The Role of Internet User Characteristics and Motives in Explaining Three Dimensions of Internet Addiction


Three Dimensions of Internet Addiction

In a little more than a decade, the Internet has revolutionized mediated communication and communication flow. With the pace of change and the emergence of new uses of the Internet (e.g., YouTube, MySpace) over this time, researchers have continued to struggle with explaining various positive and negative effects of Internet use that have garnered attention. Some have suggested that Internet use can enhance living conditions by providing access to diverse information (Bauer, Gai, Kim, Muth, & Wildman, 2002), widen users' social circles (e.g., Hampton & Wellman, 2003; Katz & Aspden, 1997; Rheingold, 1993), and enhance psychological well-being (Chen, Boase, & Wellman, 2002; Kang, 2007). Others have considered some potential negative effects of the Internet, arguing that it can be an isolating medium leading to loneliness, less social interaction with family members and friends (e.g., Kraut, Patterson, Landmark, Kielser, Mukophadhyaya, & Scherlis, 1998; Sanders, Field, Diego, & Kaplan, 2000; Stoll, 1995; Turkle, 1996), and clinical depression (Young & Rogers, 1998).

One negative effect that has received considerable attention over the last several years is the extent to which people may become addicted to the Internet. The ongoing evolution of Internet use and growth in the amount of time people spend using the Internet has fueled this concern. Researchers have used different terms to describe very similar types of behavior. These include problematic Internet use (Caplan, 2002; Davis, Besser, & Flett, 2002), pathological Internet use (Morahan-Martin, & Schumacher, 2000), Internet dependency (Anderson, 1998; Scherer, 1997), and Internet addiction (Beard & Wolf, 2001; Griffiths, 1996; Young, 1996a). In the current study we use the term Internet addiction for consistency. However, it must be noted that conceptual confusion surrounding this emotion-laden term has made it difficult to ascertain the precise psychopathology arguably associated with it (Shaffer, 2004). For example, whereas terms such as dependency and addiction have a longstanding history of being used interchangeably in the context of drug and alcohol abuse (Eisenman, Dantzker, & Ellis, 2004), in media studies such terms have very different historical meanings. For instance, dependence or reliance on a particular medium or channel has been viewed as a normal consequence of using a medium to satisfy one's communication needs (e.g., Ball-Rokeach, 1985; Rubin & Windahl, 1986), even if it is associated with heavy use and extreme affinity with the medium.

Such divergent conceptualizations of Internet addiction result in two glaring problems that researchers must remedy. First, without clarification, we are left to struggle with distinguishing between use that may reflect mere dependency on a medium (which media researchers have suggested is a normal consequence of media use), mere heavy use (which may or may not be healthy), and actual “addiction” (a pathological state as understood in contexts such as substance addiction). This, in turn, leads to a lack of clarity among professionals and policymakers who need to understand exactly what problems and symptoms, if any, they have to address. Second, divergent conceptualizations hinder the advancement of theoretical explanations about when Internet users exhibit characteristics of use that amounts to “addiction” and the identification of antecedent factors/conditions that may influence this psychological state.

In the current study, we draw on prior addiction research in an effort to synthesize prior thinking on the current subject, and attempt to conceptualize Internet addiction in a manner that is consistent with conceptualizations of addiction in other contexts, such as substance abuse. This is necessary, because if Internet addiction is a problematic phenomenon, it should have similar indicators and psychosocial risk factors as other addictions (Shaffer, 2004). Because certain traits or background characteristics have been considered to be significant predictors of addiction in both Internet and other contexts such as alcoholism (Loos, 2002; Medora & Woodward, 1991) and drug abuse (Rokach & Orzeck, 2003), we also suggest the need to explore more deliberately users' background characteristics that may make a user prone to Internet addictive behavior. We also examine whether motives for using the Internet help explain such behavior. Research conducted within the uses and gratifications perspective (U&G) over the past 3 decades has shown that background characteristics and media-use motives can enhance or mitigate media effects (e.g., Rubin, 2002). Therefore, we examine how Internet-use motives and background characteristics work together and help explain Internet addiction.

In this study we focus on the Internet, generally, rather than addiction to specific content that may be available via the Internet. The possibility that people can be addicted to general use of the Internet has been investigated in a group of previous studies (e.g., Caplan, 2002; Davis, 2001; Young, 1996b). It may be that some people turn to the Internet to fulfill needs for particular content (e.g., violence, pornography) or behavior (e.g., gambling). However, our goal here is simply to add to prior research that has suggested that people can be addicted to the Internet itself, but failed to account for differences in users' background factors and motives that may contribute to such a consequence.


A number of scholars have suggested that addiction does not necessarily have to involve abuse of a chemical intoxicant or substance (Griffiths, 1999; Young, 2004). For example, the term “addiction” has been used to refer to a range of excessive behaviors, such as gambling (Griffiths, 1990), video game playing (Keepers, 1990), eating disorders (Lesieur & Blume, 1993), physical exercise (Morgan, 1979), and media use (e.g., Horvath, 2004; Kubey, Lavin, & Barrows, 2001). Although such behavioral addictions do not involve a chemical intoxicant or substance, a group of researchers have posed that some core indicators of behavioral addiction are similar to those of chemical or substance addiction, such as loss of control, tolerance, withdrawal, and negative life consequences (Brown, 1993; Lesieur & Blume, 1993; Marks, 1990). It also has been suggested that individuals who engage in different types of addictive behaviors share similar reasons, such as relief of anxiety, boredom, and depression (Lesieur & Rosenthal, 1991; Zweben, 1987).

The Diagnostic and Statistical Manual of Mental Disorders (DSM) has been one widely used source for identifying indicators of addiction. The DSM, published by the American Psychiatric Association (APA), is a handbook that lists a diverse range of mental disorders, including addiction, and criteria for diagnosing them. The DSM-IV is the latest major revision published in 1994. It divides disorders into four categories; clinical disorders, cognitive disorders, mental retardation, and personality disorders. Although Internet addiction, specifically, has not been recognized as a disorder by the APA, it did recommend further research of overuse of the Internet and video games (American Psychiatric Association, 2006).The APA's recommendation suggests the value of exploring further whether Internet addiction can or should be categorized as another type of addiction, as promoted by some researchers (Griffiths, 1999; Young, 2004).

