Exploring Motivations and Trust Mechanisms in Knowledge Sharing: The Moderating Role of Social Alienation

Abstract

This study is based on social exchange theory and the UTAUT model to develop a research model to investigate the impact of motivations under the background of established enterprise social media (ESM) in small- and medium-sized enterprises (SMEs). Motivations like organization rewards (OR), reciprocal benefits (RB), expectation fulfillment (EF), and job relevance (JR) have different impacts on the following two dimensions of trust: affect-based trust (ABT) and technology-based trust (TBT). This study considers the trust mechanism as the mediator in the relationship between motivations and knowledge sharing (KS). It also demonstrates the psychological element of social alienation (SA) as a moderator that influences trust level and its inhibitory effect on knowledge sharing within the organization. The authors in this study collected data from managers and employees in seven enterprises, including three retail enterprises and four information technology (IT) enterprises in China through three waves. In total, 509 responses were received, and 483 valid data were used to test the research model and hypotheses through the structural equation modeling (SEM) method to evaluate the impact of the potential elements on knowledge-sharing behavior. This research found that motivations like OR and RB promote ABT, thereby encouraging knowledge sharing when employees use ESM. The motivations of EF and JR have a direct effect on TBT, as well as promoting knowledge sharing. Both ABT and TBT have mediating roles between motivations and KS. In addition, the research also revealed the negative moderation effect of SA on the relationship between the two types of trust and KS. Specifically, SA gradually weakens the effects of ABT and TBT on KS, especially when employees are experiencing high levels of SA; ABT and TBT have no impact on KS. This study attempts to advance the theoretical and practical insights of motivations on knowledge sharing by exploring mediating effects of trust mechanisms. It also reveals the importance of SA’s moderation effect on the relationship between trust mechanisms and KS under the application of ESM.

1. Introduction

Enterprises are effectively overcoming resource and capability limitations by embracing relevant technologies, as well as driving innovation and digital transformation [1,2]. Many organizations have made strategic investments in these to enhance performance and service quality [3]. Notably, more and more small- and medium-sized enterprises (SMEs) through the adoption of enterprise social media (ESM) are empowering employees to seamlessly share information, including messaging, profile creation, content editing, and network interactions [4]. Furthermore, recognizing the critical role of knowledge as a resource, small- and medium-sized enterprises are leveraging it to gain a competitive edge in the market, especially the function of technology-based knowledge sharing in organization behavior, organization management, and the establishment of enterprises’ cultures [5]. The definition of knowledge sharing (KS) is the “efforts to disseminate information, skills, ideas, experience, insights or expertise to others” [6]. This process encompasses both knowledge development and knowledge acquisition. While various drivers, such as rewards, management support, and encouragement, motivate knowledge-sharing behavior [7], there are still some obstacles for enterprises to utilize ESM for knowledge management and encouraging employees to share knowledge. It is important to acknowledge that barriers persist in the knowledge-sharing process, including issues like a lack of trust and time constraints [8]. Some researchers have noted that utilizing the advantages of ESM, such as sharing, retrieving, and storing information, to transcend information-processing boundaries and redefine formal power dynamics within an organization can be challenging [9]. Moreover, there are inherent risks associated with technology-based knowledge-sharing processes. Despite the numerous positive outcomes associated with ESM, there is a potential for detrimental behaviors, such as concealing critical information or disseminating inaccurate knowledge to colleagues, resulting in issues like information leakage and unwanted spin-offs. Similarly, previous research has emphasized the importance of establishing trustworthiness between the use of ESM and knowledge sharing in the online community’s sustainable development ([4,10,11]). Consequently, it is imperative to establish trust and to implement protective mechanisms within organization knowledge management [12].

Research spanning the United States, the United Kingdom, and China has consistently underscored the strong association between knowledge sharing and social media, particularly social networking [13]. Statistical data have further underscored the wide adoption of social media-based social networking by 53% of European organizations [4]. Prior studies have highlighted how the application of social media empowers organizations to navigate the challenges of implementing organization changes, benefiting effective communication, trust building, and increased employee engagement in the workplace [14]. As one of the collaborative technologies, social media naturally fosters content sharing, task development, and seamless communication [15]. The cumulative evidence emphasizes the essential value of ESM in enhancing an organization’s innovation capabilities [1] and improving employees’ competences [16]. In a world where digital transformation and innovation are paramount, adopting ESM is not merely optional but a strategically imperative for businesses seeking to thrive and excel in today’s competitive landscape.

In light of the preceding discussion, this study is primarily centered on the development of a conceptual framework and the application of empirical methods to scrutinize the influence of two dimensions of trustworthiness, namely affect-based trust and technology-based trust, on employees’ knowledge-sharing behaviors within the context of enterprise social media (ESM). Recently, the literature has highlighted the dynamic nature of organization members’ trust levels, which can be shaped by various motivations and complex psychological states [6]. Nonetheless, there is a scarcity of research addressing how specific psychological states affect the relationship between employee trust and knowledge-sharing behavior. Furthermore, there is a distinct value in discerning the antecedent motivations that contribute to trust building among co-workers, as this knowledge can foster the development and innovation within digital creative enterprises [17]. Previous studies have underscored the importance of comprehending the impact of trustworthiness on knowledge sharing among organization members, along with the contextual conditions that influence this relationship. Consequently, recognizing this research gap, the primary aim of this study is to investigate the following research questions:

RQ1: 

What motivations may positively influence the affect-based trust and technology-based trust under the use of ESM?

RQ2: 

Do affect-based trust and technology-based trust mediate the relationship between these motivations and knowledge sharing?

RQ3: 

How does the situation of social alienation effect the relationship between these two dimensions of trust and an employee’s knowledge-sharing behavior in the workplace?

In summary, this research has developed a comprehensive framework rooted in social exchange theory and the unified theory of acceptance and use of technology (UTAUT). This framework serves a dual purpose: First, it seeks to investigate the antecedent motivations influencing employee affect-based trust and technology-based trust, and second, it verifies their mediating role in the relationship between these trusts and knowledge-sharing behavior. Moreover, this research delves into the dynamics of employee trust in knowledge sharing with colleagues under different psychological states, particularly in scenarios involving social alienation. The remainder of this paper is organized into eight distinct sections. Section 2 provides an extensive overview of the relevant literature. The theoretical framework and research hypotheses are elaborated upon in Section 3. Section 4 and Section 5 are dedicated to the discussion of the research methodology and data analysis, respectively. The subsequent section delves into general discussions, while the final two sections address the limitations and offer the concluding remarks of this research.

