Explaining the Relationship between Internet Use and Interpersonal Trust: Taking into Account Motivation and Information Overload

Transcription

1 Journal of Computer-Mediated Communication Explaining the Relationship between Internet Use and Interpersonal Trust: Taking into Account Motivation and Information Overload Christopher E. Beaudoin Tulane University School of Public Health and Trophical Medicine Although a growing body of empirical research has assessed the relationship between Internet use and social capital, little is known about what mechanisms underlie this relationship. The current study addresses this gap in the literature by articulating and testing a multi-step model specific to the development of interpersonal trust, a critical component of social capital. In considering the influence of Internet use on interpersonal trust, this model takes into account motivation and information overload. Structural equation modeling was used to test the model with data from the 2006 Gadgets Survey of the Pew Internet & American Life Project. This analysis indicates that the effects of social resource motivation for Internet use on interpersonal trust were mediated by Internet use and perceived information overload. In addition, Internet use inversely influenced perceived information overload, Internet use influenced interpersonal trust, and perceived information overload inversely influenced interpersonal trust. These findings are considered in reference to previous literature on Internet effects, uses and gratifications, information processing, and the cognitive mediation model. doi: /j x Introduction Internet use in the United States is widespread. In 2007, it was estimated that 71% of American adults used the Internet (Pew Internet & American Life Project, 2007). The importance of this medium has been explored in different realms, including its impact on social capital, which Putnam (2000) defined as connections among individuals social networks and the norms of reciprocity and trustworthiness that arise from them (p. 19). The importance of social capital is underscored by its associations with good governance, education, economic progress, and public health (Kawachi, Kennedy, Lochner, & Prothrow-Stith, 1997; Putnam, 2000; Verba, Schlozman, & Brady, 1995). 550 Journal of Computer-Mediated Communication 13 (2008) ª 2008 International Communication Association

2 With a basis in the concept of social capital, early research in the United States found either a negative association between Internet use and social capital (Kraut et al., 1998) or no association at all (Kavanaugh & Patterson, 2001). More recent research, however, has cast a more favorable light on this relationship, demonstrating positive associations between Internet use and various social capital indicators, including social involvement (Kraut et al., 2002), social interaction (Beaudoin & Tao, 2007, in press), neighboring (Hampton & Wellman, 2003), and indices of bonding and bridging social capital (Ellison, Steinfield, & Lampe, 2007). Other research, with a general basis in the uses and gratifications approach, has examined the manner by which social capital is predicted by Internet use for specific purposes. One study found that Internet use for the purpose of information exchange was associated with interpersonal trust and civic participation (Shah, McLeod, & Yoon, 2001). Another study came to a similar conclusion, demonstrating that Internet use for the purpose of information exchange was associated with civic participation and interpersonal trust, but Internet use for the purpose of social recreation was not associated with either social capital indicator (Shah, Kwak, & Holbert, 2001). Despite the growth of research in this area, not much is known about the mechanisms that underlie the relationship between Internet use and social capital. In this light, the current study aims to signify what mechanisms are integral to the process by which Internet use influences the development of interpersonal trust, a critical component of social capital. In doing so, this study theorizes upon and empirically tests a model that includes social resource motivation for Internet use, Internet use, perceived information overload, and interpersonal trust. This model has general bases in previous research involving the uses and gratifications approach (Blumler, 1979) and messaging processing (Thorson, Reeves, & Schleuder, 1985), as well as a more specific basis in the cognitive mediation model, which postulates the interrelated roles of media use motivations, media use, and information processing in predicting knowledge (Eveland, 2001, 2002). The hypothesized model is tested with secondary analysis of data from the 2006 Gadgets Survey of the Pew Internet & American Life Project. Critical Concepts This study s hypothesized model has four main concepts: social resource motivation for Internet use, Internet use, perceived information overload, and interpersonal trust. Before articulating the hypothesized model, it is useful to provide an overview of each of these concepts. Social Resource Motivation for Internet Use Motivation has been defined in different ways, including an impetus to action (Deci & Ryan, 1985) and a point of common ground between needs, cognitions, and emotions (Reeve, 1997). Motivation is at the core of the uses and gratifications Journal of Computer-Mediated Communication 13 (2008) ª 2008 International Communication Association 551