Internet Addiction

Concerns that people can become addicted to a medium pre-date the Internet. For example, popular books such as The Plug-In Drug (Winn, 1977) referenced addictive properties of television, and researchers have explored this further in recent years (Horvath, 2004).

Regardless of medium, using an emotion-laden term such as “addiction” has been controversial. This has been the case with the Internet as well. Nonetheless, it has caught the attention of and spurred debate among the APA (American Psychiatric Association, 2006), medical professionals, and social scientists. For example, at its annual conference in June 2007, members of the APA considered a proposal to include excessive Internet use as an addiction, but decided to table it for further investigation (Mandell, 2007). Jerald J. Block, M.D., in an editorial published on The American Journal of Psychiatry, suggested that Internet addiction has become an “increasingly commonplace compulsive-impulsive disorder” and should be included as a common disorder that merits inclusion in DSM-V (p. 306). However, other medical professionals, such as Dr. Stuard Gitlow of the American Society of Addiction Medicine, have rejected such a suggestion and argued that there is not enough evidence that Internet addiction is a complex physiological state close to alcoholism or drug addiction (Martin, 2007).

There also has been a lack of agreement among social scientists. While some have promoted the notion that Internet addiction can or should be categorized as another type of addiction (Griffiths, 1999; Young, 2004), others have contended that we should focus more on other sources of maladjustment that lead people to unhealthy use of the Internet rather than on the Internet itself (e.g., Walther, 1999). The lack of agreement among the medical and scholarly communities implies that a clear definition of this disorder has yet to be developed (Shaffer, Hall, & Vander Bilt, 2000).

Even with some unresolved issues, a growing body of research has suggested that DSM-IV may offer the most promise for identifying Internet addiction (Brenner, 1997; Thatcher & Goolam, 2005;Widyanto & McMurran, 2004; Young, 1996a) or addiction to specific online content (e.g., sexual content) (Bingham & Piotrowski, 1996). Others have used the DSM-IV criteria to conceptualize and operationalize addiction to other media such as television (e.g., Horvath, 2004; Winn, 1977). Using the DSM diagnostic criteria of substance addiction (e.g., alcohol, cocaine, etc.)1, Goldberg (1996) specified four criteria for diagnosing Internet addiction: 1) one needs to increase the amount of time spent online to achieve the same effect (tolerance), 2) one experiences an unpleasant feeling when he/she is not online (withdrawal), 3) one needs to access the Internet more often and for longer periods of time (craving), and 4) one experiences conflicts between Internet use and other activities (negative life outcomes). Griffiths (1998) added three more criteria: 1) using the Internet becomes the most important activity in one's life (salience), 2) one uses the Internet to alleviate their mood (mood modification), and 3) one keeps going back to his/her old Internet use pattern with unsuccessful efforts to cut down (relapse). Young (1997) defined Internet addiction as a type of impulse control disorder. She created a 20-item Internet addiction scale based on the DSM-IV diagnostic criteria used for diagnosing substance addiction and pathological gambling. Actually, Young (1996b, 1998) found that addictive Internet users exhibited tolerance, withdrawal, and negative academic and occupational consequences that were consistent with those exhibited by substance abusers.

In light of this line of research, Kubey et al. (2001) suggested that “pathological users” of the Internet were engaged in a much more excessive form of use than mere reliance or dependence. Whereas many Internet users may spend a great deal of time online, heavy use or reliance does not necessarily reflect what may be one of the most important characteristics of Internet addiction: the loss of control. It has been suggested, for example, that those who struggle with Internet addiction are compelled to spend significant time involved with various Internet activities even though these activities cause them to neglect family, work, or school obligations. These intemperate problems reflect a user's loss of control over Internet use, increasing involvement with the Internet and an inability to curtail this involvement in spite of adverse consequences associated with such use (Shaffer, 2004). Such a loss of control is reflected in DSM IV criteria for identifying addiction in various contexts.

Unfortunately, although the DSM-IV criteria for diagnosing pathological gambling and substance addiction has provided criteria that has been used for identifying Internet addiction, most research has not been theoretically grounded. Therefore, we don't have a good overarching theoretical picture of relationships among variables that may predict Internet addiction. As Kubey et al. (2001) argued, there is a need, at a minimum, for theoretical explanations why the Internet may have a hold on some individuals. In the current study, we use an audience-centered media effects approach, uses and gratification (U&G) theory, to study Internet addiction. U&G focuses specifically on how various media user background characteristics, motives for using media, and media use patterns work in concert to influence effects. Thus, it provides a theoretical framework with which we can consider the relative contribution of social and psychological antecedent factors that have predicted addiction in other contexts (e.g., substance addiction), and media-use motive variables that have been linked to addiction to other media (e.g., television) to Internet addiction.

Uses and Gratifications Theory (U&G)

U&G suggests that an individual's underlying needs drives his/her communication behavior. Therefore, people are not viewed as being equally or uniformly purposive, motivated and active in their use of media to satisfy underlying needs. Individual factors, the nature of use, and expectations toward the media, and their content mediate outcomes of use, both intended (e.g., the satisfaction of particular needs) and unintended (e.g., addiction) (Katz, Blumer, & Gurevitch, 1974). A typical U&G model would suggest that one's social and psychological circumstances influence one's needs (perceptible in communication motives), which, in turn, influence selection and use of communication channels (e.g., mediated and interpersonal), and outcomes. Although background factors and motives influence media effects, U&G suggests that a more complete picture of the route to media effects involves various factors (i.e., psychological and social characteristics, media use motives, and media use) working together. The U&G model guiding the current study is summarized in Figure 1.

Figure 1.

Uses and Gratification Model for Internet Addiction.

Psychological and Social Characteristics of Users

Pursuant to U&G, ascertaining factors that contribute to a particular outcome of media use begins with consideration of potentially relevant background characteristics of media users. To our knowledge, there has not been any research that included a comprehensive list of characteristics that could contribute to Internet addiction. In addition, not all psychological and social characteristics potentially relevant to Internet addiction can be identified or incorporated in a single study. Nonetheless, in this study we did include several characteristics that consistently have been associated with both Internet addiction and addiction in other contexts (e.g., substance, alcohol).


