اخبار و اعلانات
آمار
| تعداد نشریات | 35 |
| تعداد شماره‌ها | 1,283 |
| تعداد مقالات | 9,275 |
| تعداد مشاهده مقاله | 8,605,331 |
| تعداد دریافت فایل اصل مقاله | 5,274,839 |
تجزیه و تحلیل بازگشت دانش بازنشستگان در زنجیره تأمین پالایشگاه اصفهان با رویکرد تلفیقی مدلسازی ساختاری تفسیری و مدلسازی معادلات ساختاری | ||
| مدیریت راهبردی دانش سازمانی | ||
| مقاله 5، دوره 8، شماره 1 - شماره پیاپی 28، فروردین 1404، صفحه 98-120 اصل مقاله (1.16 M) | ||
| نوع مقاله: مقاله پژوهشی با اصالت | ||
| شناسه دیجیتال (DOI): 10.47176/smok.2025.1833 | ||
| نویسندگان | ||
| مهران ضیائیان* 1؛ سمیه احمدزاده2؛ عبدالرسول یزدی3 | ||
| 1گروه مدیریت ، دانشکده مدیریت و نوآوری، دانشگاه شهید اشرفی اصفهانی، اصفهان، ایران | ||
| 2گروه مدیریت، دانشکده مدیریت و نوآوری،دانشگاه شهید اشرفی اصفهانی، اصفهان، ایران | ||
| 3گروه مدیریت، دانشکده مدیریت و نوآوری، دانشگاه شهید اشرفی اصفهانی، اصفهان، ایران | ||
| چکیده | ||
| هدف: علی‌رغم اهمیت دانش بازنشستگان در رفع پیچیدگی‌ها و چالش‌های مختلف در زنجیره تأمین پالایشگاه از جمله ریسک‌های ناشی از نوسانات قیمت نفت و بازار، عدم همکاری و هماهنگی میان اعضای زنجیره تأمین، چالش‌های نفتی همچون نشت و آتش‌سوزی و ...، در سالیان اخیر حجم قابل توجهی از دانش با بازنشسته شدن کارکنان خارج شده است. هدف از انجام پژوهش حاضر تجزیه و تحلیل بازگشت دانش بازنشستگان در زنجیره تأمین پالایشگاه اصفهان است. روش پژوهش: پژوهش حاضر از لحاظ هدف، کاربردی و از نظر ماهیت و روش، توصیفی- علی و از نظر شیوه گردآوری داده‌ها، مطالعه غیرآزمایشی از نوع پیمایشی مقطعی است. در ابتدا 12 عامل مؤثر بر بازگشت دانش بازنشستگان بر اساس مرور پیشینه پژوهش شناسایی و به تأیید خبرگان دانشگاهی و صنعتی (پالایشگاه اصفهان) رسید. به منظور ارائه مدل مفهومی پژوهش از رویکرد مدلسازی ساختاری تفسیری استفاده شده است. در این بخش ابتدا پرسشنامه مقایسات زوجی میان عوامل اثرگذار بر بازگشت دانش بازنشستگان در زنجیره تأمین پالایشگاه اصفهان طراحی شد. سپس با استفاده از روش نمونه‌گیری قضاوتی و نظرخواهی از 15 نفر از خبرگان دانشگاهی و صنعتی، نحوه ارتباط میان عوامل مؤثر بر بازگشت دانش بازنشستگان شناسایی و مدل مفهومی ارائه شد. به منظور تأیید یا رد مدل مفهومی از رویکرد مدلسازی معادلات ساختاری و نرم افزار Smart PLS3 استفاده شده است. با استفاده از روش نمونه‌گیری در دسترس تعداد 300 پرسشنامه میان کارکنان و مدیران پالایشگاه اصفهان توزیع که از این میان تعداد 243 پرسشنامه بازگشت داده شد. روایی پرسشنامه پژوهش با استفاده از روایی همگرا (ضرایب بار عاملی و معیار AVE) و روایی واگرا (جدول فورنل- لارکر) تأیید شده است. همچنین پایایی پرسشنامه پژوهش با استفاده از معیارهای آلفای کرونباخ و پایایی ترکیبی مورد تأیید قرار گرفته است. یافته‌ها: نتایج این پژوهش نشان داد که حمایت دولت با ضریب مسیر 495/0 و دانش و تجربه بازنشستگان با ضریب مسیر 416/0 بر حمایت مدیریت ارشد و حمایت مدیریت ارشد با ضریب مسیر 789/0 بر مشوق‌های مالی و با ضریب مسیر 854/0 بر مشوق‌های غیرمالی تأثیرگذار است. علاوه بر این نتایج پژوهش نشان داد که مشوق‌های مالی با ضریب مسیر 383/0 و مشوق‌های غیرمالی با ضریب مسیر 522/0 بر فرهنگ حفظ و ارتقاء دانش سازمانی، فرهنگ حفظ و ارتقاء دانش سازمانی با ضریب مسیر 817/0 بر استفاده از فناوری‌های پیشرفته، استفاده از فناوری‌های پیشرفته بر مطلوبیت محیط کار با ضریب مسیر 787/0، مطلوبیت محیط کار با ضریب مسیر 805/0 بر مشارکت و همکاری کارکنان و مشارکت و همکاری کارکنان با ضریب مسیر 774/0 بر انسجام و ثبات سازمانی تأثیرگذار است. از دیگر نتایج این پژوهش می‌توان به تأثیر انسجام و ثبات سازمانی با ضریب مسیر 797/0 بر تسهیل ارتباطات و تأثیر تسهیل ارتباطات بر کیفیت محصولات و خدمات با ضریب مسیر 804/0 اشاره کرد. نتیجه‌گیری: نتایج پژوهش نشان داد که بازگشت دانش بازنشستگان نقش تعیین‌کننده‌ای در رفع چالش‌ها در زنجیره تأمین پالایشگاه اصفهان دارد. همچنین بر اساس مدل مفهومی ارائه شده از رویکرد مدلسازی ساختاری تفسیری، حمایت دولت و دانش و تجربه بازنشستگان به عنوان عوامل کلیدی در بازگشت دانش بازنشستگان در زنجیره تأمین پالایشگاه اصفهان شناسایی شده‌اند. | ||
| کلیدواژه‌ها | ||
| بازگشت دانش بازنشستگان؛ دانش؛ زنجیره تأمین؛ مدیریت دانش | ||
