The Motivation for Facebook Use – Is it a Matter of Bonding or Control Over Others? Evidence From a Cross-Cultural Study Rayna Sariyska, Bernd Lachmann, Cecilia Cheng, Augusto Gnisci, Ida Sergi, Antonio Pace, Katarzyna Kaliszewska-Czeremska, Stephanie Laconi, Songfa Zhong, Demet Toraman, Mattis Geiger, and Christian Montag

Original Article

The Motivation for Facebook Use –Is it a Matter of Bonding or Control Over Others?

Evidence From a Cross-Cultural Study

Rayna Sariyska, 1

Bernd Lachmann, 1

Cecilia Cheng, 2

Augusto Gnisci, 3

Ida Sergi, 3 Antonio Pace, 3

Katarzyna Kaliszewska-Czeremska, 4

Stéphanie Laconi, 5 Songfa Zhong, 6

Demet Toraman, 7

Mattis Geiger, 1

and Christian Montag 1,8

1

Institute of Psychology and Education, Ulm University, Ulm, Germany 2

Department of Psychology, University of Hong Kong, Hong Kong 3

Department of Psychology, University of Campania “Luigi Vanvitelli”, Italy 4

Institute of Psychology, The Jesuit University Ignatianum in Krakow, Poland 5

Department of Psychology, University of Toulouse II –Le Mirail, Toulouse, France 6

Department of Economics, University of Singapore, Singapore 7

Department of Psychology, University of Bonn, Germany 8

The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China

Abstract: In the present study, we investigated individual differences in the motivation for Facebook use. In total N = 736 participants from Europe and Asia took part in the study. They filled in the Facebook questionnaire (FQ), including the two factors Attitude toward Facebook and Online Sociability, and the Unified Motive Scale (UMS-3), measuring the motives Achievement, Affiliation, Intimacy, Power, and Fear. The results showed that the Attitude toward Facebook was more positive in the subsample from Asia, but no differences could be found between the Asian and European sample with respect to the frequency of use of different activities on Facebook. The motives Fear, Power, Affiliation, and Intimacy significantly predicted the FQ factor Attitudes. Furthermore, the Attitude toward Facebook mediated the associations between the motives Power/Affiliation and Online Sociability. However, these results were only found for the European sample. The associations found suggest the important role of different motives such as Power/Affiliation for the study of Facebook use. The present work shows the possibility of motivational factors for Facebook use to differ depending on the culture. The study adds to the literature by investigating a classic motivation theory in the context of Facebook use.

Keywords: motives, Facebook, personality, cross-cultural

The increasing popularity of the Internet and one of its most prominent social network platforms –Facebook –raises numerous questions about its impact on society. As a consequence, research in Internet and in particular Facebook usage is rapidly growing in the last years. The present study aims to examine potential motivational factors underlying Facebook use. Therefore, this work tries to carve out factors explaining why humans spend much of their everyday lives on this digital platform.

Facebook is to date one of the most popular online social networking sites (SNS; see Sigerson & Cheng, 2018 for a review). It was launched in the year 2004 as a platform for Harvard University students and later expanded dramatically (Facebook, 2016), after allowing access to the general population. Facebook originated in the US, but approximately 83.6% of daily active users are outside the US and Canada (Facebook, 2016), with Facebook available in over 70 languages (Schonfeld, 2010). Because of its growing importance as a tool for communication among others, a growing body of research concentrates on exploring different factors behind the (problematic) use of Facebook like personality and psychopathological tendencies (Ross et al., 2009; Ryan & Xenos, 2011). Of particular importance are studies trying to disentangle the motivation behind Facebook use and addictive tendencies in social network use.

Among the theories on the motivations behind Internet use, the Uses and gratifications theory (shortly called U&G), a mass communication theory, has been an important theoretical platform. This theory suggests that individuals actively choose the kind of media they use, depending on their needs and goals (see Cutler & Danowski, 1980; Lin, 1977; Ruggiero, 2000). Several studies investigated the associations between U&G theory and SNS use. Ryan, Chester, Reece, and Xenos (2014) reported that the main uses and gratifications of Facebook are relationship maintenance (fostering existing relationships) and passing time. 1

Entertainment (“using Facebook to engage in socially passive activities, such as (...) playing games”, p. 136), companionship (“to avoid loneliness and gratify interpersonal needs”, p. 136), and escapism (using Facebook to escape from negative mood), although less often, were also reported to be linked to Facebook use. Chabrol, Laconi, Delfour, and Moreau (2017) reported that passing time, virtual community, 2

and entertainment were predictors of Facebook Use Disorder (FUD). 3 Bischof-Kastner, Kuntsche, and Wolstein (2014) examined the motivation for Internet use as based on the Motivational Model of Alcohol Use (Cox & Klinger, 1988), which was later applied to the context of Facebook use. The authors adapted a questionnaire, measuring motives for drinking into an Internet Motive Questionnaire for Adolescents (IMQ-A). Based on the presumption that striving for positive emotions and decreasing/eliminating negative feelings is the primal root of motivation for humans’ behavior (McClelland, 1987), the Motivational Model of Alcohol Use assesses motivation with respect to the dimensions valence (enhancing positive emotions/diminishing or eliminating negative emotions) and origin of affective change (internal or external source) (Cox & Klinger, 1988). Four motives result from this model: enhancement (increase positive feelings, internal source), coping (reduce negative feelings, internal source), conformity (reduce negative feelings, external source), and social (increase positive feelings, external source). Regarding Internet use, Bischof-Kastner et al. (2014) reported that the motives for enhancement and coping were associated with the high-risk group of Internet users, whereas the low-risk group primarily used the Internet to fulfill social needs. Marino, Vieno, Moss, et al. (2016) applied the same questionnaire in the context of FUD. The motives for coping and conformism mediated the relationship between emotional stability and FUD. Additionally, the motive enhancement directly predicted FUD. However, social motivation was not linked to FUD, suggesting different mechanisms underlying dysfunctional use of Facebook compared to using Facebook for social purposes.

The aim of the present article is to further examine the motivation for Facebook use by applying the Unified Motive Scale (UMS; Schönbrodt & Gerstenberg, 2012). The UMS questionnaire measures the motives Achievement, Power, Affiliation (McClelland, 1987), and Intimacy (McAdams, 1980). Achievement can be described as pursuing excellence in all areas of life and Power as the concern to control others. While Affiliation is often characterized as the need for social bonding to rather unfamiliar people, Intimacy focuses on closer relationships between individuals (Schönbrodt & Gerstenberg, 2012). Described in the Three Needs Theory (McClelland, 1987; and later extended by McAdams, 1980) these motives are thought to affect human’s actions and shape human’s behavior. Combining these four motives in terms of approach motivation, the UMS was designed to also measure their counterparts, incorporated in the Fear scale (e.g., Fear of failure, Fear of losing control and Fear of rejection) on the side of avoidance motivation (Schönbrodt & Gerstenberg, 2012). To the authors’ knowledge, this theoretical framework has not been investigated in the context of Facebook use so far.

Probably the most obvious motivation for using Facebook is the opportunities that it offers for socializing. In this context, the motives Affiliation and Intimacy might be of particular importance. Next to the studies reported above, Ellison, Steinfield, and Lampe (2007) showed a strong association between the use of Facebook and social capital. 4 Marino, Vieno, Pastore, et al. (2016) demonstrated the role of social influence processes such as subjective and group norms for FUD and frequency of Facebook use. In a review of over 400 articles on Facebook research, Wilson, Gosling, and Graham (2012) reported that staying connected with friends is among the most common motives for using Facebook. Moreover, the same review also indicated reducing loneliness as an important motivation for Facebook use. Wegmann and Brand (2016) demonstrated the particular importance of social aspects such as social loneliness and loss of social support for the development of Internet-Communication Disorder. These results point out the importance to also include further person and environmental factors in the context of Facebook use (disorder) and again suggest a link between the motives Affiliation/Intimacy and Facebook use.

However, personality traits linked to negative emotionality were also associated with Facebook use. Shepherd and

1

Passing time means occupying time when bored (Sheldon, 2008). 2

Virtual community describes interaction with people met online (Sheldon, 2008). 3

The terms Facebook Use Disorder and Internet-Communication Disorder (another term for SNS addiction) are derived from the latest developments in DSM-5, where Internet-Gaming Disorder was included into Section III: Conditions for further study (DSM History, 2017). 4

Social capital refers to acquiring benefits through interactions with others (Ellison et al., 2007).

Edelmann (2005) proposed that the Internet may offer an alternative for face-to-face interactions for people, who are rather anxious. Bodroža and Jovanović (2016) reported that among different personality characteristics, high social anxiety was the most robust predictor of different aspects of Facebook use such as compensatory use, self-presentation, or addiction. A couple of studies demonstrated the role of Fear of Missing out (FoMO; “(...) a pervasive apprehension that others might be having rewarding experiences from which one is absent (...)”, Przybylski, Murayama, DeHaan, & Gladwell, 2013, p. 1841) for SNS/Facebook use (disorder) (Beyens, Frison, & Eggermont, 2016; Wegmann, Oberst, Stodt, & Brand, 2017). Further research, linking individual differences in neuroticism or similar traits to individual differences in human motives explicitly shows a link between neuroticism and avoidance motivation (Engeser & Langens, 2010), as well as the motive for Fear reduction (Schönbrodt & Gerstenberg, 2012). Based on the presented empirical studies, we assume that the Fear motive might also predict Facebook use. However, despite the link between Fear and emotional instability, these concepts should not be assumed to be equal (this is also mirrored in the medium-size correlation between Fear and neuroticism as reported by Schönbrodt & Gerstenberg, 2012). It is rather assumed that personality and motivation interact to affect behavior (on the other hand, individual differences in motivational traits can be also seen as part of personality, Montag & Panksepp, 2017). Thus, investigating the motive Fear will add to the literature and further shed light on the mechanisms, which direct the use of Facebook.

While the potential link between Affiliation, Intimacy, Fear, and the use of Facebook is rather obvious, the motive Power might be another candidate to explain Facebook use. The fact that we gain a lot of information about others based on their Facebook activities and posts, might make interaction with others easier and, in turn, help to maintain interpersonal relationships. However, there is also a dark side of information sharing through social media since it can be used to control others and even for the purpose of manipulative behavior. The motivation to control others (mirrored in the Power motive) has rarely been investigated in the context of Facebook use to date. However, personality research points toward a link between narcissism, 5

psychopathy, 6

and Machiavellianism 7

(also known as the Dark Triad of personality) and different Facebook activities such as semantic content of status updates (Garcia & Sikström, 2014) or (dishonest) self-promotion through Facebook (Abell & Brewer, 2014). These results show that manipulation of others’ behavior can be performed also in the online world through (dishonest) self-presentation, embarrassing others, etc., and that SNS like Facebook offer a platform for such behaviors. Also the concept of being an influencer (vs. a follower), hence a person, who is strongly visible online and followed by many on platforms such as Facebook, Instagram, and Twitter could manifest in a higher Power motive. Thus, it is of high relevance to examine the role of motivational factors next to classic personality variables in this context.

