Abstract

Despite frequent acknowledgment that interactivity is multidimensional, previous studies have measured and treated the construct as if it were unidimensional, failing to see the differences that exist among latent dimensions. This study investigates how the latent factors of perceived interactivity differ in terms of their relationships with various social factors, including social network density and frequency of interactions, as well as with psychological factors, such as individuals' need for cognition. By conducting a factor analysis and a series of multiple regression analyses, it was found that the dimensions were differentially influenced by social and psychological indicators.

Introduction

For the past decade, the rapidly evolving computer-mediated environment has motivated scholars to study how the new media influence a variety of aspects of human communication. A central concern in studies of new media has been the construct of "interactivity," and a considerable amount of empirical research has been conducted to understand the effects of varying levels of interactivity of a message vehicle (e.g., a Web site) on individuals' information processing and decision-making (e.g., Bezjian-Avery, Calder, & Iacobucci, 1998; Cho & Leckenby, 1999; Coyle & Thorson, 2001; Fortin & Dholakia, 2003; Jee & Lee, 2002; Macias, 2003; Rodgers, 2002).

Human perception of interactivity is indispensable in studying the effects of interactive media on individuals: Whether people actually perceive a medium/vehicle as interactive is the only valid criterion for judging its interactivity. For this reason, some researchers have attempted to define and develop tools for measuring people's perceived interactivity (e.g., McMillan & Hwang, 2002; Wu, 2000). When defining a construct, it is important to determine whether it means one thing, or a collection of multiple things-the problem of dimensionality. However, the dimensional characteristics of interactivity remain controversial (Jensen, 1999). Rafaeli (1988), for example, defines interactivity narrowly as a process-oriented concept indicating the degree of sequential relatedness among messages. In this unidimensional definition, interactivity refers to "the extent to which messages in a sequence relate to each other, and especially the extent to which later messages recount the relatedness of earlier messages" (Rafaeli & Sudweeks, 1997).

In contrast, several researchers have attempted to define interactivity as a broader, multidimensional concept (Heeter, 2000). Focusing on the functional aspect of a medium, for example, Laurel (1990) defined interactivity as a concept based on three dimensions—frequency, range, and significance. In a similar approach, Steuer (1992) conceptualized interactivity based on three elements—speed, range, and mapping—facilitating users' manipulation of contents. Based on the functional approach, Coyle and Thorson (2001) identified mapping, speed, and user control as three important dimensions of Web site interactivity. Emphasizing the perceptual rather than functional aspect of interactivity, on the other hand, Wu (2000) pointed out three underlying dimensions: perceived control, perceived responsiveness, and perceived personalization. In a similar vein, McMillan and Hwang (2002) identified the most frequently mentioned elements of perceived interactivity: direction of communication, user control, and time.

Identifying the functional dimensions of a medium is equivalent to categorizing its features: dimensions like "speed" or "mapping" are the functional categories of technical features of a medium. In this sense, the dimensionality of interactivity may be reduced to the matter of range of construct definition, which is less problematic. With respect to the perceptual side of interactivity, however, the issue of dimensionality becomes more complicated, and our understanding of its dimensional characteristics remains at the conceptual level. Do people perceive the interactivity level of a medium according to multiple criteria, or a single criterion? Are the multiple criteria (dimensions) similar enough to be included under the same name? If they are similar, are the differences between them negligible?

By assuming simply that all the underlying dimensions contribute in coordination to enhance the overall perception of interactivity, past research has neglected to examine empirically the distinctive characteristics of the dimensions. In other words, it has conceptualized interactivity as consisting of heterogeneous dimensions, but treated it as a unidimensional construct in statistical analyses, failing to see the differences underlying the dimensions. Dimensions of perceived interactivity have been discussed conceptually, but have rarely been treated as a subject of empirical research.

The objective of this article is thus twofold. First, we will attempt to measure people's perceptions of the interactivity of the Web in general, and to factor-analyze the results to identify whether the perceptions consist of multiple dimensions, as previously noted by many scholars. Second, if evidence of the multidimensionality of interactivity is found, we will explore how the latent factors underlying interactivity differ in terms of their relationships with social factors such as social network density and frequency of interactions, as well as with psychological factors such as individuals' need for cognition, or the tendency to "engage in and enjoy thinking" (Caccioppo & Petty, 1982, p. 116). We measure an individual's perception of the interactivity of the Web in general, rather than of a specific Web site as has been done previously, for three primary reasons: 1) People's general perception of the interactivity of the Web is assumed to be less situation-dependent, thus, less influenced by other factors (e.g., Web site design), which are not of interest in this study; 2) With an actual Web site as a stimulus, people may not consider every relevant aspect of their experiences, but may give too much weight to certain dimensions, such as easy navigation, while nearly ignoring others; 3) Measuring people's perception of the interactivity of the Web in general may reveal more clearly each dimension's enduring relationship with other correlates of interest.

