https://jkm.ihu.ac.ir/ Strategic Management of Organizational Knowledge en daily 1 Sat, 27 Jun 2026 00:00:00 +0330 Sat, 27 Jun 2026 00:00:00 +0330 https://jkm.ihu.ac.ir/article_210828.html Purpose: This study tests a model that examines how the intensity of professional network use promotes knowledge sharing and, ultimately, social innovation, while also investigating the influence of psychological factors in this relationship.Methodology: Data were collected using a questionnaire. Face validity was established through expert evaluation by six specialists. Based on Cohen's statistical power analysis, the minimum required sample size was estimated at 68 participants. A total of 221 questionnaires were collected from professional users holding master's or doctoral degrees and employed in information technology companies in Tehran Province. Convenience sampling was employed, and the data were analyzed using partial least squares structural equation modeling (PLS-SEM) with SmartPLS 3.Results: All research hypotheses were confirmed with beta coefficients ranging from -0.288 to 0.863 and t-coefficients ranging from 7.680 to 45.324.The results showed that the intensity of professional social media use significantly increased upward social comparison. Upward social comparison positively affected benign envy  and negatively affected malicious envy. Benign envy promoted active knowledge sharing, whereas malicious envy increased reactive knowledge sharing. Both active and reactive knowledge sharing significantly enhanced social innovation.Discussion: This study advances the literature by demonstrating that the relationship between professional social media use and social innovation.Conclusion: Professional social media platforms can enhance social innovation by fostering upward social comparison, encouraging proactive knowledge sharing, and supporting organizational knowledge management through appropriate incentive and recognition mechanisms. https://jkm.ihu.ac.ir/article_210849.html Purpose: This study identifies and prioritizes the opportunities and challenges of data governance policymaking within the Iranian Vice Presidency for Science, Technology, and Knowledge-Based Economy.Methodology: A quantitative survey was conducted. Data governance components were identified through a literature review, and a researcher-developed questionnaire was distributed among managers, data specialists, and information technology staff at the Center for Transformation and Progress Cooperation. Data were analyzed using the Friedman, Spearman, Mann–Whitney, and Kruskal–Wallis tests in SPSS.Results: Ten data governance policymaking components were identified based on the Data Management Association (DAMA) framework. Data modeling and design was identified as both the greatest opportunity and the most significant challenge. Seven components showed favorable opportunity status, whereas three were relatively favorable. For challenges, seven components were unfavorable and two were relatively favorable. Significant correlations were found among all components, while demographic variables had no significant effects.Discussion: The findings indicate that data modeling and design requires the highest policy priority because of its dual role. Components with favorable opportunities should be reinforced, whereas those in relatively favorable condition should be improved before addressing more critical weaknesses. The strong relationships among components highlight the need for coordinated policymaking.Conclusion: Identifying the opportunities and challenges of data governance policymaking provides a roadmap for governmental institutions. An integrated policy model that simultaneously strengthens opportunities, mitigates challenges, and considers interactions among all components can improve organizational performance and support more effective data governance. https://jkm.ihu.ac.ir/article_210827.html Purpose: This study aimed to develop a comprehensive data-driven governance model to enhance the resilience of public organizations in responding crises and managing uncertainty.Methodology: A qualitative research design based on thematic analysis was employed. The study population consisted of academic experts and senior managers from public organizations. Using purposive sampling, 16 participants were selected. Data were collected through semi-structured interviews, transcribed verbatim, and analyzed using MAXQDA software. Instrument validity was established through expert review and pilot interviews, while reliability was ensured through independent coding by two researchers and comparison of coding results.Results: The findings revealed that effective data-driven governance is supported by several enabling factors, including robust data infrastructure, top management support, organizational capabilities, and data-oriented culture. The proposed model is operationalized through strategies such as data integration, artificial intelligence adoption, advanced data analytics, and digital governance practices. Implementing these strategies enhances organizational resilience by improving the speed and quality decision-making, strengthening crisis preparedness and response, increasing operational flexibility, and promoting more effective organizational performance under uncertain conditions.Discussion: The findings indicate that institutionalizing data-driven governance and making strategic investments in data infrastructure, digital technologies, and analytical capabilities enable public organizations to respond more proactively and effectively to complex and rapidly evolving crises.Conclusion: Data-driven governance institutionalizes evidence-based decision-making across organizational