Relationships among Internet use, personality, and social support

Relationships among Internet use, personality,and social support

Rhonda J. Swickert*, James B. Hittner, Jamie L. Harris,Jennifer A. Herring

Department of Psychology, College of Charleston, 66 George Street, Charleston, SC 29424, USA

Abstract

Competing claims have been presented in the literature regarding the impact of Internet useon social support. Some theorists have suggested that Internet use increases social interactionand support (Silverman, 1999, American Psychologist 54, 780–781), while others have argued

that it leads to decreased interaction and support (Kiesler & Kraut, 1999, American Psychol-ogist 54, 783–784). This study was designed to address this issue by examining the relation-ships among Internet use, personality, and perceived social support. Two-hundred and sixparticipants completed questionnaires that assessed Internet use, personality (agreeableness,

conscientiousness, extraversion, neuroticism, openness), and perceived social support. Usingprincipal components analysis, individual computer activities were combined into three pri-mary factors: Technical, Information Exchange, and Leisure. Correlation and regression

analyses revealed only a marginal relationship between computer use and social support.Similarly, only modest associations were found between personality and computer use. How-ever, personality did moderate the relationship between computer use and social support.

That is, on two occasions, high computer use coupled with high personality was associatedwith decreased perceived social support and on a third occasion this combination resulted inincreased perceived social support. These results help to address some of the inconsistencies

that have been reported in the literature. # 2002 Elsevier Science Ltd. All rights reserved.

Keywords: Internet; Computer use; Social support; Personality

It can be argued that the Internet has opened up a new frontier for human inter-action. Like any new frontier there are many unknown factors and challenges asso-ciated with its exploration. The Internet is no exception to this rule, especially whenone is attempting to understand the impact of online activity on social interaction.

Computers in Human Behavior 18 (2002) 437–451

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E-mail address: [email protected] (R.J. Swickert).

In particular, the role that the Internet might play in influencing an individual’ssocial support system is, as of yet, unclear. Some researchers have suggested thatonline activity might serve to facilitate an individual’s feeling of social support(Bromberg, 1996; Mickelson, 1997; Parks & Floyd, 1996; Silverman, 1999; Winzel-berg, 1997). Others have indicated that Internet use can actually degrade socialrelationships and reduce an individual’s feeling of support (Jones, 1997; Kiesler &Kraut, 1999; Kraut, Patterson, Lundmark, Kiesler, Mukopadhyay, & Scherlis,1998b). This study was designed to test these competing claims by investigating therelationship between Internet use and perceived social support.Researchers who argue that Internet use facilitates feelings of social connectedness

and social support cite a variety of factors that appear to contribute to this effect.One of the most important of these factors concerns the opportunity that the Inter-net affords individuals to meet and interact with people who have similar interests(McKenna & Bargh, 2000). Relationships formed online via chat rooms or discus-sion groups might allow individuals with mutual interests or experiences to obtaininformation and encouragement from others who are like-minded. Similarity haslong been known to contribute to friendship formation (Martin & Anderson, 1995;Newcomb, 1961) and the Internet seems to maximize this effect. Indeed, researchershave determined that it is common for individuals to form friendships with othersonline (Katz & Aspden, 1997; The UCLA Internet Report, 2000) and to considerthose relationships to be as close as face-to-face non-Internet relationships(McKenna, 1998; Parks & Floyd, 1996). Furthermore, research has demonstratedthat online relationships can be an important source of social support. For instance,Winzelberg (1997), using an archival analysis approach, analyzed the postings of aneating disorder discussion group over a 3-month period. Comments posted werecategorized into different types of social interaction. While it was found that themost common message content involved self-disclosure (31%), requests for infor-mation (23%) and the direct provision of emotional support (16%) were alsorecorded. These results are consistent with the conclusion that individuals do receive(and provide) social support through online interaction and similar research hassupported this finding (King & Moreggi, 1998; Mickelson, 1997). Unfortunatelythough, this work is based primarily on discussion group participants and thereforemay not generalize to other types of online contact (e.g., chat rooms, multiuserdungeons). In addition, other research has suggested that online interactionmay actually reduce social connections and feelings of social support (Kraut etal., 1998b).The Home Net Project (Kraut, Kiesler, Mukopadhyay, Scherlis, & Patterson,

