <--- CSE .715 .097 7.376 *** par_25

INT <--- PU .521 .090 5.764 *** par_26

INT <--- PEU .041 .068 .603 .547 par_28

GRAT <--- PU .503 .112 4.490 *** par_27

GRAT <--- PEU .257 .076 3.381 *** par_29

USE <--- INT .202 .052 3.883 *** par_30

GRAT <--- INT .405 .100 4.061 *** par_31

grat3 <--- GRAT 1.000

grat4 <--- GRAT .851 .066 12.968 *** par_1

grat6 <--- GRAT 1.075 .062 17.461 *** par_2

grat9 <--- GRAT .889 .060 14.870 *** par_3

int1 <--- INT 1.000

int2 <--- INT .886 .064 13.896 *** par_4

int3 <--- INT 1.001 .087 11.552 *** par_5

int5 <--- INT 1.102 .087 12.660 *** par_6

int7 <--- INT .973 .099 9.832 *** par_7

cse12 <--- CSE 1.000

cse11 <--- CSE .901 .068 13.334 *** par_8

cse10 <--- CSE 1.134 .065 17.486 *** par_9

cse9 <--- CSE 1.125 .069 16.356 *** par_10

cse7 <--- CSE .946 .064 14.770 *** par_11

cse4 <--- CSE .820 .072 11.306 *** par_12

pu11 <--- PU 1.000

pu7 <--- PU 1.288 .109 11.805 *** par_13

pu6 <--- PU 1.099 .091 12.079 *** par_14

pu5 <--- PU 1.396 .108 12.943 *** par_15

pu4 <--- PU 1.313 .103 12.771 *** par_16

pu3 <--- PU 1.390 .110 12.603 *** par_17

pu2 <--- PU 1.093 .092 11.846 *** par_18

peu7 <--- PEU 1.000

peu6 <--- PEU 1.131 .094 12.000 *** par_19

301


peu4 <--- PEU 1.064 .091 11.749 *** par_20

peu2 <--- PEU 1.018 .084 12.184 *** par_21

use4 <--- USE 1.000

use5 <--- USE 1.024 .094 10.861 *** par_22

use6 <--- USE 1.010 .109 9.273 *** par_23


Estimate

PU <--- CSE .629

PEU <--- CSE .570

INT <--- PU .563

INT <--- PEU .056

GRAT <--- PU .404

GRAT <--- PEU .260

USE <--- INT .318

GRAT <--- INT .301





int1 <--- INT .860

int2 <--- INT .808

int3 <--- INT .719

int5 <--- INT .781

int7 <--- INT .649

cse12 <--- CSE .865

cse11 <--- CSE .767

cse10 <--- CSE .898

cse9 <--- CSE .869

cse7 <--- CSE .816

cse4 <--- CSE .688

pu11 <--- PU .742

pu7 <--- PU .809

pu6 <--- PU .822

pu5 <--- PU .880

pu4 <--- PU .873

pu3 <--- PU .867

pu2 <--- PU .811

peu7 <--- PEU .800

peu6 <--- PEU .809

peu4 <--- PEU .782

302

Estimate

peu2 <--- PEU .817

use4 <--- USE .815

use5 <--- USE .848

use6 <--- USE .683



CSE .651 .086 7.567 *** par_32

e39 .390 .067 5.838 *** par_33

e40 .692 .112 6.168 *** par_34

e43 .362 .053 6.818 *** par_35

e41 .422 .065 6.447 *** par_36

e42 .201 .032 6.240 *** par_37

e1 .351 .048 7.338 *** par_38

e2 .578 .066 8.730 *** par_39

e3 .291 .046 6.308 *** par_40

e5 .370 .047 7.944 *** par_41

e7 .194 .029 6.586 *** par_42

e8 .230 .030 7.726 *** par_43

e9 .514 .059 8.721 *** par_44

e10 .427 .053 8.000 *** par_45

e11 .716 .080 8.997 *** par_46

e15 .219 .028 7.968 *** par_47

e16 .371 .041 9.028 *** par_48

e17 .202 .028 7.211 *** par_49

e18 .268 .034 7.911 *** par_50

e19 .292 .034 8.624 *** par_51

e20 .487 .052 9.354 *** par_52

e21 .524 .057 9.250 *** par_53

e22 .565 .064 8.894 *** par_54

e23 .374 .043 8.754 *** par_55

e24 .364 .045 8.006 *** par_56

e25 .345 .043 8.080 *** par_57

e26 .412 .050 8.212 *** par_58

e27 .400 .045 8.841 *** par_59

e30 .575 .076 7.588 *** par_60

e31 .692 .093 7.414 *** par_61

e32 .735 .094 7.805 *** par_62

e33 .527 .073 7.237 *** par_63

e35 .112 .020 5.765 *** par_64

e36 .091 .019 4.784 *** par_65

303


e37 .260 .032 8.226 *** par_66


Estimate

PEU .325

PU .395

INT .343

USE .101

GRAT .578

use6 .467

use5 .719

use4 .665

peu2 .668

peu4 .612

peu6 .654

peu7 .640

pu2 .658

pu3 .751

pu4 .763

pu5 .775

pu6 .675

pu7 .654

pu11 .551

cse4 .473

cse7 .666

cse9 .755

cse10 .806

cse11 .588

cse12 .748

int7 .421

int5 .610

int3 .518

int2 .653

int1 .740

grat9 .681

grat6 .799

grat4 .556

grat3 .740

304

Matrices (Group number 1 - Default model)