and policy processes, enabling public organizations to anticipate uncertainty, reduce structural vulnerability, strengthen adaptive capacity, and improve crisis management. Accordingly, it provides strategic foundation for redesigning public governance systems in an increasingly complex, dynamic, and data-intensive environment. https://jkm.ihu.ac.ir/article_210825.html Purpose: Despite significant investments in artificial intelligence, only a limited number of organizations achieve desired returns. This reality positions AI not merely as raw technology, but as a knowledge capability requiring the activation of internal mechanisms for tangible performance. Therefore, this study aims to investigate the impact of AI ability on firm performance in Iranian knowledge-based firms, mediated by knowledge management mechanisms.Methodology: This applied, descriptive-survey study was conducted on Iranian knowledge-based firms. A representative sample of 100 senior managers was selected via convenience sampling based on statistical power. Data were collected using a 34-item standard questionnaire and analyzed using structural equation modeling in SmartPLS 4.Results: Results indicate AI ability indirectly influences firm performance through four mechanisms: firm creativity, AI management, organizational learning, and organizational agility. Among these, organizational agility demonstrated the strongest effect, while AI management had the weakest. The proposed structural model exhibited desirable fit and predictive power.Discussion: The value of AI is realized only through its absorption into the knowledge cycle. This research clearly demonstrates that without activating internal mechanisms like agility and learning, technological investments fail to translate into tangible performance, thereby perpetuating the well-known AI productivity paradoxConclusion: Ultimately, the long-term success of knowledge-based firms in converting technological investments into sustainable competitive advantage requires the strategic integration of AI into the knowledge management cycle. Organizational agility was identified as the primary engine converting capabilities into tangible performance, highlighting the critical necessity of developing highly responsive organizational structures and dynamic and continuous learning processes. https://jkm.ihu.ac.ir/article_210824.html Purpose: This study investigates the impact of the Internet of Things Ecosystem (IOTE) and Big Data Analysis (BDA) on Organizational Strategic Intelligence (OSI), mediated by Value Creation Ecology (VCE), to optimize strategic decision-making and competitiveness.Methodology: A descriptive-correlational design employed, surveying 208 experts from knowledge-based firms via a validated 48-item questionnaire. Hypotheses were assessed using Partial Least Squares Structural Equation Modeling (PLS-SEM) via SmartPLS 4.0.Results: The PLS-SEM results showed that the independent variables explained 72.9% of the variance in strategic intelligence. All hypotheses supported: value creation ecology (β = 0.693), big data analytics (β = 0.462), and the Internet of Things (IoT) ecosystem (β = 0.375) each had a positive and statistically significant effect on strategic intelligence. Furthermore, the partial mediating role of value creation ecology confirmed using the Sobel test and VAF index.Discussion: These findings substantiate that technological infrastructure alone is insufficient for strategic superiority; the synergistic integration of IOTE and BDA through VCE is critical. VCE’s partial mediation underscores that while data generation directly bolsters intelligence, ecological alignment amplifies the translation of raw data into strategic foresight. This addresses literature gaps by operationalizing how digital ecosystems catalyze predictive intelligence, transcending traditional descriptive analytics. Consequently, organizations must foster an ecology where data-driven insights intrinsically align with value creation imperatives to fully actualize strategic intelligence.Conclusion: Cultivating OSI necessitates leveraging interconnected digital ecosystems to transform data into actionable foresight. Organizations must integrate these technologies to drive continuous learning, inter-organizational collaboration, and sustained competitive advantage within dynamic markets. https://jkm.ihu.ac.ir/article_210840.html Purpose: The aim of the research is to design a cognitive simulator framework for knowledge management learning by utilizing cognitive sciences in organizational environments.Methodology: A qualitative study with thematic analysis, semi-structured interviews with experts in knowledge management, cognitive sciences, education, and technology (snowball sampling) was conducted. Validity was confirmed through participant review, and reliability with an agreement coefficient of 82%. Data analysis was carried out using MAXQDA software and Wolcott's stages.Results: Eight main components were identified: introduction and objective, actors, design and architecture, environment, educational content, evaluation methods, development and updating, performance and efficiency. From 120 basic themes, 32 organizing themes and 8 global themes were extracted. Effective learning requires interactive activities, clear roles, knowledge sharing, continuous feedback, alignment of objectives, and a simulated environment with risk and multi-level decision-making.Discussion: This research is in line with the convergence of the three domains of knowledge management, cognitive sciences, and educational simulation, and this research is the first indigenous framework of a cognitive simulator for knowledge management learning in Iran and