Antecedents and Consequences of Perceived Interactivity:
An Exploratory Study

Joonhyung Jee, Wei-Na Lee

The University of Texas at Austin


The study reported in this paper investigated how, as antecedents, personal factors influence consumers’ perception of a web site’s interactivity in the context of making a purchase decision. A review of literature suggests three general factors – Need for Cognition (NFC), product involvement, and product expertise – and three Internet-specific factors – skills, challenges, and web shopping experience – for testing. In addition, attitude toward the web site and purchase intention are examined as consequences of perceived interactivity.

NFC was found to be a significant predictor of perceived interactivity of the web site visited. Although marginally significant, skills were also found to be a predictor. The model was supported for one of the three manufacturers’ portal sites employed in the study. Additional analysis found that consumers’ purchase intention was influenced by their attitude toward the web site, but not by the perceived interactivity of the site. This relationship was found for two of the three web sites tested. Implications and suggestions for future research are provided.


Over the past decades, rapid advancement in media technology has made using the Internet a central part of consumers’ daily lives. An essential characteristic that differentiates the Internet from traditional media is the concept of interactivity (Coyle and Thorson 2001; Hoffman and Novak 1996; Pavlik 1996). Practitioners and researchers alike have credited interactivity for helping generate various benefits for marketers and consumers. Some of these benefits include: creation of stronger brand identity (Upshaw 1995), facilitation of relationship marketing (Cuneo 1995), conversion of interested consumers to interactive customers (Berthon, Pitt, and Watson 1996) and greater control over information search and acquisition (Hoffman and Novak 1996). Ghose and Dou’s (1998) finding that web sites providing more interactive functions are more likely to be evaluated as quality sites further stresses the important role of interactivity.

However, there has been little agreement among researchers on how interactivity should be conceptualized (Heeter 2000). Therefore, it becomes difficult to develop concrete knowledge regarding its antecedents and consequences. The complex nature of interactivity itself may have contributed to the lack of agreement (Cho and Leckenby 1997; Wu 2000). Over the years, researchers have studied interactivity as a part of the communication process (e.g., Blattberg and Deighton 1991; Kirsch 1997; Milheim 1996), medium characteristic (e.g., Hoffman and Novak 1996; Steuer 1992), communication system property (e.g., Neuman 1991; Rice 1984), individual trait (e.g., Chen 1984), psychological state (e.g., Newhagen, Corders, and Levy 1995), and variable characteristic of communication settings (e.g., Rafaeli 1988). There have also been propositions that interactivity should be considered as a multidimensional construct (e.g., Fortin 1997; Heeter 1989; Williams, Rice, and Rogers 1988).

Adding another layer of complexity to the discussion, several studies have suggested that there might even be a difference between a web site’s “actual” or “objective” interactivity and the one perceived by consumers (Heeter 2000; Lee et al. 2002), and have investigated factors that might contribute to the difference. For example, Yoo and Stout (2001) found that product involvement, as well as the objective interactivity of a web site, influenced consumers’ perception of that web site’s interactivity. Wu (2000) suggested that web expertise might be an important predictor of the extent to which consumers perceive the web site to be interactive. However, these previous studies were unable to address how and why certain factors influence consumers’ perception of a web site’s interactivity.

Given the lack of theoretical development in terms of the role of interactivity, this study seeks to understand how several key personal factors might influence consumers’ perception of a web site’s interactivity. Using the framework on consumer information search behavior, personal factors are categorized into two groups and investigated in this study: general factors (Need for Cognition, product involvement, and product expertise) or Internet-specific factors (skills, challenges, and web shopping experience). The impact of these two sets of factors on perceived interactivity are then examined. In addition, this study attempts to understand how perceived interactivity influences attitudes and purchase decisions. Specifically, this study looks at the relationship between perceived web site interactivity and attitude toward the site and subsequent purchase intention.

Antecedents of Perceived Interactivity: Personal Factors

Navigating through the interlinked pages strategically designed by a corporation may create, for the consumers, discrepancies between “intended,” “perceived,” and “achieved” affordances. Affordance is defined as a set of possible actions that can operate on an object (Schuemie and van der Mast 1999). Norman (1998) suggests that affordance is not a property of an object as much as it is a relationship between an object and the action performed on the object. He further argues that “perceived affordance” gives users the impressions of what actions can be performed on the object and how to do them. Regardless of the number of interactive functions a web site provides, consumers’ perception of that web site’s interactivity may be influenced by how many interactive functions they perceive the web site to provide and their actual use experience of those interactive functions.

