The Relationship between Social Network Service Use Motives and Subjective Well-Being: The Mediating Effect of Online and Offline Social Capital

In-Pyo Hong; Sung-Man Bae

doi:10.30773/pi.2022.0008

Psychiatry Investig > Volume 20(6); 2023 > Article

Hong and Bae: The Relationship between Social Network Service Use Motives and Subjective Well-Being: The Mediating Effect of Online and Offline Social Capital

Original Article

Psychiatry Investigation 2023;20(6):493-503.

Published online: May 30, 2023

DOI: https://doi.org/10.30773/pi.2022.0008

The Relationship between Social Network Service Use Motives and Subjective Well-Being: The Mediating Effect of Online and Offline Social Capital

In-Pyo Hong¹

, Sung-Man Bae^1,²

¹Department of Psychology, Graduate School, Dankook University, Cheonan, Republic of Korea

²Department of Psychology and Psychotherapy, College of Health Science, Dankook University, Cheonan, Republic of Korea

Correspondence: Sung-Man Bae, PhD Department of Psychology and Psychotherapy, College of Health Science, Dankook University, 119 Dandae-ro, Dongnam-gu, Cheonan 31116, Republic of Korea Tel: +82-41-550-8142, Fax: +82-41-559-7852, E-mail: spirit73@hanmail.net

Received January 6, 2022 Revised September 29, 2022 Accepted January 24, 2023

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

This article has been corrected. See "The Relationship Between Social Network Service Use Motives and Subjective Well-Being: The Mediating Effect of Online and Offline Social Capital" in Volume 21 on page 110.

Abstract

Objective

The purpose of this study was to examine the mediating effect of social capital on the relationship between Social Media use motives and subjective well-being.

Methods

In the study, online self-reporting surveys were conducted with Social Media users in their 20s, and data from 445 participants were used for structural equation modeling.

Results

The main findings of the study were as follows. First, the interpersonal motives for Social Media use had an indirect effect on subjective well-being by mediating offline bonding capital and online bonding capital. In addition, interpersonal motives had an indirect effect on subjective well-being by dual-mediating online and offline bonding capital. Second, the self-expression motive for Social Media use did not directly affect subjective well-being, but it indirectly affected subjective well-being by mediating offline bonding capital. Third, the information-seeking motive for Social Media use did not directly affect subjective well-being, but it indirectly affected subjective well-being by mediating offline bonding capital.

Conclusion

This study identified a specific mechanism for how motives for using Social Media affect subjective well-being. Furthermore, the results of this study suggest that the effect of Social Media use on subjective well-being may differ depending on the motive for Social Media use.

Keywords: Social network service, Social media, Use motives, Social capital, Subjective well-being

INTRODUCTION

As of 2018, the number of Social Media users worldwide is estimated to be more than 2.95 billion, which is about one-third of the world’s total population [1]. According to the Korea Media Panel Survey [2], only 16.8% of all respondents used Social Media in 2011, but 47.7% of all respondents used Social Media in 2019. Furthermore, according to “2020 Survey on the internet Usage” in Korea, the social network service (SNS) availability ratio by age group was high in the order of those in their 20s, 30s, and 40s, and the SNS availability ratio in their 20s was the highest [3]. Early studies on Social Media focused on the negative effects of Social Media use such as emotional and interpersonal problems arising from the excessive use of Social Media [4-6]. However, it is worth noting that the majority of Social Media users do not complain of psychological or emotional problems and use Social Media in their daily lives.

Previous studies found that Social Media not only helps build and maintain relationships with others but also helps expand relationships with strangers and different groups [7,8]. The intimacy and social support that can be obtained through Social Media activities can provide a healthy and happy life [9,10]. In fact, in the 18-34-year-old group that uses Social Media a lot, the number of friends in Social Media was related to increased social support and subjective well-being [11].

Studies on the relationship between Social Media use and subjective well-being show contradictory results. According to the use and gratification theory, individuals who use the media select and use the media that satisfies their motives and needs, and the impact of media use on an individual’s psychological and emotional state can vary depending on the motivations (or purposes) with which they use the media [12,13].

Social Media use motive is used interchangeably with terms such as purpose of Social Media use and type of Social Media use. Social Media use motive is a concept related to the purposes for which users use Social Media (e.g., personal relationships, self-expression, information seeking, etc.). The use and gratification theory assumes that the way or purpose of media use reflects the user’s needs to be satisfied through the media [14]. Based on this theory, this study used the term Social Media use motive. Based on past studies, we classified the Social Media use motives as “interpersonal motive” to maintain and shape relationship, “self-expression motive” to express one’s thoughts, and “information-seeking motive” to explore or share information [15-17].

The main motive for Social Media use is interaction with others. The interpersonal motive for Social Media use has been reported to have a positive effect on subjective well-being [18]. The interpersonal motive for Social Media use may reduce depressed mood and increase mutual trust, which, in turn, can increase subjective well-being [19]. Self-expression and information-seeking motives for Social Media use have also been reported to have a positive effect on subjective well-being [20-23].

Since Social Media can establish various relationships through online networks and enable real-time communication and information sharing, the Social Media use motive can influence social capital. Social capital refers to resources that can be obtained through various social relationships [24]. Social capital theory explains that individuals can form trust, reciprocity, and norms among members through social networks [25]. Social capital can be categorized into bonding social capital and bridging social capital. Bonding social capital is a resource provided between individuals with strong ties, such as close friends and family, while bridging social capital is a resource provided between individuals or groups with weak ties, such as acquaintances or strangers [25,26]. In particular, Williams [26] emphasized that it is necessary to distinguish between online and offline social capital and divided social capital into online bonding social capital, online bridging social capital, offline bonding social capital, and offline bridging social capital.

