Next Article in Journal
Drivers of the Sharing Economy That Affect Consumers’ Usage Behavior: Moderation of Perceived Risk
Previous Article in Journal
Impact of Consumer Awareness and Behavior on Business Exits in the Hospitality, Tourism, Entertainment, and Culture Industries under the COVID-19 Pandemic
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Administrative Sciences
Volume 12
Issue 4
10.3390/admsci12040170
admsci-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Understanding Social Media Usage at Work from the Perspective of Social Capital Theory

by
Nur Muneerah Kasim
1,*,
Muhammad Ashraf Fauzi
1,*,
Walton Wider
2 and
Muhammad Fakhrul Yusuf
1
1
Faculty of Industrial Management, University Malaysia of Pahang (UMP), Gambang 26300, Malaysia
2
Faculty of Business and Communications, INTI International University, Nilai 71800, Malaysia
*
Authors to whom correspondence should be addressed.
Adm. Sci. 2022, 12(4), 170; https://doi.org/10.3390/admsci12040170
Submission received: 6 August 2022 / Revised: 25 September 2022 / Accepted: 25 September 2022 / Published: 17 November 2022
Browse Figures
Versions Notes

Abstract

:
With the pervasive use of social media (SM) in organizations, it is regarded as a relevant driver that can influence an employee’s job performance. This study fills in the gap that extends the job performance concept by discovering the role of SM in innovative performance in introducing new ideas beyond standard specifications to produce novel and valuable organizational outcomes. By adopting the social capital theory (SCT), the present study investigates the roles of social media use at work in predicting social capital (network ties, shared vision, and trust) that might promote work engagement and subsequently affect employees’ innovative job performance. The data was collected through an online survey, and 291 Malaysian employees participated. The partial least square structural equation modelling (PLS-SEM) technique was applied in data analysis for the measurement model and structural model used in this study. Findings show that SM use at work significantly predicts network ties, shared vision, and trust. Besides, network ties and trust positively promoted work engagement except for shared vision. Subsequently, work engagement was associated with innovative job performance. This study provides theoretical and practical implications for extending knowledge, as well as mitigating plans and efforts to resolve employees’ performance concerning the issues of SM use at work.

1. Introduction

Social media (SM) have increasingly become a valuable platform for communication and facilitate knowledge sharing for personal and work-life (Ahmed et al. 2019). Organizations have widely adopted it for work-related purposes (Chu 2020). Apart from individual usage, SM has been progressively embedded in the work environment. Organizations have been implementing SM tools in new management practices, starting from creating innovative business models to facilitating knowledge sharing, communication, and collaboration (Cao and Yu 2019). In addition, companies have been deliberately using such social media tools to support their employees in enhancing team and employee performance, as well as improving their business activities (Song et al. 2019; Braojos et al. 2019).
Due to the digital era, scholars have argued that face-to-face interaction in the organization was replaced by online interaction, specifically through social media, resulting in new online social capital (Park et al. 2013). Thus, several studies have explored the consequences of SM use on social capital in employees’ social network ties (Huang and Liu 2017; Park et al. 2013; Sheer and Rice 2017; Yen et al. 2020). Hence, social capital has become a key variable in understanding the use and implication of SM, especially in an organizational setting.
Social capital has provided a foundation for SM usage and employees’ social relations in organizations by focusing on its unique benefits on job performance. This framework was extensively used and well established, and has been widely adapted in SM-related studies e.g., (Ali-Hassan et al. 2015; Cao et al. 2016; Ghorbanzadeh et al. 2021; Kwon 2014; Tijunaitis et al. 2019). Moreover, the three dimensions of social capital by Nahapiet and Ghoshal (1998) align well with the complexity of SM usage in organizations due to its multi-faceted conceptualization. Ali-Hassan et al. (2015) mentioned that the cognitive and relational dimensions describe a person’s ability, while the structural dimension focuses on the availability of resources. In fact, these three dimensions are obtained through employees’ relational networks (Yan and Guan 2018).
Generally, findings from the link between SM use and job performance have been somewhat inconclusive and even contradictory. For instance, some researchers found SM usage in the workplace to be significantly associated with positive outcomes in employees’ job performance (Eid and Al-Jabri 2016; Jafar et al. 2019; Lee and Lee 2020; Song et al. 2019), while other studies state that SM use has led to a deterioration in employee performance (Brooks 2015; Yu et al. 2018; Zhang et al. 2015). Hence, the inconsistent and uncertain findings of the existing studies do not explain the real impact of SM use at the workplace, whether SM usage improves or harms the employees’ innovative job performance. Furthermore, the relevance of SM usage in the workplace has been hotly debated by scholars; whether the use of SM during work time, either for personal or work-related purposes, should be allowed, disallowed, or tolerated (Chu 2020).
Central to the present study, Yen et al. (2020) stated that studies of the exact mechanism of social capital and job performance through SM use for interaction between workers and coworkers are still scarce. The understanding of social media as social capital is still constrained by some persisting gaps in the social media literature, especially in innovative job performance (Chen et al. 2019; Ali-Hassan et al. 2015). In addition, the scope of generalizing research findings based on previous research is limited due to sample bias, affordances of SM platforms (Sheer and Rice 2017; Yen et al. 2020), and cultural differences (Cao et al. 2016). Due to the limitations, the present study aimed to explore the benefits of SM use at work on employees’ innovative performance by utilizing social capital theory (SCT), and the objective of the present study is suited to the purpose of SCT.
Therefore, this study extends the model of previous studies (Ali-Hassan et al. 2015) by incorporating work engagement in order to gain a deeper understanding of how SM use at work can foster a deep connection with social capital that directly enhances work engagement and consequently, affect employees’ innovative job performance. As Gibbs (1990) stated, social capital theory aims to explain the influence of people’s interaction in obtaining psychological and tangible benefits. In addition, social capital is relevant to collectivistic cultures due to the emphasis on social relationships in daily interactions, including during working hours (Sheer and Rice 2017). Furthermore, the addition in this study of a new variable, work engagement, can be a good predictor of an employee, team, and organizational outcomes (Bakker and Albrecht 2018). Clausen et al. (2019) stressed that social capital is an essential element in developing employee engagement, specifically work engagement. In addition, lack of studies on SM use and its influence on employee job performance in Malaysia (Radhakrishnan et al. 2018), social capital is one of the critical variables in understanding the impact and outcome of SM use at work (Sheer and Rice 2017). Consequently, the integration of SCT and work engagement will contribute to the existing body of literature in the area of SM usage and job performance from the perspective of Malaysian culture, as Malaysians in general are more collectivistic in nature (Sumaco et al. 2014; Jayasingam et al. 2021).

2. Literature Review

2.1. Social Media Use at Work

Generally, there are two major purposes of SM use by the employee while at work, which are personal or work-related reasons. Though researchers have addressed the essential use of SM for work-related reasons, the negative consequences of SM use on employee outcomes also have been identified in several studies. Initially, the use of SM for work purposes contributes to positive work morale, but excessive use of SM may lead to negative consequences on employee performance and morale (Demircioglu and Chen 2019; Yu et al. 2018). In addition, the high frequency of social media usage during work hours may unintentionally cause a pressured environment in the workplace (Bucher et al. 2013). Furthermore, employees who connect with SM for work, especially after regular working hours, could diminish the availability of time and energy of employees in fulfilling their life-role’s demands, and thus eventually generate both time-based and strain-based conflicts between their work domain and life domain (Chu 2020).
Basically, the work-related use of SM mainly means for performing work, facilitating knowledge sharing (Ahmed et al. 2019; Jafar et al. 2019), work communication between employees and organization (Zhang et al. 2019), creating content for work or collaboration with colleagues (Ali-Hassan et al. 2015), developing and strengthening contact with stakeholders for work-related issues (van den Berg and Verhoeven 2017). Furthermore, a recent study by Ghorbanzadeh et al. (2021) discovered that SM usage has positively predicted social capital and subsequently enhanced employees’ job performance. Hence, the purpose of SM usage for work-related reasons is essential to achieving organizational goals and performance enhancement. Therefore, we believe using SM in a workplace for work-related purposes has many benefits on employee performance.
In the context of this study, social media referred as a group of internet-based applications that allow users to communicate, build connections and facilitate ideas by creating, sharing, and exchanging information in other formats with multiple communities (Ahmed et al. 2019; Sheer and Rice 2017). Employees and organizations use various social media platforms for work, personal, or both purposes. This study focuses on using public social media accounts and Instant Messages (IM) to establish network ties, share information, and perform tasks or duties.
The public social accounts in this study are related to Facebook, Twitter, and LinkedIn. These platforms have been discovered to be the channel most frequently used by employees for work purposes and professional connections (Davis et al. 2020; Pekkala and van Zoonen 2022; Zoonen and Treem 2019). These platforms are typically individually owned accounts characterized as publicly available media that are free and easily accessible. In addition, Facebook, Twitter, and LinkedIn are more suited for work purposes due to the essential technical and textual features which help employees obtain or share work-related information and expand professional connections rather than using Instagram or Snapchat (Zoonen et al. 2017).
Instant Messaging (IM), particularly WhatsApp messenger and Telegram, has become an essential formal medium for work interaction in organizations. Recently, employees have used WhatsApp Messenger and Telegram extensively in Malaysia for work matters. These messengers enable employees to be quicker and more effective in sharing and discussing new ideas or products with their colleagues directly and help them engage with and manage remote teams. In addition, WhatsApp Messenger and Telegram features are user-friendly and easily adapted by employees from different age groups to accomplish individual or group tasks.
Therefore, this study measures SM use at work through Facebook, Twitter, Linked In, WhatsApp, and Telegram to discover the advantages SM use has employee’s innovative job performance in Malaysia.

