The impacts on facebook to happiness

The Impact of Facebook on Social Comparison and Happiness: Evidence from a Natural Experiment Ayala Arad Ohad Barzilay Maayan Perchick Coller School of Management Tel Aviv University Abstract The ubiquity of Facebook in modern life compels us to study its effects on well-being We study a unique sample of users and non-users in a security-related organization, where Facebook usage was manipulated by an organizational policy change, initially banning Facebook altogether and later differentially restricting its usage Thus, the assignment of the employees to the group of non-users was circumstantial rather than due to a-priori personal characteristics, which makes it possible to identify Facebook's impact on social comparison and happiness We find that Facebook usage increases users' engagement in social comparison and consequently decreases their happiness Social comparison mediates the effect of Facebook on happiness, but only for the younger half of our sample and only for those who believe that others have many more positive experiences than they Surprisingly, we find that Facebook does not cause users to overestimate the frequency of their friends' positive experiences Thus, the organization's banning of Facebook use had an overall positive effect on the employees' psychological well-being Keywords: Economics of information systems, Happiness, Facebook, Information and communication technologies, Natural experiment, Social comparison Electronic copy available at: https://ssrn.com/abstract=2916158 Introduction Facebook is currently the largest online social network, with over one billion users worldwide During the past decade, it has become an integral part of its users' everyday lives Drawing on the literature regarding the effect of information and communication technologies (ICT) on social welfare and subjective well-being (Ganju et al., 2016; Greenstein and McDevitt, 2011) and given the prominence of Facebook, we study whether using Facebook affect happiness While many studies have looked into the personality traits driving differential Facebook usage (see Nadkarni and Hofmann (2011) for a review), the effect of Facebook on the well-being of its users is little understood (Appel et al., 2016) This issue carries managerial implications considering the increasing importance of the firm in moderating its employees’ social interactions (Godes et al., 2005) Alongside the many benefits of Facebook usage, such as maintaining social connections (and possibly even reducing mortality rates (Hobbs et al., 2016)), Facebook may also have the potentially negative effect of encouraging social comparison Because Facebook users tend to post mostly positive experiences and underreport negative ones (Zhao et al., 2008; Mehdizadeh, 2010), we hypothesize that Facebook-driven comparisons with others may be undermining users' happiness This is in line with existing literature suggesting that upward social comparison (offplatform) decreases happiness (Argyle, 2013; Wood et al., 1985) Identifying a causal link from Facebook usage to happiness is a challenge in view of the inherent selection bias It is difficult to devise a proper control group of non-users because the unique individuals who choose not to use Facebook are likely to have personalities that differ from those of the Facebook users (Nadkarni and Hofmann, 2011; Ljepava et al., 2013) A similar selection problem exists in studies that compare the subjective well-being of users with different types or intensities of usage (Chou and Edge, 2012; Tandoc et al., 2015) It is possible that happy people tend to use Facebook differently, and hence one cannot identify the impact of Facebook on Electronic copy available at: https://ssrn.com/abstract=2916158 happiness by performing the above comparison Even longitudinal within-subject studies that compare subjective well-being across time and across types of usage (e.g Kross et al., 2013; Verduyn et al., 2015) are subject to the possibility that in periods when the participants are happier they also choose to use Facebook more or less intensively Some studies use path analysis (Baron and Kenny, 1986) to explore the mediating effect of envy, rumination and social comparison on depression (Tandoc et al., 2015; Feinstein et al., 2013) or on life satisfaction (Krasnova et al., 2013; Locatelli et al., 2012) However, this approach does not eliminate the possibility of underlying endogeneity (for a discussion of this issue, see Appel et al., 2016) This study investigates the impact of Facebook on the perception of others’ lives, on social comparison and on happiness, using a unique sample of users and non-users for whom not using Facebook is a circumstantial outcome rather than a result of a personal choice All the participants are employees of a security-related organization in which the use of Facebook was at first entirely forbidden (during the period 2008-2012), and then differentially restricted The restrictions, however, became contingent on the projects in which the employee is involved (rather than their function) For example, an administrator and a scientist could have identical restrictions placed on them Thus, this policy change serves as a pseudo-natural experiment Indeed, post-study interviews suggest that self-selection into the group of non-users based on individual differences is very small in magnitude (see Section 3.1) The almost exogenous assignment to each of the two groups - users and non-users - makes it possible to more cleanly measure the impact of Facebook on the above-mentioned constructs, without having to be concerned that initial differences in these constructs led to the choice of whether or not to be a Facebook user Furthermore, the unique research setting provides an additional benefit in that it allows us to account for the cumulative effect of Facebook usage 'in the wild', in contrast to lab experiments and short-lived field studies (Kross et al., 2013; Verduyn et al., 2015; Vogel et al., 2015; Lin and Sonja, 2015) Electronic copy available at: https://ssrn.com/abstract=2916158 Related Literature 2.1 ICTs and online social networks Information and communication technologies transcend boundaries from the workplace into the home and society in large (Brown, 2008; Walther 1996) Enabled by technologies such as the Internet, the Web and online social networks, computing now mediates the communication and social interactions of many (Preece and Shneiderman, 2009; Kim and Lee, 2011; Walther, 2011) As information and communication technologies become pervasive in everyday life, they influence not only the way in which one communicates, but perhaps also one's psychological well-being (Caplan, 2003) and even the well-being of nations (Ganju et al., 2016) Online social networks, such as Facebook, became popular ICTs during the first decade of the 21st century They enable users to maintain social relationships with family and friends by making it easy to share and read updates about one another Online social networks are essentially a virtualization of the offline social processes (Overby et al., 2010) Over the years, the use of social networks has broadened, and users report using online social networks also for relaxation, entertainment, and socializing (Ku et al., 2013 and Park et al., 2009) Online social networks, like their offline counterparts, have been found to fortify one’s self esteem (Gentile et al., 2012; Gonzales and Hancock, 2011; Toma and Hancock, 2013), enforce group identity (Fox and Warber, 2015; Zhao et al., 2008) and increase trust and cooperation (Bapna et al., 2016) 2.2 Online social networks and well-being Additional benefits of online social networks include increased social capital, social support, and relationship maintenance (Ellison et al., 2007; McEwan, 2013; Nabi et al., 2013) Since social capital and subjective wellbeing are strongly associated (e.g Bjørnskov, 2003; Helliwell, 2001; Leung et al., 2013), it is reasonable to assume that online social networks increase one’s social capital and, in turn, have a positive effect on one’s well-being However, there is a growing body Electronic copy available at: https://ssrn.com/abstract=2916158 of evidence to the contrary, referred to as the “Internet paradox” (Kraut et al., 1998) In other words, Internet technology appears to reduce psychological well-being, as manifested in increased depression and loneliness It is possible that Internet technology, and in particular online social networks, which are tailored to streamline information flow among users, is a two-edged sword The platform designers' control of which information is to be highlighted and in what manner affects the evolutionary dynamics of the platforms (Tiwana et al., 2010), as was demonstrated on a broad variety of contexts (e.g Dellarocas, 2005) In the case of Facebook, the design of the friends feed creates an overwhelming emphasis on others' positive experiences (Chou and Edge, 2012), which gives rise to dynamics of envy, rumination and jealousy (Tandoc et al., 2015; Feinstein et al., 2013; Fox and Moreland, 2015) Recent studies regarding the use of online social networks among adolescents, students and young adults, have found heterogeneous effects (Valkenburg et al., 2006; Radovic et al., 2017) These findings may suggest that social technologies have an amplifying effect, conditional on the user’s characteristics or on the user interactions on the platform The broad spectrum of positive, negative and mixed effects of online social technologies, and in particular Facebook, on one's well-being calls for further research This study, in line with Appel et al (2016), attempts to exploit natural experimental settings in order to understand the effect of Facebook and to identify its mechanism Methods In January 2015, 144 randomly selected employees (Mage=25.8; 40% females) filled out a penciland-paper questionnaire (hence mitigating the risk of happiness-associated volunteer bias, Heffetz and Rabin, 2013) The sample consisted of 95 users and 49 non-users (34%) of Facebook The assignment to the groups of users and non-users is described below, followed by a description of the questionnaire and the data analysis method Electronic copy available at: https://ssrn.com/abstract=2916158 3.1 Pseudo-exogenous assignment to Facebook users or non-users From 2008 to 2012, the organization's employees were not allowed to use social networks (neither at work nor at home) Employees with an existing Facebook account, including employees joining the organization during this period, were asked to delete their account In 2012, the policy was changed and became contingent on the projects in which the employee is involved (rather than their function) For example, an administrator and a scientist could have identical restrictions placed on them and two engineers might have different restrictions A very small fraction of employees were now allowed to use Facebook freely; others were allowed restricted use, i.e they were forbidden to use their full name or upload photos (including a profile picture); the rest were still forbidden to use Facebook Thus, the research, which was carried out in 2014, included both users and non-users This policy change serves as a pseudo-natural experiment: In practice, many employees who worked in the organization before 2012 did not open an account after the policy was changed, even if allowed to On the other hand, new employees, who joined the organization after 2012, tended to keep their existing Facebook account (unless forbidden to) Thus, Facebook non-users in the organization are mainly employees who are not allowed to use Facebook today and employees who were forbidden to so until 2012 We conducted post-study interviews with the organization’s non-user employees in order to understand the reasons they not use Facebook and how those reasons relate to the restrictions placed on them We interviewed 38 employees who not use Facebook: 23 who did not have an account and 15 with a non-active account When asked to explain why they not use Facebook, 32 out of the 38 subjects cited the restrictions placed on them by the organization Only six employees stated that they would not open an account for personal reasons, regardless of the organization’s policy Of those six, three Electronic copy available at: https://ssrn.com/abstract=2916158 had restrictions placed on their Facebook use Furthermore, two of those who did not have restrictions placed on them, did have them in the past In other words, they had worked in the organization when Facebook usage was totally banned Thus, their choice to not use Facebook might also have been affected by these unique circumstances The interviews suggest that our non-user group includes a small number of employees who not use Facebook out of choice For these employees, the choice not to use Facebook may be correlated with some personality trait, which in turn may be correlated with their social comparison and happiness However, self-selection into the group of non-users based on individual differences appears to be small in magnitude We maintain that the differential Facebook policy, which determined the assignment to one of the two groups, is not related to the employee’s personality We also find that the everyday experiences, as reflected in the answers to section E, are not significantly different between users and non-users in our sample (see the supplementary materials at the end of this document) The average age of non-users is greater than that of users in our sample since new employees who joined after 2012 and kept their accounts tend to be younger This is also related to the higher income of non-users We control for age and income in our analysis 3.2 The questionnaire The questionnaire (which appears in the Appendix), included sections, which were presented in the following order: A Demographics: age, gender, family status, three levels of education and five levels of income Electronic copy available at: https://ssrn.com/abstract=2916158 B Friends' experiences: We were interested in ascertaining how participants perceive others’ lives as compared to their own, but didn’t want to reveal our intention directly in the questionnaire Therefore, we separated the estimation of participant’s own life experiences from the estimation of the experiences of others The respective questions were asked on different sections as follows: In section B, the participants were asked to evaluate the frequency of various positive experiences in their friends' lives and to estimate the frequency of negative experiences in their friends’ lives In section E, which appeared few pages later, the same questions were asked with respect to the participant’s own experiences Thus, in section B participants were asked ten questions which evaluated the frequency of various positive experiences in their friends' lives (e.g how often during the course of a week they go out, read a book, watch a movie, etc.) and five questions in which they estimated the frequency of negative experiences in their friends’ lives (e.g how often during the course of a week are they in a bad mood, upset, sick, etc.) C Social comparison: Based on Scale for Social Comparison Orientation (Gibbons and Buunk, 1999) Participants were presented with eight statements and asked to indicate the degree to which they agree with each of them on a 6-point scale, from “strongly disagree” to “strongly agree” A high score indicates a high degree of social comparison The reliability of the scale was evaluated using Cronbach’s alpha measure (alpha=0.803) At the end of this section we added four questions on envy and the need to share D Happiness: Based on The Oxford Happiness Questionnaire (Hills and Argyle, 2002) Respondents were presented with eight statements and were asked to what extent they agree with each of them on a 6-point scale as described above A high score reflects a Electronic copy available at: https://ssrn.com/abstract=2916158 higher degree of satisfaction with one’s own life The reliability of the scale was evaluated using Cronbach’s alpha measure (alpha=0.715) E Personal experiences: Participants were asked about the frequency of ten positive experiences in their own lives (e.g how often during the course of a week they go out, read a book, watch a movie, etc.) and about five negative experiences in their own lives (e.g how often during the course of a week are they in a bad mood, upset, sick, etc.) In our analysis, we constructed the variables own_positive and own_negative to measure the overall frequency of positive and negative experiences in one’s life, respectively Because the answers to the various questions on experiences are on different scales, in the construction of these variables, each question’s original answer was transformed into a relative score, i.e a percentile for that question, where the variable is the average of the 10 (or 5) questions’ scores Further, using sections B and E, we constructed the difference between the perception of others’ experiences relative to one’s own for positive and negative experiences separately We then transformed the differences into percentiles for each question separately and averaged across questions The variables were given the names ∆(pos)/∆(neg), and a high value indicate a perception that others have more positive/negative experiences than oneself F Facebook use (section F): Based on Ellison et al (2007) This questionnaire asked about the frequency of Facebook use and the type of activities that users engage in For example, participants were asked how often they check their Facebook account and how often they upload photos, tag, etc Electronic copy available at: https://ssrn.com/abstract=2916158 3.3 Analysis Method We use two methods for estimating the effect of Facebook: a linear regression analysis and matching techniques In the main analysis, we measure the differences between users and nonusers (including some whose accounts are not active) but not consider the manner in which Facebook is used, which is endogenously determined by the users Subsequently, we analyze the employees’ type of usage and report on the association between intensity of usage and the study’s variables of interest Regression analysis Following the vast literature on the measurement of happiness, we use the following demographic control variables: age, gender, education, income and family status (Ferrer-i-Carbonell and Frijters, 2004; Dolan et al., 2008) In addition to age's main effect on happiness, we also included its interaction with Facebook use In line with previous findings, which showed that younger Facebook users are more susceptible to social influence than older ones (Aral and Walker, 2012), we account for potential variability in the effect of Facebook across different ages We also devise an additional covariate: the estimated proportion of a subject's friends who use Facebook This was motivated by the idea that if Facebook affects happiness and happiness is contagious (Fowler and Christakis, 2008), then one might want to control for potential peer influence (Bapna and Umyarov, 2015) while estimating Facebook's causal effect on happiness All estimations of the Facebook effect are robust to its inclusion We start with the analysis of simple models to capture the total main effect of Facebook on users’ happiness, social comparison and perception of others’ lives Then, we present the moderated mediation model (Preacher and Hayes, 2004; Hayes, 2013), which serves as the capstone of this study According to the model (Figure 1), the effect of Facebook on happiness is mediated by social comparison; Age serves as a moderator for the effect of Facebook on social comparison 10 Electronic copy available at: https://ssrn.com/abstract=2916158 Table A7: Facebook usage - Young vs old employees Considering only Facebook users in the organization, the following table compares the type of usage and intensity of usage of the group of young users (24 and younger) with that of older users (25 and older) Self vs others Association Frequency Facebook intensity Passive vs active N Mean SD 25 & older 31 0.528 0.243 24 & younger 62 0.448 0.14 25 & older 32 19.250 8.144 24 & younger 62 17.613 7.175 25 & older 32 3.97 0.933 24 & younger 62 4.18 0.915 25 & older 30 3.298 1.196 24 & younger 62 3.110 1.045 25 & older 32 2.804 1.286 24 & younger 61 3.198 1.666 t p 2.001 0.048 1.001 0.320 -1.041 0.301 0.77 0.443 -1.169 0.245 38 Electronic copy available at: https://ssrn.com/abstract=2916158 Table A8: Balancing of Treatment and Control groups with Mahalanobis distance function For robustness, we conducted additional propensity score matching analysis, using nearest neighbor method with Distance = “mahalanobis” (with replacement) The table shows that after the matching the standard deviation of the mean standard between the users and non-users is small Before Matching Means FB Users Age Gender Income Education Family 23.842 1.453 2.263 4.021 1.874 After Matching Means FB Non-Users SD FB Non-Users SD Mean Diff Means FB Non-Users SD FB Non-Users SD Mean Diff 29.65 1.306 3.082 4.571 1.531 8.969 0.466 1.656 0.791 0.504 -1.109 0.293 -0.51 -0.576 0.8192 24.253 1.432 2.274 4.147 1.853 5.384 0.503 1.721 0.977 0.36 -0.078 0.042 -0.007 -0.132 0.05 Table A9: Estimation of the effect of Facebook on balanced Treatment and Control groups We conducted weighted OLS analysis, regressing happiness, social comparison, ∆(pos) and ∆(neg) on Facebook usage and demographic covariates The specifications of the propensity score matching are shown on Table S16 The results are, again, aligned with our main analysis model showing a significant effect of Facebook on Happiness and Social Comparison, but no effect on ∆(pos) and ∆(neg) Happiness Social Comparison ∆(pos) ∆(neg) -0.266** (0.132) -0.009 (0.018) 0.455*** (0.169) -0.068*** (0.023) 0.015 (0.026) -0.005 (0.004) 0.039 (0.037) -0.005 (0.005) Gender -0.085 (0.145) -0.371** (0.186) 0.043 (0.029) 0.016 (0.041) Income 0.047 (0.053) 0.096 (0.068) 0.018* (0.011) 0.020 (0.015) Education -0.035 (0.084) 0.099 (0.108) -0.008 (0.017) -0.025 (0.024) Family -0.059 (0.171) -0.319 (0.219) 0.009 (0.034) -0.042 (0.048) R2 0.043 0.153 0.067 0.048 N 128 128 128 128 Facebook Age *p