Researchers’ views on the effect of Social Media use on social capital are largely divided into two perspectives. First, the use of Social Media will have a positive effect on the formation of social capital. Scholars holding this view argue that online interactions increase interpersonal opportunities and, consequently, can improve the quality of relationships [27]. In addition, they assume that online interactions foster communication and trust by strengthening connections between people with common interests [28]. Second, the use of Social Media will have a negative effect on the formation of social capital. Scholars who hold this view argue that the excessive use of Social Media simplifies human relationships and impedes true ties or unity [29,30]. In addition to the two perspectives, the view that the use of Social Media plays a role in maintaining and supplementing the existing social ties emerged. This view considers online relationships as an extension of offline relationships and argues that Social Media use can help maintain existing relationships [7].

Bargh and McKenna [8] argued that an intimate relationship formed online can affect offline relationships, and Kim [9] said that reciprocal interactions obtained online can help solve offline relationship problems. It means that reciprocal and reliable relationships acquired through Social Media can influence offline bonding social capital.

Previous studies have explored the relationship between Social Media use motives and social capital. The results indicate that the interpersonal motive and information-seeking motive for Social Media use have a positive effect on social capital [31], and social activities such as interpersonal interactions and information search have a positive effect on both bonding social capital and bridging social capital [32]. In addition, self-expression in Social Media can increase interpersonal connectivity [33], and the self-expression motive for Social Media use has been reported to increase the positive feedback of people, which has a positive effect on both bonding and bridging social capital [34].

Social capital was also found to be closely related to subjective well-being. According to previous studies, positive factors such as trust and volunteering of social capital have a positive effect on subjective well-being [35]. In addition, many past studies have confirmed the relationship between social capital and subjective well-being [7,36,37].

The impact of social capital on subjective well-being may vary depending on the type of social capital. It has been reported that social capital composed of intimate and close relationships, such as bonding social capital, can provide more psychological rewards to users and consequently positively affect the quality of life [38,39]. In a study by Pang [37] bonding social capital had a positive effect on subjective well-being, but bridging social capital did not significantly affect subjective well-being.

Considered together, the effect of Social Media use on subjective well-being may vary depending on Social Media use motives, and an effort is needed to understand the specific mechanisms underlying how Social Media use motives affect subjective well-being. Therefore, this study analyzes the effect of each Social Media use motive on subjective well-being and examines the mediating effects of social capital in the relationship between Social Media use motives and subjective well-being. The hypotheses of this study were as follows.

Hypothesis 1: the Social Media use motives will have a positive impact on subjective well-being. The Social Media use motives will affect subjective well-being, and it is thought that there will be a difference in type depending on each motive (interpersonal, self-expression, and information seeking).

Hypothesis 2: the Social Media use motives will have a positive impact on social capital. The Social Media use motives are expected to affect the formation of social capital. However, it is thought that there is a difference in the social capital (online bonding, online bridging, offline bonding, and offline bridging) formed by each motive (interpersonal, self-expression, and information seeking).

Hypothesis 3: there will be a mediating effect of social capital in the relationship between the Social Media use motives and subjective well-being. In the relationship between Social Media use motives and subjective well-being, the mediating effect of bonding social capital (online bonding and offline bonding) rather than bridging social capital (online bridging and offline bridging) is expected.

METHODS

Participants

In this study, an online self-reporting survey (Google questionnaire form) was conducted on Social Media users living in Gyeonggi-do, Chungcheong-do, Gyeongsang-do, and other regions. The participants were recruited through Social Media and online communities, and the survey was conducted from June 11, 2019 to June 21, 2019. It included 476 participants in the survey. Data from 445 participants were used in the final analysis, as 31 respondents either did not meet the age criteria of the study or did not respond to more than 50% of the items. They were judged to have responded unfaithfully. Of the 445 participants, 208 (46.7%) were male and 237 (53.3%) were female, and the average age was 21.8 (standard deviation=0.81) years. The regional distribution of Social Media users was as follows: 247 (55.5%) in Gyeonggi-do, 108 (24.3%) in Gyeongsang-do, 71 (15.9%) in Chungcheong-do, and 19 (4.3%) in other regions. The types of Social Media mainly used were as follows: 246 (55.3%) Instagram, 170 (38.2%) Facebook, 22 (4.9%) Twitter, 5 (1.1%) Kakao Stories, and 2 Naver Bands (0.5%). Specific demographic characteristics are presented in Table 1. All research procedures, including data collection, were approved by the Institutional Review Board of Dankook University (IRB number: 2019-09-034).

Measures

Social Media use motive

In this study, Social Media use motive was classified into interpersonal motive, self-expression motive, and information-seeking motive. The scale is composed of 26 questions, including eight questions on interpersonal motive [40-42], nine questions on self-expression motive [43,44], and nine questions of information-seeking motive [40,41,43]. Each item consisted of a 5-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree). Examples of items from each subscale are as follows. 1) Self-expression motive=“to show one’s personality or hobbies,” “to post my pictures or writings.” 2) Information-seeking motive=“to find various information,” “to get opinions on various topics.” 3) Interpersonal motive=“to keep in touch with an acquaintance,” “to talk to others.” An exploratory factorial analysis was conducted to verify the validity of the scale. The maximum likelihood method was used for factor extraction, and the direct oblimin method was applied for factor rotation. As a result of the exploratory factor analysis, a total of 17 items were selected, with five questions on interpersonal motive, six questions on self-expression motive, and six questions on information-seeking motive. Table 2 shows the results of the factor analysis. The eigenvalues of the factors were 6.04, 3.04, and 2.43, respectively, and the total cumulative explanatory variance was 67.74%. In this study, Cronbach’s alpha for the scale was 0.81 for interpersonal motive, 0.92 for self-expression motive, and 0.84 for information-seeking motive.

