Unregulated Internet Usage: Addiction, Habit, or ...

MEDIA PSYCHOLOGY, 5, 225–253 Copyright © 2003, Lawrence Erlbaum Associates, Inc.

Unregulated Internet Usage: Addiction, Habit, or Deficient Self-Regulation? Robert LaRose Department of Telecommunication Michigan State University

Carolyn A. Lin Department of Communication Cleveland State University

Matthew S. Eastin School of Journalism and Communication Ohio State University

Recent reports of problematic forms of Internet usage bring new currency to the problem of “media addictions” that have long been the subject of both popular and scholarly writings. The research in this article reconsidered such behavior as deficient self-regulation within the framework of A. Bandura’s (1991) theory of selfregulation. In this framework, behavior patterns that have been called media addictions lie at one extreme of a continuum of unregulated media behavior that extends from normally impulsive media consumption patterns to extremely problematic behavior that might properly be termed pathological. These unregulated media behaviors are the product of deficient self-regulatory processes through which media consumers monitor, judge, and adjust their own behavior, processes that may be found in all media consumers. The impact of deficient self-regulation on media behavior was examined in a sample of 465 college students. A measure of deficient self-regulation drawn from the diagnostic criteria used in past studies of pathological Internet usage was significantly and positively correlated to Internet use across the entire range of consumption, including among normal users who showed relatively few of the “symptoms.” A path analysis demonstrated that depression and media habits formed to alleviate depressed moods undermined self-regulation and led to increased Internet usage. Requests for reprints should be sent to Robert LaRose, Department of Telecommunication, 409 CAS Building, Michigan State University, East Lansing, M1 48824. E-mail: [email protected]

226

LAROSE, LIN, & EASTIN

Reports of problematic Internet consumption behavior, variously termed Internet addiction (e.g., Brenner, 1997; Fearing, 1997; Greenberg, Lewis, & Dodd, 1999; Griffiths, 2000a; Hall & Parsons, 2001; Young, 1996, 1998), pathological Internet use (Davis, 2001), Internet dependence (Scherer, 1997), or problematic Internet use (Shapira, Goldsmith, Keck, Khosla, & McElroy, 2000), bring currency to the question of media addiction that has long been the subject of both popular (e.g., Kubey & Csikszentmihalyi, 2002; Pawlowski, 2000; Winn, 1977) and scholarly (Finn, 1992; Kubey, 1996; McIlwraith, 1998; McIlwraith, Jacobvitz, Kubey, & Alexander, 1991; Smith, 1986) writings in the field of communication research. Addiction may be defined as: a repetitive habit pattern that increases the risk of disease and/or associated personal and social problems…often experienced subjectively as “loss of control” [that] continues despite volitional attempts to abstain or moderate use (Marlatt, Baer, Donovan, & Kivlahan, 1988, p. 224).

Media addictions are a type of behavioral addiction (Marks, 1990) in which there is no external1 chemical substance involved. By this definition, addicted media consumers feel compelled to consume media despite potentially negative consequences that make continued use appear irrational or out of control, even in their own eyes. As such, media addictions challenge the prevailing uses and gratifications view of media consumption that emphasizes rational and conscious seeking of media content that gratifies personal needs (e.g., Palmgreen, Wenner, & Rosengren, 1985). Noting that habit is a necessary (though not sufficient) component of the definition of addiction previously presented, media addictions would seem to conform more to a notion long lurking in the uses and gratifications literature (Rosenstein & Grant, 1997; Stone & Stone, 1990) of habitual or ritualistic (Rubin, 1984) media use. However, habit was conceived to be the result of cognitive processes (Rosenstein & Grant, 1997) or willful acts (Rubin, 1984; Stone & Stone, 1990) that seemingly could not explain the irrational and out-ofcontrol aspects of media addictions. A countervailing view is found in social psychology where habit and conscious decision making have been found to be separate and opposing processes (Aarts, Verplanken, & van Knippenberg, 1998; Landis, Triandis, & Adamopoulos, 1978; Metcalfe & Mischel, 1999; Ouellette & Wood, 1998). Research concerning problematic Internet consumption provides the opportunity for a reassessment of the media addiction issue and also basic conceptions of media attendance that involve the interplay between habit and reason.

MEDIA HABITS AND ADDICTION

227

This article synthesizes the research on computer and television addictions and critically reviews two theories that have been proposed to explain them. It offers a new theoretical model of unregulated media use that analyzes the symptoms of the so-called “media addictions” as indications of a deficiency in self-regulation (after Bandura, 1991) that leads to habit formation and, perhaps in extreme cases, to pathology. Consistent with Hall and Parsons (2001), this article also argues that the symptoms are mostly manageable “benign problems” affecting a large number of media users to some degree at various times. These symptoms are not exclusively aberrant behavior or the product of a disordered or diseased personality (e.g., McIlwraith, 1998; Smith, 1986; Young, 1999) that may require professional intervention. We acknowledge at the outset that the term “addiction” has fallen into disfavor in the field of clinical psychology where it has been replaced by “dependence” (American Psychiatric Association, 1994). Kubey (1996) pointed out that dependence, rather than addiction, should also be the preferred term to describe problematic media consumption, although he proceeded to use the two terms interchangeably. As we shall see shortly, Internet researchers have drawn upon the operational definitions of substance dependence even while coining the term “Internet addiction” to describe the syndrome. We agree with Peele (1999) that the term “addiction” may be abused to generate a sense of urgency about psychological problems that alarmists wish to profit from “curing.” The close association between Internet addiction researchers and Web sites that purport to offer therapy for the disorder (e.g., www.netaddiction.com) raises this concern anew. Still, we use “addiction” here as an umbrella term simply because it is the most prevalent term of reference in the literature. Moreover, “media dependence” would further confuse the issue, because that term has been previously defined in media studies as a goal-directed relationship between media sources and consumers (e.g., Gaziano, 1990), just the opposite of the phenomenon of interest here. We have no wish to perpetuate an imprecise term; indeed, our objective is to replace the metaphor of media addiction with a completely new concept: unregulated media usage.

THE DISEASE MODEL OF ADDICTION Conceptual definitions of “media addictions,” as previous researchers have termed them, have been predicated on a mental disease metaphor. In particular, media addictions were likened to psychiatric conditions with dependency or compulsive qualities, and the diagnostic criteria for those conditions were adapted to media consumption behavior.

228


For example, Griffiths (1991) followed the diagnostic criteria for pathological gambling (American Psychiatric Association, 1987) to define addiction to video arcade games. Others drew on the diagnostic criteria for substance abuse dependency to define television addiction (McIlwraith et al., 1991) and Internet addiction (Brenner, 1997; Scherer, 1997). Young (1998, 1999) synthesized the diagnostic criteria for psychoactive substance abuse dependency and pathological gambling (American Psychiatric Association, 1994) to define the Internet Addiction Disorder. Others used diagnostic criteria for behavioral addictions (Griffiths, 1999; 2000b; Greenberg et al., 1999; Rozin & Stoess, 1993). Still others equated them with Impulse Control Disorders (Cooper, Scherer, Boies, & Gordon, 1999; Shapira et al., 2000), the same diagnostic category that includes pathological gambling and compulsive buying (Wise & Tierney, 1994). Operational definitions (used in McIlwraith, 1998; McIlwraith et al., 1991; Smith, 1986) that were built from popular accounts of television addiction (e.g., Winn, 1977), as well as definitions of television dependence drawn from diagnostic criteria for substance dependence (Kubey, 1996), reflect the same general symptoms found in accounts of Internet addiction: Preoccupation: indicated by excessive levels of use, craving, structuring other activities around media consumption, or feeling tension or arousal while using media. Tolerance: meaning that increasingly large “doses” of the activity are needed to achieve the same effect. Relapse: the user makes repeated attempts to curb the activity but fails. Withdrawal: when the activity is not available the user experiences panic, anxiety, agitation, or other negative affect. Loss of Control: engaging in the activity for longer than intended or sensing that use is out of control or cannot be stopped. Life Consequences: reductions in the time allocated to other activities, loss of interest in them, and disregard for the disruptions in finances, family, or work that result. Concealment: the user tries to hide the extent of his or her involvement in the media activity from others.