Shyness refers to inhibition of normally expected social behavior as a result of tension, concern, feelings of awkwardness, and discomfort when one interacts with strangers or casual acquaintances (Cheek & Buss, 1981). Individuals who are shy tend to feel uncomfortable and awkward in face-to-face interaction because of their social anxiety or communication apprehension (Morahan-Martin, 2007). They may feel their social discomfort is alleviated when interacting with others online because of the Internet's greater anonymity, and continue to use the Internet instead of meeting people offline (Morahan-Martin & Schumacher, 2000). A group of studies have suggested that the higher one's level of shyness, the greater the likelihood one would be addicted to the Internet (Chak & Leung, 2004; Yuen & Lavin, 2004). In contexts of substance use and alcohol problems, research has suggested that people who were shy were more likely to use drugs and alcohol (Ensminger, Juon, & Fothergill, 2001; Santesso, Schmidt, & Fox, 2004). Thus, we expect a positive relationship between shyness and Internet addiction.


Sensation-seeking is a personality trait that reflects how willing a person is to seek novel or arousing stimuli (Perse, 1996). In the context of the Internet use, Armstrong, Phillips, and Saling (2000) found that high sensation-seekers exhibited more addictive Internet use behaviors than low sensation-seekers. Outside of the Internet context, sensation-seeking has been linked with the selection of media content, particularly arousing content such as violent and pornographic fare (e.g., Krcmar & Greene, 1999; Oliver, 2002; Perse, 1996). Sensation-seeking has been linked specifically to engaging in substance use (Wagner, 2001), alcohol use, and susceptibility to future alcohol problems (Robbins & Bryan, 2004). Thus prior research suggests a potentially positive relationship between sensation-seeking and Internet addiction.


According to McKenna and Bargh (2000), individuals who feel lonely because of their lack of good social skills try to overcome their problems through online social interactions. As in the case of shy individuals, lonely people may use the Internet for social compensation when they are not satisfied with their offline interpersonal relationships (Papacharissi & Rubin, 2000). Reliance on the Internet to alleviate loneliness may lead to problematic Internet use (Caplan, 2002, 2003; Davis, 2001). Kubey et al. (2001) also suggested a link between loneliness and Internet addiction, claiming that lonely people feel socially incompetent and tend to feel more comfortable with online activities. Outside of the Internet context, loneliness has been linked to drug use (Grunbaum, Tortolero, Weller, & Gingiss, 2000) and alcoholism (e.g., Akerlind & Hornquist, 1989; Loos, 2002; Medora & Woodward, 1991; Nerviano & Gross, 1976). Based on this prior research, we predicted a positive association between loneliness and Internet addiction.

Locus of control

Locus of control refers to an individual's belief about the extent to which he/she is in control of his/her life (i.e., internal locus of control) vis- à-vis the extent to which he/she believes external forces (e.g., other people or chance) are in control of his/her life (i.e., external locus of control) (Rotter, 1966). According to Chak and Leung (2004), individuals who believed that they had control over their lives were less likely to be addicted to the Internet, because they believed that they could maintain healthy Internet use behaviors. If that argument has merit, individuals who believe that external factors control their lives may be more susceptible to Internet addiction. In other media contexts, Wober and Gunter (1982) found that individuals who were externally controlled were heavier TV viewers than those who were internally controlled. External control also has been linked to problematic effects of television use, such as increased aggression (Haridakis, 2002). Researchers have found that high external locus of control scores in adolescents predicted heavy substance use (Bearinger & Blum, 1997) and alcohol use (Steele, Forehand, Armistead, & Brody, 1995).


According to Baumeister (1993) and Swann (1996), individuals with low self-esteem have negative evaluations about themselves and are suspicious of praise. In order to withdraw or escape from these negative evaluations and stresses, individuals with low self-esteem tend to engage in addictive behavior such as substance abuse (e.g., Craig, 1995; Hirschman, 1992; Marlatt, Baer, Donovan, & Kivlahan, 1988). In the context of Internet use behavior, Armstrong, Phillips, and Saling (2000) found that low self-esteem was a significant positive predictor of addictive Internet use. Outside of the Internet context, Peele (1985) suggested that one of the reasons people may become addicted to media use is to bolster their self-esteem. Consistent with the research results of Armstrong et al. (2000) and Peele (1985), we treat self-esteem as a possible negative predictor of Internet addiction.

Applying these findings from prior research on the relationships between diverse background characteristics and substance or behavioral addiction, the following hypotheses are posed:

H1a: Shyness, sensation-seeking, and loneliness will be positively related to Internet addiction.

H1b: Internal locus of control and self-esteem will be negatively related to Internet addiction.

Amount of Use

U&G, the theoretical framework guiding this study, suggests that exposure to a medium is an important antecedent to media effects. U&G also suggests that media use can be related to unintended consequences of use, such as Internet addiction. In fact, Widyanto and McMurran (2004) found that the higher the amount of time spent online, the greater the extent of symptoms of Internet addiction. Leung (2004) also suggested that hours spent on the Internet per day was a positive predictor of Internet addiction. Similarly, Horvath (2004) found that those who measured higher than their counterparts on a measure of television addiction tended to be heavier television viewers. The results of these studies indicate that amount of Internet use and Internet addiction have been treated as distinct but related concepts in prior Internet addiction research. If, as prior research suggest, heavier users of a medium are likely to be more prone to be addicted to the medium, the amount of use is an important variable to consider.

H2: The amount of Internet use will be positively associated with Internet addiction.

Motives for Using the Internet

Kubey et al.'s (2001) claim that addictive Internet user use the Internet to meet others suggests the importance of examining communication motives for using the Internet. Peele's (1985) claim that individuals addicted to media use them to gain a sense of control in their lives and to bolster self-esteem also suggests the importance of considering the role of motives when exploring predictors of Internet addiction. U&G has been one of the predominant theoretical frameworks used to study the influence of media use motives on media effects over the last 30 years or so. Researchers specifically have suggested that people use the Internet for a variety of interpersonal (e.g., affection, inclusion, social interaction) and media-related reasons (e.g., entertainment, information seeking, passing time, escape) (e.g., Charney & Greenberg, 2002; Ebersole, 2000; Ferguson & Perse, 2000; Kaye & Johnson, 2004; Papacharissi & Rubin, 2000). Accordingly, we assessed a range of such motives individuals may have for using the Internet.

Some researchers have considered the influence of motives for using the Internet on both Internet dependency (authors, in press) and Internet addiction (Chou & Hsiao, 2000; LaRose, Lin, & Eastin, 2003). However, there is little research truly exploring possible links between a range of motives individuals may have for Internet addiction. This is a significant gap in the research, because prior media use research suggests that motives impact effects (see Rubin, 2002 for a review of studies). Specifically, it has been suggested that more purposive and instrumental use (e.g., information seeking, control, caring others, etc.) may inhibit negative outcomes and that more habitual use (e.g., habitual entertainment, escape) enhances the likelihood of unintended, and potentially negative outcomes of use (Song, LaRose, Eastin, & Lin, 2004). We wanted to see if this was the case for Internet addiction.

Integrating all the previous research about the effects of user characteristics, media use motives, and the amount of use on addiction, the following research question is put forth;

RQ1: How do users' background characteristics, motives, and the amount of Internet use contribute to Internet addiction?

Research Methods


The sample included 203 undergraduate students ranging from freshmen to seniors from a variety of majors enrolled in a multisection course required as part of a large Midwestern U.S. university's liberal education requirement. The sample was 48% men and 52% women. The mean age was 21.5 years (SD = 5.32). Students were asked to come into a classroom and took a pen-and-paper survey. Given the exploratory nature of this research, we felt the sample was appropriate. College students tend to use a variety of Internet functions (Morahan-Martin & Schumacher, 2000). In addition, computers and the Internet were widely available across campus, and all students were required to use the Internet.


Internet addiction scale

Internet addiction was measured by asking respondents how often they engaged in each of 31 indicators of Internet addiction (1 = Never , 5 = Very Often ). This index consisted of 20 items from Young's (1996a) Internet Addiction Test (IAT) and 11 items from Horvath's (2004) Television Addiction Scale. Both measures are based on DSM-IV criteria in line with the assumption that media addiction shows symptoms that are similar to addiction to other devices/substances (e.g., drugs). The reason we chose Young's scale for this study was that it had been widely used for measuring Internet addiction in previous research (e.g., Chak & Leung, 2004; Hur, 2006; Pratarelli, Browne, & Johnson, 1999; Thatcher & Goolam, 2005). We added additional items from Horvath's Television Addiction Scales (2004), since we felt it was prudent to encompass additional DSM-IV criteria that were not included in Young's scale. Because the measure we used was comprised of items drawn from different media addiction scales, we subjected it to principle components factor analysis with varimax rotation to uncover any possible underlying component structure. Factors with eigenvalue of at least 1.0, primary loadings of at least .50 and no items that loaded significantly on another factor (i.e., a larger than .20 difference between primary and secondary loadings) were retained. Five factors showed up when all 31 items were entered into factor analysis. However, three items from escaping reality, two items from attachment, all four items from the fourth factor, and all three items from the fifth factor were removed because they were cross-loaded across more than one factor. The remaining 19 items were divided into three factors and were summed and averaged to create respective indexes of Internet addiction dimensions. These three factors explained 62.5% of the variance after rotation. Responses that loaded on each factor were summed and averaged to create indexes of each Internet addiction dimension.

Factor 1, intrusion, (eigenvalue = 8.78) explained 46.2% of the variance after rotation. Items comprising this factor reflected that using the Internet became intrusive to participants' everyday life (M = 1.66, SD = 0.77, α = .92) (e.g., “I often find that I stay online longer than I intended,”“I often neglect household chores to spend more time online”). Factor 2, escaping reality, (eigenvalue = 2.06) explained 10.8% of the variance. This factor suggested that the Internet was a tool for escaping reality (M = 2.63, SD = 0.91, α = .90) (e.g., “I often block out disturbing thoughts about my life with soothing thoughts of using the Internet,”“I often snap, yell, or act annoyed if someone bothers me while I am online”). Factor 3, attachment, (eigenvalue = 1.04) explained 5.5 % of the variance. This factor reflected a strong attachment or affinity for the Internet (M = 1.93, SD = 0.92, r = .43) (i.e., “I can't imagine living without the Internet,”“When I am unable to use the Internet, I miss it so much that I feel upset”). The final results of the factor analysis are depicted in Table 1.

Table 1.  Primary Factor Loadings of Internet Addiction Scale
ItemsFactor Loadings
IntrusionEscaping realityAttachment
 Lose track of time when I am online.75.16.24
 Stay online longer than I intended.74.07.00
 Neglect household chores to spend more time online.74.31-.01
 Check emails and Instant Messenger before doing other things.74.05-.08
 Would be more productive without going online.70.26.30
 Would enjoy more hobbies without going online.68.22.16
 Try to cut down the amount of time spent online but fail.66.43.24
 Lose sleep due to late night logins.63.44.23
 Find myself saying “Just a few more minutes” online.62.35.29
 Compared to others, I spend more time online.54.41.31
Escaping Reality
 Block out disturbing thoughts with thoughts of going online.10.83.14
 Others complain about the amount of time I spend online.33.79.11
 Form new relationships online.23.73.01
 Snap, yell, or act annoyed if others bother me when I am online.13.71.26
 Prefer going online to intimacy with friends and family.34.70.12
 Feel preoccupied with the Internet when offline.12.68.37
 Find myself anticipating going online.36.67.18
 Can't imagine living with the Internet.25.12.78
 If I cannot use Internet, I miss it so much that I am upset.05.36.73
Standard Deviation.77.91.92

Amount of the Internet use

General Internet use behavior was measured with two questions asking how much time participants spent using the Internet yesterday (the day before they participated the survey) and how much time they spent using the Internet on a typical day. These two items have been used to measure other media use research, such as television, and produced reliable estimates (Haridakis, 2002). Answers to the two questions were summed and averaged (M = 194 minutes, SD = 117.7).


Internet-use motives were measured with a 45-item Internet motives scale used in prior research (Papacharissi & Rubin, 2000). This scale taps several motives associated with using the Internet, ranging from interpersonal motives (e.g., inclusion, control, affection) to media-use motives gleaned from prior media research (e.g., entertainment, escape, pass time, information seeking). We added four additional items taken from Rubin's (1983) television motives scale that were not covered in the Internet motives scale. These items reflected using the Internet for thrill and excitement. Respondents were asked how well each of the 49 statements was like their own reasons for using the Internet (1 = Not at all, 5 = Exactly ). All items were subjected to principle components factor analysis with varimax rotation. To retain a factor, we used the same criteria used in the factor analysis of the Internet addiction measure. Eight factors emerged when all 49 items were entered into the factor analysis, but 20 items were removed. Specifically, five items from habitual entertainment, one item from seeking information, three items from escapism, four items from control, all four items from the seventh factor, and all three items from the eighth factor were removed because of their high cross-loading values. The remaining 29 items were divided into six factors and were summed and averaged to create respective indexes of motives. Six factors explaining 62.4% of the variance after rotation emerged. Responses that loaded on each factor were summed and averaged to create respective indexes of motives.

The first motive, habitual entertainment, (eigenvalue = 11.44) explained 34.7% of the variance after rotation. This factor was composed of items that reflected both habitual use and using the Internet to be entertained (M = 1.66, SD = 0.77, α = .93) (e.g., “Because it's fun just to play around and check things out,”“Because it's just a habit, just something to do”). The second motive, caring for others, (eigenvalue = 3.06) explained 9.3% of the variance. This factor reflected using the Internet to show others affection and care (M = 2.45, SD = 0.83, α = .85) (e.g., “To help others,”“To let others know I care about their feelings”). Factor 3, economical information seeking, (eigenvalue = 2.24) explained 6.8% of the variance and contained items reflecting using the Internet to search for and share information conveniently (M = 4.00, SD = 0.66, α = .78) (e.g., “To get information for free,” and “Because it is cheaper than other ways of sending information to other people”). The fourth factor, excitement, (eigenvalue = 1.51) explained 4.6% of the variance. This three-item factor reflected using the Internet to seek excitement and thrill (M = 2.72, SD = 1.10, α = .90) (e.g., “Because it is thrilling,”“Because it is exciting”). Factor 5, control, (eigenvalue = 1.27) explained 3.8% of the variance. Items comprising this factor reflected that people used the Internet to affect and control others' behavior (M = 2.69, SD = 0.67, α = .73) (e.g., “To tell others what to watch or see,”“Because I want someone to do something for me”). The final factor, escape, (eigenvalue = 1.05) explained 3.2% of the variance. This factor included two items, “So I can get away from what I'm doing,” and “So I can forget about school, work or other things” (M = 2.96, SD = 1.20, r = .67). The final results of the factor analysis of the motives scale is depicted in Table 2.

Table 2.  Primary Factor Loadings of Internet Use Motive Scale
ItemsInternet Use Motives
Habitual Entertainment (HE)
 Because it's just a habit, just something to do.
 Because it is entertaining.−.05−.03
 Because it's fun just to play around and check things out.−.04
 Because it's enjoyable.
 Because I just like to use it.
 Because it gives me something to occupy my Time.−.06.26
 When I have nothing better to do.
 Because it amuses me.
Caring for Others (CO)
 To let others know I care about their feelings.−.02.18
 To show others encouragement.15.81−.
 To belong to a group with the same interests as Mine.
 To give my input.−.02
 Because I enjoy answering other people's questions.08.58−.
 To help others−.
Economical Information Seeking (EIS)
 To search for information.13−.05.72−.07−.05−.05
 Because it is easier to get information.26−.
 To get information for free.
 Because it is easier to get information.
 Because people don't have to be there when you send messages.
 Because it provides a new and interesting way to do research.−.03−.04
Excitement (Excite)
 Because it is exciting.
 Because it is thrilling.
 Because it peps me up.
 Because I want someone to do something for Me.14.27−.
 To get something I don't have.
 Because it allows me to unwind.
 To tell others what to watch or see.
 So I can forget about school, work or other Things.34.12−.
 So I can get away from what I'm doing.39.11−.

Background characteristics

We measured locus of control with Levenson's (1974) 12-item index. These 12 items included powerful others control (e.g., “My life is controlled by powerful others”), chance control (e.g., “To a great extent, my life is controlled by accidental happenings”), and internal control (e.g., “My life is determined by my own action”). Both powerful others control and chance control items were reverse-coded so that higher scores on all 12 items represented stronger internal locus of control (Haridakis, 2002; Rubin, 1993). Responses were summed and averaged (M = 3.64, SD = 0.53, α = .78).

Rosenberg's Self-Esteem Scale (1965) was used to measure participants' self-esteem. Responses to the 10 items in the scale were summed and averaged to create the self-esteem index (M = 3.90, SD = 0.55, α = .90).

For shyness, participants answered a 9-item shyness scale developed by Cheek and Buss (1981). Responses to these items were summed and averaged to create a shyness index (M = 2.36, SD = 0.66, α = .81).

We measured sensation-seeking with Zuckerman's (1979) 40-item index. This has been a widely used measure of risk-taking behavior in communication research (e.g., Krcmar & Greene, 1999). It is comprised of four subscales: thrill seeking, experience seeking, disinhibition, and boredom susceptibility. Responses were summed and averaged to form indexes for each dimension: thrill seeking (M = 3.24, SD = 0.81, α = .86), experience seeking (M = 2.98, SD = 0.74, α = .89), disinhibition (M = 3.04, SD = 0.81, α = .78), and boredom susceptibility (M = 2.74, SD = 0.61, α = .70).

Finally, a shortened 10-item version of the UCLA loneliness scale (Russell, 1996) was used to measure loneliness in the current study. Responses were averaged (M = 1.89, SD = 0.66, α = .89). For all the six background characteristics measures, participants were asked how strongly they agreed/disagreed with each item (1 = Strongly Disagree, 5 = Strongly Agree).


Hypothesis 1a and 1b predicted that shyness, sensation-seeking, and loneliness would be positively related to Internet addiction, while internal locus of control and self-esteem would relate negatively to Internet addiction. H1a was fully supported. Shyness, sensation seeking, and loneliness related positively to all three dimensions of Internet addiction. H1b was also fully supported. Internal locus of control and self esteem were negatively related to all three dimensions of Internet addiction (Table 3).

Table 3.  Bivariate Correlation Analyses Results
 Dimensions of Internet addiction
IntrusionEscaping realityAttachment
  1. Note: *p < .05,**p < .01

Thrill seeking.20**.16*.20**
Excitement seeking.22**.18*.12*
Boredom Susceptibility.19**.22**.24**
Internal control−.39**−.41**−.32**

Hypothesis 2 posed that the amount of Internet use would be positively related to Internet addiction. This hypothesis was supported. The amount of time using the Internet was positively related to all three dimensions of Internet addiction (r = .33, p < .01 for intrusion; r = .36, p < .01 for escaping reality; r = .16, p < .05 for attachment).

Our research question (RQ1) asked about the contribution of users' background characteristics, motives, and the amount of Internet use to explaining Internet addiction. Hierarchical regression analyses were used to examine the contribution of these antecedent variables to predicting each of the three dimensions of Internet addiction we identified: Intrusion, escaping reality, and attachment. Pursuant to the assumptions of a contemporary U&G model referenced above, these antecedent factors were entered into the regression equations in the following order: users' demographic information and personality traits (step 1), Internet use motives (step 2), and the amount of Internet use (step 3). None of the predictors' variance inflation factor (VIF) values exceeded 10, a guideline for serious multicollinearity (Rawlings, Pantula, & Dickey, 1998) suggesting multicollinearity did not pose a significant problem.

The hierarchical multiple regression equation with all the variables entered accounted 46 % of the variance in intrusion [R = .68, p < .01, F(16,186) = 9.90, p < 01]. Variables entered on Step 1 (gender, self-esteem, shyness, locus of control, loneliness, and four dimensions of sensation-seeking) accounted for 22% of the variance (R2 = .22, p < .01). Entering motives on Step 2 accounted for an additional 20% of the variance (ΔR2 = .20, p < .01), and entering the amount of use on Step 3 explained additional 4.2% of the variance (ΔR2 = .042, p < .01). Specifically, locus of control and loneliness were significant negative predictors of intrusion. Using the Internet for purposes of caring for others, for excitement, to escape, and amount of Internet use were significant positive predictors. Using the Internet for habitual entertainment was a significant negative predictor.

Meanwhile, the multiple regression equation accounted for 54.6% of the variance in escaping reality [R = .74, p < .01, F(16,186) = 14.00, p < 01]. Psychological and social factors entered in Step 1 accounted for 30 % of the variance (R2 = .30, p < .01). Entering motives on Step 2 accounted for an additional 22% of the variance (ΔR2 = .22, p < .01), and entering the amount of use on Step 3 explained additional 3% of the variance (ΔR2 = .03, p < .01). Among background characteristics, locus of control was a significant negative predictor of escaping realty. Gender, shyness, habitual entertainment motivation, escape motivation, and amount of Internet use were significant positive contributors to escaping reality.

Finally, the hierarchical multiple regression equation explained 31.1% of the explained variance of attachment [R = .56, R2 = .31, F(16,186) = 5.24, p < 01]. Psychological and social factors entered in Step 1 accounted for 24 % of the variance (R2 = .24, p < .01). Entering motives on Step 2 accounted for an additional 7% of the variance (ΔR2 = .07, p < .01). However, entering amount of use on Step 3 did not increase R2 significantly (ΔR2 = .003, p = .39). Gender, thrill-seeking, and using the internet for purposes of caring for others and for excitement were significant positive contributors to attachment. Locus of control was a significant negative predictor of attachment. Final results of the hierarchical regression analyses are summarized in Table 4.

Table 4.  Summary of the Final Results of Hierarchical Regression Analyses
 Final β
IntrusionEscaping RealityAttachment
  1. Note. All βs are final βs on the last step of the regression. N = 204.

  2. *p < .05,

  3. **p < .01

Step 1 (Background characteristics)
 Locus of control−.22**−.18**−.18*
Step 2 (Internet use motives)
 Habitual entertainment−.20*.23**.02
 Caring for others.28**.08.18*
 Economical information seeking−.11−.06−.03
Step 3
 Amount of Internet use.23**.18**.06


In the present study, we conceptualized and operationalized Internet addiction by considering indicators drawn from DSM-IV criteria that have been relied upon in prior studies of addiction in substance and media-related contexts. These criteria include indicators that reflect a loss of control (e.g., a compelling need to use it though it is having negative consequences on one's life) that some researchers have alleged distinguishes mere heavy use from addictive behavior (e.g., Young, 1996b). Our exploratory factor analysis uncovered three specific dimensions of such Internet use behaviors.

Dimensions of Internet Addiction

The first dimension, intrusion, reflects a manifestation of Internet use in which users neglect activities in their everyday lives (e.g., chores, etc.) due to their unhealthy Internet use. Individuals who exhibit this form of use tend to use the Internet for longer periods than they intend. They seem aware of their problematic Internet use, but are unable to correct it satisfactorily. The second dimension, which we term escaping reality, seems to be a more intense manifestation of possible Internet addiction than either of the other two dimensions. Whereas intrusion reflects a sense that Internet is interfering with one's offline life, those whose Internet use behavior reflects escaping reality see offline activities as interfering with their online lives. Those exhibiting this form of use experience anger when others hinder their Internet use, prefer time online over time with friends and family, and are preoccupied with Internet use even when they aren't online. The final dimension, attachment, reflects a strong emotional connection to the Internet. Users exhibiting this form of Internet-use behavior could not imagine living without Internet. Although they might get upset or agitated if unable to go online, this attachment to the Internet does not seem to be as disrupting to users' offline activities as when their Internet use is manifested in the form of intrusion or escaping reality. But, it does reflect becoming upset when one cannot use the Internet that may possibly reflect a more intense feeling of loss or withdrawal than that experienced by those who simply have an affinity for or reliance on the medium.

But reaching definitive conclusions from just one study using a convenience sample would be premature and must be tempered. It is tempting, for example, to speculate that intrusion and attachment may be less intense forms of Internet use that may be precursors of the more intense form of use, escaping reality, if not negated through intervention. This might reflect that there can be a progression in Internet addiction moving from the milder to the intense level (Charlton & Danforth, 2007). It would be similar to claims made in the context of substance addiction that the use of “soft” drugs can lead to the use of “hard” drugs as addiction progresses (Hopper, 1995). It is also possible, though, that intrusion, attachment, and escaping reality are three distinct forms of Internet use and that one does not necessarily lead to the other. This would be consistent with claims made by Caplan (2002) that dimensions of addiction are distinct and not a continuum of progression. Again, though, either speculation is premature from the results of just one study. One reason we can't reach a definitive conclusions about the related or distinct nature of the different dimensions of Internet addiction is that no consensus has been reached on the dimensions or stages of Internet addiction in previous research. Another reason is that there was not a consistent set of predictors across the three different dimensions of Internet use behavior in the current study.

In addition, among this convenience sample, the mean values of each dimension (intrusion M = 1.66, escaping reality M = 2.63, attachment M = 1.94) were low. Thus, even if our measure is a valid and reliable measure of addictive behavior, on the whole, this student sample did not seem to exhibit inordinately such behaviors. Future research should target particular populations that do measure high on such indicators to see if factors identified here–intrusion, escaping reality, attachment–prove to be stable in confirmatory factor analyses, and valid and reliable when studied with other variables to which addiction should be linked theoretically.

Items composing intrusion, escaping reality, and attachment are from DSM-IV criteria that have been used for diagnosing addiction in diverse contexts. However, when applied to media contexts, we should be cautious in unabashedly interpreting the three dimensions found in the current study as addiction. Rather, they might reflect a “tendency” toward addiction or addictive behaviors. Though much more research is needed, the fact that antecedents that had been associated with addiction in different contexts were linked with the three dimensions identified in this study suggests we should at least consider the possibility that these measures reflect aspects of addictive behavior or a tendency toward it.

Background Characteristics

Specific characteristics that had linked with substance or behavioral addiction in previous research (i.e., shyness, sensation-seeking, and loneliness) were positively related to all three dimensions of Internet-use behavior in this study. Meanwhile, internal locus of control and self-esteem, which were negatively related to substance or behavioral addiction in prior research, also were negatively related to all three dimensions of Internet-use-behavior identified in this study. If these dimensions identified here do reflect addictive behavior, these results could suggest that Internet addiction may be explained and conceptualized in accordance with addiction in other contexts.

Shyness was a positive predictor of the second (escaping reality) dimension of Internet-use behavior. This supports prior research linking this personality trait with various forms of substance (e.g., Ensminger et al., 2001; Santesso et al., 2004) and behavioral (e.g., gaming, gambling) addictions (Murali & George, 2007). It also may corroborate prior research suggesting links between shyness and Internet addiction, specifically (e.g., Chak & Leung, 2004). But, again, we must be cautious in reaching such a conclusion. For example, it has been suggested that shy people find offline interaction less satisfying and supportive, so they go online to feel more comfortable interacting with others (e.g., Papacharissi & Rubin, 2000; Yuen & Lavin, 2004). Thus, those who are shy in face-to-face interaction may use the Internet as an alternative channel for social interaction. The Internet may provide them with a valuable tool for impression management, control over their self-presentations, and expressing greater communication competence than they typically have in direct face-to-face interaction. However, for some individuals (perhaps the extremely shy) there may be a darker side to their Internet use. The Internet may provide a means of escape from uncomfortable everyday offline interactions for extremely shy people and leads them to an unhealthy preference for online communication activities over their offline activities. But our results do not necessarily suggest the latter. Before concluding that shyness correlates with media use in the same way it correlates with alcohol or drug addiction, we must recognize that the underlying connections may be quite different. This has to be explored further beyond our exploratory study.

Internal locus of control was a strong negative predictor of all three dimensions of Internet-use behavior identified here. This may suggest that externally controlled Internet users may be particularly prone to developing an addiction to the Internet. In other words, individuals with high external locus of control might not believe that they can control and moderate their Internet-use behaviors. In studies of television use, research has suggested that externally controlled viewers are prone to unintended negative consequences of use such as postviewing aggression (Haridakis, 2002), cultivation effects such as fear (Wober & Gunter, 1982), and concern with safety (Haridakis & Rubin, 2005). Given its predictive strength, locus of control should continue to receive greater attention in media addiction studies. As in the case of shyness, though, results regarding the connection between locus of control and the Internet-use dimensions here should be interpreted cautiously. For some externals, the Internet may provide them with an opportunity to attempt to exercise some control in their lives that they otherwise lack (e.g., Peele, 1985). Future research should seek to differentiate between such positive effects, and the potentially unhealthy links between locus of control and Internet use that our results may suggest.

Zero-order correlation analysis suggested that loneliness related positively with all three dimensions of Internet addition. However, it was a significant negative predictor of intrusion in the multiple regression analysis. Thus, when a wider array of variables was considered, the relationship between loneliness and Internet addiction was not so straightforward. This finding suggests that prior research linking loneliness to at least some aspects of addictive behavior could be an artifact of the failure to account for other variables that may mediate that relationship. This possibility may also explain why self esteem and sensation seeking were related to dimensions of addiction, but failed to predict any specific dimension in the regression analysis.

This latter point should be stressed. We included specific background factors in this study because of their links with addiction in prior contexts. While no single study can include all of the individual differences that may impact media effects, there are numerous other possible confounding variables that could be important to assess in future research. For example, pursuant to uses and gratifications theory various psychological circumstances (e.g., depression, anxiety) and social circumstances (lack of mobility, health problems, communication disabilities) could be relevant factors that could make one more or less prone to Internet addiction or other problematic use. Accordingly, future research should examine the influence of a wider array of background factors.

Motives for Using the Internet

The second goal of this study was to ascertain whether certain motives for using the Internet might predict addiction. Prior studies have not explored systematically the potential influences of motives for using the Internet on Internet addiction. Here we found that a number of motives differentially predicted different dimensions of Internet use. The fact that different sets of motives predicted the three different dimensions of Internet addiction (as measured here) might provide some hints on distinguishing different intensity levels of addiction. Using the Internet for purposes of habitual entertainment and escape predicted allegedly the most intense dimension of Internet addiction, escaping reality. This may corroborate U&G research suggesting that more habitual use was less likely to mitigate, and at times might even enhance, the likelihood of unintended negative effects of media use (e.g., Haridakis, 2002; Rubin, 2002). On the other hand, neither of those motives predicted attachment. Instead, motives that reflected using the Internet to care for others and to seek excitement were significant predictors of attachment. Intrusion was predicted by motives to escape, seek excitement, and to care for others. Habitual entertainment was a negative predictor of intrusion, suggesting that individuals exhibiting this type of Internet-use behavior tended not to rely on the Internet for habitual entertainment.

Whereas habitual entertainment was a negative predictor of intrusion, it was a positive predictor of the Internet-use behavior we termed escaping reality. However, it is not clear whether using the Internet to escape is a form of habitual use among those exhibiting this form of Internet-use behavior. It may be that these individuals purposively use the Internet to escape. This would be consistent with research in substance abuse contexts suggesting that substance abusers use drugs to escape the problems of their everyday lives (e.g., Dole & Nyswander, 1967). Therefore, our speculation is that caring for others, seeking excitement, and perhaps escape motives (in the case of escaping reality) could be characterized as purposive uses of the Internet when users actively seek to achieve these goals from the Internet. This leads us to speculate that engaging in behaviors that evidence addiction is sometimes manifested in purposive goal-directed use of the Internet. Further investigation is required about the relationship between purposive media use motives and negative media use outcomes.

Amount of the Internet Use

Most media effects research focusing on the role of the media on negative effects (e.g., violence) suggest that exposure is a central variable contributing to the effects. Prior research also linked the amount of use with Internet addiction (e.g., Morahan-Martin & Schumacher, 2000; Young & Rogers, 1998). Here, we found that amount of Internet use correlated positively with all three dimensions of Internet-use behaviors. In the multiple regression analyses, the amount of Internet use was a significant predictor of both intrusion and escaping reality. In each instance, entering amount of Internet use into the regression equations resulted in a significant increase in the explained variance.

As with the other variables in this study, though, the relationship between amount of use and the dimensions of Internet-use behavior identified here should be explored further. If Internet use can be addictive, it is logical to assume that those who are addicted would use it extensively. But, not all heavy use is tantamount to addiction. If that were the case, all heavy use of media could be deemed addiction. On the whole, these college students used the Internet a significant amount of time, more than 3 hours per day (194 minutes). Despite the fact that they used the Internet a significant amount of time, as referenced above, the low means on the addiction scale suggests they did not on the whole exhibit a high level of addictive Internet-use behavior. Accordingly, future research should focus more deliberately on the loss of control over one's media use that is reflected in DSM-IV criteria that comprised the factors of Internet use identified here. The loss of control and the disruptive effects it may have on the user and his/her relationship with others may be a major distinguishing characteristic between mere heavy use and addiction.

Conclusion, Future Research, and Limitation

In summary, the results of this study suggest that Internet addiction may be manifested in different ways. Here we identified three possible dimensions: intrusion, escaping reality, and attachment. If, as the results suggest, some forms of Internet-use behaviors are more intense and more detrimental than others, then future research should be directed toward identifying with greater specificity exactly what background characteristics of Internet users and motives for using the Internet explain how addiction is manifested and which users are more susceptible to these different manifestations of addiction.

The results also suggest that if motives and background factors are important potential contributors to addiction that should be included in future research–particularly research that considers more specifically possible addiction to particular Internet fare or functions. It may be that some Internet users who exhibit indicators of addiction may be addicted to the Internet. It may also be that they are addicted to content the Internet permits them to access, rather than the Internet itself. Perhaps it is possible to be addicted to both the Internet and to particular fare. But more research has to focus on distinguishing between potential addiction to the medium and addiction to content that may be accessed via that medium. With respect to the former, some researchers have suggested that those who are addicted to the Internet spend more time with a variety of functions such as browsing without specific goals (e.g., Caplan, 2002; Davis, 2001). For those who are addicted to particular content, such as pornography, the Internet may simply be a delivery device in the same way that a syringe is a delivery device for a substance abuser. In addition, the Internet may only be one medium among others (e.g., videos, magazines) through which they obtain that content. Whether future research focuses on the Internet or particular content, the results here suggest that the inquiry should not ignore the important influence of motives and background characteristics of users that may make some more prone to addictive behavior than others. For example, over the years, media research has suggested that some people use and develop an affinity for media (such as television) whereas others develop an affinity for particular content (e.g., see Rubin, 2002). Future Internet addiction research should consider profiles of these different media-use orientations to see if those evidencing either are more or less prone to addiction to a medium such as the Internet and/or addiction to content available via the media.

In addition, the results of the current study also lead to a series of questions related to Internet users. Especially, how far can we generalize the findings from a study of college students, who did not exhibit a high level of problematic Internet-use behavior, to potentially more at-risk groups who may be more prone to addictive media-use behavior? Can the results here be considered applicable to other populations who do not have the level of Internet access that college students have? Finally, many of the scales measuring variables included in the current study were developed in decades preceding the Internet or adapted from research in the 1990s, when the Internet was in its infancy. The changing nature of Internet use, functions, and use environments may require more advanced and up-to-date measures of variables that may be more amenable to the study of media use and effects in ever changing media environments.


  • 1

    A maladaptive pattern of substance use, leading to clinically significant impairment or distress, as manifested by three (or more) of the following, occurring at any time in the same 12-month period.

About the Author

Junghyun Kim (Ph.D., Michigan State University) is an Assistant Professor in the School of Communication Studies at Kent State University. Her research interests include Psychological and Social Impacts of New Media; Computer-Mediated Communication.

Address: School of Communication Studies, Kent State University, P.O. Box 5190, Kent, OH. 44242 Email:

Paul M. Haridakis (Ph.D., Kent State University) is an Associate Professor in the School of Communication Studies at Kent State University. His research interests include Media Uses and Effects; New Communication Technologies; Media Law, Policy and Regulation; Freedom of Expression and media history.

Address: School of Communication Studies, Kent State University, P.O. Box 5190, Kent, OH. 44242 Email: