Corps de l’article

In today’s knowledge-intensive economy and increasingly networked global society, knowledge is considered as one of the most significant valuable assets, and strategic resources for organizations (Nguyen et al., 2020). Several researchers argue that in most organizations, knowledge management is a necessary premise for success (Balle et al., 2020; Le & Lei, 2019). Knowledge is a key source that impacts the ability of an organization to develop skills, solve challenges, improve organizational learning, and preserve the company’s sustainable growth. Therefore, improving the capacity of companies to create, gather, share, knowledge will contribute to sustained competitive advantage over other organizations (Pitafi et al., 2020; Song et al., 2019; Cao & Ali, 2018). knowledge sharing, is defined as “the exchange of knowledge and experience between and among individuals, and within and among teams, organizational units, and organizations” (King, 2006, p. 498), helps to maximize the capacity of a business to manage knowledge and encourages people to collaborate effectively and accomplish goals (Le & Lei, 2018). These insights lead some researchers to recommend using both formal and informal channels to help access knowledge, such as social media networks (Nisar et al., 2019, Jarrahi, 2018). The use of social media (SM) at workplace establishes social ties between employees and encourages “informal networks” of relationships that contribute to more open and informal discussions, which are considered crucial for employee collaboration (Lee & Lee, 2020; Ewing et al., 2019). The expansion of SM’ use within society has formally and informally permeated organizations (Nisar et al., 2019). SM is becoming a powerful medium for promoting and knowledge sharing at individual, and organizational level (Oksa et al., 2020; Ellison et al., 2015).

Nowadays, driven by new management patterns and developments, a growing number of businesses are using SM to promote communication and collaboration, and take advantage of new business opportunities (Dong et al., 2020). Consequently, SM platforms are becoming more than just mere means of communication and operate as social interaction networks for managing human resources, communication, and knowledge sharing (Sun et al., 2019; Leonardi & Vaast, 2017). While over the past few years, the notion of SM for knowledge sharing has gained increasing attention, research about SM’s use in knowledge sharing is still at an early stage of development (Ahmad et al., 2019). Particularly, there is limited research that examines the impact of SM on knowledge sharing practices among employees in the workplace within the MENA Region and especially in Saudi Arabia’s context. Therefore, the main objective of this study is to investigate the impact of SM use at the workplace on employee’s knowledge sharing in the telecommunication sector in Saudi Arabia. Consequently, this study aims to answer the question, “How does SM usage impact employee’s knowledge sharing?” This research question will be examined from the perspective of employees.

To answer this research question, the Social Theory will be applied. Specifically, in this research we are interested in understanding how SM use at workplace can foster the social capital of employees, and subsequently facilitate knowledge sharing. Thus, the paper is organized as follows: the next section presents an overview of related literature. A research model on the effect of SM use in the workplace on work performance will be proposed. The subsequent sections describe the research methodology, the data analysis, and findings. We wrap up the paper with discussion, conclusion, and potential directions for future research.

Literature review: social capital theory

SM is defined as “a group of Internet-based applications that build on the ideological and technological foundations of Web 2.0, and that allow the creation and exchange of User Generated Content” (Kaplan & Haenlien, 2010, p.61). Based on this definition, SM has two defining features. Firstly, SM promotes relationships between groups of users. This relation can be established through messages, comments, and content sharing. Secondly, SM enables social connections, interactions, and discussions to be formed between users (Capriotti et al., 2021; Hadoussa & Menif, 2019). Today, SM is an integral part of employee’s work that its use is now commonplace for communication and knowledge sharing among co-workers.

This study is guided by the Social Capital Theory, which is considered as “a resource that actors derive from specific social structures and then use to pursue their interests; it is created by changes in the relationship among actors.” Baker (1990, p. 619). Social capital is also considered as “resources embedded in relationships”, like “friends, colleagues, and more general contacts through whom you receive opportunities to use your financial and human capital” (Burt, 2009, p. 9). Thus, social capital is the set of links, interactions, and assets embedded in a social network. It is generally described as a multidimensional framework comprising three main dimensions: structural capital, relational capital, and cognitive capital (Chiu et al., 2006). In a social system, structural capital refers to the type and structure of relationships and interactions between people. Cognitive capital reflects the common goals, norms, values, and shared understanding formed through the interactions between network members, and relational capital represents the relational resources generated by these interactions (Nahapiet & Ghoshal 1998). This dimension describes qualities that are part of the social relationship, such as trust, shared identity, or reciprocity. The driving concept behind the theory of social capital is that people’s social relationships allow productive results. Social capital may, therefore, be considered an essential instrument that provides organizations with various benefits. It enables collaboration and the accomplishment of common goals and promotes the exchange and development of intellectual capital (Bhatti et al., 2021; Barlatier & Mention, 2019). The benefit of social capital is addressed through its effect on knowledge sharing, considered as a mechanism for transmitting or disseminating knowledge from one individual or group to another and acquiring and receiving knowledge from others (Hadoussa, 2020; Allameh, 2018; Lefebre et al., 2016; Chang et al., 2012).

