2006:45 master's thesis factors influencing adoption of ...1028065/fulltext01.pdf · 1.2.1...
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2006:45
M A S T E R ' S T H E S I S
Factors Influencing Adoptionof Online Ticketing
Mitra Karami
Luleå University of Technology
Master Thesis, Continuation Courses Marketing and e-commerce
Department of Business Administration and Social SciencesDivision of Industrial marketing and e-commerce
2006:45 - ISSN: 1653-0187 - ISRN: LTU-PB-EX--06/45--SE
Abstract:
This thesis attempts to analyze the factors that affect the intention to
purchase train tickets through internet. Technology acceptance model was
chosen as the basis of framework of this study to explain passengers`
acceptance through their intentions to buy tickets online and to rationalize their
intentions in terms of attitude, perceived usefulness, and perceived ease of use,
subjective norms, perceived behavioral control and trust. Survey was conducted
to gather the data. The measures and hypotheses were analyzed using partial
least square technique. Results show that social factors, perceived behavioral
control, attitude and trust significantly influence passengers` intention towards
adopting internet ticketing. The implications of the findings for theory and practice
are discussed.
Key words: e-commerce, Adoption of information Technology, online ticketing, Theory of reasoned action, Theory of planned behavior
1
Acknowledgements:
Few people are as fortunate as I have been; benefited from two of the best
supervisors; during doing this post graduate thesis. I would like to express my
sincere gratitude to my Luth supervisor, Dr. Limayem, for being very supportive
and helpful during the work process of this thesis. Also, I am also deeply grateful
to my TMU supervisor, Dr. Sepehri, for his encouragement, guidance and
invaluable comments on this thesis. He spent numerous efforts in advising me
with invaluable suggestions throughout this study. Without their assistance this
thesis would never be completed. Finally, special thanks to my family for their
support and encouragement throughout my life.
January, 2006 Mitra Karami
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Table of Contents: 1. CHAPTER ONE: INTRODUCTION ..................................................................7
1.1 Introduction.........................................................................................7
1.2 Background ........................................................................................9
1.2.1 Online ticketing .......................................................................... .10
1.2.2 Online ticketing in Iran ............................................................... .11
1.3 problem discussion and justification ................................................ .12
1.4 problem statement............................................................................13
1.5 research question............................................................................ .14
1.6 purpose of the research.................................................................. ..14
1.7 disposition of the thesis ................................................................... .14
2. CHAPTER TWO: LITERATURE REVIEWE.................................................. .16
2.1 Literature Review............................................................................ .16
2.1.1 Attitude...................................................................................... .18
2.1.2 Intention to shop online............................................................. .18
2.1.3 Perceived usefulness................................................................ .18
2.1.4 Perceived ease of use .............................................................. .19
2.1.5 Subjective norm ........................................................................ .19
2.1.6 Perceived behavioral control..................................................... .19
2.1.7 Trust.......................................................................................... .20
2.1.8. Internet usage...........................................................................21
2.1.9 Enjoyment ................................................................................. .21
2.1.10 Perceived Risk .........................................................................21
2.1.11 Experience.............................................................................. .22
2.1.12 Innovativeness .........................................................................22
2.1.13 Habit ....................................................................................... .23
2.1.14 Perceived consequences........................................................ .23
2.1.15 Demographic variables ........................................................... .24
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2.2 Theoretical framework .................................................................... .24
2.3 Adoption theories............................................................................ .25
2.3.1 Theory of reasoned action………………………………………….25
2.3.2 Theory of planned behavior ...................................................... .26
2.3.2 Technology acceptance model ................................................. .30
2.4 Difference between theories........................................................... .33
2.5 Conceptual model and hypotheses ................................................ .34
2.6 Pilot study....................................................................................... .36
2.7 Description of the research hypotheses ......................................... .38
2.7.1 Attitude...................................................................................... .39
2.7.2 Perceived ease of use .............................................................. .39
2.7.3 Perceived usefulness................................................................ .39
2.7.4 Subjective norm ........................................................................ .40
2.7.5 Perceived behavioral control..................................................... .41
2.7.6 Trust.......................................................................................... .42
2.7.8 Behavioral intention .................................................................... .42
3. CHAPTER THREE: RESEARCH METHODOLOGY..................................... .44 3.1 Research purpose……………………………………………………..…44
3.1.1 Exploratory research................................................................ .45
3.1.2 Descriptive research ................................................................ .45
3.1.3 Explanatory research ............................................................... .46
3.2 Research approach ........................................................................ .46
3.3 Deductive versus inductive............................................................. .47
3.4 Research strategy .......................................................................... .48
3.5 Defining target population............................................................... .50
3.6 Sampling technique selection......................................................... .52
3.7 Questionnaire development............................................................ .52
3.8 Data collection................................................................................ .54
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4. CHAPTER FOUR: DATA ANALYSIS ........................................................... .55 4.1 Data analysis method ..................................................................... .55
4.2 Validity and reliability ...................................................................... .56
4.3 Results ........................................................................................... .58
4.3.1 Antecedents of intention ........................................................... .59
4.3.2 Antecedents of attitude ............................................................. .61
4.3.3 Antecedents of perceived usefulness ....................................... .62
5. CHAPTER FIVE : FINDINGS AND CONCLUSION....................................... .64
5.1 Implications for the theory .............................................................. .64
5.2 Innovative part of the research. ...................................................... .65
5.3 Discussion ...................................................................................... .65
5.4 Conclusion and further research .................................................... .67
REFERENCES .................................................................................................. .69
Appendix A. Acronyms ................................................................................... .76
Appendix B. Questionnaire............................................................................. .77
Appendix C. Comparative analysis between techniques................................ .81
Appendix D. Compatibility by Research Approach ......................................... .82
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LIST OF TABLES Table 2.1: Determinants of online shopping ....................................................17
Table 3.1: Relevant Situations for Different Research Strategies................... .49
Table 3.2: Research variable and measurements .......................................... .53
Table 4.1: Weights and loadings .....................................................................57
Table 4.2: Composite reliability ...................................................................... .58
Table 4.3 Results of the hypotheses tests...................................................... .62
LIST OF FIGURES
Figure 1.1: Research structure ....................................................................... .15
Figure 2.1: Theory of reasoned action............................................................ .27
Figure 2.2: Theory of planned behavior.......................................................... .29
Figure 2.3: Technology acceptance model......................................................31
Figure 2.4: Research model ............................................................................38
Figure 4.1: Results of the hypotheses tests ................................................... .60
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Chapter One Introduction and Research Problem 1. Introduction and Research Problem In the first chapter, an introduction and a background of this research will be presented. Subsequently research problem and the disposition of the research structure are reported. 1.1 Introduction
Electronic commerce has become one of the essential characteristics in the
Internet era. According to UCLA Center for Communication Policy (2001), online
shopping has become the third most popular internet activity, immediately following e-
mail using/instant messaging and web browsing. It is even more popular than seeking out
entertainment information and news, two commonly thought of activities when
considering what Internet users do when online.
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Online shopping behavior (also called online buying behavior and Internet
hopping/buying behavior) refers to the process of purchasing products or services via the
Internet.Recent advances in technology, particularly in the field of electronics and
telecommunications, have led business and commerce in new directions over the last few
decades. New forms of trade have emerged from these advances and one area is of
particular interest: Electronic Commerce. Electronic Commerce (EC) has emerged as the
most important way of doing business for years to come. This term was first used by
Kalakota and Whinston (1996). Electronic commerce deals with the facilitation of
transactions and selling of products and services online, i.e. via the internet or any other
telecommunication network. This involves the electronic trading of physical and digital
goods, quite often encompassing all the trading steps such as online marketing, online
ordering, and electronic payment and for digital goods, online distribution (Jelassi, 2005).
This field incorporates a large number of techniques for conducting business
using electronic assistance. By far the most exciting and versatile part of electronic
commerce involve transactions over the Internet According to the United States
Department of Commerce, for the year 2001, total retail sales was US$ 3.50 trillion and
e-commerce retail sales was US$ 32.57 billion (Vijayasarathy, 2004).Electronic
Commerce has been proven to be beneficial to sellers and buyers alike. Through the
usage of electronic commerce, sellers can now access narrow market segments that may
be widely distributed geographically, thereby extending accessibility globally (Napier,
2001).Buyers reap the benefits from having access to global markets and access to a
much larger product catalogs from a wider and varied range of sellers.Kalakota and
Whinstone state that EC has two distinct forms: Business-to-business and business-to
consumer. Much of the growth in revenues from transactions over the Internet has been
achieved from business-to-business exchanges leading to the accumulation of an
impressive body of knowledge and expertise in the area of business-to-business electronic
commerce (Butler and Peppard, 1998).
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Unfortunately; this is not the case for business-to-consumer EC. With the
exception of software, hardware, travel services, and few other niche areas, shopping on
the Internet is far from universal even among people who spend long hours online.
Moreover, many companies already practicing electronic commerce are having a difficult
time generating satisfactory profits. For example, many e-companies such as
Amazon.com have successfully attracted much attention but have not been able to
convert their competitive advantage into tangible profit (Yan and Parad, 1999).
Selling in cyberspace is very different from selling in physical markets, and it
requires a critical understanding of consumer behavior and how new technologies
challenge the traditional assumptions underlying conventional theories and models.
Butler and Peppard (1998), for example, explain the failure of IBM’s sponsored Web
shopping malls by the naive comprehension of the true nature of consumer behavior on
the net.
A critical understanding of this behavior in cyberspace, as in the physical world,
can not be achieved without a good appreciation of the factors affecting the purchase
decision. Although text books and articles on internet marketing and online consumer
behavior have begun to appear, however comparatively little is known about how web
purchase behavior differs from traditional purchase behavior and whether there are any
specific web-based factors that should taken into account (Heijden et al., 2001).
1.2 Background
Since the focus of this paper is on identifying the factors that influence the
adoption of online ticketing in Iran, thus a brief explanation on online ticketing and its
situation in Iran is in order.
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1.2.1 Online Ticketing
Electronic ticketing over the Internet is a good example of Internet commerce.
The aim is to facilitate the buying or reservation of tickets online, thereby making the
process more easily accessible and convenient. Through these services tickets may be
purchased from any location and at any time, provided an Internet connection exists.
Typically, the tickets are ordered from a web site that provides both tickets information
and the purchasing or reservation service. Internet or 'online' ticketing is all about
providing a useful and efficient service to clients and customers. The aim is to make the
purchase or reservation of tickets easier. Naturally, this will encourage sales. Online
ticketing system has been used especially by firms who sell travel tickets, performing
arts, game tickets, concerts, movies and many other activities.
The use of the Internet makes buying a ticket more convenient since the service is
available at any geographical location, including your home (or even remotely via a
laptop and cellular phone) and at any time of the day, any day of the year. Online ticket
services have a further advantage by providing relevant information alongside the
service. This can aid purchasing decisions and may encourage future usage (Buford,
1998). So ticket buyers have quite an easy commute to the ticket booth these days-they
only have to get to their home personal computer and onto the internet. It beats standing
in lines (perhaps out in the rain) and day, and the only traffic one encounters is that of the
so-called information superhighway.
There are also benefits for those providing the service. New markets are being
created and ticket sales are increased. Apart from maintenance and data updates, no
manpower is required to provide the service once it has been established. The process of
recording the transactions is more automated and overhead is reduced. An important
point is that ticket providers are also providing a convenient service to customers and are
thereby improving public image and encouraging return customers. (Burford, 1998).
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Several countries across the globe are already enjoying the benefits of electronic
ticketing including the US, Canada, Australia, New Zealand, Britain, France, Mexico,
Central America, Chile, Argentina, Belgium, Venezuela and The Netherlands. In fact in
the US it has 80 per cent market penetration while in Europe it is approximately 40 per
cent. More than $350 million dollars in event tickets were sold online during 2000 in
U.S.A and the number was increased to $3.9 billion in 2004 (Bhatia, 2004).
1.2.2 Online Ticketing in Iran
In recent years with the support of the Iranian government towards IT plans,
useful steps have been taken in this field. For instance we can refer to the possibility of
payment of the water and the electricity bills from internet and also of selling online train
tickets for the first time in our country. All of these indicate the gradual growth and
development in the IT field in Iran.
Raja Train Company with establishment of the internet ticketing system to sell
tickets online has taken the first step in Iranian economy in the IT field. This company
was pioneer among those companies who wanted to enter the virtual world practically.
The internet ticketing system which is the first step taken in the e-commerce field in Iran
was established with the efforts of Iranian experts in 22 of august 2004.Iranian
passengers by buying the Saman prepaid card and connecting to the raja site
(www.raja.ir), can register in the online ticketing system and purchase train tickets
online. Purchasing tickets through internet, not only reduces the travels inside the city,
but also saves passengers’ times.
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By the time being only 10% of the total number of tickets are sold online, but if
the demand for buying tickets through the internet increases, the capacity will be
increased. So far the record of the online ticketing system for selling tickets has been 45
tickets each second (Iranian association of rail transport engineering, 2005).
1.2 Problem Discussion and Justification
Selling in cyberspace, however, is very different from selling in physical markets
and requires a critical understanding of online consumer behavior and how new
technologies challenge the traditional assumptions underlying conventional theories and
models (Limayem et al., 2000).
Online consumer behavior is defined as activities directly involved in obtaining,
consuming, and disposing of products and services online, including the decision
processes that precede and follow these actions (Engel et al., 1995). Butler and Peppard
(1998), for example, explain the failure of IBM’s sponsored Web shopping malls by the
naïve comprehension of the true nature of consumer behavior on the net. Online
consumer behavior is an emerging research area with an increasing number of
publications per year. The research articles appear in a variety of journals and conference
proceedings in the fields of Information Systems, Marketing, Management and
Psychology.
Though researchers have made noticeable progress with respect to the scope,
quality and quantity of research, there are still significant Disagreements about the
findings in this area, and the research results appear to be rather Fragmented (Llimayem
et al., 2003).this indicates the lack of good understanding of the factors affecting
consumers’ decision to buy from the Web.
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Butler and Peppard (1998) eloquently express the need for such Understanding:
“Whether in the cyber-world or the physical world, the heart of marketing
management is understanding consumers and their behavior patterns.”
This lack of understanding caused a wide confusion regarding what is really
happening, how much potential there is, and what companies should be doing to take
advantage of online shopping. As a result, commerce on the Net has turned out to be
baffling, even to experienced managers and marketers (Aldridge et al., 1997).
Critical understanding of consumer behavior in cyberspace, as in the physical
world, cannot be achieved without a good appreciation of the factors affecting the
purchase decision. If cyber marketers know how consumers make these decisions, they
can adjust their marketing strategies to fit this new way of selling in order to convert their
potential customers to real ones and then to retain them. Similarly, Web site designers,
who are faced with the difficult question of how to design pages to make them not only
popular but also effective in increasing sales, can benefit from such an understanding
(Limayem et al., 2000).
1.4 Problem Statement
The above discussion leads us to identify the following research statement:
To gain a better understanding of the online consumer behavior in Iran, that will
result in gaining knowledge regarding the factors that affect the Iranian consumers to
purchase goods and services through internet in general and specifically buying tickets
through internet.
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1.5 Research Question
The emerged research question is:
What are the main factors that influence the Iranian passengers’ intention to
purchase tickets through internet?
We propose hypothesis testing in trying to find answers to our research question.
Through literature review we will try to make a proper model to identify factors affecting
the intention to purchase tickets through internet. Identification of such factors will shed
light to the online consumer behavior in our country, Iran.
1.6 Purpose of the Research
The purpose of this research is to identify antecedents of intention to purchase
tickets through internet in Iran with the help of behavioral theories. The lack of such
understanding may cause a wide confusion regarding what is really happening, how much
potential there is, and what companies should be doing to take advantage of online
ticketing (Aldridge et al., 1997).
1.7 Disposition of the Thesis
The research paper consists of five chapters; as shown in figure 1.in the first
chapter, introduction, background, research problem and research question is presented.
The second chapter consists of the literature review, theoretical framework and the
research model.
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In chapter three the methodology used in this study will be explained. In chapter
four data analysis and results will be reported .finally, discussion, conclusion and further
research will be presented in chapter five.
Introduction
Theoretical Review
Research Methodology
Analysis and Results
Discussion and Conclusion
Figure 1.1: Research Structure
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Chapter Two Theoretical Review 2. Theoretical Review In this chapter we will review the literature concerning the online consumer behavior. We will continue by presenting the popular behavioral theories such as TRA, TPB and TAM .finally, the purposed research model for the adoption of the online ticketing will be presented. 2.1 Literature Review
Online consumer behavior is an emerging research area with an increasing
number of publications per year. The research articles appear in a variety of journals and
conference proceedings in the fields of Information Systems, Marketing, Management,
and Psychology. Though researchers have made noticeable progress with respect to the
scope, quality and quantity of research, there are still significant disagreements about the
findings in this area, and the research results appear to be rather fragmented (Limayem et
al., 2000).
16
Here we try to review the results of the researches that have been conducted
regarding the three main variables of online shopping, namely: attitude toward online
shopping, intention to shop online and online shopping behavior. Table 2.1 shows the
summary of the determinants of attitude toward online shopping, intention to shop online
and online shopping behavior.
Table 2.1. Determinants of Online Shopping
Determinants of Determinants of online Determinants of attitude
Intention to shop online shopping behavior toward online shopping
Attitude Innovativeness Trust
Perceived usefulness Experience Experience
Innovativeness Intention Perceived usefulness
Perceived behavioral control Internet usage Ease of use
Risk Perceived Risk Perceived risk
Social Norm Enjoyment Habit
Experience Perceived behavioral control Innovativeness
Perceived Consequences Demographic variables
Ease of Use
Habit Source: Limayem et al., 2000
The definition of the determinants of intention to shop online, online shopping
behavior, attitudes toward online shopping and summary or the findings of the researches
are in order:
17
2.1.1 Attitude
Attitude refers to one’s evaluation about the consequences of performing a
behavior (Athiyaman, 2002). Consistent with the findings of most IT adoption research, a
significant number of studies found that attitude is a significant antecedent of intention to
shop online (e.g., Athiyaman , 2002; Chen et al., 2002;Frini and Limayem 2000;George
2002).
2.1.2 Intention to Shop Online
Intention to shop online refers to the likelihood that a consumer actually buys
online (Chen et al., 2002).Although this variable is frequently treated as a dependent
variable, several researchers found it to be an important determinant of online shopping
behavior (e.g., Chen et al., 2002; George, 2002; Goldsmith and Goldsmith 2002;
Limayem et al., 2000).
2.1.3 Perceived Usefulness
Perceived usefulness refers to the degree to which a person believes that using a
particular system would enhance his or her job performance (Davis 1989). In the context
of online consumer behavior, Chen et al., (2002), Childers et al., (2001), and Heijden et
al.,(2001) found that perceived usefulness affects attitude toward online shopping.
Similarly, Chen et al., (2002), Gefen and Straub (2000), Heijden et al., (2001), and
Pavlou (2001) found perceived usefulness to be a significant factor affecting intention to
shop online.
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2.1.4 Perceived Ease of Use
Perceived ease of use (PEOU) refers to the degree to which a person believes that
using a particular system would be free of effort (Davis, 1989). PEOU has received
enormous attention in the IT adoption studies. Chen et al., (2002), Childers et al., (2001)
and Heijden et al., (2001) found that PEOU influences attitudes toward online shopping.
2.1.5 Subjective Norm
Subjective norm refers to one’s perception of social pressure to perform or not to
perform the behavior under consideration (Athiyaman, 2002). The association between
subjective norms and behavioral intentions has been shown in several studies. For
example, studies in organization settings have shown that subjective norm is a crucial
determinant of behavioral intention (Davis, 1993). Hartwick and Barki (1994) also
suggested the effect of subjective norms to be more significant in the initial stages of
system implementation.
2.1.6 Perceived Behavioral Control
Perceived behavioral control refers to one’s perceptions about the ease or
difficulty in performing the behavior (Athiyaman, 2002). Perceived behavioral control is
important in explaining human behavior since an individual who has the intentions of
accomplishing a certain action may be unable to do so because his or her environment
prevents the act from being performed. In the context of online shopping, computer
access, Internet access, and availability of assistance are all behavioral control factors that
are important in facilitating online shopping behavior.
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The influence of perceived behavioral control on the intention to shop online and
the actual shopping behavior has been widely considered in the area of online consumer
behavior. Most studies (Athiyaman, 2002; Limayem et al., 2000; Limayem et al., 2002,
Pavlou and Chai 2002; Skik and Limayem 2002, and Song and Zahedi 2001) found that
perceived behavioral control significantly affects intention to shop online. Limayem et
al., (2000) also found the link between perceived behavioral control and online shopping
to be significant.
2.1.7 Trust
Internet shopping is a new form of commercial activity, which tends to involve a
higher degree of uncertainty and risk when compared with traditional shopping. Internet
stores appear to be less well known to consumers, as they cannot physically examine the
quality of the products before making a purchase, nor can they fully monitor the safety
and security of sending sensitive personal and financial information through the Internet
to a party whose behaviors and motives may be hard to predict (Lee and Turban, 2001).
