effects of interactivity in a web site - CiteSeerX

EFFECTS OF INTERACTIVITY IN A WEB SITE The Moderating Effect of Need for Cognition Maria Sicilia, Salvador Ruiz, and Jose L. Munuera ABSTRACT: This paper examines how consumers process the information available, and what their experiences are, when exposed to an interactive Web site as compared with a noninteractive Web site. The experiment developed analyzes two versions of a Web site in which the capacity to interact with the message has been manipulated. The results show that the interactive Web site leads to more information processing, higher favorability toward the product and the Web site, and greater flow state intensity. In addition, the findings confirm the hypothesized moderating effect of need for cognition on information processing. Implications for new media researchers and practitioners are discussed.

The impact of the Internet on advertising has been very important in recent years, and it continues to grow (Ha 2003; Leong, Huang, and Stanners 1998; Macias 2003; Zinkhan 1998). EMarketer projects that on-line ad spending will increase from $6.4 billion in 2002 to $6.7 billion in 2003. The Internet enables active, selective exposure to advertising, thus giving consumers the discretion to attend to particular messages within the medium (Bezjian-Avery, Calder, and Iacobucci 1998; Dijkstra and van Raaij 2001; Klein 2003). However, this is not true for most formats of Internet advertising (Cho, Lee, and Tharp 2001). Ad formats such as banners, buttons, hypertexts, microbars, pop-ups, or skyscrapers are not requested by the receiver of the message. These advertisements are usually inserted in portals, searchers, or media Web sites, which reach large audiences (Ha 2003). Because involuntary exposure formats of Internet advertising are placed in a medium by a marketer with the intent of reaching a particular audience, they closely resemble traditional advertising (Hwang, MacMillan, and Lee 2003). Only when the consumer is motivated or interested does he or she click on one of these ads and begin to control the communication process, at which point, ad exposure becomes active (Chatterjee, Hoffman, and Novak 2003). The click usu-

Maria Sicilia (Ph.D., University of Murcia) is an assistant professor of marketing, School of Economics and Business, University of Murcia, Spain. Salvador Ruiz (Ph.D., University of Murcia) is an associate professor of marketing, School of Economics and Business, University of Murcia, Spain. Jose L. Munuera (Ph.D., Autonomous University of Madrid) is a professor of marketing, School of Economics and Business, University of Murcia, Spain.

ally takes the consumer to the marketer’s Web site, which is the main vehicle that a company has to inform, persuade, and remind consumers about its products and services on the Internet (Karson and Korgaonkar 2001). It has been posited that the Web site represents the future of marketing communications on the Internet (Ghose and Dou 1998), as it has the potential to provide high levels of information, in addition to creating virtual product experiences (Klein 2003). The Web site is considered a form of advertising because the Web site marketer plans both the message and the content (Hwang, MacMillan, and Lee 2003; Sheehan and Doherty 2001). In this sense, marketers are searching for ways to direct consumers to their Web sites and to send consumers a strong message (Sheehan and Doherty 2001). Moreover, current statistics indicate that most of the money spent on Internet advertising is for company-sponsored Web sites (Markham, Gatlin-Watts, and Bounds 2001). In this context, consumer experiences on a Web site are of great interest for marketers (McMillan and Hwang 2002; Smith and Sivakumar 2004). Web sites, therefore, represent the most important form of interactive advertising. Although many conceptual and empirical studies have focused on the implications of interactive media for consumers within the marketing literature (Balabanis and Vassileiou 1999; Bezjian-Avery, Calder, and Dawn 1998; Bruner and Kumar 2000; Chen and Wells 1999; Cho 1999; Cook and Coupey 1998; Coyle and Thorson 2001; Gallagher, Parsons, and Foster 2001; Stevenson, Bruner, and Kumar 2000), we still do not have a complete understanding of the processes or the consumer responses toward Web sites (Bourliataux-Lajoinie 2000; Peterson and Merino 2003) or

This research was funded by grant SEC2002-04321-C02-01 from the Spanish Ministry of Science and Technology. Journal of Advertising, vol. 34, no. 3 (Fall 2005), pp. 31–45. © 2005 American Academy of Advertising. All rights reserved. ISSN 0091-3367 / 2005 $9.50 + 0.00.

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about the persuasiveness of this communication activity (Hwang, MacMillan, and Lee 2003; Macias 2003). When visiting a Web site, the consumer has control over the flow of information, and he or she can choose what information to see and the order in which that information will be presented (Ariely 2000). Thus, research focused on the effects of interactivity in Web sites should help marketers to understand how consumers process the information available in an interactive message and what their experiences are as compared with when they are exposed to noninteractive messages. In this paper, we analyze consumer reactions to an interactive Web site compared with a noninteractive one. More specifically, we will examine how Web site processing and the consumer’s achievement of a flow state are affected by the presence of interactivity in the Web site. In addition, we will explore the role of need for cognition in moderating such relations. In addressing these issues, we hope to contribute to the aforementioned existing gaps in the marketing literature. In the following sections, the impact of interactive and noninteractive Web sites is comparatively investigated. Then, the research method is described. Next, findings from our experiment are presented to illustrate the effects of interactivity on consumers’ information processing and flow state. Finally, discussion, implications for management, and ideas for further research are reported.

ent levels of interactivity may be found (Coyle and Thorson 2001). The level of interactivity in a Web site might be critical in getting surfers involved in the communication process (Ghose and Dou 1998). People surfing the Web perceive more personal control over the information exchange process and its outcomes with high levels of interactivity as compared with low levels of interactivity (Klein 2003; Peterman, Rohem, and Haugtvedt 1999). The Web site ad format, therefore, highlights the role of the consumer in determining the effects and effectiveness of advertising, while challenging traditional assumptions about how advertising works (Pavlou and Stewart 2000). Interactivity, however, is not all positive for the consumer. Interactive media also require the user to invest processing resources in managing the information flow (Ariely 2000; Eveland and Dunwoody 2002). The need to manage the information received while making simultaneous decisions to control or respond to that information raises several questions about the navigation process in interactive Web sites. In this context, questions arise concerning how consumers process that information, along with how they feel in an interactive as compared with a noninteractive Web site environment. Information Processing from Interactive Advertising

INTERACTIVITY IN WEB SITES On the Web, navigation consists of visiting a series of Web sites and interacting with them to search for information and/ or advertising about products or consumer content, or to place an order for a product (Hoffman and Novak 1996). Web sites are based on information and communication technologies that enable easy and rapid interaction between consumers and advertisers (Coyle and Thorson 2001; Ha and James 1998). As with the Internet, the key factor of a Web site is its interactivity (Ghose and Dou 1998; Macias 2003). Interactivity represents the facility for individuals and organizations to communicate directly with one another regardless of distance or time (Berthon, Pitt, and Watson 1996). This conceptualization stems from an interpersonal communication perspective (Ha and James 1998). Another conceptualization—the one that will be used in this paper—stems from a mechanical perspective (Coyle and Thorson 2001). In a Web site, individuals can interact with the medium itself, which is called “machine interactivity” (Steuer 1992). The “machine interactivity” allows consumers to control what information will be presented, in what order, and for how long (Ariely 2000; Bezjian-Avery, Calder, and Iacobucci 1998). As with other Web ad formats, a Web site can be categorized according to the control of the consumer over the communication process (Ha 2003); therefore, differ-

According to the Elaboration Likelihood Model (ELM) (Petty, Cacioppo, and Schumann 1983), information processing is related to the elaboration of information during exposure to an ad. The study of information processing from advertising implies working with the individual cognitive responses, which are defined as any thoughts that emerge during the elaboration process (Meyers-Levy and Malaviya 1999). Elaboration indicates the amount, complexity, or range of activity occasioned by a stimulus (Mcquarrie and Mick 1999) and includes the parallel cognitive subprocesses of encoding, storage, and retrieval (Lang et al. 2002). Furthermore, as Gardial et al. (1993) pointed out, elaboration can be considered as a continuum, from a degree of minimum elaboration to a maximum level. This notion of cognitive elaboration corresponds with the idea of different levels of processing (Grunert 1996), that is, more elaboration is equivalent to a higher amount of information processing. Interactive systems can help consumers to process information, as they are able to easily reduce or eliminate unwanted or superfluous information and can organize that information in such a way that facilitates the process (Widing and Talarzyk 1993). Because the consumer can select the information and the order in which such information is presented (BezjianAvery, Calder, and Iacobucci 1998; Rodgers and Thorson 2000), control and involvement will be high (Shih 1998),

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and this will also lead to a more intense processing (Cho 1999; Dijkstra and van Raaij 2001; Eveland and Dunwoody 2002; Rodgers and Thorson 2000). In other words, interactive information needs to be structured by the consumer, and this activity requires extensive cognitive effort, which implies that the level of information processing will be high (Ariely 2000; Coupey 1994; Dijkstra and van Raaij 2001). Based on this, we propose the following hypothesis: H1: Information processing will be higher when the individual has been exposed to an interactive Web site than when he or she has been exposed to one that is noninteractive. Nevertheless, a large number of thoughts generated by a Web site does not necessarily mean that it has been successful, as thoughts may be classified as favorable, unfavorable, or neutral. We therefore also need to take into account the valence of such processing (Mackenzie, Lutz, and Belch 1986; Sauer, Dickson, and Lord 1992). The Cognitive Response Model states that persuasion in advertising reflects the net favorableness of the cognitive responses that people evoke as they elaborate on a message (Meyers-Levy and Malaviya 1999; Mick 1992). Thus, the persuasiveness of this ad format is reflected by the valence of Web site–related thoughts. In addition to the total amount of processing, interactivity can also affect the valence of such processing for many reasons. First, this may happen because interactivity increases involvement (Berthon, Pitt, and Watson 1996; Shih 1998) and the sense of presence, which leads to persuasion (Lombard and Snyder-Duch 2001). Second, when consumers are exposed to an interactive ad, they have control over the information flow (Hoffman and Novak 1996). Such control over the information exchanged has been shown to increase the pleasurability of the event itself (Ariely 2000), which will probably lead to better evaluations and responses from the consumer. A third reason comes from the fact that interactivity itself constitutes a distinct aspect that makes the ad more attractive. Recent studies show that interactive messages are perceived as more attractive than those that are noninteractive. Wu (1999) demonstrated that perceived interactivity is related to attitude toward the Web site, and Ghose and Dou (1998) and O’Keefe, O’Connor, and Kung (1998) observed that the more interactive the message, the more likely it will be considered as a top site. Moreover, Cook and Coupey (1998) stressed that interactivity may influence consumers as a peripheral cue, such as the spokesperson, music, or graphics. All of these reasons lead us to propose that: H2a: Information processing toward the Web site will be more favorable when the Web site is interactive than when the Web site is noninteractive. Concerning the product shown in the Web site, interactivity can improve information searches (Bettman, Luce, and Payne

1998), thus increasing the probability of exposure to product information. Taking into account that this information is provided by the company, it is likely to point out the advantages and strengths of the product. Consequently, this exposure will initially increase the number of positive thoughts toward the product (Nordhielm 2002). Furthermore, recent work suggests that responses to Web advertising are very similar to those in traditional media (Berthon, Pitt, and Watson 1996; Cho 1999). Through an attitude transfer mechanism (Mackenzie, Lutz, and Belch 1986), such favorableness in Web site processing may also be transferred to the product shown in the Web site. Based on this reasoning, we propose that: H2b: Information processing toward the product will be more favorable when the Web site is interactive than when the Web site is noninteractive. Interactivity and Flow State A number of researchers have suggested that flow is a useful way of describing people’s interactions with computers (Csikszentmihalyi 1990; Hoffman and Novak 1996; Smith and Sivakumar 2004; Trevino and Webster 1992). Flow is particularly relevant given the potential of the Internet for advertisers and the commercial nature of most Web sites. Hoffman and Novak (1996) suggest that the success of online marketers depends on their ability to create opportunities for consumers to experience flow. The flow state is defined as “the holistic sensation that people feel when they act with total involvement” (Csikszentmihalyi 1977). Flow is an optimal experience, one that is extremely enjoyable and one in which the individual experiences an intrinsic interest and a sense of time distortion during his or her engagement (Chen, Wigand, and Nilan 1999). This optimal experience occurs when one’s set of skills is matched with the perceived challenges of the task (Csikszentmihalyi 1977). While the flow state can be reached while engaging in numerous activities, including sports, writing, work, games, and hobbies (Novak, Hoffman, and Yung 2000), we focus on flow during consumer interaction with a Web site. Several researchers have established the validity of the flow construct in relation to computer-related activities (Ha and Chan-Olmsted 2001; Nel et al. 1999; Novak, Hoffman, and Yung 2000; Trevino and Webster 1992). When a consumer is surfing the Internet, he or she perceives two environments: the physical environment and the virtual environment. The strength of this experience is a function of the extent to which a person feels present in the mediated environment rather than in his or her immediate physical environment. The flow state is then characterized by a seamless sequence of responses facilitated by machine interactivity and is intrinsically enjoyable (Hoffman and Novak 1996). In

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such an experience, the consumer is so involved in the act of network navigation that “nothing else seems to matter” (Csikszentmihalyi 1990). Recently, Smith and Sivakumar (2004) have distinguished two characteristics of flow: intensity and duration. While flow refers to a specific state, it can also be considered a continuous variable in that different levels of flow can occur, ranging from none to intense (Hoffman and Novak 1996; Nel et al. 1999; Trevino and Webster 1992). This is the common conceptualization of flow, where a higher flow state is related to a more intense sensation (Novak, Hoffman, and Yung 2000). However, while it is comprehensible that flow duration varies from one situation to another (Smith and Sivakumar 2004), it is still not clear whether duration can be considered a dimension of flow state or a consequence of such an experience. That is, the more intense the sensation of flow, the more it will last. For example, Hoffman and Novak (1996) and Nel et al. (1999) considered it as a consequence of flow state, indicating that Web sites that facilitate flow will be visited for a longer duration. Considering the difference of opinions and the fact that we are working with the experience of flow during a controlled visit to a Web site, we refer here to flow intensity, the most common conceptualization of flow. To date, very little research has been done in the advertising literature about flow. For example, we do not know what types of ads will provoke this state or whether it will enhance advertising effectiveness (Smith and Sivakumar 2004; Zinkhan 1998). Some responses can be found in the work carried out by Webster, Trevino, and Ryan (1993), who related flow experience to expectations of future voluntary computer interactions. In a Web site environment, this means that individuals who experience flow during a visit to a Web site will want to revisit that Web site in the future. Therefore, developing a Web site capable of eliciting a flow state in visitors is vital to advertisers. More recently, Nel et al. (1999) confirmed that the flow experience makes the individual want to repeat the visit, which is a main objective of companies with a presence on the Internet. It has been established in the literature that consumers do experience flow while using the Web (Chen, Wigand, and Nilan 1999; Novak, Hoffman, and Yung 2000). More specifically, Chen, Wigand, and Nilan (1999) consider that interactivity can facilitate the occurrence of flow. This happens because information interaction gives the consumer a high level of freedom and control (Wolfinbarger and Gilly 2001). In an interactive system, individuals will experience flow with more intensity when they are allowed to interact with the Web site than when they are not. Thus, we propose the following: H3: An individual will reach a more intense flow state when he or she has been exposed to an interactive Web site than when he or she has been exposed to a noninteractive Web site.

Interaction Effects: Need for Cognition as a Moderator Variable The need for cognition (NFC) construct has received much interest from researchers in a variety of disciplines. Cacioppo, Petty, and Kao (1984) developed an 18-item scale for assessing a person’s NFC. Individuals who score high on the NFC scale like effortful thinking, such as solving puzzles, extensive deliberation, and thinking abstractly. Those individuals who are low in NFC avoid effortful thinking. In studying information processing when exposed to advertising, consumer behavior researchers have pointed out that individual differences among message recipients may lead to wide variations in the way people respond to advertising appeals (Moore, Harris, and Chen 1995). High-NFC individuals, therefore, may exhibit a tendency to engage in elaborate processing, and enjoy thinking when exposed to an ad (Cacioppo and Petty 1982), whereas low-NFC individuals may not like thinking or reasoning very much and may be less disposed to put forth much effort in processing the information (Kivetz and Simonson 2000). For that reason, NFC constitutes an individual difference variable that potentially affects a person’s motivation to process persuasive communication. Petty, Cacioppo, and Schumann (1983) suggest that individuals high in NFC are more likely to generate inferences and elaborations in response to persuasive messages than are those low in NFC. In general, high-NFC individuals have been shown to process and evaluate advertising information more thoroughly than low-NFC individuals (Batra and Stayman 1990; Mantel and Kardes 1999). Therefore, this variable determines the ability and desire to dispense cognitive efforts (Zhang and Buda 1999). In interactive environments, the substantial decrease in the price of gathering information increases the importance of understanding cognitive costs ( Johnson, Lohse, and Mandel 1999). On the one hand, interactivity offers information control, but on the other hand, it requires higher cognitive resources to manage the information flow (Ariely 2000). Literature shows that interactive designs facilitate information searches and activate consumers (Cho 1999; Rodgers and Thorson 2000). In H1, we have proposed that information processing will be higher when the individual has been exposed to an interactive Web site than when he or she has been exposed to a noninteractive Web site. However, individuals high in NFC usually engage in elaborative processing; therefore, for those individuals, the increase in processing may not be as high as the increase in processing for their low-NFC counterparts. Moreover, interactivity does not imply new information or better information, and high-NFC individuals concentrate more on the real attributes and information about the product and less on the way information is presented to them (Zhang and Buda 1999). Hence, information process-