Last but not least, since Facebook is an international online platform, it is of interest to consider the potential influence of different cultures on Facebook use. In this context Vasalou, Joinson, and Courvoisier (2010) reported that the importance of use of various Facebook activities differed with respect to country. In a recent study, Błachnio et al. (2016) pointed toward some potential differences in the associations between FUD and personality variables, depending on the culture (in this study also universal predictors of FUD were found).

Compared to prior approaches to describe motivation for Facebook use which stem from mass communication research (e.g., U&G theory) or base on models for substance use (e.g., Motivational Model of Alcohol use), in the current study we focused on a well-established psychological theory (The Three-Need Theory) and applied an elaborate (conceptually and empirically) questionnaire (UMS-3) to measure explicit motives. This study aims at helping to place results from studies, exploring different motivational factors in the context of Facebook use, into an elaborated theoretical framework. Moreover, next to the role of social motivation and avoidance motivation for the use of Facebook, the potential link between the motivation to control others (mirrored in the Power motive) and Facebook use will also be examined. Last but not least, because of its cross-cultural character, the study allows to consider potential differences in the relationship between motivation and Facebook use across different countries/cultures.

Based on the literature, presented above, we focus on the following hypotheses:

Hypothesis 1: We expect positive associations between the motives for Affiliation, Intimacy, Fear, and Facebook use, measured with the Facebook questionnaire (FQ) variables, described in detail in the section Material and Methods.

5

Narcissism is a personality characteristic that describes persons having a “(...) grandiose sense of self (...)” (Ames, Rose, & Anderson, 2006, p. 440). 6

Psychopathy describes a more impulsive personality, further characterised with low empathy (Hare, 1985). 7

Machiavellianism as a personality trait depicts a tendency to manipulative behaviour and cynism (Abell & Brewer, 2014).

Affiliation

Fear

FQ Attitudes

Intimacy FQ Online Sociability

Power

Figure 1. Proposed structural equation model on the associations between explicit motives and the Facebook questionnaire (FQ) factors.

Hypothesis 2: We expect that Power will be linked to the FQ variables. Here, we refrain from postulating a direction for this potential relationship because of sparse research in this field. Nevertheless, given the modern concept of being an influencer versus a follower, a positive association would be meaningful.

Hypothesis 3: We expect that the Attitude toward Facebook would mediate the relationship between the motives Affiliation, Intimacy, Power, Fear, and the Facebook factor Online Sociability (see Figure 1). Both Attitude and Online Sociability are assessed with the FQ.

Material and Methods

Participants

Participants from seven different countries took part in an online survey. After excluding participants due to missing values or because they did not own a Facebook account at the time the study was conducted, the whole sample resulted in N = 736 participants. The distribution of the subsamples was n = 54 from France (n = 20 males), n = 97 from Germany (n = 21 males), n = 259 from Italy (n = 79 males), n = 60 from Poland (n = 17 males), n = 105 from Turkey (n = 45 males), n = 103 from Hong Kong (n = 44 males), and n = 58 from Singapore (n = 27 males). The average age for the total sample was M = 22.97 (SD = 5.99). Seven participants reported being younger than 18 years of age. The choice of countries, which participated in the project, was based on our efforts to represent different parts of the world like Western Europe (Germany, France), Eastern Europe (Poland), Southern Europe (Italy), and Asia (Turkey, Hong Kong, Singapore). Please note, that we explain below in more detail, why the Turkish sample is part of the Asian group.

The recruitment of the participants in the different countries was accomplished by our cooperation partners mostly

at universities. Each participant was informed about the study, before giving a digital consent. Participants did not receive any monetary compensation for their participation. The study was approved by the Local Ethic Committee of the University of Bonn, Germany.

Materials

The Facebook questionnaire (FQ, Ross et al., 2009) in its original form includes 28 items on Facebook usage, which are assessed on different scale format: Response alternatives range from yes/no to 9-point scale multiple-choice options. Of particular interest for the present study are two factors, extracted by Ross et al. (2009) through a factor analysis: Attitudes (e.g., “How satisfied are you with Facebook overall?”) and Online Sociability functions (e.g., “How often do you send private messages?”). However, this questionnaire was shortened due to economic reasons (a list of the items, measuring Attitudes and Online Sociability, used in this study is presented in the Electronic Supplementary Material, ESM 1, Table 1). This resulted in reducing the number of items of the variables Attitudes (from 7 to 6) and Online Sociability (from 5 to 4). Cronbach’s α for the whole sample was satisfying: α = .83 (Attitudes) and α = .75 (Online Sociability). Internal consistencies for the subsamples can be found in ESM 1, Table 2. The Unified Motive Scale (UMS-3, Schönbrodt & Gerstenberg, 2012) is a short measure of explicit motives, consisting of three items per motive. It measures the motives Achievement, Power, Affiliation, Intimacy, and Fear. Three Fear facets can be assessed with this questionnaire: Fear of being rejected, Fear of failure, and Fear of losing control. However, for reliability reasons the authors of the questionnaire suggest to apply the whole scale since every of the Fear subscales is measured with one item respectively. Seven items are rated on a 6-point scale from 1 = strongly disagree to 6 = strongly agree and eight items are rated on a scale from 1 = not important to me to 6 = extremely important to me. Cronbach’s α for the total sample was .62 for Power, .72 for Achievement, .70 for Affiliation, .67 for Intimacy, and .70 for Fear (see detailed results for the subsamples in ESM 1, Table 2).