3 approach. This approach has four main tenets: 1) the audience is active and goaloriented; 2) motivations help explain media use; 3) people form intentions and expectations for media use; and 4) people choose a medium based on their sought motives (Blumler, 1979). Uses and gratifications research has demonstrated different dimensions of media use motivations, including surveillance, diversion, and escape (Blumler, 1979). Another common dimension involves media use for the purpose of building or maintaining social resources, social contact, and social interactions. Social dimensions of this sort have been called different things, including social resource (Bantz, 1982), interpersonal utility (Wenner, 1982), anticipated interaction (Beaudoin & Thorson, 2004b), social connection (Cho, De Zuniga, Rojas, & Shah, 2003), social utility (Kaye & Johnson, 2002), and social outcomes (LaRose, Mastro, & Eastin, 2001). Internet Use Internet use has been measured in different ways, including hours of Internet use (Kraut et al., 2002), online information seeking (Shah, Cho, Eveland, & Kwak, 2005), and synchronous and asynchronous forms of online communication (Beaudoin & Tao, 2007). There are various sub-types of Internet use, including , instant messaging, listserv or group s, and a website or blog use. Perceived Information Overload Cognitive structures determine the manner by which people attend to, process, store, and retrieve new information (Graber, 1984; Lau & Sears, 1986; Markus, 1977). Information processing has been measured in different ways, including elaboration, which is the process by which people connect new information with other information that is stored in memory (Beaudoin & Thorson, 2004b; Eveland, 2001, 2002). Also pertinent to information processing is information overload, which has been used in various studies related to Internet and computer use (Hiltz & Turoff, 1985; Hiltz & Wellman, 1997; Jones, Ravid, & Rafaeli, 2004). Information overload can be defined as the condition by which a person cannot process all communication and informational inputs, which results in ineffectiveness or terminated information processing (Rogers & Agarwala-Rogers, 1975). This concept suggests that people have limited capacity for information storing and processing (Miller, 1956; Thorson, Reeves, & Schleuder, 1985). Interpersonal Trust Interpersonal, or social, trust is a critical component of social capital. Putnam (2000) has called social trust the lubricant of the inevitable frictions of social life (p. 135). Such trust has been implemented in various empirical studies (Beaudoin & Thorson, 2004a, 2006; Putnam, 2000; Shah, Kwak, & Holbert, 2001; Shah, McLeod, & Yoon, 2001). Putnam (2000) measured interpersonal trust with a single item about trusting others. Brehm and Rahn (1997) measured it with three questions about people trying 552 Journal of Computer-Mediated Communication 13 (2008) ª 2008 International Communication Association

4 to take advantage of you, people being helpful, and people being trustworthy. Other research has measured interpersonal trust in terms of perceptions of how other people are likely to act in certain community situations (Beaudoin & Thorson, 2006). Modeling the Development of Interpersonal Trust These four concepts are the building blocks of this study s hypothesized model. The hypothesized model has a general basis in the cognitive mediation model, which postulates that learning from news results from a causal process in which motivation drives information use and processing, which, in turn, determine learning (Eveland, 2001, 2002; Eveland, Shah, & Kwak, 2003). Some research has referred to this motivation as gratifications sought (Eveland, 2001, 2002), while other research has used the term motivations for media use (Eveland, Shah, & Kwak, 2003). With the latter wording, which is used in this study, the cognitive mediation model has six integral paths: 1) from motivations for media use to news attention, 2) from motivations for media use to elaboration, 3) from news attention to elaboration, 4) from motivations for media use to knowledge, 5) from news attention to knowledge, and 6) from elaboration to knowledge. The appeal of this modeling approach is that it ties together important streams of previous research, including those on uses and gratifications, media use, media effects, and information processing. With a general basis in this model, as well as literature in the areas of social capital, Internet effects, uses and gratifications, and information processing, the current study articulates a multi-step model specific to the development of interpersonal trust. This model has six integral paths: 1) from social resource motivation for Internet use to Internet use, 2) from social resource motivation for Internet use to perceived information overload, 3) from Internet use to perceived information overload, 4) from social resource motivation for Internet use to interpersonal trust, 5) from Internet use to interpersonal trust, and 6) from perceived information overload to interpersonal trust. Path 1: From Social Resource Motivation for Internet Use to Internet Use The uses and gratifications approach holds that an active, goal-oriented audience forms intentions and expectations for media use, with such intentions and expectations predicting subsequent media use (Blumler, 1979). Empirical research has commonly found that media use motivations predict media use. For example, anticipated interaction predicts readership of trade magazines (Payne et al., 1988), as well as attention to TV and newspaper public affairs news (McLeod & McDonald, 1985). In regards to the Internet, one study found that a social connection motivation dimension was associated with and Internet use in terms of frequency and length of time (Cho, De Zuniga, Rojas, & Shah, 2003). In addition, in testing the cognitive mediation model, research has demonstrated paths from media use motivations including surveillance, anticipated interaction, and guidance to news use measures, such as news attention and news reliance (Beaudoin & Thorson, 2004b; Journal of Computer-Mediated Communication 13 (2008) ª 2008 International Communication Association 553

5 Eveland, 2001, 2002). Thus, it is generally expected that people s social resource motivation for Internet use will influence their Internet use. H1: The greater the social resource motivation for Internet use, the greater the Internet use. Path 2: From Social Resource Motivation for Internet Use to Perceived Information Overload The linkage from a media use motivation to an outcome variable, such as political knowledge or interpersonal trust, is mediated by information processing (Anderson, 1980; Schneider & Shiffrin, 1977). In regards to the current modeling approach, this would suggest a general two-step process: 1) from social resource motivation for Internet use to perceived information overload; and 2) from perceived information overload to interpersonal trust. The first step has been supported by empirical research that has found associations between an information-seeking motive and cognitive involvement (Rubin & Perse, 1987) and between an instrumental viewing motive and elaboration (Perse, 1990). In terms of the cognitive mediation model, research has demonstrated relationships between media use motivations, such as surveillance and anticipated interaction, and elaboration (Beaudoin & Thorson, 2004b; Eveland, 2001, 2002). It would, thus, be generally expected that people s social resource motivation for Internet use will inversely influence their perceptions of information overload. H2: The greater the social resource motivation for Internet use, the lower the perceived information overload. Path 3: From Internet Use to Perceived Information Overload When information is present, people strive to set order, making decisions related to what should be attended to and processed (Miller, 1956; Thorson, Reeves, & Schleuder, 1985; Zillmann & Bryant, 1985). One aspect of such information processing is information overload and attempts to control it (Hiltz & Turoff, 1985; Hiltz & Wellman, 1997; Jones, Ravid, & Rafaeli, 2004). Information control can determine the termination or continuation of information processing, as well as the subsequent effects (Hiltz & Turoff, 1985; Rogers & Agarwala-Rogers, 1975). Thus, there is support for the idea that Internet use will precede information overload and, more broadly, that information processing will mediate the relationship between media use and various outcomes (Eveland & Dunwoody, 2002). In testing the cognitive mediation model, research has demonstrated paths from news use, as measured in terms of news attention and news reliance, to elaboration (Beaudoin & Thorson, 2004b; Eveland, 2001, 2002). It would, thus, be generally expected that people s Internet use will inversely influence their perceptions of information overload. H3: The greater the Internet use, the lower the perceived information overload. 554 Journal of Computer-Mediated Communication 13 (2008) ª 2008 International Communication Association

