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INTERNET GRATIFICATIONS, DEPRESSION, SELF-EFFICACY,
AND INTERNET ADDICTION

By

lndeok Song

A THESIS

Submitted to
Michigan State University
in partial fulfillment of the requirements
for the degree of

MASTER OF ARTS
Department of Telecommunication

2003

ABSTRACT

INTERNET GRATIFICATIONS, DEPRESSION, SELF-EFFICACY,
AND INTERNET ADDICTION

By

lndeok Song

The present study applied the uses and gratifications approach and social
cognitive theory to develop an understanding of the relationship of Internet users’
gratification, depression, and Internet self-efficacy to Internet addiction tendencies in a
typical college student population. There were 7 key underlying factors of Internet
gratifications drawn from gratification items used in previous Internet research: Virtual
Community, Information Seeking, Aesthetic Experience, Monetary Compensation,
Diversion, Personal Status, and Maintenance of Relationship. Of these, Virtual
Community was interpreted as a new gratification factor that has not been in any of the
previous studies. This study also verified that Internet gratification factors could be
dichotomized into content (instrumental) gratifications or process (ritualistic)
gratifications; then it was hypothesized that only process gratifications would be
positively related to Internet addiction tendency along with depression and Internet self-
efficacy. Virtual Community, Monetary Compensation, Diversion, Personal Status,
Maintenance of Relationship gratifications, depression, and Internet self-efficacy were
found to be positively related to Internet addiction. The relationship between Internet
addiction and uses and gratifications was discussed in terms of the formation of media

habits among normal (non-addicted) populations.

To my wife Hyejung and parent,

for their unconditional support, sacrifice, and great love.

iii

ACKNOWLEDGEMENT

I feel obliged to express my gratitude to all those who helped me to complete this
thesis which is the result of my two years’ graduate study at Michigan State University. It
is a pleasant aspect that now I have the opportunity to thank all of them.

The first person I would like to thank is Dr. Robert LaRose. I have been deeply
indebted to him since I met him for the first time in his class. He encouraged me to start
this study, provided the valuable data, and then has devoted so much time and effort to
help me from the start of this research, so that my labor has never been able to match.
Without his support, I could never have expected that the initial draft of this study would
be awarded as one of the Top 3 Papers in the Communication & Technology division of
[CA in 2002. Although he could not even realize how much I have owed to him, I am
really glad that I have come to know him in my life.

I would also like to thank the other member of my MA committee, Dr. Mark Levy,
who monitored my work and took effort in reading and providing me with valuable
comments on this thesis. And, I thankfully acknowledge the contribution of my academic
advisor, Dr. Bradley S. Greenberg, who helped me by giving his advice and comments on
the initial draft of this study, which led me to have a more improved thesis. Finally, I am
grateful for Dr. Carolyn A. Lin at Cleveland State University and Dr. Matthew S. Eastin
at Ohio State University who allowed me to use their data and provided helpful
suggestions. It was a pleasure to work with them.

Once again, I thank all of them mentioned above with my deepest and sincere

gratitude for inspiring and guiding this humble being.

iv

TABLE OF CONTENTS

INTRODUCTION .............................................................................................................. 1
CHAPTER 1 LITERATURE REVIEW ............................................................................. 5
Internet Addiction: A Habit Formation ........................................................................... 5
Uses and Gratifications Theory for Internet Research .................................................... 9
Content Gratifications vs. Process Gratifications ........................................................ 13
Gratifications of Internet Use ........................................................................................ 18
Internet Gratifications and Internet Addiction .............................................................. 25
Depression and Internet Addiction ............................................................................... 32
Self-Efficacy and Internet Addiction ............................................................................ 35
CHAPTER 2 RESEARCH METHODS ........................................................................... 39
Sample ........................................................................................................................... 39
Measures ....................................................................................................................... 39
Data Analysis ................................................................................................................ 46
CHAPTER 3 RESULTS ................................................................................................... 48
CHAPTER 4 DISCUSSION ............................................................................................. 65
Limitations and Implications for Future Research ........................................................ 75
BIBLIOGRAPHY ............................................................................................................. 78

LIST OF TABLES

Table 1 Internet Gratifications in Literature ..................................................................... 24
Table 2 Internet Applications Most Utilized by Addicts and Non-Addicts ...................... 27
Table 3 Measurement Summary for Internet Gratifications ............................................. 40
Table 4 Average Time for Internet Activities ................................................................... 42
Table 5. Depression Scale ................................................................................................. 43
Table 6 Internet Self-Efficacy Scale ................................................................................. 44
Table 7 Internet Addiction Tendency Scale ...................................................................... 45
Table 8 Internet Gratifications Factors ............................................................................. 49
Table 9 Regression for Demographics Predicting Factor 1 (Virtual Community) ........... 51
Table 10 Regression for Demographics Predicting Factor 3 (Aesthetic Experience) ...... 52
Table 11 Regression for Demographics Predicting Factor 5 (Diversion) ......................... 54
Table 12 Regression for Demographics Predicting Factor 6 (Personal Status) ................ 55

Table 13 Regression for Demographics Predicting Factor 7 (Maintenance of Relationship)

................................................................................................................................... 56
Table 14 Correlations between Internet Activities and Internet Addiction Tendency ..... 57
Table 15 Differences of Gender in Internet Addiction Tendency .................................... 59
Table 16 Internet Addiction and Activities as a Function of Ethnicity ............................ 60
Table 17 Correlations between Internet Gratifications, Depression, Self-efficacy and

Internet Addiction Tendency .................................................................................... 61
Table 18 Multiple Regression: Predicting Internet Addiction .......................................... 64

vi

INTRODUCTION

Before it was available to the general public in the early 19903, the Internet was
little more than an arcane communications medium to which relatively few had access.
Its use was dominated by academic and government personnel and the primary focus was
on faculty research and communication (Weiser, 2001). However, the Internet has grown
exponentially to become an essential medium in our lives. It appears that Internet users
have embraced the Internet in daily living to “communicate, transact, entertain, educate,
and improve their connectivity and productivity” (Scruby, 1999, p. 2.).

The world Internet population grew from 280.86 million (4.63% of the total world
population) as of February in 2000 and 455.55 million (7.5%) as of February in 2001 to
544.2 million (8.96%) in February in 2002 (Nua Internet Surveys, 2002). Also, there is
no doubt that the Internet is now a mainstream activity in American life. The UCLA
Internet Project (UCLA Center for Communication Policy, 2001) found that 72.3 percent
of all Americans were online in 2001, up from 66.9 percent in 2000. It is estimated that
about 59.4 percent of all Americans had Internet access at home as of July 2001
(N ielsen/lNetratings, 2001), compared with 52.2 percent in July 2000 and 39.4 percent
the year before that. This steady growth is likely to continue as long as the costs of
computers and online services continue to decline and technological advances that allow
individuals to access the Internet from their home computers continue to be made.

As the popularity of the Internet continues to grow, it is critical to understand how
it may affect its users and society. Due to widespread use of the Internet for retail and
business applications, it is possible that the nature and scope of the family, social, and

occupational consequences may be underestimated (Young, Pistner, O'Mara, &

Buchanan, 1999). Internet technology could have a far larger and more serious impact on
our society than the introduction of any other existing media. While television primarily
involves only an allocation of leisure time, for instance, Internet technology may affect
allocation of mental and physical energy at work, school, and play, changing personal,
family, and business relationships (Ebersole, 2000).

Most people have experienced some of the benefits of the Internet (e.g., Tsai &
Lin, 2001), with rapid growth and prevalence of computer network technology. On the
Internet, for example, people can find almost any information they want, communicate
with others no matter when and where they are, and purchase almost any kind of
merchandise needed. Yet, there is also concern about the negative social impact of its use,
such as social isolation (Kraut et al., 1998), lack of involvement in prosocial behavior
(Funk & Buchman, 1996), poor school performance (Wiegman & van Schie, 1998), and
Internet addiction (Brenner, 1997; Griffiths, 1999, 2000a; Griffiths, Miller, Gillespie, &
Sparrow, 1998; Kandell, 1998; Young, 1996, 1999).

Among these negative effects of Internet use, Internet addiction (Davis, 2001a;
Young, 1998a), also known as pathological Internet use (Davis, 2001a; Morahan-Martin
& Schumacher, 2000; Young, 1997) or overuse of the Internet (Chou, 2001), may-pose a
threat to the society as well as individual users, since little is understood about the
implications of this new and often unrecognized phenomenon. Indulging in online
pornography, abandoning families for chatroom lovers, and losing one’s life savings on
online gambling are just a few of the stories covered by today’s press.

Especially, college students’ over-involvement with the Internet on campuses has

been reported (Chou & Hsiao, 2000). Some college students have expressed Internet

addiction symptoms, such as “heavy preoccupation with the Internet, excessive online
time, compulsive behavior, and time management problems” (Chou, 2001, p. 573). In
addition, poor school performance and dropout rates were found to be associated with
high Internet use (J. Young, 1998). Likewise, Young (1998b) found that a higher dropout
rate among students with high academic abilities was linked to excessive Internet use.
According to Kandell (1998), college students are more vulnerable to Internet addiction
than any other segment of users, probably because, developmentally, they have strong
drives to develop firm senses of identity, and to develop meaningful and intimate
relationships.

Besides these two major stressors, they feel “the pressure of making top grades,
fulfilling parental expectations, and upon graduation, facing fierce competition for good
jobs” (Young, 2001). It was reported that, for example, 53 percent of a college student
sample experienced depression since beginning college and that the causes of depression
included loneliness (51%), money problems (50%), relationship problems with
boyfriends or girlfriends (48%), hopelessness (26%), parental problems (25%), and
helplessness (17%) (Furr, Westefeld, McConnell, & Jenkins, 2001). And at that time,
most of college students are given free unlimited access to the Internet through their
institutions’ servers. Thus, using Internet to adapt or cope with depression symptoms may
leave them more vulnerable to Internet addiction.

As the Internet has been promoted as an abundant source of information and an
effective means of education as well as a convenient means of communication and
entertainment, uses of the Internet are growing constantly. Although the Internet is an

excellent tool for many people, dangers exist for those who make it the central focus of

their lives. In particular, over-involvement with the Internet can inhibit college students’
development of skills needed for identity and intimacy, “creating a spiral of Internet use,
difficulties in real life, followed by more Internet use as a means of avoidance and self-
medication, and the like” (Kandell, 1998). Therefore, new areas of research should
include the development of models that identify or explain the causes underlying such
abnormal online behavior. So, the main purpose of this study is, firstly, to explore the
gratifications that can be sought and obtained by Internet users and, secondly, to explore
the origins of Internet addiction in normal populations on the basis of Internet
gratifications and two important psychological variables that are closely related to the

general concept of addiction: depression and self-efficacy.

CHAPTER 1
LITERATURE REVIEW

Internet Addiction: A Habit Formation

The term “Internet addiction” was originally coined by Goldberg (1995) to
describe the effects of excessive Internet use. However, an agreement on the precise
definition of this terminology has not yet been reached (Shaffer, 1996). Thus, several
terms describing similar concepts have been used in the literature, including Internet
abuse (e.g., Brenner, 1997; Davis, 2001b), Internet dependence (e.g., Anderson, 2001),
pathological Internet use (e. g., Davis, 2001a; Morahan-Martin & Schumacher, 2000;
Young, 1997; Young & Suler, 1998), compulsive Internet use (e.g., Greenfield, 1999a,
2000), and Internet addiction (e. g., Beard & Wolf, 2001; Chou & Hsiao, 2000; Eppright,
Allwood, Stern, & Theiss, 1999; Griffiths, 1999; Griffiths et al., 1998; Kandell, 1998;
Stern, 1999; Tsai & Lin, 2001; Young, 1996, 1999; Young & Rodgers, 1998b), each with
its own conceptual definition. This study adopts “Internet addiction” or “Internet
addiction tendency” as a generic term to describe the negative effects resulting from
excessive Internet use. Although the development of the Internet addiction concept is still
in its infancy and academic investigations are few in number, some anecdotal data and
empirical studies have been accumulated and much of the discussion has centered on the
appropriateness to consider excessive use of the Internet a behavioral addiction (Petrie &
Gunn, 1998).

In general, it is well known and accepted that substances such as alcohol and
other mood-altering drugs can create a physical and/or psychological dependence. Even

certain behaviors such as compulsive gambling (Griffiths, 1990), video game playing

(Keepers, 1990), overeating (Lesieur & Blume, 1993), exercise (Morgan, 1979), and sex
or love relationships (Peele & Brodsky, 1979), and television viewing (McIlwraith, 1998)
have gained some acceptance with regard to their addictive potential. There has been an
acknowledgement that the compulsive performance of these behaviors may mimic the
addictive process found with substances such as drugs and alcohol. Thus, usage of the
Internet may be an additional behavior that can produce a compulsive pattern of use that
possibly leads to an addictive process (Greenfield, 1999a).

Marlatt, Baer, Donovan, and Kivlahan (1988) defined “addiction” as “a repetitive
habit pattern that increases the risk of disease and/or associated personal and social
problems. . ..often experienced subjectively as ‘loss of control’ [that] continues despite
volitional attempts to abstain or moderate use” (p. 224). By this definition, addicted
media users would feel compelled to consume media despite potentially negative
consequences that make continued use appear irrational or out of control, even in their
own eyes (LaRose, Lin, & Eastin, 2002). That is to say, addictions to media are a type of
behavioral addiction (Marks, 1990) in which there is no external chemical substance
involved. Technological (behavioral) addictions can either be passive (e.g., television) or
active (e. g., computer games), and usually contain inducing and reinforcing features that
may contribute to the promotion of addictive tendencies (Griffiths, 1995a, 1995b, 1995c,
1998, 20003).

Griffiths (1991) followed the Diagnostic Criteria for Pathological Gambling
(American Psychiatric Association, 1987) to define addiction to video arcade games.
Others used diagnostic criteria for behavioral addictions (Greenberg, Lewis, & Dodd,

1999; Griffiths, 1999, 2000b; Rozin & Stoess, 1993) or equated them with impulse

control disorders (Cooper, Scherer, Boies, & Gordon, 1999; Shapira, Goldsmith, Keck,
Khosla, & McElroy, 2000), the same APA diagnostic category that included behavioral
addictions such as pathological gambling and compulsive buying (Wise & Tierney, 1994).
Thus, people are not addicted to the Internet in the same way that they can be addicted to
alcohol or cocaine (Davis, 2001c) where psychoactive substances are involved. Addictive
and compulsive behavior that leads to excessive use of the Internet has also been
documented (Griffiths, 1998, 1999; Huang & Alessi, 1997; Stein, 1997; Swadley, 1995;
Wallace, 1999).

