MASTER'S THESIS. Factors Influencing Adoption of Online Ticketing

MASTER'S THESIS 2006:45 Factors Influencing Adoption of Online Ticketing Mitra Karami Luleå University of Technology Master Thesis, Continuation Courses Marketing and e-commerce Department of Business Administration and Social Sciences Division 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 2

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 3

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 4

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 5

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 6

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. 7

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). 8

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. 9

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). 10

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. 11

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. 12

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. 13

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. 14

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 15

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 Innovativeness Perceived behavioral control Experience Intention Internet usage Experience Perceived usefulness Ease of use Risk Perceived Risk Perceived risk Social Norm Enjoyment Habit Experience Perceived Consequences Ease of Use Habit Source: Limayem et al., 2000 Perceived behavioral control Demographic variables Innovativeness 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. 18

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. 19

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. 20

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 nononline 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. 21

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 noninnovators. 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). 22

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. 23

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). The person s believe that the behavior leads to certain outcomes and his/her evaluations of these outcomes Attitude toward the behavior 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 Subjective Norm Intention Behavior Source: Ajzen and Fishbein (1975) Figure 2.1: Theory of Reasoned Action 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 Behavioral Beliefs & Outcome Evaluations Attitude Normative Beliefs & Motivations to Comply Subjective Norm Intention Behavior 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 External Variables Attitude Behavior Intention Actual Behavior Perceived Ease of Use Source: (Davis et al., 1989) Figure 2.3: Technology Acceptances Model 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

Perceived Usefulness H 4 H 1 H 3 Attitude H 5 Intention H 2 Ease of Use H 8 H 9 H 6 H 7 Trust Subjective Norms Perceived Behavioral Control 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: H 5 : 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: H 1 : There is positive relationship between perceived usefulness of buying tickets online and the attitudes to buy tickets online. H 4 : 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: H 2 : There is positive relationship between perceived ease of use of buying tickets online and attitudes towards buying tickets online. 1989).Thus: PEOU also has a positive but indirect effect on attitude through PU (Davis et al., H 3 : 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: H 6: 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

H 7: 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: H 8 : There is positive relationship between passengers` Trust in buying tickets online and attitudes towards buying tickets online H 9 : 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 H 1 There is a positive relationship between perceived usefulness and attitude H 2 There is a positive relationship between perceived ease of use and attitude H 3 There is a positive relationship between perceived ease of use and perceived usefulness H 4 There is a positive relationship between perceived usefulness and intention H 5 There is a positive relationship between attitude and intention H 6 There is a positive relationship between subjective norm and intention H 7 There is a positive relationship between behavioral control and intention H 8 There is a positive relationship between trust and attitude H 9 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 Archival Analysis Who, what, where, how many, how much Who, what, where, how many, how much No No Yes 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 types: According to Saunders et al., (2000), sampling techniques can be divided into two 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) Trust Taylor and Todd (1995) 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. Table 4.1: Weights and Loadings 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 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 H 1, H 2, H 3, H 5, H 6, H 7, H 8 and H 9. 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. R 2 : 0.052 PU 0.020 t: 0.621 0.239 t: 3.636 R 2 : 0.372 0.227 t:4.116 A 0.175 t: 3.616 BI R 2 : 0.40 PEU 0.463 t: 7.141 0.119 t: 2.611 0.074 t: 1.915 0.442 t :9.680 0.249 t :4.567 T SN BC 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

Table 4.3: Results of the Hypotheses Tests 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 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. 64

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. 68

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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 76

Appendix B: Questionnaire Demographic questions: Gender: Male female Age: Less than 20 20-30 30-40 Above 40 Level of income (in Tomans ) Less 100,000 than 100,000-150,000 150,000-200,000 200,000-250,000 More 250,000 than Educational level: Middle school High school University degree Advanced degree Seminary studies 77

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 78

Appendix B: Questionnaire (Continued) Perceived ease of use: I would become confused when I use the online ticketing system Strongly Agree Neutral Disagree Strongly Agree Disagree 1 2 3 4 5 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* 79

Appendix B: Questionnaire (Continued) Facilitating conditions: I have the resources required to buy Tickets through internet Strongly Agree Neutral Disagree Strongly Agree Disagree 1 2 3 4 5 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 80

Appendix C: Comparative Analysis between Techniques Table 1, Comparative Analysis between Techniques Issue LISREL PLS Linear Regression 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 pathspecific null hypotheses of no effect. Reject a set of pathspecific null hypotheses of no effect. Objective of Variance Analysis Overall model fit, such as insignificant chisquare 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 nonmultivariate 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) 81

Appendix D: Capability by Research Approach Table 2: Capabilities by Research Approach Capabilities LISREL 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. Supported Supported Not supported Can perform a confirmatory factor analysis Supported Supported Not supported Provides a statistic to compare alternative confirmatory factor analyses models Supported Not supported Not supported Source: (Gefen, 2000) 82

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