A Social Cognitive Explanation of Internet Uses and Gratifications: Toward a New Theory of Media Attendance

 

By

 

Robert LaRose, PhD

Department of Telecommunication

Michigan State University

East Lansing, MI 48824

larose@msu.edu

 

Matthew S. Eastin, PhD

School of Journalism and Communication

Ohio State University

Columbus, OH

 

A paper submitted to the Communication and Technology Division,

International Communication Association

 

November 1, 2002

 

The authors gratefully acknowledge the assistance of the following students in collecting the data for this project: Mike Mackert, Sri Sukotjo, Yu-Chieh Lin, Jinhee Hong, Songyi Park, Wen-Ya Wu, Li-An Liu, Charintip Tungkittisuwan,  Kuang-Chiu Hang

 

 


A Social Cognitive Explanation of Internet Uses and Gratifications: Toward a New Theory of Media Attendance

 

While most of the prior research explaining Internet usage has followed the conventional uses and gratifications paradigm, some extensions and challenges to that prevailing theory of media attendance have emerged. These include the discovery of “new” gratifications overlooked in the annals of mass communication research and the introduction of powerful new explanatory variables. However, much of the extant research has focused on college students populations, while the Internet has penetrated deeply into the general population. The present study extends previous research to more diverse populations and evaluates new explanatory variables within the framework of Bandura’s (1986) Social Cognitive Theory.  Respondents from two Midwestern states were contacted by mail to complete an on-line questionnaire.  Among conventional Uses and Gratifications variables, expectations about participating in enjoyable activities online and expected social outcomes explained 23 percent of the variance in Internet usage. Habit strength, deficient self-regulation and Internet self efficacy combined in a stepwise multiple regression model that explained 40 percent of the variance in usage, in a model in which conventional Uses and Gratifications variables were not significant predictors. A new model of media attendance was proposed.

 


A Social Cognitive Explanation of Internet Uses and Gratifications: Toward a New Theory of Media Attendance

 

The addition of the Internet to the electronic media environment has renewed interest in the question of media attendance: the factors that explain and predict individual  exposure to the media. Much of the research has followed the conventions of Uses and Gratifications framework, but there have been fresh approaches to conceptualizing the problem of media attendance that have introduced new conceptual and operational approaches and new variables. However, these relationships have been explored among college student samples and must now be extended to the general online population. The present research tests a model of  media attendance inspired by Bandura’s (1986) Social Cognitive Theory (SCT) that builds upon the conventional Uses and Gratifications approach.

 

Uses and Gratifications Meet the Internet

 

. Numerous studies (e.g. Kaye, 1998; Eighmey & McCord, 1998; Perse & Greenberg- Dunn, 1998; Korgaonkar & Wolin, 1999; Lin, 1999; Parker & Plank, 2000;  Ferguson & Perse, 2000; Papacharissi & Rubin, 2000; Dimmick et al., 2000; Chou & Hsiao, 2000,  Flanagin & Metzger, 2001; Charney & Greenberg, 2001; LaRose, Mastro & Eastin, 2001; Stafford, 2001; Song, LaRose, Lin & Eastin, 2002;) have applied Uses and gratifications to the Internet, extending a well-known theory of media attendance that is arguably the dominant paradigm of media attendance (Palmgreen, Wenner & Rosengren, 1985). Collectively, these studies have generally upheld the basic proposition about media attendance from the Uses and gratifications tradition: the gratifications sought from the Internet predict individual exposure to the medium.

However, many of Internet-related studies that have examined the relationships between gratifications and media exposure (e.g. Kaye, 1998; Ferguson & Perse, 2000; Papacharissi & Rubin, 2000; Parker & Plank, 2000) have also reconfirmed a basic weakness of Uses and gratifications as a theory of media attendance: it does not predict media exposure very well. Consistent with Uses and gratifications studies of other media (cf. Palmgreen, Wenner & Rosengren, 1985), the Internet studies that hewed most closely to the concepts and operational measures of the Uses and gratifications tradition have explained less than ten percent of the variance in Internet usage from gratifications.

That the Internet is in many ways a unique medium (Morris & Ogan, 1996) has not escaped the attention of Uses and gratifications researchers who have contributed innovative variations on conventional approaches. One response has  been to expand the time-honored list of media gratifications derived from early television studies (notably Greenberg, 1974; Rubin, 1983) to explore unique facets of the Internet medium. For example, Papacharissi and Rubin (2000) developed measures of interpersonal communication gratifications, recognizing that communication functions like email and chatrooms are the dominant mode of Internet usage.  Korgaonkar and Wolin (1999) found that dimensions of information control, interactive control, and economic control, as well as more conventional escapist, social and informational gratifications, distinguished Internet users from non-users.

Other researchers reopened the basic question of “what do we use the media for” by beginning with focus groups (Charney & Greenberg, 2001) or broader theories of human behavior (Song et al., 2002) to generate gratification items. This resulted in the discovery of “new” gratifications that were either downplayed in conventional mass media Uses and gratifications research (e.g. interpersonal communication, Papacharissi & Rubin, 2000; problem solving, persuading others, relationship maintenance, status seeking, and personal insight for Flanagin & Metzger, 2001) or previously unexplored (e.g. Song et al.’s virtual community gratification, Charney & Greenberg’s coolness, sights and sounds, career, and peer identity factors).

Others have innovated with conceptual and operational definitions of gratifications, creating what might be called prospective, or expected gratifications. These ask respondents to indicate the gratifications that they expect from the Internet in the future as opposed to those that they desire or have obtained in the past. This is a departure from the gratifications sought/gratifications obtained (GS/GO) formulation that has long guided Uses and gratifications (Palmgreen et al., 1985).  Studies that have employed prospective measures (e.g., Lin, 1999; Charney & Greenberg, 2001; LaRose, Mastro & Eastin, 2001) have doubled and or tripled the amount of variance explained in Internet attendance behavior compared to conventional approaches.

A Social Cognitive Perspective of Uses and gratifications

Prospective gratification measures are consistent with a social cognitive view of media attendance derived from Bandura’s (1986, 1989) Social Cognitive Theory (SCT). In SCT, the expected outcomes of a behavior are important determinants of its performance. LaRose et al. (2001) found that expected outcomes produced superior predictions of Internet attendance compared to conventional Uses and gratifications research. They argued that expected outcomes (e.g. “when using the Internet it is likely that I will have fun”) improve upon the explanatory power of both gratifications sought and gratifications obtained. 

Unsuccessful attempts by Uses and gratifications researchers (Babrow & Swanson, 1988) to distinguish the predictive power of outcome expectations (derived from a related theory, the Theory of Planned Behavior, Ajzen, 1985) from gratifications perhaps indicated that the two are related constructs. However, the distinction between outcome expectations and gratifications is potentially consequential. Gratifications obtained (e.g. “I use the Internet to have fun”) fail to distinguish the likelihood of encountering the desired outcomes in the future. If we say we use the Internet for fun but seldom have any, then that belief is unlikely to influence our usage.  Gratifications sought (e.g. “I use the Internet because I want fun”) neglect the possibility that we may be looking for something that just is not available. So, in some instances, the gratifications sought could be a negative predictor of exposure, in others a positive one, but in the aggregate are just possibly a confounded one.  Comparing gratifications obtained with those sought may produce confounding instances (e.g. gratifications that are obtained but not sought) that may have no reliable relationship to exposure.  Outcome expectations cut through the ambiguity because they “reflect current beliefs about the outcomes of prospective future behavior but are predicated upon comparisons between incentives expected and incentives attained in the past.” (LaRose et al., 2001, p. 399)

            SCT is familiar to many media scholars in its earlier incarnation as Social Learning Theory (Bandura, 1977), as a theory of media effects. Specifically, the vicarious learning mechanism is recognized as a determinant of the effects of the media, television in particular. However, SCT is a broad theory of human behavior that may be applied to media attendance as well as to the effects on behavior that result from that exposure. SCT posits reciprocal causation among  individuals, their behavior, and their environment, mediated by human symbolizing processes that integrate stimulus-response experiences into cognitive models that guide behavior. The vicarious reinforcement mechanism of interest to media effects researchers describes how observations of others’ behavior modifies expectations of the outcomes of our own behavior.  Enactive learning is the mechanism through which we learn from our own experiences, the process by which our personal experience with the media may shape our expectations about outcomes of media exposure that determine our future levels of media attendance.

SCT has its own version of gratification categories. These are a priori categories of behavioral incentives derived from observations of behavior across wide variety of domains of human behavior.  Categories include novel sensory stimuli, monetary, social, status, activity, and self-evaluative incentives. A close analysis of these categories against Internet gratifications (LaRose et al., 2001) revealed that conventional Uses and gratifications research underemphasized status and monetary incentives that had significant positive correlations with Internet usage (see also Korgaonkar & Wolin, 1999; Flanagin & Metzger, 2001; Charney & Greenberg, 2001. When expected outcome measures reflecting the full range of these categories were subjected to exploratory factor analysis in conventional Uses and gratifications style a “new” virtual community dimension was uncovered that drew heavily on the status incentives lacking in conventional Uses and gratifications research (Song et al., 2002). Others paralleled conventional Uses and gratifications dimensions. Activity incentives, predicated on the desire to take part in enjoyable activities, correspond to the entertainment factors found in many Uses and gratifications studies. Self-evaluative incentives, which involve attempts to regulate dysphoric moods, parallel “pass time” or “boredom” gratifications. Novel sensory incentives include the search for novel information, they parallel information seeking gratifications.  Social incentives stemming from rewarding interactions with others correspond well to social gratifications.

SCT stresses the importance of self efficacy, or belief in one’s capability to organize and execute a particular course of action (Bandura, 1997). Self-efficacy is particularly relevant to the Internet since it is a somewhat troublesome medium. This is  especially so for novice users who have not as yet acquired the requisite skills to obtain useful information and deal with the discontents of life online, from viruses to balky home internet connections. Self-efficacy has proven to be robust as a significant predictor of Internet usage (Eastin & LaRose, 2000; LaRose et al., 2001).

Self-Regulation and Internet Usage

The SCT construct of self-regulation (Bandura, 1991) has also emerged as an important predictor of Internet consumption (LaRose et al., 2002).  The self-regulatory mechanism describes how individuals continually monitor their own behavior (self-monitoring), judge it in relation to relevant personal and social standards (judgmental process), and apply self-reactive incentives to moderate their behavior (self reaction). Self-regulation is an important point of distinction between SCT and “functionalist” or stimulus-response theories of human behavior in that it describes self-generated influences that free the individual from blindly following the dictates of external reinforcement. Self-regulation is perhaps what best distinguishes humans from Skinner’s (1938) pigeons: we are able to conceptualize and evaluate our own behavior and formulate and implement our own courses of action, pigeons are not.

Self-regulation may normally be expected to moderate media consumption. Indeed, an experimental manipulation of self-regulation as it is understood in SCT reduced media usage (Robinson, 1999). However, when self-regulation fails increased media consumption may be expected. This issue has been conceptualized in terms of habit and deficient self-regulation (LaRose et al., 2002).

A habit is simply a recurring behavior pattern. Habit is a well-established predictor of behavior (Triandis, 1980; Oulette & Wood, 1998) that has perhaps been somewhat overlooked in communication research (cf. Stone & Stone, 1990; Rosenstein & Grant, 1997). There, it has been associated with “ritualistic gratifications” such as passing the time (after Rubin, 1984). However, there is a growing body of research (e.g. Aarts, Verplanken, & van Knippenberg, 1998; Bargh & Gollwitzer, 1994) suggesting that habit is a form of automaticity, a pattern of behavior (e.g. checking one’s email) that is triggered by an environmental stimulus (e.g. seeing one’s computer desktop in the morning) and performed without further active consideration. This is perhaps outside the realm of active media selection processes that are presumed by Uses and gratifications theorists. At best, automatic media consumption behaviors were initially framed by such active considerations, which were eventually forgotten (cf. Stone & Stone, 1990). We may have thought carefully about our communication options the first time we used email; for example, but by the hundredth time we did not.

Within SCT we might describe this as a failure of the self-monitoring subfunction of self-regulation.  Through frequent repetition we become inattentive to the reasoning behind our media behavior, our mind no longer devotes attention resources to the consideration of such routine behavior, freeing itself for more important decisions.  In one study, a measure of habit was found to be a significant predictor of Internet usage (LaRose et al., 2001).

Deficient Self Regulation is defined as a state in which conscious self-control is relatively diminished. Working from conceptual and operational definitions of behavioral addictions, LaRose and his colleagues have shown the variable to be a powerful predictor of both e-commerce activity (LaRose & Eastin, 2002) and general Internet usage (LaRose et al., 2001) and have proposed it as an explanatory mechanism for so-called “internet addictions” (LaRose et al., 2002).

However, the relationship between habit and deficient self-regulation has not been clearly distinguished.  Addictions, including behavioral addictions, may be regarded as a form of habitual behavior (Marlatt, Baer & Kivlahan, 1988) so the two constructs overlap on a conceptual level. Since LaRose et al.’s operational definitions of deficient self- regulation were drawn from the symptoms of behavioral addictions, there is the possibility of confounding. At the operational level, the measures of habit have been underdetermined; that is, they have had too few items to produce reliable measurement. LaRose et al. (2002) were forced to conclude that they could not clearly distinguish habit from deficient self-regulation, leaving a topic for further research. The present research will assess these constructs to determine if they are empirically distinct.

Are College Students Typical Internet Users?

            Much of the extant research on Internet usage has focused on college students. The rationale often offered is that college students are a population of interest because of the ready access to the Internet they enjoy and the high incidence of users found in that population. As such, they might represent “typical” populations of users and also, as part of the first “Internet generation,” a cohort of particular interest to scholars wishing to follow the new medium from its birth. And, there is the general caveat that scholars are interested in the lawful relationships among variables that should be observable among many groups, including purposive samples of college students.

            But there are also some important ways that college students differ from the general population that may affect the relationships among variables, and these are particularly salient from the SCT perspective.  Internet usage has become such a vital part of collegiate life that students are virtually forced to embrace the medium when they enter college. But half do not begin using the Internet until after they reach college (Pew Research Center, 2002a). In SCT terms, this may create a large subclass of student users with low Internet Self Efficacy and thus exaggerate the importance of that variable in student populations. “Non- volitional” uses in which students are required to perform class-related tasks on the Internet might diminish the impact of active selection processes represented by the conventional Uses and gratifications approach. College students, and particularly the freshmen who populate the large introductory class sections from which many willing survey respondents are drawn, have relatively high levels of depression (Rich & Scovel, 1987) and depression is known to inhibit effective self-regulation (Bandura, 1991), possibly exaggerating the effect of that variable as well. One reason that freshmen are depressed is because they have been seized from the bosom of their family and friends.  That may unnaturally heighten the importance of “sociability” or “social interaction” gratifications and contribute to disproportionate usage of the Web for social support. Indeed, college students demonstrate an especially heavy reliance the Internet for social interaction and they are also more likely to engage in fun activities than other Internet users (Pew Research Center, 2002a). Now that research has compiled an ample number of studies developing psychological motives for Internet use and usage now reaches deeply into the general population, it is feasible to survey Internet users in the general population and determine whether the relationships among variables found in college populations can be replicated in broader populations.

Hypotheses

Following LaRose et al., (2001) we propose that the gratifications of the Internet, reconceptualized as outcome expectations reflecting  each of the incentive categories recognized by SCT will be positively related to Internet usage. In the present study, self-evaluative and activity outcomes, combined in the previous research to attain satisfactory reliability, will be separated to match the conceptual distinction between these two incentive categories.  Previously, self-evaluative outcomes were also found to predict Internet usage (LaRose et al., 2002).

 

H1: Internet usage will be positively related to a) novel sensory, b) activity, c) social, d) status, and e) self-evaluative outcome expectations.

 

Also consistent with earlier results, status and monetary incentives that were under-represented in prior Uses and gratifications research are expected to predict Internet usage as well:

 

H2. Internet usage will be positively related to a) status and b) monetary outcome expectations.

 

 

Although struggling novice Internet users may be especially prevalent among college freshmen, we also expect to find them in the general population. For example, Cole (2001) estimated that 7 percent of the general population of Internet users rated their Internet ability as “poor” and another 30 percent rated it only “fair.” Thus, Internet Self-Efficacy might still be related to Internet usage in a general population sample:

 

H3: Internet self-efficacy will be positively related to Internet usage.

 

On the question of the distinction between habit and deficient self-regulation, there may be a valid theoretical distinction between the two concepts. Habit could represent the failure of self-monitoring, one of the three subfunctions of self-regulation, while deficient self-regulation may represent a failure of the judgmental and self-reactive subfunctions. Individuals who are inattentive to repetitive patterns in their behavior are also unlikely to compare it to personal or social norms or to self-generate incentives (e.g. indulging in feelings of guilt or rewards for moderate behavior). However, the conceptual definition of deficient self-regulation (based on symptoms of pathological gambling and substance dependence) and its operationalization (e.g., “I feel my Internet use is out of control, I feel tense moody or irritable if I can’t get on the Web when I want”) betray an intense, even painful self-awareness of media consumption. Deficient self-regulation reflects a quite distinct state of mind from one in which we are inattentive to a repetitive behavior pattern and both might have independent effects on media attendance. A user might be painfully aware of deficient self-regulation with respect to, say, online gambling sites or Internet pornography, while still remaining blissfully unconcerned that she spends even more time on email. Thus, habit and deficient self-regulation could have independent effects:

 

H4: Internet habit strength will be positively related to Internet usage.

 

H5: Deficient Internet Self-Regulation will be positively related to Internet usage.

 

Also consistent with LaRose et al. (2001), the new variables should improve upon the predictive power of conventional demographic and Uses and gratifications dimensions. Race (Hoffman & Novak, 1998) and gender (AAUW, 2000) have been suggested as relevant demographic variables explaining Internet usage.

H6: Status and monetary incentives, habit strength, Internet self-efficacy and deficient Internet self-regulation will be positively related to Internet usage after controlling for demographic variables and conventional uses and gratifications dimensions.

 

RESEARCH METHODS

The respondents  were thus a somewhat biased sample of the respective populations from which they drawn. However, the purpose of the study was not to estimate the population distribution of the items in the survey but rather to examine lawful relationships between variables. As such, a diverse sample of adult respondents was deemed suitable for the purpose of the present study.

Procedure

Adult Internet users were the population of interest. To obtain a diverse sample of the general population of adult Internet users, respondents were recruited by mail from two Midwestern communities to complete an on-line survey.  Both of the communities surveyed included the “home town” of a major university and several surrounding counties.

A commercial mailing list vendor provided a random sample of household addresses in the designated communities. The initial mailing included a letter advising respondents of the purpose of the study and their rights as human subjects.  Half the letters requested that the survey be filled out by a male head of household and the other half by a female head of household, if such a person were available. Also included in the envelope was a nominal cash incentive and a postcard with the URL and a respondent ID for the survey. Internet users were instructed to use the card and ID number the next time they went on the Internet. Non-users were instructed to indicate their gender and year of birth and return the card by mail so that response rates could be calculated and the results compared to U.S. Census data.

Respondents

Of the 1100 solicitations sent, 170 (15%) bad addresses were returned; leaving a total usable sample of 930. A total of 334 responded to the solicitation.  One hundred and seventy-five Internet users completed the survey and 159 returned the non-Internet user postcard (36% total response rate). There were no response difference by city and thus data were collapsed.  As a total sample (N = 334) participants were 55 percent male and 45 percent female. In comparison to the general population, which consists of 50 percent female (U.S. Census, 2002). Six percent of the participants were between the ages of 18-24 (census population = 17%), 48 percent were between the ages of 25-44 (census population = 30%) 34 percent were between 45-65 years old (census population = 40%), and finally, 13 percent were over the age of 65 (census population = 14%).  The respondents were thus a somewhat biased sample of the respective populations from which they were drawn. However, a diverse sample of adult respondents was obtained and thus, this sample was deemed suitable for the purpose of this study which was to examine relationships between variables.