229

Escapism: the activity is viewed as a means of escaping or counteracting dysphoric moods such as depression, anxiety, or guilt. There is no one, dominant theory that explains the origins and development of media addiction/dependence, but two views have predominated. One posits an addictive personality type, attributing media addictions to a disordered personality. A second theory explains the progression of the disease in terms of operant conditioning processes.

THE ADDICTIVE PERSONALITY MODEL What is the personality of the media addict? Smith (1986) proposed defective ego autonomy as a possible origin of television addiction. McIlwraith (1998) theorized that it was the manifestation of an oral, neurotic, sensation-seeking, or unimaginative “addictive personality.” Commonalities among addictions (e.g., narcotics addicts also tended to abuse alcohol) have been taken as evidence of an underlying personality trait that predisposes people to addiction. Greenberg et al. (1999) found low-to-moderate correlations among television, Internet, and video game addictions, and alcohol addiction (r = .33 to .42), as well as substantial correlations between media addictions (r = .43 to .72). Personality traits, television consumption, and television addiction symptoms had statistically significant (but low) correlations (Finn, 1992; McIlwraith., 1998; McIlwraith et al., 1991). However, Finn (1992) found that physiological addictions (to marijuana and alcohol) were inversely related to television consumption and thus could not reflect the same underlying personality syndrome. Moreover, correlations among addictions could be the result of common lifestyle, cultural, or demographic factors promoting addiction in different behavioral domains, not personality per se (Rozin & Stoess, 1993). Theoretically, commonalities among addictions can be explained in terms of common learning processes across substances and activities (Marlatt et al., 1988). Thus, learning theory processes could explain away the personality correlates of addiction. Moreover, the only variable that was consistently associated with addiction, antisocial behavior (which is properly termed a behavioral tendency rather than a personality trait), has a high incidence in the general population and many addicts do not exhibit it, whereas many antisocial individuals are not addicts (Nathan, 1988). Other personality correlates, like depression and low self-esteem, appeared to be the consequences of addiction as much as their cause (Marlatt et al., 1988).

230


THE OPERANT CONDITIONING MODEL OF ADDICTION Operant conditioning formulations of the general problem of addiction (e.g., Marks, 1990; Marlatt et al., 1988) have been commonly cited by media addiction researchers (e.g., in Brenner, 1997; Davis, 2001; Griffiths, 1995, 1999; Putnam, 2000; Smith, 1986; Young, 1999). In this view, consumption behavior progresses in four phases: initiation, transition to ongoing use, addiction, and behavior change (Marlatt et al., 1988). Differing mechanisms reflecting varying mixes of habitual and consciously controlled behavior may operate in each phase. In the initiation phase, addictive behaviors are experienced as inherently pleasurable and rewarding. Genetics and family history may predispose experimentation and initial reactions, but the social and personal outcomes of the behavior play an important role. Thus, the uses and gratifications of the media in question (Palmgreen et al., 1985) or, alternatively, the perceived outcomes of media behavior (LaRose, Mastro, & Eastin, 2001; Lin, 2001) could explain media behavior at this phase. At that point, the media behavior could begin to become habitual in the sense of being automatic (Stone & Stone, 1990) or ritualistic (Lin, 1993; Rubin, 1984) while remaining consistent with conscious self-interest. The transition to problematic usage can begin if the behavior acts as an important or exclusive mechanism to relieve stress, loneliness, depression, or anxiety. When this problematic media use becomes excessive, it in turn can cause life problems, confrontations with significant others, and an inability to stop media consumption once started. Those negative life events may further heighten dysphoric moods, leading to further reliance on media consumption to relieve those undesirable moods. The transition phase is marked by diminished response to the addictive behavior (tolerance) and withdrawal symptoms in its absence. There is also a narrowing of the addictive repertoire (e.g., more time spent in chat rooms, less playing online games and other Internet activities), increased salience of facilitating behavior (e.g., neglecting family interaction to watch television), awareness of the compulsion, and relapses following periods of abstinence (Marlatt et al., 1988). It should be noted that the media usage need only be excessive relative to the individual’s own prior consumption patterns rather than in absolute terms, when compared with average consumption levels for a population. Thus, some individuals might begin the transition to addiction at levels of consumption that are far below the average for the general population. Those with consumption levels far above average need not be addicted if their media usage does not result in a cyclical pattern of self-medication of dysphoric moods with media consumption. Also,


231

the common distinction between “good” and “bad” habits requires some clarification. Behaviors that are not considered inherently harmful by nature, including media usage and exercise, can become “bad” or harmful habits when they turn into conditioned responses to dysphoria and when negative life consequences result. In the annals of communication research, the use of television to alleviate dysphoric moods (Dittmar, 1994; Zillmann & Bryant, 1985, 1994) and the relationship between stress and television addiction (Anderson, Collins, Schmitt, & Jacobvitz, 1996) were perhaps indicative of this transition phase. Reliance on the Internet to overcome loneliness (Morahan-Martin & Schumacher, 2000; Scherer, 1997; Young & Rogers, 1998), to develop a feeling of mastery, or to provide a means of escape (Morahan-Martin & Schumacher, 2000) also indicated a state in which the consumption of the medium, as opposed to content in the medium, became rewarding in itself. An initial test of unpleasant mood levels and Internet consumption indicated that unpleasant levels of excitation (e.g., boredom and stress) affected online behavior patterns (Mastro, Eastin, & Tamborini, 2002). Although conscious expectations of the consequences of behavior still play a role in the transition to habitual use, the process of classical conditioning can spiral downward further and lead the individual to enter the addiction phase. Addictive media consumption may be prompted by secondary conditioning to internal cues (such as boredom or depression) or external cues (such as the sight of a computer or the TV remote control) that then trigger the same affective response as the media stimuli themselves did initially. At this point, behavioral addictions are oneway downward spirals that can end only with a major life crisis and the solicitation of professional help (Marks, 1990). To initiate the recovery phase, abstinence from the addictive behavior, habituation to the cues that trigger it, or conditioned aversion may break the cycle of addiction, followed by social-skill training and family therapy to maintain recovery (Marlatt et al., 1988). The only completely effective therapies in the classical conditioning view are abstinence or habituation to cues that trigger the conditioned response—both highly improbable for behaviors as common and socially acceptable as media consumption behavior.

TOWARD A SOCIAL COGNITIVE MODEL OF ADDICTION Both the personality traits and the classical conditioning processes through which media addictions may be acquired are widely distributed in the population and the media stimuli are readily available in society. So, perhaps it is remarkable that there are so few Internet addicts (estimated by Hall & Parsons, 2001, to be 6% of

232


the online population) rather than so many. Moreover, neither the learning theory nor the addictive personality formulation can explain the fact that many people can overcome these so-called “addictions” without professional intervention (Hall & Parsons, 2001; Marlatt et al., 1988; Peele, 1999). Self-cures challenge the basic premise of the disease model of media addictions: If they are indeed diseases, then they should only be curable through clinical intervention in all likelihood. Indeed, learning theory fails to explain why more people are not addicted to the media, and also how people could succeed in recovering from media addictions on their own, given the constant bombardment of sensory cues reminding us of television and the Internet (see Bandura, 1999). To understand these anomalies, we propose an explanation from social-cognitive theory (Bandura, 1989)—a comprehensive model of human behavior that extends classical learning theory to account for complex human cognitions. The selfregulatory mechanism (Bandura, 1991) is of special interest here. It describes the process of self-control through the subfunctions of self-monitoring, judgmental process, and self-reaction. Self-monitoring is the observation of one’s own actions to provide diagnostic information about the impact of behavior on the self, others, and the environment. To be effective in self-monitoring, individuals must be attentive to their behavior2 and must analyze the “normalcy” in their behavior in relation to the situations in which it is performed. They also must be accurate in their selfobservations and conduct them in temporal proximity to the performance of the behavior in question. The judgmental process evaluates self-observations of behavior against personal standards or in reference to standardized group norms, social comparisons with associates, personal comparisons with prior behavior, or collective comparisons (i.e., based on individual contributions to group accomplishments). Further, the self-reactive function also provides either behavioral or psychological rewards. These types of rewards can include self-administered rewards for good behavior or self-evaluative ones such as the self-respect or self-satisfaction that are derived from accomplishing an activity that meets desired standards. In this view, deficient self-regulation is defined as a state in which conscious self-control is relatively diminished. Habits, defined as “situation-behavior sequences that are or have become automatic, so that they occur without selfinstruction” (Triandis, 1980, p. 204), form as self-regulation becomes less and less effective. Deficient self-regulation is implicit in the general definition of addiction we began with (“a repetitive habit pattern…often experienced subjectively as a ‘loss of control,’” Marlatt et al., 1988, p. 224). Deficient self-regulation was explicit in many diagnostic criteria (loss of control, relapse, preoccupation) associated with media addictions and other symptoms that strongly imply it (ignoring adverse life consequences, concealment, tolerance, and escape).


233

Unlike in the disease model, deficient self-regulation is not an all-or-nothing condition, in which one is either classified as “normal” or “addicted.” Rather, in social-cognitive theory it is possible to have varying degrees of deficient selfregulation and normal media consumers may experience lapses in self-regulation just as addicted ones do. Also, however, heavily addicted users may be able to restore self-regulation, at least temporarily, and indeed that pattern is implicit in the “relapse” symptoms associated with media (and also other) addictions. Thus, the indicators of so-called “media addictions” may be reinterpreted as markers of deficient self-regulation and the process of addiction as the struggle to maintain effective self-regulation over problematic media behavior. Among those who do not meet the diagnostic criteria for pathological dependence, a more proper term would be unregulated media behavior or, more simply, “media habits,” rather than addictive media behavior. Depression plays a seminal role in deficient self-regulation because depressed people have a negative cognitive bias that causes them to slight their own successes and blame themselves for failure (Bandura, 1991). Media usage may spiral out of control if individuals are intent on pursuing immediate relief from depressed feelings; this action can end in deepening depression if adverse life consequences result. The repetition of this cycle may then form a conditioned response linking dysphoric states with media use. Metcalfe and Mischel (1999) called this a “hot” process directly linking emotion to behavior, as opposed to the “cool” process of conscious and self-regulated behavior. From the perspective of the uses and gratifications research tradition, deficient self-regulation begins by consciously using the media to relieve boredom, lessen loneliness, “pass the time,” engage in parasocial interaction, or seek validity of social identity (e.g., Lin, 1999). Within social-cognitive theory, these motivations are recognized as self-reactive incentives (LaRose et al., 2001). Following classical conditioning processes, these self-reactive incentives motivate media consumption behavior that becomes a conditioned response to dysphoric mood states. Dysphoric moods interfere with the cognitions that maintain effective self-regulation and the pattern of problematic media consumption deepens as self-regulation becomes increasingly deficient. Each repetition of media consumption behavior increases habit strength and with it the likelihood of future usage (Landis et al., 1978). However, human information processing and learning still take place even when persons are inattentive to the behavior in question (LaBerge & Samuels, 1974), so habit strength may increase even when a behavior is not under conscious control. That is because the mind seeks to economize on the mental effort invested in executing repeated behaviors, making the consciously processed thoughts that may have originally

234


motivated consumption increasingly unavailable to memory (Bargh & Gollwitzer, 1994). As that happens, behavior is said to become automatic rather than controlled. Early conceptions of this dichotomy (e.g., Shiffrin & Schneider, 1977) posited a dual-mode model in which any particular cognitive process had to be one or the other: either controlled or automatic. More recently, it has been argued (by Bargh & Gollwitzer, 1994) that controlled and automatic thinking may be present simultaneously, in that attention, awareness, intention, and control (the defining characteristics that distinguish automatic and controlled thought) do not always co-occur. Furthermore, the concept of automaticity has been extended beyond the simple recognition and recall tasks that were the focus of early research to encompass complex sequences of behavior. The joint operation of controlled and automatic thinking might be explained in social–cognitive terms as follows: Habit formation is accompanied by decreased attention to self-monitoring, making it less likely that self-reactive incentives will be consciously applied to moderate the behavior. “Unregulated” habitual use (operantly conditioned through self-reactive incentives and no longer actively attended to) and “regulated” intentional uses (motivated by active consideration of gratification expectations) are opposing processes (Aarts et al., 1998; Ouellette & Wood, 1998). Then, as habit strength increases, conscious control, predicated on consideration of the expected outcomes (or gratification expectations) of media behavior, decreases. Thus, in present terms, unregulated habitual behavior recurs when individual acts of media behavior are no longer subjected to close selfobservation—one of the three subfunctions of self-regulation. Some users, however, may still monitor and moderate their overall media consumption by comparing it with personal or social norms. LaBerge and Samuels (1974, p. 317) first described how comparisons with personal norms, for example, could regulate automatic behavior. Moderation may be restored by the incentive value of making progress toward a goal or by generating self-reactive incentives (e.g., “I feel guilty that I am ignoring my spouse for the Internet all the time”). These judgmental and self-reactive processes, as they are known in social cognitive theory, may continue even though specific acts of media consumption (e.g., starting the day by checking e-mail) are no longer consciously evaluated. Thus, both automatic and controlled cognitions may combine to influence the performance of media behavior. Another important determinant of behavior under social-cognitive theory is self-efficacy, or beliefs in one’s capability to organize and execute a particular course of action (Bandura, 1997). In prior empirical research, Internet selfefficacy directly predicted Internet usage (Eastin & LaRose, 2000). Davis (2001) proposed that low self-efficacy might trigger both specific pathological uses of the Internet (e.g., excessive chatroom use) as well as generally excessive use.


235

The present social cognitive view of media addiction is consistent with research that showed a positive relationship between depression and Internet usage (Kraut et al., 1998; Sanders, 2000; Young & Rogers, 1998) and between television viewing and loneliness (Canary & Spitzberg, 1993). Attention deficits associated with television addiction (McIlwraith, 1998) also perhaps indicated deficient selfobservation. It has been suggested that heavy television viewing may betray a general lack of self-regulation (Kubey & Csikszentmihalyi, 1990). Moreover, many of the media “addicts” described in prior studies may not really have been addicted at all. The occurrence of severe, negative life consequences (e.g., divorce or social isolation) is necessary to distinguish addiction from behavior that is merely impulsive or habitual (Shaffer, Hall, & Vander Bilt, 2000), but that requirement has not been observed in the past media addiction literature. Thus, the significant positive correlations that have been found between the symptoms reported in the Television Addiction Scale (TAS) and self-perceptions of addiction (.26 in Smith, 1986, .41 in McIlwraith, 1998; .60 in McIlwraith, et al., 1991) generally described a relationship between deficient self-regulation (represented by the symptoms in the TAS) and television consumption (represented in the self-perceptions of addiction) instead of clinically defined addiction. These results thus reposition deficient media self-regulation in a continuum of media behavior that includes normal media consumption that has occasional lapses in self-control (a “benign problem” in the words of Hall & Parsons, 2001) as well as problematic excessive usage.

HYPOTHESES Thus, an important implication of the social-cognitive view of media addiction is that deficient self-regulation may occur to all media consumers, even among those whose media consumption patterns are generally considered normal. This means that the so-called “symptoms” of addiction, now understood to be indicators of deficient self-regulation, should be found among nonaddicted comparison groups as well (e.g., Brenner, 1997; Chou & Hsiao, 2000; Morahan-Martin & Schumacher, 2000; Smith, 1986). For example, the base rates for Internet addiction symptoms like losing sleep, escaping dysphoric moods, performance deterioration, withdrawal, unsuccessful efforts to control Internet usage, and arguments with significant others were over 20% in a college student sample (Pratarelli, Browne, & Johnson, 1999); these results then went well beyond any “true addicts” in that population. Smith (1986) found a significant correlation between the TAS and weekly viewing hours and a

236


moderate association with the proportion of one’s free time spent watching television in a general population sample. Addiction to computer games was moderately correlated with the frequency of play (Griffiths & Hunt, 1998). LaRose et al. (2001) found a high correlation between a measure of Internet addiction and a multi-item index of Internet usage among a normal population of college students. However, confounding among the independent and dependent variables could explain these relationships. The TAS (Smith, 1986) included items (e.g., “When I am watching TV, I feel like I can’t stop”; “I can’t walk away from the TV once it is on”) that were arguably alternative indicators of television exposure. The Diagnostic criteria (e.g., “Do you frequently play most days”) derived from the Diagnostic and Statistical Manual of Mental Disorders (3rd ed., rev., American Psychiatric Association, 1987) used in Griffiths and Hunt (1998), also had the potential for confounding. The Internet addiction measure in LaRose et al. (2001) used a measure of media addiction that had marginal reliability and was confounded with habit strength. Therefore, the positive relationship of deficient selfregulation to Internet usage is a likely but still untested proposition. Hypothesis 1: Deficient self-regulation will be positively related to Internet usage. However, the hypothesized relationship could still be with those users who demonstrate multiple indicators of deficient self-regulation behavior but whose Internet usage is not considered “addictive” by recommended clinical criteria to examine this scenario (e.g., Shaffer et al., 2000; Young, 1999). Hypothesis 1a: Deficient Internet self-regulation will be positively related to Internet usage even among those who fall below the criterion of Internet addiction. Deficient self-regulation was conceptualized earlier as a precursor to habit formation: Habits form as self-regulation becomes less vigilant. However, deficient self-regulation could have an independent impact on usage apart from habit strength because deficient self-regulation may also promote impulsive behavior that is neither habitual nor repetitive. Hypothesis 2: Deficient Internet self-regulation will be positively related to Internet habit strength. Hypothesis 2a: Deficient Internet self-regulation will be positively related to Internet usage after controlling for habit strength.


237

Social cognitive theory also recognizes that the expected outcomes of behavior provide incentives for its performance. Prior research found that outcome expectations directly predicted Internet usage and argued for reformulating media gratifications as expected outcomes of media usage (LaRose et al., 2001). Expected outcomes fall into several distinct categories of incentives (including social, status, monetary, activity, and novelty), but self-reactive incentives are particularly relevant here. Self-reactive incentives reflect efforts to regulate one’s own inner states; for example, engaging in media consumption that is intended to relieve boredom or depression. The pursuit of self-reactive incentives could undermine self-regulation through the “hot” (to use the terminology of Metcalfe & Mischel, 1999) classical conditioning mechanism, as well as the “cool” mechanism that links consciously expected outcomes directly to media consumption. Hypothesis 3: Self-reactive incentives will be positively related to deficient Internet self-regulation. Hypothesis 3a: Self-reactive incentives will be positively related to Internet usage. Hypothesis 3b: Deficient Internet self-regulation will be positively related to Internet usage after controlling for the direct effect of self-reactive outcome expectations. Depression could play a pivotal role in habitual media use and deficient selfregulation. Depressed people are less effective at self-regulation because of their tendency to minimize their own successes at self-control (Bandura, 1991). Depression is among the psychopathologies (also including social anxiety and substance dependence) that may trigger obsessive thoughts about the (over) use of the Internet (Davis, 2001). Mastro et al. (2002) found that individuals suffering from over- or understimulation used the Internet as a relief mechanism. Depression may also cause selective attention to media content intended to alleviate dysphoria (Helregel & Weaver, 1989; Zillmann & Bryant, 1985, 1994). In social-cognitive theory, that attention triggers self-reactive outcome expectations that media consumption will relieve depression. Hypothesis 4: Depression will be positively related to deficient self-regulation. Hypothesis 4a: Depression will be positively related to self-reactive outcome expectations.

238


Consistent with prior research (Eastin & LaRose, 2000), a direct relationship between Internet self-efficacy and Internet usage is also expected. Hypothesis 5: Internet self-efficacy will be positively related to Internet usage. Eastin and LaRose (2000) also found evidence that self-efficacy was positively related to social and informational outcome expectations. The presumption is that as Internet self-efficacy increases, users become better able to use it to achieve desired outcomes. Here, we extend that reasoning to self-reactive outcomes. Hypothesis 6: Internet self-efficacy will be positively related to self-reactive outcomes. In view of the hypothesized relationship between self-reactive outcomes and deficient self-regulation (Hypothesis 3), Internet self-efficacy might be expected to have an indirect impact on deficient self-regulation. However, the nature of the direct relationship, if any, between self-efficacy and deficient Internet self-regulation has not been explored previously, so we leave that as an open research question: RQ: What is the relationship between self-efficacy and deficient Internet selfregulation? The hypothesized relationships among Internet usage, depression, habit strength, expected self-reactive outcomes, and deficient self-regulation are summarized in the causal model shown in Figure 1. The model parallels the (as yet untested) cognitive-behavioral model of pathological Internet use proposed by

Figure 1. Initial structural model. All relationships are hypothesized to be positive except where otherwise indicated.


239

Davis (2001) in that depression contributes to maladaptive cognitions (deficient Internet self-regulation here) that cause Internet use. The present model further proposes that social-cognitive learning processes involving self-reactive incentives contribute to Internet usage and that the same processes explain normal Internet usage, as well as pathological usage.

RESEARCH METHODS Sample Data were collected from a purposive sample of 498 students in three introductory communication classes at two Midwestern universities through surveys distributed in class. College students have ready access to Internet connections and as such have been the subject of much of the prior research on Internet usage (e.g., LaRose et al., 2001; Papacharissi & Rubin, 2000) and Internet addiction (e.g., Chou & Hsiao, 2000; Morahan-Martin & Schumacher, 2000; Pratarelli et al., 1999). Thirty-three respondents were eliminated from the sample for the examination of each hypothesized relationship because of missing data on the variables (listwise deletion), yielding a sample size of 465. The sample consisted of 61% males and 39% females.3 Fifty-six percent reported family incomes of $50,000 a year or more, 16% reported incomes under $20,000, with the remainder between $20,000 and $50,000. The sample was 14% African American, 70% Caucasian, 3% Hispanic, 9% Asian, and 5% other. The average number of minutes spent on the Internet in the typical weekday was reported to be 89 (SD = 116.90), whereas typical weekend day use was 69 minutes (SD = 87.33). Operational Measures Internet usage was an additive three-item index consisting of the number of minutes participants reportedly used the Internet on a typical weekday, a typical weekend day, and the day previous to the survey (α = .80). Visual inspection of the data indicated that there were outliers on each of the three usage items that could have skewed the results. Accordingly, log10 (1+ value) transforms were applied to each value prior to summing the items in the index. Deficient Internet self-regulation consisted of seven 7-point Likert-type items (α = .86) based on operational definitions of Internet addiction used in prior research (Greenfield, 1999; Griffiths, 1999; Rozin & Stoess, 1993; Young, 1999).4 Two different sets of criteria were used to assess Hypothesis 1a. Young (1999) set a cutoff of five symptoms for a

240


diagnosis of Internet addiction (N = 85). Following Shaffer et al. (2000), however, only those who strongly agreed that their Internet usage had interfered with other activities (N = 22) could be considered likely to be truly addicted. Habit strength, also assessed on a 7-point Likert-type scale, was the sum of three items: “The Internet is part of my usual routine,” “Web surfing is a habit that I have gotten into,” and “I use the Internet without really thinking about it” (α = .69). Selfreactive outcome expectations were measured with an additive index of six 7-point Likert-type items (α = .83) assessing the degree to which respondents used the Internet to self-regulate affect.5 These items were drawn from prior Internet uses and gratifications studies but reformulated as outcome expectations in the manner recommended by LaRose et al. (2001). The 7-item short form of the Center for Epidemiological Studies Depression Scale (Mirowsky & Ross, 1992) was used to assess general level of depression (α = .82).6 Finally, the Internet self-efficacy scale (Eastin & LaRose, 2000) was replicated (α = .94) for this study.7 The means, standard deviations, and ranges of each of these variables are reported in Table 1. Data Analysis The Statistical Package for the Social Sciences (SPSS) version 10.0 (SPSS Inc., 2000) was used to analyze the data. Pearson product-moment and partial correlations were computed to test each of the main hypotheses. LISREL 8.3 was used to test the proposed path model (Jöreskog & Sörbom, 2000).

RESULTS A matrix of Pearson product-moment correlation coefficients is shown in Table 1. Bivariate and partial relationships will be presented first, followed by a path analysis. Hypothesis 1 was supported. Deficient Internet self-regulation had a significant positive relationship to Internet use (r = .45, p < .01). When only considering those who fell below the cutoff for a diagnosis of Internet addiction (i.e., who failed to agree that five or more of the items in the deficient Internet self-regulation scale applied to them; Young, 1999), a significant positive relationship was again found (r = .49, p < .01), supporting Hypothesis 1a. A similar result (r = .44, p < .01) was obtained when a more stringent criterion (i.e., strong agreement that Internet usage interfered with life activities; Shaffer et al., 2000) was used. Deficient Internet selfregulation was also positively related to Internet habit strength (Hypothesis 2,

TABLE 1 Pearson Product-Moment Correlation Matrix of Independent and Dependent Variables Variable 1. Internet use 2. Deficient Internet self-regulation 3. Habit strength 4. Self-reactive Outcome expectations 5. Depression (CES-D) 6. Internet self-efficacy

1 1.00 .45**

2

3

4

5

6

1.00

.56** .36**

.67** .41**

1.00 .46**

.08 .38**

.32** .23**

.18** .35**

1.00 .18** .25**

Note. CES-D = Center for Epidemiological Studies Depression Scale. *p < .05. **p < .01.

1.00 –.05

1.00

α

M

SD

Range

.80 .86

4.67 21.76

1.58 9.43

0.00–8.34 7.00–49.00

.69 .83

13.21 28.64

4.59 7.24

3.00–21.00 6.00–42.00

.82 .94

7.41 47.33

4.31 11.63

0.00–21.00 9.00–63.00

241

242


r = .67, p < .01). Further, after controlling for habit strength, deficient self-regulation was significantly related to Internet use (r = .12, p < .05), supporting Hypothesis 2a. Hypotheses 3 and 3a assessed the relationship between self-reactive outcome expectations and deficient self-regulation (r = .41, p < .01) and Internet usage (r = .36, p < .01); both hypotheses were supported. As predicted in Hypothesis 3b, deficient self-regulation was significantly related to Internet use (r = .35, p < .01) after controlling for the effects of self-reactive outcome expectations. When testing Hypotheses 4 and 4a, depression was found to be significantly related to both deficient Internet self-regulation (r = .32, p < .01) and self-reactive outcome expectations (r = .18, p < .01). There was also support for Hypotheses 5 and 6, which predicted that Internet self-efficacy would be significantly and positively related to Internet use (r = .38, p < .01) and to self-reactive outcomes (r = .25, p < .01). In answer to the research question about the relationship between Internet self-efficacy and deficient Internet self-regulation, a significant positive relationship was found (r = .23, p < .01). The structural model specifying the relationships among Internet use, depression, habitual Internet behavior, self-reactive outcome expectations, and deficient Internet self-regulation was not consistent with the data, χ2(5, N = 465) = 70.96, p < .001. A revised model shown in Figure 2 was found to be consistent with the data, χ2(2, N = 465) = 1.34, p > .05.8 In it, depression was the causal antecedent to deficient Internet self-regulation (β = .27) and self-reactive outcome expectations (β = .19). Internet self-efficacy was an antecedent to habitual strength (β = .18), self-reactive outcome expectations (β = .26), deficient self-regulation (β = .16) and Internet use (β = .20). Deficient self-regulation posed a direct relationship

Figure 2. Final structural model.


243

to habit strength (β = .55) and to Internet use (β = .13). Self-reactive outcome expectations were directly related to Internet use (β = .09) and indirectly related to use through habit strength (β = .19) and deficient Internet self-regulation (β = .32). Thus, the final structural model differed from the one initially proposed, in that additional direct relationships between variables were found. Links between Internet self-efficacy and both habit strength and deficient Internet self-regulation were discovered. Also, self-reactive outcomes were directly related to habit strength as well as acting on habits through deficient Internet self-regulation. The predictive power of this model is indicated by the R2 statistics shown in Figure 2. From this model, 10% of the variance in self-reactive outcome expectations, 50% of the variance in Internet habit strength and 26% of the variance in deficient Internet self-regulation were explained. Finally, 37% of the variance in Internet use was explained.

DISCUSSION Both conceptually and empirically, what others have termed “Internet addiction” can be redefined as deficient self-regulation. The so-called symptoms of Internet addiction from prior research may in fact be indicators of deficient self-regulation of Internet usage that contribute to habit formation. When assessed within a normal population, deficient Internet self-regulation was related to usage behavior across a wide range of consumption. On average, respondents scored near the collective midpoint of the seven-item measure of deficient self-regulation, suggesting that the individual symptoms/indicators were widely distributed, at least among college students. Deficient self-regulation emerged not as an all-or-nothing phenomenon that distinguished addicts from nonaddicts but as a continuous variable that was systematically related to consumption even among those who fell short of the threshold for a “diagnosis” of Internet addiction. From the current data, it appears that lapses in effective self-regulation lead to the formation of media consumption habits, but not necessarily to harmful consumption patterns that might be termed addictive. The findings also presented a significant departure from prior conceptualizations of the problem of media usage in both the uses and gratifications (Palmgreen et al., 1985) and social-cognitive (LaRose et al., 2001) paradigms. Three competing mechanisms emerged that contended with “cool” rational thought about the expected outcomes/gratifications of media usage (represented as the path from depression to self-reactive outcome expectations to usage) that both theories emphasize.

