The Role of Flow in Web Site Effectiveness
Maria Sicilia, Salvador Ruiz
University of Murcia, Spain
Abstract
Web sites are based on information and communication technologies that enable easy, rapid interactions between consumers and advertisers and thus represent more durable and common communication activity. In this context, the flow experience can provide a better explanation of consumer behavior in the context of Web sites as company communication activities. By adding the flow state to traditional advertising models, this study demonstrates the impact of flow state on Web site effectiveness. The results show that the flow state influences attitude toward the Web site, both directly and indirectly through Web site cognitions.
Introduction
In recent years, the great impact of the Internet has just continued to grow (Macias 2003; McMillan and Hwang 2002; Sheehan and Doherty 2001). In this context, Web sites represent durable, common forms of communication activity (Bart et al. 2005; Karson and Korgaonkar 2001) that combine multiple functions, such as providing information, building images, and persuading (Hwang, McMillan, and Lee 2003). A company Web site combines text and images in different pages through interactive technology to provide information and/or create branding for the company and its products.
When consumers view a company's Web site, they engage in a virtual experience. The strength of this experience is a function of the extent to which the person feels present in the mediated environment rather than in his or her immediate physical environment. According to recent research (Dailey 2004; Huang 2003; Klein 2001; Li and Browne 2006; Novak, Hoffman, and Yung 2000; Sicilia, Ruiz, and Munuera 2005; Smith and Sivakumar 2004), the flow construct offers a key variable for understanding the consumer experience in interactive environments. In such an experience, the consumer may become so involved in the act of network navigation that "nothing else seems to matter" (Csikszentmihalyi 1990, p. 4). Flow also implies abandoning oneself to a situation of good feelings (Geirland 1996). The concept of flow thus contributes to understanding how the unique characteristics of the Web mediate consumer behavior (van Beveren, Widing, and Whitwell 2002.
When visiting a Web site, the consumer suffers cognitive costs. First, consumers must manage the information, that is, choose what information appears first, for how long, and what aspects of the information will be pursued next (Ariely 2000). Second, they need to understand the information; in other words, while in a flow state on the site, consumers also concentrate on becoming orientated within the system, which indicates that message elaboration would be detrimental (Eveland and Dunwoody 2000.
The possibility of maintaining flow during the Web site experience, along with the need to manage the information received during this state, raises several questions about the influence of the flow state. For example, will the flow state contribute to or enhance elaboration of the message? Or does the flow state place the consumer in a situation of such concentration on the interaction procedure that it prevents him or her from processing the message? Finally, does the flow state give consumers such a good feeling that it positively influences their attitudes? Investigating these questions represents the main objective of this article, which helps explain whether consumer flow state increases elaboration of the message or if the virtual experience actually hampers consumer processing of information. To do so, we use traditional communication models that relate information processing and effectiveness to propose a model that includes consumer interactive experience, conceptualized as flow. This model will help both researchers and practitioners in their efforts to understand online consumer behavior by increasing comprehension of how flow experience alters traditional models.
The structure of the remainder of this article is as follows: In the next section, we review existing literature pertaining to information processing and communication effectiveness on the Internet and identify the main models that explain the relationships among cognitions, attitudes, and purchase intentions. On the basis of this review, we formulate hypotheses regarding the role of flow state. After a summary of the research methodology, we discuss the testing of the hypotheses and the results.
Literature Review
Traditional Advertising Models and The Internet
Traditional advertising models relate information processing to advertising effectiveness, using attitudes and purchase intentions as measures of effectiveness (MacKenzie, Lutz, and Belch 1986). Attitudes may be described as a person's internal evaluations of an object, such as an advertisement, and may be favorable or unfavorable (Mitchell and Olson 1981). Researchers have focused on the role of attitude toward the advertisement in the process through which advertising influences brand attitudes and purchase intention (MacKenzie, Lutz, and Belch 1986; Stevenson, Bruner, and Kumar 2000). In addition, cognitive responses, defined as any thoughts that emerge during the ad exposure, precede consumer attitudes toward advertisements, products, or brands (Lord, Lee, and Sauer 1995; Mittal 1990; Rodgers and Thorson 2000). Therefore, most models represent the theoretical linkages among cognitions, attitudes, and purchase intention.
Drawing on Petty and Cacioppo's (1981) elaboration likelihood model (ELM), MacKenzie, Lutz, and Belch (1986) explore how attitude toward the ad (Aad) mediates brand attitude (Ab) and purchase intentions by explaining and testing four alternative models. They conclude that the dual mediation hypothesis (DMH), which posits that Aad influences Ab both directly and indirectly through brand cognitions (Cb), best explains the observed relationships. This model has been empirically validated by many other studies (Brown and Stayman 1992; Homer 1990; Lord, Lee, and Sauer 1995; Mittal 1990.
Despite the differences between online and offline advertising, researchers tend to accept that traditional communication models may be applied to the Internet (Bruner and Kumar 2000; Karson and Fisher 2005a, 2005b; Stevenson, Bruner, and Kumar 2000). Balabanis and Vassileiou (1999) state that these theories can be applied to Web sites because they perform, among others, similar functions to those that ads perform. In this vein, a Web site represents a type of advertising similar to a print or television advertisement, because the message (including all sections and information) has been planned by the advertiser (Hwang, McMillan, and Lee 2003; Sheehan and Doherty 2001). However, as Rodgers and Thorson (2000) note, Web site processing likely is much more complex than information processing associated with sponsorships, banners, or pop-ups. In this context, Aws represents Aad in traditional models, because the Web site is the advertisement, and Ab may be equivalent to the attitude toward the product/brand presented on that site.
