Purpose: The acceleration of digital and data-driven technologies in high-tech industries has reshaped competitive structures, value creation, and organizational practices, making many traditional mechanisms insufficient for complex environments. The growth in data volume, process automation, and interactions among intelligent platforms highlights the need for innovative digital governance frameworks. These frameworks, based on algorithms, automated protocols, and intelligent systems, enhance transparency and coordination beyond traditional systems. Achieving effective digital governance requires technological, knowledge, and organizational capacities that many organizations are still developing. Intellectual capital including human, structural, and relational components is a key driver of digital empowerment and innovation. Employees’ digital skills, data-driven infrastructures, organizational standards, and stakeholder trust networks are critical to supporting governance. The influence of intellectual capital on digital governance is often indirect, mediated by digital transformation, which reorganizes processes, infrastructures, and work patterns to enable digital capacities. Digital governance also plays a central role in knowledge management, as the creation, storage, sharing, and utilization of knowledge depend on data transparency, information quality, and integrated knowledge flows. Weak governance can disrupt these flows and limit the effective use of intellectual capital. Human and relational capital particularly drive knowledge flows and innovation capacity, while their interaction with knowledge management strengthens value creation and competitive advantage. At the macro level, regulatory frameworks moderate the relationships among intellectual capital, digital transformation, and governance, acting as enablers or constraints. Organizations must comply with regulations controlling data, privacy, and transparency, which shape digital governance and knowledge management, aligning technology, intellectual capital, and knowledge processes. Despite extensive studies, a fully integrated theoretical model connecting intellectual capital, digital transformation, governance, regulations, and knowledge management has not yet been established. This study aims to provide such a model, offering practical and theoretical insights for managers and policymakers in high-tech industries.Methodology: The present study is a quantitative, descriptive-analytical research aimed at examining the relationships among intellectual capital, digital transformation, digital governance, and knowledge management in companies located in the Urmia Science and Technology Park. The study population comprised senior IT managers, data managers, human resources managers, digital transformation specialists, and knowledge management experts working in these companies. Initially, a sampling frame was developed based on the official list of eligible employees, with inclusion criteria requiring continuous employment in one of the park''s units, at least two years of relevant experience in IT, data, human resources, or knowledge management, and a managerial or specialist role related to digital transformation. The minimum sample size of 210 participants was determined using Daniel Supr’s (2025) formula for Structural Equation Modeling (SEM/PLS-SEM), considering the number of constructs, indicators, path complexity, and desired statistical power. A simple random sampling method was employed, with unique numeric identifiers assigned to each member and selection performed using R software (version 4.3.2) with seed = 2025. To account for non-responses, 273 questionnaires were distributed, resulting in 210 valid responses after screening. Data were collected using a standardized questionnaire based on a five-point Likert scale. The validity of the instrument was assessed through content validity (via expert evaluation), convergent validity (using Average Variance Extracted, AVE), and discriminant validity (using the Fornell-Larcker criterion). Reliability was confirmed using Cronbach’s alpha and Composite Reliability (CR), both with acceptable thresholds of 0.7. Structural Equation Modeling (SEM) using Partial Least Squares (PLS-SEM) was employed to test the research model, allowing the examination of relationships among multiple variables, measurement errors, multicollinearity, latent constructs, and hypothesized paths. Statistical analyses were performed using SPSS and Smart-PLS. Model evaluation included R² (coefficient of determination), Q² (predictive relevance), T-values (for path significance), and the overall Goodness-of-Fit (GOF) index. Thresholds were applied to interpret model fit, explanatory power, and predictive accuracy. The results indicated that the model provided robust explanatory and predictive capabilities for the relationships among intellectual capital, digital transformation, digital governance, and knowledge management in the context of the park.Results: The findings of the study indicated that all factor loadings of the questionnaire items were above 0.4 and the T-statistics exceeded 1.96, demonstrating the acceptability of the indicators and the structural validity of the instrument. Cronbach’s alpha for all constructs was above 0.7, and the composite reliability of the research variables was also higher than 0.7, confirming the adequacy and stability of the instrument. Convergent validity of the constructs was assessed using the Average Variance Extracted (AVE), with all values exceeding 0.5, indicating a satisfactory convergence of the variables with their respective indicators. For discriminant validity, the Fornell-Larcker method was applied, showing that the square root of the AVE for each construct was greater than its shared variance with other constructs, confirming acceptable discriminant validity for all constructs. The coefficient of determination (R²) for the knowledge management variable was at a strong level, while it was moderate for digital governance and digital transformation, indicating a good model fit in explaining the variance of the dependent variables. The Q² index, which measures the predictive relevance of the model for endogenous constructs, was strong for knowledge management and moderate for digital governance and digital transformation, reflecting the model’s adequate predictive capability. The overall Goodness-of-Fit (GOF) index was calculated as 0.483, indicating a strong overall fit and proper alignment between observed and predicted values. Additionally, for hypothesis acceptance, the path coefficients needed to be positive and the T-values above 1.96; the results showed that all hypothesized relationships were statistically significant, and thus all research hypotheses were supported. In summary, the results of factor analysis, reliability and validity tests, R² and Q² indices, and the overall GOF confirm the validity, explanatory power, and predictive capability of the research model in examining the relationships among intellectual capital, digital transformation, digital governance, and knowledge management in companies located in the Urmia Science and Technology Park.Discussion: Digital governance plays a pivotal role in enhancing knowledge management and facilitates the impact of intellectual capital and digital transformation on knowledge management; in such a way that without a coherent digital governance framework, effective exploitation of organizational knowledge is not possible. Digital transformation has a significant impact on digital governance and accelerates the maturation of governance structures. Digital regulations also act as moderators, weakening the impact of human and structural capital on digital governance, which indicates the need for smart and flexible policymaking.Conclusion: The study findings indicate that human, structural, and relational capital all positively influence digital governance. Human capital, including employees’ digital skills, specialized knowledge, and competencies, especially among IT managers and data specialists, is crucial for implementing effective digital standards and governance mechanisms. Structural capital, such as robust organizational frameworks, integrated information flows, and comprehensive documentation, enhances governance by enabling secure and transparent management of digital information. Relational capital, consisting of trust-based relationships and strategic collaborations, strengthens the organization’s ability to operate effectively within interconnected digital environments. Digital governance, in turn, has a significant positive effect on knowledge management. It improves the accuracy, reliability, and accessibility of organizational data, facilitating knowledge sharing, integration, and optimal use of expertise. Digital transformation positively affects governance by modernizing processes, adopting advanced technologies, and developing data-driven infrastructures. Moreover, digital governance mediates the relationship between intellectual capital and knowledge management, as well as between digital transformation and knowledge management, highlighting that the benefits of intellectual capital and transformation are realized primarily when strong governance is in place. However, digital regulations can negatively moderate these relationships, as overly restrictive frameworks limit organizational flexibility and the effective use of human, structural, and relational resources. Overall, these results emphasize digital governance as a key link connecting intellectual capital, digital transformation, and knowledge management. To enhance knowledge flows and governance while maximizing intellectual assets, organizations must invest simultaneously in human, structural, and relational capacities and implement adaptive regulatory approaches that support innovation and digital effectiveness.
Purpose: In the current era, where advanced digital technologies and artificial intelligence serve as pivotal drivers of change in organizational structures and work processes, Generation Z, as the youngest cohort in the workforce, plays a crucial and decisive role in enhancing organizational productivity. This generation, having grown up immersed in advanced technologies and digital environments, exhibits distinct behavioral traits, needs, and work patterns that are incompatible with traditional management approaches and therefore require innovative and specialized strategies. Accordingly, the objective of this study is to design and develop a comprehensive and practical scientific model aimed at improving the performance and increasing the productivity of online Generation Z employees in AI-driven work environments. The proposed model endeavors to provide an adaptive framework by precisely identifying the factors influencing motivation, job satisfaction, technological competencies, and digital interactions of this generation, maximizing their capabilities and turning emerging challenges into transformative opportunities. Ultimately, this model aims to offer a scientific and practical foundation for organizational managers and human resource professionals to foster sustainable development and success in the digital transformation era through enhanced productivity and greater job satisfaction.Methodology: The present study employs a qualitative, applied, and exploratory research design a imed at gaining a deep and comprehensive understanding of online employee productivity among Generation Z based on artificial intelligence. The research population consisted of online Generation Z employees who directly interact with AI technologies in their work environments. Samples were selected through purposive sampling to ensure that participants possessed the necessary expertise, sufficient experience, and direct relevance to the research topic, thereby enabling the collection of relevant and precise data. Data collection was conducted through semi-structured interviews with 13 experts, including Generation Z employees and specialists in the field of artificial intelligence. This interview approach allowed for the in-depth exploration of topics and the extraction of rich and diverse qualitative data. Following data collection, a systematic and staged data analysis was performed during which key concepts and categories were identified and coded. During the analysis process, special attention was given to the iterative examination and comparison of emerging concepts to develop a coherent framework consisting of causal factors, contextual elements, intervening variables, strategies, and outcomes related to Generation Z employee productivity. These analyses led to the formulation of a comprehensive model that addresses various dimensions of the topic and is applicable to online work environments. Thus, by employing purposive sampling, semi-structured interviews, and rigorous qualitative data analysis, the present research has produced reliable scientific and practical findings regarding Generation Z’s productivity based on artificial intelligence.Results: The present study employs a qualitative, applied, and exploratory research design aimed at gaining a deep and comprehensive understanding of online employee productivity among Generation Z based on artificial intelligence. The research population consisted of online Generation Z employees who directly interact with AI technologies in their work environments. Samples were selected through purposive sampling to ensure that participants possessed the necessary expertise, sufficient experience, and direct relevance to the research topic, thereby enabling the collection of relevant and precise data. Data collection was conducted through semi-structured interviews with 13 experts, including Generation Z employees and specialists in the field of artificial intelligence. This interview approach allowed for the in-depth exploration of topics and the extraction of rich and diverse qualitative data. Following data collection, a systematic and staged data analysis was performed during which key concepts and categories were identified and coded. During the analysis process, special attention was given to the iterative examination and comparison of emerging concepts to develop a coherent framework consisting of causal factors, contextual elements, intervening variables, strategies, and outcomes related to Generation Z employee productivity. These analyses led to the formulation of a comprehensive model that addresses various dimensions of the topic and is applicable to online work environments. Thus, by employing purposive sampling, semi-structured interviews, and rigorous qualitative data analysis, the present research has produced reliable scientific and practical findings regarding Generation Z’s productivity based on artificial intelligence.Discussion: This research is distinguished by its originality and innovation due to the design of the first productivity model for online Generation Z employees based on artificial intelligence, utilizing a grounded theory approach. The value of this study is highlighted in several dimensions. First, it provides a scientific and practical framework tailored to the specific characteristics and needs of Generation Z in online work environments, serving as a foundation for improving performance and human resource management in the era of advanced technologies. Second, the use of a qualitative grounded theory method combined with modern data analysis tools such as MAXQDA and Visio has enhanced the precision and depth of the analysis, leading to the identification of key productivity factors that have been less explored in this field. Moreover, this model serves as a strategic tool for organizations, not only increasing job satisfaction and skill development among Generation Z but also delivering outcomes such as task automation, improved quality and productivity, and enhanced competitiveness. Ultimately, this study can act as a starting point for future research and the development of innovative management policies in the domain of the digital workforce and new generations, significantly contributing to the sustainable growth of online businesses.Conclusion: Considering the rapid development of modern technologies, especially artificial intelligence, Generation Z as a cohort raised in the digital age holds unique expectations and needs regarding the application of technology in work environments. This study, employing a grounded theory approach and qualitative analysis, presents a comprehensive model of online employee productivity for Generation Z based on artificial intelligence, encompassing causal factors, contextual elements, intervening variables, strategies, and outcomes. The findings indicate that Generation Z possesses a positive outlook on artificial intelligence and its impact on alleviating repetitive tasks, enhancing work quality, developing skills, and fostering professional growth. Moreover, AI helps them perform work processes more simply and accurately, reducing their workload, which in turn leads to increased job satisfaction and organizational productivity. Despite these advantages, challenges and concerns such as fear of job displacement, decreased human interaction, and ethical issues are also observed among Generation Z. Addressing these requires education, cultural development, and change management. Furthermore, intervening factors like internet limitations, data security, and cultural and economic considerations play a crucial role in the acceptance and effectiveness of artificial intelligence, and managing these factors is essential for achieving optimal productivity. Additionally, organizational contextual factors such as flexible structures, advanced technological infrastructures, effective training programs, and an innovative culture provide a foundation for the successful implementation of AI in Generation Z’s work environments. The strategies designed within the model focus on simplifying technology use, developing employees’ specialized skills, fostering motivation, and removing obstacles, all of which play a decisive role in enhancing Generation Z’s productivity and job satisfaction. Ultimately, the outcomes of leveraging artificial intelligence include task automation, improved accuracy and quality, professional development and innovation, enhanced communication, and increased organizational competitiveness. Collectively, these signify a profound transformation in Generation Z’s online work environments in the era of smart technologies. This model can serve as both a practical and scientific guide for organizations in managing Generation Z’s workforce and achieving sustainable development in online businesses.