1998a) is the seminal study to date that provides evidence for the negative socialimpact of the Internet. In this study, a sample of 169 people in the Pittsburgh,Pennsylvania area were followed during their first 2 years online. Kraut et al.(1998a) reported that as participants used the Internet more their social connected-ness, as measured by contact with family and friends, was reduced. Participants’perceptions of their social support was also measured over the 2-year period.Although a negative relationship was found between Internet use and perceivedsupport, this relationship failed to meet the traditional level of statistical sig-

438 R.J. Swickert et al. / Computers in Human Behavior 18 (2002) 437–451

nificance. One reason why this effect may have failed to reach significance is that themeasure used to assess social support was an abbreviated version of a larger scale(the Interpersonal Support Evaluation List) and therefore the range of the scale mayhave been restricted, making it difficult to detect a significant effect. Also, becauseonly part of the scale was used, the measure may not have been psychometricallyreliable or valid. Because of these methodological problems the relationship betweenInternet use and perception of social support remains unclear.Given the conflicting theoretical views, inconsistent research findings, and paucity

of strong empirical evidence, further study is required to clarify the relationshipbetween Internet use and social support. We were particularly interested in deter-mining the relationship between online activity and a type of support called per-ceived social support. Measures of perceived support assess whether individualsperceive that they have others they can turn to for support (Cohen & Hoberman,1983). We chose to focus on this facet of social support because recent researchsuggests that perceived support is more psychologically salient and meaningful thanother types of support (e.g., objective or structural support; Hittner & Swickert,2002). In addition, perceived support has been shown to be more strongly associatedwith effective coping efforts than are other types of social support processes (Lakey& Drew, 1997; Mankowski & Wyer, 1997). Given the importance of this type ofsocial support, it appears reasonable to assume that if Internet use does indeedinfluence social support, then, by measuring perceived levels of support, one shouldbe able to assess this putative effect. The question remains, however, as to the natureof this effect. That is, does Internet use increase the amount of support a personperceives because he or she now has more people in their support network? Or,conversely, would it reduce the quality of the Internet user’s face-to-face socialcontacts and lead to a degraded sense of support? Addressing this issue was one goalof this study.In addition to investigating the relationship between online activity and per-

ceived social support, we were also interested in exploring the relationship betweenInternet use and personality. One potentially fruitful place to start in addressingthe relationship between personality and online activity is with the Five FactorModel (FFM) of personality. Extraversion (E) and neuroticism (N) are two of theproposed ‘‘Big Five’’ personality traits; the other big five traits include agreeable-ness (A), openness (O), and conscientiousness (C) (Costa & McCrae, 1992a). Thebig five, while not universally accepted (see Block, 1995, for a dissenting opinion),are generally viewed as the ‘‘essential’’ traits of personality (McCrae & Costa,1999), and they have been demonstrated to account for a wide variety of behaviorsfrom job performance to stress and coping (Barrick & Mount, 1991; O’Brien &DeLongis, 1996; Watson & Hubbard, 1996). Likewise, there is reason to believethat some of these personality traits may be predictive of Internet use. Forinstance, it could be argued that individuals who are high in openness, with theircurious manner and their tendency toward adventure seeking (McCrae, 1996),might be very attracted to online activity as an opportunity to explore and seek outthe new and novel. Recent research seems to bear out this prediction (Tuten &Bosnjak, 2001). Similarly, individuals high in agreeableness are often described as

R.J. Swickert et al. / Computers in Human Behavior 18 (2002) 437–451 439

very nice and easy to get along with (Costa & McCrae, 1992b). Given thesometimes hostile nature of Internet interactions (Joinson, 1998), this trait mightmake them very attractive to others when they go online and make it easier forthem to form friendships online. Likewise, individuals that are high in extraversiontend to be gregarious and are attracted to stimulating environments (Eysenck,1967). This tendency may influence the extravert to go online to seek out the newand exciting. In fact, researchers have documented, at least for males, a positiveassociation between extraversion and surfing sex web sites (Hamburger & Ben-Artizi, 2000). However, in the same study, a negative correlation was foundbetween extraversion and traditional social online activities (e.g., chat room visits,participate in discussion groups). Finally, it has been documented that individualsthat are high in neuroticism report lower levels of Internet usage (Tuten &Bosnjak, 2001), and, in particular, information based activities (e.g., utilizingsearch engines). This tendency may be due to the neurotic’s higher level ofanxiety and lowered self-efficacy in this particular domain. While these findingsare suggestive, much of this work is based on single studies employing relativelysmall numbers of subjects drawn from psychology courses. We were interested inreplicating these findings in a larger and broader-based sample. Moreover, wechose to have participants report the specific amount of time they spent engagedin online activites, rather than asking subjects to approximate their timeonline with likert-scale descriptors (1=not at all; 5=a lot). Because of thisspecificity, we believe our approach will yield a more precise assessment of Internetuse.In addition to addressing the association between Internet use and personality, a

