EXPLORING THE RELATIONSHIP BETWEEN SOCIAL INFLUENCE AND ADDICTION IN FACEBOOK USE: A COMPARATIVE STUDY

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1 EXPLORING THE RELATIONSHIP BETWEEN SOCIAL INFLUENCE AND ADDICTION IN FACEBOOK USE: A COMPARATIVE STUDY Vincentas LAMANAUSKAS Šiauliai University, Lithuania v.lamanauskas@esf.su.lt Dragos Daniel IORDACHE, Costin PRIBEANU ICI Bucharest, Romania iordache@ici.ro, pribeanu@ici.ro Abstract. Today, the social networking websites are widespread among university students. The most prominent example is Facebook that had a considerable penetration rate in the last decade, especially among young people. In the era of social learning, educators have to understand the motives that are driving the daily use as well as the positive and negative effects on the university work. Social influence has been recognized as an important factor driving the intention to use. On another hand, adoption may lead to addiction. Recent work shows that Romanian university students have large Facebook networks and spend too much time daily. This research has two main objectives. The first is to measure the social influence and the Facebook dependence on two samples of university students, one from Romania and the other from Lithuania. The second is to comparatively discuss the measures, by carrying on an invariance analysis. A model with two latent variables has been specified and tested. The results show that the model exhibits configural and metric invariance which means that the variables are perceived in the same way in both countries. The comparison of the observed scores reveals higher mean values for the Romanian sample. Keywords: social influence, Facebook dependence, invariance analysis, e-learning. JEL classification: C13 Estimation, D83 Learning, L86 Computer software 1. Introduction It is without any doubt that social networks make intercommunication easier, people become more open, and can communicate virtually. The social networking websites are more and more widespread among university students. A well-known example is Facebook with a considerable penetration rate in the last decade, especially among young people. In the context of a paradigm shift, from learner-centred to social learning, this is a challenge for educators to adapt their teaching style to the student's lifestyle. This means to understand the motives that are driving the daily use as well as its positive and negative effects on the university work [4, 5]. Social influence has been recognized as an important driver of technology acceptance [3, 19]. The fast adoption rate of Facebook is in part explained by the social influence that occurs in the social networks. On another hand, adoption may lead to addiction. Recent work shows that Romanian university students have large Facebook networks and spend too much time daily [8, 10] which may lead to dependence. This research has two main objectives. The first is to measure the social influence and the Facebook dependence on two samples of university students. The first sample has been collected in Romania (N=758) and the other in Lithuania (N=297). The second objective is to comparatively discuss the measures, by carrying on an invariance analysis. In order to do 146

2 this, a model with two inter-correlated latent variables has been specified and tested. The first variable is the social influence and the second is the Facebook dependence, measured with the withdrawal syndrome. 2. Related work Users may adopt a technology or a product not only because of their own personal beliefs but also because of the views of others [3, 6]. Social influence usually occurs when an individual s opinions or behaviors are influenced by others. Social and individual needs can vary from one person to another and constitute the main reasons why individuals use OSNs. The main social reasons for OSN use include the need to consolidate social relations, collaborate with other OSN members, participate in interest groups, and extend friendships and relationships. Bagozzi & Lee [3] pointed out that there are many paths through which the social influence may affect the intention to use a technology. Their study is based on a distinction between three broad types of social influence processes: compliance, identification, and internalization [11]. Compliance is the act of agreeing with others and can be described as the act of responding favorably to explicit or implicit requests made by others. Thus, an individual may agree with the views of others to accept a particular technology if he lacks adequate information on that technology. Identification refers to the process of being influenced by others in the same social group. Through the process of identification, individuals accept a particular technology because they want to establish or maintain a satisfying, self-defining relationship with their social group [3]. Internalization refers to the process of accepting a belief or behavior because it is consistent with one s value system. Usually, individuals accept a particular technology because it is congruent with their value system. In their study, Risselada et al. [14] addressed the social influence effects on adoption of hightechnology products. Social influence is likely to play a role because the decision to adopt a high-involvement product requires extensive information gathering from various sources. The study took into account two network metrics: tie strength and homophily. The homophily is a social network variable reflecting the similarity between consumers (in terms of sociodemographics) in a network [13]. The general idea is that the more similar people are, the more they influence one another. They found that social influence affects adoption through different social influence variables, implying that it may well be worth the effort for firms to collect information on their customers social networks. However, social networking websites can be harmful. It is necessary to accentuate, that consumer activity in social networks depends on the personality features. As shown by Papacharissi & Rubin, people feeling less satisfied and less valued in their face-to-face communication are using the Internet as an alternate space for communication, or to fill time [16]. For example, people lacking communication skills tend to spend more time in social networks, this way compensating the lack of communication in real life. In turn, this may lead to various negative effects. Several studies showed that an intense use of social networking websites may lead to negative effects upon students work [4, 5, 8]. Several conceptualizations exist that are featuring a diversity of constructs related to the overuse and addiction to social networking websites, such as withdrawal syndrome, salience, tolerance, overuse, negative effects on work, or mood modification [1, 12]. Dependence on social networks is ascribed to a psychological dependence group, quite often causing rather significant dependence/addiction. Such dependence/addiction is of two kinds. In the first case, it can develop for any pleasure-giving activity, in the other case, seeking to escape the activity causing unpleasant feelings. The research carried out in India showed that 147

3 a greater percentage of the population is in danger to get addicted or is already addicted to Facebook [15]. A similar research carried out in Malaysia, showed that social interaction, passing time, entertainment, companionship, and communication motives are related to the Facebook addiction [17]. A more recent research carried out in Taiwan, showed that university students may become high-risk groups for Facebook addictive tendency [9]. 3. Measuring the social influence and Facebook dependence The first objective of this research is to measure the social influence and Facebook dependence on two samples of university students. For the social influence (SI) a scale comprising four items has been used. The Facebook dependence was measured with three items adapted from the withdrawal scale proposed by Masur et al [12]. Students were asked to answer general questions including the size of their Facebook network and time spent daily, then to evaluate the items on a 1-7 point Likert scale. The first sample has been collected in Romania in 2015 and included 796 university students from seven universities. After checking the normality of variables with SPSS for Windows 38 observations have been eliminated thus resulting in a working sample of 758 (418 male, 340 female) with a mean age of years (SD=2.81). The network size has a mean value of (SD=816.26). The time spent in minutes/day is on average (SD=74.93). After checking the inter-item correlation one item from the social influence scale has been eliminated for collinearity reasons so the final scale has three items. The items are presented in Table 1. The second sample has been collected in Lithuania in 2016 and includes 297 university students from three universities (118 male, 179 female) with a mean age of years (SD=5.34). The network size has a mean value of (SD=222.24). The time spent in minutes/day is on average (SD=103.81). Table 1. Variables Item Statement SI1 People who influence me think that I should use Facebook SI2 People who are important to me think that I should use Facebook SI3 My colleagues think that I should use Facebook AWS1 If I am off Facebook for a longer period of time I feel nervous AWS2 When I am not online I ask myself what happens on Facebook AWS3 I feel out of touch when I haven't logged onto Facebook for a while The model has been tested with AMOS 7.0 for Windows [2], using the maximum likelihood estimation method. Convergent validity has been assessed by examining the loadings and their statistical significance through t-values, the construct reliability (composite reliability), and the average variance extracted [7]. The descriptive statistics, composite reliability, average variance extracted, and reliability is presented in Table 2. The mean values for the Facebook dependence items are small in both samples. An analysis of frequencies shows that the percentage of students that scored over the neutral value (4.00) is varying between 9.8% (for AWS3) and 17.5% (for AWS2) in the Romanian sample and between 6.1% (for AWS2) and 12.5% (for AWS3) in the Lithuanian sample. The correlation coefficient between the two latent variables is high in both countries, showing a strong relationship between the social influence and the Facebook dependence measured with the withdrawal syndrome. 148

4 Table 2. Descriptive statistics, convergent validity, and reliability Item RO sample (N=758) LT sample (N=297) M SD Estimate CR AVE Alpha M SD Estimate CR AVE Alpha SI SI SI AWS AWS AWS As it could be seen for both samples, the composite reliability is over the threshold of 0.7 and the average variance extracted is over the threshold of 0.5. The scale reliability has been analyzed checking the magnitude of Cronbach s alpha which is over 0.8 for both samples. The model testing results for the two samples are presented in Figure 1. Figure 1. Model testing results: a) Romanian sample (N=758) b) Lithuanian sample (N=297) The results revealed an acceptable fit of the model with the data: χ 2 =54.49, df=8, p=0.000, χ 2 /df=6.81, TLI=0.97, CFI=0.99, SRMR=0.025, RMSEA=0.088, for the first sample, and χ 2 =21.53, df=8, p=0.006, χ 2 /df=2.69, TLI=0.97, CFI=0.99, SRMR=0.036, RMSEA=0.076, for the second sample. 4. Comparison of results In order to analyze the differences between the two samples, a measurement invariance analysis has been carried on. This approach is a prerequisite for the model having latent variables is required since the differences could be due to different perceptions or to a different interpretation of the scale [18]. A multi-group confirmatory factor analysis (MG-CFA) has been carried on in order to analyze the measurement invariance of the model across the two samples. MG-CFA is testing a hierarchical series of nested models, starting with a baseline (unconstraint) model that fits all the samples taken together (N=1055) and provides a baseline chi-square value. Then, the nested models are obtained by adding constraints on invariance. A pre-condition is to test and validate the model on each group. This has been already done in the previous section and the results showed that in both cases the evaluation instrument exhibits configural invariance. This means that the groups have a similar perception of the two latent variables since there is the same number of factors and the same number of items measuring each factor. Then the unconstraint (baseline) model (N=1055) has been tested. The model testing results show an acceptable fit of the model with the data: χ 2 =76.022, df=16, p=0.000, χ 2 /df=4.75, 149

5 TLI=0.97, CFI=0.99, SRMR=0.036, RMSEA=0.06. The examination of the model on each group shows that both constructs have the same number of items with loadings over 0.7, thus providing evidence for the configural invariance across groups and enabling further (more indepth) comparisons. Table 3. Model comparison (N=1055) Model DF χ 2 CFI Δ df Δ χ 2 Δ CFI p Unconstraint Measurement weights Measurement intercepts The next step is to test the metric invariance between the two samples by constraining the factor loadings of like items to be invariant [18]. The results show that the evaluation instrument exhibits metric invariance (Δdf=4, Δχ 2 =1.221, p=0.875). The implication is that the observed mean scores could be compared across groups. The test for scalar invariance between groups requires constraining the item intercepts of like items [18]. The results showed a lack of scalar invariance (Δdf=6, Δχ 2 =68.839, p=0.000). The model comparison results are presented in Table 3. As it could be noticed from Table 2, Romanian students have higher perceptions as regards both the social influence on using Facebook and the Facebook dependence. A one-way ANOVA (1, 1053, 1054) showed that the differences are statistically significant for all variables: SI1 (F=18.874, p=0.000), SI2 (F=20.759, p=0.000), SI3 (F=31.279, p=0.000), AWS1 (F=13.073, p=0.000), AWS2 (F=38.534, p=0.000), and AWS3 (F=14.012, p=0.000). The largest differences are observed between the scores of the item SI3, pointing to the influence of colleagues, and between the scores of AWS2, pointing to the need to be online on Facebook. 5. Discussion and conclusions This research provides useful insights into the relationship between the social influence and the Facebook addiction measured with the withdrawal syndrome. The two variables are intercorrelated and the invariance analysis shows configural and metric invariance, which enabled a cross-country comparison. The magnitude of social influence measures is small to moderate for the Romanian students and small for the Lithuanian students. In both cases, the influence of colleagues is stronger. However, the difference between the two groups is higher for the latter measure. The differences could be explained in part by the differences between the number of Facebook friends which is more than twice larger for the first sample. For both samples, Facebook dependence is relatively small. The measures of the withdrawal syndrome have higher mean values for the Romanian sample. Surprisingly, the mean time spent daily on Facebook is higher for the Lithuanian students. Acknowledgment This work is supported in part by the national grant financed by ANCS (Romania) under COGNOTIC / References [1] Andreassen, C. S., Torsheim, T., Brunborg, G. S. & Pallesen, S. (2012). Development of a Facebook Addiction Scale. Psychological Reports, 110,

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