Thus, the concept of trust becomes very important in the context of online consumer
behavior. Trust refers to the confidence a person has in his or her favorable expectations
of what other people will do, based, in many cases, on previous interactions (Gefen,
2000). A significant number of studies (George, 2002; Heijden et al., 2001; Pavlou and
Chai 2002) found that trust is a salient determinant of online shopping attitude. Moreover,
Lynch et al., (2001) found that trust significantly affects a potential consumer’s intention
to shop online.
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2.1.8 Internet Usage
Citrin et al., (2000) and Goldsmith (2002) found that consumers who are
proficient in the use of the Internet for means other than shopping will be more likely to
adopt the Internet for shopping. This link between Internet usage and online shopping
behavior is substantiated by Goldsmith and Goldsmith (2002) and Kwak et al., (2002).
2.1.9 Enjoyment
Enjoyment refers to the extent to which the activity of using the computer is
perceived to be enjoyable in its own right, apart from any performance consequences that
may be anticipated (Teo, 2001). The importance of enjoyment in online shopping has
been challenged in the past. Koufaris (2002) did not find any difference between non-
online buyers, occasional online buyers, and frequent online buyers. However, Goldsmith
and Goldsmith (2002) found enjoyment to be an important factor determining consumer
online shopping behavior.
2.1.10 Perceived Risk
Perceived risk refers to a consumer’s perceptions of uncertainty and adverse
consequences of buying from the web (Grazioli and Jarvenpaa 2000). Prior studies
(Heijden et al., 2001; Jarvenpaa and Todd 1996) found that perceived risk had a strong
impact on attitude. Moreover, Heijden et al., (2001), Pavlou (2001) and Tan and Teo
(2000) found that perceiver risk affects intention to shop online significantly. Similarly,
Miyazaki and Fernandez (2001) found perceived risk had a significant impact on online
purchasing behavior.
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2.1.11 Experience
George (2002) and Goldsmith and Goldsmith (2002) argue that consumers who
have previous experience in online buying will be more likely to purchase online than
those who lack such experience. Hoffman et al., (1999) conclude that novice Internet
users are less likely to buy online. Further studies indicate that experience significantly
affects attitude toward online shopping and intention to shop online (French and O'Cass
2001,Vijayasarathy and Jones 2000). Thus experience is a significant determinant of
online shopping behavior (Eastin 2002, George 2002, Goldsmith and Goldsmith 2002).
2.1.12 Innovativeness
Innovativeness refers to the degree and speed of adoption of innovation by an
individual (Limayem et al., 2000). This construct has been of particular interest in
innovation diffusion research (Roger, 1995). Shopping on the Internet can be considered
as an innovative behavior because it is more likely to be adopted by innovators than non-
innovators. French and O’Cass (2001), Limeyem et al., (2000) and Limayem el al.,
(2002) found that innovativeness is a significant factor affecting attitude toward online
shopping. Further extensive research has shown that innovativeness is a significant
antecedent of intention to shop online (Goldsmith 2002, Limayem and Rowe 2001, Skik
and Limayem 2002) and that innovativeness is a significant factor of online shopping
behavior (Citrin et al., 2000, Goldsmith 2000, Goldsmith 2002, and Goldsmith and
Goldsmith 2002).
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2.1.13 Habit
Triandis (1979) defines habit as situation-behavior sequences that have become
automatic and occur without self-instruction. It is a behavior tendency developed from
historical situations that an individual experienced in the past. Such tendency will then
elicit behavioral response from the individual automatically upon a stimulus which most
likely is a situation similar to the past. In the context of online consumer behavior, several
researchers found that habit affected attitudes to shop online (e.g., Frini and Limayem
2000, Limayem et al., 2000, Limayem and Rowe 2001). However, Frini and Limayem
2000, Limayem et al., 2000, and Limayem and Rowe 2001 found the link between habit
and intention to shop online to be statistically insignificant.
2.1.14 Perceived Consequences
According to Triandis (1979), each act or behavior is perceived as having a
potential outcome that can be either positive or negative. An individual’s choice of
behavior is based on the probability that an action will provoke a specific consequence.
Limayem et al., (2000), Limayem et al., (2002), and Limayem and Rowe (2001) found
that perceived consequences significantly affect an individual’s intention to shop online.
An individual may be favorable towards online shopping, but will not adopt it if he/she
perceives some important negative consequences. This view is consistent with the
technology acceptance model (Davis et al., 1989), which posits perceived usefulness as
an antecedent to both attitude and intentions.
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2.1.15 Demographic Variables
Demographic variables include age, education, gender and income. Researchers
such as Case et al., (2001), Goldsmith and Goldsmith (2002) and Kwak et al., (2002)
found that age is not a significant determinant of online shopping behavior. Only Teo
(2001) found that age significantly affects online shopping behavior. Education is one of
the important demographic variables determining consumer buying online (Case et al.,
2001, Kwak et al., 2002). These studies argue that college students are the most active
group on the Internet. They argue that college students with considerable computer
knowledge are more likely to make online purchases than those with lesser knowledge.
A number of studies (e.g., Goldsmith and Goldsmith 2002, Kwak et al., 2002, and
Teo, 2001) found a significant impact of gender on online shopping behavior.Online
shopping has long been dominated by higher income consumers. Recent statistics,
however, show that purchases by lower and middle-income online users are on the
upswing. Case et al., (2001) and Kwak et al., (2002) found that income is an important
factor affecting online shopping behavior.
2.2 Theoretical Framework
This section of chapter two aims to give the reader a basic knowledge of adoption
theories. Since the thesis is based on the adoption theories, we believe that it is important
that the reader has basic knowledge of the adoption theories.
24
2.3 Adoption Theories
Shopping on the Internet is a voluntary individual behavior that can be explained
by behavioral theories such as the theory of reasoned action (TRA) proposed by Fishbein
and Ajzen (1975), theory of planned behavior (TPB) proposed by Ajzen (1991)
technology acceptance model (TAM) proposed by Davis(1986) , Triandis’ model
proposed by Triandis ( 1980) or diffusion of innovation theory (DOI) proposed by Rogers
( 1995).Among the theories mentioned the first three ones (TRA,TPB and TAM) have
been used more than the others in the IT adoption field .
Since TRA, TPB and TAM are the most popular theories employed to explain
online consumer behavior, hence in this paper we focus on these three adoption theories.
In this section of chapter two, we will review the Theory of reasoned action, theory of
planned behavior and technology acceptance model. Based upon these theories we
propose a model of online ticketing adoption.
2.3.1 Theory of Reasoned Action
the theory of reasoned action was introduced by Ajzen and Fishbein in 1975.The
theory of reasoned action regards a consumer’s behavior as determined by the
consumer’s behavioral intention, where behavioral intention is a function of ‘attitude
toward the behavior’ (i.e. the general feeling of favorableness or unfavorable ness for that
behavior) and ‘subjective norm (SN)’ (i.e. the perceived opinion of other people in
relation to the behavior in question) (Fishbein and Ajzen, 1975).The theory predicts
intention to perform a behavior by consumer’s attitude toward that behavior rather than
by consumer’s attitude toward a product or service.
25
Also, a consumer’s intention to perform a certain behavior may be influenced
by the normative social beliefs held by the consumer. As an example, a consumer might
have a very favorable attitude toward having a drink before dinner at a restaurant.
However, the intention to actually order the drink may be influenced by the consumer’s
beliefs about the appropriateness (i.e. the perceived social norm) of ordering a drink in
the current situation (with friends for a fun meal or on a job interview) and her/his
motivation to comply with those normative beliefs (Hawkins, et al., 2001).the theory of
reasoned action is depicted in figure 2.1.
Because of its achievement in developing a model to predict behavior, the Theory
of Reasoned Action has been the basis of researches and studies in a wide variety of
fields, including psychology, management, and marketing. One of the most important
topics in marketing research to which the theory can be applied is consumer behavior.
One of the most cited consumer behavior studies in which the Theory of Reasoned
Action played a central role was "The Theory of Reasoned Action: A Meta-Analysis of
Past Research with Recommendations for Modifications and Future Research by
Sheppard et al., 1988.
In the study, the effectiveness of the model proposed by Fishbein and Ajzen in
1975 was investigated. Two meta-analyses were conducted. The sample included 87
separate studies of the individuals' intentions and performance relationship and 87
separate studies of the individuals' attitudes and subjective norms and their intentions
relationship. The study concluded that "the model performed very well in the prediction
of goals and in forecasting activities involving an explicit choice among alternatives",
and that the predictive ability of the model was strong (Sheppard et al., 1988). Although
the study proved the effectiveness of the model developed by Ajzen and Fishbein (1980),
Sheppard et al., (1988) also found that the predictive ability of the Theory of Reasoned
Action is not valid if the behavior is not under full volitional control.
26
That is to say the theory of reasoned action is concerned with rational,
volitational, and systematic behavior (Fishbein and Ajzen, 1975), i.e. behaviors over
which the individual has control (Thompson, 1994).
Attitude toward the behavior
Subjective Norm
Intention
The person’s believe that the behavior leads to certain outcomes and his/her evaluations of these outcomes
The person’s believe that specific individuals or groups think he/she should or should not perform the behavior and his/her motivation to comply with the specific referents
Relative importance of attitudinal and normative considerations
Behavior
Figure 2.1: Theory of Reasoned Action
Source: Ajzen and Fishbein (1975)
This assumption has been widely criticized. Sheppard, Hartwick, and Warshaw
(1988) argue that researchers are often interested in situations in which the target
behavior is not completely under the consumer’s control. However, as observed by
Sheppard et al., “actions that are at least in part determined by factors beyond individuals
volitional control fall outside the boundary conditions established for the model”.
27
For example, a consumer may be prevented from buying groceries online if the
consumer perceives the purchase process as too complex or if the consumer does not
possess the resources necessary to perform the considered behavior. Such considerations
are incorporated into the theory of planned behavior (Ajzen, 1985, 1991).
2.3.2 Theory of Planned Behavior
The TPB (Ajzen, 1985) is a cognitive model of human behavior, in which the
central focus is the prediction and understanding of clearly defined behaviors. Theory of
planned behavior extends the theory of reasoned action to consider perceived behavioral
control for reflecting user perceptions regarding possible internal and external constraints
on behavior. According to Ajzen, the principal predictor of behavior is intention. People
tend to act in accordance with their intention to engage in a behavior. Intention can be
regarded as a motivation to engage in a particular behavior and represents an individual’s
expectancies about his/her behavior in a given setting.
Fishbein and Ajzen (1985) operationalzed Intention as the likelihood to act.
Intention is influenced by attitude, subjective norm, and perception of control over the
behavior. Attitude toward a particular act represents a person’s overall positive and
negative beliefs and evaluations of the behavior. In turn, attitude is derived from salient
behavioral beliefs of particular outcomes and evaluation of those outcomes. Subjective
norm is an individual’s perception of general social pressures from important others to
perform or not to perform a given behavior. It, in turn, is determined by an individual’s
normative beliefs and his/her motivation to comply with his/her referents. Lastly,
perceived behavioral control represents an individual’s perception of whether the
performance of the behavior is under one’s control; 'control’’ reflects whether the
behavior is, on the one hand, easily executed (control beliefs) and whether, on the other,
the required resources, opportunities, and specialized skills are available (perceived
control) (Conner and Abraham, 2001).
28
People are not likely to form a strong intention to perform a behavior if they
believe that they do not have any resources or opportunities to do so even if they hold
positive attitudes toward the behavior and believe that important others would approve of
the behavior. Theory of planned behavior is depicted in figure 2.2
Intention Behavior
Behavioral Beliefs & Outcome Evaluations
Attitude
Normative Beliefs & Motivations to Comply
Subjective
Norm
Control Beliefs & Perceived Facilitations
Perceived Behavioral
Control
Source: (Mathieson, 1991) Figure 2.2: Theory of Planned Behavior
TPB has been used in many different studies in the information systems literature
(e.g. Mathieson, 1991, Taylor and Todd 1995, Harrison et al., 1997).TRA and TPB have
also been the basis for several studies of internet purchasing behavior (George, 2002;
Javenpaa and Todd, 1997; Khalifa and Limayem 2003; Limayem et al., 2000; Pavilou,
2002; Song and Zahedi, 2001; Tan and Teo, 2000).
29
2.3.3 Technology Acceptance Model
Since the seventies, researchers have concentrated their efforts on identifying the
conditions or factors that could facilitate the integration of information systems into
business. Their search has produced a long list of factors that seem to influence the use of
technology (Bailey and Pearson, 1983).From the mid-eighties, IS researchers have
concentrated their efforts in developing and testing models that could help in predicting
system use. One of them, technology acceptance model (TAM) was proposed by Davis in
1989 in his doctoral thesis. Their model is an adaptation of the theory of reasoned action.
Attitude towards using (AT) and behavioral intention to use (BI) are common to TRA
and TAM, and Davis used Fishbein and Ajzen’s method to measure them. Davis chose
not to keep the variable subjective norms, because he estimated that it had negligible effect on BI. In TAM2, Venkatesh and Davis reconsidered this choice (Venkatesh, and
Davis, 2000).
The technology acceptance model (Davis 1989) is one of the most widely used
models of IT adoption. Since its introduction, the technology acceptance model (Davis
1989) has received considerable attention in the IT community. Recent studies suggest it
applies also to e-commerce and to the adoption of internet technology (Gefen and Straub,
2000).According to TAM, IT adoption is influenced by two perceptions: perceived
Usefulness and perceived ease- of- Use. Perceived usefulness is defined as “the degree to
which a person believes that using a particular system would increase his or her
performance”. Perceive ease of use, in contrast, refers to “the degree to which a person
believes that using a particular system would be free of effort” (Davis 1998).Two other
constructs in TAM are attitude towards use and behavioral intention to use. Attitude
towards Use is the user’s evaluation of the desirability of employing a particular
information systems application. Behavioral intention to use is a measure of the
likelihood a person will employ the application (Davis, 1989).
30
Tam’s dependent variable is actual usage. It has typically been a self-reported
measure of time or Frequency of employing the application. TAM postulates that external
variables intervene indirectly by influencing PEU and PU. There is no clear pattern with
respect to the choice of the external variables considered. these external variables include
factors such as Situational involvement, intrinsic involvement, prior use, argument of
change, Internal computing support, internal computing training, management support,
external computing, , external computing training, Role with regard to technology, tenure
in workforce, level of education, prior similar experiences, Participation in training, Tool
functionality, tool experience, task technology fit, task characteristics and etceteras. (Paul
Legris et al., 2003).Figure 2.3 shows the original TAM model based on Davis et al.,
1989)
Perceived Usefulness
Perceived Ease of Use
Attitude Behavior Intention
Actual Behavior
External Variables
Figure 2.3: Technology Acceptances Model
Source: (Davis et al., 1989)
31
Davis suggested that PEOU (perceived ease of use) has a positive, indirect effect
on system usage through PU (perceived usefulness). Empirical studies of TAM have
shown that usage of IS is determined by user behavioral intentions, which themselves are
jointly determined by User PU and attitudes toward using the IS (information system),
the last of which are jointly determined by user PU and PEOU. This also has a positive
but indirect effect on attitude through PU (Davis et al., 1989).
Many IS studies have been conducted based on the TAM, since PU and PEOU are
two general beliefs suited to predicting information systems usage. All relevant empirical
studies, such as the measurement of user acceptance of IT (Adams et al., 1992), and the
self-reported usage of IS (Szajna, 1996) have supported the hypothesis of TAM that PU is
directly related to IT/IS usage. Different from prior Studies (Chau, 1996; Gefen and Keil,
1998), Venkatesh and Davis (2000) have shown that PEOU has a positive, direct effect
on user acceptance of IT. However, no consistent conclusions have yet been reached
about the effect of PEOU on IS/IT usage.
Subsequent Research has expanded TAM in multiple directions. For example,
TAM2 examines the antecedents of perceived usefulness and incorporates the subjective
norm (i.e., social pressures related to adoption (Venkatesh, 2000). The impact of
computer self-efficacy, objective Usability, and experience with a system on perceived
ease of use is examined in (Venkatesh, 2000), whereas the antecedents of perceived ease
of use in terms of anchors (i.e., general beliefs about computers and computer usage) and
adjustments (beliefs shaped by direct experience with the target system) are examined in
(Venkatesh and Davis, 1996 ).
32
2.4. Differences Between Adoption Models
It’s maybe correct to say that evaluation and comparison of the different theories
reveals that they are not so different in terms of their differential predictions. Most
differences really amount to emphasis on one construct over another. Drawing upon the
theoretical foundation of TRA, Davis (1989) proposed that the theory be specially
modified for the domain of IT in form of a now widely accepted interpretation of IT
acceptance: the technology acceptance model (TAM).
In the TAM, as in the TRA, attitude predicts intention, and intentions predict
behavior. Unlike TRA, TAM does not include a subjective norm component as a
determinant of intention because of its uncertain theoretical ad empirical psychometric
status (Davis et al., 1989). Subjective norm can create the direct effects to norm on
intentions from indirect effects via attitude (Fishbein and Ajzen 1975). Comparing with
TRA, Technology Acceptance Model (TAM) is more oriented to analyze the human
behavior on using information System. TRA and TPB were formulated as generalization
of a wide area of individual behaviors, including the use of information technology.
In both theories Attitude is influenced by belief about the consequence of execute
the behavior weighted by the individual’s evaluation of each consequence. Depended
variable of interest in both theories is visible and both posit that behavior is influenced of
subjective norms. Attitude and intention have the same definition in both TAM and TPB.
Both theories predict behavior from intention. Mathieson (1991) also found TAM as a
quick and inexpensive in compare to TPB. Other suggestion about the differences is by
Mathieson (1991) found three main differences between TAM and TPB; their varying
degree of generality, TAM does not explicitly include any social variables, and finally the
models treat behavioral control differently.
33
2.5 Conceptual Model and Hypotheses
Based on the following reasons it was concluded that the TAM model is suitable
to identify the online ticketing adoption factors in our country (Iran), therefore it was
chosen to form the basis of the research model.
• TAM has been the most commonly employed model of IT usage (Taylor and Todd, 1995).
• Tam has received considerable empirical support (e.g., Davis, 1989; Davis et al.,
1989; Mathieson, 1991; Taylor and Todd, 1995).theses studies have found that TAM consistently explains a significant amount of variance (typically about 40 percent) in usage intention and behavior.
• It has been found that Tam’s ability to explain attitude toward using an
information system is better than other model’s (TRA and TPB) (Mathieson, 1991).
• Two belief factors of the TAM model (perceived ease of use and perceived
usefulness) are easy to understand and manipulate in information system design and implementation (Hung and Chang, 2004).
TAM is a very powerful and parsimonious model for explaining and predicting
much of the variance in new IT acceptance but it excludes the influence of social norms
and perceived behavioral control on behavioral intention. We believe that the proper
model for this research should include the social norm and behavioral control
factors.Subjective norm refers to one’s perception of social pressure to perform or not to
perform the behavior under consideration (Athiyaman, 2002). Considering the fact that
Iranian culture is more collectivist than individualist (Hofstede, 1980) and that
collectivists are more likely to comply with others than are individualists, we think that
the proper model of IT adoption for Iranian customers should include the subjective norm
construct. Furthermore, Hartwick and Barki (1994) suggested the effect of subjective
norms to be more significant in the initial stages of system implementation.
34
Since the online ticketing system has been developed recently so it is at the initial
stage of implementation and therefore we expect that subjective norm affect the intention
to use the online ticketing system. According to Ajzen (1991) the construct of perceived
behavioral control reflects beliefs regarding the availability of resources and
opportunities for performing the behavior as well as the existence of internal/external
factors that may impede the behavior. Perceived behavioral control is important in
explaining human behavior since an individual who has the intentions of accomplishing a
certain action may be unable to do so because his or her environment prevents the act
from being performed. In the context of online ticketing in Iran, computer access, Internet
access, Saman prepaid cards access and availability of assistance for passengers who
intend to purchase tickets online are all behavioral control factors that are important in
facilitating online ticketing behavior in our country.
That’s why we believe that the proper model for our research should include the
construct of perceived behavioral control as well. Such factors (perceived behavioral
control and subjective norm) have been found to have a significant influence on IT usage
behavior (e.g., Mathieson, 1991; Taylor and Todd, 1995 and Hartwick and Barki,
1994).these variables are also key determinants of behavior in the theory of planned
behavior (Ajzen, 1991), where social influences (subjective norm) are modeled as
determinants of behavioral intention, and perceived behavioral control is modeled as a
determinant of both intention and behavior. Hence it was concluded that adding
subjective norm (SN) and perceived behavioral control (PBC) to TAM would provide a
more complete test of the important determinants of IT adoption in general and online
ticketing adoption in specific. Buying tickets through internet in Iran is a new form of
commercial activity, which tends to involve a higher degree of uncertainty and risk when
compared with traditional way of buying tickets. Passengers who have got used in buying
tickets through traditional ways would have doubts in security of such system to do
online transactions and render trustworthy services.