Social capital

In this study, the translated and modified version of Williams [26] was used to measure social capital [45]. This scale is composed of 10 questions on online bonding social capital, 10 questions on online bridging social capital, 10 questions on offline bonding social capital, and 10 questions on offline bridging social capital. Each item consists of a 5-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree). Examples of items from each subscale are as follows. 1) Online bonding social capital=“there is someone online who I am sure can help solve my problem,” “there is someone online who can advise you on a very important decision.” 2) Online bridging social capital=“through the person I met online, I became interested in people who thought differently from me,” “meeting people online is a great opportunity to talk to new people.” 3) Offline bonding social capital=“there is someone offline who I am sure can help solve my problem,” “there is someone offline who can advise you on a very important decision.” 4) Offline bridging social capital=“through the person I met offline, I became interested in people who thought differently from me,” “meeting people offline is a great opportunity to talk to new people.” In this study, the Cronbach’s alpha of the scale was 0.84 for online bonding social capital, 0.93 for online bridging social capital, 0.94 for offline bonding social capital, and 0.92 for offline bridging social capital.

Subjective well-being

In this study, a Concise Measure of Subjective Well-Being developed by Suh and Koo [46] was used to measure subjective well-being. This scale is composed of nine questions, including three questions on life satisfaction that mean satisfaction with one’s life, three questions on positive emotion experience that asked about positive emotions felt in the past month, and three questions on negative emotion experience that asked about negative emotions felt in the past month. The items about life satisfaction consisted of a 7-point Likert scale ranging from 1 (strongly disagree) to 7 (strongly agree), and items on positive and negative emotions consisted of a 7-point Likert scale ranging from 1 (never) to 7 (always). The Cronbach’s alpha of the scale in this study was 0.85 for life satisfaction, 0.85 for positive emotion experience, and 0.72 for negative emotion experience.

Covariates

Age, sex, and types of Social Media use were added as covariates in this study. Participants were asked to choose the social media platform you use the most between Instagram, Facebook, Twitter, Kakao Story, Naver Band.

Analysis

In this study, IBM SPSS Statistics Version 24.0 (IBM Corp., Armonk, NY, USA) and AMOS 24.0 (IBM SPSS, Chicago, IL, USA) were used to analyze the collected data. A descriptive statistics analysis was conducted to identify the mean and standard deviation of the major variables. A Pearson correlation analysis was performed to verify the correlation between the major variables. The model fit was tested using the structural equation model, and the significance of the indirect effect was verified using bootstrapping. Finally, before verifying the research model, observed variables were constructed through item parceling. In the case of subjective well-being that has subscale, the content-based approach was used to consist of three observed variables: life satisfaction, positive emotion experience, and negative emotion experience. Online bonding social capital, online bridging social capital, offline bonding social capital, and offline bridging social capital which are single factors, the factorial algorithm was used to consist of three observed variables [47]. And a multiple mediator model was established to check the indirect effects of social capital subfactors between Social Media use motives and subjective well-being. Phantom variables were set to verify the indirect effects of subfactors of social capital [48]. The model’s goodness of fit was verified by comprehensively considering and various fit indices [49]. The fit of the research model is considered good, if Comparative fit Index (CFI) and Tucker-Lewis Index (TLI) were greater than 0.9, and root meansquare residual (RMSEA) was less than 0.05.

RESULTS

Descriptive statistics and correlations

The results of descriptive statistics and correlations are presented in Tables 3 and 4. Among the Social Media use motives, interpersonal motive positively correlated with subjective well-being (r=0.111, p<0.05). In relationship with social capital, interpersonal motive positively correlated with online bonding (r=0.505, p<0.01) and online bridging social capital (r=0.501, p<0.01), and negatively correlated with offline bonding social capital (r=-0.107, p<0.05). Self-expression motive positively correlated with online bonding (r=0.368, p<0.01) and online bridging social capital (r=0.429, p<0.01), and information-seeking motive positively correlated with online bonding (r=0.216, p<0.01), online bridging (r=0.239, p<0.01), and offline bridging social capital (r=0.185, p<0.01). In the relationship between social capital and subjective well-being, online bonding (r=0.131, p<0.01), offline bonding (r=0.214, p<0.01), and offline bridging social capital (r=0.216, p<0.001) positively correlated with subjective well-being. Multivariate normality was secured because the skewness value did not exceed 2 and the kurtosis value did not exceed 7.

Structural equation model

Confirmatory factor analysis

Before verifying the research model, a confirmatory factor analysis of variables was conducted after using item parceling to consider global model fit and estimation stability [50]. The verification results of the measurement model are presented in Table 5. It was found that the model fit was good for all the measurement variables (chi-square=657.324, p>0.05, df=322, CFI=0.956, TLI=0.948, RMSEA=0.048). This result means that the measurement variables properly measured the construct.