The concept of Internet addiction as a type of behavioral addiction includes
dependence on the Internet, obsessive thoughts about the Internet, tolerance, diminished
impulse control, inability to cease using the Internet, and withdrawal (Davis, 2001a;
Young, 1999). These are all symptoms commonly associated with other behavioral
addictions including gambling and alcoholism, as defined by the Fourth Edition of
Diagnostic and Statistical Manual (DSM-IV) of the American Psychiatric Association
(1995). For example, studies conducted by Young (1996) and Griffiths (1998) developed
a seven-item self test which adapted diagnostic criteria for substance dependence from
the DSM-IV. If a person answered “yes” to three or more of the seven questions, the
person was deemed to be an Internet addict.

But the problem is that participants in these Internet addiction studies might have
been excessively lenient in assessing their own addiction symptoms compared to the
judgment of clinical professionals. Moreover, one important component is missing from
self-diagnoses of Internet addiction, which assesses serious life consequences as a result

of their addictive behavior, such as the loss of employment or the dissolution of a

marriage (Shapiro, 1999). So, most of the self-identified Internet addicts described in
previous studies may not have been truly addicted in a clinical sense.

Also, it is unlikely that Internet addictions happen all at once. Rather, the
symptoms are likely to develop over time. Indeed, the diagnosis of an addiction, whether
self-reported or professionally assessed, is predicated on the accumulation of several of
the symptoms (Greenfield, 1999b; K. S. Young, 1998a). This means that it is possible to
have varying degrees of Internet addiction from being “a little bit addicted” while still
leading a normal life to being “seriously addicted” in the sense of having severe
indications of most of the addiction symptoms (Song, LaRose, Eastin, & Lin, 2002).

From the social learning perspective, addictive behaviors represent a category of
“bad habits” and are assumed to lie along a continuum of use rather than being defined in
terms of discrete or fixed categories such as excessive use (loss of control) or total
abstinence (Marlatt, 1985). This reasoning can be confirmed by a prior study (LaRose,
Mastro, & Eastin, 2001) that found a strong positive relationship between perceived (self-
identified) Internet addiction and the amount of Internet usage and suggested perceived
Internet addiction might be a useful construct in models of Internet use. LaRose et a1.
(2001) interpreted perceived addiction as an indication of habit formation that is
conceptually and empirically distinct from expected gratifications.

However, Internet researchers have conceptualized habit as a type of gratification.
Hunter (1996) first proposed “Browsing” as a distinctive gratification related to Internet
usage. Perse and Greenberg-Dunn (1998) compared respondents who chose computers as
the source of typical mass media gratifications to those who did not; they found that

perceptions that computers are a habit or are good for keeping busy were significantly

related to computer use. However, in several studies (Charney & Greenberg, 2001;
Ferguson & Perse, 2000; Kaye, 1998; Korgaonkar & Wolin, 1999) that operationalized
habit as a gratification, it emerged as only a minor component of other types of
gratifications. Since the habit gratifications were single-item measures in these studies,
their significance may have been obscured by yielding only minor loadings on multi-item
factors that also reflected other gratification dimensions.

Although habit formation (perceived addiction) and gratifications may act
independently on behavior, they may still be interrelated. Stone and Stone (1990)
proposed that media habits were the result of prior decision-making that once engaged
active uses and gratifications thought processes that had become dormant with repetition
over time. It is certain that “Internet addictions provide the opportunity for a reassessment
of the media addiction issue and also basic conceptions of media attendance that involve

the interplay between habit and reason” (LaRose et al., 2002).

Uses and Gratifications Theory for Internet Research

Uses and gratifications theory has been a dominant paradigm for explaining
media exposure in the field of communication studies. It has been applied to a wide range
of conventional mass media (Palmgreen, Wenner, & Rosengren, 1985) as well as to
interpersonal communication (R. A. Rubin, Perse, & Barbato, 1988) and now to the
Internet (Charney & Greenberg, 2001; Dimmick, Kline, & Stafford, 2000; Eighmey &
McCord, 1998; Ferguson & Perse, 2000; F lanagin & Metzger, 2001; Kaye, 1998;
Korgaonkar & Wolin, 1999; Lin, 1999a; Papacharissi & Rubin, 2000; Parker & Plank,

2000; Perse & Dunn, 1998). Many communication researchers (e. g., December, 1996;

Kuehn, 1994; Morris & Ogan, 1996; Newhagen & Rafaeli, 1996; Perse & Dunn, 1998;
Rice & Williams, 1984; Ruggiero, 2000; Williams, Strover, & Grant, 1994) suggested
that traditional models of uses and gratifications may still provide a useful framework
from which to begin to study Internet and new media consumption and a convincing
theoretical explanation for changes in media use patterns following the adoption of new
communication technologies such as the Internet.

Therefore, the present study also adopts the uses and gratifications paradigm as a
framework to better understand and to further explain Internet addiction from a
communication perspective. With its interactive and decentralized nature, the Internet is
an active two-way communication medium. This new mode of communication entails
active participation, which corresponds to the uses and gratification theory’s assumption
that the audience is active and selective in media consumption. On the Internet, the user
actively chooses or constructs information, sometimes in response to other messages or
provided information. Since the bits of information circulated on the Internet are easily
alterable, it is possible for those who were once considered “passive” to exert more
control over both the content and form of information (McCain, Morris, Green, & Al-
Najran, 1999). As an “active audience” perspective, the uses and gratifications paradigm
provides a vantage point to look at the ways that the user responds to the breadth and
depth of information that is made available by the Internet (Ebersole, 2000; Morris &
Ogan, 1996).

The uses and gratifications perspective assumes that audiences choose media
content based on certain needs or gratifications that they expect to satisfy or receive from

various media sources. It shifted the focus of inquiry from direct effects of media on

10

passive audiences to active audiences’ uses of the media: “What purposes or functions
the media serve for a body of active receivers” (Fisher, 1978, p. 159)? In other words,
uses and gratifications approach stresses individual use and choice (Rubin, 1994). A key
assumption of uses and gratifications theory is that media effects are mediated by the
intent or motivation of the user (Katz, Haas, & Gurevitch, 1973). Katz, Blumler, and

Gurevitch (1974) defined the uses and gratifications as the study of:

the social and psychological origins of needs, which generate expectations of the
mass media or other sources, which lead to differential patterns of media exposure
or engagement in other activities, resulting in need gratifications and other

consequences, perhaps mostly unintended ones (p.20).

This view makes the explicit assumption that media use is functional, or directed toward
the fulfillment of a particular need.

Uses and gratifications theory has helped to identify the various reasons why
people engage in specific types of mediated communication and to classify different
types of gratifications that can be fulfilled by users. McQuail, Blumler, and Brown (1972)
cited four categories of need gratifications: Diversion (escape from the constraints of
routine, escape from the burdens of problems, and emotional release); Personal
Relationships (companionship and social utility); Personal Identity (personal reference,
reality exploration, and value reinforcement); and Surveillance. Katz et a1. (1973) pointed

out that all media users have essentially the same five categories of needs:

11

Needs related to strengthening information, knowledge, and understanding—these
can be called cognitive needs; Needs related to strengthening aesthetic,
pleasurable, and emotional experiences—or affective needs; Needs related to
strengthening credibility, confidence, stability, and status—these combine both
cognitive and affective elements and can be labeled [personal] integrative needs;
Needs related to strengthening contact with family, friends, and the world. These
can also be seen as performing an [social] integrative function; Needs related to
escape or tension-release which we define in terms of the weakening of contact

with self and one’s social roles (pp. 166-167).

Internet users are likely to share and satisfy these needs that have been found for
other media sources such as television, radio, newspaper, book, and film because the
multimedia characteristics of the Internet can facilitate all their uses for diverse
gratifications. For instance, December (1996) argued that more traditional typologies of
mass media consumption translate appropriately to the Internet. Although the real test of
the Internet as a new medium is its ability to satisfy these needs, it would seem that the
Internet can indeed serve all five of these needs (i.e., Cognitive, Affective, Personal
Integrative, Social Integrative, and Escapist Needs) and can do so in revolutionary ways
(Hunter, 1996; Lin, 1999a).

However, there remains inconsistency between the concept of Internet addiction
discussed above and the essential concept of “active audience” in uses and gratifications
theory. According to uses and gratifications theory, people are conscious of their media

choice, know why they choose one media over another, and know why they use it in

12

retrospect (McQuail et al., 1972). Therefore, development of Internet addiction as a habit
and, at the same time, as a type of negative consequence that any user may not desire
cannot be logically explained when the user is considered to be conscious, rational, and
goal-directed in media usage. Since habit is a necessary, though not sufficient,
component of the definition of media addiction, Internet addiction seems to conform to
the notion of habitual or ritualistic (Rubin, 1984) media use (LaRose et al., 2002) which
has been long lurking in uses and gratification literature (Rosenstein & Grant, 1997;

Stone & Stone, 1990).

Content Gratifications versus Process Gratifications

Palmgreen (1984) argued that topological studies aimed at identifying motives for
media consumption are “at the core of the uses and gratifications tradition” (p.22). In fact,
the most prodigious effort has been directed toward refining an understanding of
gratifications from mass media, and gratifications studies have revealed many different
ways of categorizing the central concept. Some researches have suggested that a simple
dichotomous gratification structure might underlie most media use: immediate (i.e.,
entertainment) and delayed (i.e., informational) gratifications (Schramm, Lyle, & Parker,
1961); fantasy-escapist and informational-educational functions of media (Weiss, 1971);
content and process gratifications (Cutler & Danowski, 1980; McGuire, 1974; Stafford &
Stafford, 2001); cognitive and cultural gratifications (McQuail, 1984); cognitive and
affective gratifications (Dobos & Dimmick, 1988); and instrumental and ritual
gratifications (A. M. Rubin, 1984, 1994). Although the terms for these dichotomies differ

from one to another, their conceptual similarity is obvious.

13

Cutler and Danowski (1980) defined “content gratification” as one derived from
the use of mediated messages. For example, mediated messages may be used to gain
knowledge or understanding, to increase or reduce specific uncertainty in personal and
social situations; or the content might be perceived as useful for the defense of
predispositions. Picking up a newspaper for the latest news of the world would be a
specific example. “Process gratification,” on the other hand, is derived from the use of
mediated messages for intrinsic values that do not bear a direct link to particular
substantive characteristics of the messages; the individual receives gratification only or
mainly from being involved in the process of communication behavior, rather than from
message content. For example, watching television to pass time might be considered a
process gratification.

Similarly, Rubin (1984) distinguished between “ritualized” and “instrumental”
television viewing. He proposed that ritualized viewing is habitual and frequent television
use in search of companionship or a way to pass time, whereas instrumental viewing is
highly selective and purposeful. Cutler and Danowski’s (1980) content gratification
appears to parallel Rubin’s (1994) instrumental use, while process gratification seems to

be related more to his notion of ritualized use:

Ritualized media use is using a medium more habitually to consume time and for
diversion. It relates to greater exposure to and affinity with the medium.
Ritualized use suggests utility but an otherwise less active or less goal-directed
state. Instrumental media use is seeking certain media content for informational

reasons. It relates to greater exposure to news and informational content and

14

perceiving that content to be realistic. Instrumental use is active and purposive. It

suggests utility, intention, selectivity, and involvement (p.427).

McQuail’s (1984) distinction between “cognitive” and “cultural” parallels the dichotomy
of instrumental and ritual uses, too.

Swanson (1992) also proposed that all the gratifications, in the end, can be
categorized into two essential dimensions: “gratifications that result from the pleasurable
experience of media content and are realized during consumption [i.e., process, cultural,
or ritual gratifications], and gratifications that result from learning information from
media content and subsequently putting it to use in practical affairs” [i.e., content,
cognitive, or instrumental gratifications] (p.310).

In the study of the popular television program 60 Minutes, for an example of
empirical evidence, Rubin (1981) identified two types of viewers: a time-consuming
(habitual) information seeker who was a more frequent, generalized user of television,
and a non-time-consuming (non-habitual) entertainment-information seeker who
exhibited an affinity with the program. In accordance with these findings, Windahl (1981)
concluded that media use may be instrumental or ritualized and further proposed that
individuals tend toward one of two types of viewing. Examining older people’s television
viewing motives, Rubin and Rubin (1982) confirmed that there were two types of
television viewers depending on their motivations: Habitual viewers watched television
to fill time and for companionship, relaxation, arousal, and escape, viewed a great deal of

television, and displayed a definite affinity with the television; and selective viewers

15

sought information and watched news, talk, and magazine programs. Rubin’s (1983) third
analysis of adult television use confirmed these two viewer types, too.

Interactive communication media were found to have similar gratifications to
these conventional one-way mass media. From the early time of computer-mediated
communication, Rogers (1986); (as cited in Dimmick, Sikand, & Patterson, 1994) noted
that gratifications of the newer technologies such as email and electronic bulletin boards
fell into two categories: “socioemotional” and “task.” The socioemotional uses seem to
be similar to ritualized gratifications whereas tasks appear to be similar to the
instrumental gratifications.

Dimmick et a1. (1994) explored unique gratifications associated with the
telephone: “sociability” and “instrumentality.” According to the researchers, “the
socioemotional uses would seem to be similar to what we call sociability gratifications,
whereas those uses designated as task would appear to be similar to the instrumentality
gratifications of the telephone” (p. 659). The sociability and socioemotional gratifications
are process gratifications from media use. Communication is regarded as a purpose itself.
That is, people obtain gratifications immediately during the communication process. On
the other hand, the instrumentality and task gratifications refer to content gratifications
from media use. Here, communication is a means to achieve a certain purpose (Al-Najran
& McCain, 1999).