Moderators of consumer search behavior have generally been classified into “task” and “personal” factors (Beatty and Smith 1987). As an exploratory effort, this study focuses on personal factors. In order to be specific to the unique Internet environment, personal factors are further classified into general and Internet-specific factors. A better understanding of the relative roles of these personal factors on perceived interactivity may help further the conceptualization of a comprehensive framework.

General Factors

Past research on consumer information search behavior has identified consumer motivation and knowledge to be influential in determining the extent of their search activities (Moore and Lehmann 1980). Of the various factors related to these two constructs, Need for Cognition (NFC), involvement, and expertise have been studied extensively (e.g., Beatty and Smith 1987; Cacioppo and Petty 1982; Hawkins, Best, and Coney 1986). However, few studies to date have been devoted to investigating the impact of these constructs on consumer search behavior on the Internet. Furthermore, the relationship between these personal factors and perceived interactivity has not been dealt with fully.

Need for Cognition (NFC).

Consumers have different levels of NFC. NFC is defined as an individual’s tendency to engage in and enjoy effortful cognitive endeavors (Cacioppo and Petty 1982). In general, it has been suggested that higher NFC leads to greater decision accuracy or quality decisions. This positive outcome of high NFC can be understood in terms of the different information search strategies that consumers employ. For instance, in a choice-making experiment, Levin, Huneke, and Jasper (2000) observed that subjects with high NFC tended to adopt an “attribute-oriented” search strategy, while subjects with low NFC used an “alternative-oriented” strategy. In addition, they found that NFC was positively related to the amount of effort and depth and breadth of the information search. Thus, higher NFC led to a higher score on decision accuracy or quality. These findings are quite consistent with Mantel and Kardes’ (1999) proposition that high NFC individuals make more carefully thought-out and detail-oriented judgments, whereas low NFC individuals base their judgments on global comparisons or attitudes. Therefore, it seems reasonable to expect that high NFC consumers, when compared to low NFC consumers, are more likely to search on a web site before making a purchase decision and are more likely to be exposed to or use the interactive functions provided by the site. Consequently, the following hypothesis is proposed.

H1: Consumers with high NFC are likely to perceive the web site as having greater interactivity than consumers with low NFC.

Product Involvement.

Previous research suggests that under low product involvement conditions, consumers usually engage in minimal search. Under high product involvement conditions, however, consumers tend to search extensively (Engel and Blackwell 1982; Hawkins, Best, and Coney 1984). This could be a reflection of consumers’ subjective feelings regarding the importance of the judgment process (Mantel and Kardes 1999). Specific to the Internet shopping environment, Yoo and Stout (2001) found that consumers with a high level of product involvement tended to have more “intention to interact” with a web site. Consequently, consumers with high product involvement may engage in a more extensive search and try out more interactive functions on a web site than their low involvement counterparts. Therefore, the following hypothesis is proposed.

H2: Consumers with high product involvement will be more likely to perceive a web site as having greater interactivity than consumers with low product involvement.

Product Expertise.

Different from the facilitative influences of NFC and product involvement, past studies have found that high level of product expertise may lower the extent of information search (Alba and Hutchinson 1987; Bettman 1986; Chi, Glaser, and Rees 1982; Gardial and Biehal 1987; Maheswaran and Sternthal 1990). This could be due to the fact that consumers seek pre-purchase information less than expected (Bettman 1979). As a result, experts and novices develop inferences that are quite different from each other.

In making inferences, novices tend to use sets of “domain-independent rules” that are accessed from long-term memory (Collins and Loftus 1975). In contrast, experts are more likely to use a single “integrated rule” that has been developed through extensive practices in specific contexts or with specific objects. Thus, they are likely to perform a relevant task in a more efficient way (Newell 1973). Compared to the integrated rule that experts use, the rule novices use is prone to errors because the rule and data must be kept separately in a working memory whose capacity is limited (Anderson 1982). Therefore, novices may be more likely to try interactive functions of web sites in order to reduce errors in their decisions. Experts, on the other hand, have less need to engage in an extensive search. The following hypothesis is proposed for the study.

H3: Novice consumers will perceive the web site as having greater interactivity than experts.

Internet-specific Factors

In addition to the general factors discussed above, factors that are specific to the Internet also need to be considered due to the unique capability of the Internet. A review of literature indicates that few studies have looked at the impact of Internet-specific factors on consumer behavior on the Internet. As an initial attempt, therefore, this study incorporates three Internet-specific factors that have attracted researchers’ attention – skills, challenges, and web shopping experience – and examines their influences on consumers’ perception of interactivity.

Skills and Challenges.

According to Novak, Hoffman, and Yung (2000), skills may be defined as a consumer’s capacity for action during the online navigation process, whereas challenges are defined as his or her opportunities for action on the Internet. Although it has been found that these two constructs operate independently (Ghani and Deshpande 1994), several studies have observed that high skills and challenges led to a satisfying consumer experience on the Internet (e.g., Csikszentmihalyi 1997; Hoffman and Novak 1996). Previous studies have also suggested that consumers tend to perceive “control” and “arousal” when their skills and challenges are relatively high (Ellis, Voelkl, and Morris 1994; Massimini and Carli 1988). It is likely that this enhanced control and arousal helps produce a satisfying online experience (Ghani, Supnick, and Rooney 1991). According to Novak, Hoffman, and Yung (2000), such a satisfying experience encourages exploratory behaviors on the Internet, although their assertion was supported only in the base model that they proposed. Wu (2000) found that consumers’ web expertise, which was operationalized quite similar to skills, was positively related to their perception of interactivity. Such a relationship, however, was found only in one of the two experiments conducted. Therefore, the following two hypotheses are offered for testing in this study.

H4: Consumers with high skills tend to perceive the web site visited as having greater interactivity than consumers with low skills.

H5: Consumers with high challenges tend to perceive the web site visited as having greater interactivity than consumers with low challenges.

Web Shopping Experience.

Novak, Hoffman, and Yung (2000) found that Internet skills are positively related to how long a consumer has used the Internet. Moreover, they found that consumers who have used the Internet for longer periods of time were more likely to use the Internet for task-oriented activities, such as looking for reference material or product information, conducting research, and shopping. However, contrary to the effect of skills, consumers with more shopping experience on the Internet may have already adopted an efficient way of shopping online, such as using only limited interactive functions important for the shopping task. This expectation is in fact consistent with previous findings that as consumers become more experienced, their information search shifts from an extensive manner to a simplified one (Howard and Sheth 1969). It is likely that experienced Internet shoppers may not explore the web sites as much as those who are less experienced. Therefore, the following hypothesis is proposed.

H6: Consumers with less shopping experience on the Internet will perceive the web site visited as having greater interactivity than consumers with more Internet shopping experience.

Consequences of Perceived Interactivity: Attitude toward the Web Site and Purchase Intention

Over the years, researchers have suggested that attitude toward the ad is oftentimes a good indicator of an ad’s effectiveness (Batra and Ray 1986; Haley and Baldinger 1991; MacKenzie, Lutz, and Belch 1986; Shimp 1981). Accordingly, if today’s web sites are to resemble and reflect the characteristics of traditional ads, as suggested by various research, then attitude toward the web site should lead to consequences similar to those found in attitude research.

Wu (2000) found that perceived interactivity did have positive influences on attitudes toward the web sites, attitudes toward the brand, and purchase intention. Moreover, in an experimental study, Yoo and Stout (2001) observed that consumers’ “intention to interact” with a web site positively influenced their attitudes toward the web site and purchase intention. Although not directly studying consumers’ attitudes toward a web site, Ghose and Dou (1998) also found that greater interactivity was an important predictor of experts’ evaluation of a web site as a quality one. Therefore, the following three hypotheses are proposed.

H7: Perceived interactivity is positively related to attitude toward the web site.

H8: Perceived interactivity is positively related to purchase intention.

H9: Attitude toward the web site is positively related to purchase intention.

Figure 1 summarizes the hypotheses proposed in this study.

Figure 1. Proposed Hypotheses

Proposed Hypotheses

Study Methods

In order to test the proposed hypotheses, a correlational study was conducted in a lab using realistic web sites: Dell, Compaq, and Apple. At the time of the study, these three were the leading computer manufacturers and an independent content analysis had determined that all three web sites contained equal amount of interactive functions (Lee et al. 2002). The use of realistic web sites, compared to hypothetical ones, was also expected to better reflect the relationships of interest, since studies using hypothetical web sites may distort consumers’ behaviors on the Internet (Wu 2000).

Sample and Procedure

A total of 39 college students at a Southwestern state university were recruited for the study. The study was conducted in a laboratory equipped with laptop computers with fast connection to the Internet. Before performing the specific task, participants were asked to answer questions measuring two sets of personal factors: (1) Need for Cognition (NFC), product involvement, and product expertise; and (2) skills, challenges, and web shopping experience. They were then told that the purpose of the study was to solicit their input in making a decision about the brand and model of a laptop computer that their college was planning on requiring all students to purchase. Since Apple, Compaq, and Dell were the manufacturers under consideration, participants were asked to visit their web sites in order to make a recommendation of a computer in the price range between $1,400 and $2,400.

Participants were then given a total of 30 minutes to visit the three web sites. Afterward, they were asked to answer questions measuring their perceived interactivity of and attitude toward each web site that they had visited and their purchase intention.


Several major theoretical constructs were examined in the study. General and Internet-specific factors were operationalized via measurements adopted from past studies. Need for Cognition was measured using the Cacioppo, Petty, and Kao (1984) scale. Zaichkowsky’s (1994) short scale on product involvement was included. Also, participants’ level of expertise with laptop computers was measured using the scale developed by Kleiser and Mantel (1994). Novak, Hoffman, and Yung (2000) provided the scales to measure skills and challenges. Finally, for web shopping experience, the items used in the GVU survey (1996) were adopted.

A revised nine-item scale (Wu 2000) was used to measure perceived interactivity. This scale was developed by Wu to reflect the multidimensional nature of perceived interactivity such as perceived control, responsiveness, and personalization. To measure attitude toward the web site, Chen and Wells’ (1999) scale was incorporated. For purchase intention, participants in this study were asked to identify their brand choice at the end of the study instead of the conventional Likert-type questions.

Appendix A provides a complete list of items used to measure the theoretical constructs examined in this study.


Reliability of each measurement scale, except web shopping experience and purchase intention, was first examined using Cronbach alpha. Results from the tests indicate that all seven scales are sufficiently reliable: NFC (¥á = .85), product involvement (¥á = .92), product expertise (¥á = .86), skills (¥á = .82), challenges (¥á = .76), perceived interactivity (¥á = .89) and attitude toward the site (¥á = .80).

As mentioned earlier, this study provided participants with a realistic task environment by using three major computer manufacturers’ web sites. Accordingly, each web site (Dell, Compaq, and Apple) would serve as the general group for analysis purpose.

Antecedents of Perceived Interactivity

In order to investigate the impact of personal factors on the perception of web site interactivity, logistic regression analysis was carried out for each web site. Using median split, participants were divided into high and low perceived interactivity groups according to their scores (Apple median = 3.22; Compaq median = 3.33; Dell median = 3.44). Those personal factors of interest were included in the logistic model by using the method of “enter” so that the impact of each factor could be identified.

From the correlation matrix in Table 1, it is observed that the six factors are not correlated with each other, except that web shopping experience was correlated with skills, challenges, and expertise. This lack of interdependency between the factors provided justification for using them as separate predictors in the model.

Table 1. Correlations between the Predictors in the Logistic Model

Correlations between the Predictors in the Logistic Model

As can be see from Table 2, the proposed logistic regression model was supported for Compaq (p < .05), but not for Dell or Apple. Two additional goodness-of-fit tests – Nagelkerke R2 (.449) and Hosmer and Lemeshow test (.601) – further indicate that the proposed model was well-fitted for Compaq. From Table 3, it is observed that the classification accuracy (74.2%) of the model exceeds the benchmark accuracy rate of 64.5%, which further supports the applicability of the model to the site.

Table 2. The Logistic Regression Result for the Impacts of Personal Factors on Perceived Interactivity

The Logistic Regression Result for the Impacts of Personal Factors on Perceived Interactivity

Table 3. Classification Table for the Compaq Site

Classification Table for the Compaq Site

Regarding the impact of personal factors on perceived interactivity, NFC (p < .05) and skills (p < .10) are observed to predict participants’ perception for the Compaq site. More specifically, from exponential transformation of the coefficients, it could be interpreted that one unit increase of NFC score enhances the odds that participants perceive the Compaq web site as interactive by approximately seven times, while the odds would increase by approximately 2.6 times with one unit of increase of skills. Although not significant, the effects of product involvement, challenges, and web shopping experience were directionally supported.

In summary, among the first six proposed hypotheses, H1 was supported whereas H4 was partially supported. These hypotheses were supported only for the Compaq web site.

Consequences of Perceived Interactivity

The impact of perceived interactivity on attitude toward the web site (H7) was examined via Pearson correlation between the two constructs for each of the three web sites first. As shown in Table 4, the correlation score was statistically significant for all three web sites with p < .0001, and the relationships were all positive. This result suggests that the higher the perceived interactivity of a web site, the more positive the attitude toward the site. Therefore, H7 was supported.

Table 4. The Correlation between Perceived Interactivity and Attitudes toward the Site

The Correlation between Perceived Interactivity and Attitudes toward the Site

In order to test the effect of perceived interactivity on purchase intention, while at the same time examining the effect of attitude toward the site, logistic regressions were carried out. For each web site, participants’ purchase intention served as a dichotomous dependent variable, while the perceived interactivity of and the attitude toward the site served as independent variables. Table 5 shows that the logistic model with the two predictors was significant for Compaq and Dell, but not for Apple. Furthermore, Table 5 suggests that participants’ purchase intention could be successfully predicted by their attitudes toward the web site (Compaq: p < .05, Dell: p < .10), but not by their perceived interactivity of the web site.

Table 5. The Logistic Regression Result for the Impacts of Perceived Interactivity and Attitudes toward the Site on Brand Choice

The Logistic Regression Result for the Impacts of Perceived Interactivity and Attitudes toward the Site on Brand Choice

From exponential transformation of coefficients, it could be interpreted that the odds for participants to select Compaq as their purchase choice were approximately 9 times greater when their attitude toward the web site was increased by one unit. Similarly, the odds for choosing Dell would increase by 4.3 times with each one unit increase of participant’s attitude toward the site. Therefore, H9 is partially supported in this study whereas H8 was not supported.

In summary, regarding the relationships between perceived interactivity, attitude toward the web site, and purchase intention, Hypotheses 7 and 9 were supported. Hypothesis 8, however, was not supported. These hypotheses were supported for Compaq and Dell, but not for Apple.


This study examined how personal factors, both general and Internet-specific, influenced consumers’ perception of the interactivity of a web site. Based on past studies on consumer information search behavior, it was expected that personal factors would influence the extent to which consumers search for information on a web site. This would in turn result in differences in their perception of a web site’s interactivity.

Results from this study indicate that among the general factors tested, Need for Cognition (NFC) was a significant predictor in identifying whether a participant perceived the web site to be of high or low interactivity. Among the Internet-specific factors, skills were found to be a predictor although it was marginally significant. It is interesting to note that NFC, which has been considered an important predictor for consumers’ information search behavior in traditional media environments, still exerts a certain impact in the Internet environment. However, this impact was not observed across all three web sites examined. These inconsistent findings suggest that further research focusing on the unique Internet environment is warranted.

Compared to the traditional media that tend to have limited information capacity, the Internet enables consumers to access almost unlimited amounts of information at substantially reduced costs and efforts. Findings from this study seem to suggest that the nature of the Internet may have afforded high NFC consumers with more search activities than low NFC consumers. More importantly, this tendency to search for more information may have led to greater perceived interactivity. In a similar vein, consumers with more Internet-related skills tended to perceive the web sites visited as more interactive. It is possible that both of these observations could be explained by the expectation of and the exposure to more interactive functions of the web site according to NFC and skills levels.

However, it should be noted that the effects of other personal factors were not found in this study. This may suggest that the effects of factors other than those tested in this study should be considered in future research. For instance, Bagozzi (1993) proposed that variances in consumer behavior were better accounted for by considering “person-by-situation interactions” rather than either effect alone. Therefore, even factors that were not significant in this study might be found significant in different situations. Future studies need to investigate these factors more closely and with more depth.

Zaichkowsky (1986) differentiated product involvement from purchase involvement in that the former is enduring by nature, while the latter is initiated by a specific purchase situation. Accordingly, it could be argued that high product involvement encourages an on-going but not necessarily extensive search. In contrast, high purchase involvement prompts an extensive search under a specific situation. It is likely that the measure of product involvement used in this study did not correctly reflect participants’ information search activities that were initiated by a specific purchase situation.

It has been pointed out that the way novices make inferences in dealing with a task is relatively slow and requires sustained cognitive efforts. Therefore, unless consumers have at least a moderately strong motivation to sustain their cognitive efforts in the search process (Lee and Olshavsky 1997), their information search activities may end earlier than expected. It could be that participants in this study were not sufficiently motivated to become more exposed to those interactive functions of the web sites.

Challenges were expected to positively affect information search and perceived interactivity in this study. This expectation was based on the notion that challenges may create arousal and therefore lead to more activities on the web site. However, Novak, Hoffman, and Yung (2000) found that challenges and their resulting arousal were positively correlated with recreational uses of the Internet. This implies that the positive effects of challenges, if any, may be exhibited only in the case of experiential uses of the Internet (i.e., entertainment), rather than task-oriented uses (i.e., information search).

The context of this study may not have created an appropriate situation in which participants were properly aroused.

In terms of the role of perceived interactivity, it was found that the effect of perceived interactivity on purchase intention could be indirect, via the effect on attitude toward the web site. Specifically, it was observed that perceived interactivity was highly correlated with attitude toward the web site, but not a predictor for purchase intention. However, attitude toward the web site was found to be a good predictor for purchase intention. Future research needs to further explore the relationships between these constructs.


There is a general consensus that the unlimited “shelf space” on a web site enables marketers to put whatever amount of information they like on the Internet (Chen and Wells 1999). Given that developing a web site is an attempt to structure an environment where “affordances” (Heeter 2000) for consumers are created, it should be kept in mind that consumers may not fully experience what is provided to them on the Internet. The differences between intended, perceived, and achieved affordances may create mismatches. For example, a mismatch may be a situation where “information intensity” is perceived as “information overload” (Chen and Wells 1999).

Although beneficial for an exploratory effort, the use of realistic web sites in this study does have trade-offs in terms of internal validity. It should also be noted that the logistic model used to test personal factors and perceived interactivity was significant only for Compaq. The relationship between attitude toward the web site and purchase intention was supported for Compaq and Dell, but not for Apple. This discrepancy in the results suggests that the effects of existing brand image may need to be included in examining any relationships using realistic web sites. Furthermore, the intricate relationships among the several personal factors tested in this study may require some re-consideration.

Although the concept of perceived interactivity has been considered important, the theoretical development of a framework that clearly delineates the role of perceived interactivity and many other factors is still lacking. The study reported in this paper has attempted to incorporate several relevant factors that have been reported in the literature in order to gain a baseline understanding of the role perceived interactivity plays in the overall decision-making process on the Internet. More research is needed to help untangle these relationships and provide helpful implications for managerial strategies.


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Appendix A

Constructs and Measurement Items



Need for Cognition
(Cacioppo, Petty and Kao 1984)

1. I really enjoy a task that involves coming up with solutions to problems.
2. I would prefer a task that is intellectual, difficult, and important to one that is somewhat important but does not require much thought.
3. Learning new ways to think doesn’t excite me very much. (R)
4. I usually end up deliberating about issues even when they do affect me personally.
5. The idea of relying on thought to get my way to the top does not appeal to me. (R)
6. The notion of thinking abstractly is not appealing to me. (R)
7. I only think as hard as I have to. (R)
8. I like tasks that require little thought once I’ve learned them. (R)
9. I prefer to think about small daily projects to long-term ones. (R)
10. I would rather do something that requires little thought than something that is sure to challenge my thinking abilities. (R)
11. I find little satisfaction in deliberating hard and for long hours. (R)
12. I don’t like to have the responsibility of handling a situation that requires a lot of thinking. (R)
13. I feel relief rather than satisfaction after completing a task that required a lot of mental effort. (R)
14. Thinking is not my idea of fun. (R)
15. I try to anticipate and avoid situations where there is a likely chance I’ll have to think in depth about something. (R)
16. I prefer my life to be filled with puzzles that I must solve.
17. I would prefer complex to simple problems.
18. It’s enough for me that something gets the job done, I don’t care how or why it works. (R)

(R) = reverse coded
Measured with a 9-point scale from +4 Very Strong Agreement to -4 Very Strong Disagreement.

