Effect of social media usage on job security through social media disorder and networking behavior: a serial mediation mechanism

Abstract

Purpose

The usage of social media at the workplace has become an undeniable reality, yet the role of social media use (SMU) in job-related outcomes is still unclear. This study uncovers a chain process through which SMU may strengthen job security perception of employees through social media disorder (SMD) and networking behavior.

Design/methodology/approach

This quantitative study used ratings of 197 Emirati students enrolled in a higher education institution located in United Arab Emirates (UAE). The respondents were professionals serving in different public and private organizations in UAE.

Findings

The statistical results supported a significant serial mediation of SMD and networking behavior between SMU and job security perceptions of employees.

Practical implications

This study offers implications for employees and their supervisors about the usage of social media for strengthening their perceptions of job security.

Originality/value

This study contributed to the existing stream of research on SMU to explain a chain process through which employees may benefit from social media to strengthen their perceptions of job security.

Citation

Rajhi, S., Asadullah, M.A. and Derbel, W. (2024), "Effect of social media usage on job security through social media disorder and networking behavior: a serial mediation mechanism", PSU Research Review, Vol. 8 No. 3, pp. 794-812. https://doi.org/10.1108/PRR-04-2022-0039

1. Introduction

Social media has become an essential reality of personal and professional life today. The recent statistics (www.statista.com) demonstrate that the volume of social media users has reached 4.8 billion (almost 60% of the world population). Such a massive increase in social media use (SMU) prompted organizational members to allow the use of social media in the workplace rather than restricting it Porter et al. (2016). The recent pandemic of coronavirus disease turned social media into an essential workplace for businesses across the globe. Nevertheless, the existing stream of reviews (Sun and Zhang, 2021; Duradoni et al., 2020; D'Arienzo et al., 2019) conducted on social media addiction (SMA) or social media disorder (SMD) literature indicated that the majority of studies emphasize the individual-level psychological outcomes (well-being, life satisfaction, attachment). However, recent research has paid limited attention to the role of SMD in different individual-level workplace outcomes (Hanna et al., 2017). Further, the available literature also offers conflicting results about the role of SMD in different job-related variables related to employee perceptions, attitudes and behaviors at the workplace. There is a dichotomy in the research findings related to positive and negative outcomes of SMD. For instance, some of the commonly reported positive employee outcomes of SMD include career success, positive attitudes toward workplace politics, well-being, creativity and networking (Forret and Dougherty, 2001; Thompson, 2005; Wolff and Moser, 2009; Gibson et al., 2014; Zhou et al., 2009; Wolff and Moser, 2009; Oueslati et al., 2020; Yang et al., 2023). On the other hand, the research on social media usage has also shown various detrimental effects such as SMD (Van Den Eijnden, Lemmens and Valkenburg, 2016; Oueslati et al., 2020), unhealthy relations (Davison et al., 2014), decrease in productivity (Koch et al., 2012), life satisfaction (Sun and Zhang, 2021) and well-being (Duradoni et al., 2020).

The critics argue that versatility in social media usage by employee may ensue such inconsistent findings (Chen et al., 2022). Based on inconsistencies in the outcomes of SMD, the social media researchers criticize that the advantages of social media for different organizational members are still unclear and require further research attention (Karahanna et al., 2018; Chen and Wei, 2020). Given the research inconsistencies, new contributions for understanding how SMU may contribute to the employees' perceptions of job security may offer significant insights into the role of social media in job-related employee outcomes.

This study addressed this gap in existing research on SMD concerning employees' perceptions of job security. Some researchers also insisted that the association between SMU and NB needs further research attention to explore positive outcomes (Osatuyi, 2015). This study also addressed this research gap by examining a mediation mechanism through which SMD influences networking behavior that further affects employees' perceptions of job security. Chen et al. (2022) highlighted that mainstream research on social media ignored the different impacts of SMU even though employees use social media at work for both personal and professional activities bringing different outcomes. This study also extends the existing stream of research on SMU to the social networking domain and its impact on job security perceptions. This study also tested a serial mediation mechanism to explain how social media usage strengthens job security perceptions of social media users through a chain mediation mechanism. Figure 1 demonstrates the hypothetical framework tested in this study.

2. Theory and hypotheses

2.1 Social media use and social media disorder

SMD is evident from the excessive use of social media platforms (from 96 min in 2012 to 135 min in 2018) and mobile internet (from 32 min in 2011 to 155 min in 2021; Statista, 2019). There is consensus among the researchers investigating “dark side” of social media that unregulated use of social media platforms has negative psychological consequences including the fear of missing out (Tandon et al., 2021), anxiety (Vannucci et al., 2017), stress, dispositional anxiety and negative self-evaluations while making social comparisons (Nesi and Prinstein, 2015), ego (Andreassen et al., 2017) and compulsive online behaviors (van den Eijnden et al., 2016), low psychosocial well-being (Ryan et al., 2014) and low job satisfaction (Hanna et al., 2017). The detrimental psychological outcomes indicate a disorder resulting from excessive usage of social media. The individuals failing to control their social media usage become the victim of SMD (Ryan et al., 2014) because of unregulated mood swings, withdrawal behaviors, intolerance and conflict, and anxiety and depression (Griffiths, 2005; Bányai et al., 2017; Primack et al., 2017). Based on these harmful outcomes of social media usage, this study also hypothesized a positive association between social media usage and SMD.

H1.

The use of social media is positively related to SMD.

2.2 Social media disorder and networking behavior

This study aims to scrutinize the effect of SMD on employees` networking behavior. SMA and SMD are interchangeable constructs (Van den Eijnden et al., 2016). Although it is challenging to establish a theoretical ground for a positive linkage between SMD and networking behavior, we found empirical support from existing studies exploring the association between SMA and social connectedness. The construct of social connectedness, which refers to an individuals' ability to develop meaningful relationships in social life (Yoon and Lee, 2010), is very similar to the construct of networking behavior. In a recent investigation, Savcı and Aysan (2017) found a positive and significant relationship between SMA and social connectedness. Therefore, we also hypothesized a positive association between SMD and employees' networking behavior. Bian and Leung (2015) also found that excessive use of social media has a positive and significant association with sociability, perceived bonding, communication and social capital (Bian and Leung, 2015; Yang et al., 2023). Since excessive use of social media represents SMA, the positive association of SMD with perceived social bonding is equally possible. The excessive usage of social media leads to disorder, yet individuals use social media for maintaining communication with existing network and developing social capital (Treem et al., 2016; Yang et al., 2023). Despite the possibility of SMD, social media users obtain approval and feedback from their friends on social media sites and continue checking their posts and comments to seek positive feedbacks (Monacis et al., 2017). Thus, one may argue that SMD positively relates with networking behavior. Therefore, we theorize the following hypothesis:

H2.

SMD is positively related to networking behavior.

2.3 Networking behavior and job security

The existing research views social networking as a source of competitive advantage for employees over their coworkers (Wu, 2013) as the employees (e.g. bankers: Burt, 2004; research and development professionals: Reagans and Zuckerman, 2001) with better networking skills are more likely to obtain more favorable performance ratings from their supervisors and to receive better compensation (Burt, 1992, 2005; Podolny and Baron, 1997; Moren-Cross and Lin, 2006; Cross and Cummings, 2004). Thus, the employees with better networking behavior are more likely to secure their jobs when compared to the employees having weak networking behavior. We also agree with Castellacci and Viñas-Bardolet (2019), who contend that the employees with more social connections inside the organizations feel harder to leave (Allen, 2006; Mitchell et al., 2001; Zimmerman et al., 2012) as they feel more satisfied with their jobs. Moreover, the researchers view networking (both internal as well as external) as a more valuable career competency that leads to promotion, higher salaries and re-employment (Gould and Penley, 1984; Michael and Yukl, 1993; Cingano and Rosolia, 2012). Hence, the employees pursuing networking objectives prove enthusiastic and stay with the organizations (Hom et al., 2012).

Further, the organizational perspective of networking explains that the organizations influence employees' networking for boundary spanning (Spekman, 1979) and enhancing and promoting innovative behavior (Wang et al., 2015) to gain competitive advantage (Perry-Smith and Shalley, 2003). Thus, organizations are highly likely to retain employees with better professional networking. On the other hand, networking-conscious individuals are also less likely to quit their organizations immediately (Castellacci and Viñas-Bardolet, 2019). Thus, this study proposed that networking behavior is positively associated with job security perceptions of employees.

H3.

Networking behavior is positively related to employees' perceptions of job security.

2.4 Sequential mediation

The sequence of hypotheses discussed earlier, this study hypothesized a sequential mediation of SMD and networking behavior between social media usage and employees' perceptions of job security. Sequential mediation is a specific type of mediation mechanism in which the mediating variables link to the independent and dependent variables in a chain (Preacher et al., 2010; Hayes, 2013). There is consensus among the researchers that all forms of technological addictions, including SMA, are behavioral addictions (Kuss and Griffiths, 2011; Savcı and Aysan, 2017). Hence, SMA is also a behavioral addiction because of its detrimental psychological outcomes (e.g. mood swings, withdrawal, intolerance and conflict and anxiety and depression) (Griffiths, 2005; Primack et al., 2017; Bányai et al., 2017) of excessive use of social media truly represent the symptoms of psychological disorder. However, this SMD is further associated with their networking behavior because it leads individuals to create new connections and maintain these relationships by checking different posts and comments shared by their social media friends (Treem et al., 2016; Monacis et al., 2017). Based on this tendency, individuals are more likely to create new connections with their colleagues within and outside the organizations; thus, their social media network may develop. Further, the employees with increased networking are more likely to perceive more job security due to various positive outcomes of networking for the employees and the organizations (e.g. the organizations manipulate the networking of their employees (Spekman, 1979), as discussed in hypothesis 3. Based on this sequence of the relationship among the variables under investigation, we hypothesized that:

H4.

SMD and networking behavior sequentially mediate the relationship between SMU and employees' perceptions of job security.

3. Methodology

3.1 Design, procedure and sample

This cross-sectional study is quantitative. This study collected data from the students (final semester) enrolled in a private higher education institution (HEI) in Abu Dhabi, United Arab Emirates (UAE). The researchers used a purposive sampling method for the selection of research participants. The key participants of the study were college students with a minimum of one year of employment experience. The students were employed either in public or private organizations in the UAE. The respondents provided ratings on a paper–pencil questionnaire during class hours. The students were requested to fill out the survey questionnaire by considering their networking conduct in the previous month. We obtained data from the participants at two different time frames. Initially, we distributed 300 questionnaires among the students and received 170 filled questionnaires. The time frame of data collection was extended from January to May 2018. We removed 36 incomplete questionnaires and proceeded with 134 complete responses. Then, we distributed 150 more questionnaires among another cohort of students enrolled in the same college from September to January (the fall semester) in 2019. Seventy-two students returned the filled questionnaires. Nine students did not respond to the measure of job security. The remaining useable questionnaires in the second cohort were 63. Overall, the researchers obtained 197 useable questionnaires. The response ratio was 43.7%.

The authors determined the sample size using the recommendations of different researchers. The researchers (Hair et al., 2010; Hair et al., 2011) recommend a minimum of 100 observations for obtaining minimum acceptable statistical power for structural equation modeling. Some researchers also suggest the sample size based on the number of predictors (minimum 15 observations per predictor; Stevens, 2012). Tabachnick and Fidell (2007) introduced a mathematical formula (N > 50 + 8 m where ‘m’ stands for the number of predictors) for sample size estimation. The third criterion for sample size estimation is the SEM rule of thumb based on the ratio of observations and the number of items (1:10) included in the questionnaire (Hair et al., 2012). We also used G-power software to determine the sample size. Table 1 demonstrates that the sample size used in the current study exceeds the minimum sample size based on recommendations discussed previously.

3.2 Measures

3.2.1 Social media use (predictor)

We used a 10-item scale developed by Jenkins-Guarnieri et al. (2013) to measure the SMU. The sample item included: “I feel disconnected from friends when I have not used social media.” The respondents provided ratings on a six-point Likert-type rating scale ranging from 1(strongly disagree) to 6 (strongly agree). The reliability of the scale (Cronbach alpha) was 0.90.

3.2.2 Mediating variables

3.2.2.1 Social media disorder

We measured SMD using a 9-item scale established by Van den Eijnden et al. (2016). The sample item includes, “During the last month, have you often felt bad when you could not use social media?” The reliability of the scale (Cronbach alpha) was 0.86. The participants used a six-point Likert-type rating scale ranging from 1(never) to 6 (very often almost every day) to respond the survey items.

3.2.2.2 Networking behavior

The measure of social networking behavior consisted of a 38-items scale developed Forret and Dougherty (1997). Social networking behavior is a multidimensional scale consisting of 5-different sub-dimensions. The first dimension is “maintaining contacts” containing 5-items. The sample item includes: “Within the last month, how often have you given business contacts a phone call to keep in touch?’ The second dimension was “socializing” that consists of 7-items. The sample item was “attending social functions of your organization.” The third dimension of networking behavior was “engaging in professional activities” containing 8 items, and the sample item included: “accepting speaking engagements and attending conferences/trade shows.” The fourth dimension was “participating in community activities” that contained 4 items. The sample item included: “attending meetings of civic and social groups and clubs.” The fifth subdimension of networking behavior was “increasing internal visibility.” This dimension consists of 4 items, and the sample item included: “accepting new, highly visible work assignments.” We asked the respondents to provide ratings on a 6-point Likert-type rating scale. The overall reliability of this scale (Cronbach alpha) was 0.92.

3.2.3 Dependent variable: job security

We measured job security employing a single-item scale proposed by De Spiegelaere et al. (2014). The sample items included: “Thinking about the next 12 months, how likely do you think that you will lose your job or be laid-off.” We used the reversed scores of respondents to measure the job security perceptions of employees. The participants provided ratings response on a 6-point rating scale ranging from 1(not at all likely) to 6 (very likely).

3.2.4 Control variables

In our analysis, we control for the likely impact of business networking on the networking behavior of the employees. We employ 6-item scale established by Lau and Bruton (2011) and Yiu et al. (2007), in order to measure business networking. We obtained responses from the respondents on how closely they are connected with varied groups of individuals (e.g. government officials). For this purpose, we employed a 5-point Likert scale ranging from “not at all” to “extremely familiar,” with the scale reliability of 0.88. The complete survey instrument is provided in “Appendix” at the end of the manuscript.

4. Findings

4.1 Preliminary analysis

After testing basic assumptions of data analysis (e.g. screening, normality, multicollinearity, reliability), we performed confirmatory factor analysis (CFA) using AMOS. We performed CFA to determine the convergent and discriminant validity of the constructs. The adequacy of the CFA model was determined using the fit indices recommended by Kline (2011). These fit indices include chi-square minimum difference (CMIN/DF), comparative fit indices (CFI) and root-mean square error of approximation (RMSEA). According to the model fit criteria, the value of CMIN/Df should be less than 3; the value of CFI should be greater than 0.90, and the value of RMSEA must be less than 0.06. The initial CFA model did not fit adequately, yet after some modifications (removing four items of the SMD scale, four items of SMU and eight items of networking behavior) due to lower factor loadings (less than 0.40), the final CFA model demonstrated adequate fit (CMIN/Df = 1.593; CFI = 0.88; RMSEA = 0.066). Then, we used standardized factor loading and correlation estimates of the variables to estimate convergent and discriminant validity estimates (Table 1). The criteria for determining convergent and discriminant validity consisted of the values obtained for critical ratio (CR), average variance explained (AVE), MSV and correlations (Fornell and Larker (1981). As shown in Table 1, the values of CR exceed 0.7, and the values of AVE exceed 0.5. These estimates satisfy the condition of convergent validity. The values of MSV are smaller than the values of AVE in Table 2 demonstrating the evidence of discriminant validity of the measures used in the current study.

4.2 Hypothesis testing

We used Process Macro (Version 3.2), introduced by Hayes and Preacher (2010), for hypotheses testing. We tested run model 6 of process macro for testing the statistical results related to the entire hypothesized relationships, including the serial mediation hypothesis (H4). Process Macro facilitates mediation testing using a bootstrapping technique (Cheung and Lau, 2008; MacKinnon et al., 2012). The results demonstrated that the effect of SMU on SMD (β = 0.63; p = 0.0000) was positive and significant. This statistical result supported our first hypothesis (H1). Further, the relationship between SMD and networking behavior was positive and significant (β = 0.517; p = 0.0000). These results supported the second hypothesis (H2). Similarly, the effect of Networking Behavior (NB) on Job Security (JS) was also positive and significant (β = 0.502; p = 0.0000). These results also demonstrated statistical support for hypothesis 3 of the study. This study also found that the direct relationship between SMU and job security was insignificant (Table 3).

The results (Table 3) of Process Macro (Model 6) also provided statistics related to three different mediation relationships. First, we found that the indirect effect of SMU on JS through SMD was insignificant (β = −0.01; upperbound confidence interval = 0.2617; and lowerbound confidence interval = 0.0005). Similarly, the indirect relationship of SMU on JS through NB was also insignificant (β = −0.0259; upperbound confidence interval = 0.0684; and lowerbound confidence interval = −0.1269). However, the results demonstrated statistical support for the serial indirect effect of SMU on JS through SMD and NB as the sequential mediation was significant (β = 0.1638) as the upperbound confidence interval (0.2607) and lowerbound confidence interval (0.0824) did not include zero. Thus, hypothesis H4 was supported.

5. Discussion and implications

5.1 General discussion

The research has reported positive as well as negative job-related outcomes of social media for employees. Based on contradictory findings about the benefits of social media, this study tested the mechanism through which SMU may positively contribute to the employee perceptions of job security. The existing research has not integrated SMD in the domain of social media research as a possible intervening factor that strengthens social network of employees and their perceptions of job security. This study integrated SMD in a theoretical framework and tested how excessive use of social media strengthens the job security perceptions of employees despite developing SMD depending upon social networking behavior of employees. In this regards, we conducted a study on the use of social media in students of HEI. This study found that social media usage is positively associated with SMD; SMD is positively associated with social networking behavior of employees; and social networking behavior is positively associated with job security perceptions of employees. This study also found that SMD and networking behavior serially mediate the effect of social media usage on job security perceptions of employees.

5.2 Theoretical implications

This study also offers different theoretical implications. The positive association between SMD and social networking behavior implies the positive influence of social media positively to the networking behavior of employees. This finding also conforms to the existing social media research advocating the positive outcomes of social media usage in the workplace (Çetinkaya and Rashid, 2018) despite the significant association of excessive social media usage with SMD. Second, this study found a positive relationship between SMU and SMD. This statistical result stresses the existing literature defining SMD as an excessive and impulsive use of social media or overindulgence in online social networks (Rajhi-Oueslati et al., 2019). This study did not find the issue of multicollinearity between SMU and SMD (r = 0.53). Further, the conditions of convergent and discriminant validity were also satisfied. This study implies that SMU and SMD are theoretically distinct constructs.

Third, this study demonstrated a positive relationship between SMD and networking behavior. This finding complies with the literature advocating that the sense of connectedness and belongingness created by the excessive use of technology is imperative for high sociability (Savcı and Aysan, 2017; Bian and Leung, 2015). Contrary to the existing research reporting the detrimental individual outcomes of SMD, this study found empirical support for the positive association of SMD with social networking behavior. The positive association between SMD and networking behavior implies that although reported as a negative construct in literature, SMD may also enhance the networking behavior of employees.

The existing literature reported more positive outcomes of social networking behavior like creativity, job satisfaction and career success (Burt, 2004; Wolff and Moser, 2009) and harmful effects of SMD (increased stress and reduced well-being (Bányai et al., 2017). However, this study found that despite its association with SMD, usage of social media may enhance the social networking behavior of employees to perceive more job security. This study also found that the direct relationship between SMU and job security was insignificant. However, the indirect serial effect of SMU through the chain effect of SMD and networking behavior was significant. These findings imply despite the positive of SMD, usage of social media contributes to the job security perceptions of employees positively when it is also positively associated with the social networking behavior of employees.

5.3 Practical implications

This study also offers some practical implications for different organizational members. First, the employees need to understand that excessive use of social media is the source of SMD and truly fits its operational definition. Second, the employees also need to recognize that despite its role in causing SMD, excessive use of social media positively influences their perceptions of job security if they use social media for social networking within and outside the organization. The employees also need to understand that the more they demonstrate networking behavior, the more they may feel job security. Hence, employees should use social media for networking within and outside the organizations. Social media usage is a valuable exercise of individual freedom and the recognition that employees should enjoy human rights at work without employer interference (Mantouvalou, 2019). When SMU positively relates to the networking behavior of employees and their perceptions of job security, organizational managers should encourage them to use social media for social networking instead of banning it at the worksites. For instance, they may filter and restrict the use of social media to those social media avenues (such as LinkedIn), which help employees to develop social networks.

6. Limitations and future research directions

Some potential limitations of this study need consideration for future research. First, the findings of this study are generalizable to the population of UAE nationals (locals). Our sample represents the population of UAE nationals working in public and private sectors and does not include expatriates. However, the UAE is home to over 200 nationalities. The Emiratis hardly constitute 20% of the population (Suchitra Bajpai Chaudhary, 2019). Therefore, our sample cannot represent the whole population of social media users in the UAE. Future studies may collect data from the expatriates working in the UAE to address this limitation of our study. Future research may also replicate the findings of this study by collecting data from a large sample for generalizing results to the large population and different cultural contexts. Hence, future research should extend this study to various cultural contexts. Second, the researchers may replicate the theoretical framework of this study to integrate variables such as personality traits and thus examine the impact of different personality attributes on the employees’ perception of job security when using social media. These attributes could help predict the SMA of employees (Tang et al., 2016; Gil de Zúñiga et al., 2017). Our findings also imply that the job security perceptions fostered by social media usage through SMD and networking behavior for employee retention and talent management (Mohammed, 2016; Bolander et al., 2017). As discussed earlier, this study did not find any multicollinearity between SMU and SMD, and the minimum thresholds of discriminant and convergent validity were also satisfied. Future research may investigate their SMU and SMD.

7. Conclusion

To sum up, our work advances existing research by integrating SMD and networking behavior in an overall model to examine the influence of SMU on job security perception among employees. Based on the theory of networks, this study underlined the conditions which guarantee the benefits of social media for employees and their managers. Simultaneously, these results suggest that despite its potential detrimental effects, excessive use of social media enhances employees` perception of job security by helping them maintain and develop their social networking.

Figures

Figure 1

Hypothetical framework

Table 1

Sample size calculation

No.	Sample method	Minimum sample
1	G-power (2-tail; medium effect size (0.3); 95% confidence interval; error 0.05)	134	Sample size used in the current study = 197
2	SEM Rule of thumb (#items*5)	32*5 = 160
3	General rule of thumb (Pallant, 2020)	Small (<100); medium (100–200); large (>200)
4	Tabachnick and Fidell (N > 50 + 8 m)	50 + 8(1) = 58

Source(s): Authors' own work

Table 2

Discriminant and convergent validity of the scales

	CR	AVE	MSV	MaxR(H)	SMU	NBM	SDM	BN
SMU	0.859	0.504	0.286	0.861	0.710
NBM	0.873	0.635	0.558	0.894	0.073	0.797
SDM	0.792	0.664	0.286	0.919	0.535	0.357	0.815
BN	0.882	0.601	0.558	0.888	0.071	0.747	0.226	0.775

Source(s): Authors' own work

Table 3

Estimates for serial mediation (SMU→SMD→SNB→JS) obtained from SPSS Process Macro (Model 6)

Variables	Model 1 Dependent variable = SMD	Model 2 Dependent variable = SNB	Model 3 Dependent variable = job security (JS)
Social media use (SMU)	0.630 (0.054) 11.616***	−0.52 (0.094) −0.552	0.103 (0.075) 1.363
Social media disorder (SMD)		0.517 (0.095) 5.414***	−0.16 (0.083) −0.192
Social networking behavior (SNB)			0.502 (0.058) 8.567***
Gender	0.0485 (0.123) 0.395	−0.044 (0.162) −0.272	−0.064 (0.131) −0.488
Social status	−0.129 (0.108) −1.195	−0.129 (0.143) −0.900	0.041 (0.116) 0.357
Age	−0.044 (0.084) −0.519	0.141 (0.111) 1.268	0.098 (0.089) 1.087
Qualification	−0.089 (0.069) −1.283	0.139 (0.092) 1.506	−0.022 (0.075) −0.294
Experience	−0.083 (0.44) −1.895	0.137 (0.058) 2.362	−0.013 (0.047) −0.267
R-squared	0.438	0.2056	0.358

Indirect effects with upperbound and lowerbound confidence intervals using 5,000 bootstrapping samples
Indirect effects	Estimates	Boot standard error	Confidence intervals [lowerbound, upperbound]
Indirect effect 1: SMU→SMD→SNB	−0.010	(0.066)	Insignificant: [0.0005, 0.2617]
Indirect effect 2: SMU→SNB→JS	−0.026	(0.050)	Insignificant: [−0.1269, 0.0684]
Indirect effect 3: SMU→SMD→SNB→JS	0.164	(0.046)	Significant: [0.0824, 0.2607]

Source(s): Authors' own work

Appendix Survey questionnaire