2.2. Underpinning Theory

According to Nahapiet and Ghoshal (1998), organizational social capital is a strategic resource and a multidimensional concept which can be divided into three dimensions, namely cognitive, relational and structural dimensions. The dimensions of social capital relate to the general nature of the relationship between organization members. The cognitive dimension refers to a common understanding and view among organization members that share similar goals, visions, and languages, thus enabling collective understanding (Nahapiet and Ghoshal 1998; Ali-Hassan et al. 2015). The relational dimension is described as resources such as trust, commitment, and reciprocity embedded in a personal relationship that focus on a particular relationship such as friendship and respect, in which individuals are willing to engage in exchange (Cao et al. 2016; Nahapiet and Ghoshal 1998). The structural dimension relates to the network ties between organization members and provides access to communication (Nahapiet and Ghoshal 1998). Each of these dimensions of social capital constitutes an aspect of the social structure that facilitates the exchange and combination of resources within an organization (Hauser et al. 2016). These three dimensions must be fulfilled in order to realize social capital in the workplace.
Kwon and Adler (2014) stated that social capital must be recognized as a multidimensional concept to improve the understanding of social links because it has a value in both relations and cognition that go beyond the structural dimension. Thus, the present study conceptualized social capital developed by Nahapiet and Ghoshal (1998) that comprised three dimensions, namely the cognitive (shared vision), the structural (network ties) and the relational (trust). Hence, the three dimensions of social capital are essentially needed to examine the connection between SM use and job performance because, as Swanson et al. (2020) stated, social capital is realized when organizational members have common cognitive, relational and structural characteristics to attain the larger goals of the organization.
This study is essential to emphasize the role of SCT in the link between SM uses at work, social capital, and employee job performance in Malaysia. Moreover, several scholars stressed the importance of SM in creating social capital among employees (Men et al. 2020), as social capital has a vital role in influencing employees’ job performance, especially in innovative performance (Cappiello et al. 2020; Yan and Guan 2018). Based on dimensions of social capital by Nahapiet and Ghoshal (1998), the present study employed network ties, shared vision, and trust to gain a comprehensive view of SM use at work in influencing employees’ social capital and job performance.

2.2.1. Network Ties

With the pervasive use of technology in the digital era, the development of network ties in organizations can be accessed through SM use at work. Moreover, Cao et al. (2016) stated that social media can help employers connect with employees who share similar backgrounds and interests, which is helpful in strengthening ties with colleagues, maintaining a professional network, and discovering potential relationships. Unlike regular physical contact, SM usage can assist employees in establishing a relationship with colleagues, primarily when they work in a different place or remotely at any time (Sheer and Rice 2017). The resources included emotional, financial, information support, mutual trust, and others (Cao et al. 2016; Yen et al. 2020). Having network ties with various organization members is essential for employees to obtain resources at work. By establishing network ties in the workplace, SM enables them to reach out for support (e.g., emotional, informational, and social) from their colleagues when performing work-related tasks.

2.2.2. Shared Vision

The aspect of the cognitive dimension of interest in this study is a shared vision. A shared vision is an emergent state that develops in a team when members have access to the same information and share the same tools, work processes, and work cultures (Hinds and Mortensen 2005; Ali-Hassan et al. 2015). A shared vision facilitates amongst employees the sharing of common collective goals and aspirations of an organization that can quickly be achieved through collaboration (Chang et al. 2012; Cao et al. 2016) which may increase mutual understanding among them. Establishing a shared vision in organizations is essential to employees’ motivation and a sense of purpose (Tijunaitis et al. 2019) and may raise spirits at work (Berraies et al. 2020). Thus, employees who share the same vision have a common understanding, views, and interpretation of organizational goals that lead to better work performance.

2.2.3. Trust

The third dimension of social capital in this study is trust. Trust has become a key component in the workplace environment, both at an employee level (Clausen et al. 2019) and an organizational level (Ranjay and Sytch 2008). Since employees widely use SM in their work, relationships and trust are fostered via SM platforms. SM can assist employees in getting to know their colleagues, leading to informal relationships that may enable employees to develop trusting relationships (Hauser et al. 2016). Social media capabilities in facilitating informal, social exchange with organizational members across time and over geographic boundaries have permitted employees to reach mutual understanding and lead to the formation of trust (Valenzuela et al. 2009; Cao et al. 2016). Deepening mutual understanding can enhance trust among employees in the workplace, and it is much easier to promote trusting relationships with colleagues through various media, including SM.

2.3. Work Engagement & Innovative Performance

In the digital age, employees widely adopt SM usage for work-related purposes. However, interaction through SM use at work can either act as a resource or a hindrance in the context of work engagement. Scholars discovered that SM use at work has significantly enhanced job resources, specifically social capital (Yen et al. 2020; Huang and Liu 2017; Sheer and Rice 2017), which can increase work engagement. In fact, SM allows employees to maintain social network ties and to support colleagues (Charoensukmongkol 2014), as it can be accessed anywhere, anytime, and across organizational boundaries. Meanwhile, the direct role of SM usage in organizations is also associated with work engagement. A few studies have discovered the effects of SM usage on employee engagement. Empirically, SM use at work has significantly enhanced work engagement (Oksa et al. 2020; Zoonen et al. 2017; Men et al. 2020).
Employees who experience social capital at the workplace have extensively displayed work engagement that results in better task and contextual performance (Bhatti et al. 2018; Clausen et al. 2019). Due to the resources provided by good interpersonal relationships in organizations, employees will be engrossed in work-related tasks as they work harder to complete their work and perform better. In addition, highly engaged employees are positive and enthusiastic about their work (Bakker and Albrecht 2018), which leads to the enhancement of their creativity and innovation (Cheng et al. 2020). Further, some scholars also discovered the positive association between work engagement and innovative performance (Zyl et al. 2019; Sharma and Nambudiri 2020; Waheed et al. 2017). Thus, social capital is viewed as a job resource that leads to work engagement and consequently affects employees’ innovative job performance.
Most studies have focused on the association between SM use at work and job performance. However, Zoonen et al. (2017) stated that studies neglect the essential dimension of employees’ well-being, such as engagement. In addition, Sharma and Nambudiri (2020) suggest that considering social capital in the relationship between work engagement and its outcome could have a crucial intervening role in this association. Ali-Hassan et al. (2015) also suggested a possible extension to the association between SM use, social capital, and job performance that focuses on other potential variables. Thus, work engagement is incorporated in this study.

3. Hypothesis Development

3.1. SM Use and Network Ties

Employees are using SM to develop and maintain social relationships with their colleagues. As mentioned by Charoensukmongkol (2014), SM allows employees to create and maintain social relationships, besides acting as a mechanism for giving support or advice to colleagues. Moreover, network ties within an organization are easily expanded through SM due to the ability of SM to easily connect to employees from different places or times. In addition, several studies discovered that SM has significantly contributed to the development of social capital in the workplace represented by network ties (Babu et al. 2020; Huang and Liu 2017). Therefore, SM use at work can generate social capital by establishing network ties among employees. The first hypothesis is presented as:
Hypothesis H1. 
Social media use at work positively influence employee’s network ties.

3.2. SM Use and Shared Vision

Aside from face-to-face interaction, SM has been widely used in organizations to foster a shared vision. SM can leverage a flexible medium for employees to participate in work tasks. It potentially involves them actively through collaboration embedded within informal social interactions, making it easier for employees to develop and maintain a shared vision within an organization (Cao et al. 2016). In addition, scholars have found that a shared vision in the workplace was stronger when employees use SM at work (Babu et al. 2020; Tijunaitis et al. 2019; Ali-Hassan et al. 2015; Cao et al. 2016). These findings showed that SM use at work can promote a shared vision by facilitating employee communication and interaction. Therefore, a second hypothesis is proposed:
Hypothesis H2. 
Social media use at work positively influence employee’s shared vision.

3.3. SM Use and Trust

Recently, trust within an organization has evolved through SM usage. SM has become a platform for introducing organizational members to informal relationships that encourage the development of trust in the workplace (Tijunaitis et al. 2019). Besides, SM can help employees actively share work-related information that indirectly promotes mutual trust. Thus, well-informed employees can communicate more effectively with their colleagues and trust each other’s work (King and Lee 2016). Moreover, prior studies have demonstrated that SM use has contributed significantly to developing workplace trust among employees (Kelton and Pennington 2020; Louati and Hadoussa 2021; Hauser et al. 2016). Therefore, SM use at work can generate social capital in workplaces by establishing trust among employees without relying on face-to-face in-person contact. The third hypothesis is as follows:
Hypothesis H3. 
Social media use at work positively influence employee’s trust.

3.4. Network Ties and Work Engagement

SM allows employees to create and maintain social relationships, besides giving support or advice to colleagues (Charoensukmongkol 2014). Cao et al. (2016) stated that social media can help employers connect with employees who share similar backgrounds and interests, which is helpful for strengthening ties with colleagues, maintaining a professional network, and discovering potential relationships. Adopting social media at work will positively enhance work engagement due to the ability of SM to make it easier for employees to obtain resources from colleagues who are in their network ties. In structural social capital, individuals’ interactions with social networks provide them with various resources, such as advice, information and social support (Jutengren et al. 2020). Moreover, Oksa et al. (2020) discovered that employees who received social support from their network ties via SM interaction positively increased work engagement among the Finnish working population. Thus, employees are more engaged with their work when network ties among colleagues are forged through SM. The fourth hypothesis is:
Hypothesis H4. 
Network ties positively influence employee’s work engagement.

3.5. Shared Vision and Work Engagement

In an organization, shared vision helps create a sense of common responsibility and collective action (Mostafa 2019), which leads to an improvement in mood in the workplace (Berraies et al. 2020). It facilitates in employees the sharing of common collective goals and aspirations of an organization, which can easily be achieved through SM use at work. In addition, it directly supports workplace knowledge sharing (Chang et al. 2012). As Ali-Hassan et al. (2015) mentioned, to develop a shared vision, employees need to be familiar with their colleagues, aware of the social environment and understand their problems and solutions. Hence, a shared vision can enhance work engagement if employees have a common understanding within an organization and can achieve it through collaboration. The following hypothesis is denoted as:
Hypothesis H5. 
Shared vision positively influence employee’s work engagement.

3.6. Trust and Work Engagement

Work engagement within an organization can evolve through social support from colleagues (Bakker and Albrecht 2018). Employees might develop mutual trust with each other when they feel their relationships with colleagues are mutually supportive, which would result in high work engagement (Jutengren et al. 2020). In addition, trust is established through an excellent interpersonal relationship that develops over time through various interactions (Chiu et al. 2006). Moreover, previous studies discovered that mutual trust in relational social capital at co-worker levels was positively associated with work engagement (Strömgren et al. 2016; Mostafa 2019). Undoubtedly, trust is a significant dimension of social capital in developing employees’ work engagement. Therefore, the following hypothesis is presented as:
Hypothesis H6. 
Trust positively influences employee’s work engagement.

3.7. Work Engagement and Job Performance

Innovativeness is critical for the growth, long-term survival, and sustainability of organizations (Agarwal 2014). Employees with high innovative performance are part of the success factors for organization success. Work engagement has significantly brought creativity to organizations (Waheed et al. 2017). Some scholars have discovered a positive association between employee work engagement and innovative performance (Sharma and Nambudiri 2020; Cheng et al. 2020). Employees with work engagement have more creative ideas that enhance innovative performance because of their openness to new experiences (Gawke et al. 2017; Orth and Volmer 2017). In addition, engaged employees seek support and feedback as they manifest innovativeness through openness to new experiences and challenges (Sharma and Nambudiri 2020). It is posited that employees who display a remarkable level of work engagement show innovative job performance. The hypothesis is as follows:
Hypothesis H7. 
Work engagement positively influence employee’s innovative job performance.
The following Figure 1 presents the research model of this study.

4. Research Methodology

4.1. Sample

A quantitative approach was used by conducting a survey based on a self-administered questionnaire. The samples are Malaysian public and private employees. The Government’s success largely depends on the employees’ ability, high cognitive skills, and work performance in demonstrating their knowledge-based service. Since public sector employees are the backbone of the country in providing outstanding public services, implementing measures at the individual level may enhance an organization’s overall performance (Amran et al. 2021; Hassan et al. 2021). Despite its significance, research on public sector employees’ job performance has received little attention (Johari et al. 2019), especially on innovative performance. Furthermore, the private sector, specifically government-linked companies (GLC), also plays a significant component in Malaysia’s economy in which Government holds a certain number of shares. Money returns and profits have always been the key elements in the private sector in expanding Malaysia’s economy (Permarupan et al. 2013). Thus, this study focuses on employees from the public and private sectors.
A quota sampling frame was applied to ensure sample representation within the population. The employees were divided into government, statutory body, and GLC based on the ratio of 50:30:20, respectively. This study used the G*Power application to draw the sample size from the target population. Hair et al. (2014) recommended that with a power of 0.8 and an effect size of f2 = 0.15, plus the predictor of the variable with the highest value of 3, the minimum sample was determined as 77.

4.2. Data Collection Procedure

The online survey was utilized for data collection. The population target was employees who use SM for work-related purposes. The questionnaires were distributed through email to 1500 participants. Participants filled in a Google form attached within the mail. To increase response rate, a follow-up email was sent to the participants. Two hundred ninety-four responses were obtained within three months (November 2021 to February 2022), with a response rate of 19.6%. Data were cleaned and screened for duplicate, incomplete and inappropriate responses, resulting in 291 respondents.

4.3. Measurement

In this study, the measures of all constructs were adapted from previously validated scales and items. The 3 items for SM use at work was adapted from Cao et al. (2016). The first component of social capital, network ties, was measured with four items adapted from Cao et al. (2016). The 4 items of shared vision are adapted from Cao et al. (2016), which measure whether employees have a shared vision within an organization through virtual communities based on SM use at work. In addition, this study measured the trust variable comprising five items adapted from Cao et al. (2016) to assess the development of trust among employees through SM use at work. The measurement of the work engagement variable with 5 items was adapted from Saks (2006). Lastly, the innovative job performance scale was adapted from Ali-Hassan et al. (2015) consisting of 6 items to measure the exact process of SM use at work on employees’ innovative performance. All the items were rated on a 7-point Likert scale. The list of measurement items for the constructs is presented in Appendix A. The list of variables included with the number of items assessed in the present study is depicted in Table 1.

4.4. Pre-Test and Pilot Study

Before the questionnaires were sent to potential respondents, a pre-test and pilot test were carried out, which served as a preliminary study. The first step was pre-testing the questionnaire by experts for content validity. To determine the clarity of the questionnaire, the items were reviewed by four experts from Malaysian public universities with different fields of expertise (such as organizational behavior, human resource management, and technology adoption). Amendments to the questionnaires were then made, based on reviews and comments by these expert panels. The next stage was the pilot study, which was conducted after the items were amended based on experts’ comments. Finally, the questionnaires were distributed to employees working in the government and private sectors. Approximately 43 respondents participated in this pilot study.
For the measurement model of the pilot study, this study performed a factor analysis and reliability test using SPSS version 22. The findings discovered that for all items of this study, the construct validity of all the variables was sufficient, with all the values exceeding the threshold. Generally, the findings showed that the factor loading was more significant than 0.5 (Hulland 1999; Truong and McColl 2011), the KMO value was higher more than 0.5 (Hair et al. 2010; Kaiser 1974), total variance explained more than 60 percent (Peterson 2000), and Cronbach alpha’s value exceeded 0.7 (Taber 2018). With these results, each construct’s item remained and proceeded for the final survey. The summary of the pilot test results, including factor loading, KMO value, variance explained, and Cronbach alpha’s value, is attached in Appendix B.

4.5. Data Analysis

Before proceeding with the inferential analysis, the data analysis began with descriptive statistics, and this study used SPSS version 22 to measure the frequency of background characteristics. The study applied the Partial Least Squares Structural Equation Modeling (PLS-SEM) approach in analyzing the data using SmartPLS 3.0 software. The PLS-SEM method was helpful in estimating complex structural models with many constructs, indicators, and/or model relationships, as well as its ability to adequately use non-normal data (Hair et al. 2019; Ringle et al. 2015). In addition, it was possible to perform exploratory research in developing theory and estimate model that commonly displays a high degree of statistical power compared to the CB-SEM (Hair et al. 2011; Law and Fong 2020; Sarstedt et al. 2014). Advanced model elements, such as hierarchical component models, moderator variables, or nonlinear relationships, were handled flexibly using PLS-SEM (Chin 2010; Henseler et al. 2016; Hair et al. 2017). Thus, in exploring the association of SM and job performance in this study, PLS-SEM was deemed appropriate.

5. Results

5.1. Demographic Information

Table 2 presents the demographic information of the 291 respondents. In terms of gender, 40.5 percent (118) were male and 59.5 percent (173) were female. For the level of education, most respondents (59.5 percent) were degree holders and Masters’s degrees (23.0 percent). 28.2 percent of respondents had worked for more than 11–15 years, and 20.3 percent for 6–10 years. For SM platforms, WhatsApp (87.3 percent) and Facebook (7.6 percent) were frequently used by respondents for work-related purposes.

5.2. Common Method Bias

A statistical remedy was employed in this study to manage common method bias, which is common in behavioural research (Podsakoff et al. 2003; Podsakoff et al. 2012). In PLS-SEM, a full collinearity test was used to assess common method bias, and a variance inflation value (VIF) below 5.0 (Kock 2015) and below 3.3 (Kock and Lynn 2012) indicated the dataset did not suffer common method bias. From the test, there was no significant issue in the dataset, as the VIF values of all constructs were lower than 3.30, as shown in Table 3.

5.3. Measurement Model

The measurement model is the first stage of using PLS-SEM that specifies the relations between the latent variable (construct) and its indicator (manifest variable). The purpose of measurement model analysis is to ensure all the required relationships between the latent variables and their indicators are met by the model assessment (Hair et al. 2017). For construct reliability and validity, the convergent validity was evaluated by assessing the factor loadings and average variance extracted (AVE), and Cronbach alpha and composite reliability for the internal consistency reliability (Fauzi 2021; Chin 2010). Table 4 shows that all the factor loadings exceed the minimum of required value 0.6 for an exploratory study (Hair et al. 2017). The Cronbach alpha and composite reliability for all constructs were higher than the required value of 0.7 (Hair et al. 2017). Table 4 presents the construct validity of the measurement model for the reliability and validity analysis.
Discriminant validity is essential to ensure that each variable is distinct and not supposed to be related to each other (Chin 2010). This study applied the Heterotrait-monotrait (HTMT) ratio of correlation, which has a better performance for measuring the discriminant validity in variance-based SEM than the cross loadings and Fornell Larcker criterion (Henseler et al. 2015). Franke and Sarstedt (2019) stated that to establish the discriminant validity that reliably distinguishes between those pairs of latent variables, a cut-off value of HTMT for conceptually dissimilar constructs should be less than 0.85, while for conceptually similar constructs, it should be less than 0.9, depending on the study context. Table 5 shows that none of the respective constructs violates the cut-off HTMT value of 0.85, suggesting that the variables of this study possess satisfactory discriminant validity.

5.4. Structural Model

Before proceeding with the structural model assessment, the normality of data was measured by implementing Mardia’s multivariate kurtosis. The online tool available at http://webpower.psychstat.org/models/kurtosis/ (accessed on 15 September 2022) was used to calculate univariate/multivariate skewness and kurtosis, as suggested by Cain et al. (2017). The results indicate that data was not multivariate normal, as shown by the skewness (β = 5.479, p < 0.01) and kurtosis (β = 49.476, p < 0.01). As suggested by Hair et al. (2019), this calls for a nonparametric analysis tool to perform bootstrapping.
A 5000 bootstrapping re-sampling technique was performed to assess the structural model based on the path coefficient and statistical significance (Banjanovic and Osborne 2016). The SmartPLS 3 Version 3.6.8 bootstrapping function was used to explore the path coefficient (β–value) of exogenous to endogenous variables, t-values, squared multiple correlation (R2) values of explained variance on the endogenous variable, and to assess the predictive relevance of the model. Table 6 shows the results of R2, f2 and Q2, and Table 7 displays the structural analysis results and decision on hypotheses. Figure 2 illustrates β–value, t-value and R2 of hypotheses in the structural path.
The result shows that five of the seven proposed hypotheses were supported. As hypothesized, H1, H2, and H3 were supported, as SM use at work has a positive influence on network ties (β = 0.557, t = 12.923), shared vision (β = 0.485, t = 9.696) and trust (β = 0.412, t = 7.494). The R2 of the three variables are 0.310, 0.235 and 0.170, denoting that SM use at work explained 31%, 23.5% and 17% of the variance, respectively. Next, H4 and H6 were accepted, which posited that network ties (β = 0.203, t = 2.511) and trust (β = 0.205, t = 1.843) showed a significant positive effect on employees’ work engagement, but the effect of shared vision on employees’ work engagement was not significant (β = −0.023, t = 0.234), thus rejecting H5. The R2 was 0.128, indicating that the three predictors explained 12.8% of the variance in work engagement. Lastly, H7 (β = 0.467, t = 8.546) demonstrated that work engagement was found to be significant for innovative job performance among employees, with an R2 of 0.218 indicating 21.8% of the variance in innovative job performance.
A guideline by Cohen (1988) has provided benchmarks in reporting effect size (f2), which are small = 0.02, medium = 0.15 and large = 0.35. Table 6 presents that the effect size varies from 0.000 to 0.450. This study discovered that the path of H1 has a large effect of social media use at work on network ties; meanwhile, the effect size of the path of H2, H3 and H7 are medium. However, the study discovered the insignificant path of H5, which indicated no effect of a shared vision on work engagement.

6. Discussion

The study utilized the social capital theory to explore how SM use at work predicts social capital (network ties, shared vision & trust), which affects work engagement and, subsequently, relationships on innovative job performance. Firstly, the study discovered the new outcomes of SM use and social capital in the workplace and found SM use at work predicts network ties, shared vision, and trust among Malaysian employees. Based on the findings, H1 (t = 12.923), H2 (t = 9.696), and H3 (t = 7.494) showed a t-value of more than 1.65, and these hypotheses were supported. These findings are congruent with previous studies (Babu et al. 2020; Huang and Liu 2017; Cao et al. 2016; Tijunaitis et al. 2019; Louati and Hadoussa 2021; Ali-Hassan et al. 2015), SM has become a platform to create network ties within an organization. The integration of SM into work-life allows employees to develop and maintain social relationships, besides becoming a supportive tool in giving support or advice to colleagues. Furthermore, the deployment of SM in the workplace helps employees exchange work-related knowledge that enhances their shared vision in executing work tasks. In addition, SM use at work can assist in spreading social information with other members of organizations, which can help develop and reinforce network ties and trust among them.
Second, the findings of this study revealed that network ties and trust promoted work engagement among Malaysian employees. The H4 (t = 2.511) and H6 (t = 1.843) were accepted as a t-value of more than 1.65, positing network ties and trust encouraged employees’ work engagement. These findings align with previous studies and emphasize that employees foster high work engagement and more satisfaction with their work when network ties and trust among colleagues are connected through SM (Oksa et al. 2020; Hauser et al. 2016). In structural social capital, individuals’ interactions with social networks provide them with various resources, such as advice, information, social support and other supports (Jutengren et al. 2020). SM’s ability to provide employees with a range of benefits and resources from colleagues might enhance network ties and mutual trust with each other when they feel their relationships with colleagues are mutually supportive, resulting in high work engagement.
Meanwhile, this study found that a shared vision on employees’ work engagement was not significant as t = 0.234, t-value < 1.65, thus rejecting H5. The results indicate that a shared vision did not promote the employees’ work engagement. This finding is inconsistent with past studies (Babu et al. 2020; Tijunaitis et al. 2019; Ali-Hassan et al. 2015; Cao et al. 2016). Basically, the main components in work engagement consist of behavioral, emotional and cognitive elements, which are strongly directed toward individual work performance (Saks 2006; Zhang et al. 2019). Employees with high work engagement tend to immerse themselves in their work tasks cognitively, emotionally, and physically. In addition, a shared vision facilitates employees to share common collective goals and aspirations of an organization that can quickly be achieved through collaboration (Berraies et al. 2020). In this study, employees might feel that a shared vision could not promote work engagement due to the specific job scope already assigned to them. Also, the employees understand and share a common goal and aspiration; however, they might not feel enthusiastic and inattentive with their work activities due to years of work experience. Based on the demographic information, most of the employees had long-term work experience, between 11–20 years in their field, thus influencing their motivation and level of energy to engage with their work. Furthermore, long-term work experience may lead to boredom and a repetitive routine in completing work tasks subsequently, decreasing work engagement (van Hooff and van Hooft 2017).
Lastly, this study ascertained that work engagement was significantly associated with employees’ innovative job performance, indicating H7 (t = 8.546) t-value > 1.65. As expected, this result is in line with previous studies (Gawke et al. 2017; Orth and Volmer 2017). This result suggests that work engagement is a significant force that leads employees to perform with a strong focus on work. They can show their creative and critical thinking to produce, adopt, promote, and implement novel ideas. Engaged employees are associated with better task performance, high levels of creativity, client satisfaction, and organizational citizenship behavior (Bakker et al. 2014).

7. Implication

By employing the social capital theory, this study contributes to the theoretical understanding of the role of SM use among employees in government departments. The present study provides an essential extension of current research by offering a detailed theoretical understanding of the social relationship as resources underlying employees’ work behavior, specifically in innovative job performance. In addition, this study replicates and extends SCT and research on the area of SM within the organizational context, which is essential to understand in today’s technology-driven workplaces. The outcome provides a meaningful theoretical contribution to the literature on social relations within the organization and the effects of SM use on employee outcomes at the workplace.
Employees who use SM at work may be unaware of the consequences of SM usage. They view SM as an integral part of their daily life. The importance of understanding communication needs is essential for ensuring that employees literally engage with the information. Considering the fact that SM use has become an integral part of employees’ work life, they need to have greater insight into the role of SM usage in executing the work tasks at the workplace. In addition, this study is ground-breaking in causing employees to consider SM’s usefulness as an innovative tool that can enhance their work engagement through information delivery, knowledge sharing, and facilitating relationships.
Last, the findings of this study offer management a guide to adopting an emerging and popular technology, SM, as a medium for facilitating employees’ communication, work engagement, and job performance. Such measures can effectively provide practical insights for management to create new strategies for establishing and maintaining social interaction ties that are likely to result in even more successful job performance and better decision-making. In addition, management can strengthen existing guidelines or policies regarding the terms of SM use, which may further ensure the productive and consistent use of communication channels to generate better routines and innovative performance.