The non-Internet users (N = 159 card returns) consisted of 48 percent male and 52 percent female and their mean age was 52 years old.  Given current estimates of Internet penetration (54%, NTIA, 2002) we estimated that respondents at 504 (out of 938) of the valid addresses had access the Internet, and thus, could have completed the online survey.  Therefore, we estimate that the 175 people who completed the survey represent an Internet user response rate of 35 percent.  Of those, 42 percent were female and 55 percent were male (with 2 percent not indicating their gender) with an average age of 42 years old. Eighty-eight percent were Caucasian, five percent were African American, two percent were Latino and the remaining four percent were Asian, Pacific Islander, Native American or other. Forty-two percent of the sample indicated an average household income under $50,000; the remaining 58 percent indicated an average annual income greater than $50,000. Educationally, participants ranged between 9-22 years beyond kindergarten (M = 16, SD = 2.61).

Operational Measures

The usual procedure for analyzing gratifications in the Uses and gratifications tradition is to conduct an exploratory factor analysis of the gratification items. This procedure was not followed in the present research since a priori theoretical assumptions about the nature of the gratification variables were available, in the form of the incentive categories recognized in SCT. Instead, gratification items were collected from prior Uses and gratifications studies, rephrased as outcome expectations (i.e., “using the Internet how likely are you to..” on a scale of one to seven, where one was very unlikely and seven very likely). These statements of outcome expectations were classified into SCT incentive categories by consulting the conceptual definitions found in Bandura (1986, pp. 233ff)  and supplemented with items reflecting status and monetary incentives that were under represtentedrepresented in Uses and gratifications research (cf. LaRose et al., 2001).  Internal consistency (Cronbach alphas) were computed for each.

Six categories of expected outcomes, one representing each incentive category, were operationalized. The range, means, and standard deviations of these variables are found in Table 1. These included novel sensory outcomes[1] (a = .74), activity outcomes[2] (a = .73), social outcomes[3] (a = .89), and self-evaluative outcomes[4] (a = .77) that corresponded to Uses and gratifications dimensions common in mass communication research. Measures of status outcomes[5] (a = .75) and monetary outcomes[6] (a = .72) were also included.

Previous research (LaRose et al., 2002) had left the distinction between habit strength and deficient self -regulation unresolved. Under –determination (i.e. too few items) of the habit strength variable was a possible confounding factor. Accordingly, new items were developed by drawing upon theoretical works describing habitual behavior (Aarts et al, 1998; Oulette & Wood, 1998; Bargh & Gollwitzer, 1994). The combined pool of habit strength and deficient self-regulation items was subjected to an exploratory factor analysis using varimax rotation. Two interpretable factors emerged. On one, three of the habit strength items[7] had loadings of .6 or more and were combined into an additive index (a = .76). On the other factor, items reflecting deficient self-regulation appeared, and nine[8] with factor loadings over .6 (and no secondary loadings over .4) were combined into a measure of deficient self-regulation (a = .91). These factors seemed to reflect the distinction between the self-observation  subfunction of  self-regulation on the one hand and the judgmental process and self-reactive subfunctions on the other hand.

Internet Self Efficacy Scale (Eastin & LaRose, 2000) was replicated, but two items were deleted because of potential ceiling effects. The resulting 5-item additive scale[9] had a Cronbach alpha of .91.   Gender (1 if male, 0 if female), age (in years), race (1 if white, 0 if minority) were all assessed through single questionnaire items.

The dependent Internet usage variable was the sum of the total number of minutes spent on the Internet in the typical weekday, the typical weekend day, and the day prior to the survey.  An inspection of the distributions of responses to these items revealed that outliers were present and so a log10(1+value) transform was applied to each one before summing the three items. The resulting composite index had a Cronbach alpha of .66.

Data Analysis

            Pearson product-moment correlation coefficients, exploratory factor analysis, and multiple regression analyses were performed using SPSS version 10.1(SPSS, Inc., 2000).

            Hypothesis 6 was tested through a hierarchical stepwise regression. Following the Uses and gratifications tradition, demographic variables were entered first, followed by gratification dimensions, recast here as expectations of novel sensory, activity, self-evaluative and social outcomes. Next, monetary and status outcome expectations were entered, reflecting the two types on incentives underrepresented in conventional Uses and gratifications research. The SCT-derived variables Internet Self-Efficacy, habit strength, deficient self-regulation were added in the final step.

An inspection of the zero-order relationships (Table 1) revealed several correlations between independent variables over .6, a common rule of thumb for detecting possible multicollinearity problems, and so the SPSS multicollinearity diagnostics were run.  The maximum VIF (variance inflation factor) was 2.83 and maximum condition index was 18.5, which were deemed acceptable.  

RESULTS

The results shown in Table 2 show that Hypothesis 1 was fully supported. Internet usage was positively related to measures of a) novel sensory (r = .338, p < .001), b) activity (r = .428, p < .001), c) social (r = .429, p < .001), d) status (r = .528, p < .001), and e) self-evaluative  (r = .392, p < .001) outcome expectations. Consistent with Hypothesis 2, Internet usage was also positively related to status (r = .528, p < .001) and monetary outcome expectations (r = .266, p < .001).  Internet Self-Efficacy (r = .405, p < .001), habit strength (r = .494, p < .001), and deficient self-regulation (r = .469, p < .001) were also related to Internet usage as predicted by Hypotheses 3, 4, and 5, respectively.