third goal of this study was to determine the potential moderating role that person-ality might play between Internet use and social support. Personality factors havebeen demonstrated to affect both Internet use (Hamburger & Ben-Artzi, 2000;Kraut et al., 1998b) and perceived social support (Halamandaris & Power, 1999;Procidano, 1992; Turner, 1999). Because of this, we believe that personality andInternet use might interact to influence perceived support. To illustrate, individualshigh and low in extraversion (E) might be differentially affected by the same level ofInternet use. Whereas an individual high in E might report no change in perceivedsupport based on online social experiences, an individual low in E might reportenhanced support. This difference could be due to the fact that low E individuals,compared to high E individuals, have more to gain from these interactions becausetheir social support network is typically smaller. Determining the precise nature ofthe interaction between personality (A, C, E, N, O) and Internet use was a final goalof this study.To summarize, there were three major aims of this study. First, this study exam-

ined the relationship between Internet use and perceived social support to deter-mine what effect, if any, Internet use may have on social support. A second aim ofthe study was to determine if personality dimensions might influence frequency andtype of Internet use. Finally, a third aim of the study was to explore how person-ality might moderate the relationship between Internet use and perceived socialsupport.


1. Method

1.1. Participants

Two-hundred and six participants were recruited from computer science, politicalscience, psychology, and sociology classes at a medium-sized public liberal arts col-lege in the southeastern United States. Sixty-one percent of the participants werefemale (39% male) and their ages ranged from 18 to 45 (M=21.34). Finally, 18% ofthe participants were African-American, 1% Asian, 78% Caucasian, and 3% clas-sified themselves as other.

1.2. Materials

1.2.1. Social supportThe Interpersonal Support Evaluation List (ISEL; Cohen & Hoberman, 1983) was

used to assess the perceived availability of social support. This 48-item questionnaireassesses four types of social support and yields an overall support measure. TheAppraisal subscale assesses the perceived availability of someone to talk to aboutone’s problems; the Belonging subscale, the perceived availability of people to dothings with; the Self-esteem subscale, the perceived availability of a positive com-parison when comparing oneself to others; and the Tangible subscale, the perceivedavailability of someone to provide material aid. Individuals are asked to indicatewhether statements concerning the availability of social support are probably true orprobably false. Items associated with each subscale are summed together to yieldfour subscale totals and all of the items are added together to arrive at a total score.The subscale scores can range from 0 to 12 and the total score can range from 0 to48. The higher the total score, the higher the level of perceived support. The internalreliability of the overall scale is good (alpha=0.77). Internal reliability of theappraisal, belonging, self-esteem, and tangible subscales are adequate as well(alpha=0.77, 0.75, 0.68, and 0.71, respectively). In the present study, alpha coeffi-cients for the subscales could not be calculated because only the sum scale scores,rather than the individual items, were recorded. However, the internal reliability ofthe overall scale could be estimated by treating the four subscales as items. Theresulting alpha coefficient for the total ISEL was 0.77. Descriptive statistics, includ-ing mean, standard deviation, and range for the ISEL are reported in Table 1.Information regarding the construct and convergent validity of the ISEL can befound in Cohen and Hoberman (1983).

1.2.2. Internet useThe Computer Use Survey (CUS) was developed to assess Internet use and social

contact through online interactions. The survey requires the participant to recordthe amount of time (hours/minutes) in an average week that she or he engages in avariety of online activities including: search and do research, visit bulletin boards,visit chat rooms, create/update websites, play games, use email, use instant messag-ing, visit multiuser dungeons, and access information as a form of entertainment


(e.g., read newspaper, listen to music). Descriptive statistics for these variables arepresented in Table 1.