Product Involvement
(Zaichkowsky 1994)

To me a lap top computer is:

1. Important/Unimportant
2. Boring/Interesting
3. Relevant/Irrelevant
4. Exciting/Unexciting
5. Means nothing/Means a lot to me
6. Appealing/Unappealing
7. Fascinating/Mundane
8. Worthless/Valuable
9. Involving/Uninvolving
10. Not needed/Needed

Measured with a 7-point semantic differential scale.

Product Expertise
(Kleiser and Mantel 1994)

1. I automatically know which brand of lap top computers to buy.
2. I am loyal to one brand of lap top computers.
3. At the place of purchase, I can visually detect my preferred brand of lap top without much effort.
4. I can immediately identify my preferred brand even if it is located with other brands of lap top computers.
5. When I purchase my preferred brand, I do not pay attention to the other brands of lap top computers.
6. I enjoy learning about lap top computers.
7. I will search for the latest information on lap top computers before I purchase a brand.
8. I keep current on the most recent developments in lap top computers.
9. I consider myself knowledgeable on lap top computers.
10. My knowledge of lap top computers helps me to understand very technical information about this product.
11. I use my knowledge on lap top computers to verify that advertising claims are in fact true.
12. I can recall almost all existing brands of lap top computers from memory.
13. I can recognize almost all brand names of lap top computers.
14. I can recall product-specific attributes of lap top computers.
15. I can recall brand-specific attributes of the various brands of lap top computers.

Measured with a 7-point Likert-type scale from Strongly Disagree to Strongly Agree.

(Novak, Hoffman and Yung 2000)

1. How would you rate your skill at using the Web, compared to other things you do on the computer?
2. How would you rate your skill at using the Web, compared to the sport of game you are best at?
3. I am extremely skilled at using the Web.
4. I consider myself knowledgeable about good search techniques on the Web.
5. I know somewhat less about using the Web than most users.
6. I know how to find what I am looking for on the Web.

Items 1 & 2 were measured with a 9-point scale from Very Much Less to Very Much More; Items 3 to 6 were measured with a 9-point scale from Strongly Disagree to Strongly Agree.

(Novak, Hoffman and Yung 2000)

1. How much does the Web challenge you, compared to other things you do on the computer?
2. How much does the Web challenge you, compared to the sport or game you are best at?
3. Using the Web does not challenge me.
4. Using the Web challenges me to perform to the best of ability.
5. Using the Web provides a good test of my skills.
6. I find that using the Web stretches my capabilities to my limits.

Items 1 & 2 were measured with a 9-point scale from Very Much Less to Very Much More; Items 3 to 6 were measured with a 9-point scale from Strongly Disagree to Strongly Agree.

Web Shopping Experience
(GVU 1996)

On average, how often do you search for information from Web-based vendors about products or services you have an intention to buy at some point in the near future?
1. Don’t do at all
2. Do less than once each month
3. Do about once each month
4. Do several times each month
5. Do about once each week
6. Do several times each week
7. Do at least once each day

Perceived Interactivity
(adapted from Wu 2000)

1. I was in control of my navigation through this Web site.
2. I was in control over the content of this Web site that I wanted to see.
3. I was in control over the pace of my visit to this Web site.
4. I could communicate with the company directly for further questions about the company or its products if I wanted to.
5. The site had the ability to respond to my specific questions quickly and efficiently.
6. I could communicate in real time with other customers who shared my interest in this product category.
7. Interacting with this site is like having a conversation with a sociable, knowledgeable and warm representative from the company.
8. I felt as if this Web site talked back to me while I was navigating.
9. I perceive the Web site to be sensitive to my needs for product information.

Measured with a 5-point Likert-type scale from Strongly Disagree to Strongly Agree.

Attitudes toward the Web Site
(Chen and Wells 1999)

1. This Web site makes it easy for me to build a relationship with this company.
2. I would like to visit the Web site again in the future.
3. I am satisfied with the service provided by this Web site.
4. I feel comfortable in surfing this Web site.
5. I feel surfing this Web site is a good way to spend my time.
6. Compared with other Web sites, I would rate this one as (One of the worst / One of the best)

Measured with a 5-point Likert-type scale from Definitely Disagree to Definitely Agree.

About the Authors

Joonhyung Jee is a doctoral student in the Department of Advertising, The University of Texas at Austin.

Wei-Na Lee is an Associate Professor, Department of Advertising, College of Communication, The University of Texas at Austin, CMA 7.142, Austin, Texas 78712-1092.