the convergence of the three domains of knowledge management, cognitive sciences, and educational simulation.Conclusion: The presented framework can be used as a structured model for designing cognitive simulators in knowledge management learning and, by increasing interaction, participation, and experiential practice, significantly enhance the quality of education and the knowledge capabilities of learners. This framework helps organizations to make knowledge management learning processes realistic, person-centered, and sustainable. https://jkm.ihu.ac.ir/article_210823.html Purpose: Human resources are organizations’ most valuable asset, and strengthening employee motivation through transforming tacit knowledge into explicit knowledge is essential for improving performance. This study aimed to develop an employee motivation model based on the lived experiences of the School of Martyr General Qasem Soleimani in Iran’s public sector.Methodology: This applied, cross-sectional mixed-methods study was conducted in two phases. In the qualitative phase, the lived experiences of Martyr Qasem Soleimani were explored through in-depth interviews with 12 experts familiar with him. Data were analyzed using thematic analysis based on the externalization process of knowledge conversion. Purposive and snowball sampling were used in the qualitative phase, while managers and human resource experts from five selected ministries participated in the quantitative phase. Validity was confirmed through researcher triangulation, and reliability was established using Cohen’s kappa coefficient (0/78).Results: The motivation model comprised 35 basic themes, 35 organizing themes, and 9 overarching themes. Key dimensions included jihad-oriented human capital, a supportive organizational climate, identity-based, service-oriented, and spiritual-ethical motivation, emotional bonds with subordinates, risk-taking, delegation of authority, charisma based on moral influence, idealism, and a shared vision.Discussion: The findings indicate that the model extends conventional motivation theories by incorporating spiritual, ethical, identity-based, and value-oriented dimensions, offering a context-specific framework for Iran’s public administration.Conclusion: Transforming Martyr Soleimani’s lived experiences into explicit knowledge through training, behavioral guidelines, and managerial systems can strengthen employee motivation in public organizations; therefore, applying this model in management and training programs is recommended. https://jkm.ihu.ac.ir/article_210851.html Purpose: this study was conducted to examine the effect of artificial intelligence assistants on the innovative behavior of employees at Amer Andish Hooshmand Company, considering the mediating role of AI competencies and the moderating role of organizational readiness.Methodology: The research adopted a positivist, causal-comparative approach and was applied in nature. Its statistical population consisted of the employees of the knowledge-based company Amer Andish Hooshmand. Using random convenience sampling and Cochran’s formula, 140 individuals were selected as the sample. The collected data were analyzed using SPSS 27 and SmartPLS software.Results: The research findings show that AI assistants have a positive and significant effect on employees’ innovative behavior. Moreover, AI competencies were confirmed as a mediating variable. However, the moderating role of organizational readiness was not confirmed, which may be related to the organization’s infrastructural or cultural limitations.Discussion: The effectiveness of AI assistants on innovative behavior depends more on employees’ ability to use these tools effectively than on the availability of organizational infrastructure.Conclusion: Given that the research findings indicate the mediating role of AI competencies in the relationship between AI assistants and innovative behavior, companies should focus mainly on developing AI competencies rather than merely purchasing or deploying AI tools. https://jkm.ihu.ac.ir/article_210826.html Purpose: The integration of Artificial Intelligence and Knowledge Management  has become a strategic necessity in the digital economy. However, previous studies have lacked a comprehensive framework for explaining the systematic transformation of  KM through AI. This study aimed to develop an integrated model of the factors influencing this transformation and their interrelationships.Methodology: This qualitative study employed a meta-synthesis approach based on the seven-step model proposed by Sandelowski and Barroso. The research population comprised scientific publications on AI and KM published between 2010 and 2025. The data were analyzed through coding, categorization, and thematic synthesis.Results: A total of 371 initial codes were organized into 24 core codes and four main dimensions, namely requirements, barriers, benefits, and applications. The findings indicated that the success of AI-based KM depends on the interaction of technological, human, organizational, and cultural factors, while infrastructural and human requirements play a significant role in overcoming implementation barriers.Discussion: The findings suggest that effective KM transformation requires alignment among technological capabilities, human competencies, and organizational culture. They also provide a dynamic framework for explaining the relationships among organizational requirements, challenges, and AI-driven benefits.Conclusion: AI has transformed KM into an intelligent and dynamic cognitive system. The sustainable implementation of this transformation requires adequate infrastructure, skilled human resources, a supportive organizational