Next Article in Journal
Static Evaluation of a Midimew Connected Torus Network for Next Generation Supercomputers
Next Article in Special Issue
Think Tank Innovation-Driven Knowledge Service Ecosystems: A Conceptual Framework and Case Study Application
Previous Article in Journal
Co-Treatment of Winery and Domestic Wastewaters in Municipal Wastewater Treatment Plants: Analysis of Biodegradation Kinetics and Process Performance Impacts
Previous Article in Special Issue
Linking Sustainability-Driven Factors and Online Knowledge Sharing in Business: A PLS-SEM Analysis
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:

Knowledge Sharing through Social Media Platforms in the Silicon Age

Muhammad Zafar Yaqub
* and
Abdullah Alsabban
Department of Business Administration, Faculty of Economics & Administration, King Abdulaziz University, Jeddah 23589, Saudi Arabia
Author to whom correspondence should be addressed.
Sustainability 2023, 15(8), 6765;
Submission received: 27 February 2023 / Revised: 21 March 2023 / Accepted: 3 April 2023 / Published: 17 April 2023
(This article belongs to the Special Issue Knowledge Management and Business Development)


While making an appeal to the social exchange theory, self-determination theory and the uses and gratification theory of motivation, the study seeks to investigate the efficacy of social media platforms in promoting knowledge sharing in contemporary times, which are marked by widespread digital transformation and knowledge-driven governance. Through a PLS-based structural equation modelling performed on a dataset obtained from 450 business professionals working at disparate managerial levels in diverse organizations and institutions, it has been found that the use of social media platforms significantly amplifies knowledge sharing. It has further been discovered that the efficacy of social media platforms in maturing knowledge sharing increases both with the elevation of motivation to share knowledge and the motivation to use social media. The study is one that offers rich theory-laden conceptualization and/or explanation grounded in diverse sets of theories encompassing individual as well as collective social and behavioral antecedents and contingencies of technology–human interaction dynamics regarding knowledge sharing in virtual environments, besides offering useful insights to researchers and practitioners alike to help them better understand and/or manage knowledge sharing through social media platforms.

1. Introduction

The advents of globalization, digital transformation, and the knowledge economy have made effective knowledge sharing a forerunner to organizational competitiveness [1,2]. Sharing knowledge is essential as it effectually succors business strategy and supplements business initiatives [3]. Gainful knowledge sharing among stakeholders amasses the organization’s collective knowledge [4] and enhances the efficacy of employees and the organization in garnering a sustainable competitive advantage [5,6]. Previous studies have shown that effective knowledge sharing can improve a company’s absorption skills, productivity, performance, and competitive advantages [2,7,8]. Furthermore, prior research has revealed that knowledge donation and collection, two of the most integral components of knowledge sharing, are crucial to encouraging innovation in organizations [9]. Knowledge sharing signifies a social interaction between human subjects that involves transmitting knowledge, experience, and skills to enable future improvements [10]. According to [11], sharing knowledge means distributing it throughout an organization, and this may occur between individuals, groups, or organizations through any mode of face-to-face and/or virtual interactions [12]. Several researchers (e.g., [13,14]) have argued for the necessity of the cultivation of knowledge capabilities (especially digital KM capabilities) that could be regarded as a profoundly important precursor to boost organizational performance in this digitization- and knowledge-driven age.
In recent years, digital technology, especially social media, has emerged as a key enabler of knowledge sharing and is being progressively adopted by organizational stakeholders as the predominant channel for social interactions [13,15]. Many organizations have used social media in the past few years to create meaningful opportunities for knowledge sharing between stakeholders [16]. Knowledge sharing can be used to eliminate space and time limitations, create multi-media-based content, and provide simple interfaces that allow even non-specialists to share and connect [17,18]. Baima et al., (2022) [13] shed light on the antecedents and efficacy of knowledge sharing through social media among customers in appropriating desirable customer performance outcomes. Social media can revolutionize organizations’ business practices by encouraging knowledge sharing in a dynamic and uncertain economic environment [19]. Treem and Leonardi (2012) [20] argued that social media has a primarily knowledge-sharing effect because it fosters interactions and informal social ties between people within and across organizations. Latifah et al. (2022) [21] have attested to the role of social media networking and knowledge sharing in accelerating innovation performance. Using Web 2.0 technology, social media for knowledge sharing features more distinctive activities than traditional media [22]. Khan et al. (2014) [23] argue that social media platforms enable people to learn from others and share knowledge with others, which leads to greater gratification from social experiences. Moreover, the ability of social media to facilitate knowledge sharing and communication between employees and management can lead to a positive impact on employee performance and innovation [24]. The increased adoption of social media platforms as a tool for sharing knowledge has been noted by several scholars and has gained attention in both academia and industry in recent times [21,25].
Although the use of social media platforms for knowledge sharing has increased, attracting more attention and interest from practitioners and scholars [17], some aspects of the use of social media for knowledge sharing (especially the contextual contingencies) have gained too little attention and need to be explored further [12]. More specifically, it has been under-explored under which contingencies social media can ameliorate the flow of knowledge for individuals involved in the KS process [16]. While making an appeal to the social exchange theory (hereafter SET), self-determination theory (hereafter SDT) and uses and gratification theory (hereafter UGT), this study aims to examine the role of social media platforms in boosting knowledge sharing under a pair of critical contextual contingencies, i.e., the motivation to share knowledge and the motivation to use social media platforms. Besides expanding frontiers of knowledge about the dynamics of knowledge sharing, the findings of this study seek to offer some useful insights to the managers, which could help them to tweak their knowledge-sharing processes for better organizational outcomes.

2. Literature Review

2.1. Knowledge Sharing in Contemporary Times

Knowledge management (KM) is defined as creating, distributing, and exchanging knowledge within an organization [26]. Knowledge sharing has increasingly become an essential capability that could allow firms to execute their strategies with superior competitiveness [27] Despite various attempts in the literature to define knowledge sharing, it continues to be a source of debate among academics and practitioners, depending on the context and perspective utilized [28,29]. Knowledge sharing is defined as exchanging knowledge among employees in an organization to develop new and valuable knowledge [30]. According to [31] and [32,33], knowledge sharing is an organizational behavior involving employees sharing their knowledge with other employees to achieve organizational goals. As such, sharing knowledge creates a greater sense of involvement and cooperation between individuals, teams, and organizations [34]. Al-Kurdi et al. (2018) [28] stated that ‘knowledge transfer’ and ‘knowledge exchange’ are both interchangeable terms, with shared knowledge being a fundamental component of knowledge management [35]. Earlier studies examined knowledge sharing from the perspectives of technology, organizations, and individual behaviors [28]. In fact, the development of information technology has led to new electronic methods of knowledge sharing through online platforms such as forums, social networking, and social media [36].
Social exchange theory (SET) is widely used as a conceptual framework [37] to account for the dynamics of social interactions or exchanges. Social exchanges refer to a relationship or transaction between two or more parties that involves uncertain obligations arising from exchanging resources and expects some form of future compensation or return [38]. Indeed, knowledge-sharing between employees is essential for improving organizational efficiency; when employees develop and exchange knowledge with other employees, knowledge is generated and sustained [8]. According to [39,40] various types of knowledge, such as tacit knowledge and explicit knowledge, can be exchanged to exactitude through technology-based knowledge-management systems. Social media and knowledge management both provide new levels of knowledge and skills to be obtained through technologically based information exchanges [41]. Recent developments in social media technologies have shaped how human beings interact, communicate, collaborate, and/or share content [12]. Social media tools enable an agile two-way communication between users via videos, pictures, text messages, and podcasts [23].

2.2. Social Media Enabled Social Interactions

The increased adoption of social media as a tool for sharing knowledge has been revealed by several scholars and has gained significant scholarly and managerial attention in the last couple of decades [25]. Over time, Facebook, YouTube, and WhatsApp have emerged as the leading social media platforms for gaining and/or sharing knowledge [42]. Table 1 presents a list of the top ten social media platforms as of January 2023 [43].
Cheung et al. (2011) [44] found that most people use Facebook to stay in touch with friends in real time; when members of a group share similar values, they develop a sense of belonging and are more likely to use it. According to [45], members of Facebook groups are obligated to share knowledge when they expect to gain benefits from the group. From the uses and gratification perspective, previous studies have stated that Facebook use is motivated by entertainment, self-expression, information sharing, medium appeal, archiving, and social interaction [46]. Facebook could be a useful platform for sharing knowledge among educators, teachers, and students since they could share lecture notes and obtain up-to-date information about classes [47]. Further, several recent studies have revealed that social networking sites such as Facebook may facilitate academic knowledge sharing through collaboration, participation, interaction, resources, and information in both public and private universities [36].
Various motives of the increased use of social media platforms in organizations have been documented in recent studies. It can be used for knowledge sharing to eliminate space and time limitations, create multi-media-based content, and provide simple interfaces that allow even non-specialists to share and connect [17,18]. According to [48], social media allows workers to send messages to specific colleagues within the organization; edit, sort, and post text and files; view at any time the messages that are posted, edited, or sorted by anyone within the organization. Employers, businesses, and consumers engage in particular social media behaviors to build appropriate interactions through online communication [49]. Leaders and followers can conduct knowledge-based interactions using social media [23]. Liu et al. (2019) [50] argued that most managers of companies believe that social media can benefit organizational processes and performance [16]. Using social media as a platform for knowledge co-construction, individuals can take an active role in information consumption, feedback, reflection, and knowledge generation [51]. Ali et al. (2020) [52] mentioned that social media could be used to enhance team communication and improve innovation through knowledge management. Consequently, using social media in an organization should be recognized by employees as a constructive way to discover new ideas and build relationships [49]. Social media tools such as wikis, blogs, and intranets are being increasingly used in businesses to organize training sessions, facilitate knowledge-sharing activities, create rewards and recognition programs, enhance communication, and foster teamwork [53].

2.3. Research Gaps

Although the use of digital technologies has been revealed as a critical component triggering learning capacity and knowledge and information flows, there has not been a plentitude of investigations focusing on certain critical facets of this technology–human interaction in earlier research [19,54]. The micro-level dynamics of knowledge sharing have failed to attain a rightful amount of research attention. More specifically, there is an acute lack of empirical research testing the instrumentality of the motivation to share knowledge and the use of social media in accelerating knowledge sharing [55,56]. Through this research, we intend to bridge this gap by illustrating the relevance of these two facets of motivation on the enhancement of knowledge sharing through social media platforms. This study extends the extant literature by combining SET, SDT, and UGT to appraise the role of social media in galvanizing knowledge sharing under varying permutations of motivation to share knowledge and use social media in a South Asian context. The following section discusses in detail our conceptual model along with the theoretical frameworks that underpin it.

3. The Conceptual Framework and Hypotheses of Study

Besides structure and culture, technology is another pillar in the knowledge architecture that enables and/or drives knowledge sharing [6]. The so-called process of social contact known as knowledge sharing may be made swift, easy, and effective with the use of digital technologies [57]. Researchers found that digital technologies can help employees communicate and collaborate more effectively by facilitating knowledge sharing and retrieval in a timely manner [58,59]. In addition, an organization may keep track of its internal and external sources of information so that those who need it can find it more quickly and easily [6]. Lee (2017) [58] also contends that ICT may facilitate the quick finding, reaching, and recalling of data as well as aiding in staff cooperation. The study by [60] found that ICT may supplement knowledge-sharing platforms for enhanced sustainability. Even though a wide range of digital technologies (including social media platforms) can support knowledge-sharing processes, this can only occur if they are closely aligned with the needs, desires, and interests of the stakeholders [61]. A congruence of goals, needs, and expectations could enhance motivation to share knowledge through social media for employees, customers, or other stakeholders. We have conceptualized a model that considers the relevance of both the motivation to share knowledge and use social media in enabling knowledge sharing through social media platforms. Figure 1 presents our conceptual framework. While making an appeal to SET, SDT, and UGT frameworks, it is hypothesized that the increased use of social media platforms fosters greater knowledge sharing, contingent upon greater motivation to share knowledge as well as use of social media.
The subsequent sections would shed light upon the nature and hypothesized relationships among the subject constructs along with their background theories.

3.1. Knowledge Sharing: The Outcome

Knowledge sharing (KS) is defined as the movement of people’s accumulated experiences, skills, and information among other, different parties via formal or informal channels [27,62]. Despite the growing interest in the KS discipline, the idea of knowledge sharing remains ambiguous because there is no commonly acknowledged definition of knowledge sharing [63]. Previous studies found that many researchers confuse the concepts of knowledge sharing (hereafter KS) and knowledge transfer (hereafter KT), and these terms often overlap with each other [64,65]. As [66] pointed out, these terms are still not clearly understood by most of those interested in the application of knowledge management and are often used synonymously. However, KT is considered by many to be a wider and broader concept that involves KS practices [67]. The process of KT is comprehensively aimed at creating new knowledge, while the process of KS is considered a complementary process. Nevertheless, despite the evolution of the KS and KT concepts, activities, and procedures in their own right in certain narrow fields of theoretical investigation, the confusion between them remains, and they continue to be used interchangeably [68]. As the distinctions between them are beyond the scope of the current inquiry, this study also uses the terms interchangeably.
Knowledge sharing is part of the process of managing knowledge [40]. Knowledge sharing is described as a group activity that fosters learning and improves the ability of the organization to achieve its objectives [40,69]. Knowledge sharing has a positive effect on the performance of the knowledge provider and the innovation performance of the entire work unit. The findings of [70] show that the key to having effective KS is to rely on people’s understanding and awareness of the benefits linked with KS. Kim and Park (2017) [38] demonstrated that employees participating in knowledge sharing would be more likely to come up with and apply novel and beneficial ideas. The studies showed that employees who share knowledge and information with others who perform similar tasks are more creative [38,71]. Additionally, collaborating with coworkers increases innovation, enhancing firm performance [71,72]. Jamshed and Majeed, (2019) [62] note that healthcare teams perform better when members share their knowledge. Various studies (e.g., [73,74,75]) demonstrate that KS practices contribute to improved organizational performance.
Knowledge is not gained only through employee training; instead, it is extracted largely through social interactions [76]. Jameel and Ahmad (2020) [77] also elaborate that sharing knowledge is an interactive process that helps enriching the organization’s knowledge base by disseminating the right knowledge at the right time and through the right channels. Employees generally are more prone to share (explicit) knowledge through institutionalized systems of KS [63]. Previous studies suggest that organizational knowledge can be collected and transmitted through structured and formal knowledge-sharing routines and informal/internal and external communities sharing the same interest or formal and informal techniques as a part of its KS strategy [63]. However, organizations generally prefer to institutionalize the movement, transferring, and sharing of knowledge. Finally, the practices and procedures of knowledge sharing differ from tacit to explicit knowledge. Originations need to cultivate an environment where people are motivated to share knowledge through both formal and informal knowledge-sharing practices [40,78,79,80]. We have also employed motivation to share knowledge and use social media platforms as the critical environmental contingencies in our model.

3.2. Use of social Media Platforms: The Antecedent

Social media, the most significant facet of the contemporary digital transformation, is defined by [81] as internet applications that enable the creation and exchange of user-generated content. This includes internet-based, disentangled, and persistent channels of mass communication that facilitate interpersonal relationships through user-generated content [82]. In addition, social media usage refers to the variety of activities a person can participate in online [83]. The social media tools can include microblogs such as Twitter and personal blogs, social networking sites such as Facebook and Myspace, and video- and photo-sharing websites such as YouTube and Flickr [41]. In recent years, social media tools have been recognized as complementary to traditional communication tools, including email, phone, and video-conferencing systems, leading them to be increasingly used for knowledge capture and sharing [84].
Social media platforms are seen as a tool for facilitating information flow and knowledge sharing, as they allow for more informal communication. They play a transformative role within an organization [85]. A study by [17] showed how the informal use of social media platforms influences knowledge sharing and work processes that are integrated into an organization’s emergent informal practices. Wang et al. (2017) [71] found that by sharing knowledge, employees can acquire all kinds of knowledge and combine them, which enables them to translate new ideas into innovations. Consequently, using social media platforms in an organization should be recognized by employees as a constructive way to discover new ideas and build relationships [49] Accordingly, tools such as wikis, blogs, and intranets are used in businesses to organize training sessions, facilitate knowledge-sharing activities, create rewards and recognition programs, enhance communication, and foster teamwork. According to [86], the use of social media technology has increased productivity by 20–25 percent.
Empirical evidence indicates that social media has changed the structure and nature of social interactions, which has affected how knowledge is shared [87]. Through social media, knowledge is shared smoothly and continuously within the virtual world [12,17]. The rise of social media platforms has promoted academic interest in social capital [87,88]. These mediums enable members of digital communities to interact creatively without the need for others to be present in order to generate social capital [89]. Social media platforms provide bridges between offline and online social connections [89]. In social networks, people can link up and form connections that optimize knowledge sharing and lead to powerful individual and societal effects [35]. Recent research has found that those active in online social networks are more likely to contribute content [90]. Chiu et al. (2006) [91] explained the strength of social-interaction ties, how much time members spend involved in each other’s communities, and how often they communicate via a virtual community. Furthermore, a study conducted by [92] showed that social-interaction ties positively impact knowledge sharing through social media platforms. Following from this debate in the contemporary research, we hypothesize:
There exists a positive relationship between the use of social media platforms and knowledge sharing.

3.3. Motivation to Share Knowledge: The First Moderator

Social exchange theory [93] had been one of the leading theoretical perspectives in social psychology [94]. SET proposes that individual behavior is determined by expectations of costs and benefits resulting from interpersonal interactions [95]. Researchers have often examined knowledge sharing as a form of social exchange in organizations where employees decide to participate based on the anticipated costs and benefits of participation [96]. Interactions between employees foster trust and shared relationships, which have positive impacts on employee willingness to engage in KS activities [97]. Motivation has been recognized as a significant predictor of both general and work-related behaviors, and the research indicates that it serves as the key catalyst for knowledge sharing [55]. Firms improve by sharing knowledge (within the organization), so employees need to be motivated to exchange knowledge [40]. Goh (2002) [98] found that a lack of employee motivation results in poor KT. Empirical evidence suggests that it is desirable for organizations to encourage knowledge sharing because it is vital for organizational learning, innovation, superior performance, and maintaining competitive advantages [99]. Employees motivated to share knowledge are generally more engaged in innovative practices [69]. Numerous studies have explored the antecedents of information-sharing behavior to encourage people to engage in the knowledge-sharing process (40, 100). Studies by [100,101] have shown that utilitarian considerations play a key role in promoting knowledge sharing in the work environment.
Motivation is defined as the stimuli that induce a person to perform and achieve a desired and shared purpose [27]. Such stimuli may be intrinsic (from within the person, such as beliefs, faith, values, or principles) or extrinsic, which may monetary or non-monetary (e.g., rewards, recognition, or promotion) [57,100,102]. The most intriguing finding in the literature is that economic incentives do not always boost knowledge sharing [102]. However, the results by [40] show that when it comes to enticing employees to share their knowledge, extrinsic benefits were more successful in private organizations than intrinsic rewards were in public ones. Aleksic et al. (2021) [55] showed that personal-level openness to transfer knowledge is affected by intrinsic and extrinsic motivation. Examining the impact of both intrinsic and extrinsic motivations on the intention to share knowledge provides an intensive understanding of the core motivations for knowledge sharing among individuals [79]. Cruz et al. (2009) [64] conducted a mixed-method case study using both qualitative and quantitative approaches to analyze a non-profit organization to investigate the role of intrinsic and extrinsic motivation and its impacts on employees’ KT. The results indicated that there was no effect of extrinsic motivation on KT; however, intrinsic motivation enhanced it. Therefore, by blending both extrinsic and intrinsic incentives in unique ways, firms can create a climate which is not only instrumental in motivating people but is also difficult to imitate [40,103].
Self-determination theory (SDT) focuses more on intrinsic motivation and is a prototype example of the active integrative tendencies that exist in human nature. Thus, SDT is concerned with the nature, structure, and functioning of an active person, including the individual’s innate proactive abilities to selectively engage, understand, and act on external contexts [104]. People possess valuable information, which they may transfer to other individuals and organizations, as well as from one generation to the next. Employees who are eager to learn and discover new things require less external motivation. However, individuals may be hesitant to share their knowledge with co-workers because they worry about losing their unique value to the organization [55]. Thus, individuals who are intrinsically driven achieve considerable improvements in highly desired behavioral outcomes such as personal development, creativity, and the overall quality of learning [40]. According to [105], employees are fundamentally inspired by personal development, operational independence, and job accomplishment rather than by remuneration. Intrinsic motivation is a crucial and effective tool that overcomes obstacles that impede the transferring of knowledge between employees; thus, intrinsic motivation encourages employees to be more productive by collaborating with their co-workers, which reduces excessive competition that would otherwise impede apprenticeship, teamwork, KS, and collaborative working in general [64]. Research shows that those who actively contribute information and those who lurk are both affected by intrinsic motivations such as knowledge collaborative norms and cultural values [102]. Hau and Ho (2010) [106] state that sources of intrinsic motivation, such as common objectives, socializing, and trust, are the most important component in encouraging people to contribute their innovative knowledge. The use of vocal encouragement and positive feedback tends to boost intrinsic motivation [107]. Quite concomitant with the above scholarly discourse, we have considered motivation to share knowledge as out first critical moderating condition and have hypothesized this as:
An individual’s motivation to share knowledge significantly moderates the relationship between his use of social media platforms and knowledge sharing.

3.4. Motivation to Use Social Media Platforms: The Second Moderator

In the communication field, uses and gratification theory [108] has long been the most effective framework for understanding the reasons behind media use [109,110]. UGT consists of three key elements: achievement, enjoyment, and social interaction [111]. UGT has been widely applied to investigate audience gratification in print media and across a range of media channels and contents [112]. It is predicated on the idea that individuals are active users of various communication channels and choose channels based on their perceived needs and desires [113]. There are three major types of needs gratified by various media: social, hedonic, and cognitive [114]. Individuals generally seek a social media platform that fulfills their needs, requirements, and interests [115]. Stemming from their contextual and individual differences, individuals have different communication behaviors, which influence the gratification of their individual needs and interests when using social media [116].
UGT is an efficacious theory to understand users’ motivation for adopting technology [117]. UGT has been successfully applied to recent research on social media usage [118], social word-of-mouth spread via mobile social networks [119], social media engagement behavior [120], examining the antecedents of Facebook dependence [121], and interpersonal communication [122]. Khan et al. (2014) [23] argued that social media enables people to learn from others and share knowledge with others, which leads to greater gratification from social experiences. Researchers argue that managers and professional specialists may become motivated for knowledge sharing, but since it is a time-consuming and demanding activity, this may lessen their involvement gradually, especially in the absence of any foreseeable incentives [70]. Zhang et al. (2017) [79] examined the effects of both extrinsic and intrinsic motivation factors on the knowledge-sharing intention of online health community members. In another study, on the moderating roles of ICT in knowledge sharing, Zhang et al. (2014) [78] found rewards and reciprocity were extrinsic motivations while self-efficacy and self-enjoyment were intrinsic motivations. Finally, Aboelmaged (2018) [27] contend that hedonic motives, rather than utilitarian reasons, affect the use of enterprise social network (ESN) platforms for knowledge sharing. Following from this discourse, while employing the motivation to use social media platforms as our second moderating condition, we hypothesize:
The motivation to use social media platforms significantly moderates the relationship between use of social media platforms and knowledge sharing.

4. Methods

4.1. Measurements

The knowledge sharing construct has been measured through four items, employing scales used by [80]. The construct use of social media platform has been operationalized through five indicators while employing scales used by [123]. The first moderator motivation to share knowledge construct has been gauged through a five-item scale adapted from [124] Finally, the second moderating condition motivation to use social media platforms has been operationalized through five items proposed by [125]. The responses in all these measurements have been recorded on five-point Likert scales. The phrasing of the scales has been adapted according to the context of this study.

4.2. Data Collection

A survey using a structured questionnaire was conducted to collect data from 450 employees working at various levels of management in diverse sectors. A brief introduction to the questionnaire discussing the nature and scope of the study has been integrated into the measurement instrument. Employees were assured that the study had purely academic purposes, and their information would not be shared with any third party. They were appreciated for their time and contribution to the data-collection efforts of the researchers. A total of 463 filled-in questionnaires were received, out of which 13 were found to be inappropriate for inclusion for further analysis due to missing, incorrect, or inadequate information. Hence, ultimately the analysis was carried out on 450 data points. Table 2 summarizes the profile of the respondents.

4.3. Data Analysis

Smart PLS 3.0 [126] has been used to analyze the data collected through the survey. Following [127], PLS Algorithms and Bootstrapping (5000 samples) were framed to evaluate the reliability and (convergent and discriminant) validity of the measurement model, as well as its main effects, and the moderating effects hypothesized in the conceptual/structural model. Further, for the predictive relevance of the test, PLS blindfolding calculations were carried out.
A series of analyses have been carried out to test the hypotheses. First, individual item reliability is assessed through factor analysis. Second, internal consistency reliability has been measured through Cronbach’s alpha and composite reliability. Third, convergent validity is gauged through average variance extracted (AVE) scores. Fourth, discriminant validity is assessed through Fornell–Larcker (hereafter F–L) criterion and HTMT ratio scores. Finally, following PLS-based regression approach, the analysis of the main effects, mediation, and moderation effects was performed [128]. PLE-SEM is a suitable instrument for analyzing multiple regression equations simultaneously. The findings emerging from this analysis are presented through the next sections.

5. Results

5.1. Assessment of the Measurement Model

The appraisal of an auxiliary model entails analyses of reliability and validity, which are generally estimated by examining indicator reliability, internal consistency (i.e., construct reliability), and convergent and discriminant validity. The statistics contained in Table 2 reflect a profound conformance of the measurement model to all thresholds of acceptability.

5.1.1. Item and Construct Reliability

The assessment of indicator reliability involves scrutinizing standardized factor loadings against an acceptable threshold of ≥0.707 [127,129]. All individual indicators are reliable, as their standardized factor loading falls in a range from 0.75 to 0.80. The t-values for all loadings are also significant (at p < 0.001), showing adequate item reliability [130]. The assessment of the internal consistency of all the indicators depends on joint reliability, which is assessed by examining two common types of construct reliability: Cronbach’s alpha (α) and composite reliability (C.R) with a threshold score of ≥0.70 for joint reliability [127]. With both α and C.R. indices falling in the ranges of 0.82–0.91 and 0.88–0.93, this subsequently shows acceptable levels of internal consistency among the items, thus confirming adequate reliability.

5.1.2. Convergent and Discriminant VALIDITY

An estimation of convergent validity is generally obtained through average variance extracted (AVE), which reflects the enormity of a construct in explaining variance of its indicators. [129] recommend a reference value for this index of ≥0.50. In this study, all AVE values range from 0.62 to 0.75, suggesting that each construct features sufficient convergent validity [127]. Finally, the assessment of discriminant validity involves examining the F–L criterion and the heterotrait-monotrait (HTMT) scores. According to F–L criterion, the square root of every construct’s AVE must exceed the square of its largest correlations with fellow constructs in the research model. Relevant statistics about the indicators of both types of reliability and validity are contained in Table 3 and Table 4.

5.2. Assessment of the Structural Model

5.2.1. The Direct Effects

As can be seen in Table 5, H1 is supported (β = 0.223, t = 7.246, and p < 0.000) corroborating the significance of the relationship between USMP and KS.

5.2.2. The Moderating Effects

The relevant statistics for the moderating effects’ analysis are presented in Table 6. According to the results of H2(a) is supported (β = 0.759, t = 5.549, and p < 0.000), proving that there is a significant moderating role of MSK in the relationship between USMP and KS. Secondly, according to the results of H2(b) is also supported (β = 0.792, t = 5.562, and p < 0.000), showing that MUSM also moderates the impact of USMP on KS. The moderation effect has been quite strong in both cases.

Assessment of Effect Size and Predictive Relevance

According to [130], the value of f2 (which measures effect size) is small up to 0.02, medium up to 0.15 and from 0.35 is large. Accordingly, the f2 values of 0.031 (MUSM) and 0.041 (MSK) show a slightly-higher-than-small effect size. Moreover, the value of Q2 = 0.569 shows a strong predictive relevance as per [130].

6. Discussion

In today’s information- and digitization-driven economy, knowledge is the primary source of firms’ competitive advantage [2,8]. If firms and the team and/or individuals within those firms can create, recognize, archive, access, share, and apply knowledge in effective and creative ways, a superior organizational performance could be rightfully envisioned [5,6]. However, timeliness always remains a crucial factor in knowledge and/or information management since the same could have a profound impact on the agility of responses to the environmental challenges, especially during turbulent times. Integrating modern state-of-the art fourth generation technologies such as social media platforms in the knowledge-sharing routines and processes can enhance the speed, efficiency, and efficacy of knowledge sharing among internal and external stakeholders [131]. It may enhance individual, team, and organizational performance efficacy through enhancing their absorptive capacities. However, effectual knowledge sharing more probably takes place in an environment where people are motivated to share the knowledge and are more prone to use social media. People’s reluctance to share especially tacit knowledge or not being tech savvy makes it difficult for the management to realize the true potential of knowledge-driven governance. KS is directly connected to a firm’s competitive advantage, as unshared knowledge retards an organization’s growth.
Though it is highly important to induce or motivate people both to share knowledge and use social technologies for knowledge sharing to competitively harness the true potential of KS, the same could become quite an uphill task without a profound understanding of the needs, desires, or expectations of individuals participating in such KS endeavors [61,115]. There may be varying mix of intrinsic and extrinsic orientations, desires, and drives among individuals that may produce variable KS attitudes and behaviors. Management needs to understand these individual differences to customize the incentives. The same size does not fit all. Whereas some people may have a strong extrinsic orientation, others may be indifferent to extrinsic rewards and might be more self-motivated to share knowledge. For certain others, there may still be a need to augment extrinsic rewards to complement self-drive. An adequate adaptation of incentivization could go a long way in accelerating the knowledge-sharing drives in individuals. Such a customization of incentives may also add to the precision of efforts and could lead to better returns on KS maturing investments. The findings of this study have empirically substantiated a positive contingency effect of motivation to share knowledge on knowledge sharing through social media platforms which is quite consistent with the central tenants of self-determination theory and as evidence in the previous studies, such as [55,56,106,124].
Whereas social media or digital platforms feature enormous potential for a wide-spanning sharing of knowledge, not all individuals are tech savvy. Some may want to share knowledge through social media platforms but may either have low preparedness, capacity, or resources for it, or may have their own false apprehensions about using such platforms. Organizations need to better understand the dynamics of individuals’ adoption of new technologies to enlarge the inducement to use such technologies. This may also include making some investments in providing the necessary hardware and software support to the employees. It cannot be left up to them to use their personal devices and subscriptions, in or out of the workplace. Resource enablement could nicely augment perceived organizational support, which is an important precursor to the adoption and/or enhanced usage of such technologies. The findings of this study have corroborated the hypothesized contextual effect on knowledge sharing of the motivation to use social media through social media platforms, which is quite consistent with the assertions made by the uses and gratification theory and as highlighted in past studies, such as [13,27,79,115].

7. Conclusions

The primary aim of this study has been to empirically substantiate the contextual effects of motivation in sharing knowledge and using social media platforms in enabling knowledge transfer through social media platforms. Our findings reveal that the use of social media platforms could profoundly galvanize knowledge sharing, contingent upon significant inducement for employees to share knowledge with their fellows, as well as to be tech savvy and use social media platforms. This is quite consistent with past research in knowledge management, which recognizes technology as a primary enabler and facilitator for the practices involved. The use of emerging social technologies (e.g., social media, digital platforms) could electrify knowledge-sharing content, routines, and/or processes, leading to a profound materialization of objectives from such knowledge-sharing and/or transfer activities. However, for any technologies to produce desired effects, a favorable ecosystem is desired. The effect of contextual contingency transcends the micro, meso, and macro levels of its origination. At individual levels, it is important that employees participate in knowledge-sharing initiatives and/or processes—and a high inducement to share knowledge, especially through social technologies, is quite rudimentary for such participation and consequently for superior organizational performance in this digital age. Therefore, managers need to better understand the dynamics of individuals’ motivation to share knowledge and use social technologies to find ways to persuasively induce them to share knowledge and be technology savvy at the same time.

7.1. Theoretical Contributions

The study extends social exchange theory, self-determination theory, and use and gratification theory to knowledge management in virtual settings and the findings of this study contribute to the contemporary scholarly discourse in these academic disciplines by enhancing our understanding of the social and behavioral dynamics of knowledge sharing using social media. Its major contribution stems from its conceptualization of the dual moderation effects of motivation. Previous studies have not adequately integrated this construct, especially as a dual moderating contingency. The model brings two important facets of individuals’ motivation into the limelight as critical contextual conditions in promoting knowledge sharing using social media platforms. An explanation about a macro-level (organizational) phenomenon from a micro-level (individual) lens is yet another contribution of this study over and above its theoretical richness in making an appeal to multiple theoretical frameworks such as SET, SDT, and UGT. Its final contribution stems from its use of South Asian data that furthers the generalizability of Western theories into other cultural and geographical contexts.

7.2. Managerial Implications

New knowledge, a significant determinant of sustained organizational performance, generally spreads through meaningful interpersonal interactions, and this research presents evidence that the use of social media platforms could compound this meaningfulness through facilitating/enabling these interactions in virtual spaces. Managers must leverage the vast potential of broad-based social interactions and/or communication through promoting effectual knowledge sharing. Besides this, we found that understanding and matching people’s needs, desires, and preferences as a means to enhance motivation to share knowledge through social media is cardinal to well-founded efforts to mature such knowledge-sharing efforts. The findings of this research are beneficial to governance, executives, workers, and leadership, as they elucidate some potent factors affecting much-needed knowledge transfer and/or sharing in organizations. The findings of this paper could be useful for the organizations in establishing forcible knowledge-sharing environments through inducing individuals to share knowledge using the most proliferated contemporary means, i.e., social media platforms. Creating a climate conducive to knowledge sharing and appropriating sufficient investments and customization of incentives could help management in minting better rents from their KS-promotion efforts.

7.3. Limitations and Suggestion for Future Research

Cross-sectional studies, which generally match the survey approach and examine the phenomena in a particular period, have their own limitations. Future research may use longitudinal models to overcome these issues. Even though the model primarily aimed to study the double moderation effect, which is quite rigorous in terms of contextualization, the theoretical richness of the model could however be enhanced further by incorporating some mediating effects in the relationship between the exogenous (i.e., use of social media) and endogenous (i.e., knowledge sharing) constructs of this model. Besides this, the model specifically focuses only on knowledge sharing as the ultimate outcome. Integrating other aspects of the knowledge-management process and practice (e.g., knowledge creation and/or integration) could add to the theoretical richness and holisticism of the model. Further, a lack of diversity may impede the study from being generalized to other contexts, and the findings may not be applied to other fields. Future research could extend this model to other disciplines and/or geographical contexts (Table 7).

Author Contributions

Conceptualization, methodology, formal analysis, investigation, resources, data curation, writing—original draft preparation, M.Z.Y.; writing—review and editing, A.A. All authors have read and agreed to the published version of the manuscript.


This Research work was funded by institutional fund projects under grant no. (IFPIP: 1103-120-1442). Therefore, authors gratefully acknowledge technical and financial support from the Ministry of Education and King Abdulaziz University, DRS, Jeddah, Saudi Arabia.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

Data sharing is not applicable.

Conflicts of Interest

The authors declare no conflict of interest.


  1. Ouedraogo, A.; Leclerc, A. Job satisfaction and organizational performance: Evidence from Canadian credit union. J. Organ. Cult. Commun. Confl. 2013, 17, 35. [Google Scholar]
  2. Wu, W.-L.; Lee, Y.-C. Empowering group leaders encourages knowledge sharing: Integrating the social exchange theory and positive organizational behavior perspective. J. Knowl. Manag. 2017, 21, 474–491. [Google Scholar] [CrossRef]
  3. Harel, R.; Schwartz, D.; Kaufmann, D. Sharing knowledge processes for promoting innovation in small businesses. Eur. J. Innov. Manag. 2020. ahead-of-print. [Google Scholar] [CrossRef]
  4. Small, C.T.; Sage, A.P. Knowledge management and knowledge sharing: A review. Inf. Knowl. Syst. Manag. 2005, 5, 153–169. [Google Scholar]
  5. Harlow, H.D. Developing a knowledge management strategy for data analytics and intellectual capital. Meditari Account. Res. 2018, 26, 400–419. [Google Scholar] [CrossRef]
  6. Gold, A.H.; Malhotra, A.; Segars, A.H. Knowledge Management: An Organizational Capabilities Perspective. J. Manag. Inf. Syst. 2001, 18, 185–214. [Google Scholar] [CrossRef]
  7. Dyer, J.; Nobeoka, K. Creating and Managing A High-Performance Knowledge-Sharing Network: The Toyota Case. Strateg. Manag. J. 2000, 21, 345–367. [Google Scholar] [CrossRef]
  8. Swanson, E.; Kim, S.; Lee, S.; Yang, J.; Lee, Y. The effect of leader competencies on knowledge sharing and job performance: Social capital theory. J. Hosp. Tour. Manag. 2020, 42, 88–96. [Google Scholar] [CrossRef]
  9. Podrug, N.; Filipović, D.; Kovač, M. Knowledge sharing and firm innovation capability in Croatian ICT companies. Int. J. Manpow. 2017, 38, 632–644. [Google Scholar] [CrossRef]
  10. Rohman, M.; Marji Sudjimat, D.A.; Sugandi, R.M.; Nurhadi, D. Online Learning in Higher Education during COVID-19 Pandemic: Students’ Perceptions. J. Talent. Dev. Excell. 2020, 12, 3644–3651. [Google Scholar]
  11. Abdullah, S.; Haron, H. Knowledge Sharing System Model for Higher Learning Institutions: Case Study. In Proceedings of the 2013 International Conference on Advanced Computer Science Applications and Technologies, Kuching, Malaysia, 23–24 December 2013; pp. 97–102. [Google Scholar]
  12. Ahmed, Y.A.; Ahmad, M.N.; Ahmad, N.; Zakaria, N.H. Social media for knowledge-sharing: A systematic literature review. Telemat. Inform. 2019, 37, 72–112. [Google Scholar] [CrossRef]
  13. Baima, G.; Santoro, G.; Pellicelli, A.; Mitrega, M. Testing the antecedents of customer knowledge sharing on social media: A quantitative analysis on Italian consumers. Int. Mark. Rev. 2022, 39. [Google Scholar] [CrossRef]
  14. Koohang, A.; Paliszkiewicz, J.; Gołuchowski, J. The impact of leadership on trust, knowledge management, and organizational performance: A research model. Ind. Manag. Data Syst. 2017, 117, 521–537. [Google Scholar] [CrossRef]
  15. Tajpour, M.; Hosseini, E. Entrepreneurial Intention, and the Performance of Digital Startups: The Mediating Role of Social Media. J. Content Community Commun. 2021, 13, 2456–9011. [Google Scholar] [CrossRef]
  16. Chatterjee, S.; Rana, N.; Dwivedi, Y. Social media as a tool of knowledge sharing in academia: An empirical study using valance, instrumentality, and expectancy (VIE) approach. J. Knowl. Manag. 2020. ahead-of-print. [Google Scholar] [CrossRef]
  17. Kwayu, S.; Abubakre, M.; Lal, B. The influence of informal social media practices on knowledge sharing and work processes within organizations. Int. J. Inf. Manag. 2021, 58, 102280. [Google Scholar] [CrossRef]
  18. Alshahrani, H.; Rasmussen Pennington, D. “Why not use it more?” sources of self-efficacy in researchers’ use of social media for knowledge sharing. J. Doc. 2018, 74, 1274–1292. [Google Scholar] [CrossRef] [Green Version]
  19. Zhang, R.; Ng, F. Analysis of knowledge sharing behaviour in construction teams in Hong Kong. Constr. Manag. Econ. 2012, 30, 557–574. [Google Scholar] [CrossRef]
  20. Treem, J.W.; Leonardi, P.M. Social media use in organizations: Exploring the Affordances of visibility, Editability, persistence, and association. SSRN Electron. J. 2012, 36, 143–189. [Google Scholar] [CrossRef] [Green Version]
  21. Latifah, L.; Setiawan, D.; Aryani, Y.A.; Sadalia, I.; Al Arif, M.N.R. Human Capital, and Open Innovation: Do Social Media Networking and Knowledge Sharing Matter? J. Open Innov. Technol. Mark. Complex. 2022, 8, 116. [Google Scholar] [CrossRef]
  22. Panahi, S.; Watson, J.; Partridge, H. Conceptualising social media support for tacit knowledge sharing: Physicians’ perspectives and experiences. J. Knowl. Manag. 2016, 20, 344–363. [Google Scholar] [CrossRef]
  23. Khan, G.F.; Swar, B.; Lee, S.K. Social media risks and benefits. Soc. Sci. Comput. Rev. 2014, 32, 606–627. [Google Scholar] [CrossRef] [Green Version]
  24. Ewing, M.; Men, L.R.; O’Neil, J. Using social media to engage employees: Insights from internal communication managers. Int. J. Strateg. Commun. 2019, 13, 110–132. [Google Scholar] [CrossRef]
  25. Saifi, S.; Dillon, S.; Mcqueen, R. The relationship between face to face social networks and knowledge sharing: An exploratory study of manufacturing firms. J. Knowl. Manag. 2016, 20, 308–326. [Google Scholar] [CrossRef]
  26. Davenport, T.; Prusak, L. Working Knowledge: How Organizations Manage What They Know; Harvard Business Press: Cambridge, MA, USA, 1998. [Google Scholar] [CrossRef]
  27. Aboelmaged, M.G. Knowledge sharing through enterprise social network (ESN) systems: Motivational drivers and their impact on employees’ productivity. J. Knowl. Manag. 2018, 22, 362–383. [Google Scholar] [CrossRef]
  28. Al-Kurdi, O.; El-Haddadeh, R.; Eldabi, T. Knowledge sharing in higher education institutions: A systematic review. J. Enterp. Inf. Manag. 2018, 31, 226–246. [Google Scholar] [CrossRef] [Green Version]
  29. Wang, S.; Noe, R.A. Knowledge sharing: A review and directions for future research. Hum. Resour. Manag. Rev. 2010, 20, 115–131. [Google Scholar] [CrossRef]
  30. Le, P.B.; Lei, H. The mediating role of trust in stimulating the relationship between transformational leadership and knowledge sharing processes. J. Knowl. Manag. 2018, 22, 521–537. [Google Scholar] [CrossRef]
  31. Yi, J. A measure of knowledge sharing behavior: Scale development and validation. Knowl. Manag. Res. Pract. 2009, 7, 65–81. [Google Scholar] [CrossRef]
  32. Lee, H.; Park, J.; Lee, J. Knowledge sharing in ISD projects: Role of task interdependence and social capital. Int. J. Manag. Proj. Bus. 2020, 14, 580–599. [Google Scholar] [CrossRef]
  33. Lee, Y.; Tao, W.; Li, J.Q.; Sun, R. Enhancing employees’ knowledge sharing through diversity-oriented leadership and strategic internal communication during the COVID-19 outbreak. J. Knowl. Manag. 2020, 25, 1526–1549. [Google Scholar] [CrossRef]
  34. Hooff, B.; Schouten, A.; Simonovski, S. What one feels and what one knows: The influence of emotions on attitudes and intentions towards knowledge sharing. J. Knowl. Manag. 2012, 16, 148–158. [Google Scholar] [CrossRef] [Green Version]
  35. Ganguly, A.; Kumar, C.; Saxena, G.; Talukdar, A. Firms’ reputation for innovation: Role of marketing capability, innovation capability, and knowledge sharing. J. Inf. Knowl. Manag. 2020, 19, 2050004. [Google Scholar] [CrossRef]
  36. Nguyen, T. Four-dimensional model: A literature review in online organisational knowledge sharing. VINE J. Inf. Knowl. Manag. Syst. 2020, 51, 109–138. [Google Scholar] [CrossRef]
  37. Cropanzano, R.; Anthony, E.L.; Daniels, S.R.; Hall, A.V. Social exchange theory: A critical review with theoretical remedies. Acad. Manag. Ann. 2017, 11, 479–516. [Google Scholar] [CrossRef] [Green Version]
  38. Kim, W.; Park, J. Examining structural relationships between work engagement, organizational procedural justice, knowledge sharing, and innovative work behavior for sustainable organizations. Sustainability 2017, 9, 205. [Google Scholar] [CrossRef] [Green Version]
  39. Nonaka, I. A dynamic theory of organizational knowledge creation. Organ. Sci. 1994, 5, 14–37. [Google Scholar] [CrossRef] [Green Version]
  40. Nguyen, T.M.; Malik, A. Cognitive processes, rewards, and online knowledge sharing behaviour: The moderating effect of organisational innovation. J. Knowl. Manag. 2020, 24, 1241–1261. [Google Scholar] [CrossRef]
  41. Naeem, M. Uncovering the role of social media and cross-platform applications as tools for knowledge sharing. VINE J. Inf. Knowl. Manag. Syst. 2019, 49, 257–276. [Google Scholar] [CrossRef]
  42. Kircaburun, K.; Alhabash, S.; Tosuntaş, Ş.; Griffiths, M. Uses, and gratifications of problematic social media use among university students: A simultaneous examination of the big five of personality, social media platforms and social media use motives. Int. J. Ment. Health Addict. 2020, 18, 525–547. [Google Scholar] [CrossRef] [Green Version]
  43. Searchenginejournal. The Top 10 Social Media Sites & Platforms 2022. 2023. Available online: (accessed on 14 February 2023).
  44. Cheung, C.M.; Chiu, P.; Lee, M.K. Online social networks: Why do students use Facebook? Comput. Hum. Behav. 2011, 27, 1337–1343. [Google Scholar] [CrossRef]
  45. Pi, S.; Chou, C.; Liao, H. A study of Facebook groups members’ knowledge sharing. Comput. Hum. Behav. 2013, 29, 1971–1979. [Google Scholar] [CrossRef]
  46. Alhabash, S.; Chiang, Y.; Huang, K. MAM & U&G in Taiwan: Differences in the uses and gratifications of Facebook as a function of motivational reactivity. Comput. Hum. Behav. 2014, 35, 423–430. [Google Scholar]
  47. Moghavvemi, S.; Sharabati, M.; Paramanathan, T.; Rahin, N.M. The impact of perceived enjoyment perceived reciprocal benefits and knowledge power on students’ knowledge sharing through Facebook. Int. J. Manag. Educ. 2017, 15, 1–12. [Google Scholar] [CrossRef]
  48. Leonardi, P.; Vaast, E. Social Media and Their Affordances for Organizing: A Review and Agenda for Research. Acad. Manag. Ann. 2017, 11, 150–188. [Google Scholar] [CrossRef]
  49. Babu, S.; Hareendrakumar, V.R.; Subramoniam, S. Impact of social media on work performance at a Technopark in India. Metamorphosis 2020, 19, 59–71. [Google Scholar] [CrossRef]
  50. Liu, Q.; Shao, Z.; Tang, J.; Fan, W. Examining the influential factors for continued social media use. Ind. Manag. Data Syst. 2019, 119, 1104–1127. [Google Scholar] [CrossRef]
  51. Sigala, M.; Chalkiti, K. Knowledge management, social media, and employee creativity. Int. J. Hosp. Manag. 2015, 45, 44–58. [Google Scholar] [CrossRef]
  52. Ali, A.; Bahadur, W.; Wang, N.; Luqman, A.; Khan, A.N. Improving team innovation performance: Role of social media and team knowledge management capabilities. Technol. Soc. 2020, 61, 101259. [Google Scholar] [CrossRef]
  53. Cao, X.; Guo, X.; Vogel, D.; Zhang, X. Exploring the influence of social media on employee work performance. Internet Res. 2016, 26, 529–545. [Google Scholar] [CrossRef]
  54. Yeh, Y.; Lai, S.; Ho, C. Knowledge management enablers: A case study. Ind. Manag. Data Syst. 2006, 106, 793–810. [Google Scholar] [CrossRef]
  55. Aleksić, D.; Rangus, K.; Slavec Gomezel, A. Microfoundations of SME open innovation: The role of help, knowledge sharing and hiding. Eur. J. Innov. Manag. 2021, 25, 178–203. [Google Scholar] [CrossRef]
  56. Dai, Q.; Dai, Y.; Zhang, C.; Meng, Z.; Chen, Z.; Hu, S. The Influence of Personal Motivation and Innovative Climate on Innovative Behavior: Evidence from University Students in China. Psychol. Res. Behav. Manag. 2022, 15, 2343–2355. [Google Scholar] [CrossRef] [PubMed]
  57. Rode, H. To share or not to share: The effects of extrinsic and intrinsic motivations on knowledge- sharing in enterprise social media platforms. J. Inf. Technol. 2016, 31, 152–165. [Google Scholar] [CrossRef]
  58. Lee, H.-S. Knowledge Management Enablers and Process in Hospital Organizations. Osong Public Health Res. Perspect. 2017, 8, 26–33. [Google Scholar] [CrossRef]
  59. Lin, H.F. Knowledge sharing and firm innovation capability: An empirical study. Int. J. Manpow. 2007, 28, 315–332. [Google Scholar] [CrossRef] [Green Version]
  60. Kumari, A.; Saharan, T. Knowledge Management as a Catalyst to Sustainable Development in Banking Industry. In Proceedings of the 2020 International Conference on Computer Science, Engineering and Applications (ICCSEA), Sydney, Australia, 13–14 March 2020; pp. 1–6. [Google Scholar]
  61. Riege, A. Three-dozen knowledge-sharing barriers managers must consider. J. Knowl. Manag. 2005, 9, 18–35. [Google Scholar] [CrossRef]
  62. Jamshed, S.; Majeed, N. Relationship between team culture and team performance through lens of knowledge sharing and team emotional intelligence. J. Knowl. Manag. 2018, 23, 90–109. [Google Scholar] [CrossRef]
  63. Turulja, L.; Cinjarevic, M.; Veselinovic, L. Information technology and knowledge sharing for better health care: An emerging economy context. J. Knowl. Manag. 2020, 25, 559–572. [Google Scholar] [CrossRef]
  64. Cruz, N.M.; Pérez, V.M.; Trevilla, C.T. The influence of employee motivation on knowledge transfer. J. Knowl. Manag. 2009, 13, 478–490. [Google Scholar] [CrossRef]
  65. Shahbaznezhad, H.; Rashidirad, M.; Vaghefi, I. A systematic review of the antecedents of knowledge transfer: An actant-object view. Eur. Bus. Rev. 2019, 31, 970–995. [Google Scholar] [CrossRef]
  66. Ishak, N.; Islam, M.; Sumardi, W. Human resource management practices in creating a committed workforce for fostering knowledge transfer: A theoretical framework. VINE J. Inf. Knowl. Manag. Syst. 2011. [CrossRef]
  67. Tangaraja, G.; Rasdi, R.M.; Abu Samah, B.; Ismail, M. Knowledge sharing is knowledge transfer: A misconception in the literature. J. Knowl. Manag. 2016, 20, 653–670. [Google Scholar] [CrossRef]
  68. Chou, C.; Tang, T. Exploring the Distinction between Knowledge Transfer and Knowledge Sharing by Bibliometric Method. J. Ind. Intell. Inf. 2014, 2, 179–183. [Google Scholar] [CrossRef] [Green Version]
  69. Farooq, R. Developing a conceptual framework of knowledge management. Int. J. Innov. Sci. 2018, 11, 139–160. [Google Scholar] [CrossRef]
  70. Chen, C.-J.; Huang, J.-W.; Hsiao, Y.-C. Knowledge management and innovativeness: The role of organizational climate and structure. Int. J. Manpow. 2010, 31, 848–870. [Google Scholar] [CrossRef]
  71. Wang, Z.; Wang, N. Knowledge Sharing, Innovation and Firm Performance. Expert Syst. Appl. 2012, 39, 8899–8908. [Google Scholar] [CrossRef]
  72. Singh, S.K.; Gupta, S.; Busso, D.; Kamboj, S. Top management knowledge value, knowledge sharing practices, open innovation, and organizational performance. J. Bus. Res. 2021, 128, 788–798. [Google Scholar] [CrossRef]
  73. Gupta, A.K.; Govindarajan, V. Knowledge Flows within Multinational Corporations. Strateg. Manag. J. 2000, 21, 473–496. [Google Scholar] [CrossRef]
  74. Olivera, F. Memory Systems In Organizations: An Empirical Investigation Of Mechanisms For Knowledge Collection, Storage And Access. J. Manag. Stud. 2000, 37, 811–832. [Google Scholar] [CrossRef]
  75. Yang, J.T. Knowledge Sharing: Investigating Appropriate Leadership Roles and Collaborative Culture. Tour. Manag. 2007, 28, 530–543. [Google Scholar] [CrossRef]
  76. Diab, Y. The concept of knowledge sharing in organizations (studying the personal and organizational Factors and their effect on knowledge management). Manag. Stud. Econ. Syst. 2021, 6, 91–100. [Google Scholar]
  77. Jameel, S.; Ahmad, A.R. The Role of Information and Communication Technology on Knowledge Sharing among the Academic Staff during COVID-19 Pandemic. In Proceedings of the 2020 2nd Annual International Conference on Information and Sciences (AiCIS), Fallujah, Iraq, 24–25 November 2020; pp. 141–147. [Google Scholar]
  78. Zhang, X.; De Pablos, P.O.; Xu, Q. Culture effects on the knowledge sharing in multi- national virtual classes: A mixed method. Comput. Hum. Behav. 2014, 31, 491–498. [Google Scholar] [CrossRef]
  79. Zhang, X.; Liu, S.; Deng, Z.; Chen, X. Knowledge sharing motivations in online health communities: A comparative study of health professionals and normal users. Comput. Hum. Behav. 2017, 75, 797–810. [Google Scholar] [CrossRef]
  80. Bock, G.W.; Zmud, R.W.; Kim, Y.G.; Lee, J.N. Behavioral intention formation in knowledge sharing: Examining the roles of extrinsic motivators, social-psychological forces, and organizational climate. MIS Q. 2015, 9, 87–111. [Google Scholar] [CrossRef]
  81. Kaplan, A.M.; Haenlein, M. Users of the world, unite! The challenges and opportunities of social media. Bus. Horiz. 2010, 53, 59–68. [Google Scholar] [CrossRef]
  82. Carr, C.T.; Hayes, R.A. Social Media: Defining, Developing, and Divining. Atl. J. Commun. 2015, 23, 46–65. [Google Scholar] [CrossRef]
  83. Smith, B.; Gallicano, T. Terms of engagement: Analyzing public engagement with organizations through social media. Comput. Hum. Behav. 2015, 53, 82–90. [Google Scholar] [CrossRef]
  84. Sarka, P.; Ipsen, C. Knowledge sharing via social media in software development: A systematic literature review. Knowl. Manag. Res. Pract. 2017, 15, 594–609. [Google Scholar] [CrossRef]
  85. Santos, R.F.; Oliveira, M.; Curado, C. The effects of the relational dimension of social capital on tacit and explicit knowledge sharing: A mixed-methods approach. VINE J. Inf. Knowl. Manag. Syst. 2021, 53, 43–63. [Google Scholar] [CrossRef]
  86. Al-Mawali, H.; Al-Busaidi, K.A. Knowledge Sharing Through Enterprise Social Media in a Telecommunications Context. Int. J. Knowl. Manag. 2022, 18, 27. [Google Scholar] [CrossRef]
  87. De Zúñiga, H.G.; Barnidge, M.; Scherman, A. Social media social capital, offline social capital, and citizenship: Exploring asymmetrical social capital effects. Political Commun. 2017, 34, 44–68. [Google Scholar] [CrossRef]
  88. Skoric, M.M.; Zhu, Q. Social media and offline political participation: Uncovering the paths from digital to physical. Int. J. Public Opin. Res. 2016, 28, 415–427. [Google Scholar] [CrossRef]
  89. De Zúñiga, G.H.; Jung, N.; Valenzuela, S. Social media use for news and individuals’ social capital, civic engagement, and political participation. J. Comput. -Mediat. Commun. 2012, 17, 319–336. [Google Scholar] [CrossRef] [Green Version]
  90. Xu, W.; Saxton, G.D. Does stakeholder engagement pay off on social media? A social capital perspective. Nonprofit Volunt. Sect. Q. 2018, 48, 28–49. [Google Scholar] [CrossRef] [Green Version]
  91. Chiu, C.; Hsu, M.; Wang, E.T. Understanding knowledge sharing in virtual communities: An integration of social capital and social cognitive theories. Decis. Support Syst. 2006, 42, 1872–1888. [Google Scholar] [CrossRef]
  92. Okazaki, S.; Andreu, L.; Campo, S. Knowledge sharing among tourists via social media: A comparison between Facebook and Tripadvisor. Int. J. Tour. Res. 2016, 19, 107–119. [Google Scholar] [CrossRef]
  93. Homans, G.C. Social Behavior: Its Elementary Forms; Harcourt, Brace: San Diego, CA, USA, 1961. [Google Scholar]
  94. Cook, K.S.; Cheshire, C.; Rice, E.R.; Nakagawa, S. Social exchange theory. In Handbook of Social Psychology; Springer: Dordrecht, The Netherlands, 2013; pp. 61–88. [Google Scholar]
  95. Blau, P. Exchange and Power in Social Life; Routledge: Abingdon, UK, 2017. [Google Scholar]
  96. Kankanhalli, A.; Tan, B.C.; Wei, K.K. Contributing knowledge to electronic knowledge repositories: An empirical investigation. MIS Q. 2005, 29, 113. [Google Scholar] [CrossRef] [Green Version]
  97. Zhou, Q.; Deng, X.; Wang, G.; Mahmoudi, A. Linking elements to outcomes of knowledge transfer in the project environment: Current review and future direction. Front. Eng. Manag. 2022, 9, 221–238. [Google Scholar] [CrossRef]
  98. Goh, S.C. Managing effective knowledge transfer: An integrative framework and some practice implications. J. Knowl. Manag. 2002, 6, 23–30. [Google Scholar] [CrossRef] [Green Version]
  99. Han, J.; Pashouwers, R. Willingness to share knowledge in healthcare organisations: The role of relational perception. Knowl. Manag. Res. Pract. 2018, 16, 42–50. [Google Scholar] [CrossRef]
  100. Bartol, K.M.; Srivastava, A. Encouraging Knowledge Sharing: The Role of Organizational Reward Systems. J. Leadersh. Organ. Stud. 2002, 9, 64–76. [Google Scholar] [CrossRef]
  101. Yeon, K.; Wong, S.F.; Chang, Y.; Park, M.C. Knowledge sharing behavior among community members in professional research information centers. Inf. Dev. 2016, 32, 655–672. [Google Scholar] [CrossRef]
  102. Hung, S.Y.; Lai, H.M.; Chang, W.W. Knowledge-sharing motivations affecting R&D employees’ acceptance of electronic knowledge repository. Behav. Inf. Technol. 2011, 30, 213–230. [Google Scholar]
  103. Zhang, X.; Bartol, K.M. Linking empowering leadership and employee creativity: The influence of psychological empowerment, intrinsic motivation, and creative process engagement. Acad. Manag. J. 2010, 53, 107–128. [Google Scholar] [CrossRef] [Green Version]
  104. Ryan, R.M.; Deci, E.L. Intrinsic and extrinsic motivations: Classic definitions and new directions. Contemp. Educ. Psychol. 2000, 25, 54–67. [Google Scholar] [CrossRef]
  105. Tampoe, M. Motivating Knowledge workers—The challenge for the 1990s. In Knowledge Management and Organizational Design; Myers, P.S., Ed.; Butterworth-Heinemann: Boston, MA, USA, 1996; pp. 179–190. [Google Scholar]
  106. Hau, K.-T.; Ho, I.T. Chinese students’ motivation and achievement. In The Oxford Handbook of Chinese Psychology; Bond, M.H., Ed.; Oxford University Press: Oxford, UK, 2010; pp. 187–204. [Google Scholar]
  107. Deci, E.L. Effects of externally mediated rewards on intrinsic motivation. J. Personal. Soc. Psychol. 1971, 18, 105. [Google Scholar] [CrossRef]
  108. Katz, E.; Blumler, J.G.; Gurevitch, M. Uses, and gratifications research. Public Opin. Q. 1973, 37, 509–523. [Google Scholar] [CrossRef]
  109. Gan, C. Understanding WeChat users’ liking behavior: An empirical study in China. Comput. Hum. Behav. 2017, 68, 30–39. [Google Scholar] [CrossRef]
  110. LaRose, R.; Eastin, M.S. A social cognitive theory of internet uses and gratifications: Toward a new model of media attendance. J. Broadcast. Electron. Media 2004, 48, 358–377. [Google Scholar] [CrossRef]
  111. Chuang, Y. Toward an understanding of uses and gratifications theory and the sense of virtual community on knowledge sharing in online game communities. Int. J. Inf. Educ. Technol. 2015, 5, 472–476. [Google Scholar] [CrossRef] [Green Version]
  112. Lee, C.S.; Ma, L. News sharing in social media: The effect of gratifications and prior experience. Comput. Hum. Behav. 2012, 28, 331–339. [Google Scholar] [CrossRef]
  113. Baxter, L.; Egbert, N.; Ho, E. Everyday health communication experiences of college students. J. Am. Coll. Health 2008, 56, 427–436. [Google Scholar] [CrossRef]
  114. Ali-Hassan, H.; Nevo, D.; Wade, M. Linking dimensions of social media use to job performance: The role of social capital. J. Strateg. Inf. Syst. 2015, 24, 65–89. [Google Scholar] [CrossRef]
  115. Wu, J.; Wang, S.; Tsai, H. Falling in love with online games: The uses and gratifications perspective. Comput. Hum. Behav. 2010, 26, 1862–1871. [Google Scholar] [CrossRef]
  116. Rubin, A.M. The uses and gratifications perspective of media effects. In Media Effects: Advances in Theory and Research; Bryant, U.J., Zillmann, I.D., Eds.; Routledge: Abingdon, UK, 2002; pp. 525–548. [Google Scholar]
  117. Lubis, A.H.; Syed Idrus, S.Z.; Sarji, A. ICT usage amongst lecturers and its impact towards learning process quality. J. Komun. Malays. J. Commun. 2018, 34, 284–299. [Google Scholar] [CrossRef] [Green Version]
  118. Korhan, O.; Ersoy, M. Usability, and functionality factors of the social network site application users from the perspective of uses and gratification theory. Qual. Quant. 2015, 50, 1799–1816. [Google Scholar] [CrossRef]
  119. Lin, Y.H.; Hsu, C.L.; Chen, M.F.; Fang, C.H. New gratifications for social word-of-mouth spread via mobile SNSs: Uses and gratifications approach with a perspective of media technology. Telemat. Inform. 2017, 34, 382–397. [Google Scholar] [CrossRef]
  120. Dolan, R.; Conduit, J.; Fahy, J.; Goodman, S. Social media engagement behaviour: A uses and gratifications perspective. J. Strateg. Mark. 2016, 24, 261–277. [Google Scholar] [CrossRef] [Green Version]
  121. Ferris, A.L.; Hollenbaugh, E.E. A Uses and Gratifications Approach to Exploring Antecedents to Facebook Dependency. J. Broadcast. Electron. Media 2018, 62, 51–70. [Google Scholar] [CrossRef]
  122. Hollenbaugh, E.E.; Ferris, A.L.; Casey, D.J. How Do Social Media Impact Interpersonal Communication Competence? A Uses and Gratifications Approach. In The Psychology and Dynamics behind Social Media Interactions; IGI Global: Hershey, PA, USA, 2020; pp. 137–163. [Google Scholar]
  123. Kuegler, M.; Smolnik, S.; Kane, G. What’s in IT for employees? Understanding the relationship between use and performance in enterprise social software. J. Strateg. Inf. Syst. 2015, 24, 90–112. [Google Scholar] [CrossRef]
  124. Andreeva, T.; Sergeeva, A. The more the better … or is it? The contradictory effects of HR practices on knowledge-sharing motivation and behaviour. Hum. Resour. Manag. J. 2016, 26, 151–171. [Google Scholar] [CrossRef] [Green Version]
  125. Wasko, M.M.; Faraj, S. Why should I share? Examining social capital and knowledge contribution in electronic networks of practice. MIS Q. 2005, 29, 35–57. [Google Scholar] [CrossRef]
  126. Ringle, C.M.; Wende, S.; Becker, J.M. SmartPLS 3. SmartPLS GmbH: Boenningstedt, Germany. 2015. Available online: (accessed on 24 December 2022).
  127. Hair, J.F.; Hult, G.T.M.; Ringle, C.M.; Sarstedt, M. A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM), 3rd ed.; Sage: Thousand Oaks, CA, USA, 2022. [Google Scholar]
  128. Hayes, J.C. Translation, Subjectivity, and Culture in France and England, 1600–1800; Stanford University Press: Stanford, CA, USA, 2022. [Google Scholar]
  129. Fornell, C.; Larcker, D.F. Evaluating structural equation models with unobservable variables and measurement error. J. Mark. Res. 1981, 18, 39–50. [Google Scholar] [CrossRef]
  130. Henseler, J.; Dijkstra, T.K.; Sarstedt, M.; Ringle, C.M.; Diamantopoulos, A.; Straub, D.W.; Ketchen, D.J., Jr.; Hair, J.F.; Hult, G.T.M.; Calantone, R.J. Common beliefs and reality about PLS: Comments on Rönkkö and Evermann (2013). Organ. Res. Methods 2014, 17, 182–209. [Google Scholar] [CrossRef] [Green Version]
  131. Sanchez-Franco, M.J.; Roldán, J.L. Expressive aesthetics to ease perceived community support: Exploring personal innovativeness and routinised behaviour as moderators in Tuenti. Comput. Hum. Behav. 2010, 26, 1445–1457. [Google Scholar] [CrossRef]
Figure 1. The Conceptual Model.
Figure 1. The Conceptual Model.
Sustainability 15 06765 g001
Table 1. Top Ten Social Media Platforms.
Table 1. Top Ten Social Media Platforms.
RankSM PlatformLaunchedHeadquartersMAUAnnual Revenue
1Facebook2004Menlo Park, CA2.9 billion$85.96 billion
2YouTube2005San Bruno, CA2.2 billion$28.8 billion
3WhatsApp2009Menlo Park, CA2 billion$5.5 billion
4Instagram2010Menlo Park, CA2 billion$24 billion
5TikTok2016Culver City, CA1 billion$11 billion
6Snapchat2011Los Angeles, CA538 million$1.06 billion
7Pinterest2005San Francisco, CA444 million$575 million
8Reddit2010San Francisco, CA430 million$289.9 million
9LinkedIn2006San Francisco, CA250 million$12. 4 billion
10Twitter2003Mountain View, CA217 million$5.42 billion
Adapted from accessed on 16 February 2023.
Table 2. The Sample Profile.
Table 2. The Sample Profile.
AgeLess than 307617
30–40 years18541
40–50 years10824
More than 50 years8118
Experienceless than 5 years6314
5–10 years9922
10–15 years14933
More than 15 years12127
No response184
Areas Marketing/Sales10824
Operations/Supply chains/logistics11726
No response133
Table 3. Assessment of the Measurement Model.
Table 3. Assessment of the Measurement Model.
VariablesItemsFactor LoadingsαC.R.AVE
Motivation toMUSM10.9360.9180.9390.758
Use socialMUSM20.854
Media PlatformsMUSM30.751
(MUSM)MUSM4 0.848
Use of Social USMP10.860.8590.8940.629
Media PlatformsUSMP20.669
Motivation to MSK10.8440.8910.920.697
Table 4. Discriminant Validity (HTMT).
Table 4. Discriminant Validity (HTMT).
KS = Knowledge Sharing, MUSM = Motivation to use Social Media, USMP = Use of social media Platform, MSK = Motivation to Share Knowledge.
Table 5. Assessment of Direct Effects.
Table 5. Assessment of Direct Effects.
Direct EffectsΒS.D.t-Valuep-Value Conclusion
USMP → KS0.2230.0317.2460.000Supported
KS = Knowledge Sharing, USMP = Use of Social Media Platform.
Table 6. Assessment of Moderation Effects.
Table 6. Assessment of Moderation Effects.
MSK Mod USMP → KS 0.7590.1415.5490.000Supported
MUSM Mod USMP → KS0.7920.1395.6290.000Supported
KS = Knowledge Sharing, MUS = Motivation to use Social Media, USMP = Use of Social Media Platform, and MSK = Motivation to Share Knowledge.
Table 7. The Measurement Scales.
Table 7. The Measurement Scales.
Knowledge Sharing (KS): Adapted from [80]
KS1. I intend to share my experience or know-how from work with other organizational members more frequently in the future.
KS2. I will always provide my know-where or know-whom at the request of other organizational members.
KS3. I will try to share my expertise from my education or training with other organizational members in a more effective way.
KS4. I will share my work reports and official documents with members of my organization more frequently in the future.
Use of Social Media Platforms (USMP): Adapted from [123].
USMP1: Social media platform helps and supports sharing knowledge with co-workers.
USMP2: Social media platform helps and supports sharing knowledge with managers.
USMP3: Social media platform helps and supports sharing knowledge with subordinates.
USMP4: Social media platform helps and supports sharing knowledge with outside partners.
USMP5: Social media platform helps and supports sharing knowledge with suppliers.
Motivation to Use Social Media (MUSM): Adapted from [125].
MUSM1: I earn respect from others by participating in the social media platforms.
MUSM2: I feel that participation in social media platforms improves my status in the profession.
MUSM3: I participate in the social media platforms to improve my reputation in the profession.
MUSM4: It feels good to help others solve their problems.
MUSM5: I enjoy helping others in the social media platforms.
Motivation to Share Knowledge (MSK). Adapted from [124]
MSK1: I like sharing knowledge.
MSK2: I think sharing knowledge is an important part of my job.
MSK3: I find it personally satisfying.
MSK4: I share knowledge because I want my supervisor to praise me. I share knowledge because I want to get a reward.
MSK5: I share knowledge because it might help me get promoted.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Yaqub, M.Z.; Alsabban, A. Knowledge Sharing through Social Media Platforms in the Silicon Age. Sustainability 2023, 15, 6765.

AMA Style

Yaqub MZ, Alsabban A. Knowledge Sharing through Social Media Platforms in the Silicon Age. Sustainability. 2023; 15(8):6765.

Chicago/Turabian Style

Yaqub, Muhammad Zafar, and Abdullah Alsabban. 2023. "Knowledge Sharing through Social Media Platforms in the Silicon Age" Sustainability 15, no. 8: 6765.

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