Stepwise multiple regression results are shown in Table 2. The prediction equation that resulted after each block of variables was entered is shown, along with the associated regression statistics.  None of the demographic variables emerged as significant predictors, although ethnicity had a significant, but low, zero-order correlation with the dependent variable (r = -.157 p < .05). After the outcome expectation variables corresponding the most closely to conventional Uses and gratifications factors were added, a significant (F2,164 = 26.023, p < .001, R = .491, corrected R2 =  .232) regression equation was obtained (labeled “Uses and gratifications” in Table 2). Social (b = ..290, t = 3.76, p < .001), and Activity (b = .282, t = 3.65, p < .001) outcome expectations were significant predictors. In the next model (labeled “Uses and gratifications+” F3,163 = 22.355, p < .001, R = .540, corrected R2 = .278), to which gratification dimensions atypical of conventional Uses and gratifications research were added, status outcome expectations were the only remaining significant predictor of Internet usage (b = .350, t = 3.41, p < .001),  producing a  significant increase in the overall variance explained (Rsq change = .051, p < .001). After SCT variables were introduced a final prediction equation was obtained (F6,160 = 19.386, p < .001, R = .649, corrected R2 = .399 ) in which Internet self-efficacy (b = .152, t = 2.16, p < .05), deficient self-regulation (b = ..218, t = 3.02, p < .01), and habit strength (b = .239, t = 3.23, p < .001) were significant predictors.

DISCUSSION

The present results both affirm and extend the prevailing paradigm of media attendance and exposure in communication studies, adding both to our understanding of the factors that predict Internet usage and our understanding of underlying theories of media attendance. A basic implication of Uses and gratifications, that media exposure may be predicted from media gratifications was again upheld.  However, new variables and new operational definitions from SCT greatly improved -- and in the end subsumed ---  the predictive power of media gratifications, here re-construed as outcome expectations.

Using dimensions that paralleled those common to Uses and gratifications studies of the Internet, but changing the conceptual and operational focus from gratifications sought to outcomes expected, resulted in explaining nearly three times the variance in Internet usage previously reported (e.g., 9 percent for Papacharissi & Rubin, 2000, compared to 28 percent here).  Expected activity outcomes, which closely parallel entertainment gratifications in Uses and gratifications research, and social outcomes/gratifications were significant predictors, much as they had been in prior research involving college students (e.g., Papacharissi & Rubin, 2000; LaRose et al., 2001; Kaye, 1998).

Status outcomes, a gratification/outcome dimension identified by SCT but underrepresented in prior Uses and gratifications research in which predictions of exposure levels were the focus, further added to our ability to explain Internet attendance. Indeed, this variable subsumed the effects of the two conventional gratification dimensions on Internet usage. The perceived ability of the Internet to improve one’s lot in life thus emerges as a powerful motivating factor for the use of the medium.

Uses and gratifications research, including Internet studies, have tended to subsume habit in other gratifications dimensions, usually under either an entertainment or “pass time” factor. Here, it emerged as a powerful and independent predictor of media exposure even after the effects of gratifications sought/expected outcomes had been accounted for. This lends credence credibility to the initial supposition that the significance of habit strength had been previously overlooked owing to underidentification of the variable: the items used in previous studies were perhaps too few or too ambiguously worded to properly distinguish this variable.  This finding lends credence to the conceptualization of habit strength as a distinct construct from gratifications/expected outcomes. The low-to-moderate zero-order correlations between habit and expected outcomes perhaps indicated the availability of memories of past active media selection processes, in the form anticipated by Uses and gratifications research, that had become dormant with repetition. In this vein, it is interesting to note that among newer Internet users (those who had been online less than three years) the correlations between expected outcomes and usage were higher than among those with more experience. For example, the correlation between activity outcomes and usage was .545 for new users, compared to .345 for the more experienced ones. This could indicate that the newer users were still making active media selection decisions on the basis of expected outcomes while veteran users had lapsed into more habitual modes of Internet consumption.

The relationship of habit and deficient self-regulation was also further clarified. Habit perhaps indicates a failure of the first of the three subfunctions of self-regulation proposed by SCT, self-observation. Habitual media attendance means engaging in media consumption behavior in direct response to environmental stimuli, without engaging in  (or at least without replicating) the active analytic thought processes assumed by Uses and gratifications. As such, this aspect of unregulated media behavior is closely related to notions of automaticity (Bargh & Gollwitzer, 1994). Deficient self-regulation derives from the failure of the judgmental and self-reactive subprocesses of self-regulation. It reflects a conscious failure of self-control wherein individuals struggle with themselves to judge their own behavior against appropriate standards and to apply incentives to moderate their consumption. The two variables are theoretically related in that excessive habitual usage might trigger the struggle for self control. However, habitual behavior is inherently automatic and unobserved, while individuals with high levels of deficient self-regulation are keenly, even perhaps painfully, aware of their behavior. Both variables  were unique significant predictors of usage in the present study. This suggests that these two constructs are in fact different, confirming the notion proposed by LaRose et al. (forthcoming).

