MASTER'S THESIS. Factors Influencing Adoption of Online Ticketing

Transcription

1 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: ISRN: LTU-PB-EX--06/45--SE

2 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

3 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

4 Table of Contents: 1. CHAPTER ONE: INTRODUCTION Introduction Background Online ticketing Online ticketing in Iran problem discussion and justification problem statement research question purpose of the research disposition of the thesis CHAPTER TWO: LITERATURE REVIEWE Literature Review Attitude Intention to shop online Perceived usefulness Perceived ease of use Subjective norm Perceived behavioral control Trust Internet usage Enjoyment Perceived Risk Experience Innovativeness Habit Perceived consequences Demographic variables

5 2.2 Theoretical framework Adoption theories Theory of reasoned action Theory of planned behavior Technology acceptance model Difference between theories Conceptual model and hypotheses Pilot study Description of the research hypotheses Attitude Perceived ease of use Perceived usefulness Subjective norm Perceived behavioral control Trust Behavioral intention CHAPTER THREE: RESEARCH METHODOLOGY Research purpose Exploratory research Descriptive research Explanatory research Research approach Deductive versus inductive Research strategy Defining target population Sampling technique selection Questionnaire development Data collection

6 4. CHAPTER FOUR: DATA ANALYSIS Data analysis method Validity and reliability Results Antecedents of intention Antecedents of attitude Antecedents of perceived usefulness CHAPTER FIVE : FINDINGS AND CONCLUSION Implications for the theory Innovative part of the research Discussion 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

7 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

8 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

9 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$ 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

10 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

11 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

12 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) 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 ( 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

13 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

14 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

15 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

16 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

17 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

18 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

19 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) 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) 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

20 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 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 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

21 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 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

22 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) 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 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

23 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) 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

24 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 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

25 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

26 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 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

27 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., 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

28 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

29 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) 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

30 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

31 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

32 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

33 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

34 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

35 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

36 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

37 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