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ing by high-NFC individuals will not be strongly affected by interactivity. On the other hand, low-NFC individuals may rely more readily on ad characteristics or on the way the information is presented (Meyers-Levy and Peracchio 1992). Consequently, we expect that the increase in processing derived from interactivity will be higher for low-NFC individuals than for high-NFC individuals. That is to say, an interactive Web site will result in a higher increase in information processing for low-NFC individuals as compared with the increase produced for high-NFC individuals. Our fourth hypothesis is the following: H4: The increase in information processing for an interactive versus a noninteractive Web site will be smaller for high-NFC than for low-NFC individuals (i.e., the effect of interactivity on processing is moderated by NFC). While we hypothesize a moderating effect for NFC, we see no reason to expect a main effect of NFC on either the valence of processing toward the Web site or the product, since NFC is expected to affect the depth or amount of elaboration and not the favorableness or valence of such elaboration (Batra and Stayman 1990). Thus, although high-NFC individuals process more thoroughly than low-NFC individuals, nothing can be deduced about the valence of such processing. The same can be said about the interaction effect. Although it is reasonable to assume greater favorableness in the interactive condition (H2a and H2b) for both high- and low-NFC participants, it may happen for different reasons, such as higher involvement and information searches (for high-NFC individuals) or greater Web site attractiveness (for low-NFC individuals, because they use heuristic processing). However, we have no reasons to expect a higher increase in favorableness for highor for low-NFC participants. Therefore, no interaction effect is expected regarding NFC and favorableness. Flow experience has been shown to require high levels of concentration from individuals (Csikszentmihalyi 1990; Hoffman and Novak 1996). Recent studies suggest that certain personal characteristics may enable individuals to engage in flow experiences more often, more intensely, and for longer periods than others. For example, Smith and Sivakumar (2004) have proposed that some consumer-related factors (perceived risk, willingness to buy, consumer self-confidence) moderate the relation between Internet shopping behaviors and flow experience. Identifying critical individual differences in the tendency to experience flow will help us further isolate the construct (Klein 2001). According to the characteristics of low-NFC individuals, they have been shown to engage in more heuristic processing (Batra and Stayman 1990) and concentrate less during processing. For this reason, they are less likely to engage in a very intense flow experience. They may find an intense flow state overwhelming and difficult to maintain. Therefore, although

interactivity enhances flow state, it can be said that the increase in the intensity of this experience will be higher for high-NFC individuals than for low-NFC individuals: H5: The increase in flow state intensity for an interactive versus a noninteractive Web site will be higher for high-NFC individuals than for low-NFC individuals (i.e., NFC moderates the effect of interactivity on flow state intensity). METHOD Design and Participants The study design consisted of two Web site environments, one that was interactive and one that was noninteractive (between subjects). A convenience sample of 233 students at a Spanish university was recruited from different undergraduate introductory marketing classes. Students participated in exchange for extra credit. After examining responses for inconsistencies or incomplete questionnaires, 213 questionnaires were usable. The participants ranged in age from 18 to 49 (M = 22.49; SD = 3.63), with women having slightly higher representation (54%) than men. Stimuli A personal computer was selected as the product for this experiment. A personal computer is a product in high demand by the target population and has been used in previous experimental studies (Meyers-Levy and Peracchio 1992). A computer is also a product in which information is an important attribute (McMillan and Hwang 2002; Sheehan and Doherty 2001), and it can therefore benefit from the Web. Computers are dominated by product attributes for which full information can be acquired prior to purchase, and are categorized as search goods (Klein 1998). According to Klein, the incremental value of the new media for these goods will be the provision of information in a more accessible, less costly, and more customizable format. Given that information prior to purchase is plentiful, interacting with the medium allows the consumer to select the information that he or she needs or wants. For this reason, the Web is dominated by product categories where some kind of interaction is needed, such as computers, audio and video equipment, or automobiles (Huffman and Kahn 1998). These products can exploit the informationdriven nature of the Web (Hwang, MacMillan, and Lee 2003). In summary, a personal computer is an appropriate product to be used in the experiment for both sample and communication medium reasons. The Web site was specifically developed for this experiment and was based on real computer Web sites that were reviewed before the creation of the two versions of the Web

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site. Instead of developing an entire company’s Web site, a simplified Web site was created based on one product, a computer. To avoid bias, we did not use a recognized brand name. The Web site contained information about the processor, memory, Internet connection, technical assistance, and product guarantee. A short description of the company was offered in the introduction section. The Web site was screen size, as is typically encountered when consumers click on a banner or other involuntary exposure Web ad format, but smaller than a full Web site because it focused exclusively on the information about one product. Two versions of the Web site were created, an interactive version and a noninteractive version (a copy of each version’s homepage is presented in Appendix 1). Following Dijkstra and van Raaij (2001), information remained constant in both versions. The Web site creation began by using the same message and the same images; only the capacity to interact with the message was manipulated. The two versions were distinguished based on criteria set forth in the works of Ha and James (1998) and McMillan and Hwang (2002). The interactive Web site, which was made up of six pages connected by hyperlinks, included a telephone number, an e-mail address, and a fictitious link to other sections of the Web site. The interactive version offered the possibility to interact with the message, as the participants could select the order of the information they wanted to see at each moment. In contrast, the noninteractive version was set up like a print ad: The entire message was on one Web page and there were no hyperlinks. There was a table of contents at the beginning, and the full text of the message and images followed. It could be read by scrolling down the page without changing the main URL. The only way of communicating with the company was via a telephone number included at the bottom. The noninteractive version had less connectedness (presence of hyperlinks) and less potential for reciprocal communication (presence of response mechanisms), as recommended by Ha and James (1998). In agreement with McMillan and Hwang (2002), the less interactive Web site was designed to have fewer interactive features and fewer opportunities for interactive exchange. Procedure Participants were randomly assigned to one of the two conditions. On arrival at the computer laboratory, participants were informed about the procedure. Based on Hauser, Urban, and Weinberg (1993), we did several pretests to determine the time of exposure required to give the participants enough time to see the entire message. After being assigned to a condition, participants first answered questions about their knowledge of computers and the Internet, and completed the NFC scale. They were then

