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 been carried out by followers of the uses and
gratifications tradition, who anticipated the medium as an exemplar of
active media selection that could further validate the core tenets of
that paradigm (Morris & Ogan, 1996; Newhagen & Rafaeli, 1996;
Ruggerio, 2000). Instead, Internet research has introduced new
conceptual and operational approaches and new variables that now
challenge some of the basic assumptions, procedures, and findings of
uses and gratifications. However, these findings have yet to be
integrated into a comprehensive model of media attendance. Moreover,
these relationships have been explored among college student samples and
must now be extended to the general online population. The present
research proposes and tests a model of media attendance inspired by
Bandura's (1986) Social Cognitive Theory (SCT) that builds upon the
conventional uses and gratifications approach by clarifying important
explanatory constructs and identifying new ones.
Uses and Gratifications Meet the Internet
Numerous studies (e.g. Charney & Greenberg, 2001; Chou &
Hsiao, 2000; Dimmick, Kline & Stafford, 2000; Eighmey & McCord,
1998; Ferguson & Perse, 2000; Flanagin & Metzger, 2001; Kaye,
1998; Korgaonkar & Wolin, 1999; LaRose, Mastro & Eastin, 2001;
Lin, 1999; Papacharissi & Rubin, 2000; Parker & Plank, 2000;
Perse & Greenberg-Dunn, 1998; Song, LaRose, Eastin & Lin, 2004;
Stafford & Stafford, 2001) have applied uses and gratifications to
the Internet. Collectively, these studies upheld one of the model's
basic propositions (Palmgreen, Wenner & Rosengren, 1985), that
gratifications sought explain individual media exposure. However, many
Internet-related studies have also reconfirmed a basic weakness of uses
and gratifications: They did not explain 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 uses and gratifications tradition have
explained less than 10% of the variance in Internet usage from
gratifications (e.g., Ferguson & Perse, 2000; Kaye, 1998;
Papacharissi & Rubin, 2000; Parker & Plank, 2000).
That the Internet is in many ways a unique medium has not escaped
the attention of researchers. The time-honored list of gratifications
derived from early television studies (notably, Greenberg, 1974; Rubin,
1983) has been expanded to explore unique facets of the Internet medium.
For example, Papacharissi and Rubin (2000) proposed interpersonal
communication gratifications, recognizing that communication functions
like e-mail and chatrooms are common modes of Internet usage. Korgaonkar
and Wolin (1999) found dimensions of information, interactive, and
economic control. Other new gratification dimensions have included:
problem solving, persuading others, relationship maintenance, status
seeking, and personal insight (Flanagin & Metzger, 2001); Song et
al.'s (2004) virtual community gratification; Charney and
Greenberg's (2001) coolness, sights and sounds, career, and peer
identity factors; and Stafford and Stafford's (2001) search and
cognitive factors. Stafford and Stafford (2001) achieved a modest
increase (to 21%) in the variance explained in Internet usage, mostly
from the addition of a search factor (i.e., that accessing search
engines was an important motivation for using the Internet) to more
conventional information seeking and entertainment gratifications.
Others innovated with conceptual and operational definitions,
creating what might be called prospective, or expected, gratifications.
These ask respondents what they expect from the Internet in the future
as opposed to those that they seek in the present 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., Charney & Greenberg, 2001;
LaRose, Mastro, & Eastin, 2001; Lin, 1999) have consistently
doubled, tripled, or quadrupled the amount of variance explained in
Internet attendance behavior compared to conventional approaches.
A Social Cognitive Perspective of Uses and Gratifications
Prospective gratification measures were initially an innovative
means of understanding the medium before it was widely distributed in
the population (e.g., Lin, 1999). However, they are also consistent with
a view of media attendance derived from Bandura's (1986, 1989)
Social Cognitive Theory (SCT), which offers a theoretical explanation
for the often-observed (for example, Papacharissi & Rubin, 2000)
empirical relationship between media gratifications and media usage. SCT
is familiar to media scholars in its earlier incarnation as Social
Learning Theory (Bandura, 1977), as a theory of media effects. However,
SCT is a broad theory of human behavior that may be applied to media
attendance as well. SCT posits reciprocal causation among individuals,
their behavior, and their environment. Within SCT, behavior is an
observable act and the performance of behavior is determined, in large
part, by the expected outcomes of behavior, expectations formed by our
own direct experience or mediated by vicarious reinforcement observed
through others. Thus, media usage is overt media consumption behavior
(usage of the Internet in the present case), and it is determined by the
expected outcomes that follow from consumption. Since expected
gratification outcomes may be formulated from vicarious observation of
others' behavior (Eastin, 2002) they can explain consumption both
among prospective future users of the Internet (as in Lin, 1999) and
current users.