Conceptual model and research hypotheses

The impact of SM use at work on social capital

Since the key characteristic of social capital is its emphasis on individual relationships (Nahapiet & Ghoshal, 1998), and since SM is the primary source of connection among people, many researchers claim that SM use plays a role in promoting and fostering the growth of social capital (Marengo et al., 2020; Williams, 2019; Cao et al., 2016), as it encourages social interaction and sharing of knowledge (Ellison et al., 2015; 2014). SM consist of various working and communication tools and platforms, providing users means to search and share information as well as to communicate and develop relationships among different users in work environments (Oksa et al., 2020; Ewing et al., 2019). The use of SM is appropriate for the accumulation and preservation of social capital by users as it promotes contact, strengthens relationships, reinforces connections with peers, preserves and enhances their social networks (Cao & Ali, 2018; Carlson et al., 2016). Several research consider a common classification of social capital which is categorized into three dimensions: structural capital, cognitive capital, and relational capital, to explore the relationship between SM use and social capital. This dimensional analysis, particularly applied in the field of knowledge management, is important because, each dimension has significant and independent effects (Berraies et al., 2020; Ganguly et al., 2019).

First, the structural dimension of social capital refers mainly to “the overall pattern of connections and interactions between individuals and is characterized by the number and strength of the existing network ties between individuals and by the network’s configuration” (Ali-Hassan et al., 2015; p. 68). It reflects the breadth of the relationships, as well as the amount of time spent, and communication frequency between members (Chang et al., 2012). Several researchers argue that employees who participate in online social interactions with coworkers through SM tend to build close network ties (Sun et al., 2019). SM networks have made profound changes in the way people communicate and interact. In today’s digital age, people have more and more interactions with others online as opposed to in-person. The deployment of SM strengthens working relationships and facilitates the development of new working relationships with other peers, which can help to establish and enhance employee social network ties. Thus, we hypothesize that:

H1: SM use at work has a positive impact on structural social capital.

H1a: SM use at work has a positive effect on social interaction ties.

The relational dimension of social capital focuses on the character and nature of the connection among people and refers to important aspects that are embedded in interactions, such as trust, norms of reciprocity, and identification (Kamboj et al., 2017). Identification is “an individual’s sense of belonging and positive feeling toward a virtual community” (Chiu et al., 2006, p.1877). It refers to the perception of belonging, membership, and attachment to a specific human community or a group of people (Tsai & Hsu, 2019). Individuals may identify with a certain category based on their similarity with other group members and on the importance or distinctiveness of their membership in the group (Ashforth & Mael, 1989). SM users often use these networks to connect with their peers on concerns of mutual interest, and are likely to continue to connect with each other, which leads to the creation of specific connections and relationships that contribute to a sense of status-group membership. Discovering and debating topics of mutual interest promotes cooperation with other workers and creates a sense of belonging and develops identity. Besides, norms of reciprocity are the standards of human behavior, which refer to the mutual expectation that favor or benefit currently provided that should be compensated in the future (Le & Lei, 2018). The face-to-face contacts establish norms of reciprocity because, through these exchanges, expectations about others’ reactions are built up and transmitted. However, even if face-to-face interaction is different from online communication through SM, norms of reciprocity can be also formed online, since individuals are constantly exposed to social interaction with other connected users and then expectations are established and norms of reciprocity are formed (Pai & Tsai, 2016). Finally, Trust is defined as: “the belief that the results of somebody’s intended action will be appropriate from our point of view” (Misztal, 1996, p. 9–10). Trust is one of the key facets of relational social capital that can be improved through repeated social exchanges among people (Nahapiet & Ghoshal, 1998). The use of SM at work enables relational social capital to be created, as it strengthens overall connections with employees and provides an adequate context in which social relations can take place (Ali-Hassan et al., 2015). SM is mainly important for the generation of trust at the workplace, as it allows for more regular and frequent social connections and relationships among co-workers, enabling them to recognize each other, reducing uncertainty and generating a positive attitude, and ultimately, reinforcing their trust (Cao et al., 2016). Consequently, we assume that SM use at the workplace is likely to promote identification within the virtual community. Based on the above-mentioned arguments, the following hypothesis is proposed:

H2: SM use at work has a positive impact on relational social capital.

H2a: SM use at work has a positive effect on norms of reciprocity.

H2b: SM use at work has a positive effect on identification.

H2c: SM use at work has a positive effect on trust.

