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Is internet the cherry on top or a crutch? Offline social support as moderator of the outcomes of online social support on Problematic Internet Use
Computers in Human Behavior 56 (2016) 369e374
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Computers in Human Behavior journal homepage: www.elsevier.com/locate/comphumbeh
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Is internet the cherry on top or a crutch? Offline social support as moderator of the outcomes of online social support on Problematic Internet Use Elvis Mazzoni a, *, Lucia Baiocco a, Davide Cannata a, Isabel Dimas b a b
Alma Mater Studiorum - University of Bologna Italy University of Aveiro Portugal
a r t i c l e i n f o
a b s t r a c t
Article history: Received 22 January 2015 Received in revised form 2 November 2015 Accepted 17 November 2015 Available online 18 December 2015
The study is part of a research whose goal is identifying what predictors determine either a positive or a dysfunctional use of Internet. The factor at stake is here social support. Specifically our study, carried out through an online questionnaire, hypothesized a moderation of Offline Social Support in the relationship between Online Social Support, Problematic Internet Use, and Life Satisfaction. The study found that while Offline Social Support reduces the chances of developing a Problematic Internet Use, Online Social Support increases them. Furthermore the data supported the moderation of Offline Social Support in the outcomes of Online Social Support: when the first is low, as the latter increases the Problematic Internet Use gets higher; when Offline Social Support is high, an increase in Online Social Support determines a decrease in Problematic Internet Use. By contrast the moderation of Offline Social Support on the relationship between Online Social Support and Life Satisfaction was not confirmed. Our research show that when investigating psychological constructs related to Internet activity these must be considered in their offline and online variations to provide an answer to the debate on psychological outcomes of undertaking social interactions in Internet land. Our results suggest that the usage of the Web may become dysfunctional when it is meant to compensate for lacks of the “offline life”. © 2015 Elsevier Ltd. All rights reserved.
Keywords: Online social support Offline social support Problematic internet use Life satisfaction Compensation Enhancement
1. Introduction Internet is that powerful tool that allows us to have a conversation with someone living some time zones away as if he/she were right there, sitting in front of us. As well as this, some people, as Sherry Turkle (2012) in her Ted talk, claim that communications in the era of the Web 2.0 make us more lonely than ever. The findings here reported are part of a study that considers the Web as an undeniable resource, a tool that helps people achieving their scopes and agrees with defining Internet a functional organ (Frozzi & Mazzoni, 2011; Mazzoni & Iannone, 2014; Mazzoni, Baiocco, & Benvenuti, 2015). In addition, we believe that the risk associated with Internet usage is the possibility for the user to develop a dysfunctional use. In this case, the Web loses its function of tool and, by contrast, it is the user to metaphorically become the instrument of Internet: such phenomenon is called inverse
* Corresponding author. P.zza A. Moro, 90, 47521 Cesena, FC, Italy. E-mail address: [email protected] (E. Mazzoni). http://dx.doi.org/10.1016/j.chb.2015.11.032 0747-5632/© 2015 Elsevier Ltd. All rights reserved.
instrumentality (Ekbia & Nardi, 2012). The broad goal of the research is identifying what are the key factors that determine whether Internet represents a resource for the person or it rather becomes a problem impairing one's quality of life. The part of the study here considered focuses on social support as a predictor of the quality of one's use of internet. Social Support has been defined as “the resources provided by another person” (Cohen & Syme, 1985, p.4). Can these resources be conveyed through the Internet? A growing stream of researchers have investigated the relationship between Internet usage and Social Support. Some scholars tried to verify whether making use of the Web affects how much supported people feel. Swickert, Hittner, Harris, and Herring (2002) showed that there isn't a main effect of accessing the Web on the perception of Social Support. They further found that personality plays a moderating role and Internet Usage would enhance perceived Social Support only for users with certain personality traits. Eastin and LaRose (2005) found that the size of one's Online Social Network has a positive relation with the perception of Social Support. A recent study of Rozzell, Piercy, Carr, King, Lane, Tornes, Johnson and Wright (2014) investigated the
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perceived support internet users feel from close and nonclose ties, respectively. Their findings showed that both type of online social connections provide equitable support. Taken together these studies give a positive answer: the usage of Internet does have the potential of enhancing perceived Social Support. As a psychological resource, Social Support has been proven to buffer the effect of stressful events and to have a main effect on one's well-being (Cohen & Wills, 1985). Can we assume that because the Web boosts the perception of Social Support, making use of it leads to an increased well-being? Kim and Lee (2011) followed this line of reasoning and hypothesized a mediating effect of Social Support between the number of Facebook friends and one's Well-being. While the main positive effect of the number of Facebook friends on the Well-being was found, the data did not support the mediation hypothesis. By contrast, a recent study of Oh, Ozkaya, and LaRose (2014) lead to opposite findings testing a similar hypothesis. The path analysis they performed on a sample of 339 adults showed that having a greater number of Social Networking Sites friends increases the amount of supportive interactions undertaken, which in turn enhance the positive affect experienced: Social Support mediates the relationship between positive affect and Well-being. In particular, it would be the “Perceived Companionship” factor of Social Support to directly increase Life Satisfaction1. The evidence that social resources received online positively impact on one's Well-being was corroborated by Grieve, Indian, Witteveen, Tolan, and Marrington (2013), who tested the relationship between Social Connectedness, another measure of the quality of one's social network, and Well-being and found that Facebook Social Connectedness explains additional variance over the offline construct. On the other hand, a recent study (Chan, 2015) concluded that a greater deal of online interactions may in some case be detrimental to Well-being. This finding is not new in the literature: Leung and Lee (2005) showed that whereas Social Support has a positive relationship with Life Satisfaction, the latter is negatively associated with the use of the Web for social interaction. These mixed evidences keep the question open: does online support increase/decrease or not have an impact at all on one's Well-being? Caplan (2003) found that engaging in social interactions in online land may lead to developing a Problematic Internet Use. Specifically he showed that preferring online social interaction over face-to-face interaction increases the negative outcomes of Internet on one's life. Many scholars focused on the addictive potential of Social Networking Sites (Harfuch, Murguía, Lever, & Andrade, 2010; Andreassen, Torsheim, Brunborg & Palessen, 2012; Koc & Gulyagci, 2013). These evidences suggest that the usage of internet for social interaction could be linked to Problematic Internet Use or to Internet Addiction. Such a perspective opens the way to the research question: does Online Social Support lead to develop a dysfunctional usage of the Web? While Casale, Fioravanti, Flett, and Hewitt (2014) found that perceiving low Social Support increases the chances of developing a Problematic Internet Use, Wang and Wang (2013) focused both on the impact of Online and Offline Social Support on Internet Addiction. Their study carried out in Taiwan found that the former
1 The current study will refer indiscriminately to Well-being and Life Satisfaction, as previous researches treated the two constructs as equitable. See, for instance, Valkenburg, Peter, and Schouten (2006), who used Life Satisfaction scale (Diener et al., 1985) as measure of Well-being.
is positively associated with Internet addiction, whereas Offline Social Support has a negative relationship with Internet Addiction. 1.1. This study: offline and online social support, well-being and problematic internet use The present study answers Wang and Wang (2013)’s call for a research that investigates the same variables in a different cultural background. By contrast, we decided to focus on Problematic Internet Use rather than focusing on Internet Addiction2. We hypothesize that: H1. Offline Social Support (OffSS) negatively predicts Problematic Internet Use (PIU) H2. Online Social Support (OnSS) positively predicts Problematic Internet Use (PIU) In addition to address this issue, the research aims to shed light on the mixed findings in the literature: is online social support linked to Life Sastisfaction and/or to PIU? “Much remains unknown regarding the benefits and the drawbacks of online social support” (Mitchell, Lebow, Uribe, Grathouse, & Shoger, 2011, p.1858). It is here claimed that studies investigating the social dimension at stake may have led to inconsistent evidences because they treated Social Support as an unitary construct. Drawing on Wang and Wang (2013)’s distinction between Offline and Online Social Support, we propose that in order to investigate the effects of Social Support these two dimension must be considered as distinct. Furthermore, we suggest that they must be simultaneously taken into account. Namely, the present study hypothesizes a moderating effect of Offline Social Support on Online Social Support in determining the effects of the latter on Problematic Internet Use and Well-being. In doing so we agree with Swickert et al. (2002), who believe that, in studying the relationship between Internet and Social Support, moderating effects must be investigated. The same view is supported by Oh et al. (2014), who suggest that the studies about psychological outcomes of online networking may have brought to mixed findings because moderator and mediator effects were not considered. H3. OffSS is a moderator between OnSS and PIU and Life Satisfaction. We believe that the outcomes of online social support on the recipients are either positive or negative depending on how strong is his/her perception of support in the offline life. We'll refer to this view as the “social compensation vs enhancement hypothesis”, as already defined by Zywica and Danowski (2008). That is, if seeking online social support is meant to compensate for weak offline social networks, the user will tend to develop a Problematic Internet Use. H3.a. OnSS positively predicts PIU only when OffSS is low. By contrast we hypothesize that those who are strong in their perception of Offline Social Support will benefit from receiving Online Social Support (“social enhancement hypothesis”). H3.b. OnSS predicts Life Satisfaction only when OffSS is high. 2. Method 2.1. Data collection and participants Data have been gathered through an anonymous online questionnaire, available in Italian in a website made up for the study and spread via e-mail and Social Networking Sites. Considering only the respondents who fully answered the questionnaire, the sample
2 We agree with Moreno, Jelenchick, and Christakis (2013), who argue that while the term “addiction” refers to a disease implying loss of control and withdrawal symptoms and overuse, the expression of “Problematic Internet Use” broaden the concept to a usage of Internet that negatively interferes with the offline life.
E. Mazzoni et al. / Computers in Human Behavior 56 (2016) 369e374 Table 1 Dimensions considered for Offline life, Online life and control variables. Offline life
Online life
Control variables
Offline Social Support Well-being
Online Social Support Problematic Internet Use
Gender Age
consists of 535 females and 284 males ranging from 18 to 64 years (M ¼ 27.17 years). 2.2. Measures The broader research this study is part of aims to investigate the relationship between constructs referred to one's “offline life” and some dimensions related to people's online activity3, taking into account control variables (Table 1). Offline and Online Social Support have been assessed translating in Italian Wang and Wang's Offline and Online Social Support Scales (Wang & Wang, 2013). Both consist of 11 items addressing the question “How often is each of the following kinds of support available to you if you need it?” (either offline or in Internet land), the answer is given by selecting one among four points. Although Wang and Wang (2013) treated the scales as unidimensional, to create the instrument they had adopted Leung and Lee's inventory (2005), which distinguishes three factors Emotional and Informational (EI), Positive Social Interaction (PSI), Affectionate (AF). Confirmatory factor analyses with the three factors explained by the 11 items produced acceptable fits both for Offline Social Support (c2 (37) ¼ 212.36, p ¼ .00, CFI ¼ .977, RMSEA ¼ .07) and Online Social Support (c2 (38) ¼ 197.97, p ¼ .00, CFI ¼ .984, RMSEA ¼ .07). Both the scales and all their factors are reliable (Table 2). Well-being has been evaluated using the Satisfaction With Life Scale (Diener, Emmons, Larsen, & Griffin, 1985). This consists of a short 5 points likert scale (5 items, a ¼ .850). The degree of agreement to be expressed range from “agree entirely” to “disagree entirely”. Problematic Internet Use (PIU) was assessed through the Italian version of the Generalized Problematic Internet Use Scale (Caplan, 2002): the one translated and validated by Fioravanti, Primi, and Casale (2013). The participant rates based on 8 points his/her degree of agreement with the statements, which are 15 items (a ¼ .911). The analyses are run considering gender and age as control variables. The scholars who studied variables related to one's online activity did, indeed, show an effect of gender and age on them (Kraut et al., 2002; Valkenburg, Schouten, & Peter, 2005; Mottram & Fleming, 2009; Sherman, 2011). Life Satisfaction may as well be related to Gender and Age (Shmotkin, 1990), therefore the same control variables are taken into account when testing predictors of it. 2.3. Results4 All factors of OffSS and OnSS are correlated among one another (Table 3). Further, they all correlate positively with Well-Being however, while factors characterizing OffSS are negatively related to PIU, those characterizing OnSS correlate positively with it. Because of the correlation among the predictors, Hypothesis 1 and Hypothesis 2 are tested in the same regression model. The
3 Only the scales used for the sake of this specific part of the study will be reported. 4 The software used to run the analyses is IBM SPSS Statistics 21.
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control variables are entered at Step 1 (Model 1), in the second step (Model 2) OffSS factors are added and lastly the analysis is performed (Model 3) considering as predictors also the factors of OnSS (Table 4). Supporting our predictions, OffSS negatively predicts PIU and OnSS positively affects it. As to the former, the dimensions that impact on the dependent variable are Positive Social Interaction and Affectionate. With respect to OnSS only the Positive Social Interaction factor predicts PIU. To test the moderation hypothesis (H3) of OffSS on the outcomes of OnSS, we centered all the factors these constructs consist of in order to prevent multicollinearity and computed 9 products with the obtained variables, considering each possible interaction among the factors of the predictor and the moderator. H3.a. is tested running a hierarchical multiple regression having PIU as dependent variable. The control variables are entered at step 1, at step 2 are added, centered, all the factors of OffSS and OnSS, respectively, and finally, at step 3, the interaction terms are considered (Table 5). The results show that entering these products improves the model and that the only significant interaction is the one between OffSS and OnSS Affectionate. The same procedure followed for H3.a is used to test H3.b. The hierarchical multiple regression revealed main positive effects of Emotional-Informational and Affectionate OffSS and of Positive Interaction OnSS on Well-being (Table 6). Three interaction terms are significant: - OffSS Emotional and Informational OnSS Emotional and Informational; - OffSS Emotional and Informational OnSS Affectionate; - OffSS Affectionate OnSS Emotional and Informational. Nevertheless entering the moderation products in step 3 does not significantly change R2, therefore assuming that the moderation occurs its effects are negligible. 3. Discussion Our study aimed to shed light on mixed findings regarding the effects of Online Social Support. Our research questions were: Does Online Social Support lead to Problematic Internet Use and/or to Wellbeing? Is there a moderation of Offline Social Support on the effects of this predictor? Supporting Hypothesis 1 and 2 we found a main negative effect of Offline Social Support and a main positive effect of Online Social Support on Problematic Internet Use. We confirmed Wang and Wang (2013) and answered to their call for a repetition of the study in a Western-society. Although the Taiwanese study treated these variables as unidimensional, we analyzed the effect of each factor proposed by the original scale (Leung & Lee, 2005) and found that while Emotional-Informational and Affectionate Offline Social Support negatively predict Problematic Internet Use, this dependent variable is negatively predicted by Positive Social Interaction Online Social Support. The analysis run to test these hypotheses found, moreover, a negative effect of Age on Problematic Internet Use and did not prove any significant Gender effect.The moderation hypothesis (Hypothesis 3) has only partially been confirmed. Low levels of Affectionate Offline Social Support do indeed associate greater Affectionate Online Social Support to greater Problematic Internet Use (Hypothesis 3.a, Fig. 1), but high levels of Offline Social Support lead none of Online Social Support's factors to cause Life Satisfaction (Hypothesis 3.b). Specifically, we found that among the factors of Social Support is the Affectionate dimension, the one most related to “feeling loved”
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E. Mazzoni et al. / Computers in Human Behavior 56 (2016) 369e374 Table 2 Cronbach's Alfa of offline and online social support. Items
a
Offline Social Support Emotional and Informational Someone whose advice you really want Someone who can give you good advice about a crisis Someone who can give you information to help you understand a situation Someone you can turn to for suggestions about how to deal with a personal problem Positive Social Interaction Someone you can get together with for relaxation Someone you can do something enjoyable with Someone you can do things with to help you get your mind off things Affectionate Someone who shows you love and affection Someone who wants you and makes you feel wanted Someone who comforts sincerely Someone you can count on to listen to you when you need to talk Online social Support Emotional and Informational Someone whose advice you really want Someone who can give you good advice about a crisis Someone who can give you information to help you understand a situation Someone you can turn to for suggestions about how to deal with a personal problem Positive Social Interaction Someone you can get together with for relaxation Someone you can do something enjoyable with Someone you can do things with to help you get your mind off things Affectionate Someone who shows you love and affection Someone who wants you and makes you feel wanted Someone who comforts sincerely Someone you can count on to listen to you when you need to talk
.930 .911
.913
.905
.961 .948
.935
.938
Table 3 Correlations of the measured dimensions. Measure
1
2
3
4
5
6
7
1. 2. 3. 4. 5. 6. 7. 8.
.549** .646** .318** .183** .286** .326** .163**
.592** .247** .276** .261** .305** .192**
.179** .124** .285** .404** .220**
.709** .804** .083* .189**
.708** .107** .213**
.112** .161**
.257**
Offline EI Offline PSI Offline AF Online EI Online PSI Online AF Well-being Problematic Internet Use *
Note p < .05
**
p < .01.
Table 4 Hierarchical multiple regression, dependent variable: Problematic Internet Use. Model 1
b Gender Age Offline EI Offline PSI Offline AF Online EI Online PSI Online AF R2 F for change in R2 Note *p < .05
**
8
.057 .123**
.018 7.580**
Model 2 t 1.649 3.551
b .013 .155** .020 .131** .132**
.077 17.285**
Table 5 Hierarchical multiple regression, dependent variable: Problematic Internet Use. Model 1
Model 3 t .217 2.945 1.567 4.188 2.409
b .007 .100** .073 .183** .117* .117 .161** .033 .147 22.177**
t .217 2.945 1.567 4.188 2.409 1.945 3.204 .545
p < .01.
that plays a role in determining the outcomes of receiving Support online. Those who seek in Internet land the affection that they lack in the “real life” are the ones who are harmed by receiving online this kind of support. For people who score strong in Affectionate Offline Social Support the tendency seems to be just the opposite:
Gender Age Offline EI Offline PSI Offline AF Online EI Online PSI Online AF OffEI OnEI OffEI OnPSI OffEI OnAF OffPSI OnEI OffPSI OnPSI OffPSI OnAF OffAF OnEI OffAF OnPSI OffAF OffAF R2 F for change in R2 Note *p < .05
**
Model 2
Model 3
b
t
b
t
.057 .123**
1.649 3.551
.007 .100** .073 .183** .117* .117 .161** .033
.217 2.945 1.567 4.188 2.409 1.945 3.204 .545
.018 7.580**
p < .01.
.077 17.285**
b .004 .109** .085 .161** .154** .124 .141** .071 .126 .089 .114 .096 .035 .094 .138 .012 .215** .147 22.177**
t .112 3.205 1.758 3.592 2.997 2.018 2.770 1.126 1.609 1.340 1.429 1.249 .596 1.217 1.852 .180 2.950
E. Mazzoni et al. / Computers in Human Behavior 56 (2016) 369e374 Table 6 Hierarchical multiple regression, dependent variable: Life Satisfaction.
Gender Age Offline EI Offline PSI Offline AF Online EI Online PSI Online AF OffEI OnEI OffEI OnPSI OffEI OnAF OffPSI OnEI OffPSI OnPSI OffPSI OnAF OffAF OnEI OffAF OnPSI OffAF OffAF R2 F for change in R2 Note *p < .05
**
Model 1
Model 2
b
b
.006 .067
.004 1.839
t .174 1.907
Model 3 t
**
.099 .113** .129** .075 .306** .050 .115* .041
.201 33.109**
3.039 3.435 2.878 1.768 6.532 .852 2.353 .695
b
t **
.103 .111** .146** .071 .293** .059 .113* .038 .197* .005 .177* .008 .073 .070 -.166* .048 .076 .213 1.402
3.160 3.362 3.098 1.617 5.852 .980 2.285 .614 2.598 .081 2.275 .113 1.280 .938 2.298 .712 1.075
p < .01.
Fig. 1. The relation between Affectionate Social Support (Online and Offline) and Problematic Internet Use.
higher levels of Affectionate Online Social Support are associated to lower levels of Problematic Internet Use.We named our moderation hypothesis “compensation vs enhancement hypothesis”. This view is not new in the literature regarding psychological aspects of the online activity. Zywica and Danowski (2008) proposed that “those with more developed offline social networks enhance them with more extensive online social networks” (Social Enhancement), whereas the Social Compensation hypothesis suggests that “those who perceive their offline social networks to be inadequate compensate for them with more extensive online social networks” (p.3). While these scholars found that the compensation and the enhancement do occur they did not investigate their effects. Kuss and Griffiths (2011) called for a better understanding of these phenomena and claimed that both would lead to develop a dysfunctional usage of the Web. Although they did not refer at the phenomenon with this label, Indian and Grieve (2014) provided evidences that Social Compensation occurs with respect to Social Support: socially anxious individuals score lower in Social Support but similarly in Facebook Social Support to non-anxious individuals. The scholars further showed that while for the former Facebook Social Support explained additional variance in WellBeing, for the latter the support received in the SNS does not add variance over and above Offline Social Support. This view clashes
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with other studies that claim and demonstrate that the usage of Web for social interaction may be detrimental for people with weak offline social networks. “It could be suggested that those with poor social connectedness are more likely to use the Internet for a social purpose, however they do not necessarily receive psychosocial benefits for doing this” (McIntyre, Wiener, & Saliba, 2015, p.570). Our data display a similar discrepancy regarding the outcomes of Social Compensation. Namely, we found a main effect of Positive Social Interaction Online Social Support on Life Satisfaction and at the same time that this factor positively correlates with Problematic Internet Use. The current study contributes to the debate on the outcomes of social support received online by suggesting that Well-being and Problematic Internet Use can be both consequences of seeking social resources in Internet land. We showed that moderating variables play a fundamental role in determining what consequences the usage of Internet leads to and, namely, that compensating for weak social networks increases the chances of developing a dysfunctional Internet use and that having strong networks buffers this risk. 4. Conclusion The current study belongs to the stream of researches that reckon that the quality of the social interactions undertaken online is more critical of the frequency of usage on the psychological outcomes of Internet activity (Oh et al., 2014). Specifically, we claim that the motives that drive Internet usage are crucial in determining its effects. In addition, our research brings some methodological contribution suggesting that researches investigating psychological outcomes of Internet usage may have led to inconsistent findings because they did not consider that people develop an “online identity” (Kim, Zheng, & Gupta, 2011), and therefore did not appraise certain constructs (such as Social Support) in their offline and online variations. Furthermore, we agree with Oh et al. (2014) that “Prior work combined dimensions of social support in a unitary construct. However, a major limitation with such application is overseeing the differences in the gravity of each factor on enhancing (..) life satisfaction” (Oh et al., 2014, p.76). Finally, we propose that from our study some practical implication could be inferred. The possibility given by new technologies (smartphones, tablets, etc.) to access the internet wherever, whenever, have led to an increasing concern about the effects of such massive usage. Our findings show that the Web is not addictive or risky per-se. By contrast, its effects are strongly related to the user's offline activity. We therefore believe that interventions aimed to reduce dysfunctional outcomes of Internet should work on fostering aspects of the offline life rather than focus on the usage of devices to access the Web. 4.1. Limits The current research presents some limits that future scholars interested in the topic should address. The study we conducted is cross-sectional and in order to confirm the causal links we think that a longitudinal study should be carried out. Moreover although our population are the people who use the Internet, the sample is not representative of it because the channels through which the questionnaire has been spread out are mainly connected to the university environment. Finally, there could have been a further bias in sorting the participants. The research-team thoroughly elaborated a type of feedback to be given to the respondents that would not influence the kind of person who would answer the questionnaire: providing, for instance, the participants with a
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feedback regarding their risk of developing a Problematic Internet Use would have attracted people who feel themselves to have some issues in their usage of Internet. The compromise found has been telling them how much they use internet comparing to other people belonging to their same geographical area. The sample could be still have been biased by such an offered feedback. References Andreassen, C. S., Torsheim, T., Brunborg, G. S., & Pallesen, S. (2012). Development of a facebook addiction scale 1, 2. Psychological reports, 110(2), 501e517. Caplan, S. E. (2002). Problematic internet use and psychosocial well-being: Development of a theory-based cognitiveebehavioral measurement instrument. Computers in human behavior, 18(5), 553e575. Caplan, S. E. (2003). Preference for online social interaction a theory of problematic internet use and psychosocial well-being. Communication Research, 30(6), 625e648. Casale, S., Fioravanti, G., Flett, G. L., & Hewitt, P. L. (2014). From socially prescribed perfectionism to problematic use of internet communicative services: The mediating roles of perceived social support and the fear of negative evaluation. Addictive behaviors, 39(12), 1816e1822. Chan, M. (2015). Multimodal connectedness and quality of life: Examining the influences of technology adoption and interpersonal communication on wellbeing across the life span. Journal of Computer-Mediated Communication, 20(1), 3e18. Cohen, S. E., & Syme, S. (1985). Social support and health. Academic Press. Cohen, S., & Wills, T. A. (1985). Stress, social support, and the buffering hypothesis. Psychological bulletin, 98(2), 310. Diener, E. D., Emmons, R. A., Larsen, R. J., & Griffin, S. (1985). The satisfaction with life scale. Journal of personality assessment, 49(1), 71e75. Eastin, M. S., & LaRose, R. (2005). Alt. support: Modeling social support online. Computers in Human Behavior, 21(6), 977e992. Ekbia, H., & Nardi, B. (2012). Inverse instrumentality: how technologies objectify patients and players. In P. Leonardi, B. Nardi, & J. Kallinikos (Eds.), Materiality and organizing: Social interaction in a technological world (pp. 157e176). Oxford: Oxford University Press. Fioravanti, G., Primi, C., & Casale, S. (2013). Psychometric evaluation of the generalized problematic internet use scale 2 in an italian sample. Cyberpsychology, Behavior and Social Networking, 16(10), 761e766. Frozzi, G., & Mazzoni, E. (2011). On the importance of social network sites in the transitions which characterize ‘Emerging adulthood’. ICST Transactions on eEducation and e-Learning, 11, 1e11. Grieve, R., Indian, M., Witteveen, K., Tolan, G. A., & Marrington, J. (2013). Face-toface or Facebook: Can social connectedness be derived online? Computers in Human Behavior, 29(3), 604e609. Harfuch, M. F. H., Murguía, M. P. P., Lever, J. P., & Andrade, D. Z. (2010). Facebook addiction related to low self-esteem, depression and lack of social skills. Psicología Iberoamericana, 18(1), 6e18. Indian, M., & Grieve, R. (2014). When facebook is easier than face-to-face: Social support derived from facebook in socially anxious individuals. Personality and Individual Differences, 59, 102e106. Kim, H. W., Zheng, J. R., & Gupta, S. (2011). Examining knowledge contribution from the perspective of an online identity in blogging communities. Computers in Human Behavior, 27(5), 1760e1770. Koc, M., & Gulyagci, S. (2013). Facebook addiction among turkish college students: The role of psychological health, demographic, and usage characteristics.
Cyberpsychology, Behavior, and Social Networking, 16(4), 279e284. Kraut, R., Kiesler, S., Boneva, B., Cummings, J., Helgeson, V., & Crawford, A. (2002). Internet paradox revisited. Journal of social issues, 58(1), 49e74. Kuss, D. J., & Griffiths, M. D. (2011). Online social networking and addictionda review of the psychological literature. International journal of environmental research and public health, 8(9), 3528e3552. Leung, L., & Lee, P. S. (2005). Multiple determinants of life quality: The roles of internet activities, use of new media, social support, and leisure activities. Telematics and Informatics, 22(3), 161e180. Mazzoni, E., Baiocco, L., & Benvenuti, M. (2015). Online life: Between inverse instrumentality and functional organs. MOJ Addiction Medicine & Therapy, 1(2), 00008. Mazzoni, E., & Iannone, M. (2014). From high school to university: Impact of social networking sites on social capital in the transitions of emerging adults. British Journal of Educational Technology, 45(2), 303e315. McIntyre, E., Wiener, K. K., & Saliba, A. J. (2015). Compulsive internet use and relations between social connectedness, and introversion. Computers in Human Behavior, 48, 569e574. Mitchell, M. E., Lebow, J. R., Uribe, R., Grathouse, H., & Shoger, W. (2011). Internet use, happiness, social support and introversion: A more fine grained analysis of person variables and internet activity. Computers in Human Behavior, 27(5), 1857e1861. Moreno, M. A., Jelenchick, L. A., & Christakis, D. A. (2013). Problematic internet use among older adolescents: A conceptual framework. Computers in Human Behavior, 29(4), 1879e1887. Mottram, A. J., & Fleming, M. J. (2009). Extraversion, impulsivity, and online group membership as predictors of problematic internet use. CyberPsychology & Behavior, 12(3), 319e321. Oh, H. J., Ozkaya, E., & LaRose, R. (2014). How does online social networking enhance life satisfaction? the relationships among online supportive interaction, affect, perceived social support, sense of community, and life satisfaction. Computers in Human Behavior, 30, 69e78. Rozzell, B., Piercy, C. W., Carr, C. T., King, S., Lane, B. L., Tornes, M., et al. (2014). Notification pending: Online social support from close and nonclose relational ties via Facebook. Computers in Human Behavior, 38, 272e280. Sherman, E. (2011). Facebook Addiction: Factors Influencing an Individual's Addiction. Shmotkin, D. (1990). Subjective well-being as a function of age and gender: A multivariate look for differentiated trends. Social Indicators Research, 23(3), 201e230. Swickert, R. J., Hittner, J. B., Harris, J. L., & Herring, J. A. (2002). Relationships among internet use, personality, and social support. Computers in human behavior, 18(4), 437e451. Turkle, S. (2012). Connected but alone? [Video file]. Retrieved from 2012, February http://www.ted.com/talks/sherry_turkle_alone_together?language¼en. Valkenburg, P. M., Peter, J., & Schouten, A. P. (2006). Friend networking sites and their relationship to adolescents' well-being and social self-esteem. CyberPsychology & Behavior, 9(5), 584e590. Valkenburg, P. M., Schouten, A. P., & Peter, J. (2005). Adolescents' identity experiments on the Internet. New Media & Society, 7(3), 383e402. Wang, E. S. T., & Wang, M. C. H. (2013). Social support and social interaction ties on internet addiction: Integrating online and offline contexts. Cyberpsychology, Behavior, and Social Networking, 16(11), 843e849. Zywica, J., & Danowski, J. (2008). The faces of Facebookers: Investigating social enhancement and social compensation hypotheses; predicting Facebook™ and offline popularity from sociability and self-esteem, and mapping the meanings of popularity with semantic networks. Journal of Computer-Mediated Communication, 14(1), 1e34.
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Full length article
Is internet the cherry on top or a crutch? Offline social support as moderator of the outcomes of online social support on Problematic Internet Use Elvis Mazzoni a, *, Lucia Baiocco a, Davide Cannata a, Isabel Dimas b a b
Alma Mater Studiorum - University of Bologna Italy University of Aveiro Portugal
a r t i c l e i n f o
a b s t r a c t
Article history: Received 22 January 2015 Received in revised form 2 November 2015 Accepted 17 November 2015 Available online 18 December 2015
The study is part of a research whose goal is identifying what predictors determine either a positive or a dysfunctional use of Internet. The factor at stake is here social support. Specifically our study, carried out through an online questionnaire, hypothesized a moderation of Offline Social Support in the relationship between Online Social Support, Problematic Internet Use, and Life Satisfaction. The study found that while Offline Social Support reduces the chances of developing a Problematic Internet Use, Online Social Support increases them. Furthermore the data supported the moderation of Offline Social Support in the outcomes of Online Social Support: when the first is low, as the latter increases the Problematic Internet Use gets higher; when Offline Social Support is high, an increase in Online Social Support determines a decrease in Problematic Internet Use. By contrast the moderation of Offline Social Support on the relationship between Online Social Support and Life Satisfaction was not confirmed. Our research show that when investigating psychological constructs related to Internet activity these must be considered in their offline and online variations to provide an answer to the debate on psychological outcomes of undertaking social interactions in Internet land. Our results suggest that the usage of the Web may become dysfunctional when it is meant to compensate for lacks of the “offline life”. © 2015 Elsevier Ltd. All rights reserved.
Keywords: Online social support Offline social support Problematic internet use Life satisfaction Compensation Enhancement
1. Introduction Internet is that powerful tool that allows us to have a conversation with someone living some time zones away as if he/she were right there, sitting in front of us. As well as this, some people, as Sherry Turkle (2012) in her Ted talk, claim that communications in the era of the Web 2.0 make us more lonely than ever. The findings here reported are part of a study that considers the Web as an undeniable resource, a tool that helps people achieving their scopes and agrees with defining Internet a functional organ (Frozzi & Mazzoni, 2011; Mazzoni & Iannone, 2014; Mazzoni, Baiocco, & Benvenuti, 2015). In addition, we believe that the risk associated with Internet usage is the possibility for the user to develop a dysfunctional use. In this case, the Web loses its function of tool and, by contrast, it is the user to metaphorically become the instrument of Internet: such phenomenon is called inverse
* Corresponding author. P.zza A. Moro, 90, 47521 Cesena, FC, Italy. E-mail address: [email protected] (E. Mazzoni). http://dx.doi.org/10.1016/j.chb.2015.11.032 0747-5632/© 2015 Elsevier Ltd. All rights reserved.
instrumentality (Ekbia & Nardi, 2012). The broad goal of the research is identifying what are the key factors that determine whether Internet represents a resource for the person or it rather becomes a problem impairing one's quality of life. The part of the study here considered focuses on social support as a predictor of the quality of one's use of internet. Social Support has been defined as “the resources provided by another person” (Cohen & Syme, 1985, p.4). Can these resources be conveyed through the Internet? A growing stream of researchers have investigated the relationship between Internet usage and Social Support. Some scholars tried to verify whether making use of the Web affects how much supported people feel. Swickert, Hittner, Harris, and Herring (2002) showed that there isn't a main effect of accessing the Web on the perception of Social Support. They further found that personality plays a moderating role and Internet Usage would enhance perceived Social Support only for users with certain personality traits. Eastin and LaRose (2005) found that the size of one's Online Social Network has a positive relation with the perception of Social Support. A recent study of Rozzell, Piercy, Carr, King, Lane, Tornes, Johnson and Wright (2014) investigated the
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perceived support internet users feel from close and nonclose ties, respectively. Their findings showed that both type of online social connections provide equitable support. Taken together these studies give a positive answer: the usage of Internet does have the potential of enhancing perceived Social Support. As a psychological resource, Social Support has been proven to buffer the effect of stressful events and to have a main effect on one's well-being (Cohen & Wills, 1985). Can we assume that because the Web boosts the perception of Social Support, making use of it leads to an increased well-being? Kim and Lee (2011) followed this line of reasoning and hypothesized a mediating effect of Social Support between the number of Facebook friends and one's Well-being. While the main positive effect of the number of Facebook friends on the Well-being was found, the data did not support the mediation hypothesis. By contrast, a recent study of Oh, Ozkaya, and LaRose (2014) lead to opposite findings testing a similar hypothesis. The path analysis they performed on a sample of 339 adults showed that having a greater number of Social Networking Sites friends increases the amount of supportive interactions undertaken, which in turn enhance the positive affect experienced: Social Support mediates the relationship between positive affect and Well-being. In particular, it would be the “Perceived Companionship” factor of Social Support to directly increase Life Satisfaction1. The evidence that social resources received online positively impact on one's Well-being was corroborated by Grieve, Indian, Witteveen, Tolan, and Marrington (2013), who tested the relationship between Social Connectedness, another measure of the quality of one's social network, and Well-being and found that Facebook Social Connectedness explains additional variance over the offline construct. On the other hand, a recent study (Chan, 2015) concluded that a greater deal of online interactions may in some case be detrimental to Well-being. This finding is not new in the literature: Leung and Lee (2005) showed that whereas Social Support has a positive relationship with Life Satisfaction, the latter is negatively associated with the use of the Web for social interaction. These mixed evidences keep the question open: does online support increase/decrease or not have an impact at all on one's Well-being? Caplan (2003) found that engaging in social interactions in online land may lead to developing a Problematic Internet Use. Specifically he showed that preferring online social interaction over face-to-face interaction increases the negative outcomes of Internet on one's life. Many scholars focused on the addictive potential of Social Networking Sites (Harfuch, Murguía, Lever, & Andrade, 2010; Andreassen, Torsheim, Brunborg & Palessen, 2012; Koc & Gulyagci, 2013). These evidences suggest that the usage of internet for social interaction could be linked to Problematic Internet Use or to Internet Addiction. Such a perspective opens the way to the research question: does Online Social Support lead to develop a dysfunctional usage of the Web? While Casale, Fioravanti, Flett, and Hewitt (2014) found that perceiving low Social Support increases the chances of developing a Problematic Internet Use, Wang and Wang (2013) focused both on the impact of Online and Offline Social Support on Internet Addiction. Their study carried out in Taiwan found that the former
1 The current study will refer indiscriminately to Well-being and Life Satisfaction, as previous researches treated the two constructs as equitable. See, for instance, Valkenburg, Peter, and Schouten (2006), who used Life Satisfaction scale (Diener et al., 1985) as measure of Well-being.
is positively associated with Internet addiction, whereas Offline Social Support has a negative relationship with Internet Addiction. 1.1. This study: offline and online social support, well-being and problematic internet use The present study answers Wang and Wang (2013)’s call for a research that investigates the same variables in a different cultural background. By contrast, we decided to focus on Problematic Internet Use rather than focusing on Internet Addiction2. We hypothesize that: H1. Offline Social Support (OffSS) negatively predicts Problematic Internet Use (PIU) H2. Online Social Support (OnSS) positively predicts Problematic Internet Use (PIU) In addition to address this issue, the research aims to shed light on the mixed findings in the literature: is online social support linked to Life Sastisfaction and/or to PIU? “Much remains unknown regarding the benefits and the drawbacks of online social support” (Mitchell, Lebow, Uribe, Grathouse, & Shoger, 2011, p.1858). It is here claimed that studies investigating the social dimension at stake may have led to inconsistent evidences because they treated Social Support as an unitary construct. Drawing on Wang and Wang (2013)’s distinction between Offline and Online Social Support, we propose that in order to investigate the effects of Social Support these two dimension must be considered as distinct. Furthermore, we suggest that they must be simultaneously taken into account. Namely, the present study hypothesizes a moderating effect of Offline Social Support on Online Social Support in determining the effects of the latter on Problematic Internet Use and Well-being. In doing so we agree with Swickert et al. (2002), who believe that, in studying the relationship between Internet and Social Support, moderating effects must be investigated. The same view is supported by Oh et al. (2014), who suggest that the studies about psychological outcomes of online networking may have brought to mixed findings because moderator and mediator effects were not considered. H3. OffSS is a moderator between OnSS and PIU and Life Satisfaction. We believe that the outcomes of online social support on the recipients are either positive or negative depending on how strong is his/her perception of support in the offline life. We'll refer to this view as the “social compensation vs enhancement hypothesis”, as already defined by Zywica and Danowski (2008). That is, if seeking online social support is meant to compensate for weak offline social networks, the user will tend to develop a Problematic Internet Use. H3.a. OnSS positively predicts PIU only when OffSS is low. By contrast we hypothesize that those who are strong in their perception of Offline Social Support will benefit from receiving Online Social Support (“social enhancement hypothesis”). H3.b. OnSS predicts Life Satisfaction only when OffSS is high. 2. Method 2.1. Data collection and participants Data have been gathered through an anonymous online questionnaire, available in Italian in a website made up for the study and spread via e-mail and Social Networking Sites. Considering only the respondents who fully answered the questionnaire, the sample
2 We agree with Moreno, Jelenchick, and Christakis (2013), who argue that while the term “addiction” refers to a disease implying loss of control and withdrawal symptoms and overuse, the expression of “Problematic Internet Use” broaden the concept to a usage of Internet that negatively interferes with the offline life.
E. Mazzoni et al. / Computers in Human Behavior 56 (2016) 369e374 Table 1 Dimensions considered for Offline life, Online life and control variables. Offline life
Online life
Control variables
Offline Social Support Well-being
Online Social Support Problematic Internet Use
Gender Age
consists of 535 females and 284 males ranging from 18 to 64 years (M ¼ 27.17 years). 2.2. Measures The broader research this study is part of aims to investigate the relationship between constructs referred to one's “offline life” and some dimensions related to people's online activity3, taking into account control variables (Table 1). Offline and Online Social Support have been assessed translating in Italian Wang and Wang's Offline and Online Social Support Scales (Wang & Wang, 2013). Both consist of 11 items addressing the question “How often is each of the following kinds of support available to you if you need it?” (either offline or in Internet land), the answer is given by selecting one among four points. Although Wang and Wang (2013) treated the scales as unidimensional, to create the instrument they had adopted Leung and Lee's inventory (2005), which distinguishes three factors Emotional and Informational (EI), Positive Social Interaction (PSI), Affectionate (AF). Confirmatory factor analyses with the three factors explained by the 11 items produced acceptable fits both for Offline Social Support (c2 (37) ¼ 212.36, p ¼ .00, CFI ¼ .977, RMSEA ¼ .07) and Online Social Support (c2 (38) ¼ 197.97, p ¼ .00, CFI ¼ .984, RMSEA ¼ .07). Both the scales and all their factors are reliable (Table 2). Well-being has been evaluated using the Satisfaction With Life Scale (Diener, Emmons, Larsen, & Griffin, 1985). This consists of a short 5 points likert scale (5 items, a ¼ .850). The degree of agreement to be expressed range from “agree entirely” to “disagree entirely”. Problematic Internet Use (PIU) was assessed through the Italian version of the Generalized Problematic Internet Use Scale (Caplan, 2002): the one translated and validated by Fioravanti, Primi, and Casale (2013). The participant rates based on 8 points his/her degree of agreement with the statements, which are 15 items (a ¼ .911). The analyses are run considering gender and age as control variables. The scholars who studied variables related to one's online activity did, indeed, show an effect of gender and age on them (Kraut et al., 2002; Valkenburg, Schouten, & Peter, 2005; Mottram & Fleming, 2009; Sherman, 2011). Life Satisfaction may as well be related to Gender and Age (Shmotkin, 1990), therefore the same control variables are taken into account when testing predictors of it. 2.3. Results4 All factors of OffSS and OnSS are correlated among one another (Table 3). Further, they all correlate positively with Well-Being however, while factors characterizing OffSS are negatively related to PIU, those characterizing OnSS correlate positively with it. Because of the correlation among the predictors, Hypothesis 1 and Hypothesis 2 are tested in the same regression model. The
3 Only the scales used for the sake of this specific part of the study will be reported. 4 The software used to run the analyses is IBM SPSS Statistics 21.
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control variables are entered at Step 1 (Model 1), in the second step (Model 2) OffSS factors are added and lastly the analysis is performed (Model 3) considering as predictors also the factors of OnSS (Table 4). Supporting our predictions, OffSS negatively predicts PIU and OnSS positively affects it. As to the former, the dimensions that impact on the dependent variable are Positive Social Interaction and Affectionate. With respect to OnSS only the Positive Social Interaction factor predicts PIU. To test the moderation hypothesis (H3) of OffSS on the outcomes of OnSS, we centered all the factors these constructs consist of in order to prevent multicollinearity and computed 9 products with the obtained variables, considering each possible interaction among the factors of the predictor and the moderator. H3.a. is tested running a hierarchical multiple regression having PIU as dependent variable. The control variables are entered at step 1, at step 2 are added, centered, all the factors of OffSS and OnSS, respectively, and finally, at step 3, the interaction terms are considered (Table 5). The results show that entering these products improves the model and that the only significant interaction is the one between OffSS and OnSS Affectionate. The same procedure followed for H3.a is used to test H3.b. The hierarchical multiple regression revealed main positive effects of Emotional-Informational and Affectionate OffSS and of Positive Interaction OnSS on Well-being (Table 6). Three interaction terms are significant: - OffSS Emotional and Informational OnSS Emotional and Informational; - OffSS Emotional and Informational OnSS Affectionate; - OffSS Affectionate OnSS Emotional and Informational. Nevertheless entering the moderation products in step 3 does not significantly change R2, therefore assuming that the moderation occurs its effects are negligible. 3. Discussion Our study aimed to shed light on mixed findings regarding the effects of Online Social Support. Our research questions were: Does Online Social Support lead to Problematic Internet Use and/or to Wellbeing? Is there a moderation of Offline Social Support on the effects of this predictor? Supporting Hypothesis 1 and 2 we found a main negative effect of Offline Social Support and a main positive effect of Online Social Support on Problematic Internet Use. We confirmed Wang and Wang (2013) and answered to their call for a repetition of the study in a Western-society. Although the Taiwanese study treated these variables as unidimensional, we analyzed the effect of each factor proposed by the original scale (Leung & Lee, 2005) and found that while Emotional-Informational and Affectionate Offline Social Support negatively predict Problematic Internet Use, this dependent variable is negatively predicted by Positive Social Interaction Online Social Support. The analysis run to test these hypotheses found, moreover, a negative effect of Age on Problematic Internet Use and did not prove any significant Gender effect.The moderation hypothesis (Hypothesis 3) has only partially been confirmed. Low levels of Affectionate Offline Social Support do indeed associate greater Affectionate Online Social Support to greater Problematic Internet Use (Hypothesis 3.a, Fig. 1), but high levels of Offline Social Support lead none of Online Social Support's factors to cause Life Satisfaction (Hypothesis 3.b). Specifically, we found that among the factors of Social Support is the Affectionate dimension, the one most related to “feeling loved”
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E. Mazzoni et al. / Computers in Human Behavior 56 (2016) 369e374 Table 2 Cronbach's Alfa of offline and online social support. Items
a
Offline Social Support Emotional and Informational Someone whose advice you really want Someone who can give you good advice about a crisis Someone who can give you information to help you understand a situation Someone you can turn to for suggestions about how to deal with a personal problem Positive Social Interaction Someone you can get together with for relaxation Someone you can do something enjoyable with Someone you can do things with to help you get your mind off things Affectionate Someone who shows you love and affection Someone who wants you and makes you feel wanted Someone who comforts sincerely Someone you can count on to listen to you when you need to talk Online social Support Emotional and Informational Someone whose advice you really want Someone who can give you good advice about a crisis Someone who can give you information to help you understand a situation Someone you can turn to for suggestions about how to deal with a personal problem Positive Social Interaction Someone you can get together with for relaxation Someone you can do something enjoyable with Someone you can do things with to help you get your mind off things Affectionate Someone who shows you love and affection Someone who wants you and makes you feel wanted Someone who comforts sincerely Someone you can count on to listen to you when you need to talk
.930 .911
.913
.905
.961 .948
.935
.938
Table 3 Correlations of the measured dimensions. Measure
1
2
3
4
5
6
7
1. 2. 3. 4. 5. 6. 7. 8.
.549** .646** .318** .183** .286** .326** .163**
.592** .247** .276** .261** .305** .192**
.179** .124** .285** .404** .220**
.709** .804** .083* .189**
.708** .107** .213**
.112** .161**
.257**
Offline EI Offline PSI Offline AF Online EI Online PSI Online AF Well-being Problematic Internet Use *
Note p < .05
**
p < .01.
Table 4 Hierarchical multiple regression, dependent variable: Problematic Internet Use. Model 1
b Gender Age Offline EI Offline PSI Offline AF Online EI Online PSI Online AF R2 F for change in R2 Note *p < .05
**
8
.057 .123**
.018 7.580**
Model 2 t 1.649 3.551
b .013 .155** .020 .131** .132**
.077 17.285**
Table 5 Hierarchical multiple regression, dependent variable: Problematic Internet Use. Model 1
Model 3 t .217 2.945 1.567 4.188 2.409
b .007 .100** .073 .183** .117* .117 .161** .033 .147 22.177**
t .217 2.945 1.567 4.188 2.409 1.945 3.204 .545
p < .01.
that plays a role in determining the outcomes of receiving Support online. Those who seek in Internet land the affection that they lack in the “real life” are the ones who are harmed by receiving online this kind of support. For people who score strong in Affectionate Offline Social Support the tendency seems to be just the opposite:
Gender Age Offline EI Offline PSI Offline AF Online EI Online PSI Online AF OffEI OnEI OffEI OnPSI OffEI OnAF OffPSI OnEI OffPSI OnPSI OffPSI OnAF OffAF OnEI OffAF OnPSI OffAF OffAF R2 F for change in R2 Note *p < .05
**
Model 2
Model 3
b
t
b
t
.057 .123**
1.649 3.551
.007 .100** .073 .183** .117* .117 .161** .033
.217 2.945 1.567 4.188 2.409 1.945 3.204 .545
.018 7.580**
p < .01.
.077 17.285**
b .004 .109** .085 .161** .154** .124 .141** .071 .126 .089 .114 .096 .035 .094 .138 .012 .215** .147 22.177**
t .112 3.205 1.758 3.592 2.997 2.018 2.770 1.126 1.609 1.340 1.429 1.249 .596 1.217 1.852 .180 2.950
E. Mazzoni et al. / Computers in Human Behavior 56 (2016) 369e374 Table 6 Hierarchical multiple regression, dependent variable: Life Satisfaction.
Gender Age Offline EI Offline PSI Offline AF Online EI Online PSI Online AF OffEI OnEI OffEI OnPSI OffEI OnAF OffPSI OnEI OffPSI OnPSI OffPSI OnAF OffAF OnEI OffAF OnPSI OffAF OffAF R2 F for change in R2 Note *p < .05
**
Model 1
Model 2
b
b
.006 .067
.004 1.839
t .174 1.907
Model 3 t
**
.099 .113** .129** .075 .306** .050 .115* .041
.201 33.109**
3.039 3.435 2.878 1.768 6.532 .852 2.353 .695
b
t **
.103 .111** .146** .071 .293** .059 .113* .038 .197* .005 .177* .008 .073 .070 -.166* .048 .076 .213 1.402
3.160 3.362 3.098 1.617 5.852 .980 2.285 .614 2.598 .081 2.275 .113 1.280 .938 2.298 .712 1.075
p < .01.
Fig. 1. The relation between Affectionate Social Support (Online and Offline) and Problematic Internet Use.
higher levels of Affectionate Online Social Support are associated to lower levels of Problematic Internet Use.We named our moderation hypothesis “compensation vs enhancement hypothesis”. This view is not new in the literature regarding psychological aspects of the online activity. Zywica and Danowski (2008) proposed that “those with more developed offline social networks enhance them with more extensive online social networks” (Social Enhancement), whereas the Social Compensation hypothesis suggests that “those who perceive their offline social networks to be inadequate compensate for them with more extensive online social networks” (p.3). While these scholars found that the compensation and the enhancement do occur they did not investigate their effects. Kuss and Griffiths (2011) called for a better understanding of these phenomena and claimed that both would lead to develop a dysfunctional usage of the Web. Although they did not refer at the phenomenon with this label, Indian and Grieve (2014) provided evidences that Social Compensation occurs with respect to Social Support: socially anxious individuals score lower in Social Support but similarly in Facebook Social Support to non-anxious individuals. The scholars further showed that while for the former Facebook Social Support explained additional variance in WellBeing, for the latter the support received in the SNS does not add variance over and above Offline Social Support. This view clashes
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with other studies that claim and demonstrate that the usage of Web for social interaction may be detrimental for people with weak offline social networks. “It could be suggested that those with poor social connectedness are more likely to use the Internet for a social purpose, however they do not necessarily receive psychosocial benefits for doing this” (McIntyre, Wiener, & Saliba, 2015, p.570). Our data display a similar discrepancy regarding the outcomes of Social Compensation. Namely, we found a main effect of Positive Social Interaction Online Social Support on Life Satisfaction and at the same time that this factor positively correlates with Problematic Internet Use. The current study contributes to the debate on the outcomes of social support received online by suggesting that Well-being and Problematic Internet Use can be both consequences of seeking social resources in Internet land. We showed that moderating variables play a fundamental role in determining what consequences the usage of Internet leads to and, namely, that compensating for weak social networks increases the chances of developing a dysfunctional Internet use and that having strong networks buffers this risk. 4. Conclusion The current study belongs to the stream of researches that reckon that the quality of the social interactions undertaken online is more critical of the frequency of usage on the psychological outcomes of Internet activity (Oh et al., 2014). Specifically, we claim that the motives that drive Internet usage are crucial in determining its effects. In addition, our research brings some methodological contribution suggesting that researches investigating psychological outcomes of Internet usage may have led to inconsistent findings because they did not consider that people develop an “online identity” (Kim, Zheng, & Gupta, 2011), and therefore did not appraise certain constructs (such as Social Support) in their offline and online variations. Furthermore, we agree with Oh et al. (2014) that “Prior work combined dimensions of social support in a unitary construct. However, a major limitation with such application is overseeing the differences in the gravity of each factor on enhancing (..) life satisfaction” (Oh et al., 2014, p.76). Finally, we propose that from our study some practical implication could be inferred. The possibility given by new technologies (smartphones, tablets, etc.) to access the internet wherever, whenever, have led to an increasing concern about the effects of such massive usage. Our findings show that the Web is not addictive or risky per-se. By contrast, its effects are strongly related to the user's offline activity. We therefore believe that interventions aimed to reduce dysfunctional outcomes of Internet should work on fostering aspects of the offline life rather than focus on the usage of devices to access the Web. 4.1. Limits The current research presents some limits that future scholars interested in the topic should address. The study we conducted is cross-sectional and in order to confirm the causal links we think that a longitudinal study should be carried out. Moreover although our population are the people who use the Internet, the sample is not representative of it because the channels through which the questionnaire has been spread out are mainly connected to the university environment. Finally, there could have been a further bias in sorting the participants. The research-team thoroughly elaborated a type of feedback to be given to the respondents that would not influence the kind of person who would answer the questionnaire: providing, for instance, the participants with a
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