The Multidimensionality of Interactivity

For this research, an online cross-sectional survey was conducted. Despite its convenience and efficiency, online survey data collection is considered problematic for two reasons: 1) difficulty in conducting probabilistic sampling, and 2) low response rate (Sheehan, 2002). As a result, many studies dealing with Internet-related issues have relied on data collected from convenience samples. As a partial way to improve the quality of data collected online, this study employed the method of cluster sampling. Cluster sampling is a technique that has often been used when the sampling frame is grouped by certain rules, such as geographical regions.

Using the cluster sampling method seems appropriate and useful for this study in two respects. First, since Internet users are dispersed geographically, random sampling from the population is extremely difficult, if not impossible. Geographic classification may serve as a cost-effective criterion for sampling from a widely dispersed population. Second, one of the principles of cluster sampling is that the elements within a cluster should be heterogeneous, while there should be homogeneity among clusters. Evidently, there is no reason to believe that Internet users in Ohio are different from those in Washington, but it may be expected that individuals living in each state are substantially different from one another. Additionally, many e-mail search engines allow one to search for individuals geographically.

Given this rationale, eight states out of the total 50 states in the U.S. were randomly selected, and 30 cities from each state were randomly chosen. The e-mail addresses of prospective participants living in the 240 cities were found using the e-mail search engines at Yahoo.com and Lycos.com, which own the largest e-mail databases. A recruiting electronic message for our online survey was then sent to the prospective participants, directing interested people to the URL of the online questionnaire. The total number of respondents for this study was 108, out of a total of 2320 e-mails sent, for a response rate of 4.7%. This low response rate reflects the declining trend in response rates to online surveys that has been pointed out by many researchers (e.g., Sheehan, 2002). Since it is important to see the composition of respondents, when the sample is collected non-randomly or the response rate is low (Witte, Amoroso, & Howard, 2000), the principal social characteristics of the respondents are reported, though they are not directly relevant to the purpose of this study.

Respondents were asked a series of questions about general Internet usage, their perceptions of the interactivity of the Web in general, their social relationships with others, and their levels of need for cognition. Perceived interactivity of the Web was measured by ten Likert-type items with a 5-point scale (1 = Strongly Disagree, 5 = Strongly Agree), which were modified from the items originally developed by Wu (2000) to measure people's perceptions of the interactivity of a particular Web site.¹ The Cronbach alpha reliability score on this modified scale was .89.

With the data measured, a factor analysis with a varimax rotation method was conducted. Tables 2, 3, and 4 illustrate the results of a factor analysis of the items for measuring people's perceptions of the interactivity of the Web in general. As shown in Table 3, three principal components were derived based on the ten items used, with 29.3% of the total variance explained by the first component, 22.7% explained by the second, and 17.8% by the third principal component. In sum, the total variance explained by the three components was approximately 70% (69.7%), which makes it reasonable to regard the three as principal dimensions of perceived interactivity.

Table 4 shows all observed variables with higher factor loadings both in the original and rotated component matrices. As shown in the "rotated" column of the table, five items with higher loadings (selection criterion was .5) belonged to the first factor, three items to the second factor, and the remaining two items to the third factor. Close examination of the items in Table 4 reveals that each factor consisted of relatively homogenous items; the first component consisted of five items reflecting perceived controllability with respect to Internet usage, the second component consisted of three items reflecting Internet users' perceptions of the sensitivity and responsiveness of the Web in general, and the third component was composed of two items reflecting people's perceptions of the efficacy of the Web for communicating with others.

These findings seem similar to Wu's (2000) identification of three dimensions of perceived interactivity. However, Wu did not explicitly examine the distinctive aspects of the latent factors, but instead combined the factors to form a group of measurements. In fact, the approach of combining multiple dimensions into a group of measurements for a single construct may lead to the loss of a considerable amount of information, masking the unique aspects of the underlying latent factors in terms of their relationships with other correlates of interest. In this study, therefore, the three dimensions extracted are not combined, but regarded as three new composite variables, which are named respectively as follows: control, responsiveness, and interaction efficacy.