244


The first alternative mechanism (depression to self-reactive outcomes to deficient Internet self-regulation to habit strength to usage) reflected a conceptualization of habit formation found “lurking” in the uses and gratifications literature (Stone & Stone, 1990): that habitual behavior is the residue of repetitive, prior conscious decision making. Drawing on social-cognitive theory, we proposed that the repeated use of the media to relieve dysphoric moods can lead to deficient selfregulation as the media consumer economizes on the mental energy required to make decisions about repeated behavior. As self-regulation becomes less vigilant, the media behavior in question becomes automatic and habitual; indeed, the loss of self-control is one of the recognized preconditions for automatic, habitual behavior (Bargh & Gollwitzer, 1994). In this model, depression also acted directly on deficient self-regulation, which we interpret as the depressive cognitive bias that impairs self-regulation (described in Bandura, 1991). We also hypothesized a direct link from deficient self-regulation to usage because impulsive as well as habitual behavior may result from deficient selfregulation. We found evidence of that mechanism as well, although it was a relatively weak one. However, the nature of the criterion usage variable, which included two items asking about “typical” usage patterns, may have underestimated the impact of atypical, impulsive behavior. An unexpected third path (depression to self-reactive outcomes to habit strength to usage) also emerged. This may reflect a second type of automatic thinking (cf. Bargh & Gollwitzer, 1994) in which the user is inattentive to the behavior in question, making it increasingly inaccessible to self-regulation. That also suggests a direct “hot” link between emotion and repetitive behavior (see Metcalfe & Mishel, 1999), a classical conditioning mechanism that short circuits conscious choice of media alternatives. In that case, habits could build through direct stimulus–response associations between media stimuli and the emotional responses they produce, without the formulation or execution of conscious consumption decisions. Internet self-efficacy had positive, prior relationships to both deficient selfregulation and habit strength, as well as the previously examined (in Eastin & LaRose, 2000) relationships to outcome expectations and usage. It may be that in the amount of time required to establish Internet self-efficacy (some 2 years, cf. LaRose, Eastin, & Gregg, 2001), Internet usage patterns had already become habitual, thus resulting in the self-perceived positive relationship between selfefficacy and deficient self-regulation and habit strength. Depression may play an important and heretofore underappreciated role in media consumption behavior. Depression was a precursor to seeking self-reactive incentives but also directly impacted deficient self-regulation. The latter finding


245

substantiates the proposition (Bandura, 1991) that depressed people are less capable of engaging in effective self-regulation. Thus, depression may trigger both the rational, logical “high road” to habitual media selection and also the emotional, uncontrollable “low road” to unregulated media usage. By equating Internet addiction with deficient self-regulation and habit, we do not wish to dismiss the possibility of “true” Internet dependency. Indeed, there are case studies (e.g., in Greenfield, 1999; Hall & Parsons, 2001; Young, 1998) that may well meet the diagnostic criteria for psychiatric illnesses. Our own data were consistent with the hypothesis that true dependency exists at the extreme end of the spectrum of deficient self-regulation. Comparing those meeting the criterion for “Internet addiction” (five or more symptoms, as defined by Young, 1999) with those who did not, the “addicted” respondents had higher levels of Internet usage, t(485) = 4.00, p < .001, habit strength, t(494) = 9.63, p < .001, self-reactive outcome expectations, t(496) = 5.62, p < .001, Internet self-efficacy, t(492) = 2.92, p < .01, and depression, t(492) = 5.11, p < .001. We would expect Internet-dependent individuals to score higher on these indices than normal Internet users. We would contend, though, that many of the “addicts” and “pathological Internet users” identified in previous survey studies (e.g., Chou & Hsiao, 2000; Young, 1996) of Internet addiction fell well short of the clinical definition that requires a professional assessment of harmful life consequences (Shaffer et al., 2000). Rather, we argue that those studies parallel ours in studying the relationship between indicators of deficient self-regulation and usage in predominantly nonpathological populations. For true pathology to exist, serious personal and social problems must be experienced, and that should be left for trained clinicians rather than for social scientists to evaluate. We do not think that answering “strongly agree” to a survey item that asks whether Internet usage interferes with other life activities (as 4% of our sample did) is an adequate indication of the presence of serious life problems as they would be judged by a clinical psychologist. Making such items harder to “pass” (e.g., by specifying “serious” interference with life activities or asking about specific crises provoked by excessive media consumption) still leaves the issue in the realm of self-diagnosis, which all beginning psychology students are warned not to do. Limitations Several factors limit the validity of the present study. The results may not be generalizable to other populations that have lower levels of Internet usage and experience than college students do. In particular, differing results may be observed

246


among populations of novice users where habitual patterns of Internet use have yet to emerge. Also, college students experience higher levels of depression than the general population (Rich & Scovel, 1987), so the mechanisms relating depression to media usage may vary. Finally, although causal modeling can test assumptions about the direction of causality, experimental or time series analysis is required to establish causal relationships conclusively. Several of the relationships tested in the this study (e.g., among depression, deficient Internet self-regulation, and usage) may be reciprocal in nature and hence should be further examined with time series data. Implications for Future Research Thus, self-regulation is a potentially important new variable with which to explain habitual media exposure. The phenomenon formerly known as media dependence or media addiction—but now understood as deficient self-regulation—is potentially of central interest to a field of study concerned with media use in society, rather than an aberration that is the province of clinical psychology. Our findings pose two challenges to the prevailing uses and gratifications paradigm. First, it appears that habit may have been identified as a type of media use gratification in an overly broad manner in the tradition of uses and gratifications research. In present research, habit was found to be a conceptually and empirically distinct variable that acted in concert with the active decision-making process that weighs media choices against perceived needs. Second, it appears that the “cool” consideration of the needs fulfilled by media consumption behavior may coexist alongside the “hot” operant conditioning mechanism—a condition that was neglected in the basic conceptualization of uses and gratifications as a reaction against functionalism (Palmgreen et al., 1985). The notion that automatic and controlled thinking may jointly affect behavior is also consistent with current conceptions of automatic thought (Bargh & Gollwitzer, 1994), in contrast to earlier research that stressed an either/or relationship between the two modes of cognition (e.g., Shiffrin & Schneider, 1977). Still, there was a cognitive component to habit formation that traced through expected self-reactive outcome gratifications and self-regulation. It is conceivable that other types of expected gratification outcomes (e.g., for social integration) could be related to habit strength as well. This is a question for future research. Understanding habit as deficient self-regulation opens new avenues of research into the origins and control of habitual behavior through the self-regulatory subfunctions of self-observation, judgment, and self-reactive influence. In highlighting the importance of habit and self-regulation as new explanatory variables,


247

operational definitions of media usage should be revisited. Many of the formulations common in media research, including those used in this study, referred to “typical” patterns of media usage (e.g., in the typical or average week, weekday, or weekend day). These might be construed as measures of habit strength in that they are retrospective in nature and bespeak a consistency in past behavior across time and situations. As such, they could be confounded with self-reports of habits (cf. Ouellette & Wood, 1998) as much as indicators of current media usage. If so, a contending explanation emerges for the relative inability (cf. Palmgreen et al., 1985) of uses and gratifications to explain media exposure; that is, that the measures of media consumption they used were in effect measures of habit strength instead. Thus, the “cool” conscious mechanism associated with the uses and gratifications paradigm did not explain habitual behavior very well because habits are conditioned by “hot” automatic mechanisms. By changing the dependent variable to prospective use (as in Lin, 1999), expected gratifications/outcomes might assume increased predictive power. By extension, our model of media behavior might be reformulated (as in Metcalfe & Mischel, 2000) to treat habits and behavioral intentions as separate but parallel predictors of actual behavior. The distinction between habit and deficient self-regulation should also be further explored. Both theoretically and empirically, deficient self-regulation was found to precede habit formation here. However, habit strength severely attenuated the relationship between deficient self-regulation and consumption, reducing the r value from .45 to .12, and the two were moderate to highly correlated (r = .67) with one another. This raises the possibility that the two measures were tapping the same underlying concept. Future research should attempt to replicate our findings with alternative operational measures of habit strength (e.g., Aarts et al., 1998). The current model could be further refined by adding self-regulatory selfefficacy, or a belief in one’s ability to moderate one’s own Internet behavior, to the general measure of Internet self-efficacy. Self-regulatory self-efficacy plays a pivotal role in social-cognitive explanations of addictions (Bandura, 1997). A singleitem measure of the construct (“I am confident that I can keep my Internet use under control”) was included in this study, but it was subject to a ceiling effect. Future research should develop a reliable multi-item measure of this construct.

NOTES 1

However, chemicals naturally produced in the brain are involved in the addiction process (see Marlatt et al., 1988).

248


2

Along a number of dimensions including the quality, rate, originality, sociability, morality, and deviancy of their performance. 3 The overrepresentation of males reflected the composition of the classes included in the study. 4 The items were as follows: I use the Internet so much it interferes with other activities; I get strong urges to be on the Internet; I have to keep using the Internet more and more to get my thrill; I feel my Internet use is out of control; I would miss the Internet if I could no longer go online; I often spend longer on the Internet than I intend to when I start; I would go out of my way to satisfy my Internet urges. 5 The items were as follows: Relieve boredom; Feel relaxed; Feel less lonely; Forget my problems; Feel a sense of accomplishment; Find something that challenges me; Find a way to pass time. 6 The items to complete the sentence “During the past week…” were as follows: I felt that I could not shake the blues even with help from my family or friends, I had trouble keeping my mind on what I was doing. I felt everything I did was an effort, my sleep was restless, I felt lonely, I felt sad, I could not get “going.” Responses were scored on a 4-point scale, where 0 = rarely/none (less than 1 day), 1 = some/little (1–2 days), 2 = occasionally/moderate (3–4 days), and 3 = most/all (5–7 days). Mirowsky and Ross (1992) previously reported α = .83 and a correlation of .92 with the long form of the CES-D. 7 The items were each scored on a 7-point Likert-type scale ranging from strongly agree (7) to strongly disagree (1). The items to complete the sentence “I feel confident...” were as follows: understanding terms/words relating to Internet hardware; understanding terms/words relating to Internet software; troubleshooting Internet problems; explaining why a task will not run on the Internet; using the Internet to gather data; learning advanced skills within a specific Internet program; turning to an online discussion group when help is needed. Eastin and LaRose (2000) reported α = .93. 8 A second alternative model suggested by one of our reviewers, in which deficient preceded depression, did not fit these data, χ2(3, N = 465) = 8.75, p < .05.

REFERENCES Aarts, H., Verplanken, B., & van Knippenberg, A. (1998). Predicting behavior from actions in the past: Repeated decision making or a matter of habit? Journal of Applied Social Psychology, 28, 1355–1374. American Psychiatric Association. (1987). Diagnostic and statistical manual of mental disorders (3rd ed, rev.). Washington, DC: Author. American Psychiatric Association. (1994). Diagnostic and statistical manual of mental disorders (4th ed.). Washington, DC: Author. Anderson, D. R., Collins, P. A., Schmitt, K. L., & Jacobvitz, R. S. (1996). Stressful life events and television viewing. Communication Research, 23, 243–260.


249

Bandura, A. (1989). Human agency in social cognitive theory. American Psychologist, 44, 1175–1184. Bandura, A. (1991). Social cognitive theory of self-regulation. Organizational Behavior and Human Decision Processes, 50, 248–287. Bandura, A. (1997). Self-efficacy: The exercise of control. New York: Freeman. Bandura, A. (1999). A sociocognitive analysis of substance abuse: An agentic perspective. Psychological Science, 10, 214–217. Bargh, J. A., & Gollwitzer, P. M. (1994). Environmental control of goal directed action: Automatic and strategic contingencies between situation and behavior. In W. D. Spaulding (Ed.), Nebraska symposium on motivation (Vol. 41, pp. 71—124). Lincoln: University of Nebraska Press. Brenner, V. (1997). Psychology of computer use XLVII: Parameters of Internet use, abuse and addiction: The first 90 days of the Internet usage survey. Psychological Reports, 80, 879–882. Canary, D. J., & Spitzberg, B. H. (1993). Loneliness and media gratification. Communication Research, 20, 800–821. Chou, C., & Hsiao, M. C. (2000). Internet addiction, usage, gratification, and pleasure experience: The Taiwan college students’ case. Computers & Education, 35, 65–80 Cooper, A., Scherer, C., Boies, S. C., & Gordon, B. (1999). Sexuality on the Internet: From sexual exploration to pathological expression. Professional Psychology: Research and Practice, 30, 154–164. Davis, R. A. (2001). A cognitive–behavioral model of pathological Internet use. Computers in Human Behavior, 17, 187–195. Dittmar, M. E. (1994). Relations among depression, gender, and television viewing of college students. Journal of Social Behavior and Personality, 9, 317–328. Eastin, M. S., & LaRose, R. (2000). Internet self-efficacy and the psychology of the digital divide. Journal of Computer Mediated Communication, 6. Retrieved September 30, 2001, from http://www.asusc.org/jcmc/vo16/issue1/eastin .html Fearing, J. (1997) Hooked on the net. Retrieved September 15, 2000, from http://www.nationalcounseling.com/cmpadict.html Finn, S. (1992). Television addiction—An evaluation of four competing mediause models. Journalism Quarterly, 69, 422–435. Gaziano, C. (1990). Media dependence for news: Some neglected groups. Mass Communication Review, 17, 2. Greenberg, J. L., Lewis, S. E., & Dodd, D. K. (1999). Overlapping addictions and self-esteem among college men and women. Addictive Behaviors, 24, 565–571.

250


Greenfield, D. N. (1999). Virtual addiction. Oakland, CA: New Harbinger Publications. Griffiths, M. (1991). Amusement machine playing in childhood and adolescence: A comparative analysis of video games and fruit machines. Journal of Adolescence, 14, 53–73. Griffiths, M. (1995). Technological addictions. Clinical Psychology Forum, 71, 14–19. Griffiths, M. (1999). Internet addiction: Fact or fiction? Psychologist, 12, 246–250. Griffiths, M. (2000a). Does Internet and computer “addiction” exist? Some case study evidence. CyberPsychology and Behavior, 3, 211–218. Griffiths, M. (2000b). Excessive Internet use: Implications for sexual behavior. CyberPsychology and Behavior, 3, 537–552. Griffiths, M. D., & Hunt, N. (1998). Dependence on computer games by adolescents. Psychological Reports, 82, 475–480. Hall, A. S., & Parsons, J. (2001). Internet addiction: College student case studying best practices in cognitive behavior therapy. Journal of Mental Health Counseling, 23, 312–327. Helregel, B., & Weaver, J. (1989). Mood-management during pregnancy through selective exposure to television. Journal of Broadcasting and Electronic Media, 33, 15–33. Jöreskog, K., & Sörbom, D. (2000). LISREL 8.30 (Computer Software). Scientific Software International. Kraut, R., Patterson, M., Lundmark, V., Kiesler, S., Mukophadhyay, T., & Scherlis, W. (1998). Internet paradox: A social technology that reduces social involvement and psychological well-being? American Psychologist, 53, 1017–1031. Kubey, R. (1996). Television dependence, diagnosis and prevention: With commentary on video games, pornography, and media education. In T. MacBeth (Ed.), Tuning in to young viewers: social science perspectives on television (pp. 221–260). Newbury Park, CA: Sage. Kubey, R., & Csikszentmihalyi, M. (1990). Television and the quality of life. Hillsdale, NJ: Lawrence Erlbaum Associates. Kubey, R., & Csikszentmihalyi, M. (2002). Television addiction. Scientific American, 286, 74–81. LaBerge, D., & Samuels, S. J. (1974). Toward a theory of automatic information processing in reading. Cognitive Psychology, 6, 293–323. Landis D., Triandis, H. C., & Adamopoulos, J. (1978). Habit and behavioral intentions as predictors of social-behavior. Journal of Social Psychology, 106, 227–237.


251

LaRose, R., Eastin, M. S., & Gregg, J. (2001). Reformulating the Internet paradox: Social cognitive explanations of Internet use and depression. Journal of Online Behavior, 1. Retrieved September 30, 2001, from http://www .behavior.net/JOB/v1n2/paradox.html LaRose, R., Mastro, D. A., & Eastin, M. A. (2001). Understanding Internet usage: A social cognitive approach to uses and gratifications. Social Science Computer Review, 19, 395–413. Lin, C. A. (1993). Adolescents’ viewing activity and gratifications in a new media environment. Mass Communication Review, 20, 39–50. Lin, C. A. (1999). Predicting online service adoption likelihood among potential subscribers: A motivational approach. Journal of Advertising Research, 39, 79–89. Lin, C. A. (2001). Audience attributes, media supplementation and likely online service adoption. Mass Communication and Society, 4, 19–38. Marks, I. (1990). Behavioural (non-chemical) addictions. British Journal of Addiction, 85, 1389–1394. Marlatt, G. A., Baer, J. S., Donovan, D. M., & Kivlahan, D. R. (1988). Addictive behaviors: Etiology and treatment. Annual Review of Psychology, 39, 223–252. Mastro, D., Eastin, M. S., & Tamborini, R. (2002). Internet search behavior and mood alteration: Replication and expansion of selective exposure theory. Media Psychology, 4, 157–172. McIlwraith, R. D. (1998). “I’m addicted to television”: The personality, imagination and TV watching pattern of self-identified TV addicts. Journal of Broadcasting and Electronic Media, 42, 371–386. McIlwraith, R., Jacobvitz, R. S., Kubey, R., & Alexander, A. (1991). Television addiction—Theories and data behind the ubiquitous metaphor. American Behavioral Scientist, 35, 104–121. Metcalfe, J., & Mischel, W. (1999). A hot/cool-system analysis of delay of gratification: Dynamics of willpower. Psychological Review, 106, 3–19. Mirowsky, J., & Ross, C. E. (1992). Age and depression. Journal of Health and Social Behavior, 33, 187–205. Morahan-Martin, J., & Schumacher, P. (2000). Incidence and correlates of pathological Internet use among college students. Computers in Human Behavior, 16, 13–29. Nathan, P. E. (1988). The addictive personality is the behavior of the addict. Journal of Consulting and Clinical Psychology, 56, 183–188. Ouellette, J. A., & Wood, W. (1998). Habit and intention in everyday life: The multiple processes by which past behavior predicts future behavior. Psychological Bulletin, 124, 54–74.

252


Palmgreen, P., Wenner, L., & Rosengren, K. (1985). Uses and gratifications research: The past ten years. In K. Rosengren, L. Wenner, & P. Palmgreen (Eds.), Media gratifications research (pp. 11–37). Beverly Hills, CA: Sage. Papacharissi, Z., & Rubin, A. M. (2000). Predictors of Internet usage. Journal of Broadcasting and Electronic Media, 44, 175–196. Pawlowski, C. (2000). Glued to the tube: The threat of television addiction to today’s family. New York: Sourcebooks. Peele, S. (1999). Diseasing of America: How we allowed recovery zealots and the treatment industry to convince us we are out of control. San Francisco: Jossey-Bass. Pratarelli, M. E., Browne, B. L., & Johnson, K. (1999). The bits and bytes of computer Internet addiction: A factor analytic approach. Behavior Research Methods Instruments & Computers, 31, 305–314. Putnam, D. E. (2000). Initiation and maintenance of online sexual compulsivity: Implications for assessment and treatment. CyberPsychology and Behavior, 3, 553–564. Rich, A. R., & Scovel, M. (1987). Causes of depression in college students: A cross-lagged panel correlational analysis. Psychological Reports, 60, 27–30. Rosenstein, A. W., & Grant, A. E. (1997). Reconceptualizing the role of habit: A new model of television audience activity. Journal of Broadcasting and Electronic Media, 41, 324–344. Rozin, P., & Stoess, C. (1993). Is there a general tendency to become addicted? Addictive Behaviors, 18, 81–87. Rubin, A. M. (1984). Ritualized and instrumental television viewing. Journal of Communication, 34, 67–77. Sanders, C. E. (2000). The relationship of Internet use to depression and social isolation among adolescents. Adolescence, 35, 237–242. Scherer, K. (1997). College life on-line: Healthy and unhealthy Internet use. Journal of College Student Development, 38, 655–664. Shaffer, H. J., Hall, M. N., & Vander Bilt, J. (2000). “Computer addiction”: A critical consideration. American Journal of Orthopsychiatry, 70, 162–168. Shapira, N. A., Goldsmith, T. D., Keck, P. E., Khosla, U. M., & McElroy, S. L. (2000). Psychiatric features of individuals with problematic Internet use. Journal of Affective Disorders, 57, 267–272. Shiffrin, R. M., & Schneider, W. (1977). Controlled and automatic human information processing: II. Perceptual learning, automatic attending, and a general theory. Psychological Review, 84, 127–190.


253

Smith, R. (1986). Television addiction. In J. Bryant & D. Anderson (Eds.), Perspectives on media effects (pp. 109–128). Hillsdale, NJ: Lawrence Erlbaum Associates. SPSS, Inc. (2000). Statistical Package for the Social Sciences (Version 10.0) [Computer software]. Chicago, IL: SPSS, Inc. Stone, G., & Stone, D. (1990). Lurking in the literature: Another look at media use habits. Mass Communication Review, 17, 25–33. Triandis, H. C. (1980). Values, attitudes and interpersonal behavior. In H. E. Howe & M. Page (Eds.), Nebraska symposium on motivation (Vol. 37, pp. 195–259). Lincoln: University of Nebraska Press. Winn, M. (1977). The plug-in drug. New York: Bantam. Wise, M. G., & Tierney, J. G. (1994). Impulse control disorders not elsewhere classified. In R. E. Hales, S. C. Yudofsky, & J. A. Talbott (Eds.), Textbook of psychiatry (2nd Ed., pp. 681–699). Washington, DC: American Psychiatric Press. Young, K. S. (1996). Psychology of computer use XL: Addictive use of the Internet: A case that breaks the stereotype. Psychological Reports, 79, 899–902. Young, K. S. (1998). Caught in the net. New York: Wiley. Young, K. S. (1999). Evaluation and treatment of Internet addiction. In L. VandeCreek & T. Jackson (Eds.), Innovations in clinical practice: A sourcebook (Vol. 17, pp. 19–31). Sarasota, FL: Professional Resource Press. Young, K. S., & Rogers, R. C. (1998). The relationship between depression and Internet addiction. Cyberpsychology and Behavior, 1, 25–36. Zillmann, D., & Bryant, J. (1985). Affect, mood and emotion as determinants of selective media exposure. In D. Zillmann & J. Bryant (Eds.), Selective exposure to communication (pp. 157–190). Hillsdale, NJ: Lawrence Erlbaum Associates. Zillmann, D., & Bryant, J. (1994). Entertainment as media effects. In D. Zillmann & J. Bryant (Eds.), Media effects (pp. 157–190). Hillsdale, NJ: Lawrence Erlbaum Associates.