2. Theoretical Framework and Literature Review

2.1. Social Exchange Theory

Social exchange theory is related to the “voluntary actions of individuals that are motivated by the returns they are expected to bring and typically do in fact bring from others” [18]. It postulates that individuals tend to act in their own self-interest, calculating benefits and sacrifices in interpersonal exchange activities [19]. This theory underscores the idea that social behavior often involves two parties seeking to maximize benefits while minimizing costs [20]. Previous research has demonstrated that various resources, such as economic resources, socioemotional resources [21], and trust resources, play significant roles in interpersonal exchange, ultimately influencing engagement and knowledge sharing in online communities [22]. In essence, people can be motivated by factors such as rewards, reciprocity, expectations, costs, and resources, depending on the specific conditions of the exchange [18]. For example, suppliers’ anticipation of future dependence can drive relational and learning motives, thereby encouraging knowledge sharing [23] Perceived benefits of sharing knowledge through social media significantly enhance students’ academic growth [20]. Therefore, in the context of knowledge sharing within organizations, professional-cultural knowledge sharing is influenced by different exchange modes, including negotiated exchange, reciprocal exchange, generalized exchange, and productive exchange [24]. These exchange modes are predominantly determined by intrinsic benefits (e.g., a sense of self-worth, knowledge self-efficacy, enjoyment in helping others, and influencing the company), extrinsic benefits (e.g., reputation, social support, and social trust), and associated costs (e.g., cognitive costs and executional costs) [25]. Furthermore, the growing motivation to share knowledge and the motivation to use social media platforms also enhance the efficacy of social media in knowledge sharing [26]. As a result, digital creative enterprises are increasingly adopting social media technology as a means to reduce social alienation and to encourage employees to transition from personal knowledge ownership to the active practice of knowledge sharing [17]. Therefore, based on social exchange theory, people’s willingness to share is influenced by different types of trust, motivation, resource support, and social psychology when using ESM.

2.2. UTAUT Model

Researchers often employ the unified theory of acceptance and usage of technology (UTAUT) model, which is an extension of the technology acceptance model (TAM), to investigate the behavioral motivations behind enterprises’ technology adoption for knowledge sharing. This model is frequently adapted in conjunction with other theories, with researchers making reasonable modifications to align its potential variables with the specific characteristics of their research subject [27]. The UTAUT model encompasses four fundamental paradigms: performance expectation, effort expectation, social impact, and facilitating conditions [28]. Additionally, it accounts for individual characteristics such as age, gender, experience, and voluntariness. The UTAUT primarily focuses on the micro-level exploration of individual technology acceptance and usage within organization settings [29]. The existing literature indicates that users’ attitudes and behavioral intentions toward using social media platforms are typically influenced by the fulfillment of their expectations, technology performance, and social factors [30]. In particular, employees’ expectations serve as pivotal determinants of their motivation behaviors, closely related to their beliefs about valence and instrumentality [31]. When the expectations of an individual are met, it fosters personal engagement, as it becomes intertwined with the achievement of desired performance, consequently leading to positive behavioral outcomes [32].

Furthermore, an array of recent research endeavors has introduced novel variables to extend the unified theory of acceptance and usage of technology (UTAUT) in predicting technology usage and behavioral intentions. These extensions encompass the incorporation of new endogenous mechanisms, exogenous mechanisms, and moderation mechanisms that enrich the model’s explanatory power [29]. For instance, Altamimi, A. and colleagues [33] posited that perceived cost and perceived convenience directly impact users’ intentions and attitudes towards adopting blockchain technology for e-learning [33]. Mechanisms like self-efficacy, trust, and job relevance have also been considered to be antecedents that influence technology adoption in enterprises and employees’ intentions to use technology [34]. In the context of social media-based knowledge sharing, the intention to share knowledge has witnessed a notable surge, serving as a potent motivator for knowledge-sharing behaviors among members of organizations. This upswing can be attributed to the fulfillment of expectations and social needs [35]. Nonetheless, the existing literature largely explores the UTAUT model in the context of the direct influence of predictors on users’ acceptance or purchase behavior through technology adoption. Few studies have applied this theory to the domain of knowledge sharing. Furthermore, recent investigations based on the UTAUT model have predominantly focused on social impact, without adequately emphasizing the importance of social psychology. Thus, this study takes a unique approach by integrating the UTAUT model with social psychological elements to unravel their combined effects on knowledge-sharing behavior.

2.3. Enterprise Social Media in Knowledge Sharing

Enterprise social media (ESM), functioning as an information management tool, has primarily served as a means for the transfer, storage, and retrieval of knowledge [9]. It is a versatile suite of tools that support communication and coordination among members within an organization [16]. Grounded in the perspective of affordance theory, ESM not only contributes to the development of human capital but also facilitates the establishment of vital social connections. In doing so, it aids organizations in achieving availability management ([4,36]). As an efficient tool, social media naturally encourages and facilitates knowledge sharing. For instance, ESM empowers organization members to engage in visible and personalized communication with specific individuals, making it easier to identify peers with similar activities, backgrounds, and interests by visiting their personal profiles [37]. Furthermore, ESM provides the affordances necessary for exchanging messages, posting and collecting files, tagging specific individuals, and viewing and responding to others’ comments, all without time limitations ([4,38]).

The influence of ESM extends beyond the realm of cognitive and structural capital and collective organization knowledge; it also plays a crucial role in promoting organization participation [39]. The current landscape of ESM emphasizes that social media-based knowledge-sharing intentions are positively bolstered by the fulfillment of expectations and the impact of social influence, thereby fostering the development of authentic leadership [35]. Additionally, studies such as Jami Pour, M. and Taheri, F. [40] have affirmed the association between individuals’ knowledge-sharing behaviors, personal traits, trust, and subjective well-being through the use of social media. On the organizational front, enterprises harness social media to surmount the challenges posed by the implementation of organization changes. Research has demonstrated that social media applications can enhance trust levels and reduce resistance to change, thereby fostering knowledge sharing and improving effective communication during the formulation and implementation of changes [14]. This is attributed to the positive impact of social networking on organizational factors, including informal relationships, collaboration, and the cultivation of a knowledge-sharing culture [41]. In light of these multifaceted contributions, ESM emerges as an indispensable element in the construction and development of enterprises.

2.4. Motivations in Knowledge Sharing

From the perspective of self-determination theory, employees’ psychological needs are closely linked to their motivational orientations. Motivation is a complex and multifaceted concept, which is understood through various psychological elements. It is essential to distinguish between two fundamental motivational approaches, i.e., intrinsic factors and external constructs. Intrinsic motivations are intricately tied to internal goals and self-perception [42]. This implies that individuals are driven by the inherent satisfaction derived from accomplishing core tasks [43]. Consequently, intrinsic motivations enable individuals to derive enjoyment from fulfilling their innate psychological needs and validating their capabilities [44]. In contrast to intrinsic motivations, extrinsic motivations are associated with a person’s predetermined interest in specific outcomes, such as rewards or reciprocal benefits [43]. Previous research has revealed the significant impacts of both intrinsic and extrinsic motivations on knowledge-sharing attitudes and behaviors under various circumstances. For instance, Mao, B. et al. [45] found that intrinsic motivational factors, like an individual’s health status, trait anxiety, use of active media, and frequency of information seeking, directly influenced the dissemination of knowledge [45]. Specifically, the willingness of individuals to contribute knowledge was an inevitable outcome of the promotion of intrinsic motivation [44]. In essence, intrinsic motivation acts as a fundamental driver, motivating individuals to initiate the flow of information to other members within an organization [43].

Existing research has explicitly acknowledged that knowledge sharing is an active and proactive behavior, which demands that employees possess a high level of proactive motivation. This motivation is contingent on factors such as employees having a high degree of psychological safety and experiencing workplace dignity [46]. The motivation for knowledge sharing in contemporary organizations is essentially driven by three interrelated motivational processes, namely the “can do”, “reason to”, and “energized to” motivations [47]. Within this framework, motivations for knowledge-sharing behavior in today’s organizations can be attributed to factors like satisfaction, attention, relevance, and confidence. These motivations are particularly pronounced in knowledge-sharing behavior supported by innovative technologies such as social media and blockchain [48]. In essence, knowledge dissemination is facilitated by the creation of knowledge awareness, which is closely linked to the motivations of “learning by doing” and “learning by interaction” [5]. This implies that individuals are motivated to transform experimental and socialized information into internalized knowledge to realize personal value through knowledge sharing, ultimately contributing to the enhancement of authentic leadership and the development of social ties ([5,35]).

In the realm of social media-based knowledge sharing, the adoption of technology enables organizations to achieve the goals of knowledge creation, communication, and sharing by fulfilling performance expectancy, effort expectancy, and social influence factors [35]. Research has underscored that the implementation of social media not only paves the way for employees to contribute to their teams but also enhances team knowledge sharing, team efficacy, and the cohesion of team members [49]. From the perspective of professional identity, the interplay between an individual’s social-self and personal-self dynamics contributes to changes in interpersonal knowledge sharing, encompassing the exchange of ideas, experiences, and opinions [50]. A key factor in this dynamic is the positive influence of individual identification on trust within social networking sites [10]. Conversely, enterprise social media assists employees in recognizing their colleagues’ job performances, promoting collaboration and fostering trust among team members [4]. Therefore, the implementation of social media stands as an innovative application that significantly influences information sharing within organizations.

3. Conceptual Model and Hypothesis Design

3.1. Model Development

The theoretical framework of this research is depicted in Figure 1. It elucidates the intricate relationships among motivation variables, namely organization rewards, reciprocal benefits, expectation fulfillment, and job relevance, and the mediating variables, including affect-based trust and technology-based trust. These variables collectively influence employees’ knowledge-sharing behaviors through the use of enterprise social media (ESM). Additionally, the framework illustrates the moderating effect of social alienation on the relationship between trust and behavior. Further elaboration on the theoretical foundations and hypotheses is provided in the following sections.

3.2. Hypothesis Design

3.2.1. Technology-Based Trust, Affect-Based Trust, and Knowledge Sharing

Trust, as defined in the relevant literature, is characterized as “a willingness to rely on an exchange partner in whom one has confidence” [51]. Within the framework of trust building, it is primarily associated with the following three key elements: antecedents, processes, and outcomes [10]. Notably, trust beliefs, which encompass the perception of trustworthiness in the object of trust, constitute the central component of trust building [52]. These beliefs are influenced by individual characteristics such as ability, benevolence, integrity, and reputation [53], as well as organizational and societal factors [8], including organizational culture [54] and organizational structure [51]. Furthermore, trust can also be shaped by environmental and technological mechanisms. For example, social interaction ties, the norm of reciprocity [10], and the adoption of enterprise social media and blockchain technologies play pivotal roles in influencing trust. In prior trust research within virtual communities [4], the concepts of person-to-technology and person-to-organization have been extensively discussed to underscore the significance of trust in establishing sustainable knowledge-sharing virtual communities [11].

Trust serves as the cornerstone for individuals to establish meaningful relationships within an organization. Previous research has underscored persistent challenges related to trust issues among colleagues and concerns about the misuse of knowledge, which continue to impede knowledge sharing within organizations [8]. Kunttu and Neuvo [55] emphasized that the cultivation of mutual trust in personal relationships was a crucial process for mitigating information barriers and fostering knowledge sharing [55]. According to the interpersonal trust theory, trustworthiness among peers can be assessed along two dimensions, i.e., affect-based trust and cognition-based trust [56]. In more detail, affect-based trust pertains to the concept wherein organization members emotionally choose individuals to whom they feel emotionally attached, based on their experiences of reciprocity in relationships [4], whereas, cognition-based trust is established through observations or experiences of others, such as assessing an individual’s expertise, abilities, talents, and skills [4]. McAllister, D.J [56] regarded cognitive-based trust as the driving force behind affect-based trust within organizations. In this context, affect-based trust develops through natural interactions and is driven by motivations, enabling the prediction of behavior. For instance, organizational culture elements like adhocracy, clan, hierarchy, and market can enhance co-workers’ propensity to share knowledge by bolstering affect-based trust [54]. Therefore, affect-based trust plays a positive and prominent role in influencing knowledge sharing and can serve as a mediating element to accelerate employees’ knowledge-sharing behaviors.

H1. 

Affect-based trust has a positive effect on an employee’s knowledge-sharing behavior.

H2. 

Affect-based trust has a mediated effect between motivations and an employee’s knowledge-sharing behavior.

The influence of trust on the internal sharing of information within organizations is unequivocal and has gained increasing importance in the digital media and technology landscape [57]. The evidence, as observed in HP labs, indicates that a high level of trust among employees can significantly enhance knowledge sharing within an organization, driven by motivations aimed at improving decision making, fostering innovation, and enhancing overall coordination [12]. It is essential to note that trust in technology differs from trust in individuals, as it is rooted solely in amoral and non-volitional factors. In the recent literature, technology-based trust is defined as an individual’s relationship with a specific technology, reflecting the favorable attributes associated with that technology [23]. Specifically, within the realm of information system (IS) trust, it is often related to the disclosure of private information. This is because the sharing process may entail potential risks, such as information leakage or negative feedback [36]. Furthermore, an individual’s cognitive constructs, including perceived fairness, accountability, transparency, and explainability, can be considered antecedents of trust in technology [58]. Similarly, the establishment of technology-based trust is intertwined with both external and intrinsic motivations. These motivations encompass expectancy fulfillment, social influence, job relevance, self-efficacy, and compatibility within the context of enterprise technology application [34]. Consequently, technology-based trust not only shapes employees’ knowledge-sharing behaviors but also serves as a mediating element that bridges the gap between motivations and knowledge-sharing tendencies.

H3. 

Technology-based trust has a positive effect on an employee’s knowledge-sharing behavior.

H4. 

Technology-based trust has a mediated effect between motivations and an employee’s knowledge-sharing behavior.

3.2.2. Motivations on Affect-Based Trust and Technology-Based Trust

Building on prior research, it becomes evident that the utilization of rewards and reciprocity to stimulate knowledge sharing within organizations is an effective means of encouraging employees to engage with their colleagues [43]. In contrast to one-way communication, organization reward and reciprocity enhance the value of interpersonal relationships, thereby fostering greater engagement in knowledge sharing [59]. As colleagues’ relationships grow closer, they tend to engage in behaviors that benefit one another, driven by the presence of a certain level of trust and strong interpersonal bonds [60]. The cultivation of interpersonal trust is rooted in motivational factors such as expectation and purpose, which enable employees to adopt innovative approaches in addressing complex work situations and facilitating knowledge communication [61]. Particularly, individuals are inclined to develop affect-based trust in situations where there is a reciprocal exchange of experiences within their relationships [4]. Consequently, the growth of affect-based trust among colleagues is closely intertwined with the utilization of rewards and reciprocity. In light of these insights, we posit the following hypotheses:

H5. 

Organization rewards have a positive impact on affect-based trust.

H6. 

Reciprocal benefits have a positive impact on affect-based trust.

Expectation fulfillment is intricately linked to the usability of technology within the framework of a user’s experience, specifically in terms of how well the technology’s functions align with the user’s expectations [62]. In the context of this study, the concept of expectation fulfillment primarily encompasses the realization of both performance expectation and effort expectation. Performance expectation is defined as “the extent to which an individual believes that using the system will lead to improvements in job performance” [28], while effort expectation relates to “the perceived ease of using new technology” [34]. Technology-based trust is generally associated with sub-elements such as information authenticity, information security, property rights, and system stability. It relies on the dependable attributes of technology, including transparency, openness, and immutability [62]. Previous research has substantiated that the application of social media technology not only reduces resistance to the establishment of informal relationships and enhances collaboration among colleagues [41] but also fosters effective communication, knowledge sharing, employee engagement, trust, support, and participation [14]. Additionally, social media users perceive it as an easy-to-use tool for information sharing, leading to tangible benefits [63]. With this context in mind, the following hypotheses are proposed:

H7. 

Expectation fulfillment can improve technology-based trust.

Job relevance, as a concept, pertains to “the extent to which an individual perceives that the target system is applicable to their job” [34]. Recent studies have emphasized the importance of considering job relevance, as it is crucial for investigating both an individual’s trust in using technology and their belief that the technology can enhance the quality of their work life [64]. Given employees’ needs for knowledge sharing, transfer, communication, and acquisition, emerging business intelligence technologies based on social media have influenced knowledge sharing by providing monitoring and listening capabilities that keep organization members updated with the latest conversations and trends [39]. Haque, A. et al. [20] have also argued that social media’s technological support is closely linked to the development of an individual’s career and personal skills. Therefore, the following hypothesis is proposed:

H8. 

Job relevance has a positive influence on technology-based trust.

3.2.3. The Moderation Role of Social Alienation

Social alienation is a concept that is rooted in psychology and is pertinent in management studies. It encompasses detrimental feelings related to job satisfaction, work effort, motivation, work environment, social networks, and personal performance [65]. This state reflects individuals’ psychological experiences within their social relationships and signifies a sense of detachment from others [66]. Zhang, G. et al. [65] posited that creative employees, who possess strong networking abilities, are more inclined to exhibit sharing behaviors in organizations, allowing them to effectively seek informative advice and emotional support, thereby mitigating the experience of social alienation. However, employees with a high degree of psychological ownership over their personal knowledge may be more susceptible to social alienation, which, in turn, hinders knowledge sharing [17].

Additionally, social alienation is significantly and negatively associated with trust and communication quality [67]. Employees who perceive negative workplace gossip are more likely to reduce their knowledge-sharing behavior with colleagues [68]. This is because organization members place emphasis on factors such as the value of information from others, the sensitivity of information if leaked, employee trustworthiness, and legal protection mechanisms, all of which impact the knowledge-sharing process [12]. Thus, the establishment of mutual trust, which is based on personal-level relationships, can strike a balance between learning and knowledge protection, thereby reducing information barriers and enhancing collaboration [55]. Previous studies have revealed that knowledge sharing is influenced not only by factors such as management support, reward systems, and organizational culture but also by key elements like technology and trust [69]. The introduction of technologies like enterprise social media has alleviated situations of social alienation by promoting trust building and enhancing network ties, which, in turn, improve knowledge sharing and employee competence [16]. Consequently, we propose the following hypotheses:

H9. 

Social alienation lowers the effect of affect-based trust on an employee’s knowledge-sharing behavior.

H10. 

Social alienation lowers the effect of technology-based trust on an employee’s knowledge-sharing behavior.

4. Methodology

4.1. Measurement and Instrument

A survey method was employed to address the research objectives, which involved exploring the moderation effect of social alienation, investigating the positive impact of motivations on affect-based trust and technology-based trust, and estimating the mediating role of trust mechanisms in the relationship between motivations and knowledge sharing. A three-wave questionnaire was conducted to fulfill the research gap and to achieve a comprehensive understanding of the relationships under investigation. In the first wave, we recruited 30 participants to test the measurement items through an online platform to ensure the questionnaire’s reliability and logical validity. Each item was measured using a seven-point Likert scale, where “1” represented “strongly disagree”, “4” indicated the midpoint, and “7” denoted “strongly agree”. As presented in

Table 1. Measurement items and sources.

Items	Description	Sources
OR 1	I will receive a higher salary in return for my knowledge sharing	[43]
OR 2	I will receive a higher bonus in return for my knowledge sharing
OR 3	I will receive increased promotion opportunities in return for my knowledge sharing
OR 4	I will receive increased job security in return for my knowledge sharing
RB 1	I strengthen ties between existing members of the organization and myself	[43]
RB 2	I expand the scope of my association with other organization members
RB 3	I expect to receive knowledge in return when necessary
RB 4	I believe that my future requests for knowledge will be answered
ABT 1	I have a sharing relationship with the members of my work team. We can all freely share our ideas	[4]
ABT 2	I can talk freely with my colleagues about difficulties I am having with my work
ABT 3	If I share my problems with my colleagues, I know that they will respond constructively and caring
ABT 4	I believe that the members of my work team have made considerable emotional investments in our working relationship
KS 1	I share knowledge learned from my own experience	[5]
KS 2	I have the opportunity to learn from others’ experiences
KS 3	Colleagues include me in discussions about best practices
KS 4	Colleagues share new ideas with me
TBT 1	Social media technology is trustworthy	[34]
TBT 2	Social media technology is honest
TBT 3	Social media technology is transparent and visible
TBT 4	Social media technology prevents opportunists from making profits
EF 1	Using social media would enhance my effectiveness in job-related activities	[34]
EF 2	Using social media would enhance the efficiency of my job
EF 3	I would find it easy to use social media for job-related activities
EF 4	It would be easy for me to become skillful at using social media technology
JR 1	In the knowledge-sharing process, social media can be massively used	[34]
JR 2	In the knowledge-sharing process, social media usage is relevant
JR 3	Social media is relevant for future knowledge-sharing services
JR 4	The future of knowledge sharing in work-related activities based on social media technology
SA 1	I feel I am outside the network of resources needed to get practical support to accomplish my mission	[17]
SA 2	I feel alienated from my colleagues
SA 3	I feel that people around me are just out for themselves and do not really care for anyone else
SA 4	I feel that there is interpersonal isolation and even a reluctance to communicate within the company actively

Notes: OR, organization rewards; RB, reciprocal benefits; EF, expectation fulfillment; JR, job relevance; ABT, affect-based trust; TBT, technology-based trust; SA, social alienation; KS, knowledge sharing.

, each variable was assessed using four items, which were adapted from the existing literature. To mitigate language bias, we employed a back-translation method to translate each item into Chinese, following the recommendation of Than, S. T. et al. [1].

In the second wave, between 3 May 2023, and 10 May 2023, we proactively contacted various target enterprises to obtain research permissions and to verify that they met two fundamental conditions, namely using SEM and the presence of an independent IT or e-commerce department. Ultimately, four IT enterprises and three retail enterprises met these criteria and expressed their willingness to participate in this study. We engaged in direct communication with the leaders of these firms through personalized invitations, clearly explaining the research’s objectives, scope, and how it related to their businesses. Data were collected from 7 key departmental personnel or heads in each participating firm who possessed a comprehensive understanding of the research’s objectives, the survey’s content, and their firm’s operational context.

In the third stage, we collected a total of 509 responses through emails and WeChat messages that were sent by department leaders, from 15 May 2023 to 20 June 2023. Coffee coupons were offered as an incentive to maximize participation. After data validation, we obtained 484 valid responses, resulting in an 88.6% response rate, which exceeded the recommended threshold of 80% for a valid response rate [70]. Further details regarding the sample’s respondents are provided in

Table 2. Demographics of the respondents.

Variables	Features	Frequency	Percentage (%)
Gender	Male	252	52.07
	Female	232	47.93
Age (years)	18–30	171	35.33
	31–40	158	32.64
	41–50	86	17.77
	51 and above	69	14.26
Education Background	Junior college or less	57	11.78
	Undergraduate degree	301	62.19
	Master’s degree	112	23.14
	Doctoral degree and above	14	2.89
Position	Non-managerial employees	364	75.21
	Manager	93	19.21
	Senior/executive manager	27	5.58
Job experience	5 years and below	90	18.60
	5–10 years	168	34.71
	Above 10 years	226	46.69

.

To fulfill the research objectives and to evaluate the conceptual model, an empirical investigation was conducted that involved managers and employees within China’s emerging small- and medium-sized enterprises (SMEs) who had adopted enterprise social media (ESM) technology. This study encompassed three retail enterprises and four information technology (IT) enterprises. Specifically, the three retail enterprises were located in Gansu province, Shanxi province, and Jiangsu province, while two IT enterprises were established in Shanghai and Shenzhen, and the remaining two IT enterprises were located in Zhejiang province. These enterprises were randomly selected from a pool of 102 enterprises using the Ketao platform (https://curtao.eedatek.com (accessed on 3 March 2023)), adhering to specific criteria, including industry (information transmission, software and information technology services/retail), years of establishment (3–5 years), company size (100–499 employees), legal structure (Limited Liability Company/China), and operational status (existing). A total of seven enterprises expressed their willingness to participate in this research. The Ketao platform allows custom filtering and provides detailed enterprise information, including contact names, phone numbers, and email addresses.

These criteria were established for several reasons. SMEs hold a significant position in the economic and commercial landscape, and knowledge sharing within these organizations plays a crucial role in achieving sustainable development and fostering innovation. Managers and employees from IT, e-commerce, or R&D units within these enterprises were both selected as the sample population. Previous research has emphasized the knowledge-intensive nature of IT and software departments, where knowledge workers have been required to possess specialized skills to perform complex tasks within their teams [6]. Such tasks have included testing, operating, designing, and installing network systems to support their companies’ management program services for other businesses and customers. Retail enterprises were included as the target sample due to the rapid development of e-commerce and widespread adoption of electronic payment methods in China, which enabled the retail industry to consider the power of knowledge transformation and the destructive nature of social barriers. Furthermore, the selected samples were deemed suitable for this research because these enterprises actively encouraged their employees to share information through ESM to access both technical and social supports. Consequently, these organizations provided an ideal context for exploring the motivations behind employees’ trust levels and the knowledge-sharing process.

4.2. Data Collection

Regarding the survey design, it encompassed the primary demographic features of the sample, which included gender, age, educational background, job position, and work experience. The statistical overview presented in Table 2 reveals that 52.07% of the respondents were male, while 47.93% were female. The age group between 31 and 50 years constituted the majority, making up 50.41% of the total sample, and nearly half of the respondents (46.69%) had over 10 years of job experience. Furthermore, a significant proportion of the participants, specifically 75.21%, held non-managerial positions within their respective organizations. Among the respondents, 62.19% possessed an undergraduate degree, with nearly a quarter of the total sample having achieved higher academic degrees, such as master’s or doctoral degrees.

5. Data Analysis

The data were organized and processed using the IBM SPSS version 26.0 and AMOS software version 26.0. These two programs were employed for conducting descriptive statistics, examining correlations among variables, and assessing the validity and reliability of the data. In line with previously established practices in the literature, structural equation modeling (SEM) was utilized to evaluate multiple statistical relationships, facilitating visualization and model validation [43]. Furthermore, confirmatory factor analysis (CFA) proved to be a robust statistical technique for examining relationships among various factors and their features [71]. As a result, the employment of SEM and CFA methods was considered to be the most appropriate approach in the analytical process, allowing for flexible assumptions.

5.1. Common Method Bias

As the literature has recommended, time-lagged multi-wave data were used in this study to avoid CMB [72]. The risk of CMB in the analysis results still existed because of the self-reported nature of the data. According to the method in prior studies, Haman’s single-factor test was used in this study [4]. The results presented an overall variance of 23.6%, which was less than the threshold limit of 50%, indicating that our data did not have an issue of CMB.

5.2. The Measurement Model

We initiated this research by conducting confirmatory factor analyses to establish the distinctiveness of the constructs, as prescribed by the underlying theory. Confirmatory factor analysis (CFA) was the chosen method to verify the reliability, convergence validity, and discriminant validity, consistent with prior literature [71].

Specifically, we employed composite reliability (CR) to illustrate the confidence and validity of the scale. The results demonstrated that the CR exceeded 0.9, signifying strong internal consistency within the model. As presented in

Table 3. Item reliability statistics.

Variables	Items	Factor Loading	AVE	CR
Organizational rewards	OR 1	0.911	0.794	0.939
	OR 2	0.873
	OR 3	0.870
	OR 4	0.909
Reciprocal benefits	RB 1	0.881	0.789	0.937
	RB 2	0.881
	RB 3	0.903
	RB 4	0.887
Affect-based trust	ABT 1	0.893	0.787	0.936
	ABT 2	0.883
	ABT 3	0.896
	ABT 4	0.875
Knowledge sharing	KS 1	0.892	0.803	0.942
	KS 2	0.895
	KS 3	0.897
	KS 4	0.900
Technology-based trust	TBT 1	0.894	0.778	0.933
	TBT 2	0.898
	TBT 3	0.872
	TBT 4	0.862
Expectation fulfillment	EF 1	0.889	0.761	0.927
	EF 2	0.855
	EF 3	0.866
	EF 4	0.878
Job relevance	JR 1	0.867	0.803	0.942
	JR 2	0.909
	JR 3	0.920
	JR 4	0.888
Social alienation	SA 1	0.897	0.819	0.948
	SA 2	0.885
	SA 3	0.924
	SA 4	0.913

, the lowest factor loading observed was 0.862, exceeding the accepted minimum threshold of 0.5. Furthermore, the average variance extracted (AVE) for each variable surpassed the standard value of 0.5, indicating that the measurement model exhibited a high degree of reliability.

Then, the data in

Table 4. Correlation coefficient matrix and AVE square root values.

	OR	RB	ABT	KS	TBT	EF	JR
OR	0.891
RB	0.151 **	0.888
ABT	0.170 **	0.191 **	0.887
KS	0.265 **	0.213 **	0.271 **	0.896
TBT	0.135 **	0.056	0.192 **	0.240 **	0.882
EF	0.165 **	0.077	0.103 *	0.259 **	0.294 **	0.872
JR	0.190 **	0.041	0.170 **	0.262 **	0.180 **	0.191 **	0.896

Notes: * p < 0.05, ** p < 0.01; OR, organization rewards; RB, reciprocal benefits; EF, expectation fulfillment; JR, job relevance; ABT, affect-based trust; TBT, technology-based trust; KS, knowledge sharing.

present the variable correlation results and discriminant validity. In detail, all the correlation results are significant (p < 0.01). Diagonal numbers are AVE square root values. This means that the model has adequate discriminant validity because the AVE’s square root values of all latent variables are higher than the maximum absolute value of the correlation coefficient between factors [73]. CFA can be used to confirm the trustworthiness of collected data and to evaluate the adequacy of the measuring instrument. Therefore, we can establish structure modeling to estimate the hypotheses put forward earlier.

5.3. The Structural Model and Hypothesis Testing

In assessing the model fit, we adopted the approach of comparing actual values to recommended thresholds for various model fit indices. Following recent methodological recommendations, we employed maximum likelihood estimation to derive estimates for the model [1]. The results, as displayed in

Table 5. Recommended and actual values of the model fit indices.

Fit Index	χ2/df	PGFI	GFI	AGFI	RMSEA	NFI	RFI	CFI
Recommended value	<3	>0.5	>0.90	>0.90	<0.08	>0.90	>0.90	>0.90
Actual value	1.09	0.779	0.944	0.932	0.014	0.967	0.962	0.997

, reveal that the proposed model exhibits a favorable fit. The fit indices of χ²/df ratio, GFI, PGFI, AGFI, RMSEA, NFI, RFI, and CFI are used to estimate the model. To elaborate, the χ²/df ratio is 1.09 < 3, which means the sample size is acceptable. If the sample size is too small, the χ²/df ratio will increase. With an increase in the GFI, the proportion that can explain the total variation increases and the fitting becomes better (GFI = 0.944 > 0.9). In addition, the other index values are PGFI of 0.779, AGFI of 0.932, RMSEA of 0.014, NFI of 0.967, RFI of 0.962, and CFI of 0.997. These values meet the acceptable thresholds for each index.

The results of the path coefficients and their respective significance levels were computed utilizing AMOS, and the findings are outlined in

Table 6. Path coefficients and hypothesis testing.

Path	Hypothesis	Path Coefficient	β	T Stats	p-Value	VIF	Results
ABT→KS	H1	0.203	0.055	3.701	***	1.107	Supported
TBT→KS	H3	0.131	0.054	2.413	0.016 *	1.144	Supported
OR→ABT	H5	0.129	0.039	3.270	0.001 **	1.021	Supported
RB→ABT	H6	0.160	0.043	3.752	***	1.023	Supported
EF→TBT	H7	0.287	0.048	5.970	***	1.038	Supported
JR→TBT	H8	0.128	0.044	2.890	0.004 **	1.036	Supported

Notes: * p < 0.05, ** p < 0.01, *** p < 0.001. OR, organization rewards; RB, reciprocal benefits; EF, expectation fulfillment; JR, job relevance; ABT, affect-based trust; TBT, technology-based trust; KS, knowledge sharing.

. In line with the recommendation of Asghar, M. Z. et al. [35], we employed the β coefficient to evaluate the hypotheses. The β coefficient represents the impact of a one-unit change in the exogenous construct on the endogenous construct. The hypotheses were assessed for significance using t-statistics and p-values via 5000 bootstrappings. The structural model, depicted in Figure 2, underwent rigorous empirical testing, and all six hypotheses were found to be statistically significant (p < 0.05).

Specifically, ABT exerts a positive effect on KS (β = 0.055, t = 3.701, p < 0.001), while TBT also positively influences KS (β = 0.054, t = 2.413, p < 0.05); therefore, H1 and H2 are supported. Furthermore, OR (β = 0.039, t = 3.270, p < 0.01) and RB (β = 0.043, t = 3.752, p < 0.001) exhibit positive impacts on ABT; therefore, H5 and H6 are supported. Similarly, EF (β = 0.048, t = 5.970, p < 0.001) and JR (β = 0.044, t = 2.890, p < 0.01) also positively influence TBT; therefore, H7 and H8 are supported. To ensure the absence of collinearity issues, it is essential that the VIF values remain below the suggested threshold of 5, as proposed by Asghar, M. Z. et al. [35]. Notably, all VIF statistics met this criterion, indicating the absence of collinearity concerns.

5.4. Indirect Effects Test

The primary objective of this research is to furnish empirical evidence regarding the mediating roles of ABT and TBT in the relationship between motivation and knowledge-sharing behavior. In accordance with the recommendations of Than, S. T. et al. [1], we sought to verify both the magnitude and statistical significance of the indirect effects. This study used the bootstrap method of the Amos software version 26.0 for mediating effect testing. The sample size was set to 5000, and the confidence level of the interval was set to 95% (usually 90%, 95%, or 99%). The bias-corrected confidence interval was used as the standard, and its upper and lower limits were observed. When the bias-corrected confidence interval for indirect effects does not include zero, it indicates the existence of mediating effects. As illustrated in

Table 7. Indirect effects of the bootstrapping test.

Indirect Effects	Estimated Value	95%CI Lower	95%CI Upper	p	Conclusion (with or without Intermediary)
OR→ABT→KS	0.026	0.009	0.054	0.001 **	Yes
RB→ABT→KS	0.030	0.013	0.058	0.001 **	Yes
EF→TBT→KS	0.033	0.007	0.068	0.015 **	Yes
JR→TBT→KS	0.015	0.002	0.039	0.014 **	Yes

Notes: ** p < 0.01. OR, organization rewards; RB, reciprocal benefits; EF, expectation fulfillment; JR, job relevance; ABT, affect-based trust; TBT, technology-based trust; KS, knowledge sharing.

, all paths examined within the study yield significant p-values that fall well within the confines of the confidence interval. In detail, ABT has mediating effects in OR and KS (estimated value is 0.026, confidence interval is [0.009, 0.054], p < 0.01) as well as RB and KS (estimated value is 0.033, confidence interval is [0.013, 0.058], p < 0.01). TBT has mediating effects in EF and KS (estimated value is 0.033, confidence interval is [0.007, 0.068], p < 0.01), as well as JR and KS (estimated value is 0.015, confidence interval is [0.002, 0.039], p < 0.01).

5.5. Moderating Effects of Social Alienation

According to the research of Zhang, M. et al. [71], the moderating effect of SA on the correlation between ABT, TBT, and KS (H9 and H10) was tested by the method of two-group comparison of SEM via AMOS. First of all, as

Table 8. Grouping statistics.

Group	Mean	Number	Mean	S.D.	t-Test	p
Low SA	<3.6828	261	2.4933	0.60463	−34.263	0.000
High SA	>3.6828	223	5.0751	1.02679

Notes: SA, social alienation.

indicates, the sample was divided into two groups (mean SA = 3.6828) representing low social alienation (low SA < 3.6828) and social alienation (high SA > 3.6828). The sample number in the low SA group was 261 (N₁ = 261) and in the high SA group the sample number was 223 (N₂ = 223). As shown in Table 8, according to the independent samples t-test, there are significant differences in the SA scores between the low SA and high SA groups (mean low SA = 2.4933 vs. mean high SA = 5.0751, T = −34.263, p < 0.001), which means group differentiation has statistical significance. Then, an unconstrained multi-group structural equation model was employed to test our hypotheses. The statistics were calculated via the following formula [71]:

$$S_{pooled}=\sqrt{\{[(N_1-1)/(N_1+N_2-2)]\times S{E}_1^2+[(N_2-2)/(N_1+N_2-2)]\times S{E}_2^2\}}$$

$$t=({\beta }_1-{\beta }_2)/\left[S_{pooled}\times \sqrt{(1/N_1+1/N_2)}\right]$$

In detail, S_pooled is the pooled estimator of the variance, the t index is the t-statistic with (N₁ + N₂ − 2) degrees of freedom, N_i represents the sample size of the data set for group i, SE_i signifies the standard error of path for group i, and β_i is group i’s path coefficient in the structural model.

According to

Table 9. The results of moderating effect testing.

Social Alienation	N1	N2	β1	SE1	β2	SE2	Difference	Spooled	t
ABT→KS	261	223	0.421 ***	0.76	−0.447	0.71	0.468	0.737	6.967
TBT→KS			0.269 ***	0.77	−0.026	0.69	0.295	0.734	4.410

Notes: *** p < 0.001, ABT, affect-based trust; TBT, technology-based trust; KS, knowledge sharing.

, when an employee is in the low SA situation, the effect of ABT is important on KS (β₁ = 0.421, p < 0.001), but when an employee is in the high SA situation, ABT has no influence on KS (β₂ = −0.447, p > 0.05). Similarly, the impact of TBT becomes less important when the SA becomes high ((β₁ = 0.269, p < 0.001; β₂ = −0.026, p > 0.05, diff = 0.165, t = 2.362). This means that an employee’s ABT and TBT on their KS gradually weakened when the SA increased. Therefore, H9 and H10 are approved.

6. Discussion and Implications

6.1. General Discussion

This research has introduced a theoretical model that provides substantial evidence regarding the influential variables affecting employees’ knowledge sharing through ESM technology. This study delves into the realm of employee knowledge sharing with a focus on potential motivations and trust. The theoretical significance of this research arises from the amalgamation of social exchange theory and the UTAUT model to explore the influencing factors in the knowledge-sharing process. This model not only elucidates how employee motivations impact their trust, consequently influencing their knowledge-sharing behavior in the context of innovative technology, but also delineates the shift in employees’ trust levels in knowledge sharing when they experience social alienation. Furthermore, this model offers practical insights into how enterprises can encourage employees to share knowledge with their colleagues, emphasizing the role of potential motivations.

In our empirical analysis, first, we examined the direct impact of motivation on both ABT and TBT. The statistical findings indicate that OR and RB exert significant influences on ABT, with RB having a particularly strong impact on ABT. Similarly, TBT is influenced by the motivations of EF and JR, with EF emerging as the most influential variable in shaping TBT. These results suggest that rewards and reciprocal benefits play crucial roles in enhancing employees’ social and emotional support. Additionally, employees’ trust in technology depends on whether ESM fulfills their usability expectations and the fitness of task. This aligns with previous research, such as the utilization of ESM as a participant variable influencing employees’ interpersonal trust in the workplace [4]. Furthermore, studies by Koranteng, F. N. et al. [10] have highlighted the importance of elements like social interaction ties, reciprocity, and identification in the trust exchange process. Moreover, the usability of technology, including the ability to perform job-related tasks and the fulfillment of expectations, is closely associated with the establishment of users’ trust [34].

Secondly, in line with our RQ2, we investigated the potential mediating effects of ABT and TBT on the relationship between motivations and knowledge-sharing behavior. We began by confirming the significant positive impact of both ABT and TBT on employees’ knowledge-sharing behaviors, underscoring their crucial roles in the knowledge-sharing process. Subsequently, we examined the mediating roles of ABT and TBT, as presented in Table 7. The results indicate that ABT fully mediated the relationships between OR, RB, and KS. Similarly, TBT also played a significant mediating role between EF, JR, and KS. These findings underscore the importance of ABT and TBT as mediating mechanisms, aligning with prior research. For instance, Ng, K. Y. N. [54] has pointed out that ABT serves as a complementary mediator between organizational cultural elements and knowledge-sharing tendencies. Previous studies have also highlighted factors such as interaction frequency and citizenship behavior as predictors of ABT [56]. Additionally, empirical evidence from Jami Pour, M., and Taheri, F. [40] emphasized the mediating role of trust in knowledge sharing through social media.

Thirdly, in this study, we investigated the moderating effect of social alienation on the relationship between trust mechanisms and knowledge sharing. The results reveal that both ABT and TBT are progressively weakened when employees experience high levels of social alienation (SA). This suggests that SA leads to a decrease in employees’ trust in their colleagues and the technology, subsequently diminishing the occurrence of knowledge sharing in the workplace. This finding aligns with prior literature, which has consistently highlighted the negative influence of employees’ psychological issues such as SA on their knowledge-sharing behaviors [17]. Moreover, Zhang, G. et al. [65] noted that employees’ social abilities and motivations for harmony played pivotal roles in determining the level of social alienation they experienced. Thus, the results of this research reasonably illustrate the detrimental impact of social alienation on the relationship between trust and knowledge-sharing behavior.

6.2. Theoretical Contributions

First and foremost, the existing literature has predominantly concentrated on examining the predictors of enterprise social media (ESM) adoption, as well as the positive and negative impacts of ESM use on knowledge sharing within organizations [5]. This recent study also responds to the call for exploring the direct impact of motivations on knowledge sharing [23]. Building upon the distinctive features of ESM, we have integrated the theories of social exchange and the Unified Theory of Acceptance and Use of Technology (UTAUT) to specifically investigate the influence of motivations on trust mechanisms and knowledge sharing within the workplace. Furthermore, our research goes beyond the existing limited studies by introducing employees’ psychological states in knowledge sharing under the context of ESM, particularly focusing on different dimensions of trust and social obstacles. In doing so, we provide a novel perspective on the role of psychological elements in knowledge management.

Secondly, this research model is constructed to analyze the determinants, including motivations and trust, in the knowledge-sharing processes among organization members. In particular, it delves into the impact of different types of trust, namely affect-based trust (ABT) and technology-based trust (TBT), on knowledge sharing (KS) and explores their mediating role in the relationship between motivations (organization reward, reciprocal benefit, effort expectation, and job relevance) and KS. There has been little research on explaining the mediating variables of ABT and TBT. Previous studies have only demonstrated the positive impact like ESM applications and organizational culture on ABT ([4,54]). These prior studies have solely looked at the positive impact of trust as a holistic concept on KS [12] and have stated that increasing trust helps enterprises to realize knowledge-sharing strategies [2]. The empirical findings in this study shed new light on the theoretical understanding of how and why organization rewards (OR) and reciprocal benefits (RB) directly influence ABT, and how effort expectation (EF) and job relevance (JR) significantly affect TBT when employees engage in knowledge sharing through enterprise social media (ESM). Thus, this research not only contributes to the existing literature by examining the potential drivers of ABT and TBT but also by elucidating their roles and relationships in the context of knowledge sharing.

Moreover, this study offers valuable insights into understanding the changes in the knowledge-sharing process when employees experience social alienation within enterprises. While recent research has indicated that work alienation is often associated with factors such as physical distance in the work environment and educational levels [74], there is limited literature that has explored the influence of alienation on employees’ trust and knowledge-sharing behavior. Moreover, it is important to note that the recent research has only alluded to the negative impact of SA on KS and primarily regarded it as a mediating variable in the context of psychological ownership of personal knowledge and its relationship with KS [17]. The moderating role of SA in the relationship between trust mechanisms and KS remains an underexplored facet in the existing literature. Thus, the findings of this research demonstrate that social alienation (SA) can attenuate the impact of affect-based trust (ABT) and technology-based trust (TBT) on employees’ knowledge-sharing behavior. Particularly, when organization members’ psychological states reach high levels of social alienation, ABT and TBT no longer influence knowledge sharing in the workplace. Thus, this study contributes to the existing literature by revealing the moderating role of social alienation in the relationship between trust mechanisms and knowledge sharing, thereby providing a theoretical foundation for recognizing the significance of employees’ psychological factors in organization management.

6.3. Practical Implications

This study provides valuable practical implications and strategies for managers of small- and medium-sized enterprises (SMEs) seeking to enhance knowledge sharing among their organization members through enterprise social media (ESM). To begin with, in light of the substantial impacts of affect-based trust (ABT) and technology-based trust (TBT) on employees’ knowledge-sharing behaviors, enterprise managers should prioritize the establishment of trust mechanisms among their employees. The use of ESM can bring about trust-related challenges, such as the absence of face-to-face interactions or potential negative emotional reactions [4]. Given the positive influence of these two trust dimensions on KS, SMEs should consider implementing additional measures to facilitate both formal and informal social interactions and should emphasize the advantages of utilizing relevant technologies, such as ESM, within the workplace.

In more detail, organizations should take into consideration the influence of organization rewards (OR) and reciprocal benefits (RB) on affect-based trust (ABT). This implies that managers should establish a structured reward system to incentivize team members to share knowledge and information. Additionally, it is advisable to designate employees with expertise in various professional skills to encourage mutual learning within the team. Furthermore, it is recommended that organizations develop innovative and suitable technologies to engage employees in their use. This is based on the premise that the more functionalities a technology offers to meet users’ demands, the more readily it will be accepted. Therefore, we recommend that managers of small- and medium-sized enterprises invest in advanced technologies to achieve objectives such as information security, knowledge sharing, information transformation, and the enhancement of work and self-efficiency. In doing so, this study elaborates on strategies for sustainable development that align with the outcomes of different trust dimensions on knowledge sharing (KS).

Furthermore, the findings of this study indicate that social alienation (SA) negatively moderates the relationship between affect-based trust (ABT), technology-based trust (TBT), and knowledge sharing (KS). While enterprise social media (ESM) is primarily employed for knowledge sharing, storage, and retrieval, there is a lack of evidence on how to leverage it to foster new knowledge practices and to overcome knowledge barriers, particularly when organization members are grappling with situations of social alienation [9].

We recommend that managers take proactive steps to cultivate a workplace environment characterized by relaxation and warmth, thereby encouraging increased communication and interaction among employees. Embracing a human-centered management approach in the daily routine is vital to counteract social alienation. Moreover, managers should pay special attention to the control of knowledge content to boost individual engagement, ensuring that information is vivid and fluent. It is worth noting that employee engagement can be positively influenced by the quality of content, encompassing aspects of informativeness, relational appeal, and entertainment value. Conversely, engagement can be undermined by content that is overly serious and sensitive, causing discomfort [75]. In light of these findings, enterprises should emphasize the importance of humanistic care and understanding toward employees, fostering a sense of personal belonging and connection [17]. This approach is essential in mitigating the adverse effects of social alienation and nurturing a conducive environment for knowledge sharing and collaboration.

7. Limitations and Future Research

This study acknowledges several limitations that can guide future sustainable research. Firstly, the study primarily focused on two dimensions of trust mechanisms, i.e., affect-based trust (ABT) and technology-based trust (TBT), as mediating variables to explore the factors influencing knowledge sharing (KS). However, there exist various other elements such as psychological factors, information management-related variables, and technology-related aspects, which could also significantly impact KS. These unexplored variables present promising avenues for future research as potential mediators or moderators of the relationship between motivations, trust mechanisms, and KS. Furthermore, this study’s focus was limited to small- and medium-sized enterprises in China, which may restrict the generalizability and practical applicability of the findings to other regions, markets, and big enterprise’s knowledge management. To enhance the external validity of research outcomes, it is advisable to expand the sample size and incorporate a more diverse range of regional and organizational types. Lastly, this study was grounded in social exchange theory and the UTAUT model. To deepen the understanding of KS within organizations, future research endeavors should consider incorporating specific dimensions and variables that may provide a more comprehensive view of the knowledge-sharing process. This expanded perspective could further enrich the existing body of knowledge on KS.

8. Conclusions

Building upon the foundational principles of social exchange theory and the unified theory of acceptance and use of technology (UTAUT) model, this study introduces an extended model aimed at investigating the factors influencing employees’ knowledge-sharing behaviors within a professional setting, facilitated by enterprise social media (ESM). This research extends beyond the existing body of knowledge on knowledge sharing by closely examining two dimensions of trust, namely affect-based trust (ABT) and technology-based trust (TBT), while considering the moderating role of social alienation in shaping the knowledge-sharing process. A quantitative approach was adopted, utilizing a questionnaire-based survey distributed across seven SMEs in China, encompassing three retail enterprises and four information technology (IT) enterprises. The study garnered valid responses from 484 employees, providing the empirical basis for its findings. The results of this study indicate that employees’ motivations, particularly organization rewards (OR) and reciprocal benefits (RB), exhibit a positive influence on knowledge sharing (KS) through the mediating effect of ABT. Similarly, the motivations of extrinsic factors (EF) and intrinsic rewards (JR) exert a favorable impact on KS through the mediating element of TBT. Furthermore, social alienation (SA) is identified as a pivotal factor with a moderating influence on the relationships among these variables. Notably, the influence of ABT and TBT on KS is attenuated as employees perceive higher levels of SA, with these dimensions having no significant impact on KS in situations characterized by elevated SA. In this paper, we delve into the managerial and practical implications of knowledge sharing, highlighting the significance of the variables under investigation. We also present the study’s limitations and suggests potential avenues for future research.