Factors Affecting Perceived Interactivity

For several decades, communication researchers have examined people's media usage behaviors in relation to a variety of psychological and social factors. As frequently mentioned in the literature based on the uses-and-gratifications perspective, an individual's needs, desires, and motives may determine in part his/her patterns of media usage (Katz, Blumler, & Gurevitch, 1973). Based on this perspective, communication researchers have tried to illuminate the social and psychological origins of needs and motives for media adoption and usage by examining elements like personality and lifestyle, and some contemporary studies have applied this perspective to studies of new media (e.g., Eighmey, 1997; Jeffres & Atkin, 1996; Korgaonkar & Wolin, 1999; Lin, 1999; 2003).

Among the psychological factors examined previously, this study employed people's need for cognition (NFC) as an important predictor of their overall interactivity perception of the Web, because an individual's personality characteristics may influence his/her media adoption and usage (Rubin, 1994). Need for cognition reflects the tendency to "engage in and enjoy thinking" (Caccioppo & Petty, 1982, p. 116). This construct has been employed in many persuasion-related studies based on the Elaboration Likelihood Model (ELM) as an important moderator determining the routes of information processing (central vs. peripheral). In those studies, individuals with high NFC have been found to be prone to processing incoming information intensively, while those with low NFC tend to focus on the peripheral aspects of messages (Petty & Caccioppo, 1986).

The Internet organizes and structures diverse information in particular ways (e.g., hyperlinks). In such an information-oriented environment (Schlosser, 2003), individuals should have a certain level of familiarity with or knowledge of the content structures to find the information they need. Thus, individuals with higher capability/motivation to process information may perceive using the Internet as more rewarding and interactive than those with lower ability/motivation. Indeed, Jee and Lee (2002) found NFC to be a significant predictor for the perceived interactivity of Web sites. In our study, this variable was measured based on 10 items with 5-point scales developed by Cacioppo and Petty (1982). Cronbach alpha of this measure was .92.

In addition to these psychological factors, the influence of the social environment on media usage behaviors has also been recognized (e.g., Elliotte, 1974; Johnstone, 1974). For example, Fulk (1993) argued that the meaning of a new communication technology should be constructed through the social interaction process among individuals. This argument implies that a communication technology is not independent of, but embedded into the ongoing social interactions/relationships among individuals. An individual's adoption of new media is actually a response to needs arising from communicating with others, not purely to the individual's own interests (Lin, 2003).

It has been widely acknowledged that people's decision to adopt new technology is influenced substantially by social communications with others, including opinion leaders or innovators (Rogers, 2003), since a technology is introduced not to individuals in isolation, but to networks of individuals. One's perception and evaluation of a medium, therefore, is influenced by the characteristics of the social networks to which s/he belongs: If an individual is relatively isolated from and rarely communicates with others, s/he may underestimate the value of adopting new communication media.

In order to measure the social surroundings of an individual, this study employed a concept called social network density, a quantitative indicator of an individual's personal network properties. Density refers to "the general level of linkage among the points in a graph" (Scott, 2000, p. 69). In social network analysis, a point refers to an individual node and a graph to a graphical representation of the linkages among points. Marsden (1990) provides the operational definition of network density as "the mean strength of connections among units in a network" (p. 453). That is, network density reflects the overall proportion/strength of connections among network members (Wasserman & Faust, 1994).

To measure this construct, respondents were asked to report five persons with whom they typically engaged in "highly valued interactions" (e.g., information exchange, emotional support, etc.) through the Internet (e.g., e-mail, electronic bulletin boards, etc.).² Then, they reported the strength of the social relationship between the focal individual and each network member (e.g., how close is this person to you?) as well as among the members. For calculating network density, the relationships between a focal individual and five network members were not included, and only the linkages between network members were considered. The direct social ties reported by a focal individual were excluded because they reflect the degree of one's personal intimacy with others, rather than the structural aspects of a network (Scott, 2000). Based on the information reported, one's social network density was calculated by using the following mathematical formula:³

Network Density: where n = the number of nodes = the maximum possible value of relationship strength xij = relationship strength between node i and j

Network density reflects only overall closeness among network members, but does not show a focal individual's role in communication process within a network. For example, consumers who primarily send out messages to their network members may utilize the Web more actively and perceive it as more interactive than those who primarily receive messages from others. In other words, examining contact frequency and role structure in communication process may illuminate aspects that cannot be indicated solely by network density.

