The Internet has rapidly evolved into a global phenomenon and is affecting our workplaces and the marketplace, and has totally changed the ways people do business (Rowley, 1996). It enables instantaneous communication and interaction among individuals and organizations and allows real-time global access to information, products, and services. The Internet is changing the way consumers shop and many companies have started using the Internet with the aim of cutting marketing costs, thereby reducing the price of their products and services to stay ahead in highly competitive markets (Roselius, 1971). Companies also use the Internet to convey, communicate, and disseminate information, to sell products, to acquire feedback, and also to conduct satisfaction surveys with customers. Customers use the Internet not only to buy products online, but also to compare prices, product features, and after-sale service facilities they will receive if they purchase the product from a particular store. Many experts are optimistic about online businesses.

Some of the biggest companies today have grown by taking advantage of the efficient, low-cost advertising, and commerce available through the Internet, also known as e-commerce. It is the fastest way to spread information simultaneously to a large number of people.

Internet shopping is the process consumers go through to purchase products or services over the Internet. Internet shopping technologies are essentially self-service, offering the benefits and convenience of 24-hour, ubiquitous availability, time and money savings, and reduced anxiety caused by judgmental service representatives (Bitner, 2001; Meuter, Ostrom, Roundtree, & Bitner, 2000). An online shop, Internet shop, web shop, or online store evokes the physical analogy of buying products or services at a bricks-and-mortar retailer. It is an electronic commerce application used for both the business to business and business to consumer marketplace. Some issues of concern about Internet shopping include fluctuating exchange rates for foreign currencies, local and international laws, and delivery methods.

With some exceptions (Grazioli & Jarvenpaa, 2002; van der Heijden, Verhagen, & Creemers, 2001), the function of the perceived risk in online shopping consumer behavior has not been examined as an explanatory indicator of the slow uptake in particular buying situations. Consumer purchase intentions can be examined as an outcome of the decision process (Bettman, 1979; McGaughey & Mason, 1998) through the relative value or risk presented by the purchase experience (Novak, 2002). Perceived consumer risk has been the subject of numerous studies over the last four decades (Mitchell, 1999; van der Heijden et al., 2001). These researchers modeled the role of perceived risk as an indirect influence on consumer online purchase intentions, feeding through to consumer attitudes and affecting the willingness to purchase.

In the past, the emphasis in research was almost always on cost-benefit analysis as a risk factor. In earlier work perceived risk was shown to consist of many types, including financial, physical, social, and time-loss risk (Jacoby & Kaplan, 1972; Lu, Hsu, & Hsu, 2005; Roselius, 1971; Yi & Davis, 2003), and the importance of risk was associated with the opportunity cost of making a purchase decision. Dowling and Staelin (1994) found that consumers required more information to make riskier decisions. Donthu (1991) found a negative relationship between risk-averse consumers and Internet shopping tendencies. In fact, in a recent study Liu and Wei (2003) pointed out that when considering goods as compared to services online, consumers’ e-commerce adoption decisions were influenced more strongly by their risk perceptions. In his seminal work on risk taking, Bauer (1967) propounded the idea that consumer behavior involves risk in the same sense that any action by a consumer will produce outcomes that he or she views with some degree of uncertainty.

Our aim in this study was to discuss which risk factors affect consumers who shop online. We wanted to establish what is the most emphatic perceived risk factor affecting the attitude toward online shopping. In this research we considered seven types of risks of shopping on Internet websites; convenience risk, financial risk, performance risk, physical risk, physiological risk, social risk, and time risk.

Literature Review

Perceived Risk

The theory of perceived risk is an attempt to answer some of the questions related to consumer decision-making that can help in understanding consumer behavior. As defined by Field (1986), perceived risk is the psychological sensation of risk experienced by individuals when making a decision in a less than certain state. Consumer perceptions of risk have been widely dealt with in previous literature and have been shown to shape all purchase decisions to varying degrees, thereby influencing consumer behavior (Bauer, 1967; Bettman, 1979; Chaughuri, 1997; Cunningham, 1967; Mitchell, 1999). Research on perceived risk in different buying contexts has developed into advocacy of risk-taking behavior as a possible measure of consumer attitudes towards a purchase (Bauer), so that any action of a consumer will produce outcomes that he or she views with some degree of uncertainty.

Perceived risk is usually measured as a multidimensional construct made up of physical loss, financial loss, psychological loss, time loss, performance risk, and social risk (Jacoby & Kaplan, 1972; Pires, Stanton, & Eckford, 2006). Perceived risk theories have been applied to different consumer behavior contexts (Mitchell, 1999), but only recently has attention turned to using perceived risk in explaining consumer reluctance to use the Internet for various purchasing tasks. In this paper, we have assumed the perceived risk of purchase intention, and our focus has turned to the issue of how perceived risk may be affected by other elements of the buying situation once the store website has been selected.

Huang and Min (2007) considered how perceived risk would affect bidders’ conformity, and they suggested that marketers can take specific actions, such as positive word-of-mouth communication, to create a positive impression. Perceived risk has also been shown to have some effect on the herding behavior of organizations (Huang & Chen, 2008). Lin and Fang (2006) examined the effects of perceived risk on the sender and the receiver of word-of-mouth (WOM) communications. They found that people will avoid WOM communications when a product is inherently risky, as the outcome of purchasing may be more serious than when a product is not risky and making a purchase may result in a feeling of regret and guilt.

Jarvenpaa and Todd (1996) reported that perceived risk influenced attitudes toward online shopping but not the intention to shop online. However, Vijayasarathy and Jones (2000) found perceived risk influenced both attitudes toward online shopping and intention to shop online. In other similar studies perceived risk has been found to have negatively influenced consumers’ attitudes or intentions to shop online (Liu & Wei, 2003; van der Heijden, Verhagen, & Creemers, 2003).

Intention to increase the frequency of online shopping The intention to increase the frequency of online shopping is the ultimate dependent variable in this model. In this research we regarded the intention to increase frequency as the consumer’s expression of support for online shopping and as the recognition that she or he is responsible for purchases made barring unforeseen events (Ajzen & Fishbein, 1977). Therefore, we regarded the intention to increase chiefly to be functioning as a control variable that would enable us to assess whether this research model accurately predicts changes in the intention to increase the frequency of online shopping.
H1: The more positive a consumer’s attitude towards online shopping, the greater will be the intention to increase the frequency of online shopping.

Attitude toward online shopping We assumed that a positive attitude towards online shopping would positively influence the intention to increase the frequency of online shopping. The relationship between attitude and intention is based on the theory of reasoned action (TRA), in which it is stated that the beliefs about an outcome shape the attitude towards performing that behavior. Attitude, in turn, influences the intention to perform the behavior and ultimately, influences the behavior itself (Wixom & Todd, 2005). Therefore, we reasoned that the more positive the attitude towards shopping, the greater will be the intention to increase the frequency of online shopping. This relationship has been empirically tested in numerous studies, especially those focusing on the technology acceptance model.
H2: The higher the perceived risk of online shopping, the more negative will be the consumer’s attitude towards online shopping.

Convenience risk Defined as the convenience dimension in this study, convenience risk refers to consumers’ perceived usefulness of B2C E-commerce. It includes the inconvenience incurred during online transactions, often resulting from difficulty in navigation and/or submitting orders, or delay in receiving products (Forsythe, Liu, Shannon, & Gardner, 2006). Consumers view the Internet as an instrument of convenience. Convenience is the primary benefit cited by respondents as a result of shopping on the Internet.
H3: The higher the convenience risk, the higher the consumer’s overall perceived risk of online shopping will be.

Financial risk Defined as the risk that the actual cost may exceed the planned costs of online shopping, financial risk is the practice of creating value in a firm using financial instruments to manage exposure to risk. Similar to general risk management, financial risk management requires that the sources of risk be identified, that the risk is measured, and plans are put in place to address them. Financial risk can be caused by manipulation or misuse of financial information by the intended recipients of credit card numbers or by interception of credit card numbers by hackers.
H4: The higher the financial risk, the higher the consumer’s overall perceived risk of online shopping will be.

Physical risk Defined as the physical risk of consulting services within high-risk business operating environments. It relates to the safety and health of the individual. Physical risk involves the potential threat to a consumer’s safety or physical health and well-being.
H5: The higher the physical risk, the higher the consumer’s overall perceived risk of online shopping will be.

Psychological risk Defined as the risk that the decision to shop online has a negative effect on the responsible consumer’s peace of mind or self-perception, the feeling of a lack of control over the access others may have to personal information during the online navigation process is a psychological risk. This may prevent many consumers from providing information to web providers in exchange for access to information offered on the site (Jacoby & Kaplan, 1972).
H6: The higher the psychological risk, the higher the consumer’s overall perceived risk of online shopping will be.

Performance risk Defined as the risk that the service provided by the vendor will not be delivered as expected through online shopping, this manifests itself as the loss incurred when a product or brand does not perform as expected. Performance risk is the uncertainty and the consequence of a product not functioning at the expected level. Sometimes referred to as quality risk, it is based on the belief that a product will not perform as well as expected or will not provide the desired benefits.
H7: The higher the performance risk, the higher the consumer’s overall perceived risk of online shopping will be.

Social risk Defined as the disappointment in the individual by his friends in the case of poor product or service choices, this risk is evident when the consumer perceives that the purchase of a product or service may not meet the standards that is important to others, resulting in social embarrassment.
H8: The higher the social risk, the higher the consumer’s overall perceived risk of online shopping will be.

Time risk Defined as the time spent on the purchase of a product and the time wasted in the case of a poor product or service choice, this is made up of the time required for finding a suitable website, searching for information, and processing the transaction.
H9: The higher the time risk, the higher the consumer’s overall perceived risk of online shopping will be.

These hypotheses are summarized as the research concept in Figure 1.

Method

The sample population for this study was drawn from graduate and undergraduate students who had experienced online shopping in Taiwan.

Data Analysis

Descriptive statistics in this study were compiled using SPSS version 13.0 to analyze both the sample distribution and the answers from the survey. Through structural equation modeling (SEM), and SEM processing of the SEM analysis, we used the goodness of fit to verify the fit of sample and model, and to examine the factorial validity and path analysis of each factor. Finally, we verified the validity of the model. The main method of data collection for this study was a survey. The data were gathered from the survey and then analyzed using the partial least squares (PLS) approach. PLS is an implementation of SEM that has been gaining interest from information systems researchers in recent years because of the ability to model latent constructs under conditions of nonnormality and small to medium sample sizes with PLS (Chin, 1998).

Results

After the survey instrument was finalized and 300 questionnaires had been distributed, we received 222 responses, yielding a response rate of 74%. Table 1 shows the demographic description of respondents.

Table 2 shows the composite reliability values ranges from 0.795 to 0.944, above the acceptable value. For average variance extracted (AVE), a score of 0.5 indicates an acceptable level. Table 2 shows AVE in the range from 0.627 to 0.850, exceeding the recommended value.

Discriminant validity is the degree of distinction between items in constructs. Each item should correlate more highly with other items of the same construct than with items of other constructs. To assess discriminant validity (Agarwal & Karahanna, 2002), the square root of AVE from the construct should be greater than the variance shared between the construct and other constructs in the model (i.e., the square root of the AVE by a construct from its indicators should exceed the construct’s correlation with other constructs; Ajzen & Fishbein, 1977) and indicators should load more highly on their corresponding construct than on other constructs in the model (i.e., loadings should be higher than cross-loadings; Agarwal, 2002; Chin, 1998; Yi & Davis, 2003).

Table 3 displays the correlations of latent variables. These values should exceed the interconstruct correlations for adequate discriminant validity. Our hypotheses were tested using PLS. Discriminant validity was also acceptable, as shown by the square root of the AVE being larger than any of the correlations among the constructs. The bold diagonal elements are the square root of the variance shared between the latent constructs. For strong discriminant validity, the diagonal elements should be larger than any other corresponding row or column entry.

In our study, the adequacy of indicators in the measurement model enabled us to evaluate the explanatory power of the entire model as well as the predictive power of the independent variables. The explanatory power was examined by looking at the R-square of the dependent variables. As can be inferred from Figure 2, 39.7% (R² = 0.397) of the variation in perceived risk is explained by convenience, financial, physical, psychological, performance, social, and time risks. Moreover, 5.4% of the variation in attitude toward online shopping is explained by perceived risk. The R² value for the intention to increase the level of online shopping (R² = 0.473) was also encouragingly high. The results of the PLS estimates for testing this hypothesis are shown in Figure 2.

Conclusion

Gewald, Wullenweber, and Weitzel (2006) used five types of perceived risk in their research. Their data analysis revealed that all hypotheses were supported with results showing significant loadings. According to our results, perceived risk has a strongly negative influence on the attitude toward online shopping. Thus, Hypothesis 2 was fully supported. In our research we identified seven different types of risk: convenience, financial, physical, psychological, performance, social, and time.

Our findings support the inconsistency revealed in the review of relevant literature and add to the limited amount of perceived risk descriptions reported in our summary of all previous investigations regarding Internet-shopping-site users. The majority of the sample group in our study gave responses that tend to indicate divergent levels of perceived risk. Our main overall hypothesis was that perceived risk negatively affects the attitude toward online shopping. Taiwan is a country that has already achieved a high level of information technology development. The perception of risk is a behavioral belief and an important antecedent of the attitude toward online shopping. Therefore, perceived risk in Taiwan directly impacts on attitude, and indirectly influences the intention to increase the level of online shopping through the effect of attitude on intention.

The perceived risk factors that most strongly affected the attitude toward online shopping were the convenience, financial, physical, performance, and social factors. Our respondents perceived those five risks to be significant. Currently, the retail environment in Taiwan is a buyer’s market as consumers are being offered a wide choice of shopping experiences through several retail formats. However, no one format can offer all the shopping experiences desired by consumers.

As to the limitations of this study, there are certain points to keep in mind when interpreting the results of this research. In this study we focused on the consumer in Taiwan. Therefore, the results may not be representative of other cultures. The emphasis of our study was on perceived risk in this country. An analysis of exactly who makes online purchases in years to come may reveal a change in the group of people who shop online.

The contribution of this study lies in the confirmation of the importance to consumers’ of perceived risk of online shopping in Taiwan. In particular, we found perceived risk had a strong negative influence on the attitude toward online shopping in Taiwan. Time risk and psychological were not found to be significant statistically but still need further exploration in future studies.

In particular, psychological risk has always been an important consideration in consumer behavior research. Therefore, there is a need to examine psychological risk in relation to different shopping websites, brands of products, and product categories. Investigations could also be carried out with groups of people who have different lifestyles, to establish the possible differences of the perceptions of risk in relation to online shopping.

References

Agarwal, R., & Karahanna, E. (2000). Time flies when you’re having fun: Cognitive absorption and beliefs about information technology usage. MIS Quarterly, 24(4), 665-694.

Ajzen, I., & Fishbein, M. (1977). Attitude-behavior relations: A theoretical analysis and review of empirical research. Psychological Bulletin, 84(5), 888-918.

Bauer, R. (1967). Consumer behavior as risk taking. In D. Cox (Ed.), Risk taking and information handling in consumer behavior (pp. 389-398). Cambridge, MA: Harvard University Press.

Bettman, R. (1979). An information processing theory of consumer choice. Reading, MA: Addison Wesley.

Bitner, M. J. (2001). Self-service technologies: What do customers expect? Marketing Management, 10(1), 10-11.

Chaughuri, A. (1997). Consumption emotion and perceived risk: A macro-analytic approach. Journal of Business Research, 39(2), 81-92.

Chin, W. W. (1998). The partial least squares approach for structural equation modeling. In G. A. Marcoulides (Ed.), Modern methods for business research (pp. 295-336). Hillsdale, NJ: Erlbaum.

Cunningham, S. M. (1967). Perceived risk and brand loyalty. In D. F. Cox (Ed.), Risk-taking and information handling in consumer behavior (pp. 507-523). Boston, MA: Boston University Press.

Donthu, N. (1991). Quality control in the services industry. Journal of Professional Service Marketing, 7(1), 31-54.

Donthu, N., & Garcia, A. (1999). The internet shopper. Journal of Advertising Research, 39(3), 52-63. Dowling, G. R., & Staelin, R. (1994). A model of perceived risk and intended risk-handling activity. The Journal of Consumer Research, 21(1), 119-134.

Field, D. M. (1986). Determinants in the adoption of the idea component of an innovation: Identifying symbolic adopters of the home video ordering system. Unpublished doctoral dissertation, University of Arkansas, Fayetteville, AR, USA.

Forsythe, S., Liu, C., Shannon, D., & Gardner, L. C. (2006). Development of a scale to measure the perceived benefits and risks of online shopping. Journal of Interactive Marketing, 20(2), 55-75.

Gewald, H., Wullenweber, K., & Weitzel, T. (2006). The influence of perceived risks on banking mangers’ intention to outsource business processes: A study of the German banking and finance industry. Journal of Electronic Commerce Research, 7(2), 78-96.

Grazioli, S., & Jarvenpaa, S. L. (2000). Perils of internet fraud: An empirical investigation of deception and trust with experienced internet consumers. IEEE Transactions on Systems, Man and Cybernetics, 30(4), 395-410.

Huang, J., & Chen, Y. (2006). Herding in online product choice. Psychology and Marketing, 23(5), 413-428.

Huang, C. H., & Min, J. C. (2007). A research note of online bidders’ conformity. Social Behavior and Personality: An international journal, 35(8), 1033-1034.

Jarvenpaa, S. L., & Todd, P. A. (1996). Consumer reactions to electronic shopping on the world wide web. International Journal of Electronic Commerce, 1(2), 59-88.

Jacoby, J., & Kaplan, L. B. (1972). The components of perceived risk. Advances in Consumer Research, 1(1), 328-393.

Liu, X., & Wei, K. K. (2003). An empirical study of product differences in consumers’ e-commerce adoption behavior. Electronic Commerce Research and Applications, 2(3), 229-239.

Lin, T. M. Y., & Fang, C. H. (2006). The effects of perceived risk on the word-of-mouth communication dyad. Social Behavior and Personality: An international journal, 34(10), 1207-1216.

Lu, H. P., Hsu, C.-L., & Hsu, H.-Y. (2005). An empirical study of the effect of perceived risk upon intention to use online applications. Information Management & Computer Security, 13(2), 106- 132.

McGaughey, R. E., & Mason, K. H. (1998). The internet as a marketing tool. Journal of Marketing: Theory and Practice, 6(3), 1-11.

Meuter, M. L., Ostrom, A. L., Roundtree, R. I., & Bitner, M. J. (2000). Self-service technologies: Understanding customer satisfaction with technology-based service encounters. Journal of Marketing, 64(3), 50-64.

Mitchell, V.-W. (1999). Consumer perceived risk: Conceptualization and models. European Journal of Marketing, 33(1/2), 163-195.

Novak, T. P., Hoffman, D. L., & Yung, Y.-F. (2000). Measuring the customer experience in online environments: A structural modeling approach. Marketing Science, 19(1), 22-42.

Pires, G., Stanton, J., & Eckford, A. (2006). Influence on the perceived risk of purchasing online. Journal of Consumer Behavior, 4(2), 118-131.

Roselius, T. (1971). Consumer ranking of risk reduction methods. Journal of Marketing, 35(1), 56-61. Rowley, J. (1996). Retailing and shopping on the Internet. International Journal of Retail & Distribution Management, 3, 26-31.

van der Heijden, H., Verhagen, T., & Creemers, M. (2001). Predicting online purchasing behavior: Replications and tests of competing models. System Sciences. Proceedings of the 34th Annual Hawaii International Conference on, 2001.

Vijayasarathy, L. R., & Jones, J. M. (2000). Print and Internet catalog shopping: Assessing attitudes and intentions. Internet Research, 10(3), 191-202.

Wixom, B. H., & Todd, P. A. (2005). A theoretical integration of user satisfaction and technology acceptance. Information Systems Research, 16(1), 85-102.

Yi, M. Y., & Davis, F. D. (2003). Developing and validating an observational learning model of computer software training and skill acquisition. Information Systems Research, 14(2), 146-169.

Agarwal, R., & Karahanna, E. (2000). Time flies when you’re having fun: Cognitive absorption and beliefs about information technology usage. MIS Quarterly, 24(4), 665-694.

Ajzen, I., & Fishbein, M. (1977). Attitude-behavior relations: A theoretical analysis and review of empirical research. Psychological Bulletin, 84(5), 888-918.

Bauer, R. (1967). Consumer behavior as risk taking. In D. Cox (Ed.), Risk taking and information handling in consumer behavior (pp. 389-398). Cambridge, MA: Harvard University Press.

Bettman, R. (1979). An information processing theory of consumer choice. Reading, MA: Addison Wesley.

Bitner, M. J. (2001). Self-service technologies: What do customers expect? Marketing Management, 10(1), 10-11.

Chaughuri, A. (1997). Consumption emotion and perceived risk: A macro-analytic approach. Journal of Business Research, 39(2), 81-92.

Chin, W. W. (1998). The partial least squares approach for structural equation modeling. In G. A. Marcoulides (Ed.), Modern methods for business research (pp. 295-336). Hillsdale, NJ: Erlbaum.

Cunningham, S. M. (1967). Perceived risk and brand loyalty. In D. F. Cox (Ed.), Risk-taking and information handling in consumer behavior (pp. 507-523). Boston, MA: Boston University Press.

Donthu, N. (1991). Quality control in the services industry. Journal of Professional Service Marketing, 7(1), 31-54.

Donthu, N., & Garcia, A. (1999). The internet shopper. Journal of Advertising Research, 39(3), 52-63. Dowling, G. R., & Staelin, R. (1994). A model of perceived risk and intended risk-handling activity. The Journal of Consumer Research, 21(1), 119-134.

Field, D. M. (1986). Determinants in the adoption of the idea component of an innovation: Identifying symbolic adopters of the home video ordering system. Unpublished doctoral dissertation, University of Arkansas, Fayetteville, AR, USA.

Forsythe, S., Liu, C., Shannon, D., & Gardner, L. C. (2006). Development of a scale to measure the perceived benefits and risks of online shopping. Journal of Interactive Marketing, 20(2), 55-75.

Gewald, H., Wullenweber, K., & Weitzel, T. (2006). The influence of perceived risks on banking mangers’ intention to outsource business processes: A study of the German banking and finance industry. Journal of Electronic Commerce Research, 7(2), 78-96.

Grazioli, S., & Jarvenpaa, S. L. (2000). Perils of internet fraud: An empirical investigation of deception and trust with experienced internet consumers. IEEE Transactions on Systems, Man and Cybernetics, 30(4), 395-410.

Huang, J., & Chen, Y. (2006). Herding in online product choice. Psychology and Marketing, 23(5), 413-428.

Huang, C. H., & Min, J. C. (2007). A research note of online bidders’ conformity. Social Behavior and Personality: An international journal, 35(8), 1033-1034.

Jarvenpaa, S. L., & Todd, P. A. (1996). Consumer reactions to electronic shopping on the world wide web. International Journal of Electronic Commerce, 1(2), 59-88.

Jacoby, J., & Kaplan, L. B. (1972). The components of perceived risk. Advances in Consumer Research, 1(1), 328-393.

Liu, X., & Wei, K. K. (2003). An empirical study of product differences in consumers’ e-commerce adoption behavior. Electronic Commerce Research and Applications, 2(3), 229-239.

Lin, T. M. Y., & Fang, C. H. (2006). The effects of perceived risk on the word-of-mouth communication dyad. Social Behavior and Personality: An international journal, 34(10), 1207-1216.

Lu, H. P., Hsu, C.-L., & Hsu, H.-Y. (2005). An empirical study of the effect of perceived risk upon intention to use online applications. Information Management & Computer Security, 13(2), 106- 132.

McGaughey, R. E., & Mason, K. H. (1998). The internet as a marketing tool. Journal of Marketing: Theory and Practice, 6(3), 1-11.

Meuter, M. L., Ostrom, A. L., Roundtree, R. I., & Bitner, M. J. (2000). Self-service technologies: Understanding customer satisfaction with technology-based service encounters. Journal of Marketing, 64(3), 50-64.

Mitchell, V.-W. (1999). Consumer perceived risk: Conceptualization and models. European Journal of Marketing, 33(1/2), 163-195.

Novak, T. P., Hoffman, D. L., & Yung, Y.-F. (2000). Measuring the customer experience in online environments: A structural modeling approach. Marketing Science, 19(1), 22-42.

Pires, G., Stanton, J., & Eckford, A. (2006). Influence on the perceived risk of purchasing online. Journal of Consumer Behavior, 4(2), 118-131.

Roselius, T. (1971). Consumer ranking of risk reduction methods. Journal of Marketing, 35(1), 56-61. Rowley, J. (1996). Retailing and shopping on the Internet. International Journal of Retail & Distribution Management, 3, 26-31.

van der Heijden, H., Verhagen, T., & Creemers, M. (2001). Predicting online purchasing behavior: Replications and tests of competing models. System Sciences. Proceedings of the 34th Annual Hawaii International Conference on, 2001.

Vijayasarathy, L. R., & Jones, J. M. (2000). Print and Internet catalog shopping: Assessing attitudes and intentions. Internet Research, 10(3), 191-202.

Wixom, B. H., & Todd, P. A. (2005). A theoretical integration of user satisfaction and technology acceptance. Information Systems Research, 16(1), 85-102.

Yi, M. Y., & Davis, F. D. (2003). Developing and validating an observational learning model of computer software training and skill acquisition. Information Systems Research, 14(2), 146-169.

Appreciation is due to anonymous reviewers.

Shi-Ming Pi, Department of Information Management, Chung Yuan Christian University, 200, Chung Pei Rd., Chung Li, 32023, Taiwan, ROC. Phone: +886-3-2355406; Fax: +886-3-2655499; Email: [email protected]