8. Limitation and Future Work

Although this study offers valuable insights, certain limitations should also be acknowledged and addressed in future studies. Given the pervasiveness of SM use at work and its effects on employees’ innovative job performance, our findings imply that future research should pay closer attention to social capital, its antecedents, and its outcomes in the other type of organizational settings (i.e., private, large corporate organization) or specific industry such as manufacturing, services, and education. Different contexts would provide a detailed and diverse understanding of adapting to SM usage that may influence employees’ job performance.
Second, this study highlighted only the positive consequences of SM use in the workplace. Future studies should explore the harmful and benefits of SM use at work by integrating two theories/models (e.g., the SSO model and social capital theory). We believe that researchers in other disciplines can improve the understanding of SM use at work and employees’ outcomes from another perspective through a combination of theories.
Third, this study collected data from a single source by obtaining the responses from an internet survey only. Although the statistical result showed no issues in common method bias, respondents might be unable to inform the actual situation or condition in answering the sensitive questions related to personal relation, trust, and vision. Future scholars should consider applying a mixed-method design by adding interview sessions or observation to measure employees’ social ties and innovative job performance accurately.

9. Conclusions

As employees have increased connectivity and diverse social groups on SM platforms across personal and professional settings, SM usage at work has facilitated employees to maintain social relations with co-workers. Several scholars have accepted that social capital through SM platforms has significantly played a critical role in maximizing work engagement and employee performance. The present study adopts the SCT theory to explain how SM use at work predicts social capital that influences work engagement and, subsequently, employees’ innovative job performance in the workplace. The findings of this study provide an essential extension of prior knowledge for the conceptual relationships for social capital that were empirically validated in terms of work engagement and employees’ outcomes. Moreover, the outcome of this study will be of immense benefit to employees and employers in adapting SM use at work to promote employees’ innovative job performance.

Author Contributions

Conceptualization, N.M.K. and M.A.F.; methodology, N.M.K. and M.A.F.; software, N.M.K. and M.A.F.; validation, N.M.K. and M.A.F.; formal analysis, N.M.K. and M.A.F.; investigation, N.M.K. and M.A.F.; resources, N.M.K. and M.A.F.; data curation, N.M.K.; writing—original draft preparation, N.M.K.; writing—review and editing, M.A.F., W.W. and M.F.Y.; visualization, N.M.K. and M.A.F.; supervision, M.A.F.; project administration, N.M.K.; funding acquisition, M.A.F. All authors have read and agreed to the published version of the manuscript.

Funding

Ministry of Higher Education Malaysia under the Fundamental Research Grant Scheme FRGS RACER/1/2019/SS03/UMP//1 (University Grant no. RDU192619).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A

Table
Table A1. Research instrument.
Table A1. Research instrument.
ItemOriginal Scale ItemModified Scale Item
Social media use at work
SM 1I often use social media to obtain work-related information and knowledge.
SM 2I regularly use social media to maintain and strengthen communication with colleges in my work.
SM 3What is your frequency of usage of social media in the work? not at all (1)–frequently(7)How you rate the usage of Social Media? not at all (1)–frequently(7)
Network ties
NT1I maintain close social relationships with my colleges.I maintain close social relationships with my colleagues.
NT2I spend a lot of time interacting with my colleges through social media.I spend a lot of time interacting with my colleagues through social media.
NT33I know some colleges through social media on a personal level.I know some colleagues through social media on a personal level.
NT4I have frequent communication with my colleges through social media.I have frequent communication with my colleagues through social media.
Shared vision
SV1Members in the virtual community created by social media share the vision of helping others solve their professional problems
SV2Members in the virtual community created by social media share the same goal of learning from each other.
SV3Members in the virtual community created by social media share the same value that helping others is pleasant.
Trust
TR1I assumed that members in the virtual community created by social media would always look out for my interests.
TR2I assumed that members in the virtual community created by social media would go out of their ways to make sure I was not damaged or harmed.
TR3I felt like members in the virtual community created by social media cared what happened to me.
TR4I believed that members in the virtual community created by social media approached their jobs with professionalism and dedication.
TR5Given members in the virtual community created by social media track record, I saw no reason to doubt their competence and preparation.
Work Engagement
WE1I really “throw” myself into my job.
WE2Sometimes I am so into my job that I lose track of time.
WE3This job is all consuming, I am totally into it.
WE4My mind often wanders and I think of other things when doing my job (R). My mind often focuses and concentrates on my job.
WE5I am highly engaged in this job.
Innovative job performance
IP1Create new ideas for improvements.I always create new ideas for improvements.
IP2Mobilize support for innovative ideas.I always mobilize support for innovative ideas.
IP3Search out novel working methods.I always search out novel working methods.
IP4Transform innovative ideas into useful applications.I always transform innovative ideas into useful applications.
IP5Generate original solutions to problems.I always generate original solutions to problems.
IP6Introduce innovative ideas.I always introduce innovative ideas.

Appendix B

Table
Table A2. Summary of the pilot test results.
Table A2. Summary of the pilot test results.
ConstructItemsFactor LoadingKMO% Variance ExplainedCronbach’s Alpha
SM useSM10.8880.72580.9240.882
SM20.927
SM30.884
Network tiesNT10.8580.80374.790.883
NT20.793
NT30.904
NT40.899
Shared visionSV10.9180.67987.330.926
SV20.97
SV30.915
TrustTR10.7450.72971.3720.895
TR20.904
TR30.869
TR40.846
TR50.853
Work engagementWE10.7860.74871.1790.892
WE20.722
WE30.872
WE40.903
WE50.919
Innovative job performanceIJP10.8710.87477.8750.943
IJP20.912
IJP30.852
IJP40.894
IJP50.864
IJP60.901

References

  1. Agarwal, Upasna A. 2014. Examining the Impact of Social Exchange Relationships on Innovative Work Behaviour. Team Performance Management 20: 102–20. [Google Scholar] [CrossRef]
  2. Ahmed, Yunis Ali, Mohammad Nazir Ahmad, Norasnita Ahmad, and Nor Hidayati Zakaria. 2019. Social Media for Knowledge-Sharing: A Systematic Literature Review. Telematics and Informatics 37: 72–112. [Google Scholar] [CrossRef]
  3. Ali-Hassan, Hossam, Dorit Nevo, and Michael Wade. 2015. Linking Dimensions of Social Media Use to Job Performance: The Role of Social Capital. The Journal of Strategic Information Systems 24: 65–89. [Google Scholar] [CrossRef]
  4. Amran, Noor Afza, Halimah Nasibah Ahmad, and Nor Laili Hassan. 2021. Malaysian Public Sector Size: A Comparison with Other Asean Countries. Journal of Business Management and Accounting 11: 1–20. [Google Scholar] [CrossRef]
  5. Babu, Srishti, V. R. Hareendrakumar, and Suresh Subramoniam. 2020. Impact of Social Media on Work Performance at a Technopark in India. Metamorphosis: A Journal of Management Research 19: 59–71. [Google Scholar] [CrossRef]
  6. Bakker, Arnold B., and Simon Albrecht. 2018. Work Engagement: Current Trends. Career Development International 23: 4–11. [Google Scholar] [CrossRef]
  7. Bakker, Arnold B., Evangelia Demerouti, and Ana Isabel Sanz-Vergel. 2014. Burnout and Work Engagement: The JD–R Approach. Annual Review of Organizational Psychology and Organizational Behavior 1: 389–411. [Google Scholar] [CrossRef]
  8. Banjanovic, Erin S., and Jason W. Osborne. 2016. Confidence Intervals for Effect Sizes: Applying Bootstrap Resampling. Practical Assessment, Research and Evaluation 21: 5. [Google Scholar] [CrossRef]
  9. Berraies, Sarra, Rym Lajili, and Rached Chtioui. 2020. Social Capital, Employees’ Well-Being and Knowledge Sharing: Does Enterprise Social Networks Use Matter? Case of Tunisian Knowledge-Intensive Firms. Journal of Intellectual Capital 21: 1153–83. [Google Scholar] [CrossRef]
  10. Bhatti, Muhammad Awais, Norazuwa Mat, and Ariff Syah Juhari. 2018. Effects of Job Resources Factors on Nurses Job Performance (Mediating Role of Work Engagement). International Journal of Health Care Quality Assurance 31: 1000–13. [Google Scholar] [CrossRef]
  11. Braojos, Jessica, Jose Benitez, and Javier Llorens. 2019. How Do Social Commerce-IT Capabilities Influence Firm Performance? Theory and Empirical Evidence. Information & Management 56: 155–71. [Google Scholar] [CrossRef]
  12. Brooks, Stoney. 2015. Does Personal Social Media Usage Affect Efficiency and Well-Being? Computers in Human Behavior 46: 26–37. [Google Scholar] [CrossRef]
  13. Bucher, Eliane, Christian Fieseler, and Anne Suphan. 2013. The Stress Potential of Social Media in the Workplace. Information, Communication & Society 16: 1639–67. [Google Scholar] [CrossRef]
  14. Cain, Meghan K., Zhiyong Zhang, and Ke-Hai Yuan. 2017. Univariate and multivariate skewness and kurtosis for measuring nonnormality: Prevalence, influence and estimation. Behavior Research Methods 49: 1716–35. [Google Scholar] [CrossRef] [Green Version]
  15. Cao, Xiongfei, and Lingling Yu. 2019. Exploring the Influence of Excessive Social Media Use at Work: A Three-Dimension Usage Perspective. International Journal of Information Management 46: 83–92. [Google Scholar] [CrossRef]
  16. Cao, Xiongfei, Xitong Guo, Douglas Vogel, and Xi Zhang. 2016. Exploring the Influence of Social Media on Employee Work Performance. Edited by Professor Pan Wang, Professor Sohail Chaudhry, Professor Ling Li. Internet Research 26: 529–45. [Google Scholar] [CrossRef]
  17. Cappiello, Giuseppe, Federica Giordani, and Marco Visentin. 2020. Social Capital and Its Effect on Networked Firm Innovation and Competitiveness. Industrial Marketing Management 89: 422–30. [Google Scholar] [CrossRef]
  18. Chang, Chia-Wen, Heng-Chiang Huang, Chi-Yun Chiang, Chiu-Ping Hsu, and Chia-Chen Chang. 2012. Social Capital and Knowledge Sharing: Effects on Patient Safety. Journal of Advanced Nursing 68: 1793–803. [Google Scholar] [CrossRef]
  19. Charoensukmongkol, Peerayuth. 2014. Effects of Support and Job Demands on Social Media Use and Work Outcomes. Computers in Human Behavior 36: 340–49. [Google Scholar] [CrossRef]
  20. Chen, Xiayu, Shaobo Wei, Robert M. Davison, and Ronald E. Rice. 2019. How Do Enterprise Social Media Affordances Affect Social Network Ties and Job Performance? Information Technology & People 33: 361–88. [Google Scholar] [CrossRef]
  21. Cheng, Tan, Pan Zhang, Yuping Wen, and Liyin Wang. 2020. Social Media Use and Employee Innovative Performance: Work Engagement as a Mediator. Social Behavior and Personality: An International Journal 48: 1–9. [Google Scholar] [CrossRef]
  22. Chin, Wynne W. 2010. Handbook of Partial Least Squares. Edited by Vincenzo Esposito Vinzi, Wynne W. Chin, Jörg Henseler and Huiwen Wang. Berlin/Heidelberg: Springer. [Google Scholar] [CrossRef]
  23. Chiu, Chao-Min, Meng-Hsiang Hsu, and Eric T. G. Wang. 2006. Understanding Knowledge Sharing in Virtual Communities: An Integration of Social Capital and Social Cognitive Theories. Decision Support Systems 42: 1872–88. [Google Scholar] [CrossRef]
  24. Chu, Tsz Hang. 2020. A Meta-Analytic Review of the Relationship between Social Media Use and Employee Outcomes. Telematics and Informatics 50: 101379. [Google Scholar] [CrossRef]
  25. Clausen, Thomas, Annette Meng, and Vilhem Borg. 2019. Does Social Capital in the Workplace Predict Job Performance, Work Engagement, and Psychological Well-Being? A Prospective Analysis. Journal of Occupational & Environmental Medicine 61: 800–5. [Google Scholar] [CrossRef]
  26. Cohen, Jacob. 1988. Statistical power analysis for the behavioral science. Technometrics 31: 499–500. [Google Scholar]
  27. Davis, Joanna, Hans-Georg Wolff, Monica L. Forret, and Sherry E. Sullivan. 2020. Networking via LinkedIn: An Examination of Usage and Career Benefits. Journal of Vocational Behavior 118: 103396. [Google Scholar] [CrossRef]
  28. Demircioglu, Mehmet Akif, and Chung-An Chen. 2019. Public Employees’ Use of Social Media: Its Impact on Need Satisfaction and Intrinsic Work Motivation. Government Information Quarterly 36: 51–60. [Google Scholar] [CrossRef]
  29. Eid, Mustafa I. M., and Ibrahim M. Al-Jabri. 2016. Social Networking, Knowledge Sharing, and Student Learning: The Case of University Students. Computers & Education 99: 14–27. [Google Scholar] [CrossRef]
  30. Fauzi, Muhammad Ashraf. 2021. Research vs. Non-Research Universities: Knowledge Sharing and Research Engagement among Academicians. Asia Pacific Education Review, 1–15. [Google Scholar] [CrossRef]
  31. Franke, George, and Marko Sarstedt. 2019. Heuristics versus Statistics in Discriminant Validity Testing: A Comparison of Four Procedures. Internet Research 29: 430–47. [Google Scholar] [CrossRef]
  32. Gawke, Jason C., Marjan J. Gorgievski, and Arnold B. Bakker. 2017. Employee intrapreneurship and work engagement: A latent change score approach. Journal of Vocational Behavior 100: 88–100. [Google Scholar] [CrossRef]
  33. Ghorbanzadeh, Davood, Valery Ivanovich Khoruzhy, Irina Viktorovna Safonova, and Ivan Vladimirovich Morozov. 2021. Relationships between social media usage, social capital and job performance: The case of hotel employees in Iran. Information Development, 02666669211030553. [Google Scholar] [CrossRef]
  34. Gibbs, J. P. 1990. Foundations of Social Theory. Cambridge: Harvard University Press. [Google Scholar]
  35. Hair, J. F., W. C. Black, B. J. Babin, and R. E. Anderson. 2010. Multivariate Data Analysis, 7th ed. New York: Pearson. [Google Scholar]
  36. Hair, Joe F., Christian M. Ringle, and Marko Sarstedt. 2011. PLS-SEM: Indeed a Silver Bullet. Journal of Marketing Theory and Practice 19: 139–52. [Google Scholar] [CrossRef]
  37. Hair, Joe F., Jr., Lucy M. Matthews, Ryan L. Matthews, and Marko Sarstedt. 2017. PLS-SEM or CB-SEM: Updated Guidelines on Which Method to Use. International Journal of Multivariate Data Analysis 1: 107. [Google Scholar] [CrossRef]
  38. Hair, Joe F., Jr., Marko Sarstedt, Lucas Hopkins, and Volker G. Kuppelwieser. 2014. Partial least squares structural equation modeling (PLS-SEM): An emerging tool in business research. European Business Review 26: 106–21. [Google Scholar] [CrossRef]
  39. Hair, Joseph F., Jeffrey J. Risher, Marko Sarstedt, and Christian M. Ringle. 2019. When to Use and How to Report the Results of PLS-SEM. European Business Review 31: 2–24. [Google Scholar] [CrossRef]
  40. Hassan, Zaiton, Jing Samuel Tnay, Siti Murtiyani Sukardi Yososudarmo, and Surena Sabil. 2021. The Relationship between Workplace Spirituality and Work-to-Family Enrichment in Selected Public Sector Organizations in Malaysia. Journal of Religion and Health 60: 4132–50. [Google Scholar] [CrossRef]
  41. Hauser, Christoph, Urban Perkmann, Sibylle Puntscher, Janette Walde, and Gottfried Tappeiner. 2016. Trust Works! Sources and Effects of Social Capital in the Workplace. Social Indicators Research 128: 589–608. [Google Scholar] [CrossRef]