6 Path 4: From Social Resource Motivation for Internet Use to Interpersonal Trust Previous research has theorized upon the role that media motivations play in predicting outcomes. For example, Blumler (1979) contended that a cognitive motive would facilitate information gain, while diversion or escape motives would facilitate the adoption of social perceptions. Empirical research has provided support for the role of media gratifications in predicting knowledge (Gantz, 1978; McLeod & McDonald, 1985; Neuman, 1976; Vincent & Basil, 1997). Of greater pertinence to the current study is previous social capital research involving Internet use for a specific purpose. Research in this area has found that Internet use for the purpose of information exchange was associated with interpersonal trust and civic participation, while Internet use for the purpose of social recreation was not associated with either social capital indicator (Shah, Kwak, & Holbert, 2001; Shah, McLeod, & Yoon, 2001). The cognitive mediation model holds that the influence of a media gratification on knowledge will be mediated by media use and information processing. Such mediation has received at least partial support from previous research involving the model (Beaudoin & Thorson, 2004b; Eveland, 2001; Eveland & Dunwoody, 2002). In regards to the current study, it would generally be expected that the influence of social resource motivation for Internet use on interpersonal trust will be mediated by Internet use and perceived information overload. H4: The influence of social resource motivation for Internet use on interpersonal trust will be mediated by Internet use and perceived information overload. Path 5: From Internet Use to Interpersonal Trust The linkage between Internet use and interpersonal trust can be theorized upon with reference to two of social cognitive theory s primary means to learning: symbolization and vicarious learning (Bandura, 2002). Via symbolization, cognition affects what external stimuli will be attended to, how they will be processed, whether they will be stored in memory, and whether they will affect attitudes and behavior (Bandura, 2002). Via such symbolization, people process personal and vicarious experiences, including those modeled in the mass media. These two concepts symbolization and vicarious learning align closely with research that has theorized that news use influences social capital development via a symbolic-informational dichotomy (Shah, McLeod, & Yoon, 2001). In a symbolic sense, the news media can help construct self- and collective-identity and self- and collective-efficacy. In this light, the news media help develop bonds between people and between social groups. In terms of information, the news media disseminate a wealth of facts, opinions, and statistics related to community, which can mobilize people and urge them to interact, participate in civic affairs, and develop strong social bonds (Newton, 1999). A growing line of research has found positive associations between different forms of Internet use and various social capital indicators, including social involvement (Kraut et al., 2002), social interaction (Beaudoin & Tao, 2007, in press), neighboring (Hampton & Wellman, 2003), and indices of bonding and bridging Journal of Computer-Mediated Communication 13 (2008) ª 2008 International Communication Association 555

7 social capital (Ellison, Steinfield, & Lampe, 2007). In addition, two studies in the context of cancer have found that Internet effects vary by type of use. These two studies found that asynchronous online communication was associated with social interaction, but that interpersonal trust was not predicted by asynchronous online communication, synchronous online communication, or online information seeking (Beaudoin & Tao, 2007, in press). While most research in this area has relied on cross-sectional data and, thus, could only make broad assumptions of causality, two recent studies have employed panel data as a means to strengthening causal inferences about the direction of influence from media use and, specifically, Internet use to social capital (Beaudoin, 2007; Shah, Cho, Eveland, & Kwak, 2005). Thus, it would be generally expected that people s Internet use will influence their interpersonal trust. H5: The greater the Internet use, the greater the interpersonal trust. Path 6: From Perceived Information Overload to Interpersonal Trust Information overload affects whether information processing is continued or terminated, as well as what effects it will have (Hiltz & Turoff, 1985; Rogers & Agarwala- Rogers, 1975). In testing the cognitive mediation model, there is general support for the effects of information processing, as measured in terms of elaboration, on outcome measures, including public affairs knowledge and political knowledge (Beaudoin & Thorson, 2004b; Eveland, 2001, 2002). In terms of interpersonal trust, it is expected that people who do not experience information overload will be most likely to accrue benefits from Internet use. In this light, we would expect people s perceptions of information overload to inversely influence their interpersonal trust. H6: The greater the perceived information overload, the lower the interpersonal trust. Methods The six hypotheses were tested with secondary analysis of data from the 2006 Gadgets Survey of the Pew Internet & American Life Project. The survey was of 4,001 adults (=.18 years old) in the United States. The telephone survey interviews were conducted from February 15 to April 6, 2006, by Princeton Survey Research Associates International. The survey had a random digit sample. The reported response rate was 29.8%. Measurement Demographics were gender, household income, education, age, and ethnicity. Household income was measured on an 8-point scale. The income categories were as follows: less than $10,000; $10,000 to under $20,000; $20,000 to under $30,000; $30,000 to under $40,000; $40,000 to under $50,000; $50,000 to under $75,000; $75,000 to under $100,000; and $100,000 or more. Education was measured on a 556 Journal of Computer-Mediated Communication 13 (2008) ª 2008 International Communication Association

8 7-point scale, from 1 (none or grades 1 8) to 7 (post-graduate training/professional school after college). Ethnicity was specific to non-hispanic Whites versus others. Social resource motivation for Internet use was measured in two steps. First, group membership was assessed with the following question: On a different topic, do you happen to belong to any groups that relate to your hobbies or personal or professional interests? Second, the following question was asked to respondents who answered the previous question in the affirmative: Thinking about the group in which you are most active, how do you keep in touch with group members or keep track of group activities? Do you ever keep in touch with this group through [various forms of communication]? There was a dichotomous response (Y = 1) for each of the following four forms of Internet use: 1) , 2) instant messaging, 3) listserv or group s, and 4) a group website or blog. Factor analysis (principal components) indicated one dimension to social resource motivation for Internet use (eigenvalue = 2.05, variance explained =.51, a =.68). An index was created by adding the items and dividing by four. There were three items for Internet use. They were specific to the following locations for Internet use: home, work, or someplace other than home or work. The question was as follows: About how often do you use the internet or from [home/work/someplace other than home or work] several times a day, about once a day, 3 5 days a week, 1 2 days a week, every few weeks, or less often? Thus, there were three items with responses on a 7-point scale from 1 (never) to 7 (several times a day). An index of Internet use was created by adding the three items. There were two items for perceived information overload. The first item read as follows: Some people say they feel overloaded with information these days, considering all the TV news shows, magazines, newspapers, and computer information services. Others say they like having so much information to choose from. How about you. do you feel overloaded, or do you like having so much information available? Responses were as follows: 1 (like having so much information available), 2 (don t know/refused), and 3 (feel overloaded). The second item was as follows: Overall, do you think that computers and technology give people more control over their lives, less control over their lives, or don t you think it makes any difference? The responses were as follows: 1 (more), 2 (no difference/don t know/refused), and 3 (less). An index for perceived information overload was created by adding the items and dividing by two (r =.71, p,.001). To measure interpersonal trust, the following question was used: Generally speaking, would you say that most people can be trusted or that you can t be too careful in dealing with people? Responses were as follows: 1 (you can t be too careful), 2 (depends), and 3 (most people can be trusted). Statistical Procedure Analyses were conducted on the sub-sample of respondents (n = 1,240) who indicated that they belong to any groups that relate to [their] hobbies or personal or professional interests. Structural equation modeling (SEM) was implemented with Journal of Computer-Mediated Communication 13 (2008) ª 2008 International Communication Association 557

9 AMOS 16. In this covariance structure analysis, maximum likelihood (ML) method of estimation was used. Close fit was signified by a non-significant p-value for the x 2 statistic, a comparative fit index (CFI) value of.95 or higher, and a root mean square error of approximation (RMSEA) value of close to.06 or less (Hu & Bentler, 1999). In SEM, demographics served as exogenous variables. The SEM included four endogenous variables: social resource motivation for Internet use, Internet use, perceived information overload, and interpersonal trust. Paths were initially drawn from exogenous variables to endogenous variables. In addition, paths were drawn from social resource motivation for Internet use to the other three endogenous variables: Internet use, perceived information overload, and interpersonal trust. Paths were also drawn from Internet use to both perceived information overload and interpersonal trust and from perceived information overload to interpersonal trust. Non-significant paths from exogenous variables were then pruned. Finally, in cases of potential mediation, an additional procedure was performed (Holbert & Stephenson, 2003; MacKinnon, Lockwood, Hoffman, West, & Sheets, 2002). In this procedure, z-score products were calculated, with their significance (Craig, 1936) used to determine mediation. Results Descriptive statistics for the sub-sample of group members (n = 1,240) are depicted in Table 1. The mean age was about 52 years, and of the sub-sample, 46% was male and 90% was White. The mean household income was above $50,000, and the mean education was some college. The mean for social resource motivation for Internet use indicates that respondents used about half of the four types of Internet use to keep in touch and maintain group memberships. The mean for Internet use indicates that respondents had an average Internet use of a slightly more than every few weeks in the three places. The mean for perceived information overload was toward Table 1 Descriptive Statistics for Respondents with Group Membership (n = 1,240) Mean SD Gender a Income Education Age White a Social Resource Motivation for Internet Use Internet Use Perceived Information Overload Interpersonal Trust a Represents a frequency for this dichotomous variable. 558 Journal of Computer-Mediated Communication 13 (2008) ª 2008 International Communication Association

10 the lower end of the scale. The mean for interpersonal trust fell near the middle of the 3-point scale. The SEM is shown in Figure 1. Of the six depicted paths, five were significant. The model had close fit, CFI =.997; RMSEA =.025 (90% confidence interval: ); x 2 (36, 1,240) = , p =.094. The model accounted for the following variance: social resource motivation for Internet use,.23; Internet use,.56; perceived information overload,.06; and interpersonal trust,.05. Table 2 depicts the direct and indirect predictors of the model s endogenous variables. The direct effects of social resource motivation for Internet use, Internet use, and perceived information overload are identical to those in Figure 1. In terms of the indirect effects of these endogenous variables, social resource motivation for Internet use had a negative indirect effect on perceived information overload and a positive indirect effect on interpersonal trust, and Internet use had a positive indirect effect on interpersonal trust. In terms of demographics, Whites had higher levels of Internet use and interpersonal trust, but lower levels of perceived information overload. Older respondents had lower levels of social resource motivation for Internet use and Internet use, but higher levels of perceived information overload. In addition, age had a positive direct effect on interpersonal trust, but negative indirect effect. Male respondents, as well as those with higher education and higher income, had higher levels of social resource motivation for Internet use, Internet use, and interpersonal trust, but lower levels of perceived information overload. Internet Use.42* -.09*.17* Social Resource Motivation for Internet Use -.05 Interpersonal Trust -.10* -.06* * p <.05 Perceived Information Overload Figure 1 SEM of the Development of Interpersonal Trust Journal of Computer-Mediated Communication 13 (2008) ª 2008 International Communication Association 559

11 Table 2 Predictors of Endogenous Variables in SEM Social Resource Motivation for Internet Use Effects Sources of Influence White Age Education Gender Income Direct Indirect Social Resource Motivation for Internet Use Internet Use Internet Use Direct Indirect Perceived Direct Indirect Information Overload Interpersonal Direct Indirect Trust Perceived Information Overload Coefficients are standardized. Effects are significant at.05-level. 560 Journal of Computer-Mediated Communication 13 (2008) ª 2008 International Communication Association

12 Hypothesis 1 posited that the greater the social resource motivation for Internet use, the greater the Internet use. As shown in Figure 1 and Table 2, social resource motivation for Internet use was associated with Internet use (b =.42). Thus, Hypothesis 1 is supported. Hypothesis 2 predicted that the greater the social resource motivation for Internet use, the lower the perceived information overload. As depicted in Figure 1 and Table 2, social resource motivation for Internet use was inversely associated with perceived information overload (b = 2.10). Thus, Hypothesis 2 is supported. Hypothesis 3 held that the greater the Internet use, the lower the perceived information overload. As depicted in Figure 1 and Table 2, Internet use was inversely associated with perceived information overload (b = 2.09). Thus, Hypothesis 3 is supported. Hypothesis 4 predicted that the influence of social resource motivation for Internet use on interpersonal trust will be mediated by Internet use and perceived information overload. The SEM (see Figure 1) provides general support for the two related mediation frameworks: 1) from social resource motivation for Internet use to Internet use to interpersonal trust; and 2) from social resource motivation for Internet use to perceived information overload to interpersonal trust. In addition, the direct path from social resource motivation for Internet use to interpersonal trust was non-significant. For the two aforementioned potential mediation frameworks, z-score products were calculated (Holbert & Stephenson, 2003). In the first case, the z-score product was [(.543/.131)3(.023/.006)]. In the second case, the z-score product was 5.17 [(2.046/.017)3(2.086/.045)]. These paths were significant at the.001-level (Craig, 1936), which indicates mediation. Thus, Hypothesis 4 is supported. Hypothesis 5 posited that the greater the Internet use, the greater the interpersonal trust. As depicted in Figure 1 and Table 1, Internet use was associated with interpersonal trust (b =.17). Thus, Hypothesis 5 is supported. Hypothesis 6 predicted that the greater the perceived information overload, the lower the interpersonal trust. As depicted in Figure 1 and Table 1, perceived information overload was inversely associated with interpersonal trust (b = 2.06). Thus, Hypothesis 6 is supported. Discussion A growing body of research has explored various relationships, some causal, between Internet use and different social capital indicators. Other research has explored relationships between Internet use for specific purposes and social capital. The current study aimed to explore what mechanisms explain these relationships, with a specific focus on the development of interpersonal trust, a critical component of social capital. The hypothesized model is grafted from research on the cognitive mediation model, with bases in Internet effects, uses and gratifications, and information processing. Journal of Computer-Mediated Communication 13 (2008) ª 2008 International Communication Association 561

13 The paths of the tested model are generally in line with this study s expectations. The path from social resource motivation for Internet use to Internet use provides general support for research in the uses and gratifications tradition (Blumler, 1979). More specifically, this finding is consistent with research that has demonstrated the role of social resource-like media use motivation in predicting Internet use and other forms of media use (Beaudoin & Thorson, 2004b; Cho, De Zuniga, Rojas, & Shah, 2003; McLeod & McDonald, 1985; Payne, Severn, & Dozier, 1988). It is clear that a social resource motivation to use the Internet predicts actual use of the medium. In addition, the significant path from social resource motivation for Internet use to perceived information overload is supportive of research that has demonstrated that the linkage from motivation to an outcome variable is mediated by information processing (Anderson, 1980; Schneider & Shiffrin, 1977). Generally consistent with research that has demonstrated relationships between media use motivations and elaboration (Beaudoin & Thorson, 2004b; Eveland, 2001, 2002), this path indicates that people with higher levels of an Internet use motivation are less likely to encounter information overload. The finding that Internet use predicts interpersonal trust underscores the capacity of the Internet to foster via symbolic and informational means (Shah, McLeod, & Yoon, 2001) the development of community, social interaction, and open debate (Wellman, 2001; Wellman, Quan Haase, Witte, & Hampton, 2001). Another rationale for this linkage can be found in research that has demonstrated that Internet use allows people to mitigate uncertainty (Tidwell & Walther, 2002), solve problems (Campbell & Stasser, 2006), control communication (Trevino & Webster, 1992), and communicate effectively (Harasirn, 1993). This finding is generally consistent with previous research that has demonstrated positive associations between Internet use and various social capital indicators, including social involvement (Kraut et al., 2002), social interaction (Beaudoin & Tao, 2007, in press), neighboring (Hampton & Wellman, 2003), and indices of bonding and bridging social capital (Ellison, Steinfield, & Lampe, 2007). This finding, however, is inconsistent with research in the context of cancer that found that interpersonal trust was not predicted by asynchronous online communication, synchronous online communication, or online information seeking (Beaudoin & Tao, 2007, in press). This difference in findings may result from differing contexts and differing forms of Internet use measurement (ie, general Internet use versus cancer-related Internet use). That Internet use inversely predicts perceived information overload is supportive of research that has commented on information overload and related attempts to control it (Hiltz & Turoff, 1985; Hiltz & Wellman, 1997; Jones, Ravid, & Rafaeli, 2004). Internet users undergo assessments of whether they can handle and control information, which can result in the termination or continuance of information processing (Hiltz & Turoff, 1985; Rogers & Agarwala-Rogers, 1975). Furthermore, people who use the Internet often are likely to perceive fewer problems and confront fewer obstacles in terms of information overload. 562 Journal of Computer-Mediated Communication 13 (2008) ª 2008 International Communication Association

14 The inverse path from perceived information overload to interpersonal trust makes sense in that people s perceptions of information overload can lead to the termination and diminished effects of information processing (Hiltz & Turoff, 1985; Rogers & Agarwala-Rogers, 1975). While Internet use predicts interpersonal trust, it appears that this development can be undermined during instances of information overload. It is also important to consider broader aspects of the tested model. In using SEM, which encompasses the testing of simultaneous regression analyses, this study can depict a multi-step, systematic process. SEM indicates a multi-step model for the development of interpersonal trust. This demonstration makes an important contribution to the literature by helping explain the mechanisms that underlie Internet effects on social capital. Instead of demonstrating bivariate relationships (e.g., from Internet use to social capital or from Internet use for specific purposes to social capital), this study finds support for a multi-step developmental process including Internet use gratifications, Internet use, and perceived information overload. Critical to this modeling is mediation. The finding that Internet use and perceived information overload mediate the effects of social resource motivation for Internet use on interpersonal trust is generally consistent with the cognitive mediation model (Eveland, 2001, 2002). The cognitive mediation model postulates that the effects of media use motivations on knowledge are mediated by news use and information processing. The current study grafts this general modeling approach from that of knowledge to a different realm the development of interpersonal trust. Thus, there appears to be a common framework for the development of knowledge and interpersonal trust. The current study finds that media use motivations have an important influence, one that is mediated by Internet use and perceived information overload. This finding suggests that a motivation for Internet use does not by itself bring about a change in interpersonal trust. Four limitations of this study should be noted. The first three relate to measurement, with such limitations not uncommon in secondary data analysis. First, measurement of social resource motivation for Internet use is related to the group a respondent is most active in. Thus, this measurement is limited to one primary group membership and not comprehensive of all group memberships. Second, there is a slight change of Internet use type when considering the measure of Internet use and the measure of social resource motivation for Internet use. The Internet use measure is specific to use of the Internet and , while the social resource motivation for Internet use measure is specific to and the three other forms of Internet use: instant messaging, listserv or group s, and a group website or blog. It would be better to have a perfect match between the items for Internet use and those for social resource motivation for Internet use. Thus, in light of the four measures of social resource motivation for Internet use, it would be favorable to measure Internet use with four similar items specific to the frequency of respondent use of , instant messaging, listserv or group s, and a group website or blog. Regrettably, the 2006 Gadgets Survey does not include such detailed measurement. Journal of Computer-Mediated Communication 13 (2008) ª 2008 International Communication Association 563

15 Despite this limitation, it can be argued that the frequency of Internet and use would encapsulate the four distinct forms of Internet use ( , instant messaging, listserv or group s, and a group website or blog). Third, while the 2006 Gadgets Survey measures the frequency of Internet and use, it does not take into account what type of content is encountered in such use. Media content varies in an assortment of ways, including news type (hard news versus soft news), credibility, and tone. For example, the credibility of online content has been shown to influence credibility perceptions (Hong, 2006b), which, in turn, influence Internet effects (Hong, 2006a). In addition, flames and other types of destructive posts can create a negative tone in online discussion groups, which can prevent the development of positive outcomes (Kayany, 1998). A fourth limitation relates to causation. Although SEM suggests a direction of influence, it cannot conclusively demonstrate causation. That said, research using panel data has strengthened inferences of causality in two important regards: 1) the influence of media use and, specifically, Internet use on various social capital indicators (Beaudoin, 2007; Shah, Cho, Eveland, & Kwak, 2005); and 2) the causal nature of the cognitive mediation model (Eveland, Shah, & Kwak, 2003). This study has implications for future research and practice. Future scholarship should continue to explore mechanisms that underlie Internet effects on social capital indicators, including interpersonal trust. It is important that such research further consider the roles of different media use gratifications, different forms of information processing, and different types of Internet use, including those that are content specific. Thus, while most previous research has focused on the associations between Internet use and social capital indicators, it is important that we probe deeper and explore what mechanisms are at work. In addition, related models should be tested in other contexts and other countries. This study examined an Internet process in the United States, but there is a possibility of variance in other contexts and cultures. These findings also have ramifications for practice. The Internet can play a positive role in society, influencing the development of social capital, which, in turn, has been linked to important societal outcomes, including good governance, economic progress, education, and public health. The role of perceived information overload in the SEM points out the need to streamline and simplify Internet content and related use. References Anderson, J. R. (1980). Cognitive psychology and its applications. San Francisco: Freeman. Bandura, A. (2002). Social cognitive theory of mass communication. In J. Bryant & D. Zillmann (Eds.), Media effects: Advances in theory and research (pp ). Mahwah, NJ: Lawrence Erlbaum. Bantz, C. (1982). Exploring uses and gratifications: A comparison of reported uses of television and reported uses of favorite program type. Communication Research, 9(3), Journal of Computer-Mediated Communication 13 (2008) ª 2008 International Communication Association

16 Beaudoin, C. E. (2007). Mass media use, neighborliness, and social support: Assessing causal links with panel data. Communication Research, 34(6), Beaudoin, C. E., & Tao, C. C. (2007). Benefiting from social capital in online support groups: An empirical study of cancer patients. CyberPsychology & Behavior, 10(4), Beaudoin, C. E., & Tao, C. C. (in press). The impact of online cancer resources on the supporters of cancer patients. New Media & Society. Beaudoin, C. E., & Thorson, E. (2004a). Social capital in rural and urban communities: Testing differences in media effects and models. Journalism & Mass Communication Quarterly, 81(2), Beaudoin, C. E., & Thorson, E. (2004b). Testing the cognitive mediation model: The roles of news reliance and three gratifications sought. Communication Research, 31(4), Beaudoin, C. E., & Thorson, E. (2006). The social capital of Blacks and Whites: Differing effects of the mass media in the United States. Human Communication Research, 32(2), Blumler, J. G. (1979). The role of theory in uses-and-gratifications research. Communication Research, 6(1), Brehm, J., & Rahn, W. (1997). Individual-level evidence for the causes and consequences of social capital. American Journal of Political Science, 41(3), Campbell, J., & Stasser, G. (2006). The influence of time and task demonstrability on decision-making in computer-mediated and face-to-face groups. Small Group Research, 37, Cho, J., De Zuniga, H. G., Rojas, H., & Shah, D. V. (2003). Beyond access: The digital divide and Internet uses and gratifications. IT & Society, 1(4), Craig, C. C. (1936). On the frequency of function xy. Annals of Mathematical Statistics, 7(1), Deci, E. L., & Ryan, R. M. (1985). Intrinsic motivation and self-determination in human behavior. New York: Plenum. Ellison, N. B., Steinfield, C., & Lampe, C. (2007). The benefits of Facebook Friends: Social capital and college students use of online social network sites. Journal of Computer-Mediated Communication, 12(4). Eveland, W. P., Jr. (2001). The cognitive mediation model of learning from the news: Evidence from nonelection, off-year election, and presidential election contexts. Communication Research, 28(5), Eveland, W. P., Jr. (2002). News information processing as mediator of the relationsip between motivations and political knowledge. Journalism & Mass Communication Quarterly, 79(1), Eveland, W. P., Jr., & Dunwoody, S. (2002). An investigation of elaboration and selective scanning as mediators of learning from the Web versus print. Journal of Broadcasting & Electronic Media, 46(1), Eveland, W. P., Jr., Shah, D. V., & Kwak, N. (2003). Accessing causality in the cognitive mediation model: Panel study of motivations, information processing, and learning during campaign Communication Research, 30(4), Gantz, W. (1978). How uses and gratifications affect recall of television news. Journalism Quarterly, 55, Hampton, K., & Wellman, B. (2003). Neighboring in Netville: How the Internet supports community and social capital in a wired suburb. City & Community, 2(4), Journal of Computer-Mediated Communication 13 (2008) ª 2008 International Communication Association 565

