Read The Impact of Internet Knowledge on Online BuyingAttitudes, Behavior, and Future Intentions: A StructuralModeling Approach text version

The Impact of Internet Knowledge on Online Buying Attitudes, Behavior, and Future Intentions: A Structural Modeling Approach

Leisa Reinecke Flynn, Florida State University Ronald E. Goldsmith, Florida State University

Abstract This paper describes a model of the influence of perceived knowledge of the Internet on selected Internet-related attitudes, behaviors, and future intentions. Based on the Theory of Reasoned Action and on previous studies of online shopping, the theoretical model was tested by fitting it to data gathered from 566 students who completed a self-administered questionnaire containing items measuring their perceived knowledge of the their attitudes toward shopping and buying over the Internet, measures of their online buying activity, and a measure of their future intentions to buy online. Structural equation modeling was used to assess the fit of these multiple indicators of ,-he constructs. The results showed that the model fit the data well, demonstrating the explanatory power of perceived knowledge to 2xplain the other constructs through a direct association with attitudes. Introduction It is interesting that the rush to join the ranks of e-tailers has just as quickly turned into a rush for the doors (Buckman, 2001). Irrational exuberance has transformed into harsh reality. Well known web retailers such as eToys Inc. and many lesser-known consumer sites have turned off their servers. Still, fourth quarter e-commerce retail sales for 2000 were 67.1% higher than for the same period in 1999 for a total of $8.686 billion (2-16-01 United States Department of Commerce News). Retail sales on the web accounted for 0.8% of total U.S. retail sales for 2000, but that percentage shows a steady growth trend. There is much change occurring in an area of consumer behavior that we are just beginning to understand. A growing body of research tries to identify and to understand Internet shoppers in terms of demographic and general shopping related characteristics. For instance, Donthu and Garcia (1999) found that Internet shoppers were different from general Internet users. "The Internet shopper is older and makes more money than the typical Internet user" (Donthu and Garcia, 1999, p. 52). One particularly relevant finding was that Internet shoppers held more favorable attitudes toward direct marketing in general (Donthu and Garcia, 1999). Goldsmith and Bridges (2000) compared the attitudes of Internet buyers and non-buyers. They found buyers to have generally more positive attitudes towards aspects of Internet shopping and buying than the nonbuyers. For example, those who had made a purchase over the Internet were more likely to agree to, "It is easy to find what I need on the Internet." Non-buyers were more likely to agree with, "I would rather stand in line than place an order over the Internet" (Goldsmith and Bridges, 2000).

There have been, however, few attempts to model Internet shopping behavior. The complex model developed by Novak et al. (2000) explains use of the web but not online shopping per se. Citrin et al. (2000) use innovativeness and Internet usage to predict online shopping in a small model. A recent model predicts online buying using the volume of Internet usage, involvement with the Internet and consumer innovativeness characteristics (Goldsmith, 2001). One of the most comprehensive Internet shopping models is based on a variant of the Theory of Reasoned Action. It includes a measure of behavioral control, which is based on the notion of self-efficacy. This study concluded that behavioral control was related both to intentions to shop on the Internet and to actual Internet shopping (Limayem, Khalifa, and Frini, 2000). Our study seeks to further this effort by modeling the influence of knowledge. Alba et al. (1997) do not offer a model but a thorough discussion of the forces they see as driving internet usage for shopping. To this end they make a number of predictions about who will shop the Internet and why. One of the primary motivators of web use for shopping is information search (Alba et al., 1997). They hypothesize that consumers with a greater amount of preexisting brand knowledge will find shopping on the Internet more advantageous than less knowledgeable consumers. More knowledgeable consumers find their cost of search for product related information on the web even lower as they are more efficient at gathering data (Alba et al., 1997). The primary value of e-shopping is information, and consumers with a stronger knowledge base can better use that information (Alba et al.. 1997; Brucks, 1985). One cluster of related constructs appears in a preponderance of the Internet shopping literature. Many models or descriptions of online shopping contain some version of consumer confidence, experience, efficacy, or skill in using the Internet. All of these variables are roughly substitutable for the perceived ability to successfully use the Internet. Novak et al. (2000) call it "skill/control." They found that more perceived skill leads to greater "flow," which in turn leads eventually to more exploratory behavior on the Internet. In a study examining the use of online information in the purchase of a home, consumers with a greater self perceived awareness of online information resources reported greater usage of the Internet in home search (Littlefield, Bao, and Cook, 2000). Goldsmith and Bridges (2000) found that consumers who felt that it was easy to buy over the web were more likely to buy, implying that confidence leads to greater purchase likelihood. A positive relationship between Internet experience/confidence and amount of shopping is thus found in three studies (Goldsmith, 2001; Goldsmith and Bridges, 2000; Limayern et al,, 2000) This constellation of characteristics all centered on the consumer's perceived ability to perform some task has been examined extensively over the years (Alba and Hutchinson, 1987; Brucks, 1985; Flynn and Goldsmith, 1998; Raju, Lonial, and Mangold, 1995). It is generally accepted that expertise, objective knowledge, and perceived knowledge are three separate constructs (Brucks, 1985; Flynn and Goldsmith, 1998; Park, Feick, and Mothersbaugh, 1992) with experience and perceived knowledge more closely correlated than experience and objective knowledge or objective and subjective knowledge (Park, Mothersbaugh, and Feick, 1994).