  42. Henseler, Jörg, Christian M. Ringle, and Marko Sarstedt. 2015. A New Criterion for Assessing Discriminant Validity in Variance-Based Structural Equation Modeling. Journal of the Academy of Marketing Science 43: 115–35. [Google Scholar] [CrossRef] [Green Version]
  43. Henseler, Jörg, Geoffrey Hubona, and Pauline Ash Ray. 2016. Using PLS Path Modeling in New Technology Research: Updated Guidelines. Industrial Management & Data Systems 116: 2–20. [Google Scholar] [CrossRef]
  44. Hinds, Pamela J., and Mark Mortensen. 2005. Understanding conflict in geographically distributed teams: The moderating effects of shared identity, shared context, and spontaneous communication. Organization Science 16: 290–307. [Google Scholar] [CrossRef]
  45. Huang, Lei Vincent, and Piper Liping Liu. 2017. Ties That Work: Investigating the Relationships among Coworker Connections, Work-Related Facebook Utility, Online Social Capital, and Employee Outcomes. Computers in Human Behavior 72: 512–24. [Google Scholar] [CrossRef]
  46. Hulland, John. 1999. Use of partial least squares (PLS) in strategic management research: A review of four recent studies. Strategic Management Journal 20: 195–204. [Google Scholar] [CrossRef]
  47. Jafar, Rana Muhammad Sohail, Shuang Geng, Wasim Ahmad, Ben Niu, and Felix T. S. Chan. 2019. Social Media Usage and Employee’s Job Performance. Industrial Management & Data Systems 119: 1908–25. [Google Scholar] [CrossRef]
  48. Jayasingam, Sharmila, Su Teng Lee, and Khairuddin Naim Mohd Zain. 2021. Demystifying the Life Domain in Work-Life Balance: A Malaysian Perspective. Current Psychology, 1–2. [Google Scholar] [CrossRef]
  49. Johari, Johanim, Faridahwati Mohd Shamsudin, Tan Fee Yean, Khulida Kirana Yahya, and Zurina Adnan. 2019. Job characteristics, employee well-being, and job performance of public sector employees in Malaysia. International Journal of Public Sector Management 32: 102–19. [Google Scholar] [CrossRef]
  50. Jutengren, Göran, Ellen Jaldestad, Lotta Dellve, and Andrea Eriksson. 2020. The Potential Importance of Social Capital and Job Crafting for Work Engagement and Job Satisfaction among Health-Care Employees. International Journal of Environmental Research and Public Health 17: 4272. [Google Scholar] [CrossRef]
  51. Kaiser, Henry F. 1974. An index of factorial simplicity. Psychometrika 39: 31–36. [Google Scholar] [CrossRef]
  52. Kelton, Andrea Seaton, and Robin R. Pennington. 2020. If You Tweet, They Will Follow: CEO Tweets, Social Capital, and Investor Say-on-Pay Judgments. Journal of Information Systems 34: 105–22. [Google Scholar] [CrossRef]
  53. King, Ceridwyn, and Hyemi Lee. 2016. Enhancing Internal Communication to Build Social Capital amongst Hospitality Employees—The Role of Social Media. International Journal of Contemporary Hospitality Management 28: 2675–95. [Google Scholar] [CrossRef]
  54. Kock, Ned. 2015. Common Method Bias in PLS-SEM. International Journal of E-Collaboration 11: 1–10. [Google Scholar] [CrossRef] [Green Version]
  55. Kock, Ned, and Gary Lynn. 2012. Lateral Collinearity and Misleading Results in Variance-Based SEM: An Illustration and Recommendations. Journal of the Association for Information Systems 13: 546–80. [Google Scholar] [CrossRef] [Green Version]
  56. Kwon, Illoong. 2014. Motivation, discretion, and corruption. Journal of Public Administration Research and Theory 24: 765–94. [Google Scholar] [CrossRef]
  57. Kwon, Seok-Woo, and Paul S. Adler. 2014. Social capital: Maturation of a field of research. Academy of Management Review 39: 412–22. [Google Scholar] [CrossRef] [Green Version]
  58. Law, Locky, and Natalie Fong. 2020. Applying Partial Least Squares Structural Equation Modeling (PLS-SEM) in an Investigation of Undergraduate Students’ Learning Transfer of Academic English. Journal of English for Academic Purposes 46: 100884. [Google Scholar] [CrossRef]
  59. Lee, Seung Yeop, and Sang Woo Lee. 2020. Social Media Use and Job Performance in the Workplace: The Effects of Facebook and KakaoTalk Use on Job Performance in South Korea. Sustainability 12: 4052. [Google Scholar] [CrossRef]
  60. Louati, Hanen, and Slim Hadoussa. 2021. Study of Social Media Impacts on Social Capital and Employee Performance—Evidence from Tunisia Telecom. Journal of Decision Systems 30: 118–49. [Google Scholar] [CrossRef]
  61. Men, Linjuan Rita, Julie O’Neil, and Michele Ewing. 2020. Examining the Effects of Internal Social Media Usage on Employee Engagement. Public Relations Review 46: 101880. [Google Scholar] [CrossRef]
  62. Mostafa, Ahmed Mohammed Sayed. 2019. Transformational Leadership and Restaurant Employees Customer-Oriented Behaviours. International Journal of Contemporary Hospitality Management 31: 1166–82. [Google Scholar] [CrossRef]
  63. Nahapiet, Janine, and Sumantra Ghoshal. 1998. Social Capital, Intellectual Capital, and the Organizational Advantage. The Academy of Management Review 23: 242. [Google Scholar] [CrossRef]
  64. Oksa, Reetta, Markus Kaakinen, Nina Savela, Noora Ellonen, and Atte Oksanen. 2020. Professional Social Media Usage: Work Engagement Perspective. New Media & Society 23: 2303–26. [Google Scholar] [CrossRef]
  65. Orth, Maximilian, and Judith Volmer. 2017. Daily within-person effects of job autonomy and work engagement on innovative behaviour: The cross-level moderating role of creative self-efficacy. European Journal of Work and Organizational Psychology 26: 601–12. [Google Scholar] [CrossRef]
  66. Park, Kyung-gook, Sehee Han, and Lynda Lee Kaid. 2013. Does Social Networking Service Usage Mediate the Association between Smartphone Usage and Social Capital? New Media & Society 15: 1077–93. [Google Scholar] [CrossRef]
  67. Pekkala, Kaisa, and Ward van Zoonen. 2022. Work-Related Social Media Use: The Mediating Role of Social Media Communication Self-Efficacy. European Management Journal 40: 67–76. [Google Scholar] [CrossRef]
  68. Permarupan, P. Yukthamarani, Abdullah Al-Mamun, and Roselina Ahmad Saufi. 2013. Quality of Work Life on Employees Job Involvement and Affective Commitment between the Public and Private Sector in Malaysia. Asian Social Science 9: 268–78. [Google Scholar] [CrossRef]
  69. Peterson, Robert A. 2000. A meta-analysis of variance accounted for and factor loadings in exploratory factor analysis. Marketing Letters 11: 261–75. [Google Scholar] [CrossRef]
  70. Podsakoff, Philip M., Scott B. MacKenzie, and Nathan P. Podsakoff. 2012. Sources of Method Bias in Social Science Research and Recommendations on How to Control It. Annual Review of Psychology 63: 539–69. [Google Scholar] [CrossRef] [Green Version]
  71. Podsakoff, Philip M., Scott B. MacKenzie, Jeong-Yeon Lee, and Nathan P. Podsakoff. 2003. Common Method Biases in Behavioral Research: A Critical Review of the Literature and Recommended Remedies. Journal of Applied Psychology 88: 879–903. [Google Scholar] [CrossRef]
  72. Radhakrishnan, Grace Shalini A.P., Abdul Basit, and Zubair Hassan. 2018. The Impact of Social Media Usage on Employee and Organization Performance: A Study on Social Media Tools Used by an IT Multinational in Malaysia. Journal of Marketing and Consumer Behaviour in Emerging Markets 1: 48–65. [Google Scholar] [CrossRef]
  73. Ranjay, Gulati, and Maxim Sytch. 2008. Does Familiarity Breed Trust? Revisiting the Antecedents of Trust. Managerial and Decision Economics 29: 165–90. [Google Scholar] [CrossRef]
  74. Ringle, C. M., S. Wende, and J. M. Becker. 2015. SmartPLS 3. SmartPLS GmbH, Boenningstedt. Available online: http://www.smartpls.com (accessed on 24 September 2022).
  75. Saks, Alan M. 2006. Antecedents and Consequences of Employee Engagement. Journal of Managerial Psychology 21: 600–19. [Google Scholar] [CrossRef] [Green Version]
  76. Sarstedt, Marko, Christian M. Ringle, Jörg Henseler, and Joseph F. Hair. 2014. On the Emancipation of PLS-SEM: A Commentary on Rigdon (2012). Long Range Planning 47: 154–60. [Google Scholar] [CrossRef]
  77. Sharma, Anupama, and Ranjeet Nambudiri. 2020. Work Engagement, Job Crafting and Innovativeness in the Indian IT Industry. Personnel Review 49: 1381–97. [Google Scholar] [CrossRef]
  78. Sheer, Vivian C., and Ronald E. Rice. 2017. Mobile Instant Messaging Use and Social Capital: Direct and Indirect Associations with Employee Outcomes. Information and Management 54: 90–102. [Google Scholar] [CrossRef] [Green Version]
  79. Song, Qi, Yi Wang, Yang Chen, Jose Benitez, and Jiang Hu. 2019. Impact of the Usage of Social Media in the Workplace on Team and Employee Performance. Information & Management 56: 103160. [Google Scholar] [CrossRef]
  80. Strömgren, Marcus, Andrea Eriksson, David Bergman, and Lotta Dellve. 2016. Social Capital among Healthcare Professionals: A Prospective Study of Its Importance for Job Satisfaction, Work Engagement and Engagement in Clinical Improvements. International Journal of Nursing Studies 53: 116–25. [Google Scholar] [CrossRef] [PubMed]