shown one of the two Web site versions. The Web site exposure was controlled and lasted five minutes (Ariely 2000; Meyers-Levy and Peracchio 1992). Participants in the interactive condition were free to click on any topic or link on the Web site; exposure was thus completely defined by these participants. Participants in the noninteractive condition also viewed the Web site at their own pace, moving up and down the page according to their interests. After Web site exposure, participants reported all the thoughts that came to mind while they were watching the site. A few minutes later, participants responded to the flow state measure and reported their attitudes toward the Web site and the product. The full experiment lasted approximately 20 minutes. Measurement Product knowledge, navigation experience, and NFC were measured first. Product knowledge was measured with items from Smith and Park’s (1992) seven-point Likert scale. The specific items used were “I feel very knowledgeable about this product”; “If a friend asked me about this product, I could give them advice about different brands”; “If I had to purchase this product today, I would need to gather very little information in order to make a wise decision”; and “I feel very confident about my ability to tell the difference in quality among different brands of this product.” Navigation experience was measured by asking participants how many hours per week they use the Web (Bruner and Kumar 2000; Macias 2003). NFC was assessed using the 18-item scale proposed by Cacioppo, Petty, and Kao (1984), a shortened version of the original 34-item scale (Cacioppo and Petty 1982). To measure processing, participants were instructed to write down all the thoughts they could recall going through their mind during the time they were exposed to the message (Haugtvedt and Wegener 1994; Mackenzie, Lutz, and Belch 1986). This process is called thought elicitation. Seven lines were provided for them to complete this task. We directly measured flow in the present study with a twoitem scale following a narrative description of flow. Several researchers have successfully used this approach in eliciting examples for experiences of flow among Web consumers (Chen, Wigand, and Nilan 1999; Novak, Hoffman, and Yung 2000; Rettie 2000). The narrative description used in this work was developed by Novak, Hoffman, and Yung (2000) through qualitative methods. When applied to their study, however, flow was treated as a general state (all their questions related to the general experience of Internet use) that may emerge during network navigation. This treatment of flow conflicts with Csikszentmihalyi’s situation-specific use of the flow concept (Rettie 2000). For example, one of Novak, Hoffman, and Yung’s (2000) questions was: “Do you think you have ever experienced flow on the Web?” Therefore, to measure flow

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state during the Web site exposure, we adapted Novak, Hoffman, and Yung’s scale (2000). In contrast to Novak and colleagues’ (2000) goal to determine whether participants had ever experienced this state on the Internet, the objective of the present study was to determine to what extent participants had experienced this state during the Web site exposure. The final measure is presented in Appendix 2. Attitude toward the ad was measured by asking participants to indicate their overall evaluation of the Web site using a seven-point semantic differential scale (good/bad, pleasant/unpleasant, and favorable/unfavorable). Attitude toward the product was assessed with another seven-point differential scale (attractive/ unattractive, I like it/I do not like it, it is good/it is bad). RESULTS One hundred and eight participants were exposed to the interactive Web site, and 105 to the noninteractive Web site. Product knowledge and navigation experience were similar between the two groups, and the differences were not statistically significant ( p > .05). Cronbach’s α coefficients were computed to assess the reliabilities of the multi-item measures used in the study. The αs for all scales ranged from .81 to .94, well above the .70 level that has been recommended.

as a sentence, a clause of a sentence, or a phrase. Sometimes a single-word sentence may represent a thought. After segmentation, each thought was categorized depending on whether it was focused on the Web site, the product, or other matters (Briñol, Petty, and Zakary 2004; Mackenzie and Spreng 1992; Nordhielm 2002). Two judges, unaware of experimental conditions, independently categorized all thoughts and coded them as favorable, unfavorable, or neutral toward the product or the Web site (Nordhielm 2002). Judges agreed on 82% of the thoughts coded, and a third judge resolved disagreements. Need for Cognition A median split was used to separate participants into highand low-NFC groups based on a summed measure (Mantel and Kardes 1999; Meyers-Levy and Peracchio 1992; MeyersLevy and Tybout 1997; Zhang 1996). The resulting mean composite NFC scores were significantly different between the high- and low-NFC groups ( p < .001). Furthermore, in both conditions (interactive and noninteractive), the proportion of high- and low-NFC participants was very similar. In Table 1, we have presented the cell means for NFC and number of participants in each treatment condition. Total Amount of Processing

Manipulation Check An additional study was conducted to test whether the two Web sites had different interactivity rates. Sixty-six undergraduates from the same university participated in partial fulfillment of an introductory marketing course requirement. The sample for the manipulation check was composed of participants from the same population as the main study. Based on McMillan and Hwang’s (2002) work, we measured perceived interactivity using a seven-point Likert scale (interactive, enables two-way communication, active, keeps my attention). Principal factor analysis revealed that all items loaded on a single factor and were highly correlated with a coefficient α of .94. The success of the manipulation for the level of Web interactivity was determined by running an ANOVA (analysis of variance) for the perceived interactivity variable. Results showed that the difference in the perceived interactivity level of the two Web sites was statistically significant, M = 4.37 for the interactive Web site and M = 2.87 for the noninteractive site, F(1, 64) = 19.93; p < .001. Categorization of Thoughts The thoughts reported by individuals were segmented into “thought” units (Eveland and Dunwoody 2000; Sauer, Dickson, and Lord 1992). Typically, a thought may be operationalized

The measure for this variable was obtained from the total number of Web site- and product-related thoughts (Nordhielm 2002). An examination of the thought protocols indicated that a total of 370 thoughts were reported (228 in the interactive condition, and 142 in the noninteractive condition). Table 2 reveals that participants in the interactive condition reported more thoughts (M = 2.11) than participants in the noninteractive condition (M = 1.35) (F = 14.20; p < .001). This greater number of thoughts in the interactive condition is due mainly to a greater number of Web site– related thoughts (M = 1.64 versus M = .99; F = 13.85, p < .001), while the number of thoughts related to the product remained constant in the two conditions (M = .47 versus M = .36; F = .91, p > .05). H1 is therefore supported. The main effect of NFC is nonsignificant (F = 1.56, p > .05), although there is a significant interaction effect between NFC and the presence of interactivity that confirms H4 (F = 5.59, p < .05). As shown in Figure 1, NFC moderates the effect of interactivity on total processing. We can see that both groups of individuals increase their processing when exposed to an interactive Web site, but the size of the increase is much greater for low-NFC individuals than for high-NFC individuals, even surpassing the total processing of high-NFC individuals. For low-NFC individuals, the level of processing rose from 1.00 thought in the noninteractive condition to 2.22 thoughts in the interactive condition (p < .01); for high-NFC participants,

38 The Journal of Advertising TABLE 1 NFC Scores and Number of Individuals in Each Treatment Condition Interactive (n = 108) Noninteractive (n = 105)

High NFC

Low NFC

129.29 n = 54 130.64 n = 51

102.64 n = 54 106.31 n = 54

Notes: NFC = need for cognition; n = number of individuals in the treatment. Because two between-subjects factors (interactivity and NFC) were employed, ANOVA (analyses of variance) were performed (see Table 2 for means and standard deviations, and Table 3 for the analysis of variance) to test the hypotheses.

TABLE 2 Means and Standard Deviations for Total Processing, Valence of Processing, and Flow Intensity Condition

Interactive

Noninteractive

Total processing = Web site- + product-related thoughts High NFC Low NFC Total High NFC Low NFC Total

2.00 2.22 2.11 1.72 1.00 1.35

= 1.51 = 1.78 = 1.64 = 1.32 = 1.68 = .99

+ .49 (1.14) + .44 (1.73) + .47 (1.46) + . 40 (1.73) + .32 (1.10) + .36 (1.48)

Valence of processing (Web site) .05 .15 .10 –1.00 –.68 –.83

(1.52) (1.51) (1.51) (1.84) (1.68) (1.76)

Valence of processing (product) .14 .18 .16 –.02 .18 0

(.67) (.48) (.58) (.38) (.36) (.37)

Flow intensity 6.96 6.65 6.80 5.66 5.78 5.73

(3.17) (3.07) (3.11) (3.31) (3.00) (3.14)

Notes: NFC = need for cognition; numbers in parentheses represent standard deviations.

the level of processing increased from 1.72 to 2.00 (nonsignificant). Furthermore, the data shows that the large increase in processing from the noninteractive to the interactive Web site for low-NFC individuals was mainly due to Web site–related thoughts (M = .68 versus M = 1.78; F = 6.88, p < .05, for the interaction between NFC and the presence of interactivity), while product-related thoughts remained essentially constant (M = .32 versus M = .44; F = .32, p > .05, for the interaction between NFC and the presence of interactivity).

uct shown on the Web site, the mean level of valence of processing was significantly higher for participants exposed to the interactive Web site (M = .16) than for participants exposed to the noninteractive Web site, whose processing was completely neutral (M = .00) (F = 6.01, p < .05). Therefore, H2 is also supported. As expected, no main effect was observed for NFC and there were no significant interactions.

Valence of Processing

According to H3, participants’ flow state intensity would be higher in the interactive condition than in the noninteractive condition. The resulting levels of flow state intensity were higher for participants exposed to the interactive Web site (M = 6.80) than for participants exposed to the noninteractive Web site (M = 5.73). This difference is significant (F = 6.27, p < .05), supporting H3. Finally, in H5 we proposed that the increase in flow intensity would be greater for high-NFC participants than for low-NFC participants. For high-NFC participants, flow intensity increased from 5.66 to 6.96 ( p < .01); for low-NFC participants, flow intensity increased from 5.78 in the noninteractive condition to 6.65 in the interactive condition (nonsignificant). However, the interaction effect was nonsignificant (F < 1). H5 is therefore only partially

To assess whether the Web site had persuaded participants, the valence of Web site-/product-related thoughts was compared between conditions. Valence of processing was calculated as the number of participants’ favorable thoughts, minus the number of unfavorable thoughts related to the Web site (the ad) or the product (Krishnamurthy and Sivaraman 2002; Mackenzie, Lutz, and Belch 1986). Participants exposed to the interactive Web site were more likely to process the message favorably (M = .10) than participants exposed to the noninteractive Web site, whose mean level for valence of processing was negative (M = −.83) (F = 17.64, p < .001), which means that unfavorable thoughts were predominant. Regarding the prod-

Flow State

Fall 2005 39

FIGURE 1 Interaction Between NFC and Experiment Design on Total Processing

FIGURE 2 Interaction Between NFC and Experiment Design on Flow Intensity Flow Intensity Scores

Total Processing Scores 2.5

7.5

7.0 2.0

6.5 Low NFC High NFC

1.5

Low NFC High NFC 6.0

1.0

5.5

0.5

5.0 Noninteractive

Interactive

supported. These effects are portrayed visually in Figure 2. These results are reinforced by the attitudinal data. Attitude toward the site was more positive in the interactive condition than in the noninteractive condition (M = 4.77 versus M = 4.43; p < .01), which is in agreement with previous work that suggests that interactivity may enhance Web site evaluations (Ghose and Dou 1998; McMillan and Hwang 2002; O’Keefe, O’Connor, and Kung 1998; Wu 1999). To examine whether interactivity has a direct effect on Web site attitudes in addition to that of valence of Web site processing, we conducted a hierarchical regression analysis to predict Web site attitudes with three independent variables: valence of Web site processing, flow intensity, and site interactivity.1 Results of this analysis indicated that valence of Web site processing ( β = .43, p < .01) and flow intensity (β = .16, p < .01) were significant predictors, whereas site interactivity was not significantly related to Web site attitudes (β = .02, p > .05) (all reported β’s are standardized). Given that we have shown that site interactivity positively influences valence of Web site processing and flow intensity, the effect of interactivity on attitudes may be mediated by Web site–related thoughts and flow intensity. The series of regression analyses recommended by Baron and Kenny (1986) were therefore employed to specifically test the mediation effect. The influence of site interactivity on Web site attitude was statistically significant (β = .17, p < .05) when the former variable was introduced as the only independent variable, but this influence became insignificant when both the valence of Web site processing and flow intensity (as shown above) or either of these variables were introduced in the regression analysis. Nonetheless, only the mediated effect through the valence of the Web site processing was significant (t = 3.99, p < .01), while the mediated effect through flow intensity was not (t = .93,

Noninteractive

Interactive

p > .05). According to these findings, it can be said that Web site–related thoughts completely mediate the effect of interactivity on Web site attitudes. Based on the persuasion literature (Briñol, Petty, and Tormala 2004; Mackenzie, Lutz, and Belch 1986) and on previous work on the effects of interactivity (Schlosser 2003), different predictors (Web site attitudes, valence of product processing, and interactivity) were entered into a hierarchical regression analysis to evaluate whether site interactivity would relate to product attitudes beyond what site attitudes could explain. The effect of interactivity, however, was again nonsignificant ( β = −.08, p > .05). Consistent with the mediating role of attitude toward the ad (Mackenzie and Spreng 1992; Mackenzie, Lutz, and Belch 1986) and with the Elaboration Likelihood Model (Cho 1999; Petty, Cacioppo, and Schumann 1983), product attitudes are best explained by Web site attitudes ( β = .30, p < .05) and the valence of product-related thoughts (β = .26, p < .05).2 As a whole, these results are consistent with existing studies relating valence of processing with attitudes (Briñol, Petty, and Tormala 2004; Mackenzie, Lutz, and Belch 1986). DISCUSSION Our study examined whether information processing and flow state intensity differ among individuals exposed to an interactive Web site as compared with those individuals exposed to a noninteractive Web site. The study also included an analysis of NFC as a possible moderator variable. The data provide insights into the predicted increase in elaboration when the Web site is presented in an interactive format. Individuals exposed to an interactive Web site processed information more thoroughly than individuals exposed to a noninteractive Web site. These results confirmed previ-

40 The Journal of Advertising TABLE 3 Analysis of Variance for Total Processing, Valence of Processing, and Flow Intensity Dependent variable Total processing

Valence of processing toward the Web site Valence of processing toward the product Flow intensity

Source

F value

Design NFC Design × NFC Design NFC Design × NFC Design NFC Design × NFC Design NFC Design × NFC

14.20 1.56 5.59 17.64 .82 .24 6.01 .33 0 6.27 .04 .27

p value 0 .213 .019 .000 .365 .623 .015 .564 .993 .013 .835 .600

Notes: NFC = need for cognition; df = 1.209. As suggested by one of the reviewers and the editor, another analysis of variance was conducted for each dependent variable, including product knowledge and navigation experience as covariates. Results showed that none of the effects due to the covariates was significant.

ous assumptions about processing in interactive systems (Cho 1999; Rodgers and Thorson 2000). Some researchers, however, suggested that the opposite could also be true. For example, Bezjian-Avery, Calder, and Iacobucci (1998) stated that under certain conditions, interactivity may inhibit processing and suggested that the persuasion process could be affected, although they did not measure information processing. In their opinion, when a consumer uses an interactive system, the link between retrieval and yielding to persuasion may be broken. The present study, however, finds that processing increases as interactivity increases. The reason for this discrepancy may be the treatment of interactivity. In Bezjian-Avery, Calder, and Iacobucci’s 1998 work, participants could not choose what product information they wished to see. Interactivity in their study resulted from several choices about the products that participants would see (ad sequence). This study worked with just one product, but included a Web site that allowed individuals to choose what information they wanted to see and in what order. The results also indicate that interactivity is especially important in increasing information processing for low-NFC individuals. Individuals processed much more information in the interactive Web site design, which suggests that their motivation to process increases under interactive conditions. Moreover, the large increase in processing from the noninteractive to the interactive Web site for low-NFC individuals was mainly due to Web site–related thoughts, while product-related thoughts remained essentially constant. This result suggests that low-NFC individuals tend to focus on the peripheral aspects of the message rather than on product attributes, and that interactivity of the Web site might be acting as a peripheral cue. However, a significant effect of NFC

on total information processing was not found, although this relation has been demonstrated in previous studies (Batra and Stayman 1990). A possible explanation is that our target message was relatively short, and even low-NFC participants were sufficiently motivated to process it, as the cognitive effort required was relatively low (Haugtvedt, Petty, and Cacioppo 1992). Interactivity also has implications for the valence of processing. The interactive Web site design was processed more favorably than the noninteractive Web site. It seems that control over information flow is good for both consumers and advertisers. On the one hand, interactive systems enhance a consumer’s freedom, control, and fun (Wolfinbarger and Gilly 2001). On the other hand, having control over information exchange has been shown to impact a consumer’s ability to integrate, remember, and understand inputs to their judgments, thus increasing the effectiveness of ads (Ariely 2000). Although the data appear to confirm a similar favorableness effect among high- and low-NFC participants, we propose that the persuasion effect occurs due to different reasons for both groups. High-NFC individuals are more likely to be influenced by the freedom and control derived from information interactivity, whereas low-NFC individuals may be more affected by the attractiveness of the interactive Web site, due to the heuristic processing characteristic of the latter group of individuals. This theory is supported by the results obtained for low-NFC participants, whose increase in processing was mainly due to Web site (ad)–related thoughts. Aside from resulting in differences in information processing, there was also the question of whether interactivity would affect the consumer’s experience with the Web site. We therefore studied the effects of interactivity on flow state intensity.

Fall 2005 41

Recently, Novak, Hoffman, and Yung (2000) found that interactivity is an antecedent of flow; however, they considered interactivity as equivalent to system speed, whereas in the present study we are dealing with the information within the system. Our findings demonstrate the importance of interactivity when evaluating consumer experiences on the Web. Individual flow intensity is increased when the Web site is interactive, suggesting that this variable is very useful when defining consumer experiences in interactive environments. As flow intensity is enhanced by interactivity, previous work describing the consequences of this experience should be tested in interactive environments. The moderating role of NFC between interactivity and flow intensity was also explored. The data were in the expected direction, although the interaction effect was not significant. As previous research has confirmed (McMillan and Hwang 2002), we found that interactivity may enhance advertising effectiveness in terms of attitudes. Our contribution is that we have shown that the effect of interactivity on attitude toward the Web site is mainly due to the influence of interactivity on information processing and flow intensity. The main implications of our results can be stated in four points. First, it has been shown that interactivity enhances participants’ processing of Web sites. Given that higher levels of processing are related to more persistent attitudes and memory (Haugtvedt and Wegener 1994), the fact that interactivity increases the total amount of processing is relevant because of its influence on advertising effectiveness through favorable processing (MacInnis and Jaworski 1989; MacInnis, Moorman, and Jaworski 1991). Second, interactivity also enhances flow intensity, and therefore influences advertising effectiveness. Third, the results obtained for low-NFC individuals are very interesting for companies. When designing their Web pages, adding interactive properties to their Web sites, especially if their target audience is not very high in NFC, can enhance motivation to process information. In summary, interactivity seems to be positive for the advertiser, as it increases both information processing and persuasiveness of the Web site. With regard to the consumer experience, since interactive Web sites tend to increase the intensity of flow state, marketers need to account for other consumers’ benefits of such experience apart from that of attitudes. Some consequences are future visits to the Web site (Webster, Trevino, and Ryan 1993), feelings of pleasure (Csikszentmihalyi 1990), and exploratory behavior (Hoffman and Novak 1996). LIMITATIONS AND FURTHER RESEARCH A limitation of this study is that we worked with just one product category, the computer. We may have obtained different results with other types of products, such as hedonic

products. Determining whether these results are applicable to other categories is one avenue of additional research. It will help explicate the possible generalization of these findings. Another limitation of our study is a potential confound resulting from the difference in design of the interactive and noninteractive Web sites. The additional images in the buttons of the interactive site may have enhanced the visual appeal of the interactive site and led participants in this condition to have more interest in exploring the site. Nonetheless, the relatively simple design of these images and their small size lead us to believe that the confounding effect, if it does exist, may be insignificant. Furthermore, the interactive condition may have lacked the actual level of interactivity of some Web sites. Coyle and Thorson (2001) undertook a very interesting study dealing with different levels of interactivity, although they did not focus on information processing. With very high levels of interactivity, the overhead caused by demanding systems can cause processing demands to exceed capacity (Ariely 2000). As this study probably did not replicate such a high level of interactivity, our conclusions may be restricted to low or medium levels of interactivity. Such interactivity levels are very common among Web sites of small and medium enterprises, partly because of financial constraints (O’Keefe, O’Connor, and Kung 1998) and partly because of the low level of importance accorded to Web sites by such companies (Hwang, MacMillan, and Lee 2003). Most large companies, however, have also been slow to adopt the use of interactive options. Thus, despite its importance in Web management, the effective use of interactivity still remains a challenge in many companies (Huang 2003). Further research should explore the intensity of flow state appropriate for particular advertising objectives. For example, the most intense flow state may not be equally as effective in facilitating the enjoyment of the Web site as in facilitating direct shopping behavior (Smith and Sivakumar 2004). Finally, we did not examine the effects of interactivity in the communication process between a company and its customers; we chose to focus on “machine interactivity.” Interactivity has many other dimensions (Ariely 2000), and we have only concentrated on one aspect of interactive communications, namely, controlling the information flow. NOTES 1. Traditional persuasion models contend that attitude change depends on both the amount and the direction or valence of processing (Briñol, Petty, and Tormala 2004). Valence has shown an effect on attitude favorableness, while amount of processing has shown an effect on attitude strength but not on attitude favorableness (Haugtvedt, Petty, and Cacioppo 1994). This variable was therefore not included in the regression as attitude levels are mainly determined by the valence of processing. Given the positive consequences that have been attributed to flow state (Novak,

42 The Journal of Advertising

Hoffman, and Yung 2000; Webster, Trevino, and Ryan 1993)— for example, between flow rating and the intention to return to the Web site (Nel, Berthon, and Davies 1999)—flow intensity was also included in the regression to explore whether this variable could increase Web site attitudes. 2. Due to the lack of research about flow consequences on advertising effectiveness (Smith and Sivakumar 2004; Zinkhan 1998), flow intensity was also included in the first stage of a hierarchical regression; however, the direct effect of this variable on attitude toward the product was nonsignificant ( β = −.02, p > .05).

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APPENDIX 1 Homepage of the Interactive Web Site

Homepage of the Noninteractive Web Site

APPENDIX 2 Measurement of Flow State Narrative description The word “flow” is used to describe a state of mind sometimes experienced by people who are deeply involved in some activity. One example of flow is the case where a professional athlete is playing exceptionally well and achieves a state of mind where nothing else matters but the game; he or she is completely and totally immersed in it. The experience is not exclusive to athletics: Many people report this state of mind when playing games, engaging in hobbies, or working. Activities that lead to flow completely captivate a person for some period of time. When one is in flow, time may seem to stand still, and nothing else seems to matter. Flow may not last for a long time on any particular occasion, but it may come and go over time. Flow has been described as an intrinsically enjoyable experience.

Thinking about the Web site you have just visited, respond to the following: Yes, I am sure I have experienced flow state./No, I have not experienced it.

(seven points)

It was a very intense sensation./It was a nonintense sensation.

(seven points)