The cognitive dimension is related to shared values and meanings as well as common assumptions among parties. Particularly shared vision is commonly mentioned as the main element of the cognitive dimension of social capital. The shared vision “embodies the collective goals and aspirations of the members of an organization” (Tsai & Ghoshal, 1998, p. 467). It reflects the members’ common goals and purposes within the organization. In virtual settings, number of studies outline that the use of SM supports the creation and maintenance of a shared vision (Yen et al., 2020; Tijunaitis et al., 2019; Cao et al., 2016; Ali-Hassan et al., 2015). In the workplace, SM promote the creation of informal relationships between co-workers, which fosters collaboration, and develops a shared vision for team members (Sun et al., 2019; Cao et al., 2016). Therefore, we hypothesize that:

H3: SM use at work has a positive effect on cognitive social capital.

H3a: SM use at work has a positive effect on shared vision.

The impact of SM use at work on knowledge sharing through social capital dimensions

Knowledge sharing is an integral aspect of knowledge management, resulting in several benefits at the individual and organizational levels. It is defined as a process that includes two dimensions: collecting and donating, tacit and explicit knowledge (Al-Husseini & Elbeltagi, 2018). Knowledge donating is “communicating to others what one’s personal intellectual capital”, whereas knowledge collecting is the fact of “consulting colleagues in order to get them share their intellectual capital” (Van Den Hooff & De Ridder, 2004, p. 118). Several previous studies have examined the effect of social capital dimensions on knowledge sharing (Ganguly et al., 2019; Polyviou et al., 2019; Allameh, 2018). Todo et al., (2016) emphasized the importance of social networks ties (i.e. structural Social Capital), for encouraging organizational knowledge sharing. Social interaction ties, reflecting the connections between members in the social network, promote inter-member social interactions, as well as the exchange of information between individuals and the amount of time and energy people need to access information sources, which facilitates knowledge sharing among people (Chui et al., 2006). Therefore, we hypothesize that:

H4: Structural social capital has a positive impact on Knowledge sharing

H4a: Social interaction ties have a positive impact on Knowledge donating

H4b: Social interaction ties have a positive impact on Knowledge collecting

Furthermore, many researchers consider that individuals are more likely to provide and share valuable knowledge when relationships of trust exist (Le & Lei, 2018; Razak et al., 2016). This can be explained by the fact that when individuals trust each other, they are more likely to cooperate and share resources without worrying that their peers will profit from them. It may, also, reduce the adverse effects of differences between partners (Lavie et al., 2012). Trust is, then, viewed as “a central characteristic of relationships that promotes effective knowledge creation” (Abrams et al., 2003, p.65). Trust creates and retains exchange relationships that, in turn, lead to knowledge sharing. Additionally, knowledge sharing is recognized to be the consequence of the profit and cost analysis, meaning that people “will not share unless they perceive the benefits of sharing, such as reciprocal benefits, rewards, and stronger interpersonal ties” (Chen & Hsieh, 2015, p.814). Thus, individuals expect to receive reciprocal benefits that justify their expense in terms of time and effort spent when sharing their knowledge (Moghavvemi et al., 2018). Moreover, in online networks, norms of reciprocity are one of the main factors that influence students to share knowledge through Facebook groups (Moghavvemi et al., 2017). In addition, identification acts as a resource influencing the motivation to combine and exchange knowledge (Nahapiet & Ghoshal, 1998). In fact, when a person develops strong identification with the organization, she/he is likely to make efforts for the organization’s benefit. These efforts are manifested in such collaborative behaviors as knowledge sharing. In social networks, users tend to search for people with whom they have connections, and they share knowledge for reasons of identification and social integration. As a result, identifying with a group or community explains the readiness to remain an active member of the network and is considered as important in stimulating knowledge sharing behaviors (Chiu, et al., 2006). It is therefore expected that within SM networks, employees will share their knowledge when they feel a sense of belongingness to a virtual community. So, it is hypothesized that:

H5: Relational social capital has a positive impact on Knowledge sharing

H5a: Identification has a positive impact on Knowledge donating.

H5b: Identification has a positive impact on Knowledge collecting

H5c: Norms of reciprocity have a positive impact on Knowledge donating.

H5d: Norms of reciprocity have a positive impact on Knowledge collecting.

H5e: Trust has a positive impact on Knowledge donating.

H5f: Trust has a positive impact on Knowledge collecting.

Finally, the cognitive dimension of social capital has also been recognized as a predictor of knowledge sharing among network members. Several studies use the notion of shared language or culture and shared vision or common goals to represent this dimension (Hadoussa et al., 2022). In this study, the cognitive dimension focuses on the shared vision of the members of a social network. Previous researchers argue that when employees have a common vision, it is easier for them to share knowledge. In fact, the employee’s common goals and vision lay to a strong base for the exchange and integration of resources into the community. Organization members who share a vision are more expected to become partners in sharing knowledge (Tsai & Ghoshal, 1998). Consequently, mutual vision can be seen as the mechanism that connects individuals together and inspires them to share their knowledge (Todo et al., 2016).