PEU .715 .000 .000 .000 .000 .000

PU .625 .000 .000 .000 .000 .000

INT .355 .041 .521 .000 .000 .000

USE .072 .008 .105 .202 .000 .000

GRAT .642 .273 .714 .405 .000 .000

use6 .072 .008 .106 .204 1.010 .000

use5 .074 .008 .108 .207 1.024 .000

use4 .072 .008 .105 .202 1.000 .000

peu2 .727 1.018 .000 .000 .000 .000

peu4 .760 1.064 .000 .000 .000 .000

peu6 .809 1.131 .000 .000 .000 .000

peu7 .715 1.000 .000 .000 .000 .000

pu2 .683 .000 1.093 .000 .000 .000

pu3 .869 .000 1.390 .000 .000 .000

pu4 .821 .000 1.313 .000 .000 .000

pu5 .873 .000 1.396 .000 .000 .000

pu6 .687 .000 1.099 .000 .000 .000

pu7 .806 .000 1.288 .000 .000 .000

pu11 .625 .000 1.000 .000 .000 .000

cse4 .820 .000 .000 .000 .000 .000

cse7 .946 .000 .000 .000 .000 .000

cse9 1.125 .000 .000 .000 .000 .000

cse10 1.134 .000 .000 .000 .000 .000

cse11 .901 .000 .000 .000 .000 .000

cse12 1.000 .000 .000 .000 .000 .000

int7 .345 .040 .507 .973 .000 .000

int5 .391 .045 .574 1.102 .000 .000

int3 .355 .041 .521 1.001 .000 .000

int2 .315 .036 .462 .886 .000 .000

int1 .355 .041 .521 1.000 .000 .000

grat9 .571 .243 .635 .360 .000 .889

grat6 .690 .294 .768 .436 .000 1.075

grat4 .546 .233 .608 .345 .000 .851

grat3 .642 .273 .714 .405 .000 1.000

305



PEU .570 .000 .000 .000 .000 .000

PU .629 .000 .000 .000 .000 .000

INT .386 .056 .563 .000 .000 .000

USE .123 .018 .179 .318 .000 .000

GRAT .518 .277 .573 .301 .000 .000

use6 .084 .012 .122 .217 .683 .000

use5 .104 .015 .152 .269 .848 .000

use4 .100 .014 .146 .259 .815 .000

peu2 .466 .817 .000 .000 .000 .000

peu4 .446 .782 .000 .000 .000 .000

peu6 .461 .809 .000 .000 .000 .000

peu7 .456 .800 .000 .000 .000 .000

pu2 .510 .000 .811 .000 .000 .000

pu3 .545 .000 .867 .000 .000 .000

pu4 .549 .000 .873 .000 .000 .000

pu5 .553 .000 .880 .000 .000 .000

pu6 .517 .000 .822 .000 .000 .000

pu7 .508 .000 .809 .000 .000 .000

pu11 .467 .000 .742 .000 .000 .000

cse4 .688 .000 .000 .000 .000 .000

cse7 .816 .000 .000 .000 .000 .000

cse9 .869 .000 .000 .000 .000 .000

cse10 .898 .000 .000 .000 .000 .000

cse11 .767 .000 .000 .000 .000 .000

cse12 .865 .000 .000 .000 .000 .000

int7 .251 .036 .366 .649 .000 .000

int5 .302 .044 .440 .781 .000 .000

int3 .278 .040 .405 .719 .000 .000

int2 .312 .045 .455 .808 .000 .000

int1 .332 .048 .485 .860 .000 .000

grat9 .427 .228 .473 .248 .000 .825

grat6 .463 .247 .512 .269 .000 .894

grat4 .386 .206 .427 .224 .000 .746

grat3 .445 .238 .493 .259 .000 .860



PEU .715 .000 .000 .000 .000 .000

PU .625 .000 .000 .000 .000 .000

INT .000 .041 .521 .000 .000 .000

USE .000 .000 .000 .202 .000 .000

306


GRAT .000 .257 .503 .405 .000 .000

use6 .000 .000 .000 .000 1.010 .000

use5 .000 .000 .000 .000 1.024 .000

use4 .000 .000 .000 .000 1.000 .000

peu2 .000 1.018 .000 .000 .000 .000

peu4 .000 1.064 .000 .000 .000 .000

peu6 .000 1.131 .000 .000 .000 .000

peu7 .000 1.000 .000 .000 .000 .000

pu2 .000 .000 1.093 .000 .000 .000

pu3 .000 .000 1.390 .000 .000 .000

pu4 .000 .000 1.313 .000 .000 .000

pu5 .000 .000 1.396 .000 .000 .000

pu6 .000 .000 1.099 .000 .000 .000

pu7 .000 .000 1.288 .000 .000 .000

pu11 .000 .000 1.000 .000 .000 .000

cse4 .820 .000 .000 .000 .000 .000

cse7 .946 .000 .000 .000 .000 .000

cse9 1.125 .000 .000 .000 .000 .000

cse10 1.134 .000 .000 .000 .000 .000

cse11 .901 .000 .000 .000 .000 .000

cse12 1.000 .000 .000 .000 .000 .000

int7 .000 .000 .000 .973 .000 .000

int5 .000 .000 .000 1.102 .000 .000

int3 .000 .000 .000 1.001 .000 .000

int2 .000 .000 .000 .886 .000 .000

int1 .000 .000 .000 1.000 .000 .000

grat9 .000 .000 .000 .000 .000 .889

grat6 .000 .000 .000 .000 .000 1.075

grat4 .000 .000 .000 .000 .000 .851

grat3 .000 .000 .000 .000 .000 1.000



PEU .570 .000 .000 .000 .000 .000

PU .629 .000 .000 .000 .000 .000

INT .000 .056 .563 .000 .000 .000

USE .000 .000 .000 .318 .000 .000

GRAT .000 .260 .404 .301 .000 .000

use6 .000 .000 .000 .000 .683 .000

use5 .000 .000 .000 .000 .848 .000

use4 .000 .000 .000 .000 .815 .000

peu2 .000 .817 .000 .000 .000 .000

peu4 .000 .782 .000 .000 .000 .000

307


peu6 .000 .809 .000 .000 .000 .000

peu7 .000 .800 .000 .000 .000 .000

pu2 .000 .000 .811 .000 .000 .000

pu3 .000 .000 .867 .000 .000 .000

pu4 .000 .000 .873 .000 .000 .000

pu5 .000 .000 .880 .000 .000 .000

pu6 .000 .000 .822 .000 .000 .000

pu7 .000 .000 .809 .000 .000 .000

pu11 .000 .000 .742 .000 .000 .000

cse4 .688 .000 .000 .000 .000 .000

cse7 .816 .000 .000 .000 .000 .000

cse9 .869 .000 .000 .000 .000 .000

cse10 .898 .000 .000 .000 .000 .000

cse11 .767 .000 .000 .000 .000 .000

cse12 .865 .000 .000 .000 .000 .000

int7 .000 .000 .000 .649 .000 .000

int5 .000 .000 .000 .781 .000 .000

int3 .000 .000 .000 .719 .000 .000

int2 .000 .000 .000 .808 .000 .000

int1 .000 .000 .000 .860 .000 .000

grat9 .000 .000 .000 .000 .000 .825

grat6 .000 .000 .000 .000 .000 .894

grat4 .000 .000 .000 .000 .000 .746

grat3 .000 .000 .000 .000 .000 .860



PEU .000 .000 .000 .000 .000 .000

PU .000 .000 .000 .000 .000 .000

INT .355 .000 .000 .000 .000 .000

USE .072 .008 .105 .000 .000 .000

GRAT .642 .017 .211 .000 .000 .000

use6 .072 .008 .106 .204 .000 .000

use5 .074 .008 .108 .207 .000 .000

use4 .072 .008 .105 .202 .000 .000

peu2 .727 .000 .000 .000 .000 .000

peu4 .760 .000 .000 .000 .000 .000

peu6 .809 .000 .000 .000 .000 .000

peu7 .715 .000 .000 .000 .000 .000

pu2 .683 .000 .000 .000 .000 .000

pu3 .869 .000 .000 .000 .000 .000

pu4 .821 .000 .000 .000 .000 .000

pu5 .873 .000 .000 .000 .000 .000

308


pu6 .687 .000 .000 .000 .000 .000

pu7 .806 .000 .000 .000 .000 .000

pu11 .625 .000 .000 .000 .000 .000

cse4 .000 .000 .000 .000 .000 .000

cse7 .000 .000 .000 .000 .000 .000

cse9 .000 .000 .000 .000 .000 .000

cse10 .000 .000 .000 .000 .000 .000

cse11 .000 .000 .000 .000 .000 .000

cse12 .000 .000 .000 .000 .000 .000

int7 .345 .040 .507 .000 .000 .000

int5 .391 .045 .574 .000 .000 .000

int3 .355 .041 .521 .000 .000 .000

int2 .315 .036 .462 .000 .000 .000

int1 .355 .041 .521 .000 .000 .000

grat9 .571 .243 .635 .360 .000 .000

grat6 .690 .294 .768 .436 .000 .000

grat4 .546 .233 .608 .345 .000 .000

grat3 .642 .273 .714 .405 .000 .000



PEU .000 .000 .000 .000 .000 .000

PU .000 .000 .000 .000 .000 .000

INT .386 .000 .000 .000 .000 .000

USE .123 .018 .179 .000 .000 .000

GRAT .518 .017 .170 .000 .000 .000

use6 .084 .012 .122 .217 .000 .000

use5 .104 .015 .152 .269 .000 .000

use4 .100 .014 .146 .259 .000 .000

peu2 .466 .000 .000 .000 .000 .000

peu4 .446 .000 .000 .000 .000 .000

peu6 .461 .000 .000 .000 .000 .000

peu7 .456 .000 .000 .000 .000 .000

pu2 .510 .000 .000 .000 .000 .000

pu3 .545 .000 .000 .000 .000 .000

pu4 .549 .000 .000 .000 .000 .000

pu5 .553 .000 .000 .000 .000 .000

pu6 .517 .000 .000 .000 .000 .000

pu7 .508 .000 .000 .000 .000 .000

pu11 .467 .000 .000 .000 .000 .000

cse4 .000 .000 .000 .000 .000 .000

cse7 .000 .000 .000 .000 .000 .000

cse9 .000 .000 .000 .000 .000 .000

309


cse10 .000 .000 .000 .000 .000 .000

cse11 .000 .000 .000 .000 .000 .000

cse12 .000 .000 .000 .000 .000 .000

int7 .251 .036 .366 .000 .000 .000

int5 .302 .044 .440 .000 .000 .000

int3 .278 .040 .405 .000 .000 .000

int2 .312 .045 .455 .000 .000 .000

int1 .332 .048 .485 .000 .000 .000

grat9 .427 .228 .473 .248 .000 .000

grat6 .463 .247 .512 .269 .000 .000

grat4 .386 .206 .427 .224 .000 .000

grat3 .445 .238 .493 .259 .000 .000



M.I. Par Change

e39 <--> e40 42.821 .285 e43 <--> CSE 42.500 .255 e43 <--> e40 23.599 -.210 e43 <--> e39 29.546 -.169 e37 <--> e43 8.027 .073 e33 <--> e39 4.113 .079 e33 <--> e37 4.221 -.066 e31 <--> e39 21.624 .206 e31 <--> e43 6.033 -.108 e27 <--> e40 4.661 .094 e27 <--> e30 6.983 .106 e26 <--> CSE 4.962 -.092 e26 <--> e43 4.486 -.069 e25 <--> e31 5.972 .105 e25 <--> e27 12.258 -.105 e25 <--> e26 8.995 .094 e24 <--> e43 4.304 -.065 e24 <--> e31 4.392 .093 e24 <--> e27 4.089 .063 e23 <--> e40 5.673 -.101 e23 <--> e41 6.473 -.086 e23 <--> e35 5.416 .043 e23 <--> e31 7.601 -.119 e23 <--> e24 4.192 -.062 e22 <--> e40 6.173 .129 e22 <--> e41 4.288 .086 e22 <--> e32 5.750 -.127 e21 <--> e26 9.866 -.116 e21 <--> e23 8.587 .100 e20 <--> e40 6.223 -.117