1.2.3. PersonalityThe NEO Five-Factor Inventory (NEO-FFI; Costa & McCrae, 1992b) was used

to assess agreeableness (A), conscientiousness (C), extraversion (E), neuroticism (N),and openness (O). It consists of 60 items that participants respond to using a five-point likert scale format (strongly disagree to strongly agree). Each factor is madeup of 12 items that collectively yield a score of 0–48. In each case, higher numbersare associated with higher levels of the personality factor. Internal consistency ofthis scale was calculated using coefficient alpha. Coefficients for A, C, E, N, and Owere 0.68, 0.81, 0.77, 0.86, and 0.73, respectively. Construct validity for this test isreported in the NEO PI-R Manual (Costa & McCrae, 1992b). Descriptive statisticsfor the NEO-FFI can be found in Table 1.

1.3. Procedure

Participants were tested while in class, at various sites on campus, typically in agroup of 20–25, during 45 min testing sessions. All testing occurred between thehours of 9.00 a.m. and 3.00 p.m. Participants were told that the purpose of the study

Table 1

Descriptive statistics for the ISEL, the CUS, and the NEO-FFI

Variable Mean Standard Deviation Range

Social support

Appraisal 10.16 2.46 0–12

Belonging 8.26 2.59 0–12

Self-esteem 8.55 2.21 0–12

Tangible 10.67 1.90 0–12

ISEL total 37.62 7.08 10–48

Computer usea

Search/research 131.17 204.83 0–2100

Bulletin board 12.51 51.54 0–600

Chat room 18.03 56.95 0–390

Create webpage 8.52 35.34 0–300

Play games 60.54 204.07 0–2100

Email 160.19 244.89 0–2400

Instant messaging 77.65 176.70 0–1500

Multiuser dungeon 2.95 23.10 0–300

Access information 150.16 230.49 0–1200

Personality

Agreeableness 31.30 6.57 6–44

Conscientious 30.74 7.04 11–46

Extraversion 30.87 6.40 3–43

Neuroticism 21.77 8.77 2–42

Openness 28.81 5.72 15–44

a Internet use is reported in minutes.


was to examine factors associated with Internet use. The assessment packets werethen administered to the participants in the following order: the demographic form,the ISEL, the CUS, and the NEO-FFI. After completing the study, participantswere thanked for their participation and debriefed.

2. Results

Prior to addressing the major aims of the study, a variety of preliminary analyseswere conducted to reduce the data, transform skewed variables, and screen formultivariate outliers. Regarding the data reduction procedure, the nine Internet usevariables were subjected to a principal components factor analysis with varimaxrotation and kaiser normalization. Inspection of the scree plot indicated a three-factor solution and the types of Internet use loading on each factor are as follows(Cronbach alpha values in parentheses): (1) bulletin board use, chat room visitation,creating web pages, and multiuser dungeon visitation (0.69), (2) search/research,email use, and accessing information (0.60), and (3) utilizing instant messenger andplaying games (0.75). In light of the low Cronbach alpha coefficient for factor No. 2,we examined the intercorrelations among the three Internet use variables. Althoughthe correlation between email and accessing information was moderately large inmagnitude (r=0.54), the correlations between these two variables and search/research were considerably smaller (rs of 0.23 and 0.19 for email and accessinginformation, respectively). Given these results, we excluded search/research from thesecond factor and recalculated the Cronbach alpha. The new alpha coefficient was0.70 and the three principal component factors accounted for 70% of the variance inthe Internet use intercorrelation matrix. Three Internet use factor variables werethen created by summing the individual Internet use variables within each factor.Upon visual inspection of the component variables, we decided to label the firstfactor Technical. This factor was made up of bulletin board use, chat room visita-tion, creating web pages, and multiuser dungeon visitation. While these activitiesappear to be quite diverse, we believe that there is a common underlying theme forthis factor. That is, one must be fairly technologically savvy to be able to engage inall of these online activities, hence, the label of Technical seemed appropriate. Thesecond factor is comprised of email use and accessing information. Both of theseInternet activities involve either generating (email) or receiving (accessing) informa-tion. Therefore, we chose to label this factor Information Exchange. Finally, thethird factor, Leisure, is made up of utilizing instant messaging and playing games.We felt that both of these activities were consistent with relaxing and having funthrough playing games and interacting with others online.While these three factors collectively seemed to effectively capture online compu-

ter use, not all of our participants engaged in these online activities to an equaldegree. In fact, upon inspection of the factors it was found that many participantsreported that they did not engage in one or more of these types of online activities.For instance, out of 206 participants only 70 individuals reported computer useconsistent with the Technical factor, 183 participants reported computer use


consistent with the Information Exchange factor, and 122 participants reportedcomputer use consistent with the Leisure factor. In an attempt to effectively dealwith this issue we decided to exclude non-users from the analyses. We reasoned thatit would be irrelevant to ask if computer use was influencing social support for theseindividuals given the fact that they were not participating in these types of onlineactivities. Therefore, all subsequent analyses were conducted solely on participantswho reported use consistent with each factor (Technical—70 participants, Informa-tion Exchange—183 participants, Leisure—122 participants).After completing the principal components analyses and excluding individuals

who reported no computer use by type of online activity, the normality of the dis-tributions for each of the three computer use factors was examined. Due to the sig-nificant positive skewness of all three factors, each distribution was logarithmicallytransformed to increase normality. We also screened for multivariate outliers beforeconducting multiple regression analyses. In particular, all participants who evi-denced statistically significant Mahalanobis D2 values were excluded from theregression analyses. Finally, because so little is known about the relationshipsamong specific types of Internet use, personality and perceived social support, wefelt it was important to not overlook potential associations among these variables.Therefore, in order to minimize the likelihood of committing Type II errors, we setour critical P-value for all analyses at a more liberal value of P<0.10. We report allof our findings on the basis of this value, however, we label P-values between 0.06and 0.10 as marginally significant.The first aim of the study was to determine if Internet use is related to perceived

social support. Correlational procedures were used to investigate this issue. No sig-nificant correlations were found between two of the three types of Internet use andthe ISEL. Specifically, nonsignificant associations were found between Technicaland the ISEL (r=�0.11, P=0.18) and Information Exchange and the ISEL(r=0.03, P=0.32). However, a marginally significant correlation was found betweenLeisure and the ISEL (r=0.13, P=0.08). To further examine this issue a simulta-neous multiple regression analysis (SMR) was conducted by entering all onlineactivities in one block to predict ISEL. No significant effects were found in thisanalysis.To explore the second aim of the study, the relationship between personality, as

measured by the NEO-FFI, and Internet use was examined. No significant correla-tions were found between personality and Technical. However, personality was sig-nificantly correlated with Information Exchange and Leisure. RegardingInformation Exchange, both neuroticism (r=�0.11, P=0.07) and agreeableness(r=�0.10, P=0.09) evidenced marginally significant correlations. A SMR analysiswas conducted by entering all five personality traits in one block to predict Infor-mation Exchange. The results from this analysis were somewhat consistent with thecorrelational findings in that there was a marginally significant effect for neuroticism[t (175)=�1.69, P=0.09, �=�0.140]. However, no significant effect was found foragreeableness. Regarding the computer use factor of Leisure, significant correlationswere found with neuroticism (r=�0.16, P=0.04) and conscientiousness (r=0.15,P=0.05), and a marginally significant association was found with extraversion


(r=0.13, P=0.08). However, results of a SMR analysis failed to reveal any sig-nificant personality predictors of Leisure.To explore the third aim of the study, hierarchical multiple regression analyses

were conducted to examine the interactive effects of Internet use and personality onperceived social support. The first set of analyses explored the interactive effects ofTechnical and personality in predicting the ISEL. The main effects of personality (A,C, E, N, and O) and Technical were entered in the first block of the equation and theinteractions between personality and Technical (A�Technical, C�Technical,E�Technical, N�Technical, O�Technical) were entered in the second block of theequation. Results of these analyses demonstrated a significant main effect for extra-version [t(60)=4.74, P=0.001, �=0.587] and a marginal main effect for openness[t(60)=�1.91, P=0.06, �=�0.195]. In addition, a marginally significant interactioneffect was found between neuroticism and Technical [t(55)=�1.75, P=0.09,�=�0.957]. In order to explore the nature of the interaction effect, we plotted thefour mean ISEL scores that are obtained by factorially crossing the neuroticism andTechnical factor (i.e., Low N, Low T; Low N, High T; High N, Low T; High N,High T). These group means indicated that individuals who are high in neuroticismand high in Technical have lower levels of perceived social support than any othergroup (Fig. 1).The second analysis examined the interactive effects of Information Exchange and

personality in predicting the ISEL. The same procedure as mentioned above wasutilized in entering the variables into the equation to predict the ISEL. Significantmain effects were found for neuroticism [t(167)=�2.49, P=0.01, �=�0.181],extraversion [t(167)=5.58, P=0.001, �=0.425], and openness [t(167)=�2.03,

Fig. 1. Mean perceived social support scores by Technical computer use and Neuroticism.


P=0.04, �=�0.137]. A marginally significant interaction effect was found for neu-roticism and Information Exchange [t(167)=�1.82, P=0.07, �=�0.707]. Visualinspection of the mean ISEL scores for the four groups indicated that individualshigh in neuroticism and high in Information Exchange reported lower levels of per-ceived social support than any other group (Fig. 2).The third analysis examined the interactive effects of Leisure and personality in

predicting the ISEL. The same procedure as noted above was utilized in entering thevariables into the equation to predict the ISEL. Once again, significant main effectswere found for neuroticism [t(110)=�2.71, P=0.01, �=�0.246] and extraversion[t(110)=4.75, P=0.001, �=0.438). A significant interaction effect was also found foragreeableness and Leisure [t(105)=2.38, P=0.02, �=1.691]. An examination of thefour group means indicated that individuals high in agreeableness and high in Leisurereported higher levels of perceived social support than any other group (Fig. 3).

3. Discussion

There were three major aims of this project. First, this study attempted to deter-mine the association between Internet use and perceived availability of social sup-port. Results of this study indicated no strong relationships between these variables.However, a marginally significant positive correlation was found between Leisureand the ISEL. The factor of Leisure involves ‘‘social’’ Internet activities like instantmessaging and playing games with others online. The positive relationship between

Fig. 2. Mean perceived social support scores by Information Exchange computer use and Neuroticism.


these variables indicates that individuals who reported higher Leisure use perceivedgreater social support when compared with individuals who reported less Leisure-based online activity. Although this positive correlation is suggestive, it is importantto note that this finding is only marginally significant and SMR analyses did notreplicate this association.The second aim of this project was to determine the relationship between Internet

use and five basic personality factors. There were no significant relationships foundbetween Technical Internet use and any of the personality traits. However, person-ality was marginally related to Information Exchange (email and accessing infor-mation) and Leisure (instant messaging and playing games). The personalitydimension of neuroticism seemed to be most consistently related to these types ofonline activities. While other personality traits were correlated with InformationExchange (agreeableness) and Leisure (conscientiousness, extraversion), these cor-relations were not supported by regression analyses and therefore do not merit fur-ther discussion. Regarding the effects of neuroticism, both correlation andregression analyses revealed marginally significant negative associations betweenneuroticism and Information Exchange and neuroticism and Leisure. These findingsindicate that individuals who are high in neuroticism are less likely to utilize thesetypes of Internet activities. While these findings are consistent with some researchpresented in the literature (Tuten & Bosnjak, 2001), these results contradict otherpublished results. Specifically, although Hamburger and Ben-Artzi (2000) reported apositive relationship between neuroticism and social-leisure activities, our researchwas not supportive of this finding. We found a negative relationship between

Fig. 3. Mean perceived social support scores by Leisure computer use and Agreeableness.


neuroticism and Leisure activity. One explanation for the inconsistency between thepresent results and past research concerns the degree of measurement specificity thathas been utilized when assessing Internet activity. In particular, whereas previousstudies have generally examined only global measures of activity on the Internet, thepresent study measured online activities much more precisely (e.g., reported minutesonline). In addition, whereas most previous work has examined individual Internetuse variables as the unit of analysis, this study utilized principal component factorsin all inferential statistical analyses. This approach is generally regarded by psycho-metricians as being superior to individual variable analyses because principal com-ponents are typically more reliable than individual variables (Tabachnick & Fidell,1989). Regardless of the merits of this study, the association between neuroticismand leisure Internet use requires further attention in order to clarify the incon-sistencies in the literature.The third aim of this study was to examine whether personality serves to moderate

the association between Internet use and perceived social support. Both significantand marginally significant interaction effects were found between personality andInternet use. Regarding the marginal effects, neuroticism was found to interact withTechnical Internet use (bulletin board, chat room, web page, multiuser dungeon) inthat individuals high in neuroticism and high in Technical use reported lower per-ceived support than any other group. This same trend was found between neuroti-cism and Information Exchange. Individuals high in neuroticism and high inInformation Exchange reported lower perceived support compared with the othergroups. These effects imply that highly neurotic individuals who use these types ofInternet activities do seem to be at risk for lowered perception of social support.However, the causal direction of this effect may not be so clear, and in fact, mayactually operate in a reverse manner. That is, highly neurotic individuals who havevery low levels of perceived support might seek out these types of Internet activitiesin an effort to compensate for their lowered sense of support. While this issue isbeyond the purpose and scope of this study, future work in this area should try toelucidate the nature and causal direction of the associations between neuroticismand these types of Internet activities.Finally, a significant interaction effect was found between agreeableness and

Leisure. Participants who reported high levels of agreeableness and high levels ofLeisure Internet use perceived themselves as having higher levels of social support,compared to the other groups. While the current study does not allow for any defi-nitive explanation of this effect, perhaps it is the case that highly agreeable indivi-duals experience more positive interactions when engaging in instant messaging andonline games, which leads to higher quality social interactions and higher levels ofperceived support. Obviously the merits of this ‘‘positive social interaction’’hypothesis cannot be discerned from the current study and hence necessitates furtherresearch. It is also unclear as to why agreeableness was the only personality factor tointeract with Leisure in this manner. This issue would likewise benefit from furtherresearch.In summary, while this study seems to invite as many questions as it addresses,

one should not be surprised by this, given that we are exploring a new area of


research, a new frontier. However, what can be said about these findings is thatalthough Internet use alone may not strongly influence perceived social support, itdoes seem to interact with personality in an important way to influence perceptionsof support. Furthermore, these findings help to address some of the inconsistenciesthat have been reported in the literature. Specifically, research in this area hasalternately indicated that Internet use either facilitates or degrades social relation-ships and social support (Kraut et al., 1998a, 1998b; Parks & Floyd, 1996). Whatthis study demonstrates is that both of these effects can occur, it is not simply aquestion of one or the other. To illustrate, high levels of neuroticism, when com-bined with high levels of specific types of Internet use, are associated with reducedfeelings of social support. In contrast, high levels of agreeableness, coupled withhigh levels of Internet use, lead to an enhancement of perceived support.While this study may help to address some of the inconsistencies in the Internet-

social support literature, we acknowledge that there are some limitations of thepresent study that should be considered. First, while the participants were selected torepresent a wide variety of majors, the sample used in this study is still based oncollege students and therefore is somewhat limited in its generalizability. Also, whilethis study assessed Internet use more precisely than past studies, the measurement ofthis variable was based on a self-report approach, rather than on objective beha-vioral criteria. Therefore, the assessment of Internet use may be somewhat biased dueto memory errors. In addressing these limitations, future research should attempt tosurvey a more representative sample that includes both college students as well astraditional adults. Furthermore, rather than relying on self-reported Internet use, abehavioral measure (e.g., a computer program that records time spent online) couldbe employed which would perhaps yield a more reliable measurement of Internetactivity.To conclude, while this study has several limitations that need to be addressed in

future work, it nevertheless makes an important contribution to our understandingof the effects of Internet use. Specifically, these findings indicate that researchers canno longer look at bivariate relationships or simple main effects of online activity onsocial support and expect to understand the complexity of the association betweenthese two constructs. Other relevant variables, such as personality factors, shouldalso be considered as they might exert important moderating effects. Future workshould attempt to understand why certain personality traits are associated withbeneficial effects of Internet use (enhanced support) while other personality factorsseem to be associated with more problematic experiences (degraded support). Infocusing on such issues, a more accurate understanding of the relationship betweenInternet use and social support may be possible.

Acknowledgements

The authors would like to thank Andy Abrams, Von Bakanic, and Walter Pharrfor allowing us to recruit participants in their classes.


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Documents

Relationships among Internet use, personality, and social support