Purpose: The rapid advancement of emerging technologies, particularly artificial intelligence (AI), has profoundly transformed traditional work structures and professional environments. In the education sector, while AI offers significant opportunities to enhance teaching and learning processes, it has simultaneously generated substantial concerns among teachers regarding job security, professional identity, and future career prospects. Existing literature has largely focused on the technological benefits of AI, with limited attention given to its psychological and behavioral consequences. This gap is particularly critical in private educational settings, where job insecurity and competitive pressures are more pronounced. Therefore, a significant research gap exists in understanding the psychological mechanisms linking AI awareness to teachers’ work-related behaviors. This study aims to investigate the impact of threat-based AI awareness on teachers’ work withdrawal and to examine the mediating roles of negative work-related rumination and emotional exhaustion within the framework of Conservation of Resources (COR) theory. The study seeks to answer whether AI awareness can indirectly increase teachers’ withdrawal behaviors through cognitive and emotional processes.Methodology: This study is applied in nature and uses a quantitative, descriptive-correlational design with an inductive approach. The theoretical framework is based on COR theory, which emphasizes the role of perceived threats to psychological resources in shaping behavioral responses. The statistical population consisted of teachers from non-governmental elementary schools in Mashhad, Iran. A sample of 200 participants was selected using stratified random sampling. Data were collected using a standardized questionnaire consisting of 29 items measuring four constructs: awareness of AI, negative work-related rumination, emotional exhaustion, and disengagement from work. Validity was confirmed by confirmatory factor analysis and reliability was confirmed using Cronbach's alpha and composite reliability. Data analysis was performed using partial least squares structural equation modeling (PLS-SEM) with SmartPLS4 software. The model fit was evaluated with indices such as SRMR, NFI, and R², and hypothesis testing was performed using bootstrap resampling’s.Results: The results obtained from the structural equation modeling analysis indicate that the proposed research model demonstrates an acceptable and robust level of fit with the empirical data. Key model fit indices, including the Standardized Root Mean Square Residual (SRMR = 0.061) and the Normed Fit Index (NFI = 0.91), confirm the adequacy of the model’s overall structure. In addition, the coefficients of determination (R²) for the endogenous constructs reveal substantial explanatory power. Specifically, the model explains 89% of the variance in work withdrawal (R² = 0.89), 82.6% of the variance in emotional exhaustion, and 77.6% of the variance in negative work-related rumination. These high explanatory values indicate that the model provides a strong and reliable representation of the relationships among the studied variables.At the level of direct effects, the findings show that threat-based AI awareness has a positive and statistically significant impact on teachers’ work withdrawal (β = 0.299, t = 4.729). This result suggests that as teachers increasingly perceive artificial intelligence as a threat to their professional roles and job security, their tendency to engage in withdrawal behaviors—such as reduced participation, psychological disengagement, and diminished effort—significantly increases. Furthermore, AI awareness exerts a very strong and significant effect on emotional exhaustion (β = 0.909, t = 92.598), highlighting its critical role as a major psychological stressor that depletes teachers’ emotional and cognitive resources. Emotional exhaustion, in turn, is found to have a positive and significant effect on work withdrawal (β = 0.287, t = 3.878), confirming its role as a key predictor of disengagement-related behaviors.In terms of cognitive pathways, the results demonstrate that AI awareness has a strong and significant positive effect on negative work-related rumination (β = 0.881, t = 67.469). This finding indicates that heightened awareness of AI-related threats leads to persistent and repetitive negative thinking about work-related issues, particularly concerning job insecurity and professional identity. Additionally, negative rumination significantly predicts work withdrawal (β = 0.390, t = 6.014), suggesting that continuous cognitive preoccupation with work-related stressors prevents psychological recovery and contributes to behavioral disengagement.At the level of indirect effects, the findings confirm that both emotional exhaustion and negative rumination serve as significant mediating mechanisms in the relationship between AI awareness and work withdrawal. The indirect effect of AI awareness on work withdrawal through emotional exhaustion is estimated at 0.260 (t = 3.625), while the indirect effect through negative rumination is stronger, at 0.343 (t = 5.122). This comparison indicates that cognitive processes (i.e., rumination) play a more dominant role than emotional processes in transmitting the effects of perceived AI threats to withdrawal behaviors. In other words, the way individuals cognitively process and internalize perceived threats appears to be more influential than emotional depletion alone in shaping behavioral outcomes.Overall, the pattern of results suggests that AI awareness influences work withdrawal not only directly but also indirectly through simultaneous cognitive and emotional mechanisms. These findings point to a cascading resource depletion process, in which perceived technological threats activate negative cognitive cycles and emotional strain, ultimately leading to reduced work engagement and increased withdrawal behaviors. This integrated mechanism highlights the complex interplay between cognition and emotion in shaping employees’ responses to technological change. It also underscores the importance of considering both psychological dimensions when examining the organizational consequences of emerging technologies such as artificial intelligence.Discussion: Within the framework of COR theory, the findings suggest that AI awareness functions as a job stressor that threatens teachers’ psychological resources, triggering cognitive (rumination) and emotional (exhaustion) responses. These processes lead to resource depletion, ultimately resulting in withdrawal behaviors as a protective mechanism. The results are consistent with prior research indicating that perceived technological threats can increase anxiety, reduce motivation, and lead to disengagement. Theoretically, this study contributes by integrating cognitive and emotional mediators into a comprehensive model explaining AI-related behavioral outcomes. Practically, the findings emphasize the importance of addressing the psychological dimensions of digital transformation in educational settings.Conclusion: This study demonstrates that threat-based AI awareness, in the absence of adequate organizational and psychological support, can undermine teachers’ job stability and knowledge sustainability. By identifying the mediating roles of rumination and emotional exhaustion, the research provides a deeper understanding of how AI-related perceptions influence work behaviors. A key contribution is the development of an integrated model for analyzing the psychological impacts of AI in educational contexts. However, limitations such as the focus on a single city and occupational group restrict generalizability. Future research is recommended to adopt longitudinal designs and explore diverse educational and organizational settings. Ultimately, the study underscores the necessity of implementing supportive interventions, enhancing technological literacy, and strengthening teachers’ psychological resilience in the face of emerging technologies.
Purpose: Today, productivity as a continuous process provides the power and ability necessary to solve many problems and studies the existing inadequacies in the country such as low profitability, increased waste, inability to compete, lack of maximum utilization of production capacity and generally lack of proper understanding of a system. Governments and companies try to get as close as possible to these indicators by using appropriate techniques and methods. Factors such as employee training and employment, multi-skill training programs, employee motivation, job satisfaction, bonuses paid to employees and organizational culture are effective in increasing employee productivity. On the other hand, according to some researchers, knowledge management is known as a strategic lever for improving organizational productivity. Knowledge management is a set of processes for identifying, storing, sharing, and utilizing the organization's valuable knowledge. Empirical studies show that employees who enjoy a higher level of well-being are more likely to participate in knowledge sharing and creation processes, which can improve organizational performance. In the oil industry, the nature of its activities is often based on specialized knowledge and technical skills, the importance of knowledge management for optimizing human resources and large volumes of data and work experience is particularly evident. On the other hand, ignoring employee well-being can lead to reduced motivation, inefficient knowledge management processes, and consequently a decline in organizational productivity. Organizations that make meaningful investments in the area of human resource well-being usually enjoy a higher level of motivation, productivity, and competitive advantage. In high-risk and complex industries such as the oil industry, the importance of employee well-being is doubly important. This industry is inherently tied to difficult working conditions, environmental pressures, technical complexities, and high productivity requirements. In such environments, improving well-being can increase job satisfaction and work output in addition to reducing job stress and physical and psychological pressures. The technical knowledge of experienced and sometimes retired employees in the oil industry is a valuable resource that must be preserved and utilized. If employees feel a sense of well-being and value, they are more likely to share their knowledge and experiences with others. Indeed, by improving the overall welfare of employees within the organization, and through identifying and preserving knowledge, acquiring, transferring, sharing, and applying it, organizational productivity will subsequently be enhanced. The present study was conducted under the title "The Effect of Employee welfare on Organizational Productivity Components Considering the Mediating Role of Knowledge Management in the Oil Industry". This study sought to answer the general question: Does employee welfare have an effect on organizational productivity components (including motivation, feedback, organizational support, and social status) in the oil industry? Does knowledge management play a mediating role in the relationship between employee welfare and organizational productivity components?Methodology: The present study is an applied research in terms of its purpose and uses a descriptive-survey method in terms of data collection. In this study, structural equation modeling analysis using the PLS method in SmartPLS4 software was used to test the hypotheses in the inferential statistics section. The data collection tool was standard questionnaires, which were used to examine the validity of the questionnaire questions using convergent validity and divergent validity indices, and to examine the reliability using Cronbach's alpha and composite reliability indices. The statistical population of this research consisted of 265 experts and human resource managers of the Ministry of Petroleum in Tehran. The sample size was calculated as 157 using Cochran's formula.Results: The findings of the research based on the fitted structural equation model in the case of significant values at the 95% confidence level showed that employee well-being with significant numbers of 36.93, 5.27, 2.74, 2.23 has a direct and positive effect on organizational productivity components (including motivation, feedback, organizational support and social status) in the oil industry. The results also showed that knowledge management with significant numbers of 4.85, 4.82, 2.11, 5.39 plays a mediating role in the relationship between employee well-being and organizational productivity components, meaning that improving employee well-being can lead to increased productivity if knowledge management is improved.Discussion: The findings reveal that employee welfare has a significant direct positive impact on all key components of organizational productivity (motivation, feedback, organizational support, and social status) in Iran’s oil industry, with the strongest effect observed on employee motivation. More importantly, knowledge management plays a strong and significant mediating role in this relationship. When employees experience higher levels of well-being, they are more willing to share and apply their knowledge, which in turn enhances organizational productivity.In the high-risk, knowledge-intensive oil sector, employee welfare and knowledge management function as two sides of the same coin. Simultaneous investment in both creates a virtuous cycle that not only boosts productivity but also fosters a healthier, more engaged, and sustainable workforce. This study is among the first to empirically examine the mediating role of knowledge management between employee welfare and organizational productivity in Iran’s oil industry, highlighting that balanced attention to “people” and “knowledge” is essential for achieving sustainable performance in this strategic sector.Conclusion: Oil industry managers should adopt policies that promote both employee well-being and knowledge management simultaneously, and subsequently increase employee satisfaction and engagement, and organizational productivity. This study is the first to focus on the importance of the relationship between employee well-being and organizational productivity, considering the mediating role of knowledge management in the oil industry in a country.
Purpose: In the knowledge era, higher education is recognized as one of the most important domains for knowledge creation, sharing, and management. However, knowledge management processes in these environments often face challenges such as knowledge fragmentation, resource heterogeneity, hierarchical structures, and weak interactions among actors. Conventional approaches to knowledge management mostly focus on human and structured dimensions, paying less attention to non-human elements—such as technology, physical spaces, or even policies—within knowledge networks. This research aims to investigate the implications of Bruno Latour’s Actor-Network Theory (ANT) elements in knowledge management within higher education, seeking to provide a comprehensive and interactive perspective that enables more efficient utilization of all actors—both human and non-human—in university settings.Methodology: Considering the purpose of this study — analyzing the implications of Bruno Latour’s Actor-Network Theory in knowledge management within higher education — the most suitable method is "speculation" or "Speculative essay". This qualitative and non-traditional approach enables in-depth analysis, the integration of theories, a critique of existing frameworks, and the presentation of innovative perspectives. Data were collected through a comprehensive review of scholarly texts, research articles, and relevant theoretical sources, and then analyzed through reflective and critical thinking. As a preliminary step toward theory building, this method enables the researcher to introduce innovative ideas scientifically and systematically.Results: The findings of this theoretical research indicate that in the field of higher education, both human and non-human actors (such as individuals, organizations, technologies, and objects) play a crucial role in knowledge management through dynamic networks. These heterogeneous networks influence all four dimensions of knowledge management—creation, application, transfer, and retention—and are the driving force behind these processes. In other words, knowledge creation is the result of the interaction and synergy between various actors. Additionally, the application and transfer of knowledge cannot occur without the involvement of non-human elements (such as digital systems, documents, and organizational procedures) alongside human actors. Knowledge retention (preservation and storage) also depends on the collaboration between human actors (such as knowledge managers or librarians) and material-technological infrastructures (such as databases and archives). Therefore, knowledge processes in universities are not merely human actions but are the outcome of networking and reciprocal interactions between human and non-human elements.According to Actor-Network Theory, the process of "translation" plays a central role in explaining these patterns. This concept suggests that actors continuously redefine their interests and roles within the network, transforming and re-producing reality and knowledge through their interactions. In other words, some actors act as active mediators (having a role in mediation), reshaping the content of knowledge or relationships. In contrast, others merely serve as intermediaries, transferring knowledge or instructions without altering their nature. Furthermore, it was observed that the stable networks formed in this context gradually turn into "black boxes"—complex structures and processes that, due to their stability and acceptance, are assumed to be self-evident, and their internal details become invisible to the actors. Consequently, many established practices, technologies, or institutions function as black boxes, and their stable performance makes it difficult to question their inner workings. Collectively, these concepts demonstrate that knowledge management in higher education is the result of networked actions among various factors (human and non-human) through continuous translations and mediations, and its sustainability depends on networks that eventually solidify into black boxes.Discussion: This paper examines the implications of Actor-Network Theory for knowledge management in higher education, providing insights into the complex interactions between human and non-human actors in educational processes. It highlights the significance of dynamic networks and novel concepts such as translation, mediation, and black-boxing in the facilitation and preservation of knowledge. The findings provide valuable insights for universities to improve their knowledge management practices through these theoretical frameworks.Conclusion: Knowledge is not a static, human-centered asset, but a dynamic product of heterogeneous networks of human and non-human actors. This view poses a serious challenge to humanistic traditions that view technology and objects as passive instruments; whereas actor-network theory suggests that digital systems, documents, procedures, and even academic spaces are active co-constructors of knowledge realities. This study extends the application of this theory to the organizational-educational domain and shows that blurring the line between human and non-human agents provides a deeper understanding of the complexity of the contemporary university. From a practical perspective, if knowledge management is a product of dynamic networking, it is not enough to rely solely on “strengthening human capacity” or “upgrading technology”; rather, flexible networks must be designed that allow for the opening of black boxes and prevent the process from becoming fixed. Ultimately, universities must create spaces that enable not only knowledge production, but also the ongoing review of knowledge production mechanisms.
