Assessing the Influence of the Knowledge Management Cycle on Job Satisfaction and Organizational Culture Considering the Interplay of Employee Engagement

Abstract

This study leveraged the social exchange theory to explore the influence of the knowledge management cycle on employee engagement, job satisfaction, and organizational culture within the Ghana Highway Authority (GHA). The structural equation modeling (SEM) software (AMOS version 23) was employed to analyze data from 300 GHA employees. The study used snowball sampling and a descriptive cross-sectional methodology to collect data through printed and electronic questionnaires. The findings demonstrated a substantial and positive impact of the knowledge management cycle on employee engagement, job satisfaction, and organizational commitment. Employee engagement also exhibited a significant and positive influence on both job satisfaction and organizational commitment. Furthermore, employee engagement partially mediated the relationships between the knowledge management cycle, job satisfaction, and organizational commitment. This study’s findings underscore the practical implications of allocating resources to knowledge management activities. Such allocation can promote organizational culture and employee satisfaction, enabling firms to achieve real gains.

1. Introduction

The Ghana Highway Authority (GHA) holds a pivotal role in Ghana’s infrastructure and economic growth [1] as it is responsible for the maintenance and development of the country’s road network. The effectiveness of the GHA can be significantly enhanced through the strategic implementation of knowledge management cycle (KMC), organizational culture (OC), job satisfaction (JS), and employee engagement (EE) strategies. Robust knowledge management (KM) practices, for instance, can improve decision making and resource allocation, leading to enhanced operational efficiency and project outcomes [2]. A positive OC fosters collaboration, innovation, and employee well-being, all of which are crucial for delivering high-quality infrastructure projects [3]. Prioritizing JS among GHA employees can boost motivation, productivity, and performance [4]. Engaging employees through open communication and empowerment can drive continuous improvement and impactful outcomes [5]. By focusing on these areas, the GHA can optimize its operations and contribute more effectively to Ghana’s development and regional connectivity.

In the context of the GHA, JS takes on a unique significance. It encompasses an individual’s personal assessment of their work environment [6], including factors such as job stability, salary, connections with co-workers and superiors, growth prospects, and the meaningfulness of the work itself [7]. The GHA recognizes the significant influence of promoting JS, as it is directly linked to productivity, innovation, and employee loyalty [8]. Content employees in the GHA play a vital role in fostering a positive workplace culture defined by trust, teamwork, and a common purpose [9]. However, employees in the GHA also encounter distinct obstacles in their positions, emphasizing the importance of identifying and addressing issues that impact their JS [10]. This is crucial for enhancing organizational performance and achieving the GHA’s goal of promoting economic development and societal advancement.

OC is the collective set of values, attitudes, and behaviors that shape how individuals inside an organization cooperate and make decisions [11]. OC has a crucial impact on changing employee attitudes and behaviors, affecting their JS and the firm’s overall effectiveness [12]. The GHA must clearly understand its existing OC to pinpoint areas needing development and cultivate a pleasant and supportive atmosphere. Furthermore, KM is increasingly important for organizations like the GHA operating in an economy reliant on knowledge. Effective KM strategies enable organizations to leverage expertise, improve operational efficiency, and promote innovation [13]. In the GHA, where expertise in transportation infrastructure design and management is crucial, KM plays a significant role in ensuring the quality and safety of road networks [14].

The relationship between JS, OC, and KM has been extensively studied by researchers such as [15,16,17,18]. These studies, while comprehensive, have not fully explored the influence of the KMC on JS and OC. Nevertheless, the current research highlights the need for more investigation into these mechanisms by indicating that higher engagement levels might promote a better OC and boost JS [19,20]. Furthermore, the mediating role of EE in this relationship remains underexplored in the current research. Addressing these gaps can provide valuable insights into optimizing workplace dynamics and enhancing organizational outcomes, making this study a significant contribution to the field.

This study aims to investigate several key questions to fill these gaps: What is the direct influence of the KMC on JS and OC? What is the mediating role of EE in this relationship? Additionally, what is the direct influence of EE on both JS and OC? Through empirical investigation at the GHA, this study seeks to contribute to the literature in the following ways: First, by empirically investigating the influence of the KMC on JS and OC, this study aims to provide concrete evidence and insights into specific stages that impact these outcomes. In order to improve their productivity, creativity, and overall performance in the current competitive market, firms must recognize the crucial relationship between EE and information sharing. The former fosters positive workplace environments, while the latter enhances EE.

Second, by examining the mediating role of EE in translating KMC into tangible outcomes related to JS and OC. Effective mediation is crucial for organizations aiming to improve employee motivation, foster a favorable OC, and increase JS. Consequently, this increases cooperation, creativity, and efficiency, promoting sustained success and competitiveness in a fast-changing business landscape.

Third, by exploring the direct effect of EE on JS and OC, this study aims to provide valuable insights into these critical workplace dynamics. Engaged workers are directly associated with favorable workplace dynamics, JS, and the ability to innovate, boost productivity, and improve overall performance. Interdependence is crucial for ensuring sustainability and achieving long-term growth.

Studying how the KMC impacts JS and OC can optimize knowledge utilization, enhancing JS, productivity, and overall effectiveness. Insights from this study, when applied to the GHA, can inform strategic decision making, enabling the organization to align KM with the desired culture and engagement. Positive JS and OC, as a result, can support employee retention and engagement, fostering innovation and continuous improvement, thereby significantly impacting the GHA’s operations.

2. Literature Review

2.1. Theoretical Background of the Study

When examining how the KMC affects JS and OC, it is crucial to consider the theoretical frameworks that explain these connections. The study utilizes the social exchange theory (SET) to clarify how EE mediates the relationship between KMC techniques and organizational outcomes.

The Self-Determination Theory (SDT), a commonly included theory in comparable studies, underscores the importance of intrinsic motivation in fostering EE, JS, and alignment with OC [21]. Still, one problem with SDT is that it focuses too much on internal psychological needs, which means it might miss the importance of outside factors like resource transfers and reciprocal relationships that are very important to how the workplace works. When comparing SDT and SET, it is evident that SET provides a more comprehensive explanation of how mutual obligations, trust, and reciprocity influence employee behavior. Hence, SET is better suited for understanding how the KMC impacts JS and OC through EE. SET acknowledges the interactive nature of employee–employer relationships, which is crucial in promoting engagement and satisfaction within a particular OC.

The SET suggests that individuals participate in relationships and interactions based on their perception of the balance between the benefits and drawbacks [22]. In the context of KM in businesses, employees are more likely to engage in knowledge-sharing activities if they perceive significant advantages, such as recognition, skill development opportunities, and prospects for career progression. When firms effectively manage knowledge by providing employees access to helpful information and fostering a culture prioritizing ongoing learning and development, employees are not only more inclined to participate in knowledge-sharing activities but also contribute to the overall growth and success of the organization [23].

Engaged personnel play a crucial role in fostering a healthy OC. They inherently seek to utilize their expertise and abilities, expecting mutual benefits from the organization. This mutual exchange often manifests as increased JS and a supportive work environment. A supportive environment that is advantageous to OC facilitates collaboration and knowledge sharing among employees [24].

From the social exchange theory perspective, the KMC establishes a mutually beneficial relationship between employees and the organization. It is the organization’s responsibility to strengthen and promote a culture of continuous learning and innovation by creating an environment that values and incentivizes information sharing [25]. Emphasizing KM enhances organizational efficiency and leads to long-term success by cultivating a committed and motivated workforce that shares the firm’s objectives and principles.

2.2. Knowledge Management Cycle

KM involves recognizing and leveraging the collective information within an organization to enhance its competitive advantage. The body of research on KM classifies KMC in various ways. Nonaka et al. [26] categorize KMC into three main areas: knowledge creation, incorporation, and distribution. Demarest [27] identifies four KMCs: knowledge construction, embodiment, dissemination, and usage. Alavi and Leidner [28] examine cycles such as knowledge production, storage and retrieval, transfer, and application. Additionally, KM involves the infrastructure, capabilities, and management activities that facilitate and improve these knowledge processes [29]. Overall, the literature commonly recognizes four to six interconnected knowledge processes that occur in a cyclical manner [26,27,28].

This study aims to investigate the potential impact of the KMC framework proposed by [30] on JS, OC, and EE in a manner similar to the aforementioned viewpoints. Sağsan [30] defines the KMC as a series of five consecutive steps: knowledge creation, sharing, structuring, utilization, and auditing. According to Sağsan [30], KMC begins with knowledge creation, where novel knowledge is generated through methods like research, innovation, or collaboration. Subsequently, techniques such as documentation, databases, or digital archives capture and preserve this knowledge for future utilization. The next stage involves organizing the accumulated knowledge systematically to improve the retrieval and distribution processes.

Once organized, knowledge is shared among the relevant stakeholders through channels like training sessions, conferences, or the internet. Sharing knowledge fosters teamwork and learning, allowing individuals to benefit from diverse perspectives and insights. Subsequently, practical contexts apply knowledge to address issues, make decisions, or enhance procedures within the organization.

After knowledge is put to use, it is assessed to evaluate its effectiveness and impact on organizational goals and outcomes. This assessment identifies areas for improvement and provides insights for future KM endeavors. Ultimately, the cycle concludes with knowledge refinement, where the insights gained from the evaluation are used to enhance the existing information and procedures, perpetually improving the organization’s KM methods.

2.3. Job Satisfaction

JS is a concept that has attracted significant attention in management, social psychology, and academic practice. It is a multi-faceted construct, with researchers offering various definitions. It is the favorable and enjoyable emotional state individuals experience towards their employment. Kalleberg [31] characterized JS as an employee’s attitude towards their job, evaluating their happiness or dissatisfaction across different job aspects. Dorta-Afonso et al. [32] described JS as employees’ comprehensive attitude towards their work, reflecting contentment or joy in their occupation.

JS is not just a theoretical concept, but a critical factor that directly influences employee productivity, motivation, and engagement. Research has shown that increased JS leads to higher productivity and motivation [33], thereby improving work performance. Moreover, JS can significantly enhance EE, resulting in better customer service and overall organizational success [34]. To foster a satisfied workforce, organizations need to grasp employees’ priorities and create supportive work environments that promote appreciation [35].

JS is not just a concept for academic discussion, but a practical metric used by various stakeholders. Employees use it to assess their satisfaction and the rewarding aspects of their work [35], while employers leverage it to evaluate performance and predict employee retention. Researchers, on the other hand, utilize JS to gain insights into employee attitudes and their impact on productivity [33].

2.4. Organizational Culture

OC is widely studied across various disciplines, such as anthropology, sociology, and management sciences. Schein [36] defines it as acquired behaviors that shape how an organization perceives itself and its environment. This includes formal elements like leadership and structure and informal aspects such as behavioral norms, values, and organizational mythology, which could be stories about the company’s founding or key figures [20].

Contemporary definitions by [37] bring to light the importance of values, leadership styles, language, and symbols in OC. However, despite the scholarly efforts of [38] and others to develop integrative frameworks, a comprehensive theory of OC remains elusive, sparking ongoing debates. Alvesson [38] categorizes eight metaphors representing different perspectives on corporate culture, from ‘exchange regulator’ to ‘world closure’.

Building a robust OC necessitates consistent communication, active listening to employee concerns, providing valuable feedback, and maintaining situational consistency [39]. The commitment and long-term focus of leaders can facilitate cultural change efforts despite the considerable investment required. Establishing a strong OC from the outset reaps enduring benefits and fosters economic growth over time [36].

2.5. Employee Engagement

EE has become a prominent focus in human resource (HR) consulting and academic studies, although interpretations vary. It is often praised for its potential to drive positive financial outcomes by boosting productivity, sales, and employee retention, thereby contributing to profitability. According to [40], engagement occurs when employees fully commit to their roles, displaying physical, cognitive, and emotional involvement. Fait et al. [41] define it as a deep emotional and intellectual dedication to the organization, exemplified by activities like ‘Say, Stay, and Strive’ [42].

Researchers like [43] characterize engagement as the mental state accompanying the personal investment of energy, while [44] add a spiritual dimension, describing it as captivating employees’ minds, emotions, and inner beings. Gallup [45] classifies engagement into three categories—engaged, not engaged, and actively disengaged—each impacting organizational performance differently. Saks [46] expands engagement to encompass job and organizational aspects influenced by job features, perceived support, gratification, and fairness.

Engagement outcomes include work satisfaction, commitment, turnover intention, and organizational citizenship behavior [47]. Studies suggest that increased engagement substantially reduces turnover intention [47,48]. Joshi and Sodhi [49] identify six critical management practices influencing executives’ engagement: job nature, salary, work–life balance, upper-level interactions, career progression, and team dynamics. This study aims to comprehensively analyze the factors influencing EE to develop a robust model.

2.6. Hypothesis Development

2.6.1. Influence of KMC on JS

KM ensures that employees are equipped with the necessary information, tools, and resources to fulfill their job responsibilities efficiently [23]. This finding was based on a comprehensive study that analyzed the impact of KM on the various aspects of job performance. When employees have convenient access to knowledge and expertise within the organization, they experience greater empowerment and capability, contributing to JS. An effective KM system promotes continuous learning and skill enhancement. This was demonstrated in a study by [50], which found that employees who have opportunities to acquire additional information and skills are more likely to feel engaged and satisfied with their jobs, especially when they perceive these opportunities as beneficial for their professional development.

Implementing KM techniques that prioritize and recognize employees’ contributions to knowledge sharing and collaboration can significantly improve JS [51]. When individuals are not just acknowledged but truly valued for their expertise and contributions to organizational knowledge, their JS is positively impacted. KM not only enhances problem solving and decision making but also provides access to valuable insights and lessons learned from previous experiences [52]. Having a reliable knowledge base for employees to rely on when encountering challenges and making decisions is a practical benefit of KM that promotes JS by reducing frustration and boosting confidence in their abilities.

Knowledge sharing, a key aspect of KM, not only fosters collaboration and teamwork among employees but also promotes a sense of camaraderie and accomplishment. When individuals can easily collaborate with colleagues and leverage collective knowledge to achieve common goals, it fosters a sense of belonging and accomplishment, ultimately contributing to JS [53]. Sivagnanam et al. [54] investigated utilizing KM processes, infrastructure, and system quality as effective solutions to address the challenges posed by the COVID-19 pandemic. Their findings suggest a positive correlation between KM processes and both performance and EE, highlighting the social benefits of KM in addition to its impact on JS.

Li et al. [55] conducted a study on the effects of remote auditing on audit quality, audit efficiency, and auditors’ JS. Their findings demonstrate that remote work significantly enhances audit efficiency. The competence of auditors in managing flexibility contributes to the quality and efficiency of remote audits. Additionally, audit efficiency and auditors’ JS during remote audits positively correlate with a physical work environment that supports a focus on audit duties. Based on these discussions, hypotheses were formulated as follows:

H1a. 

KMC significantly and positively influences JS.

2.6.2. Influence of KMC on OC

OC enables the effective implementation of KMC, prioritizing sharing, collaboration, and continuous learning and contributing to highly valued innovation and adaptation [56]. The promotion of knowledge and idea-sharing among employees cultivates a culture that not only nurtures creativity and innovation but also sparks excitement for new ideas and solutions. Efficient KM fosters transparent communication and collaboration among teams and departments, creating a culture of openness, teamwork, and mutual assistance [57].

KMC can empower employees by strengthening organizational values and norms through knowledge sharing, valuing expertise, and committing to continuous learning. This transformative influence on the entire OC is a testament to the power of KM. Techniques that enable employees to access knowledge and resources foster a culture of trust and empowerment [23]. When employees feel appreciated and confident, it positively impacts the organizational climate, inspiring a sense of personal and professional growth.

KM facilitates effective problem solving and decision making by leveraging collective expertise and insights [52]. This promotes a culture of data-driven decision making and proactive problem solving. However, it is important to note that implementing KM practices can be challenging, especially in organizations with a hierarchical structure or a culture that does not prioritize knowledge sharing. Organizations with strong KMCs are better equipped to adapt to change and respond to evolving market conditions, fostering a culture of flexibility, resilience, and continuous improvement [58].

KM is essential for ensuring an OC aligns with its strategic goals and values [29]. By organizing and sharing knowledge, KM programs strengthen the cultural norms that align with the organization’s goals, vision, and core values. This alignment improves EE, dedication, and overall organizational success [59]. The role of leadership in fostering a KM-driven culture is crucial. Alavi and Leidner [28] found that KM initiatives substantially impact OC by encouraging knowledge exchange and creating a collaborative work environment. When firms adopt KM systems and methods, they often establish cultural norms prioritizing knowledge sharing, collaboration, and continuous learning.

Research by [60] highlights the significance of information generation and sharing in influencing OC. Advocates argue that firms with robust KM practices are likelier to foster cultures that promote transparency, confidence, and creativity [57]. The increased interdepartmental and hierarchical collaboration among employees fosters a culture characterized by shared knowledge and collective intelligence [61], instilling a sense of trust in the reliability and credibility of the information shared. Additionally, the concept that OC plays a crucial role in determining the success of KM initiatives in a company is substantiated by empirical findings that indicate that it influences the effectiveness of KMC in fostering learning and adaptability [62]. By implementing KM strategies, businesses can gather and utilize knowledge, enhancing their ability to adapt to internal and external changes. This flexibility fosters a culture of creativity and adaptability, encouraging experimentation, learning from mistakes, and continuous process improvement.

Li et al.’s [63] empirical study, conducted in the manufacturing industry, shows that OC significantly and favorably influences KM. The available empirical research demonstrates that OC plays a crucial and advantageous role in KM, emphasizing its ability to promote innovation and facilitate the exchange of information. These studies’ limited generalizability is attributed to their tendency to concentrate on specific industries or geographic regions. Furthermore, studies on how distinct cultural elements in different organizational settings impact the KMC are scarce.

Similarly, Tang [64]’s research, which focused on healthcare organizations, examined the impact of KM on OC and its effectiveness in medicine and health sciences, demonstrating a strong and positive relationship between KM and OC. These discussions warrant the formulation of the following hypothesis:

H1b. 

KMC significantly and positively influences OC.

2.6.3. Influence of the KMC on EE

An effectively executed KMC ensures that personnel have access to pertinent information, resources, and skills. This access enables employees to carry out their tasks more efficiently, potentially enhancing their level of involvement. KM approaches prioritizing ongoing learning and development allow employees to improve their skills and knowledge [65]. When employees see that they receive assistance and encouragement in their professional development, they are more inclined to be actively involved and dedicated to their responsibilities.

Efficient knowledge dissemination not only promotes cooperation and synergy among staff members but also fosters a culture of recognition and appreciation. Encouraging employees to work together on projects and exchange knowledge between teams fosters a feeling of inclusion and shared success, boosting EE [66]. A KM culture that appreciates and acknowledges employees’ efforts in sharing knowledge and fostering innovation can enhance EE [57]. When employees experience a sense of recognition and gratitude for their contributions, they are more inclined to participate and be driven actively.

Implementing KM strategies that enable employees to make decisions and contribute ideas can enhance their sense of autonomy and ownership. Empowered employees demonstrate higher levels of engagement and commitment to their work. Effective KM not only enables employees to understand how their work directly contributes to the organization’s goals and objectives but also fosters a culture of open feedback and communication [16,29]. This alignment has the potential to enhance motivation and foster greater involvement. KMC promotes open feedback and communication channels to foster conversations between employees and management. When employees see that their opinions and contributions are not only acknowledged but also actively sought, it positively affects their level of involvement and commitment.

Research indicates that successful KM programs positively influence EE by promoting a culture of cooperation, learning, and empowerment [28]. This indicates that effective KM programs facilitate EE by promoting self-determination, education, and collaboration. Nevertheless, the study often fails to consider the degree to which these impacts differ among various industries and sizes of organizations, thereby limiting their applicability to a broader context. Furthermore, there is a paucity of studies that specifically examine the long-term viability of these projects. When firms use KM systems and promote information-sharing behaviors, employees experience increased engagement with their work and the organization [51]. KM approaches promote the exchange of knowledge among employees, resulting in enhanced collaboration and involvement [26]. KM activities facilitate sharing expertise and best practices, allowing employees to contribute valuable contributions toward organizational goals [67]. This enhances their sense of purpose and engagement.

Rahman [68] examined how KM might improve EE. The results indicated that the use of KM can effectively improve EE. This finding has significant implications for organizations, suggesting that implementing KM strategies can lead to a more engaged workforce. Naim et al. [69] conducted a study investigating the correlation between KM variables and EE. The study’s findings indicated that all the variables analyzed, including KM, could benefit EE. This suggests that organizations can enhance EE by incorporating KM principles into their management practices.

Chantsaldulam [70] studied the impact of KM on EE on JS among public officers. The findings suggest that EE, OC, and knowledge management positively impact JS. Unfortunately, most research is tailored to specific industries, restricting its application scope. Furthermore, the understanding of the interaction between these components over time is still limited, which prompts the inquiry of how these elements will ultimately influence the business’s effectiveness and its employees’ contentment.

Based on the discussion above, we can conclude that effective KM can lead to increased EE, which, in turn, can enhance JS. This hypothesis provides a clear direction for future research in this area.

H1c. 

KMC significantly and positively influences EE.

2.6.4. Influence of EE on JS

Engaged employees often find a greater sense of purpose and significance in their work, leading to increased JS [35]. They are not just workers, but individuals who strongly connect to the organization’s mission and values, finding happiness in their roles. Their active involvement and commitment play a significant role in establishing a favorable work atmosphere defined by teamwork, assistance, and reciprocal admiration [71]. An inclusive work environment improves JS by cultivating a feeling of camaraderie and overall wellness. Engaged individuals frequently encounter a feeling of independence and authority in their jobs. Organizations that empower employees to make decisions and assume responsibility for tasks contribute to increased JS levels [34].

Engaged employees are more inclined to actively pursue and take advantage of opportunities for personal and professional growth and advancement within their current positions. According to [24], the employees’ perception of advancement and learning positively impacts their JS. Committed personnel highly regard acknowledgment and gratitude for their contributions. When employees are not just acknowledged, but truly recognized for their efforts and accomplishments, their JS and positive behaviors are significantly boosted. Employees who are engaged in their work tend to develop strong relationships with their colleagues and supervisors, forming a supportive network inside the workplace [66]. Positive social relationships enhance JS by cultivating a feeling of solidarity and promoting effective teamwork.

Committed employees are dedicated to accomplishing the goals and objectives of the organization. When employees see that their contributions positively impact the organization’s achievements, it enhances JS and motivation [43]. Engaged employees frequently place high importance on achieving a balance between their job and personal life and prioritizing their overall well-being. Organizations that not only promote but actively support work–life balance programs enhance employees’ JS. Work engagement is characterized by enthusiasm and focus, which enable employees to utilize their maximum capabilities and improve the quality of their primary job duties [40].

Leal-Rodríguez et al. [39] assert that EE has a positive impact on productivity, overall performance, and work atmosphere and minimizes absenteeism and turnover. Engaged employees want effective communication with their superiors, personally meaningful and inspiring work, and a secure working environment. When these circumstances are met, employees become actively involved and achieve improved financial outcomes, exhibit pride in their firms, and display enthusiasm [39].

Saks [46] conducted research with a sample size of 500 employees from various industries, suggesting that work engagement is an intermediary between the variables that impact JS and EE. Nevertheless, research often focuses on particular user groups or organizations, restricting its applicability to a broader population. Moreover, further investigation is necessary to thoroughly comprehend the enduring consequences of job engagement and its many impacts in distinct organizational settings.

Saks [46] has verified that JS is a mediator in the connection between job engagement and organizational success. Saks [72] reviewed the research by [46] and found evidence supporting the notion that job engagement is a predictor of JS. According to [73], job engagement has a favorable and significant impact on JS. Therefore, we formulated the following hypothesis:

H2a. 

EE significantly and positively influences job satisfaction.

2.6.5. Influence of EE on OC

EE has not just become a crucial issue, but a necessity for firms in today’s fast-changing corporate environment. Committed, motivated, and enthusiastic personnel are not just beneficial but essential for enhancing productivity, fostering innovation, and promoting growth within firms [74].

While previous empirical studies [75,76] have mainly examined how OC affects EE, this study takes a different direction by investigating the reverse relationship, with a particular emphasis on how EE influences OC. Previous research has provided insights into the positive outcomes linked to engaged employees, such as increased productivity, greater customer satisfaction, and reduced turnover rates [77]. However, it is critical to thoroughly understand how EE influences OC.

Engaged personnel improve an organization’s performance by understanding and embodying its expectations. Actively engaging in organizational tasks promotes a sense of commitment and efficiency, resulting in reduced employee turnover and a happier workforce [43]. Academic researchers and professionals in the field of organizational behavior and human resources play a crucial role in shaping the OC. An affirmative and encouraging culture amplifies involvement, but a detrimental or poisonous culture can hinder it [78]. Cultivating a culture of EE necessitates deliberate efforts to ensure that employees feel valued and supported. Effective communication, employee recognition programs, the promotion of work–life balance, and leadership development activities are vital elements in establishing an engaged workforce [9].

Studies provide evidence of the significant influence of OC on EE and organizational outcomes. The Culture Factor [79] presented research findings from the Harvard Business Review and Deloitte, which showed that organizations with favorable cultures had lower employee turnover rates and considerably higher revenue growth. Moreover, Deloitte’s research revealed that organizations that engaged their employees in decision making had greater revenue and enhanced efficiency. From the ongoing discussions among our research team, which included a review of these studies and our own observations, we developed the following hypothesis:

H2b. 

EE significantly and positively influences OC.

2.6.6. Mediating Role of EE in the Relationship between the KMC and Both JS and OC

The KMC encompasses knowledge generation, retention, dissemination, and utilization. Engaged employees, empowered by their active participation in knowledge-sharing activities, make valuable contributions with their ideas and experience [69]. This empowerment cultivates a feeling of participation and responsibility, which favorably influences both JS and OC. Employees actively involved in KM activities tend to have higher levels of JS. These individuals find fulfillment in the chance to make significant contributions to the objectives of the business by sharing and applying their knowledge [80]. This sense of contribution promotes JS by linking individual efforts with company objectives.

EE influences OC and JS by fostering cooperation, creativity, and a growth-oriented attitude. Engaged personnel prioritize ongoing enhancement and actively pursue opportunities to disseminate and implement knowledge [24]. This behavior fosters a culture that places importance on learning, adaptation, and the application of information. EE implemented in KM facilitates enhanced communication and collaboration among different teams and departments. Employees who are actively engaged in their work increase the sharing of knowledge, break down barriers between departments, and encourage collaboration across different functions, positively affecting OC [25].

According to ref. [81], engaged employees experience a strong sense of purpose and alignment with company goals as a result of their participation in KM. This alignment strengthens the impact of JS by emphasizing the importance of individual contributions to the business’s success and promoting a positive OC centered on shared goals. Importantly, employees who are actively engaged in their work provide valuable input on KM procedures and practices. This feedback loop not only helps drive ongoing improvement but also enhances OC by fostering transparency, responsiveness, and adaptation.

Employees who are actively engaged in their work, have a sense of purpose, are given independence, and have opportunities to improve and progress tend to express greater JS and make positive contributions to the corporate culture [82]. Therefore, we formulated the following hypothesis from the above discussion:

H3a. 

EE mediates the relationship between KMC and job satisfaction.

H3b. 

EE mediates the relationship between KMC and OC.

From the literature review and hypothesis development, the research model was developed (Figure 1).

3. Materials and Methods

3.1. Research Design and Strategy

The research design, meticulously crafted, lays out the procedures for gathering and analyzing data to achieve the study goals [83,84]. This research used a descriptive cross-sectional approach which captures information about variables and their connections at a specific point in time [85]. The goal was to provide a comprehensive picture of the research population’s features by gaining insights into the current variables and relationships.

One highly effective way to approach a research challenge and achieve research objectives is through a research strategy. The researchers in this study employed standardized questionnaires, a proven method, to collect data from a sample. The goal was to gather quantitative data from a large sample promptly to learn how people felt and acted about the study’s subject [86].

3.2. Population of the Study

The study specifically targeted the personnel of the GHA, a governmental organization in southern Ghana tasked with constructing and managing the country’s road infrastructure. This organization plays a crucial role in ensuring secure and effective transportation throughout Ghana, which significantly influences the country’s development and economic expansion [1]. The research had a clear objective of addressing the practical challenges faced by employees, achieved by analyzing the impact of the KMC on JS and OC. The study aimed to identify effective techniques for enhancing working conditions, improving employee morale, and increasing productivity within the GHA. The study aimed to provide practical insights that could be implemented in the workplace to bring about tangible improvements.

3.3. Sample Size

The total number of employees at the GHA in southern Ghana was unknown. We employed the [87] method in this study to determine the sample size to ensure it was representative and adequate. The Cochran formula is expressed as follows:

$$n={\displaystyle \frac{Z^2}{{4e}^2}}$$

“n” is the required sample size;

“Z” is the Z-score corresponding to the desired confidence level;

“e” is the desired margin of error.

$$n={\displaystyle \frac{{1.96}^2}{{4\left(0.05\right)}^2}}$$

$$n={\displaystyle \frac{3.8416}{0.01}}$$

$$n=384$$

Out of the 384 questionnaires distributed, 300 were deemed suitable for analysis, ensuring the data’s quality and dependability. Incomplete and invalid responses were excluded to maintain data integrity for rigorous statistical analysis. Focusing on these 300 responses allowed us to conduct a thorough analysis, optimizing the use of resources and ensuring reliable results. The response rate for our survey was 78.13%, calculated from the 300 valid responses obtained out of the 384 questionnaires administered.

3.4. Sampling Technique

We gathered the data using physical and electronic questionnaires, and the electronic questionnaire was designed using Google Forms. To overcome geographical obstacles in reaching all the GHA employees in the southern region, we utilized a snowball sampling technique. Snowball sampling engenders biases, including sample homogeneity and an overdependence on social networks that may not adequately reflect the general population. To address these biases, we used a wide range of referral sources to improve the representativeness of the sample. We also used both purposive and snowball selection methods to guarantee a diverse range of viewpoints.

Initially, we distributed physical questionnaires to those who were easily accessible, and they subsequently shared the electronic survey link with their co-workers. We iteratively repeated this approach, increasing our sample size and achieving a more comprehensive representation of the personnel throughout the region.

3.5. Measures and Scales

We employed [30] KMC, encompassing the creation, sharing, structuring, use, and auditing stages, to guide our analysis of knowledge practices at the GHA. These steps were chosen to comprehensively assess KMC within the organization, examining how knowledge about JS and OC is generated, shared, organized, utilized, and evaluated. This approach enabled us to explore the KMC and its influence on critical outcomes within the organization.

The questionnaire items for knowledge creation (6 items) were adapted from [88], knowledge sharing (6 items) were sourced from [89], knowledge structuring (6 items) were based on [90]’s framework, knowledge using (6 items) were derived from [90], and knowledge auditing (6 items) were obtained from [91].

EE was assessed using a nine-item scale based on [92]. JS was measured using a five-item scale from [93]. OC was evaluated using a seven-item scale from [94]. These instruments (refer Appendix A) employed a 5-point Likert scale adapted from [95], where the respondents rated their agreement on a scale from 1 (strongly disagree) to 5 (strongly agree).

3.6. Data Analysis

We analyzed the participants’ collected data using structural equation modeling (SEM) in AMOS version 23. Cronbach’s alpha (α) and composite reliability (CR), along with the average variance extracted (AVE) test and standardized loadings, were utilized to assess the model’s reliability and consistency. A confirmatory factor analysis was conducted, and all the standard loadings below the minimum value of 0.6 were removed. Following [96]’s recommendation, the items with standard loadings below 0.6 were excluded from the confirmatory factor analysis (CFA). This process removed the items associated with knowledge auditing (AUD4, AUD5, and AUD6), organizational culture (ORC5, ORC6, and ORC7), and employee engagement (EPE8 and EPE9). By focusing on the items demonstrating strong factor loadings above 0.6, the reliability and validity of the measurement model were enhanced. This approach ensures that only the most reliable indicators for the constructs under investigation are retained, contributing to the accuracy and robustness of the subsequent SEM analysis.

4. Results and Discussion

4.1. Descriptive Statistics

Table 1. Descriptive statistics and matrix correlation.

Variables	N	Descriptive Statistics		Matrix Correlation
		Mean	Std. Deviation	1	2	3	4
(1) KMC	300	4.5494	0.41010	1
(2) JS	300	4.6300	0.49048	0.556 **	1
(3) OC	300	3.4067	1.11880	0.018	0.012	1
(4) EE	300	3.1562	1.08462	0.054	0.011	0.285	1

** correlation is significant at the 0.01 level (2-tailed).

displays the descriptive statistics and a correlation matrix for the studied variables. The high mean score for KMC suggests that the participants perceive effective organizational KM with good access to information and resources. The favorable mean score for JS indicates that the participants generally experience high JS, likely influenced by positive KM practices. Conversely, lower mean scores for OC and EE suggest less favorable perceptions of OC and EE, potentially indicating leadership, communication, and empowerment challenges.

The study followed the approach of previous research by [97,98,99,100] who utilized correlation matrix analysis to assess multicollinearity. They considered multicollinearity an issue if the correlation coefficient between independent variables exceeded 0.70. The findings of this study indicate that there is no multicollinearity concern, as the correlation coefficient between KMC and EE is not greater than 0.70.

Table 2. Assessment of the measurement model fitness.

Fit Indices	Recommended Value	Obtained Value
CMIN/df	3–5	3.457
GFI	>0.90	0.918
CFI	>0.90	0.953
TLI	>0.90	0.911
SRMR	<0.08	0.058
RMSEA	<0.08	0.065

presents a rigorous and comprehensive assessment of the study’s measurement model fit, employing six distinct evaluation approaches. The evaluation commenced with the CMIN/df function, a well-established metric. Notably, previous studies by [101] have indicated a well-fitting model with a CMIN/df range of 3 to 5. In our study, the CMIN/df value was 3.457, falling within this acceptable range and thereby affirming a satisfactory model fit.

Furthermore, the model’s fitness was rigorously evaluated using the Tucker–Lewis Index (TLI), Comparative Fit Index (CFI), and Goodness-of-Fit Index (GFI). This multi-index approach, as suggested by [102], provides a comprehensive view of the model’s fitness. Importantly, the TLI, CFI, and GFI scores all exceeded the threshold of 0.90, further affirming the sufficiency of the model’s fitness.

The assessment also encompassed the SRMR (standardized root mean square residual) and RMSEA (root mean square error of approximation). As proposed by [103,104], a model fit is deemed excellent when the SRMR and RMSEA values are below 0.080. Our research revealed that the SRMR and RMSEA values were indeed lower than this specified threshold, thereby confirming the appropriateness of the model. In summary, the use of six methodologies to evaluate model fitness, consistently meeting predefined thresholds, provides robust evidence of the model’s satisfactory degree of fitness.

4.2. Assessment of Normality, Reliability, and Validity Test Results

Table 3. Factor loadings, and normality, reliability, and validity test results.

Item	Loadings	CA	Skewness	Kurtosis	CR	AVE	MSV
KAUD1	0.947	0.970	−1.06	0.874	0.970	0.915	0.171
KAUD2	0.953		−0.789	−0.822
KAUD3	0.970		−0.789	−0.822
KCEA1	0.916	0.981	−1.299	0.502	0.981	0.895	0.241
KCEA2	0.975		−1.342	0.633
KCEA3	0.925		−1.544	1.42
KCEA4	0.943		−1.342	0.633
KCEA5	0.964		−1.299	0.502
KCEA6	0.951		−1.342	0.633
KSHA1	0.919	0.973	−1.363	0.701	0.974	0.863	0.401
KSHA2	0.964		−1.342	0.633
KSHA3	0.857		−1.846	1.855
KSHA4	0.947		−1.386	0.772
KSHA5	0.952		−1.299	0.502
KSHA6	0.931		−1.367	0.786
KSTR1	0.951	0.973	−1.411	0.926	0.973	0.855	0.241
KSTR2	0.962		−1.299	0.502
KSTR3	0.943		−1.434	0.999
KSTR4	0.848		−1.408	0.964
KSTR5	0.884		−1.174	0.975
KSTR6	0.955		−1.846	0.855
KUSE1	0.957	0.955	−0.961	1.565	0.968	0.838	0.135
KUSE2	0.603		−1.015	0.485
KUSE3	0.956		−1.06	1.871
KUSE4	0.984		−1.078	0.902
KUSE5	0.985		−1.092	0.609
KUSE6	0.948		−0.947	1.442
EPE1	0.774	0.895	−0.271	−1.245	0.896	0.552	0.110
EPE2	0.697		0.017	−1.279
EPE3	0.729		−0.135	−1.235
EPE4	0.726		−0.104	−1.222
EPE5	0.731		0.066	−1.228
EPE6	0.758		−0.154	−1.138
EPE7	0.780		−0.077	−1.193
JSAT1	0.971	0.986	−0.741	−0.904	0.986	0.936	0.181
JSAT2	0.963		−0.805	−0.792
JSAT3	0.950		−0.789	−0.822
JSAT4	0.978		−0.741	−0.904
JSAT5	0.974		−0.789	−0.822
ORCU1	0.816	0.849	−0.2	−1.468	0.850	0.586	0.110
ORCU2	0.714		0.247	−1.42
ORCU3	0.767		0.031	−1.509
ORCU4	0.762		−0.022	−1.505

provides an in-depth evaluation of the results obtained from the normality, convergent validity, and reliability tests conducted in the study. We evaluated the collected data sets for normal distribution using the skewness and kurtosis criteria.

4.2.1. Normality Assessment

Skewness indicates the degree of asymmetry in a distribution, while kurtosis quantifies the extent to which it deviates from a normal distribution [105]. These metrics enhance statistical analysis’s rigor, accuracy, and dependability, aiding in making informed decisions in research and practical contexts. Mermi et al. [106] suggested that skewness and kurtosis values within the range of ±1.96 indicate a high level of adherence to the normal distribution. All the questions in the questionnaire exhibited skewness and kurtosis values within an acceptable range, suggesting that the data set follows a normal distribution and enabling the utilization of parametric analysis.

4.2.2. Convergent Validity Assessment

Convergent validity, crucial for ensuring measurement reliability, was assessed using standardized loadings, average variance extracted (AVE), and maximum shared variance (MSV). This evaluation ensures reliable and consistent information about the subject under investigation, increasing confidence in the research findings. Convergent validity is considered satisfactory when loadings are above 0.50, AVE is above 0.50, and MSV is lower than the AVE [107]. The loading, AVE, and MSV values exceeded the set standards, indicating a high level of convergent validity. Figure 2 displays the standardized loading results.

4.2.3. Reliability Assessments

We thoroughly assessed the internal consistency and composite reliability to ensure the accuracy and reliability of the measurements. High dependability is the ability to consistently and reliably obtain accurate readings. CR and α coefficients exceeding 0.70 signify robust reliability, while lower values suggest diminished reliability [108]. The constructs’ CR and α values exceeded 0.70, demonstrating robust dependability and strengthening trust in the research findings.

4.3. Assessment of Discriminant Validity

Table 4. Discriminant validity test results.

	USE	AUD	STR	SHA	CRE	EM_ENG	JSA	ORG_CUL
USE	0.916
AUD	0.367	0.957
STR	0.213	0.370	0.925
SHA	0.212	0.367	0.001	0.929
CRE	0.221	0.362	0.312	0.403	0.946
EE	0.035	0.019	0.050	0.035	0.047	0.743
JS	0.355	0.009	0.373	0.370	0.365	0.010	0.967
OC	−0.007	0.023	0.025	0.013	0.036	0.331	0.016	0.766

USE—knowledge use, AUD—knowledge auditing, STR—knowledge structuring, SHA—knowledge sharing, and CRE—knowledge creating.

presents the results of the [109] criterion-based discriminant validity assessment. This criterion involves comparing the square root of the AVE for each construct with the correlations between constructs [110]. According to this criterion, the square root of the AVE value for each parameter must exceed the correlations with all the other constructs in the model.

The results indicate that the AVE for each construct is higher than the correlations with the other constructs, demonstrating discriminant validity. Specifically, the AVE for USE (0.916) exceeds the correlation values for AUD (0.367), STR (0.213), SHA (0.212), CRE (0.221), EE (0.035), JS (0.355), and OC (−0.007). This outcome underscores the uniqueness of each component in the model, suggesting that each one assesses a distinct aspect of the underlying event.

These results provide compelling evidence of discriminant validity among the dimensions, highlighting their independence and bolstering the validity of the study’s measurement paradigm.

4.4. Hypothesis Testing

Direct Effect Results

Table 5. Assessment of the structural model fitness.

Fit Indices	Recommended Value	Obtained Value
CMIN/df	3–5	3.457
GFI	>0.90	0.956
CFI	>0.90	0.972
TLI	>0.90	0.934
SRMR	<0.08	0.041
RMSEA	<0.08	0.052

displays the adequacy outcomes of the structural model. We assessed the fitness of the structural model using the following criteria: CMIN/df (3.457), TLI (0.934), CFI (0.972), GFI (0.956), SRMR (0.041), and RMSEA (0.052). According to [101], TLI, CFI, and GFI values above 0.90 indicate satisfactory model fitness. The TLI, CFI, and GFI scores exceeded the threshold of 0.90, indicating that the model’s fitness is adequate. Similarly, the SRMR (0.041) and RMSEA (0.052) values met the threshold values suggested by [102] of below 0.080.

We conducted path analysis to test the direct and mediating effects using the bootstrapping technique with a bootstrap size of 5000 and a 5% confidence interval [111].

Table 6. Direct effect test results.

Relationship	Hypothesis	β	S.E.	t-Value	Beta	p-Value	Decision
JS <--- KMC	H1a	0.241 ***	0.052	4.617	0.225	0.000	Accepted
OC <--- KMC	H1b	0.402 ***	0.061	6.563	0.36	0.000	Accepted
EE <--- KMC	H1c	0.783 ***	0.036	22.001	0.786	0.000	Accepted
JS <--- EE	H2a	0.713 ***	0.052	13.605	0.664	0.000	Accepted
OC <--- EE	H2b	0.556 ***	0.061	9.058	0.497	0.000	Accepted
R-square (EE) = 0.618
R-square (JS) = 0.728
R-square (OC) = 0.657

*** p < 0.01.

displays the direct effects of the model.

The study found that there is a significant connection between KMC and JS (β = 0.2410, t = 4.617, p < 0.01). This supports the first hypothesis (H1a) that KMC significantly influences JS. Again, there is strong statistical evidence (β = 0.402, t = 6.563, p < 0.01) that demonstrates the significant influence of KMC on OC. This finding supports H1b that KMC significantly influences OC. Further, the study found that KMC has a substantial impact on EE (β = 0.783, t = 22.001, p < 0.01), providing support for H1c, which indicates that KMC has a significant influence on EE. Additionally, the study found that EE has a substantial impact on JS (β = 0.713, t = 13.605, p < 0.01), providing support for H2a, which states that EE has a significant influence on JS. Furthermore, the study found that EE has a substantial impact on OC (β = 0.556, t = 9.058, p < 0.01), providing support for H2b, which states that EE has a significant influence on OC.

Table 5 also presents the squared multiple correlations, which provide crucial information about the factors’ ability to explain the observed outcomes. KMC can account for approximately 61.8% of the variation in EE, as indicated by the R-square coefficient of determination (0.618). Additionally, the combined impact of KMC and EE accounts for approximately 72.8% of the variability in JS (R-square = 0.728) and approximately 65.7% in OC (R-square = 0.657).

4.5. Mediation Analysis

Table 7. Indirect effect test results.

Relationship	β	Std. Error	t-Value	Confidence Interval		Sig.	Conclusion
				Lower Bounds	Upper Bounds
JS <--- EE <--- KMC	0.558	0.072 ***	7.750	0.443	0.683	0.000	Partial mediation
OC <--- EE <--- KMC	0.436	0.062 ***	7.032	0.305	0.591	0.000	Partial mediation
Unstandardized Coefficients (B), Standardized Coefficients (Beta), Confidence interval of 95%, and bootstrap sample of 5000.

*** p < 0.01.

presents the mediation analysis of the study. This study examined the role of EE in mediating the relationship between KMC and JS, and OC. We used the bootstrapping technique with a confidence level of 95% and a two-tailed test to analyze the data. The results of the mediation study indicate that EE (β = 0.558, t = 7.750, p < 0.01) serves as a partial mediator in the association between KMC and JS, confirming H3a that EE mediates the relationship between KMC and JS. Also, the results of the mediation study indicate that EE (β = 0.436, t = 7.032, p < 0.01) serves as a partial mediator in the association between KMC and OC, supporting H3 that states that EE mediates the relationship between KMC and OC

5. Discussion of the Findings

This study provides additional evidence that EE can serve as the mechanism by which KMC influences JS and OC. The study’s findings demonstrate a significant and positive influence of KMC on JS, confirming hypothesis H1a. This positive impact supports the concept of SET, as the mutual sharing of knowledge improves employee satisfaction.

The findings of this study align with previous research by [54,112], highlighting the importance of efficient KM strategies, such as knowledge-sharing platforms, training programs, and collaborative initiatives, in enhancing employee JS [113]. Employees at GHA are likely to experience fulfillment and satisfaction when they have access to relevant knowledge resources, learning opportunities, and mechanisms for sharing expertise.

Additionally, the positive impact of KMC on JS suggests that organizations prioritizing KM are better positioned to create a nurturing and stimulating work environment [114] where employees feel valued, empowered, and supported in their professional development [74].

Furthermore, the study reveals a substantial and positive influence of KMC on OC, underscoring the critical role of KM practices in shaping OC. This result is consistent with the SET, as it illustrates how mutual knowledge exchange can influence and strengthen cultural values. These findings support the research by [64] and emphasize that successful KM efforts, such as knowledge-sharing platforms and collaboration tools, can contribute to developing a robust and unified OC. Effective KMC can promote a culture of knowledge exchange, transparency, and innovation, influencing organizational norms, values, and behaviors [115] which, in turn, affects employee attitudes, decision-making processes, and overall performance.

Organizations prioritizing KM are more likely to foster a culture that values ongoing learning, collaboration, and adaptability, resulting in benefits such as increased employee involvement, JS, and organizational effectiveness [116]. These findings suggest that organizations should consider investing in KM practices and fostering a culture of knowledge exchange and innovation to enhance EE, JS, and the overall OC.

Moreover, the study validates the significant and positive influence of KMC on EE, highlighting the critical role of KM strategies in promoting EE and commitment. This outcome aligns with SET, demonstrating how mutual exchanges can lead to increased EE and commitment. The effective management of information resources and support for knowledge-sharing processes can enhance EE levels among GHA employees [117]. This finding is consistent with prior research by [118], emphasizing the importance of providing employees with relevant information, resources, and support systems to foster empowerment, motivation, and dedication. KM programs encouraging collaboration, learning, and skill enhancement can further enhance EE.

Additionally, the study reveals EE’s significant and positive influence on JS, supporting hypothesis H2a. This positive influence reinforces the concept of SET by emphasizing how the mutual interaction between EE and JS enhances the connections within an organization. This underscores the crucial role of EE in shaping employee job satisfaction.

Consistent with [38,73], deeply engaged employees are more likely to experience higher levels of contentment, gratification, and motivation, leading to increased dedication, efficiency, and a stronger sense of meaning in their work. Organizations can enhance JS levels by implementing strategies that promote higher levels of EE, including skills enhancement, autonomy, favorable work environments, and the recognition of employee efforts.

Moreover, the study reveals a significant and positive influence of EE on OC, supporting hypothesis H2b. This result is in accordance with the SET concept, illustrating how mutually beneficial interactions between committed employees and the company contribute to developing and maintaining a robust OC. This finding emphasizes the mutual influence and interdependence of EE and OC. While the existing literature often examines the direct effect of OC on EE, this study provides evidence of a bidirectional relationship. Engaged employees positively influence the broader culture by fostering cooperation, innovation, and a sense of belonging [119]. When profoundly engaged and committed to their work, employees are more likely to internalize and exemplify the organization’s principles and standards, contributing to a robust and unified culture. The correlation between EE and OC suggests that organizations can leverage EE efforts to cultivate and strengthen desirable cultural characteristics, fostering an environment where employees feel empowered, motivated, and aligned with the organization’s mission and values [120].

Furthermore, the study demonstrates that EE plays a crucial role in mediating the relationship between KMC and JS, supporting the study’s hypothesis. EE as a mediator in this connection supports the SET since it illustrates how the reciprocal sharing of knowledge promotes engagement, subsequently leading to higher levels of JS.

This suggests that the level of EE within GHA is not just an outcome, but a key factor that influences the impact of KM practices on job satisfaction. Actively engaged employees are not just beneficiaries, but active participants in translating the benefits of KM initiatives into improved JS [121]. The presence of partial mediation indicates that while EE contributes to the connection between KM and JS, additional factors likely influence this relationship. Nevertheless, the mediation effect underscores the importance of implementing effective KM systems and fostering a culture of EE [122], prioritizing programs that promote employee involvement, motivation, and dedication.

Finally, the study reveals that EE partially mediates the relationship between KMC and OC, aligning with the study’s hypothesis. The study’s discovery that EE acts as a mediator in this relationship is consistent with the SET, emphasizing how the exchange and engagement facilitated by KM methods contribute to strengthening and influencing the OC. This finding suggests that the level of EE within GHA partially influences the impact of KM practices on OC. Engaged employees are essential for effectively implementing KM projects and shaping OC. The concept of partial mediation suggests that while EE explains part of the connection between KMC and OC, other factors also contribute to this relationship. However, the mediation effect highlights the importance of considering EE as a means through which KM promotes OC, emphasizing the need to cultivate a culture of active EE [51,123]. Organizations should prioritize efforts that encourage EE, motivation, and dedication, as these elements play a crucial role in maximizing the benefits of KM in shaping OC.

6. Conclusions

This study investigated the relationships among KMC, EE, JS, and OC at the GHA. Data from 300 employees in southern Ghana were collected using a snowball sampling technique and analyzed using SEM.

The results shed light on the intricate interactions among these variables and their implications for organizational success. Firstly, the study revealed that KMC significantly and positively influences JS and OC. This underscores the importance of implementing effective KM strategies to enhance employee satisfaction and OC within the GHA.

Moreover, the findings highlighted that EE substantially impacts both JS and OC. This underscores the critical role of EE in promoting JS and shaping OC within the GHA. Additionally, the study found that EE partially mediates the relationship between KM and both JS and OC. This underscores the importance of fostering EE as a mechanism through which KM initiatives can impact JS and OC.

These findings provide valuable insights for organizations like the GHA, emphasizing the importance of prioritizing KM and EE practices to enhance employee satisfaction, strengthen OC, and ultimately drive organizational success.

7. Theoretical Implications

The study highlights the significance of implementing efficient KMC systems in influencing JS and OC. Organizations can utilize knowledge production, sharing, and utilization to improve employee happiness and cultivate a healthy OC. The results emphasize the crucial impact of EE in shaping JS and OC. The employees who are actively involved and committed to their work positively impact the firm’s overall performance. Therefore, organizations must emphasize implementing tactics that enhance employee engagement.

The study proposes that EE functions as an intermediary between KMC and JS. This suggests that by promoting effective execution, the impact of KM initiatives on organizational outcomes can be enhanced. Organizations should use a comprehensive approach that combines efficient KM procedures with measures to enhance EE. Implementing this comprehensive approach can result in enhanced JS and a favorable OC, and increase the firm’s overall effectiveness.

8. Managerial Implication

The results of this study hold important implications for businesses, particularly those like the GHA that prioritize efficient KMC, EE, and OC. First and foremost, the study underscores the significance of allocating resources towards KMC activities within the GHA. For instance, by adopting KMC methods that facilitate knowledge exchange, documentation, and innovation, the organization can enhance work characteristics such as skill diversity, task identity, and autonomy. This could lead to higher employee satisfaction and engagement, as employees feel more empowered and involved in their work. Therefore, managers must prioritize developing and implementing KM systems and procedures that enable employees to contribute effectively towards organizational goals.

Additionally, the research underscores the critical importance of EE in mediating the relationship between KMC and both JS and OC. Managers at the GHA should focus on fostering an engaged culture by setting clear objectives, providing supportive guidance, and establishing avenues for feedback and recognition. Engaged employees are more likely to support and engage in KMC initiatives, collaborate effectively, and contribute significantly to a positive corporate culture characterized by trust, innovation, and adaptability.

Moreover, the study suggests that organizations can leverage KMC practices to not just enhance JS and OC, but to drive performance excellence. By aligning KMC strategies with strategic company goals and values, managers can create an environment that promotes employee empowerment, motivation, and commitment to achieving success. This potential for driving performance excellence should inspire GHA managers to integrate KM practices into their strategic planning processes.

This entails GHA managers incorporating KM objectives into strategic planning processes, allocating resources for KM training and infrastructure, and cultivating a supportive culture emphasizing knowledge sharing and collaboration. However, it is important to note that implementing KMC and EE initiatives may face challenges such as resistance to change or a lack of technological infrastructure. Therefore, implementing regular evaluation and feedback systems can facilitate the monitoring of KMC initiatives’ effectiveness and identify areas for improvement.

This study underscores the significance of viewing KMC as a strategic resource. By integrating KM with a focus on employee engagement, organizations can achieve success and effectively fulfill the objectives of the GHA.

9. Limitations and Directions for Future Research

While this study provides intriguing insights into the relationships among KMC, EE, JS, and OC within the GHA, several limitations must be acknowledged. Firstly, convenience sampling may introduce bias into the sample, limiting the generalizability of the findings. Also, the Cochran formula was used to derive the original recommended sample size of 384. However, the final sample was reduced by excluding specific responses found to be invalid or incomplete throughout the data entry procedure. Consequently, the ultimate sample size was decreased to 300. Future research could employ random or stratified sampling techniques to achieve a more representative sample of GHA personnel.

Secondly, the cross-sectional nature of the study and collecting data at a single point in time makes it challenging to establish causality or draw conclusions about long-term effects. Longitudinal studies would provide a more comprehensive understanding of how KM practices influence EE, JS, and OC over time.

Thirdly, relying on self-reported data through online surveys may introduce response bias and social desirability effects, potentially leading to inflated or inaccurate results. Incorporating diverse data collection methods, such as interviews or observations, could enhance the validity of the findings.

Furthermore, the study focused specifically on KMC, EE, JS, and OC variables, potentially overlooking the other factors that could influence these constructs. Future research could explore additional mechanisms, such as leadership styles, organizational structures, or external environmental factors, to gain a more holistic understanding of organizational dynamics.

Lastly, the study’s findings are based on data from the GHA, which may not apply to other organizations or sectors. OC, and economic milieus variations may limit the generalizability of results from one country to other nations. Future studies should investigate similar dynamics in a wide range of settings and demographics to improve generalizability, considering differences in business practices and cultural norms that could impact the relationships under investigation.