310

M.I. Par Change

e20 <--> e43 10.586 .110 e19 <--> e43 7.195 .072 e19 <--> e27 6.115 -.067 e19 <--> e26 5.847 -.069 e19 <--> e21 5.472 .071 e19 <--> e20 14.171 .110 e18 <--> e42 4.117 .041 e18 <--> e32 8.341 .111 e18 <--> e25 5.458 -.061 e17 <--> e31 10.343 -.111 e17 <--> e30 6.665 .081 e17 <--> e27 9.723 .076 e17 <--> e18 5.555 .048 e16 <--> e33 9.241 .113 e16 <--> e32 4.692 -.093 e15 <--> e27 4.831 -.054 e11 <--> CSE 8.686 .153 e11 <--> e40 22.743 .274 e11 <--> e39 8.016 .117 e11 <--> e43 11.037 -.136 e11 <--> e22 5.012 .110 e11 <--> e20 5.532 -.105 e11 <--> e16 8.176 .114 e10 <--> e36 8.299 -.056 e10 <--> e35 4.009 .041 e10 <--> e21 4.265 .078 e10 <--> e20 5.651 -.086 e10 <--> e18 8.183 .083 e10 <--> e11 5.419 .103 e9 <--> e40 7.201 -.133 e9 <--> e20 18.413 .166 e9 <--> e18 5.512 -.072 e9 <--> e17 4.887 -.062 e9 <--> e15 7.733 .077 e8 <--> e40 12.693 -.124 e8 <--> e39 13.041 -.091 e8 <--> e30 4.552 -.068 e8 <--> e24 8.820 -.075 e8 <--> e20 4.248 .056 e8 <--> e19 18.575 .094 e8 <--> e11 7.747 -.092 e7 <--> e27 6.907 .065 e7 <--> e18 5.691 -.051 e7 <--> e17 15.041 .074 e5 <--> CSE 4.074 .080 e5 <--> e43 4.551 .067 e5 <--> e23 6.517 .078 e5 <--> e17 4.308 .051 e5 <--> e10 9.610 .105 e3 <--> CSE 4.504 -.082 e3 <--> e41 4.572 .070

311

M.I. Par Change

e3 <--> e27 5.814 -.075 e3 <--> e26 13.318 .118 e3 <--> e23 7.154 -.080 e2 <--> CSE 4.436 .100 e2 <--> e41 7.474 -.114 e2 <--> e30 4.535 .103 e2 <--> e26 4.403 -.083 e2 <--> e24 8.600 .111 e2 <--> e5 5.156 -.085 e1 <--> e30 4.263 -.084 e1 <--> e11 4.574 -.090 e1 <--> e5 4.317 -.064


M.I. Par Change


M.I. Par Change

PEU <--- PU 24.090 .411 PU <--- PEU 26.532 .255 INT <--- CSE 42.500 .391 use6 <--- INT 6.776 .144 use6 <--- int5 5.667 .088 use6 <--- int3 8.973 .113 use6 <--- int1 6.571 .115 use5 <--- int5 9.783 -.082 use4 <--- peu2 4.441 .048 use4 <--- pu6 4.072 .054 peu2 <--- use6 4.708 -.182 peu4 <--- cse9 7.080 .171 peu6 <--- PU 9.286 .260 peu6 <--- pu3 12.033 .179 peu6 <--- pu4 13.517 .203 peu6 <--- pu5 12.332 .184 peu6 <--- pu7 10.680 .170 peu6 <--- pu11 5.834 .149 pu2 <--- PEU 6.142 .123 pu2 <--- peu4 6.275 .086 pu2 <--- peu7 10.782 .123 pu2 <--- cse10 5.503 .109 pu3 <--- CSE 4.962 -.141 pu3 <--- pu11 4.198 -.094 pu3 <--- cse4 8.042 -.146 pu3 <--- cse7 9.438 -.163 pu3 <--- cse12 4.749 -.116 pu4 <--- cse9 7.347 -.118 pu4 <--- cse10 6.118 -.111 pu5 <--- int2 7.628 -.160 pu6 <--- use4 4.351 .165

312

M.I. Par Change

pu6 <--- peu6 5.756 -.078 pu6 <--- grat6 5.156 -.087 pu7 <--- PEU 4.718 .128 pu7 <--- peu2 4.072 .090 pu7 <--- peu6 6.079 .098 pu7 <--- peu7 6.483 .113 pu7 <--- grat4 4.044 .099 pu7 <--- grat3 4.204 .099 cse4 <--- INT 4.189 .148 cse4 <--- peu2 4.843 -.089 cse4 <--- peu7 5.707 -.096 cse4 <--- cse7 4.189 .111 cse4 <--- int3 17.719 .208 cse4 <--- int2 7.076 .166 cse4 <--- int1 5.528 .139 cse7 <--- pu2 5.167 -.086 cse7 <--- pu3 4.285 -.066 cse7 <--- cse4 7.120 .113 cse7 <--- int2 10.998 .166 cse9 <--- peu4 4.279 .061 cse10 <--- peu6 4.776 -.057 cse10 <--- int1 7.108 .114 cse11 <--- peu2 4.463 .076 cse11 <--- int7 8.691 .120 cse12 <--- int3 5.405 .083 int7 <--- CSE 8.686 .235 int7 <--- PEU 30.037 .358 int7 <--- PU 15.738 .316 int7 <--- GRAT 4.250 .134 int7 <--- peu2 26.066 .252 int7 <--- peu4 19.199 .198 int7 <--- peu6 20.575 .200 int7 <--- peu7 26.608 .254 int7 <--- pu2 13.549 .212 int7 <--- pu3 11.677 .165 int7 <--- pu4 7.723 .144 int7 <--- pu5 14.671 .188 int7 <--- pu6 15.328 .227 int7 <--- pu7 19.128 .213 int7 <--- pu11 10.352 .185 int7 <--- cse9 10.608 .194 int7 <--- cse10 4.805 .134 int7 <--- cse11 15.562 .259 int7 <--- grat4 5.997 .134 int5 <--- cse9 6.214 .120 int5 <--- grat9 8.529 .137 int3 <--- PEU 5.201 -.129 int3 <--- peu2 7.304 -.115 int3 <--- cse4 8.050 .159 int2 <--- PEU 5.346 -.092 int2 <--- PU 4.767 -.106

313

M.I. Par Change

int2 <--- peu6 4.558 -.057 int2 <--- peu7 8.327 -.086 int2 <--- pu2 4.625 -.075 int2 <--- pu3 4.545 -.063 int2 <--- pu5 9.901 -.093 int2 <--- pu7 7.090 -.079 int2 <--- cse4 4.055 .079 int2 <--- cse7 8.981 .121 int2 <--- int7 4.230 -.070 int1 <--- cse4 4.689 .083 int1 <--- cse10 8.092 .103 grat9 <--- CSE 4.074 .122 grat9 <--- INT 4.966 .150 grat9 <--- pu6 4.687 .096 grat9 <--- cse9 5.084 .102 grat9 <--- cse10 6.491 .119 grat9 <--- int5 11.731 .155 grat9 <--- int1 4.034 .110 grat6 <--- CSE 4.504 -.126 grat6 <--- pu2 6.360 -.108 grat6 <--- pu6 6.963 -.114 grat6 <--- cse10 6.273 -.114 grat6 <--- cse11 6.443 -.124 grat6 <--- int1 4.825 -.118 grat4 <--- CSE 4.436 .153 grat4 <--- peu7 4.434 .095 grat4 <--- pu2 5.468 .123 grat4 <--- pu5 8.413 .130 grat4 <--- pu7 5.013 .100 grat4 <--- cse4 4.725 .128 grat4 <--- cse10 4.472 .118 grat4 <--- cse11 5.089 .135 grat4 <--- int7 5.006 .114 grat3 <--- int7 5.418 -.100

“The Outputs of the Revised TAG Model for Chinese Participants”

Estimates (Group number 1 - Default model)

Scalar Estimates (Group number 1 - Default model)

Maximum Likelihood Estimates



PU <--- CSE .370 .077 4.802 *** par_24

PEU <--- CSE .432 .098 4.382 *** par_25

314


INT <--- PU .061 .128 .474 .636 par_26

INT <--- PEU .500 .147 3.412 *** par_28

GRAT <--- PU .587 .125 4.690 *** par_27

GRAT <--- PEU .327 .137 2.383 .017 par_29

USE <--- INT .402 .087 4.640 *** par_30

GRAT <--- INT .161 .085 1.893 .058 par_31

grat3 <--- GRAT 1.000

grat4 <--- GRAT 1.076 .091 11.851 *** par_1

grat6 <--- GRAT 1.143 .100 11.457 *** par_2

grat9 <--- GRAT 1.092 .093 11.792 *** par_3

int1 <--- INT 1.000

int2 <--- INT .827 .086 9.610 *** par_4

int3 <--- INT .923 .093 9.885 *** par_5

int5 <--- INT 1.007 .084 11.943 *** par_6

int7 <--- INT .953 .079 12.088 *** par_7

cse12 <--- CSE 1.000

cse11 <--- CSE 1.108 .064 17.221 *** par_8

cse10 <--- CSE 1.023 .062 16.392 *** par_9

cse9 <--- CSE 1.118 .065 17.203 *** par_10

cse7 <--- CSE 1.037 .064 16.194 *** par_11

cse4 <--- CSE .844 .070 12.088 *** par_12

pu11 <--- PU 1.000

pu7 <--- PU 1.425 .150 9.497 *** par_13

pu6 <--- PU 1.448 .147 9.857 *** par_14

pu5 <--- PU 1.401 .139 10.096 *** par_15

pu4 <--- PU 1.410 .144 9.792 *** par_16

pu3 <--- PU 1.436 .142 10.093 *** par_17

pu2 <--- PU 1.359 .143 9.487 *** par_18

peu7 <--- PEU 1.000

peu6 <--- PEU 1.144 .168 6.797 *** par_19

peu4 <--- PEU 1.049 .169 6.221 *** par_20

peu2 <--- PEU .822 .135 6.091 *** par_21

use4 <--- USE 1.000

use5 <--- USE 1.060 .138 7.674 *** par_22

use6 <--- USE .874 .116 7.534 *** par_23


Estimate

PU <--- CSE .388

PEU <--- CSE .437

INT <--- PU .054

INT <--- PEU .464

GRAT <--- PU .501

315

Estimate

GRAT <--- PEU .289

USE <--- INT .422

GRAT <--- INT .153





int1 <--- INT .808

int2 <--- INT .656

int3 <--- INT .673

int5 <--- INT .805

int7 <--- INT .812

cse12 <--- CSE .865

cse11 <--- CSE .890

cse10 <--- CSE .863

cse9 <--- CSE .893

cse7 <--- CSE .862

cse4 <--- CSE .724

pu11 <--- PU .624

pu7 <--- PU .825

pu6 <--- PU .869

pu5 <--- PU .900

pu4 <--- PU .865

pu3 <--- PU .900

pu2 <--- PU .820

peu7 <--- PEU .685

peu6 <--- PEU .707

peu4 <--- PEU .655

peu2 <--- PEU .533

use4 <--- USE .642

use5 <--- USE .846

use6 <--- USE .691



CSE .693 .092 7.509 *** par_32

e39 .533 .112 4.757 *** par_33

e40 .546 .118 4.646 *** par_34

e43 .607 .102 5.936 *** par_35

e41 .458 .085 5.402 *** par_36

e42 .586 .134 4.366 *** par_37

e1 .776 .092 8.436 *** par_38

e2 .453 .065 6.990 *** par_39

316


e3 .669 .087 7.650 *** par_40

e5 .518 .071 7.281 *** par_41

e7 .417 .058 7.233 *** par_42

e8 .710 .080 8.823 *** par_43

e9 .808 .092 8.751 *** par_44

e10 .433 .059 7.343 *** par_45

e11 .368 .051 7.214 *** par_46

e15 .234 .029 8.137 *** par_47

e16 .222 .029 7.692 *** par_48

e17 .249 .030 8.179 *** par_49

e18 .221 .029 7.628 *** par_50

e19 .257 .031 8.225 *** par_51

e20 .448 .048 9.256 *** par_52

e21 .986 .103 9.549 *** par_53

e22 .599 .069 8.715 *** par_54

e23 .426 .052 8.265 *** par_55

e24 .291 .038 7.681 *** par_56

e25 .422 .051 8.314 *** par_57

e26 .304 .040 7.679 *** par_58

e27 .565 .064 8.801 *** par_59

e30 .764 .113 6.776 *** par_60

e31 .884 .140 6.307 *** par_61

e32 .992 .139 7.120 *** par_62

e33 1.148 .132 8.722 *** par_63

e35 1.016 .127 7.993 *** par_64

e36 .318 .087 3.643 *** par_65

e37 .597 .084 7.116 *** par_66


Estimate

PEU .191

PU .151

INT .227

USE .178

GRAT .470

use6 .477

use5 .716

use4 .412

peu2 .285

peu4 .428

peu6 .500

peu7 .469

pu2 .672

317

Estimate

pu3 .810

pu4 .747

pu5 .809

pu6 .755

pu7 .680

pu11 .389

cse4 .524

cse7 .743

cse9 .797

cse10 .744

cse11 .793

cse12 .748

int7 .659

int5 .648

int3 .453

int2 .430

int1 .653

grat9 .665

grat6 .628

grat4 .688

grat3 .526



PEU .432 .000 .000 .000 .000 .000

PU .370 .000 .000 .000 .000 .000

INT .238 .500 .061 .000 .000 .000

USE .096 .201 .024 .402 .000 .000

GRAT .397 .407 .597 .161 .000 .000

use6 .084 .176 .021 .351 .874 .000

use5 .102 .213 .026 .426 1.060 .000

use4 .096 .201 .024 .402 1.000 .000

peu2 .355 .822 .000 .000 .000 .000

peu4 .453 1.049 .000 .000 .000 .000

peu6 .494 1.144 .000 .000 .000 .000

peu7 .432 1.000 .000 .000 .000 .000

pu2 .503 .000 1.359 .000 .000 .000

pu3 .531 .000 1.436 .000 .000 .000

pu4 .522 .000 1.410 .000 .000 .000

pu5 .518 .000 1.401 .000 .000 .000

pu6 .535 .000 1.448 .000 .000 .000

pu7 .527 .000 1.425 .000 .000 .000

pu11 .370 .000 1.000 .000 .000 .000

318


cse4 .844 .000 .000 .000 .000 .000

cse7 1.037 .000 .000 .000 .000 .000

cse9 1.118 .000 .000 .000 .000 .000

cse10 1.023 .000 .000 .000 .000 .000

cse11 1.108 .000 .000 .000 .000 .000

cse12 1.000 .000 .000 .000 .000 .000

int7 .227 .477 .058 .953 .000 .000

int5 .240 .504 .061 1.007 .000 .000

int3 .220 .462 .056 .923 .000 .000

int2 .197 .414 .050 .827 .000 .000

int1 .238 .500 .061 1.000 .000 .000

grat9 .433 .445 .652 .175 .000 1.092

grat6 .453 .466 .683 .184 .000 1.143

grat4 .427 .438 .642 .173 .000 1.076

grat3 .397 .407 .597 .161 .000 1.000



PEU .437 .000 .000 .000 .000 .000

PU .388 .000 .000 .000 .000 .000

INT .224 .464 .054 .000 .000 .000

USE .094 .196 .023 .422 .000 .000

GRAT .355 .360 .509 .153 .000 .000

use6 .065 .135 .016 .291 .691 .000

use5 .080 .166 .019 .357 .846 .000

use4 .061 .126 .015 .271 .642 .000

peu2 .233 .533 .000 .000 .000 .000

peu4 .286 .655 .000 .000 .000 .000

peu6 .309 .707 .000 .000 .000 .000

peu7 .299 .685 .000 .000 .000 .000

pu2 .319 .000 .820 .000 .000 .000

pu3 .350 .000 .900 .000 .000 .000

pu4 .336 .000 .865 .000 .000 .000

pu5 .349 .000 .900 .000 .000 .000

pu6 .338 .000 .869 .000 .000 .000

pu7 .320 .000 .825 .000 .000 .000

pu11 .242 .000 .624 .000 .000 .000

cse4 .724 .000 .000 .000 .000 .000

cse7 .862 .000 .000 .000 .000 .000

cse9 .893 .000 .000 .000 .000 .000

cse10 .863 .000 .000 .000 .000 .000

cse11 .890 .000 .000 .000 .000 .000

cse12 .865 .000 .000 .000 .000 .000

319


int7 .182 .377 .044 .812 .000 .000

int5 .180 .373 .044 .805 .000 .000

int3 .151 .312 .037 .673 .000 .000

int2 .147 .304 .036 .656 .000 .000

int1 .181 .375 .044 .808 .000 .000

grat9 .290 .294 .415 .125 .000 .816

grat6 .281 .285 .404 .121 .000 .792

grat4 .295 .299 .423 .127 .000 .830

grat3 .258 .261 .370 .111 .000 .726



PEU .432 .000 .000 .000 .000 .000

PU .370 .000 .000 .000 .000 .000

INT .000 .500 .061 .000 .000 .000

USE .000 .000 .000 .402 .000 .000

GRAT .000 .327 .587 .161 .000 .000

use6 .000 .000 .000 .000 .874 .000

use5 .000 .000 .000 .000 1.060 .000

use4 .000 .000 .000 .000 1.000 .000

peu2 .000 .822 .000 .000 .000 .000

peu4 .000 1.049 .000 .000 .000 .000

peu6 .000 1.144 .000 .000 .000 .000

peu7 .000 1.000 .000 .000 .000 .000

pu2 .000 .000 1.359 .000 .000 .000

pu3 .000 .000 1.436 .000 .000 .000

pu4 .000 .000 1.410 .000 .000 .000

pu5 .000 .000 1.401 .000 .000 .000

pu6 .000 .000 1.448 .000 .000 .000

pu7 .000 .000 1.425 .000 .000 .000

pu11 .000 .000 1.000 .000 .000 .000

cse4 .844 .000 .000 .000 .000 .000

cse7 1.037 .000 .000 .000 .000 .000

cse9 1.118 .000 .000 .000 .000 .000

cse10 1.023 .000 .000 .000 .000 .000

cse11 1.108 .000 .000 .000 .000 .000

cse12 1.000 .000 .000 .000 .000 .000

int7 .000 .000 .000 .953 .000 .000

int5 .000 .000 .000 1.007 .000 .000

int3 .000 .000 .000 .923 .000 .000

int2 .000 .000 .000 .827 .000 .000

int1 .000 .000 .000 1.000 .000 .000

grat9 .000 .000 .000 .000 .000 1.092

320


grat6 .000 .000 .000 .000 .000 1.143

grat4 .000 .000 .000 .000 .000 1.076

grat3 .000 .000 .000 .000 .000 1.000



PEU .437 .000 .000 .000 .000 .000

PU .388 .000 .000 .000 .000 .000

INT .000 .464 .054 .000 .000 .000

USE .000 .000 .000 .422 .000 .000

GRAT .000 .289 .501 .153 .000 .000

use6 .000 .000 .000 .000 .691 .000

use5 .000 .000 .000 .000 .846 .000

use4 .000 .000 .000 .000 .642 .000

peu2 .000 .533 .000 .000 .000 .000

peu4 .000 .655 .000 .000 .000 .000

peu6 .000 .707 .000 .000 .000 .000

peu7 .000 .685 .000 .000 .000 .000

pu2 .000 .000 .820 .000 .000 .000

pu3 .000 .000 .900 .000 .000 .000

pu4 .000 .000 .865 .000 .000 .000

pu5 .000 .000 .900 .000 .000 .000

pu6 .000 .000 .869 .000 .000 .000

pu7 .000 .000 .825 .000 .000 .000

pu11 .000 .000 .624 .000 .000 .000

cse4 .724 .000 .000 .000 .000 .000

cse7 .862 .000 .000 .000 .000 .000

cse9 .893 .000 .000 .000 .000 .000

cse10 .863 .000 .000 .000 .000 .000

cse11 .890 .000 .000 .000 .000 .000

cse12 .865 .000 .000 .000 .000 .000

int7 .000 .000 .000 .812 .000 .000

int5 .000 .000 .000 .805 .000 .000

int3 .000 .000 .000 .673 .000 .000

int2 .000 .000 .000 .656 .000 .000

int1 .000 .000 .000 .808 .000 .000

grat9 .000 .000 .000 .000 .000 .816

grat6 .000 .000 .000 .000 .000 .792

grat4 .000 .000 .000 .000 .000 .830

grat3 .000 .000 .000 .000 .000 .726

321



PEU .000 .000 .000 .000 .000 .000

PU .000 .000 .000 .000 .000 .000

INT .238 .000 .000 .000 .000 .000

USE .096 .201 .024 .000 .000 .000

GRAT .397 .080 .010 .000 .000 .000

use6 .084 .176 .021 .351 .000 .000

use5 .102 .213 .026 .426 .000 .000

use4 .096 .201 .024 .402 .000 .000

peu2 .355 .000 .000 .000 .000 .000

peu4 .453 .000 .000 .000 .000 .000

peu6 .494 .000 .000 .000 .000 .000

peu7 .432 .000 .000 .000 .000 .000

pu2 .503 .000 .000 .000 .000 .000

pu3 .531 .000 .000 .000 .000 .000

pu4 .522 .000 .000 .000 .000 .000

pu5 .518 .000 .000 .000 .000 .000

pu6 .535 .000 .000 .000 .000 .000

pu7 .527 .000 .000 .000 .000 .000

pu11 .370 .000 .000 .000 .000 .000

cse4 .000 .000 .000 .000 .000 .000

cse7 .000 .000 .000 .000 .000 .000

cse9 .000 .000 .000 .000 .000 .000

cse10 .000 .000 .000 .000 .000 .000

cse11 .000 .000 .000 .000 .000 .000

cse12 .000 .000 .000 .000 .000 .000

int7 .227 .477 .058 .000 .000 .000

int5 .240 .504 .061 .000 .000 .000

int3 .220 .462 .056 .000 .000 .000

int2 .197 .414 .050 .000 .000 .000

int1 .238 .500 .061 .000 .000 .000

grat9 .433 .445 .652 .175 .000 .000

grat6 .453 .466 .683 .184 .000 .000

grat4 .427 .438 .642 .173 .000 .000

grat3 .397 .407 .597 .161 .000 .000



PEU .000 .000 .000 .000 .000 .000

322


PU .000 .000 .000 .000 .000 .000

INT .224 .000 .000 .000 .000 .000

USE .094 .196 .023 .000 .000 .000

GRAT .355 .071 .008 .000 .000 .000

use6 .065 .135 .016 .291 .000 .000

use5 .080 .166 .019 .357 .000 .000

use4 .061 .126 .015 .271 .000 .000

peu2 .233 .000 .000 .000 .000 .000

peu4 .286 .000 .000 .000 .000 .000

peu6 .309 .000 .000 .000 .000 .000

peu7 .299 .000 .000 .000 .000 .000

pu2 .319 .000 .000 .000 .000 .000

pu3 .350 .000 .000 .000 .000 .000

pu4 .336 .000 .000 .000 .000 .000

pu5 .349 .000 .000 .000 .000 .000

pu6 .338 .000 .000 .000 .000 .000

pu7 .320 .000 .000 .000 .000 .000

pu11 .242 .000 .000 .000 .000 .000

cse4 .000 .000 .000 .000 .000 .000

cse7 .000 .000 .000 .000 .000 .000

cse9 .000 .000 .000 .000 .000 .000

cse10 .000 .000 .000 .000 .000 .000

cse11 .000 .000 .000 .000 .000 .000

cse12 .000 .000 .000 .000 .000 .000

int7 .182 .377 .044 .000 .000 .000

int5 .180 .373 .044 .000 .000 .000

int3 .151 .312 .037 .000 .000 .000

int2 .147 .304 .036 .000 .000 .000

int1 .181 .375 .044 .000 .000 .000

grat9 .290 .294 .415 .125 .000 .000

grat6 .281 .285 .404 .121 .000 .000

grat4 .295 .299 .423 .127 .000 .000

grat3 .258 .261 .370 .111 .000 .000


M.I. Par Change

e39 <--> e40 87.398 .438

e43 <--> CSE 23.824 .259

e43 <--> e40 7.193 -.143

e43 <--> e39 4.288 -.097

e42 <--> CSE 17.967 .228

e36 <--> CSE 4.874 .105

323

M.I. Par Change

e33 <--> CSE 4.434 -.144

e33 <--> e39 18.386 .260

e32 <--> e37 6.248 -.165

e32 <--> e36 5.010 .135

e32 <--> e35 4.203 -.173

e31 <--> CSE 4.530 -.138

e31 <--> e39 22.699 .274

e31 <--> e43 10.895 -.216

e31 <--> e32 11.958 .279

e30 <--> e32 9.747 -.231

e27 <--> e43 12.648 .175

e27 <--> e41 4.660 -.096

e27 <--> e37 5.595 .114

e27 <--> e31 5.866 -.146

e27 <--> e30 14.043 .207

e26 <--> e41 6.021 .085

e26 <--> e31 5.159 .107

e26 <--> e30 15.658 -.170

e25 <--> CSE 4.496 .091

e25 <--> e30 7.753 .136

e24 <--> e41 6.783 -.088

e24 <--> e31 6.674 .119

e24 <--> e25 4.637 .064

e23 <--> CSE 4.069 -.087

e23 <--> e40 5.088 .099

e23 <--> e33 4.056 .114

e22 <--> CSE 7.167 -.134

e22 <--> e40 4.470 .107

e22 <--> e43 4.008 -.102

e22 <--> e41 16.658 .186

e22 <--> e27 6.926 -.121

e22 <--> e24 4.900 -.077

e22 <--> e23 8.713 .121

e21 <--> CSE 24.814 .308

e21 <--> e40 4.173 -.128

e21 <--> e39 4.458 -.115

e21 <--> e42 12.977 .229

e21 <--> e36 4.152 .114

e19 <--> e39 4.032 -.060

e19 <--> e26 9.436 -.075

e19 <--> e25 4.996 .062

e17 <--> e32 9.228 .128

e17 <--> e23 6.749 -.071

324

M.I. Par Change

e16 <--> e39 10.311 -.092

e16 <--> e43 6.052 .081

e16 <--> e32 4.345 -.086

e16 <--> e31 4.782 -.088

e15 <--> e24 6.011 .056

e15 <--> e17 6.415 .051

e11 <--> e42 4.894 -.096

e11 <--> e25 4.498 -.073

e11 <--> e24 4.964 .066

e10 <--> e11 5.098 .080

e9 <--> e37 4.740 .125

e9 <--> e27 6.821 .140

e8 <--> e40 4.116 -.111

e8 <--> e31 12.413 -.236

e8 <--> e30 4.117 .125

e8 <--> e25 5.843 .107

e8 <--> e9 4.497 .126

e5 <--> e31 4.197 -.128

e5 <--> e22 4.669 .103

e3 <--> e37 4.441 -.117

e3 <--> e36 4.361 .106

e3 <--> e32 7.340 .192

e3 <--> e27 6.966 -.136

e3 <--> e22 13.982 .199

e2 <--> e27 8.047 .125

e2 <--> e22 7.868 -.128

325

TAG Model (University of Malaya in Malaysia_Unconstrain) Configural Invariance analysis

326

TAG Model (Jiaxing University in China_Unconstrain) Configural Invariance analysis

327

TAG Model (UM_constrain) Metric Invariance analysis

328

TAG Model (JU_constrain) Metric Invariance analysis

329

Appendix J The Survey Questionnaire (English version)

General Guidelines

This questionnaire attempts to investigate “Development and Validation of the

Technology Adoption and Gratification (TAG) Model to Assess Lecturers’ ICT

Usage: An Empirical Study”.

Direction

The questionnaire has seven sections. For all multiple choice questions please indicate

your response by placing a tick (/) in the appropriate box. A seven-point Likert scale

questions, please circle the number of your choice. If you wish to comment on any

question or qualify your answer, please feel free to use the space in the margin or you

may write your comments on separate sheet of paper. All information that you provide

will be kept strictly confidential. Thanks for your gracious cooperation

Section I: Demographic Information

1. Gender: Male

Female

2. Age: 25-29 years old

30-34 years old

35-39 years old

40-44 years old

45 ears above

3. Number of years working at the university

1. 1-5 years

2. 6-10 years

3. 11-15 years

4. 16-20 years

5. 21 years above

4. Highest level of educational background

a. Undergraduate

b. Master

c. PhD

5. Designation:

a. Professor

b. Associate Professor

c. Assistant Professor/Senior Lecturer

d. Lecturer

6. Faculty/School______________________________

330

7. Nationality_________________________________

Section II: Use of ICT facilities

For questions 1-16, rate the likelihood of each using the following scale:

1= Never (N) 4= Weekly (W)

2= Once a semester (OS) 5= Sometimes (SO)

3= Monthly (M) 6= Daily (D)

7 = Always (A)

No.

How often do you use the following ICT

facilities for research and academic-related

activities?

N OS M W SO D A

1 The University online research

databases/Online library research

databases. 1 2 3 4 5 6 7

2 Laptop or Desktop computers. 1 2 3 4 5 6 7

3 Wireless Internet. 1 2 3 4 5 6 7

4 Web browser (www). 1 2 3 4 5 6 7

5 Search engine (e.g. Google, Yahoo, etc.). 1 2 3 4 5 6 7

6 Microsoft Office applications. 1 2 3 4 5 6 7

7 Research related software to analyze the

data. 1 2 3 4 5 6 7

8 Document processing (e.g. PDF). 1 2 3 4 5 6 7

9 Computer graphics. 1 2 3 4 5 6 7

10 Email. 1 2 3 4 5 6 7

11 Document editing/composing. 1 2 3 4 5 6 7

12 Computer labs. 1 2 3 4 5 6 7

13 Multimedia facilities (e.g. CD-ROM, VCD,

DVD etc.). 1 2 3 4 5 6 7

14 I use ICT for preparing the course and

lecture notes. 1 2 3 4 5 6 7

15 I use ICT facilities for making

presentations in the course. 1 2 3 4 5 6 7

16 I use ICT facilities for carrying out studies

in laboratories or workshops, and making

experiments.

1 2 3 4 5 6 7

331

Section III: Perceived Ease of Use of ICT Facilities

For questions 1-15, rate how much you agree with each statement using the following

scale:

1= Strongly Disagree (SD) 4= Neither agree nor disagree (N)

2= Moderately Disagree (MD) 5= Slightly Agree (SLA)

3= Slightly Disagree (SLD) 6= Moderately Agree (MA)

7= Strongly Agree (SA)

No.

Items SD MD SLD N SLA MA SA

1 I find the university ICT facilities

easy to use. 1 2 3 4 5 6 7

2 I find it easy to access the

university research databases. 1 2 3 4 5 6 7

3 It is easy for me to become skilful

at using university databases for

conducting research. 1 2 3 4 5 6 7

4 Interacting with the research

databases system requires minimal

effort on my part. 1 2 3 4 5 6 7

5 I find it easy to get the research

databases to help facilitate my

research. 1 2 3 4 5 6 7

6 Interacting with the university

research databases system is very

stimulating for me.

1 2 3 4 5 6 7

7 I find it easy to select


categories using research databases

(Education/Engineering/Business,

etc.).

1 2 3 4 5 6 7

8 With Wireless Internet, I find it

easy to access university databases

to do research. 1 2 3 4 5 6 7

9 Wireless Internet allows me to

access research and learning

materials from Web Browser

(WWW).

1 2 3 4 5 6 7

10 Wireless Internet is easy to use for

teaching and research. 1 2 3 4 5 6 7

11 I find it easy to use computers

provided by the university. 1 2 3 4 5 6 7

12 My interaction with university ICT

services available is clear and

understandable.

1 2 3 4 5 6 7

332

13 I find it easy to download teaching,

learning and research materials

using Wireless Internet in terms of

speed.

1 2 3 4 5 6 7

14 It is easy for me to use multimedia

facilities at the university. 1 2 3 4 5 6 7

15 I find it easy to use Microsoft office

for teaching and research purposes. 1 2 3 4 5 6 7

Section IV: Perceived Usefulness of ICT Facilities


scale:





No.


1 Using the ICT facilities at the

university enables me to

accomplish tasks more quickly. 1 2 3 4 5 6 7

2 Using the ICT services available at

the university increases my

research productivity. 1 2 3 4 5 6 7

3 The current university ICT system

makes work more interesting. 1 2 3 4 5 6 7

4 ICT facilities at the university

improve the quality of the work I

do. 1 2 3 4 5 6 7

5 Using the university ICT facilities

enhances my research skills. 1 2 3 4 5 6 7


would make it easier for me to find

information.

1 2 3 4 5 6 7

7 ICT services at the university make

information always available to

users. 1 2 3 4 5 6 7

8 Using the ICT facilities helps me

write my journal articles. 1 2 3 4 5 6 7

9 My job would be difficult to

perform without the ICT facilities. 1 2 3 4 5 6 7

333

10 Using the ICT facilities saves my

time. 1 2 3 4 5 6 7


provides me with the latest

information on specific areas of

research.

1 2 3 4 5 6 7

12 I can use ICT facilities from

anywhere, anytime at the campus. 1 2 3 4 5 6 7

13 I believe that the use of ICT in the

classroom enhances student

learning in my discipline. 1 2 3 4 5 6 7

14 I believe that email and other forms

of electronic communication are

important tools in faculty/student

communication.

1 2 3 4 5 6 7

15 I believe that web-based

instructional materials enhance

student learning. 1 2 3 4 5 6 7

16 Using the ICT services enables me

to download teaching, learning and

research materials from the internet. 1 2 3 4 5 6 7

Section V: Computer Self-Efficacy of ICT facilities


scale:





No.


1 I have the skills required to use

computer applications for writing

my research papers.

1 2 3 4 5 6 7

2 I have the skills and knowledge

required to use computer

applications for demonstrating

specific concepts in class.

1 2 3 4 5 6 7

3 I have the skills required to use

computer applications for

presenting lectures. 1 2 3 4 5 6 7

334

4 I have the skills required to

communicate electronically with

my colleagues and students. 1 2 3 4 5 6 7

5 I have the ability to e-mail, chat,

download teaching, learning and

research materials, and search

different websites.

1 2 3 4 5 6 7

6 I have the ability to use the


by the university.

1 2 3 4 5 6 7

7 I have the skills required to use ICT

facilities to enhance the



1 2 3 4 5 6 7

8 I feel capable of using the

university research databases for

writing journal papers. 1 2 3 4 5 6 7

9 I have the ability to navigate my

way through the ICT facilities. 1 2 3 4 5 6 7

10 I have the skills required to use the

ICT facilities to enhance the quality

of my research works. 1 2 3 4 5 6 7

11 I have the ability to save and print

journals/articles from the research

databases.

1 2 3 4 5 6 7

12 I have the knowledge and skills

required to benefit from using the

university ICT facilities. 1 2 3 4 5 6 7

13 I am capable of accessing the

research databases from the

university website. 1 2 3 4 5 6 7

14 I am capable to download and

install research related software

using ICT facilities at the

university.

1 2 3 4 5 6 7

15 I am capable of using multimedia

facilities for delivering lecturers. 1 2 3 4 5 6 7

335

Section VI: Intention to Use of ICT Facilities

1= Very Unlikely (VU) 4= Neither like or unlike (N)

2= Moderately Unlikely (MU) 5= Slightly Likely (SL)

3= Slightly Unlikely (SU) 6= Moderately Likely (ML)

7= Very likely (VL)

No.

Items VU MU SU N SL ML VL

1 I will use the ICT facilities to carry

out my research activities. 1 2 3 4 5 6 7

2 I will use computer applications to

present lecture materials in class. 1 2 3 4 5 6 7

3 I will use Microsoft office


papers. 1 2 3 4 5 6 7

4 I will teach a course that will be

delivered totally on the world wide

web. 1 2 3 4 5 6 7

5 I intend to use ICT facilities for


purposes frequently. 1 2 3 4 5 6 7

6 I intend to download research

materials from the university

databases frequently.

1 2 3 4 5 6 7

7 I will use the university research

databases to update myself on the

research areas I am pursuing. 1 2 3 4 5 6 7

8 I intend to use the ICT facilities to

upgrade my research performance. 1 2 3 4 5 6 7

9 I will use the university research

databases to write outstanding

journal articles. 1 2 3 4 5 6 7

10 I intend to use research related

software to analyze the data. 1 2 3 4 5 6 7

336

Section VII: Gratification of ICT Facilities Usage


scale:

1= Very Unsatisfied (VU) 4= Not Sure (NS)

2= Moderately Unsatisfied (MU) 5= Slightly Satisfied (SLS)

3= Slightly Unsatisfied (SLU) 6= Moderately Satisfied (MS)

7= Very Satisfied (VS)

No.

Items VU MU SLU NS SLS MS VS

1 Overall I am satisfied with the

ease of completing my task using

ICT facilities. 1 2 3 4 5 6 7

2 I am satisfied with the ICT

facilities provided by the

university. 1 2 3 4 5 6 7

3 I am satisfied with the ease of use

of the ICT facilities. 1 2 3 4 5 6 7

4 The university ICT facilities have

greatly affected the way I search

for information and conduct my

research.

1 2 3 4 5 6 7

5 Providing the ICT is an

indispensable services provided

by the university. 1 2 3 4 5 6 7



complete my task.

1 2 3 4 5 6 7

7 I am satisfied with the structure of

accessible information (available

as categories of research domain,

or by date of issue—of journals in

particular, or as full-texts or

abstracts of theses and

dissertations) of the university

research databases.

1 2 3 4 5 6 7

8 I am satisfied with the support

information provided by the

university’s ICT facilities. 1 2 3 4 5 6 7

9 I am satisfied in using ICT


and research. 1 2 3 4 5 6 7



at the university. 1 2 3 4 5 6 7

337

(Chinese Version)

使用指南使用指南使用指南使用指南

该问卷旨在调查““““科技应用的接受性和满意性理论模型科技应用的接受性和满意性理论模型科技应用的接受性和满意性理论模型科技应用的接受性和满意性理论模型（（（（TAG））））在评估教师使用信息和通信技在评估教师使用信息和通信技在评估教师使用信息和通信技在评估教师使用信息和通信技

术这一领域的发展及有效性术这一领域的发展及有效性术这一领域的发展及有效性术这一领域的发展及有效性：：：：实证性研究实证性研究实证性研究实证性研究””””。。。。

说明说明说明说明

该问卷由6个小部分组成。衡量方式采用李克特七点尺度量表，请在每一道题后面的选项中用

(√)标出您的选择。如果您对任何问题有不同的看法或有不同的答案，您可以写在对应题号左右

的空白处，或写在另外纸上的空白处。您所填的个人信息将会被严格保密。谢谢您的合作。

第一部分第一部分第一部分第一部分：：：：个人信息个人信息个人信息个人信息

1.性别: 男女

2.年龄： 25 – 29 岁

30 – 34 岁

35 – 39 岁

40 – 44 岁

45岁及以上

3.大学的工作经历：

a. 1 – 5 年

b. 6 – 10 年

c. 11 – 15 年

d. 16 – 20 年

e. 21年及以上

第二部分第二部分第二部分第二部分：：：：信息和通信设备的信息和通信设备的信息和通信设备的信息和通信设备的使用频率使用频率使用频率使用频率

问题1 – 16，用以下几种标准来评定你对每一项的认同程度, 请用(√√√√)标出您的选择。

1=从不 2=每学期 3=每月 4=每周 5=有时候 6=每天 7=经常

序序序序

号号号号

您使用下列信息与通信设施来做与研究和学术相关您使用下列信息与通信设施来做与研究和学术相关您使用下列信息与通信设施来做与研究和学术相关您使用下列信息与通信设施来做与研究和学术相关

的活动的频率的活动的频率的活动的频率的活动的频率？？？？

从

不

每

学

期

每

月

每

周

有

时

候

每

天

经

常

1 学校的研究性数据库。 1 2 3 4 5 6 7

2 笔记本电脑或台式电脑。 1 2 3 4 5 6 7

3 无线网络。 1 2 3 4 5 6 7

4 网络浏览器（www万维网）。 1 2 3 4 5 6 7

5 搜索引擎（比如：谷歌，雅虎，百度等）。 1 2 3 4 5 6 7

6 微软办公应用（Microsoft Office:Word, Excel, PPT

等）。 1 2 3 4 5 6 7

7 与研究相关的用来分析数据的软件。 1 2 3 4 5 6 7

8 文件处理应用（比如PDF转换器） 1 2 3 4 5 6 7

9 计算机绘图应用 1 2 3 4 5 6 7

10 邮件。 1 2 3 4 5 6 7

11 文档编辑/排版应用。 1 2 3 4 5 6 7

4.本人已获得的最高学历：

a. 本科

b. 硕士

c. 博士

5.职称：

a. 教授

b. 副教授

c. 助理教授

d. 高级讲师

e. 讲师

6.所在院系：_________________________________

7.国籍： _________________________________

338

12 计算机实验室。 1 2 3 4 5 6 7

13 多媒体设施（光驱，光盘，视频播放器等）。 1 2 3 4 5 6 7

14 我使用信息与通信设施来备课和准备讲义。 1 2 3 4 5 6 7

15 教学时，我使用信息和通信设施演示课程和讲义。 1 2 3 4 5 6 7

16 在实验室或研讨会上，我使用信息与通信设施来开

展教学研究。 1 2 3 4 5 6 7

第三部分第三部分第三部分第三部分：：：：使用信息和通信设备的使用信息和通信设备的使用信息和通信设备的使用信息和通信设备的易用性感知易用性感知易用性感知易用性感知

问题1 – 15，用以下几种标准来评定你对每一项的认同程度，请用(√√√√)标出您的选择。

1=强烈不赞同 2=中度不赞同 3=略不赞同 4=既不赞同也不反对

5=稍赞同 6=中度赞同 7=强烈赞同

序序序序

号号号号陈述项目陈述项目陈述项目陈述项目

强

烈

不

赞

同

中

度

不

赞

同

略

不

赞

同

既

不

赞

同

也

不

反

对

稍

赞

同

中

度

赞

同

强

烈

赞

同

1 我觉得学校的信息和通信设备容易使用。 1 2 3 4 5 6 7

2 我觉得访问学校的研究性数据库是容易的。 1 2 3 4 5 6 7

3 对我来说，我能容易的就变得熟练地使用学校数据库来做研

究。 1 2 3 4 5 6 7

4 与研究性数据库系统互动需要我很少的努力。 1 2 3 4 5 6 7

5 我觉得使用数据库来帮助促进我的研究是容易的。 1 2 3 4 5 6 7

6 对我来说，与学校研究性数据库进行互动很令人兴奋。 1 2 3 4 5 6 7

7 我觉得使用研究性数据库来选择不同类别的文章/期刊是容

易的（比如：教育类/工程类/商务类/语言类等）。 1 2 3 4 5 6 7

8 我觉得使用无线网络访问学校数据库做研究是容易的。 1 2 3 4 5 6 7

9 无线网络允许我通过网页浏览器（WWW万维网）访问研究

和学习资源。 1 2 3 4 5 6 7

10 使用无线网络来进行教学和研究是容易的。 1 2 3 4 5 6 7

11 我觉得学校提供的计算机使用起来是容易的。 1 2 3 4 5 6 7

12 对我来说，与学校可获得的信息和通信服务的互动过程是明

确的、易懂的。 1 2 3 4 5 6 7

13 在网络方面，我觉得使用无线网络下载教学、学习和研究资

料是容易的。 1 2 3 4 5 6 7

14 对我来说，在学校使用多媒体设施是容易的或方便的。 1 2 3 4 5 6 7

15 我觉得微软办公应用软件（Microsoft Office）在以教学和研

究为目的的应用中是容易的。 1 2 3 4 5 6 7

339

第四部分第四部分第四部分第四部分：：：：信息和通信设备的实用性感知信息和通信设备的实用性感知信息和通信设备的实用性感知信息和通信设备的实用性感知




序序序序


强

烈

不

赞

同

中

度

不

赞

同

略

不

赞

同

既

不

赞

同

也

不

反

对

稍

赞

同

中

度

赞

同

强

烈

赞

同

1 使用学校的信息和通信设施能够使我更快地完成任务。 1 2 3 4 5 6 7

2 使用学校可获得的信息和通信服务提高我的研究生产力。 1 2 3 4 5 6 7

3 当前学校的信息与通信系统使工作变得更加有趣。 1 2 3 4 5 6 7

4 学校的信息与通信设施提高我的工作质量。 1 2 3 4 5 6 7

5 使用学校的信息与通信设施增强我的研究技能。 1 2 3 4 5 6 7

6 对我来说，使用学校的信息与通信设施寻找信息会更容

易。 1 2 3 4 5 6 7

7 学校的信息与通信服务让用户始终可获得信息。 1 2 3 4 5 6 7

8 使用信息与通信设施有助于我写期刊文章。 1 2 3 4 5 6 7

9 没有信息与通信设施，我的工作会难以执行。 1 2 3 4 5 6 7

10 使用信息与通信设施节省我的时间。 1 2 3 4 5 6 7

11 使用学校的信息与通信设施给我提供特定研究领域的最新

信息。 1 2 3 4 5 6 7

12 我可以在校园里的任意时间和任何地方使用信息与通信设

施。 1 2 3 4 5 6 7

13 在我的课上，我认为信息与通信设施的使用提高学生的学

习能力。 1 2 3 4 5 6 7

14 我认为电子邮件和其他形式的电子通讯是老师/学生交流沟

通的重要工具。 1 2 3 4 5 6 7

15 我认为基于网络的教学材料提高学生的学习能力。 1 2 3 4 5 6 7

16 使用信息与通信服务允许我从英特网上下载教学，学习和

研究资料。 1 2 3 4 5 6 7

340

第五部分第五部分第五部分第五部分：：：：自我效能自我效能自我效能自我效能




序序序序


强烈不

赞同

中

度

不

赞

同

略不

赞同

既不赞同

也不反对

稍

赞

同

中度

赞同

强烈

赞同

1 我拥有所需的技能使用相关计算机应用来

写我的研究论文。 1 2 3 4 5 6 7

2 我拥有所需的技能和知识使用相关计算机

应用来在课堂上展现出特定具体的概念。 1 2 3 4 5 6 7

3 我拥有所需的技能使用相关计算机应用来

授课。 1 2 3 4 5 6 7

4 我拥有所需的技能来与我的同事和学生进

行电子通讯交流。 1 2 3 4 5 6 7

5 我有能力发邮件，聊天，下载教学、学习

和研究资料，以及搜索不同的网站。 1 2 3 4 5 6 7

6 我有能力使用学校提供的无线网络服务。 1 2 3 4 5 6 7

7 我拥有所需的技能使用信息与通信设备来

提高我的教学、学习与研究效益。 1 2 3 4 5 6 7

8 我相信我有能力使用学校的研究性数据库

来写期刊论文。 1 2 3 4 5 6 7

9 我有能力通过我的方式来使用信息与通信

设施。 1 2 3 4 5 6 7

10 我拥有所需的技能使用信息与通信设施来

提高我研究工作的质量。 1 2 3 4 5 6 7

11 我有能力从研究性数据库中保存和打印期

刊/文章。 1 2 3 4 5 6 7

12 我拥有所需的知识和技能从学校信息与通

信设施的使用中获益。 1 2 3 4 5 6 7

13 我能够从学校网站上访问研究性数据库资

源。 1 2 3 4 5 6 7

14 我能够使用学校的信息与通信设施下载和

安装与研究相关的软件。 1 2 3 4 5 6 7

15 我能够使用多媒体设施来授课。 1 2 3 4 5 6 7

第六部分第六部分第六部分第六部分：：：：使用信息和通信设备的使用信息和通信设备的使用信息和通信设备的使用信息和通信设备的意向意向意向意向


1=非常不可能 2=中度不可能 3=略不可能 4=不确定 5=稍可能 6=中度可能 7=非常可能

序序序序


非

常

不

可

能

很不

可能

略不

可能

不

确

定

稍

可

能

很

可

能

非常

可能

1 我将使用信息与通信设施来开展我的研究活

动。 1 2 3 4 5 6 7

2 在课堂上，我将使用相关的计算机应用来展示 1 2 3 4 5 6 7

341

课件/讲义材料。

3 我将使用微软办公应用软件（Microsoft Office）

来写我的研究论文。 1 2 3 4 5 6 7

4 我将完全在万维网（WWW）上教授一门课程。

（远程教育） 1 2 3 4 5 6 7

5 我打算经常使用信息与通信设施来教学，学习

和做研究。 1 2 3 4 5 6 7

6 我打算经常从学校的数据库中下载研究资料。 1 2 3 4 5 6 7

7 我将使用学校的研究性数据库在自己所从事的

的研究领域更新自己。 1 2 3 4 5 6 7

8 我打算使用信息与通信设施来提升我的研究

（绩效，成果）表现。 1 2 3 4 5 6 7

9 我将使用学校的研究性数据库来写出色的期刊

文章。 1 2 3 4 5 6 7

10 我打算使用与研究相关的软件来分析数据。 1 2 3 4 5 6 7

第七部分第七部分第七部分第七部分：：：：对信息和通信设备的对信息和通信设备的对信息和通信设备的对信息和通信设备的满意度满意度满意度满意度


1=非常不满意 2=中度不满意 3=略不满意 4=不确定 5=稍满意 6=中度满意 7=非常满意

序号序号序号序号陈述项目陈述项目陈述项目陈述项目

非常

不满

意

中度

不满

意

略

不

满

意

不

确

定

稍

满

意

中

度

满

意

非

常

满

意

1 总体上，我对使用信息与通信设施完成任务的简易

性表示满意。 1 2 3 4 5 6 7

2 我满意于学校提供的信息与通信设施。 1 2 3 4 5 6 7

3 我满意于使用信息与通信设施的简易性。 1 2 3 4 5 6 7

4 学校的信息与通信设施已经很大程度上影响了我搜

索信息和开展研究的方式。 1 2 3 4 5 6 7

5 信息与通信服务是学校提供的必不可少的服务。 1 2 3 4 5 6 7

6 总体上，我满意于完成任务所花的时间量。 1 2 3 4 5 6 7

7

我满意于学校数据库可访问信息的布局构造（以研

究领域分类，或以特定期刊、论文全文或摘要的发

表日期呈现）。

1 2 3 4 5 6 7

8 我满意于学校的信息与通信设施所提供的支持信

息。 1 2 3 4 5 6 7

9 我满意于使用信息与通信设施来教学，学习和做研

究。 1 2 3 4 5 6 7

10 总体上，我满意于学校所提供的无线网络服务。 1 2 3 4 5 6 7

phd thesis a.y.m. atiquil islam submission...

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