Examining the directions of communication between the focal individual and other network members enables us to investigate how an individual's role in communicating with others affects his/her perception of interactivity of the Web in general. As a partial indicator of communication directions, we measured separately the frequencies of sending and receiving messages to each network member. Then, for the purpose of revealing the overall degree of the focal individual's potential communication activity in his/her information exchange network, a variable subtracting the frequency of receiving messages from the frequency of sending messages was constructed. Measures for frequency of sending and receiving messages were calculated by summing five items indicating either sending or receiving frequency between a focal individual and other network members.

Differential Characteristics of Interactivity Dimensions: Multiple Regression Results

With the three new composite variables as dependent measures, multiple regression analyses were conducted. Because respondents' technical expertise and familiarity with Web usage may have confounding effects on the dependent measures, the variables indicating the amount of time spent in Web usage and respondents' age, which were binary-coded, were controlled. Table 5 illustrates the multiple regression analysis results showing the relationships between perceived control and various social/psychological variables. NFC was the only statistically significant predictor for perceived control, while other variables indicating social relationships, time for Internet usage, and age were not. This result shows that perceived control was related more to personal psychological aspects than social correlates. With this model, 18% of variance was explained.

Table 6 shows that the social indicators as well as the need for cognition variable were significant predictors for the perceived responsiveness dimension. First, an individual's social network density was found to influence significantly his/her perception of responsiveness of the Web. That is, an individual tends to perceive the Web as more responsive when s/he maintains a dense rather than a sparse social network with others. Since network density was calculated from the information of the relationships among network members, not from the direct linkages between a focal individual and others, it reflects one's perceived external social environment. This finding implies that an individual's perception of the responsiveness of a communication medium can be influenced by the social surroundings consisting of others with whom s/he communicates socially.

In addition, although the communication frequency among people is evidently correlated with the closeness among them, the difference variable between frequency of sending and receiving messages proved to be a significant factor with the presence of the network density variable. This indicates that the contact frequency/direction is not the mirror image of the closeness among people (Marsden & Campbell, 1984), but may reveal other aspects of social communication processes that are not explained by relationship strength. Positive values for the variable indicate that the individual tends to play a role as a message sender more frequently in his/her communication network, while negative values show that s/he is more oriented to receiving rather than sending messages. As shown in the table, the variable had a positive regression coefficient, which indicates that the frequency of sending (receiving) messages was positively (negatively) associated with one's perceived responsiveness of the Web. Based on these results, it is possible to infer that active interpersonal communicators are likely to perceive the Web as more responsive than are passive communicators. With this model, 23% of variance was explained.

Finally, Table 7 shows that NFC and the amount of time spent in Web usage were significant predictors for the "interaction efficacy" component. With these results, we may infer that people with a higher level of NFC and those who spend more time using the Web tend to think online social interaction with others is more doable and comfortable. This model explains 13% of the total variance.

Discussion

The primary focus of this study was to examine the distinct characteristics underlying principal components of people's perceptions of the interactivity of the Web, and the relationships between the components and other social/psychological correlates. The results of the factor analysis and multiple regression analyses revealed that each principal component of interactivity had different relationships with other correlates. As shown in Table 5, perceived control was found to be a dimension closely related to personal psychological characteristics, such as NFC, rather than social variables. This means that the degree of control an individual perceives while using the Web is not solely a function of the technological features of a medium, but is affected jointly by his/her enduring psychological characteristics. Some researchers have already investigated the associations between personal psychological attributes and perceived interactivity. For example, Sohn and Leckenby (2001) found significant effects of an individual's personality characteristics-locus of control-on perceptions of the interactivity of the Web. Also, Jee and Lee (2002) empirically found significant effects of NFC and Web usage skills on perceived interactivity in relation to commercial Web sites. This result not only replicates the findings of previous studies, but also reveals the close relationship between the dimension of control and the NFC variable.