Purpose: This study aims to identify, analyze knowledge-based governance strategies in science and technology parks using a meta-synthesis approach, emphasizing the multi-level and multi-dimensional nature of governance in knowledge-intensive innovation ecosystems. With the rapid advancement of digital technologies, the growing complexity of knowledge flows, and the increasing interdependence among universities, industries, and government agencies, traditional linear and hierarchical governance models have proven insufficient for addressing dynamic and heterogeneous challenges. Knowledge-based governance, therefore, is not merely a managerial or administrative mechanism but functions as an integrative platform for institutionalizing intelligent knowledge flows, fostering transparency, trust, accountability, and collaborative innovation, while also ensuring sustainable social, economic, and environmental outcomes across diverse stakeholders. This study provides a theoretically grounded and empirically informed framework for governance in science and technology parks, highlighting the alignment of institutional, organizational, digital, and international dimensions to enhance decision-making, knowledge sharing, learning, and ecosystemic resilience, ultimately contributing to innovation-driven regional development.Methodology: Grounded in an interpretive qualitative paradigm, this research employs the meta-synthesis method to systematically integrate and re-interpret findings from prior empirical and theoretical studies related to knowledge-based governance in science and technology parks. The study adopts Sandelowski and Barroso’s seven-step meta-synthesis framework, encompassing: (1) formulation of research questions; (2) comprehensive literature review; (3) systematic source identification and selection based on relevance and rigor; (4) detailed extraction of relevant data and concepts; (5) qualitative analysis and synthesis of primary, secondary, and final codes; (6) rigorous quality control to ensure the reliability, validity, and transparency of the synthesis process; and (7) presentation of synthesized findings in a structured framework. Through this methodology, the study develops a multi-dimensional, integrative set of governance strategies that reflects the interactions among institutional structures, organizational processes, digital platforms, policy frameworks, and international collaboration, providing a holistic understanding of governance mechanisms in science and technology parks. A total of 152 primary studies were examined, from which 78 secondary codes were generated and subsequently distilled into 9 overarching strategic dimensions, capturing the complexity and diversity of knowledge-based governance practices.Results: The meta-synthesis identifies nine interrelated strategic dimensions central to effective knowledge-based governance in science and technology parks:Transparency and accountability: This dimension encompasses institutional and legal transparency, open access to data, stakeholder responsiveness, and mechanisms for continuous reporting and feedback. The findings emphasize that transparency, coupled with stakeholder accountability, establishes the foundation for trust, collective learning, and decision-making legitimacy within multi-stakeholder environments. 2. Capacity building and empowerment: Effective governance requires both institutional capacity development and human capital empowerment. Key strategies include organizational strengthening, knowledge management systems, specialized training programs, intra- and inter-park knowledge sharing, and leadership development initiatives. These mechanisms enhance absorptive capacity, enable systematic learning, and facilitate the alignment of organizational objectives with broader governance goals. 3. Innovation and networking: The study underscores the role of open innovation, co-creation, and ecosystem-based networking as critical enablers of knowledge circulation and technology commercialization. By fostering interactions between startups, universities, research institutes, and industry partners, parks can support collaborative innovation, bridge gaps between knowledge producers and users, and accelerate the translation of knowledge into tangible economic and social value. 4. Digitalization and intelligent governance: Digital infrastructure, platform-based governance, and data-driven decision-making constitute the core of this dimension. The findings highlight how artificial intelligence, Internet of Things (IoT), and other digital technologies can optimize knowledge flows, enhance process efficiency, and support adaptive governance, while also enabling predictive analytics for strategic human capital planning and operational management within parks. 5. Smart policy-making: Adaptive and evidence-based policy frameworks, regulatory flexibility, and incentive structures are identified as essential for effective governance. Policies that integrate top-down guidance with participatory mechanisms allow for iterative learning, mitigate governance gaps, and align stakeholder interests while facilitating innovation and sustainable development. 6. Sustainable development and social responsibility: Governance strategies must incorporate environmental sustainability, ethical innovation, and social inclusion. Green technologies, low-carbon solutions, equitable access to resources, and socially responsible practices strengthen the legitimacy and long-term viability of parks, ensuring that knowledge-based growth is not solely economic but also socially and environmentally balanced. 7. Scientific diplomacy and international collaboration: Parks serve as nodes for global knowledge exchange, facilitating international technology transfer, cross-border learning, and collaborative innovation networks. Strategic partnerships at regional and international levels enhance the competitiveness of parks, allow for contextualized learning from diverse environments, and support the global positioning of national innovation systems. 8. Resilience and foresight: Governance frameworks must integrate adaptive capacities, risk management, strategic planning, and foresight mechanisms to address technological uncertainty, economic fluctuations, and evolving stakeholder expectations. Resilience is achieved not merely through robustness but by enabling rapid reconfiguration of resources, processes, and policies in response to emerging challenges. 9. Ecosystem convergence: Finally, the study highlights the convergence of university-industry-government networks and cross-sectoral collaborations as a central mechanism for integrated knowledge-based governance. Multi-level interactions among diverse actors facilitate trust, reduce conflicts of interest, accelerate knowledge transfer, and foster systemic innovation, ensuring that science and technology parks function as dynamic, learning-driven ecosystems rather than isolated entities.Collectively, these nine dimensions provide a multi-level framework that guides the design, implementation, and evaluation of knowledge-based governance strategies in science and technology parks. They emphasize the interplay between institutional structures, digital tools, human capital development, policy frameworks, and international cooperation, establishing a foundation for participatory, transparent, and sustainable governance capable of enhancing regional innovation capacity.Discussion: The findings of this meta-synthesis highlight that knowledge-based governance in science and technology parks is inherently multi-dimensional, dynamic, and deeply embedded in complex innovation ecosystems. The identification of nine interrelated strategic dimensions demonstrates that effective governance extends beyond traditional administrative or hierarchical approaches and requires an integrated framework combining institutional transparency, organizational capacity building, digital transformation, and collaborative innovation. These results are consistent with prior studies emphasizing the shift from linear governance models toward networked, participatory, and knowledge-driven systems.A key contribution of this study lies in revealing the synergistic interaction among governance dimensions. For instance, transparency and accountability not only enhance trust but also enable more effective knowledge sharing and stakeholder engagement, which in turn strengthens innovation networks and ecosystem convergence. Similarly, digitalization and intelligent governance act as enablers that amplify the effectiveness of other dimensions by facilitating data-driven decision-making and optimizing knowledge flows. This interconnectedness suggests that governance strategies cannot be implemented in isolation but must be designed as part of a coherent and adaptive system.Moreover, the findings underscore the critical role of contextual factors such as institutional structures, policy environments, and regional innovation capacities. The heterogeneity observed across science and technology parks indicates that while the proposed framework provides a comprehensive reference, its practical implementation should be tailored to local conditions. This aligns with the broader literature that emphasizes context-sensitive governance models in innovation ecosystems.Importantly, this study bridges a significant gap in the literature by integrating fragmented perspectives on knowledge management, innovation, and governance into a unified analytical framework. It advances the understanding of how knowledge-based governance can simultaneously promote innovation, resilience, and sustainable development. The emphasis on dimensions such as scientific diplomacy, sustainability, and foresight further expands the scope of governance beyond economic performance toward long-term societal and environmental impact.Conclusion: This research contributes to the literature by presenting a novel, multi-dimensional, and integrative framework for knowledge-based governance in science and technology parks. Unlike prior studies that primarily focus on operational efficiency or sector-specific interventions, this study emphasizes governance as a participatory, dynamic, and sustainability-oriented system. By detailing strategic dimensions across institutional, organizational, digital, policy, and international domains, the findings provide actionable guidance for designing resilient, transparent, and socially responsible governance mechanisms. The study enhances understanding of how knowledge-based governance can simultaneously support innovation, learning, and sustainable development, offering a roadmap for the strategic transformation of science and technology parks into knowledge-intensive ecosystems that balance economic, social, and environmental objectives.
Purpose: Knowledge management (KM) has been a cornerstone of human progress, evolving from oral traditions to structured systems for preserving, transferring, and leveraging knowledge. Over time, humanity has sought innovative methods to harness knowledge as a strategic asset, particularly in organizational contexts. The advent of modern technologies, such as artificial intelligence (AI), machine learning (ML), the Internet of Things (IoT), and the metaverse, has fundamentally transformed how knowledge is managed, shared, and created. These advancements have ushered in what is termed the "fourth generation" of knowledge management, a paradigm that responds directly to the rapid and pervasive developments in the digital era. Unlike previous generations, which focused on codifying explicit knowledge, fostering human interactions, or aligning knowledge with strategic objectives, the fourth generation leverages cutting-edge technologies to create intelligent, dynamic, and scalable knowledge ecosystems. However, despite these technological strides, the scientific literature on knowledge management lacks a comprehensive framework to fully articulate the characteristics of this fourth generation and its integration with emerging technologies, particularly Artificial General Intelligence (AGI). AGI, with its ability to mimic human cognitive capabilities, promises to revolutionize knowledge processes by automating complex tasks, personalizing learning, and uncovering hidden patterns in data. This study addresses this gap by delineating clear distinctions between the generations of knowledge management and proposing a novel model for the fourth generation. Specifically, it revisits and redefines the Nonaka and Takeuchi SECI model (Socialization, Externalization, Combination, Internalization) in the context of AGI, offering a framework that aligns with the demands of the digital age. By doing so, this research provides both theoretical insights and practical guidance for organizations seeking to harness advanced technologies for sustainable competitive advantage.Methodology: This qualitative research was conducted using a systematic literature review (SLR) approach, adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) standards to ensure rigor and transparency. The study targeted peer-reviewed articles published between 2019 and 2024, sourced from reputable scientific databases, including Scopus, Web of Science, SID, Noormags, and Civilica. These databases were selected for their comprehensive coverage of both international and regional (Persian-language) scholarship, ensuring a broad and inclusive perspective. The research process began with the identification of 1,550 relevant sources through a combination of keyword searches and logical operators tailored to knowledge management, AGI, and emerging technologies. A three-stage screening process was employed: first, 100 duplicate articles were removed; second, 100 unrelated articles were excluded based on title and abstract screening; and third, 1,100 articles were further eliminated using the Critical Appraisal Skills Programme (CASP) and AMSTAR quality assessment tools to ensure methodological rigor. This process resulted in the selection of 64 high-quality articles for in-depth analysis. Data extraction was performed using a standardized form to capture key details such as study objectives, methodologies, findings, and relevance to the fourth generation of KM. The extracted data were analyzed through two complementary methods: thematic analysis using NVivo software to identify recurring themes and patterns, and bibliometric analysis using VOSviewer software to map research clusters, author networks, and citation trends. To enhance the study’s validity, the triangulation method was employed, cross-referencing findings from multiple sources. Reliability was ensured through independent coding by two researchers, with discrepancies resolved through consensus. This rigorous methodology allowed for a robust synthesis of the current state of knowledge management and its evolution into the fourth generation.Findings: The study’s findings highlight three key insights into the fourth generation of knowledge management and its integration with emerging technologies. First, AGI significantly enhances organizational knowledge creation by processing unstructured data and uncovering complex patterns that were previously inaccessible. Unlike traditional AI, which is limited to specific tasks, AGI’s ability to emulate human-like reasoning enables it to analyze vast datasets, identify trends, and generate actionable insights, thereby fostering innovation and agility. For instance, AGI can extract meaningful knowledge from diverse sources, such as social media, IoT-generated data, and organizational repositories, enabling organizations to respond swiftly to market changes. Second, emerging technologies, including AGI, IoT, and augmented/virtual reality (AR/VR), present both opportunities and challenges for knowledge management. Opportunities include enhanced organizational agility, personalized learning experiences tailored to individual needs, and improved decision-making through real-time data analysis. For example, IoT facilitates the collection of real-time data from interconnected devices, while AR/VR creates immersive environments for knowledge transfer, such as virtual training simulations. However, these technologies also introduce challenges, such as algorithmic biases, which may skew decision-making, privacy concerns related to data collection, and infrastructure limitations that hinder scalability. Organizations must address these challenges through robust ethical frameworks and investments in digital infrastructure. Third, the study’s revision of the Nonaka and Takeuchi SECI model demonstrates how AGI transforms its four stages. In the socialization phase, AGI simulates human interactions in virtual environments, enabling knowledge sharing without physical presence. In externalization, AGI’s advanced natural language processing capabilities convert tacit knowledge into explicit forms, such as reports or models, with greater accuracy and cultural sensitivity. In the combination phase, AGI integrates diverse knowledge sources to create novel insights, uncovering interdisciplinary connections that enhance innovation. Finally, in internalization, AGI supports experiential learning through adaptive simulations, allowing individuals to internalize explicit knowledge as tacit expertise. These advancements mark a significant departure from the third generation of KM, positioning the fourth generation as a dynamic, intelligent, and technology-driven paradigm.Research limitations/implications: Despite its comprehensive approach, this study faced several methodological limitations. The focus on the 2019–2024 period, chosen to capture recent trends in AGI and the metaverse, may have excluded foundational studies from earlier periods. Additionally, the inclusion of only Persian and English-language articles, driven by publication volume and researcher accessibility, omitted potentially valuable contributions in other languages, such as Chinese or German. The reliance on literature review methodology, without incorporating empirical data from interviews or surveys, limited the depth of analysis. Furthermore, the use of specific databases (e.g., Scopus, SID) may have overlooked sources in less prominent repositories. To address these limitations, future research should adopt hybrid methodologies, combining literature reviews with empirical data, and incorporate multilingual sources to enhance inclusivity. Despite these constraints, the study’s findings have significant implications for both theory and practice, offering a foundation for further exploration of the fourth generation of KM.Practical implications: The research underscores the importance of leveraging AGI, IoT, and AR/VR to build intelligent knowledge management systems. Organizations should invest in digital infrastructure to support these technologies, fostering a culture of human-machine collaboration to maximize their potential. For instance, AGI can personalize employee learning by analyzing individual preferences and performance, while IoT can optimize knowledge flows in supply chains. To mitigate challenges like algorithmic biases and privacy concerns, organizations should adopt technologies like blockchain for secure and transparent knowledge sharing. By rethinking the SECI model through the lens of AGI, organizations can enhance knowledge processes, achieve greater agility, and drive innovation, ultimately securing a sustainable competitive advantage in dynamic markets.Originality/value: This research offers significant originality and scientific value by providing a novel framework for the fourth generation of knowledge management. Focusing on General Artificial Intelligence (AGI) and emerging technologies, it establishes a clear distinction between the third and fourth generations of knowledge management and redefines the Nonaka and Takeuchi SECI model within the context of AGI. This approach addresses a critical research gap in the knowledge management literature and introduces an innovative framework for managing knowledge in the digital era, which has not been previously explored. The study's value lies in offering practical guidance for organizations to leverage advanced technologies to enhance knowledge processes and achieve a sustainable competitive advantage.
Purpose: In the contemporary knowledge-based economy, private companies in developing nations, particularly Iran, face profound challenges in leveraging intellectual resources to secure sustainable competitive advantages. Traditional factors of production, such as labor, land, and physical capital, have yielded intangible assets such as organizational knowledge, innovation capabilities, and adaptive learning. This shift demands that firms reconfigure their strategies to prioritize knowledge synergy; however, many private enterprises grapple with resource limitations, inadequate infrastructure, and misalignment between knowledge initiatives and business goals. These barriers not only stifle innovation but also erode resilience in the face of environmental volatility and technological disruptions.This study investigates how knowledge management mechanisms enable private companies to cultivate and maintain competitive advantages during the transition to a knowledge-based economy. Researchers have focused on the structural, cultural, and strategic enablers that foster knowledge integration within resource-constrained settings. By addressing the core problem of underdeveloped knowledge infrastructures, this study illuminates pathways for overcoming systemic constraints, such as limited access to advanced technologies and skilled talent. It posits that effective knowledge management serves as a pivotal strategic lever, transforming intellectual assets into drivers of long-term value creation and organizational agility.This study addresses critical gaps in the literature, where prior studies often overlook the unique challenges faced by private firms in sanctioned environments such as Iran. Through targeted exploration, this study answers the following key question: Which knowledge management processes significantly enhance competitive advantages? How do synergy mechanisms operate under resource scarcity conditions? What structural and cultural factors underpin successful knowledge management? How do policy frameworks bolster innovation capacities? Ultimately, this study empowers private enterprises to harness knowledge as a cornerstone of strategic differentiation, fostering economic transformation at both the firm and national levels.Design/methodology/approach: This study adopted a qualitative, exploratory approach to uncover latent patterns and relationships in the existing literature, employing the meta-synthesis method as the core framework. This methodology draws on Sandelowski and Barroso's (2007) seven-step model, which facilitates the structured integration of diverse qualitative findings into cohesive themes. The process begins with formulating research questions, followed by systematic literature reviews, source selection, data extraction, synthesis, quality control and presentation of results.Data collection targeted secondary library sources, including Persian and English scientific articles, policy reports, and industry analyses. For Persian sources, the temporal scope spanned 1390–1404 (solar calendar, approximately 2011–2025 Gregorian), capturing long-term domestic trends in knowledge-based development in Iran. English sources covered 2021–2025, emphasizing recent global advancements in knowledge management and digital transformation. The inclusion criteria prioritized topical relevance (knowledge management and competitive advantage in private firms), scientific rigor (peer-reviewed publications in reputable journals), and temporal alignment. The exclusion criteria eliminated irrelevant, low-quality, or out-of-scope materials.The research population comprised 243 primary sources, from which purposive and systematic sampling yielded 65 high-quality samples for analysis. Analysts conducted a thematic analysis through three-stage coding: open coding extracted 410 conceptual codes from the texts, axial coding grouped these into 23 subthemes, and selective coding synthesized them into four main themes. Validation measures included expert panel reviews for content validity, source triangulation for comprehensiveness, and inter-coder agreement (85% reliability coefficient). All coding and analysis were performed manually, without software, to preserve interpretive depth and ensure methodological transparency.This design aligns with the exploratory goal of generating a localized framework tailored to Iranian private firms, integrating interdisciplinary insights from economics, strategy and technology. By synthesizing fragmented literature, the approach not only identifies emergent patterns but also constructs a robust analytical model for knowledge-driven competitivenessFinding: The meta-synthesis revealed that knowledge sharing emerged as the most salient subtheme, accounting for 15% of the conceptual codes and playing a central role in bolstering organizational resilience. Analysts identified four overarching themes: processes of organizational knowledge management, mechanisms for competitive advantage, structural and cultural enablers in the knowledge-based economy, and strategic and policy frameworks.Under knowledge management processes, subthemes such as knowledge creation, sharing, application, and preservation dominated, with the SECI model (Nonaka & Takeuchi, 1995) being highlighted as a key mechanism for converting tacit to explicit knowledge. These processes enhance innovation and adaptability, particularly in volatile market conditions. Dynamic capabilities, green innovation, and strategic flexibility stood out as competitive advantage mechanisms, enabling firms to respond to environmental changes and differentiate their products. Structural and cultural enablers encompass human capital development, organizational redesign for knowledge flow, and cultural values promoting trust and collaboration, which collectively mitigate resource constraints.Strategic and policy frameworks emphasize performance evaluation indices, supportive policies for knowledge-based firms, and alignment between national goals and firm strategies. These findings align with global benchmarks, such as the OECD countries' 2.7% GDP allocation to R&D in 2023 and Iran's growth in knowledge-based firms from 5,000 to over 10,115 units between 2019 and 2024. The radar chart visualization of themes underscores the prominence of knowledge sharing, with evaluation performance (frequency 33) as a cross-cutting driver.Overall, this study demonstrates that integrating knowledge management processes fosters sustainable competitive advantages, even under sanctions, by amplifying innovation and resilience. These insights validate the strategic imperative of investing in knowledge infrastructure for private firms navigating economic transitions.Research limitations/implications: The primary limitation of this study stems from restricted access to proprietary data from private companies, driven by confidentiality, competitive, and security concerns. This barrier hinders in-depth operational analyses, rendering some knowledge management dimensions less observable in real-world contexts. Additionally, the absence of standardized performance indicators for knowledge initiatives in private enterprises compromises comparative validity and generalizability. Economic sanctions and technological restrictions have further exacerbized these challenges, limiting firms' adoption of advanced tools and impeding digital transformation pathways. The rapid evolution of technology and policy landscapes has also introduced volatility, necessitating ongoing revisions to maintain relevance.Despite these constraints, the findings have significant implications for theory and practice. Theoretically, this study advances the knowledge management literature by proposing a context-specific model for resource-constrained environments, bridging gaps in interdisciplinary integration. Practically, it guides private firms to prioritize knowledge sharing and SECI processes for resilience building, while policymakers can leverage insights to craft supportive frameworks, such as incentives for R&D and human capital development.Future research should pursue comparative studies between the public and private sectors to uncover scalable models, longitudinal evaluations of the impact of knowledge management on sustained advantages, and cross-cultural analyses to contextualize theories. The integration of emerging technologies, such as AI, blockchain, and big data, into knowledge systems warrants exploration, as it has the potential to accelerate digital capabilities and innovation. These directions promise empirically grounded strategies that are resilient to market fluctuations.Originality/value: This research pioneers a localized meta-synthesis framework customized for Iranian private firms, diverging from conventional quantitative paradigms to offer a nuanced and interdisciplinary analytical model. By synthesizing economic, strategic, and technological perspectives, it challenges fragmented methodologies and introduces novel explorations of advanced technologies—such as AI and quantum computing—in knowledge practices under sanction-impacted conditions. This approach, which is largely underexplored in the domestic literature, opens new avenues for holistic, context-sensitive organizational research and knowledge-based policy discourse.This study’s value lies in its actionable insights for enhancing competitiveness amid resource scarcity, providing a blueprint for firms to align knowledge initiatives with strategic objectives. By addressing overlooked dimensions such as cultural enablers and policy synergies, this study contributes to global knowledge managemenat theory while fostering Iran's transition to a resilient, innovation-driven economy.
Purpose: Knowledge is the primary capital of every organization, and by managing it effectively, an organization ensures its survival and growth. Knowledge is the heartbeat of every decision made in an organization. Product development, service improvement, customer interaction, and overall organizational operations cannot function without such knowledge. Organizational knowledge identity refers to the beliefs, values, tacit and explicit knowledge, learning culture, and behavioral patterns that define how an organization manages, shares, and utilizes knowledge. This identity encompasses what the organization knows about knowledge, how it creates, stores, and transfers knowledge, and how it applies knowledge in decision-making and daily operations. Organizational knowledge identity reflects a company’s cultural and strategic distinctiveness and serves as a foundation for developing cognitive capabilities and organizational innovation. In today’s digital and globalized era, characterized by rapid change, intense competition, and increasing environmental complexity, having a clear and strong knowledge identity has become essential for organizational survival. Knowledge-based organizations, as key drivers of innovation and scientific-economic development in a country, require intelligent knowledge management and utilization of digital capabilities. Organizational knowledge identity, as a measure of how an organization engages with knowledge, creates the necessary foundation for enhanced performance in the field of knowledge intelligence. Simultaneously, digital knowledge empowerment plays a critical role in facilitating rapid and up-to-date access to the knowledge required for strategic decision-making. However, a comprehensive understanding of how these concepts interact to enhance strategic flexibility, particularly within local contexts such as knowledge-based companies in Ilam Province, remains limited. This gap in the literature highlights the need for localized, empirically grounded research. This study, which focuses on knowledge-based companies in Ilam Province, examines the relationship between organizational knowledge identity and strategic flexibility while considering the mediating roles of knowledge intelligence and digital knowledge empowerment. This research not only deepens the theoretical understanding of these interrelated concepts but also provides practical insights for managers of knowledge-based firms seeking to strengthen knowledge- and technology-driven strategies. Therefore, the main research question is as follows: What is the impact of organizational knowledge identity on strategic flexibility, considering the mediating roles of knowledge intelligence and digital knowledge empowerment in knowledge-based companies in Ilam Province?Research Method: This study is a quantitative research that is applied in nature, as its findings can be utilized in decision-making processes by managers of knowledge-based companies in the province of Ilam. In terms of design and methodology, this analytical-correlational study examines the relationships among the variables using Structural Equation Modeling (SEM). The statistical population includes all employees of knowledge-based companies in Ilam Province who are involved in knowledge-driven and innovative activities. These individuals play key roles in knowledge management, strategic decision-making, and the utilization of digital technologies. The sample size consisted of 200 participants selected from these companies through simple random sampling. The sample members were senior managers, middle managers, knowledge specialists, and information technology experts. Data were collected using a standardized questionnaire. The questionnaire content was designed based on a five-point Likert scale ranging from "Strongly Agree" to "Strongly Disagree." To measure the organizational knowledge identity variable, we used the standardized scale developed by Gupta and Govindarajan (2021). For strategic flexibility, a standardized questionnaire by Jiang and Cheng (2021) was employed. The organizational knowledge intelligence variable was assessed using the validated scale developed by Ranjan et al. (2023), and the digital knowledge empowerment variable was measured using the standardized instrument developed by Tiss (2018). The content validity of the questionnaire was confirmed through expert reviews by faculty members and specialists in the fields of knowledge and strategic management. Additionally, the Average Variance Extracted (AVE) values were used to assess construct validity. Since the AVE values for all the research variables exceeded the recommended threshold of 0.50, convergent validity was established. The reliability of the measurement instrument was evaluated using Cronbach’s alpha coefficient and composite reliability. The obtained values for both Cronbach’s alpha and composite reliability were above 0.70, indicating acceptable internal consistency and reliability of the scale. For data analysis, Structural Equation Modeling (SEM) was conducted using Visual PLS software.Findings: The results indicate that organizational knowledge identity has a significant positive effect on knowledge intelligence (β = 0.896, t > 1.96), digital knowledge empowerment (β = 0.845, t > 1.96), and strategic flexibility (β = 0.481, t > 1.96). Furthermore, both knowledge intelligence (β = 0.859, t > 1.96) and digital knowledge empowerment (β = 0.872, t > 1.96) significantly and positively influenced strategic flexibility. The mediation analysis reveals that both knowledge intelligence and digital knowledge empowerment significantly mediate the relationship between organizational knowledge identity and strategic flexibility, independently and completely.Results: The findings indicate that organizational knowledge identity enhances strategic flexibility directly and indirectly by fostering intelligent and digital capabilities in knowledge management. This highlights the importance of integrating cultural factors (i.e., knowledge identity) with technological enablers (i.e., digital empowerment) in knowledge-oriented organizations. Overall, this study proposes a comprehensive theoretical framework that demonstrates that organizational knowledge identity, as the most fundamental form of intangible capital, can effectively drive strategic flexibility through the enhancement of knowledge intelligence and digital knowledge empowerment. These findings enrich the theoretical literature on knowledge and strategy and offer valuable practical implications for organizations seeking sustainability and competitiveness in dynamic and uncertain environments.Originality and Value: This study contributes to the literature on knowledge management and organizational strategy by integrating the concepts of organizational knowledge identity, knowledge intelligence, digital knowledge empowerment, and strategic flexibility into a unified theoretical framework. Moreover, conducting this research in knowledge-based firms located in an underdeveloped and resource-constrained province such as Ilam adds significant value, demonstrating that even under limited resources, focusing on intangible knowledge and digital assets can enhance the strategic performance. These findings offer practical and valuable implications for managers of knowledge-based companies and regional policymakers aiming to improve organizational effectiveness.The findings of this study offer valuable practical implications for managers and leaders of knowledge-based companies particularly those operating in underprivileged or less-developed regions such as Ilam Province. Given the limitations of financial resources, technological infrastructure, and access to specialized human capital in such areas, focusing on intangible organizational assets such as knowledge identity, knowledge intelligence, and digital knowledge empowerment can serve as a low-cost yet highly effective strategy to enhance strategic flexibility and, consequently, organizational survival and competitiveness.
Purpose: The primary aim of this study is to explore the role of artificial intelligence (AI) in enhancing human resource management practices and strengthening knowledge management in this domain to improve organizational outcomes and employee well-being. In recent years, AI has attracted significant attention due to its clear potential to revolutionize business operations in organizations. In human resource management, the integration of AI can enhance decision-making processes, increase efficiency, and promote fairness in talent management. This study also examines how AI interacts with positive human resource management practices, particularly how this technology can guide human resource processes in a personalized, effective, and supportive manner to help employees thrive and optimize knowledge management in organizations. Moreover, this research aims to provide perspectives and a conceptual framework for integrating AI into human resource practices that benefit both organizations and employees, while facilitating knowledge management in organizations. While the impact of AI on operational efficiency and productivity is well-documented, this study also emphasizes the importance of employee well-being in achieving sustainable success and how it can be improved through positive human resource knowledge management practices. In fact, this study investigates how AI can not only increase productivity but also enhance employee experience and organizational culture. The findings aim to help develop human resource systems that are not only efficient but also human-centered, creating an environment where employees can flourish both personally and professionally. Focusing on the dual goals of organizational success and employee flourishing, this study provides practical recommendations for human resource professionals and organizational leaders interested in leveraging AI to create positive and productive work environments.Methodology: This study employs a mixed-methods approach, incorporating both qualitative and quantitative research methods to comprehensively analyze the role of AI in human resource management. The first phase of the research involved an extensive literature review on AI in human resources, organizational psychology, and employee well-being using text mining techniques and web-based tools like Viante and RapidMiner software. The literature review helped identify key factors influencing the successful implementation of AI in human resource practices, such as transparency, fairness, and personalized employee experiences. The second phase included the use of fuzzy DEMATEL (Decision-Making Trial and Evaluation Laboratory) and fuzzy cognitive mapping methods, which are widely used in research analyzing complex relationships between factors. The fuzzy DEMATEL method was used to identify key factors and barriers to the successful adoption of AI in positive human resource management, while fuzzy cognitive mapping was employed to model and visualize the causal relationships between various factors affecting the adoption of AI in positive human resource knowledge management. These methods are specifically designed to analyze and assess the interdependencies and complex relationships among factors. This research involved a case study from a leading organization in human resources to deeply examine how AI is integrated into human resource practices and its impact on organizational outcomes, employee flourishing, and well-being. This combination of methods allowed the researchers to comprehensively examine fuzzy relationships and averages, providing a practical model for AI application in human resources.Findings: This research used fuzzy cognitive mapping and fuzzy DEMATEL methods to analyze the application of AI in improving human resource processes and its impact on employee well-being. Initially, 188 final articles were reviewed to extract key indicators. After converting the article content into text format and removing irrelevant words, Viante was used to extract the most frequently mentioned terms. These terms were manually filtered and clustered, leading to the selection of 22 key indicators for the conceptual model. Next, RapidMiner software was used to extract key concepts, and with expert opinions and data refinement, these concepts were transformed into the final conceptual model. Then, using the fuzzy DEMATEL method, causal relationships between the indicators were identified, and the relationships between the factors were analyzed based on their influence and impact. Finally, fuzzy cognitive mapping was used to create a visual network of the indicators and their relationships. The findings showed that among the key indicators, strategic human resource modeling plays a crucial role in strengthening human resource processes and enhancing employee flourishing. Furthermore, the use of AI in performance evaluation, workforce needs prediction, and career development processes significantly improved organizational performance and employee job satisfaction.Research limitations/implications: Despite its valuable insights, this study has several limitations. The research is based on a single case study, which may limit the generalizability of the findings to other industries or organizations. Additionally, while the study highlights the potential benefits of AI in HRM, it does not fully explore the long-term implications of AI adoption on employee well-being and organizational performance. Further research is needed to examine the long-term effects of AI on employee satisfaction, retention, and overall organizational culture. Moreover, the study primarily focuses on the technical and operational aspects of AI adoption, while the ethical implications of AI in HRM are only briefly touched upon. As AI continues to evolve, it is crucial to explore the ethical considerations of using AI in human resource practices, such as data privacy, bias, and accountability.Practical implications: The findings of this study have several practical implications for HR professionals and organizational leaders. First, organizations should prioritize transparency and fairness when implementing AI-powered HR systems. Ensuring that AI systems are transparent and explainable can help build trust among employees and mitigate concerns about bias or discrimination. Second, organizations should invest in training programs to ensure that HR professionals and employees understand how AI works and how it can be used to augment HR practices. Furthermore, the study emphasizes the importance of a holistic approach to AI adoption in HRM. Organizations should not only focus on the technological aspects of AI but also consider the organizational culture and employee experience. By integrating AI into PHR practices in a way that supports employee well-being and promotes a positive work environment, organizations can create a culture of growth, engagement, and innovation.Originality/value: This research makes a significant contribution to the field of HRM by providing a novel framework for integrating AI into PHR practices with a focus on employee well-being. While much of the existing research on AI in HRM focuses on operational efficiency and productivity, this study highlights the importance of AI in creating a supportive and positive work environment. The findings contribute to the growing body of knowledge on the intersection of technology and human resource management, offering a new perspective on how AI can be used to promote both organizational success and employee flourishing.
Purpose: In the knowledge-based era, knowledge as an intangible asset plays a vital role in the success of organizations. Knowledge flow, which relies on information exchange and collaboration among individuals, facilitates the updating of knowledge and the advancement of new research. Knowledge-based companies, as key players in the knowledge-driven economy, require effective management of knowledge flow to maintain competitiveness and foster innovation. This study aims to identify and prioritize the factors influencing knowledge flow in knowledge-based companies located in the Yazd Science and Technology Park.Methodology: This research employs multi-criteria decision-making (MCDM) techniques, including SWARA, ARAS, and COCOSO. Initially, factors affecting knowledge flow were extracted through a comprehensive review of literature and prior research. Subsequently, questionnaires were designed and distributed to experts, including managers, employees of knowledge-based companies, and academic professionals. The statistical population of this study consisted of managers and employees of knowledge-based companies in the Yazd Science and Technology Park, as well as academic experts. The collected data were analyzed and ranked using the aforementioned techniques. Finally, the results were integrated using the rank averaging method.Findings: The results revealed that the most significant factors influencing knowledge flow in knowledge-based companies are management support and commitment (C8), continuous training (C4), IT infrastructure (E1), teamwork (A2), work ethic (A1), and support for teamwork (B5). These factors, in order of priority, play a key role in facilitating knowledge flow. Management support and commitment emerged as the most critical factor, highlighting the significant impact of leadership in fostering an organizational culture conducive to knowledge exchange. Continuous training and IT infrastructure were also identified as vital factors, enabling access to and updating of knowledge. Teamwork and work ethic, as human factors, enhance interaction and collaboration among employees.`Research limitations/implications: While this study employs an innovative combination of multi-criteria methods to comprehensively analyze knowledge flow factors, it has certain limitations, including its focus on companies in Yazd Science and Technology Park (which necessitates additional studies to generalize findings to other regions) and partial reliance on expert opinions (which may be subject to cognitive biases). Nevertheless, the findings can serve as a foundation for designing policy models in technology parks, developing training programs to enhance human factors affecting knowledge flow, and improving technological infrastructure in knowledge-based companies. Furthermore, the proposed hybrid methodology can provide a framework for future research in other knowledge management domains.Practical implications: The findings of this study can significantly assist managers of knowledge-based companies in developing operational strategies to enhance knowledge flow. Specifically, highlighting the crucial role of "management support and commitment" underscores the need for senior executives to foster an organizational culture conducive to knowledge sharing. Additionally, identifying key factors such as "continuous training" and "IT infrastructure" provides clear directions for future investments. The study recommends that policymakers in science and technology parks design specialized support programs to strengthen teamwork and develop knowledge infrastructure. On a broader scale, the proposed model can serve as a framework for evaluating the effectiveness of national-level initiatives aimed at developing knowledge-based ecosystems.Originality/value: This study offers unique scientific originality and value from multiple perspectives. Methodologically, the innovative integration of three multi-criteria decision-making techniques (SWARA, ARAS, and COCOSO) for analyzing knowledge flow factors presents a pioneering approach in knowledge management literature, enhancing result accuracy and reliability while enabling comprehensive findings comparison. The research's focus on knowledge-based companies in Yazd Science and Technology Park as a distinctive sample of Iran's innovation ecosystems addresses existing gaps in regional studies. The practical findings, particularly identifying "management support and commitment" as a key factor, not only emphasize leadership's vital role in shaping knowledge-oriented culture but also provide an operational framework for policymaking in other science and technology parks nationwide. Furthermore, the study bridges theory and practice through empirical evidence of simultaneous impacts from human factors (e.g., work ethics) and technological factors (e.g., IT infrastructure) on knowledge flow, transcending traditional boundaries in knowledge management research. Notably, this represents the first study simultaneously applying SWARA, ARAS, and COCOSO methods to analyze knowledge flow in Iranian knowledge-based companies, significantly enhancing its scientific value and innovation.
Purpose: In the era of a knowledge-based economy, knowledge sharing is recognized as a key factor shaping organizational capabilities and enhancing organizational performance. The advancement of emerging technologies, such as artificial intelligence (AI), has highlighted their role in creating sustainable competitive advantages. This study aims to examine the trends in scientific studies on the impact of knowledge sharing on organizational capabilities and organizational performance over the past 25 years (2000 to 2025) using bibliometric methods. The goal was to provide a conceptual map of the evolution of this field and identify research gaps. By analyzing 1719 research articles extracted from the Scopus database, this study offers comprehensive insights into the path of research development, key trends, main sources, and influential authors. This bibliometric analysis not only maps the quantitative growth in publications but also explores the integration of knowledge sharing with modern technologies such as AI, which acts as a catalyst for improving performance and innovation in organizations. Furthermore, it emphasizes how knowledge quality and organizational culture reinforce each other to achieve sustainable competitive advantages. The research question guiding this study is: "What has been the trend of research in the field of knowledge management regarding its impact on organizational capabilities over the last 25 years?" This inquiry is rooted in resource-based view (RBV) theory, which posits that intangible assets, such as knowledge, culture, and innovation, are primary sources of competitive advantage. Intangible assets, such as organizational learning and knowledge, can lead to greater profitability than physical or financial resources. For instance, organizational culture, as a core competency, plays a vital role in maintaining competitive advantage by fostering an environment in which knowledge is shared and innovation is strengthened.Design/methodology/approach: This quantitative study employs a bibliometric approach, focusing on metadata from research articles, keywords, and citations. The population consisted of all relevant articles in the Scopus scientific database, one of the most reputable international sources containing over 80 million articles and 16 million authors. The sample includes 1719 articles, selected through targeted keyword searches within the time frame of 2000 to 2025. The data collection tool comprised output files from the database, including metadata such as publication year, authors, abstracts, keywords, citations, and DOIs. The validity of the research was ensured by selecting the reputable Scopus database, and its reliability was confirmed through the repeatability of the search and the analysis methods.Two primary keyword searches were conducted to gather data. The first search used: "Knowledge management" OR sharing AND organization AND capability, yielding 508 articles within the specified period. The second search used: "Knowledge sharing" AND organization AND performance, resulting in 1281 articles. After integrating these results and removing duplicates, a final CSV file with 1719 records was created. Each record represents an article and includes details such as authors, full author names, author IDs, article title, publication year, journal title, citation count, affiliations, link, abstract, keywords, references, article language, title summary, document type, publication stage, ISSN code, and DOI identifier.Data analysis involved descriptive statistical analyses (e.g., publication trends, top countries, and authors) using Excel software and its Power Query add-on. Network analyses, such as keyword co-occurrence, co-authorship, and source citation, were performed using VOSviewer software. This combination allowed us to examine the temporal changes in research topics. For the co-citation analysis of authors, a minimum of 100 citations was set to identify influential researchers. Similarly, for journals, co-citation networks were drawn with a minimum of 300 citations to focus on high-impact journals. Keyword trends were analyzed using overlay visualizations in VOSviewer, where color spectra indicate the recency of terms (darker colors for older terms and brighter colors for more recent ones). An analysis of keyword frequency over time was conducted for the top 30 keywords, revealing shifts in the research focus. This methodological rigor ensured a systematic and reproducible examination of the literature, aligning with established bibliometric practices.Findings: The findings indicate that The number of articles from 2000 to 2025 has shown an upward trend, reaching over 200 articles per year in recent years (post-2020). Descriptive statistical analysis in Excel revealed that the United States and China account for more than 40% of the articles, making them the top countries. Authors such as Nonaka, with over 50 citations, are among the most influential in this field. In the VOSviewer analysis, the keyword co-occurrence network, comprising over 500 main keywords, identified clusters such as "artificial intelligence" (with a linkage strength of 150), "organizational culture" (linkage strength of 120), and "innovation" (linkage strength of 180), which constituted more than 60% of occurrences in the studied period. The trend analysis of topic changes also confirmed a 35% increase in the focus on integrating knowledge sharing with emerging technologies, such as artificial intelligence, in the last decade.Publication trends show a significant increase starting from 2007, peaking in 2024 with 172 articles, although a slight decline to 89 in 2025. The top active authors include Salleh Kalsom (8 articles, 125 citations) and Kianto Aino (7 articles, 937 citations). The co-authorship network highlights clusters where Nonaka I. is central and has the highest total link strength. Journal co-citation analysis positions the Journal of Knowledge Management as the most cited journal, with strong connections to other management and innovation journals. The top 20 most-cited articles include works such as "Knowledge sharing and firm innovation capability: An empirical study" by Lin (2007, 1161 citations) and "Knowledge networks: Explaining effective knowledge sharing in multiunit companies" by Hansen (2002, 1132 citations).The subject area distribution shows dominance in Business, Management and Accounting (824 articles) and Computer Science (597 articles). Keyword trends reveal consistent growth in terms like "knowledge sharing" and "innovation," but recent years show emergence of "knowledge hiding," "leadership," and "social network analysis." The overlay visualization confirms that AI-related terms are recent additions, clustered with business performance, tacit knowledge, and sustainability. The AI cluster is strongly linked to supply chain management, indicating a focus on this area, whereas other business domains show research gaps. Overall, the integration of AI with knowledge sharing is emerging as a driver of ambidexterity, enhancing both exploitative and explorative capabilities, with knowledge quality mediating these effects.Conclusion: This study concludes that integrating knowledge sharing with artificial intelligence, as a key driver, improves organizational performance by strengthening innovation and ambidextrous capabilities, with knowledge quality and organizational culture playing reinforcing roles. Explicit knowledge sharing impacts exploitative ambidexterity, whereas tacit knowledge sharing enhances explorative innovation. Organizational culture fosters trust and collaboration, leading to better decision-making and competitive advantage. Recent trends highlight knowledge hiding as a barrier and social networks as enablers of KCP. This evolution suggests a shift towards digital knowledge economies, where AI automates processes and combines with human interaction for the creation of new knowledge. Policymakers and managers should prioritize AI integration in knowledge management to maintain competitiveness.Originality/value: This study is the first to provide a comprehensive bibliometric analysis of the 25-year trend in this field, with a focus on AI integration. It offers novel evidence of crossing traditional methodological boundaries (combining quantitative and network analysis) and interdisciplinary borders (knowledge management and information technology), proposing new ideas for organizational policymaking in the digital economy. Unlike prior studies, this study identifies gaps in AI applications beyond supply chains and emphasizes emerging concepts such as knowledge hiding, adding value for researchers and practitioners in advancing sustainable, innovative organizations.
Purpose: The integration of AI and knowledge management refers to the strategic use of technology to increase the creation, sharing, and use of knowledge in an organization. Implementing artificial intelligence in knowledge management poses challenges and limitations for organizations, especially government departments that are tasked with providing services to the public. To understand the relationship between knowledge management and AI, it is necessary to carefully examine how it affects important human resource variables of organizations, including "performance". The aim of this study is to investigate the simultaneous impact of knowledge management and AI on the performance of human resources in the public sector.Methodology: The present study is applied in terms of orientation and quantitative in terms of methodology, which was conducted using a descriptive-correlation strategy. The study period is winter 2024 and spring 2025. The research population is the employees of the public sector in Markazi Province, from which 150 managers and experts were selected using snowball sampling. A questionnaire was used to collect data, and its reliability was confirmed based on Cronbach's alpha and combined reliability criteria, and validity were confirmed based on AVE and Cross-factor loading indices. Structural Equation Modeling was used to analyze the data, and Smart PLS 3.0 software was used to perform its calculations.Findings: Data analysis showed that various applications of artificial intelligence have a direct relationship with knowledge management processes. Documenting tacit knowledge has a positive and significant effect on knowledge conversion, transfer, and application; personalizing access to knowledge has a positive and significant effect on knowledge retention and maintenance; and intelligent prediction and decision-making has a positive and significant effect on knowledge creation and application. Also, the integration of artificial intelligence and knowledge management has a direct relationship with the performance of human resources in the public sector. Knowledge creation has a positive and significant effect on participation; knowledge retention and maintenance has a positive and significant effect on satisfaction; knowledge conversion and transfer has a positive and significant effect on participation; and knowledge application has a positive and significant effect on retention and maintenance, satisfaction, and training costs. The highest path coefficient (0.641) is related to the effect of knowledge application on satisfaction, and the lowest path coefficient (0.174) is related to the effect of prediction and intelligent decision-making on knowledge application.Research limitations/implications: The following are some of the limitations of the present study:1. Limited use of artificial intelligence tools in the country's public sector.2. Limited access to service sector employees who use artificial intelligence tools, resulting in a limited sample size.3. Limited data analysis methods and tools.The following suggestions are made for future research:1. The challenges and obstacles to implementing artificial intelligence and knowledge management in executive agencies should be studied.2. The impact of using new technologies on various organizational performance measures should be examined.3. The scope of the study should be expanded at the national level and the comparative study should be expanded at the international level.4. Qualitative methodology should be used to find the implementation pattern of artificial intelligence and knowledge management in the public sector.Practical implications: Comprehensive training programs should be launched to strengthen the skills and awareness of employees in the field of artificial intelligence and knowledge management. The formation of multidisciplinary teams that are a combination of employees from different departments of the organization can help in creating a culture of artificial intelligence and knowledge management in the organization. There should be standard processes for collecting, organizing and converting data into a usable format. Security measures should be considered, including the use of strong encryption algorithms to protect data, applying access policies and restrictions, and reviewing and identifying security threats. Organizations should design appropriate monitoring mechanisms to ensure the quality of shared data.One of the application areas where artificial intelligence has many capabilities is knowledge management. Using artificial intelligence tools and techniques such as machine learning, artificial neural networks, natural language processing and recommender systems, knowledge can be collected, organized, extracted and shared in a structured way. This improves access to knowledge, increases the efficiency and speed of knowledge management processes, and makes better organizational decisions. However, to successfully implement artificial intelligence in knowledge management, we need to face related challenges. One of the benefits of artificial intelligence for knowledge management is the power of prediction and analysis. Using machine learning algorithms and artificial neural networks, it is possible to recognize patterns and trends in data and provide more accurate predictions about future behaviors and changes. This capability is of great importance for organizations, because they can make better decisions for the future and achieve success and sustainable growth. Artificial intelligence can also be used to aggregate knowledge. By using hybrid algorithms and intelligent decision-making systems, information and knowledge in an organization can be collected from various sources and a comprehensive picture of organizational knowledge can be obtained. This helps the organization to improve its strategies and decisions based on existing knowledge and achieve better results. In addition, artificial intelligence can play a role in increasing cooperation and interaction between people in different departments of the organization. By using recommender systems and data analysis, more effective communication and collaboration can be established between members of the organization. These systems can give individuals suggestions that increase interaction and cooperation between team members and improve the performance and creativity of groups. Another benefit of artificial intelligence for knowledge management is improving access to knowledge. Using machine learning algorithms, systems can be developed that are capable of searching and extracting knowledge from various sources, which allows for quick and easy access to the required knowledge at any time and place. Artificial intelligence can also play an important role in knowledge sharing. AI-based recommender systems can help employees in an organization share their knowledge and experiences with colleagues. These systems can recommend appropriate materials and resources to employees based on past experiences and individual profiles, and accelerate the knowledge sharing process. Using natural language processing, artificial intelligence can be used to analyze texts and information available in an organization. This technology can help identify topics, extract useful information, and summarize texts. Natural language processing systems can also be used in knowledge management automation processes. For example, automation systems can automatically categorize and assign relevant tags to related content. This speeds up the process of categorizing and organizing knowledge, improving its accessibility and usability.Originality/value: Knowledge management provides the conditions for knowledge understanding to occur, while AI provides the capabilities to expand, use, and create knowledge in new and efficient ways. By effectively integrating technology with knowledge management practices, organizations can improve decision-making, innovation, and overall organizational performance. Training and development of human resources specialized in AI and knowledge management is critical to the successful implementation of this technology.
Purpose: Public sector organizations today operate under intensifying demands to uphold ethical standards, mitigate corruption, and ensure public trust. Whistleblowing—the deliberate, principled disclosure of sensitive internal information such as financial irregularities, misconduct, or policy violations—serves as a vital corrective tool. However, the decision to blow the whistle is not driven by a single stimulus; instead, it emerges from a complex nexus of factors. An individual’s moral inclination, shaped by personal values and integrity, provides the foundational readiness to report wrongdoing. Simultaneously, the prevailing ethical knowledge sharing culture within an organization either encourages or stifles such disclosures by signaling whether employees will be supported or ostracized. Finally, structural barriers—bureaucratic hurdles, opaque reporting procedures, fear of retaliation, and lack of legal safeguards—directly impede the act of disclosure. While prior research has examined these dimensions in isolation and largely through linear models, the present study addresses a critical gap by modeling their joint, potentially non linear influences on whistleblowing intention. Moreover, the advent of advanced language models, such as Grok 3, affords a unique opportunity to compare algorithmic predictions against actual human behavior. Accordingly, this research aims to (1) develop a comprehensive, non linear quantitative model capturing the combined effects of moral inclination, ethical culture, and structural barriers on whistleblowing intention among public service employees in Lorestan Province, Iran, and (2) evaluate the comparative predictive accuracy and limitations of human survey data versus Grok 3’s outputs under identical vignette scenarios.Methodology: An applied, descriptive experimental field study was undertaken, employing Central Composite Design (CCD) in conjunction with Response Surface Methodology (RSM) to explore main, interaction, and quadratic effects. From a population of 700 service sector employees, a stratified random sample of 385 was determined via Cochran’s formula, ensuring sufficient statistical power. Data collection used a scenario based questionnaire featuring fifteen unique vignettes, each systematically varying three independent variables—individual moral inclination (A), ethical knowledge sharing culture (C), and structural barriers (B)—across low, medium, and high levels. Participants rated their likelihood to disclose sensitive knowledge on a seven point Likert scale (1 = “definitely would not” to 7 = “definitely would”). Content validity was confirmed through expert review by five scholars in knowledge management and organizational behavior, and internal consistency reliability was established with Cronbach’s alpha = 0.88 in a pilot test of 30 employees. In parallel, the same vignette scenarios were input into Grok 3 to generate AI based intention scores. Four regression frameworks—purely linear, two way interaction, quadratic (second degree), and cubic (third degree)—were fitted for both human and AI datasets using Design Expert 13. Model selection criteria included coefficient of determination (R²), adjusted R², ANOVA F tests, lack-of-fit tests, and sum of squared errors (SSE). The quadratic model demonstrated superior explanatory power (R² = 0.95 human; R² = 0.97 AI) with non significant lack-of-fit and was selected for detailed analysis. A composite utility function was then applied to pinpoint the optimal factor combination that maximizes whistleblowing intention.Findings: The human‐based quadratic model explained 95% of variance in whistleblowing intention, with highly significant coefficients. Moral inclination (A) exhibited the most pronounced positive linear effect (coefficient = 0.96, p < 0.01), highlighting its central motivational role. Its accompanying negative quadratic term (–0.76, p < 0.05) revealed a saturation threshold beyond which additional moral motivation produced diminishing increases in disclosure intent—an effect seldom captured in linear analyses. Ethical knowledge sharing culture (C) registered a robust positive linear coefficient of 0.95 (p < 0.01) across all levels, with no evidence of saturation, signifying its consistent enabling function. Structural barriers (B) exerted a significant negative linear effect (–0.59, p < 0.01), indicating that each incremental barrier unit steadily suppresses willingness to report. Under the optimal conditions (A = 7, C = 7, B = 1), predicted human intention reached 5.55 on the seven point scale with a composite utility of 0.87. Grok 3’s quadratic model paralleled these trends but with distinct magnitudes: A’s linear coefficient was 1.64 (p < 0.01), B = –0.60 (p < 0.01), and C = 0.80 (p < 0.01). Notably, AI identified significant interactions: A×B (–0.375, p < 0.05) suggested that high moral drive combined with strong barriers markedly dampens intention, while A×C (0.225, p < 0.05) signified synergistic gains when moral drive aligns with a supportive culture. The AI quadratic A² term (–0.43, p < 0.05) reaffirmed saturation in moral motivation. Grok 3’s optimal predicted intention soared to 7.00, approximately 26% higher than human respondents, reflecting AI’s lack of psychosocial risk aversion and highlighting the complexity of real‐world disclosure decisions.Research limitations/implications: The cross‐sectional vignette approach, though experimentally robust, cannot fully emulate the emotional stakes, group dynamics, and organizational politics of authentic whistleblowing. The geographic focus on a single province reduces the generalizability to distinct cultural, regulatory, or institutional contexts. Self‐report measures risk social desirability bias and cognitive fatigue, especially over multiple scenario evaluations. Grok 3’s opaque proprietary architecture precludes in‐depth understanding of how it weights and processes scenario information. Future research should incorporate longitudinal designs tracking actual disclosure behaviors, broaden samples across diverse settings, integrate objective whistleblowing records, and explore moderating influences such as employees’ perceived organizational support and individual risk tolerance.Practical implications: This study furnishes a multi‐lever, evidence‐based roadmap for public sector management. First, cultivate intrinsic moral motivation via scenario‐based ethics training, peer support networks, and visible ethical leadership—but calibrate intensity to avoid motivational saturation. Second, strengthen an ethical knowledge‐sharing culture by embedding transparency in organizational mission statements, celebrating exemplary whistleblowers, and maintaining open communication channels for ethical concerns. Third, dismantle structural barriers through simplified, confidential reporting procedures, robust anti‐retaliation policies, and clear legal safeguards. While AI models like Grok 3 can support scenario planning and policy simulations, they must complement—rather than replace—human judgment, particularly where psychosocial and cultural nuances dictate employee risk calculations.Originality/value: This research represents the first Iranian application of an integrated CCD RSM experimental design combined with a cutting‐edge large language model to analyze whistleblowing intentions. Departing from conventional linear regression, it uncovers novel saturation dynamics in moral motivation and maps intricate factor interactions. The introduction of a utility optimization metric offers actionable guidelines for calibrating moral, cultural, and structural interventions. By juxtaposing human survey data with AI predictions, the study elucidates both the promise and the limitations of AI in ethically sensitive organizational contexts, thereby advancing methodological frontiers in the study of organizational ethics and public governance.
Purpose: Despite the rapid growth in the production and utilization of big data and its vast potential for enhancing innovation and improving performance, many Iranian knowledge-based enterprises (KBEs) still face serious challenges in effectively leveraging this strategic resource. If such limitations persist, they are likely to weaken these firms’ competitive positions in today’s turbulent business environment by reducing product commercialization. A review of the existing literature indicates that most prior studies have been conducted in developed countries, and their findings are not necessarily applicable to Iran’s local context. Moreover, much of the research has focused only on partial relationships among the variables, with limited attention given to developing and testing a comprehensive conceptual model that explains the entire value-creation pathway from big data knowledge management capabilities (BDKMC) to business performance (BP). Accordingly, this study seeks to address this research gap and provide empirical evidence within the context of Iranian KBEs. Therefore, this study examines the impact of BDKMC on BP by clarifying the mediating roles of innovation capability (IC), business process innovation (PI), and competitive advantage (CA). This study aims to deliver an integrated and precise picture of how big data can be intelligently harnessed to foster innovation, build CA, and enhance BP.Methodology: This study was designed and conducted within a positivist paradigm, following a deductive reasoning approach. In terms of purpose, it is categorized as applied research, while methodologically, it is descriptive in nature and implemented as a cross-sectional survey. The population of interest comprised 5,048 knowledge-based companies in Tehran Province. To ensure adequate statistical precision and reduce the likelihood of Type I and Type II errors, the minimum sample size was estimated using G*Power 3. Based on four predictor variables, a significance level of 0.05, an effect size of 0.05, and a statistical power of 0.90, the required sample size was determined to be 313. The unit of analysis was company managers, with one questionnaire administered to each firm in the sample. Sampling was conducted using a simple random sampling procedure using the random sampling function in SPSS. Data were collected using a standardized instrument consisting of 47 items. The research model was analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) in SmartPLS 3. In the preliminary stage, factor loadings were inspected to ensure that all exceeded the minimum threshold of 0.40. The model evaluation was conducted in three steps: measurement, structural, and overall models. Within the measurement model, internal consistency reliability was assessed using Cronbach’s alpha, rho_A, and composite reliability (CR) values. Convergent validity was examined using the average variance extracted (AVE), while discriminant validity was assessed using both the Fornell–Larcker criterion and the Heterotrait–Monotrait ratio (HTMT). In the structural model, the predictive capability was first evaluated through the explained variance (R²) and Stone–Geisser’s Q² (predictive relevance). The study hypotheses were then tested. Finally, the overall model fit was assessed using three key indices: the root mean square residual covariance (RMStheta), standardized root mean square residual (SRMR), and goodness-of-fit (GOF) index. Together, these indices ensured the robustness and reliability of our proposed conceptual model.Findings: The results revealed that BDKMC significantly affected IC (β = 0.329, t = 4.669), BPI (path coefficient = 0.239, t = 3.010), and CA (β = 0.425, t = 6.749). However, the direct effect of BDKMC on BP was not supported (β = 0.052, t = 0.985). Furthermore, IC has significant positive effects on BPI (β = 0.536, t = 9.632), CA (β = 0.443, t = 7.562), and BP (β = 0.299, t = 3.514). In addition, BPI significantly influenced both CA (β = 0.165, t = 2.360) and P (β = 0.146, t = 2.071) in this study. Finally, CA had a strong and significant impact on BP (path coefficient = 0.342, t = 5.517). Regarding the mediation effects, the mediating roles of IC and BPI were not supported (β = 0.098 and 0.035; t = 1.812 and 0.794, respectively). However, the mediating role of CA was confirmed (β = 0.145, t = 2.013).Research limitations: Despite its theoretical and practical contributions, this study is subject to several limitations, which provide avenues for future research. First, reliance on self-reported data from managers of knowledge-based firms may introduce response bias. Future work could enhance validity by employing multi-method approaches, such as in-depth case studies and semi-structured interviews, to reveal the more nuanced mechanisms of value creation through big data knowledge management. Second, the geographical and industrial scope—focusing on firms in Tehran Province and analyzing heterogeneous industries without differentiation—may limit the generalizability of the findings. Expanding geographical coverage, conducting cross-national comparisons, and pursuing industry-specific studies could address this issue while exploring variations across technological domains and organizational life-cycle stages. Third, the cross-sectional design restricts causal inference and leaves room for reverse or bidirectional effects to be observed. Longitudinal designs can offer stronger causal insights. Fourth, the dynamic nature of environments and the rapid evolution of big data technologies may constrain the measurement validity. Data mining, document and social media content analysis, updated measurement instruments, and novel theoretical perspectives may help mitigate this concern. Fifth, the lack of distinction among firms in terms of size, age, technological focus, and business models may obscure relevant differences. Future research could employ cluster analysis, multigroup analysis, or multilevel modeling to uncover subgroup-specific patterns and test the conceptual framework accordingly. Finally, the insignificant mediating effects highlight the need for further investigation to capture the complexity of relationships and provide a more holistic understanding of the value creation process.Practical implications: This study’s findings offer several practical insights for managers and policymakers in KBEs. First, they highlight the critical role of BDKMC in enhancing innovation and creating CA, suggesting that firms should strategically invest in knowledge management systems and processes to fully leverage their data resources. Second, the results indicate that IC and the BPI serve as key mechanisms through which the BDKMC impacts BP. Therefore, managers should focus not only on technological adoption but also on fostering a culture of continuous innovation and process improvement to translate data-driven insights into tangible performance results. Third, the confirmed mediating role of CA underscores the importance of aligning innovation and process initiatives with strategic objectives to sustain superior performance in dynamic business environments. Collectively, these insights provide actionable guidance for KBEs aiming to optimize their big data strategies, strengthen their innovation pipelines, and enhance their overall organizational competitiveness and performance.Originality/value: This study, for the first time, investigates the impact of BDKMC on the performance of Iranian KBEs and provides unique empirical evidence from these companies. The findings address existing gaps in both theoretical and practical literature and offer novel insights into the dynamics of innovation and competitive advantage within this context.
Background/Objectives: Over the past few decades, lean thinking has emerged as a method for optimizing and continuously improving organizations, ensuring their sustainable development when successfully implemented. While rooted in Taiichi Ohno’s lean philosophy within Toyota’s automotive industry, lean principles are now gaining traction in educational institutions and universities as a strategy for continuous improvement. Universities and higher education centers, facing shifts from traditional paradigms, grapple with numerous challenges, including: inconsistent or delayed implementation of appropriate academic actions, evolving stakeholder needs and expectations (and a corresponding lack of responsiveness), industry demands and declining output quality, flawed understanding of university processes, technological advancements, changing university processes over time, rising costs of university outputs, insufficient government funding, and persistent pressure from society, employers, students, and other stakeholders (Cudney et al., 2020; Cox et al., 2020). Consequently, universities require staff familiar with lean approaches (Bumjaid et al., 2019) who can transform challenges into opportunities through continuous process improvement and the pursuit of excellence (Sfakianaki et al., 2019).Given the advantages of lean thinking in higher education, its adoption in universities is crucial. A key initial step is developing a lean higher education model and assessing its alignment with the core dimensions of the university as a learning and knowledge-driven organization (Balzer, 2020). While characteristics and components of a lean model have been identified in some universities, this model has not been designed and developed from the perspective of experts in the central headquarters of the Ministry of Science, Research, and Technology. This ministry is responsible for policymaking, planning, oversight, and evaluation of university affairs and should provide the necessary platform for universities to become “lean.” Understanding the perspectives of managers and experts at the Ministry headquarters is therefore very important and influential. By accessing a model from the perspective of these headquarters experts, it can be compared with past studies conducted “in the field” (at universities), and similarities and differences can be identified from the viewpoint of headquarters and field personnel. It’s also important to recognize that becoming lean in organizations, especially in universities as educational and research institutions, requires organizational changes, including changes in structures and work processes, perspectives, culture, the style of leadership support, and, ultimately, the development of information and communication technology. Of course, these changes should be focused on a continuous improvement approach (NaranjiSani et al., 2017). Lean thinking, as an operational and improvement-oriented approach, utilizes critical thinking to provide the basis for eliminating waste and enhancing value in educational systems through in-depth analysis, identifying the root causes of problems, and continuously evaluating processes. A literature review revealed limited research on developing lean higher education models in Iran. Therefore, given the importance of the lean approach and the numerous benefits of implementing this method in higher education, this research aims to answer the question: What is the lean higher education model, and what is the prioritization of lean higher education dimensions from the perspective of experts in the central headquarters of the Ministry of Science, Research, and Technology?Research Methods: This research aims to present a model for lean higher education from the perspective of experts in the central headquarters of the Ministry of Science, Research, and Technology. To achieve this goal, detailed qualitative data is required. Therefore, a qualitative approach was chosen for the study. This study also falls under the category of exploratory-analytical methods. The statistical population of this research includes all experts in the headquarters of the Ministry of Science, Research, and Technology who have educational backgrounds related to education, especially higher education, and also have executive and managerial experience in the headquarters of the Ministry. Purposive sampling was used in this research. Experts were selected for interviews who have experience in teaching and training in the field of lean management, as well as executive and managerial experience in higher education and human resource management at the university and ministry levels. The number of samples in the qualitative phase was determined based on the principles of theoretical saturation. Based on this principle, theoretical data saturation was achieved by conducting interviews with 12 experts in the central and executive areas. In this study, semi-structured interviews consisting of five questions were used to identify the components of lean higher education and develop a suitable model for lean higher education. The analysis of the data obtained from the semi-structured interviews focused on the Strauss-Corbin method, which was performed by establishing a connection based on questioning and continuous comparison. In axial coding, the variety of extracted codes indicates the type of relationships. For example, to compare one category with another, the question might be asked: Is category “A” a consequence of the strategies for category “B”? When the data confirmed the question, the relationship between the two categories was determined and could be converted into a proposition, thus the coding process was formed. After ensuring that no new code could be extracted from the experts’ responses and the coding process led to the repetition of results, the stage of theoretical saturation and coding cessation occurred.Research Findings: Through the coding process, components related to lean higher education in public universities were identified, and its conceptual model was developed. According to the results of thematic analysis of interviews with key stakeholders, experts, and key experts, the proposed model for the implementation of lean higher education in the Ministry of Science, Research, and Technology has been schematically drawn. The findings obtained in the target community showed that five components – structural, managerial, human resources, financial-economic, and infrastructural-technological – are the most important factors of the lean higher education model from the perspective of experts and managers of the Ministry of Science, Research, and Technology. The managerial dimension includes five components: quality monitoring, university-community interaction, independent decision-making, needs-based services, and university management and leadership. The structural dimension includes four components: structural reform of the university, higher education planning and reform of faculty promotion regulations, governance of lean attitude and thinking, and reduced dependence on the government. The human resources dimension includes three components: recruitment, development and empowerment, specialized capabilities, and individual and personality characteristics. The financial-economic dimension includes two components: securing financial resources and strengthening links with industry. Finally, the infrastructural-technological dimension includes two components: infrastructure development and the use of innovative and transformative technologies. To analyze the data obtained from semi-structured interviews, the focus was on the Strauss-Corbin method and the theme analysis technique was used, which was done by creating a connection based on the question design and making continuous comparisons, and NVivo software was used.Conclusion: The human resources component, with 49 codes, the structural component, with 35 codes, the managerial component, with 24 codes, the infrastructural-technological component, with 19 codes, and finally the financial-economic component, with 12 extracted codes, were the most important and prioritized, respectively. The findings of the structural dimension are consistent with the results of the research of (Ijtihad & et al., 2007) and QureshiKhorasgani, 2016), and clearly show that the inappropriate structure in the higher education system can cause tension and conflict in the organization and prevent the initiative and creativity that is necessary for an academic organization. The findings of the managerial dimension are in line with the results of studies (Emiliani, 2015), which show that components such as the spirit of cooperation with employees and problem-solving skills are key factors in the lean university. Also, the results of research (Hajkhazimeh & et al., 2019; Jafari & et al., 2007; Akbari & et al., 2020) emphasize that the use of participatory management, empowerment and meritocracy is directly consistent with the components of the role model and participatory management. The findings of the human resources dimension are consistent with the results of the research of (Kucheryavenko & et al., 2019; Cano & et al., 2020; Moore & et al., 2007) and show the importance of the budget and financial and economic resources components in the readiness to implement the components of the lean higher education system. Finally, in the infrastructural-technological dimension, the analysis of the findings shows that the use of advanced technologies such as artificial intelligence can have significant effects on the lean transformation of the higher education system. In other words, considering the influence of technology in society and its impact on the structure of infrastructure, the higher education system and its related processes have faced structural changes, alignment with these changes seems necessary in the current situation.Originality/Value: By presenting the lean higher education model from the perspective of experts from the Ministry of Science, Research and Technology, this research provides the necessary platform for localizing international theoretical models in order to implement the principles and approaches of lean higher education in the country's universities, and helps bridge the gap between macro policymaking and operational implementation.
Purpose: The primary objective of This study examines knowledge management in relation to the sustainability and long-term development of businesses, with a particular focus on women-owned enterprises, and designs a comprehensive knowledge management framework using the Delphi technique. Knowledge management is a critical driver of organizational success, enabling businesses to leverage internal and external knowledge to achieve competitive advantage, innovation, and operational efficiency. In a dynamic business environment, organizations that effectively implement knowledge management practices are better equipped to respond to market changes, technological advancements, and evolving customer demands. Businesses are increasingly significant contributors to the national economy but face unique challenges, including limited access to financial resources, insufficient market networks, and barriers to professional training and mentorship. This study aims to identify modern tools, methods, and techniques of knowledge management that enhance organizational performance, foster innovation, and strengthen sustainability. Beyond assessing the impact of knowledge management practices, this study explores how these practices influence long-term business growth and resilience. Specifically, this study evaluates how knowledge management supports sustainable development by facilitating knowledge creation, dissemination, utilization, and retention. It investigates how businesses can capture valuable knowledge from employees, particularly key personnel, and transform it into actionable strategies that improve productivity, innovation, and market competitiveness of the company. The central goal is to propose a practical and actionable framework grounded in real-world organizational conditions that supports continuous improvement and sustainable growth. This study also addresses the gaps in the literature. While many studies focus on large organizations, fewer examine SMEs, particularly women-owned enterprises, which represent a growing and important economic segment. By focusing on this group, this study provides context-specific insights to guide policymakers, business leaders, and knowledge management practitioners in implementing effective strategies. The developed framework serves as a roadmap for organizations seeking to leverage knowledge assets to achieve strategic objectives, improve business processes, and ensure long-term sustainability.Design/methodology/approach: This applied qualitative study employs a descriptive-analytical approach to understand complex organizational processes. The Delphi survey technique was used to gather expert insights and develop the knowledge management framework, as it allows for iterative rounds of feedback and consensus-building among experts. In the first stage, key components, indicators, and constructs were identified through an extensive review of the academic literature, industry reports, and case studies. These were structured into a questionnaire based on a five-point Likert scale, capturing both quantitative and qualitative expert perspectives. The statistical population included 30 experts and professors in knowledge management, business management, and entrepreneurship, selected through purposive sampling to ensure their sufficient expertise. The questionnaire covered 11 major components and 128 items, including the objectives of knowledge utilization, information technologies, human resources, knowledge sharing, knowledge development, and evaluation. External factors—individual, organizational, governmental, economic, and socio-environmental—were also incorporated to capture the multidimensional nature of knowledge management in supporting sustainability. Validity and reliability were confirmed through expert review and Cronbach’s alpha. Data analysis was conducted using SPSS version 25, employing descriptive and inferential statistics to identify the trends and critical components. Multiple iterative rounds of the Delphi method ensured that the final framework reflected the most impactful knowledge management practices for female-owned businesses.Findings: The findings identified key components crucial to knowledge management and sustainability of women-owned businesses. These include the objectives of knowledge utilization, application of information technology, human resource management, systematic knowledge sharing, knowledge development, and continuous evaluation. External factors, such as individual capabilities, organizational culture, governmental policies, economic conditions, and socio-environmental influences, significantly shape the effectiveness of knowledge management initiatives. Knowledge management is a central mechanism for promoting sustainability and growth, enabling the systematic capture, organization, and application of knowledge. This enhances operational efficiency, supports informed decision-making, and creates a long-term value. Among the specific objectives, improving organizational productivity (average rating 4.30), fostering innovation and creativity, transferring employee experiences, and enhancing market competitiveness (average rating 4) were prioritized. These results demonstrate that organizations view knowledge management not only as an efficiency tool but also as a strategic asset for growth and competitiveness. This study also highlights the importance of building long-term relationships with customers, preserving and developing human capital, adapting to market changes, and fostering strong leadership. The integration of knowledge management with advanced technologies, systematic documentation, and retention of key employees were identified as essential strategies for enhancing business processes and cultivating a learning culture. Knowledge sharing, facilitated through regular meetings, workshops, and collaborative platforms, supports employee engagement and skills development. Knowledge development programs, including training, mentorship, and experiential learning, equip employees to drive innovation and contribute to business growth. External enablers, such as governmental support, financial incentives, insurance programs, and social security benefits, were also identified as crucial for the effective implementation of knowledge management practices. Sales and marketing skills, combined with effective customer relationship management, are emphasized as key drivers of business sustainability. Organizations that integrate robust knowledge management with strong marketing and customer engagement strategies are better positioned to respond to market dynamics, attract and retain customers, and achieve their long-term objectives.Conclusion: This study demonstrates that knowledge management plays a pivotal role in the sustainability and long-term development of female-owned businesses. It contributes to enhanced productivity, increased innovation, strengthened competitiveness, and overall improved organizational performance. Effective knowledge management requires a holistic approach that integrates human resources, technological tools, knowledge sharing, documentation, and leadership practices. Retaining key employees, developing technical and managerial skills, and fostering continuous learning are essential for effective knowledge utilization, enabling businesses to adapt, innovate and sustain growth over time. Advanced technologies such as knowledge management software, data analytics, and collaborative platforms facilitate knowledge capture, storage, dissemination, and application. External factors, including governmental policies, economic conditions, and socio-environmental considerations, play a significant role in supporting knowledge management initiatives. Providing financial support, insurance programs, and social security benefits strengthens the organizational capacity to implement knowledge management effectively. Ethical standards and corporate social responsibility initiatives reinforce trust, credibility, and long-term sustainability. Knowledge management is not an isolated activity but an integrated organizational strategy that interacts with multiple dimensions of business operation. Aligning knowledge management practices with strategic objectives enhances productivity, encourages innovation, maintains competitive advantage, and supports sustainable growth. The proposed framework offers a practical roadmap for women entrepreneurs and business leaders to implement effective knowledge management practices, ensuring resilience, long-term success, and contributions to the national economy. Thus, knowledge management serves as a strategic asset that enables sustainable organizational development and long-term business growth in contemporary economic contexts.Research limitations/implications: This study is subject to several limitations: Sample Size and Scope: The findings are based on a relatively small purposive sample of 25 experts, which may affect the generalizability of the results. Methodological Constraints: While the Delphi technique is robust, its reliance on consensus may marginalize dissenting or highly innovative viewpoints. Contextual Specificity: The research was conducted within a specific geographic and economic context (Iran), potentially limiting the direct applicability of the proposed framework to other environments. Subjective Assessment: Data collection based on expert judgment using Likert scales carries an inherent risk of subjective bias in evaluating the components. Temporal Limitation: The rapid evolution of knowledge management technologies and market conditions means that the findings reflect a specific point in time.Practical implications: This study provides a validated framework for implementing knowledge management (KM) to achieve sustainable business practices. Managers should prioritize documenting key employees' knowledge and fostering regular knowledge-sharing sessions. Invest in information technology, particularly decision support systems, to facilitate knowledge storage and access. Focus on developing employees' technical and managerial skills based on the assessed training needs. These actions directly enhance productivity, innovation, and long-term competitive advantages.Originality/value: This study confirms the pivotal role of knowledge management in ensuring the sustainability and long-term development of businesses. The purposeful application of knowledge management leads to increased productivity, process improvement, enhanced innovation, and the creation of competitive advantage. Advanced technologies, knowledge documentation, and retention of key employees were identified as critical factors for improving organizational performance. Additionally, sales and marketing skills, along with establishing sustainable customer relationships, play a significant role in long-term business growth and success. Based on these findings, managers and policymakers should strengthen the culture of knowledge management and develop the necessary infrastructure to provide a conducive environment for sustainable growth and the empowerment of entrepreneurs.
Purpose: In today's world, knowledge is a fundamental element in defining the value and necessity of human capital as the main factor for the survival and maintenance of the superiority of organizations. In this era, where overall quality and sustainable development are of primary importance for competition, efficiency is only possible through the successful use of human resource knowledge. Human resources, with their knowledge, skills, and talents, cause the progress and development of organizations. Material assets are not considered the main wealth of organizations, but human resource knowledge is recognized as the main and strategic asset of organizations. Knowledge is a set of awareness, understanding, or information obtained through experience or study and is the source of achieving efficiency, solving problems, and making decisions in human resources. In addition to creating distinctions between individuals, human resource knowledge can be a valuable resource for increasing the competitive advantage of the organization and helping to improve the strategy and performance of the organization to achieve organizational goals. In fact, the vital part that turns human resource knowledge into the wealth of organizations is that human resources must use their knowledge completely and correctly to advance the organization's programs and transfer it to others. Since human behavior is largely unpredictable and is influenced by various factors, organizations are always faced with people who, despite the efforts of the organization's management to optimize internal knowledge and create a knowledge-based environment with sustainable progress and growth for employees, cause deviations and sabotage behaviors in the organization. The presence of people with sabotage behaviors can reduce the productivity of the organization and other employees. One of these behavioral deviations that exists among human resources today and has caused great damage to organizational structures is known as knowledge sabotage. Knowledge sabotage occurs when an employee intentionally hides or misrepresents specific knowledge they possess, knowing that the knowledge is essential for the other party. Such individuals often seek to gain or maintain power, authority, influence, or some kind of preferential or favored behavior with the intention of undermining management or one of their colleagues who deliberately commits knowledge sabotage. They fear that if they fully disclose their knowledge to others, they will lose their power because they tend to dominate others through their knowledge. Knowledge sabotage behaviors affect the overall image of the organization and may cause negative publicity for the organization, degrade its position in today's highly competitive environment, and reduce the organization's chances of attracting and retaining the best human resources.Methodology: The present study was designed using the research onion model of Saunders et al. (2016) and a mixed approach (qualitative-quantitative) to identify and validate the knowledge sabotage pattern in government organizations in Lorestan province. In the research philosophy layer, this study was placed within the framework of the pragmatism paradigm, which allows the use of interpretive aspects for the qualitative part (identifying the pattern by examining meanings and subjective experiences) and positivist aspects for the quantitative part (statistical validation of the pattern). Pragmatism resolves the philosophical contradictions between qualitative and quantitative approaches by focusing on practical problem-solving. From the perspective of the approach, the research was of an exploratory-explanatory type, which first proceeded with an inductive approach to identify the pattern and then with a deductive approach to validate it, as follows: This research is categorized as qualitative research. The research participants were university professors and senior and middle managers of government organizations in Lorestan Province, who were selected using the theoretical sampling method. Therefore, based on the theoretical saturation rule, this important issue was addressed in the present study using 12 interviews. The required data were collected using semi-structured interviews, and data analysis was conducted using Clark and Brown’s content analysis method.Findings: Data analysis resulted in the formation of 132 primary codes, 16 subthemes, and three main themes. The main themes identified were individual, organizational, and environmental factors. The subthemes identified were: self-centered personality traits, emotional incongruence, power-seeking motives, lack of communication skills, habit of professional isolation, weak ethical values, competitive organizational culture, ineffective reward systems, lack of knowledge management training, poor performance monitoring, limited organizational resources, lack of organizational empathy, organizational distrust, economic pressures, competitive culture of society, and political and factional relations Research.limitations/implications: Regarding the limitations of the present study, it is necessary to explain, considering the purpose and type of research that the interview process and the opinions of experts had to be used, so this included a limitation in the number of samples and difficulty in accessing experts. Additionally, among the most important limitations of this study, it can be noted that it is new and there are no studies on this subject in the country, which means that researchers were faced with research limitations in this field. Another limitation of this study was that it was conducted within a specific time period; thus, it reflects the opinions of the respondents within a limited time period. Knowledge sabotage in government organizations in Lorestan Province is a serious structural and cultural challenge that not only hinders progress and innovation but also directly affects the quality of service delivery to the public. This phenomenon means the failure to transfer, improper storage, or even concealment and deliberate prevention of the effective sharing of information, experiences, and best practices within and between different units of the organization. In a government environment whose main capital is organizational knowledge and employee experience, this "sabotage" leads to a waste of financial and human resources, the repetition of past costly mistakes, a decrease in the speed of adaptation to changes, and ultimately, a significant decline in organizational productivity and legitimacy. This study aims to provide a comprehensive analysis of the three factors (individual, organizational, and environmental) affecting this phenomenon in the context of government organizations in Lorestan Province and to provide operational solutions to combat it.Originality/value: This study not only helps to better understand knowledge degradation in the local context but also provides a framework for designing local solutions that can be applied in other regions with similar characteristics.
Purpose: Technology-induced stress can be a significant factor in reducing the effectiveness of knowledge management in work environments. Pressures from new technologies, such as the need for continuous adaptation to rapid changes, the complexity of digital tools, and the increasing volume of processed information, may lead to job burnout, reduced individual focus, and ultimately, a decreased willingness of employees to share knowledge. In such situations, employees may have a greater tendency to conceal their tacit knowledge due to stress and work pressures, which can negatively impact organizational productivity and reduce access to knowledge repositories and the organization's ability to leverage explicit and tacit knowledge. This issue can ultimately limit organizations' ability to create effective communities of practice and organizational learning. Therefore, the present study aims to investigate the effect of technology stress on knowledge hiding, and in this regard, the mediating role of job burnout and the moderating role of job autonomy are evaluated. Examining these factors can lead to a deeper understanding of how technology affects employees' knowledge-based behaviors and provide solutions for better stress management and improved knowledge interactions in organizations.Methodology: This study is descriptive-survey and examines the effects of technology-induced stress on knowledge hiding in the workplace. The statistical population of this research consisted of 250 employees of Saman Electronic Payment Company. To determine the sample size, Cochran's formula was used, based on which 150 people were selected as the statistical sample. This sample was randomly selected from among the organization's employees to ensure the generalizability of the research results to the larger population. The tools used in this research included four standard questionnaires that respectively assessed technology stress, knowledge hiding, job burnout, and job autonomy. To measure the reliability of these questionnaires, Cronbach's alpha coefficient was used. The results showed that the reliability for the technology stress questionnaire was 0.746, for the job autonomy questionnaire 0.561, for the job burnout questionnaire 0.714, and for the knowledge hiding questionnaire 0.837. These values indicate acceptable reliability of the research tools and contribute to the validity of the research results. In addition, the validity of the questionnaires was examined using the average variance extracted method, and the results showed that the construct validity was confirmed. Also, the Fornell-Larcker method was used to evaluate convergent and discriminant validity, and the results of this method also confirmed the appropriate validity of the research tools.Results: The results of statistical inference showed that technology stress has a significant impact on knowledge hiding and job burnout among employees. Job burnout had a significant impact on knowledge hiding among employees. The mediating role of job burnout was confirmed, but the moderating role of job autonomy was rejected.Conclusion: The research results showed that technology-induced stress indirectly reduces the willingness to share knowledge among employees by increasing job burnout. Pressures from new technologies and their complexities can create stress in the workplace, which leads to job burnout. This job burnout, in turn, prevents employees from actively participating in knowledge-sharing processes, as they feel unable to manage these pressures and interact with complex systems. This process will ultimately negatively impact team and organizational performance and reduce the effectiveness of teamwork. Additionally, job autonomy has been introduced as a moderating factor that can reduce the negative effects of technology stress and job burnout. In other words, when employees have more freedom in choosing how to perform their tasks and feel more control over their work environment, they experience less stress and their job burnout decreases. The results show that creating conditions that strengthen job autonomy in the organizational environment can reduce stress and job burnout. Furthermore, this may increase employees' motivation to share knowledge and ultimately help improve overall organizational performance, although these effects will vary depending on different environmental and organizational conditions.Originality/Value: It is essential that the managers of Saman Electronic Payment Company develop and implement comprehensive and well-structured programs to increase job autonomy and personal control of employees in the workplace. These programs can include creating flexible work structures that allow employees to choose how to perform their tasks according to their work style and personal abilities. Flexibility in work scheduling, the possibility of remote work, and delegation of authority in daily task-related decisions are among the solutions that can give employees a greater sense of control and, as a result, reduce the level of stress and job burnout. Also, providing rewards and incentives for independent and creative performance can lead to increased employee productivity and strengthen their sense of responsibility.In addition, the use of efficient and simple technologies that do not require complex and time-consuming interactions with difficult systems can play an important role in reducing technology-induced stress. Using user-friendly software, optimizing organizational processes, and providing the necessary training to effectively use these tools can help employees increase their productivity and reduce unnecessary pressures from complex technologies. Conducting training courses in stress management and optimal use of new technologies is also an important step in empowering employees. These measures not only reduce job burnout and increase the motivation to share knowledge among employees but also help improve overall organizational performance, increase team interactions, and promote organizational culture.
Purpose: This article was done with the aim of Identifying and explaining the mechanism of tacit knowledge sharing among University of Tehran employees in the development of successors. .This research is an applied-developmental research because it seeks to identify and explain the mechanisms of sharing tacit knowledge to employees in the field of successor development at the University of Tehran. The University of Tehran has had many administrative and research experiences, but the use and utilization of these experiences has become an important concern of the university. On the one hand, the university has benefited from experienced and knowledgeable specialists over a period of time, and after a period of time, it has lost them for reasons such as retirement, being project workers, etc., and on the other hand, it has not prepared documentation and lessons learned from previous projects in a systematic manner. Therefore, in order to reduce the negative effects of the university's knowledge workers leaving and also extracting lessons learned from administrative projects, mechanizing the sharing of tacit knowledge seemed necessary. In mechanization, the effort is to document the sharing of tacit knowledge, experiences, and knowledge of individuals in understandable ways and make them available to other university managers and employees. In this case, even without the presence of knowledgeable individuals in the organization, their knowledge can be transferred. Also, realizing the mission of the University of Tehran to advance the overall goals of a leading university at the level of the Islamic Revolution requires identifying, mechanizing, and streamlining the sharing of tacit knowledge to its managers and employees.Methodology: This research is a mixed-method, sequential, exploratory study. The research population in the qualitative section is managers, deputy heads of departments, and employees of the University of Tehran. Purposeful sampling and snowball sampling techniques were used, and interviews continued until theoretical saturation, and accordingly, 22 people participated in the interview. This research was conducted using the qualitative data-based research method or grounded theory based on the Strauss and Corbin approach through 3 types of coding, open coding, axial coding and selective coding. The grounded theory method is a method for determining, analyzing and expressing the main categories of the phenomenon under study. This method organizes qualitative data and describes it in detail. For analysis The qualitative part used MAXQDA software. The reliability of the qualitative part was determined using the Holst coefficient (poa = 0.712). In the quantitative part, the statistical population includes managers and employees of the University of Tehran. For the validation of the model in the quantitative part, the sample size was estimated to be 140 people using the Cohen power analysis rule (1992) and G*Power software. A semi-structured interview and a questionnaire extracted from the qualitative part were used to collect data. The paradigmatic model of the research was validated using PLS SMART software.Findings: The research findings showed that the presented paradigmatic model includes causal conditions (organizational knowledge management, organizational justice, organizational policy-making, and organizational structure), a central phenomenon (sharing tacit knowledge with employees), driving factors (organizational culture and values, organizational leadership), inhibiting factors (human resource resistance to knowledge sharing (soft factors) and resistance in processes and technologies to knowledge sharing (hard factors)), strategies (human resource empowerment), and consequences (development of successors and growth and sustainability of the university). The validity of the research paradigmatic model was confirmed using SMART PLS software, and the model has a good fit. In the quantitative part, the statistical population includes managers and employees of the University of Tehran. For the validation of the model in the quantitative part, the sample size was estimated to be 140 people using the Cohen power analysis rule (1992) and G*Power software. A semi-structured interview and a questionnaire extracted from the qualitative part were used to collect data. The paradigmatic model of the research was validated using SMART PLS software.Conclusion: Paying attention to and applying the model identified in this study can help policymakers, senior and middle managers, and decision makers to routinely conduct continuous knowledge exchange in the internal and external environment of the organization, which increases employee knowledge, optimizes the use of technology, and changes the organizational culture approach, is justice-oriented, and ultimately leads to effectiveness and efficiency.Research limitations/implications: Undoubtedly, every research is faced with limitations and problems along the way, and this research is no exception, some of which are mentioned below. In collecting research data, in addition to using interviews as suggested in the data-driven strategy, attention was also paid to direct observations and studying documents. In this study, it was not possible to study all the organization's documents and observe the behavior of employees for a long period of time. It can be expected that observing the phenomenon of sharing tacit knowledge with employees in the context of research and over a longer period of time will provide deeper insight into this phenomenon. According to experts, the results obtained from the data-driven theory method can only be generalized to theoretical propositions, not analytical generalization and the possibility of generalization to the entire society (statistical generalization) is not available. The subjective nature of the code definitions in data coding in the qualitative data analysis section requires qualified and motivated individuals who, unfortunately, like many internal studies, were faced with a lack of real experts and the opportunity to interact and benefit from their opinions. Some individuals who, in the researcher's view, could have added to the richness of the research materials refused to cooperate in the study, and those who agreed to cooperate did not have the appropriate opportunity for in-depth discussion and analysis as the researcher intended due to their busy schedules. Like most studies based on data-driven theory, the findings of this study were obtained by relying on the views and experiences of a relatively limited number of individuals, and this inadequacy can limit the theoretical generalizability of the research findings.Originality/value: Due to the lack of use of the grounded theory method in previous studies, the general components of none of the models of tacit knowledge sharing to employees of the University of Tehran in the field of successor development are compatible with the components of this model, but conceptually, relationships and comparisons can be made between them. Using a mixed approach of grounded theory in the first phase and quantitative methods in the second phase of the research is one of the innovations of this study.