Internet self-efficacy was also a significant predictor of Internet usage, although it was not as powerful a predictor as it was in previous studies involving college student populations (LaRose et al., 2001).  The substantial correlations observed between Internet self-efficacy and novel sensory outcomes (r = .496) and status outcomes (r =. 488) perhaps suggest that self-efficacy building is an on-going process.  Even after basic Internet skills are acquired, users must continue to develop skills and confidence in using the Internet to obtain useful information (i.e., obtain novel sensory experiences)  and improve their social position. Further, as virtual environments become more prevalent or as technological convergence technologically advances, self-efficacy will theoretically play an important role in the adoption and utilization process.

The Internet emerges from the present study as something of a distinctive medium, but perhaps not in ways previously described. That the Internet is a medium of social interaction is indisputable, but a question now arises as to the purpose of the social interaction. Prior research, especially that surrounding the so-called Internet Paradox (Kraut, Patterson, Lundmark, Kiesler, Mukophadhyay, & Scherlis, 1998) focused on social interaction as a means of securing social support and thereby improving psychological well-being. Now it appears that social status, not social support might be the prime mover in Internet usage. And, the enjoyable activities pursued on the Internet may also be a means of achieving status, such as the “bragging rights” to the “coolest” selection of MP3 recordings.  Perhaps by finding like-minded individuals on the Internet and expressing ourselves in those venues we enhance our social status. Or, recalling Turkle’s (1995) Life on the Screen ethnography, perhaps the Internet is a means of constantly exploring and trying out new, improved versions of our selves. From this we should begin to empirically explore the alternative dimensions of social expectations such as social development (as self or with virtual other) and social maintenance (a support mechanism).

The failure of demographic variables to explain Internet usage may seem somewhat surprising in view of the extensive attention that the Digital Divide has received. However, it may be that once disadvantaged groups gain access they are able to to close the gap in usage. Indeed, one way to interpret the negative correlations between race and outcome expectations (recalling that effects coding was used, in which Anglos were coded as 1 and minorities as 0) is that minorities have their expectations met better by the Internet than Anglos. This may be especially true of status outcomes.  However, there was a positive correlation between gender (with males coded 1, females 0) and Internet self-efficacy (r = .239, p < .05), suggesting a confidence gap still may exist between male and female users. The inferior forms of access that females experience (AAUW, 2000) is a possible explanation for this deficit.

Limitations

The generalizability of the present research is limited due to its limited geographic scope. The Internet user sample contained disproportionately small representations of young people and males. As a one-shot survey study, the direction of causation cannot be established. Indeed, within SCT reciprocal causation is recognized. For example, self-efficacy is a precondition for successful performance of a behavior but successful performance also increases self-efficacy. However, given that this process is ongoing, conventional longitudinal research methods, which evaluates perceptions several times a year, may miss subtle changes over time. Here, experimental lab based research that allows the user to continually mark perceived expectations and self beliefs would provide a more enlightening picture of the reciprocal process.

Implications for Further Research

            Habit strength, deficient self-regulation, and self-efficacy might be productively applied to the study of other media. Television addiction (Kubey & Csikszentmihalyi, 2002) has been described in the same terms of behavioral addiction that underlie the present conceptualization of deficient self-regulation, for example. A wide variety of media consumption behaviors (from reading the newspaper over breakfast to tuning in TV comedians at bedtime) would seem to be habit-prone on their face in that they recur in a consistent context, perhaps with little active re-evaluation each time the behavior is repeated. While few mass media consumption behaviors require skills as complex as those needed to surf the Web, there are perhaps parallel media self-efficacy constraints. Anyone who has ever put down a book because it was “too deep” or turned away from a television drama that was “too disturbing” might be said to have experienced a self-efficacy constraint.  Considering television or more relevant advanced television systems, self-efficacy could be a users perceived ability to manage their viewing needs.

            The present research suggests some new departures for the Uses and gratifications tradition. It appears that redefining gratifications as expected outcomes may have merit, on both a conceptual and operational level. Secondly, the process of continually recycling gratification dimensions from previous (mostly mass media oriented) research may have left out some potentially important variables, particularly regarding the status that media consumption may confer. Third, habit strength appears to be a conceptually and empirically distinct construct from gratifications. Early conceptualizations of Uses and gratifications (e.g. Palmgreen et al., 1985, p. 17) observed that distinction but it appears to have been lost over the years, buried in the factor structure of “entertainment” and “pass time” gratifications. Given this, scholars would be well served to revisit research comparing models of displacement between television and the Internet. Such analysis (or reanalysis) of these constructs would offer a more accurate picture regarding how users are viewing each media’s ability to fulfill expectations.

            Some departures from Uses and gratifications are perhaps also in order. The present findings are consistent with the view that active selection of media content and media channels takes place only at the habit formation stage. That might happen either when, as the case of the Internet, a new medium is introduced or when there is some disruption of personal routines. Thereafter, media consumption is primarily habitual and automatic as the once-active media selection thought processes fade into memory. There is still active monitoring of media consumption taking place, but not the type of active seeking of gratifications that Uses and gratifications posits. Instead, only the general levels of media consumption are being monitored. That is, once habitual consumption patterns are established users no longer think much about whether the Internet or a phone call is a better way of “gratifying” a need for social interaction. Perhaps explaining why teens and young adults no longer prefer the telephone over computer-mediated-communication (Pew Research Center, (2002b). They  Users do perhaps still monitor their overall level of Internet usage and apply self-reactive incentives to either increase or decrease the amount of usage to appropriate levels. But some users may lose the power to self-regulate their own consumption as well, perhaps through a process of operant conditioning (cf. LaRose et al., 2002) at which point they might be said to have a media addiction, or media dependency. SCT provides a framework for integrating Uses and gratifications mechanisms with these competing influences on individual media attendance.

 

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Table 1  Pearson Product-Moment Correlations of Independent and Dependent Variables

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 Variable

1

2

3

4

5

6

7

8

9

10

11

12

Min

Max

Mean

S.D.

1. Internet Usage

1.00

 

 

 

 

 

 

 

 

 

 

 

1.04

8.44

5.2323

1.61706

2. Age in years

-.072

1.00

 

 

 

 

 

 

 

 

 

 

19.00

78.00

41.8313

12.94951

3. Sex

.066

.044

1.00

 

 

 

 

 

 

 

 

 

.00

1.00

.5774

.49545

4. Race

-.157*

.136

.051

1.00

 

 

 

 

 

 

 

 

.00

1.00

.8889

.31519

5. Activity Outcomes

.428**

-.188*

.044

-.183

1.00

 

 

 

 

 

 

 

7.00

28.00

19.5745

5.01349

6. Novel Sensory Outcomes

.338**

-.060

.076

-.181

.436**

1.00

 

 

 

 

 

 

10.00

28.00

22.7386

3.91390

7. Self-evaluative Outcomes

.392**

-.192*

.076

-.216*

.650**

.260**

1.00

 

 

 

 

 

3.00

21.00

13.3547

4.25699

8. Social Outcomes

.429**

-.007

-.049

-.166*

.475**

.364**

.507**

1.00

 

 

 

 

4.00

28.00

15.0235

6.40724

9. Monetary Outcomes

.266**

-.056

.012

-.168*

.400**

.528**

.366**

.320**

1.00

 

 

 

4.00

28.00

18.1190

5.08000

10. Status Outcomes

.528**

-.109

.020

-.248**

.650**

.444**

.585**

.673**

.437**

1.00

 

 

4.00

27.00

17.1749

4.87411

11. Internet Self Efficacy

.405**

-.153*

.239*

-.114

.332**

.496**

.312**

.330**

.379**

.488**

1.00

 

5.00

35.00

24.1632

7.38008

12. Habit Strength

.494**

-.119

.077

-.117

.360**

.363**

.386**

.457**

.335**

.421**

.351**

1.00

3.00

21.00

14.0468

4.78382

13. Deficient Self Regulation

.469**

-.155*

.171*

-.158*

.320**

.174*

.411**

.406**

.093

.450**

.201*

.447**

9.00

54.00

17.5327

10.49921

 

*  Correlation is significant at the 0.05 level (2-tailed).

**  Correlation is significant at the 0.001 level (2-tailed).



Table 2 Stepwise Multiple Regression of Uses and Gratifications and Social Cognitive Variables on Internet Usage

 

 

 

 

 

 

 

 

 

Model

Variable

Uses and gratifications

Uses and gratifications+

SCT

Social Outcomes

.290**

.126

.012

Activity Outcomes

.282**

.134

.089

Status Outcomes

 

.350**

.180

Habit Strength

 

 

.239**

Deficient Self Regulation

 

 

.218*

Internet Self Efficacy

 

 

.152*

Multiple R

.491

.540

.649

Adjusted R2

.232

.278

.399

F

26.023**

22.355**

19.386**

 

Note: Table entries are standardized beta weights.

*   Significant at the .05 level

** Significant at the .001 level

 

 



[1] The items were: Obtain information that I can’t find elsewhere, get immediate knowledge of big news events, find a wealth of information, solve a problem.

[2] Hear music I like, feel entertained, have fun, play a game I like.

[3] Feel like I belong to a group, find something to talk about, get support from others, maintain a relationship I value.

[4] Forget my problems, find a way to pass the time, relieve boredom

[5] Improve my future prospects in life, find people like me, find others who respect my views, get up to date with new technology

[6] Save time shopping, find bargains on products and services, get free information that would otherwise cost me money, get products for free

[7] The Internet is part of my usual routine, I find myself going online about the same time each day, I would miss the Internet if I could no longer go online

[8] I have a hard time keeping my Internet use under control, I sometimes have to struggle with myself to limit my time online, I have to keep using the Internet more and more to get my thrill, I have tried unsuccessfully to cut down on the amount of time I spend online, I feel my Internet use is out of control, I get tense, moody, or irritable if I can’t get on the Web when I want, I often think about the Internet even when I am not online, I sometimes try to conceal how much time I spend online from my family or friends, I would go out of my way to satisfy my Internet urges.

[9]