Abstract
Considering the prevalence and increased use of online social networking sites (SNSs), the present study investigated the association between visiting SNSs and users’ subjective wellbeing. Data were collected from 251 participants and were analyzed using partial least square-based structural equation modeling. The findings showed that there was no significant direct influence of SNS use on users’ subjective wellbeing. Additionally, the study found a significant mediating influence of passion on the association between SNS visits and subjective wellbeing. Moreover, the study did not find any significant negative mediating impact of obsession with SNS visits and association with subjective wellbeing. Among various personality traits, openness to experience had a positive moderating impact and neuroticism had a negative moderating impact on the association between SNS visits and subjective wellbeing among SNS users. The study provides implications for managers and parents regarding improved SNS use and increased subjective wellbeing.
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Introduction
Social networking sites (SNSs) such as Facebook, Linked-in, Instagram, WhatsApp, and Twitter, are now a part of daily life and individuals frequently use multiple SNSs for different purposes with varying motives (Bastos et al., 2015). SNSs refer to social media platforms where the users create their own public profiles and connect with friends and others with similar interests (Griffiths et al., 2014). The use of SNSs has increased significantly and the number of active social media at the time of writing was 4.2 billion (Statista, 2022). The multifaceted features of social network sites provide endless opportunities for continuous interaction, gathering large volumes of information to meet the needs and objectives of user groups (Chang & Hsu, 2016; Chang et al., 2015; Uddin et al., 2021).
Due to distinct human personalities, values, and attitudes, users’ interactions are assumed to be diverse, and therefore, the impact of different SNSs on individuals may vary (Huang, 2019; Islam et al., 2020a, b). Studies have shown that the presence and increasing use of social networks can impact human lives both positively and negatively (Stephanidis et al., 2019; Tandon et al., 2021). Considering the widespread presence of SNSs, the present study investigated their impact on users’ subjective wellbeing, and more specifically, assessed the relationship between the extent of SNS visits and the subjective wellbeing of SNSs users (Kaur et al., 2021).
Subjective well-being is an ideal mental state that individuals aspire to experience, such as happiness and life satisfaction, and this domain has become an important field of study in economic and psychological research due to the increasing use of SNSs (Wang et al., 2017; Wenninger et al., 2019). Because technologies are abstract and their use and impact depend on users’ personal attributes, the present study also examined the influence of user habits and personality traits on the association between SNS visit frequency and subjective wellbeing (Khan, 2021).
Previous studies have shown both the positive and negative effects of SNS use on various facets of human life (Bhatti et al., 2020; Han & Myers, 2018; Kaur et al., 2021). However, the underlying reasons and process of SNS influences on individuals’ lives is comparatively less known in both academic and policy research. Although users encounter various types of information while using SNSs, little of it has a negative impact (Han & Myers, 2018; Saridakis et al., 2016; Whaite et al., 2018). Users’ intentions for using SNSs can also assist and improve users’ subjective wellbeing (Stephanidis et al., 2019). Moreover, user habits and personality traits have a significant role in the use of SNSs and can impact the users’ lives and wellbeing (Gao et al., 2017).
Therefore, the present study formulated the underlying research questions to examine the influence of SNSs on users’ subjective wellbeing: (i) Does subjective wellbeing among SNS users have any association with SNS visit frequency? and (ii) Do user habits mediate, and personality traits moderate the association between SNS visit frequency and subjective wellbeing? The findings will help policymakers provide interventions regarding SNSs information sources, usage magnitude, and thoughtfulness associated with personality traits to improve subjective wellbeing of individuals. It is also envisaged that the findings of the study could provide empirical evidence of impact of SNS visits and subsequently help to improve wellbeing of human lives. While in many cases, SNS visits are often considered as detrimental to employees’ work performance, the findings could initiate a rethinking of such notions, and organizations could allow employees to visit SNSs as part of fulfilment of psychological and social needs which even could help to improve the individuals’ wellbeing and organizational performance.
Theoretical Background and Hypothesis Development
The present study’s objectives and hypotheses are rooted in the tenets of Social Presence Theory (Short et al., 1976), which proposes that individuals engage in social interaction to participate in and facilitate interpersonal communication as well as exchange information with one another (Gao et al., 2017; Oztok et al., 2015). As long as presence in social space (e.g., SNS sites) provides “intimacy” (i.e., a degree of attachment) and “immediacy” (i.e., a degree of reaction and responsiveness) to users, they feel motivated and encouraged to engage and interact with those objects and fellow users (Nowak, 2013). Virtual SNSs provide open platforms where users can meet and interact with others, gather desired information, and exchange ideas concerning their preferred domains and interests (Kaye & Quinn, 2020). On SNSs, users frequently follow one another’s posts, comments and activities, and they react using reciprocal comments, “likes,” and other indicative information (Kim et al., 2019).
Such communication and interaction signal intimacy and immediacy among virtual communities increases the level of presence in social spaces (Oztok et al., 2015). Therefore, increased presence on SNSs promotes more social interactions, which ultimately influences human behavior and subsequent outcomes (Chen et al., 2019; Nowak, 2013; Zych et al., 2021). Each individual has their own life values, attitudes, perceptions, and preferences. Whenever they interact frequently on SNSs, such interactions shape others’ subsequent psychological behavior and outcomes either knowingly or unknowingly (Chang & Hsu, 2016). Therefore, considering individuals’ psychological outcome while using SNSs, the present study investigated the association between SNS visits and users’ subjective wellbeing. Also, when individuals engage in reciprocal interactions with social networks, their personality traits and user habits may facilitate either positive or negative feelings regarding life and may aggravate psychological wellbeing (Brand et al., 2016). Since SNS use has become part of individuals’ daily lives, the influence of personality traits and user habits also need to be investigated carefully to improve SNS efficacy and user benefits.
The present study considered SNS visit frequency (SNSVF) as the independent variable and subjective wellbeing as the dependent variable. From a social presence theory perspective, visits to SNSs provide opportunities of both “intimacy” and “immediacy” in social space and influence individuals’ psychological subjective wellbeing. In the process of influencing subjective wellbeing, personality is assumed to play a significant role as shown in earlier studies (Etxeberria et al., 2019; Stead & Bibby, 2017). Therefore, the present study further considered personality as the moderating variable in the hypothesized relationship between SNS visit frequency and subjective wellbeing. Figure 1 illustrates the conceptual framework of the study.
Social Networking Sites Visit and Subjective Wellbeing
Online SNSs are common social spaces for individuals worldwide and provide users with opportunities to meet new individuals and interact more frequently (Dhir et al., 2018; Huang et al., 2021). Extended exposure to various SNSs provides more widespread information and helps individuals enjoy improved social relationships (Kaye & Quinn, 2020). However, as each individual is unique, possessing distinct thoughts and cognitive capacities, extended exposure to SNSs may develop into a passion or excessive obsession, and could therefore positively and/or negatively impact their psychological health and personal wellbeing (Stephanidis et al., 2019). Prior research has indicated that young people’s Facebook use encourages closer social connection, i.e., effective peer relationships and subsequent satisfaction in life (Huang, 2017; Shi et al., 2019) and similar findings have also been observed between older people’s social media use and life satisfaction (Gaia et al., 2021). A meta-analysis by Verduyn et al. (2017) reported a positive relationship between active SNS use (i.e., providing feedback on other SNS users’ messages, comments or pictures) and higher levels of subjective wellbeing. However, some researchers have identified social networks’ negative influence on individuals, which was expressed by such feelings as solitude, depression, degraded social skills, and other negative psychological aspects of life (Kim et al., 2019; Koç & Turan, 2021).
A study by Pontes et al. (2015) also noted that non-controlled or problematic use of internet over controlled use of internet, including SNSs, has a negative impact on users’ quality of life. Kross et al. (2013) reported a U-shape relationship between social network visits and wellbeing. More specifically, they reported that users initially feel good when visiting SNSs, but that the good feelings slowly diminish over repeated SNS visits. Huang (2017) noted that, to date, the association between time spent on SNSs and users’ wellbeing is diminutive. Therefore, the SNS uses are assumed to impact on users’ wellbeing and other psychological aspects (Lee et al., 2010; Shi et al., 2019; Wheatley & Buglass, 2019).
The Mediating Role of User Habits on the Social Network-Wellbeing Relationship
From a psychological perspective, users’ visiting habits could be explained using the dualistic model of passion comprising harmonious passion and obsessive passion (Vallerand et al., 2010). Harmonious passion posits that individuals might involve themselves in any activity or event enthusiastically through their own choice without any adverse concomitant effects (Vallerand, 2008). On the other hand, obsessive passion can compete with individuals’ other life accomplishments if the passion becomes too much (Vallerand et al., 2010). Therefore, harmonious passion has been found to prognosticate subjective wellbeing, appreciation for life, positive feelings, life satisfaction, and viability (Carpentier et al., 2012). Carbonell and Panova (2017) showed that SNSs use can be considered as an object of passion and posit that individuals who frequently visit SNSs become more passionately involved.
However, sometimes passion can lead to conflict with other activities (e.g., being too passionate for an activity might result in delaying or postponing another) which leads to obstinate stubbornness and in turn impede attaining a balanced and prosperous life (Vallerand et al., 2010). Other authors have emphasized that obsessive passion could be the reason users face undesirable consequences, such as burnout and exhaustion, when involved in SNS activities (Al-Kandari & Al-Sejari, 2020; Vallerand et al., 2010). Therefore, obsessive passion can occasionally facilitate negative sentiments and despair. Ultimately, obsession with such activities can negatively affect users’ life satisfaction and subsequently reduce subjective wellbeing (Philippe et al., 2009).
Previous research has shown that passion plays a substantial role in an individual’s wellbeing (Marsh et al., 2013). Social networking site visit frequency could also facilitate individuals’ habits of passion or obsession in relation to SNS use which could subsequently affect their subjective wellbeing. Błachnio and Przepiorka (2016) found that individuals who were more obsessed with the internet, in particular, Facebook, demonstrated the characteristics found among neurasthenic and introverted individuals. Although many concepts have helped identify and clarify the factors that result in an improved life, research demonstrates that the concept of passion deserves special attention (Vallerand et al., 2010).
Personality Traits as Moderators in the SNS Visit Frequency-Wellbeing Relationship
Among various personality trait categories, the Big Five framework comprising extraversion, neuroticism, conscientiousness, openness, and agreeableness is the most extensively used typology in information technology research (Huang, 2019). Stead and Bibby (2017) asserted that problematic internet use negatively influences SNS users’ subjective wellbeing. Moreover, studies have reported the positive association between problematic internet use and neuroticism, whereas agreeableness, extraversion, and openness to new practices appear in the absence of problematic internet use (Kayiş et al., 2016; Montag et al., 2021). Among the Big Five traits, research has shown that neuroticism and openness traits are most commonly associated with internet and information technology use (Mark & Ganzach, 2014; Wu et al., 2018). Hastings and O’Neill (2009) found that neurotic individuals are comparatively more inclined to spiral into negative feelings quickly (i.e., to be angry, worried, and depressed). Yao et al. (2013) reinforced this finding by reporting a positive association between internet obsession and neuroticism.
A study by López-Fernández et al. (2020) reported that neuroticism had a significant positive influence on adolescents’ excessive use of the internet. Other authors also confirm and support the association between neuroticism and being inclined to engage more frequently in online activities (Charlton & Danforth, 2010). Jordan et al. (2015) asserted that openness drives individuals to participate in more online activity. In summary, SNSs users with higher neuroticism and openness tend to influence subjective wellbeing more than SNSs users with lower neuroticism and openness. Based on the all the aforementioned literature, it was hypothesized that:
H1. Social network users’ subjective wellbeing would have a positive association with the SNS visit frequency.
H2. Passionate social network use would mediate the influence of SNS visit frequency on subjective wellbeing.
H3. Obsessive social network use would mediate the influence of SNS visit frequency on subjective wellbeing.
H4. Openness would moderate users’ SNS visit frequency and subjective wellbeing relationship.
H5. Neuroticism would moderate users’ SNS visit frequency and subjective wellbeing relationship.
Methods
Participants and Data Collection
The study sample participants were students in different universities (i.e., undergraduate and postgraduate students in various Kazakhstani universities). Purposive sampling was used because young university students are the most common SNS users (Qin et al., 2018). A total of 455 printed surveys were distributed to students through the deployment of research assistants, and 260 replies were received. A sample size of 200 is adequate for structural equation modeling (Hoelter, 1983; Loehlin, 2004). Moreover, further adequacy of the sample size was ascertained by two important determinants: (i) sample size (n) and the number of predictors (k) utilizing the “10 times rule” (Pituch & Stevens, 2016) which means a minimum of 70 participants would be needed, and (ii) five cases per parameter estimate which means 145 participants would be needed (Bentler & Chou, 1987). Therefore, the number of participants in the present study exceeded all of these guideline estimates.
After discarding responses that were unmatched, missing, or had outlier issues, a total of 251 surveys remained for analysis. The response rate for totally completed surveys was 55.16% which is moderately higher than the average response rate in similar research (Uddin et al., 2021). The demographic profile of the participants yielded a higher percentage of female participants (62.5%) than male (37.5%). Responses were collected across disciplines: 44 came from those with accounting majors (17.5%), 153 from finance majors (61.0%), and the remainder from marketing and management majors (21.5%). The majority of participants were undergraduates (69.3%). Notably, the largest segment of participants was under 25 years old (68.9%) (Table 1).
Measures
The present study used the constructs from prior studies published in English. The survey excluded any identifiable detail of participants’ identities to ensure accurate responses. The participants were requested to express an appropriate response to each item in the survey. Appendix A includes the survey measures used in this survey. The survey also included participants’ demographic information, such as age, studied major, gender, and education. In line with Beaton et al. (2000) suggestion, a few modifications to the statements in each scale were made for the cultural adaptation in Kazakhstan context with the help of five faculty members and ten potential participants. Additionally, in line with the guidelines by Brislin (1970), a back-translation procedure was followed to translate the instrument from English into native Kazakh language to elicit accurate responses from Kazakh participants. This back-translation process was iterated until no significant difference in understanding between the original and native language was observed. In this translation process, voluntary assistance was provided by five academics and ten university graduates who were proficient in both English and Kazakh languages.
Neuroticism
The four-item Neuroticism subscale from the Brief HEXACO Inventory (de Vries, 2013) was used to assess neuroticism. Items (e.g., “While watching or enjoying sad or romantic movies, I cannot control my emotions”) assessed on a six-point scale from 1 (strongly disagree) to 6 (strongly agree). Higher scores reflect higher emotional instability. The Cronbach alpha for neuroticism in the present study was 0.842.
Openness
The four-item Openness subscale from the Brief HEXACO Inventory (de Vries, 2013) was used to assess openness. Items (e.g., “I often express criticism without considering the consequences”) were assessed on a six-point scale from 1 (strongly disagree) to 6 (strongly agree). Higher scores reflect higher openness. The Cronbach alpha for openness in the present study was 0.831.
Passionate Social Networking Use
An adapted version of the seven-item Passion subscale (Vallerand et al., 2003) was used to assess passionate social networking use. The word “activity” or “it” was replaced by the words “social networking.” Items (e.g., “Social networking help me to develop/understand good qualities of life”) were assessed on a six-point scale from 1 (strongly disagree) to 6 (strongly agree). Higher scores indicate higher passionate use of social networking. The Cronbach alpha for passion in the present study was 0.90.
Obsessive Social Networking Use
An adapted version of the seven-item Obsession subscale (Vallerand et al., 2003) was used to assess obsessive social networking use. Items (e.g., “I feel emotional and mental dependence on social networking”) were assessed on a six-point scale from 1 (strongly disagree) to 6 (strongly agree). Higher scores indicate higher obsession with the use of social networking. The Cronbach alpha for passion in the present study was 0.856.
Subjective Wellbeing
Six items from 10-item Personal Wellbeing Index of the Organization for Economic Co-operation and Development (2013) were used to assess subjective wellbeing. Items (e.g., “How satisfied are you with what you are achieving in life?”) are assessed on a six-point scale from 1 (very dissatisfied) to 6 (very satisfied). Higher scores indicate greater subjective wellbeing. The Cronbach alpha for passion in the present study was 0.876.
SNS Visit Frequency
SNS visit frequency was assessed using a single item adapted from Coyne et al. (2020). The item (“How many times do you visit social networking sites on a typical day?”) is assessed on a six-point scale from 1 (too few times such as 0–2 times a day) to 6 (too many times, such as more than 10 times a day).
Data Analysis
The present study used partial least square-based structural equation modeling (PLS-SEM), a second-generation regression technique. PLS-SEM using SmartPLS2 was chosen over other analytical tools because of its ability to estimate the overall predictability and strength of the model in an integrated manner (Hair Jr et al., 2014). PLS-SEM integrates measurement and structural models through using confirmatory factor analysis and path (structural) analysis (Fan et al., 2019). The analysis followed the bootstrapping procedure with 5000 cases to generate t-statistics and the significance levels (Das et al., 2019; Hair Jr et al., 2017; Yi et al., 2019).
Measurement Issues
In its measurement model, the study tested its variables and found them suitable for representing distinctive constructs. Accordingly, composite reliability, convergent validity, and discriminant validity were estimated for all the constructs. Composite reliability assesses an item’s consistency in underlying a construct; convergent validity and discriminant validity assert items’ suitability to represent or be distinct from the intended construct (Azim et al., 2019; Uddin et al., 2019). Whereas convergent validity reflects the higher correlation among items representing a construct, discriminant validity demonstrates the distinctiveness of a construct from other constructs (Hair Jr. et al., 2017). Three obsession items and two passion and subjective wellbeing items were dropped due to poor regression weights. In Table 2, minimum composite reliability and Cronbach’s alpha scores are 0.880 (neuroticism) and 0.831 (openness), which are adequate for SEM applications and considered acceptable for SEM. To assess convergent validity, average variance extracted (AVE) was estimated, yielding the lowest AVE for any given construct as 0.650 (neuroticism), which is above the minimum threshold limit (0.50) (Das et al., 2019; Fan et al., 2019; Yi et al., 2019). Data normality was also tested. In line with the findings of Shan et al. (2013), the data were normally distributed because both skewness and kurtosis were within the limits of 1.70 and 3.00, respectively.
Estimates in Table 3 demonstrate the discriminant validity following Fornell-Larcker’s criterion. The estimates show that the square root of a particular construct’s AVE is higher than its correlation with other constructs. Therefore, no concern in discriminant validity existed among the constructs (Fornell & Larcker, 1981). Additionally, confirmatory factor analysis estimated in Table 3 shows that items were highly loaded to their respective constructs than others (Chin, 1998). No items were loaded to variables other than those within their own construct (Table 4).
The structural model estimated the coefficient of determination (R2), effect size, variance inflation factor (VIF), and coefficient (β). The coefficient of determination demonstrates the structural model’s overall predictive power, and the beta-coefficient indicates the influence that exogenous variables have on endogenous variables (Mahmood et al., 2019; Uddin et al., 2019). To ensure the model’s robustness, the data were further checked for collinearity issues. Previous studies show that a VIF above 10 might jeopardize the model and its predictability (Field, 2018). However, Table 5 shows that the maximum VIF was 1.171 (subjective wellbeing). Therefore, collinearity was not a major concern.
Additionally, effect size accounts for the variance in endogenous variables that their exogenous variables may cause. An effect size (R2) value > 0.02 is considered in the medium category, while > 0.15 is considered large. Additionally, an effect size larger than 0.20 is adequate in the management science discipline (Hair Jr. et al., 2017). Figure 2 shows the path relationships, p-value, and R2 estimates. The estimates in Fig. 2 demonstrated mixed results, containing both significant and non-significant path relationships. It was observed that independent variables jointly accounted for a 51% (R2) change in subjective wellbeing that satisfies the threshold limit (Cohen, 1988; Hair Jr et al., 2014). Social networking explained a 10% (R2) change in passion and a 0.3% (R2) change in obsession.
Results
Figure 2 shows that social network use frequency was not positively associated with users’ subjective wellbeing (β = 0.065, p = 0.381). Therefore, the empirical findings do not support H1. In H2 and H3, it was hypothesized that passion and obsession would mediate the association between SNS visit frequency and subjective wellbeing. Table 6 shows the estimates of the mediating effects of passion and obsession. The analysis tested the influence (c) of users’ SNS visit frequency on subjective wellbeing before running the mediating effect in H2. Accordingly, the analysis tested the indirect effects from users’ visit frequency on SNSs to passion and passion to subjective wellbeing after running the mediating variable (passion). Since all indirect effects (“a” and “b”) were significantly associated, the analysis further examined the direct effect (c/) after using a mediating variable, which was found to be non-significant. In line with explanations by Hair Jr. et al. (2017), Azim et al. (2019), Fan et al. (2019), and Uddin et al. (2019), full mediation existed. Therefore, H2 was supported. While examining the mediating influence of obsession in H3, it was observed that neither indirect effect of users’ SNS visit frequency on SWB nor the direct effect (c/) of users’ SNS visit frequency on SWB were found to be significant. Consequently, H3 was not supported.
To examine the moderating effect of neuroticism and openness, the study followed the procedure by Lowry and Gaskin (2014). Figure 2 shows that neither neuroticism (β = 0.037, p = 0.705) nor SNS visit frequency (β = 0.047, p = 0.381) had any significant influence on users’ subjective wellbeing. Therefore, the condition for the existing significant effects of the moderating variables and predictor variables to ensure the moderating effect on the dependent variable did not exist. Therefore, the moderating effect of neuroticism on subjective wellbeing (H4) was not supported. Finally, the analysis examined the moderating effect of openness on the association between SNS visit frequency and subjective wellbeing. Findings in Fig. 3 highlight the moderating effect of openness on the impact of SNS visit frequency on subjective wellbeing was also non-significant (β = 0.033, p = 0.524). Therefore, H5 was supported.
The interaction effect is shown in Fig. 4, which shows the positive moderating effect of openness on passion’s impact on subjective wellbeing. The findings showed that when openness was high, high passion led to higher subjective wellbeing. On the other hand, when openness was low, high passion did not change users’ subjective wellbeing. This moderating influence was non-significant (β = 0.340, p = 0.620) with 5000 bootstrapped resamples and 95% confidence intervals.
Discussion
The present study’s main objective was to investigate the association of SNS use with subjective wellbeing among young adults. In contrast to previous findings, the findings showed no significant association of SNS visit frequency with young people’s lives nor their wellbeing. This generation grew up amid technological innovations, which caused them to not only understand the technologies but to be aware of their pitfalls. Despite constant exposure to SNS visit frequency, they feel good when they receive and interact with intended and unintended information on social network sites.
SNSs continuously update and feed users’ new information, which ignites their inherent human curiosity and entices the users to visit the sites as frequently as possible (Chang et al., 2015). Social networking technologies’ virtual platforms expand individuals’ horizons in meeting with others and sharing or exchanging opinions and feelings that they deem stimulating and interesting (Mark & Ganzach, 2014). Each SNS has unique features and offers different utilities to its users. Many technological platforms for social networking sites exist, and most individuals use multiple social networking sites simultaneously for various reasons. As long as users get diverse and user-friendly information using SNS sites, they further continue using SNS sites. Therefore, the study’s findings are in agreement with prior research (Clark et al., 2018; Ha et al., 2015). Additionally, Ha et al. (2015) showed that internet and social networking sites facilitate social interactions and pleasant feelings among users.
The present study also investigated the mediating role of passion and obsession toward SNS visits. The empirical findings showed that passionate use of SNS positively mediateds SNS visit frequency and subjective wellbeing. It seems that as long as users are enthusiastic and find the content and information interesting, they will continue visiting the sites without experiencing negative feelings. Believing that they are obtaining relevant and necessary information may lead them to feel better about their SNS use. Meanwhile, no substantial effect of obsession on subjective wellbeing exists which negates previous research because obsession to SNS, such as overuse or underuse of SNS, negatively impacts various facets of lives (Han & Myers, 2018). Individuals might reconsider their choices of SNS between “necessary means” and “unnecessary evil” in their different aspects of lives.
Additionally, the research further investigated the moderating effect of neuroticism and openness on the association between SNS visit frequency and subjective wellbeing. The findings showed that the traits of openness to experience and neuroticism did not have any moderating impact on the association of SNS visit frequency and subjective wellbeing. However, the direct effect of passion on users’ subjective wellbeing was significant. These findings align with previous findings with respect to the use of various SNSs (Blackwell et al., 2017; Kayiş et al., 2016). Openness and agreeableness tend to facilitate engagement, which in consequence contributes to a more positive aptitude (Tov et al., 2016). Although neuroticism may occasionally create fear of missing out from their loops or SNS users, it does not seem to impact wellbeing as long as users are passionate about and aware of their SNS use and its successive impacts (Blackwell et al., 2017).
Contributions of the Study
From a theoretical perspective, the findings contribute to both the information management and social psychology literature. First, the study found a mediated influence of passion in the association between extended SNS use and subjective wellbeing. Consequently, the findings of the study reiterate applicability and validity of SPT in context of new SNS technology use. In the era of a highly individualized society, the proliferation and penetration of information technology and social networks provide a sense of belongingness for users who interact in these virtual platforms. As SNSs appear to increase feelings of intimacy and immediacy among the younger generation, passionate use of SNSs could improve their subjective wellbeing. Therefore, it could be considered as an activity that fulfills their social needs. Second, the study showed the mediating effect of passion and obsession that opens potential avenues for linking SPT through psychological mechanisms, such as the theory of reasoned action. Integration could allow a strengthening of the information technology and life satisfaction relationship and bring greater positive attributes to daily lives.
Implications for Managers and Parents
SNS use is often considered negatively. For instance, most organizations prevent employees from using SNSs during work hours. The present study found a non-significant association between SNS use and subjective wellbeing, therefore it is suggested that managers might perhaps rethink how using SNSs could enhance employees’ wellbeing with a mediated mechanism of passion rather than limiting SNS use in the workplace. Similarly, parents should not prohibit their adolescent children using SNSs given that SNS use in moderation is an outlet for them to interact with friends and acquaintances and helps adolescents to meet their social needs. Considering the nature of the jobs and the type of businesses, responsible managers must identify appropriate interventions to optimize SNS use benefitting both employees and organizations (Qin et al., 2018). Similarly, parents should be educated on the practical benefits for their adolescent children given that SNS use could be useful for the adolescents to improve their social lives.
Limitations and Directions for Future Research
Although several measures were taken to limit response bias, such as back-translation of the scales (Brislin, 1970; Qin et al., 2018), Harman’s one factor test (Harman, 1967), and inter-scale correlation (Bagozzi et al., 1991; Pavlou et al., 2007), the present study has some limitations that restrict the robustness of the findings. First, it examined the association of SNS visits in with users’ subjective wellbeing but the duration of the visits was not assessed. Future studies should therefore examine the impact SNS visit time duration on users’ subjective wellbeing. Second, the study was conducted in only one country and the degree of SNS use intensity and sensitivity might be varied in different cultural differences (Qin et al., 2018). Future studies could contain samples from different countries, which could help produce more reliable, generalized findings. Third, the study only collected cross-sectional data from Kazakhstan and this prevents the generalization of the findings. Future research using larger sample sizes with longitudinal survey data could be useful to understand better the relationship between SNS visit frequency on user subjective wellbeing. Fourth, the study relied on participants’ retrospective report of their SNS use and visiting time. Prior research has shown that users provide inaccurate responses of retrospective information of SNS use and visiting time when self-reporting (Ellis et al., 2019). Future research using the real-time objective information of SNS use habit and visiting time could provide more accurate and reliable results concerning the impact of SNS on human behavior. Additionally, the present study only investigated the mediating roles of passion and obsession and moderating effects of openness and neuroticism, but other personality types, particularly conscientiousness, were not investigated and this might influence the relationships between SNS use and subjective wellbeing. Thereby, future studies might consider other control variables, which would provide a holistic understanding of SNS use and its subsequent impact on subjective wellbeing. The findings of the present study add new evidence to the literature on SNS use and subjective wellbeing. It showed that passionate use of SNS was positive in relation to subjective wellbeing, and suggests that behavioral interventions are needed for reducing obsessive use of SNS use in daily life.
Data Availability
The data will be made available by the first author on reasonable request.
References
Al-Kandari, Y. Y., & Al-Sejari, M. M. (2020). Social isolation, social support and their relationship with smartphone addiction. Information, Communication & Society, 1–19. https://doi.org/10.1080/1369118X.2020.1749698
Azim, M. T., Fan, L., Uddin, M. A., Jilani, M. M. A. K., & Begum, S. (2019). Linking transformational leadership with employees’ engagement in the creative process. Management Research Review, 42(7), 837–858. https://doi.org/10.1108/MRR-08-2018-0286
Bagozzi, R. P., Yi, Y., & Phillips, L. W. (1991). Assessing construct validity in organizational research. Administrative Science Quarterly, 36(3), 421–458. https://doi.org/10.2307/2393203
Bastos, M., Mercea, D., & Charpentier, A. (2015). Tents, tweets, and events: The interplay between ongoing protests and social media. Journal of Communication, 65(2), 320–350. https://doi.org/10.1111/jcom.12145
Beaton, D. E., Bombardier, C., Guillemin, F., & Ferraz, M. B. (2000). Guidelines for the process of cross-cultural adaptation of self-report measures. Spine, 25(24), 3186–3191. https://doi.org/10.1097/00007632-200012150-00014
Bentler, P. M., & Chou, C. -P. (1987). Practical issues in structural modeling. Sociological Methods & Research, 16(1), 78–117. https://doi.org/10.1177/0049124187016001004
Bhatti, Z. A., Arain, G. A., Akram, M. S., Fang, Y. -H., & Yasin, H. M. (2020). Constructive voice behavior for social change on social networking sites: A reflection of moral identity. Technological Forecasting and Social Change, 157, 120101. https://doi.org/10.1016/j.techfore.2020.120101
Błachnio, A., & Przepiorka, A. (2016). Personality and positive orientation in Internet and Facebook addiction. An empirical report from Poland. Computers in Human Behavior, 59, 230–236. https://doi.org/10.1016/j.chb.2016.02.018
Blackwell, D., Leaman, C., Tramposch, R., Osborne, C., & Liss, M. (2017). Extraversion, neuroticism, attachment style and fear of missing out as predictors of social media use and addiction. Personality and Individual Differences, 116, 69–72. https://doi.org/10.1016/j.paid.2017.04.039
Brand, M., Young, K. S., Laier, C., Wölfling, K., & Potenza, M. N. (2016). Integrating psychological and neurobiological considerations regarding the development and maintenance of specific Internet-use disorders: An interaction of person-affect-cognition-execution (I-PACE) model. Neuroscience & Biobehavioral Reviews, 71, 252–266. https://doi.org/10.1016/j.neubiorev.2016.08.033
Brislin, R. (1970). Back-translation for cross-cultural research. Journal of Cross-Cultural Psychology, 1(3), 185–216. https://doi.org/10.1177/135910457000100301
Carbonell, X., & Panova, T. (2017). A critical consideration of social networking sites’ addiction potential. Addiction Research & Theory, 25(1), 48–57. https://doi.org/10.1080/16066359.2016.1197915
Carpentier, J., Mageau, G. A., & Vallerand, R. J. (2012). Ruminations and flow: Why do people with a more harmonious passion experience higher well-being? Journal of Happiness Studies, 13(3), 501–518. https://doi.org/10.1007/s10902-011-9276-4
Chang, C. -C., Hung, S. -W., Cheng, M. -J., & Wu, C. -Y. (2015). Exploring the intention to continue using social networking sites: The case of Facebook. Technological Forecasting and Social Change, 95, 48–56. https://doi.org/10.1016/j.techfore.2014.03.012
Chang, C. -M., & Hsu, M. -H. (2016). Understanding the determinants of users’ subjective well-being in social networking sites: An integration of social capital theory and social presence theory. Behaviour & Information Technology, 35(9), 720–729. https://doi.org/10.1080/0144929X.2016.1141321
Charlton, J. P., & Danforth, I. D. W. (2010). Validating the distinction between computer addiction and engagement: Online game playing and personality. Behaviour & Information Technology, 29(6), 601–613. https://doi.org/10.1080/01449290903401978
Chen, S., Shao, B. -J., & Zhi, K. -Y. (2019). Examining the effects of passive WeChat use in China. International Journal of Human-Computer Interaction, 35(17), 1630–1644. https://doi.org/10.1080/10447318.2018.1559535
Chin, W. W. (1998). Commentary: Issues and opinion on structural equation modeling. MIS Quarterly, 22(1), vii–xvi.
Clark, J. L., Algoe, S. B., & Green, M. C. (2018). Social network sites and well-being: The role of social connection. Current Directions in Psychological Science, 27(1), 32–37. https://doi.org/10.1177/0963721417730833
Cohen, J. (1988). Statistical power analysis for the behavioral sciences. Lawrence Erlbaum Associates.
Coyne, S. M., Rogers, A. A., Zurcher, J. D., Stockdale, L., & Booth, M. (2020). Does time spent using social media impact mental health?: An eight year longitudinal study. Computers in Human Behavior, 104, 106160. https://doi.org/10.1016/j.chb.2019.106160
Das, A. K., Biswas, S. R., Jilani, M. M. A. K., & Uddin, M. A. (2019). Corporate environmental strategy and voluntary environmental behavior—Mediating effect of psychological green climate. Sustainability, 11(11), 3123–3139. https://doi.org/10.3390/su11113123
de Vries, R. E. (2013). The 24-item Brief HEXACO Inventory (BHI). Journal of Research in Personality, 47(6), 871–880. https://doi.org/10.1016/j.jrp.2013.09.003
Dhir, A., Yossatorn, Y., Kaur, P., & Chen, S. (2018). Online social media fatigue and psychological wellbeing—A study of compulsive use, fear of missing out, fatigue, anxiety and depression. International Journal of Information Management, 40, 141–152. https://doi.org/10.1016/j.ijinfomgt.2018.01.012
Ellis, D. A., Davidson, B. I., Shaw, H., & Geyer, K. (2019). Do smartphone usage scales predict behavior? International Journal of Human-Computer Studies, 130, 86–92. https://doi.org/10.1016/j.ijhcs.2019.05.004
Etxeberria, I., Etxebarria, I., & Urdaneta, E. (2019). Subjective well-being among the oldest old: The role of personality traits. Personality and Individual Differences, 146, 209–216. https://doi.org/10.1016/j.paid.2018.04.042
Fan, L., Mahmood, M., & Uddin, M. A. (2019). Supportive Chinese supervisor, innovative international students: A social exchange theory perspective. Asia Pacific Education Review, 20(1), 101–115. https://doi.org/10.1007/s12564-018-9572-3
Field, A. (2018). Discovering statistics using IBM SPSS statistics (5th ed.). Sage Publications Inc.
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50. https://doi.org/10.1177/002224378101800104
Gaia, A., Sala, E., & Cerati, G. (2021). Social networking sites use and life satisfaction. A quantitative study on older people living in Europe. European Societies, 23(1), 98–118. https://doi.org/10.1080/14616696.2020.1762910
Gao, W., Liu, Z., & Li, J. (2017). How does social presence influence SNS addiction? A belongingness theory perspective. Computers in Human Behavior, 77, 347–355. https://doi.org/10.1016/j.chb.2017.09.002
Griffiths, M. D., Kuss, D. J., & Demetrovics, Z. (2014). Social networking addiction: An overview of preliminary findings. In K. P. Rosenberg & L. C. Feder (Eds.), Behavioral addictions: Criteria, evidence, and treatment (pp. 119–141). Academic Press.
Ha, Y. W., Kim, J., Libaque-Saenz, C. F., Chang, Y., & Park, M. -C. (2015). Use and gratifications of mobile SNSs: Facebook and KakaoTalk in Korea. Telematics and Informatics, 32(3), 425–438. https://doi.org/10.1016/j.tele.2014.10.006
Hair, J. F., Jr., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2014). A primer on partial least squares structural equation modeling (PLS-SEM). SAGE Publications Inc.
Hair, J. F., Jr., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2017). A primer on partial least squares structural equation modeling (PLS-SEM). Sage Publication.
Han, B., & Myers, C. (2018). Perceptions of overuse, underuse, and change of use of a social media site: Definition, measurement instrument, and their managerial impacts. Behaviour & Information Technology, 37(3), 247–257. https://doi.org/10.1080/0144929X.2018.1432687
Harman, D. (1967). A single factor test of common method variance. Journal of Psychology, 35(1967), 359–378.
Hastings, S. E., & O’Neill, T. A. (2009). Predicting workplace deviance using broad versus narrow personality variables. Personality and Individual Differences, 47(4), 289–293. https://doi.org/10.1016/j.paid.2009.03.015
Hoelter, J. W. (1983). The analysis of covariance structures: Goodness-of-fit indices. Sociological Methods & Research, 11(3), 325–344. https://doi.org/10.1177/0049124183011003003
Huang, C. (2017). Time spent on social network sites and psychological well-being: A meta-analysis. Cyberpsychology, Behavior, and Social Networking, 20(6), 346–354. https://doi.org/10.1089/cyber.2016.0758
Huang, C. (2019). Social network site use and Big Five personality traits: A meta-analysis. Computers in Human Behavior, 97, 280–290. https://doi.org/10.1016/j.chb.2019.03.009
Huang, L., Zheng, D., & Fan, W. (2021). Do social networking sites promote life satisfaction? The explanation from an online and offline social capital transformation. Information Technology & People, ahead-of-print(ahead-of-print). https://doi.org/10.1108/ITP-03-2020-0143
Islam, A. K. M. N., Laato, S., Talukder, S., & Sutinen, E. (2020a). Misinformation sharing and social media fatigue during COVID-19: An affordance and cognitive load perspective. Technological Forecasting and Social Change, 159, 12020a1. https://doi.org/10.1016/j.techfore.2020.120201
Islam, M. S., Sujan, M. S. H., Tasnim, R., Ferdous, M. Z., Masud, J. H. B., Kundu, S., ... & Griffiths, M. D. (2020b). Problematic internet use among young and adult population in Bangladesh: Correlates with lifestyle and online activities during the COVID-19 pandemic. Addictive Behaviors Reports, 12, 100311. https://doi.org/10.1016/j.abrep.2020.100311
Jordan, G., Pope, M., Wallis, P., & Iyer, S. (2015). The relationship between openness to experience and willingness to engage in online political participation is influenced by news consumption. Social Science Computer Review, 33(2), 181–197. https://doi.org/10.1177/0894439314534590
Kaur, P., Islam, N., Tandon, A., & Dhir, A. (2021). Social media users’ online subjective well-being and fatigue: A network heterogeneity perspective. Technological Forecasting and Social Change, 172, 121039. https://doi.org/10.1016/j.techfore.2021.121039
Kaye, L. K., & Quinn, S. (2020). Psychosocial outcomes associated with engagement with online chat systems. International Journal of Human-Computer Interaction, 36(2), 190–198. https://doi.org/10.1080/10447318.2019.1620524
Kayiş, A. R., Satici, S. A., Yilmaz, M. F., Şimşek, D., Ceyhan, E., & Bakioğlu, F. (2016). Big five-personality trait and internet addiction: A meta-analytic review. Computers in Human Behavior, 63, 35–40. https://doi.org/10.1016/j.chb.2016.05.012
Khan, A. N. (2021). A diary study of psychological effects of misinformation and COVID-19 threat on work engagement of working from home employees. Technological Forecasting and Social Change, 171, 120968. https://doi.org/10.1016/j.techfore.2021.120968
Kim, J., Kim, J., & Yang, H. (2019). Loneliness and the use of social media to follow celebrities: A moderating role of social presence. The Social Science Journal, 56(1), 21–29. https://doi.org/10.1016/j.soscij.2018.12.007
Koç, T., & Turan, A. H. (2021). The relationships among social media intensity, smartphone addiction, and subjective wellbeing of Turkish college students. Applied Research in Quality of Life, 16(5), 1999–2021. https://doi.org/10.1007/s11482-020-09857-8
Kross, E., Verduyn, P., Demiralp, E., Park, J., Lee, D. S., Lin, N., Ybarra, O. (2013). Facebook use predicts declines in subjective well-being in young adults. PloS One, 8(8), e69841. https://doi.org/10.1371/journal.pone.0069841
Lee, G., Lee, J., & Kwon, S. (2010). Use of social-networking sites and subjective well-being: A study in South Korea. Cyberpsychology, Behavior, and Social Networking, 14(3), 151–155. https://doi.org/10.1089/cyber.2009.0382
Loehlin, J. C. (2004). Latent variable models: An introduction to factor, path, and structural equation analysis. Lawrence Erlbaum Associates Inc.
López-Fernández, F. J., Mezquita, L., Etkin, P., Griffiths, M. D., Ortet, G., & Ibáñez, M. I. (2020). The role of violent video game exposure, personality, and deviant peers in aggressive behaviors among adolescents: A two-wave longitudinal study. Cyberpsychology, Behavior, and Social Networking, 24(1), 32–40. https://doi.org/10.1089/cyber.2020.0030
Lowry, P. B., & Gaskin, J. (2014). Partial least squares (pls) structural equation modeling (sem) for building and testing behavioral causal theory: When to choose it and how to use it. IEEE Transactions on Professional Communication, 57(2), 123–146. https://doi.org/10.1109/TPC.2014.2312452
Mahmood, M., Uddin, M. A., & Luo, F. (2019). Influence of transformational leadership on employees’ creative process engagement: A multi-level analysis. Management Decision, 57(3), 741–764. https://doi.org/10.1108/MD-07-2017-0707
Mark, G., & Ganzach, Y. (2014). Personality and Internet usage: A large-scale representative study of young adults. Computers in Human Behavior, 36, 274–281. https://doi.org/10.1016/j.chb.2014.03.060
Marsh, H. W., Vallerand, R. J., Lafrenière, M. -A. K., Parker, P., Morin, A. J. S., Carbonneau, N., & Paquet, Y. (2013). Passion: Does one scale fit all? Construct validity of two-factor passion scale and psychometric invariance over different activities and languages. Psychological Assessment, 25(3), 796–809. https://doi.org/10.1037/a0032573
Montag, C., Wegmann, E., Sariyska, R., Demetrovics, Z., & Brand, M. (2021). How to overcome taxonomical problems in the study of Internet use disorders and what to do with “smartphone addiction”? Journal of Behavioral Addictions, 9(4), 908–914. https://doi.org/10.1556/2006.8.2019.59
Nowak, K. L. (2013). Choosing Buddy Icons that look like me or represent my personality: Using Buddy Icons for social presence. Computers in Human Behavior, 29(4), 1456–1464. https://doi.org/10.1016/j.chb.2013.01.027
Oztok, M., Zingaro, D., Makos, A., Brett, C., & Hewitt, J. (2015). Capitalizing on social presence: The relationship between social capital and social presence. The Internet and Higher Education, 26, 19–24. https://doi.org/10.1016/j.iheduc.2015.04.002
Pavlou, P. A., Liang, H., & Xue, Y. (2007). Understanding and mitigating uncertainty in online exchange relationships: A principal-agent perspective. MIS Quarterly, 31(1), 105–136. https://doi.org/10.2307/25148783
Philippe, F. L., Vallerand, R. J., Andrianarisoa, J. l., & Brunel, P. (2009). Passion in referees: Examining their affective and cognitive experiences in sport situations. Journal of Sport and Exercise Psychology, 31(1), 77–96. https://doi.org/10.1123/jsep.31.1.77
Pituch, K. A., & Stevens, J. P. (2016). Applied multivariate statistics for the social sciences. Routledge, Taylor & Francis Group, New York.
Pontes, H. M., Szabo, A., & Griffiths, M. D. (2015). The impact of internet-based specific activities on the perceptions of internet addiction, quality of life, and excessive usage: A cross-sectional study. Addictive Behaviors Reports, 1, 19–25. https://doi.org/10.1016/j.abrep.2015.03.002
Qin, L., Kim, Y., & Tan, X. (2018). Understanding the intention of using mobile social networking apps across cultures. International Journal of Human-Computer Interaction, 34(12), 1183–1193. https://doi.org/10.1080/10447318.2018.1428262
Saridakis, G., Benson, V., Ezingeard, J. -N., & Tennakoon, H. (2016). Individual information security, user behaviour and cyber victimisation: An empirical study of social networking users. Technological Forecasting and Social Change, 102, 320–330. https://doi.org/10.1016/j.techfore.2015.08.012
Shan, S., Zhao, Q., & Hua, F. (2013). Impact of quality management practices on the knowledge creation process: The Chinese aviation firm perspective. Computers & Industrial Engineering, 64(1), 211–223.
Shi, Y., Luo, Y. L. L., Liu, Y., & Yang, Z. (2019). Affective experience on social networking sites predicts psychological well-being off-line. Psychological Reports, 122(5), 1666–1677. https://doi.org/10.1177/0033294118789039
Short, J., Williams, E., & Christie, B. (1976). The social psychology of telecommunications: Wiley. London; New York.
Statista (2022). Social media - Statistics & facts. Retrieved March 15, 2022, from https://www.statista.com/topics/1164/social-networks/#dossierKeyfigures
Stead, H., & Bibby, P. A. (2017). Personality, fear of missing out and problematic internet use and their relationship to subjective well-being. Computers in Human Behavior, 76, 534–540. https://doi.org/10.1016/j.chb.2017.08.016
Stephanidis, C., Salvendy, G., Antona, M., Chen, J. Y. C., Dong, J., Duffy, V. G., & Zhou, J. (2019). Seven HCI grand challenges. International Journal of Human–Computer Interaction, 35(14), 1229–1269. https://doi.org/10.1080/10447318.2019.1619259
Tandon, A., Dhir, A., Talwar, S., Kaur, P., & Mäntymäki, M. (2021). Dark consequences of social media-induced fear of missing out (FoMO): Social media stalking, comparisons, and fatigue. Technological Forecasting and Social Change, 171, 120931. https://doi.org/10.1016/j.techfore.2021.120931
Tov, W., Nai, Z. L., & Lee, H. W. (2016). Extraversion and agreeableness: Divergent routes to daily satisfaction with social relationships. Journal of Personality, 84(1), 121–134.
Uddin, M. A., Mahmood, M., & Fan, L. (2019). Why individual employee engagement matters for team performance? Mediating effects of employee commitment and organizational citizenship behaviour. Team Performance Management: An International Journal, 25(1/2), 47–68. https://doi.org/10.1108/TPM-12-2017-0078
Uddin, M. A., Mahmood, M., Ostrovskiy, A., & Hwang, H. J. (2021). Can social networks improve user subjective wellbeing? Role of passion and personality traits. Journal of Global Information Management (JGIM), 29(4), 53–77. https://doi.org/10.4018/JGIM.20210701.oa3
Vallerand, R. J. (2008). On the psychology of passion: In search of what makes people’s lives most worth living. Canadian Psychology/psychologie Canadienne, 49(1), 1–13. https://doi.org/10.1037/0708-5591.49.1.1
Vallerand, R. J., Blanchard, C., Mageau, G. A., Koestner, R., Ratelle, C., Léonard, M., ... & Marsolais, J. (2003). Les passions de l'ame: On obsessive and harmonious passion. Journal of Personality and Social Psychology, 85(4), 756–767. https://doi.org/10.1037/0022-3514.85.4.756
Vallerand, R. J., Paquet, Y., Philippe, F. L., & Charest, J. (2010). On the role of passion for work in burnout: A process model. Journal of Personality, 78(1), 289–312.
Verduyn, P., Ybarra, O., Résibois, M., Jonides, J., & Kross, E. (2017). Do social network sites enhance or undermine subjective well-being? A critical review. Social Issues and Policy Review, 11(1), 274–302. https://doi.org/10.1111/sipr.12033
Wang, J. -L., Gaskin, J., Rost, D. H., & Gentile, D. A. (2017). The reciprocal relationship between passive social networking site (SNS) usage and users’ subjective well-being. Social Science Computer Review, 36(5), 511–522. https://doi.org/10.1177/0894439317721981
Wenninger, H., Krasnova, H., & Buxmann, P. (2019). Understanding the role of social networking sites in the subjective well-being of users: A diary study. European Journal of Information Systems, 28(2), 126–148. https://doi.org/10.1080/0960085X.2018.1496883
Whaite, E. O., Shensa, A., Sidani, J. E., Colditz, J. B., & Primack, B. A. (2018). Social media use, personality characteristics, and social isolation among young adults in the United States. Personality and Individual Differences, 124, 45–50. https://doi.org/10.1016/j.paid.2017.10.030
Wheatley, D., & Buglass, S. L. (2019). Social network engagement and subjective well-being: A life-course perspective. The British Journal of Sociology, 70(5), 1971–1995. https://doi.org/10.1111/1468-4446.12644
Wu, Y., Cui, T., Liu, N., Deng, Y., & Guo, J. (2018). Demystifying continuous participation in game applications at social networking sites. Internet Research, 28(2), 374–392. https://doi.org/10.1108/IntR-11-2016-0347
Yao, M. Z., He, J., Ko, D. M., & Pang, K. (2013). The influence of personality, parental behaviors, and self-esteem on internet addiction: A study of chinese college students. Cyberpsychology, Behavior, and Social Networking, 17(2), 104–110. https://doi.org/10.1089/cyber.2012.0710
Yi, L., Uddin, M. A., Das, A. K., Mahmood, M., & Sohel, S. M. (2019). Do transformational leaders engage employees in sustainable innovative work behaviour? Perspective from a Developing Country. Sustainability, 11(9), 2485–2502. https://doi.org/10.3390/su11092485
Zych, I., Kaakinen, M., Savolainen, I., Sirola, A., Paek, H. -J., & Oksanen, A. (2021). The role of impulsivity, social relations online and offline, and compulsive Internet use in cyberaggression: A four-country study. New media & society, 14614448211009459. https://doi.org/10.1177/14614448211009459
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The research project: Social Network Use and Subjective Well-being received the ethical approval (Ref No. BCB/RO-023/2020) from the Ethical Board of Bang College of Business, KIMEP University, Kazakhstan. The study was conducted following the 1964 Declaration of Helsinki and its subsequent amendments. The aims, benefits, and potential risks were communicated to the participants. Further, the participation of study was voluntary and the participants were given full rights of refusal to respond to the survey. The confidentiality and anonymity of the data were assured and preserved.
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Appendix A: Survey measures
Appendix A: Survey measures
Social networking—Passion |
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Social networking allows me to feel different experiences of life |
Social networking brings new things to me that I do not find in other sources |
Social networking creates a variety of good memories for me |
Social networking helps me to develop/understand good qualities of life |
Social networking engagement matches with my routine activities |
Social networking use is hobby for me, and I am able to control use of it |
I am happy with my engagement in social networking |
Social networking—Obsession |
Sometimes, life without social networking seems to be boring |
It is difficult to imagine life without social networking |
I feel disappointment/frustration without social networking |
I feel emotional and mental dependence on social networking |
I experience difficulties in controlling social networking usage |
I think, I am using social networking too much |
My mood changes completely because of social networking presence or absence |
Personality—Neuroticism |
I am scared of feeling stress |
In difficult situation, I feel less nervous than my friends. (Reverse scored) |
While watching or enjoying sad or romantic movies, I cannot control my emotions |
I can control my emotions or bad behavior even when someone treats me badly. (Reverse scored) |
Personality—Openness |
I like people with creative and new ideas |
I postpone complicated tasks as long as possible. (Reverse scored) |
I often express criticism without considering the consequences |
I am not a big fan of scientific experiments. (Reverse scored) |
Subjective wellbeing |
How satisfied are you with what you are achieving in life? |
How satisfied are you with your physical health? |
How satisfied are you with your psychological/mental well-being? |
How satisfied are you with your personal relationships? |
How satisfied are you with your financial condition? |
How satisfied are you with your academic achievement/result? |
SNS visit frequency |
How many times do you visit social networking sites on a typical day? |
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Mahmood, M., Uddin, M., Hwang, H.J. et al. Social Networking Site Use, Personality, User Habit, and Subjective Wellbeing: A Kazakhstani Pilot Study. J. technol. behav. sci. 8, 182–195 (2023). https://doi.org/10.1007/s41347-023-00312-7
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DOI: https://doi.org/10.1007/s41347-023-00312-7