Uses and gratifications can be understood in socio-cognitive terms.
Where uses and gratification researchers have explored gratifications,
SCT proposes expected outcomes and where uses and gratifications
researchers posit needs, SCT proposes behavioral incentives. Expected
positive outcomes of Internet exposure should cause further exposure.
What people have gotten in the past from the Internet is an important
part of the basis for their current expectations about it. However,
expectations are also shaped by vicarious learning, based on
observations of the experiences of others, and also self-efficacy (see
below). However, it is the current expectation about outcomes of
behavior that best determines behavior.
The expected outcomes are organized around six basic types of
incentives for human behavior: novel sensory, social, status, monetary,
enjoyable activity, and self-reactive incentives (Bandura, 1986, pp.
232-240) and these are theoretically constructed rather than
statistical[y derived from exploratory factor analysis as in the uses
and gratifications tradition. An 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 a]so Charney & Greenberg, 2001; Flanagin & Metzger,
2001; Krgaonkar & Wolin, 1999). When expected outcome measures
reflecting the full range of these incentive categories were subjected
to exploratory factor analysis, a "new" virtual community
dimension was uncovered that drew heavily on the status incentives
lacking in conventional uses and gratifications research (Song et al.,
2004). Despite few differences (notably the inclusion of measures of
habit strength in gratification dimensions), other SCT incentive
categories parallel conventional uses and gratifications dimensions.
Activity incentives, predicated on the desire to take part in enjoyable
activities, correspond to the entertainment gratifications.
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, and they are similar to information seeking gratifications.
Social incentives stemming from rewarding interactions with others
correspond to social gratifications.
However, the gratifications sought-gratifications obtained
formulation (Palmgreen, et al, 1985) does not precisely match the
concept of outcome expectations, or the subjective probability that a
particular outcome will be obtained for future behavior. Gratifications
sought reflect wished-for outcomes (e.g., I hope to find an e-mail from
home) but not necessarily expectations of achieving the outcome through
our present behavior (but the folks e-mailed yesterday, so I don't
expect one today). Comparing gratifications sought with those obtained
reflects the outcomes achieved in the past but not necessarily the
likelihood that they will be repeated in the present by engaging in
further media consumption. Rather, SCT assumes that outcome expectations
are continually updated as a result of self-observation of our own
experience and (vicarious) observation of the behavioral consequences
that occur to others.
The socio-cognitive mechanism can be perceived in "A General
Media Gratifications Model" (found in Palmgreen et al. 1985, p. 17)
in which media consumption affects perceptions of gratifications
obtained, which feed back to beliefs and expectations about media
alternatives, which determine gratifications sought, which determine
media consumption behavior. From the SCT perspective, the expectations
about media alternatives--specifically the outcomes that our media
consumption behavior produces, organized according to the incentives
that motivate human behavior--are what determine further media
consumption. GS and GO are thus imprecise, and perhaps superfluous, ways
of describing the construct of outcome expectations. The SCT formulation
differs in that these expected consequences are themselves the
psychological origins of media behavior. The expected outcomes thus
produce the "need" for media attendance.
This imprecise match between outcome expectations, GS, and GS-GO
formulations might explain the pattern of weak relationships observed
between gratifications and media consumption (e.g., Papacharissi &
Rubin, 2000). Unsuccessful attempts by researchers (Babrow &
Swanson, 1988) to distinguish 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. Some gratifications sought could be
negative predictors of media behavior (if we don't expect to
achieve them), others positive ones, but in the aggregate are just
possibly confounded ones. Comparing gratifications obtained with those
sought compounds the problem (e.g., with gratifications that are
obtained but not sought, those that are sought but never realistically
expected) that may have no reliable relationship to media behavior.
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).
Extending Uses and Gratifications
SCT also suggests new concepts that may extend our understanding of
uses and gratifications and their impact on media behavior. Two
additional mechanisms of SCT, self-efficacy and self-regulation, are
particularly heuristic.
Self-efficacy is belief in one's capability to organize and
execute a particular course of action (Bandura, 1986). Self-efficacy is
particularly relevant for novice users who have not yet acquired the
requisite skills to obtain useful information and deal with the
discontents of life online, from viruses to balky home Internet
connections. It was directly related to Internet usage (Eastin &
LaRose, 2000; LaRose et al., 2001), and also acted on usage indirectly,
through expected outcomes. In other words, as Internet users become more
self-efficacious, their expectations that they will obtain specific
outcomes (e.g., finding useful information) also increase, and that
encourages more usage. Prior experience with the Internet in turn
causally preceded Internet self-efficacy (Eastin & LaRose, 2000),
probably through the process of enactive mastery (Bandura, 1986), in
which users gradually master complex tasks.
The SCT construct of self-regulation (Bandura, 1991) describes how
individuals monitor their own behavior (self-monitoring), judge it in
relation to 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 stimulus-response theories of human behavior in that
self-generated influences free the individual from blindly following the
dictates of external reinforcement. 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,
Lin, & Eastin, 2003).
In simplest terms, a habit is a recurring behavior pattern. Habit
is a well-established predictor of behavior (Oulette & Wood, 1998;
Triandis, 1980). Although long overlooked in communication research (cf.
Rosenstein & Grant, 1997; Stone & Stone, 1990), recent
qualitative research suggests that a great deal of media behavior is
habitual (Adams, 2000). Uses and gratifications researchers have
associated habit with "ritualistic gratifications," such as
passing time (Rubin, 1984), that are conceptually still part of an
active selection process. However, recent research (e.g., Aarts,
Verplanken, & van Knippenberg, 1998; Bargh & Gollwitzer, 1994)
suggests that habit is a form of automaticity, a pattern of behavior
(e.g., checking one's e-mail) that follows a fixed cognitive
schema, triggered by an environmental stimulus (e.g., seeing one's
computer desktop in the morning) or by recalling a goal (e.g., keeping
up with one's associates), and performed without further
self-instruction. This is outside the realm of active media selection
presumed in uses and gratifications research At best, automatic media
consumption behaviors were initially framed by active considerations
that were eventually forgotten (cf. Stone & Stone, 1990): We
carefully evaluated our options the first time we used e-mail but by the
hundredth time we did not. Within SCT habit is a failure of the
self-monitoring subfunction of self-regulation. Through repetition we
become inattentive to the reasoning behind our media behavior, our mind
no longer devotes attention resources to evaluating it, freeing itself
for more important decisions.
Habit strength is not a catch-all for uses and gratifications but
rather represents patterns of behavior established by past thinking
about outcome expectations/ gratifications that is no longer repeated in
the present. Habit strength is expected to influence ongoing behavior,
independent of current active thinking about expected (gratification)
outcomes. Habit should be causally determined by outcome expectations,
which precede habit in time. Habit strength should be preceded by
self-efficacy, since users are unlikely to be inattentive to a behavior
they are still mastering. These relationships were confirmed in LaRose
et al., 2003).
Deficient Self Regulation is defined as a state in which conscious
self-control is diminished. It has been proposed as an explanatory
mechanism for so-called "Internet addictions," more properly
called "problematic Internet use" (LaRose et al., 2003). In
that research, deficient self-regulation was directly related to
Internet usage and also contributed to usage indirectly, through habit
strength. As deficient self-regulation comes into effect, media behavior
tends to become an end unto itself and no longer subject to active
consideration of its expected outcomes. One important exception are
self-reactive outcomes, through which users counteract the negative
affect that results from personal problems that intensify with excessive
media usage, part of a self-reinforcing "downward spiral" into
problematic or addictive usage.
Still, habit and deficient self-regulation have not been clearly
distinguished. Addictions, including behavioral addictions, are a form
of habitual behavior (Marlatt, Baer, & Kivlahan, 1988) so the two
constructs overlap. At the operational level, measures of habit lacked
sufficient reliability and exhibited some degree of multi-collinearity
with deficient self-regulation so LaRose et al. concluded that they had
not clearly distinguished the two. They proposed a possible theoretical
distinction: Habit could represent the failure of self-monitoring, while
deficient self-regulation might represent a failure of the judgmental
and self-reactive subfunctions. Both the conceptual definition of
deficient self-regulation and its operationalization (based on symptoms
of pathological gambling and substance dependence, e.g., "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. Thus, deficient self-regulation reflects a distinct state
of mind from one in which media consumers are inattentive, explaining
how both might have independent effects on media attendance. Yet, the
two should be related in that persons with deficient self-control may
also be expected to engage in habitual behavior.
Are College Students Typical Internet Users?
Research on Internet usage has focused on college students, often
with the reassurance 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 and these are particularly salient from the SCT perspective.
College students, and particularly the freshmen who populate large
introductory classes, 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. One reason that freshmen are depressed is separation from
their family and friends, perhaps heightening the importance of social
and mood-elevating self-reactive outcomes. Indeed, college students
demonstrate an especially heavy reliance on the Internet for social
interaction and fun activities (Pew Research Center, 2002). Will the
same motives found among college students affect Internet attendance in
broader populations?
Relationships among deficient self-regulation, Internet
self-efficacy, habit strength, and Internet usage were explored
previously in research on so-called "Internet addictions"
among college students (LaRose et al., 2003) and can be summarized as
follows:
H1: Internet self-efficacy will be directly related to Internet
usage.
H2: Internet habit strength will be directly related to Internet
usage.
H3: Deficient Internet self-regulation will be directly related to
Internet usage.
H4: Deficient Internet self-regulation will be directly related to
Internet habit strength.
H5: Internet self-efficacy will be directly related to Internet
habit strength.
The causal relationship of prior Internet experience to Internet
self-efficacy has also been verified in college student populations
(Eastin & LaRose, 2000). Self-efficacy is conceived as the product
of progressive mastery of behavior that increases with experience. If
habit is simply a recurring behavior pattern, then the amount of prior
experience with the Internet should be directly related to habit
strength. Theoretically, repetition fosters a growing inattentiveness to
behavior, undermining self-monitoring. Triandis (1980) pointed out that
the best predictor of behavior is past behavior. Indeed, this truism may
be largely responsible for the scant attention paid to habits in media
research, since it has the ring of a tautology: One measure of behavior
predicts another measure of behavior (and so what?). However, we propose
that the impact of prior experience on current behavior is explainable
entirely through the causal paths among socio-cognitive constructs.
H6: Prior Internet experience will be directly related to Internet
self-efficacy.
H7: Prior Internet experience will be directly related to Internet
habit strength.
To complete the model of Internet usage, the role of outcome
expectations (a.k.a uses and gratifications) also must be specified.
LaRose et al. (2003) examined a single type of outcome expectation,
self-reactive outcomes, which uses and gratifications researchers would
perhaps recognize as "pass time" gratifications. They found
that self-reactive outcome expectations were causally related to
Internet usage and also acted on Internet usage through deficient
self-regulation. Self-reactive outcome expectations were themselves
preceded by Internet self-efficacy and followed by habit strength.
However, other types of outcome expectations should also explain
Internet usage. LaRose et al. (2001) found that the gratifications of
the Internet, reconceptualized as outcome expectations reflecting other
incentive categories recognized by SCT, were positively related to
Internet usage. However, these relationships were not arrayed in an
overall causal model and self-reactive and activity outcomes were
combined to attain satisfactory reliability. Thus, the self-reactive and
activity constructs will be separated to match the conceptual
distinction between these two incentive categories.
Self-efficacy should precede each of the the various types of
outcomes and habit strength should follow them. Users need to learn how
to successfully obtain social, status, activity, novelty, and monetary
gratifications as much as self-reactive ones. But once they achieve
satisfactory means for attaining those outcomes, they should become
increasingly inattentive to specific behaviors that support them.
Past research expended a great deal of effort distinguishing
gratification dimensions, but from the SCT perspective expected outcomes
are a unitary construct, representing the mechanism of learning through
experience. Thus, we represent gratifications, now understood to be
different types of expected outcomes grouped into six incentive
categories, as first-order concepts that are part of a second-order
construct, outcome expectations.
H8: Expected a) activity, b) social, c) status, d) novel, e)
self-reactive, and f) monetary Internet outcomes will be directly
related to Internet usage.
H9: Internet serf-efficacy will be directly related to expected
Internet outcomes.
H10: Expected Internet outcomes will be directly related to
Internet habit strength.
An important exception is noted for the relationship between
self-reactive outcomes and deficient self-regulation. Self-reactive
outcomes bear a unique relationship to deficient self-regulation. The
use of the media to adjust internal states should be the main type of
incentive susceptible to triggering the spiral of excessive usage and
dysphoria thought to lead to problematic media usage. Thus,
H11: Self-reactive outcomes of Internet usage will be positively
related to deficient Internet self-regulation.
These hypothesized relationships are represented in the path
diagram shown in Figure 1. The final path analysis (see Results, below)
was identical except for one path that was proposed but not
statistically significant, another that narrowly missed significance
(both indicated by light dotted lines) and another that was not
initially proposed but uncovered during analysis (indicated by a heavy
dotted line), in the interest of space only the final path diagram is
shown. For the sake of clarity the word "Internet" has been
omitted from the labels in figures and tables.
[FIGURE 1 OMITTED]
Research Methods
Procedure
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 online survey in April and May of 2002. Both
communities included a major university and 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 331 responded
to the solicitation. One hundred and seventy-two Internet users
completed the survey and 159 returned the non-Internet user postcard
(36% total response rate). Recent research assessing response rates (Yun
& Trumbo, 2000) indicates that the present rates were consistent
with methods employing Web surveys. A rule of thumb for structural
equation modeling, and also for regression analysis, is that there be 10
respondents for each link/ relationship in the model. The proposed model
has 16 links so the sample size was deemed adequate. There were no
response difference by city and thus data were collapsed. As a total
sample (N = 331) participants were 55% male and 45% female. The general
population in the counties surveyed was 50% female (U.S. Census, 2002).
Six percent of the participants were between the ages of 18-24 (census
population = 17%), 48% were between the ages of 25-44 (census population
= 30%) 34% were between 45-65 years old (census population = 40%), and
finally, 13% 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 therefore this sample was deemed
suitable for the purpose of this study which was to examine
relationships between variables.
The non-Internet users (N = 159) were 48% male and 52% 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 to the Internet, and thus,
could have completed the online survey. Therefore, we estimate that the
172 people who completed the survey represent an Internet user response
rate of 34%. Of those, 41% were female and 58% were male (with 1% not
indicating their gender) with an average age of 42 years old.
Eighty-nine percent were Caucasian, 5% were African American, 2% were
Latino and the remaining 4% were Asian, Pacific Islander, Native
American, or other. Forty-two percent of the sample had average
household incomes under $50,000; the remaining 58% had incomes greater
than $50,000. Educationally, participants ranged between 9-22 years
beyond kindergarten (Mean = 16, S.D. = 2.61). Six respondents were
removed from the sample for incomplete data, yielding a final sample of
167. A common rule of thumb in structural equation modeling is to have
10 cases for each link in the model. The proposed model had 16 links and
so the size of the sample was deemed adequate.
Compared to an NTIA (2002) study completed in fall of 2001, the
present sample tended to be older, better educated (including more with
advanced degrees and fewer with high school diplomas), and
disproportionately male. There were no income differences. There were
few significant relationships between demographic variables and the main
explanatory variables (females had lower self-efficacy, r = .26, and
less deficient self regulation, r = .20, than males, and age was
negatively, r = -.15, related to self-efficacy), and no significant
correlations with the dependent variable so sample bias was not deemed
an important issue. Within Social Cognitive Theory, demographic
differences are attributed to explanatory variables (e.g., females have
lower Internet self-efficacy due to the nature of their past experiences
with the Internet).
Operational Measures
The questionnaire was introduced with the following description of
Internet use: "Internet use includes sending or receiving
electronic mail, visiting chatrooms, participating in discussion groups
and visiting locations on the World-Wide Web." No distinction was
made between work and leisure activity.
The usual procedure for analyzing gratifications in the uses and
gratifications tradition is to conduct an exploratory factor analysis of
the gratification items. However, in the present research a priori
theoretical assumptions about the nature of the expected outcomes were
available, in the form of the incentive categories recognized in SCT.
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 1-7, where 1 was very unlikely
and 7 very likely, cf. Ajzen, 1985). These statements of outcome
expectations were classified into SCT incentive categories by consulting
the conceptual definitions found in Bandura (1986, pp. 233) and
supplemented with items reflecting status and monetary incentives that
were underrepresented in uses and gratifications research (cf. LaRose et
al., 2001). Six categories of expected outcomes, one representing each
incentive category, were subjected to confirmatory factor analyis. The
means and standard deviations of the scales and their component items,
along with confirmatory factor analysis results and their alpha
coefficients, are found in Table 1.
Previous research (LaRose et al., 2003) left the distinction
between habit strength and deficient self-regulation unclear.
Accordingly, new items were developed by drawing upon theoretical works
describing habitual behavior (Aarts et al, 1998; Bargh & Gollwitzer,
1994; Oulette & Wood, 1998) and LaRose et al.'s previous
description of deficient self-regulation. The pool of items was
subjected to an exploratory principal components factor analysis using
varimax rotation. Two interpretable factors emerged, also shown in Table
1. 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.
The Internet Self-Efficacy Scale (Eastin & LaRose, 2000) was
replicated (Table 1). Also from that study, a measure of Internet
experience was computed by asking the number of years and months it had
been since the respondent first started using the Internet.
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 (M = 5.17, S.D. = 1.59).
Data Analysis
Pearson product-moment correlation coefficients and exploratory
factor analyses were calculated using SPSS version 11.5 (SPSS, Inc.,
2002). Structural equation analysis was completed with Amos version 4.0
(Arbuckle, 1999).
Results
Pearson product-moment correlations among the independent and
dependent variables are shown in Table 2 and the results of structural
equation modeling are shown in Figure 1. The model shown was a good fit
to the data ([chi square] = 62.3, df = 34, RMSEA = .994, CFI = .071). As
hypothesized, Internet usage was directly predicted by expected Internet
outcomes ([rho] = .29), Internet habit strength ([rho] = .26), and
deficient Internet self-regulation ([rho] = .15). The individual
activity (r = .40), monetary (r = .27), novel (r = .36), social (r =
.44), self-reactive (r = .46), and status (r = .53) expected outcome
categories all had significant (p < .001) zero-order correlations
with usage. Expected Internet outcomes were a second-order factor
consisting of status, activity, self-reactive, social, novel sensory,
and monetary incentives, preceded by Internet self-efficacy ([rho] =
.55). Internet habit strength was predicted by expected Internet
outcomes ([rho] = .26). Deficient self-regulation predicted Internet
habit strength ([rho] = .39) and was itself causally determined by
self-reactive outcomes ([rho] = .43). Finally, Internet self-efficacy
was also predicted by prior Internet experience ([rho] = .38). However,
the hypothesized relationships between prior experience and Internet
habit strength was not significant ([rho] = .03, p = .63). The
relationship between Internet self-efficacy and habit strength ([rho] =
.14, p = .069) narrowly missed significance.
An inspection of the modification indices suggested a causal link
from self-efficacy to novel expected outcomes. Since this outcome
category represents information seeking on the Internet and the task of
seeking useful information is likely to require a high degree of
confidence in one's ability, this was accepted as a logical
extension to the model. Correlated error terms between self-reactive
outcomes and both activity (r = .44) and social outcomes (r = .18), and
between novel and monetary outcomes (r = .37), not shown, were added to
improve fit. The model explained 42.2% of the variance in the dependent
variable, Internet usage.
Discussion
The present results both affirm the uses and gratifications
paradigm and extend it to a theory of media attendance grounded in
Social Cognitive Theory. A basic implication of uses and gratifications,
that media exposure may be predicted from media gratifications, was
upheld. Indeed, by instituting new operational measures of expected
gratifications, it was possible to predict media consumption to an
unprecedented degree. However, new variables from SCT improved the
explanatory power of gratifications, here reconstrued as outcome
expectations.
Expected activity outcomes, which closely parallel entertainment
gratifications in uses and gratifications research, and social
outcomes/gratifications were significantly related to usage, as they had
been in prior uses and gratifications research involving college
students (e.g., Kaye, 1998; Papacharissi & Rubin, 2000), but with
more variance explained by the current expected outcomes formulation.
Unlike the previous studies, novel outcomes (paralleling information
gratifications), self-reactive outcomes (parallel to "pass
time" gratifications) were also significantly related to usage,
when conceived as expected outcomes of Internet usage. Monetary
outcomes, somewhat overlooked in previous research, were also
significantly related to usage. Status outcomes, a gratification/outcome
dimension identified by SCT but underrepresented in prior uses and
gratifications research, had the highest zero-correlations with Internet
usage of all. 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 finding supports the conceptualization of habit
strength as a distinct construct from gratifications/expected outcomes.
