A Unique Problem or the Manifestation of a Preexisting Disorder? The Mediating Role of Problematic Internet Use in the Relationships Between Psychosocial Problems and Functional Impairment

Abstract

Concerns about the problematic nature of internet use have been discussed since the inception of the internet. Internet addiction, problematic internet use (PIU), and the deficient self-regulation of internet use are some issues studied in this domain. Some regard these conditions as genuine disorders that cause disruptions in one’s life. Others criticize their legitimacy, claiming that functional impairment associated with internet use is indicative of primary psychosocial problems and has little to do with the internet. The purpose of this investigation was to understand whether cognitive preoccupation and uncontrolled use, components of PIU, are part of a unique disorder or are symptomatic of underlying psychosocial problems. This research tested the mediating role of PIU in the relationships between psychosocial problems (i.e., social anxiety, loneliness, and depression) and impairment of interpersonal relationships and vocational performance in two studies. Different conclusions were reached based on the methodological design of the study; however, the findings generally supported the mediation of PIU.

Keywords

problematic internet use internet addiction social anxiety loneliness depression social impairment vocational impairment longitudinal analysis

Discussions on the “addictive” qualities of media use date back to the early 1940s when Preston (1941) wrote about the ostensible addictions children can develop to motion pictures and radio programs. She and other media scholars (e.g., Rowland, 1944) viewed specific content in films and radio programs as the stimulus to which people formed addictions. This media addiction was described as “giving oneself over to a habit-forming practice very difficult to overcome, no matter how the aftereffects are dreaded” (pp. 147-148). In the extensive tradition of media addiction research that followed, the notion of addiction has been applied to a wide range of contexts, including television viewing (Finn, 1992; Kubey & Csikszentmihalyi, 2002; McIlwraith, 1998), video game play (Griffiths, 1991, 1992; Soper & Miller, 1983), and computer use (Murray, 1996). It is not then surprising that internet technologies, in their pervasive reach, have been indicted for their addictive potential (e.g., Beard & Wolf, 2001; Griffiths, 1999; Kandell, 1998; Young, 1998).

Problematic internet use (PIU), also called internet addiction, compulsive internet use, and the deficient self-regulation of internet use, has been studied predominately in the past two decades. PIU generally refers to “maladaptive cognitions and behaviors involving Internet use” (Caplan, 2003, p. 626). Communication research has made significant contributions to the study of PIU by identifying important antecedents and outcomes and refining how to understand and interpret this construct. Recent evidence suggests that PIU is best characterized as a form of deficient self-regulation and moves away from the disease model approach, which classifies PIU as an addiction or pathology (e.g., Caplan, 2010; LaRose & Eastin, 2004; LaRose, Lin, & Eastin, 2003; Tokunaga & Rains, 2010). Despite the progress made in understanding PIU, skepticism remains about the legitimacy of the condition. Shaffer, Hall, and Vander Bilt (2000) voice criticisms about PIU by arguing that it may not be a unique disorder but symptomatic of other primary disorders, such as psychosocial problems or chronic boredom. Ha et al. (2006) summarize some of the confusion by stating that PIU is inconsistently viewed as “a genuine diagnosis, a new symptom manifestation of underlying disorders or psychosocial problems in adjusting to a new medium” (pp. 821-822).

Criticisms concerning the authenticity of problematic media use are not unique to internet technologies; questions about the legitimacy of other media addictions have also been articulated. Some argue that media addictions are the product of media hysteria and are often misinterpreted (Wood, 2008). They propose media addictions are mere indicators of underlying psychosocial vulnerabilities, such as psychological problems or social isolation (Livingstone, 1999). The controversy involving the legitimacy of media addictions, such as PIU, has obscured the interpretation of this construct and puts into question the contribution of research and theory on PIU. The present investigation endeavors to understand the mediating role of PIU in the relationships between psychosocial problems and functional impairment in an effort to reconcile the conflicting views. Addressing the relationships between PIU, psychosocial problems, and functional impairment in one’s life may bring clarity to discussions regarding whether PIU is a genuine condition. If psychosocial problems influence functional impairment through PIU, it may be argued that PIU is an important factor in disruptions to daily life that people experience from internet use. The absence of mediation demonstrates that psychosocial problems largely account for the challenges in one’s life and downplays the significance of PIU. In the following section, the relationships between psychosocial problems, PIU, and functional impairment are described.

Psychosocial Problems as an Individual-Level Vulnerability Factor

Psychosocial problems are any mental health disruptions that involve cognitive, emotional, or behavioral symptoms (Segrin, 2001). These disruptions, which stem from distressing life experiences, can range in duration from transitory to persistent problems in one’s life. The diathesis-stress model explains that a preexisting vulnerability determined by genetics or biology, in concert with a stressor originating from the environment, has the ability to hasten the development or instantiation of psychosocial problems (Abramson, Metalsky, & Alloy, 1989; Meehl, 1962; Rosenthal, 1963). Both stress and the diathesis must be present for psychosocial problems to surface. Segrin and Flora (2000) demonstrate that social skills deficits can act as a diathesis, and when presented with a stressor, can initiate feelings of social anxiety, loneliness, and depression. These three psychosocial problems are commonly presented as individual vulnerability factors of PIU (e.g., Caplan, 2002, 2007; Kim, LaRose, & Peng, 2009; LaRose et al., 2003; Morahan-Martin & Schumacher, 2003; Tokunaga & Rains, 2010).

Social Anxiety

Social anxiety refers to apprehension emerging from the possibility or presence of negative personal evaluations in genuine or imagined social settings (Schlenker & Leary, 1982). These social settings can be interpersonal, group, or public interactions wherein people wish to make positive impressions on others but are concerned with the potential receipt of negative criticisms. Social anxiety can range in duration from short periods of state anxiety to long-term periods of trait anxiety (Holt, Heimberg, Hope, & Liebowitz, 1992; Segrin, 2001). Individuals with chronic social anxiety often experience somatic symptoms (i.e., physical reactions), which are accompanied by unpleasant moods and apprehensive thoughts, when confronted with social interactions (Leary, 1995). Escaping or avoiding social interactions altogether is one way the socially anxious manage their apprehension.

People with social anxiety use the internet to fulfill their needs for socializing with others because these technologies are imbued with qualities that make online social interactions less risky than in-person communication (Shepherd & Edelmann, 2005). The lowered risk is reflected in the reduced cues that may convey negative evaluations or social rejection (McKenna, Green, & Gleason, 2002; Siegel, Dubrovsky, Kiesler, & McGuire, 1986). Asynchronous text-based communication available on the internet also allows socially anxious individuals to manage the information they present about themselves in a deliberate and low-risk manner. Caplan (2007) explains that people with social anxiety initially develop a preference for online social interactions because they feel more effective and confident through internet channels. The preference for socializing over the internet eventually develops into the maladaptive cognitions and behaviors symptomatic of PIU.

Loneliness

Loneliness is a subjective evaluation of inadequacy or deficiency in one’s social network (Perlman & Peplau, 1981). This inadequacy is experienced when people feel there are too few members in their social network or the quality of their relationships is unsatisfactory. Because loneliness is often triggered by unmet companionate needs (Russell, Peplau, & Cutrona, 1980; Segrin & Flora, 2000), some believe lonely people are motivated to seek social interactions despite the anxiety that may be associated with in-person communication (Sullivan, 1953; Weiss, 1973). The need to belong is a powerful and pervasive desire in people’s lives, that influences cognitions, emotions, and social behaviors (Baumeister & Leary, 1995). Satisfying social relationships are thus sought to mitigate feelings of loneliness (Heinrich & Gullone, 2006).