The distinction between content (cognitive and instrumental) and process (cultural
and ritual) gratifications may also be applied to the Internet. Morris and Ogan (1996)
argued that the concept of active audience can be applied to both instrumental (content-

oriented) and ritualized (process-oriented) use of the Internet, and thus, it should continue

16

to be included in current and future Internet research. Reasons for using the Internet may
differ from person to person. Some users may be goal-directed and want to complete a
specific task through browsing intended Web pages. On the other hand, others may feel
bored and surf Web sites randomly just for fun. According to Stafford and Stafford
(2001), for example, Web site use can be characterized by process gratifications in
recognition of the “Web surfing” phenomenon. “Aimless surfing is an apt Internet
characterization of McGuire’s (1974) process gratifications, but bookmarking a site,
might be more representative of motivations arising from content gratifications. . ..this is
probably indicative of strong content interest” (Stafford & Stafford, 2001, p. 24). Even if
one bookmarks an online casino site and visit it regularly, for instance, his or her Internet
use might be considered content-oriented as far as he or she has specific expectations
from the gambling site. In a study of adolescents’ uses of the Internet, Ebersole (1999)
also could find two factors similar to both content and process gratifications by an
alternative two-factor solution: “Education” and “Diversion” motives (or gratifications).

In the present study, while process-oriented use of the Internet might be
understood as to be done by those users who usually browse the Internet looking for
something to catch their interest, a more content-oriented approach might be taken by
those users who access the Internet looking for a specific bit of information that can
fulfill their specific needs (Ebersole, 1999). In other words, content gratifications may
entail use of the message or content carried by the Internet, and are thus mainly related to
cognitive-related gratifications, whereas process gratifications are related to enjoyment of
the act of using the medium itself. It is important to notice that the basic distinction

between content-oriented and process-oriented use may not represent types of people or

17

their fixed usage patterns. Instead, it indicates a progression in the long run, and is thus
diffith to distinguish one from the other. For example, people may begin as content-
oriented (instrumental) users and become process-oriented (ritualized) users as their

active seeking for specific outcomes changes to mindless habit (of. Stone & Stone, 1990).

Gratifications of Internet Use

Thus, within the uses and gratifications perspective as discussed so far, all forms
of communication media serve as important sources of need satisfaction for users
(Schramm et al., 1961; Weiss, 1971), and how the user will be affected by a
communications medium depends primarily on their goals and purposes for using that
medium (Blumler & Katz, 1974; E. Katz, 1959). Extrapolating this approach to the
Internet, development of Internet addiction as an adverse consequence of its use may
therefore depend on one’s various purposes or goals for using it. Thus, to answer the
question “how does Internet addiction develop” one must first answer to the question:
What are the functions underlying Internet use?

As early as in the middle of 1980’s when the World Wide Web (WWW) was not
woven yet, Garrarnone, Harris, and Anderson (1986) found that the needs for
Surveillance, Personal Identity, and Diversion in using electronic bulletin boards, which
were the precursors to today’s chatrooms, listserves, and discussion groups on the
Internet. Rafaeli (1986) found that users of electronic bulletin boards gave Reaction,
Diversion, and Entertainment as the principal motivations for use, followed by
Communications and Learning What Others Think. James, Wotring, and Forrest (1995)

found that Informational Leaming and Socialization were essential psychological motives

l8

for using electronic bulletin boards, followed by Communication Medium Appeal,
Business, and Entertainment. The Internet was originally founded for government and
educational use. Thus, it is no surprise that its earlier use was mainly for information
exchange and education. Today, however, people use the Internet for a variety of reasons
beyond content-oriented (instrumental) purposes.

The abundant opportunities for social interaction that set the Internet apart from
the conventional mass media have figured prominently in discussion of the usage
motivations for the new medium. Ha and James (1998) defined interactivity as “the
extent to which the communicator and the audience respond to each other’s
communication needs” (p. 457). Since its inception, the Internet has connected
individuals all over the world. Through the Internet, individuals are communicating with
each other, creating new relationships, and exchanging social support, information, and
knowledge (Wellman & Gulia, 1999; Wellman et al., 1996).

Kuehn (1994) emphasized this interactive capacity of computer-mediated
communication and suggested a typology of gratifications including Convenience,
Diversion, Relationship Development, and Intellectual Appeal. Miller (1996) argued that
online activity was mainly motivated by seeking gratifications in Interaction along with
Escape, Entertainment, and Surveillance. December (1996) theorized Interaction as one
of the three factors including Communication and Information for why people used the
Internet. Papacharissi and Rubin (2000) found that a Social Interaction factor and that
was the only one that predicted Internet usage.

Interactive applications such as discussion groups, chatrooms, and email were

found to fulfill the need for social interaction, including the formation of new

19

relationships and maintaining existing ones (Al-Najran & McCain, 1999). In the UCLA
study (UCLA Center for Communication Policy, 2001), more than 80 percent of email
users agreed that email allowed them to communicate with people they could not
normally talk to. Typically, checking one’s email was the first thing one did after
establishing a connection to the Internet (Kraut et al., 1998). Moreover, Internet relay
chats (IRCs), multi-user dungeons (MUDs), as well as newsgroups, listserves, and other
kinds of synchronous discussion forums allow users to meet new people, communicate
with other, and discuss anything they want (Weiser, 2001).

However, entertainment and other process gratifications may be at least as
important as social interaction as a motivation for Internet use. LaRose et a1. (2001)
operationalized gratifications as the “expected outcomes” of Internet use and determined
that the enjoyable activities found on the Web predicted Internet usage. Ferguson and
Perse (2000) studied the WWW as a functional alternative to television. In their study,
Entertaimnent was found to be the most salient motive for visiting the Web, followed by
Pass Time, Social Information, and Relaxation/Escape respectively.

A study of college students’ WWW usage (Kaye, 1998) yielded six motivational
categories: Entertainment, Social Interaction, Passing the Time, Escape, Information, and
Web Site Preference. Similarly, for Parker and Plank (2000), Relaxation and Escape
factors were found to be key factors to predict Internet usage, explaining approximately
nine percent of the variance.

In a pilot study, Yoo (1996) found six dimensions of gratifications relating to use
of the Internet: Entertainment, Information, Sociability-Building, Sociability-Maintaining,

Transaction-General, and Transaction-Task. Of these, Entertainment, Information,

20

Transaction-General, and Sociability-Building were related to WWW usage. By the same
token, Lin (2000) discovered seven different gratification-expectation dimensions:
Companionship; Learning; Interpersonal Communication; Entertainment; Habituality;
Surveillance; and Diversion. The latter four dimensions were able to explain 26 percent
of the total variance in online content access level. More recently, Lin (2001) found
Escape/Interaction, Informational Learning, and Entertainment factors as Internet motives.

Other empirical studies stressed gratifications that were more distinctly content-
oriented (instrumental). According to the UCLA study (UCLA Center for
Communication Policy, 2001), at the top of the list of the primary reasons respondents
started to use the Internet was “to get information quickly” (25.0%), followed by “for
work” (11.6%), “to get e-mail” (10.6%), “it seemed to be new and interesting” (9.3%),
“for school” (8.8%), “my friends” (4.6%), “to learn more about things that interest me”
(4.5%), “to be up-to-date” (3.8%), “was given access” (3.1%), and “other” (19.0%).

Katz and Aspden (1997) found that all categories of users (i.e., longtime users,
new users, nonusers, and former users) ranked Seeking Information as number one or two
as a reason for being online. Charney and Greenberg (2001) established eight
gratification factors for the Internet, which were Keep Informed, Diversion/Entertainment,
Peer Identity, Good Feelings, Communication, Sights and Sounds, Career, and Coolness,
and found two of these (Keep Informed and Communication) explained 36 percent of the
variance in weekly time spent on the Internet. Stafford and Stafford (2001) identified
motivations for the use of commercial Web sites and found Search, Cognitive, New and
Unique, Social, and Entertainment gratifications. McCain et a1. (1999) found Information

Seeking, Maintaining Social Contacts, What’s New/Cool, and Social Interaction factors.

21

In Ebersole’s (2000) study participants gave the following reasons for using the
WWW: “for research and learning” (52%); “to communicate with other people” (7%);
“for access to material otherwise unavailable” (5%); “to find something fun or exciting”
(8%); “for something to do when I’m bored” (5%); “for sports and game information”
(6%); and “for shopping and consumer information” (1%). However, while 52 percent of
the students reported their purpose for using the WWW “for research and learning,” they
were found to be visiting commercial sites at a much higher proportion than those in
other domains.

Korgaonkar and Wolin (1999) found the factor of Economic motivation in
Internet usage along with Escapism, Information Control, Interactive Control (relating to
the ability to control the presentation of information), and finally Socialization.
Anecdotal evidence also showed that smaller, but still large, proportions of Internet users
were engaging in e-commerce by buying products or services online. It was estimated
from 36 percent (N ie & Erbring, 2000) to 50.7 percent (UCLA Center for
Communication Policy, 2001) in the US, 33 percent in the UK. (National Statistics
Omnibus, 2000), and 19 percent of households with access to the Internet in Canada
(Statistics Canada, 2000) bought goods or services on the Internet.

Although the distinction between content and process gratifications may not be
clear due to the progressive characteristic mentioned above, the findings in the extant
Internet uses and gratifications literature imply that the basic audience motivations for
using either traditional media or the Internet appear to be similar. This means that “the
motivations for using the media are stable and may not be media dependent” (Parker &

Plank, 2000, p. 48). This assumption is upheld by Lin’s (1999a) study, which found that

22

the motivations for Internet use and television viewing were identical, even though the
motivations for seeking specific types of Internet or TV content diverged. These findings
conformed with Dimmick and Wallschlager’s (1986) view that, although the new media
offered a broader variety of content, the audience motives were similar to those of
preexisting mass media use.

Table 1 presents the gratifications categories of the Internet on the basis of Katz et
al.’s (1973) categories of needs. It is interesting that the list of gratifications of the
Internet looks similar to those found by researchers exploring the uses and gratifications
of old media. Cognitive, Affective, Personal Integrative, Social Integrative, and finally
Escapist gratifications are present.

However, there is also a wide disparity of findings that may depend upon the
selection of gratification items utilized for the various studies. Those studies introduced
in Table 1 have mixed origins of gratification items: some drawn from prior research on
television gratifications (Ferguson & Perse, 2000); others from analyses of the Internet
(Papacharissi & Rubin, 2000); and others still from the responses of actual users
(Charney & Greenberg, 2001). Therefore, the task that still remains left is to create a

unified list of Internet gratifications by asking:

RQI .' What are the gratifications of the Internet?

A firm understanding why users are attracted to the Internet, as well as what they do on

the Internet will help media researchers to better understand the general effects of

Internet usage as well as Internet addiction.

23

Table 1 Internet Gratifications in Literature

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Gratifications
Study Cognitive Affective Personal Social. Escapist Others
Integrative Integrative
Garramone et Surveillance Personal D'v .
al. (1986) Identity ‘ "5w“
. Learning . . . .
Rafaeli . * Communication; Drversron;
(1986) whatritficfirers Entertarnment Reaction Entertainment“
Entertainment";
James etal. Informational Communication . . . . .. .
(1995) Learning Medium Socralrzatron Entertainment Busmess
Appeal
. . . . Escape:
1: 9
Mrller (1996) Surveillance Entertainment Interaction Entertainment"
December Information Communication;
( l 996) Interaction
Hunter(1996) Information Entertainment" Entertainment‘ Browsing
Seeking
Sociability- Transaction-
Yoo(l996) Information Entertainment" Building; Entertainment" General;
Socrabrlrty- Transaction-
Maintaining Task
. Passing Time; .
Kaye (1998) Information Entertainment“ Socral Escape; Web Srte
Interaction . ,, Preference
Entertainment
Korgaonkar Information Socialization; .
. Control, . . Economic
& Wolm . Interactive Escapism M . .
(1 999) Interactive Control" otrvatron
Control"
Maintaining
McCain et al. Information What’s Social Contacts;
( 1999) Seeking New/Cool Social
Interaction
For research
and learning;
For access to To find
. . To . For
material something fun . For somethrng to .
Ebersole . .. . communicate , shopprng&
otherwrse or exerting, For . do when] m
(2000) . . With other consumer
unavailable, sports & game eo le bored information
For sports & information“ p p
game
information"
. . Passing Time'
Ferguson & Socral . Socral . ’
Perse (2000) Information" Entertainment Information" Relgxatron &
scape
. . Companionship; . L
Lin (2000) Learning, Entertainment" Interpersonal Entertainment ’ Habituality
Surveillance . . Diversron
Communication
Papacharissi Social
& Rubin ( Interaction

 

 

24

Table l (cont’d)

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Gratifications
Study Cognitive Affective Personal Social. Escapist Others
ntegratrve Integrative
Parker & Surveérkllance Surveillance & Corgpggrcoithrp Relaxation &
' t
Plank (2000) Excitement" Excitement Relationships Escape
Diversion &
Entertainment";
Charney & Keep . Good Feelings; . Peer Identity; Diversion &
Greenberg lnforrned, . Career . . . *
* Sights and Communication Entertainment
(2001) Career .
Sounds,
Coolness
. Escape &
Lin (2001) 11152233201: Entertainment" I:::$Ofi* Interaction";
g Entertainment"
Stafford & . . . . .
Stafford Cognitive, New & .Unrque, Social Entertainment" Search"
Search Entertainment"
(2001)
Pleasing
M183; 3 al. 52:52“ Sensory; Status Social Self-reactive Monetary
ry Activity
To get
information
quickly; For
13:21ng It seemed to be To be it To et email: To learn more Was given
UCLA (2001) ’ new and E. g . ’ about things that access;
learn more . . to-date My friends . *
. interesting interest me Other
about things
that interest
me"; To be
up-to-date"

 

Note. " Denotes gratifications factors overlapped through 2 categories

Internet Gratifications and Internet Addiction

Theoretically, we should expect process gratifications to be related to media

addictions including Internet addiction. This supposition can be supported by operant

conditioning formulations of the general problem of addiction. In this view, the

individual performs a behavior and gets either rewarded or punished for the behavior. If a

person wants certain kinds of rewards, mainly process gratifications such as excitement,

25

 

emotional comfort, escape, and fun, etc. (Ferris, 1997), and learns that a specific medium
will gratify that needs, “the behavior could begin to become habitual in the sense of being
automatic (Stone & Stone, 1990) or ritualistic (Lin, 1993; A. M. Rubin, 1984) while
remaining consistent with rational and conscious self-interest” (LaRose et al., 2002). Of
course, the initial rational and conscious use could become irrational and addictive
behavior over time. Since perceived outcomes of one’s behavior or gratifications play an
important role in this process, it seems reasonable to explain Internet addiction from the
uses and gratifications perspective.

