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What is available about technology acceptance of e-learning software and systems?

A review and comprehension paper

Mustafa DEĞERLİ * md.mustafadegerli@gmail.com

June 11, 2010

* Graduate Student in Department of Information Systems, Informatics Institute, METU

The speedy advances in information and communication technologies (ICT)

have led to their amplified exploitation in teaching and learning contexts (Cappel and

Hayen, 2004). Additionally, International Data Corporation (IDC) estimates that the

value of the e-learning market worth will be between $21 billion and $28 billion by

2008 (Brown, 2006). In this context, Mackay and Stockport (2006) mention that

according to IDC, the revenue from synchronous e-learning exceeded $5 billion by

2006. Stemmed from these facts, applying technology by means of e-learning software

and systems (e-LSS) to facilitate and support learning is an imperative and interested in

application area recently.

Nevertheless, another imperative concern intended for this context is surely the

technology acceptance (TA) of these e-LSS by people, especially by students and

teachers. Even though there are studies conducted in this subject with respect to various

contexts, there is lacking a paper that reviews and summarizes previous studies and by

this way provides a comprehensive guide to let people know about the TA of e-LSS.

This paper aims to compensate this lack for the interested readers wanting to know

about not only the TA concepts, but also about the preceding TA of e-LSS studies.

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In this study, searches were conducted using the online databases ABI/INFORM

Complete, Academic Search Complete, Cambridge Journals Online, Computers &

Applied Sciences Complete, EBSCOhost Databases, Education Research Complete,

Emerald Management Xtra, ERIC, IEL-IEEE/IEE Electronic Library, Library,

Information Science & Technology Abstracts with Full Text, and World Higher

Education Database; with the keywords „„Technology Acceptance Model”, „„TAM”,

„„TAM2”, „„UTAUT,”, „„Universal Theory of Acceptance and Use of Technology”,

“TPB”, “Theory of Planned Behavior”, “IDT”, “Innovation Diffusion Theory”, “e-

learning”, “adoption”, “acceptance”, “educational software”, “e-teaching”, “online

learning”, “online teaching”, and “educational computer systems”.

In this context, apparent non-e-learning and non-technology results were

detached first, and after this, the abstracts of all left behind results were read. Thus, full

versions of all articles that were possibly relevant were retrieved and read. For each

retrieved article, a search of references that might meet inclusion criteria was conducted,

and any of these relevant articles retrieved and the same procedure of analyzing was

applied to these articles. As a consequence of this process, sixteen studies published in

or after 2000 are included in this review.

Before reviewing and summarizing preceding studies and providing a

comprehensive guide on the subject of TA of e-LSS, it is compulsory to have a look at

concept of TA and underlying principles and models related with TA. As indicated by

Dillon and Morris (1996), TA is the user acceptance that is defined as the demonstrable

willingness of the users to employ information technology (IT) for the tasks that it is

intended to support. They argue that demonstrable willingness of the users to use related

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IT must be reached for TA. Moreover, Dillon and Morris also note that every TA

process of IT for intended purposes can be modeled and predicted. In fact, this is a

promising statement, as they argue that thanks to TA theory it is possible to model and

predict any intended ITs' TA. Additionally, in this context, Davis (1993) suggests that

TA is the key factor that determines whether an information system (IS) or IT project is

to be successful or not. Surely, IT or IS projects will be useless and meaningless unless

they are accepted by the intended users for intended purposes.

There are models and theories trying to explain and shape the TA process and its

characteristics. For example, as said by Rogers (1995), innovation diffusion theory

(IDT) says that there are five characteristics of a technology that determine an IT‟s or

IS‟s TA. These are relative advantage, compatibility, complexity, trialability and

observability. According to Rogers, as long as these five concerns are took seriously and

managed well, related IT or IS is to be accepted by intended users for intended purposes.

Furthermore, Technology Acceptance Model (TAM) of Davis, et al. (1989),

Theory of Planned Behavior (TPB) of Ajzen (1991), Technology Acceptance Model 2

(TAM2) of Venkatesh and Davis (2000), and Universal Theory of Acceptance and Use

of Technology (UTAUT) of Venkatesh, et al. (2003) are the models in the literature

mostly used to design, implement and test TA of IT or IS.

Of these models, the most usually cited one is the TAM of Davis, et al. Their

work not only provides major contribution to TA literature, but this model is used as a

reference by other studies. TAM of Davis, et al. predicts that TA of any IT is determined

by two factors. These are perceived usefulness (PU) and perceived ease of use (PEOU).

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PU is defined as the extent to which users believe that using the system will enhance his

or her performance regarding the intended purpose. Moreover, PEOU is defined as the

extent to which the users believe that using the system will be free from effort. In

accordance with TAM, both PU and PEOU have major impact on a users‟ attitude

toward using the IT and determining its TA.

The illustrations of the models related with TA, TAM of Davis, et al. (1989),

TPB of Ajzen (1991), TAM2 of Venkatesh and Davis (2000), and UTAUT of

Venkatesh, et al. (2003), are provided below in Figures 1-4. In addition, definitions of

the variables used in these figures are provided in Table 1 below.

As these TA models are crucial to understand the TA studies for TA of e-LSS, it

is a good idea to examine the below figures and the table.

Figure 1: Illustration of TAM

Attitude

Behavioral

Intention to Use

(Acceptance) Actual Use

Perceived

Usefulness

Perceived

Ease of Use

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Figure 2: Illustration of TPB

Figure 3: Illustration of TAM2

Attitude

Behavioral

Intention Behaviour Subjective

Norm

Perceived

Behavioral

Control

Behavioral

Beliefs

Normative

Beliefs

Control

Beliefs

Subjective

Norm Behavioral

Intention to Use

(Acceptance) Actual Use

Perceived

Usefulness

Perceived

Ease of Use

Image

Job Relevance

Output Quality

Results

Demonstrability

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Figure 4: Illustration of UTAUT

Variable Definition

Behavior Use (BU) The action, specific or general, whose prediction is of

interest

Behavioral Intention (BI) One specific behavior of interest performed by individuals

with regard to some IT system

Attitude (ATT) An individual‟s evaluative judgment of the target behavior

on some dimension (e.g., good/bad, harmful/beneficial,

pleasant/unpleasant)

Perceived Ease of Use

(PEOU)

An individual‟s perception that using an IT system will be

free of effort

Performance

Expectancy

Behavioral

Intention to Use

(Acceptance) Actual Use

Effort

Expectancy

Social Influence

Facilitating

Conditions

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Perceived Usefulness

(PU)

An individual‟s perception that using an IT system will

enhance job performance

Subjective Norm (SN) An individual‟s perception of the degree to which

important other people approve or disapprove of the target

Perceived Behavioral

Control (PBC)

An individual‟s perception of how easy or difficult it will

be to perform the target behavior (self-efficacy), of factors

that impede or facilitate the behavior (facilitating

conditions), or of the amount of control that one has over

performing the behavior (controllability)

Effort Expectancy An individual‟s perception that using an IT system will be

free of effort

Performance Expectancy An individual‟s perception that using an IT system will

enhance job performance

Social Influence An individual‟s perception of the degree to which

important other people approve or disapprove of the target

Facilitating Conditions An individual‟s perception of how easy or difficult it will

be to perform the target behavior (self-efficacy), of factors

that impede or facilitate the behavior (facilitating

conditions), or of the amount of control that one has over

performing the behavior (controllability)

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Image The degree to which one perceives the use of the

technology as a means of enhancing one's status within a

social group

Job Relevance An individual's perception of the degree to which the

technology is applicable to his or her job

Output Quality An individual's perception of how well a system performs

tasks necessary to his or her job.

Results Demonstrability The tangibility of the results of using the technology

Behavioral Beliefs An individual‟s belief about consequences of particular

behavior

Normative Beliefs An individual‟s perception about the particular behavior,

which is influenced by the judgment of significant others

Control Beliefs An individual's beliefs about the presence of factors that

may facilitate or impede performance of the behavior

Table 1: Definitions of the Variables Used in TA Models

The first study examined in the context of reviewing papers in TA of e-LSS is

Martinez-Torres, et al.‟s “A technological acceptance of e-learning tools used in

practical and laboratory teaching, according to the European higher education area”

titled study. The objective of their study is to examine the effectiveness of TAM of web-

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based e-LSS used in practical and laboratory teaching. In the study, Martinez-Torres, et

al. tried to empirically validate the research hypotheses derived from TAM, whose

illustration is provided in Figure 1 above, using the responses to a survey on e-LSS

usage among 220 users. The obtained results of their study strongly support the

extended TAM in predicting users‟ intention to use e-LSS and define a set of external

variables with a major influence in the original TAM variables. However, they found

out that PEOU did not create a significant impact on users‟ attitude or intention towards

e-LSS usage. Martinez-Torres, et al. integrated new factors related to human and social

change processes to the initial TAM to adapt it for the study of e-LSS. These factors

refer to providing students with a new channel to learn, such as providing interactivity

and control, feedback, communicativeness); others refer to factors that can influence

users‟ motivations to use the tool, such as enjoyment, user tools, diffusion,

methodology, user adaptation. To sum up, Martinez-Torres, et al.‟s study concluded that

TAM is there to use to provide TA of e-LSS with some additional extensions.

The second study examined in the context of reviewing papers in TA of e-LSS is

Park‟s “An Analysis of the Technology Acceptance Model in Understanding University

Students’ Behavioral Intention to Use e-Learning” titled study. A sample of 628

university students took part in the related research. In Park‟s study, the general

structural model including e-learning self-efficacy, subjective norm, system

accessibility, perceived usefulness, perceived ease of use, attitude, and behavioral

intention to use e-LSS, is developed based on the TAM. The results of the study are

proved TAM to be a good theoretical tool to understand users‟ acceptance of e-LSS.

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Additionally, Park noted that e-learning self-efficacy was the most important construct,

followed by subjective norm in explicating the causal process in the model.

The third study examined in the context of reviewing papers in TA of e-LSS is

Hsia and Tseng‟s “An enhanced technology acceptance model for e-learning systems in

high-tech companies in Taiwan: analyzed by structural equation modeling” titled study.

In their study, Hsia and Tseng‟s efforts aimed to integrate two constructs, perceived

flexibility and computer self-efficacy, to examine the applicability of TAM in

explaining employees‟ decisions to accept e-LSS. Their study is based on a sample of

233 employees from 16 high-tech companies at Hsinchu Science Park in Taiwan. The

result of their study significantly supports the extended TAM in predicting employees‟

behavioral intention to use e-LSS. Additionally, results of this study showed that ee-LSS

must be flexible in any time and place. That is perceived flexibility has the most

significant direct and total effect on behavioral intention to use e-LSS. Moreover, Hsia

and Tseng‟s study also showed that computer self-efficacy had a positive effect on

perceived ease of use, perceived usefulness, and perceived flexibility in the context of

TA of e-LSS.

The fourth study examined in the context of reviewing papers in TA of e-LSS is

Liu, et al.‟s “Applying the technology acceptance model and flow theory to online e-

learning users’ acceptance behavior” titled study. In their study, Liu, et al. tested

constructs from IS, TAM, and Human Behavior and Psychology (Flow Theory) in an

integrated theoretical framework of online e-learning users‟ acceptance behavior. Their

study concludes that the most media-rich presentation interface (text-audio-video based

presentations) generated higher levels of PU and concentration than text-audio and

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audio-video based presentations. Additionally, they note that PU and concentration

influence user intentions. Consequently, the study concludes that the TA rate of text-

audio-video based presentations is high thanks to not only its PU but also owing to that

it generates the highest user concentration.

The fifth study examined in the context of reviewing papers in TA of e-LSS is

Khan and Iyer‟s “ELAM: A Model for Acceptance and Use of E-learning by Teachers

and Students” titled study. In their study, Khan and Iyer propose a conceptual

framework for understanding TA of e-LSS. Their model, namely e-learning acceptance

model (ELAM), is based on the UTAUT of Venkatesh, et al. (2003). ELAM identifies

the key factors in TA of e-LSS as measured by behavioral intention to use the

technology and actual usage. The four determinants of TA of e-LSS are performance

expectancy, effort expectancy, social influence, and facilitating conditions. Specifically,

the following factors are included in facilitating conditions variable in ELAM: reliable

infrastructure, institutional policies, training and support. Additionally, Khan and Iyer

note that since e-learning is associated with individualization of the teaching and

learning process, the learning style of the student and teaching style of the teacher is an

essential factor affecting the TA process for e-LSS.

The sixth study examined in the context of reviewing papers in TA of e-LSS is

Maldonado, at al.‟s “E-learning motivation, Students’ Acceptance/Use of Educational

Portal in Developing Countries” titled study. In their study, Maldonado, at al. tried to

adopt and modify UTAUT model of Venkatesh, et al. by adding a new construct of e-

learning motivation and they applied it to Peruvian context for prediction of the role of

e-learning motivation in TA and use. Furthermore, they found that e-learning motivation

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plays a decisive role in the adoption and use of e-LSS and they demonstrated that e-

learning motivation is different from conventional learning motivation by means of

adding technology characteristics (like effort expectancy) to traditional motivational

construct. What is more, Maldonado, at al. examined the cyclic effect of the technology

use on e-learning motivation, and they found that e-educational portal use simulates

students‟ e-learning motivation. They also confirmed the importance of influence of

teachers, parent and other peers in TA of e-LSS in schools in Peru context and they used

region and gender as moderating variables in their study.

The seventh study examined in the context of reviewing papers in TA of e-LSS

is Yuen and Ma‟s “Exploring teacher acceptance of e-learning technology” titled study.

In their study, Yuen and Ma attempted to explore a model to understand teachers‟ TA of

e-LSS. In the related study, a self-reported questionnaire was used to examine teacher

acceptance and attitude towards e-LSS. Data were collected from 152 in-service

teachers who were studying in a part-time teacher education program in Hong Kong.

Additionally, TAM was used as the core framework in favor of analysis while additional

constructs were added in order to find a better model to understand teacher acceptance

of e-learning technology. A composite model including five constructs, specifically,

intention to use, perceived usefulness, perceived ease of use, subjective norm and

computer self-efficacy, were formed and tested in the study. It was found that subjective

norm and computer self-efficacy serve as the two significant perception commentators

of the fundamental constructs in TAM. However, contrary to previous literature, PEOU

became the sole determinant to the prediction of intention to use, while perceived

usefulness was non-significant to the prediction of intention to use. In my opinion, the

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reason for this is that the target is not students but teachers for the study. This seems to

indicate that the perceived ease of use amongst teachers is extremely important. As well,

this is just because teachers are different from students with some major respects.

The eighth study examined in the context of reviewing papers in TA of e-LSS is

Moghadam and Bairamzadeh‟s “Extending the Technology Acceptance Model for E-

learning: A Case Study of Iran” titled study. In their study, Moghadam and

Bairamzadeh attempted to extend the TAM to include subjective norm, personal

innovativeness in domain of information technology and self-efficacy to evaluate TA of

e-LSS. Responses from 155 university students were collected to evaluate the proposed

structural model. The results indicated that personal innovativeness in domain of IT has

a direct effect on self-efficacy. Both personal innovativeness in domain of IT and self-

efficacy have unswerving effect on perceived ease of use. Perceived usefulness has a

direct effect on intention of students‟ to accept an e-LSS. Additionally, the study

suggested that e-LSS should include functions that add to efficiency and effectiveness of

teaching and learning, and also to promote the belief of being easy to use. Furthermore,

in their study, Moghadam and Bairamzadeh illustrated the role of personality traits in

TA of e-LSS.