culture, and ethical governance, thereby strengthening knowledge-based competitive advantage in the digital economy. https://jkm.ihu.ac.ir/article_210848.html Purpose: In recent years, the American Psychological Association (APA) and the World Health Organization (WHO) have identified gaslighting as a major contributor to job stress and covert bullying in the workplace. Gaslighting in the workplace is a form of psychological manipulation in which an individual (often in a position of higher power) systematically distorts, denies, or distorts reality in order to cause the victim to doubt their perceptions, memories, and judgments. The concept has its roots in the 1944 film Gaslight, in which a man makes his wife doubt her sanity by dimming and increasing the light of gaslights. Unlike overt bullying, which is characterized by aggressive behavior, gaslighting erodes the victim’s self-confidence and ability to make professional judgments through ambiguity, gradual changes in reality, and covert acts of control. In other words, perceptual psychosis is a type of maladaptive and destructive communication pattern in which one party attempts to undermine the other party’s perception of reality. Gaslighting manifests itself in the workplace through the following tactics: denial of past statements or actions by the supervisor, even when documented; invalidating the employee’s feelings and experiences (you are too sensitive); constantly changing expectations and requirements without prior notice; blaming the employee for problems they did not cause; attributing the employee’s achievements to themselves; socially isolating the victim from colleagues; and using constant criticism in the guise of constructive feedback. This process, which takes place gradually and over time, aims to create and reinforce doubt about the individual’s abilities, judgments, and perceptions of themselves and the realities of the workplace. In workplaces, this phenomenon can manifest itself through supervisors distorting facts, denying or rewriting past events, and presenting contradictory messages, such that employees gradually begin to doubt their memory, judgment, and perceptions of work situations. Knowledge erosion is one of the most significant intangible damages that gaslighting can cause to an organization’s intellectual capital. Harvard Business Review noted in 2023 that the sudden departure of passive, value-creating employees or their failure to share knowledge due to a weakening of organizational trust can destroy half of an organization’s intellectual capital. In organizations where gaslighting is prevalent, organizational silence prevails; the workplace becomes an environment in which originality is considered a risk and knowledge becomes a commodity to be hidden. This knowledge hiding occurs in response to organizational injustice and feeds a negative vicious cycle. Therefore, the present study was designed to rank the drivers of human resource gaslighting based on knowledge erosion dimensions at Lorestan University.Methodology: Given that the present study was conducted with the aim of solving the problem raised, it is applied in terms of purpose and is classified as exploratory research in terms of data collection method. This research is a mixed research and because it has an inductive approach in the qualitative part and a deductive approach in the quantitative part, it is classified as inductive deductive research. The statistical population of this study is limited to Lorestan University and includes experts consisting of employees working in this university and university professors, 12 of whom were selected using the purposive sampling method and based on the principle of theoretical saturation. The criteria for selecting experts were having work experience in the field of management and human resources, having direct management experience or human resources consulting, and having at least a master's degree. The data collection tools in this study are interviews and questionnaires. Given that the main goal of the research is to rank the stimuli of human resources' gaslighting based on knowledge erosion dimensions at Lorestan University, to achieve this goal, a combined fuzzy Delphi approach (for screening and consensus building of experts in identifying effective stimuli) and the Marcos method (for final prioritization and quantitative ranking) have been used.Results: The final ranking showed that the exercise of power through informal networks parallel to the official structure (score = 0.969), the lack of any independent, secure, and supra-organizational reporting system (score = 0.939), the institutionalization of a culture of concealment and denial of mistakes instead of correcting them (score = 0.929), the dependence of career advancement on political relations and internal lobbying (0.909), and the pervasive fear of the consequences of exposing systemic flaws for senior managers (0.908) are, in order, the most important drivers of gaslighting based on knowledge erosion dimensions at Lorestan University.Discussion: The findings from the combined Delphi Fuzzy and Marcos rankings show that in Lorestan University (as the case study), the exercise of power through informal networks parallel to the formal structure is, with a slight difference from other stimuli, the most important stimulus for cognitive neurosis based on the knowledge erosion dimensions. This finding is important in several ways. First, informal power networks - which are usually based on family, tribal, party or old friendship relationships - not only bypass formal decision-making processes, but also gradually do not leave a single and transparent narrative of organizational realities. Employees