H6: Cognitive social capital has a positive effect on knowledge sharing.

H6a: Shared vision has a positive effect on knowledge donating.

H6b: Shared vision has a positive effect on knowledge collecting.

Figure 1 illustrates the proposed research model.

Figure 1

Research Model: SM effects on knowledge sharing

Research Model: SM effects on knowledge sharing

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Research methodology

This study applies a positivist paradigm as it is more suitable for theory testing rather than theory generation to test the proposed hypotheses and their causal relationships and the scale validation (De Vaus, 2002). This study is based on a quantitative research methodology that explains an issue or phenomenon by gathering data in numerical form and analyzing it with the assistance of mathematical methods and statistics (Aliaga & Gunderson, 2006). Quantitative research starts with a statement of a problem, generating a research question, reviewing related literature to construct a research model, formulating a hypothesis, collecting data, and conducting a quantitative analysis (Williams, 2007). To test our research question, we conducted a survey at a Saudi Telecom Company which is a leading telecom and technology services provider for individuals and businesses based in Saudi Arabia and founded on 1998. The company was owned by the Saudi Government who sold 30% of its shares in 2002. In the following paragraphs we expose relevant details regarding data collection and hypothesis tests using the structural equation modeling (SEM) method with SPSS and AMOS statistical package.

Study sample and data collection

According to Sukamolson (2007), the survey encompasses the use of scientific sampling method with a designed questionnaire (Appendix 1) to measure the population’s characteristics using statistical methods. An online survey was developed and administered to a population of 500 employees of the company. The respondents are professionals who used SM for job-related purposes at work. The participation to the survey is anonymous and voluntary. After eliminating the invalid responses, a final sample of 288 exploitable responses (57.6% response rate), is retained. The respondents’ demographic profiles show that our sample is composed of 66% male and 34% female employees which is explained by a local culture of male favoritism in job market. The sample’s median age is in the range of 25–35 with 61.6%. Most respondents have an experience below than five years (61.6%) and get access to their position with a bachelor’s degree (61.9%). More details about the respondents are available in the Table I (Appendix 2).

Measures

To measure the different constructs of our conceptual framework, we diffused an online survey. The questionnaire is a research instrument consisting of a series of questions to gather relevant information from respondents. The online questionnaire provides a quick, cheap, and efficient way to obtain responses to our research question. It adapts items from previous studies, based on a 5-point Likert-type scale; ranging from “strongly disagree”, to “strongly agree”. Appendix 1 presents the questionnaire and lists all the constructs and their related sources as well as the number of items used per construct. Regarding the research context, all items were translated into Arabic language by a professional translator.

Data analysis and results

The data analysis is performed through structural equation modeling (SEM). For this purpose, two statistical software are used: SPSS V.28 is used to solve the basic encoding process and descriptive statistics analysis, and AMOS V.28 is used to test the fit of the model and for estimating causal models with latent variables and proposed hypotheses. SEM can simultaneously test a set of interrelated hypotheses by estimating the relationships between multiple independent and dependent variables in a structural model.

Appendix 2: Table I

Sample Description

Sample Description

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Scales reliability and Constructs validity

To determine the scales’ reliability and constructs validity, a scale purification is carried out. To eliminate weak and non-representative items, Crombach’s Alpha and item-to-total correlations are calculated (Churchill, 1979). Therefore, the evaluation of scales reliability and constructs validity is based on three criteria: (1) composite reliability (CR) and Cronbach’s Alpha (α) should be greater than 0.7 in reliability testing; (2) all communalities should exceed 0.4, and the average variance extraction (AVE) of the convergence validity test should exceed 0.5; (3) the square root of each AVE should be greater than the inter-structure correlation for discriminant validity testing (Costello and Osborne, 2005). Table II (Appendix 2) shows the reliability and convergence validity results. The Cronbach’s Alpha for all variables ranged from 0.731 to 0.946, and the composite reliability was between 0.678 and 0.843, indicating satisfactory reliability. In addition, all communalities were above 0.4 and the AVEs were above 0.5, indicating an acceptable results and favorable convergence validity (Fornell & Larcker, 1981). Finally, according to Table II (Appendix 2), the square root of the AVEs (the number on the diagonal of the matrix) was greater than the correlation between the constructs in all cases, indicating sufficient discriminant validity. Furthermore, to test the factorial structure of the scales, a confirmatory factor analysis is conducted. The construct validity indicates the extent to which a given construct is different from other variables. With reference to Fornell & Larcker, (1981), we calculate the discriminant validity through AMOS V.28. The Criteria of discriminant validity is to examine whether the square root of the AVE for each construct exceeds the correlation shared between the construct and other constructs in the model. As shown in Table III Appendix 2, all diagonal values exceeded the inter-construct correlations, thereby demonstrating adequate discriminant validity of all constructs and confirming our previously presented results. Finally, the results indicate that eight factors (SM use at work, Social interaction ties, identification, norms of reciprocity, trust, shared vision, knowledge donating, and knowledge collecting) are identified and used subsequently as the latent variables for the analysis conducted (Table III, Appendix 2).

Appendix 2: Table II

Measurement Construct

Measurement Construct

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appendix 2: Table III

Discriminant validity of the theoretical construct measurement

Discriminant validity of the theoretical construct measurement

Diagonal elements are the square root of average variance extracted (AVE) between the constructs and their measures.

Off diagonal elements are correlations between constructs.

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Structural Model

This study used Structural Equation Modelling (SEM) with Amos V.28 to apply the inferential analysis and test the hypotheses. This study uses p-value criteria with a 95% confidence level that should be less or equal to 0.05 for the significance result to test the different hypotheses using all available observations in group analysis. Furthermore, as suggested by previous authors (Byrne, 2016; Hair et al., 2016), this study refers to some relevant indices (absolute, incremental and parsimony) to evaluate the overall fitness to test the Model Fit. These indicators consist of Goodness of Fit Indices, Adjusted Goodness of Fit Index, Comparative Fit Index, Incremental Fit Index, and Normed Fit Index (GFI, AGFI, CFI, IFI, and NFI > 0.90), Root Mean Square Error of Approximation (RMSEA) (0.05 ≤ RMSEA ≤ 0.08), RMR with the smaller value (Yen et al., 2014). The model fit represents the fitness of the data collected from the survey with the theoretical model. The observed indices compared to the recommended values (Fornell, 1987) indicate a good fit of the optimal model. The Chi Square/df ratio is less than 5 (3,118). The observed absolute fit indices GFI (0.934 > 0.90), AGFI (0.929 > 0.90), RMR (0.009), and RMSEA (0.062) indicate very acceptable results and provide sufficient information to evaluate the ‘s fitness (Hair et al., 2016). Moreover, the observed incremental indices NFI (0.958), CFI (0.961), and IFI (0.946) indicate a good and acceptable values (>0.90) as suggested by (Hair et al., 2016). In addition, the observed parsimony indices measured by the normed χ2 (χ2/ddl) indicates a value of 3,118 < 5 (recommended value). According to these results summarized in Table IV (Appendix 2), the goodness of fit of the structural model can be confirmed. Thus, the observed model is accepted as the study’s optimal measurement model as shown in Figure 2.

After examining the relationships between the causes and the latent variables, the results agree with the previous literature mentioned above and highlight the positive relationship and causality between the use of SM at work, the social capital’ dimensions and knowledge sharing.

appendix 2: Table IV

Adequation indices – AMOS V.28 Output

Adequation indices – AMOS V.28 Output

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Figure 2

Optimal Model Fit – AMOS V.28 Output

Optimal Model Fit – AMOS V.28 Output

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As shown in Table V (Appendix 2), the results of the structural model emphasize the validation of 11 hypotheses. The results in Table V, show that the SM use at work (SMUW) has a positive impact on social interaction ties (SOITER) (β = 0.610, t-value = 6.095), identification (IDENT) (β = 0.773, t-value = 7.002), norms of reciprocity (NRECIP) (β = 0.417, t-value = 6.090), Trust (TRUST) (β = 0.652, t-value = 6.423) and shared vision (SHAV) (β = 0.596, t-value = 6.710). Based on these results, H1a, H2a, H2b, H2c, H3a are supported. These findings corroborate previous research (Cao & Ali, 2018; Cao et al., 2016; Ali et al., 2015; Ellison et al., 2014; Carlson et al., 2012). In addition, the results indicate that most of the social capital dimensions are strongly correlated with knowledge sharing dimensions. The results demonstrate an important impact of shared vision (SHAV) on both employees’ knowledge donating (KDON) (β = 0.374, t-value = 3.654) and knowledge collection (KCOLL) (β = 0.205, t-value = 2.768). Moreover, the results highlight the positive impacts of trust (TRUST) on knowledge donating (KDON) and knowledge collecting (KCOLL) with respectively β = 0.227, t-value = 3.265, p<0.001; β = 0.084, t-value = 2.412, p<0.005. However, the results illustrate that identification (IDENT) only impact positively knowledge collection (KCOLL) with β = 0.140, t-value = 2.337, p<0.005. Besides, the results emphasize only the positive impact of norms of reciprocity (NRECIP) on knowledge donation (KDON) with β = 0.207, t-value = 2.956, p<0.001. These findings allow to support only hypotheses H5b, H5c, H5d, H5e, H5f, H6a, and H6b. The research findings corroborate with some previous studies (Berraies et al., 2020; Cao et al., 2016; Chiu et al., 2006). Only four hypotheses H4a, H4b, H5a, and H5d are not supported. In fact, the results show that there are no causality and no implications of identification on knowledge donation, and norms of reciprocity on knowledge collection. Furthermore, instead of different previous studies, the findings show that social interactions ties have no causality and no implications on knowledge sharing dimensions. Table V (Appendix 2) below summarizes the details of the hypotheses test.

appendix 2: Table V

Results of the structural model – Hypothesis Test

Results of the structural model – Hypothesis Test

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Discussions and implications

This study contributes to MIS and HR literature by investigating the impacts of using SM at work on knowledge sharing through implications on social capital’ dimensions. The study’s findings here support the preceding literature (Leonardi, 2014) that SM use is associated with employees’ knowledge sharing measured by knowledge donation and knowledge collection (Faraj et al., 2016; Aksoy et al., 2016).

Nonetheless, this study complements earlier research in business digitalization and the integration of SM at work which enable organization to modernize their practices and provide them competitive advantage (Theiri & Hadoussa, 2023; Hadoussa, 2022; Mazurchenko & Maršíková, 2019; Barlatier & Mention, 2019). By applying a confirmatory approach, the findings enhance the social capital theory within the diffusion and use of SM at work (Ellison et al., 2015 and 2014). The contribution of this study is to emphasize the significant improvement of knowledge management resources especially the employees’ knowledge sharing practices through SM use. This study extends knowledge in the relatively understudied area of SM use at work and its effects on HR practices, especially within knowledge sharing practices at work.

This study reveals that all the hypotheses are supported except for four hypotheses: social interaction which was found to have no causality and no implications on knowledge donation and collection (H4a and H4b); identification which was found to have a significant relationship with only knowledge collecting and no causality with knowledge donating (H5a), which means that the assumption of the effect of relational capital dimension on knowledge sharing is partially supported. Moreover, norms of reciprocity which also was found to have only a significant implication with knowledge donating and no causality with knowledge collection (H5d).

The findings validate the assumptions made by researchers in digital management (Capriotti et al., 2021; Berraies, et al., 2020; Cao & Ali, 2018; Cao et al., 2016; Carlson et al., 2012). The findings support that the social characteristics of SM promote employees’ social capital dimensions (Lu & Dzikria, 2020; Ellison et al., 2015). Accordingly, a SM professional environment characterized by an important shared vision, trust, norms of reciprocity, and identification, enhances the knowledge sharing practices between employees. The study highlights the crucial role of shared vision as the most important factor affecting knowledge sharing. This cognitive dimension of social capital means that team members are more likely to share their knowledge when they think that they have the same vision, which confirms Chiu et al., (2006) findings. In addition, shared vision occupies an important place in the Middle Eastern culture specially in the Saudi economic tissue and government institutions. This issue explains the fact that shared vision is the most important factor impacting employees’ knowledge sharing practices.

This study reveals also that trust enables knowledge sharing. In fact, trust affects the employees’ willingness to donate their knowledge and cooperate with other organizational members via social networks. Moreover, employees believe that donating and collecting knowledge with their colleagues require strong confidence and trust that their contribution will not be misused against their interests (Kim, 2019). In other words, within a SM environment, if one does trust another member, it does mean that she/he will seek knowledge from her/him. This finding runs to the results of the studies conducted by Chang et al., (2012) and Hau et al., (2013), which found that a high level of trust among workers has a significant effect on knowledge sharing. Furthermore, greater norms of reciprocity enhance knowledge sharing within the use of SM at work. Previous studies in the higher education context in Saudi Arabia highlight this issue (Hadoussa & Menif, 2019). As demonstrated by Fraj et al., (2016), this study shows that SM create significant economic and relational value for its users when there are strong norms of reciprocity among communities’ members. These relational values enhance the feeling of identification and trust (Chang & Chuang, 2011). Nevertheless, the no-causality between social interaction ties and knowledge collection and donation could be explained by the working environment in Saudi Arabia in which gender separation still persists and in which bureaucracy and hierarchy occupy an important place. Also, such results could be explained by the important number of young respondents were recruited at the company and who did not get sufficient time to discover its organizational culture and make contact with other colleagues to enhance social interactions. These findings do not corroborate with many previous studies (Huong & Truong, 2021; Berraies, 2020; Ewing et al., 2019; Allameh, 2018; Chung et al., 2016) which demonstrate that once an employee builds up stronger ties with his/her colleagues using SM at work, more she/he will collect and donate knowledge and so contribute to the knowledge sharing experience within her/his organization. Besides, this study exposes a weak result regarding identification with no implication on knowledge collection. In fact, identification presents just an impact on knowledge donation. This issue could be explained by cultural aspects related to Saudi locals’ behavior known by having difficulties in asking for information contrary to donating information. Mainly, these findings are consistent with prior research, which consider norms of reciprocity, identification, and trust as an important social capital dimensions encouraging knowledge sharing at work (Pitafi et al., 2020; Ricciardelli et al., 2020; Lu & Dzikria, 2020; Capiriotti et al., 2020; Song et al., 2019; Kim, 2019; Allameh, 2018; Lefebre et al., 2016; Chang et al., 2012).

To summarize, this study’s findings corroborate partially with many previous studies showing that SM can serve as a good platform for sharing knowledge and acquiring direct and tacit knowledge, which are difficult to obtain in one-to-one communication (Leonardi, 2014). Thus, we note that users with higher connectivity and issue involvement are better at influencing information and knowledge flow, and that social content shared by directors at the organization had greater influence than those by middle managers and individual users. This observation is consistent with previous research findings (Xu et al., 2014).

Theoretical Contributions

This study offers significant literary contributions, especially for academics and practitioners in digital and HR Management and interested in the effects of SM use at work. Guided by the Social Capital Theory, this study proposes a linear research model connecting SM professional use and the forms of knowledge sharing through the dimensions of social capital. Thus, this study extends the previous studies on SM at work by understanding in depth the complex human electronic relationships that impact knowledge sharing practices. In addition, few studies investigated in depth the impacts of each dimension of the Social Capital on knowledge sharing (Song et al., 2019; Cao & Ali, 2018). While some researchers studied the relations between Social Capital and knowledge sharing, they commonly recognize the relational dimension of social capital by only one variable which is “trust”. Few empirical studies examined the simultaneous effect of trust, shared vision, norms of reciprocity, identification, and social interactions on knowledge sharing within SM networks. Nevertheless, the study of the effect of each dimension is crucial for researchers and practitioners to master and engage in practical actions assisting managers with applying specific policies regarding the diffusion and use of SM at work. Since the intensive use of SM at work especially in the MENA Region and in Saudi Arabia, this research contributes to clarify the importance of the different dimensions of Social Capital and the role that they could have in enhancing profitable knowledge sharing practices. Finally, although researchers recognize the importance of distinguishing between two knowledge sharing behaviors: knowledge collecting – consulting other colleagues to get them to share their knowledge – and knowledge donating – communicating one’s knowledge to others – (Van den Hooff & De Ridder, 2004), this issue is generally ignored in the literature. Most of the research has conceptualized knowledge sharing as a unidimensional construct (Kim, 2019; Yang, 2010). The findings of this study clearly show the difference in employees’ interpretation between the two knowledge sharing behaviors.

Managerial Implications

SM is still an emergent technology at the workplace. Previous studies pay attention to SM use and its impacts on organizations (Cao et al., 2018; Cao et al., 2016). Different studies stressed their effects at work on employee performance. Although some studies consider SM as a source of stress, anxiety (Lee-Won et al., 2015), distraction in academic settings (Chang et al., 2020; Feng et al., 2019) and at work (Marengo et al., 2020; Rozgonjuk et al., 2020; Song et al., 2019; Braojos et al., 2019); other few studies deem SM as a platform for knowledge sharing and communication (Pitafi et al., 2020; Song et al., 2019; Leonardi, 2014; Turban et al., 2011). This study provides a substantial contribution to HR and digital management practices with the proposal of a valid empirical instrument measuring the positive relationship that exists between SM use at work and employees’ knowledge sharing practices. This empirical instrument could be used to evaluate any specific SM contribution to knowledge sharing within professional virtual communities. The study findings reveal the significant contribution of SM use to the development of Social Capital among employees, which enhance their practices of work-related knowledge sharing and professional relations. This indicates that SM support different knowledge management activities and practices at the workplace as demonstrated by Moqbel & Nah (2017). Thus, to take advantage of SM networking and promote the exchange of knowledge sharing practices, organizations need to enable their employees to use and optimize the managerial opportunities offered by these tools. This could be enhanced by providing clear policies and ethical rules on their use for work purposes, as well as supporting knowledge-related activities that are coordinated through these tools by strengthening the shared business vision. In addition, HR managers could encourage their employees to participate regularly in professional discussions through SM networks to build especially social interaction ties, to enhance the shared vision, trust, norms of reciprocity, and identification values that will result in more successful knowledge sharing practices. For this purpose, HR managers and directors could introduce specific policies promoting reciprocity, identification between the group and the organization, and for sure the shared vision of the organizational culture. This could be done by implementing a financial and/or motivational reward system for active employer who shares her/his knowledge with the group. Also, this would be an opportunity to make each member’s knowledge contribution noticeable, visible, valuable, and will help her/him assesses social exchange fairness. In addition, such digital practices could enhance the shared vision and social identification within the organizational environment and culture especially for young employees, and propose them a frame that assists and guides them in their professional lifes. HR Direction may, therefore, create clear goals and objectives by strengthening discussions and social interactions between network members within the organization, to enhance the knowledge sharing practices and make more successful the professional practice community. As demonstrated in this research, shared vision and trust are crucial for the success of knowledge sharing practices within social networks. Thus, HR managers who aim to enhance knowledge donating and collecting activities through SM, have to encourage and foster activities developing human behaviors of trust between employees such as encouraging regular social interactions and fair discussions allowing employees to better know and understand each other by having at the same time a professional shared vision. This issue could be challenging especially in the Saudi social context because of the fact of gender separation that exists between male and female employees, and which was stressed by many respondents who found some difficulties in sharing (donating and collecting) knowledge at work. The social and cultural context in Saudi Arabia is still conservator and many cultural and social barriers still exist and have pressures on the working environment which could be problematic (Hadoussa et al., 2022). Nevertheless, SM use at work represents a real opportunity for Saudi employees and especially the youth generation to go beyond these social pressures (Hadoussa & Menif, 2019). SM at work in the Saudi context are very diffused and used to facilitate communication between employees. This issue strengthens the shared vision and could be an opportunity to build and develop social interaction ties which may increase the knowledge sharing practices. Furthermore, regarding the local culture and social habits, HR managers can ensure an acceptable level of visibility of the employee’s personal information, especially women who share knowledge on SM, such as the employee’s name, and her/his professional profile, which may increase also the level of trust among organizational members and thus lead to an honest sharing of knowledge, as well as a sense of social responsibility.

Conclusions, Limitations and Future Research

In the past few years, SM has come close to the people and is taking part in almost all spheres of life (Hadoussa & Menif, 2019; Zaki, 2019). SM has gained wide popularity at the workplace and has especially affected the HR management field (Louati & Hadoussa, 2021; Yen et al., 2020; Nguyen et al., 2020; Rozgonjuk et al., 2020; Pitafi et al., 2020; Song et al., 2019; Braojos et al., 2019). Therefore, these network tools provide interconnected platforms for communication, collaboration, and promotion of the exchange of knowledge (Cao & Ali, 2018). These tools create and enhance knowledge sharing practices and the development of communities of practice in the wave of today’s business digital transformation (Zaki, 2019; Jarrahi, 2018). In the healthcare sector, SM allow the essay and rapid dissemination and sharing of information and knowledge to much wider audiences than traditional methods of communication (Hadoussa, 2022; Chan et al., 2020). In the education sector, SM help in creating a useful learning climate for students as they inspire users/learners to participate in online discussions (Chatti & Hadoussa, 2021; Hadoussa, 2020). Nevertheless, the rise of SM has sparked discussions regarding its use in the workplace and especially its impact on HR management and knowledge sharing (Braojos et al., 2019; Song et al., 2019). Many companies are blocking employee access to these tools in the workplace. Banning the use of those sites could lead companies to lose a huge number of opportunities for business development as improving communication, knowledge sharing, and even employees’ performance (Ali-Hassan et al., 2015). The insights obtained by this study are useful for researchers and HR practitioners. The findings of this study stress important issues enriching the scientific debate on the effect of using SM in today’s digital workplaces. This study highlights that SM encourage knowledge donating and collecting. The study’s findings illustrate that SM enhance knowledge sharing practices within professional communities of practice (Rasheed et al., 2020; Ricciardelli et al., 2020; Dong et al., 2020; Cao & Ali, 2018). Furthermore, this study offers effective and valuable HR policies regarding the use of SM at the workplace within the MENA social context. The study’s findings are beneficial to HR managers for establishing a strategic policy to optimize the use of SM at the workplace and gain in terms of knowledge sharing and capitalization which may enhance the creativity and innovation practices within businesses (Nguyen et al., 2020; Lee & Lee, 2020; Lee, 2018). Although this study has found important results and insights regarding SM diffusion and use at the workplace within the wave of digitalization, the findings should be seen in terms of its limitations. For instance, the study is conducted and limited to one company in a specific sector which is telecommunication. In addition, although the sampling method and the use of structural equation modeling provide interesting data, considering the use of additional sources of data, such as interviews and focus groups can cross-validate the results and emphasize the importance of social and cultural aspects dealing with the Saudi and Arab culture. Future research may include various other sectors (health care, education, industrial, etc.) to increase the generalizability of the study’s findings. Also, future studies can be carried out in other countries and regions (Europe, North America, etc.) and should assess the effect of cultural differences.