| عنوان مقاله [English] | ||
| Analysis of retirees' knowledge return in the supply chain of Isfahan Refinery with a combined approach of interpretive structural modeling and structural equation modeling | ||
| نویسندگان [English] | ||
| Mehran Ziaeian1؛ Somayeh Ahmadzadeh2؛ Abdolrasoul Yazdi3 | ||
| 1Department of Management, Faculty of Management and Innovation, Shahid Ashrafi Esfahani University, Isfahan, Iran | ||
| 2Department of Management, Faculty of Management and Innovation, Shahid Ashrafi Esfahani University, Isfahan, Iran | ||
| 3Department of Management, Faculty of Management and Innovation, Shahid Ashrafi Esfahani University, Isfahan, Iran | ||
| چکیده [English] | ||
| Purpose: Despite the importance of retirees' knowledge in resolving various complexities and challenges in the refinery supply chain, including risks arising from oil price and market fluctuations, lack of cooperation and coordination among supply chain members, oil challenges such as leaks and fires, etc., in recent years, a significant amount of knowledge has been lost with the retirement of employees. The purpose of this study is to analyze the return of retirees' knowledge in the Isfahan Refinery supply chain. Design/methodology/approach: The present study is applied in terms of its purpose, descriptive-causal in terms of its nature and method, and a cross-sectional survey-type non-experimental study in terms of data collection. Initially, 12 factors affecting the return of retirees' knowledge were identified based on a literature review and research background and were approved by academic and industrial experts (Isfahan Refinery). In order to present the conceptual model of the research, the interpretive structural modeling approach was used. In this section, a paired comparison questionnaire was designed among the factors affecting the return of retirees' knowledge in the Isfahan Refinery supply chain. Then, using the judgmental sampling method and opinion polls from 15 academic and industrial experts, the relationship between the factors affecting the return of knowledge of retirees was identified and a conceptual model was presented. In order to confirm or reject the conceptual model, the structural equation modeling approach and Smart PLS3 software were used. Using the convenience sampling method, 300 questionnaires were distributed among the employees and managers of Isfahan Refinery, of which 243 questionnaires were returned. The validity of the research questionnaire was confirmed using convergent validity (factor loading coefficients and AVE criterion) and divergent validity (Fornell-Larker table). Also, the reliability of the research questionnaire was confirmed using Cronbach's alpha and composite reliability criteria. Findings: The results of this study showed that government support with a path coefficient of 0.495 and knowledge and experience of retirees with a path coefficient of 0.416 have an effect on top management support, top management support with a path coefficient of 0.789 on financial incentives, and with a path coefficient of 0.854 on non-financial incentives. In addition, the results of the study showed that financial incentives with a path coefficient of 0.383 and non-financial incentives with a path coefficient of 0.522 have an effect on the culture of maintaining and promoting organizational knowledge, the culture of maintaining and promoting organizational knowledge with a path coefficient of 0.817 on the use of advanced technologies, the use of advanced technologies on the desirability of working environment with a path coefficient of 0.787, the desirability of the work environment with a path coefficient of 0.805 on employee participation and collaboration, and employee participation and collaboration with a path coefficient of 0.774 on organizational coherence and stability. Other results of this study include the effect of organizational cohesion and stability with a path coefficient of 0.797 on facilitating communication and the effect of communication facilitation on the quality of products and services with a path coefficient of 0.804. In addition, the results of the study showed that the return of knowledge of retirees plays a decisive role in resolving challenges in the supply chain of Isfahan Refinery. In addition, based on the conceptual model presented from the interpretive decision-making modeling approach, government support and the knowledge and experience of retirees have been identified as key factors in the return of knowledge of retirees in the supply chain of Isfahan Refinery. Research limitations/implications: The present study, like many studies, has limitations in the field of conducting research processes. Among these limitations, we can mention the lack of examination of different cycles between stimuli, which has always existed as a structural limitation in the structural equation technique, and it is suggested that in future research, the return paths between the relationships formed in this study be examined. Also, considering the use of a questionnaire as a data collection tool and its distribution among Isfahan Refinery employees and managers and academic experts, their mindset and attitude in completing the questionnaire may have affected the results of the present study. In addition, the present study was related to the Isfahan Refinery supply chain, and its results should be generalized with caution to other similar organizations and industries. Practical implications: This research has various implications for the refinery supply chain. Using retirees' knowledge can reduce various problems in the refinery supply chain, including oil field exploration, oil price fluctuations, lack of coordination and cooperation between supply chain members, high repair and maintenance costs, transportation costs, etc. In addition, the results shown in this research based on the use of retirees' knowledge in solving various supply chain problems and challenges can encourage other industries to use their retirees' knowledge. Also, the return of retirees’ knowledge can lead to improved crisis management and reduced risks in the supply chain, as these individuals usually have experience in critical situations and can help design and implement successful strategies. This process can lead to increased productivity and reduced costs in the supply chain, which is of particular importance for the refinery’s competitiveness. Furthermore, fostering collaboration between active employees and retirees can promote a culture of continuous learning and knowledge sharing that is critical to adapting to industry change. This engagement not only helps to facilitate the onboarding process for new employees, but also helps to preserve organizational knowledge that might otherwise be lost. Involving retirees in advisory roles can also help to strengthen organizational resilience, as their guidance can be invaluable during times of crisis or transition. Ultimately, these outcomes lead to increased productivity, reduced operating costs, and improved competitiveness, better positioning the refinery for sustained success in a dynamic marketplace. Originality/value: Using the experiences and knowledge of retirees in the Isfahan Refinery supply chain can bring various benefits such as reducing costs and time wasted due to trial and error, reducing rework, improving performance, etc. Therefore, it is necessary to identify key factors in the return of retirees' knowledge, given the large amount of knowledge that is lost when people retire from the Isfahan Refinery. | ||