Conversely, the results in Table 6 show that social indicators as well as NFC proved to be significant predictors for perceived responsiveness. This demonstrates that perceived responsiveness was a distinct component mirroring the social aspects of Internet users. First, it was found that people who belonged to dense online social networks were likely to perceive the Web as more sensitive and responsive than those who were in sparse social networks. The specific reason for the association between network density and perceived responsiveness of the Web requires further empirical investigation. However, we may infer from this result that an individual's way of perceiving the Web is related to the way that s/he communicates with others through it, because people's expectations regarding communication activities are formed through their ongoing social communication practices. In that the concept of network density reflects an individual's perceived social environment, this finding implies that one's perception of a communication medium is not determined solely by the dyadic relationships between an individual user and the medium, but is embedded in a long-term social structural environment.

Sociologists define the term "structure" as recurring patterns of relationships that govern rules and resources guiding individuals' actions (e.g., Giddens, 1984). People usually tend to communicate not with everyone, but with those within the boundary allowed by existing social structures, and it may be speculated that an individual's expectations, knowledge, and experiences regarding communication are deeply rooted in the underlying long-term social structure. What should be noted is that a "new" medium is introduced to individuals within ongoing social relationships, not to isolated individuals separately. As such, an individual's perception of a medium (e.g., the Web) should be understood as originating from prior knowledge and experiences conditioned by social structures.

The study also found that the variable reflecting people's degree of online communication activities was a significant predictor for perceived responsiveness of the Web. As shown in Table 6, the difference variable between frequency of sending and receiving messages was positively associated with the level of perceived responsiveness. That is, individuals' active participation in social communication processes (e.g., initiating communication by sending messages) should contribute to increasing their perceptions of the Web's responsiveness. Active social communicators may tend to perceive communication as rewarding and to have higher expectations as regards Web usage than others. While the finding of the effects of social network density illuminates structural influences on individuals, this result highlights the importance of individuals' roles in communication processes. Based on the discussion above, it is possible to conclude that the dimension of perceived responsiveness may be related to people's social communication contexts.

As shown in Table 7, the hypothesized relationships between social network variables and the dimension of interaction efficacy were not verified. Instead, NFC and the amount of time spent using the Web were found to be significant predictors for the dimension. This means that people with higher NFC and more experience with Web usage tended to think of interactions with others through online channels as comfortable and viable. Overall, NFC was found to be the only predictor significantly influencing the three dimensions altogether, but social indicators and time spent in Web usage affected the dimensions differentially.

Implications for Future Research

Interactivity is a vital concept for Internet-related studies because it holds the key to understanding the dynamics of interactive communication processes on the Internet. Despite the research efforts of the past decade, the concept remains elusive. No consensus about the definition of the concept has been reached, let alone its relationships with a variety of other relevant factors. For example, Bucy (2004) suggests that interactivity is best understood as "a perceptual variable that involves communication mediated by technology" (p. 377). As a counter-argument to this suggestion, Sundar (2004) points out that perceived interactivity is confounded with perceived usability. He proposes that interactivity should be regarded as an attribute of communication technology.

Fundamentally, what should be noted is that this is not a matter of choice. Rather, both the functional and perceptual aspects are essential elements of human-computer interaction (HCI). Without either the functionality of the medium or human perception, the concept of interactivity cannot be defined or even scientifically studied, because interactivity is an emergent outcome from the intersection of the two. What we, as social scientists, should do is not to reduce the concept to either a technological attribute or a personal characteristic, but rather to understand the relationships between the two. Knowing the internal structure of each concept—whether it be perception or medium functionality—is a necessary first step in pursuing this goal.

The findings of this study may serve as an indication that perception of the interactivity of the Web consists of multiple dimensions, and that each dimension has distinctive characteristics that require further empirical investigation in relation to various correlates. To acknowledge the unique aspects of each dimension, a different strategy for studying interactivity than viewing interactivity as a unified/measurable construct is recommended: considering interactivity as a meta-concept that can be indirectly inferred from unidimensional indicators. With this approach, interactivity is understood as an integrative outcome of the underlying dimensions, but the empirical research focus is directed towards certain dimensions of interest.

For example, Ariely (2000) conducted a series of experiments to examine the effects of different levels of information control on consumers' decision quality, memory, and so forth. He found that allowing more information control to consumers in decision contexts had positive effects on their decision quality and memory to some extent, but negative effects beyond certain levels, due to consumers' limited capacity for information utilization. A unidimensional treatment of interactivity implicitly requires researchers to assume that other dimensions (e.g., responsiveness and interaction efficacy) would have similar relationships with the dependent variables observed, which may not be true in actuality. By investigating further the dimensions' relationships with other correlates, interesting similarities and differences among the dimensions' characteristics may be revealed.

This approach also has implications for communication practice on the Web. A number of studies have emphasized the importance of designing a highly interactive Web site for successful communication with audiences, but few have suggested concrete guidelines for doing so. Systematic examinations of the underlying dimensions of interactivity may enable designers to figure out the relationships among them, and to find optimal combinations appropriate for their communication objectives. For instance, some Web site developers focus on enhancing controllability of their Web sites (e.g., making navigation easy, providing customizable options), while others give greater weight to facilitating direct communication with end users by providing various channels and routes for them to express their opinions and ideas. This variation of focus is, of course, dependent on one's primary communication objectives. Understanding the similarities and differences among the dimensions of interactivity would be a necessary step not only toward establishing concrete objectives of communication, but toward finding systematic ways to transform the objectives into tangible outcomes.

Limitations and Extension

This study is exploratory and has several limitations. First, the primary focus of the study was to examine whether the underlying dimensions of interactivity were distinguishable in relation to other correlates of interest, not to explain why such relationships exist. As a result, only tentative speculations were provided for the findings of the multiple regression analyses. For example, this study did not explore deeply the associations between social network indicators and perceived responsiveness, and some critical questions remained unanswered: Why is perceived responsiveness of the Web the only dimension related to social indicators? How are people's perceptions of the Web's responsiveness socially conditioned? What are the mediating processes underlying the relationships found? To answer the remaining questions, more sophisticated examinations of the issues are necessary.

Second, this study was confined to examining ego-centric networks, which were based on focal individuals' self-reported social relationships. Conceivably, self-reported network properties might be biased by the ambiguousness of respondents' subjective criteria. To remove the potential error of self-reporting, a whole network approach rather than an ego-centric network can be used. The whole network approach relies not on a focal individual's report, but observes and measures the linkages between nodes in a specified population (e.g., clubs, classroom).

Third, this study confines the number of network members reported by a focal individual to five people, which might distort the overall structure of a personal social network. "Omission of pertinent elements or arbitrary delineation of boundaries can lead to misleading or artifactual results" (Marsden, 1990, p. 439). Although limiting the number of network members has been widely employed in various network research (Wasserman & Faust, 1994), the problems of boundary specification require further attention.

Notes

1. Wu (2000) originally developed nine items for measuring people's perceived interactivity of a Web site. To use the items for measuring people's overall interactivity perception of the Web, some of the inappropriate items were modified or removed at the authors' discretion. For example, an item stating "I just had a personal conversation with a sociable, knowledgeable and warm representative from the company" seemed improper for the present context, and was excluded. Instead, a new item stating "The Web gives me back positive outcomes" was included, which reflected more appropriately people's overall perceptions of the Web as a communication medium. Also, an item reflecting perceived controllability ("While surfing the Web, I am always aware of where I am"), adopted from Wu (1999), was included. The remaining 8 items were re-stated to be general rather than specific.
2. Social network analysis consists of two different approaches: the ego-centric approach and the whole network approach. An ego-centric network refers to a social network self-reported by a focal individual. On the other hand, the whole network approach focuses on a network of a certain population, which is based on boundary specifications. Examples include clubs and formal organizations (Scott, 2000). This study employs the ego-centric (or personal) approach because the network variables measured from the ego-centric approach can be used with other traditional attribute-based variables. The virtue of this approach is that it enables researchers to investigate various individual attributes in the context of ongoing social relationships.
3. With binary data indicating the existence/non-existence of a link, x_ij simply becomes the total number of links existing in a network (denoted by L), and becomes 1 because 1 is the highest value in binary data. When the directionality of linkage is examined, the denominator of the density formula changes to n(n-1). As a result, the network density formula with binary-coded information is = L/n(n-1). The range of density value is 0 ? ? 1

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About the Authors

Dongyoung Sohn is a doctoral candidate in the Department of Advertising at The University of Texas at Austin. His research interests are in sociological approaches to a variety of issues including the impact of new communication technologies on consumer information processing and exchange behavior.
Address: Department of Advertising, College of Communication, The University of Texas at Austin, CMA 7.142, 1 University Station A1200, Austin, TX 78712-1092 USA

Byung-Kwan Lee earned his Ph.D. in Advertising at the University of Texas at Austin. His research has focused on consumer information processing and advertising effectiveness on the Internet.
Address: Department of Advertising, College of Communication, The University of Texas at Austin, CMA 7.142, 1 University Station A1200, Austin, TX 78712-1092 USA