As recently shown by Karson and Fisher (2005a, 2005b), DMH still provides a good background for exploring the relationships between information processing and Web site effectiveness. These authors first add a new path from attitude toward the site to purchase intention to extend the DMH (Karson and Fisher 2005a), then include intention to return to the site instead of purchase intentions (Karson and Fisher 2005b). However, in addition to making these adaptations in the original model, additional research needs to consider the interactive nature of this medium to improve the capacity of the DMH explanation; therefore, we extend the DMH by including the flow state and take into account consumer interactions with the Web site.
The Influence of Flow State in Web Site Effectiveness
Various researchers suggest that flow is a useful way to describe people's interactions with computers (Csikszentmihalyi 1990; Hoffman and Novak 1996; Smith and Sivakumar 2004; Trevino and Webster 1992). Flow can be defined as "the holistic sensation that people feel when they act with total involvement" (Csikszentmihalyi 1977, p. 36). However, the concept of flow differs from that of involvement. Although both involvement and flow include an interest component (whether enduring or situationally motivated), flow has a control quality that is not characteristic of involvement and an intrinsic enjoyment that is not always present in the involvement construct (for a better distinction between the concepts, see Huang 2006). Flow is an extremely enjoyable experience in which the person encounters an intrinsic interest and a sense of time distortion during the engagement (Chen, Wigand, and Nilan 1999; Huang 2003; Novak, Hoffman, and Yung 2000).
This optimal experience occurs when a person's set of skills matches the perceived challenges of the task (Csikszentmihalyi 1977). Although the flow state can be reached during engagement in numerous activities, including sports, writing, work, games, and hobbies (Novak, Hoffman, and Yung 2000), the focus here is flow during consumer interactions with a Web site. Previous research has established the validity of the flow construct in relation to computer-related activities (Chen, Wigand, and Nilan 1999; Nel et al. 1999; Novak, Hoffman, and Duhachek 2003; Novak, Hoffman, and Yung 2000; Trevino and Webster 1992).
Web visits can vary in the degree of situational involvement, depending on the personal relevancy for the user. That is, a person can usually experience flow on the Web but might not experience such a state when navigating through a specific Web site. In this case, his or her attention will be not focused; consumers may become bored or anxious and likely to leave the site in search of more challenging sites or activities (Huang 2006). Thus, rather than considering the overall flow experienced while using the Web, as most studies dealing with flow have (Chen, Wigand, and Nilan 1999; Hoffman and Novak 1996; Huang 2006; Li and Browne 2006; Novak, Hoffman, and Yung 2000), this study considers the influence of the flow state during a visit to a Web site on the effectiveness of that particular site. Although theoretically appealing, the usefulness of the flow concept when applied to the full range of online consumer research has been called into question, especially with regard to its consequences on attitudes and behaviors (Li and Browne 2006; Mathwick and Rigdon 2004).
Hoffman and Novak (1996) explain that flow consists of a process state that requires a set of antecedents to occur and results in a set of consequences. However, both theoretically and empirically, different studies do not agree about the identification of the antecedents and consequences of a flow state. Most studies focus on the antecedents of flow, though some variables considered antecedents in some studies (e.g., personal control, focused attention) are treated as dimensions of the flow construct in others (Siekpe 2005).
A few studies center on the consequences of flow, but none analyzes the underlying process (i.e., influence on consumer responses) that could help researchers explain its influence. Webster, Trevino, and Ryan (1993) directly relate flow experience to expectations of future voluntary computer interactions, and Nel and colleagues (1999) confirm that the flow experience makes a person want to repeat the visit. Huang (2003) also observes that the more intense the flow state during the visit, the higher the performance of the site is (i.e., consumers consider the site more useful and pleasant). Hoffman and Novak (1996) further demonstrate that flow is beneficial and causes increased learning, exploratory and positive behavior, and a positive subjective experience. Finally, Siekpe (2005) shows its influence on intention to purchase and intention to return to the site.
Moreover, most companies use their Web sites as a communication tool instead of for transactions, but the consequences of flow on information processing and attitudes remain largely unexplored (Siekpe 2005). Therefore, the flow state needs to be clarified to determine if it is appropriate for communication and persuasion objectives.
The sense of time distortion, along with the intrinsic interest associated with flow, suggests that this state must have an influence on consumer processing, that is, on cognitive responses. Recognizing the influence of flow state on consumer processing could enable marketers to use the variable to predict the degree to which consumers elaborate on information. Elaboration indicates the amount, complexity, or range of activity occasioned by a stimulus. Two alternative arguments can be inferred from existing literature about this influence. First, the person becomes so absorbed by the navigation experience that the information is not elaborated upon, because involvement with the virtual experience impedes information processing. For example, Eveland and Dunwoody (2000) conclude that most processing when using the Web focuses on maintaining an orientation to the structure and content of the site, thus reducing elaboration of information. Second, an alternative point of view states that flow enhances focused attention on the task (Webster, Trevino, and Ryan 1993) and that such a state provokes a high level of concentration and involvement (Csikszentmihalyi 1990; Hoffman and Novak 1996).