35
This implies the concept of trust which has been found to be one of the most
important impediments of the online shopping. Trust refers to the confidence a person has
in his or her favorable expectations of what other people will do, based, in many cases, on
previous interactions (Gefen, 2000). A significant number of studies (George 2002,
Heijden et al., 2001, Jarvenpaa et al., 2000, Pavlou and Chai 2002) found that trust is a
salient determinant of online shopping attitude. Morever, Lynch et al., (2001) found that
trust significantly affects a potential consumers` intention to shop online. We believe that
adding the concept of trust to our model will improve the predictive ability of the model
to investigate the driving factors of online ticketing adoption in our country.
2.6 Pilot Study
To customize the research model and make sure that it is proper to identify the
main factors driving online ticketing adoption in Iran, it was necessary to be aware of
what the train passengers think about such factors, thereby, verifying if the proposed
model included such factors. For this purpose in depth interviews were conducted. A
depth interview is an unstructured, direct, personal interview in which a single respondent
is probed by an experienced interviewer to uncover underlying motivations, beliefs,
attitudes and feelings on a topic (Harris, 1996).
Seven interviews were conducted. The interviewees were those train passengers
who used the train frequently. The objective of the research was explained clearly for
each interviewee and since they were not familiar with the process through which they
could buy the train tickets online, complete information about know/how of the online
ticketing system was given.
36
Then the interviewees were asked about the main factors that affected their
intention to adopt online ticketing system to buy tickets thorough internet.the
interviewees verified the usefulness of buying tickets online but expressed their concerns
about factors such as lack of resources (internet, computer, Saman prepaid card) and
knowledge necessary for buying train tickets through internet and their disability to
purchase the tickets online by themselves and without any one else help. The respondents
verified the important role of mass media in informing the passengers about development
and availability of online ticketing system. They also emphasized on importance of
interacting with the system easily.
The other important factor that almost all of the interviewees mentioned was
regarding the perceived risk and lack of trust regarding the online transaction and the
quality of the services or products bought through internet. They simply compared this
new way of buying tickets with the traditional way of buying tickets and explained the
new way not trustworthy since they could not monitor security of the financial transaction
and quality of the service rendered. comparing the proposed model of this research with
the beliefs of customers, verifies the appropriateness of the proposed model for
investigating factors that influence the adoption of online ticketing in Iran.
The emerging model (shown in figure 2.4) was chosen as the research model of
this study. This study will not examine the intention-behavior relation since it is a cross
sectional research. Further, considering that internet ticketing in Iran is still relatively
new, it is reasonable for the present study to focus on the behavioral intentions to use
online ticketing system for purchasing the train tickets in Iran.
37
Attitude
Ease of Use
Perceived Usefulness
Intention
Subjective Norms
Perceived Behavioral Control
Trust
H4
H7
H5
H8
H2
H6
H1
H3
H9
Figure 2.4: Research model
2.7 Description of the Research Hypotheses
So far we reviewed the three main behavioral theories and with the help of them
we made the research model for online ticketing. In this part, we try to explain and
describe meaning of the hypothesized linked of the model in the context of online
ticketing.
38
2.7.1 Attitude
Attitude refers to one’s evaluation about the consequences of performing a
behavior (Athiyaman, 2002). In this research attitude represents passengers’ positive or
negative feelings about buying tickets through internet that affects the intention to buy
tickets online. As such, we suggest:
H5: There is positive relationship between Attitude towards buying tickets through
internet and intention to buy tickets through the internet
2.7.2 Perceived Usefulness
Perceived usefulness refers to the degree to which a person believes that using a
particular system would enhance his or her job performance (Davis 1989). In this study
perceived usefulness represents the degree to which train passengers believe in positive
consequences of using online ticketing system. As such we suggest:
H1: There is positive relationship between perceived usefulness of buying tickets
online and the attitudes to buy tickets online.
H4: There is positive relationship between perceived usefulness of buying tickets
online and the intention to buy tickets online.
2.7.3 Perceived Ease of Use
Perceived ease of use (PEOU) refers to the degree to which a person believes that
using a particular system would be free of effort (Davis 1989).
39
In this study perceived ease of use refers to the degree to which passengers
believe that using the online ticketing system for buying tickets through internet would be
easy and free of effort. Thus, we suggest:
H2: There is positive relationship between perceived ease of use of buying tickets
online and attitudes towards buying tickets online.
PEOU also has a positive but indirect effect on attitude through PU (Davis et al.,
1989).Thus:
H3: There is positive relationship between perceived ease of use of buying tickets
online and perceived usefulness of buying tickets online.
2.7.4 Subjective Norm
Subjective norm refers to one’s perception of social pressure to perform or not to
perform the behavior under consideration (Athiyaman, 2002). Considering the fact that
Iranian culture is more collectivist than individualist (hofstede, 1980) and that
collectivists are more likely to comply with others than are individualists, we expect that
Iranian train passengers under the influence of those referent groups (e.g. friends, family
members and …) that promote the idea of buying tickets through the internet will comply
with group norms and thereby intend to buy tickets through internet. Since the online
ticketing system has been developed recently so it is at the initial stage of implementation
and therefore we expect that subjective norm have a strong positive influence on the
intention to adopt online ticketing.
40
Hence, we suggest:
H6: There is a positive relationship between subjective norm and intention to
purchase the tickets through internet.
2.7.5 Perceived Behavioral Control
According to Ajzen (1991) the construct of perceived behavioral control reflects
beliefs regarding the availability of resources and opportunities for performing the
behavior as well as the existence of internal/external factors that may impede the
behavior. Hence, we agree with Taylor and Todd’s (1995) decomposition of perceived
behavioral control into” facilitating conditions” and the internal notion of individual
“self-efficacy”. Self efficacy indicates an individual’s self confidence in hi or her ability
to perform the behavior. In terms of internet purchasing, if an individual is self confident
about engaging in activities related to purchasing online, he or she should feel positive his
or her behavioral control (George, 2004).
Facilitating condition is defined as the degree to which an individual believes that
an organizational or technical infrastructure exists to support use of the system
(Venkatesh, 2003).Perceived behavioral control is important in explaining human
behavior since an individual who has the intentions of accomplishing a certain action may
be unable to do so because his or her environment prevents the act from being performed.
In the context of online ticketing in Iran, computer access, Internet access, Saman prepaid
cards access (facilitating conditions) and availability of assistance for passengers who
intend to purchase tickets online (self efficacy) are all behavioral control factors that are
important in facilitating online ticketing behavior in our country. Thus we suggest:
41
H7: There is a positive relationship between behavioral control and intention to
purchase the tickets through internet.
2.7.6 Trust
Internet shopping is a new form of commercial activity, which tends to involve a
higher degree of uncertainty and risk when compared with traditional shopping. Trust
refers to the confidence a person has in his or her favorable expectations of what other
people will do, based, in many cases, on previous interactions (Gefen, 2000). In this study
trust refers to the confidence passenger have in online transaction and consequences of
purchasing tickets through internet. Hence, we suggest:
H8: There is positive relationship between passengers` Trust in buying tickets
online and attitudes towards buying tickets online
H9: There is positive relationship between passengers` Trust in buying tickets
online and intention to buy tickets online
2.7.7 Behavioral Intention
Behavioral intention refers to “instructions that people give to themselves to
behave in certain ways” (Triandis, 1980). In our model, behavioral intention refers to
potential passengers’ intention to adopt online ticketing services. Considering that
internet ticketing in Iran is still relatively new, it is reasonable for the present study to
focus on the behavioral intentions to use online ticketing system for purchasing the train
tickets in Iran. Thereby, the link between intention and actual behavior is not tested in
this study. Summary of the research hypotheses are shown in table 2.2.
42
Table 2.2: Research Hypotheses
Table 2.2: Research Hypotheses
Hypotheses Description
H1 There is a positive relationship between perceived usefulness and attitude
H2 There is a positive relationship between perceived ease of use and attitude
H3 There is a positive relationship between perceived ease of use and perceived usefulness
H4 There is a positive relationship between perceived usefulness and intention
H5 There is a positive relationship between attitude and intention
H6 There is a positive relationship between subjective norm and intention
H7 There is a positive relationship between behavioral control and intention
H8 There is a positive relationship between trust and attitude
H9 There is a positive relationship between trust and intention
43
Chapter Three Research Methodology 3. Research Methodology In this chapter, we outline the methodology to be used in our research and the theoretical basis behind the approaches and their definitions for the understanding of the reader. We start by identifying the differences between the exploratory, descriptive, and exploratory research approaches and identify our research in this category. We also highlight the difference between deductive vs. inductive research, identify our research strategy. Data analysis methods and instruments are chosen and defined. 3.1 Research Purpose
Every researcher has his/her own personal motivation to perform a scientific study
while in general according to yin (1994), the types of research purpose can be classified
in three categories: exploratory research, descriptive research and explanatory (or casual)
research.
44
3.1.1 Exploratory Research
Exploratory research is characterized by its flexibility. When a problem is broad
and not specifically defined, the researches use exploratory research as a preliminary
step. By an exploratory research we mean a study of a new phenomenon exploratory
studies are a valuable means of finding" what is happening; to seek new insights; to ask
questions and to asses phenomenon in a new light (Yin 1994).Exploratory research has
the goal of formulating problems more precisely, clarifying concepts, gathering
explanations, gaining insight, eliminating impractical ideas, and forming hypothesis. It
can be performed using a literature research, surveying certain people about their
experiences, focus groups and case studies. For instance, when surveying people,
exploratory research studies would not try to acquire a representative sample, but rather,
seek to interview those who are acknowledgeable and who might be able to provide
insight concerning the relationship among variables. Case studies can include contrasting
situations or benchmarking against an organization known for its excellence. Exploratory
research may develop hypothesis, but it does not seek to test them.
3.1.2 Descriptive Research
When a particular phenomenon of a nature is under study, it is understandable
that, research is needed to describe it, to explain its properties and inner
relationships( Huczynski and Buchana 1991).the object of descriptive research is " to
portray an accurate profile of persons, events or situations (Robson , 1993). In academic
research, descriptive research is more rigid than exploratory research. When conducting a
management or business research, it seeks to describe users of a product or service,
determine the proportion of the population that uses a product or service, or predict future
demand for product or service.
45
As opposed to exploratory research, descriptive research should define questions,
people surveyed and the method of analysis prior to beginning of data collection. In other
words, the who, what, where, when, why and how aspects of the research should be
defined. Such preparation allows one the opportunity to make any required changes
before the process of data collection has begun. However, descriptive research should be
thought of as a means to an end rather than an end to itself.
Our research purpose and research questions reveal that this study is primarily
descriptive. Large -scale survey studies will be conducted to identify the main factors that
affect the Iranian passengers to buy the train tickets through the internet. The related data
will be collected and analyzed to verify the hypotheses of the research.
3.1.3 Explanatory Research
The study can be explanatory when the focus is on cause-effect relationships,
explaining what causes produces what effects (Yin 1994).explanatory (or causal) research
seeks to find cause and affect relationships between variables. It accomplishes this goal
through laboratory and field experiments.
3.2 Research Approach
In this part, we are going to find the right way to address the matter we focus on.
There are two main research approaches to choose from when conducting research in
social science: quantitative or qualitative method (yin, 1994).the most important
difference between the two approaches is to use the numbers and statistics you get the
choice of research approach naturally depends on the defined research problem and the
data needed for solving this problem.
46
Qualitative focus on the research that will have a better understanding of the
studies objects, they also have to be relative flexible. in addition, qualitative research is
the search for knowledge that is supposed to investigate, interpret and understand the
problem phenomenon by the means of an inside perspective ( Patel and Tebelius,
1987).the characteristics of qualitative studies are that they are based largely on the
researcher’s own description, emotions and reactions( yin, 1994). The qualitative
approach also includes a great closeness to the respondents or to the source that the data
are being collected from. Quantitative has a characteristic that tend to be more structured
and formalized. .the research tries to explain phenomenon with numbers to obtain results,
thereby basing the conclusion on the data that can be quantified. This approach is
especially useful when conducting a wide investigation that contains many units (Holme
and Solvang, 1995).
After comparing two research approaches, quantitative approach was chosen for
our thesis. The goal of this research is to identify the factors that influence the Iranian
passengers to purchase train tickets online .for doing so we have chosen a structured
framework. We have made a model by reviewing the related literature, thereby making
our research hypotheses. In fact we are trying to explain the online ticketing adoption
phenomenon with numbers, thereby basing our conclusion on the data that can be
quantified. We are going analyze the data collected from sample passengers and
generalize the data to the whole population. All the characteristics mentioned indicate
that the quantitative approach should be used in our research.
3.3 Deductive vs. Inductive
According to Saunders (2000), the research should use the inductive approach,
where the author would collect data and develop theory as a result of the data analysis;
47
While the deductive approach where the authors develop a theory and hypothesis
(or hypotheses) and design a research strategy to test the hypotheses. Deductive reasoning
works from the more general to the more specific. Sometimes this is informally called a
“top-down” approach; inductive reasoning works the other way, moving from specific
observations to border generalizations and theories. Informally, we sometimes call this
approach a “bottom-up” approach (Trochim 2002).
In this study begins with thinking up a proper research model about our topic of
interest (online ticketing adoption). Then we try to narrow that down into more specific
hypotheses that we can test. So we narrow down even further when we collect related
data to address the hypotheses. This ultimately leads us to be able to test the hypotheses
with specific data, resulting in confirmation or verification of our original theories. So we
draw on our research approach with deductive trait.
3.4 Research Strategy
There are three distinct conditions that will affect the choice of research strategy:
the type of research questions asked, the extent of control an investigator has over actual
behavioral events and the degree of focus on contemporary events.
According to Yin (1994) there are five different strategies for the research, of
course each one has both advantages and disadvantages. The five ones are an experiment,
a survey; history, an analysis of archival records and a case study. These are shown in
table 3.1
48
.
Table 3.1: Relevant Situations for Different Research Strategies
Research Strategy
Form of Research Question
Required Control Over Behavioral
Systems
Focus on Contemporary
Events
Experiment How, why Yes Yes
Survey Who, what, where,
how many, how much
No Yes
Archival Analysis Who, what, where,
how many, how much
No Yes/No
History How, why No No
Case Study How, why No No
Source: (Yin, 1994)
Since the aim of this study was to collect the answers from a large scale of
passengers who have not bought tickets online and formulate the main factors that affect
the intention to adopt online ticketing system, we have mainly chosen a survey as our
research strategy. This choice is partly determined by our research approach, which to
most extent is of quantitative nature. A survey is an appropriate strategy due to the fact
that the aim is to answer who, where, how many, or how much or what questions. There
is no faster, more affordable way to conduct a survey irrespective of size. Furthermore,
due to the quantitative nature of this study, a survey is appropriate because of its
quantitative character.
49
3.5 Defining the Target Population
Sampling design begins by specifying the target population. This is the collection
of elements or objects that possess the information sought by the researcher and about
which inferences are to be made (Malhotra and Briks 1999).Considering the fact that
online ticketing system is at its infancy stage in our country, and a trivial number of
passengers have used the system for buying tickets online, it was decided to target only
those passengers who had never used the system (inexperienced users of the system).
Since we were interested in the concept of intention, the fact that the respondents
are inexperienced users of the online ticketing system, does not disturb the result of this
study. Testing the behavioral models based on the data gathered from inexperienced users
is not something unusual and has been seen the literature review. Taylor and Todd in
1995, Conducted a study to assess the role of prior experience in assessing IT usage.
They tested the predictive ability of the Augmented TAM model based upon the data
gathered from two distinct groups of experienced and inexperienced users of the
computer resource center separately and compared the results to assess the role of
experience. Taylor and Todd (1995) encouraged the researchers to test:
1- Whether models such as TAM are predictive of behavior for inexperienced users of the information technology.
2- Whether the determinants of IT usage are the same for experienced and
inexperienced users of a system.
Furthermore, Yu et al., (2005), who conducted a study to verify TAM for to t-
commerce, used two distinct groups of samples of inexperienced and experienced users
of the t-commerce and compared the results. In an attempt to see if it’s possible to make a
comparison between experienced and inexperienced users on the online ticketing system
in Iran, we tried to take a sample from experienced users.
50
This was done with Raja Company cooperation, giving us the access to email
address of users of the system, but unfortunately the response rate was too low and the
size of the sample was too small to let us compare the results between two groups of
experienced and inexperienced users of the online ticketing system. Based on the
literature review, the current situation of online ticketing in Iran and the focus of study
which is on intention, it was decided to target the inexperienced users of the system.
Based on the above explanations we continue to define the target population of
this study. The target population should be defined in terms of elements, sampling units,
extent and time (Malhotra and Briks, 1999).An element is the object about which or from
which the information is desired. In survey research, the element is usually the
respondent. A sampling unit is an element, or a unit containing the element, that is
available for selection at some stage of the sampling process.
Extent refers to the geographical boundaries of the research and the time refers to
the period under consideration. . (Malhotra and Briks 1999) Raja passengers train
company has five main local traveling roots (Azarbayejan, Khorasan, Khozestan,
Golestan and Hormozgan) and three main international travelling roots (Tehran-Istambul,
Tehran-Damescue, Tehran-Van and Zahedan-Koveyte),
According to the explanations mentioned above, the target population of this
study is defined as:
-Elements: inexperienced users of the online ticketing system -Sampling units: trains traveling in the main traveling roots -Extent: trains traveling through the five main roots locally (inside Iran). -Time: 22 of the May 2005 to 23 June 2005.
51
3.6 Sampling Technique Selection
According to Saunders et al., (2000), sampling techniques can be divided into two
types:
• Probability or representative sampling • Non-probability or judgmental sampling
In probability sampling, sampling units are selected by chance. Probability
sampling is most commonly associated with survey-based research. This method of
sampling permits the researcher to make inferences or projections about the target
population from which the sample was drawn.
Non probability sampling relies on the personal judgment of the researcher
rather than on chance to select sample elements. Non probability samples may yield
good estimates of the population characteristics, but they do not allow for objective
evaluation of the precision of the sample results. (Malhotra and Briks 1999).since in
this study we want to generalize the results to the whole inexperienced passengers’
population, so the probability sampling method was chosen.
3.7 Questionnaire Development
In order to ensure that a comprehensive list of items was included, an extensive
review of previous work was conducted. To ensure reliability while operationalizing our
research constructs, we tried to choose those items that had been validated in previous
research. Table 3.2 shows the source of measures used for making questions. The
questionnaire consists of questions that relate to possible factors affecting adoption of
online ticketing system.
52
Likert five point scales ranging from “strongly agree” to “strongly disagree” were
used as a basis of questions. This scale has been used in previous e-commerce adoption
research.
Table 3.2: Research Variables and Measurements
Construct Source
Attitude Taylor and Todd (1995)
Intention Taylor and Todd (1995)
Perceived Ease of Use Davis (1989)
Perceived Usefulness Davis (1989)
Subjective Norm Taylor and Todd (1995)
Perceived Behavioral Control (self efficacy+ facilitating
conditions) Taylor and Todd (1995)
Trust Vijayasarathy (2004) and Jieun Yu et al.,(2005)
The questionnaire was translated to Farsi language .after translating the
questionnaire, a pilot study was conducted. At this stage 10 train passengers who had
never experienced using the online ticketing system, answered the questions these
passengers were asked to mention any ambiguity points in the questions.
53
With the help of the pilot study the original questionnaire was refined and some
corrections were made. A copy of the survey questionnaire is presented in Appendix B.
3.8 Data Collection
A survey was conducted to verify the research model. The sample was taken
randomly from inexperienced users of the online ticketing system in Iran. Inexperienced
users were defined as passengers who had never experienced purchasing the train tickets
through the internet. A team consisting of the university students who regularly traveled
with trains was made to distribute the questionnaires over the period 22 of the may 2005
to 23 June 2005. The purpose of the study was explained to the team members and they
were trained how to distribute the questionnaires and treat with the interviewees.
Since the respondents were inexperienced users who were not familiar with the
know-how of using the online ticketing system it was necessary to inform them about the requirements of using such system and the way it works. The interviewers were trained to
give a brief description of how the system works to respondents while showing them the
real web site pages one would see while interacting with the online ticketing system. The
respondents could see the process through which they could purchase tickets online, in
the lap top screens. After viewing the screens, the passengers were asked to answer the
questions. Total number of questionnaires distributed was equal to 600, from which 174
were incomplete and were excluded for analysis. This yields a response rate of 71%.this
means that the sample size of this study is equal to 426.
54
Chapter Four Data Analysis 4. Data Analysis In this chapter we will analyze the data collected based on the basis of frame of reference of this thesis. The partial least square method will be applied for analyzing the collected data. 4.1 Data Analysis Method
Analysis of the data was done by using the PLS (partial least squares method),
which is one of the SEM techniques. Structural Equation Modeling (SEM) techniques
such as Lisrel and Partial Least Squares (PLS) are second generation data techniques that
can be used to test the extent to which IS research meets recognized standards for high
quality statistical analysis (Gefen, 2000).SEM enables researchers to answer a set of
interrelated research questions in a single, systematic and comprehensive analysis by
modeling the relationships among multiple and dependent constructs simultaneously.
This capability for simultaneous analysis differs greatly from most first generation
regression models such as linear regression, ANOVA, and MANOVA, which can analyze
only one layer of linkages between independent and independent variable at a time.
55
(In appendix C comparison of the capability of these three approaches Lisrel, PLS
and Linear Regression is provided). Unlike First generation regression tools, SEM not
only assesses the Structural Model, the assumed causation among a set of dependent and
independent constructs, but in the same analysis, also evaluates the measurement model–
loadings of observed items (measurements) on their expected latent (constructs). The
result is a more rigorous analysis of the proposed research model and, very often, a better
methodological assessment tool (Gefen, 2000). (Summaries of the objective behind each
technique and limitations relating to sample size and distribution are provided in
appendix D).Due to the formative nature of some of the measures used and non normality
of the data, LISREL analysis was not appropriate for data analysis of this study (Chin and
Gopal, 1995). Thus, the Visual PLS 0.98 b software was chosen to perform the analysis.
4.2 Validity and Reliability
For reflective measures, all items are viewed as parallel measures capturing the
same construct of interests. Thus, the standard approach for evaluation, where all path
loadings from construct to measures are expected to be strong (i.e., 0.70 or higher), is
used (limayem et al., 2000). In the case of formative measures, all item measures can be
independent of one another since they are viewed as items that create the “emergent
factor.” Thus, high loadings are not necessarily true and reliability assessments such as
Cronbach’s alpha are not applicable. Under this situation, Chin (1998) suggests that the
weights of each item be used to assess how much it contributes to the overall factor. For
the reflective measures, rather than using Cronbach’s alpha, which represents a lower
bound estimate of internal consistency due to its assumption of equal weightings of items,
a better estimate can be gained using the composite reliability formula (Erlbaum Assoc.,
1998).All the measurements in this study are reflective, except the measurements of the
perceived behavioral control.
56
In this case the two concepts of “self efficacy” and “facilitating conditions’ form
the behavioral control concept. Thereby, behavioral control measurements are considered
to be formative. Table 4.1 provides information concerning the weights and loadings of
the measures to their respective constructs. The loadings of all the reflective measures of
this study area above 0.7, which indicates a good level of convergent validity.
Construct Indicator Loading Weight Attitude ATT1 -0.8164 ATT2 -0.9287 Intention INT1 -0.8621 INT2 -0.7401 PBC1 -0.6314 Perceived Behavioral PBC2 -0.5614 Control PBC3 -0.3361 PBC4 -0.3003 Subjective Norm SN1 -0.9022 SN2 -0.8324 PEOU1 0.9423 Perceived Ease of Use PEOU2 0.9457 PEOU3 0.939 PU1 -0.7199 Perceived Usefulness PU2 -0.8124 PU3 -0.8662 Trust TR1 -0.885 TR2 -0.957
Table 4.1: Weights and Loadings
57
Measurements of reliability for all scales are included in table 4.2. The composite
reliability was estimated to evaluate the internal consistency of the measurement model.
The composite reliability for all the constructs of this study was greater than the level of
0.60 which is recommended by Bagozzi and Yi (1995), as a good level for internal
consistency.
Table 4.2 : Composite Reliability
Construct Composite Reliability
Attitude 0.812559
Intention 0.783557
Perceived Behavioral Control 0.647661
Subjective Norm 0.804402
Perceived Ease Of Use 0.959642
Perceived Usefulness 0.843
Trust 0.918516
4.3 Results
The statistical significance of all the paths in the model was tested using the
bootstrap resampling procedure. (Cotterman and Senn, 1992).
58
Using one-tailed tests, eight of the nine paths were significant at p< 0.01 level,
providing support for H1, H2, H3, H5, H6, H7, H8 and H9. Figure 3.4 provides the results of
testing the structural links of the proposed research model using PLS analysis. These
results represent yet another confirmation of the appropriateness of the TAM for
explaining voluntary individual behavior of potential users of information technology
systems. The results also provide strong support for the new links added to the TAM
representing the effects of PBC, SN and Trust. In this part we try to explain the results in
the form of analyzing the antecedents of intention and attitude and perceived usefulness
with the help of statistical results.
4.3.1 Antecedents of Intention
As suggested by the t statistics and path coefficient values, subjective norm, trust,
attitudes toward online ticketing and perceived behavioral control had a positive
significant effect on intention to purchase tickets online whereas for perceived usefulness
the path was not found to be significant. The path between subjective norm and intention
was found to be significant (path coefficient= 0.44, p< 0.01), thereby supporting the
hypothesis 6.this is consistent with the findings of Taylor and Todd (1995) and Yu et al.,
(2004), who verified existence of a positive significant relationship between subjective
norm and the intention for inexperience users of the information technology systems.
The path between perceived behavioral control and intention was found to be
significant (path coefficient= 0.249, p< 0.01), thereby supporting the hypothesis 7.this is
consistent with the findings of Taylor and Todd (1995), who reported the existence of a
positive significant relationship between behavioral control and the intention for
inexperienced users of the information technology.
59
The path between attitude and intention was found to be significant (path
coefficient= 0.175, p< 0.01), thereby supporting the hypothesis 5.this is consistent with
the findings of Yu et al., (2004), who verified the existence of a positive significant
relationship between attitude and the intention for inexperienced users of the information
technology. But it is inconsistent with the findings of Taylor and Todd (1995), who
reported the existence of an insignificance relationship between attitude and the intention
for inexperienced users of the information technology.
0.020 t: 0.621
0.175 t: 3.616
0.119 t: 2.611
0.239 t: 3.636
0.227 t:4.116
R2: 0.40
R2: 0.372
R2: 0.052
BI A
T BC SN
PU
PEU0.074 t: 1.915
0.442 t :9.680 0.249
t :4.567
0.463 t: 7.141
A, attitude; PU, perceived usefulness; PEOU, perceived ease of use; SN, subjective norm; BC, perceived behavioral control, BI, behavioral intention; T, trust
Figure 4.1: Results of Testing the Hypothesized Links
60
The path between perceived usefulness and intention was not found to be
significant (path coefficient= 0.020, p> 0.05), thereby rejecting the hypothesis 4. This is
consistent with the findings of Yu et al., (2004), who verified the existence of an
insignificant relationship between perceived usefulness and the intention for
inexperienced users of the information technology. But it is inconsistent with the findings
of Taylor and Todd (1995), who reported the existence of a significance relationship
between perceived usefulness and the intention for inexperienced users of the information
technology. The path between trust and intention was found to be significant (path
coefficient= 0.074, p< 0.05), thereby supporting the hypothesis 9.this is inconsistent with
the findings of Yu et al., (2004), who reported the existence of an insignificance
relationship between trust and the intention for inexperienced users of the information
technology. The effects of the antecedents of intention accounted for 40% of the variance
in this variable. This is an indication of quite a good explanatory power of the model for
intention. The path coefficients showed that subjective norm was a more significant
determinant of intention relative to other determinants of intention. This shows the
importance of social influence in forming the potential users’ intention towards using the
online ticketing system.
4.3.2 Antecedents of Attitude
As suggested by the t statistics and path coefficient values, trust, perceived
usefulness and perceived ease of use had a positive significant effect on attitude towards
online ticketing. The path between trust and attitude was found to be significant (path
coefficient= 0.119, p< 0.01), thereby supporting the hypothesis 8.This is inconsistent with
the findings of Yu et al., (2004), who reported the existence of an insignificant
relationship between trust and attitude for inexperienced users of the information
technology systems. The path between perceived usefulness and attitude was found to be
significant (path coefficient= 0.239, p< 0.01), thereby supporting the hypothesis 1.
61
This is consistent with the findings of Taylor and Todd (1995) and Yu et al.,
(2004), who verified existence of a positive significant relationship between perceived
usefulness and attitude for inexperienced users of the information technology systems.
The path between perceived ease of use and attitude was found to be significant (path
coefficient= 0.463, p< 0.01), thereby supporting the hypothesis 2. This is inconsistent
with the findings of Yu et al., (2004), who verified the existence of an insignificant
relationship between perceived ease of use and the attitude for inexperienced users of the
information technology. But it is consistent with the findings of Taylor and Todd (1995),
who reported the existence of a significance relationship perceived ease of use and the
attitude for inexperienced users of the information technology. The effects of the three
antecedents of attitude (i.e., trust, perceived usefulness and perceived ease of use)
accounted for over 37% of the variance in this variable. This is an indication of the good
explanatory power of the model for attitude. Perceived ease of use had the strongest
effect with a path coefficient of 0.46 emphasizing the important role of ease of use in
driving attitude toward online ticketing. The results of the hypotheses tests are
summarized in table 4.3.
4.3.3 Antecedents of Perceived Usefulness
As suggested by the t statistics and path coefficient values, perceived ease of use
of online ticketing had a positive significant effect on perceived usefulness. Online
ticketing. The path between perceived ease of use and perceived usefulness was found to
be significant (path coefficient= 0.227, p< 0.01), thereby supporting the hypothesis 3.This
is consistent with the findings of Taylor and Todd (1995) and Yu et al., (2004), who
reported the existence of a significant relationship between perceived ease of use and
perceived usefulness for inexperienced users of the information technology systems.
Perceived ease of use accounted for over 0.05% of the variance in perceived usefulness.
62
Hypothesis Effects Structural Coefficient
t Statistic Remarks
H1 PU--->A 0.239 3.636* S
H2 EOU--->A 0.463 7.141* S
H3 EOU--->PU 0.227 4.116* S
H4 PU--->BI 0.02 0.621 N.S
H5 A--->BI 0.175 3.616* S
H6 SN--->BI 0.442 9.68* S
H7 PBC--->BI 0.249 4.567* S
H8 T--->A 0.119 2.611* S
H9 T--->BI 0.074 1.915** S
A, attitude; PU, perceived usefulness; PEOU, perceived ease of use; SN, subjective norm; PBC, perceived behavioral control, BI, behavioral intention; T, trust; S, supported, N.S; not supported *p<0.01 **p<0.05
Table 4.3: Results of the Hypotheses Tests
The effects of the three antecedents of attitude (i.e., trust, perceived usefulness
and perceived ease of use) accounted for over 37% of the variance in this variable. This is
an indication of the good explanatory power of the model for attitude. Perceived ease of
use had the strongest effect with a path coefficient of 0.46 emphasizing the important role
of ease of use in driving his/her attitude toward online ticketing.
63
Chapter Five Findings and Conclusions 5. Findings and Conclusions In this chapter, we are going to present what we have found from our research, so we could answer our research question. Furthermore, we will also give the conclusions of the research the innovative part of the research and what researchers can do for the future study. 5.1 Implications for the Theory
The aim of this research has been to increase the understanding of online
consumer behavior in Iran by answering the research question of this study. In this study,
we think we have contributed to the theory regarded applying existing theories
concerning online consumer behavior and verify their validity. Regarded our research
question, the majority of the findings for this study, supported the existing theories.
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5.2 Innovative Part of the Research
This research is amongst the first studies in Iran to investigate the antecedents of
online shopping in general and online ticketing in specific. Considering the choice of
suitable research model we tried to be innovative and added the construct of trust to the
augmented technology acceptance model.
5.3 Discussions
The research shows that perhaps the most important factor influencing online
ticketing adoption is the subjective norm. Following the subjective norm, Perceived
behavioral control, attitude and trust were found to be other important determinants of the
online ticketing intention, respectively. The effect of subjective norm on intention was
even stronger than the effect of the perceived behavioral control, trust and attitude on
intention. This may be due to the fact that the online ticketing system has been introduced
recently in Iran; hence most of the passengers as well as referent groups that influence the
passengers` intention are not aware of existence o such a system.
This indicates the relative importance of the social influence on potential users
with no prior experience. This finding has implication for marketers. It indicates that
marketing tools such as advertisements in media or press play important roles in forming
the intention of the potential users of the online ticketing system. The online ticketing
system is developed recently in Iran. Thereby, most of the passengers were even not
aware that such a system exists! This suggests that efficient advertisement programs in
press and media about the online ticketing system would motivate passengers to use the
online ticketing system. Perceived behavioral control was found to be another important
antecedent of the online ticketing intention. This result was expected since passengers can
not use the online ticketing system if they don not have the resources and the knowledge
necessary for using the system.
65
The weights of facilitating condition was more than the weight of self efficacy (in
absolute terms), indicating the importance of facilitating condition as compared to self
efficacy. Passengers should have access to computer, internet and payment cards that can
be used for internet purchase. By the time being, the system only works with one type of
payment card (Saman prepaid cards), and other payment cards are not accepted. This is
one of the main obstacles hindering passengers from buying tickets online. Online
ticketing system should be flexible enough to allow the customers to use it with different
payment cards, thereby extending its reach. Information technology systems such as web
kiosks will solve the computer and internet access problems. These web kiosks shall be
located in main train stations, thereby giving comfortable access to the passengers. The
web kiosks should also be equipped with a toll free telephone number to give the
passengers the confidence and overcome possible confusions while using the system.
Perceived usefulness did not affect the intention to buy tickets online, directly. It had an
indirect effect on intention through attitude.
The relationship of trust with both attitude and intention was significant,
indicating the importance of trust in forming the attitude and intention of the
inexperienced users of the online ticketing system. This suggests that passengers will not
try the system unless they are assured of the security of the system in online transaction
process and trustworthiness of the tickets issued. there should be some kind of guarantee
(e.g. insurance), for such issues, so that customers are feel confidence about the security
of the system and thereby trust the online ticketing system and replace the traditional way
of buying tickets with the new online ticketing system .Attitude was found to have a
significant positive impact on intention. Among the antecedents of the attitude, perceived
ease of use had the strongest effect on attitude. Following the perceived ease of use,
perceived usefulness and trust had significance positive effect on attitude, respectively.
This suggests the important role of the perceived ease of use in online ticketing attitude
formation for the potential users of the online ticketing system. This has implications for
design and implementation of online ticketing systems.
66
Those specialists involved in designing the online ticketing systems should
design the systems with technical features and instructions that allow the novice users to
use them without being confused. Though being significant, the role of trust was not
found to be as important as the other two antecedents of attitude (perceived ease of use
and perceived usefulness). This means that concepts such as perceived ease of use and
perceived usefulness are more important than the concept of trust in forming the attitudes
of potential users of the online ticketing system towards using such a system. Marketers
involved in promoting usage of online ticketing system should target potential users with
designing special advertisement programs that focuses on ease of use, usefulness and
security of such systems, respectively, thereby affecting the attitude of these groups of
customers towards using online ticketing system.
5.4 Conclusions and Further Research
The conclusion of this study is not revolutionizing finding that could be summed
up in two or three point. However, many interesting aspects have been found. The
purpose of this study was to use and refine TAM in order to investigate factors that
motivate online ticketing adoption. The findings indicate that the TAM model is quite a
good predictor of behavior for inexperienced users of the information technology.
The results also showed strong support for the importance of considering the
concepts of subjective norm, perceived behavioral control and trust in adopting the online
ticketing system. In general, the result supports that the subjective norm, and later
perceived behavioral control, attitude and trust (respectively) are the most significant
factors that affect online ticketing adoption by inexperienced users. The results also had
implications for marketers of online ticketing systems.
67
This study, like all others, is not without its limitations:
1. Like most of the empirical studies in the online consumer behavior area, this
study is cross sectional. Therefore, it doesn’t capture the essence of the online shopping
phenomenon. Moreover many researchers emphasize the need for longitudinal studies to
better understand online shopping. Longitudinal studies allow the researchers to measure
both intention to buy and actual buying behavior. So researchers are encouraged to
conduct longitudinal researches about the online ticketing adoption factors in Iran to
better understand the essence of this area of study.
2-approximately 60% of the variance in the behavioral intention remains
unexplained. Future research should use more elaborate model in cooperating additional
antecedents factors beyond those mentioned in this study.
3-the respondents were selected randomly from passengers who had never
experienced buying tickets online. Given that we were interested in the perception of
intention to adopt, we were comfortable that these people were nonusers of the online
ticketing system. Another study can be conducted which specifically target people who
use online ticketing system. Even researchers can conduct studies to compare the
adoption factors between the experienced and non experienced users of the online
ticketing system.
4-this study asked respondents about the influence of referent groups in forming
their intention to use online ticketing system. Since subjective norm was found to be the
most important determinant of the intention, thereby, researchers are encouraged to
identify the identity of these referent groups that influence the passengers’ decision to use
online ticketing system.
5-this study concentrated on analyzing one service category (only tickets).this
could mean that result may suffer from lack of generalizability when other product or
service categories are considered. The result should be interpreted carefully when applied
to predict online shopping behavior in other product or service categories.
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Appendix A: Acronyms AT: Attitude BI: Behavioral Intention EC: Electronic Commerce PBC: Perceived Behavioral Control PEOU: Perceived Ease of Use PU: Perceived Usefulness S: Supported SEM: Structure equation modeling SN: Subjective Norms NS: Not Supported T: Trust TAM: Technology Acceptance Model TPB: Theory of Planned Behavior TRA: Theory of Reasoned Action
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Appendix B: Questionnaire Demographic questions: Gender: Male female Age: Less than 20 20-30 30-40 Above 40 Level of income (in Tomans ) Less than 100,000
100,000-150,000
150,000-200,000
200,000-250,000
More than 250,000
Educational level: Middle school High school University degree Advanced degree Seminary studies
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Appendix B: Questionnaire (Continued) For each of the following, please answer by an x in the box that best represents your level of agreement or disagreement. Strongly Agree Neutral Disagree Strongly Agree Disagree 1 2 3 4 5 Attitude: It is a good idea to buy tickets through internet I like the idea of buying tickets through internet Intention: I intend to purchase tickets through Internet in the near future (i.e. next three months) It is likely that I will purchase tickets
through internet in the near future (i.e. next three months) Perceived Usefulness: Online ticketing system will enable Me to save time Online ticketing system will make It easier to buy tickets
Online ticketing system will enable
Me to buy tickets more quickly
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Appendix B: Questionnaire (Continued) Strongly Agree Neutral Disagree Strongly Agree Disagree 1 2 3 4 5 Perceived ease of use:
I would become confused when I use the online ticketing system Learning to use the online ticketing
System would be easy for me Overall I would find online ticketing System easy to use
Subjective Norm: People who are important to me
Would think that I should buy Tickets through internet People who influence my behavior
Would think that I should buy tickets through internet Trust:
Making payments on the internet Is secure I think tickets purchased by using
The online ticketing system will be Trust worthy I can trust the online ticketing system to safeguard my privacy*
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Appendix B: Questionnaire (Continued) Strongly Agree Neutral Disagree Strongly Agree Disagree 1 2 3 4 5 Facilitating conditions: I have the resources required to buy
Tickets through internet
I have knowledge and ability Necessary to buy tickets through Internet Self Efficacy:
I would feel comfortable buying Tickets through internet
I would be able to buy tickets Through internet even if there was
No one around to show me how to * indicates that the item was deleted because of its low loading
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Appendix C: Comparative Analysis between Techniques
Table 1, Comparative Analysis between Techniques
LISREL PLS Linear Regression Issue
Objective of Overall Analysis
Show that the null hypothesis of the entire proposed model is plausible, while rejecting path-specific null hypotheses of no effect.
Reject a set of path-specific null hypotheses of no effect.
Reject a set of path-specific null hypotheses of no effect.
Objective of Variance Analysis
Overall model fit, such as insignificant chi-square or high AGFI.
Variance explanation (high R-square)
Variance explanation (high R-square)
Required Theory Base
Requires sound theory base. Supports confirmatory research.
Does not necessarily require sound theory base. Supports both exploratory and confirmatory research.
Does not necessarily require sound theory base. Supports both exploratory and confirmatory research.
Assumed Distribution
Multivariate normal, if estimation is through ML. Deviations from multivariate normal are supported with other estimation techniques.
Relatively robust to deviations from a multivariate distribution.
Relatively robust to deviations from a multivariate distribution, with established methods of handling non-multivariate distributions.
Required Minimal Sample Size
At least 100-150 cases.
At least 10 times the number of items in the most complex constructs.
Supports smaller sample sizes, although a sample of at least 30 is required.
Source: (Gefen, 2000)
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Appendix D: Capability by Research Approach
Capabilities LISREL
Table 2: Capabilities by Research Approach
PLS Regression
Maps paths to many dependent (latent or observed) variables in the same research model and analyze all the paths simultaneously rather than one at a time.
Supported Supported Not supported
Maps specific and error variance of the observed variables into the research model.
Supported Not supported Not supported
Maps reflective observed variables Supported Supported Supported
Maps formative observed variables Not supported Supported Not supported
Permits rigorous analysis of all the variance components of each observed variable (common, specific, and error) as an integral part of assessing the structural model.
Supported Not supported Not supported
Allows setting of non-common variance of an observed variable to a given value in the research model.
Supported Not supported
Supported by adjusting the correlation matrix.
Analyzes all the paths, both measurement and structural, in one analysis.
Not supported Supported Supported
Not supported Can perform a confirmatory factor analysis Supported Supported
Provides a statistic to compare alternative confirmatory factor analyses models
Supported Not supported Not supported
Source: (Gefen, 2000)
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