outside these networks never get a complete picture of the evaluation criteria, resource allocation or even strategic priorities. This systematic ambiguity is recognized by experts as the most important platform for gaslighting: network managers can undermine the employee's perception of justice and competence by relying on behind-the-scenes negotiations or unwritten decisions. In such a situation, the employee, in order to maintain his position, never dares to share his critical knowledge or creative ideas, because he knows that this knowledge may be interpreted against him in informal networks. According to experts' judgment, this is a direct consequence of the process of extensive knowledge concealment. This finding is in line with Ebrahimi's (1404) research, which pointed to the role of behavioral controls and speech suppression, but beyond that, it introduces the specific mechanism of informal power networks as the most key factor in gaslighting from the experts' perspective.Conclusion: The results clearly showed that at Lorestan University, gaslighting is not a purely behavioral phenomenon resulting from individual characteristics or simple supervisor-subordinate interactions; rather, it is deeply rooted in organizational layers (informal power structures, institutional weakness in monitoring, and a culture of concealment). However, identifying triggers at three etiological layers (organizational, managerial, and interactional) shows that destructive behaviors at lower levels (such as false promises and personnel threats) act as connecting links and implementation mechanisms for this systemic damage. In other words, unhealthy organizational structures provide a context in which gaslighting behaviors of human resources are manifested at the level of middle managers and supervisors, causing employees to doubt and be confused, and ultimately leading to the erosion of the organization's knowledge capital. https://jkm.ihu.ac.ir/article_210829.html Purpose: This research aims to design a "Systematic Model of a Smart Organization" employing a systemic approach, thereby providing an operational framework for organizations to transition from traditional structures to adaptable ecosystems in the era of complexity and big data.Methodology: This is an applied, qualitative study with an exploratory approach. Data were collected through a systematic review of 33 peer-reviewed domestic (2011–2025) and international (2010–2025) documents, alongside semi-structured interviews with 9 experts selected via snowball sampling. Validity was confirmed using data triangulation, and thematic analysis was conducted utilizing MAXQDA 2024 software.Results: The analysis identified 6 main dimensions and 19 key components, formulated into a multi-layered systemic model. These include "technology and infrastructure" and "culture and structure" (as enablers), "human and social capital" and "knowledge management" (as processes), and "strategic leadership" and "education and research" (as strategic outcomes). Furthermore, a feedback loop ensures the system's dynamism and self-correction.Discussion: A smart organization operates as an integrated, living system where the synergy between technological infrastructure and organizational culture provides the foundation for knowledge application by human capital. This network-based interaction not only fosters strategic leadership and research capacities but also significantly enhances the organization's adaptability and resilience in the face of rapid environmental changes.Conclusion: Organizational intelligence is achieved through the systemic integration of its components rather than the mere accumulation of technology. Implementing this proposed model equips senior executives to transition from reactive management to proactive agency within turbulent environments. https://jkm.ihu.ac.ir/article_210850.html Purpose: Generative artificial intelligence has created opportunities for knowledge management and human resource management, yet a model that synergistically integrates employee flourishing and strategic knowledge creation is rare. This study aims to design a symbiotic knowledge system model based on generative artificial intelligence and to explain its causal relationships.Methodology: An exploratory sequential mixed‑method approach was adopted in two phases. In the qualitative phase, fuzzy Delphi with 15 experts extracted the initial components. In the quantitative phase, fuzzy DEMATEL analyzed causal relationships, and fuzzy cognitive mapping combined with agent‑based simulation over a five‑year horizon modeled system dynamics. Content validity was confirmed using CVR, and reliability was confirmed using Kendall's coefficient (0.73).Results: Five components were identified: algorithmic transparency, cognitive diversity preservation, human‑AI interaction feedback, generative personalized learning, and ethical AI governance. "Cognitive diversity preservation" with the highest causality (+3.78) was the strongest causal driver. Simulation showed the full system increases strategic knowledge creation by 87%, reduces burnout from 34% to 12%, and improves decision‑making accuracy by 48%.Discussion: Contrary to traditional approaches, cognitive diversity preservation drives knowledge‑wellbeing synergy. Algorithmic transparency enables trust and feedback, while personalized learning operates under ethical governance.Conclusion: This study provides an integrated framework linking positive HRM, generative AI, and strategic collective intelligence. Organizations should prioritize cognitive diversity preservation and algorithmic transparency in implementation. Generalizability is limited to Iranian knowledge‑based industries.