Journal Search Engine
Search Advanced Search Adode Reader(link)
Download PDF Export Citaion korean bibliography PMC previewer
ISSN : 2288-4637(Print)
ISSN : 2288-4645(Online)
The Journal of Asian Finance, Economics and Business Vol.6 No.1 pp.249-259
DOI : http://doi.org/10.13106/jafeb.2019.vol6.no1.249

Social Network Services Addiction in the Workplace

Youngkeun Choi1, Kyounghee Chu2, Eun-Jung Choi3
1 First Author, Associate Professor of HR, College of Business & Economics, Sangmyung University, South Korea. E-mail:
2 Co-author, Assistant Professor of Marketing, College of Business, Chosun University, South Korea. E-mail: khjoo777@chosun.ac.kr
3 Corresponding Author. Professor of Marketing, College of Business & Economics, Sangmyung University, South Korea. Tel:+82-2-781-7507. E-mail: choiej@smu.ac.kr
April 12, 2018. November 07, 2018. February 10, 2019

Abstract

Studies looking at many aspects of SNS addiction have dramatically increased in recent years. Most of the SNS addiction research has focused on individual behaviors. There is little academic research about SNS addiction in the workplace. This study, therefore, plans to examine the organizational behaviors related to SNS addiction in the workplace. We investigate whether negative behaviors in the workplace induce SNS addiction, and how SNS addiction influences the organizational or social attitudes of employees. We also explore the possible mediating effect of SNS addiction. We use an online survey and collected 285 responses from office workers in South Korea. The results tested by a structural equation modeling indicate, first, that both abusive supervision and workplace bullying have aroused SNS addiction among employees; second, employees’ SNS addiction increases both from work-to-family-conflicts and family-to-work-conflicts; and third, SNS addiction fully mediates the relationship between abusive supervision and workplace bullying, as well as the relationship among abusive supervision, workplace bullying, and work-family conflicts. The study finds that abusive supervision and workplace bullying are important antecedents of SNS addiction, and that SNS addiction affects conflicts in both work-to-family and family-to-work situations. Therefore, companies should be cognizant of potential mediating influences in monitoring employees’ SNS usage in order to improve their work environments.

JEL Classification Code: C12, C83, M12, M14.

초록


1. Introduction

 

Social networking services (hereby called SNS) have gained huge popularity in the last decade. Surprisingly, around 70 percent of Internet use at work has nothing to do with their job because of the addictive social networking media (Michael, 2017). There is a report that if 1,000 employees spent every day 1 hour on cyberloafing activity, the company could have $35 million loss annually in USA (Gaille, 2017). Due to the emerging problematic issues from SNS addiction, many academia have taken their attention in the decade.

Most of the previous SNS addiction studies focused on some general perspectives. First, SNS addiction is a disorder that requires clinical therapy for individuals addicted to social media experience with symptoms similar to those of other eco-addict behaviors (e.g., Block, 2008; Brenner, 1997). However, most of the previous studies focused on individual behavior when investigating the excessive consumption of these new media. In fact, very few academic studies are done at work on the results of SNS addiction. The nature of SNS addiction is different from other addicts as employees use SNS more than working hard, it is a big issue at most companies in the workplace context. Because of the advent of SNS use, it seems to exist several problems at work. Most of the recent studies suggest that people may feel they should maintain an online social network. This can cause SNS to be overused in some situations including workplace (e.g., Kang, Shin, & Park, 2013).

Prior SNS addiction studies in workplace started being reported recently. There are a few studies about SNS addiction in workplace, which focus on some antecedents of SNS and some effects of SNS in workplace (e.g., Choi, 2018a; Choi, 2018b). The literature of SNS addiction seems to face at an early stage. Due to the lack of study, this study, thus, aims to investigate organizational behaviors related to SNS addiction in the workplace context.

 

2. Theoretical Background and Hypothesis Development

 

The literature about SNS addiction generally focused on three perspectives. The first is that SNS addiction is a disorder that requires clinical therapy. It is caused by the initial obsession with the benefits of psychosocial substances in the clinical field (i.e., pharmacology, medicine, neuropharmacology, etc.) where drug addiction is traditionally studied, and by the initial obsession with the benefits of psychotropic substances. The second is that people addicted to SNS are more likely to suffer from addictive substance or other behavior unlike the eco-psychological structure and addiction syndrome model (Griffiths, 2005), which are generally used for explanation of addiction. They call the excessive use of SNS addictive SNS consumption. The third is that the treatment of SNS addiction is different from that of other addiction. Unlike other addicts, the goal of SNS addiction therapy is not to completely abstain from using the Internet as SNS usage is an essential part of today's leisure culture (Echeburua & de Corral, 2010; Kuss & Griffiths, 2011).

Recently, some studies started focusing on examinations about SNS impact on a workplace context. For example, Choi (2018a) reported that SNS addiction reduces organizational commitment and augments turnover intention, which are well-known indicators for employees’ performance. SNS addiction may not be longer a personal behavior issue. It should be considered one of serious organizational issues at work today.

 

2.1. Antecedents of SNS Addiction

 

There are three important theoretical perspectives on the recent formation of social network addiction. First, the cognitive behavioral model highlights the fact that irregular social networking is based on maladaptive cognition and is magnified by a variety of environmental factors, leading to addictive social networking patterns. Second, the social skill model in the study is likely to lead to unusual social networking outcomes, resulting in social networking obsession as people prefer to interact with self-expressive technologies, or as a result of the inability to interact with them. Insufficient Internet autonomy will eventually lead to addictive social networking behavior. Third, the socio-cognitive model emphasizes the fact that unregulated social networking websites is determined by outcome expectancy, high self-efficacy, and low control which develop compulsive behaviors and result in negative outcomes for the users (Turel & Serenko, 2012). 

Much of the associated research focuses on social and technical models or socio-aware models. Psychological profiles of addictive users may be more specific. Consequently, it appeared that a similar set of personality traits such as low self-esteem and low life satisfaction was associated with excessive use on SNS (Amichai-Hamburger & Vinitzky, 2010). Some studies have shown a cognitive behavioral model perspective. Xu and Tan (2012) argues that social networking could be a main mechanism to relieve loneliness, stress, or depression. They showed that poor people often engage in social networking in social life. Social media can give these people continuous rewards such as self-efficacy and life satisfaction. Ultimately, they engage in activities and ultimately lead to a number of problems (e.g., neglecting real-life relationships, professional issues, and educational conflicts).

Choi (2018b) suggested salience, withdrawal and tolerance as antecedents of SNS addiction. Especially, the negative impact of abusive supervision on work outcomes has been heavily reported as this strongly influences business environments and performances. For example, Breaux, Perrewé, Hall, Frink, and Hochwarter (2008) examined interactive relationship of abusive supervision and accountability on work outcomes. The results showed that perceived abuse interact with accountability. That is, the control-depleting property of abuse makes employees’ job satisfaction decline and their tension and exhaustion increase. In addition, the recent Choi’s research (2018a) argues that abusive supervision is an apparent factor affecting SNS addiction and the effect of abusive supervision on SNS addiction is weaken by employees’ perceived organizational support. Thus, the following hypothesis has been established.

 

H1: Abusive supervision is positively associated with SNS addiction.

 

Bullying is "all situations where one or more persons feel subjected to negative behavior from others in the workplace over a period of time and in a situation where they for different reasons are unable to defend themselves against these actions” (Einarsen, 2005, p.2). Extensive studies have been conducted to introduce the effect of workplace violence on the very damaging outcomes (Astrauskaite, Perminas, & Kern, 2010; Hogh, Mikkelsen, & Hansen, 2011). The early research reported that the exposure to bullying can affect detrimentally targets’ psychological health. Those studies also found that some targets suffer from only moderate levels of stress such as depression or PTSD.

According to the Einstein stress model, the nature and seriousness of the emotional response after being exposed to bullied and violent supervision is a function of the dynamic interaction between the incident characteristics and the individual evaluation and handling process (Lazarus, 1999; Zapf & Einarsen, 2003). These characteristics can configure very stressful situations with limited control. Bullying and violent coach at work can aggravate an individual's undesirable mood. It encourages individuals to participate in social networking to reduce unpleasant feelings. As a result, social network users become more psychologically dependent on social networking as they repeat the circular pattern, which relieves unwanted surroundings by using social media. Thus, the following hypothesis has been established.

 

H2: Workplace bullying is positively associated with SNS addiction.

 

2.2. Consequences of SNS Addiction

 

Excessive use of SNS can not only cause relationship problems in a variety of situations but also lead to decrease actual community participations and academic performance (Kirschinner & Karpinski, 2010; Nyland, Marvez, & Beck, 2007). Especially, Facebook can also negatively affect romantic relationships. If a large amount of personal information is released on Facebook pages, including status updates, comments, photos, and new friends, you might see other people's cyber spies (Tokunaga, 2011). It seemed to lead to jealousy (Muise, Christofides, & Desmarais, 2009; Persch, 2007), but in the most extreme cases, divorce-related actions were taken.

Spending more time using and committing SNS at work can have negative consequences at work. In the work environment, we present family conflicts as the organizational or social attitude of our employees, one of which negatively affects SNS addiction. Family conflicts are a form of conflict between the roles in which pressure on work and family domains are not compatible in some respects. That is, as a result of participating in the role of each family (or workplace) versus family (or family), it is more difficult to engage in the role of family (Greenhaus & Beutell, 1985).

Work–family conflict is one of inter-role conflicts pressuring from a work domain and a family domain, and these two may be incompatible in some respects. The participation role in the work (or family) domain could be made more difficult by virtue of the participation role in the family (or work) domain (Greenhaus & Beutell, 1985). These conflicts could exist in both directions—work-to-family (WFC) and family-to-work (FWC) (Frone, Barnes, & Ferrell, 1994; Carlson, Kacmar, & Williams, 2000). Considering this bi-directional relationships in the work-family conflict, the following hypotheses have been established.

 

H3: SNS addiction is positively associated with work-to-family conflict

H4: SNS addiction is positively associated with family-to-work conflict

 

2.3. The Mediating Effect of SNS Addiction

 

The Conservation of Resources (COR) theory has explained that abusive supervision and harassment can increase family conflicts. COR theory indicates that people are trying to secure and maintain resources that will help increase their goals, and the actual or intimidating loss of resources causes stress (based on actual or threat loss). Hobfoll (1989) stated that resources can take the form of various objects, conditions (married status and tenure), individual characteristics (self-esteem), and energy (time, money, and knowledge). When an actual loss or a potential loss occurs, people are motivated to participate in the effort to avoid further loss.

Unlike the COR theory, the Transaction Stress Model describes the nature and seriousness of the emotional response to harassment and abuse surveillance at work as a function of event characteristics and dynamic feelings between individual assessment and response processes (Lazarus & Folkman, 1984). Considering the transaction-stress model for the workplace environment, we focus on the transaction-related stress model instead of the COR theory in order to explain the effects of stress from abuse supervision and harassment on individuals involved.

From the point of view of the transaction-stress model, we investigate the possibility of the mediation effect of SNS addiction between abuse supervision and stress. According to the Choi’s empirical research (2018b), SNS addiction can mediate the relationship between abusive supervision and employees’ turnover. As the same logic, the stress from violent directors and harassment might cause employees to be addicted to social networking services, and then increase family conflicts. By doing so, SNS addiction mediates the relationship between workplace harassment and family conflicts. Thus, theses hypotheses have been established as follows.

 

H5a: SNS addiction mediates the relationship between abusive supervision and work-to-family conflicts.

H5b: SNS addiction mediates the relationship between abusive supervision and family-to- work conflicts.

H6a: SNS addiction mediates the relationship between workplace bullying and work-to-family conflicts.

H6b: SNS addiction mediates the relationship between workplace bullying and family-to-work conflicts.

 

 

 

 

3. Research Methodology

 

3.1. Procedure

 

The purpose of this study is to identify the factors in organizational behavior associated with SNS addiction through empirical test. Factors in organizational behavior could be identified by measuring the perception of organizational members in the workplace context. We utilized the online survey method using a convenient sampling for collecting data. This is very useful for collecting data from large numbers of individuals at a relatively low cost.

All participants and business associates received an email explaining the purpose of the survey, highlighting voluntary participation, and requesting the online survey via an email. The survey questionnaire is composed of three parts: In the first part, participants were asked to read the purpose of the survey. The next section included measuring the perception of respondents, including key variables of this study (abusive supervision, workplace bullying, social networking service addiction, family to work conflict, and work to family conflict. The third part of the study consisted of basic information on the characteristics of the company and respondents (e.g. demographics, etc.).

 

3.2. Participants 

 

A self-completion questionnaire by using online survey was administered to office workers (20s~50s) in Korea. Through the procedure mentioned earlier, 285 complete responses have been collected. The characteristics and nature of respondents are shown in Table 1 (e.g., age, gender, etc.).

 

 

 

 

3.3. Measurement

 

The questionnaire was developed in a four-stage process. First, we reviewed the relevant literature for the key constructs of this research. We developed an initial and theory-based questionnaire. Second, we modified the initial questionnaire to accommodate the office workers' comments after we conducted the interviews with nine office workers, using a structured interview protocol. Next, the revised second version of questionnaire was revalidated with nine office workers in the M.B.A. program in the university. In the third stage, we developed a third version of the questionnaire drawing on office workers’ feedbacks. Lastly, we reviewed the questionnaire, ranked items according to their content validity, and modified the identified issues in the wording expressions, order structure, and layout of the items. The final measures for this study are listed in Appendix 1.

 

4. Results

 

Data analysis was conducted in three steps. First, confirmatory factor analysis (CFA) was used. Second, we tested the common method bias issues. In the third step, a structural equation modeling approach was used.

 

4.1. Measurement Model Assessment

 

As we demonstrated the validity and reliability of the multi-scale used in this study, we identified five factors in our analysis, namely, abuse supervision, workplace bullying, social network site addiction, work to family conflicts, and family to work conflicts. The second-order configuration was tested separately from the first-order factors. We have found the second-order factors for workplace bullying (task-related bullying, personal bullying, and threatening bullying) and social networking services addiction (salience, euphoria, immersion, compulsion, and association). The second-order factor loadings were significant (p < .001), indicating that it is reasonable to use it.

Table 2 shows the validity of all items. The factors associated with them are applied with attention to their corresponding potential components (BagozzI, Yi, & Phillips, 1991) and the statistics are suitable. Overall, the factor loading values of all items range between 0.719 and 0.908, which exceeds the 0.5 threshold and indicates an acceptable model fit (χ2 /df= 1.949; RMR = .046; GFI = .841; CFI = .932; IFI = .932; TFI = .925; RMSEA = .058).

The indexes justify further examinations of the structural model. The means and standard deviation of each construct are presented in Table 3. Table 3 shows the fit statistics of convergent validity on three criteria. First, all of the standardized path loadings were significant and greater than 0.7. Second, the average variance extracted (AVE) for each construct was greater than exceeded 0.5. Third, all path loadings exceeded 0.7. The values of composite reliability (CR) were greater than 0.7, and all of the AVEs exceeded 0.5. Therefore, convergent validity of our model was well established.

Next, for discriminant validity of all constructs, it was validated through comparing between the χ2 (the value of Chi-square) of the original measurement model with the five latent variables and the one of an alternative model with the four latent variables where two constructs were combined. We conducted this for all combinations of any two constructs in this study(Anderson, 1987; Bagozzi & Fornell, 1982). The significance test of the χ2 difference was conducted for the original and constrained model. Model comparison results showed that the χ2 differences were all statistically significant, which indicated that the value of χ2 of the original model with five latent variables was superior to the one of any possible combination models. This meant that the original five-factor model of our study was better than any alternative models. As another evidence for discriminant validity of all constructs, the square root of AVE for each factor was greater than any other correlations between a construct and the other constructs as Table 3 shows. Therefore, the discriminant validity of the five constructs in this study was well established. Overall, the CFA results show the high level in terms of discriminant and convergent validity. Additionally, the reliability analysis was also assessed by composite reliability (CR) and Cronbach’s alpha. The results indicates that the values of composite reliability and Cronbach’s alpha were larger than 0.7.

 

 

 

 

 

 

4.2. Hypotheses Testing Results

 

4.2.1. Main effect 

 

The hypotheses in our research model were tested, estimating standardized path coefficients in the structural equation modeling. The overall model fit was estimated and our hypothesized research model fits the data well (χ2 /df= 1.949; RMR = .046; GFI = .841; CFI = .932; IFI = .932; TFI = .925; RMSEA = .058). Table 4 indicates the parameter estimates for H1~H4. The results supported all main effects of our study. Specifically, for H1, abusive supervision showed a significant negative effect on SNS addiction (coefficient: 0.171, p < .05). Next, for H2, workplace bullying showed a significant negative effect on SNS addiction (coefficient: 0.249, p<.01). For H3, SNS addiction showed a significant positive effect on work-to-family conflict (coefficient: 0.164, p<.05). For H4, SNS addiction showed a significant positive effect on family-to-work conflict (coefficient: 0.469, p<.001).

 

4.2.2. The tests of mediating effect 

 

H5a suggests that the relationship between abusive supervision and work-to-family conflict is fully mediated by SNS addiction. Recent research has advocated the structural equation modeling approach as a better method for testing mediation effects than Baron and Kenny's (1986) method (Iacobucci, Saldanha, & Deng, 2007; Iacobucci, 2008). This study followed Iacobucci et al.'s (2007) suggestions to fit one structural model which has all direct and indirect paths together. Then, the statistical significance tests of all effects (mediation, direct, and indirect one) were conducted by means of a bootstrapping resampling method to identify the 95% bias-corrected confidence intervals. The confidence interval which does not include zero indicates that the mediation effect is statistically significant. Using the 95% bias-corrected bootstrapping confidence intervals have been recently preferred as a better method in that this method have more statistical power relative to Baron and Kenny's and Sobel test (Cheung & Lau, 2009; Sobel, 1982).

Table 5 indicates the direct path between the independent variable (i.e., abusive supervision) and the dependent variable (work-to-family conflict) was significant statistically. If the both paths (the independent variable → the mediator  & the mediator → the dependent variable) are all statistically significant with the significant direct path from the independent variable (abusive supervision) to the dependent variable (work-to-family conflict), some mediation may exist (Goodwin, Groth, & Frenkel, 2011). In this case, as indicated by the 95% bias-corrected bootstrapping confidence intervals: β abusive supervision → SNS addiction = .179, p< .05; β SNS addiction → work-to-family conflict = .288, p< .01, the total standardized indirect effect was .052, p<.05. Thus, we conclude that SNS addiction partially mediates the relationship between abusive supervision and work-to-family conflict. Therefore, Hypothesis H5a is partially supported (see Table 5).

H5b suggests that the relationship between abusive supervision and family-to-work conflict is mediated by SNS addiction. Table 6 shows the direct path between the independent variable (abusive supervision) and the dependent variable (family-to-work conflict) was not statistically significant. However, this direct path is not a prerequisite for a mediation effect (Shrout & Bolger, 2002). Rather, if all direct paths both from the independent variable (abusive supervision) to the mediator (SNS addiction) and from the mediator (SNS addiction) to the dependent variable (family-to-work conflict) are statistically significant with the statistically insignificant direct path between the independent variable (abusive supervision) and the dependent variable (family-to-work conflict), some full mediation may exist. In this case, as indicated by the 95% bootstrapped bias-corrected confidence intervals: β abusive supervision → SNS addiction = .179, p< .05; β SNS addiction → family-work conflict =.401, p<0.01, the total standardized indirect effect was .072, p<.05. Thus, we conclude that SNS addiction fully mediates the relationship between abusive supervision and family-to-work conflict. Therefore, Hypothesis H5b is supported (see Table 6).

H6a suggests that the relationship between workplace bullying and work-to-family conflict is mediated by SNS addiction. Table 5 shows the direct path between the independent variable (workplace bullying) and the dependent variable (work-to-family conflict) was not statistically significant. But, this path is not a prerequisite for a mediation effect. Rather, if all paths both from the independent variable (workplace bullying) to the mediator (SNS addiction) and from the mediator (SNS addiction) to the dependent variable (work-to-family conflict) are statistically significant with the insignificant direct path between the independent variable (workplace bullying) and the dependent variable (work-to-family conflict), some full mediation may exist (Goodwin et al., 2011). In this case, as indicated by the 95% bootstrapped bias-corrected confidence intervals: β workplace bullying → SNS addiction =.480, p<.001; β SNS addiction → work-to-family conflict =.288, p<0.01, the total standardized indirect effect was .070, p<.05. Thus, we conclude that SNS addiction fully mediates the relationship between workplace bullying and work-to-family conflict. Therefore, Hypothesis H6a is supported (see Table 5).

H6b suggests that the relationship between workplace bullying and family-to-work conflict is mediated by SNS addiction. Table 6 shows the direct path between the independent variable (workplace bullying) and the dependent variable (family-to-work conflict) was not statistically significant. Rather, if the paths both from the independent variable (workplace bullying) to the mediator (SNS addiction) and from the mediator (SNS addiction) to the dependent variable (family-to-work conflict) are statistically significant with the statistically insignificant direct path between the independent variable (workplace bullying) and the dependent variable (family-to-work conflict), some full mediation may exist. In this case, as indicated by the 95% bootstrapped bias-corrected confidence intervals: βworkplace bullying → SNS addiction = .480, p<.001; β SNS addiction → family-work conflict =.401, p<0.01, the total standardized indirect effect was .098, p<.05. Thus, we conclude that SNS addiction fully mediates the relationship between workplace bullying and family-to-work conflict. Therefore, Hypothesis H6b is supported (see Table 6).

 

 

 

 

 

5. Discussion and Conclusion

 

5.1. Discussion

 

Considering the lack of studies investigating SNS addiction impacts at work, it is a pioneering study on the prognosis of SNS addiction and its results at work. The study examined the effects of abusive supervision and workplace bullying on employees' SNS use and the effects of SNS addiction on work-to-family (family-to-work) conflicts. This research found some dynamic relationships among abusive supervision, workplace bullying, SNS addictions, and work-to-family (family-to-work) conflicts. The key results of the research are summarized as follows.

First, abusive supervision (H1) and work bullying (H2) cause SNS addiction. These results are consistent with the Einstein stress model explaining the nature and seriousness of the undesirable emotional response after being exposed to bullied and violent supervision (Lazarus, 1999; Zapf & Einarsen, 2003). Second, the results reveal that employees' SNS addiction can increase work-to-family conflicts (H3) and family-to-work conflicts (H4). This initial examination is significant because this study confirmed SNS addiction negative impact work-to-family and family-to-work domains as well as in the workplace domain. This result confirmed SNS addiction influences the bi-directional family-work conflict. Finally, this study found that SNS addiction mediates the relationship between abusive supervision and work-family conflicts (H5). SNS addiction mediates the relationship between workplace bullying and work-family conflicts (H6). That is, this study found that abusive supervision of directors and workplace bullying are the major antecedent factors in SNS addiction, and then SNS addiction affects conflicts between work and family.

From a theoretical perspective, this study provides five contributions. First, we introduced SNS addiction at work and investigated organized behaviors related to it. The study initially added SNS, one of the problems with media usage, to the behavior of the organization in question. This is important as most previous SNS addiction studies focused on the perspective of individual behavior. SNS addiction should be considered seriously at work when considering SNS use throughout our lives, including the workplace situation.

Second, our study is the first to verify abusive supervision as one of consequences of SNS addiction at work. In previous studies, the abusive supervision was associated with various stress-related results such as occupational tension, anxiety, psychological health, insomnia, drinking problems, burn-out, and emotional fatigue (e.g., Breaux et al., 2008; Bowling & Michel, 2011; Yagil, 2006). As a result, 

the study expanded the literature that emphasizes the importance of SNS addiction into the workplace domain.

Third, our research has found that SNS addiction is a strong mediator in the workplace where there are abusive supervision and bullying in the workplace. The results are consisted with the previous studies on the family domains that worked in SNS addiction environments (Carlson et al., 2000; Frone et al., 1994).

Forth, we introduced a transaction stress model to explain the appearance of SNS addiction at work from a theoretical perspective. Our studies clearly indicate that SNS addiction is closely linked to stress factors from harsh supervision and workplace bullying can make them participate in social networking behavior in a way that relieves unpleasant feelings. Thus, this study identified the trading stress model by empirical validation of our hypotheses. This is consistent with study from Xu and Tan (2012) indicating out a switch from normal networking to problematic social networking usage when social networking is considered as a tool to alleviate stress, loneliness, or depression.

Lastly, this is important as this study first proves that SNS addiction in workplace affects not only workplace domain as well as the work-family domain. Thus, the domain of SNS addiction study has been expanded by this study.

 

5.2. Managerial Implications

 

The study reveals the negative side of SNS. SNS addiction actually occurs at work. Addiction can often be overlooked in the workplace as it pertains to only personal matters. SNS addiction should be handled not only as a personal issue but also as an organizational issue. Companies, thus, need to apply insights from this study results into their organizations as following.

First, companies need to build up a SNS addiction monitoring system. They need to monitor their employees’ serious additive behaviors in social media in order to improve their working environment. That is, human resource management teams might develop how to monitor their employees’ social media usages.

Second, abusive supervision and bullying creating SNS addiction often occur in the corporate workplace. The abusive supervision and bullying lead to increasing conflicts among employees as well. Corporate executives should monitor employees against SNS addiction and try to identify the presence of abusive supervision of directors or workplace bullying.

Third, LaRose and Eastin's theory of social identity (2004) pointed out the expected outcomes and self-regulating mechanisms are important predictors of media usage in these situations. Corporate executives and human resources management teams must develop regulations for SNS use. They also need to train the propagation of the preferred usage guidelines for social media in order to help their employees to establish its own regulatory mechanism for SNS use.

 

5.3. Limitations and Future Research Directions

 

The results of this study show some insight into the relationship between organizational behaviors and SNS addiction. Nevertheless, there are the following limitations, too. First, we collected answers from employees working in Korean business establishments. There may be some national cultural issues in an organizational context. Future studies should be conducted at different sites in different countries to ensure the reliability of these results. Second, as all key variables are measured at the same time, we cannot be sure that the relationship is consistent. Therefore, to be sure that a causal relationship exists between the variables, future research where the key variables are measured at different time periods (time 1, time 2) will be needed. Third, although this survey was observed at one point, all types of adding behavior in media consumption were developed during a particular period of time. Consequently, it is interesting to carry out an end-to-end study in future studies to track problem behavior at work. The end-to-end studies provide deep understanding of particular event characteristics and the lack of self-control and/or functional capabilities of the dynamic interactions between individual assessment and business processes.

Figure

Table

Reference

  1. Anderson, J. C. (1987). An approach for confirmatory measurement and structural equation modeling of organizational properties. Management Science, 33(4), 525-541.
  2. Astrauskaite, M., Perminas, A., & Kern, R. M. (2010). Sickness, colleagues’ harassment in teachers’ work and emotional exhaustion. Medicina (Kaunas), 46(9), 628-634.
  3. Bagozzi, R. P., Yi, Y., & Phillips, L. W. (1991). Assessing construct validity in organizational research. Administrative Science Quarterly, 36(3), 421-458.
  4. Bamberger, P. A., & Bacharach, S. B. (2006). Abusive supervision and subordinate problem drinking: Taking resistance, stress and subordinate personality into account. Human Relations, 59(6), 723-752.
  5. Baron, R. M., & Kenny, D. A. (1986). The moderator-mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51(6), 1173−1182.
  6. Block, J. J. (2008). Issues for DSM-V: Internet addiction. American Journal of Psychiatry, 165(3), 306-307.
  7. Bowling, N., & Michel, J. (2011). Why do you treat me badly? The role of attributions regarding the cause of abuse in subordinates' responses to abusive supervision. Work and Stress, 25(4), 309–320.
  8. Breaux, D., Perrewé, P., Hall, A., Frink, D., & Hochwarter, W. (2008). Time to try a little tenderness? The detrimental effects of accountability when coupled with abusive supervision. Journal of Leadership & Organizational Studies, 15(2), 111–122.
  9. Brenner, V. (1997). Psychology of computer use: XLVII. Parameters of internet use, abuse and addiction: the first 90 days of the internet usage survey. Psychological Reports, 80(June), 879-82.
  10. Carlson, D. S., Kacmar, K. M., & Williams, L. (2000). Construction and initial validation of a multidimensional measure of work–family conflict. Journal of Vocational Behavior, 56(2), 249–276.
  11. Carlson, D., Ferguson, M., Hunter, E., & Whitten, D. (2012). Abusive supervision and work–family conflict: The path through emotional labor and burnout. The Leadership Quarterly, 23(5), 849–859.
  12. Cheung, G. W., & Lau, R. S. (2009). Testing mediation and suppression effects of latent variables. Organizational Research Methods, 11(2), 296−325.
  13. Choi, Y. (2018a). Abusive supervision and social network service addiction. Social Behavior and Personality: An International Journal, 46(2), 189-198.
  14. Choi, Y. (2018b). Narcissism and social media addiction in workplace. The Journal of Asian Finance, Economics and Business, 5(2), 95-104.
  15. Echeburua, E., & de Corral, P. (2010). Addiction to new technologies and to online social networking in young people: A new challenge. Addictions, 22(2), 91-95.
  16. Einarsen, S. (2005). The nature, causes and consequences of bullying at work: The Norwegian experience. Perspectives interdisciplinaires sur le travail et la santé, (7-3).
  17. Frone, M. R., Barnes, G. M., & Farrell, M. P. (1994). Relationship of work–family conflict to substance use among employed mothers: The role of negative affect. Journal of Marriage and Family, 56(4), 1019–1030.
  18. Gaille, B. (2017, May 23). 29 Social Networking Addiction Statistics. Retrieved from https://brandongaille.com/28-social-networking-addiction-statistics/
  19. Goodwin, R. E., Groth, M., & Frenkel, S. J. (2011). Relationships between emotional labor, job performance, and turnover. Journal of Vocational Behavior, 79(2), 538-548.
  20. Grandey, A. A., & Cropanzano, R. (1999). The conservation of resources model applied to work–family conflict and strain. Journal of Vocational Behavior, 54(2), 350–370
  21. Greenhaus, J., & Beutell, N. (1985) Sources of conflict between work and family roles. Academy of Management Review, 10(1), 76–88.
  22. Griffiths, M. D. (2005) A components model of addiction within a biopsychosocial framework. Journal Substance Use, 10(4), 191-197.
  23. Hobfoll, S. E. (1989). Conservation of resources: A new attempt at conceptualizing stress. The American Psychologist, 44(3), 513–524.
  24. Hogh, A., Mikkelsen, E. G., & Hansen, A. M. (2011). Individual consequences of workplace bullying/mobbing. In S. Einarsen, H. Hoel, D. Zapf, & C. L. Cooper (Ed.), Bullying and harassment in the workplace: Developments in theory, research and practice (pp. 107-128). London, UK: Taylor & Francis.
  25. Iacobucci, D. (2008). Mediation Analysis. Thousand Oaks, CA: Sage.
  26. Iacobucci, D., Saldanha, N., & Deng, X. (2007). A meditation on mediation: Evidence that structural equations models perform better than regressions. Journal of Consumer Psychology, 17(2), 139−153.
  27. Kang, I., Shin, M. M., & Park, C. (2013). Internet addiction as a manageable resource: a focus on social network services. Online Information Review, 37(1), 28-41.
  28. Kirschner, P. A., & Karpinski, A. C. (2010). Facebook and academic performance. Computer Human Behavior, 26(6), 1237-1245.
  29. Kuss, D. J., & Griffiths, M. D. (2011). Online social networking and addiction--a review of the psychological literature. International Journal of Environmental Research Public Health, 8(9), 3528-3552.
  30. LaRose, R., & Eastin, M. S. (2004). A social cognitive theory of Internet uses and gratifications: Toward a new model of media attendance. Journal of Broadcasting and Electronic Media, 48(3), 358-377.
  31. Lazarus, R. S. (1999). Stress and e motion: A new synthesis. New York, NY: Springer.
  32. Lazarus, R. S., & Folkman, S. (1984). Stress, appraisal, and coping. New York, NY: Springer.
  33. Luscombe, B. (2009). Social norms: Facebook and divorce. Time, 173, 93-94.
  34. Michael, K. (2017, February 23). Facts and figures: the rise of social media addiction. PC World. Retrieved from https://www.pcworld.idg.com.au/article/614696/facts-figures-rise-social-media-addiction/
  35. Muise, A., Christofides, E., & Desmarais, S. (2009). More information than you ever wanted: Does facebook bring out the green-eyed monster of jealousy? Cyber Psychology Behavior, 12(4), 441-444.
  36. Nyland, R., Marvez, R., & Beck, J. (2007, February). Social networking or social isolation? In Proceedings of the Midwinter Conference of the Association for Education in Journalism and Mass Communication, Reno.
  37. Persch, J. A. (2007). Jealous Much? MySpace, Facebook Can Spark It. The MSNBC Digital Network. Retrieved August 18, 2011 from http://www.msnbc.msn.com/ id/20431006/ (accessed on 18 August 2011).
  38. Rafferty, A., Restubog, S., & Jimmieson, N. (2010). Losing sleep: Examining the cascading effects of supervisors' experience of injustice on subordinates' psychological health. Work and Stress, 24(1), 36–55.
  39. Shrout, P. E., & Bolger, N. (2002). Mediation in experimental and nonexperimental studies: New procedures and recommendations. Psychological Methods, 7(4), 422−445.
  40. Sobel, M. E. (1982). Asymptotic confidence intervals for indirect effects in structural equation models. Sociological Methodology, 13, 290−312.
  41. Tepper, B. J. (2000). Consequences of abusive supervision. Academy of Management Journal, 43(2), 178–190.
  42. Turel, O., & Serenko, A. (2012). The benefits and dangers of enjoyment with social networking websites. European Journal of Information Systems, 21(5), 512-528.
  43. Xu, H., & Tan, B. C. Y. (2012, December). Why Do I Keep Checking Facebook? Effects of Message Characteristics on the Formation of Social Network Services Addiction. Proceedings of the International Conference on Information Systems (ICIS), Orlando (Florida), USA.
  44. Yagil, D. (2006). The relationship of abusive and supportive workplace supervision to employee burnout and upward influence tactics. Journal of Emotional Abuse, 6(1), 49–65.
  45. Zapf, D., & Einarsen, S. (2003). Individual antecedents of bullying: victims and perpetrators. In S. Einarsen, H. Hoel, D. Zapf, & C. Cooper (Ed), Bullying and Emotional Abuse in the Workplace: International Perspectives in Research and Practice. London, UK: Taylor & Francis.