Conceptual and Measurement Equivalence

First, we examined possible measurement variability with respect to country/language. Conceptual equivalence was established with the help of experts in the area of psychology in the different countries under investigation. Translation and back-translation in English were conducted by psychologists, whose native language is the language of the particular country and who are also fluent in English.

The reason for this course of action was that experts in psychology would deliver most precise translation because of their expertise. When receiving the back-translation we were able to communicate problems, if any, concerning the constructs measured and the way they are understood in the particular country. Measurement invariance 8

was examined using the package Lavaan in R (Rosseel, 2012). As our samples per nation were not large enough to conduct invariance testing, we merged them to larger groups. Here, we focused on samples stemming from similar geographical regions, in order to take into account potential cultural differences. Moreover, samples were combined when their composition was similar with regard to gender and age distribution. Following this approach, we combined samples from France, Germany, Italy, Poland to one group (referred to as “Europe,” n = 470) and samples from Hong Kong, Singapore, Turkey 9

to another group (we will refer to it as “Asia,” n = 266). The following analyses were conducted, using those two samples.

Statistical Analyses

As part of assessing measurement invariance, confirmatory factor analyses (CFAs) were conducted for every questionnaire and every sample. Structural equation modeling (SEM) was used to test Hypothesis 3. The CFA and SEM were conducted, using R. To assess the model fit we used root mean square error of approximation (RMSEA), the standardized root mean square residual (SRMR), and comparative fit index (CFI). CFI values > .90 represent an acceptable fit, > .95 a good fit. RMSEA values close to .06 represent a good model fit while values < .08 indicate an acceptable model fit. SRMR values close to .08 indicate a good fit (Hu & Bentler, 1995, 1999; MacCallum, Browne, & Sugawara, 1996). To evaluate measurement invariance, the following cut-off criteria were applied for loading invariance for samples with n > 300 a change of
.010 in CFI, a change of
.015 in RMSEA, and a change of
.030 in SRMR indicate measurement variance. For n < 300 a change of
.005 in CFI, a change of
.010 in RMSEA, and a change of
.025 in SRMR indicate measurement variance (Chen, 2007). Descriptive statistics on demographics, FQ variables, and motives were computed. Then, variables were assessed for normal distribution, by evaluating the skewness and kurtosis of the variables (Miles & Shevlin, 2001; see ESM 1, Table 3). Correlation analyses were conducted using the latent variables. When testing for mediating effects as part of the SEM model, it was required that all variables correlated with each other.

Results

CFA and Measurement Invariance

First, we combined the samples as described in the Conceptual and Measurement Equivalence section. CFAs were conducted for each sample separately. Here, for the UMS-3 10

questionnaire, the following results were observed: for the European sample w 2 (79) = 219.501, p < .001, CFI = .901, RMSEA = .062, SRMR = .054; for the Asian sample w 2 (79) = 204.590, p < .001, CFI = .906, RMSEA = .077, SRMR = .067. For the Facebook questionnaire (two-factor solution), the following results were observed: for the European sample w 2 (34) = 128.274, p < .001, CFI = .933, RMSEA = .077, SRMR = .064; for the Asian sample w 2 (34) = 101.068, p < .001, CFI = .936, RMSEA = .086, SRMR = .061. Thus, the models for both questionnaires showed an acceptable fit in both samples. Next, we tested for measurement invariance (fit indices are presented in Table 1). Here, results showed that metric invariance was fulfilled for both the UMS-3 and the Facebook questionnaires (see cut-off criteria, described in the Statistical Analyses section). Here the factor loadings were set to be equal across both the European and Asian samples. Further analyses showed that the requirements for scalar 11

and strict 12

invariance were not fulfilled. However, since metric invariance was given, we proceeded with testing our hypotheses based on the same measurement models (Chen, 2007).

Descriptive Statistics

In total 47% of participants in the European sample and 41% of participants in the Asian sample spent one or more hours daily on Facebook. Descriptive statistics for all of the

8

Measurement invariance implies that “(...) an instrument has the same psychometric properties across heterogenous groups (...)” (Chen, 2007, p. 465). 9

We are aware of the fact that a small part of Turkey is located in Europe. However, according to our cooperation partner, responsible for recruiting the Turkish sample, participants were recruited mostly in cities, positioned on the Asian continent (Ankara and Samsun) and some of the participants in Istanbul. Nevertheless, for transparency reasons we report the results for the Turkish sample separately in the ESM 1 (Table 4). 10

In order to achieve an acceptable model fit and based on content similarities, we allowed the residuals of the items “I like to have the final say.” and “I become scared when I lose control over things.” to correlate. 11

Here the intercepts are set to be equal in different groups (Chen, 2007). 12

Here residuals are set to be equal in different groups (Chen, 2007).

Model

w2 (df) RMSEA SRMR CFI Change CFI Sign.

UMS-3

Configural 424.09 (158) .068 .055 .903 n/a n/a Metric 436.89 (168) .066 .058 .902 .001 .235 Scalar 532.39 (178) .074 .067 .871 .031 .000 Strict 759.15 (183) .092 .095 .790 .081 .000 FQ

Configural 229.34 (68) .080 .058 .934 n/a n/a Metric 240.12 (76) .077 .062 .933 .001 .215 Scalar 284.64 (84) .081 .067 .918 .015 .000 Strict 299.19 (86) .082 .073 .913 .005 .000

Note. Sign. = Significance; UMS-3 = Unified Motive Scale; FQ = Facebook Questionnaire.

Table 2. Descriptive statistics and comparison of means for the variables Power, Achievement, Affiliation, Intimacy, Fear, Attitudes and Online Sociability for both subsamples under investigation

Group Power Achievement Affiliation Intimacy Fear Attitudes Online Sociability

Europe (470) 10.56 (3.10) 14.07 (2.46) 13.37 (2.79) 14.72 (2.60) 11.76 (3.36) 2.87 (0.76) 5.59 (1.66) Asia (266) 11.83 (2.65) 13.15 (2.86) 12.85 (2.83) 13.14 (2.62) 10.83 (3.35) 3.07 (0.86) 5.45 (1.81) t-test p < .01 p < .01 p < .05 p < .01 p < .01 p < .01 ns

Note. p values are two-tailed.

Table 3. Associations between the latent variables

Power Achievement Affiliation Intimacy Fear

Europe (470)

Attitudes .170** .076 .229** .016 .187**

Online Sociability .214** .111 .133*

–.081

–.007

Asia (266)

Attitudes .141 .013 .076 .160 .188*

Online Sociability .062

–.085 .110

–.079

–.056

Note. **p < .01, *p < .05 (all two-tailed).

variables under investigation, including mean, standard deviations and comparison of the means for both samples (using t-test since the variables were normally distributed) are presented in Table 2. Here, significant differences between both samples were found regarding all motives and for the FQ factor Attitudes. The samples did not differ in the frequency of use of different activities on Facebook (Online Sociability).

Facebook Use and Motivation (Hypotheses 1 and 2)

The results from the correlation analyses with latent variables are presented in Table 3. Affiliation and Power were positively linked to the FQ variables in the European

sample. Fear was positively linked to Attitudes in both samples. The associations between the latent variables Attitudes and Online Sociability were r = .574 (p < .01) for the European sample and r = .512 (p < .01) for the Asian sample. Thus, we found partial support for Hypotheses 1 and 2.

SEM (Hypothesis 3)

Next, we tested the structural model depicted in Figure 1. Since the condition for mediation analysis was that all of the variables are correlated, we only tested the mediation between Power –Attitudes –Online Sociability and Affiliation –Attitudes –Online Sociability, respectively (see Table 3). The motives Fear and Intimacy were only set to predict Attitudes. The structural model was tested separately for the

Affiliation

Fear

Intimacy FQ Attitudes

.557

FQ Online Sociability

Power

Figure 2. Structural equation modeling results for the European sample. FQ = Facebook Questionnaire; solid line p < .05, dashed line p > .05, p values are two-tailed.

Affiliation

Fear

Intimacy FQ Attitudes

.522

FQ Online Sociability

Power

Figure 3. Structural equation modeling results for the Asian sample. FQ = Facebook Questionnaire; solid line p < .05, dashed line p > .05, p values are two-tailed.

European and the Asian sample. Despite mostly nonsignificant associations between the variables in the Asian sample, the structural equation model was conducted for the purpose of consistency.

The results for the European and Asian sample are presented in Figures 2 and 3, respectively. The proposed model showed the following fit for the European sample: w 2 (195) = 466.141, p < .001, CFI = .894, RMSEA = .054, SRMR = .057. Both, the indirect effect for Power and Affiliation gained significance: β = .078, p < .05 and β = .162, p < .01, respectively. Moreover, Fear and Intimacy significantly predicted the FQ factor Attitudes. The model fit for the Asian sample was as follows: w 2 (195) = 414.269, p < .001, CFI = .888, RMSEA = .065, SRMR = .072. Here none of the indirect effects for Power and Affiliation gained significance (p = .677 and p = .731, respectively). Furthermore, none of the associations between motives and the FQ factors gained significance.

Discussion

The aim of the present study was to investigate the role of classic motives in the context of Facebook use and, thus, provide a grounded theoretical framework for the relationship between motivation and the use of Facebook. The results of the study showed that the motives Affiliation, Power, and Fear were positively and Intimacy negatively associated with the FQ factor Attitudes in the European sample. In addition, the FQ variable Attitudes significantly mediated the relationship between the motives Affiliation, Power and the FQ variable Online Sociability. These results could not be replicated for the Asian sample where mostly nonsignificant associations were found.

In general, the positive association between the motive Affiliation and the FQ factors, reported for the European sample, confirms already existing findings (Ellison et al., 2007; Marino, Vieno, Pastore, et al., 2016; Ryan et al., 2014). In the present study, the motive Affiliation was directly related to the Attitude toward Facebook and indirectly to Online Sociability. Thus, participants who are motivated to maintain relationships with others (high Affiliation motive) have a rather positive Attitude toward Facebook and consequently use it more often for different social activities. However, the association between Attitude and the motive Intimacy was negative. This could be explained through the fact that, although both motives describe the desire to be part of social groups and to belong, the motive Affiliation is more concerned with (loose) relationships with rather unfamiliar people, whereas Intimacy focuses more on close relationships (Sokolowski & Heckhausen, 2008). These findings further shed light on the kind of relationships, maintained through Facebook, adding to studies, having reported an off-to-online manner 13