Process (ritualized) gratifications, as conceptualized in the previous section, are
focused on the consumption of the medium itself and pull the user away from the outside
world more forcefully relative to content (instrumental) gratifications. This would
logically lead to a pattern of mounting use. Rubin (1984) found that ritualized (process-
oriented) use of a medium involved more habitual use of it for diversionary reasons and a
greater attachment with the media, while instrumental (content-oriented) use was
associated with overall lower levels of consumption and affinity, but typically linked to
greater information use. Television viewing out of habit was correlated significantly with
all other television viewing motives except viewing for information. On the other hand,
content gratifications impel the user in the opposite direction in that the needs being met
by the media are inherently connected to the world outside the media system, although
content-oriented (instrumental) uses could also eventually graduate into process-oriented
(ritualized) uses.

This premise is born out by the results of a number of studies. For instance,

Young (1996) contrasted the applications favored by Internet addicts and non-addicts.

26

She found that non-addicts predominantly used those aspects of the Internet that allowed
them to gather information and send and receive email. On the other hand, Internet
addicts did not seem to regard the Internet as an informational tool (e. g., Young, 1997).

They predominantly used the two-way communication functions available on the Internet

(Table 2).

 

Table 2 Internet Applications Most Utilized by Addicts and Non-Addicts

 

 

Application Addicts Non-addicts
Chat Rooms 35% 7%
MUDs 28% 5%
News Groups 15% 10%
E-Mail 13% 30%
WWW 7% 25%
Information Protocols 2% 24%
Total 100% 101%

 

Note. This table was drawn from Young’s (1996) study, N=396. Results

do not add to 100% due to rounding.

Chatrooms and multi-user dungeons (MUDs) were the two most utilized
applications by Internet addicts. Newsgroups or bulletin board systems (BBSs) were the

third most utilized application among addicts. Internet addicts mainly used email to

27

arrange dates to meet someone online or to keep in touch between real time interactions
with new found online friends. Online relationships were often seen as highly intimate,
confidential, and less threatening than real life friendships and reduce the addicts’
loneliness. With similar findings in later studies, Young and her colleagues (1999)
categorized five general subtypes of Internet addiction on the basis of the most
problematic types of online applications. They were addictions to cyber-sex, cyber-
relationships, online stock trading or gambling, information surfing, and computer games.

Perse and Greenberg-Dunn (1998) found that the more people went online, the
more they did it for entertainment and to pass time. Kandell (1998) observed that MUD
games, Internet relay chat (IRC), chatrooms, extended Web surfing, and email were
major activities that led people to Internet addiction. In a study of Taiwan college
students’ Internet uses (Chou, Chou, & Tyan, 1999), it was found that Internet addiction
was associated positively with Escape, Interpersonal Relationship, and Total
Communication needs, and that Internet addicted students spent significantly more hours
on BBSs and IRCs than non-addicted students.

Chou and Hsiao (2000) reported that Internet addicts spent more time on
electronic bulletin board systems (BBSs) and email than non-addicts. In this study, BBS
and e-mail use hours were two powerful factors in predicting Internet addiction. Internet
addicts were more likely to use the Internet to meet new people, get emotional support,
talk to others with the same interest, play interactive games, gamble, and engage in net-
sex (Morahan-Martin & Schumacher, 2000). Chen, Chen, and Paul (2001) found that
there were significant differences between Internet dependents and non-dependents in the

way they used the Internet. They confirmed the fact that dependent users were more

28

likely to use interactive applications such as online games and chatrooms than non-
dependent users.

Although there is a little disagreement in findings among several studies
introduced above, all of them provide a basis for a hypothesis that there will be certain
relations between various activities on the Internet to different levels of Internet addiction
tendency. However, since the intemet entails both addictive and non-addictive activities,
gross measures of intemet use should not be related to addiction. Thus, the present study

advances the following specific research hypotheses:

H] -A: The amount of time spent in Web browsing will not be related to Internet
addiction tendencies; however,

H] —B: The amount of time spent in using e-mail will be positively related to
Internet addiction tendencies;

H1 -C.' The amount of time spent in chatrooms will be positively related to Internet
addiction tendencies; and

HI-D: The amount of time spent in newsgroups flistserves) will be positively

related to Internet addiction tendencies.

In regard to demographic differences in Internet addiction, age and education
appear to have influence on potential Internet addiction tendency. That is, young or well-
educated people are more likely to be found among Internet addicts. However, recent
studies have consistently found that other demographic factors, especially gender and

ethnicity that were once considered important variables to predict Internet addiction (e.g.,

29

Petrie & Gunn, 1998; Young, 1996), do not make significant differences any longer
between addicts and non-addicts. For example, Brenner (1997) found that younger users
tended to have experienced more problems related to addictive use of the Internet than
older users, while spending the same amount of time online, and that there were no
significant differences between men and women in either time online or number of
addictive problems experienced.

It was also found that problematic Internet users had an average 15 years
education, which seems consistent with the results of other studies (e.g., Shapira et al.,
2000; Young, 1996; Young & Rodgers, 1998a). Similarly, Kraut et al. (1998) found that
teenagers accessed the Internet more often than their parents and that there were no
significant racial differences in Internet use. Nie and Erbring (2000) concluded that
younger populations were more likely than older adults to use the Internet, but failed to
validate that men and whites were more susceptible than women and minorities to
Internet addiction. To verify the above results that age and education, but not gender or
ethnicity, had relationships with Internet addiction, this study is going to test the

following hypotheses:

H2-A: Internet user’s age will be negatively related to Internet addiction tendency;

HZ-B: Internet user’s education will be positively related to Internet addiction
tendency; but

H2-C: There will be no difference of gender in Internet addiction tendency; and

H2-D: There will be no ethnic diflerences in Internet addiction tendency.

30

 

It is important to note that the Internet itself is not addictive. Specific types of
applications available on the Internet play different roles in the development of Internet
addiction, depending on their different interactive features (Young, 1996) and personal
characteristics and situations of individuals. As Griffiths (1993) pointed out, the
structural characteristics of particular activities are responsible for reinforcement,
gratifications, and excessiveness of Internet use. In other words, Internet addiction is
determined by the cluster of needs that Internet use fulfills and how those needs are met
(Suler, 1999).

Understanding that many of the respondents in previous Internet addiction studies
were not true addicts, but within the bounds of normal Internet behavior, we would
expect the same relationships within general populations of users. Thus, this study
formulates a grand hypothesis that the degree of Internet users’ addiction will have a
positive relation only to the degree of process gratifications (i.e., Affective, Personal
Integrative, Social Integrative, and Escapist gratifications) but not to content
gratifications (i.e., Cognitive gratifications such as information seeking and completing

online transactions):

H3 ~A : Process gratifications will be positively related to Internet addiction

tendencies; but

H3-B: Content gratifications will be unrelated to Internet addiction tendencies.

31

Depression and Internet Addiction

While gratifications play an important role in the development of habitual use of
the media and even addiction, addictive media consumption may be prompted by
conditioning to internal cues such as boredom and depression (LaRose et al., 2002).
According to the stress reduction theory (Holmes & Rahe, 1967) that has been discussed
mainly in psychiatry, stress contributes to addictive behaviors. Addicts tend to cling to
certain substances such as alcohol and drug as well as certain behaviors such as gambling
and electronic games to get rid of daily-life stress. The behavior itself momentarily
allows the person to forget problems, and this may be a useful way to cope with the stress
(Young, 1999).

The concept of stress in this theory includes a wide scope of mental derangement
from depression to drug dependency (Rahe, 1979). In fact, previous research in the
addiction field showed that psychiatric illness such as depression was often associated
with alcoholism and drug addiction (Capuzzi & Lecoq, 1983), and that other addictive
behaviors overlapped with depression (Young & Rodgers, 1998b). Although the distinct
causal relationship between depression and addiction still remains obscure for some
scholars (cf. Marlatt et al., 1988), the same premise holds for Internet addiction.

For example, Petrie and Gunn (1998) found that there was a significant
relationship between high Internet use and self-reported depression, supporting the view
that Internet addicts are more likely to be suffering from depression. Suggesting a
cognitive-behavioral model of pathological Internet use, Davis (2001a) proposed that
depression is a distal necessary cause of Internet addiction, which must first be present in

order to Internet addiction to occur but does not in itself result in Internet addiction.

32

Young and Rodgers (1998b) also suggested that maladaptive cognitions such as
depression were significant factors in the development of Internet addiction. They
hypothesized that those who suffer from deeper psychological problems may be the ones
who are drawn the most to the anonymous interactive applications of the Internet in order
to overcome perceived inadequacies. Results showed that those who met criteria for
Internet addiction yielded high scores on a depression inventory, and these findings were
interpreted as an illustration of a high incidence of depression among Internet addicts.
Kraut and his colleagues (1998) concluded that the amount of Internet use was related to
increased levels of depression. The results showed that greater use of the Internet resulted
in a small but significant increase in depression and loneliness, and a decrease in social
engagement.

According to Kiesler, Siegal, and McGuire (1984), computer-mediated
communication weakens social influence by the absence of such nonverbal behavior as
talking in the head set, speaking loudly, staring, touching, and gesturing. Therefore, the
disappearance of facial expression, voice inflection, and eye contact makes electronic
communication less threatening, thereby helping the depressed to overcome the initial
awkwardness and intimidation in meeting and speaking with others (Young & Rodgers,
1998b). However, perhaps online activities are ultimately not as satisfying as the
corresponding real world behaviors, leading to a continuing search, one that may pull the
depressed people further away from the real world. At best, they can only give the user
immediate gratification that reduces depressed feelings. This convenient remedy for
depression through online activity is problematic in the sense that the relief from

depression is only temporary and may produce feelings of guilt or negative affect (Lavoie

33

 

& Pychyl, 2001), and thus deepen depression. Kraut et al. (1998) described this as

“paradox”:

The intemet is a social technology used for communication with
individuals and groups, but it is associated with declines in social
involvement and the psychological well-being that goes with social
involvement. Perhaps, by using the Internet, people are substituting poorer
quality social relationships for better relationships, that is, substituting

weak ties for strong ones (p. 1029).

In this view, it is plausible to propose a research hypothesis that relates depression to

Internet addiction as follows:

H4: Internet user’s degree of depression will be positively related to Internet

addiction tendency.

In addition, this hypothetical relationship between depression and Internet addiction will

be supported by such a mechanism as:

H4-A: Internet user’s degree of depression will be positively related to process
gratifications; and
H4-B: Internet user ’5 degree of depression will be negatively related to content

gratification.

34

 

Self-Efficacy and Internet Addiction

According to Bandura’s (1997) social cognitive theory, individuals possess a self-
system that enables them to exercise a measure of control over their thoughts, feelings,
motivations, and actions. This self-system provides reference mechanisms and a set of
sub-functions for perceiving, regulating, and evaluating behavior, which results from the
interplay between the system and environmental sources of influence. In this self-referent
process, self-efficacy plays a key role in mediating subsequent behaviors (Bandura, 1977,
1982, 1986, 1997; Bandura, Adams, & Beyer, 1977; Bandura, Jeffrey, & Gajdos, 1975).

Self-efficacy is defined as “beliefs in one’s capabilities to organize and execute
the courses of action required to produce given attainments” (Bandura, 1997, p. 3).
However, in this study, the concept of self-efficacy is limited to the specific situation, in
which the individual is using the Internet, since “self-efficacy is domain specific in that
perceptions are associated with specific tasks” (Hofstetter, Zuniga, & Dozier, 2001, p.
62). For example, a person may exhibit high levels of self-efficacy in using the Internet,
while at the same time exhibiting low self-efficacy in other activities such as schooling or
playing sports. Likewise, the level and strength of perceived efficacy in using the Internet
may differ depending on the applications available on the Internet, the format of content,
and the types of people to interact with.

Self-efficacy determines exposure to a medium that many users may find
troublesome in accomplishing useful tasks (LaRose et al., 2001). People’s beliefs in their
personal efficacy influence what courses of action they choose to pursue, how much
effort they will invest in activities, how long they will persevere in the face of obstacles

and failure experiences, and their resiliency following setbacks (Bandura, 1994b, 1997).

35

People who have strong beliefs in their capabilities approach difficult tasks as
challenges to be mastered rather than as threats to be avoided. Such an affirmative
outlook fosters intrinsic interest and deep engrossment in activities. They set themselves
challenging goals and maintain strong commitment to them. They heighten and sustain
their efforts in the face of failures or setback. They quickly recover their sense of efficacy
after failures or setbacks. They attribute failure to insufficient effort or deficient i

knowledge and skills which are acquirable (Bandura, 1994a, 1997). In addition, persons

 

who are efficacious regarding a task are more likely to perform the task than those who
are not efficacious (Bandura, 1977, 1997). On the other hand, unless people believe that
they can produce desired results by their actions they have little incentive to act or to
persevere in the face of difficulties (Bandura, 1999).

Early research (Shutton, 1989, 1991) indicated that Internet or computer addicts
tend to be technologically savvy. The importance of self-efficacy in explaining computer
use was also demonstrated by Hill, Smith, and Mann (1987). They found that computer
self-efficacy affected whether individuals chose to use computers or not. Compeau and
Higgins (Compeau & Higgins, 1995) tested several hypotheses related to a hypothetical
linear model of computer use based on social cognitive theory. In their study, individuals
with high self-efficacy used computers more than those with low self-efficacy.

Hill and Hannafin (1997) studied the influence of perceived self-efficacy on
strategies employed in an Internet search. Participant’s self-efficacy beliefs in using
computer technologies and in using information searching systems affected both the
number and types of strategies they employed. Those with stronger self-efficacy explored

the system more vigorously, while those with lower self-efficacy retreated or

36

concentrated on simply locating information. Al-Khaldi and Al-Jabri (1998) also reported
that confidence (self-efficacy) in using computers was a significant positive predictor of
frequency and intensity of computer use.

Eastin and LaRose (2000) showed that Internet self-efficacy directly predicted
overall levels of Internet usage. Teo (2001) found that perceived ease of use had
significant positive relationship with messaging, browsing, and downloading activities.
Also, Tsai and Lin (2001) found that Intemet-addicted adolescents perceived better
control about using the Internet than non-addicts.

Based on the above discussion and abundant empirical evidence, it can be
hypothesized that persons who believe that they can gain information they want or
communicate with others effectively on the Internet will use the Internet applications

more overall than others, thus increasing the likelihood of being addicted to the Internet:

H5 .' Internet user’s self-eflicacy will be positively related to Internet addiction

tendencies.

In addition, since self-efficacy is task specific as explained in the beginning of
this section, it can be also hypothesized that Internet self-efficacy is more strongly related
to specific applications available on the Internet than to generalized use of the Internet.
However, little evidence has accrued which can demonstrate the influence of self-efficacy
on the use of various online applications. Instead, it was reported that Internet self-
efficacy was positively related to expected gratifications (e. g., Ebersole, 1999; LaRose et

al., 2001) irrespective of whether they were process or content gratifications. This may be

37

due to the fact that “user friendly browsers make the Internet easy to use while the almost
infinite pool of entertainment and other information available makes the Internet
enjoyable to use” (Teo, 2001, p. 134). Thus, the present study proposes the following

hypotheses:

H5-A: Internet user’s self-eflicacy will be positively related to general Internet

gratifications (both content and process gratifications).

38

 

CHAPTER 2
RESEARCH METHODS
Sample

Data were collected from a purposive sample of 498 students in three introductory
communication classes at two Midwestern universities through surveys distributed in
class. Respondents picked up the questionnaire in class and returned it later. Respondents
have ready access to Internet connections and as such have been the subject of much of
the prior research on Internet usage (e. g., Papacharissi & Rubin, 2000) and Internet
addiction (e. g., Chou & Hsiao, 2000; Morahan-Martin & Schumacher, 2000; Pratarelli,
Browne, & Johnson, 1999).

The sample consisted of 61 percent (n = 303) males and 39 percent (n =194)
females. The average age of the respondents was 25.24 (SD = 15.33) and their average
years of education was 13.74 (SD = 2.26). Among all respondents, 66 people (13.3%)
were African Americans and 342 (68.7%) were White American people consisting of the
majority. In addition, there were 42 (8.4%) Asians and 23 (4.6%) Hispanics and the other
20 respondents (4.0%) were categorized into others. Fifty-four percent (n = 269) reported
family incomes of $50,000 a year or more, 15.3 percent (n = 76) under $20,000, and the

remainder (n = 140) between $20,000 and $50,000.

Measures
A comprehensive pool of Internet gratifications items was assembled from prior
Internet gratification studies (Charney & Greenberg, 2001; Ferguson & Perse, 2000;

Kaye, I998; Korgaonkar & Wolin, 1999; LaRose et al., 2001; Lin, 1999b; Papacharissi &

39

 

Rubin, 2000). Items were edited for duplication and consistency in wording. The
resulting pool of items was checked for completeness against an alternative typology of
behavioral motivations found in Bandura’s (1986) social cognitive theory. Additional
items were developed to reflect status and monetary incentives that appeared to be

underrepresented in previous uses and gratifications studies (cf. LaRose et al., 2001).

Table 3 Measurement Summary for Internet Gratifications

 

 

 

 

Incentive Statements M SD n
Categories
Using the Internet, how likely are you to....
See Web pages with pleasing color schemes 5.94 1.11 497
Find cool new Web pages 5.85 1.21 498
Pleasing sensory Find new interactive features 5.64 1.34 498
incentives Find Web pages that are easy to navigate 5.79 1.23 497
See attractive graphics 5.95 1.15 498
Find cluttered Web pages ® 2.52 1.45 498
Obtain information that is new to me 6.06 .79 497
Get useful information about products or services 5.98 1.02 498
Get immediate knowledge of big news events 6.14 1.10 496
Get information I can trust 5.03 1.05 498
Encounter controversial information 5.63 1.27 498
Obtain current news, sports and weather 6.33 1.22 497

Get information about local community and government 5.22 1.08 498
Novel sensory programs

 

incentives Find employment listings that fit me 5.13 1.53 497
Learn things to improve myself 5.11 1.47 496

Find a wealth of information 5.87 1.51 498

Solve a problem 5.22 1.32 495

Get information to improve my health 5.36 1.43 496

Find useless information ® 2.27 1.65 497

Learn about local community events 4.60 1.67 498

Get useful housinanformation 4.93 1.64 498

Hear music I like 6.10 1.32 498

Feel excited 5.1 1 1.48 497

Have fun 5.58 1.33 497

Activity incentives Feel entertained 5.72 1.32 497
Fulfill a fantasy 3.63 1.94 498

Have a bad time ® 4.68 1.54 497

Pursue a hobby 5.15 1.63 498

 

4o

Table 3 (cont’d)

 

 

 

£2332; Statements M SD n
Using the Internet, how likely are you to....
Relieve boredom 5.80 1.38 498
Feel relaxed 5.22 1.54 498
Save time 4.95 1.73 497
Self-reactive Feel less lonely 3.84 1.96 498
incentives Forget my problems 3.74 1.89 498
Feel a sense of accomplishment 4.48 1.70 498
Find something that challenges me 4.79 1.70 497
Find a way to pass the time 5.91 1.33 497

 

4.25 2.00 495
3.24 1.92 495
3.67 1.99 494
5.80 1.52 494
2.70 1.93 494

Maintain a relationship I value

Find companionship

Meet new friends

Get in touch with people I know

Meet someone in person who I met on the lntemet

 

Provide help to others 4 11 1 83 490
. . . Get through to someone who is hard to reach ' '
Socral mcentrves . 5.04 1.79 494
Feel like I belong to a group
. . 3.29 1.92 494
F 1nd something to talk about
. . . . 4.40 1.95 491
Do something together With famrly or friends
3.80 1.97 494
Get support from others 3 87 l 94 495
Cut myself off from family and friends ® ' '
Develop a romantic relationship 5‘51 L79 495
2.22 1.68 493

Frnd more interesting people than in real life 257 1.86 494

 

2.63 1.92 495

 

Get people to think I in cool 2. 61 1.79 491
Improve my standrng 1n the world 2 68 1 82 495
Gain respect from the people who matter most me ‘ '
Status incentives . . p y S 3.99 1.97 493
Find people like me
. . . 3.92 2.03 493
Find information that reflects my culture 4 59 1 92 494
Get up to date with new technology ' '
. . 5.33 1.68 494
Improve my future prospects in life
F llik Id ’tb l 1' ® 4.34 1.83 494
cc e on eong on me 5.64 1.69 495
Find bargains on product and services 5.29 1.75 495
Find ways to make more money 4.33 1.99 495
Get free music 6.02 1.67 495
Monetary Get free information of interest to me 6.05 1.25 497
incentives Get products for free 4.44 1.97 495
Save money on phone calls 4.68 2.12 497
Waste money on things I don’t need ® 4.18 2.16 497
Find profitable financial information 4.21 2.03 497

 

Note. Incentive categories are from Bandura (1986). Items were measured on 7-point scale (1 = very
unlikely, 7 = very likely). ® denotes reflected items

41

Following the procedure recommended by LaRose et al. (2001), respondents were asked
how likely they were to obtain each gratification from using the lntemet from “1 = very
unlikely” to “7 = very likely.” The means and standard deviations of these items are
shown in Table 3.

The amount of time spent in each activity using certain applications (Web
browsing, e-mail, chatrooms, newsgroups, and others) was measured by asking the
respondent “how many minutes he or she spent in each of the activities on the lntemet
yesterday.” The average time spent in Web browsing was 26.87 (SD = 35.53) minutes,

that was found to be the most common activity of the sample (Table 4).

Table 4 Average Time for Internet Activities

 

 

 

lntemet Activities M SD N
Weekday 91.05 117.21 489
Weekend 70.83 87.5 1 490

Web Browsing 26.87 35.53 437
E-mail 19.99 21.11 437
Chatrooms 6.15 20.97 43 7
Newsgroups 3.75 12.91 437
Other Activities 21.69 49.59 437

 

Except aggregate other activities, e-mailing was found to be the second with the

average 19.99 (SD = 21.11) minutes spent. Considering that these respondents’ average

42

amount of time spent on the Internet in the typical weekday was reported to be 91.05
minutes (SD = 117.21) and typical weekend day use was 70.83 minutes (SD = 87.51), it
can be said that almost half of the time using the lntemet was spent in both Web
browsing and e-mailing. However, chatrooms (M = 6.15, SD = 20.97) and newsgroups
(M = 3.75, SD = 12.91) were found to be relatively less utilized applications by the
sample of the study.

The seven-item short form of the Center for Epidemiologic Studies Depression
(CES-D) scale (Mirowsky & Ross, 1992) was used to assess general level of depression
(a = .82). This scale includes seven items and they were measured on a four-point scale

ranging from 0 to 3. Table 5 shows the means and standard deviations of these items.

Table 5. Depression Scale

 

 

Items for Depression M SD
I felt that I could not shake the blues even with help from my .79 .84
family or friends
I had trouble keeping my mind on what I was doing 1.28 .86
I felt that everything I did was an effort 1.35 .89
My sleep was restless 1.11 .97
I felt lonely .89 .89
I felt sad .87 .85
I could not get “going” 1.06 .91

 

Note. Items were measured on 4-point scale (0 = rarely/none, 1 = some/little, 2 =

occasionally/moderate, 3 = most/all). a = .82

43

To measure respondents’ levels of lntemet self-efficacy, Eastin and LaRose’s
(2000) lntemet self-efficacy scale was utilized for this study. This scale consisted of eight
items that showed a high level of reliability (0. = .93) shown in Table 6. It measured the
level of agreement with each statement on the seven-point Likert scale that ranged from

“1 = strongly disagree” to “7 = strongly agree.”

Table 6 lntemet Self-Efficacy Scale

 

 

Items for Internet Self-Efficacy M SD
I feel confident...
understanding terms/words relating to lntemet hardware 5.43 1.59
understanding terms/words relating to lntemet software 5.40 1.60
describing functions of lntemet hardware 5.00 1.70
trouble shooting lntemet problems 4.67 1.79
explaining why a task will not run on the lntemet 4.48 1.79
using the lntemet to gather data 6.1 1 1.24
learning advanced skills within a specific lntemet program 5.36 1.55
turning to an online discussion group when help is needed 4.69 1.86

 

Note. Items were measured on 7—point scale (1 = strongly disagree, 7 = strongly

agree). a = .93

44

lntemet addiction items were drawn from a variety of sources including Rozin

and Stoess (1993), Young (1996; 1999), Greenfield (1999b), Griffiths (1999), Pratarelli

et al. (1999) and Morahan-Martin and Schumacher (2000). These were formatted as

seven-point Likert-type items ranging from “1 = strongly disagree” to “7 = strongly

agree.” These items were reduced through factor analysis (see below) to the six items (or

= .80) shown in Table 7. Recalling that these indicators were assessed within a normal

population of college students in which “true” lntemet addicts were not expected to be

prevalent, this study calls this variable lntemet Addiction Tendency.

Table 7 lntemet Addiction Tendency Scale

 

 

Items for Internet Addiction Tendency M SD
The lntemet is part of my usual routine 4.77 2.02
I use the Internet so much that it interferes with other activities 3.07 1.89
Web surfing is a habit I have gotten into 4.01 1.89
I use the lntemet without really thinking why 4.38 1.99
I would miss the lntemet if I could no longer go online 4.72 2.04
I often spend much longer on line than I intended to when I started 3.96 1.98

 

Note. Items were measured on 7-point scale (1 = strongly disagree, 7 = strongly

agree). a = .80

45

Data Analysis

All analyses were performed using the Statistical Package for the Social Science
(SPS S) version 11.5. Firstly, factor analyses were conducted to extract factors related to
lntemet Addiction Tendency and gratifications of the lntemet. Secondly, for the purpose
of exploratory analysis, since the lntemet gratifications items were gathered into a new
scale for the present study, each of the gratification factors produced by the factor
analysis was entered into a multiple regression analysis as the dependent variable that
would be explained by several essential demographic variables including gender, age,
education, ethnicity, and household income.

The factor analysis used a principal component solution and varimax rotation to
find variable groupings, and specified the retention of factors with eigenvalues greater
than 1.0. To extract more precise factors, however, a rigorous criterion was applied by
which an item was considered significant, for example, if it had a primary loading at .50
or higher on one factor, and no secondary loading above a value of .40 on any other
factor. For this reason, items with less than .50 factor loading points in their primary
factors and items with more than .40 factor loading points in any other factors were
omitted. Factors with less than .60 Cronbach ’s alpha and items that negatively affected
the reliability coefficient of each factor were omitted, too.

To test all hypotheses proposed in the present study, Pearson product-moment
correlation (with pair-wise exclusion of missing cases), t-test, and AN OVA analysis were
utilized. And then, to assess the relative predictive value of the various independent

variables (i.e., demographic variables, lntemet activities, gratifications of the lntemet,

46

 

Depression, and lntemet Self-Efficacy), they were entered into a multiple regression
analysis with lntemet Addiction Tendency as the dependent variable.

For regression analyses, each categorical variable was coded as follows: Gender
was coded as “1 = female” and “2 = male”; Household income was coded as “I = under
$20,000”, “2 = $20,000 to $34,999’, “3 = $35,000 to $49,999”, and “4 = $50,000 or
more "; Ethnicity was recoded into dummy variables which had two values (“0 = No ”

and “1 = Yes ”) in each ethnicity group.

47

CHAPTER 3
RESULTS

To answer Research Question 1 about what the gratifications of the lntemet are,
seven factors with eigenvalues above 1.0, which accounted for 67.4 percent of the total
variance, were extracted from the initial set of Internet gratifications. Table 8 presents the
final results of factor analysis and reliability coefficients. For each factor, multiple linear
regression analyses were conducted to examine their predictability from basic
demographic parameters such as gender, age, education, ethnicity, and household income.

Factor 1 was characterized as Virtual Community (eigenvalue = 5.61, variance
after rotation = 16.02%, a = .92). This factor represented a new variety of social
interaction gratification one can seek exclusively online. It reflected both Social-
Integrative and Personal-Integrative related gratifications as well as Social Incentives and
Status Incentives proposed by LaRose et al. (2001). In other words, this factor suggested
that lntemet users seek to make a new self-image that is totally different from that in real
life (e. g. “get people to think I’m cool” and “improve my standing in the world”) through
forming new personal relationships on the lntemet. However, it differes from the concept
of parasocial interaction (for more discussion, see in Giles, 2002) in that the interactions
are with real people, as opposed to symbolic interactions with media personalities. The
factor of Virtual Community might best be categorized as a process (or ritualized)
gratification because of the combination of Social-Integrative and Personal-Integrative

related gratifications.

48

Table 8 lntemet Gratifications Factors

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Factor 1: Virtual Community Factor Loading
Develop a romantic relationship .858
Find more interesting people than in real life .818
Get people to think I’m “cool” .811
Meet someone in person who I met on the lntemet .778
Find companionship .764
Meet new friends .745
Improve my standing in the world .720
Feel like I belong to a group .692
Get support from others .530
Eigenvalue 5.61
Variance explained 16.02%
Reliability (Cronbach ’5 Alpha) .92
Factor 2: Information Seeking Factor Loading
Learn about local community events .815
Get useful housing information .795
Find employment listings that fit me .753
Get information about local community and government .750
programs
Get information to improve my health .701
Get immediate knowledge of big news events .509
Get useful information about products or services .504
Eigenvalue 4.08
Variance explained 11.67%
Reliability (Cronbach '5 Alpha) .88
Factor 3: Aesthetic Experience Factor Loadirg__
Find cool new Web pages .845
See attractive graphics .827
Find new interactive features .785
See Web pages with pleasing color schemes .777
Find Web pages that easy to navigate .745
Eigenvalue 3 .86
Variance explained 11.03%
Reliability (Cronbach ’s Alpha) .89
Factor 4: Monetary Compensation Factor Loading
Find bargains on product and services .769
Find ways to make more money .738
Get products for free .715
Find profitable financial information .653
Save money on phone calls .590
Eigenvalue 3.08
Variance explained 8.79%
Reliability (Cronbach '3 Alpha) .85
Factor 5: Diversion Factor Loading
Have fun .826
Feel excited .804
Feel entertained .782
Feel relaxed .614
Eigenvalue 2.96
Variance explained 8.45%
Reliability (Cronbach '5 Alpha) .87

 

49

 

Table 8 (cont’d)

 

 

 

 

 

 

 

Factor 6: Personal Status Factor Loading
Improve my future prospects in life .722
Get up to date with new technology .689
Find information that reflects my culture .676
Eigenvalue 2.27
Variance explained 6.50%
Reliability (Cronbach 's Alpha) .80
Factor 7: Maintenance of Relationship Factor Loading
Get in touch with people I know .807
Get through to someone who is hard to reach .701
Eigenvalue 1.75 ‘
Variance explained 4.99%
Reliability (Cronbach ’s Alpha) .63
Total Variance explained 67.4%

 

For the first factor of Virtual Community, the linear regression model was
significant, F (9, 466) = 4.983, p < .001, explaining 8.8 percent of the total variance in
Factor 1 (Table 9). Being white was the most significant predictor of Virtual Community
gratification, B = -.596, p < .01, indicating whites were less likely to expect Virtual
Community gratification. Education was the second significant predictor, B = -.142, p
< .01. Also, males than females ([3 = .115,p < .05) were found to seek more this
gratification on the lntemet. It appears that non-white males who had lower education
level were more satisfied with Virtual Community function of the lntemet than the others.

Factor 2 represented a theme of Information Seeking (eigenvalue = 4.08, variance
after rotation = 11.67%, a = .88). All the items were clearly related to contexts external to
the lntemet, such as community events, housing and the news of the world (e.g., “learn
about local community events” and “get immediate knowledge of big news events”).

Cognitive-related gratifications such as these may be categorized as a content (or

50

instrumental) gratification. However, the regression model for Factor 2, Information
Seeking, was found to be non-significant, F (9, 469) = 1.840, us.

The third factor was termed Aesthetic Experience (eigenvalue = 3.86, variance
after rotation = 11.03%, a = .89), because all the items loaded on it seemed to signify
artistic aspects related to the design of Web sites on the lntemet (e. g., “see attractive
graphics” and “see Web pages with pleasing color schemes”). Looking back upon Katz et
al.’s (1973) reference that Affective needs are related to strengthening aesthetic,
pleasurable, and emotional experiences, by and large, this factor appears to be an

Affective-related gratification, and consequently can be considered a process gratification.

Table 9 Regression for Demographics Predicting Factor 1 (Virtual Community)

 

 

Independent Variables [I t p
Gender .115 2.518 .012
Age .085 1.884 .060
Education -. 142 -3. 125 .002
Household Income .004 .089 .929
Ethnicity

African American -.328 -1.939 .053
White American -.596 -2.637 .009
Asian —.222 -1.549 .122
Hispanic -.194 -1.746 .082
Others -.146 -1.423 .155

 

Note. Table reports standardized Betas. Gender was measured as “I = Female ” and “2 ==
Male. " Household Income was measured as “1 = Under $20, 000, ” “2 = $20,000 to

$34, 999, ” “3 = $35,000 to $49,999, ” and “4 = $50, 000 or more. ” Ethnicity was recoded
into dummy variables which had two values (“0 = No” and “I = Yes”) in each group. R

= .296. R2 = .088, Adjusted R2 = .070. F(9, 466) = 4.983, p < .001.

51

For Factor 3, Aesthetic Experience, the linear regression model was significant, F
(9, 469) = 2.626, p < .01, and 4.8 percent of the variance was explained by the model
(Table 10). Two demographic variables—education (B = -.152, p = .001) and gender ([3 =
-.104, p < .05)——were found to be significant indicators in predicting Aesthetic

Experience gratification factor. That is to say, less educated people were more likely to

 

r
be gratified with their aesthetic experiences on the lntemet than well educated people.
And, females were more likely to seek and to be satisfied with their aesthetic experiences 5

0
on the lntemet than males. ; ..

Table 10 Regression for Demographics Predicting Factor 3 (Aesthetic Experience)

 

 

Independent Variables [I t p
Gender -. 104 -2.241 .026
Age .053 1.145 .253
Education -. 152 -3 .285 .001
Household Income .004 .094 .925
Ethnicity

African American -.112 -.648 .517
White American -.160 -.692 .489
Asian -.146 -1.006 .315
Hispanic -.046 -.41 1 .682
Others -.028 -.260 .795

 

Note. Table reports standardized Betas. Gender was measured as “I = Female” and “2 =
Male. ” Household Income was measured as “I = Under $20, 000, ” “2 = $20,000 to
$34,999, ” “3 = $35,000 to $49,999, ” and “4 = $50, 000 or more. ” Ethnicity was recoded
into dummy variables which had two values (“0 = No” and “I = Yes”) in each group. R

= .219. R2 = .048, Adjusted R2 = .030. F(9, 469) = 2.626,p < .01.

52

Factor 4 was named Monetary Compensation (eigenvalue = 3.08, variance after
rotation = 8.79%, 0. = .85). It reflected using the lntemet simply for the purposes of
financial gain (e. g., “find ways to make more money” and “get products for free”). Since
the monetary benefits are obtained in the real world outside of the lntemet, this should be
considered a content gratification. For Monetary Compensation factor, prediction using
the linear regression model failed, F (9, 469) = 1.675, ns, since the significance level was
greater than .05.

The fifth factor appeared to be a Diversion gratification (eigenvalue =, 2.96,
variance after rotation = 8.45%, or = .87). This factor included such items as “have fun”
and “feel relaxed.” This is the exact case of process gratification that “results from the
pleasurable experience of media content and is realized during consumption” (Swanson,
1992, p. 310). Thus, it should be also categorized into a process gratification.

The regression model predicting this factor was significant, F (9, 469) = 2.494, p
< .01, and explained 4.6 percent of the total variance in Diversion gratification (Table 11).
But education alone was found to be a significant predictor, B = -.188, p < .001, in the
prediction model, while other demographic variables were not. The model shows that
those who had lower education levels were likely to expect and thus be more gratified
with diversion on the lntemet than were those who had higher levels of education.

The sixth factor named as Personal Status (eigenvalue = 2.27, variance after
rotation = 6.50%, a = .80) reflected Personal Integrative Needs, defined by Katz et al.
(1973) as related to strengthening credibility, confidence, stability, and status of the
individual (e.g., “improve my future prospects in life”). Therefore, this factor was

included in the category of process gratifications, too.

53

Table 11 Regression for Demographics Predicting Factor 5 (Diversion)

 

 

Independent Variables B t p
Gender .024 .522 .602
Age .058 1.263 .207
Education -. 1 88 -4.068 .000
Household Income -.014 -.291 .771
Ethnicity

African American -.013 -.075 .940
White American -.088 -.381 .704
Asian .029 .200 .842
Hispanic -.064 -.568 .570
Others -.044 -.413 .680

 

Note. Table reports standardized Betas. Gender was measured as “I = Female ” and “2 =
Male. ” Household Income was measured as “I = Under $20, 000, ” “2 = $20, 000 to

$34, 999, ” “3 = $35, 000 to $49, 999, ” and “4 = $50, 000 or more. ” Ethnicity was recoded
into dummy variables which had two values (“0 = No” and “I = Yes”) in each group. R

= .214. R2 = .046, Adjusted R2 = .027. F(9, 469) = 2.494,p < .01.

Explaining 8.8 percent of the total variance in the factor, the linear regression
model was found to be significant in predicting Personal Status factor, F (9, 466) = 5.009,
p < .001 (Table 12). Education (B = -.124, p < .01) was the most significant predictors of
Personal Status gratification, followed by gender (B = .122, p < .01) and age (B = .111, p
< .05). In other words, less educated and older people used more the Internet for Personal
Status gratification than more educated and younger people. It is also shown that males

were more likely to seek to enhance personal status on the lntemet than females.

54

Table 12 Regression for Demographics Predicting Factor 6 (Personal Status)

 

 

Independent Variables B t p
Gender .122 2.666 .008
Age .111 2.456 .014
Education -. 124 -2.726 .007 '
Household Income -.070 -1.526 .128
Ethnicity

African American .111 .659 .510
White American -.057 -.254 .800
Asian .092 .640 .522
Hispanic -.007 -.061 .951
Others .098 .954 .341

 

Note. Table reports standardized Betas. Gender was measured as “1 = Female” and “2 =
Male. " Household Income was measured as “I = Under $20, 000, " “2 = $20, 000 to

$34, 999, ” “3 = $35, 000 to $49, 999, ” and “4 = $50, 000 or more. ” Ethnicity was recoded
into dummy variables which had two values (“0 = No” and “I = Yes”) in each group. R

= .297. R2 = .088, Adjusted R2 = .071. F (9, 466) = 5.009, p < .001.

The final factor, named as Maintenance of Relationship (eigenvalue = 1.75,
variance after rotation = 4.99%, a = .63), contained two items: “get in touch with people I
know” and “get through to someone who is hard to reach.” It reflected the thought that
the lntemet is a useful tool for remaining communications and relationships with the
already-known acquaintance. In contrast to the first factor, Virtual Community, that
emphasized communication with new people who were newly met on the Internet not in
real life, Maintenance of Relationship focuses on maintaining relationships with existing
acquaintances. However, in the broad sense that social interaction among people is a

continuing process, it also might be characterized as a process gratification.

55

For Maintenance of Relationship, the regression model accounts 4.1 percent of
the total variance and was significant, F (9, 466) = 2.230, p < .05 (Table 13). As shown in
the table, age was found to be a significant indicator, B = .107, p < .05, in the prediction
model. Education was another significant predictor, B = -.096, p < .05. In other words, the
gratification of Maintenance of Relationship was affected by lntemet user’s age and
education among various demographic variables so that the older and the less educated
was more likely to seek and obtain Maintenance of Relationship gratification than the

younger and the highly educated.

Table 13 Regression for Demographics Predicting Factor 7 (Maintenance of

 

 

Relationship)

Independent Variables B t p
Gender -.O79 -1.686 .092
Age .107 2.300 .022
Education -.096 -2.064 .040
Household Income -.053 -1.126 .261
Ethnicity

African American .124 .714 .475
White American .209 .904 .366
Asian .233 1.589 .113
Hispanic .070 .618 .537
Others .049 .462 .644

 

Note. Table reports standardized Betas. Gender was measured as “I = Female ” and “2 =
Male Household Income was measured as “I = Under $20, 000, " “2 = $20, 000 to

$34, 999, ” “3 = $35, 000 to $49, 999, ” and “4 = $50, 000 or more. ” Ethnicity was recoded
into dummy variables which had two values (“0 = No” and “I = Yes”) in each group. R

= .203. R2 = .041, Adjusted R2 = .023. F(9, 466) = 2.230, p < .05.

56

Hypotheses concerning how the amount of time spent in doing various Internet

activities (H1) and demographic variables such as lntemet user’s age (H2-A) and

education (HZ-B) were examined through the correlation matrix as shown in Table 14.

And then, in terms of H2-C and H2-D that predicted no differences of gender and

ethnicity in lntemet addiction tendency, a t-test (Table 15) and an ANOVA (Table 16)

were conducted respectively.

Table 14 Correlations between Internet Activities and Internet Addiction Tendency

 

 

l 2 3 4 5 6 7 M SD

IAT .11" -.04 .07 .30""" .24*"" .16"" .16” 4.15 1.39
1. Age .03 .00 .07 .06 -.04 -.01 25.24 15.33
2. Edu -.03 .09 .08 .05 .19""" 13.74 2.26
3. H1 -.02 .02 -.03 -.07 3.10 1.15
4. WB 24"" .l8"** .l8**" 26.87 35.53
5. EM .37""" .24""" 19.99 21.11
6. CR .33""* 6.15 20.97
7. NG 3.75 12.91

 

Note. IAT = lntemet Addiction Tendency, Edu = Education, HI = Household
Income, WB = Web Browsing, EM = E-Mail, CR = Chatrooms, NG = Newsgroups.

*p <05. "p <01. *** p < .001

Table 14 shows that, while age did not affect any of the various lntemet activities,

education had positive relationships with newsgroups activity. That is, the higher one’s

57

level of education was, the more amount of time he or she spent in newsgroups (r = .19, p
< .001). It was also found that there are significant correlations among four types of
lntemet activities.

In terms of the hypothesized relationships between lntemet activities and Internet
Addiction Tendency, it was found that all kinds of activities were positively related to
lntemet addiction, leading to rejection of Hl-A that predicted no significant relationship
between the amounts of time spent Web browsing and lntemet Addiction Tendency.
However, the amount of time using e-mail (r = .24, p < .001), chatrooms (r = .16, p < .01),
and newsgroups (r = .16, p < .01) were found to have positive relations to lntemet
Addiction Tendency as expected. Thus, Hl-B, Hl-C, and Hl-D were supported.

Table 14 also shows that H2-A that lntemet user’s age will be negatively related
to lntemet Addiction Tendency should be rejected, because age was found to be
positively related to Internet addiction (r = .11, p < .05) contrary to the expectation. In
addition, education was found not to have positive relationship with lntemet Addiction
Tendency (r = -.04, ns), so that H2-B was not supported, either.

According to the results of a t-test (Table 15), the amounts of time in Web
browsing was significantly different between male (M = 31.11, SD = 38.76) and female
(M = 19.54, SD = 27.67), t (412.03) = 3.61, p < .001 (two-tailed). E-mail was also
different in the amounts of using time between male (M = 18.28, SD = 22.26) and female
(M = 22.97, SD = 18.68), t (374.48) = -2.35, p < .05 (two-tailed). However, there was no
gender difference in the other activities related to chatrooms and newsgroups. For
lntemet Addiction Tendency, different from the hypothesis, male respondents (M = 4.33,

SD = 1.30) reported higher levels of lntemet Addiction Tendency than did female

58

respondents (M = 3.86, SD = 1.47), t (372.70) = 3.59, p < .001 (two-tailed). Thus, H2-C

that predicted no gender difference in lntemet addiction was rejected.

Table 15 Differences of Gender in lntemet Addiction Tendency

 

 

Mean (standard deviation) Male Female t
Internet Addiction Tendency 4.33 (1.30) 3.86 (1.47) 3.59"""
lntemet Activities

Web browsing 31.11 (38.76) 19.54 (27.67) 3.61 """
E-mail 18.28 (22.26) 22.97 (18.68) ~2.24"
Chatrooms 5.92 (19.57) 6.59 (23.35) -.32
Newsgroups 4.21 (14.09) 2.96 (10.57) .98

 

"p < .05. *"p < .01. "*"p < .001.

Table 16 presents the mean score of lntemet Addiction Tendency and lntemet

Activities for each type of ethnicity. In terms of lntemet activities, there were significant

ethnical differences only in using e-mail, F (4, 427) = 3.908, p < .01, while there was no

difference among different ethnical groups in using Web browsing, F (4, 427) = .676, ns,

chatrooms, F (4, 427) = 2.005, ns, and newsgroups, F (4, 427) = .560, ns. For e-mail

activity, African Americans (n = 65) were found to spend more time than any other group.

Their mean time spending in e-mail activity for a day was 27.96 minutes (SD = 28.19).

On the other hand, Hispanics (n = 23) spent less time than others in e-mail, M = 15.95,

SD = 14.73.

59

Table 16 lntemet Addiction and Activities as a Function of Ethnicity

 

lntemet Activities

 

 

Race IAT N
WB EM CR NG
AA 3.87 (1.55) 27.96 (39.52) 27.96 (28.19) 8.72 (20.93) 4.93 (11.66) 65
WA 4.09 (1.33) 26.08 (34.55) 18.00 (17.30) 4.42 (19.17) 3.37 (13.23) 342
Asian 5.05 (1.17) 35.02 (43.10) 26.14 (22.49) 12.45 (26.48) 6.07 (16.05) 42
Hispanic 4.04 (1.49) 22.74 (32.03) 15.95 (14.73) 5.42 (13.75) 2.26 (4.89) 23
Others 4.12 (1.58) 24.33 (29.85) 27.13 (48.79) 11.33 (26.69) 3.67 (8.12) 20
Total 4.14 (1.39) 26.94 (35.71) 20.08 (21.16) 5.94 (20.36) 3.76 (12.97) 492
F 5447"" .676 3.908"" 2.005 .560

 

Note. Values enclosed in parentheses represent standard deviations for the means. AA = African

American, WA = White American, IAT = lntemet Addiction Tendency, WB = Web browsing,

EM = E-mail, CR = Chatrooms, NG = Newsgroups. "p < .05. ""p < .01. """p < .001.

As shown in Table 16, for the relationship between ethnicity and lntemet
Addiction Tendency which was hypothesized into H2-D, the result of one—way analysis
of variance shows that there were significant differences among five ethnical groups in
lntemet Addiction Tendency, F (4, 487) = 5.447, p < .001. Thus, H2-D that expected no
ethnical difference in lntemet Addiction Tendency was rejected, too. After Tukey post
hoc test, it was found that Asian group’s average level of lntemet addiction (M = 5.05,
SD = 1.17) was higher than other ethnic groups except ‘others’ at 95 percent confidence
leveL

Hypotheses concerning how process and content gratifications are linked to

lntemet addiction (H3-A and H3-B), how depression and lntemet self-efficacy are

60

associated to lntemet addiction (H4 and H5), as well as how depression and lntemet self-
efficacy are related to each of the two types of Internet gratifications (H4-A, H4-B, and

H5-A) were examined through the correlation matrix in Table 18.

Table 17 Correlations between Internet Gratifications, Depression, Self-efficacy and
lntemet Addiction Tendency

 

 

1 2 3 4 5 6 7 8 9
IAT .45*** .08 .09 .27*** .39*** .31*** .27*** .27*** .37***
1. vc .17*** .12" 44*" 39*" .48""" .26""" .26""" 19*"
2. IS .48""" 52*" 44*" 40*" .36""" -.11* 25*"
3. AB 35*" .46""" 34*" 29*" -.06 24*"
4. MC .46""" 53*" .26""" -.02 33*”
5. Div 50*" .33": _094 ‘30:"
6. PS 37*“ .06 32*"
7. MR .00 ,29m
8. Dep _.04
9. ISE

 

Note. IAT = lntemet Addiction Tendency, VC = Virtual Community, IS = Information Seeking,
AB = Aesthetic Experience, MC = Monetary Compensation, Div = Diversion, PS = Personal
Status, MR = Maintenance of Relationship, Dep = Depression, and ISE = lntemet Self-Efficacy.

*p <05. "p <01. mp < .001.

Both Hypotheses H3-A and H3 -B received mixed support. The Virtual
Community (r = .45, p < .001), Diversion (r = .39, p < .001), Personal Status (r = .31,p

< .001), and Maintenance of Relationship (r = .27, p < .001) process gratifications were

61

positively related to lntemet Addiction Tendency, but the Aesthetic Experience (r = .09,
ns) process gratification was not. The Information Seeking gratification was unrelated to
lntemet Addiction Tendency (r = .08, ns) as predicted in H3-B. However, the Monetary

Compensation factor was positively related to Internet Addiction Tendency (r = .27, p

< .001), contrary to Hypothesis 3-B.

Hypothesis 4 that presumed positive relationship between Depression and lntemet
Addiction Tendency was supported (r = .27, p < .001). Respondents’ levels of Depression
were positively related to Virtual Community (r = .26, p < .001), Diversion (r = .09, p
< .05). However, there were no significant relationships between Depression and the
other process gratifications such as Aesthetic Experience (r = -.06, ns), Personal Status (r
= .06, ns), and Maintenance of Relationship (r = .00, ns). Also, it was found that
Depression was negatively associated with Information Seeking (r = -.1 1, p < .05) as
predicted by Hypothesis 4-B, but not with Monetary Compensation (r = .02, ns). Thus,
both Hypothesis 4-A and Hypothesis 4-B were partially sustained.

Hypothesis 5 that predicted lntemet user’s Self-Efficacy would be positively
related to lntemet Addiction Tendency was supported for this sample (r = .37, p < .001).
In addition, this relationship between lntemet Self-Efficacy and lntemet Addiction
Tendency seemed to be supported by the fact that lntemet users’ beliefs in their self-
efficacy was positively related to both process and content gratifications: Virtual
Community (r = .19, p < .001); Information Seeking (r = .25, p < .001); Aesthetic

Experience (r = .24, p < .001); Monetary Compensation (r = .36, p < .001); Diversion (r

.30, p < .001); Personal Status (r = .32, p < .001); and Maintenance of Relationship (r

.29, p < .001). Thus, hypothesis 5-A received full support.

62

Finally, to gain an overall understanding of the relationships between all the
independent variables including demographic characteristics, Internet activities, lntemet
gratifications, depression, and lntemet self-efficacy (which were all inter-correlated,
Table 8) and Internet Addiction Tendency, a multiple regression analysis was performed.
The regression model for lntemet addiction is introduced in Table 18.

The combination of demographic variables, four lntemet activities, seven lntemet
gratifications, Depression, and lntemet Self-Efficacy significantly predicted lntemet
Addiction Tendency, F (22, 393) = 10.901, p < .001, explaining 37.9 percent of the total
variance in lntemet Addiction Tendency. After controlling other variables, household
income (B = .090, p = .031) among demographic variables was found to be the only
significant predictor of lntemet Addiction Tendency. Among lntemet activities, Web
browsing (B = .177, p < .001) and e-mail (B = .141, p < .01) were positively related to
lntemet addiction.

In terms of Internet gratifications, Virtual Community (B = .198, p < .001) and
Diversion (B = .198, p < .001) were significantly related to lntemet Addiction Tendency.
Finally, both Depression (B = .220, p < .001) and lntemet self-efficacy (B = .168, p

< .001) were found to be significant indicators in predicting lntemet Addiction Tendency.

63

Table 18 Multiple Regression: Predicting Internet Addiction

 

 

Independent Variables B t p
Demographic Variables
Gender .001 .019 .985
Age .041 .994 .321
Education -.067 -1 .571 .1 17
Household Income .090 2.169 .03 1
Ethnicity
African American -.116 —.876 .382
White American -.161 -.852 .395
Asian .012 .094 .925
Hispanic -.062 -.664 .507
Others -.045 -.538 .591
lntemet Activities
Web Browsing .177 4.112 .000
E-mail .141 3.098 .002
Chatrooms -.074 -1 .640 .102
Newsgroups .051 1 .196 .232
lntemet Gratifications
Virtual Community .198 3.806 .000
Information Seeking -.094 -1.778 .076
Aesthetic Experience -.091 -1.793 .074
Monetary Compensation .040 .745 .457
Diversion .177 3 .479 .001
Personal Status -.018 -.347 .729
Maintenance of Relationship .053 1.148 .252
Depression .220 5.13 5 .000
Internet Self-Efficacy .168 3.523 .000

 

Note. Table reports standardized Betas. Gender was measured as “l = Female” and “2 = Male. "
Household Income was measured as “I = Under $20,000,” "2 = $20,000 to $34,999,” “3 =
$35, 000 to $49, 999, ” and “4 = $50, 000 or more. ” Ethnicity was recoded as dummy variables which
had two values (“0 = No” and “I = Yes”) in each group. R = .616. R2 = .379, Adjusted R’
= .344. F (22, 393) = 10.901,p < .001.

64

CHAPTER 4
DISCUSSION

The present study applied the uses and gratifications approach and social
cognitive theory to develop an understanding of the relationships between lntemet users’
gratifications, depression, as well as lntemet self-efficacy and their tendencies to become
addicted to the lntemet. The primary result of this study is that several lntemet
gratifications, depression, and lntemet self-efficacy were found to be positively related to
indicators of lntemet Addiction Tendency. This provided support for the initial
conceptualization that habitual lntemet use could be equated with emerging lntemet
addictions and that depression and self-efficacy play a catalytic role in the development
of habitual use. In other words, active seeking of the gratifications on the lntemet, which
is also mediated by psychological conditions such as depression and self-efficacy, may
lead users to become out of control over their online behavior as the amount of time
spending online increases. This verifies Stone and Stone’s (1990) notion that habitual
media use is often the result of prior active media selection.

An exploratory factor analysis identified key underlying factors of lntemet
gratifications: Virtual Community, Information Seeking, Aesthetic Experience, Monetary
Compensation, Diversion, Personal Status, and Maintenance of Relationship. These
gratifications explain 67.4 percent of the total variance. Of these, Virtual Community had
no exact parallel in previous research. It expands on social process gratifications found in
prior research to reflect both Social Integrative Needs and Personal Integrative Needs
simultaneously. This means that forming new personal relationships using a fictitious

handle allows one to transform himself or herself mentally into a new person on the

65

lntemet (Young, 1997). In fact, a lot of people use the lntemet to form new relationships
that are totally different from their real-life relationships (Parks & Floyd, 1996). This
factor also finds precedent in Turkle’s (1995) ethnographic research that detailed how
lntemet users use the medium to explore new social identities. For this reason, it is
plausible that social integrative needs are subdivided into creation of new relationships
and maintenance of existing relationships, which are two distinctive factors.

Different from the results of lntemet gratifications studies in the functional
alternative approach (e. g., Ferguson & Perse, 2000), which view the lntemet as a
functional alternative to television and suggest lntemet uses may be more motivated by a
desire for entertainment or diversion than by any other motivation, especially information
seeking motivation, this study shows that the Internet still functions well as an
information or educational tool for the college students population. Information Seeking
factor was found to be the second important factor explaining 11.67 percent of the total
variance, while Diversion was the fifth factor that explained 8.45 percent of the total
variance in lntemet gratifications and followed Monetary Compensation. This result
seems to be rather consistent with the results of empirical studies (Charney & Greenberg,
2001; J. E. Katz & Aspden, 1997; Stafford & Stafford, 2001; UCLA Center for
Communication Policy, 2001) that stressed more distinctly content-oriented gratifications.
This suggests a possibility that, for college students, the lntemet might be considered as a
different medium from television so that they may be utilized for totally different
purposes, for example, television for entertainment and diversion and the lntemet for

information seeking and surveillance.