| کلیدواژه‌ها [English] | ||
| Retired knowledge return, Knowledge, Supply Chain, Knowledge Management | ||
| مراجع | ||
|
Abdelwhab Ali, A., Panneer selvam, D. D. D., Paris, L., & Gunasekaran, A. (2019). Key factors influencing knowledge sharing practices and its relationship with organizational performance within the oil and gas industry. Journal of knowledge management, 23(9), 1806-1837. https://doi.org/10.1108/JKM-06-2018-0394 Abdullahi, H., Sharifzadeh, F., & Haji, J. (2019). An Appraisal of the Effect of Social Capital on Knowledge Sharing among Faculty Members (Case Study: Allameh Tabatabaee University of Tehran). Strategic Management of Organizational Knowledge, 2(3), 47-75. https://doi.org/10.47176/smok.2019.1083 [In persian] Abubakar, S., Esther, G. Y., & Angonimi, O. (2020). The Effect of Financial and Non-Financial Incentives on Staff Performance. Journal of Business and Management (IOSR-JBM), 22(6), 26-32. https://doi.org/10.9790/487X-2206112632 Aghababaei, R. (2024). The effect of synergistic leadership behaviors on the knowledge hiding of kashan teachers: the mediating role of professional learning community and organizational voice. Strategic Management of Organizational Knowledge, 7(3), 11-28. https://doi.org/10.47176/smok.2024.1788 [In Persian] Akbari, E., Rasekh, B., Maal, K. B., Karbasiun, F., Yazdian, F., Emami-Karvani, Z., & Peighami, R. (2021). A novel biosurfactant producing Kocuria rosea ABR6 as potential strain in oil sludge recovery and lubrication. AMB Express, 11, 1-10. https://doi.org/10.1186/s13568-021-01283-9 Alizadeh, M., & Karimi, B. (2024). A dynamic two-phased approach for planning downstream oil supply chain network under uncertainty. Computers & Industrial Engineering, 194, 110374. https://doi.org/10.1016/j.cie.2024.110374 Allameh, G., & Saidi-Mehrabad, M. (2021). Pricing decisions in a decentralized biofuel supply chain with RIN mechanism considering environmental impacts. Biomass and Bioenergy, 150, 106090. https://doi.org/10.1016/j.biombioe.2021.106090 Al-Qubaisi, S. S., & Ajmal, M. (2018). Determinants of operational efficiency in the oil and gas sector: A Balanced scorecards perspective. Benchmarking: An International Journal, 25(9), 3357-3385. https://doi.org/10.1108/BIJ-04-2017-0079 Bahrami, S., & Khadivar, A. (2020). A Model for Selecting the Knowledge Management Strategy in a Supply Chain. Strategic Management of Organizational Knowledge, 3(2), 97-136. https://doi.org/10.47176/smok.2020.1161 [In persian] Bamakan, S. M. H., Soleymanizadeh, H., & Ziaeian, M. (2024). Sterategic management of Organizational knowledge. 7 (2). 133-164.{In persian}. https://doi.org/10.47176/smok.2024.1723 Basak, D., Bose, A., Roy, S., Chowdhury, I. R., & Sarkar, B. C. (2021). Understanding sustainable homestay tourism as a driving factor of tourist's satisfaction through structural equation modelling: A case of Darjeeling Himalayan region, India. Current Research in Environmental Sustainability, 3, 100098. https://doi.org/10.1016/j.crsust.2021.100098 Bhosale, V. A., & Kant, R. (2016). An integrated ISM fuzzy MICMAC approach for modelling the supply chain knowledge flow enablers. International Journal of Production Research, 54(24), 7374-7399. https://doi.org/10.1080/00207543.2016.1189102 Biglari, M., & Abdul Razak, R. (2023). Resource Imperialism and Resistance: Labour, Security and Social Reproduction after Iranian Oil Nationalisation. Journal of Energy History, 9. http://www.energyhistory.eu/en/special-issue/resource-imperialism-and-resistance-labour-security-and-social-reproduction-after Bucher-Koenen, T., Ferrari, I., & Pettinicchi, Y. (2019). The role of pension knowledge in labour supply decisions: Evidence from Europe. Retrieved from https://www.pensjonsforum.net/nyheter/the-role-of-pension-knowledge-in-labour-supply-decisions-evidence-from-europe Capello, R., Caragliu, A., & Nijkamp, P. (2011). Territorial capital and regional growth: increasing returns in knowledge use. Tijdschrift voor economische en sociale geografie, 102(4), 385-405. https://doi.org/10.1111/j.1467-9663.2010.00613.x Chen, Y., & Jin, W. (2024). Grounded Research on Influence Mechanism of Knowledge-based Retirees’ Returning to Hometown. Paper presented at the 2024 9th International Conference on Social Sciences and Economic Development (ICSSED 2024). https://doi.org/10.2991/978-94-6463-459-4_83 Copacino, W. C. (2019). Supply chain management: The basics and beyond: Routledge. https://doi.org/10.4324/9780203737859 Cunningham, C., Patton, M. C., & Reed, R. R. (2016). Heterogeneous returns to knowledge exchange: Evidence from the urban wage premium. Journal of Economic Behavior & Organization, 126, 120-139. https://doi.org/10.1016/j.jebo.2016.01.001 Di Vaio, A., Palladino, R., Pezzi, A., & Kalisz, D. E. (2021). The role of digital innovation in knowledge management systems: A systematic literature review. Journal of business research, 123, 220-231. https://doi.org/10.1016/j.jbusres.2020.09.042 Donate, M. J., González-Mohíno, M., Appio, F. P., & Bernhard, F. (2022). Dealing with knowledge hiding to improve innovation capabilities in the hotel industry: The unconventional role of knowledge-oriented leadership. Journal of business research, 144, 572-586. https://doi.org/10.1016/j.jbusres.2022.02.001 Dong, C., Boute, R., McKinnon, A., & Verelst, M. (2018). Investigating synchromodality from a supply chain perspective. Transportation Research Part D: Transport and Environment, 61, 42-57. https://doi.org/10.1016/j.trd.2017.05.011 Edwards, J. S. (2022). Where knowledge management and information management meet: Research directions. International Journal of Information Management, 63, 102458. https://doi.org/10.1016/j.ijinfomgt.2021.102458 Elkington, R. W. T. (2013). Transferring experiential knowledge from the near-retirement generation to the next generation. Ensslin, L., Carneiro Mussi, C., Rolim Ensslin, S., Dutra, A., & Pereira Bez Fontana, L. (2020). Organizational knowledge retention management using a constructivist multi-criteria model. Journal of knowledge management, 24(5), 985-1004. https://doi.org/10.1108/JKM-12-2019-0689 Etemadi, A., & Kasraei, A. (2019). The Effect of Knowledge Management on Supply Chain Performance in the Offshore Sector of Oil and Gas (Case study: Iranian Offshore Engineering and Construction Company). Strategic Management of Organizational Knowledge, 1(3), 185-205. https://doi.org/10.47176/smok.2018.1004 [In persian] Eugene Jennex, M. (2014). A proposed method for assessing knowledge loss risk with departing personnel. VINE: The journal of information and knowledge management systems, 44(2), 185-209. https://doi.org/10.1108/VINE-07-2012-0028 Ferreira, N. N., Martins, M. R., de Figueiredo, M. A. G., & Gagno, V. H. (2020). Guidelines for life extension process management in oil and gas facilities. Journal of Loss Prevention in the Process Industries, 68, 104290. https://doi.org/10.1016/j.jlp.2020.104290 Ghorbanali, H., Mousa Khani, M., & Daneshfard, K. (2023). Presenting a model of evidence-based policies to attract and retain knowledge workers in Iran's aviation industry. Geography (Regional Planning), 13(51), 97-111. : https://10.22034/JGEOQ.2023.297233.3223 Hamilton, R., & Sodeman, W. A. (2020). The questions we ask: Opportunities and challenges for using big data analytics to strategically manage human capital resources. Business Horizons, 63(1), 85-95. https://doi.org/10.1016/j.bushor.2019.10.001 Homan, H. (2017). Modeling structural equations using Lisrel software, Samit Publications. Tehran. [In persian] Hosseinzadeh, A. R., Mohammad Pourzarandi, M. E., & Afsharkazemi, M. A. (2023). Prioritizing Strategies Based on the Identification and Ranking of Supply Chain Risks (Case Study: Oil and Gas Operating Company). Iranian Journal Of Supply Chain Management, 25(79), 1-15. http://iieshrm.ir/article-1-1454-en.html [In persian] Idrees, H., Xu, J., Haider, S. A., & Tehseen, S. (2023). A systematic review of knowledge management and new product development projects: Trends, issues, and challenges. Journal of Innovation & Knowledge, 8(2), 100350. https://doi.org/10.1016/j.jik.2023.100350 Jafari, M., & Khanachah, S. N. (2024). Integrated knowledge management in the supply chain: Assessment of knowledge adoption solutions through a comprehensive CoCoSo method under uncertainty. Journal of Industrial Information Integration, 39, 100581. https://doi.org/10.1016/j.jii.2024.100581 Jansukpum, K., Chernbumroong, S., Intawong, K., Sureephong, P., & Puritat, K. (2024). Gamified Virtual Reality for Library Services: The Effect of Gamification on Enhancing Knowledge Retention and User Engagement. New Review of Academic Librarianship, 1-21. https://doi.org/10.1080/13614533.2024.2381509 Jayawickrama, U., Liu, S., Hudson Smith, M., Akhtar, P., & Al Bashir, M. (2019). Knowledge retention in ERP implementations: the context of UK SMEs. PRODUCTION PLANNING & CONTROL, 30(10-12), 1032-1047. https://doi.org/10.1080/09537287.2019.1582107 Jessani, N. S., Valmeekanathan, A., Babcock, C. M., & Ling, B. (2020). Academic incentives for enhancing faculty engagement with decision-makers—considerations and recommendations from one School of Public Health. Humanities and Social Sciences Communications, 7(1), 1-13. https://doi.org/10.1057/s41599-020-00629-1 Jonsson, R. (2021). Retaining the Aging Workforce: Studies of the interplay between individual and organizational capability in the context of prolonged working lives. Kahn, S., & MacGarvie, M. (2016). Do return requirements increase international knowledge diffusion? Evidence from the Fulbright program. Research Policy, 45(6), 1304-1322. https://doi.org/10.1016/j.respol.2016.02.002 Kalashi, A. D., & Kasrai, A. R. (2018). Identification, investigation and prioritization of factors affecting the strategies of non-governmental oil Refining Companies using FAHP Software. Revista Publicando, 5(14 (2)), 1-26. Kamal, M. M., Mamat, R., Mangla, S. K., Kumar, P., Despoudi, S., Dora, M., & Tjahjono, B. (2022). Immediate return in circular economy: Business to consumer product return information sharing framework to support sustainable manufacturing in small and medium enterprises. Journal of business research, 151, 379-396. https://doi.org/10.1016/j.jbusres.2022.06.021 Karimi-Ghartemani, S., Khani, N., & Nasr Isfahani, A. (2022). Designing a conceptual model for organizational stupidity using a combination of grounded theory and interpretive structural modeling. Interdisciplinary Journal of Management Studies (Formerly known as Iranian Journal of Management Studies), 15(3), 549-568. https://doi.org/10.22059/ijms.2021.317451.674409 Khalili, S. M. , Pooya, A. , Kazemi, M. and Fakoor Saghih, A. M. (2022). Designing a Sustainable and Resilient Gasoline Supply Chain Network under Uncertainty (Case study: Gasoline Supply Chain Network of Khorasan Razavi Province). Industrial Management Journal, 14(1), 27-79. https://doi.org/10.22059/imj.2022.334524.1007896 Kitchot, S., Siengthai, S., & Sukhotu, V. (2021). The mediating effects of HRM practices on the relationship between SCM and SMEs firm performance in Thailand. Supply Chain Management: An International Journal, 26(1), 87-101. https://doi.org/10.1108/SCM-05-2019-0177 Kosklin, R., Lammintakanen, J., & Kivinen, T. (2023). Knowledge management effects and performance in health care: a systematic literature review. Knowledge Management Research & Practice, 21(4), 738-748. https://doi.org/10.1080/14778238.2022.2032434 Lambert, D. M., & Enz, M. G. (2017). Issues in supply chain management: Progress and potential. Industrial marketing management, 62, 1-16. https://doi.org/10.1016/j.indmarman.2016.12.002 Lee, S. M., & Trimi, S. (2018). Innovation for creating a smart future. Journal of Innovation & Knowledge, 3(1), 1-8. https://doi.org/10.1016/j.jik.2016.11.001 Levallet, N., & Chan, Y. E. (2019). Organizational knowledge retention and knowledge loss. Journal of knowledge management, 23(1), 176-199. https://doi.org/10.1108/JKM-08-2017-0358 MacCarthy, B. L., Ahmed, W. A., & Demirel, G. (2022). Mapping the supply chain: Why, what and how? International Journal of Production Economics, 250, 108688. https://doi.org/10.1016/j.ijpe.2022.108688 Maheshwarkar, M., & Sohani, N. (2019). Knowledge management evaluation criteria for industries: identification and interpretive structural modelling. International Journal of Knowledge Management Studies, 10(3), 227-250. https://doi.org/10.1504/IJKMS.2019.101482 Malekinejad, P, Mirfakhredini, S. H, Marvoti Sharifabadi, A, & Zanjechi, Seyed Mahmoud. (2023). Designing scenarios for the use of knowledge block chains in the closed loop supply chain of knowledge-based companies (case study: Yazd Science and Technology Park). Sterategic management of Organizational knowledge, 6(4), 181-216. [In persian] Marinelli, E. (2013). Sub-national graduate mobility and knowledge flows: An exploratory analysis of onward-and return-migrants in Italy. Regional Studies, 47(10), 1618-1633. https://doi.org/10.1080/00343404.2012.709608 Mazorodze, A. H., & Buckley, S. (2020). A review of knowledge transfer tools in knowledge-intensive organisations. South African Journal of Information Management, 22(1), 1-6. https://doi.org/10.4102/sajim.v22i1.1135 Min, S., Zacharia, Z. G., & Smith, C. D. (2019). Defining supply chain management: In the past, present, and future. Journal of business logistics, 40(1), 44-55. https://doi.org/10.1111/jbl.12201 Mun, J. (2022). Optimizing warfighters ‘intellectual capability: return on investment of military education and research. Defense AR Journal, 29(3), 192-245. Natek, S., & Lesjak, D. (2021). Knowledge management systems and tacit knowledge. International Journal of Innovation and Learning, 29(2), 166-180. https://doi.org/10.1504/IJIL.2021.112994 Oliveira, M., Curado, C., Balle, A. R., & Kianto, A. (2020). Knowledge sharing, intellectual capital and organizational results in SMES: are they related? Journal of Intellectual Capital, 21(6), 893-911. https://doi.org/10.1108/JIC-04-2019-0077 Raudeliūnienė, J., Davidavičienė, V., & Petrusevičius, R. (2018). Factors influencing knowledge retention process: case of Lithuanian armed forces. Technological and Economic Development of Economy, 24(3), 1104-1124. https://doi.org/10.3846/20294913.2017.1309598 Sahebi, H., Ashayeri, J., Mousakazemi, S. S., & Rahiminejad, S. (2024). A sustainable integrated crude oil supply chain network design with consideration of social performance and environmental laws in a case study. Petroleum Science and Technology, 42(23), 3442-3465. https://doi.org/10.1080/10916466.2023.2208595 Santos, R. F., Oliveira, M., & Curado, C. (2023). The effects of the relational dimension of social capital on tacit and explicit knowledge sharing: A mixed-methods approach. VINE Journal of Information and Knowledge Management Systems, 53(1), 43-63. https://doi.org/10.1108/VJIKMS-05-2020-0094 Shadmanfar, M. H., & Makvandi, F. (2024). Identifying barriers and facilitators of organizational knowledge sharing. International Journal of Innovation in Management, Economics and Social Sciences, 4(2), 23–43. https://doi.org/10.59615/ijimes.4.2.23 Sharifian, H., Abedi, Z., Amidpour, M., Hemmasi, A., & Ghaffarzadeh, H. (2021). Energy footprint assessment in oil refineries based on green productivity techniques and tools, case study: Iran. International Journal of Environmental Science and Technology, 1-18. Singh, M. K., & Gupta, V. (2021). Critical types of knowledge loss in military organisations. VINE Journal of Information and Knowledge Management Systems, 51(4), 618-635. https://doi.org/10.1108/VJIKMS-09-2019-0152 Slocum, A. H., & Gessel, D. J. (2022). Evolving from a hydrocarbon-based to a sustainable economy: Starting with a case study for Iran. Renewable and Sustainable Energy Reviews, 154, 111750. https://doi.org/10.1016/j.rser.2021.111750 Sousa, I. C., Ramos, S., & Carvalho, H. (2019). Age-diversity practices and retirement preferences among older workers: A moderated mediation model of work engagement and work ability. Frontiers in psychology, 10, 1937. https://doi.org/10.3389/fpsyg.2019.01937 Sumbal, M. S., Tsui, E., Durst, S., Shujahat, M., Irfan, I., & Ali, S. M. (2020). A framework to retain the knowledge of departing knowledge workers in the manufacturing industry. VINE Journal of Information and Knowledge Management Systems, 50(4), 631-651. https://doi.org/10.1108/VJIKMS-06-2019-0086 Talaie, H., Ziaeian, M., & Malekinejad, P. (2022). Designing the establishment and implementation model of quality 4.0 with the integrated approach of interpretive structural modeling and structural equation modeling. Journal of Quality Engineering and Management, 12(1), 51-68. [In persian] Thomas, A., & Gupta, V. (2021). Social capital theory, social exchange theory, social cognitive theory, financial literacy, and the role of knowledge sharing as a moderator in enhancing financial well-being: from bibliometric analysis to a conceptual framework model. Frontiers in psychology, 12, 664638. https://doi.org/10.3389/fpsyg.2021.664638 Wang, L., Li, X., Zhu, H., & Zhao, Y. (2023). Influencing factors of livestream selling of fresh food based on a push-pull model: A two-stage approach combining structural equation modeling (SEM) and artificial neural network (ANN). Expert Systems with Applications, 212, 118799. https://doi.org/10.1016/j.eswa.2022.118799 Weiss, P. A. (2016). Knowledge transfer preferences of expert employees nearing retirement: Walden University. Yazdanparast, R., Jolai, F., Pishvaee, M., & Keramati, A. (2022). A resilient drop-in biofuel supply chain integrated with existing petroleum infrastructure: Toward more sustainable transport fuel solutions. Renewable Energy, 184, 799-819. https://doi.org/10.1016/j.renene.2021.11.081 Yu, W., Jacobs, M. A., Chavez, R., & Yang, J. (2019). Dynamism, disruption orientation, and resilience in the supply chain and the impacts on financial performance: A dynamic capabilities perspective. International Journal of Production Economics, 218, 352-362. https://doi.org/10.1016/j.ijpe.2019.07.013 Zouk, H., Yu, W., Oza, A., Hawley, M., Kumar, P. K. V., Koch, C., . . . Karlson, E. W. (2022). Reanalysis of eMERGE phase III sequence variants in 10,500 participants and infrastructure to support the automated return of knowledge updates. Genetics in Medicine, 24(2), 454-462. https://doi.org/10.1016/j.gim.2021.10.010 | ||
|
آمار تعداد مشاهده مقاله: 96 تعداد دریافت فایل اصل مقاله: 58 |
||