The ninth study examined in the context of reviewing papers in TA of e-LSS is

Liu, at al.‟s “Impact of media richness and flow on e-learning technology acceptance”

titled study. In their study, Liu, at al. tried to propose an integrated theoretical

framework for the user‟s acceptance behaviour of web-based streaming media for e-

LSS. In their related study, they tested concepts from TAM and human behaviour and

psychology (flow theory) with reference to the TA of e-LSS. In addition to the TAM,

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flow theory was used to study the influence of user concentration on task activity. The

related study concluded that the most media-rich presentation interface (text–audio–

video presentation) always generates higher levels of PU and concentration than text–

audio-based or audio–video-based presentations. This study further confirms that course

materials that use rich media can promote higher user acceptance through stimulating a

higher PU and concentration.

The tenth study examined in the context of reviewing papers in TA of e-LSS is

Zayim‟s “Instructional technology adoption of medical school faculty in teaching and

learning: faculty characteristics and differentiating factors in adopter categories” titled

study. In her study, Zayim used a mix-method research design, a quantitative

methodology (survey) in conjunction with qualitative methodology (in-depth interviews)

for the purpose of gathering data about characteristics and adoption patterns of medical

school faculty from 155 teaching personnel. The findings provided an evidence for

similarities between adoption patterns of medical school faculty and other higher

education faculty; relatively new tools associated with instruction were not adopted by

majority of the faculty. In this study, additionally it is noted that some differences were

found between early adopters and mainstream faculty in terms of individual

characteristics, adoption patterns, perceived barriers and incentives to adoption and

preferred methods of learning about technology and support.

The eleventh study examined in the context of reviewing papers in TA of e-LSS

is Işık‟s “Perceptions of students and teachers about the use of e - learning / sharing

portal in educational activities” titled study. In his study, Işık conducted a questionnaire

with 200 students of 6th and 7th grade students. In the study, he investigated the

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perceptions in terms of three aspects: effects of the use of this technology on their

perceived motivation, the perceived usefulness and the perceived ease of use of this

technology. The findings of the study indicated that the students and the teachers

perceived that e-learning / sharing portal technology is a useful and they easy to use

technology for targeted people. In the study, it was found out that the students and the

teachers are satisfied with advantages of the use of this new technology in their learning

environment. In the same way, the teachers and the students stated that using the system

effected students‟ perceived motivation towards the educational activities in a positive

way.

The twelfth study examined in the context of reviewing papers in TA of e-LSS is

Özdemir‟s “The effect of educational ideologies on technology acceptance” titled study.

In his study, Özdemir tried to investigate the effect of both students‟ and academics‟

educational ideologies on TA, and to find out whether there are differences in the PEOU

of technology, PU of technology, attitudes toward technology, and the frequency of use

of technology in education in terms of their educational ideologies. In the study, a

survey design was used. The questionnaire used in the study was developed by making

use of the related literature, and it was administered to 58 academic personnel and 320

students. The results of the study demonstrated that academics‟ educational ideologies

affect their acceptance of technology; specifically they affect the perceived usefulness of

educational technology. Furthermore, there is an effect of students‟ educational

ideologies on the frequency of their use of educational technologies. Educational

ideology is a factor affecting academics‟ perceptions of the usefulness of technology,

and it is a factor affecting the students‟ the frequency of use of educational technology.

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The thirteenth study examined in the context of reviewing papers in TA of e-LSS

is Tseng and Hsia‟s “The impact of internal locus of control on perceived usefulness and

perceived ease of use in e-learning: an extension of the technology acceptance model”

titled study. In their study, Tseng and Hsia are aimed to broaden the TAM to include

variables related to human factor. Therefore, their mainly effort was to integrate internal

locus of control (ILOC) and computer self-efficacy (CSE), to examine the applicability

of the TAM in explaining employees‟ decisions about TA of e-LSS. Based on a sample

of 204 employees taken from 12 high-tech companies in Taiwan, the results strongly

supported the extended TAM in predicting employees‟ behavioral intention to use e-

learning. It is seen that PU has the most significant direct effect on behavioral intention

to use e-LSS. TAM has been extended in an e-learning context. Specifically, CSE had a

positive effect on PEOU and behavioral intention to use.

The fourteenth study examined in the context of reviewing papers in TA of e-

LSS is Henderson and Steward‟s “The Influence of Computer and Internet Access on E-

learning Technology Acceptance” titled study. In their study, Henderson and Steward

tried to investigate whether computer and Internet access influence TA of e-LSS. The

related instrument was administered to 583 business students at two universities in the

Southeast. Regression analysis revealed that computer and Internet access affected the

degree to which students expect Blackboard and the Internet to be easy to use. Computer

and Internet access also affected their attitude towards these technologies. Additional

findings revealed that socioeconomic status and race influenced computer ownership,

convincingly.

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The fifteenth study examined in the context of reviewing papers in TA of e-LSS

is Roca, at al.‟s “Understanding e-learning continuance intention: An extension of the

Technology Acceptance Model” titled study. In their study, Roca, at al. proposed model

in which the perceived performance component is decomposed into perceived quality

and perceived usability. A sample of 172 respondents took part in this study. The results

suggest that users‟ continuation intention is determined by satisfaction, which in turn is

jointly determined by PU, information quality, confirmation, service quality, system

quality, PEOU and cognitive absorption. More importantly, this study found that the

influence of perceived quality, which is information quality, service quality and system

quality, on confirmation and satisfaction was strong. The empirical results of the related

study showed that information quality had a strong influence on confirmation, and the

effect of information quality on satisfaction was stronger than service quality and system

quality on satisfaction.

The sixteenth study examined in the context of reviewing papers in TA of e-LSS

is Saadé, at al.‟s “Viability of the Technology Acceptance Model in Multimedia Learning

Environments: a Comparative Study” titled study. In their study, Saadé, at al. conducted

a comparative study consisting of 362 students. The related study‟s results suggest that

TAM is a solid theoretical model where its validity can extend to the multimedia and e-

learning context. The study provides a more intensive view of the multimedia learning

system (MMLS) users and is an important step towards a better understanding of the

user behavior on the system and a multimedia acceptance model. The results showed

that PU has a significant impact on student attitude towards using MMLS. Attitude is

confirmed to play an essential role of affecting behavioral intention to use MMLS. The

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findings validate the TAM as basis for this new model and support the value of attitude

toward MMLS in student acceptance.

Above, all sixteen reviewed studies‟ details provided and explained.

Nonetheless, in below Table 2, studies reviewed and their details are provided, and

purposely the extension variables of these studies on TAM are listed correspondingly.

For a comparative and contrastive, and a general view the below table shall be referred.

#

Title of Study

Sample

Size

Referenced

TA Model

Added / Extension Variables

1 A technological acceptance

of e-learning tools used in

practical and laboratory

teaching, according to the

European higher education

area

220 TAM New channel to learn, such as

providing interactivity and

control, feedback,

communicativeness); factors

that can influence users‟

motivations to use the tool,

such as enjoyment, user tools,

diffusion, methodology, user

adaptation.

2 An Analysis of the

Technology Acceptance

Model in Understanding

University Students‟

628 TAM E-learning self-efficacy,

subjective norm, system

accessibility, perceived

usefulness, perceived ease of

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Behavioral Intention to

Use e-Learning

use, attitude, and behavioral

intention

3 An enhanced technology

acceptance model for e-

learning systems in high-

tech companies in Taiwan:

analyzed by structural

equation modeling

233 TAM Perceived flexibility and

computer self-efficacy

4 Applying the technology

acceptance model and flow

theory to online e-learning

users‟ acceptance behavior

102 TAM The most media-rich

presentation interface,

perceived usefulness, and

concentration

5 ELAM: A Model for

Acceptance and Use of E-

learning by Teachers and

Students

NA UTAUT Performance expectancy, effort

expectancy, social influence,

and facilitating conditions.

Specifically, the following

factors are included in

facilitating conditions variable:

reliable infrastructure,

institutional policies, training

and support.

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6 E-learning motivation,

Students‟ Acceptance/Use

of Educational Portal in

Developing Countries

150 UTAUT E-learning motivation,

influence of teachers, parent

and other peers, region and

gender

7 Exploring teacher

acceptance of e-learning

technology

152 TAM Intention to use, perceived

usefulness, perceived ease of

use, subjective norm and

computer self-efficacy

8 Extending the Technology

Acceptance Model for E-

learning: A Case Study of

Iran

155 TAM subjective norm, personal

innovativeness in domain of

information technology and

self-efficacy

9 Impact of media richness

and flow on e-learning

technology acceptance

NA TAM The most media-rich

presentation interface (text–

audio–video presentation), user

concentration, perceives

usefulness

10 Instructional technology

adoption of medical school

faculty in teaching and

learning: faculty

155 NA Individual characteristics,

adoption patterns, perceived

barriers and incentives to

adoption and preferred

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characteristics and

differentiating factors in

adopter categories

methods of learning about

technology and support.

11 Perceptions of students and

teachers about the use of e

- learning / sharing portal

in educational activities

200 NA Perceived motivation, the

perceived usefulness and the

perceived ease of use

12 The effect of educational

ideologies on technology

acceptance

378 IDT Educational ideologies

13 The impact of internal

locus of control on

perceived usefulness and

perceived ease of use in e-

learning: an extension of

the technology acceptance

model

204 TAM Integrate internal locus of

control (ILOC) and computer

self-efficacy (CSE)

14 The Influence of Computer

and Internet Access on E-

learning Technology

Acceptance

583 TAM Computer and Internet access

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15 Understanding e-learning

continuance intention: An

extension of the

Technology Acceptance

Model

172 TAM Perceived usefulness,

information quality,

confirmation, service quality,

system quality, perceived ease

of use and cognitive absorption

16 Viability of the

Technology Acceptance

Model in Multimedia

Learning Environments: a

Comparative Study

362 TAM Attitude

Table 2: Studies Reviewed and Their Details

As sixteen studies reviewed above showed, it is seen that most of the extension

studies referred the TAM to provide a model in order to understand, implement and test

the TA of e-LSS. Moreover, it is seen that the TAM is a venerated theory of TA and it

has a use that has been widely researched in IT practices, and it is an important

theoretical tool for e-LSS research and studies.

Nevertheless, all these studies tried to extend the TAM or any other fundamental

TA models from diverse perspectives. This is just because of the fact that it is necessary

to take into consideration the intended people and intended purpose. As long as intended

people and intended purpose are recognized wholly, by using the fundamental models

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and principles in relation with TA explained above, it is possible to model and generate

any sort of TA process for e-LSS.

Teachers, students, academicians, designers, purchasers, and all others involved

with e-LSS projects are consistently advised to take into account the fundamental TA

models and TA of e-LSS studies to give support to the design or purchasing process,

training and informational sessions, implementation, and other activities in these

contexts. Surely, to the degree that the factors predicting TA for e-LSS are controllable,

they can be salient levers meant for acceptance and use.

However, there is also a need to continue exploring new theoretically motivated

variables and relationships that can be added to fundamental TA models, or extended

ones. Moreover, it is necessary for researchers to conduct studies for the purpose of

identifying prominent beliefs that actors in e-LSS have on the subject of using e-LSS.

In a word, this paper is written for the interested readers wanting to know about

not only the TA concepts, but also about the preceding TA of e-LSS studies.

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