The internet can be used to seek social interactions and, in turn, create satisfying interpersonal relationships necessary to relieve lonely feelings. Morahan-Martin and Schumacher (2003) suggest that lonely people are drawn to the internet to expand their social networks and capitalize on some favorable characteristics of internet-based communication, such as anonymity and the lack of physical presence. These characteristics among others allow people with loneliness to have control over their social interactions, thereby reducing self-consciousness and anxiety. Lonely people are better able to “express their real selves with others on the internet than they can with those they know offline” (McKenna et al., 2002, p. 28). The anonymity of certain text-based social interactions, such as support groups and instant messaging, encourages uninhibited behaviors by making it easier for the lonely to divorce their in-person and online identities (Joinson, 1998). Any interpersonal failures experienced over the internet, accordingly have little consequence on the personal identity of the lonely person.

Depression

Depression is characterized as an affective disorder in which depressed individuals experience a host of symptoms, including mood alterations, negative self-concept, the desire to inflict self-punishments, and changes in levels of activity (Beck & Alford, 2009). Depression can range in severity from minor bouts to major depression (Goldman, Nielsen, & Champion, 1999). Coyne (1976a, 1976b) asserts that the social interactions of depressed people tend to elicit negative appraisals and social rejection from others that then cause more depression, propelling the depressed into a vicious cycle. The negative evaluations and rejection stem from poor interactional skills (Lewinsohn, 1975; Segrin & Dillard, 1992).

The internet serves many important functions for depressed individuals, which explains why depression is often discussed in relation to internet use. Caplan (2003, 2005) suggests that the greater potential for anonymous communication, increased control over interactions, and lowered risk make socializing over the internet a desirable choice for those who fear rejection from others. Although rejection does sometimes occur through internet communication, negative social experiences can be easily resolved by moving on to others with whom the depressed person can socialize. Internet technologies are able to provide gratifications in the form of mood management and attention diversion to users with depression and other psychosocial problems (Chou & Hsiao, 2000; Song, LaRose, Eastin, & Lin, 2004). The ability to cope with negative moods using the internet is a seductive quality for individuals suffering from depression (Caplan, 2010).

Problematic Internet Use

PIU is used to describe maladjusted thoughts and behaviors involving some aspect of internet use (Caplan, 2002). This construct is related to internet addiction and compulsive internet use; however, PIU is not viewed as a disease or pathology but rather lapses in the effective management of internet use that can be self-corrected (LaRose et al., 2003; Tokunaga & Rains, 2010). The characterization of PIU adopted in this investigation is consistent with the deficient self-regulation of internet use perspective, which interprets PIU as a “state in which conscious self-control is relatively diminished” (LaRose et al., 2003, p. 232).

PIU is a bidimensional construct comprised of two elements: cognitive preoccupation and uncontrolled internet use. Cognitive preoccupation is a psychological disruption wherein a person becomes fixed to some aspect of internet use (Caplan, 2010). Preoccupation exists as persistent or even obsessive thoughts and feelings of anxiety or discomfort while away from the internet. For people with preoccupation, the internet is seen as an irresistible medium that serves critical functions in their lives (Shapira, Goldsmith, Keck, Khosla, & McElroy, 2000). These cognitive distortions may also accompany anticipatory thoughts about returning to the internet. Uncontrolled internet use, described as “an inability to control one’s online activity along with feelings of guilt about the lack of control” (Caplan, 2003, p. 626), is the behavior component of PIU. Kim and Davis (2009) argue that healthy internet use transitions to PIU when internet consumption falls out of one’s volitional control.

Functional Impairment

An important element distinguishing unhealthy from healthy internet use is the experience of functional impairment (Scherer, 1997; Shapira et al., 2000). Functional impairment, also called negative outcomes or consequences, is described as interferences in accomplishing daily routines or difficulties participating in customary life activities (LaRose, Lai, Lange, Love, & Wu, 2005; Tokunaga, 2011). These impairments often stem from diminished psychological or social health (Spitzer et al., 1995), adding to some of the confusion about the legitimacy of PIU (Ha et al., 2006; Shaffer et al., 2000). Although functional impairment manifests in many forms (e.g., financial impairment in relation to pathological gambling), social and vocational problems are the primary disruptions that emerge with PIU (Liu & Peng, 2008).

Social impairment refers to the diminished quality of offline interpersonal relationships (Kraut et al., 1998). These impairments occur most often in friend and family relationships, which are identified by strong social ties. The online relationships people create and maintain through internet technologies may be at the expense of their in-person social relationships (Kraut et al., 1998). Subjective evaluations of the reduction in quality of offline relationships, at the hands of internet use, are noticeable with some users (Aboujaoude, Koran, Gamel, Large, & Serpe, 2006). In addition to declining family and friend relationships, people experience vocational impairment in academic or occupational settings (Shapira et al., 2000). Perceptible losses in productivity of school work, such as declining grades, or occupational responsibilities are reported (Kubey, Lavin, & Barrows, 2001; Thatcher, Wretschko, & Fisher, 2008). Impairment occurs when people put off necessary tasks to participate in or think about internet activities.

The Mediating Role of Problematic Internet Use Between Psychosocial Problems and Functional Impairment

Psychosocial problems, such as depression, are initiated by changes in affect, cognitions, or behaviors caused by environmental stimuli (Lewinsohn, Rohde, & Seeley, 1998). The shift in thoughts and behaviors may lead to poor social interactions, and, if prolonged, result in unpleasant moods. Unpleasant moods, characterized by uneasiness or discomfort, are associated with many psychosocial problems (Rapaport & Judd, 1995). These mood states are, in part, responsible for the development of PIU.

Two related explanations are proposed for the relationships between psychosocial problems and PIU. Young (1998, 1999) asserts that the socially anxious, lonely, and depressed seek internet technologies to manage their psychosocial problems. People strategically organize their media consumption habits to manage the moods associated with psychosocial problems in an effort to avoid or terminate aversive states and sustain gratifying, pleasurable experiences (Knobloch, 2003; Zillmann, 1988, 2000; Zillmann & Bryant, 1985). Internet media hold this ability to divert attention from unpleasant moods and raise pleasing mood states (Leung, 2004). To the extent that mood management using the internet is effective, individuals with psychosocial problems form PIU through a conditioned response that cognitively links the reduction of unpleasant moods to media use (Caplan, 2010; LaRose et al., 2003; Morahan-Martin & Schumacher, 2000). Bandura (1999) explains in social cognitive theory that unpleasant moods related to psychosocial problems can lead to the deficient self-regulation of behaviors through a process of self-slighting. Negative biases associated with self-slighting make it difficult to maintain control over behaviors or correct existing unregulated behaviors. The deficient self-regulation of internet use, interpreted as a marker of PIU, grows from psychosocial problems because aversive mood states motivate negative cognitive biases that rebuff attempts at regaining lost control over internet use (LaRose et al., 2003).

The origin of functional impairment related to internet use has been the concern of scholarly work since the advent of the internet. Displacement theorists have long argued that the internet shifts time previously saved for offline activities, such as socializing with others, to time spent using the internet (Kraut et al., 1998; Nie & Erbing, 2000; Nie, Hillygus, & Erbing, 2002). The displacement of in-person relationships for internet-based relationships is viewed then as a sign that one’s interpersonal relationships are deteriorating (Aboujaoude et al., 2006; Anderson, 2001; Mesch, 2003). Internet use also dislocates time for completing academic or work responsibilities, which in turn leads to vocational impairment (Kubey et al., 2001). Mixed and inconclusive results in research testing displacement theory of internet use have tempered some of the early attention and appeal of its theoretical propositions. A recent meta-analysis of the relationship between internet use and functional impairment finds little support for the relationship (Tokunaga, 2011).

A more tenable explanation for functional impairment from internet use resides in PIU. It is not that time spent using the internet causes functional impairment; people spend copious time online each day without ever experiencing difficulties or disruptions to their daily routines. Numerous studies also demonstrate that people can increase their subjective well-being through time spent participating in various internet activities (e.g., Selfhout, Branje, Delsing, ter Bogt, & Meeus, 2009; Valkenburg & Peter, 2007). Instead, it is the preoccupation and uncontrolled internet use that are responsible for the functional impairment (Caplan, 2003; Caplan & High, 2006; Kerkhof, Finkenauer, & Muusses, 2011; Nalwa & Anand, 2003; Suhail & Bargees, 2006; Tokunaga, 2011).

Preoccupation to and unregulated uses of the internet can serve as an obstacle in accomplishing daily activities and lead to perceptible impairment. Persistent thoughts about the internet have the capacity to consume individuals, reducing their focus and retention of information in school or work, and contribute to poorer academic or occupational performance. The loss of behavioral control and related feelings of guilt may also uniquely explain the decision to forego in-person social interactions because PIU sufferers are unwilling or unable to cease their use of the internet even temporarily. LaRose et al. (2005) explains that unregulated media use can “interfere with normal life activities, producing negative real world consequences (e.g., faltering relationships, failing grades) that in turn lead to deeper negative effects and a spiral of mounting media usage” (p. 6).

Research and theory on PIU suggest that cognitive preoccupation and uncontrolled internet use play important mediating roles between psychosocial problems and functional impairment. Testing this mediational model may bring clarity to the controversy surrounding the legitimacy of PIU by identifying the (un)necessary function of PIU in the development of impairments. It is argued that if PIU is a genuine condition, the indirect effects of psychosocial problems on functional impairment through PIU would be significant. If, as others have argued, the impairments people face from internet use are mere manifestations of underlying psychological or social disorders (e.g., Ha et al., 2006; Shaffer et al., 2000), then PIU would not mediate these relationships. Accordingly, the relative unimportance or even nonexistence of PIU is captured in an unsubstantiated mediation.

Hypothesis 1 (H1): Problematic internet use mediates the relationship between social anxiety and impairment in (a) academic/occupational work, (b) friendships, and (c) family relationships.

Hypothesis 2 (H2): Problematic internet use mediates the relationship between loneliness and impairment in (a) academic/occupational work, (b) friendships, and (c) family relationships.

Hypothesis 3 (H3): Problematic internet use mediates the relationship between depression and impairment in (a) academic/occupational work, (b) friendships, and (c) family relationships.

Study 1

Methods

Procedures and Participants

Three methods were used to collect participants for Study 1. Undergraduate students from a university in the Southwest were asked to participate in the study in exchange for research credit. In an effort to expand diversity of the sample in age, education, internet exposure, and internet experience, students were asked to refer nonstudent adults who were willing to participate. The response rate of the nonstudent adult referrals was 36.8%. Last, a letter of recruitment was placed on popular internet gaming websites, general interest websites, and virtual message boards read by self-identified internet addicts and PIU sufferers. The solicitation asked internet users to contribute their thoughts about internet use through an internet-based questionnaire.

A sample of 179 (63 males, 116 females) internet users completed the survey in full. The sample included 85 students from the university pool and 94 nonstudent adults from student referrals and the invitations placed on various websites. Several t tests conducted between the student and nonstudent adult subsamples, using PIU and impairments as separate dependent variables, were nonsignificant. These difference tests provided confidence that the subsamples could be aggregated and treated as a larger sample. The mean age of participants in the sample was 28.6 years (SD = 13.6, range = 18 to 86). The ethnic or racial makeup of this sample was as follows: 77.1% White/Caucasian, 10.1% Latino/a, 3.4% Asian, 1.1% African American/Black, 1.1% Pacific Islander, and 0.6% Native American. The additional 6.6% of the sample identified their race/ethnicity as mixed or Other.

Instruments

Social anxiety

Trait levels of social anxiety were measured using Leary’s (1983) Interactional Anxiousness Scale (IAS). The 15-item measure includes items such as “I wish I had more confidence in social situations,” and “In general, I’m a shy person.” The one-factor IAS evaluates social anxiety in several different social contexts, including work or school, small social interactions, and large gatherings. The IAS was measured on a 7-point Likert-type scale, with larger values representing greater perceived anxiety in social interactions.

Loneliness

The 20-item UCLA Loneliness Scale (Version 3; Russell, 1996) was used to measure participants’ level of appraised loneliness. The instrument serves as an index of how often people feel excluded from groups, how often they lack companionship, and whether there is anyone to whom they genuinely feel close (e.g., “How often do you feel that you are no longer close to anyone?”). Previous psychometric research demonstrates the good fit of a three-factor model specifying a single bipolar global loneliness factor, with two method factors for lonely and nonlonely items (Russell, 1996). These findings suggest that it is appropriate to reverse code nonlonely items and treat the UCLA Loneliness Scale as a unidimensional bipolar construct. The scale was measured on a 7-point scale (1 = never, 7 = always), with larger values corresponding to greater feelings of loneliness.

Depression

Participants’ level of depression was measured with Beck’s Depression Inventory-II (BDI-II; Beck, Steer, & Brown, 1996). The factor structure of the BDI-II is regarded as more stable than its predecessor, BDI-I (Dozois, Dobson, & Ahnberg, 1998). Participants are given 21 groups of 4 statements (e.g., “I am sad all the time,” “I feel I am a total failure as a person”) and asked to pick the statement that best reflects their present state. Depression is treated as a continuous unidimensional construct rather than a diagnostic tool with set cutoff points. Higher values on the BDI-II reflect elevated levels of depression.

Problematic internet use

The Generalized Problematic Internet Use Scale (GPIUS; Caplan, 2002) and LaRose et al.’s (2003) Deficient Self-Regulation Measure were combined to form an index of participants’ maladaptive cognitions and behaviors involving internet use. Four items measured the cognitive preoccupation component, and three items were used to evaluate unregulated internet use. Because the available information on the factor structure of the GPIUS in insufficient, a confirmatory factor analysis (CFA) of the two-dimensional measurement model was conducted. The bidimensional measurement model demonstrated good fit to the data, χ²(13) = 19.08, p = .12, comparative fit index (CFI) = .99, root mean square error of approximation (RMSEA) = .05, standardized root mean square residual (SRMR) = .05. The items were assessed with a 7-point scale; higher values represent more pervasive cognitive preoccupation and a greater inability to control internet behaviors.

Functional impairment

Three subscales of functional impairment were created for the purpose of this investigation. A four-item scale was used to evaluate impairment in school or work (e.g., “I have noticed that I have more difficulty with completing my school work/occupational duties”). Impairment in friend relationships was assessed with three items (e.g., “I have noticed that my relationships with offline friends have declined”). Finally, impairment in family relationships was measured with a four-item scale (e.g., “I am beginning to feel more detached from my family members”). Items that evaluate positive gains from internet use, taken from social compensation measures, were included to disguise the purpose of this scale and avoid test sensitization. A CFA of the functional impairment scale, with three unique dimensions reflecting impairment in academic/occupational work, friendships, and family relationships, showed good fit of the measurement model, χ²(32) = 72.85, p > .05, CFI = .97, RMSEA = .08, SRMR = .05.