The correlation between habit strength 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, 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 .54 for new users, compared to .34 for
the more experienced ones. This could well indicate that the newer users
were 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 serf-regulation was further
clarified. Habit perhaps indicates a failure of the first of the three
subfunctions of serf-regulation proposed by SCT, self-observation. 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 findings supported the
proposed theoretical relationship between the two constructs wherein
deficient self-regulation adds to habit strength.
Internet self-efficacy causally preceded Internet usage and was in
turn determined by prior Internet experience as it was in previous
college student samples (Eastin & LaRose, 2000). The hypothesized
relationship between self-efficacy and habit strength was not observed,
falling just short of statistical significance. It appears that
self-efficacy acts on habit strength through expected outcomes. The
inattentiveness to one's own behavior that signals habit formation
thus could be more determined by a gradual cessation of active thinking
about outcomes/gratifications rather than inattentiveness accompanying
task mastery.
The lack of a causal connection between experience and habit
strength, despite a significant zero-order correlation (r = .30, p <
.001), belies the rival "tautology hypothesis" about the role
of habit in human behavior; namely, that habit is "just" prior
behavior and that relationships between two measures of behavior are not
theoretically meaningful. An alternative causal model in which a direct
link from experience (prior behavior) to usage (current behavior) was
hypothesize also failed to produce a significant relationship, and did
not diminish the causal link between habit and usage, again despite a
highly significant zero-order correlation (r = .35, p < .001) between
experience and usage. These findings suggest that habit strength indeed
has an impact on behavior that can be described in social cognitive
terms.
The Internet emerges from the present study as something of a
distinctive medium, but perhaps not in ways previously described. It is
not primarily a social medium (e.g., Papacharissi & Rubin, 2000),
but also a medium through which enjoyable activity, self-reactive,
monetary, novel (i.e., informational), and, above all, status incentives
can be obtained. 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 surrounding the 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.
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 online personal development (as self or with virtual
others) as well as social maintenance (a support mechanism).
Limitations
The generalizability of the present research is limited by the
geographic scope of the sample. The 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.
Implications for Further Research
Internet usage was broadly defined. Future research might
distinguish Internet applications (e.g., e-mail vs online chat),
functions (e.g., entertainment vs news Web sites), or settings (e.g.,
work vs leisure). However, in keeping with the operational procedures
recommended in SCT, it will be important to match the explanatory
constructs (e.g., expected e-mail outcomes, e-mail self-efficacy, e-mail
habits, etc.) to achieve satisfactory results.
Habit strength, deficient self-regulation, and self-efficacy might
extend to other forms of media attendance. Many media consumption
behaviors (e.g., tuning in the evening news) would seem to be
habit-prone. While few mass media require skills as complex as the
Internet, there are perhaps parallel media self-efficacy constraints.
Anyone who has ever given up recording a favorite show because
programming the recorder was "too much hassle" may be said to
suffer from a self-efficacy deficit. Television addiction has been
described (by Kubey & Csikszentmihalyi, 2002) in the same terms of
behavioral addiction that underlie deficient self-regulation. Here, we
found the construct useful in explaining media attendance in a normal
population of media consumers; perhaps that would extend to other media
as well.
The present research suggests new departures from uses and
gratifications traditions. Redefining gratifications as expected
outcomes may have merit on both conceptual and operational levels.
Secondly, gratification dimensions from previous research may have
neglected some potentially important variables, particularly the status
that media attendance may confer. Third, habit strength appears to be a
distinct construct from gratifications, as early conceptualizations
(e.g., Palmgreen et al., 1985, p. 17) observed, but later research
neglected.
More fundamentally, the present findings suggest that active
selection of media that best meet personal needs is not the sole
mechanism explaining media attendance. Active selection dominates when
new media alternatives appear or when personal routines are disrupted.
Self-efficacy beliefs about one's ability to utilize alternative
media channels may also contribute to media selection. Thereafter,
repeated consumption is increasingly habitual and automatic as we turn
our attention elsewhere. Once habits are established, users no longer
think through whether one alternative or another is a better way of
obtaining a particular outcome. Users still monitor their overall level
of Internet usage and apply self-reactive incentives to adjust the
amount to appropriate levels, as defined by personal or social norms.
But some users may lose the power to self-regulate, perhaps through a
process of operant conditioning (cf. LaRose et al., 2003) to
self-reactive outcomes, and in extreme cases they might develop a media
dependency. SCT provi