How Positive Informational Social Influence Affects Consumers Decision of Internet Shopping?

How Positive Informational Social Influence Affects Consumers Decision of Internet Shopping? Matthew K.O. Lee Department of Information Systems City University of Hong Kong Tel: (852) 2788-7348 Fax: (852) 2788-8694 Email: ismatlee@cityu.edu.hk Christy M.K. Cheung Department of Information Systems City University of Hong Kong Tel: (852) 2784-4745 Fax: (852) 2788-8694 Email: iscc@cityu.edu.hk Choon Ling Sia Department of Information Systems City University of Hong Kong Tel: (852) 2788-7544 Fax: (852) 2788-8694 Email: iscl@cityu.edu.hk Kai H. Lim Department of Information Systems City University of Hong Kong Tel: (852) 2788-8045 Fax: (852) 2788-8694 Email: iskl@cityu.edu.hk Abstract Given the amount of perceived risk involved in Internet shopping, many potential Internet shoppers tend to wait and observe the experiences of others who have tried it before considering adopting it. This study explores how positive informational social influence affects consumers decision of Internet shopping using a laboratory experiment. The research results reveal that positive informational social influence reinforces the relationships between perceived ease of use and consumer attitude toward Internet shopping, as well as attitude and their intention to shop. Implications for the current investigation and future research directions are provided. Keywords: Internet shopping, positive informational social influence, belief, attitude, behavioral intention. 1. Introduction Varadarajan and Yadav [38] conceptualized the electronic marketplace as a networked information system that serves as an enabling infrastructure for buyers and sellers to exchange information, transact, and perform other activities related to the transaction before, during, and after the transaction (p.297). The connectivity nature of the Internet allows one-tomany and many-to-many communications among consumers that help diminish information asymmetry. For instance, consumers can interact, exchange ideas, and compare experiences with other consumers in online chat rooms, consumer discussion forums, and online newsgroups. Informational social influence is especially important to Internet shopping because of the amount of perceived risk involved [3][17][29]. Many potential Internet shoppers would tend to wait and observe the experiences of others who have tried it before considering adopting it. While informational social group influence could be positive (successful cases) or negative (bad experiences), we focus on positive informational social group influence in this study because we are interested in the facilitation of Internet shopping. Past research often modeled informational social influence as having a direct impact on adoption intention [31]. Drawing from recent findings in group-decision research, this study proposes that informational social influence could be viewed as a moderating variable that interacts with beliefs in determining attitude, as well as with attitude in determining behavioral intention. Thus, the key objective of this paper is to investigate the moderating role of positive informational social influence on consumers beliefs, attitudes, and behavioral intention of Internet shopping. The paper begins with the theoretical background and hypotheses development of this study. We then describe the research design and methodology. After discussing the findings, the paper highlights implications for both research and practice and points toward promising areas for future research. 2. Theoretical Background and Hypotheses Development A critical review of research on online consumer behavior revealed that the conceptual framework. 0-7695-2507-5/06/$20.00 (C) 2006 IEEE 1

proposed by Fishbein and Ajzen [13] that relates belief, attitude, and behavioral intention is the most widely accepted framework for the study of Internet shopping adoption. This framework suggests that attitude toward some object depends on the direct effects of beliefs about the object, while attitude has a direct positive impact on behavioral intention. As shown in Figure 1, consumers beliefs of Internet shopping features (perceived usefulness, perceived ease of use, and perceived enjoyment) determine their attitudes toward Internet shopping, and the attitudes formed, in turn affect consumers intention to shop online. In addition to these basic variables, Monsuwe et al. [27] suggested that there exist exogenous factors moderating the relationships between the core constructs in the framework of Internet shopping. Given the connectivity nature of the Internet, consumers can easily interact and exchange shopping experiences with other consumers using online discussion forums. In this study, we focus primarily on the moderating role of positive informational social influence in the relationships among the key variables in the conceptual framework of Internet shopping. The following sections will elaborate on the theory base and derive the hypotheses. Beliefs Usefulness Ease of Use Enjoyment Positive Informational Social Influence H1 H2 H3 H4 Figure 1: Research Model and Hypotheses Behavioral Intention Direct effect Moderating effect 2.1. Usefulness, Ease of Use, and Enjoyment of Internet Shopping Beliefs refer to a person s subjective probability judgments concerning some discriminable aspect of his world; they deal with the person s understanding of himself and his environment. (p. 131,[13]). Beliefs are the building blocks of a person s conceptual structure, and they serve as the informational base that ultimately determines a person s attitudes, intentions, and behavior. In the context of consumer-based electronic commerce, studies have shown that perceived usefulness and perceived ease of use are two of the most widely studied beliefs in Internet shopping [19]. usefulness refers to the degree to which a person believes that using a particular system would enhance his or her job performance [10]. ease of use refers to the degree to which a person believes that using a particular system would be free of effort [19]. Lee et al. [25] conducted a thorough review on the literature of technology acceptance, and they found that the impacts of these two beliefs on user attitude toward the innovation remain consistent and significant across different settings. Thus, we believe that these relationships also apply to the context of Internet shopping. Some empirical evidence (e.g. [6][18][40]) have already shown that if consumers find Internet shopping beneficial or easy to use, they will form a favorable feeling toward Internet shopping. usefulness and perceived ease of use represent the functional and utilitarian aspects of Internet shopping, recent studies ([24][27][33]) however suggested that there is a need to include the emotional and hedonic perspectives so as to provide a more holistic understanding of consumers adoption of e-commerce, as well as to improve the specificity and explanatory power of the research model. enjoyment refers to the extent to which the activity itself is perceived to be enjoyable in its own sake, apart from any other consequence that may result [11]. To some extent, perceived enjoyment is a form of intrinsic motivation, reflecting the emotional and hedonic aspects of Internet shopping. In the context of e-commerce, when consumers find Internet shopping fun and enjoyable, they will have a favorable feeling toward Internet shopping. There exists a great deal of empirical evidence supporting the role of intrinsic motivator (e.g. perceived enjoyment) in explaining the adoption of new technologies [11][37][39]. 2.2. toward Internet Shopping can be described as a learned predisposition to respond in a consistently favorable or unfavorable manner with respect to a given object (p. 6, [13]). An individual s attitude toward an object is basically determined by the set of his or her beliefs about the objects. However, research demonstrated that only a relative small number of beliefs serve as determinants of his or her attitude at any given time. For instances, the belief of an individual is usually determined by no more than five to nine beliefs about the object [13]. On the other hand, attitude toward an object is related to the individual s intentions to 2

perform the behaviors. Consistent with findings of most IT adoption studies, research in consumer-based electronic commerce found that attitude has a strong, direct, and positive effect on behavioral intention of Internet shopping (i.e. [6][21][40]). 2.3. Behavioral Intention of Internet Shopping Behavioral intention refers to a person s subjective probability that he will perform some behavior (p. 288, [13]). In the context of electronic commerce, behavioral intention refers to the likelihood that a consumer actually buys online. As mentioned before, there exists a strong relationship between attitude and intention. The more favorable an individual s attitude toward Internet shopping, the more likely he or she will intend to shop online. Although intention to shop is currently treated as a dependent variable in the research model (as shown in Figure 1), several researchers suggested that understanding behavioral intention is necessary for predicting behavior from attitudes. Past research [6] found that consumers intention to shop is an important factor determining consumers Internet shopping behavior. 2.4. Positive Informational Social Influence Social influence comes in two forms: Normative social influence (more commonly referred to as subjective norms) and informational social influence [2]. Subjective norms refer to the perceived social pressure on individuals to perform or not to perform a behavior, regardless of their individual beliefs and attitudes toward the behavior [1]. For example, some people may feel that by not adopting a particular technology, they may be perceived by others as old fashioned. This mindset creates a pressure for people to adopt the technology regardless of whether they have a positive or negative attitude toward the technology. On the other hand, information social group influence is a learning process in which people observe the successful experiences of their social groups with an innovation, before deciding whether to adopt it. In particular, research has indicated that people tend not to adopt an innovation until after they learn of social others (such as peers) successful experiences with the innovation [30]. As noted earlier, past research in group decision indicates that informational social influence could actually have a moderating impact on innovation adoption. Though theory and research in group decision have acknowledged the moderating role of informational social influence on innovation adoption, the moderating effect of informational social influence has not been studied with regard to Internet shopping. Previous research suggested that information from external sources (e.g. online discussion forum) can enhance consumers confidence in their beliefs or attitudes toward some object [28]. Spreng and Page [35] suggested that the more confidence an individual is in his or her belief, the more likely it is the belief will influence attitude formation. In a similar fashion, Fazio and Zanna [12] suggested that the more confidence an individual is in his or her attitude toward some object, the more likely it is that this attitude will guide later behavior toward the object. Building upon these arguments, we believe that positive informational social influence can be viewed as a moderating variable strengthening the relationship between beliefs and attitude, as well as the relationship between attitude and behavioral intention. Hypothesis 1: The positive relationship between perceived usefulness and attitude toward Internet shopping will be stronger when consumers have the positive informational social influence. Hypothesis 2: The positive relationship between perceived ease of use and attitude toward Internet shopping will be stronger when consumers have the positive informational social influence. Hypothesis 3: The positive relationship between perceived enjoyment and attitude toward Internet shopping will be stronger when consumers have the positive informational social influence. Hypothesis 4: The positive relationship between attitude toward Internet shopping and intention to shop online will be stronger when consumers have the positive informational social influence. 3. Research Method The objective of this study is to understand the moderating role of positive informational social influence on consumers decision on Internet shopping. The study tested the impact using a laboratory experiment. The sections below describe in detail the experimental task, the participant profile, the experimental procedures, and the measurement. 3.1. Experimental Task The experimental task required subjects to decide whether to make online purchases of tickets in a cinema website. To augment realism in the experimental task [16], we used a real cinema website 3

(www.cinema.com.hk). Screen shot of the homepage of the cinema website is shown in Figure 2. included in this experiment. Of the 104 individuals participating in the study, 48% were males and 52% were females. Over 50% of them aged 20-25. 52% were business students, the others majored in Sciences and Engineering (24%), Social Studies and Languages (15%) and others (9%). Undergraduate students comprised around 90% of the participants, the remainder were graduate students. Most of the participants were experienced Internet users, with over 80% have used the Internet for more than 4 years. All of them used the Internet regularly, with 94% using the Internet more than once per day. The sampling unit of this study is believed to be the most active Internet user group 1. 3.3. Experimental Procedures Figure 2: Screen shot of the Homepage of the Cinema Website. There were two experimental groups in this study, the treatment group and the control group. For the treatment group, participants were required to login and browse through an online discussion forum comprised of other consumers messages about their positive online purchasing experiences with the cinema website. The online discussion forum and the messages were created by the research assistants. Screen shot of the online discussion forum is shown in Figure 3. There were a total of six sessions (Three sessions for the treatment group and three sessions for the control group). Two sessions (one treatment group and one control group) were held at one time and each session held in a computer laboratory had around 20 participants. Participants were first randomly assigned to an experimental session and a computer. The experimental administrator then introduced the task. For the control group, participants were told to view the cinema website and decide whether they will make online purchases through the cinema website. The participants then completed an online questionnaire containing measures of research variables and their demographic information. For the treatment group, apart from browsing the cinema website, participants had to login and browse through the online discussion forum for 10 minutes before they filled in the online questionnaire. No communication was allowed with one another during the experiment. Responses were audited with respect to the time spent by participants on the website, the online discussion forum (for treatment group), and the completion of the subsequent online questionnaire. 3.4. Measures Figure 3: Screen shot of the Online Discussion Forum 3.2. Participants An invitation to participate in this study was via email broadcasting, posters, and flyers inside the campus in a local university. Participants were provided with around US$7 for their time spent in the experiment. A total of 104 individuals were finally Table 1 presents a summary of constructs and measures used for this study. We used items that had been validated in prior research, but modified the wording of the questionnaire in order to fit this particular context of online ticket purchases. Measures of perceived usefulness, perceived ease of use, attitude, and behavioral intention were borrowed from Taylor and 1 http://www.clickz.com/stats/sectors/demographics/article.php/3455741 4

Todd [36], Gefen and Straub [19], and Venkatesh [39]. All these measures were phrased as questions on a seven-point Likert scales, from 1 = strongly disagree to 7 = strongly disagree. Usefulness [36] PU1 Purchasing tickets through the Broadway Circuit Website will be benefit to me. PU2 The advantages of using the Broadway Circuit Website to purchase tickets will outweigh the disadvantages. PU3 Overall, using the Broadway Circuit Website will be advantageous. Ease of Use [19] EOU1 The Broadway Circuit Website is easy to use. EOU2 It is easy to become skillful at using the Broadway Circuit Website. EOU3 It is easy to interact with the Broadway Circuit Website. Enjoyment [39] ENJOY1 I find using the Broadway Circuit Website to be enjoyable. ENJOY2 The actual process of using the Broadway Circuit Website is pleasant. ENJOY3 I have fun using the Broadway Circuit Website. [36] ATT1 Buying tickets at the Broadway Circuit Website is a good idea. ATT2 Buying tickets at the Broadway Circuit Website is a wise idea. ATT3 I like the idea of using the Broadway Circuit Website to buy tickets. ATT4 Using the Broadway Circuit Website to buy tickets would be pleasant. Behavioral Intention [36][19] BI1 I am very likely to buy tickets from the Broadway Circuit Website. BI2 I intended to use the Broadway Circuit Website to buy tickets. BI3 I intended to use the Broadway Circuit Website frequently to buy tickets. BI4 I would seriously contemplate buying from the Broadway Circuit Website. BI5 It is likely that I am going to buy from the Broadway Circuit Website. Table 1: List of Measures 4. Analysis and Results The data analyses were carried out using structural equation modeling, a powerful second generation multivariate analysis technique that permits the estimation of multiple and interrelated dependence relationships, has the ability to represent unobserved concepts in these relationships, and account for measurement errors in the estimation process [20]. It is an important tool for studying causal models [14], such as the one proposed in this study. Before we tested our research model, we first performed the manipulation checks to ensure the treatment was successful. 4.1. Manipulation Checks The manipulation on positive informational social group influence was checked. Participants were requested to indicate the extent to which they agreed with the seven statements as shown in Table 2. For instances, we would expect the treatment group who were exposed to the positive informational social group were more likely to agree that the online consumer group displays positive messages about the cinema websites from satisfied customers (t=6.67, p<0.001), and that the online consumer group shows successful online purchase experiences with the cinema websites from existing customers (t=5.74, p<0.001). These results suggested that the experimental manipulation between the treatment group and control group was successful. Online Discussion Forum The online consumer group displays positive messages about the Broadway Circuit Website from satisfied customers. The online consumer group shows successful online purchase experiences with the Broadway Circuit Website from existing customers. I can see from the online consumer group that existing customers are satisfied with the Broadway Circuit Website. Some messages in the online consumer group contain the details about the online ticket buying process. I notice that other customers use different languages to describe their online experiences with the Broadway Circuit Website in the online consumer group. I notice that most customers mentioned their happy experiences with the Broadway Circuit Website in the online consumer group. The online consumer group includes messages about special offers provided by VISA when they tickets using VISA credit card. Table 2: Manipulation Checks (Note: ** p<0.001, * p<0.01) 4.2. PLS Analyses Control Group (Mean) Treatment Group (Mean) Mean Diff. 4.83 6.10 1.27** 4.77 5.98 1.21** 4.52 6.35 1.83** 4.79 5.50 0.71* 4.58 5.52 0.94** 4.10 6.35 2.25** 4.37 5.85 1.48** We chose PLS-Graph (Partial Least Squares) version 3.00 [8] to perform the analysis in this study. PLS an implementation of structural equation modeling that has been gaining interest among IS researchers in recent years because of its ability to model latent constructs under conditions of nonnormality and small to medium sample sizes [9][41]. Following the two-step analytical procedures [20], we first examined the measurement model, then the structural model. The rationale of this two-step approach is to ensure our conclusion on structural relationship is drawn from a set of measurement instruments with desirable psychometric properties. 5

4.2.1. Measurement Models. Convergent validity indicates the extent to which the items of a scale that are theoretically related should be related in reality. A composite reliability of 0.70 or above and an average variance extracted of more than 0.50 are deemed acceptable [15]. Table 3 summarizes the factor loadings, composite reliability, and average variance extracted of the measures for the control group and treatment group. All items have significant path loadings at the 0.01 level and fulfill the recommended levels of the composite reliability and average variance extracted, with composite reliability at 0.71 or above and average variance extracted at 0.50 or above. Based on the criteria mentioned above, the measures of the constructs in this study had adequate convergent validity. Usefulness Control Treatment Item Loading PU1 0.79 0.89 PU2 0.85 0.74 PU3 0.88 0.91 Composite Reliability 0.88 0.88 Average Variance 0.71 0.72 Extracted Ease of Use Control Treatment Item Loading EOU1 0.52 0.86 EOU2 0.52 0.68 EOU3 0.93 0.84 Composite Reliability 0.71 0.84 Average Variance 0.50 0.63 Extracted Enjoyment Control Treatment Item Loading ENJOY1 0.94 0.86 ENJOY2 0.86 0.87 ENJOY3 0.89 0.75 Composite Reliability 0.93 0.87 Average Variance 0.81 0.69 Extracted Control Treatment Item Loading ATT1 0.88 0.84 ATT2 0.78 0.71 ATT3 0.81 0.86 ATT4 0.91 0.86 Composite Reliability 0.91 0.89 Average Variance 0.72 0.68 Extracted Behavioral Intention Control Treatment Item Loading BI1 0.89 0.89 BI2 0.90 0.93 BI3 0.85 0.84 BI4 0.84 0.91 BI5 0.86 0.88 Composite Reliability 0.94 0.95 Average Variance Extracted 0.76 0.79 Table 3: Convergent Validity of the Measures Testing for discriminant validity involves checking whether the items measure the construct in question or other (related) constructs. Discriminant validity was verified with the squared root of the average variance extracted for each construct higher than the correlations between it and all other constructs [15]. As shown in Table 4, each construct shares greater variance with its own block of measures than with the other constructs representing a different block of measures. Control Group PU EOU ENJOY ATT BI PU 0.84 Treatment Group EOU 0.10 0.71 ENJOY 0.27 0.26 0.90 ATT 0.64 0.20 0.42 0.85 BI 0.74 0.14 0.38 0.65 0.87 PU 0.85 PU EOU ENJOY ATT BI EOU 0.62 0.80 ENJOY 0.56 0.55 0.83 ATT 0.71 0.63 0.54 0.82 BI 0.40 0.40 0.45 0.71 0.89 Table 4: Correlations between Constructs (Diagonal Elements are Square Roots of the Average Variance Extracted) Overall, these results provide strong empirical support for the convergent validity and discriminant validity of the scales of our research model. 4.2.2. Structural Models. Figures 4 and 5 present the results of our study with overall explanatory power, estimated path coefficients (all significant paths are indicated with an asterisk), and associated t- value of the paths for the control group and treatment group respectively. Tests of significance of all paths were performed using the bootstrap resampling procedure. As shown in Figure 4, the structural model for control group explains 42.7% variance. toward Internet shopping exhibits a strong and significant effect on intention ( =0.65, t=10.93). Among the three beliefs, perceived usefulness posits the strongest impact ( =0.56, t=4.64) on attitude toward Internet shopping, followed by perceived enjoyment ( =0.25, t=2.27). However, perceived ease of use does not have any significant effect on consumers attitude toward Internet shopping ( =0.07, t=0.34). Figure 5 shows the result of the structural model for the treatment group. The model explains 51.0% variance. Similar to the control group, attitude toward Internet shopping has a very strong and significant effect on intention ( =0.71, t=11.39). Among the three beliefs, perceived usefulness also posits the strongest impact ( =0.47, t=4.12) on attitude toward 6

Internet shopping. ease of use becomes a significant factor determining attitude ( =0.27, t=2.27). However, perceived enjoyment becomes statistically insignificant ( =0.13, t=1.13). Usefulness Ease of Use Enjoyment 0.560** 0.073 0.253** 0.654** Behavioral Intention R 2 =0.477 R 2 =0.427 Control Group Sample size: 52 Figure 4: Structural Model of the Control Group Usefulness Ease of Use Enjoyment 0.467** 0.273** 0.129 0.714** Behavioral Intention R 2 =0.571 R 2 =0.510 Treatment Group Sample size: 52 Figure 5: Structural Model for the Treatment Group 4.3. Hypotheses Testing Hypotheses on the impact of positive informational group can be tested by statistically comparing corresponding path coefficients between the two structural models. The statistical comparison was carried out using the procedure (See Appendix A) suggested in Keil et al. [23]. Table 5 summarizes the comparisons. Results show that among the three belief variables, positive informational social influence only enhances the relationship between respondents perceived ease of use and attitude toward Internet shopping. The path coefficient from perceived ease of use to attitude for the treatment group was significantly stronger than the corresponding path coefficient for the control group, providing support to Hypothesis 2. However, the path coefficients from perceived usefulness to attitude, and from perceived enjoyment to attitude for the treatment group, was significantly weaker than the corresponding paths for the control group. The results do not provide support to Hypothesis 1 and Hypothesis 3. Results also show that positive informational social influence enhances the relationship between respondents attitude toward Internet shopping and their intention to shop online. The path coefficient from attitude to behavioral intention for the treatment group was significantly stronger than the corresponding path for the control group, providing support to Hypothesis 4. To further explore how positive informational social influence consumers intention to shop online, a t-test for mean difference between the treatment group (mean = 4.50, std dev = 1.24) and control group (mean = 3.88, std dev = 1.31) was carried out. The results showed that the two groups had significant difference in terms of their intention to shop online (t=2.44, p=0.02). Thus, we believe that positive informational social influence can enhance consumers intention to shop online. Path Usefulness -> Ease of Use -> Enjoyment -> -> Behavioral Intention Control Group Treatment Group Conclusion 0.56 0.47 H1 is not supported 0.07# 0.27 H2 is supported (with t- statistics =34.06**) 0.25 0.13# H3 is not supported 0.65 0.71 H4 is supported (with t- statistics =81.37**) Table 5: Path Comparisons between the Control Group and Treatment Group (**p<0.001) (#Note: Path coefficient is not statistically significant) 5. Conclusion and Discussion Motivated by the need to understand consumers decision of Internet shopping, this study examines the moderating effect of positive informational social influence on the relationships among consumers beliefs, attitudes, and behavioral intention of Internet shopping. Our findings showed that positive informational social influence strengthens the relationship between consumers attitude toward Internet shopping and their intention to shop, as well as the relationship between consumers perceived ease of use and their attitude. Surprisingly, positive informational social influence does not enhance the relationships between the other two beliefs (perceived usefulness and perceived enjoyment) and attitude toward Internet shopping. The findings of this study have several implications for research and practice that are discussed next. 7

5.1. Implications for Theory and Research The main theoretical contribution of this research is that while past studies on Internet shopping have focused largely on the impact of beliefs and attitudes on consumers intention to shop online [27], this study further investigates how an exogenous variable, positive informational social influence, could affect consumer adoption of Internet shopping. Instead of modeled informational social influence as having a direct impact on adoption intention, this study proposes that positive informational social influence would have a moderating impact on the relationships between consumers beliefs of Internet shopping features and their attitude toward Internet shopping, as well as their attitude and intention to shop online. By taking a contingency approach in this study, we found a significant increase in the amount of variance explained of the model for Internet shopping. The model for the control group (without the exposure to the positive informational social influence) explains 42.7% of the variance in their intention to shop online, while the model for the treatment group (with the exposure to the positive informational social influence) explains 51.0% of the variance. Consistent with past literature, positive informational social influence is found significant in moderating the link between attitude and behavioral intention. However, among the three beliefs, positive informational social influence only significantly moderates the link between perceived ease of use and attitude toward Internet shopping. One possible explanation for our results is that most messages in the online discussion forum are pertinent to the easy to use feature of the cinema website. These messages reinforce respondents pre-existing beliefs about the ease of use feature of Internet shopping, enhance their confidence about their perception of this particular feature, and thus respondents are more likely to depend on this belief to form their attitude toward Internet shopping. Another important implication of this study is that we considered both utilitarian and hedonic perspectives in understanding consumers adoption of Internet shopping. The results are rather consistent with other studies in the adoption of new technology, perceived usefulness remains as the dominant factor affecting attitude toward Internet shopping (in both the control group and treatment group). Moreover, our results show that positive attitude is an important intermediate step to the formation of intention to shop online. The results of this study further reaffirm that the conceptual framework that relates beliefs, attitude, and behavioral intention [13] is also applicable to the electronic commerce context. 5.2 Implications for Practice While this study leads to several interesting implications for theory and research, it is also relevant for Internet marketing and website design practitioners. This study highlights how successful experiences from other consumers affect consumers decision of Internet shopping. The results suggest that positive informational social influence could promote Internet shopping behavior. Internet merchants should therefore, target their marketing efforts at the beliefs and attitudes of potential shoppers, through emphasizing the successful experiences of their social groups. For instances, Internet merchants could organize and maintain a virtual community, a kind of online social entity comprising of both existing and potential customers, that facilitates consumers to share opinions or exchange shopping experiences among themselves. Among the three beliefs of Internet shopping features, perceived usefulness of Internet shopping is consistently a dominant factor affecting the formation of consumers attitude toward Internet shopping. Internet merchants should therefore emphasize how the Internet offers a range of advantages that can collectively attract massive interest in Internet shopping. For instance, the Internet enables consumers to shop or do transactions 24 hours a day, all year around from almost any location. It also provides consumers more choices and allows them to have quick comparisons. Moreover, it allows consumers to interact, exchange ideas, and to compare experiences with other customers in the electronic communities. 5.3 Limitations and Future Research One potential limitation of this study is related to the fact that data was collected from relatively homogeneous student subjects of a collectivistic culture [22]. Culture has a significant impact on country-level Internet shopping rates [26]. In addition, people from the collectivistic cultures tend to view ingroup members more positively than out-group members. Sia et al. [34] found that customer endorsement can build consumer trust in Internet shopping more effective in the collectivistic culture (Hong Kong) than in the individualistic culture (Australia). Thus, it is possible that the impact of positive informational social influence on consumers 8

decision of Internet shopping may be stronger in the collectivistic culture than in the individualistic culture. Replication of this study to other Internet users and other countries is necessary before the results can be generalized. Another limitation is that we have not manipulated the messages in the online discussion forum. Thus, one direction for future research would be to extend this study through manipulating the messages in the online discussion forum. For instance, respondents in each treatment group should expose to the messages related to one particular Internet shopping feature, we will then examine whether these messages would interact significantly with that particular belief in determining attitude toward Internet shopping. Finally, this study only focuses on the impact of positive informational social influence on consumers decision in Internet shopping. Indeed, in conventional shopping, negative word of mouth travels farther and faster than positive word of mouth travels [4]. There is a growing interest of negative asymmetry in the field of IS [5]. Recent literature suggested that events that are negatively valenced will have longer lasting and more intense consequences than positively valenced events of the same type [7]. Future research may focus on the negativity asymmetry in informational social influence. Acknowledgement The work described in this paper was supported by a grant from City University of Hong Kong (Project No. 7001160). References [1] Ajzen, I. "The theroy of planned behavior," Organizational Behavior and Human Decision Processes (50:2) 1991, pp 179-211. [2] Bearden, W.O., Calcich, S.E., Netemeyer, R., and Teel, J.E. "An exploratory investigation of consumer innovativeness and interpersonal influences," Advances in Consumer Research (13:1) 1986, pp 77-82. [3] Bhatnagar, A., and Ghose, S. "Segmenting consumers based on the benefits and risks of Internet shopping," Journal of Business Research (57:12) 2004, pp 1352-1360. [4] Cafferky, M.E. Let your customers do the talking: Word-of-mouth marketing tactics Upstart Publishing, 1995, p. 228. [5] Cenfetalli, R.T. "Inhibitors and enablers as dual factor concepts in technology usage," Journal of the Association for Information Systems (5:11) 2004. [6] Chen, L.D., Gillenson, M.L., and Sherrell, D.L. "Enticting online consumers: An extended technology acceptance perspective," Information & Management (39:8) 2002, pp 705-719. [7] Cheung C.M.K., and Lee, M.K.O., The Asymmetric Impact of Website Attribute Performance on User Satisfaction: An Empirical Study, e-service Journal, 3(3), Summer 2004/05. [8] Chin, W.W. PLS Graph Manual, 1994. [9] Chin, W.W. "The partial least squares approach for structural equation modeling.," in: Modern Methods for Business Research, Marcoulides G.A. Hillsdale, NJ: :awrence Erlbaum Associates, 1998, pp. 295-336. [10] Davis, F.D. " usefulness, perceived ease of use, and user acceptance of information technology," MIS Quarterly (13:3) 1989, pp 319-340. [11] Davis, F.D., Bagozzi, R.P., and Warshaw, P.R. "Extrinsic and intrinsic motivation to use computers in the workplace," Journal of Applied Social Psychology (22:14) 1992, pp 1111-1132. [12] Fazio, R.H., and Zanna, M.P. "Direct experience and attitude-behavior consistency.," Advances in Experimental Social Psychology (14) 1981, pp 161-202. [13] Fishbein, M., and Ajzen, I. Belief,, Intention and Behavior: An Introduction to Theory and Research Addison-Wesley, MA, 1975. [14] Fornell, C. Second Generation of Multivariate Analysis, Methods: Volume 1 Praeger Publishers, New York, 1982. [15] Fornell, C., and Larcker, D. "Evaluating structural equation models with unobservable variables and measurement error," Journal of Marketing Research (18:1) 1987, pp 39-50. [16] Fromkin, H.L., and Streufert, S. "Laboratory experimentation," in: Handbook of Industrial and Organizational Psychology, Rand McNally Publishing Company, Chicago, 1976, pp. 415-465. [17] Garbarino, E., and Strahilevitz, M. "Gender differences in the perceived risk of buying online and the effects of receiving a site recommendation," Journal of Business Research (57:7) 2004, pp 768-775. [18] Gefen, D., Karahanna, E., and Straub, D.W. "Trust and TAM in Online Shopping: An Integrated Model," MIS Quarterly (27:1) 2003, pp 51-90. [19] Gefen, D., and Straub, D. "The relative importance of perceived ease of use in IS adoption: A study of e- commerce adoption," Journal of Association of Information Systems (1) 2000. [20] Hair, J.F., Anderson, R.E., Tatham, R.L., and Black, W.C. Multivariate Data Analysis Prentice-Hall Englewood Cliffs, NJ, 1998. [21] Hansen, T., Jensen, J.M., and Solgaard, H.S. "Predicting online grocery buying intention: a comparison of the theory of reasoned action and the theory of planned behavior," International Journal of Information Management (24:6) 2004, pp 539-550. [22] Hofstede, G. Culture's Consequences Thousand Oaks, CA, 2001. [23] Keil, M., Tan, B.C.Y., Wei, K.-K., Saarinen, T., Tuunainen, V., and Wassenaar, A. "A cross-cultural study on escalation of commitment behavior in software projects," MIS Quarterly (24:2) 2000, pp 299-325. [24] Kourfaris, M. "Applying the technology acceptance model and flow theory to online consumer behavior," Information Systems Research (13:2) 2002, pp 205-223. 9

[25] Lee, Y., Kozar, K.A., and Larsen, K.R.T. "The technology acceptance model: Past, present, and future," Communications of the Association for Information Systems (12) 2003, pp 752-780. [26] Lim, K.H., Leung, K., Sia, C.L., and Lee, M.K.O. "Is ecommerce boundary-less? Effect of individualismcollectivism and uncertainty avoidance on Internet shopping," Journal of International Business Studies (35:6), 2004, pp. 545-559. [27] Monsuwe, T.P., Dellaert, B.G.C., and Ruyter, K.d. "What drives consumers to shop online? A literature review," International Journal of Service Industry Management (15:1) 2004, pp 102-121. [28] Oliver, R.L. "A cognitive model of the antecedents and consequence of satisfaction decisions," Journal of Marketing Research (XVII:November) 1980, pp 460-469. [29] Pires, G., Stanton, J., and Eckford, A. "Influences on the perceived risk of purchasing online," Journal of Consumer Behavior (4:2) 2004, pp 118-131. [30] Rogers, E. Communication Technology: The New Media in Society The Free Press, New York NY, 1986. [31] Rogers, E. Diffusion of Innovations, (Fourth ed.) The Free Press, New York, 1995. [32] Ryan, M.J. "Behavioral intention formation: The interdependency of attitudinal and social influence variables," The Journal of Consumer Research (9:3) 1982, pp 263-278. [33] Shang, R.-A., Chen, Y.-C., and Chen, L. "Extrinsic versus intrinsic motivations for consumers to shop on-line," Information and Management (42:3) 2005, pp 401-413. [34] Sia, C.L., Lim, K.H., Lee, M.K.O., Leung, K., Huang, W., and Benbasat, I. "Web strategies to promote Internet shopping: Uniformity versus cultural-customization," Working paper) 2004. [35] Spreng, R.A., and Thomas J. Page, J. "The Impact of Confidence in Expectations on Consumer Satisfaction," Psychology & Marketing (18:11), November 2001 2001, pp 1187-1204. [36] Taylor, S., and Todd, P.A. "Understanding information technology usage: A test of competing models," Information Systems Research (6:2) 1995, pp 144-176. [37] Van der Heijden, H. "User Acceptance of Hedonic Information Systems," MIS Quarterly (28:4) 2004, pp 695-704. [38] Varadarajan, P.R., and Yadav, M.S. "Marketing strategy and the Internet: An organizing framework," Journal of the Academy of Marketing Science (30:4) 2002, pp 296-312. [39] Venkatesh, V. "Determinants of perceived ease of use: Integrating control, intrinsic motivation, and emotion into the technology acceptance model," Information Systems Research (11:4) 2000, pp 342-365. [40] Vijayasarathy, L.R. "Predicting consumer intentions to use on-line shopping: the case for an augmented technology acceptance model," Information & Management (41:6) 2004, pp 747-762. [41] Wold, H. "Systems under indirect observation using PLS," in: A Second Generation of Multivariate Analysis Methods, C. Fornell (ed.), Praeger, New York, 1982. Appendix A Statistics developed by Wynne Chin to Compare Corresponding Paths S pooled = {[N 1 1) / (N 1 + N 2 2)] x SE 1 2 + [(N 2 1) / (N 1 + N 2 2)] x SE 2 2 } t spooled = (PC 1 PC 2 ) / [S pooled x (1/N 1 + 1/N 2 )] where S pooled is the pooled estimator for the variance t spooled refers to the t-statistic with (N1 + N2 2) degrees of freedom Ni is the sample size of dataset for culture i SEi is the standard error of path in structural model of culture i PCi is the path coefficient in structural model of culture i 10