Results

The mediating role of PIU in the relationships between psychosocial problems and functional impairment was tested using a bootstrap (i.e., asymptotic resampling procedure) method for evaluating indirect effects. Bootstrapping provides an estimate for and corrects for the bias of $\hat{θ}$ , which in this case represents the point estimates and standard errors of the indirect effects. This procedure also provides the confidence intervals around the parameter being estimated, θ (Mooney & Duval, 1993). The nonparametric bootstrapping method, which does not assume the distributional shape of individual variables or a sampling distribution, is a way to derive confidence intervals of the point estimates for the indirect effects empirically using an iterative bootstrapped sampling distribution (Preacher & Hayes, 2004).

The mediator models were tested in two ways. First, cognitive preoccupation and uncontrolled internet use were treated as individual mediators in the models to assess the indirect effects of each mediating variable in the absence of the other. Second, the indicators of PIU were included in multiple mediator models. The mediation tests were conducted using the INDIRECT macros created by Preacher and Hayes. Multiple mediator models are often favored over testing multiple mediators in a series of single-mediator models because multiple-mediator models are able to test whether a group of variables mediates the relationship between the independent and dependent variables (Preacher & Hayes, 2008). The bias in the parameter estimates of the regression coefficients is also reduced by including key variables in the model. However, given the high collinearity of cognitive preoccupation and uncontrolled internet use (i.e., r = .70), the effects of the mediators on the dependent variable may be attenuated when included together.

The bias-corrected and bias-corrected-and-accelerated 95% confidence intervals (CIs) for the indirect effect estimates obtained from the 5,000-bootstrap resamples procedure are presented in Tables 1 to 3. The indirect effect estimates and CIs for the mediator models that use social anxiety, loneliness, and depression as independent variables are provided in Tables 1, 2, and 3, respectively. The indirect effect, and thus the mediation, is substantiated when the bias-corrected-and-accelerated 95% CIs do not include 0. To interpret the tables, the first two mediators in each block represent the results of separate mediator models. The second set of mediators in each block, which group CP (i.e., cognitive preoccupation), UU (i.e., uncontrolled use), and Total, illustrates the results of multiple mediator models. The “Total” represents the summative effect of the independent variable on the dependent variable through the mediators. Table 4 presents the means, standard deviations, and a complete zero-order correlation matrix of variables used in this project.

Table 1.

Mediation of the Effect of Social Anxiety on Functional Impairment Through Problematic Internet Use.

					Bootstrapping
					BC 95% CI		BCa 95% CI
Independent Variable	Dependent Variable	Mediator	Point Estimate	SE	Lower	Upper	Lower	Upper
Social anxiety	Academic/occupational
		CP	0.083	0.035	0.029	0.169	0.027	0.162
		UU	0.116	0.035	0.059	0.198	0.058	0.197
		CP	0.023	0.036	−0.041	0.106	−0.041	0.105
		UU	0.099	0.039	0.034	0.188	0.033	0.187
		TOTAL	0.122	0.038	0.057	0.208	0.056	0.207
	Friendship relationships
		CP	0.070	0.032	0.020	0.150	0.019	0.147
		UU	0.085	0.034	0.029	0.165	0.029	0.166
		CP	0.035	0.037	−0.024	0.127	−0.025	0.125
		UU	0.059	0.042	−0.015	0.152	−0.013	0.156
		TOTAL	0.094	0.036	0.031	0.177	0.031	0.176
	Family relationships
		CP	0.060	0.028	0.018	0.126	0.017	0.125
		UU	0.074	0.029	0.027	0.140	0.027	0.141
		CP	0.028	0.031	−0.024	0.105	−0.025	0.104
		UU	0.053	0.035	−0.008	0.130	−0.001	0.132
		TOTAL	0.081	0.032	0.028	0.157	0.028	0.157

Note: BC = Bias corrected; BCa = Bias corrected and accelerated; CP = Cognitive Preoccupation; UU = Uncontrolled Use; 95% CI = 95% confidence interval. The initial CP/UU in each block represents separate mediator models; the latter CP/UU/TOTAL represents results from multiple mediator models.

Table 2.

Mediation of the Effect of Loneliness on Functional Impairment Through Problematic Internet Use.

					Bootstrapping
					BC 95% CI		BCa 95% CI
Independent Variable	Dependent Variable	Mediator	Point Estimate	SE	Lower	Upper	Lower	Upper
Loneliness	Academic/occupational
		CP	0.091	0.043	0.021	0.197	0.020	0.193
		UU	0.126	0.042	0.056	0.227	0.055	0.225
		CP	0.016	0.052	−0.087	0.123	−0.089	0.117
		UU	0.115	0.049	0.029	0.219	0.030	0.220
		TOTAL	0.131	0.050	0.051	0.252	0.049	0.250
	Friendship relationships
		CP	0.042	0.038	−0.023	0.128	−0.024	0.126
		UU	0.053	0.039	−0.012	0.144	−0.013	0.143
		CP	0.013	0.054	−0.095	0.122	−0.106	0.114
		UU	0.045	0.056	−0.066	0.158	−0.059	0.166
		TOTAL	0.057	0.044	−0.013	0.164	−0.014	0.162
	Family relationships
		CP	0.043	0.031	−0.007	0.115	−0.007	0.115
		UU	0.056	0.031	0.006	0.131	0.006	0.131
		CP	0.013	0.043	−0.072	0.099	−0.079	0.092
		UU	0.047	0.043	−0.032	0.137	−0.027	0.143
		TOTAL	0.060	0.035	0.007	0.147	0.007	0.147

Table 3.

Mediation of the Effect of Depression on Functional Impairment Through Problematic Internet Use.

					Bootstrapping
					BC 95% CI		BCa 95% CI
Independent Variable	Dependent Variable	Mediator	Point Estimate	SE	Lower	Upper	Lower	Upper
Depression	Academic/occupational
		CP	0.300	0.131	0.098	0.625	0.090	0.614
		UU	0.366	0.124	0.162	0.656	0.160	0.654
		CP	0.050	0.134	−0.171	0.370	−0.170	0.370
		UU	0.336	0.133	0.125	0.682	0.123	0.669
		TOTAL	0.386	0.142	0.141	0.701	0.138	0.695
	Friendship relationships
		CP	0.287	0.133	0.087	0.617	0.077	0.599
		UU	0.295	0.117	0.113	0.585	0.111	0.581
		CP	0.123	0.146	−0.108	0.487	−0.112	0.473
		UU	0.221	0.133	0.005	0.530	0.010	0.536
		TOTAL	0.343	0.139	0.125	0.701	0.119	0.689
	Family relationships
		CP	0.238	0.109	0.073	0.521	0.065	0.507
		UU	0.251	0.010	0.098	0.506	0.095	0.498
		CP	0.092	0.122	−0.104	0.388	−0.106	0.383
		UU	0.195	0.108	0.023	0.465	0.024	0.468
		TOTAL	0.288	0.118	0.097	0.573	0.095	0.568

Table 4.

Reliabilities, Means, Standard Deviations, and Zero-Order Correlation Matrix .for Variables in Study 1.

Variable	α	M	SD	1	2	3	4	5	6	7	8
1. Cognitive preoccupation	.70	1.99	1.09	—
2. Uncontrolled internet use	.74	1.72	0.89	.70	—
3. FI: Academic/occupation	.90	1.85	1.27	.34	.40	—
4. FI: Friendships	.92	1.80	1.27	.28	.31	.68	—
5. FI: Family	.87	1.63	1.02	.30	.33	.66	.79	—
6. Social anxiety	.96	2.57	1.18	.31	.34	.36	.31	.33	—
7. Loneliness	.94	2.73	1.01	.36	.36	.44	.57	.55	.32	—
8. Depression	.91	1.31	0.32	.32	.29	.38	.25	.29	.40	.34	—

Note: FI = Functional impairment.

Table 1 shows that cognitive preoccupation and uncontrolled internet use independently mediate the relationship between social anxiety and vocational impairment (H1). The cumulative indirect effects of social anxiety on vocational impairment through PIU were also significant. The patterns were consistent for the relationships between social anxiety and social impairments through PIU. Cognitive preoccupation and uncontrolled internet use independently mediated the relationships between social anxiety and friendship impairment, and social anxiety and declining family relationships. The total indirect effects for the two multiple mediator models were also significant.

The tests for the mediation of PIU in the relationships between loneliness and functional impairment (H2), displayed in Table 2, were mixed. Cognitive preoccupation and uncontrolled internet use separately mediated the relationship between loneliness and vocational impairment. The total indirect effects of the two mediators in a combined model were also statistically significant. However, the indicators of PIU did not mediate the relationship between loneliness and impairment in friendships. Neither the individual nor collective indirect effects of loneliness on friendship impairment through cognitive preoccupation and uncontrolled internet use were significant. Last, uncontrolled internet use mediated the relationship between loneliness and social impairment in family relationships; however, cognitive preoccupation was not a significant mediator in the single-mediator or multiple-mediator models. The cumulative indirect effects of loneliness on family impairment through PIU were substantiated even though neither PIU indicator was independently significant in the multiple mediator model.

Table 3 provides the estimates for the effects of depression on functional impairment through cognitive preoccupation and uncontrolled internet use (H3). The PIU indicators individually mediated the relationship between depression and vocational impairment. The linear combination of the estimates for the indirect effects was also significant. In addition, the effects of depression on interpersonal impairment, including friendships and family relationships, through PIU were statistically significant. The cumulative indirect effects for both types of social impairments were substantiated, demonstrating the importance of PIU in the causal chain between depression and difficulties maintaining interpersonal relationships.

Post Hoc Exploratory Investigation of Alternative Models

Several mediator models were tested to see whether plausible alternative explanations of the relationships among variables used in this investigation are consistent with the data. The initial series of tests explored reverse causation; functional impairment was modeled as the independent variable, PIU as the mediator, and psychosocial problems as the dependent variable. Table 5 presents the point estimates for the indirect effects. The estimates indicate that cognitive preoccupation and uncontrolled internet use do not mediate the relationship between loneliness and functional impairment. In relational impairment but not vocational impairment, uncontrolled internet use mediates the relationship between social anxiety and functional impairment. Last, depression leads to vocational and social impairment through cognitive preoccupation but not uncontrolled internet use. No definitive conclusions about reverse causality can be drawn from the mixed findings.

Table 5.

The Effects of Functional Impairment on Psychosocial Problems Through Problematic Internet Use.

					Bootstrapping
					BC 95% CI		BCa 95% CI
Independent Variable	Dependent Variable	Mediator	Point Estimate	SE	Lower	Upper	Lower	Upper
Academic/occupational	Social anxiety
		CP	0.036	0.039	−0.031	0.128	−0.033	0.125
		UU	0.058	0.042	−0.010	0.159	−0.010	0.159
		TOTAL	0.094	0.035	0.036	0.177	0.036	0.175
	Loneliness
		CP	0.045	0.037	−0.015	0.134	−0.012	0.142
		UU	0.034	0.039	−0.045	0.112	−0.045	0.111
		TOTAL	0.079	0.033	0.029	0.166	0.028	0.164
	Depression
		CP	0.017	0.011	0.001	0.042	0.0004	0.041
		UU	0.004	0.013	−0.021	0.033	−0.019	0.036
		TOTAL	0.021	0.013	−0.0003	0.052	0.002	0.056
Friendship relationships	Social anxiety
		CP	0.030	0.034	−0.030	0.106	−0.032	0.104
		UU	0.056	0.033	0.004	0.138	0.004	0.138
		TOTAL	0.086	0.030	0.040	0.160	0.039	0.158
	Loneliness
		CP	0.031	0.031	−0.022	0.103	−0.018	0.111
		UU	0.028	0.030	−0.031	0.089	−0.033	0.087
		TOTAL	0.058	0.026	0.021	0.128	0.020	0.124
	Depression
		CP	0.015	0.010	0.002	0.040	0.001	0.037
		UU	0.008	0.010	−0.009	0.032	−0.008	0.034
		TOTAL	0.023	0.011	0.007	0.051	0.008	0.052
Family relationships	Social anxiety
		CP	0.039	0.044	−0.041	0.136	−0.042	0.133
		UU	0.072	0.045	0.001	0.189	0.0003	0.189
		TOTAL	0.110	0.039	0.049	0.205	0.048	0.202
	Loneliness
		CP	0.042	0.040	−0.027	0.132	−0.022	0.144
		UU	0.035	0.040	−0.039	0.121	−0.041	0.119
		TOTAL	0.077	0.032	0.027	0.159	0.026	0.157
	Depression
		CP	0.019	0.012	0.002	0.049	0.0003	0.047
		UU	0.009	0.013	−0.015	0.038	−0.013	0.040
		TOTAL	0.029	0.014	0.007	0.062	0.008	0.064

Note: BC = Bias corrected; BCa = Bias corrected and accelerated; CP = Cognitive Preoccupation; UU = Uncontrolled Use; 95% CI = 95% confidence interval.

The second set of mediator models in the post hoc analysis tested the influence of psychosocial problems on PIU through functional impairment. These models were tested because psychosocial problems are measured as trait-like variables as opposed to PIU and functional impairment, both interpreted as transitory variables. The estimates of the indirect effects, provided in Table 6, show that vocational impairment mediates the relationship between psychosocial problems and uncontrolled internet use; all other point estimates for the indirect effects in which functional impairment mediates the relationship between psychosocial problems and PIU are statistically nonsignificant.

Table 6.

The Effects of Psychosocial Problems on Problematic Internet Use Through Functional Impairment.

					Bootstrapping
					BC 95% CI		BCa 95% CI
Independent Variable	Dependent Variable	Mediator	Point Estimate	SE	Lower	Upper	Lower	Upper
Social anxiety	Cognitive preoccupation
		Academic/occupational	0.061	0.044	−0.014	0.152	−0.026	0.141
		Family relationships	0.006	0.039	−0.063	0.090	−0.058	0.102
		Friendship relationships	0.030	0.040	−0.046	0.113	−0.044	0.116
		TOTAL	0.096	0.036	0.035	0.172	0.033	0.169
	Uncontrolled use
		Academic/occupational	0.075	0.039	0.012	0.163	0.004	0.151
		Family relationships	−0.005	0.034	−0.068	0.070	−0.060	0.086
		Friendship relationships	0.024	0.032	−0.038	0.088	−0.035	0.091
		TOTAL	0.094	0.031	0.043	0.165	0.042	0.163
Loneliness	Cognitive preoccupation
		Academic/occupational	0.105	0.060	−0.010	0.228	−0.029	0.213
		Family relationships	−0.039	0.094	−0.224	0.146	−0.213	0.162
		Friendship relationships	0.037	0.080	−0.121	0.198	−0.118	0.203
		TOTAL	0.103	0.071	−0.030	0.250	−0.041	0.238
	Uncontrolled use
		Academic/occupational	0.123	0.054	0.037	0.245	0.021	0.223
		Family relationships	−0.049	0.080	−0.206	0.110	−0.192	0.129
		Friendship relationships	0.032	0.065	−0.106	0.153	0.099	0.158
		TOTAL	0.106	0.057	0.003	0.230	−0.001	0.227
Depression	Cognitive preoccupation
		Academic/occupational	0.194	0.160	−0.102	0.524	−0.144	0.498
		Family relationships	0.037	0.120	−0.186	0.297	−0.167	0.339
		Friendship relationships	0.100	0.131	−0.133	0.396	−0.124	0.406
		TOTAL	0.330	0.137	0.106	0.643	0.097	0.623
	Uncontrolled use
		Academic/occupational	0.282	0.149	0.301	0.597	−0.004	0.561
		Family relationships	−0.002	0.104	−0.193	0.231	−0.172	0.274
		Friendship relationships	0.086	0.105	−0.117	0.310	−0.110	0.321
		TOTAL	0.366	0.128	0.161	0.658	0.152	0.641

Note: BC = Bias corrected. BCa = Bias corrected and accelerated; 95% CI = 95% confidence interval.

Study 2

Procedures and Participants

Participants for Study 2 were incoming freshmen undergraduate students at a large university in the Southwest. The undergraduates were recruited for this three-wave panel study in the summer session preceding their inaugural year in college. Contact information for the incoming freshmen was provided by the university admission’s office. Incoming freshmen were asked to participate in exchange for an opportunity to win one of twenty-four US$25 cash awards through a drawing that took place at the end of the study. Students could not participate if they were under 18 years of age at the start of the project. Participants were asked to complete an identical internet-based questionnaire at three different time points: the summer before they started college, the middle of their first semester, and the end of the first semester. The numbers of weeks between each wave was equivalent; efforts were made to avoid recruitment near conventional midterm and final exam dates.

In total, 170 incoming undergraduate students completed the first wave of the study. The level of attrition between Time 1 and Time 2 was 11%. Eight percent of the sample participants dropped out of the study from Time 2 to Time 3. A total of 139 freshmen (44 males, 95 females) completed the study in full. The sample of participants who remained in the study was compared to those who dropped out of the study at each wave, using a series of t tests. Individual psychosocial problem, PIU, and functional impairment variables were used as the dependent variable in the tests. The results of these t tests demonstrated no significant departures in scores between returnees and dropouts. Missing data from individuals who completed the study at the three waves were estimated using multiple imputation (MI). This method, which assumes data are missing at random, replaces missing values with plausible estimates. Because the missingness followed a monotoned missing data pattern, a parametric regression method was used for the MI.

Measures

Social anxiety

Trait anxiety in social interactions was measured using Leary’s (1983) Interactional Anxiousness Scale (IAS). Seven items were dropped from the measure because they contributed to poor model fit and their standardized residuals were greater than 2.0 in absolute magnitude. The unidimensional measurement model for the IAS was invariant across waves. The fit for the measurement model at Time 1, χ²(9) = 16.16, p = .06, CFI = .99, RMSEA = .06, SRMR = .02; at Time 2, χ²(9) = 8.94, p = .44, CFI = 1.00, RMSEA = .001, SRMR = .02; and at Time 3, χ²(9) = 33.25, p < .001, CFI = .98, RMSEA = .14, SRMR = .03, was satisfactory. The same indicators for the unidimensional factor were used at each time point to avoid potential confounds from measurement differences (see Little, Preacher, Selig, & Card, 2007). No Cronbach’s alpha reliabilities fell below the acceptable threshold of .70. All scales were averaged into a composite parcel that represented a single index of a measure at a given time point.

Loneliness

The 10-item UCLA Loneliness Scale (Version 3; Russell, 1996) was used to measure the extent to which participants experienced loneliness. Four items from the loneliness measure were dropped because their large standardized residuals contributed to poor model fit. The measurement model, specifying a unidimensional factor structure, fit the data well at Time 1, χ²(9) = 8.54, p = .48, CFI = 1.00, RMSEA = .001, SRMR = .02; at Time 2, χ²(9) = 29.04, p = .001, CFI = .97, RMSEA = .07, SRMR = .03; and at Time 3, χ²(9) = 14.92, p = .09, CFI = .99, RMSEA = .07, SRMR = .03.

Depression

Participants’ level of depression was measured with the Center for Epidemiological Studies Depression Scale (CES-D; Radloff, 1977). This measure was selected because it is easier to score for participants yet functionally equivalent to the BDI-II. The trimmed nine-item measure assesses changes in moods (e.g., increased lethargy) and behaviors (e.g., sleep patterns) indicative of depression. The unidimensional factor structure of the CES-D fit the data at all three waves: the fit indices for Time 1 were χ²(27) = 28.63, p = .40, CFI = .99, RMSEA = .02, SRMR = .03; for Time 2 were χ²(27) = 76.07, p < .001, CFI = .93, RMSEA = .10, SRMR = .05; and for Time 3 were χ²(27) = 53.24, p = .002, CFI = .96, RMSEA = .08, SRMR = .04.

Problematic internet use

The Generalized Problematic Internet Use Scale (GPIUS; Caplan, 2002) and LaRose et al.’s (2003) Deficient Self-Regulation Measure were used to evaluate PIU. Five items assessed cognitive preoccupation and five items were used to estimate one’s unregulated internet use. The bidimensional measurement model demonstrated good fit to the data at Time 1, χ²(34) = 81.86, p < .001, CFI = .95, RMSEA = .06, SRMR = .06; at Time 2, χ²(34) = 78.02, p < .001, CFI = .96, RMSEA = .06, SRMR = .05; and at Time 3, χ²(34) = 66.83, p = .001, CFI = .97, RMSEA = .05, SRMR = .04.

Functional impairment

Three subscales of functional impairment were taken from Study 1. The vocational impairment, impairment in friendships, and impairment in family relationships scales each comprised of three items. In line with Study 1, a measure of social compensation and an adapted measure of vocational compensation were included to mask the objective of this scale. The CFA of the functional impairment scale demonstrated good internal consistency and parallelism among the factors at Time 1, χ²(24) = 53.00, p = .001, CFI = .98, RMSEA = .08, SRMR = .04; at Time 2, χ²(24) = 76.22, p < .001, CFI = .96, RMSEA = .10, SRMR = .04; and at Time 3, χ²(24) = 83.78, p < .001, CFI = .96, RMSEA = .13, SRMR = .06.

Results

For Study 2, the mediation of PIU in the relationship between psychosocial problems and functional impairment was tested using a cross-lagged panel data autoregressive model (Cole & Maxwell, 2003; Little et al., 2007). The structural equation model (SEM) specified psychosocial problems as a latent factor of social anxiety, loneliness, and depression. PIU was measured by cognitive preoccupation and uncontrolled internet use. Last, vocational, friendship, and family relationship impairment were indicative of a functional impairment latent variable. To test the mediation, a path was estimated between psychosocial problems at Time 1 and PIU at Time 2 while controlling for the influence of PIU at Time 1, and a path was included between PIU at Time 2 and functional impairment at Time 3, again controlling for functional impairment at Time 2. A path between psychosocial problems at Time 1 and functional impairment at Time 3 was also included to measure the direct effect of the independent variable on the dependent variable. In addition, path coefficients between PIU at Time 1 and functional impairment at Time 2 and between PIU at Time 2 and functional impairment at Time 3 were estimated.

The results of the SEM, conducted using the SAS System’s CALIS procedure (SAS Institute Inc., 1989), are presented in Figure 1. The fit of the proposed autoregressive model to the data was acceptable, χ²(210) = 409.05, p < .001, CFI = .93, RMSEA = .08 [.07, .09], SRMR = .08, power = .97. To demonstrate the mediation of PIU in the relationship between psychosocial problems and functional impairment, the individual path coefficients were examined. Psychosocial problems at Time 1 significantly predicted PIU at Time 2 while holding PIU at Time 1 constant, β = .18, p < .05. PIU at Time 2 significantly predicted functional impairment at Time 3 while controlling for functional impairment at Time 2, β = .19, p < .05. Psychosocial problems at Time 1 was not associated with functional impairment at Time 3 when the mediator, PIU, was modeled in, β = -.01, ns. Although the two parameter estimates that identify the mediating paths between psychosocial problems and functional impairment through PIU are significant, it neither substantiates mediation nor provides a rigorous test for mediation.

Figure 1.

Results of the autoregressive structural equation model testing the upstream paths in the relationships between psychosocial problems, problematic internet use, and functional impairment at three time points.

The indirect effect for the mediation of PIU in the relationship between psychosocial problems and functional impairment was evaluated using the asymmetric distribution of products test (MacKinnon, Lockwood, Hoffman, West, & Sheets, 2002; MacKinnon, Lockwood, & Williams, 2004). The test, which uses unstandardized path coefficients and standard errors, provides confidence intervals around the product of the coefficients (i.e., the indirect effects). The results demonstrate that the point estimate for the psychosocial problems (Time 1)-PIU (Time 2)-functional impairment (Time 3) indirect effect significantly differs from zero (.05, 95% [.0001, .12]), which provides support for the mediation.

Post Hoc Analysis Evaluating Reverse Causation

The model that specified reverse causation in the relationships between psychosocial problems, PIU, and functional impairment could not be decisively rejected by the cross-sectional data in Study 1. This model was therefore tested to see whether it fits the longitudinal data in Study 2. The model estimating the upstream paths, illustrated in Figure 2, fits the data reasonably well, χ²(210) = 424.21, p < .001, CFI = .92, RMSEA = .09 [.08, .10], SRMR = .09, power = .99. The standardized path coefficient from functional impairment at Time 1 to PIU at Time 2 was nonsignificant, β = .02, ns, and the path between PIU at Time 2 and psychosocial problems at Time 3 was also nonsignificant, β = -.06, ns, providing little support for the mediation. Last, the path from functional impairment at Time 1 did not directly predict psychosocial problems at Time 3, β = -.03, ns. The point estimate for the indirect effect (.002, 95% [-.02, .03]), using MacKinnon et al.’s (2002, 2004) “product of coefficients” approach, was not different from zero. Therefore, the data do not support the reverse causal relationship of functional impairment on psychosocial problems through PIU.

Figure 2.

A final test was conducted to study whether the causal effects implied by the mediation model are collectively substantiated by the data. Although the two models tested in this project are not hierarchically related to each other, they are nested under a fuller model. This full model, which specifies all cross-paths (i.e., upstream and downstream) between the latent variables, was tested. The results of the SEM indicated moderate fit of the full model, χ²(205) = 399.43, p < .001, CFI = .93, RMSEA = .08 [.07, .10], SRMR = .07, power = .97. The chi-square for the full model was compared to the chi-square for the two nested models. A significant chi-square change (χ²_Δ) between the reverse-causal model and the full model indicates support for the causal relationship in which psychosocial problems lead to PIU, which in turn causes functional impairment (for review, see Cole & Maxwell, 2003). The results demonstrated a significant difference between the full model and model indicative of reverse causality, χ²_Δ(5) = 24.78, p = .0002. This provided further support for the mediation of PIU in the original model. The paths indicative of reverse causation were not significant in the test of the full model and the original model, χ²_Δ(5) = 9.62, p = .09. This test provided additional evidence that the reverse causality argument is not supported in Study 2.

Discussion

Previous research and theory on media addictions have been criticized for the way they characterize these problems. The issues range from carelessly confounding media and message factors to deficient explanations about the self-correctable nature of these “addictions” (see Hall & Parsons, 2001; LaRose et al., 2003). One of the most frequently cited concerns from these critics is whether the ostensible addictions people form to media, such as communication technologies, are genuine disorders or manifestations of primary psychosocial problems (e.g., Ha et al., 2006; Hall & Parsons, 2001; Livingstone, 1999; Shaffer et al., 2000). There is general agreement that functional impairment in one’s life is what makes PIU “problematic” (Scherer, 1997; Shapira et al., 2000); however, it is unknown whether this impairment stems from a unique set of maladaptive cognitions and behaviors involving the internet or is purely motivated by the psychosocial vulnerabilities happening contemporaneously with PIU.

Criticisms about the legitimacy of the PIU construct have, in turn, given rise to questions about the validity and (un)importance of the body of literature accumulated on this topic over the past two decades. The weight and relative importance of PIU research hinge on whether the effects of PIU can be disentangled from the psychosocial problems that begin the causal chain. This investigation attempts to study the authenticity of the PIU construct by determining its role in the relationships between psychosocial problems and functional impairment. Although investigations on several theoretical frameworks and models of PIU have studied the relationships between psychosocial problems, PIU, and functional impairment, the majority have not substantively tested the mediation. Another contribution of this study is the comparison of various methodological procedures used to examine the relationships among these variables. The limitations of cross-sectional data are exposed and ways to improve research on PIU are discussed.

The Effects of Psychosocial Problems on Functional Impairments Through PIU

The findings reveal that psychosocial problems, such as social anxiety and depression, can initiate cognitive preoccupation to and uncontrolled uses of the internet. Unpleasant moods that accompany these psychosocial problems may be avoided or at least managed through internet technology use, which then promotes PIU (LaRose et al., 2003). In addition to the conditioned responses users generate between mood regulation and internet use, the unpleasant moods associated with psychosocial problems elicit cognitive biases that slight the effortful self-regulation of internet use. These unregulated behaviors, represented by uncontrolled internet use, accompany cognitive preoccupation, a construct understood to be entirely independent from the negative cognitive biases.

The development of PIU may lead to noticeable difficulties in one’s life. These difficulties can emerge in the form of relational impairment with friends and family members or the degeneration of academic and occupational work performance. The personal and interpersonal sacrifices made on the road to unregulated internet use are responsible for the initial and perhaps sustained loss of contact with family members and friends (Beard & Wolf, 2001; Kerkhof et al., 2011; Whang, Lee, & Chang, 2003; Young, 1998). A reduction in physical or mediated (e.g., telephone) contact with family members and friends is accompanied by social strains and the global perception that interpersonal relationships with formerly close others have suffered. The inability to focus on school or occupational work (from cognitive preoccupation) and the procrastination of responsibilities (from uncontrolled internet use) are reasonable explanations for perceived vocational problems (Kubey et al., 2001; Nalwa & Anand, 2003; Thatcher et al., 2008).

