APPLYING A MODEL OF THE DYNAMICS OF PURCHASING FROM VIRTUAL STORES TO UAE

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1 APPLYING A MODEL OF THE DYNAMICS OF PURCHASING FROM VIRTUAL STORES TO UAE, Department of MIS, American University of Sharjah, Sharjah, UAE Rbarkhi@aus.edu (On leave from Virginia Tech, USA) Abstract Virtual stores influence the dynamics of consumer choice-making. We develop and empirically test a model, using data from United States, that describes consumer purchase decisions in a virtual store and propose a refined model to be tested with data from United Arab Emirates, specifically Dubai. The model helps in the design of virtual stores by describing how individuals who visit such stores can be induced to purchase from virtual stores. It describes that perceived usefulness, perceived behavioral control, and perceived peer influence impact attitude toward purchasing from a virtual store. Attitude toward purchasing from a virtual store, in turn, influence the actual purchasing from a virtual store. The results of this study have implications for the design of virtual stores that lead to purchase decisions. Key Words: Electronic Commerce, Ease of Use, Usefulness, Peer Influence, Structured Equation Modeling, Theory of Reasoned Action, Theory of Planned Behavior. 1 INTRODUCTION With the widespread use of the Internet, the virtual store innovation stands above many other technological innovations in that it can increase the speed, efficiency, and effectiveness of business transactions (Diamond, 1998). E-commerce sales in the second quarter of 2004 were approximately $15.7 billion, an increase of 23.1 percent from the second quarter of 2003 (U.S. Census Bureau, 2003). Forrester Research projects that U.S. consumers will spend approximately $217.8 billion online in Clearly, knowledge of the determinants of virtual store purchase is increasingly important to business success. While virtual store purchase in the United Arab Emirates (UAE) market may be less widespread than in the United States, the tremendous growth in UAE, specifically Dubai, with the economic expansion and modernization is likely to make virtual stores more prevalent in the UAE market in the future. Several companies such as Souq.com, Tejari.com, Brownbag.ae, and BurjMall.com are successfully crafting their company strategies to fit the needs of online consumers in the UAE online market. QuickDubai.com is a site that provides the opportunity for consumers to buy many different categories fo products online. Also, Brownbag.ae site not anly allows cunsumer to purchase products online, they can also rent movies online and there is a one hour delivery service. Another example is the BurjMall.com that allows online UAE consumers to purchase online. Given the widespread proliferation of online shopping and increased economic activity that occurs online, it is important to understand the dynamics of online shopping behavior. While previous research has generally focused on one-to-one marketing on the Web and has focused on the effect of each factor that induces consumers to purchase online, we focus in this study on building a more comprehensive model that captures the consumer decision to purchase online. We extend existing research to identify factors influencing the decision to purchase from virtual stores. We use empirical 1

2 data from United States and evaluate the model and refine it accordingly to capture the dynamics of the purchase decision in a virtual store. We suggest future research to evaluate the models of online shopping for UAE market. Today s Internet technologies such as cookies can record consumer decision patterns for each individual, and therefore, personalize environments to each individual who visits a virtual store. For example, when a consumer visits the Amazon.com, or Brownbag.ae virtual store, the program that runs the virtual store makes recommendations based on his/her past purchases to tailor a unique experience for that particular consumer. This has resulted in such concepts as one-to-one marketing (Peppers, Rogers, and Dorf, 1999, Smith, 2000); the objective is to develop offers through the analysis of consumer information that are personalized to each consumer s unique needs and incorporate cultureal dynamics in the decision process. The changes that virtual stores have brought with them are influencing the dynamics of consumer choice-making, and a model that can describe the decision dynamics of purchasing from virtual stores in different clutures can be useful. We propose a generic model and test it with data from U.S. and extend this basic model for the Dubai online consuer market. 2 RESEARCH MODEL The model we discuss was first introduced by Barkhi et al. (2008) and is based on Technology Acceptance Model (TAM) developed by Davis (Davis, 1989). TAM is an adaptation of the theory of reasoned action (Ajzen and Fishbein, 1980) that focuses on acceptance and usage behavior of information systems. TAM postulates that perceived usefulness is an important determinant of user attitude about acceptance of technologies that can lead to intention to use the technology and actual usage. The Theory of Reasoned Action and its extensions such as the Theory of Planned Behavior suggest that attitude toward a behavior motivates an intention that leads to a behavioral outcome (Ajzen, 1991, Fishbein and Ajzen, 1975). Further, attitudes are defined as the positive or negative feelings of an individual toward a specific behavior, and these are induced by individual beliefs. While many models of consumer choice making focus on the decision cycles, we focus on antecedents and consequents of behavior that lead to a reasoned action. The decision cycle in the virtual store is influenced by the fact that information is more easily searchable providing greater ability to compare information and find products with desired attributes at the lowest price. Despite some advantages, the virtual store should enhance consumer confidence by providing the ability to stimulate the consumer interest and positive attitude to encourage a purchase decision from a virtual store. In this paper, we develop and test a model depicted in Figure 1 for virtual store that can use consumer attitude to explain their purchase decision. Figure 1 suggests that perceived peer influence, perceived behavioral control, and perceived usefulness form attitude toward purchasing from virtual stores; attitude, in turn, predicts actual purchase from virtual store. The links in Figure 1 result in four testable hypotheses shown next to each link Please Insert Figure 1 Here RESEARCH METHOD 3.1 Sample I administered 300 surveys in classes to senior undergraduates and graduate students at a university in the United States. Completion of the survey was voluntary. There were 277 usable responses (where no data were missing). The survey took the students about 30 minutes to fill out and they received bonus credit for participation. To tie the exercise to the class material, the results were presented to them to discuss overall electronic commerce issues. The average age of the respondents was 21 years old. The median amount of computer experience in the sample was five years. Almost all the respondents reported prior Web experience, and all used , the Web, and word processing at least several times a week. 2

3 3.2. Instrument Validation The instrument was validated for reliability and construct validity. To assess reliability of the scales, Cronbach s alphas were computed. The final reliability scores for each scale substantially exceed 0.70, which is a commonly used cutoff for acceptable reliability (Nunnally, 1978). Construct validity was assessed using confirmatory factor analysis. 3.3.Model Testing Given that most of the indicators indicate the model fit is adequate, it is appropriate to test individual hypotheses related to each path on the model. These tests determine whether the coefficient associated with the tested path is significantly different from zero. Table 1 shows the standardized direct effects and the p value for each path. Any p values in excess of 0.01 can be interpreted as meaning that the associated path loading is not significantly different from zero Please Insert Table 1 Here The results given in Table 1 show that all hypotheses were supported. The support for hypothesis H1 suggests that a consumer s attitude toward purchasing from a virtual store describes the increased likelihood that he or she will purchase from a virtual store. The support for hypotheses H2, H3, and H4 suggest that the higher the perceived peer influence, perceived behavioral control, and perceived usefulness, the more positive is the attitude toward purchasing from a virtual store. The results of the statistical analyses is depicted in Figure Please Insert Figure 2 Here SUMMARY AND IMPLICATIONS This study developed and tested a model of online consumer behavior that links the actual decision to purchase from virtual stores to attitude towards virtual stores. Attitude was modeled based on a set of antecedents, namely perceived peer influence, perceived behavioral control, and perceived usefulness. All these factors were found to be significant predictors of attitude that in turn described the purchase decision. We briefly describe some implications of the results Academic and Practitioner Implications The model proposed in this study captures the dynamics of a purchase decision from virtual stores for subjects who are not novices about this technology. Consumers using virtual stores generally work on the Internet frequently, receive many s every day, and believe that the Internet and other developments in communication technology have improved their online interactions. Also, these consumers turn to the Internet to search for information, are not novices in the use of the Internet, and may be more familiar with security issues. The results of this study suggest that those who generally use the Internet (i.e., for their school or work) will form an attitude to purchase from a virtual store if they perceive that to be useful, if they are influenced by their peers, and if they perceive to have behavioral control. Peer influence can be conveyed online by providing feedback forums that are available to other consumers. For example, a consumer who wants to interact with a virtual store may observe how other consumers have rated this particular virtual store. The implications for practice include the observation that companies need to identify what is considered useful to the consumer as this influences their buying behavior. For example, consumers become brand loyal when they are treated as special customers. Technology can be used to increase customer satisfaction by learning about consumer preferences and using these in future transactions. 3

4 A loyal customer may expect exemplary customer service, speedier return privileges, and access to the newest products as quickly as possible. That is, the loyal customer perceives value when the virtual store understands his or her needs and makes recommendations of new products that he or she is likely to be interested in. For example, when the consumer logs into Amazon, the system will make recommendations based on the pattern of the books that the consumer has purchased before. This way, the consumer perceives buying from the virtual store useful. The virtual store cannot only provide excellent customer service through one-to-one marketing that only small bookstores typically offer, but also should offer lower prices that typically only large chain stores can afford. Marketers provide usefulness in terms of recognition and reward, and customers respond with larger and more frequent purchases. Customer relationship management technology offers many personalization opportunities and special-privilege benefits. Still, the ultimate success will come not from technology but from tactics that influence the perception that the consumer develops about usefulness. Perceived behavioral control was also found to be a strong predictor of attitude toward purchasing from a virtual store. Perceived behavioral control can be strengthened by utilizing appropriate decision technology into the design of websites. For example, perceived behavioral control can be mitigated by introducing decision tools into interactive sites that allow consumers to comparison shop; the consumers develop a positive attitude about purchasing from virtual stores because they have choices and perceive behavioral control over their decision process. Decision tools can also be incorporated into interactive websites to allow consumers to conduct cost-benefit analysis and compare the value of products with other available products. These comparisons can enhance the perception of behavioral control. In addition to decision technologies, suppliers can provide guarantees for high quality and timely delivery of products and services to assure the consumers that they have control over their transactions with the virtual store. In addition, decision technologies that allow capturing online feedback forums in the form of organizational memory of past experiences can systematically capture the peer influence effect. Future research should study the cultural effects on the decision dynamics of online purchasing. Cultural values are antecedents to perceived risk, perceived self-efficacy, and subjective norm (Kuhlmeier and Knight, 2005). National culture is the collective programming of the mind which distinguishes the members of one human group from another (Hofstede, 1980). National culture can describe consumption behavior (Clark, 1990). Given the economic growth and modernization of United Arab Emirates as a major hub for shopping and trade, virtual stores in this region are expected to flourish at increasing rates. Companies like Tejari.com, UAEMall.com, UAESale.com, and Souq.com, BurjMall.com, and Brownbag.ae are already competing actively in the virtual space. Future research should investigate how the models developed in the literature can be modified to fit the UAE virtual environments. A survey conducted in November 2007, during GITEX computer show in Dubai, by Symantec which makes internet security software, suggests that 75 percent of UAE shoppers do not consider shopping online because they perceive it to be unsafe and too risky. The results of surveys like this can be used to extend the model proposed in this study to UAE market and develop a model that can incorporate the constructs that are relevant in the UAE online shopping environments. One of the variables that are likely to strongly affect the decision to purchase online, hence, is perceived risk of buying online (Kuhlmeier and Knight, 2005). Customers can overcome the barriers to perceived risk if they perceive sufficient trust online (Harridge-March, 2005). Research has shown that trust is a significant factor that can influence the decision to purchase online (Akhtar, Hobbs, and Maamar, 2004). While the consumers are protected though litigation process often in the United States, it is less common in many other countries, and hence, trust may play a more significant role in describing the tendency to purchase online in UAE than in the US. We extend the research model tested in this study for consumers in UAE where the culture places a strong emphasis on trust. Another factor that can describe online shopping in UAE is the web use that is a key indicator of how sticky is the 4

5 site (Venkataesh and Agrawal, 2006). This construct can be operationalized by such measures as the number of hours of internet use per week, and the number of purchases made online (Kraut et al, 1999). The research model to be tested in the future with UAE data is depicted in Figure 3. Future research can add more constructs to this basic model to uniquely capture the decision dynamics of online shopping in the UAE market. References Ajzen, I., Fishbein, M Understanding Attitudes and Predicting Social Behavior, Upper Saddle, New Jersey: Prentice Hall. Ajzen, I The theory of planned behavior, Organizational Behavior and Human Decision Processes, 5(2), Akhtar, F., Hobbs, D., Maamar, Z Determining the Factors which Engender Customer Trust in Business-to-Consumer (B2C) Electronic Commerce, Proceedings of the IEEE International Conference on E-Commerce Technology. Barkhi, R. Belanger, F., Hicks, J A Model of the Determinants of Purchasing From Virtual Stores, Journal of Organizational Computing and Electronic Commerce, Forthcoming. Clark, T International marketing and national character: A review and proposal for an integrative theory, Journal of Marketing 54(4), Davis, F Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology, MIS Quarterly, 21(4), Diamond, J Guns, Germs, and Steel: The Fates of Human Societies, New York: W.W. Norton. Fishbein, M. and Ajzen, I Belied, attitude, intention and behavior: An introduction to theory and research. Reading, MA: Addison-Wesley. Hofstede, G Culture s consequences: International differences in work related values. London: Sage Publications. Harridge-March, S Can the building of trust overcome consumer perceived risk online? Marketing Intelligence & Planning 24(7), Kraut, R. Mukhopadhyay, T., Szczypula, J., Kiesler, S., Scherlis, B Information and communication: Alternative uses of the Internet in households, Information Systems Research 10(4), Kuhlmeier, D., Knight, G Antecedents to Internet-based purchasing: a Multinational Study, International Marketing Review, 22(4), Nunnally, J Psychometric Theory, New York: McGraw-Hill. Peppers, D., Rogers, M., Dorf, B The One-to-One Field Book: The Complete Toolkit for Implementing a One-to-One Marketing Program (New York: Bantam Doubleday Dell Publishing Group, Inc.). 5

6 Smith, E.R e-loyalty: How to Keep Customers Coming Back to Your Website, New York: Harper Business Publishers, U.S. Census Bureau Estimated Quarterly U.S. Retail E-Commerce Sales: 4 th Quarter nd Quarter United States Department of Commerce ( Venkatesh, V., Agarwal, R Turning Visitors into Customers: A Usability-Centric Perspective on Purchase Behavior in Electronic Channels, Management Science 52(3),

7 List of Figures Peer influence H2 Behavioral control Usefulness H3 H4 Attitude toward purchasing H1 Actual purchase Figure 1. Research Model Peer influence Behavioral control Usefulness Attitude toward purchasing Actual purchase Figure 2. Refined Model Peer influence Behavioral control Usefulness Attitude toward purchasing Actual purchase Web Use Trust Figure 3. UAE Online Shopping Model 7

8 List of Tables Hypothesis Path Standardized Direct Effects St. Dev. P H1 Actual Purchase Attitude < H2 Attitude Peer Influence < H3 Attitude Behav. Control < H4 Attitude Usefulness < Table 1. Testing The Model 8