Imam Hossein University Strategic Management of Organizational Knowledge 2645-4262 9 2 2026 06 27 A Knowledge-Sharing Model to Enhance Social Innovation: Evidence from a professional social network A Knowledge-Sharing Model to Enhance Social Innovation: Evidence from a professional social network 1 45 210828 10.47176/SMOK.2026.1994 FA Omid Dehghann Ph.D. Student, Faculty of Management and Economics, Tarbiat Modares University, Iran, omid.dehghan@modares.ac.ir 0009-0004-1234-0421 Abolghasem Sarabadani Assistant Professor, Faculty of Management and Economics, Tarbiat Modares University, Iran, a.sarabadani@modares.ac.ir 0000-0002-5915-6363 Journal Article 2025 10 11 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. 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_210828_5383003af20c968dfb1eccb56a204b15.pdf

Imam Hossein University Strategic Management of Organizational Knowledge 2645-4262 9 2 2026 06 22 Data-driven governance model: resilience of government organizations in the face of crisis Data-driven governance model: resilience of government organizations in the face of crisis 46 70 210827 10.47176/SMOK.2026.1990 FA Abdullah Saedi Assistant Professor, Department of Management, Faculty of Management and Economics, Lorestan University, Khorramabad, Iran, Saedi.a@lu.ac.ir 0000-0001-7812-1978 Mohammad Tavassoli Assistant Professor, Department of Management, Faculty of Management and Economics, Lorestan University, Khorramabad, Iran, Tavassoli.m@lu.ac.ir 0000-0003-2536-4698 Journal Article 2025 12 28 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. 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_210827_736db1565da03cd29c48c2b1f50e917f.pdf

Imam Hossein University Strategic Management of Organizational Knowledge 2645-4262 9 2 2026 06 22 Investigating the impact of the internet of things ecosystem (IOTE) and big data analysis (BDA) on organizational strategic intelligence with the mediation of value creation ecology in the knowledge-based companies Investigating the impact of the internet of things ecosystem (IOTE) and big data analysis (BDA) on organizational strategic intelligence with the mediation of value creation ecology in the knowledge-based companies 71 101 210824 10.47176/SMOK.2026.1978 FA Reza ُSaeedi Associate Professor of Management, Faculty of Management & Economy, University of Shahid Bahonar Kerman, Kerman, Iran, Reza.Saeidi@uk.ac.ir 0000-0001-9592-9848 Behnam Karamshahi Associate Professor of Accounting, Baft Higher education complex, , Shahid Bahonar University of Kerman, Kerman, Iran, Behnamkaramshahi @uk.ac.ir 0000-0003-0759-043X Journal Article 2025 11 12 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. 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_210824_cfb3fe24979302288361619db11feac6.pdf

Imam Hossein University Strategic Management of Organizational Knowledge 2645-4262 9 2 2026 06 22 Presenting a model for employee motivation based on the lived experience of haj Qasem Soleimani in the public sector: Utilizing a tacit-to-explicit knowledge conversion approach Presenting a model for employee motivation based on the lived experience of haj Qasem Soleimani in the public sector: Utilizing a tacit-to-explicit knowledge conversion approach 102 127 210823 10.47176/SMOK.2026.2010 FA Afshin Bazgir Ph.D. candidate in Public Administration, Department of Management, Bo.C., Islamic Azad University, Borujerd, Iran, afbazgir@iau.ac.ir 0000-0001-5942-9547 Mohamadreza Jaberansari Assistant Professor of Public Administration, Department of Management, Bo.C., Islamic Azad University, Borujerd, Iran, j.ansari@iau.ac.ir 0000-0001-8680-3856 Mohamadreza Taheri Rozbehani Assistant Professor of Public Administration, Department of Management, Bo.C., Islamic Azad University, Borujerd, Iran, mo.taheri1351@iau.ac.ir 0000-0001-8680-3856 Hojat Taheri Godarzi Assistant Professor of Public Administration, Department of Management, Bo.C., Islamic Azad University, Borujerd, Iran, h_taheri47@iau.ac.ir 0000-0002-7184-8856 Journal Article 2026 04 19 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. 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_210823_e326550cc69b7a817819a0b3af9bf176.pdf

Imam Hossein University Strategic Management of Organizational Knowledge 2645-4262 9 2 2026 06 22 The transformation of knowledge management with artificial intelligence: A synthesis of requirements, challenges and AI technologies in knowledge management The transformation of knowledge management with artificial intelligence: A synthesis of requirements, challenges and AI technologies in knowledge management 128 158 210826 10.47176/SMOK.2026.1987 FA Hadise Soofi Master's degree student in educational management , Faculty of Literature and Humanities, Malayer University, Malayer, Iran, Hadise.soofi@stu.malayeru.ac.ir 0009-0009-9062-811X Abbas Khakpour Associate professor, Faculty of Literature and Humanities, Malayer University, Malayer, Iran, Khakpour@malayeru.ac.ir 0000-0002-0349-796X Sajad Gharloghi Assistant professor, Faculty of Literature and Humanities, Malayer University, Malayer, Iran, s.gharloghi@malayeru.ac.ir 0000-0002-5594-4151 Journal Article 2025 12 23 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. 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_210826_85e00b395adcf2b8af71af9bd42abaaa.pdf

Imam Hossein University Strategic Management of Organizational Knowledge 2645-4262 9 2 2026 06 22 Systemic model of intelligent organization Systemic model of intelligent organization 159 185 210829 10.47176/SMOK.2026.2002 FA Gholamreza Salmanpoor Siavoshi Assistant Professor, Imam Hossein Officer and Guard Training University, Tehran, Iran , r.salmanpour01@sndu.ac.ir 0009-0003-9460-0356 Behrooz Kameli Assistant Professor, Supreme National Defense University, Tehran, Iran , behroozkameli@gmail.com 0000-0003-2284-2877 Journal Article 2026 02 10 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. 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_210829_b95cd7bcf8772862035051de4385a2bf.pdf