17 Harasirn, L. M. (1993). Networlds: Networks as social space. In L. M. Harasirn (Ed.), Global networks: Computers and international communication (pp ). Cambridge, MA: MIT Press. Hiltz, S. R., & Turoff, M. (1985). Structuring computer-mediated communication systems to avoid information overload. Communications of the ACM, 28(7), Hiltz, S. R., & Wellman, B. (1997). Asynchronous learning networks as a virtual classroom. Communications of the ACM, 40(9), Holbert, R. L., & Stephenson, M. T. (2003). The importance of indirect effects in media effects research: Testing for mediation in structural equation modeling. Journal of Broadcasting & Electronic Media, 47(4), Hong, T. (2006a). Contributing factors to the use of health-related websites. Journal of Health Communication, 11(2), Hong, T. (2006b). The influence of structural and message features on website credibility. Journal of the American Society for Information Science and Technology, 57, Hu, L.-T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternative. Structural Equation Modeling, 6(1), Jones, Q., Ravid, G., & Rafaeli, S. (2004). Information overload and the message dynamics of online interaction spaces: A theoretical model and empirical exploration. Information Systems Research, 15(2), Kavanaugh, A., & Patterson, S. (2001). The impact of community computer networks on social capital and community involvement. American Behavioral Scientist, 45, Kawachi, I., Kennedy, B. P., Lochner, K., & Prothrow-Stith, D. (1997). Social capital, income inequality, and mortality. American Journal of Public Health, 87, Kayany, J. M. (1998). Contexts of uninhibited online behavior: Flaming in social newsgroups on Usenet. Journal of the American Society of Information Science, 49(12), Kaye, B. K., & Johnson, T. J. (2002). Online and in the know: Uses and gratifications of the Web for political information. Journal of Broadcasting & Electronic Media, 46(1), Kraut, R., Kiesler, S., Boneva, B., Cummings, J., Helgeson, V., & Crawford, A. (2002). Internet paradox revisited. Journal of Social Issues, 58, Kraut, R., Patterson, M., Lundmark, V., Kiesler, S., Mukopadhyay, T., & Scherlis, W. (1998). Internet paradox: A social technology that reduces social involvement and psychological well-being? American Psychologist, 53, LaRose, R., Mastro, D., & Eastin, M. S. (2001). Understanding Internet usage: A social-cognitive approach to uses and gratifications. Social Science Computer Review, 19(4), MacKinnon, D. P., Lockwood, C. M., Hoffman, J. M., West, S. G., & Sheets, V. (2002). A comparison of methods to test mediation and other intervening variable effects. Psychological Methods, 7(1), McLeod, J. M., & McDonald, D. G. (1985). Beyond simple exposure: Media orientations and their impact on political processes. Communication Research, 12, Miller, G. A. (1956). The magical number seven, plus or minus two: Some limits on our capacity to process information. Psychological Review, Neuman, W. R. (1976). Patterns of recall among television news viewers. Public Opinion Quarterly, 40, Journal of Computer-Mediated Communication 13 (2008) ª 2008 International Communication Association

18 Newton, K. (1999). Mass media effects: Mobilization or media malaise? British Journal of Political Science, 29(4), Payne, G. A., Severn, J. H., & Dozier, D. M. (1988). Uses and gratifications: Motives as indicators of magazine readership. Journalism Quarterly, 65, , 959. Perse, E. M. (1990). Audience selectivity and involvement in the newer media environment. Communication Research, 17(5), Pew Internet & American Life Project. (2007). February 15 March 7, 2007 Tracking Survey. Washington, DC: Pew Internet & American Life Project. Putnam, R. D. (2000). Bowling alone: The collapse and revival of American community. New York: Simon and Schuster. Reeve, J. (1997). Understanding motivation and emotion (2 ed.). Fort Worth: Harcourt Brace College Publishers. Rogers, E. M., & Agarwala-Rogers, R. (1975). Organizational communication. In G. L. Hanneman & W. J. McEwen (Eds.), Communication behaviour (pp ). Reading, MA: Addision Wesley. Rubin, A. M., & Perse, E. M. (1987). Audience activity and television news gratifications. Communication Research, 14, Schneider, W., & Shiffrin, R. M. (1977). Controlled and automatic human information processing: I. Detection, search, and attention. Psychological Review, 84, Shah, D. V., Cho, J., Eveland, W. P., Jr., & Kwak, N. (2005). Information and expression in a Digital Age: Modeling Internet effects on civic participation. Communication Research, 32(5), Shah, D. V., Kwak, N., & Holbert, R. L. (2001). Connecting and disconnecting with civic life: Patterns of Internet use and the production of social capital. Political Communication, 18, Shah, D. V., McLeod, J. M., & Yoon, S.-H. (2001). Communication, context, and community: An exploration of print, broadcast, and Internet influences. Communication Research, 28(4), Thorson, E., Reeves, B., & Schleuder, J. (1985). Message complexity and attention to television. Communication Research, 12, Tidwell, L. C., & Walther, J. B. (2002). Computer-mediated communication effects on disclosure, impressions, and interpersonal evaluations: Getting to know one another a bit at a time. Human Communication Research, 28(3), Trevino, L. K., & Webster, J. (1992). Flow in computer-mediated communication. Communication Research, 19, Verba, S., Schlozman, K. L., & Brady, H. E. (1995). Voice and equality: Civic voluntarism in American politics. Cambridge, MA: Harvard University Press. Vincent, R. C., & Basil, M. D. (1997). College students news gratifications, media use, and current events knowledge. Journal of Broadcasting & Electronic Media, 41(3), Wellman, B. (2001). Computer networks as social networks. Science, 293, Wellman, B., Quan Haase, A., Witte, J., & Hampton, K. (2001). Does the Internet increase, decrease, or supplement social capital? Social networks, participation, and community commitment. American Behavioral Scientist, 45(3), Wenner, L. A. (1982). Gratifications sought and obtained in program dependency: A study of network evening news programs and 60 Minutes. Communication Research, 9(4), Journal of Computer-Mediated Communication 13 (2008) ª 2008 International Communication Association 567