The results of this study complement what has already been established in empirical research on the relationship between psychosocial problems and functional impairment. For instance, Caplan (2003) demonstrated through the use of hierarchical regression models that indicators of PIU, including maladaptive cognitions (e.g., withdrawal) and unregulated use, uniquely account for a significant proportion of the variance in the functional impairment construct above and beyond psychosocial problems. This evidence provided initial support for describing PIU as a genuine condition that is able to be discriminated from psychosocial problems. Furthermore, Study 2 finds that there is no relationship between psychosocial problems and functional impairment when PIU is included as a mediator in the model. It is imprudent to argue against the direct effect of psychosocial problems on functional impairment in the absence of PIU given the wealth of evidence to suggest the causal relationship (e.g., Jaffe, Froom, & Galambos, 1994); however, this study recognizes PIU as an important pathway to the development of vocational and social impairment for socially anxious, lonely, or depressed individuals. The validation of the indirect effects of psychosocial problems and functional impairment through PIU in both Study 1 and 2 offers considerable evidence for characterizing PIU as a legitimate and genuine condition.

The Comparison of Cross-Sectional and Longitudinal Data

The results presented in this project are taken from independent samples using two different methodological designs: a cross-sectional survey (Study 1) and a longitudinal panel analysis (Study 2). The relationships tested in the cross-sectional study are correlational by nature, which suggests directional causality cannot be determined. The problems with attempting to infer causality between variables using cross-sectional data are underscored in the post hoc analysis of equivalent models in Study 1. Testing alternative explanations of the relationships between psychosocial problems, PIU, and functional impairment with cross-sectional data yields other empirically defensible models, such as the model that argues for reverse causality.

The results of Study 1 emphasize a serious weakness in the majority of research on PIU. An overwhelming number of PIU studies (i.e., over 90%) employ cross-sectional designs (Tokunaga & Rains, 2010), and data from these surveys are regularly used to test path or SEMs. Causal conclusions are drawn from the findings of these models without fulfilling necessary prerequisites for making tentative causal statements from cross-sectional data, such as replicating the tests across independent samples and eliminating alternative equivalent explanations (Bullock, Harlow, & Mulaik, 1994). The post hoc analysis of alternative equivalent models in Study 1 demonstrates that some models cannot be eliminated. This necessitated Study 2 wherein the proposed mediation was tested with longitudinal data. The results of Study 2 not only provided support for the mediation of PIU in the relationship between psychosocial problems and functional impairment but eliminated the possibility of reverse causation as well, something Study 1 could not accomplish. Study 1, however, offered enough evidence to suggest that functional impairment likely does not mediate the psychosocial problem-PIU relationship.

Theoretical and Practical Implications

The small corpus of recent work focused on examining the relationship between PIU and functional impairment over time (i.e., longitudinally) indicates that PIU at an earlier time point predicts change in functional impairment at a later time point (e.g., Kerkhof et al., 2011). Support for this time-ordered relationship is important for legitimizing previous PIU research that used “negative outcomes” or “negative consequences” in reference to functional impairment without evidence to suggest these were, in fact, outcomes or consequences of internet use. Model and theories of PIU often do not include impairment from internet use because no strong support exists for the causal relationship between PIU and functional impairment. Nevertheless, PIU models and theories can now integrate, with more confidence, functional impairment as a dependent variable in the causal chain of PIU. This is not to say that functional impairment should be considered the only or main dependent variable in PIU models. Findings reliably show that time spent using the internet also increases proportionally to PIU (LaRose et al., 2003; LaRose & Eastin, 2004; Tokunaga & Rains, 2010).

The findings from this study have practical implications for internet use among incoming college freshmen. College students are at an increased likelihood for developing PIU because they have large blocks of free time, regular access to the internet, and lofty expectations about internet gratifications (Kandell, 1998). Because the rather profound adjustment for incoming college students can initiate stress and activate preexisting psychosocial problems (Segrin & Flora, 2000), freshmen represent a particularly vulnerable population for PIU development. In addition, at the school from which the sample for this study was drawn, internet use is not only strongly encouraged but mandated by university policy. Students must use their e-mails, and thus the internet, on a regular basis to remain current with faculty and administration communication. These policies are not exclusive to this university either; mandatory internet use policies are being implemented in colleges and universities nationwide. Therefore, the internet use policies at universities may lead to PIU development in a small subset of students who would not otherwise use the internet on a daily basis.

Limitations and Suggestions for Future Research

The findings from this research must be viewed in light of the limitations associated with the study design. First, Caplan (2003) concludes that the PIU process, from development to manifestation, should be considered a “specific cycle of innate dysfunction leading to internet use that in turn exacerbates the dysfunction” (p. 627). It may no longer be theoretically and empirically defensible to argue for reverse causation, where functional impairment leads to PIU, which in turn results in psychosocial problems. However, there may be reason to consider including in the model a feedback loop in which functional impairment leads back to psychosocial problems. Nonrecursive models can test feedback loops, but more waves should be included in the panel design to test exhaustively the cyclical nature of psychosocial problems, PIU, and functional impairment.

Second, although this study attempts to address issues relevant to the validity of PIU, there are even more basic questions that must be answered before PIU can be understood in the context of these findings. As Hall and Parsons (2001) contend, a rather substantial weakness of the PIU construct is that models and operational measures of PIU confound medium and message factors, making it impossible to know whether people are forming cognitive preoccupation to and unregulated uses of the contextual components of internet technologies or the content delivered through the technologies (e.g., social interactions, games, pornography, etc.). This is perhaps the most fundamental shortcoming of PIU research because it places into question every claim of the supposed importance of this nebulous “Internet” in PIU. It is therefore necessary for future research and theory on PIU to clarify the stimulus that PIU sufferers are forming cognition preoccupation to during lapses in effective self-regulation.

Third, evidence consistently indicates that the internet has both positive and negative effects on people with psychosocial problems. Shaw and Gant (2002), for instance, find that increased time spent using the internet moderated feelings of loneliness and increased perceptions of self-esteem and social support over time. Alternatively, the results from this and others studies (e.g., Caplan, 2003) show that internet use can lead to problematic media effects in the form of functional impairment for users with psychosocial or mental health problems. The paradoxical findings between these two camps remain at odds, with little attempt to reconcile the central processes that determine why some internet users with psychosocial problems experience gains from internet use while others suffer losses. Recent evidence from Selfhout et al. (2009) may bring transparency to the dissonant findings. Their data show that individual-level differences in psychosocial health, in concert with the specific activities performed over the internet, determine whether the well-being of internet users improves or declines. Improvements are experienced for people with already established levels of social support and use the internet to socialize with others. Conversely, impairments are perceived by those with poor social support and use the internet for leisure activities, such as surfing the internet. There is an inherent need for future research to test these processes in a unified model for conceptual clarity.

Concluding Remarks

The controversy surrounding issues of legitimacy and validity of the PIU construct represent one of myriad concerns with research in this domain. Although PIU research has flourished in the past two decades and now endeavors to model and test complex relationships, the literature has ignored basic questions critical to our understanding of this condition. The present investigation and others like it make efforts to uncover and address the criticisms raised about this phenomenon. This study represents one attempt to revisit formative questions, making it possible to appreciate and interpret current findings in PIU research.

Footnotes

Declaration of Conflicting Interests

The author declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author received no financial support for the research, authorship, and/or publication of this article.

Author Biography

Robert S. Tokunaga (PhD, University of Arizona) is an assistant professor in the Department of Communicology at the University of Hawai‘i at Mānoa. His research examines the social and psychological impact of electronic and digital technology use. He has published papers in the past on cyberbullying victimization, lateral surveillance, and the deficient self-regulation of Internet use.

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