Structural equation modeling

The path between self-expression motive and subjective well-being, and the path between information-seeking motive and subjective well-being were deleted from the final model because the correlation between the variables was not significant.

The analysis of the research model showed the following: chi-square=1,068.995, p<0.05, df=531, CFI=0.946, TLI=0.940, RMSEA=0.048. The research model is presented in Figure 1.

In this study, an alternative model was established by adding a path from online binding capital to offline binding capital. The alternative model was a nested model of the research model, and the two models were compared using the chi-square difference test presented by Bentler and Bonett [51]. The result showed that the fit of model was good (chi-square=1,057.498, p<0.05, df=530, CFI=0.947, TLI=0.941, RMSEA=0.047). In addition, it was found that the chi-square value of the alternative model decreased by more than 3.84 compared to the decrease of the degree of freedom by 1 in the research model (χ²=11.497), which was determined to be more suitable for the alternative model than the research model at a significance level of 0.05. Thus, the alternative models (Figure 2) were determined as the final model.

The results of the analysis of the pathways are presented in Table 6. It was found that interpersonal motive did not significantly affect subjective well-being.

Online bonding social capital (β=0.29, p<0.01) and offline bonding social capital (β=0.23, p<0.05) had a positive effect on subjective well-being, but online bridging social capital (β=-0.21, p<0.05) had a negative effect on subjective well-being and offline bridging social capital did not significantly affect subjective well-being. These results indicate that the more the bonding social capital, the more the subjective well-being increases.

Interpersonal motive had a positive effect on online bonding social capital (β=0.68, p<0.001) and online bridging social capital (β=0.53, p<0.001) but had a negative effect on offline bonding social capital (β=-0.56, p<0.001) and offline bridging social capital (β=-0.28, p<0.01). Self-expression motive had a positive effect on the online bridging social capital (β=0.14, p<0.05), offline bonding social capital (β=0.26, p<0.001), and offline bridging social capital (β=0.21, p<0.01) but did not affect online bonding social capital. Information-seeking motive had a positive effect on offline bonding social capital (β=0.24, p<0.001) and offline bridging social capital (β=0.28, p<0.001), but it did not affect online bonding social capital and online bridging social capital.

Mediating effect of social capital

A bootstrapping test was conducted to verify the mediating effect of social capital on the relationship between Social Media use motives and subjective well-being. When using AMOS 24.0 (IBM SPSS) to verify a multiple mediator model, the total indirect effect can be verified, but each indirect effect cannot be verified. Therefore, in this study, a method of estimating the indirect effects of measurement variables using a virtual phantom variable was used. The method was proposed by Macho and Ledermann [48] and Cheung [52] to verify the indirect effect in a multiple mediator model by generating a phantom variable with a variance of 0. The phantom variables added to the research model are indicators of each indirect effect. The results regarding the mediating effects are presented in Table 7.

The analysis showed statistically significant indirect effects of interpersonal motive affecting subjective well-being by mediating online bonding (B=0.727, p<0.001), online bridging social capital (B=-0.409, p<0.05), and offline bonding social capital (B=-0.481, p<0.05). The indirect effects of interpersonal motive affecting subjective well-being through offline bridging social capital were not statistically significant. Meanwhile, the indirect effect of interpersonal motive on subjective well-being by dual-mediating online bonding social capital and offline bonding social capital was found to be statistically significant (B=0.097, p<0.05). Although the indirect effect of self-expression motive on subjective well-being through offline bonding social capital was statistically significant (B=0.148, p<0.05), the indirect effects of self-expression motive on subjective well-being through online bonding social capital, online bridging social capital, and offline bridging social capital were not statistically significant. The indirect effect of information-seeking motive on subjective well-being through offline bonding social capital was statistically significant (B=0.177, p<0.05), but it had indirect effects on subjective wellbeing through online bonding social capital, online bridging social capital, and offline bridging social capital, which were not statistically significant.

DISCUSSION

The purpose of this study was to verify the effect of Social Media use motives on subjective well-being and to test the mediating effect of social capital on the relationship between Social Media use motives and subjective well-being.

First, an indirect effect of offline bonding social capital was found in the relationship between the interpersonal motive for Social Media use and subjective well-being. In particular, interpersonal motive was found to have a negative effect on offline bonding social capital, and decreased offline bonding capital negatively affected subjective well-being. This is the result of supporting the argument that interaction through the Internet has a negative effect on subjective well-being by reducing offline interaction [30,53]. Turkle [30] argued that media dependence such as excessive Social Media use can hinder true human relationships. The interpersonal motive for using Social Media can reduce intimate relationships such as family relationships or close friendships, and decreased offline bonding capital can lead to a decrease in subjective well-being.

Interestingly, the interpersonal motive was found to have a positive effect on subjective well-being through online bonding social capital and had a positive effect on subjective well-being by dual-mediating online and offline bonding social capital. Social Media can help remove barriers between users, thus enabling them to build new social capital, but it can also help maintain existing social relationships [7]. Bargh and McKenna [8] argued that an intimate relationship formed online can affect offline relationships, and Kim [9] said that reciprocal interactions obtained online can help solve offline relationship problems. In this context, it can be interpreted that reciprocal and reliable relationships acquired through Social Media can influence offline bonding social capital, which in turn can increase subjective well-being.

Second, there was no direct effect in the relationship between self-expression motive and subjective well-being, but an indirect effect through offline bonding social capital was observed. Self-expression through Social Media can increase social connectivity by increasing self-exposure and, consequently, may increase subjective well-being [22,54]. Third, although there was no direct effect in the relationship between information-seeking motive and subjective well-being, there was an indirect effect through the offline bonding social capital. In other words, the information-seeking motive for Social Media use can increase offline bonding social capital, thereby enhancing subjective well-being. This result is supported by previous studies, which found that information search activities have a positive effect on subjective well-being through social capital [55]. The information-seeking motive for Social Media use can help to share important information in daily life (e.g., health, economics) and help solve individual or daily problems, resulting in increased satisfaction in life.

The contributions and implications of this study are as follows. First, this study explained the specific mechanism by which Social Media use motives affect subjective well-being by mediating social capital. Second, past studies mainly focused on integrated social capital or offline social capital. However, with the development of the Internet, many people are forming social capital online [26]. Therefore, in this study, social capital was classified according to characteristics (bonding and bridging) and background (online and offline), and the specific relationships with Social Media use motives are verified.

The limitations of this study and suggestions for future studies are as follows. First, this study was conducted on Social Media users in their 20s, and there is a limitation that the results cannot be generalized to users of all ages. Second, because this study was designed as a cross-sectional study, it is important to be careful not to assert a causal relationship between each variable.

Notes

Availability of Data and Material

The datasets generated or analyzed during the study are available from the corresponding author on reasonable request.

Conflicts of Interest

The authors have no potential conflicts of interest to disclose.

Author Contributions

Conceptualization: In-Pyo Hong, Sung-Man Bae. Data curation: In-Pyo Hong. Formal analysis: In-Pyo Hong. Investigation: In-Pyo Hong. Methodology: In-Pyo Hong, Sung-Man Bae. Project administration: In-Pyo Hong, Sung-Man Bae. Resources: In-Pyo Hong. Software: In-Pyo Hong. Supervision: Sung-Man Bae. Validation: Sung-Man Bae. Visualization: In-Pyo Hong. Writing—original draft: In-Pyo Hong. Writing—review & editing: Sung-Man Bae.

Funding Statement

None

Figure 1.

Research model for structure equation model. ^*p<0.05; ^**p<0.01; ^***p<0.001.

Figure 2.

Alternative model established by adding a path from online binding capital to offline binding capital. ^*p<0.05; ^**p<0.01; ^***p<0.001.

Table 1.

Demographic characteristic of the participants

Variable	Value (N=445)
Age
19-21 yr	250 (56.2)
22-24 yr	137 (30.8)
25-27 yr	41 (9.2)
28-29 yr	17 (3.8)
Sex
Male	208 (46.7)
Female	237 (53.3)
Occupation
College or graduate student	371 (83.4)
Office workers	44 (9.9)
Self-employed	1 (0.2)
Inoccupation	15 (3.4)
Others	14 (3.1)
Regional distribution
Gyeonggi-do	247 (55.5)
Gyeongsang-do	108 (24.3)
Chungcheong-do	71 (15.9)
Other regions	19 (4.3)
Mainly used Social Media
Instagram	246 (55.3)
Facebook	170 (38.2)
Twitter	22 (4.9)
Kakao Story	5 (1.1)
Never Band	2 (0.5)
Hours of use
<30 min	91 (20.5)
30-60 min	121 (27.2)
1-2 hr	98 (22.0)
2-3 hr	66 (14.8)
3-4 hr	36 (8.1)
>4 hr	33 (7.4)

Values are presented as number (%)

Table 2.

Exploratory factor analysis for Social Media use motives

		Factor 1	Factor 2	Factor 3
Self-expression motive	1. To talk about me	0.938
	2. To post my pictures or writings	0.867
	3. To express one-self	0.861
	4. To express one’s mood	0.746
	5. To show one’s personality or hobbies	0.740
	6. To inform people of my special interests	0.686
Information-seeking motive	1. To get information for free		0.829
	2. To find various information		0.778
	3. To get useful information about services and products		0.694
	4. To receive external news		0.640
	5. To find interesting pictures or videos		0.574
	6. To get opinions on various topics		0.518
Interpersonal motive	1. To keep in touch with an acquaintance			0.805
	2. To keep in touch with the family			0.786
	3. To talk to others			0.580
	4. To meet new people			0.560
	5. To communicate with friends living far away			0.530
Eigenvalue		6.041	3.041	2.434
Explanatory variance		35.532	17.887	14.318
Cumulative explanatory variance		35.532	53.420	67.738
Reliability		0.922	0.839	0.810
Total reliability			0.882

Factor 1: self-expression motive to express one’s thoughts. Factor 2: information-seeking motive to explore or share information. Factor 3: interpersonal motive to maintain and shape relationship

Table 3.

Correlation coefficients for all variables

	1	2	3	4	5	6	7	8
1. Interpersonal motive	-
2. Self-expression motive	0.545^***	-
3. Information-seeking motive	0.380^**	0.180^**	-
4. Online bonding social capital	0.505^**	0.368^**	0.216^**	-
5. Online bridging social capital	0.501^**	0.429^**	0.239^**	0.760^**	-
6. Offline bonding social capital	-0.107^*	0.026	0.083	-0.071	-0.087	-
7. Offline bridging social capital	0.049	0.090	0.185^**	-0.065	-0.036	0.707^**	-
8. Subjective well-being	0.111^*	0.037	0.019	0.131^**	0.034	0.214^**	0.216^***	-

^* p<0.05;

^** p<0.01;

^*** p<0.001

Table 4.

Descriptive statistics for all variables

	Interpersonal motive	Self-expression motive	Information-seeking motive	Online bonding social capital	Online bridging social capital	Offline bonding social capital	Offline bridging social capital	Subjective well-being
Mean	13.03	16.98	20.96	22.27	24.37	40.06	37.16	17.34
Standard deviation	4.28	6.77	5.24	9.56	9.53	8.87	8.27	7.70

Table 5.

Confirmatory factor analysis for all variables

Variable	Observed variable	B	β	SE	C.R.	AVE	CR
Interpersonal motive	Interpersonal motive 1	1	0.777	-	-	0.506	0.835
	Interpersonal motive 2	0.951	0.715	0.083	11.417^***
	Interpersonal motive 3	0.915	0.761	0.074	12.299^***
	Interpersonal motive 4	0.802	0.607	0.083	9.615^***
	Interpersonalmotive 5	0.991	0.632	0.090	11.053^***
Self-expression motive	Self-expression motive 1	1	0.896	-	-	0.667	0.923
	Self-expression motive 2	0.955	0.823	0.041	23.392^***
	Self-expression motive 3	0.930	0.836	0.039	24.100^***
	Self-expression motive 4	0.864	0.810	0.038	22.692^***
	Self-expression motive 5	0.860	0.792	0.039	21.779^***
	Self-expression motive 6	0.779	0.734	0.041	19.134^***
Information-seeking motive	Information-seeking motive 1	1	0.833	-	-	0.516	0.863
	Information-seeking motive 2	0.947	0.813	0.057	16.731^***
	Information-seeking motive 3	0.852	0.699	0.061	14.072^***
	Information-seeking motive 4	0.766	0.653	0.059	13.000^***
	Information-seeking motive 5	0.688	0.612	0.057	12.029^***
	Information-seeking motive 6	0.658	0.673	0.049	13.452^***
Online bond social capital	Online bond social capital 1	1	0.914	-	-	0.851	0.945
	Online bond social capital 2	0.923	0.928	0.028	33.294^***
	Online bond social capital 3	0.916	0.926	0.028	33.056^***
Online bridge social capital	Online bridge social capital 1	1	0.931	-	-	0.808	0.927
	Online bridge social capital 2	0.975	0.888	0.033	29.837^***
	Online bridge social capital 3	0.932	0.877	0.032	28.989^***
Offline bond social capital	Offline bond social capital 1	1	0.945	-	-	0.868	0.952
	Offline bond social capital 2	0.946	0.919	0.026	35.938^***
	Offline bond social capital 3	0.963	0.930	0.026	37.440^***
Offline bridge social capital	Offline bridge social capital 1	1	0.924	-	-	0.780	0.914
	Offline bridge social capital 2	0.985	0.928	0.031	31.942^***
	Offline bridge social capital 3	0.961	0.791	0.042	22.708^***
Subjective well-being	Subjective well-being 1	1	0.875	-	-	0.556	0.787
	Subjective well-being 2	0.755	0.686	0.087	8.707^***
	Subjective well-being 3	0.660	0.658	0.066	5.615^***

^*** p<0.001.

SE, standard error; C.R., critial ratio; AVE, average variance extracted; CR, construct reliability

Table 6.

Path coefficients of final model

Path	B	β	SE	C.R.
Interpersonal motive → Subjective well-being	0.454	0.124	0.312	1.457
Online bonding social capital → Subjective well-being	0.889	0.292	0.324	2.746^**
Online bridging social capital → Subjective well-being	-0.714	-0.212	0.347	-2.056^*
Offline bonding social capital → Subjective well-being	0.817	0.233	0.332	2.463^*
Offline bridging social capital → Subjective well-being	0.612	0.163	0.347	1.764
Online bonding social capital → Offline bonding social capital	0.145	0.167	0.044	3.323^***
Interpersonal motive → Online bonding social capital	0.818	0.680	0.106	7.735^***
Interpersonal motive → Online bridging social capital	0.573	0.526	0.090	6.393^***
Interpersonal motive → Offline bonding social capital	-0.589	-0.562	0.111	-5.315^***
Interpersonal motive → Offline bridging social capital	-0.274	-0.281	0.084	-3.252^**
Self-expression motive → Online bonding social capital	-0.015	-0.019	0.051	-0.299
Self-expression motive → Online bridging social capital	0.101	0.139	0.045	2.257^*
Self-expression motive → Offline bonding social capital	0.181	0.259	0.050	3.620^***
Self-expression motive → Offline bridging social capital	0.139	0.213	0.046	3.058^**
Information-seeking motive → Online bonding social capital	-0.080	-0.078	0.059	-1.361
Information-seeking motive → Online bridging social capital	0.002	0.002	0.052	0.034
Information-seeking motive → Offline bonding social capital	0.217	0.242	0.059	3.662^***
Information-seeking motive → Offline bridging social capital	0.238	0.284	0.054	4.374^***

^* p<0.05;

^** p<0.01;

^*** p<0.001.

SE, standard error; C.R., critial ratio

Table 7.

Mediating effect of social capital

Path		Effect	SE	95% CI
Interpersonal motive → Online bonding social capital → Subjective well-being	p2	0.727^***	0.288	0.211 to 1.348
Interpersonal motive → Online bridging social capital → Subjective well-being	p4	-0.409^*	0.210	-0.849 to -0.010
Interpersonal motive → Offline bonding social capital → Subjective well-being	p6	-0.481^*	0.269	-1.132 to -0.017
Interpersonal motive → Offline bridging social capital → Subjective well-being	p8	-0.168	0.126	-0.456 to 0.050
Self-expression motive → Online bonding social capital → Subjective well-being	p10	-0.014	0.062	-0.150 to 0.109
Self-expression motive → Online bridging social capital → Subjective well-being	p12	-0.072	0.057	-0.201 to 0.012
Self-expression motive → Offline bonding social capital → Subjective well-being	p14	0.148^*	0.095	0.002 to 0.365
Self-expression motive → Offline bridging social capital → Subjective well-being	p16	0.085	0.065	-0.027 to 0.230
Information-seeking motive → Online bonding social capital → Subjective well-being	p18	-0.071	0.068	-0.234 to 0.041
Information-seeking motive → Online bridging social capital → Subjective well-being	p20	-0.001	0.042	-0.096 to 0.075
Information-seeking motive → Offline bonding social capital → Subjective well-being	p22	0.177^*	0.112	0.005 to 0.455
Information-seeking motive → Offline bridging social capital → Subjective well-being	p24	0.145	0.103	-0.048 to 0.367
Interpersonal motive → Online bonding social capital → Offline bonding social capital → Subjective well-being	p26	0.097^*	0.069	0.002 to 0.264

^* p<0.05;

^*** p<0.001.

SE, standard error; CI, confidence interval

REFERENCES

1. Statista. Number of global social network users 2017-2027. Available at: https://www.statista.com/statistics/278414/number-of-worldwidesocial-network-users/. Accessed March 5, 2023.

2. KISDI. Korea Information Society Development Institute Stat Report: 2019 Korea media panel. Available at: https://stat.kisdi.re.kr/kor/board/BoardList.html?board_class=BOARD35. Accessed March 5, 2023.

3. National Information Society Agency. Ministry of science and ICT 2020 survey on the internet usage. Available at: https://www.nia.or.kr/site/nia_kor/ex/bbs/View.do?cbIdx=99870&bcIdx=23213&parentSeq=23213. Accessed March 5, 2023.

4. Kim J, LaRose R, Peng W. Loneliness as the cause and the effect of problematic internet use: the relationship between internet use and psychological well-being. Cyberpsychol Behav 2009;12:451-455.

5. Pantic I, Damjanovic A, Todorovic J, Topalovic D, Bojovic-Jovic D, Ristic S, et al. Association between online social networking and depression in high school students: behavioral physiology viewpoint. Psychiatr Danub 2012;24:90-93.

6. Wu AM, Cheung VI, Ku L, Hung EP. Psychological risk factors of addiction to social networking sites among Chinese smartphone users. J Behav Addict 2013;2:160-166.

7. Ellison NB, Steinfield C, Lampe C. The benefits of Facebook “friends:” social capital and college students’ use of online social network sites. J Comput Mediat Commun 2007;12:1143-1168.

8. Bargh JA, McKenna KY. The internet and social life. Annu Rev Psychol 2004;55:573-590.

9. Kim MA. Making of social capital on cyberspace: analysis of the online network of university students in Korea. Inf Soc Media 2007;11:25-59.

10. Diener E, Lucas RE, Oishi S. Subjective well-being: the science of happiness and life satisfaction. In: Snyder CR, Lopez SJ, editor. Handbook of positive psychology. New York: Oxford University Press, 2002, p. 63-73.

11. Chan M. Mobile-mediated multimodal communications, relationship quality and subjective well-being: an analysis of smartphone use from a life course perspective. Comput Hum Behav 2018;87:254-262.

12. Blumler JG, Katz E. The uses of mass communications: current perspectives on gratifications research. Beverly Hills, CA: Sage Publications, Inc; 1974.

13. Blumler JG. The role of theory in uses and gratifications studies. Commun Res 1979;6:9-36.

14. Katz E, Haas H, Gurevitch M. On the use of the mass media for important things. Am Sociol Rev 1973;38:164-181.

15. Raacke J, Bonds-Raacke J. MySpace and Facebook: applying the uses and gratifications theory to exploring friend-networking sites. Cyberpsychol Behav 2008;11:169-174.

16. Valenzuela S, Park N, Kee KF. Is there social capital in a social network site?: Facebook use and college students’ life satisfaction, trust, and participation. J Comput Mediat Commun 2009;14:875-901.

17. Kim HS. A study on use motivation of SNS and communication behavior. J Korea Acad Ind Coop Soc 2012;13:548-553.

18. Wang JL, Jackson LA, Gaskin J, Wang HZ. The effects of social networking site (SNS) use on college students’ friendship and well-being. Comput Hum Behav 2014;37:229-236.

19. Baek YM, Bae Y, Jang H. Social and parasocial relationships on social network sites and their differential relationships with users’ psychological well-being. Cyberpsychol Behav Soc Netw 2013;16:512-517.

20. Hwang HJ, Hwang YS. Gaps and reasons of digital divide within the elderly in Korea: focusing on household composition. J Soc Sci 2017;24:359-386.

21. Ko HC, Kuo FY. Can blogging enhance subjective well-being through self-disclosure? Cyberpsychol Behav 2009;12:75-79.

22. Lee G, Lee J, Kwon S. Use of social-networking sites and subjective well-being: a study in South Korea. Cyberpsychol Behav Soc Netw 2011;14:151-155.

23. Liu RD, Shen CX, Xu L, Gao Q. Children’s internet information seeking, life satisfaction, and loneliness: the mediating and moderating role of self-esteem. Comput Educ 2013;68:21-28.

24. Lin N. Building a network theory of social capital. In: Lin N, Cook K, Burt RS, editor. Social capital. New York: Routledge, 2017, p. 3-28.

25. Putnam RD. Bowling alone: the collapse and revival of American community. New York: Simon and Schuster; 2000.

26. Williams D. On and off the’Net: scales for social capital in an online era. J Comput Mediat Commun 2006;11:593-628.

27. Valkenburg PM, Peter J. Online communication and adolescent well-being: testing the stimulation versus the displacement hypothesis. J Comput Mediat Commun 2007;12:1169-1182.

28. Wellman B, Gulia M. Net-surfers don’t ride alone: virtual communities as communities. In: Wellman B, editor. Networks in the global village. New York: Routledge, 1999, p. 311-366.

29. Kraut R, Patterson M, Lundmark V, Kiesler S, Mukopadhyay T, Scherlis W. Internet paradox. A social technology that reduces social involvement and psychological well-being? Am Psychol 1998;53:1017-1031.

30. Turkle S. Alone together: why we expect more from technology and less from each other. New York: Basic Books; 2017.

31. Park JW, Kim DJ. Effect of social media use motive and credibility on social capital. J Korea Soc Comput Inf 2014;19:207-219.

32. Ahmad S, Mustafa M, Ullah A. Association of demographics, motives and intensity of using social networking sites with the formation of bonding and bridging social capital in Pakistan. Comput Hum Behav 2016;57:107-114.

33. Utz S. The function of self-disclosure on social network sites: not only intimate, but also positive and entertaining self-disclosures increase the feeling of connection. Comput Hum Behav 2015;45:1-10.

34. Liu D, Brown BB. Self-disclosure on social networking sites, positive feedback, and social capital among Chinese college students. Comput Hum Behav 2014;38:213-219.

35. Matsushima M, Matsunaga Y. Social capital and subjective well-being in Japan. Voluntas 2015;26:1016-1045.

36. Magsamen-Conrad K, Billotte-Verhoff C, Greene K. Technology addiction’s contribution to mental wellbeing: the positive effect of online social capital. Comput Hum Behav 2014;40:23-30.

37. Pang H. Exploring the beneficial effects of social networking site use on Chinese students’ perceptions of social capital and psychological well-being in Germany. Int J Intercult Relat 2018;67:1-11.

38. Misra N, Dangi S, Patel S. Gender differences in usage of social networking sites and perceived online social support on psychological well-being of youth. Int J Indian Psychol 2015;3:63-74.

39. Sheer VC, Rice RE. Mobile instant messaging use and social capital: direct and indirect associations with employee outcomes. Inf Manag 2017;54:90-102.

40. Papacharissi Z, Rubin AM. Predictors of internet use. J Broadcast Electron Media 2000;44:175-196.

41. Park N, Kee KF, Valenzuela S. Being immersed in social networking environment: Facebook groups, uses and gratifications, and social outcomes. Cyberpsychol Behav 2009;12:729-733.

42. Smock AD, Ellison NB, Lampe C, Wohn DY. Facebook as a toolkit: a uses and gratification approach to unbundling feature use. Comput Hum Behav 2011;27:2322-2329.

43. Gülnar B, Balci Ş, Çakir V. Motivations of Facebook, You Tube and similar web sites users. Bilig 2010;54:161-184.

44. Son YH. The effect of Facebook users’ levels of self-disclosure, motives for self-expression and social interaction on subjective happiness. Korean J Soc Sci 2018;37:115-150.

45. Kim G. An exploratory study on the impacts of online activities about social capital formation of online and offline: focused on the undergraduate. J Cybercommunication Acad Soc 2010;27:5-44.

46. Suh EK, Koo JS. A concise measure of subjective well-being (COMOSWB): scale development and validation. Korean J Soc Pers Psychol 2011;25:96-113.

47. Little TD, Rhemtulla M, Gibson K, Schoemann AM. Why the items versus parcels controversy needn›t be one. Psychol Methods 2013;18:285-300.

48. Macho S, Ledermann T. Estimating, testing, and comparing specific effects in structural equation models: the phantom model approach. Psychol Methods 2011;16:34-43.

49. Hong SH. The criteria for selecting appropriate fit indices in structural equation modeling and their rationales. Kor J Clin Psychol 2000;19:161-177.

50. Matsunaga M. Item parceling in structural equation modeling: a primer. Commun Methods Meas 2008;2:260-293.

51. Bentler PM, Bonett DG. Significance tests and goodness of fit in the analysis of covariance structures. Psychol Bull 1980;88:588-606.

52. Cheung MW. Comparison of approaches to constructing confidence intervals for mediating effects using structural equation models. Struct Equ Model 2007;14:227-246.

53. Kraut R, Patterson M, Lundmark V, Kiesler S, Mukopadhyay T, Scherlis W. Internet paradox: a social technology that reduces social involvement and psychological well-being? Am Psychol 1998;53:1017-1031.

54. Kim J, Lee JE. The Facebook paths to happiness: effects of the number of Facebook friends and self-presentation on subjective well-being. Cyberpsychol Behav Soc Netw 2011;14:359-364.

55. Sum S, Mathews MR, Pourghasem M, Hughes I. Internet technology and social capital: how the internet affects seniors’ social capital and wellbeing. J Comput Mediat Commun 2008;14:202-220.