of relationships through Facebook (Ellison et al., 2007). The Fear motive, also embodying the dimension of avoidance motivation, was positively related to the FQ factor Attitudes. This means, the stronger the fear of losing control or being rejected (in the real world), the more positive the Attitude toward Facebook. As outlined in the introductory section of this article, people might exert preference for online communication, because this allows more distance to the communication partner, anonymity and, thus, less direct vulnerability to negative feedback (Caplan, 2010). In the context of Internet Use Disorder, Caplan (2010) demonstrated that the preference for online social interaction predicted using the Internet for mood regulation, deficient self-regulation and, in turn, negative outcomes due to Internet use. The model by Caplan (2010) has also shown its applicability in the context of Facebook

use (see Marino, Vieno, Altoè, & Spada, 2016; Marino, Vieno, Moss, et al., 2016; Marino, Vieno, Pastore, et al., 2016). Thus, it is possible that avoidance motivation (here measured with the Fear motive) might lead to a positive Attitude toward SNS and to a preference for online social interaction, which might then result in more frequent SNS use/addiction. However, this assumption is beyond the scope of this article and should be tested in future studies. Interestingly, Fear was associated with the Attitude toward Facebook, but not with the frequency of social Facebook use (Online Sociability). Future studies should incorporate more variables next to the motives investigated in the current study to be able to reliably predict behavior (see recently proposed I-PACE model for specific Internet Use Disorder by Brand, Young, Laier, Wölfling, & Potenza, 2016). In this context, Graham and Gosling (2013) investigated the link between personality and motives for playing World of Warcraft and reported different personality profiles, depending on the motivation for playing. For instance, social motivation was positively linked to extraversion, agreeableness, neuroticism, and openness, whereas achievement motivation was negatively linked to agreeableness, conscientiousness, and openness as well as positively linked to extraversion and neuroticism. Similar investigations might be of interest in the area of Facebook use (disorder).

The results of the present study also support the assumption that people are motivated to use Facebook to control others, since Power was associated with Attitudes and Online Sociability (in the European sample). Thus, next to the opportunity to communicate with others and participate in online social activities, Facebook also offers a platform where information is exchanged, which can be used to control or manipulate behavior. These results also add to studies showing a link between (the Dark Triad of) personality and Facebook use. Discussing the exact mechanisms of exerting control over others through Facebook is beyond the scope of this article. However, existing literature stemming from personality research suggests a link between the use of “cheater strategies” like editing pictures before posting or relational aggression (e.g., embarrassing people on Facebook through posts) and the Dark Triad of personality (Abell & Brewer, 2014; Fox & Rooney, 2015). Here again, investigating the interplay between the Power motive and personality variables might shed more light on manipulative behavior on Facebook.

It is important to notice that the FQ variables were related to the motives Affiliation, Intimacy, Power, and Fear only in the European sample. As outlined in the Introduction of this article, a couple of studies already reported cultural differences regarding Facebook use (Błachnio et al., 2016; Vasalou et al., 2010; Wilson et al., 2012). Moreover, in the current study differences between the European

and Asian sample were reported with respect to motivation (see Table 2). Thus, it is possible that patterns of motivation for Facebook use differ depending on the culture. However, these results need to be replicated in future studies.

Last but not least, different kinds of external motivation/pressure, supported by Facebook, such as the possibility to add content or the birthday reminder function to name a few were shown to have an impact on Facebook use (see Wilson et al., 2012). Thus, the combined investigation of external and internal motivational factors might be important to fully understand what drives people to spend large amount of time using this social networking platform.

Strengths and Limitations

One strength of the present study is the foundation of the research question on a theoretical framework concerning the relationship between Facebook use and motivation (McClelland, 1987) incorporated in the model of Schönbrodt and Gerstenberg (2012). In addition, this approach is, to some extent, suited to organize repetitive results of earlier studies in a more comprehensive way. Another strength of this study is its cross-cultural design with multiple samples, which allows cross-cultural comparisons. However, since the assignment to one of the two samples was based on geographical location and sample composition, the cultural comparison results need to be interpreted with caution. Future studies might consider applying a measure of culture to further justify the combination of samples to larger ones. A limitation of the present study is its cross-sectional design, which does not allow for causal implications. Moreover, some samples were relatively small and homogenous (most of the participants were students), hence, possibly restraining the generalizability of the effects to some extent. Further, self-report measures are used and these measures are susceptible to response biases such as social desirability and acquiescence bias (Sigerson & Cheng, 2018), and future studies may replicate the present study with objective data such as application programming interface and objective logs.

Conclusion

In summary, for the European sample a stronger motivation for social bonding (Affiliation –concerning rather loose relationships with acquaintances) and the need to control others (Power) were linked to a positive Attitude toward Facebook. Furthermore, the Attitude toward Facebook mediated

the relationship between the motives Affiliation/Power and the frequency of Facebook use (Online Sociability). Fear was positively linked to Attitudes, whereas the motivation for bonding with significant others (mirrored in the motive Intimacy) was negatively linked to the Attitude toward Facebook. Regarding the Asian sample, no significant associations between explicit motives and Facebook use could be found in the Structural Equation Model. Future investigations are needed to replicate the results of the current study.

Acknowledgments

Funding: German Research Foundation (DFG, MO 2363/2-1). The position of CM is funded by a Heisenberg grant awarded to him by the German Research Foundation (DFG, MO2363/3-2).

Electronic Supplementary Material

The electronic supplementary material is available with the online version of the article at https://doi.org/10.1027/ 1614-0001/a000273

ESM 1. Tables 1–4 (.xlsx) Reliabilities, skewness/kurtosis and additional structural equation modeling results for the Turkish sample.

References

Abell, L., & Brewer, G. (2014). Machiavellianism, self-monitoring, self-promotion and relational aggression on Facebook. Computers in Human Behavior, 36, 258–262. https://doi.org/ 10.1016/j.chb.2014.03.076 Ames, D. R., Rose, P., & Anderson, C. P. (2006). The NPI-16 as a short measure of narcissism. Journal of Research in Personality, 40, 440–450. https://doi.org/10.1016/j.jrp.2005.03.002 Beyens, I., Frison, E., & Eggermont, S. (2016). “I don’t want to miss a thing”: Adolescents’ fear of missing out and its relationship to adolescents’ social needs, Facebook use, and Facebook related stress. Computers in Human Behavior, 64, 1 –8. https://doi.org/ 10.1016/j.chb.2016.05.083 Bischof-Kastner, C., Kuntsche, E., & Wolstein, J. (2014). Identifying problematic internet users: Development and validation of the internet motive questionnaire for adolescents (IMQ-A). Journal of Medical Internet Research, 16, e230. https://doi.org/ 10.2196/jmir.3398 Błachnio, A., Przepiorka, A., Benvenuti, M., Cannata, D., Ciobanu, A. M., Senol-Durak, E., ... Popa, C. (2016). Cultural and personality predictors of Facebook intrusion: A cross-cultural study. Frontiers in Psychology, 7, 1 –9. https://doi.org/10.3389/ fpsyg.2016.01895 Bodroža, B., & Jovanović, T. (2016). Validation of the new scale for measuring behaviors of Facebook users: Psycho-social aspects of Facebook use (PSAFU). Computers in Human Behavior, 54, 425–435. https://doi.org/10.1016/j.chb.2015.07.032

Journal of Individual Differences (2019), 40(1), 26–35 Brand, M., Young, K. S., Laier, C., Wölfling, K., & Potenza, M. N. (2016). Integrating psychological and neurobiological considerations regarding the development and maintenance of specific internet-use disorders: An interaction of person-affect-cognition-execution (I-PACE) model. Neuroscience and Biobehavioral Reviews, 71 , 252–266. https://doi.org/10.1016/j.neubiorev. 2016.08.033 Caplan, S. E. (2010). Theory and measurement of generalized problematic internet use: A two-step approach. Computers in Human Behavior, 26, 1089–1097. https://doi.org/10.1016/j. chb.2010.03.012 Chabrol, H., Laconi, S., Delfour, M., & Moreau, A. (2017). Contributions of psychopathological and interpersonal variables to problematic Facebook use in adolescents and young adults. International Journal of High Risk Behaviors and Addiction, 6, e32773. https://doi.org/10.5812/ijhrba. 32773 Chen, F. F. (2007). Sensitivity of goodness of fit indexes to lack of measurement invariance. Structural Equation Modeling, 14, 464–504. https://doi.org/10.1080/10705510701301834 Cox, W. M., & Klinger, E. (1988). A motivational model of alcohol use. Journal of Abnormal Psychology, 97, 168–180. https://doi. org/10.1037/0021-843X.97.2.168 Cutler, N. E., & Danowski, J. A. (1980). Process gratification in aging cohorts. Journalism and Mass Communication Quarterly, 57, 269–276. https://doi.org/10.1177/107769908005700210 DSM History. (2017). DSM-5 table of contents. Retrieved April 10, 2017 from https://www.psychiatry.org/psychiatrists/practice/ dsm/history-of-the-dsm 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, 1143–1168. https://doi.org/10.1111/ j.1083-6101.2007.00367.x Engeser, S., & Langens, T. (2010). Mapping explicit social motives of achievement, power, and affiliation onto the five-factor model of personality. Scandinavian Journal of Psychology, 51 , 309–318. https://doi.org/10.1111/j.1467-9450.2009.00773.x Facebook. (2016). Facebook: Company info. Retrieved from https://newsroom.fb.com/company-info/ Fox, J., & Rooney, M. C. (2015). The dark triad and trait selfobjectification as predictors of men’s use and self-presentation behaviors on social networking sites. Personality and Individual Differences, 76, 161 –165. https://doi.org/10.1016/j.paid.2014. 12.017 Garcia, D., & Sikström, S. (2014). The dark side of Facebook: Semantic representations of status updates predict the dark triad of personality. Personality and Individual Differences, 67, 92–96. https://doi.org/10.1016/j.paid.2013.10.001 Graham, L. T., & Gosling, S. D. (2013). Personality profiles associated with different motivations for playing World of Warcraft. Cyberpsychology, Behavior, and Social Networking, 16, 189–193. https://doi.org/10.1089/cyber.2012.0090 Hare, R. D. (1985). Comparison of procedures for the assessment of psychopathy. Journal of Consulting and Clinical Psychology, 53, 7–16. https://doi.org/10.1037/0022-006X.53.1.7 Hu, L., & Bentler, P. (1995). Evaluating model fit. In R. Hoyle (Ed.), Structural equation modeling concepts, issues, and applications (pp. 76–99). London, UK: Sage Publications. Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6, 1 –55. https://doi.org/10.1080/10705519909540118 Lin, N. (1977). Communication effects: Review and commentary. Communication Yearbook, 1 , 55–72. https://doi.org/10.1080/ 23808985.1977.11923670

Marino, C., Vieno, A., Altoè, G., & Spada, M. M. (2016). Factorial validity of the problematic Facebook use scale for adolescents and young adults. Journal of Behavioral Addictions, 6, 5–10. https://doi.org/10.1556/2006.6.2017.004 Marino, C., Vieno, A., Moss, A. C., Caselli, G., Nikčević, A. V., & Spada, M. M. (2016). Personality, motives and metacognitions as predictors of problematic Facebook use in university students. Personality and Individual Differences, 101 , 70–77. https://doi.org/10.1016/j.paid.2016.05.053 Marino, C., Vieno, A., Pastore, M., Albery, I. P., Frings, D., & Spada, M. M. (2016). Modeling the contribution of personality, social identity and social norms to problematic Facebook use in adolescents. Addictive Behaviors, 63, 51 –56. https://doi.org/ 10.1016/j.addbeh.2016.07.001 McAdams, D. P. (1980). A thematic coding system for the intimacy motive. Journal of Research in Personality, 14, 413–432. https://doi.org/10.1016/0092-6566(80)90001-X MacCallum, R. C., Browne, M. W., & Sugawara, H. M. (1996). Power analysis and determination of sample size for covariance structure modeling. Psychological Methods, 1 , 130–149. https://doi.org/10.1037/1082-989X.1.2.130 McClelland, D. C. (1987). Human motivation. CUP Archive. Miles, J., & Shevlin, M. (2001). Applying regression and correlation: A guide for students and researchers. London, UK: Sage Publications. Montag, C., & Panksepp, J. (2017). Primary emotional systems and personality: An evolutionary perspective. Frontiers in Psychology, 8, 1 –15. https://doi.org/10.3389/fpsyg.2017.00464 Przybylski, A. K., Murayama, K., DeHaan, C. R., & Gladwell, V. (2013). Motivational, emotional, and behavioral correlates of fear of missing out. Computers in Human Behavior, 29, 1841 –1848. https://doi.org/10.1016/j.chb.2013.02.014 Ross, C., Orr, E. S., Sisic, M., Arseneault, J. M., Simmering, M. G., & Orr, R. R. (2009). Personality and motivations associated with Facebook use. Computers in Human Behavior, 25, 578–586. https://doi.org/10.1016/j.chb.2008.12.024 Rosseel, Y. (2012). Lavaan: An R package for structural equation modeling. Journal of Statistical Software, 48, 1 –36. Ruggiero, T. E. (2000). Uses and gratifications theory in the 21st century. Mass Communication & Society, 3, 3–37. https://doi. org/10.1207/S15327825MCS0301_02 Ryan, T., Chester, A., Reece, J., & Xenos, S. (2014). The uses and abuses of Facebook: A review of Facebook addiction. Journal of Behavioral Addictions, 3, 133–148. https://doi.org/10.1556/ JBA.3.2014.016 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, 1658–1664. https://doi.org/10.1016/j.chb.2011.02.004 Schönbrodt, F. D., & Gerstenberg, F. X. (2012). An IRT analysis of motive questionnaires: The unified motive scales. Journal of Research in Personality, 46, 725–742. https://doi.org/10.1016/ j.jrp.2012.08.010 35

Schonfeld, E. (2010). Facebook closing in on 500 million visitors a month. TechCrunch. Retrieved from https://techcrunch.com/ 2010/04/21/facebook-500-million-visitors-comscore/ Sheldon, P. (2008). The relationship between unwillingness-tocommunicate and students’ Facebook use. Journal of Media Psychology, 20, 67–75. https://doi.org/10.1027/1864- 1105.20.2.67 Shepherd, R., & Edelmann, R. J. (2005). Reasons for internet use and social anxiety. Personality and Individual Differences, 39, 949–958. https://doi.org/10.1016/j.paid.2005.04.001 Sigerson, L., & Cheng, C. (2018). Scales for measuring user engagement with social network sites: A systematic review of psychometric properties. Computers in Human Behavior, 83, 87–105. https://doi.org/10.1016/j.chb.2018.01.023 Sokolowski, K., & Heckhausen, H. (2008). Social bonding: Affiliation motivation and intimacy motivation. In J. Heckhausen & H. Heckhausen (Eds.), Motivation and action (pp. 184–201). New York, NY: Cambridge University Press. Vasalou, A., Joinson, A. N., & Courvoisier, D. (2010). Cultural differences, experience with social networks and the nature of “true commitment” in Facebook. International Journal of Human-Computer Studies, 68, 719–728. https://doi.org/ 10.1016/j.ijhcs.2010.06.002 Wegmann, E., & Brand, M. (2016). Internet-communication disorder: It’s a matter of social aspects, coping, and internet-use expectancies. Frontiers in Psychology, 7, 1 –13. https://doi.org/ 10.3389/fpsyg.2016.01747 Wegmann, E., Oberst, U., Stodt, B., & Brand, M. (2017). Onlinespecific fear of missing out and internet-use expectancies contribute to symptoms of internet-communication disorder. Addictive Behaviors Reports, 5, 33–42. https://doi.org/10.1016/ j.abrep.2017.04.001 Wilson, R. E., Gosling, S. D., & Graham, L. T. (2012). A review of Facebook research in the social sciences. Perspectives on Psychological Science, 7, 203–220. https://doi.org/10.1177/ 1745691612442904

Received November 5, 2016 Revision received February 9, 2018 Accepted March 8, 2018 Published online November 16, 2018

Rayna Sariyska Institute of Psychology and Education Ulm University 1.40 Helmholtzstr. 8/1 89081 Ulm Germany rayna.sariyska@uni-ulm.de