The Internet has changed how information is communicated and processed. People use the Internet for different purposes, including communicating with friends, researching and monitoring online stock prices, trading stock, paying bills, banking, and shopping. Growing numbers of consumers purchase goods and services, gather product information, or just to browse online. Online shopping is the process whereby consumers buy goods or services directly from a seller in real time, without an intermediary service, over the Internet. Online shopping environments are, therefore, playing an increasing role in the relationship between marketers and their consumers (Demangeot & Broderick, 2007).

The interactive nature of the Internet has increased the convenience of shopping: however, information processing in online shopping environments has challenged the consumer’s knowledge, experience, and cognitive ability (Li, 2010). For example, consumers purchasing online cannot inspect the service environment nor can they see or physically inspect the products. There is, thus, more risk in purchasing from an Internet store than from a physical store (Chiou & Pan, 2009). Trust has always been an important element in marketing, and is especially important in e-commerce (Urban, Sultan, & Qualls, 2000). Trustworthy images of an Internet retailer are very important to consumers when they are making purchases (Rust, Kannan, & Peng, 2002). Without trust, the efficiency of exchange on the Internet will be significantly reduced. The behavior of a customer when shopping online is − for the most part − explains that customer’s beliefs about the contents of the website and the perceived utilitarian and hedonic values of the website. When consumers like the perceived utilitarian and hedonic values of a website, the seller on the Internet will think about strategies such as ease of use, safety of online shopping, and appearance of the page to increase the trust and generate a positive attitude in the customer (Babin & Attaway, 2000). However, the amount of time spent online has a significant moderating effect on the process of online shopping behaviors. We were interested in determining whether or not a user’s online experience moderates their behavioral intentions and actual behavior. Thus, our purpose in this study was to explore the moderating effects of experience on customer’s perceived usefulness, trust, behavioral intentions, and on actual behavior, using survey data collected from undergraduate students in Taiwan. Our aim was to increase knowledge of how and why undergraduate students use online shopping, a topic that few researchers have investigated to date.

Theoretical Framework

Perceived Ease of Use

The link between perceived usefulness and compatibility has been identified by previous researchers (see e.g., Rogers, 2003). For example, in the technology acceptance model, causal links as defined in the theory of reasoned action (TRA) are used to explain individual information technology (IT) acceptance. It is argued that perceived usefulness (PU) and perceived ease of use (PEOU) of IT are major determinants of its usage. PU is the extent to which a person believes that using a particular system will enhance his or her job performance. PEOU is the degree to which a person believes that using a particular system will cost little in terms of effort (Davis, 1989). Both PU and PEOU influence consumer online shopping intentions. Although online shopping has been surmised to have beneficial outcomes, using an interactive website could prove to be daunting for some consumers. If this negative perception of the process outweighs the perceived benefits of purchasing using the Internet (e.g., long download times, poorly designed formatting), then potential Internet shoppers are likely to continue purchasing using conventional channels. In other words, if there are barriers that reduce perceptions of ease of use of Internet shopping, Internet users may develop a negative attitude toward Internet shopping. We formed the following hypotheses to test this proposition:
H1: Perceived ease of use will have a positive impact on customer intention to shop online.
H2: Perceived ease of use will have a positive impact on customer attitudes about online shopping.

Perceived Usefulness

The antecedents of attitude toward websites include consumers’ beliefs in the availability, design attractiveness, and structure of information on those websites (Luna, Peracchio, & Juan, 2002; Yilmaz, 2004). Since behavioral intentions depend on cognitive choice, a potential online shopper can respond either favorably or unfavorably towards the idea of engaging in online purchasing. Davis (1989) states that the power to attract online shoppers lies in the technology’s usability and usefulness, and he defines perceived usefulness (PU) as the belief that using the application will increase one’s performance. Researchers have found that PU influences intention to use Internet shopping (Koufaris, 2002). Venkatesh and Davis (2000) and Moon and Kim (2001) also reported that PU had a significantly positive influence on trust, attitude, and behavioral intentions. Hence, we developed the following hypotheses:
H3: Perceived usefulness will have a positive impact on customer intention to shop online.
H4: Perceived usefulness will have a positive impact on customer attitudes about online shopping.
H5: Perceived ease of use will have a positive impact on customer behavioral intentions concerning shopping online.

Attitude

According to the CAB model (Martínez-López, Luna, & Martínez, 2005), attitudes are compounded by beliefs or cognitions (C), affect (A), and behavior (B). The CAB model has been found to have validity in explaining online shopping use when the consumer is highly involved in the Internet experience of processing persuasive messages. According to the CAB model, it is assumed that consumers base their beliefs on their accumulated knowledge of some key attributes of the object. Affect (A) should be formed based on one’s firmly held beliefs about the object, and will then be followed by behavior (B). Babin, Darden, and Griffin (1994) found that an enjoyment attitude is a hedonic type of shopping value as perceived by consumers, and results in emotional arousal, enjoyment, and playfulness during the shopping process. Based on this concept, we developed the following hypotheses:
H6: Attitude will have a positive impact on customer intention to shop online.
H7: Attitude will have a positive impact on customer behavioral intentions concerning shopping online.

Trust

The importance of trust in online shoppers’ satisfaction may be different for infrequent versus frequent shoppers (Chiou & Pan, 2009). Castelfranchi and Tan (2002) argued that online shoppers will not engage in a transaction on the Internet unless their level of perceived trust exceeds a minimum level of acceptability. Frequent shoppers normally spend more time on the Internet than do infrequent shoppers and have more understanding of the potential risks (Gefen, Karahanna, & Straub, 2003; Hoffman, Novak, & Peralta, 1998; Reichheld & Schefter, 2000). Consumers with higher levels of positive affect toward the object tend to be more favorably disposed toward shopping online and are more likely to choose this method (Chen & Lee, 2008). If customers perceive service quality favorably, they will have more confidence in the Internet retailer, and this will increase their trust of that Internet retailer (Chiou & Pan, 2009). Chaudhuri and Holbrook (2001) found that brand trust and brand affect are related to customers’ loyalty and behavioral intentions. Trust helps mitigate customers worries about risk and security and encourages them to participate in online activities (Salo & Karjaluoto, 2007). Martínez-López et al. (2005) argued that it is extremely important for online businesses to generate trust and brand equity so that consumers develop intention to purchase from their websites. Actual behaviors represent all positive behaviors, such as a willingness to stay, explore, work, and affiliate in an environment (Bitner, 1992). Therefore, consumers’ attitudes toward websites, as well as their use of a web system that supports online commercial exchanges, should have a significant influence on consumers’ trust in a specific online shopping transaction. Thus, based on the foregoing discussion, we developed the following hypotheses:
H8: Trust will have a positive impact on customer behavioral intentions regarding shopping online.
H9: Trust will have a positive impact on customer actual behavior regarding shopping online.

Behavioral Intentions

Rational behavior is an actual behavior determined by a person’s behavioral intention, in the sense that attitudes accurately reflect beliefs, intentions accurately reflect attitudes, and behaviors accurately reflect intentions (Fishbein & Ajzen, 1975). Warshaw and Davis (1985) define behavioral intention as “the degree to which a person has formulated conscious plans to perform, or not perform, some specified future behavior” (p. 214). This is in line with the theory of reasoned action (Fishbein & Ajzen, 1975) and its successor the theory of planned behavior (Ajzen, 1991), in which it is contended that behavioral intention is a strong predictor of actual behavior. Hartwick and Barki (1994) also found strong causal relationships between behavioral intentions and actual behavior. In relation to this, the following hypothesis was developed:
H10: Behavioral intentions will have a positive impact on customer intention to shop online.

Moderating Effects of Online Shopping Experience

Adams, Weinberg, Masztal, and Surette (2005) claimed that people in a workplace with a high-speed Internet connection, and also people with more online shopping experience, were more likely than other people to shop online at work. Consumers who have experience with e-banking services such as automatic teller machines and telephone banking were also found to be more likely to use online banking than were those who do not (Cai, Yang, & Cude, 2008). Therefore, customers with more online shopping experience tend to choose the Internet as a platform for shopping. Based on these findings, we developed the following hypotheses:
H11: The level of a customer’s online experience will moderate the influence of perceived usefulness on behavioral intentions to shop online.
H12: The level of a customer’s online experience will moderate the influence of trust on behavioral intentions concerning shopping online.
H13: The level of a customer’s online experience will moderate the influence of trust on actual behavior regarding shopping online.

Method

Participants

We studied the online shopping behavior of undergraduates in Taiwan. The respondents were guaranteed anonymity. With the assistance of a marketing research firm in Taiwan, we sent questionnaires to 900 students at universities in Taiwan. Of these, 600 individuals completed and mailed back their questionnaires, yielding a response rate of 66.67%. Our sampling method was successful in gathering up a group of responses from individuals with a variety of personal characteristics representative, in terms of age and gender, of the general population in Taiwan. Of our respondents 65.5% were female and 34.5 were male, 45.8% were less than 20 years of age, 42.7% were aged between 21 and 24, and 11.4% were older than 25.

Measures

Because the participants were Taiwanese, we carried out a pretest with 30 undergraduates before conducting the formal survey, to confirm that the questionnaire had no semantic problems. As shown in Table 1, the formal questionnaire consisted of two items about perceived ease of use and perceived usefulness taken from the study by Wang, Lin, and Luarn (2006), two items about trust in online shopping taken from the study by Salo and Karjaluoto (2007), two items about attitude toward the Internet taken from the study by Kempf (1999), three items about behavioral intentions taken from the study by Kraft, Rise, Sutton, and Roysamb (2005), one item on actual behavior taken from the study by Wang (2007), and one item about online experience taken from the study by Venkatesh, Morris, Davis, and Davis (2003). All questions, except those in the first section, were measured using a 7-point Likert-type scale (1 = strongly disagree to 7 = strongly agree).

Analysis

In addition to descriptive statistics, validity and reliability tests and factor analysis were carried out for each construct. Structural equation modeling (SEM) was employed to assess the nomological validity for our theoretical framework (Kline, 2005; Schumacker & Lomax, 2004). We used maximum likelihood estimation estimates because our data had no missing values and were continuous variables with acceptable skewness and kurtosis. Therefore, standard error and chi-square tests were considered appropriate (Schumacker & Lomax, 2004).

Results and Discussion

Measurement Reliability and Validity

To verify the dimensionality and reliability of the measurement we performed construct and confirmatory factor analysis, tested for composite reliability, and calculated Cronbach’s alpha coefficients. The results of these analyses are shown in Table 2. The results indicated that factor loadings of the research constructs ranged from 0.72 to 0.95 and Cronbach’s alpha coefficients ranged from 0.822 to 0.993. Composite reliability ranged from 0.813 to 0.933 and item-to-total correlation coefficients for each research factor were all higher than 0.53. Therefore, the measurement scales for each construct were found to be reliable and valid. Thus, the summated scores for each construct were used to test the hypotheses. The detailed results of factor analysis and reliability test are shown in Table 2.

The Path Estimate

The results of the path estimate are shown in Figure 1. The ratio of chi-square and degree of freedom was 12 (root mean square error of approximation = 0.09). The nonsignificance of the chi-square test leads to the conclusion that the hypothesized model mirrored the pattern of covariance contained within the raw data. Goodness of fit = 0.97, adjusted goodness of fit = 0.95, comparative fit index = 0.98, and normed fit index = 0.97 were all higher than the recommended criterion of 0.9, confirming that our theoretical framework was appropriate.

In addition, perceived ease of use had a significant and positive impact on perceived usefulness (β = 0.58, p < .0001) and attitude toward online shopping (β = .10, p < .01). Perceived ease of use had a significant and positive impact on trust in online shopping (β = .57, p < .0001), attitude toward online shopping (β = .71, p < .001), and on behavioral intentions (β = 0.34, p < .001). Attitude had a significant and positive impact on trust in online shopping (β = 0.42, p < .001) and on behavioral intentions (β = .11, p < .01). Finally, trust in online shopping had a significant and positive impact on behavioral intentions (β = 0.44, p < .0001), and a significant and positive impact on actual behavior (β = .28, p < .001). Behavioral intentions had a significant and positive impact on actual online shopping behavior (β = .53, p < .001). Online experience moderated the influence of both perceived usefulness and trust on behavioral intentions (β = .12, p < .0001 and β = .14, p < .0001, respectively). However, the interaction term (online experience trust) was not significant (β = -.03, p < .05), indicating that conscientiousness has a significant effect on the influence of a central route for online shopping website content on online experience.

In this study our aim was to assess the importance of online shopping behaviors for undergraduates in Taiwan. Using a theoretical framework, we explored the moderating effects of the online shopping experience. Except for H13, the hypothesized relationships were corroborated in this study. Although online shopping experience positively moderated the relationship between perceived usefulness and behavioral intentions, and that between trust and behavioral intentions, it did not significantly moderate trust and actual behavior. There are several possible explanations for this result. For example, customers with high levels of trust and online experience know more about the details or cheating behaviors associated with Internet purchasing, so they behave with circumspection and caution when shopping online.

A number of caveats need to be noted regarding the present study. Our findings must be interpreted with caution because the sample size was small and participants were all from Taiwan. The findings, therefore, might not be generalizable to other countries and cultures. In addition, participants were undergraduate students, so the sample may suffer from the homologous phenomenon. Thirdly, this survey instrument involved subjective responses that may be affected by common source bias. Future researchers should use more objective data collection methods to survey the respondents and could also use a longitudinal method first to collect information on the independent variables, and then test the dependent variables such as behavioral intentions and actual behavior at a later time. This could be a more effective method when investigating causation.

Adams, S. M., Weinberg, B. D., Masztal, J. J., & Surette, D. M. (2005). This time it is personal: Employee online shopping at work. Interactive Marketing, 6(4), 326-336. http://doi.org/cjr

Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179-211. http://doi.org/cc3

Babin, B. J., & Attaway, J. S. (2000). Atmospheric affect as a tool for creating value and gaining share of customer. Journal of Business Research, 49(2), 91-99. http://doi.org/cjs

Babin, B. J., Darden, W. R., & Griffin, M. (1994). Work and/or fun: Measuring hedonic and utilitarian shopping value. The Journal of Consumer Research, 20(4), 644-656. http://doi.org/cjt

Bitner, M. J. (1992). Servicescapes: The impact of physical surroundings on customers and employees. The Journal of Marketing, 56(2), 57-71. http://doi.org/cjv

Cai, Y., Yang, Y., & Cude, B. (2008). Inconsistencies in U.S. consumers’ attitudes toward, and use of, electronic banking: An empirical investigation. Journal of Financial Services Marketing, 13(2), 150-163. http://doi.org/cjw

Castelfranchi, C., & Tan, Y. H. (2002). The role of trust and deception in virtual societies. International Journal of Electronic Commerce, 6(3), 55-70. http://doi.org/cjx

Chaudhuri, A., & Holbrook, M. B. (2001). The chain of effects from brand trust and brand affect to brand performance: The role of brand loyalty. The Journal of Marketing, 65(2), 81-95. http://doi.org/cjz

Chen, S. H., & Lee, K. P. (2008). The role of personality traits and perceived values in persuasion: An elaboration likelihood model perspective on online shopping. Social Behavior and Personality: An international journal, 36(10), 1379-1400. http://doi.org/cj2

Chiou, J. S., & Pan, L. Y. (2009). Antecedents of Internet retailing loyalty: Differences between heavy versus light shoppers. Journal of Business and Psychology, 24, 327-339. http://doi.org/cj3

Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-340. http://doi.org/cc6

Demangeot, C., & Broderick, A. J. (2007). Conceptualising consumer behaviour in online shopping environments. International Journal of Retail and Distribution Management, 35(11), 878-894. http://doi.org/cj4

Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention, and behavior: An introduction to theory and research. Boston, MA: Addison-Wesley.

Gefen, D., Karahanna, E., & Straub, D. W. (2003). Trust and TAM in online shopping: An integrated model. MIS Quarterly, 27(1), 51-90.

Hartwick, J., & Barki, H. (1994). Explaining the role of user participation in information system use. Management Science, 40(4), 440-465. http://doi.org/cj5

Hoffman, D. L., Novak, T. P., & Peralta, M. (1998). Building consumer trust online. Communications of the ACM, 42, 80-85. http://doi.org/cj6

Kempf, D. (1999). Attitude formation from product trial: Distinct roles of cognition and affect for hedonic and functional products. Psychology and Marketing, 16(1), 35-50. http://doi.org/cj7

Kline, R. B. (2005). Principles and practice of structural equation modeling (2nd ed.). New York: Guildford.

Koufaris, M. (2002). Applying the technology acceptance model and flow theory to online consumer behavior. Information Systems Research, 13(2), 205-223. http://doi.org/cj8

Kraft, P., Rise, J., Sutton, S., & Roysamb, E. (2005). Perceived difficulty in the theory of planned behaviour: Perceived behavioural control or affective attitude? British Journal of Social Psychology, 44(3), 479-496. http://doi.org/cj9

Li, P. F. (2010). Effects of individual differences on choice strategy in goal-directed online shopping. Journal of American Academy of Business, 15(2), 186-192.

Luna, D., Peracchio, L. A., & Juan, M. D. D. (2002). Cross-cultural and cognitive aspects of website navigation. Journal of the Academy of Marketing Science, 30(4), 397-410. http://doi.org/ckb

Martínez-López, F. J., Luna, P., & Martínez, F. J. (2005). Online shopping, the standard learning hierarchy, and consumers’ Internet expertise: An American-Spanish comparison. Internet Research, 15(3), 312-334. http://doi.org/ckc

Moon, J., & Kim, Y. (2001). Extending the TAM for a World-Wide-Web context. Information and Management, 38, 217-230. http://doi.org/ckd

Reichheld, F. F., & Schefter, P. (2000). E-loyalty: Your secret weapon on the web. Harvard Business Review, 78(4), 105-113.

Rogers, E. M. (2003). Diffusion of innovations (5th ed.). New York: Free Press.

Rust, R. T., Kannan, P. K., & Peng, N. (2002). The customer economics of Internet privacy. Journal of the Academy of Marketing Science, 30(4), 455-464. http://doi.org/ckf

Salo, J., & Karjaluoto, H. (2007). A conceptual model of trust in the online environment. Online Information Review, 31(5), 604-621. http://doi.org/ckg

Schumacker, R. E., & Lomax, R. G. (2004). A beginner’s guide to structural equation modeling (2nd ed.). Mahwah, NJ: Erlbaum.

Urban, G., Sultan, F., & Qualls, W. (2000). Placing trust at the center of your Internet strategy. Sloan Management Review, 42(1), 39-48.

Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186-204. http://doi.org/cdv

Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425-478.

Wang, Y. (2003). Assessment of learner satisfaction with asynchronous electronic learning systems. Information and Management, 41(1), 75-86. http://doi.org/ckh

Wang, Y. (2007). Development and validation of a mobile computer anxiety scale. British Journal of Educational Technology, 38(6), 990-1009. http://doi.org/ckj

Wang, Y., Lin, H., & Luarn, P. (2006). Predicting consumer intention to use mobile service. Information Systems Journal, 16(2), 157-179. http://doi.org/ckk

Warshaw, P. R., & Davis, F. D. (1985). Disentangling behavioral intention and behavioral expectation. Journal of Experimental Social Psychology, 21, 213-228. http://doi.org/ckm

Yilmaz, V. (2004). Consumer behavior in shopping center choice. Social Behavior and Personality: An international journal, 32(8), 783-790. http://doi.org/ckn

Appreciation is due to reviewers including

Bijou Yang Lester

Drexel University

PA

USA

Email

[email protected]

Pei-Wen Liao, Department of Applied Technology and Human Resource Development, National Taiwan Normal University, No. 162, Sec. 1, Ho-Ping E. Road, Taipei, Taiwan, ROC. Email: [email protected]