Because of the similarity of the constructs of experience and subjective knowledge and because subjective knowledge is often a better predictor of consumer behavior than objective knowledge (Flynn and Goldsmith, 1998; Raju et al., 1995) we have chosen to model perceived knowledge of Internet use as the antecedent in our model of Internet purchase attitudes, behaviors and future intentions (Bang, Ellinger, Hadjimarcou, and Traichal, 2000). Model and Hypotheses Our model (see Figure 1) is based upon the modified Theory of Reasoned Action as specified by Bentler and Speckhart (1981). In their version attitude is modeled as concurrent with behavior. This model also includes a direct link between prior behavior and behavioral intention (see Foxall, Goldsmith, and Brown, 1998, p. 110). To test the effect of subjective knowledge on attitudes towards shopping on the Internet we model subjective knowledge as an antecedent of the impacts of attitude and behavior on future intentions (Bang et al., 2000). A number of researchers have found that knowledge of a category is related to attitudes about that category. Microeconomic theory's view of the consumer as "economic man" expects the buyer to have complete knowledge of all alternatives and to be able to accurately rank alternatives to make the optimum purchase decision. An optimum purchase outcome implies a positive attitude. The less knowledgeable consumer is more likely to make inefficient use of his or her search time (Alba and Hutchinson, 1987). This inefficiency may lead to frustration, poorer purchase outcomes, and less positive attitudes about the experience. Brucks (1985) calls this same phenomenon inappropriate search. We expect greater subjective knowledge to lead to more positive attitudes about the online shopping experience (Alba et al., 1997; Bang et at., 2000; Ward and Lee, 2000). Method Sample A convenience sample of 566 students at a large public university in the southeastern US provided the data for this study. While this sample will not provide point and interval estimates, we feel that young consumers will provide valuable information because of their role in the future of e-commerce (Hogg, Bruce, and Hill, 1998). The sample included 46.5% men and 53.5% women with ages ranging from 18 to 50 with a mean of 22,6 years (sd = 4.9). The racial makeup of the sample reflects that of the university with 74% white, 11% African-Americans, 7.4% Hispanics, and 7% others. A check for age/gender and age/race interaction showed no significant differences in age for any of those classifications.

Questionnaire The questionnaire contained three items from the five-item perceived knowledge scale developed by Flynn and Goldsmith (1998). We limited the items from this scale to those that had the same direction of wording. Also included were four items designed to measure enjoyment of shopping on the Internet, and three items assessing perceptions of the safety of online buying Goldsmith (2001). All of these appeared as Likert-type items with a five-point response format. In addition, the survey included two items that asked about the frequency of purchasing online. Future purchase intentions were measured with one item asking about the likelihood of buying over the Internet in the future, The questionnaire forms contained other measures that were not used in this study. The items used in this study are included in Table I - I

Results We examined the psychometric properties of all scalar measures. Descriptive statistics and correlations appear in Table 2. The shortened subjective knowledge scale was unidimensional under exploratory factor analysis. The three items had an internal consistency of .87 as measured by Cronbach's alpha. The shopping fun and safety scales were both unidimensional and had alphas of.74 and .76, respectively. For the purposes of modeling the hypothesized relationships, multiple measures were used for each construct. The three knowledge items were used W indicators of perceived knowledge. The fun and safety scale5 were summed and these total scores were used as two measures of attitude toward online buying. The two individual buying items measured behavior, and future intentions were measured with the single item. For the SEM analysis the error variance of this itern was fixed at. 15.

We developed and tested a series of three models using LISREL 8 (see Figure 1). The measurement model fit well. The chi-square was 10.35 (p =.797) on 15 degrees of freedom. All fit indices were near unity (AGFI = 0.951 and RMSR = 0. 184). The largest standardized residual was 11.7331. All the t-values were statistically significant. The second model displayed in Figure I is the complete model with a path from subjective knowledge of the Internet to attitudes about shopping on the Internet. This model also fit extremely well. The chi-square was 15.35 on 17 degrees of freedom with a p-value of 0.57. The AGFI was 0.986 and the RMSR was 0.0401. All paths were statistically significant and the total effects of knowledge on the other The third model removed the path from subjective knowledge to attitudes. Thus, with 18 degrees of freedom the chi-square increased to 59.034 and the p-value became significant. The gain of a degree of freedom yielded a positive change of 43.687 in the chi-square. This is a significant change. The AGFI fell to 0.951 and the RMSR rose to 0.184. Moreover, if additional direct paths were added from perceived knowledge to behavior and to future intentions their path coefficients

were small and not statistically significant, indicating that those alternative models did not fit the data as well as the hypothesized model. Discussion The purpose of our study was to test a conceptual model of the influence of perceived Internet knowledge on Internet-related attitudes, online buying, and buying intentions. Data from a convenience sample of 566 students were used to test the fit of the model to self-reported measures of perceived knowledge, attitudes toward online buying, online buying behavior, and online buying intentions. The fit statistics showed that the model provided a good fit to the data. Perceived knowledge of the Internet seems to positively influence e-commerce via its influence on attitudes toward online buying. This conclusion is consistent with both attitude theory and previous empirical studies. The findings reinforce the traditional attitude theories that play such an important role in consumer behavior and marketing. It should come as no surprise that attitudes behave in cyberspace as they do in the brick and mortar world. The findings especially highlight the importance of the knowledge construct in explaining some aspects of consumer behavior. As consumers gain knowledge of a product field they are likely to buy more and feel more secure in their purchases. This is likely a reciprocal relationship, in that greater knowledge leads to more buying, which in turn increases knowledge of the product category. The study makes a unique contribution to understanding consumer behavior because it is one of the few to explicitly model the impact of knowledge, suggesting that the influence of this construct on behavior may be mediated exclusively by attitudes. The influence of knowledge on buying presents unique challenges. Increasing levels of knowledge lead to unique consumer behaviors. More knowledgeable buyers are more discriminating and make better use of available information to make choices. They are also likely to be product innovators in the product category. The findings of our study suggest that a way for cyber marketers to promote online buying is to provide information to consumers that increases their knowledge of ecommerce and of the Web. The information should stress the fun and safety of Internet. As consumers learn more about online buying their safety concerns should decrease, thus leading them to shop online. This suggestion is consistent with a recent report by Market Facts Inc. (2001) suggesting that consumers are becoming more comfortable providing credit card information over the Web, thus increasing their propensity to buy online. The study is limited by the convenience sample that limits the generalizability of the findings. Future studies should use different data sources to expand scope of the findings to other types of consumers. The findings are limited to the measures used. Future studies can meliorate this monomethod bias by using different measures of the constructs. Additional constructs should be added to the model to supplement its limited scope. This effort should especially include other types of product category knowledge, such as objective knowledge and experience. The model should be applied in future studies to other product categories to enhance it generalizability. To conclude, the concept of consumer knowledge has a long history in the fields of consumer behavior and marketing, The findings from this and other studies suggest that it has a promising

future as well, owing to the fact that it seems to play a crucial role in the formation of both attitudes and behaviors. As the concept of knowledge is studied more intently, the relative contributions of real versus subjective knowledge will become more highly defined. Incorporating the effects of knowledge into e-commerce marketing strategies as well as strategies in the brick and mortar world should make them more effective.

References Alba, J., Hutchinson, J. W.: Dimensions of Consumer Expertise. Journal of Consumer Research, 13 (1987): 411-454. Alba, J., Lynch, J., Weitz, B. Janiszewski, C.: Interactive Home Shopping: Consumer, Retailer, and Manufacturer Incentives to Participate in Electronic Marketplaces. Journal of Marketing, 61, 3 (1997): 38-53. Bang, H., Ellinger, A. E., Hadjimarcou, J,, Traichal, P. A.: Consumer Concern, Knowledge, Belief, and Attitude toward Renewable Energy: An Application of the Theory of Reasoned Action. Psychology and Marketing 17, 6 (2000): 449-468. Bentler, P. M. Speckhart, G.: Attitudes "Cause" Behaviors: A Structural Equations Analysis. Journal of Personality and Social Psychology 40 (1981): 226-38. Brucks, M.: The Effects of Product Class Knowledge on Information Search Behavior. Journal of Consumer Research, 12 (1985): 1-16. Buckman, R.: Who Caused the Dot-Corn Crash? Wall Street Journal, March 5, 200 1, B 1. Citrin, A. V., Sprott, D. E., Silverman, S. N., Stem, D. E., Jr.: Adoption of Internet Shopping: The Role of Consumer Innovativeness. Industrial Management & Data Systems 100,7 (2000): 294-300. Donthu, N, Garcia, A.: The Internet Shopper. Journal of Raju, P Advertising Research 39, 3 (1999): 52-58. Flynn, L. R., Goldsmith, R. E.: A Short, Reliable Measure of Subjective Knowledge. Journal of Business Research 46, 1 (1998): 57-66. Foxall, G. R., Goldsmith, R. E., and Brown, S.: Consumer Psychology for Marketing. London: International Thompson Business Press: (1998).

Goldsmith, R. E.: Explaining and Predicting Consumer Intention to Purchase over the Internet: An Exploratory Study. Association of Marketing Theory and Practice Proceedings, (200 1 ). Goldsmith, R. E., Bridges, E.: E-tailing vs. Retailing: Using Attitudes to Predict Online Buyer Behavior. Quarterly Journal of Electronic Commerce 1, 3 (2000): 245-253. Hoffman, D. L., Novak, T. P.: A New Marketing Paradigm for Electronic Commerce. Information Soc, Special Issue on Electronic Commerce 13 January-March (1996): 4354. Hogg, M. K., Bruce, M., Hill, A. J.: Fashion Brand Preferences among Young Consumers. International Journal of Retail & Distribution Management 26, 8 (1998): 293300. Limayern, M., Khalifa, M., Frini, A.,: What Makes Consumers Buy from Internet? A longitudinal Study of Online Shopping. 1EE Transactions on Systems, Man, and CyberneticsPart A: Systems and Humans 30, 4 (2000) 421-432. Littlefield, J. E., Bao, Y., Cook, D. L.: Internet Real Estate Information: Are Home Purchasers Paying Attention to it? Journal of Consumer Marketing 17, 7 (2000): 575590. Market Facts Inc.: U.S. Online Consumers Now More Comfortable Providing Credit Card and Personal Information Online Than Over the Phone. www. market facts. com/news (2001). Novak, T. P., Hoffman, D. L., Yung, Y.: Measuring the Customer Experience in Online Environments: A Structural Modeling Approach. Marketing Science 19, 1 (2000): 22-42. Park, C. W., Feick, L., Mothersbaugh, D. L.: Consumer Knowledge Assessment: How Product Experience and Knowledge of Brands, Attributes, and Features Affects What We Think. Proceedings of the Association for Consumer Research (1992): 193-198, Park, C. W., Mothersbaugh, D.L., and Feick, L.: Consumer Knowledge Assessment. Journal of Consumer Research, 21, (1994): 71-82. S., Lonial, S. C., Mangold, W. G.: Differential Effects of Subjective Knowledge, Objective Knowledge, and Usage Experience on Decision Making: An Exploratory Investigation. Journal of Consumer Psychology, 4, (1995): 153-180. Ward, M. R., Lee, M. J.: Internet Shopping, Consumer Search and Product Branding. Journal of Product and Brand Management, 9, 1 (2000): 6-20. U.S. Census Bureau: Monthly Retail Trade Survey., 2-15-01


The Impact of Internet Knowledge on Online BuyingAttitudes, Behavior, and Future Intentions: A StructuralModeling Approach

8 pages

Report File (DMCA)

Our content is added by our users. We aim to remove reported files within 1 working day. Please use this link to notify us:

Report this file as copyright or inappropriate


You might also be interested in

A Model of Male Consumer Behaviour in Buying Skin Care Products in Thailand
The Impact of Internet Knowledge on Online BuyingAttitudes, Behavior, and Future Intentions: A StructuralModeling Approach