66

Although the results of this study cannot be generalized to the general population
due to the distinctive characteristics of college students, it is still interesting and
worthwhile to note some implications of demographic variables in uses and gratifications
of the lntemet. Firstly, after conducting multiple regression analyses for each of the seven
lntemet gratification factors, an interesting pattern was observed. The linear regression
equations, which included demographic variables such as gender, age, education,
household income, and ethnicity as independent variables to predict each lntemet
gratification, were significant only for the process gratifications but not for the content
gratifications. Neither the regression model for Information Seeking, F (9, 469) = 1.840,
nor the regression model for Monetary Compensation, F (9, 469) = 1.675, was significant,
while all the other regression models for process gratifications were statistically
significant. This might mean that instrumental lntemet use for a specific goal such as
Information Seeking and Monetary Compensation can be conducted by all lntemet users
regardless of their demographic characteristics only when certain needs arise but not
always. On the other hand, process-oriented uses are more likely to be driven by needs
based on socio-biological conditions such as gender, age, education, household income,
and ethnicity (i.e., demographic characteristics). This result also seems to provide a good
support for the main assumption of the study that lntemet addiction is related to using the
lntemet for process gratifications but not for content gratifications.

With respect to gender, male users are more likely to use the lntemet for Virtual
Community and Personal Status gratifications, spending more time in Web browsing than
female lntemet users. On the other hand, female users tend to use the lntemet for

Aesthetic Experience than male users. This result indicates that gender difference still

67

exists in some aspects even in the college environment. Thus, gender differences in
lntemet gratifications may be larger when the general population is considered. This
suggests that there might be other psychological variables (e.g., sensation seeking) that
are mediated by gender in explaining the difference in lntemet gratifications and lntemet
activities (especially Web browsing in this study) between males and females.

Age was found to be a significant demographic factor only in predicting Personal
Status and Maintenance of Relationship. Results showed that there were positive
relationships between age and gratifications of Personal Status as well as Maintenance of
Relationship. However, there were no age effects in the amount of time spent in different
lntemet activities. These results need to be interpreted with caution, along with education
variable, because the data were gathered from college students so that the difference in
age (the average age of the respondent is 25.24 and its standard deviation is 15.33) might
be diluted by the small amount of variation in their education (the average years of
education is 13.74 years and its standard deviation is only 2.26 years). Interestingly,
education was found to have negative relations to all of the process gratifications. That is,
the less educated the user is, the more likely to use the lntemet for process gratifications
he or she is. Also, the less educated user tends to spend relatively more time in Web
browsing and using newsgroups. Given the limited time of college student’s campus life,
it can be easily understood that senior students use the lntemet more for Personal Status
gratification, searching for some useful information on the Web and Maintenance of
Relationship gratification sharing information with other people in newsgroups, in
preparing for career and social lives after their graduation. And, the negative relationships

of education to the process gratifications suggest that those with high levels of education

68

may tend to be more critical of the lntemet and use the lntemet only for specific
outcomes. Or, seniors may know how to do research more effectively than freshmen.
According to this view, the hypothesis (H2-B) proposed in this study needs to be revised
into “Internet user’s education will be negatively related to lntemet addiction tendencies”
and examined in terms of educational differences in process gratifications in the future.

Difference in household income did not show up for any of the lntemet
gratifications and lntemet activities. There was no relationship between household
income and lntemet activities either. Although it was not hypothesized due to the lack of
consistent results, this result indicates that there is no gap between haves and have-nets
among college students in using the lntemet. This results can be attributed to the distinct
characteristics of college environment that is favorable to students’ uses of the lntemet.

Except for Virtual Community and the amount of time using e-mail, ethnical
differences did not show up for lntemet gratifications and lntemet activities respectively.
White Americans were found to be less motivated and less use the lntemet for Virtual
Community than any other racial group. In terms of e-mail usage, Afi'ican American
people spent the most amount of time, almost double than that of Hispanic people. It may
suggest that the so—called digital divide based on ethnic differences may not exist once
lntemet access has been achieved, at least not in the USA where the lntemet has become
an important communication medium for everyone’s daily lives as access to the lntemet
has become easier.

Considering the relationships between demographic variables and lntemet
addiction tendencies, gender, age, and ethnicity were found to have influence on potential

lntemet addiction tendency. Specifically, it seems that male users more than female users

69

and old (i.e. college seniors) users more than young users (i.e., college freshmen) are
more vulnerable to lntemet addiction. Also, potential Internet addicts are most likely to
be found among Asian people followed by other racial groups, White American, Hispanic,
and African American people in order. These results seem to run counter to the results of
recent studies (Brenner, 1997; Nie & Erbring, 2000; Petrie & Gunn, 1998; Young, 1996)
that found there were no significant differences between men and women or among
different ethnicities. These results suggests that demographic variables may be
fundamental and pre-dispositional factors that can be used to explain and predict people’s
degrees of lntemet addiction, but it should be noted that these causal relationships from
demographic variables to lntemet addiction tendencies can be fully understood only
when various lntemet gratifications and lntemet activities are examined as moderators of
the causal relationships.

The seven lntemet gratifications found in this study were dichotomized into
process and content gratifications on the basis of distinctions between ritual and
instrumental gratifications (Cutler & Danowski, 1980; McGuire, 1974; A. M. Rubin,
1984, 1994; Stafford & Stafford, 1998). Process gratifications are social and emotional
dimensions which included Virtual Community, Aesthetic Experience, Diversion,
Personal Status, and Maintenance of Relationship. These gratifications addressed the
needs that can be gratified in the process of using the medium in order to look for
something interesting. On the other hand, content gratifications are more outcome-
oriented which included Information Seeking and Monetary Compensation. These
gratifications refer to more purposive uses of the medium in order to fulfill specific needs.

In this study, although the process gratifications were found to drive the majority of

70

lntemet use among college students, the content-oriented motivations for Information
Seeking and Monetary Compensation were also so important that could not be neglected.
This means that college students’ lntemet uses should not be viewed as only process-
oriented (ritualistic) or only content-oriented (instrumental) since the lntemet
gratifications for college students encompass both dimensions.

However, it must be mentioned that the dichotomy of content (instrumental) and
process (ritualized) gratifications needs to be reconsidered on the basis of inconsistent
results of continuing research. The hypotheses based on the arbitrary distinction between
content and process gratifications were not well supported as expected. This can be
attributed to the inherent invalidity of that distinction in the lntemet medium. In fact, the
distinction between content and process gratifications may disappear on the Internet since
there is no clear boundary between the real world and the so-called virtual reality.
Moreover, when we consider that an increasing number of real world transactions,
including education, shopping and financial transactions are mediated through the
lntemet, the distinction between processes internal to media consumption and external
outcomes (contents) perhaps becomes somewhat irrelevant. Therefore, there is a
possibility that each use might fulfill both process and content gratifications at the same
time. For instance, one might be satisfied with information seeking process itself as well
as the content or outcome of the online behavior. This is consistent with Lin’s (1993)
findings that certain gratification-expectations were associated with dualistic motivational
goals and behavioral outcomes.

Another possible explanation is that, for example, Monetary Compensation might

be a process gratification in the sense that the transactions in question are completed

71

entirely inside the lntemet media system, even though they secure instrumental outcomes
in the real world. On the other hand, the Internet as an interactive medium allows people
to select specific content from a variety of data, some of which is useful in fulfilling
certain gratifications but much of which is not. Thus, the process of searching for
worthwhile information, even if it is for process gratifications, can become an
instrumental and outcome-oriented behavior in itself. In this sense, Aesthetic Experience
might be termed a content gratification because it clearly implies attempts to attain
desired goals (e.g., “find cool new Web pages” and “See attractive graphics”) which will
not be sought any more once they are achieved immediately at a given moment. Had that
categorization been made at the outset, both hypotheses of H3-A and H3 -B would have
been fully confirmed.

Indeed, all gratifications could be considered content-oriented in the sense of
“seeking a desired outcome.” Even diversionary gratifications such as seeking
entertainment or relieving boredom may be seen in this light. From a social cognitive
perspective (Bandura, 1986) these reflect the pursuit of self-reactive incentives: efforts to
adjust our own internal states. Prevailing conceptualizations of the origins of addictions
(e.g., Marks, 1990; Marlatt et al., 1988) stress the role played by operant conditioning,
also known as instrumental learning. In effect, the addictive behavior becomes
conditioned by repeated association with pleasurable feelings that may result from
obtaining a desired gratification from the lntemet (see further discussion in LaRose et al.,
2002). Whether the pleasurable outcomes are obtained completely “inside” the media
system, in a fulfillment of a process gratification, or instrumentally “through” the media

system is ultimately irrelevant. Thus, both process and content gratifications could lead to

72

media addiction. The inconsistent relations of Depression to each of content gratifications
and process gratifications and the consistent relations of lntemet Self-Efficacy to each
gratification factor corroborate this view.

The data analyzed in this study supported the view that there is a positive
relationship between depression and lntemet addiction tendency. It was found that those
college students who had higher degree of depression were more likely to show higher
degree of lntemet addiction tendencies. However, due to the limitation of research design,
any causal relationship should not be drawn to the conclusion that depression must be
present for some of the lntemet addiction symptoms to be developed. Still unclear is
whether depression leads people to lntemet addiction as explained by the stress reduction
theory (Holmes & Rahe, 1967) and the cognitive-behavioral model of pathological
Internet use (Davis, 2001 a; Young & Rodgers, 1998b) or, contrarily, excessive use of the
lntemet leads people to experience depression symptoms as shown in Kraut et al.’s
(1998) lntemet Paradox study.

While it is obvious that this results suggests the need for a more elaborated and
systematic research design such as the longitudinal study, it should be noted that
depression and Internet addiction tendency are interactive factors that are working
together in a reciprocal way. Those who suffer from psychological problems may be
more easily enticed with the distinctive characteristics of the Internet use, such as
anonymity and interactivity, while they are trying to get out of the aversive psychological
state of depression. The Internet may be more convenient compared to the other methods
of mood management. Also, extremely stimulating content, such as pornography, that is

not readily available in other media can be obtained on the Internet. These characteristics

73

of the lntemet may lead stressed and depressed people to a temporary relief. However,
increased use of the Internet also may further aggravate addicts’ depression by producing
feelings of guilt and negative affect about their excessive uses and inability to control
themselves. In this way, the relation of depression to Internet addiction becomes an
endless process in which the depressed and addicted user of the Internet continuously
seeks out for more sensational stimulus. As LaRose and his colleagues (2002) argued,
“media usage may spiral out of control if individuals are intent on pursuing immediate
relief from depressed feelings; this action can end in deepening depression if adverse life
consequences result. The repetition of this cycle may then form a conditioned response
linking dysphoria states with media use” (p. 12).

In light of these findings, it is suggested that people select stimuli that can help
adjust the post-exposure psychological state, and that a certain kinds of stimuli have the
potential to affect the user’s dependency on them. It should be noted, however, that the
type of stimulus could be the medium itself in a broad sense as well as the specific kind
of content available in a certain medium. That is, different kinds of media may have
different effects on users’ psychological well-being due to their distinctive media
characteristics and potential for mood management. Given the ubiquity of the lntemet in
people’s everyday lives and its use the lntemet for the purpose of entertainment (mood
management), it is imperative to examine more closely the relationship between

depression and lntemet use as well as lntemet addiction.

74

Limitations and Implications for Future Research

The generalizability of the present findings is limited by the nature of the
convenience sample of college students. Samples drawn from other populations,
especially those including larger portions of novice users who have not as yet formed
lntemet habits, could yield different results. There may also be reciprocal relationships
among variables. Therefore, time series data is necessary to determine the direction of
causality. For example, those with high levels of depression may use the lntemet more to
get out of distress, thus perceiving more gratifications of the lntemet because they are
more familiar with the capabilities of the Internet.

A new lntemet gratification discovered in this study (i.e., Virtual Community)
indicates some potential limitations in the uses and gratification research tradition.
Specifically, a single set of gratifications for the television medium (i.e., A. M. Rubin,
1983) has been dominant for two decades, which prevented certain new gratifications
relevant to new media from being probed. In addition, the gratifications have been
examined through post hoc factor analysis and built into a typology rather than deduced
from theoretical frameworks. On the other hand, the endeavor to generate a new
dimension of gratifications has been made through some studies that examined the unique
attributes of the new medium (e.g., Papacharissi & Rubin, 2000) or induced lntemet users
to answer what they think as the needs gratified by the lntemet (Charney & Greenberg,
2001). Furthermore, some other items were derived from theoretical framework outside
of the uses and gratifications tradition which is related to behavioral incentives: e.g., the
status and social incentive categories originated from social cognitive theory (Bandura,

1986). These cases suggest some guidelines for future research of uses and gratification

75

tradition; some innovative approaches might generate considerable benefit for this
research tradition both operationally and conceptually.

Further research could also help to identify crucial points where intervention may
be advisable so that users can realize the Internet’s full potential while avoiding harmful
abuse. The present results suggest some intriguing possibilities. If information seeking is
to some extent the “antidote” for the lntemet “drug,” then interventions that train users to
use the medium more effectively to seek personally relevant information might counter
the formation of habitual usage patterns. Also, improvements in the aesthetic qualities of
information-oriented sites could help attract users away from Internet venues such as
chatrooms and multi-user environments with relatively lean production qualities.

From a scholarly perspective, it is perhaps reassuring to know that we may
understand Internet addiction tendencies in terms of a well-established theory of media
attendance and exposure, that is the uses and gratifications theory. But it is also alarming
to realize that normal media usage motivations may be the breeding ground for harmful
behavioral addictions which may beset generations of lntemet users to come. The
learning theory mechanisms that underlie behavioral addictions are present in everyone,
and the media stimuli that may condition media addictions are virtually unavoidable. So,
an intriguing question for further research is why everyone isn’t an lntemet addict, and
what factors lead some to lose control while many others learn to moderate their own
media consumption behavior.

In sum, the present study has repositioned the question of lntemet addiction fi'om
the fringes of psychiatry and psychopathology to one of the central concerns of the field

of communication research: the motivations of media use. In the circumstances that

76

studies on lntemet addiction have focused on mainly psychological and pathological
factors, this kind of exploratory study that approaches the problem with a communication
theory is of much significance. If we are able to learn more about the phenomenon of
lntemet addiction, properties of the lntemet through the uses and gratifications approach,
or other forms of communication research, then we will be better able to use this new

medium in our own lives and for the benefit of society.

77

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