  81. Sumaco, Fanggy T., Brian Charles Imrie, and Kashif Hussain. 2014. The Consequence of Malaysian National Culture Values on Hotel Branding. Procedia Social and Behavioral Sciences 144: 91–101. [Google Scholar] [CrossRef] [Green Version]
  82. Swanson, Eric, Sally Kim, Sae-Mi Lee, Jae-Jang Yang, and Yong-Ki Lee. 2020. The Effect of Leader Competencies on Knowledge Sharing and Job Performance: Social Capital Theory. Journal of Hospitality and Tourism Management 42: 88–96. [Google Scholar] [CrossRef]
  83. Taber, Keith S. 2018. The Use of Cronbach’s Alpha When Developing and Reporting Research Instruments in Science Education. Research in Science Education 48: 1273–96. [Google Scholar] [CrossRef] [Green Version]
  84. Tijunaitis, Karolis, Debora Jeske, and Kenneth S. Shultz. 2019. Virtuality at Work and Social Media Use among Dispersed Workers. Employee Relations: The International Journal 41: 358–73. [Google Scholar] [CrossRef]
  85. Truong, Yann, and Rod McColl. 2011. Intrinsic motivations, self-esteem, and luxury goods consumption. Journal of Retailing and Consumer Services 18: 555–61. [Google Scholar] [CrossRef]
  86. Valenzuela, Sebastián, Namsu Park, and Kerk F. Kee. 2009. Is There Social Capital in a Social Network Site?: Facebook Use and College Students’ Life Satisfaction, Trust, and Participation. Journal of Computer-Mediated Communication 14: 875–901. [Google Scholar] [CrossRef] [Green Version]
  87. van den Berg, Annelieke C., and Joost W. M. Verhoeven. 2017. Understanding Social Media Governance: Seizing Opportunities, Staying out of Trouble. Corporate Communications 22: 149–64. [Google Scholar] [CrossRef]
  88. van Hooff, Madelon L. M., and Edwin A. J. van Hooft. 2017. Boredom at Work: Towards a Dynamic Spillover Model of Need Satisfaction, Work Motivation, and Work-Related Boredom. European Journal of Work and Organizational Psychology 26: 133–48. [Google Scholar] [CrossRef] [Green Version]
  89. Waheed, Abdul, Miao Xiao-Ming, Naveed Ahmad, and Salma Waheed. 2017. Impact of Work Engagement and Innovative Work Behavior on Organizational Performance; Moderating Role of Perceived Distributive Fairness. Paper presented at the 2017 International Conference on Management Science and Engineering (ICMSE), Nomi, Japan, August 17–20; pp. 127–33. [Google Scholar] [CrossRef]
  90. Yan, Yan, and JianCheng Guan. 2018. Social Capital, Exploitative and Exploratory Innovations: The Mediating Roles of Ego-Network Dynamics. Technological Forecasting and Social Change 126: 244–58. [Google Scholar] [CrossRef]
  91. Yen, Yung-Shen, Mei-Chun Chen, and Chun-Hsiung Su. 2020. Social Capital Affects Job Performance through Social Media. Industrial Management & Data Systems 120: 903–22. [Google Scholar] [CrossRef]
  92. Yu, Lingling, Xiongfei Cao, Zhiying Liu, and Junkai Wang. 2018. Excessive Social Media Use at Work. Information Technology & People 31: 1091–112. [Google Scholar] [CrossRef] [Green Version]
  93. Zhang, Xi, Yang Gao, Hui Chen, Yongqiang Sun, and Patricia Ordenaz De Pablos. 2015. Enhancing Creativity or Wasting Time?: The Mediating Role of Adaptability on Social Media—Job Performance Relationship. Paper presented at the Pacific Asia Conference on Information Systems, PACIS 2015—Proceedings, Singapore, July 5–9. [Google Scholar]
  94. Zhang, Xin, Liang Ma, Bo Xu, and Feng Xu. 2019. How Social Media Usage Affects Employees’ Job Satisfaction and Turnover Intention: An Empirical Study in China. Information & Management 56: 103136. [Google Scholar] [CrossRef]
  95. Zoonen, Ward van, and Jeffrey W. Treem. 2019. The Role of Organizational Identification and the Desire to Succeed in Employees’ Use of Personal Twitter Accounts for Work. Computers in Human Behavior 100: 26–34. [Google Scholar] [CrossRef]
  96. Zoonen, Ward van, Joost W.M. Verhoeven, and Rens Vliegenthart. 2017. Understanding the Consequences of Public Social Media Use for Work. European Management Journal 35: 595–605. [Google Scholar] [CrossRef]
  97. Zyl, Llewellyn Ellardus van, Amber van Oort, Sonja Rispens, and Chantal Olckers. 2019. Work Engagement and Task Performance within a Global Dutch ICT-Consulting Firm: The Mediating Role of Innovative Work Behaviors. Current Psychology 40: 4012–23. [Google Scholar] [CrossRef]
Admsci 12 00170 g001 550
Figure 1. Research Model.
Figure 1. Research Model.
Admsci 12 00170 g001
Admsci 12 00170 g002 550
Figure 2. Structural Model.
Figure 2. Structural Model.
Admsci 12 00170 g002
Table
Table 1. The list of variables measured in this study.
Table 1. The list of variables measured in this study.
ConstructsNumber of ItemsSource AdaptionUnit Analysis
Social media use at work3Cao et al. (2016) Professional employees (Higher-educational institutions) in China.
Network ties4Cao et al. (2016)
Shared vision3Cao et al. (2016)
Trust5Cao et al. (2016)
Work engagement5Saks (2006)Employees working in a variety of jobs and organizations, Canada.
Innovative job performance6Ali-Hassan et al. (2015)Employees of multinational Information Technology company.
Total26
Table
Table 2. Demographic Information.
Table 2. Demographic Information.
CategoriesTypeFrequency (n)Percentage (%)
GenderMale11840.5
Female17359.5
AgeBelow 2520.7
25–304114.1
31–354916.7
36–407024.1
41–456823.4
46–503010.3
51–55124.1
56–60186.2
Above 6010.3
Level of educationSPM/A-level/Certificate82.7
STPM31.0
Diploma3411.7
Bachelor’s17359.5
Master’s6723.0
PhD62.1
Years of working5 years and below4415.1
6–10 years5920.3
11–15 years8228.2
16–20 years5518.9
21–25 years227.6
26–30 years124.1
More than 30 years175.8
SectorGovernment14650
Statutory body8730
GLC5820
SM platformsWhatsApp25487.3
Telegram20.7
Facebook227.6
Twitter31.0
Instagram31.0
Others72.4
Table
Table 3. Full Collinearity test.
Table 3. Full Collinearity test.
ConstructSM UseNetwork TiesShared VisionTrustWork EngagementInnovative Job Performance
VIF1.4962.8122.5702.3251.3761.333
VIF = variance inflation factor.
Table
Table 4. Reliability and convergent validity analysis.
Table 4. Reliability and convergent validity analysis.
ConstructItemsLoadingsCronbachComposite Reliability (CR)Average Variance Extracted (AVE).
SM useSM10.8490.8500.9090.770
SM20.904
SM30.879
Network tiesNT10.8420.8740.9130.725
NT20.873
NT30.798
NT40.890
Shared visionSV10.9460.9530.9700.915
SV20.967
SV30.957
TrustTR10.8500.9050.9290.725
TR20.876
TR30.836
TR40.836
TR50.858
Work engagementWE10.7760.8870.9160.688
WE20.692
WE30.857
WE40.884
WE50.917
Innovative performanceIP10.9080.9420.9540.778
IP20.904
IP30.773
IP40.928
IP50.878
IP60.891
Table
Table 5. Heterotrait-Monotrait Ratio of Correlations (HTMT).
Table 5. Heterotrait-Monotrait Ratio of Correlations (HTMT).
ConstructInnovative PerformanceNetworking TiesShared VisionSM UseTrustWork Engagement
Innovative performance
Networking ties0.272
Shared vision0.2450.796
SM use0.2590.6380.537
Trust0.3060.7770.7470.466
Work engagement0.4790.3710.2890.2670.359
Table
Table 6. The result of the coefficient determination (R Square), Effect Size (f Square), and predictive relevance (Q Square).
Table 6. The result of the coefficient determination (R Square), Effect Size (f Square), and predictive relevance (Q Square).
HypothesisRelationshipR2f2Q2
H1SM → Network ties0.3100.4500.210
H2SM → Shared vision0.2350.3080.202
H3SM → Trust0.1700.2040.114
H4Network ties → Work engagement0.1280.0190.078
H5Shared vision → Work engagement0.000
H6Trust → Work engagement0.021
H7Work engagement → innovative job performance0.2180.2780.155
Table
Table 7. Hypothesis Testing.
Table 7. Hypothesis Testing.
HypothesisRelationshipPath Coefficient (β)Std Devt-Valuep-ValueBCI LLUCI LLDecision
H1SM → Network ties0.5570.04312.9230.0000.4760.621Accepted
H2SM → Shared vision0.4850.0509.6960.0000.3980.562Accepted
H3SM → Trust0.4120.0557.4940.0000.3160.495Accepted
H4Network ties → Work engagement0.2030.0812.5110.0060.0690.332Accepted
H5Shared vision → Work engagement−0.0230.1000.2340.408−0.1860.144Rejected
H6Trust → Work engagement0.2050.1111.8430.0330.0130.378Accepted
H7Work engagement → innovative job performance0.4670.0558.5460.0000.3670.546Accepted
BCI LL = Bias confidence interval lower limit, BCI UL = Bias confidence interval upper limit.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Kasim, N.M.; Fauzi, M.A.; Wider, W.; Yusuf, M.F. Understanding Social Media Usage at Work from the Perspective of Social Capital Theory. Adm. Sci. 2022, 12, 170. https://doi.org/10.3390/admsci12040170

AMA Style

Kasim NM, Fauzi MA, Wider W, Yusuf MF. Understanding Social Media Usage at Work from the Perspective of Social Capital Theory. Administrative Sciences. 2022; 12(4):170. https://doi.org/10.3390/admsci12040170

Chicago/Turabian Style

Kasim, Nur Muneerah, Muhammad Ashraf Fauzi, Walton Wider, and Muhammad Fakhrul Yusuf. 2022. "Understanding Social Media Usage at Work from the Perspective of Social Capital Theory" Administrative Sciences 12, no. 4: 170. https://doi.org/10.3390/admsci12040170

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop