Predicting Social Networking Site (SNS) use ...

Personality and Individual Differences 80 (2015) 119–124

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Predicting Social Networking Site (SNS) use: Personality, attitudes, motivation and Internet self-efficacy Jin-Liang Wang a,⇑, Linda A. Jackson b, Hai-Zhen Wang c, James Gaskin d a

Center for Mental Health Education, Faculty of Psychology, Southwest University, No. 2 Tiansheng Road, Beibei District, Chongqing City 400715, China Department of Psychology, Michigan State University, 262 Psychology Rd, East Lansing, MI 48824, USA c Department of Tourism and Art for Humanity, Chongqing Youth & Vocational Technical College, No. 1 Yanjingba, Bebei District, Chongqing City 400712, China d Marriott School of Management, Brigham Young University, 785 TNRB, Provo, UT 84602, USA b

a r t i c l e

i n f o

Article history: Received 3 November 2014 Received in revised form 4 February 2015 Accepted 16 February 2015 Available online 11 March 2015 Keywords: Social Networking Sites Personality traits Attitudes Motivations Internet self-efficacy

a b s t r a c t Few studies have considered the personal characteristics that may predict the use of Social Networking Sites (SNSs). We examined the prediction of SNS use from distinct personality traits (i.e., sociability and shyness), attitudes toward SNS use, motivations for SNS use and Internet self-efficacy. Participants were 352 Chinese college students who used the QQ zone. Results indicated that sociability, shyness, attitudes, motivations and self-efficacy predicted SNS use, prediction depending on the function of SNS use. Specifically, sociability, attitudes, social interaction motivation, entertainment motivation and self-efficacy were significant predictors of SNS’s social function. In contrast, shyness, attitudes, relaxing entertainment motivation and self-efficacy were significant predictors of its recreational function. Ó 2015 Elsevier Ltd. All rights reserved.

1. Introduction The past two decades have witnessed a rapid increase in the number of young adults with access to the Internet and a proliferation of Internet applications available for social and recreational functions. One such application is the Social Networking Site (SNS), a virtual community that allows users to communicate with each other, engage in a variety of social and recreational activities and, to a lesser extent, acquire needed or desired information. Decades of psychological research indicates that the primary developmental task of young adults is to establish close relationships with peers and intimate relationships with a romantic partner (Brown & Larson, 2009). Young adults’ peer groups are their most important source of social support, more important than their families (Brown & Larson, 2009). Researchers, recognizing the potential importance of SNS use to young adults’ development, have been working to identify the personal characteristics that best predict use (Deng, Liu, Li, & Hu, 2013; Lee, Ahn, & Kim, 2014; Nadkarni & Hofmann, 2012; Seidman, 2013). However, a review of the literature reveals that our knowledge of predictors use is still limited for a number of ⇑ Corresponding author. E-mail addresses: [email protected] (J.-L. Wang), [email protected] (L.A. Jackson), [email protected] (H.-Z. Wang), [email protected] (J. Gaskin). http://dx.doi.org/10.1016/j.paid.2015.02.016 0191-8869/Ó 2015 Elsevier Ltd. All rights reserved.

reasons. First, predictors identified in previous research have been, at best, only weak predictors of SNS use. The Big Five personality factors are among the most frequently examined predictors but findings indicate that they explain only a small percentage of the variance in use (Hughes, Rowe, Batey, & Lee, 2012; Orr et al., 2009). This may be attributable to the fact that the Big Five factors are too broad to render predictions of specific behavior. More narrow and distinctive personality traits, such as sociability and shyness, may be better at predicting SNS use. Second, research that has examined the influence of motivations on SNS use suggests that motivations should be included along with personality factors in predicting use. According to Uses and Gratifications (U&G) Theory (Katz, Blumler, & Gurevitch, 1974), motivations should be even more important predictors of SNS use than personality traits because motivations are more proximal causes of behavior than personality (Ajzen, 1991). Thus, examining motivational and personality influences in the same design will deepen our understanding of SNS use. Third, within the SNS research there are concerns about how use has been measured. Use is often measured by a single item or by the total amount of time spent on SNSs. These single-item measures are of questionable reliability. One exception is a study by Smock, Ellison, Lampe, and Wohn (2011), where use of specific Facebook features was measured by a set of items about frequency of use. However, measures used in that study focused only the social activity of SNS use, such as status updates, while other

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important functions, such as the recreational function (e.g., relaxing entertainment) were not considered. Although there are also several studies which have measured SNSs use with multiple-item measures, the types of SNSs activities were not differentiated (Kwon & Wen, 2010; Wang, 2013). Measures of SNS that include the multiple motives that may be satisfied by SNS use would provide a more complete understanding of why so many people spend so much time on SNSs. The present research was designed to overcome the limitations of previous research and thus extend our knowledge of the predictors of SNS use. Using a sample of young adults in China (college students), we examined the influence of two distinct personality traits – sociability and shyness, multiple motivations for SNS use, attitudes toward SNS use and Internet self-efficacy on SNS usage.

1.1. The QQ zone in China The QQ zone is one of the most popular SNSs among Chinese young adults. Based on our own pilot study, Chinese college students use the QQ zone for the two functions discussed earlier – a social function (e.g., social interaction) and a recreational function (e.g., relaxing entertainment). These two functions encompass the top seven functions of SNSs reported by the most recent CNNIC report (2013). Specifically, the report indicates that keeping in touch with old friends, reading news, passing time, playing games, searching for old friends, making new friends, writing blogs and uploading photos are the top features that attract young adults to SNS use.

1.2. Predictors of SNSs use 1.2.1. Personality traits: sociability and shyness Sociability is frequently included in research on SNS use because of its presumed importance to the development and maintenance of virtual communities. Individuals high in sociability tend to be conversational and interpersonally active and enjoy being the focus of others’ attention (Lee & Ashton, 2004), suggesting that they have a strong need to belong to groups such as SNS groups. Research supports the view that sociability predicts online socializing. For example, a study by Gangadharbatla (2008) found that individuals who were high in the ‘‘desire to belong’’ were more likely to use SNSs than individuals low in the belongingness desire. Therefore we hypothesized that sociability will predict SNS use for its social function. Greater sociability will predict greater SNS use (Hypothesis 1). Shyness is defined as discomfort and inhibition that occurs during normally expected social behavior in the presence of strangers or casual acquaintances. Shy individuals feel insecure and anxious about being evaluated or rejected by others during face-to-face social interactions (Ebeling-Witte, Frank, & Lester, 2007; Ryan & Xenos, 2011). Previous studies found that shy people tend to use SNSs more than do non-shy people (Orr et al., 2009; Sheldon, 2008). In another study by Ryan and Xenos (2011), however, shyness was negatively related to chat room activity and positively related to passive engagement activities. It is reasonable that shy people may use SNSs more for their recreational function because, although anonymity is assumed on SNSs, it is not guaranteed. There are often user-identifying factors that make social judgments of those who use SNSs possible. This may hinder the shy individual from engaging in any of the social functions available on SNSs. Therefore, we hypothesized that shyness will be negatively related to using the social activities available on SNSs (Hypothesis 2) and positively related to using its recreational function activities (Hypothesis 3).

1.2.2. Motivations for SNS use The U&G Theory’s holds that individuals are active and goal-directed in their media use and intentionally choose media and content to gratify psychological needs or motivations (Katz et al., 1974). These psychological needs or motivations may include relaxation, social interactions, companionship, habitual pass time, professional advancement, expressive information sharing, or escape (e.g., Smock et al., 2011). U&G suggests that multiple media compete for users’ attention, and users select the medium that satisfy their needs. Hence, it is rational that individuals use certain SNSs features because they obtain something out of that experience. Weibull (1985) suggested that individual needs lead people to select certain media to satisfy those needs, which in turn leads them to use that medium again because using it was gratifying. The U&G takes into account not only the gratifications people search for in a media but also the attitudes of the audience towards the medium and its contents (Fagerlind & Kihlman, 2000). Additionally, it underlines the role of individual differences (e.g., personality traits) in selecting media, and holds that users utilize media to gratify their felt needs arising from social and psychological conditions. Moreover, U&G considers the role of self-efficacy in determining users’ behaviors, because Palmgreen and Rayburn (1979) proposed that gratifications sought and obtained can be regarded as an expectancy value in explaining how needs influence media use selection, which is closely tied to self-efficacy. Prior studies have applied U&G Theory to investigate the relationship between motivations and SNS use and found that SNSs can satisfy different needs among users (Smock et al., 2011). Smock et al. (2011) examined the predictive effects of users’ motivations on collection of Facebook features. They found a correspondence between users’ motivations and the SNS feature they used. The motivation for social interaction predicted commenting, private messaging and chatting. For users with social interaction motives these one-to-one communication features of SNSs can fulfill their social interaction needs because they promote more direct communication with another or others (Smock et al., 2011). Considering the evidence that a variety of motivations can be satisfied by using different SNS features, we hypothesized that motivations will predict which features of SNSs will be used. Social motivations will predict greater use of the social features of SNSs whereas recreational motivation will predict greater use of the recreational features of SNSs (Hypothesis 4). 1.2.3. Attitudes for SNS use According to Theory of Reasoned Action, individuals’ attitude toward a behavior would influence his or her intention towards a specific behavior, which in turn influence the target behavior (Ajzen & Fishbein, 1980). Research has confirmed a positive relationship between attitudes toward the Internet and Internet use (Poude, Zamani, & Abedi, 2011; Tsai et al., 2009). Shin and Shin (2011) found that attitude plays an influential role in users’ intention to use SNS. Therefore, we hypothesized that attitudes toward SNS use will predict actual SNS use (Hypothesis 5). 1.2.4. Internet self-efficacy and SNS use Self-efficacy is defined as a belief in one’s ability to perform a given task (Bandura, 2000). Individuals perceived higher capabilities often approach difficult tasks as challenges to be mastered, while those with lower confidence in their abilities hold themselves back from such tasks. In terms of Internet self-efficacy, it was found to be related to Internet use, with higher levels of self-efficacy predicting higher levels of use (Eastin & LaRose, 2000). When using Internet, individuals need to master skills in establishing and maintaining Internet connection, as well as using the applications offered on Internet. This might scare off those users with little computer experience (Igbaria & Iivari, 1995). For

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example, Park, Oh, and Kang (2012) found that self-confidence about uploading content and having the intellectual resources needed to participate in online encyclopedia editing were positively related to intentions to upload content to Wikipedia. In a more recent study, effort expectancy was found to be predictor of SNS continuance intention towards online social networks (Sun, Liu, Peng, Dong, & Barnes, 2013). In the context of using SNSs, individuals’ confidence about updating status, posting comments, sharing information and their skills in interacting with online friends are likely to be positively related to their SNSs use. Thus we hypothesized that Internet self-efficacy will be positively related to SNS use for both its social function and its recreational function (Hypothesis 6).

2011) along two dimensions including basic self-efficacy and advanced self-efficacy (a = .93 and .90 respectively). All these four scales used a Likert-type response format, with most having five options ranging from ‘‘Strongly Disagree’’ (1) to ‘‘Strongly Agree’’ (5). SNS use was measured by a newly developed scale by the authors. It has eleven items which assess participants’ SNS use for both its social function (a = .87) and its recreational function (a = .84). Participants were asked to indicate how often they used these SNS features during the previous week.

2. Participants and measures

Hierarchical multiple regression analysis was used to predict the social function of SNS use. Three models were tested. Model 1 consisted of only the socio-demographic characteristics – gender, age and household income (Block 1). None predicted the social function of SNS use (R2 = 0.01). They were nevertheless included in all subsequent models to control for the small amount of variance they did account for in Model 1. Model 2 consisted of the two distinct personality traits – sociability and shyness, attitudes and self-efficacy (Block 2). Model 3 added motivations for SNS use, entered in Block 3. Block 2 was entered before these motivational factors because personality traits, attitudes and Internet self-efficacy have been shown to influence motivation in previous research (Amiel & Sargent, 2004; Coffin & MacIntyre, 1999; Zimmerman, 2000) (See Table 1). The personality trait sociability marginally predicted use of SNSs for their social function. However, shyness was not negatively related to social function use. Instead, attitudes and Internet selfefficacy were positively related to social function use. Thus, results support Model 2, when motivations for SNS use are not included in the analyses. Result to test Model 3, also presented in Table 1. Motivation for social interaction was positively related to social features of SNS. However, motivation for companionship was not. Instead, the motivation for relaxing entertainment was the only additional motivational predictor of SNS use for its social features. Self-efficacy was also positively related to social function use. Socio-demographic characteristics made no contribution to the prediction of SNS use for its social function (F(3, 321) = 0.64, p > .10). Attitudes toward SNS use and Internet self-efficacy were the strongest predictors, and the personality trait sociability was a marginal predictor of SNS use for its social function

3. Results 3.1. Predicting the social features of SNS

2.1. Participants and procedures Participants were from two large undergraduate universities in Southwestern China. Participants were recruited during Spring semester, 2013, by advertisements distributed in a variety of classrooms that briefly described the nature of the survey study and the monetary compensation for participating (¥10-about $1.5 U.S. dollars). Of the 402 potential participants, 352 completed the survey (response rate = 87.56%), ranging in age from 18 to 24 years old, with an average age of 19.82 (SD = 1.42). 109 (31.2%) participants identified themselves as males, 232 (66.5%) as females, and 11 failed to report their genders. Surveys were completed in a large lecture hall during four sessions of 20 min each. 2.2. Measures The survey questionnaire measured sociability, shyness, attitudes towards Internet use, motivation for using SNSs, Internet self-efficacy, and SNS usage. The sociability and shyness were measured with the Sociability Scale (Cheek & Buss, 1981) (a = .70) and Shyness Scale (Cheek & Buss, 1981) (a = .79), which were both onedimensional scales. Attitude was measured with Attitudes toward Internet use Scale (Ellison, Steinfield, & Lampe, 2007) (a = .83) along a single dimension. Motivation was measured by Motivations Scale (Papacharissi & Mendelson, 2010) along five dimensions including relaxing entertainment, escapism, companionship, passing time, and social interaction (a coefficients for each dimensions ranged from .83 to .89). Internet self-efficacy was assessed using the Internet Self-Efficacy Scale (Liang, Wu, & Tsai,

Table 1 Regression analysis to predict SNS use for its social function (n = 352). Model 1

Gender Age Income R2 Attitudes Sociability Shyness R2 Escapism Relaxing entertainment Companionship To pass the time Social interaction R2 Note: bs are standardized coefficients.

Model 2

b

t

p

.04 .01 .06 .01

.76 .10 1.10

.45 .92 .28

Model 3 t

b

p

t

b

p

.03 .01 .02

.70 .14 .36

.49 .89 .72

.04 .01 .02

.95 .31 .60

.34 .75 .54

.43 .09 .02

8.93 1.73 .51

.00 .08 .61

.29 .05 .02

5.81 .99 .52

.00 .32 .60

.01 .26 .03 .04 .18

.15 4.68 .58 .82 3.71

.89 .00 .56 .41 .00

.34

.44

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Table 2 Regression analysis to predict SNS use for its recreation function (n = 352). Model 1 b Gender Age Income R2 Attitudes Sociability Shyness R2 Escapism Relaxing entertainment Companionship To pass the time Social interaction R2

.08 .02 .01 .00

Model 2 t

p

1.40 .28 .12

.16 .78 .91

Model 3 t

b

p

t

b

p

.02 .01 .06

.32 .24 1.23

.75 .81 .22

.02 .00 .06

.33 .03 1.29

.74 .98 .20

.32 .03 .12 .22

6.09 .60 2.31

.00 .55 .02

.23 .02 .11

3.95 .28 2.19

.00 .78 .03

.04 .18 .03 .00 .07

.71 2.76 .51 .00 1.15

.48 .01 .61 .99 .25

.26

Note: bs are standardized coefficients.

(F(4, 317) = 23.46, p < .001, R2change = .34). Motivational factors for social interaction made an additional contribution (F(5, 312) = 20.18, p < .001, R2change = .96), as did motivation for relaxing entertainment (F(5, 312) = 4.68, p < .01). 3.2. Predicting the recreational function of SNS use Hierarchical multiple regression an analysis was similarly used to predict SNS use for its recreational function. Results are presented in Table 2. Once again, socio-demographic characteristics (Block 1) did not predict SNS use (F(3, 321) = .68, p > .05, R2 = .01). Table 2 shows that shyness was positively related to use SNS for their recreational function. Recreational motives predicted SNS use for its recreational function (F(5, 312) = 8.98, p < .01, R2change = .38), but this effect was significant only for the motive relaxing entertainment. Motivations for escapism, companionship and passing time did not predict SNS use for its recreational function. Attitudes toward SNS use and Internet self-efficacy predicted SNS use for its recreational function (F(4, 317) = 21.53, p < .001, R2change = .21). Participants with more positive attitudes toward SNS use and greater Internet self-efficacy were more likely to use SNSs for their recreational function than were participants with less positive attitudes and lower Internet self-efficacy, respectively. 4. Discussion Findings of this research extend our understanding of the predictors of SNS use. We found that personality variables, attitude, self-efficacy and motivations were predictors of SNS use. Findings provided support or partial support for 5 of our hypotheses. With respect to sociability, we found that sociability can influence social function use. This has supported Hypothesis 1 that sociability can predict social information use and is in line with findings in previous research (Orchard, Fullwood, Galbraith, & Morris, 2014). Orchard and Fullwood (2010) suggest that personality translates to the Internet in an expected way and users engage in online activities to satisfy their predisposed needs. Sociable individuals are conversational and interpersonally active (Lee & Ashton, 2004) and therefore, they have stronger requirements for social interaction. When U&G theory is considered, it fits well that sociable individuals would prefer SNS as their needs for interaction can be fulfilled. Not in line with Hypothesis 2, shyness was not negatively related to SNS use for social function. Our Hypothesis 3 was confirmed. We found that shy participants were more likely to use SNS for recreation than were less shy participants, and this is consistent with previous research (Ryan & Xenos, 2011). Our result regarding

Hypothesis 2 is not surprising when the mixed findings on the relationship between shyness and SNSs use are considered in literature (Orr et al., 2009; Ryan & Xenos, 2011; Sheldon, 2008). A possible reason for the inconsistency might be the different degrees of anonymity on the various SNSs involved. In the present study, QQ zone users are mostly offline friends and social judgments occur, which may prohibit shy participants from social interaction there. However, users can turn to the recreational function for relaxation where no social judgments exist. In support of Hypothesis 4, motivation for social interaction predicted SNS use for its social function. However, the motivation for companionship, expected to predict use for this function, did not. Instead, and unexpectedly, the motivation for relaxing entertainment was the only other motivation to predict SNS use for its social function. It may be that this motivation is satisfied by both the social and recreational functions of SNS use. Relaxing entertainment may render the user more receptive to social interaction on SNSs, and perhaps in offline life as well. Motivations for escapism and passing time were not expected to influence SNS use for its social function, and they did not. Further supporting Hypothesis 4, participants higher in entertainment motivation used SNS more than did participants lower in this motivation. Contrary to predictions, however, motivations for escapism and passing time did not predict SNS use for its recreational function. Companionship and social interaction were not expected to predict SNS for its recreational function and they did not. Attitudes toward SNS use and Internet self-efficacy have significant influences on social interaction function and recreational function, supporting Hypotheses 5 and 6, respectively. This is in line with previous studies which found that attitude towards Internet would lead to more frequent use of Internet (Poude et al., 2011; Tsai et al., 2009). According to Reasoned Action Theory, individuals’ attitudes toward an object would influence his or her behavioral intentions regarding the object. Consequently, these intentions affect actual behaviors towards the object (Ajzen & Fishbein, 1980). Self-efficacy was found to be a predictor of two types of SNS use, which has supported our Hypothesis 6 and is in line with previous findings (Hsiao, Tu, & Chung, 2012). According to Bandura (2000), self-efficacy is individuals’ perception of and confidence in their own abilities to successfully perform a behavior. Individuals’ selfefficacy levels influence their ability to acquire skills, choice of activities, and willingness to continue in a course of action. Therefore, a positive relationship between Internet self-efficacy and SNS use is reasonable. As is true of most research, the present study is not without limitations. First, as just discussed, the sample is relatively homogeneous with respect to age and educational level, falling short of

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J.-L. Wang et al. / Personality and Individual Differences 80 (2015) 119–124 Appendix Table Descriptive statistics and correlations among distinct personality traits, attitudes, motivations and Internet self-efficacy with SNS use (n = 352). Variable 1 – Sociability 2 – Shyness 3 – Self-efficacy 4 – Attitudes 5 – Escapism 6 – Entertainment 7 – Companionship 8 – Pass time 9 – Social interaction 10 – Social usage 11 – Recreation usage

M 3.50 2.45 3.87 2.81 1.86 3.25 2.59 3.02 3.28 18.00 12.52

SD .91 .62 .75 .95 .98 .88 1.03 .97 .96 5.44 3.66

1 1 .18** .27** .25** .12* .22** .01 .08 .23** .23** .16**

2

3 **

.18 1

.18** .01 .07 .01 .06 .07 .06 .01 .08

4 **

.27 .18** 1 .23** .10 .36** .08 .15** .27** .35** .29**

5 **

.25 .01 .23** 1 .01 .46** .24** .27** .30** .50** .38**

6 *

.12 .07 .10 .01 1 .01 .34** .28** .04 .01 .06

**

.22 .01 .36** .46** .01 1 .39** .42** .40** .53** .39**

7

8

.01 .06 .08 .24** .34** .39** 1 .45** .26** .27** .19**

.08 .07 .15** .27** .28** .42** .45** 1 .31** .26** .23**

9 .23** .06 .27** .30** .04 .40** .26** .31** 1 .42** .26**

As it was shown this Table, sociability, self-efficacy, attitude, motivations for entertainment, companionship, passing time, and social interaction were positively correlated to both social and recreational function use of SNS. ** p < .01. * p < .05.

representing the diverse population of SNS users in China. Thus, care must be taken in generalizing our results to other age groups and countries. They nevertheless provide insights into which personal characteristics might be considered in future research aimed at predicting SNS use. Second, only one type of SNS was examined in the present study. Future research should consider comparisons among different types of SNSs to determine whether the same or different user characteristics are important in predicting their use. In China, Renren and SinaWeibo are popular SNSs which may attract different types of users than we observed for the QQ zone site considered in this research. 5. Conclusions In conclusion our findings suggest that distinct personality traits (i.e., sociability, shyness), motivations, attitudes toward SNSs and Internet self-efficacy influence SNS use by Chinese college students, their influence depending to some extent on whether the focus of use is on the social or recreational functions. SNS use for its social function seems more attractive to individuals who are more sociable, have positive attitudes toward SNSs, high levels of motivation for social interaction and high levels of Internet self-efficacy. SNS use for both its social and recreational functions seems more attractive to those who have positive attitudes toward SNS, high motivation for relaxing entertainment and high Internet self-efficacy. This research points to directions for future research aimed at understanding the popularity of this global phenomenon and why people take time from real life to participate in these virtual worlds. Acknowledgements This study has been funded by the Planning Projects for the National Education Science: Grant No. CBA140146. Grant title is The Influence of Social Networking Sites Use on Adolescents’ and Young Adults’ Mental Health and its Interventions. Appendix A. See Appendix Table References Ajzen, I. (1991). Theory of planned behavior. Organizational Behavior and Human Decision Processes, 50, 179–211. Ajzen, I., & Fishbein, M. (1980). Understanding attitudes and predicting social behavior (Vol. 278). Englewood Cliffs, NY: Prentice Hall, p. 278.

Amiel, T., & Sargent, S. L. (2004). Individual differences in Internet usage motives. Computers in Human Behavior, 20(6), 711–726. Bandura, A. (2000). Cultivate self-efficacy for personal and organizational effectiveness. In: Handbook of principles of organization behavior (pp. 120–136). Brown, B. B., & Larson, J. (2009). Peer relationships in adolescence. In R. M. Lerner & L. Steinberg (Eds.), Handbook of Adolescent Psychology. New York, NY: Wiley. Cheek, J. M., & Buss, A. H. (1981). Shyness and sociability. Journal of Personality and Social Psychology, 41(2), 330–339. Coffin, R. J., & MacIntyre, P. D. (1999). Motivational influences on computer-related affective states. Computers in Human Behavior, 15(5), 549–569. Deng, S., Liu, Y., Li, H., & Hu, F. (2013). How does personality matter? An investigation of the impact of extraversion on individuals’ SNS use. Cyberpsychology, Behavior and Social Networking, 16(8), 575–581. Eastin, M. S., & LaRose, R. (2000). Internet self-efficacy and the psychology of the digital divide. Journal of Computer-Mediated Communication, 6(4). Ebeling-Witte, S., Frank, M. L., & Lester, D. (2007). Shyness, Internet Use, and Personality. Cyberpsychology & Behavior, 10(5), 713–716. Ellison, N. B., Steinfield, C., & Lampe, C. (2007). The benefits of Facebook ‘‘Friends:’’ Social capital and college students’ use of online social network sites. Journal of Computer-Mediated Communication, 12(4), 1143–1168. Fagerlind, Hanna, Kihlman, D. (2000). How do they use Internet? The use of Internet among youth in a gender perspective. Paper Presented at HTU 2001. Gangadharbatla, H. (2008). Facebook me: Collective self-esteem, need to belong, and Internet self-efficacy as predictors of the igeneration’s attitudes toward social networking sites. Journal of Interactive Advertising, 8, 5–15. Hsiao, H.-C., Tu, Y.-L., & Chung, H.-N. (2012). Perceived social supports, computer self-efficacy, and computer use among high school students. Turkish Online Journal of Educational Technology, 11, 167–177. Hughes, D. J., Rowe, M., Batey, M., & Lee, A. (2012). A tale of two sites: Twitter vs. Facebook and the personality predictors of social media usage. Computers in Human Behavior, 28(2), 561–569. Igbaria, M., & Iivari, J. (1995). The effects of self-efficacy on computer usage. Omega, 23, 587–605. Katz, E., Blumler, J. G., & Gurevitch, M. (1974). The uses of mass communication: Current perspectives on gratifications research. Sociology The Journal Of The British Sociological Association, 19–32. Kwon, O., & Wen, Y. (2010). An empirical study of the factors affecting social network service use. Computers in Human Behavior, 26(2), 254–263. Lee, E., Ahn, J., & Kim, Y. J. (2014). Personality traits and self-presentation at Facebook. Personality and Individual Differences, 69, 162–167. Lee, K., & Ashton, M. C. (2004). Psychometric properties of the HEXACO personality inventory. Multivariate Behavioral Research. Liang, J.-C., Wu, S.-H., & Tsai, C.-C. (2011). Nurses’ Internet self-efficacy and attitudes toward web-based continuing learning. Nurse Education Today, 31(8), 768–773. Nadkarni, A., & Hofmann, S. G. (2012). Why do people use Facebook? Personality and Individual Differences, 52(3), 243–249. Orchard, L. J., & Fullwood, C. (2010). Current perspectives on personality and Internet use. Social Science Computer Review, 28, 155–169. Orchard, L. J., Fullwood, C., Galbraith, N., & Morris, N. (2014). Individual differences as predictors of social networking. Journal of Computer-Mediated Communication, 19(3), 388–402. Orr, E. S., Sisic, M., Ross, C., Simmering, M. G., Arseneault, J. M., & Orr, R. R. (2009). The influence of shyness on the use of Facebook in an undergraduate sample. CyberPsychology & Behavior, 12(3), 337–340. Palmgreen, P., & Rayburn, J. D. II., (1979). Uses and gratifications and exposure to public television: A discrepancy approach. Communication Research, 6, 155–180. Papacharissi, Z., & Mendelson, A. (2010). Toward a new (er) sociability: uses, gratifications and social capital on Facebook. In Media perspectives for the 21st century, p. 212. Park, N., Oh, H. S., & Kang, N. (2012). Factors influencing intention to upload content on Wikipedia in South Korea: The effects of social norms and individual differences. Computers in Human Behavior, 28(3), 898–905.

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J.-L. Wang et al. / Personality and Individual Differences 80 (2015) 119–124

Poude, L. D., Zamani, B. E., & Abedi, A. (2011). Relation between Iranian students’ attitudes subscale with kind of internet usage in universities. Procedia Social and Behavioral Sciences, 15, 2694–2698. Ryan, T., & Xenos, S. (2011). Who uses Facebook? An investigation into the relationship between the Big Five, shyness, narcissism, loneliness, and Facebook usage. Computers in Human Behavior, 27(5), 1658–1664. Seidman, G. (2013). Self-presentation and belonging on Facebook: How personality influences social media use and motivations. Personality and Individual Differences, 54(3), 402–407. Sheldon, P. (2008). The Relationship Between Unwillingness-to-Communicate and Students’ Facebook Use. Journal of Media Psychology, 20(2), 67–75. Shin, D.-H., & Shin, Y.-J. (2011). Why do people play social network games? Computers in Human Behavior, 27(2), 852–861. Smock, A. D., Ellison, N. B., Lampe, C., & Wohn, D. Y. (2011). Facebook as a toolkit: A uses and gratification approach to unbundling feature use. Computers in Human Behavior, 27(6), 2322–2329.

Sun, Y., Liu, L., Peng, X., Dong, Y., & Barnes, S. J. (2013). Understanding Chinese users’ continuance intention toward online social networks: An integrative theoretical model. Electronic Markets, 24(1), 57–66. Tsai, H. F., Cheng, S. H., Yeh, T. L., Shih, C.-C., Chen, K. C., Yang, Y. C., et al. (2009). The risk factors of Internet addiction – A survey of university freshmen. Psychiatry Research, 167(3), 294–299. Wang, S. S. (2013). ‘‘I share, therefore I am’’: Personality traits, life satisfaction, and Facebook check-ins. Cyberpsychology, Behavior and Social Networking, 16(12), 870–877. Weibull, L. (1985). Structural factors in gratifications research. In K. E. Rosengren, L. A. Wenner, & P. Palmgreen (Eds.), Media gratifications research: Current perspectives (pp. 123–157). Beverly Hills, CA: Sage. Zimmerman, B. J. (2000). Self-efficacy: An essential motive to learn. Contemporary Educational Psychology, 25, 82–91.