As long as consumers have an information-seeking goal, this second perspective should hold, because concentration will be focused on the task (information seeking). In addition, Novak, Hoffman, and Duhachek (2003) find more evidence of flow for task-oriented than for experiential activities. Therefore, a user will read through and elaborate on the information, allowing nothing (not even the virtual experience) to distract him or her from such activity. Thus, according to this second perspective, the more intense the flow state as the consumer navigates the Web site, the greater attention he or she will pay to the interaction and the greater his or her concentration and involvement during the visit.
Such attention and concentration associated with the flow state will favor the likelihood that the consumer reads and processes all the information contained in the Web site. As a result, the intensity of this experience should enhance consumer information processing of the information included in the Web site, whether related to the site or to the products shown on that site. On the basis of this reasoning,
H1: Flow state intensity relates positively to Web site cognitions.
H2: Flow state intensity relates positively to product cognitions.
Flow response is quite typical of most descriptions of how people feel when they are thoroughly involved in something that is enjoyable (Geirland 1996). Because having control over the information exchanged increases the pleasure of the event itself (Ariely 2000), consumers in a flow state likely perceive positive subjective experiences (Csikszentmihalyi 1977; Novak, Hoffman, and Yung 2000) and positive affect (Chen, Wigand, and Nilan 1999). In summary, flow is a cognitively pleasant state (Dailey 2004; Huang 2003; Webster, Trevino, and Ryan 1993).
When a person feels pleasant, this mood likely influences his or her attitudes. Various theorists suggest that affective responses play an important role in a person's perceptions (Bargh 1996; Isen 1984; Zajonc 1980). For example, affective conditioning theory suggests that a pleasant experience can transfer directly to attitudes (Madden, Allen, and Twible 1988). The applicability of classical conditioning to the Web site thus seems likely, as consumers transfer positive (or negative) feelings from interaction with the Web site to their attitudes toward it.
In addition, because control is desirable to consumers, if consumers perceive the Web site as enhancing their control, their attitude toward it may be more favorable (Peterman, Rohem, and Haugtvedt 1999), because flow also has been theoretically associated with attitudes (Csikszentmihalyi 1977). As a result, when the consumer experiences high levels of a flow state, he or she will feel more attracted to or manifest a more favorable attitude toward the Web site; therefore,
H3: Flow state intensity relates positively to attitude toward the Web site.
On the basis of the relationships established in the DMH, and taking into account the influence of flow state on information processing and attitudes, the proposed model extends traditional models to incorporate particular characteristics of the Internet (Figure 1).
FIGURE 1. Proposed Model
In this model, Cws and Cb represent Web site- and product- (or brand-) related cognitions. They have a positive effect on attitude toward the Web site (Aws) and attitude toward the brand (Ab), respectively, and are influenced by flow state intensity, as proposed in H1 and H2. Flow state also influences attitude toward the Web site (Aws) positively, as suggested by H3. The rest of the relationships are derived from the DMH, proposed initially by MacKenzie, Lutz, and Belch (1986) and recently tested by Karson and Fisher (2005a, 2005b), in which Aws (Aad in traditional advertising models) influences Ab both directly and indirectly through brand cognitions (Cb). Central processing is represented by the Cb-Ab link, and peripheral processing is reflected by the Aws-Ab link (MacKenzie, Lutz and Belch, 1986). Following Karson and Fisher (2005a), the model also incorporates the path from Aws to purchase intention, which suggests that Internet communication environments (Web sites) can have a direct effect on purchase intentions, in addition to the traditional route through brand attitudes. Finally, as in most communication models, Ab influences purchase intention.
Methodology
The sample of students recruited for this study come from various undergraduate classes; 240 students participated in exchange for extra credit. This sample is appropriate for the study objectives for several reasons. First, most university students are experienced Internet users. Their knowledge and heavy usage of the Internet justifies the interest in this category of Internet users as potential commercial customers (Bourdeau, Chebat, and Couturier 2002). Second, many advertising studies have used students, who provide a homogeneous sample (Brown and Stayman 1992; Coyle and Gould 2002; Li and Browne 2006). Because the Web sites often are dominated by product categories that require some kind of interaction, such as computers, audio and video equipment, or automobiles (Huffman and Kahn 1998), a personal computer serves as the focal product for this experiment. In addition, a computer is a product in high demand among students, which is important for this experiment, because highly involved consumers seek product information to augment product knowledge as well as to experience pleasure (Mathwick and Rigdon 2004).
The Web site specifically developed for this experiment is based on real computer Web sites. It consists of several pages connected by hyperlinks, which connect information and enable users to explore the Web site in the order they desire at any moment (Park and Kim 2000). Thus, the study design allows for both interaction and consumer control of the information exchange, two of the most important antecedents of flow (Chen, Wigand, and Nilan 1999; Huang 2003; Novak, Hoffman, and Yung 2000). A short description of the company appears in the introduction to the Web site, which also contains information about the processor, the memory, Internet connection, technical assistance, product guarantee, and an e-mail address for potential contacts with the company. To avoid possible bias, the focal computer does not display a familiar brand name. This overall design thus helps facilitate flow experiences.
The sessions took place in a laboratory fitted with personal computers. Upon arrival at the laboratory, students were instructed to visit the Web site as if they were in the process of buying a computer and then interact with it at their own pace. After their Web site exposure, subjects completed the questionnaire.
First, students answered questions about their knowledge of computers and the Internet. These variables help control for possible external influences on the proposed model (see Appendix 1). Second, a two-item scale measures flow, following a narrative description of flow. Several researchers successfully use this approach to elicit examples for experiences of flow among Web consumers (Chen, Wigand, and Nilan 1999; Novak, Hoffman, and Yung 2000; see Appendix 2). The two-item scale used to measure the intensity of the flow state during the Web site exposure comes from Sicilia, Ruiz, and Munuera's (2005) study. Third, subjects reported all the thoughts that came to their minds while they were visiting the Web site (Krishnamurthy and Sivaraman 2002) on seven lines provided for them to complete this task.
A few minutes later, subjects responded to questions related to their attitudes toward the Web site (Aws), the product (Ab), and their purchase intentions (PI). These variables rely on traditional scales (Bruner and Kumar 2000; Stevenson, Bruner, and Kumar 2000; Zhang 1996). The measurement of these variables appears in Appendix 3.
Analysis and Results
Sample Descriptions and Contruct Validation
The sample consists of 52% male students and 48% female students, with ages ranging from 18 to 29 years. Those surveyed tended to spend approximately four hours or more per week on the Web, and their knowledge of the product category was slightly lower than the mean (3.8 on a 7-point scale).
The consumers' expressed thoughts provide the processing variables. A total of 813 thoughts were reported. Two judges, unaware of study objectives, independently categorized all thoughts and rated them as favorable, unfavorable, or neutral toward the product (Cb) or Web site (Cws). Some irrelevant thoughts (183) were disregarded for further analysis, but their relationship with flow requires examination. Irrelevant thoughts may emerge as a consequence of lack of concentration on the information contained on the Web site; for example, the respondent might think about what he or she is going to do after the visit or how much work he or she has to do (i.e., thoughts about actions, images, or concepts not related to the visit at all). For flow to occur in a Web environment, consumers must focus on the interaction and narrow their awareness to filter out irrelevant thoughts (Hoffman and Novak 1996; Huang 2006). The greater the number of operations of this type, the less the probability that the person has been immersed in a flow state (not acting with total involvement). Results confirm this negative relationship; the correlation between flow and irrelevant thoughts is -.39 (p < .001).
Following MacKenzie, Lutz, and Belch (1986) and Krishnamurthy and Sivaraman (2002), the computation of Web site-/product-related cognitions involves subtracting the number of unfavorable thoughts in each protocol from the number of favorable thoughts. Each indicator, Cws and Cb, represents the net valence of the cognitions included in its respective category. Judges agreed on 80% of the thoughts coded, and a third judge resolved any disagreements.
Confirmatory factor analysis serves to evaluate the reliability and validity of the constructs measured with more than one indicator (i.e., flow, Aws, Ab, and PI). A completely standardized solution produced by the maximum likelihood method (Jöreskog and Sörbom 1996) shows that all 11 indicators depend highly on their corresponding factors, in support of the independence of the constructs and strong empirical evidence of their validity. Overall fit statistics of the measurement model (χ2(38) = 51.56; p < .1) are as follows: confirmatory fit index (CFI) = .99; goodness-of-fit index (GFI) = .96; normed fit index (NFI) = .96; adjusted goodness-of-fit index (AGFI) = .94; and root mean squared error of approximation (RMSEA) = .03. Therefore, the proposed measurement model with four constructs and 11 indicators reveals an adequate fit of the data (Anderson and Gerbing 1988). In addition, for each construct (flow, Aws, Ab, and PI), the scale composite reliability (.90, .89, .81, and.73, respectively) and average variance extracted (.82, .73, .60, and .48, respectively) are satisfactory, because the former exceed the threshold value of .70 (Nunnally and Bernstein 1994), and the latter exceed the threshold value of .50 (Fornell and Larker 1981), with the exception of PI, which reaches a score very close to .50.
To check for discriminant validity, we determine whether the correlations among the latent constructs are significantly less than 1. The Φ matrix (correlations between constructs) appears in Table 1. None of the confidence intervals of the φ values (± two standard errors) includes the value of 1 (Bagozzi and Yi 1988), so this test provides evidence of discriminant validity. In addition, the test of discriminant validity suggested by Fornell and Larcker (1981) supports discriminant validity when the average variance extracted by the underlying construct is greater than the shared variance (i.e., φ2 value) with other latent constructs. This condition is satisfied in all cases (Table 1). In summary, the selected items result in reliable and valid measures for the four constructs.
TABLE 1. PHI Matrix and Test of Discriminant Validity
According to an examination of the influence of the control variables on the variables in the proposed model (i.e., cognitions, flow state, attitudes, and purchase intention), neither product knowledge nor Internet experience has a significant effect (p > .05).
We test the proposed model using structural equation modeling. Weighted least squares serves as the estimation method, because the two endogenous latent constructs, Web site and product cognitions (Cws and Cb), are cognitive response counts, which are not normally distributed variables (Jöreskog 2004). The structural model's fit is satisfactory: χ2(58) = 112.08, p < .01; AGFI = .96; GFI = .97; and RMSEA = .06.
Table 2 reveals that two of the hypotheses are supported: Flow positively influences Web site cognitions and Aws, in support of H1 and H3, but it does not have a significant direct effect on product cognitions, so H2 must be rejected. The results also support all the effects established in the DMH, as well as the additional path proposed by Karson and Fisher (2005a). Consequently, the proposed model, applied to Web site communication, receives support. As cognitions (Cws and Cb) influence attitudes (Aws and Ab, respectively), Aws enhances Ab both directly and indirectly through Cb, and both attitudes (Aws and Ab) positively influence PI.
The coefficients in Table 2 provide important information about the linkages of the theoretical model. Flow state exerts a strong influence on Aws (.37) in two ways. First, it directly influences Aws (.26), which indicates that the more intense the flow state, the more favorable Aws is. Second, it has an indirect influence (.11) through Cws, which means that flow state does not impede, but rather enhances, Web site processing. However, flow is not related to Cb, so its influence mainly is directed toward the Web site (Cws and Aws).
The results also demonstrate that Cws wields a strong influence on Aws (.46) and thus lend further support to the idea that the former is an important antecedent and mediator of Web site attitude formation in Web site settings. Aws exerts a small and positive influence on Cb (.13), working as a persuasion cue that enhances product acceptance. In addition, Aws influences Ab (.28), highlighting the relevance of the Web site as a communication vehicle for brands and products. Finally, both Ab and Aws show a strong influence on PI (.33 and .31, respectively).
TABLE 2. Model Comparison
In addition to these results, we estimate a nested model to compare the proposed model with the DMH, which previously has been tested to explain advertising effectiveness (Brown and Stayman 1992; Lord, Lee, and Sauer 1995). The nested model includes the same constructs but constrains the paths from flow to Cws, Cb, and Aws to 0. It also constrains the path from Aws to PI to 0, because that path is not in the DMH model. Model fit depends on five indices: the chi-square test, GFI, AGFI, RMSEA, and the expected cross-validation index (ECVI). The preferred model minimizes the value of ECVI relative to other models (Brown and Cudeck 1993). The ECVI for our model is .74, and the nested model also shows a satisfactory fit for the data: χ2(61) = 127.44, p < .01; AGFI = .96; GFI = .97; RMSEA = .07; and ECVI = .78.
According to the results, the proposed model, which includes flow effects, fits the data significantly better than the DMH model, as mainly reflected through two indices: Δχ2(3) = 15.36, p < .01, and ΔECVI = .04. The increase in χ2 reflects the improvement in the explanation provided in the proposed model compared with the DMH model, and the smaller value of the ECVI for the proposed model indicates that it has greater potential for replication (van Birgelen, Ruyter, and Wetzels 2003).
The results in Table 2 also depict a comparison between the proposed model and a third model based on Karson and Fisher's (2005a) extended DMH model. This latter model is nested within the proposed model and shows a satisfactory fit of the data: χ2(60) = 122.93, p < .01; AGFI = .96; GFI = .97; RMSEA = .07, and ECVI = .77. The fit comparison shows an increase in χ2 of 10.85 with 2 degrees of freedom (p < .01), which demonstrates the better fit of the proposed model. In addition, the proposed model offers greater potential for replication, because its ECVI is also smaller.
Discussion of Findings and Applications
The continually increasing popularity of the Internet leads many organizations to develop and communicate through Web sites, adopting them as communication and marketing tools. However, many marketers still doubt whether a Web presence will pay off (Bellizzi 2000; Lee and Park 2004). This study reaffirms the findings of a range of researchers who argue and demonstrate that the more fun, interesting, and relevant a company's Web site is, the more effective it will become.
This study also makes a significant contribution to marketing literature by providing insight into the role of the flow state on Web site effectiveness. This variable has proved very useful when describing human interaction with computers. The results demonstrate the influence of flow in Web site effectiveness, as measured by cognitions, attitudes, and purchase intentions, which are as valid for Web sites as they are for measuring advertising effectiveness in traditional media (Karson and Fisher 2005a).
Moreover, this study proposes and tests a model that explains how communication through Web sites works. The results confirm previous research relating Aws, Ab, and purchase intention (Balabanis and Vassileiou 1999; Stevenson, Bruner, and Kumar 2000). Karson and Fisher's (2005a) works follow this method and indicate support for the DMH online, though they observe that the inclusion of a new path from Aws to purchase intention fits the data best. Such a relationship is supported herein; Web sites thus appear to provide Internet shopping environments that are particularly important for brands or companies that are unknown to consumers.
More important than confirming the relationships established in the extended DMH for Web site communication activities, this article also contributes to the literature by including the flow state in a traditional DMH model. Thus, it takes into account the interactive nature of the environment and provides a better understanding of the process, which offers a detailed examination of how communication effectiveness occurs in Web site environments and what role flow plays. As stated by previous research (Hoffman and Novak 1996; Huang 2003; Novak, Hoffman, and Yung 2000), consideration of this variable is important for understanding consumer behavior on a Web site, and the proposed model has proven to fit better than both the traditional DMH and Karson and Fisher's extension. In addition, by including flow state as an antecedent of Aws, this study contributes to the explanation of Aws, which in turn influences Ab and purchase intentions. Flow intensity experienced during the interaction with the Web site positively influences Aws, which can be related to the affective state associated with flow situations (Chen, Wigand, and Nilan 1999; Huang 2003). This influence occurs both directly and indirectly through Web site cognitions. The latter can be related to the cognitive consequences of flow state (Huang 2003). Thus, consumers who are completely absorbed in navigating the Web site are more likely to process the information about the Web site's characteristics and design favorably. However, the direct relationship between flow state and product cognitions is not significant, which indicates that the flow concept relates more to affect than to cognition. These results match Huang's (2003) work, which reveals that flow is both cognitive and affective in nature, though the affective part is more important. Thus, this variable favors Web site persuasion, which means that the more intense the flow state an individual experiences, the greater the Web site effectiveness will be.
In summary, by finding support for H1 and H3 (but not H2), this research shows that having a Web site that causes consumers to experience flow likely fosters positive comments (and, it is hoped, word of mouth) about the Web site and an improved attitude toward it, though positive product or brand beliefs are not affected by this state. An engaging Web site does not guarantee positive product beliefs or brand attitudes, but it contributes directly to liking of the site, which can foster product acceptance and increase purchase intentions. The overall findings thus clarify that intense flow states are more likely to enhance, rather than impede, favorable information processing. This relevant result contradicts many noncommunication experts (e.g., MBAs who manage firms) who may be more inclined to believe that sparse, straightforward presentations of information are best.
In turn, these results have important implications for managers. Success in communicating through Web sites depends on the ability to create opportunities for consumers to experience flow. Because Web sites represent an information-laden medium, the flow experience conceivably might distract consumers from processing the information contained in the Web site, because this variable has been related to entertainment, playfulness, and exploratory behavior (Huang 2003; Novak, Hoffman, and Duhachek 2003). However, our results show that achieving an intense flow state enhances consumer information processing, which is important for managers who want consumers to process and favorably evaluate the information contained in their Web sites. Therefore, Web sites reflect communication vehicles whose features can be used by managers not only to aid users in making decisions but also as tools to enhance online processing and enjoyment.
In trying to get consumers involved with the act of navigation, marketers can provide consumers with an interesting and enjoyable experience by offering relevant information and demanding interactive options. Providing large amounts of information or irrelevant information could lead to distraction or worse results if the Web site cannot generate flow experience in the consumer (we observe a negative correlation between flow intensity and irrelevant thoughts). Therefore, as suggested by Huang (2003), a Web site must be able to use its features to satisfy both the informational and entertainment needs of the consumers. For that reason, Geirland (1996) states that Web sites are not about navigating content but about staging and experience.
Because marketers can influence consumers' opportunities to experience flow, further research should critically determine which Web site characteristics actually enhance the flow state for consumers. According to recent research (Chen, Wigand, and Nilan 1999; Geirland 1996; Dailey 2004; Huang 2003; Mathwick and Rigdon 2004), Web sites should be challenging and competitive, stimulate control, and include interactive Web activities such as discussion groups, uploading and downloading options, and reading and posting capacities for newsgroups, e-mail, and games to encourage flow. By knowing the extent to which each of these factors stimulates flow state on a Web site, managers would be further motivated to facilitate this experience as an alternative way to enhance communication effectiveness.
Finally, some limitations in this study should be addressed by additional research. First, the data come from a student sample, which is not representative of all online consumer groups, though its usage herein is consistent with previous studies, especially in the advertising area. The use of such a sample is also recommended on the Internet, because of students' greater experience compared with other target groups (Bourdeau, Chebat, and Couturier 2002). Thus, though any generalization of the findings should be made with caution, the increasing Internet experience of consumers implies that this sample has a high potential for generalization. Second, subjects visit just one particular site, which enables us to examine the influence of flow within a specific Web site on its communication effectiveness, whereas previous studies treat it as a more general state (Li and Browne 2006; Novak, Hoffman, and Yung 2000). However, even though flow researchers should use the operationalization of a construct that fits best with the study objectives, it could be interesting to investigate the relationships between the two conceptualizations of flow, which may reveal that the perception of flow at a particular site is affected by the global experience of that person on the Web.
References
Anderson, James C., and David W. Gerbing (1988), "Structural Equation Modeling in Practice: A Review and Recommended Two-Step Approach,," Psychological Bulletin, 103 (3), 411-423.
Ariely, Dan (2000), "Controlling the Information Flow: Effects on Consumers' Decision Making and Preferences," Journal of Consumer Research, 27 (2), 233-248.
Bagozzi, Richard P., and Youjae Yi (1988), "On the Evaluation of Structural Equation Models," Journal of the Academy of Marketing Science, 16 (1), 74-94.
Balabanis, George, and Stefanos Vassileiou (1999), "Some Attitudinal Predictors of Home-Shopping through the Internet," Journal of Marketing Management, 15 (5), 361-85.
Bargh, John A. (1996), "Automaticity in Social Psychology," in Social Psychology, Handbook of Basic Principles, T. Higgins and A. Kruglanski, eds. New York: The Guilford Press, 169-183.
Bart, Yakov, Venkatesh Shankar, Fareena Sultan, and Glen L. Urban (2005), "Are the Drivers and Role of Online Trust the same for all Web Sites and Consumers? A Large-Scale Exploratory Empirical Study," Journal of Marketing, 69 (October), 133-152.
Bellizzi, Joseph A. (2000), "Drawing Prospects to e-Commerce Web Sites," Journal of Advertising Research, 40 (1/2), 43-53.
Bourdeau, Laurent, Jean Charles Chebat, and Christian Couturier (2002), "Internet Consumer Value of University Students: e-Mails Versus Web Users," Journal of Retailing and Consumer Services, 9, 61-69.
Brown, Michael W., and Robert Cudeck (1993), "Alternative Ways of Assessing Model Fit," in Testing Structural Equation Models, K.A. Bollen and J.S. Long, eds. Newbury Park, CA: Sage, 136-162.
Brown, Steven P. and Douglas Stayman (1992), "Antecedents and Consequences of Attitude toward the Ad: A Meta-Analysis," Journal of Consumer Research, 19 (1), 34-51.
Bruner, Gordon C., II, and Anand Kumar (2000), "Web Commercials and Advertising Hierarchy-of-Effects," Journal of Advertising Research, 40 (1/2), 35-42.
Chen, Hsiang, Rolf T. Wigand, and Michael Nilan (1999), "Exploring Web Users' Optimal Flow Experiences," Computers in Human Behavior, 15 (5), 585-608.
Coyle, James R., and Stephen J. Gould (2002), "How Consumers Generate Clickstreams through Web Sites: An Empirical Investigation of Hypertext, Schema, and Mapping Theoretical Explanations," Journal of Interactive Advertising, 2 (2), available at (accessed 12/02/2004).
Csikszentmihalyi, Mihaly (1977), Beyond Boredom and Anxiety. San Francisco, CA: Jossey-Bass United States.
---- (1990), The Psychology of Optimal Experience. New York: Harper and Row.
Dailey, Lynn (2004), "Navigational Web Atmospherics. Explaining the Influence of Restrictive Navigation Cues," Journal of Business Research, 57 (2), 795-803.
Eveland, William P., Jr. and Sharon Dunwoody (2000), "Examining Information Processing on the World Wide Web Using Think Aloud Protocols," Media Psychology, 2, 219-244.
Fornell, Claes, and David F. Larcker (1981), "Evaluating Structural Equation Models with Unobservable Variables and Measurement Error," Journal of Marketing Research, 18 (February), 39-50.
Geirland, John (1996), "Go with the Flow," Wired Magazine, 4 (9), 160-161.
Hoffman, Donna L., and Thomas P. Novak (1996), "Marketing in Hypermedia Computer-Mediated Environments: Conceptual Foundations," Journal of Marketing, 60 (July), 50-68.
Homer, Pamela M. (1990), "The Mediating Role of Attitude toward the Ad: Some Additional Evidence," Journal of Marketing Research, 17 (February), 78-86.
Huang, Ming-Hui (2003), "Designing Web Site Attributes to Induce Experiential Encounters," Computers in Human Behavior, 19 (4), 425-442.
---- (2006), "Flow, Enduring, and Situational Involvement in the Web Environment: A Tripartite Second-Order Examination," Psychology & Marketing, 23 (5), 383-411.
Huffman, Cynthia, and Barbara E. Kahn (1998), "Variety for Sale: Mass Customization or Mass Confusion?" Journal of Retailing, 74 (4), 491-513.
Hwang, Jang-Sun, Sally J. MacMillan, and Guiohk Lee (2003), "Corporate Web Sites as Advertising: an Analysis of Function, Audience and Message Strategy," Journal of Interactive Advertising, 3 (2), available at (accessed 12/10/2004).
Isen, Alice M. (1984), "Toward Understanding the Role of Affect in Cognition," in Handbook of Social Cognition, Vol. 3, R. S. Wyer and T. K. Srull, eds. Hillsdale, NJ: Erlbaum, 179-236.
Jöreskog, Karl (2004), "Multivariate Censored Regression," available at (accessed 10/01/2006).
---- and Dag Sörbom (1996), LISREL 8: User's Reference Guide, 2d ed. Chicago: Scientific Software International.
Karson, Eric J. and Robert J. Fisher (2005a), "Reexamining and Extending the Dual Mediation Hypothesis in an On-line Advertising Context," Psychology & Marketing, 22 (4), 333-351.
--- and --- (2005b), "Predicting Intentions to Return to the Web Site: Extending the Dual Mediation Hypothesis," Journal of Interactive Marketing, 19 (3), 2-14.
--- and Pradeep K. Korgaonkar (2001), "An Experimental Investigation of Internet Advertising and the Elaboration Likelihood Model," Journal of Current Issues and Research in Advertising, 23 (2), 53-72.
Klein, Lisa (2001), "The Influence of Individual Factors on the Antecedents of Flow," Advances in Consumer Research, 28, 252.
Krishnamurthy, Parthasarathy and Anuradha Sivaraman (2002), "Counterfactual Thinking and Advertising Responses," Journal of Consumer Research, 28 (March), 650-658.
Lee, Jung-Gyo and Jae-Lin Park (2004), "Consequences of Commercial Web Presence: An Exploratory Study of South Korean Business Adopters of Web Sites," International Journal of Advertising, 23 (2), 253-276.
Li, Dahui, and Glenn J. Browne (2006), "The Role of Need for Cognition and Mood in Online Flow Experience," Journal of Computer Information Systems, 46 (3), 11-17.
Lord, Kenneth R., Myung-Soo Lee, and Paul L. Sauer (1995), "The Combined Influence Hypothesis: Central and Peripheral Antecedents of Attitude toward the Ad," Journal of Advertising, 24 (1), 73-85.
Macias, Wendy (2003), "A Preliminary Structural Equation Model of Comprehension and Persuasion of Interactive Advertising Brand Web Sites," Journal of Interactive Advertising, 3 (2), available at (accessed 05/02/2005).
MacKenzie, Scott B., Richard J. Lutz, and George E. Belch (1986), "The Role of Attitude toward the Ad as a Mediator of Advertising Effectiveness: A Test of Competing Explanations," Journal of Marketing Research, 23 (May), 130-143.
Madden, Thomas J., Chris T. Allen, and Jacquelyn I. Twible (1988), "Attitude toward the Ad: An Assessment of Diverse Measurement Indices under Different Processing Sets," Journal of Marketing Research, 25 (3), 242-252.
Mathwick, Charla and Eduard Rigdon (2004), "Play, Flow, and the Online Search Experience," Journal of Consumer Research, 31 (2), 324-332.
McMillan, Sally J. and Jang-Sun Hwang (2002), "Measures of Perceived Interactivity: An Exploration of the Role of Direction of Communication, User Control, and Time in Shaping Perceptions of Interactivity," Journal of Advertising, 31 (3), 29-42.
Mitchell, Andrew A. and Jerry C. Olson (1981), "Are Product Attribute Beliefs the only Mediator of Advertising Effects on Brand Attitude?" Journal of Marketing Research, 18 (August), 318-332.
Mittal, Banwari (1990), "The Relative Roles of Brand Beliefs and Attitude toward the Ad as Mediators of Brand Attitude: A Second Look," Journal of Marketing Research, 27 (May), 209-219.
Nel, Deon, Raymond van Niekerk, Jean-Paul Berthon, and Tony Davies (1999), "Going with the Flow: Web Sites and Customer Involvement," Internet Research: Electronic Networking Applications and Policy, 9 (2), 109-116.
Novak, Thomas P., Donna L. Hoffman, and Adam Duhachek (2003), "The Influence of Goal-Directed and Experiential Activities on Online Flow Experiences," Journal of Consumer Psychology, 13 (1&2), 3-16.
---, ---, and Yiu-Fai Yung (2000), "Measuring the Customer Experience in Online Environments: A Structural Modeling Approach," Marketing Science, 19 (1), 22-42.
Nunnally, Jum C. and Ira H. Bernstein (1994), Psychometric Theory. New York: McGraw-Hill.
Park, Joonah and Jinwoo Kim (2000), "Contextual Navigation Aids for Two World Wide Web Systems," International Journal of Human-Computer Interaction, 12, 193-217.
Peterman, Michelle L., Harper A. Rohem Jr., and Curtis P. Haugtvedt (1999), "An Exploratory Attribution Analysis of Attitudes toward the WWW as a Product Information Source," Advances in Consumer Research, 26, 75-79.
Petty, Richard E. and John T. Cacioppo (1981), Attitudes and Persuasion: Classic and Contemporary Approaches. Dubuque, IA: Wm. C. Brown.
Rodgers, Shelly and Esther Thorson (2000), "The Interactive Advertising Model: How Users Perceive and Process Online Ads," Journal of Interactive Advertising, 1 (1), available at (accessed 10/08/2002).
Sheehan, Kim B. and Caitlin Doherty (2001), "Re-weaving the Web: Integrating Print and Online Communications," Journal of Interactive Marketing, 15 (2), 47-59.
Sicilia, Maria, Salvador Ruiz, and Jose L. Munuera (2005), "Effects of Interactivity in a Web Site: The Moderating Effect of Need for Cognition," Journal of Advertising, 34 (3), 31-44.
Siekpe, Jeffrey S. (2005), "An Examination of the Multidimensionality of Flow Construct in a Computer-Mediated Environment," Journal of Electronic Commerce Research, 6 (1), 31-43.
Smith, Daniel C., and C. Whan Park (1992), "The Effects of Brand Extensions on Market Share and Advertising Efficiency," Journal of Marketing Research, 29 (August), 296-313.
Smith, Donnavieve, and K. Sivakumar (2004), "Flow and Internet Shopping Behavior: A Conceptual Model and Research Propositions," Journal of Business Research, 57 (10), 1199-1208.
Stevenson, Julie, Gordon C. Bruner II, and Anand Kumar (2000), "Webpage Background and Viewer Attitudes," Journal of Advertising Research, 40 (1/2), 29-34.
Trevino, Linda K. and Jane Webster (1992), "Flow in Computer-Mediated Communications," Communication Research, 19 (5), 411-426.
van Beveren, John A., Robert E. Widing, and Gregory J. Whitwell (2002), "A Mindset and Flow Model of Consumer Search Behavior on the Web," Winter Educators´ Conference MK Theory and Applications, Vol. 13. Chicago: American Marketing Association.
van Birgelen, Marcel, Ko de Ruyter, and Martin Wetzels (2003), "The Impact of Attitude Strength on Customer-Oriented Priority Setting by Decision-Makers: An Empirical Investigation," Journal of Economic Psychology, 24, 763-783.
Webster, Jane, Linda K. Trevino, and L. Ryan (1993), "The Dimensionality and Correlates of Flow in Human Computer Interactions," Computers in Human Behavior, 9 (4), 411-426.
Zajonc, Robert B. (1980), "Feeling and Thinking: Preferences Need no Inferences," American Psychologist, 35, 151-175.
Zhang, Yong (1996), "Responses to Humorous Advertising: The Moderating Effect of Need for Cognition," Journal of Advertising, 25 (1), 15-32.
Appendix 1: Measurement of Product Knowledge and Internet Experience
Appendix 2: Measurement of Flow State
Appendix 3: Measurement of Attitudes and Purchase Intention
About the Authors
Maria Sicilia (Ph.D., University of Murcia) is an Associate Professor in the Department of Marketing at the University of Murcia (Spain). Her research interest focuses on consumer behavior, interactive advertising, and marketing communications.
Salvador Ruiz (Ph.D., University of Murcia ) is a Marketing Professor at the University of Murcia (Spain). His research interest focuses on consumer behavior, family decisions, consumer and technology and consumer emotions.
Acknowledgement: This research was funded by a grant SEJ2005-09358/ECON from the Spanish
Ministry of Science & Technology and FEDER. Authors also thank Fundacion Cajamurcia for its support.
