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ارائه مدل بهینه سازی برون‌سپاری مدیریت دانش با استفاده از الگوریتم ژنتیک | ||
| مدیریت راهبردی دانش سازمانی | ||
| مقاله 3، دوره 8، شماره 2 - شماره پیاپی 29، تیر 1404، صفحه 67-85 اصل مقاله (1.59 M) | ||
| نوع مقاله: مقاله پژوهشی با اصالت | ||
| شناسه دیجیتال (DOI): 10.47176/smok.2025.1846 | ||
| نویسندگان | ||
| آمنه خدیور* 1؛ فاطمه عباسی2؛ شیدا اکبریان3 | ||
| 1دانشیار، گروه مدیریت، دانشکده علوم اجتماعی و اقتصاد، دانشگاه الزهرا، تهران، ایران | ||
| 2استادیار، گروه مدیریت صنعتی و مدیریت فناوری اطلاعات، دانشگاه شهید بهشتی، تهران، ایران | ||
| 3دانشجوی کارشناسی ارشد، گروه مدیریت، دانشکده علوم اجتماعی و اقتصاد، دانشگاه الزهرا، تهران، ایران | ||
| تاریخ دریافت: 29 آذر 1403، تاریخ بازنگری: 25 اسفند 1403، تاریخ پذیرش: 03 تیر 1404 | ||
| چکیده | ||
| هدف: مدیریت دانش همچنان به عنوان یک منبع اصلی برای کسب مزیت رقابتی در سازمان‌ها اهمیت دارد. این پژوهش با هدف ارائه مدل بهینه‌سازی برون‌سپاری مدیریت دانش با استفاده از الگوریتم ژنتیک انجام شده است. روش پژوهش: این تحقیق از نظر هدف، توسعه‌ای-کاربردی و از نظر روش گردآوری داده‌ها، توصیفی-پیمایشی است. جامعه آماری شامل هشت نفر از خبرگان و اساتید دانشگاهی و صنعتی در حوزه مدیریت دانش و شش شرکت پیمانکاری بود. نمونه‌گیری به روش گلوله برفی انجام شد و ابزار اصلی جمع‌آوری اطلاعات، پرسشنامه بود. یافته‌ها: در گام نخست، با بررسی مطالعات پیشین و نظرات خبرگان، 28 معیار برای ارزیابی ارائه‌دهندگان برون‌سپاری شناسایی شد. سپس، این معیارها با روش فازی تأیید و چارچوب نهایی طراحی شد. در مرحله بعد، پیمانکاران با استفاده از الگوریتم ژنتیک و بر اساس معیارهای تأییدشده ارزیابی شدند. نتیجه‌گیری: نتایج نشان داد که یکی از پیمانکاران بالاترین امتیاز را کسب کرده است. مدل پیشنهادی می‌تواند به سازمان‌ها در انتخاب بهینه ارائه‌دهندگان خدمات مدیریت دانش و بهبود تصمیم‌گیری در این زمینه کمک کند. این پژوهش با استفاده از الگوریتم ژنتیک و رویکرد فازی به تحلیل و ارزیابی ارائه‌دهندگان خدمات برون‌سپاری مدیریت دانش پرداخته است که در ادبیات موجود کمتر مورد توجه قرار گرفته است. | ||
| کلیدواژه‌ها | ||
| مدیریت دانش؛ برون‌سپاری؛ الگوریتم ژنتیک؛ انتخاب پیمانکار | ||
| عنوان مقاله [English] | ||
| A Model For Optimization of Knowledge Management Outsourcing Decision Using Genetic Algorithm | ||
| نویسندگان [English] | ||
| Ameneh Khadivar1؛ Fatemeh Abbasi2؛ Sheyda Akbarian3 | ||
| 1Associate Professor, Department of Management, Faculty of Social Sciences and Economics, Alzahra University, Tehran, Iran | ||
| 2Assistant Professor, Department of Industrial Management and Information Technology, Shahid Beheshti University, Tehrani, Iran | ||
| 3M.Sc. Student, Department of Management, Faculty of Social Sciences and Economics, Alzahra University, Tehran, Iran | ||
| چکیده [English] | ||
| Purpose: The purpose of this study is to develop an optimization model for knowledge management outsourcing using a genetic algorithm. This research aims to provide a framework for evaluating and selecting knowledge management service providers, enhancing decision-making processes for organizational managers. Methodology: This research is developmental-applied in terms of its objective and descriptive-survey in terms of data collection method. It analyzes the criteria for selecting knowledge management outsourcing providers using a genetic algorithm. In the identification of criteria, the statistical population included eight experts and professors from academia and industry in the field of knowledge management and six contracting companies. Snowball sampling was done and the main data collection tool was a questionnaire.The primary data collection tool was a questionnaire with 33 questions, specifically designed to evaluate the selection criteria for outsourcing providers. The Delphi technique was employed for validation, involving four rounds of expert feedback to reach a consensus. Data from the questionnaires were processed using MATLAB, where the genetic algorithm was applied. The algorithm used a random initial population, with operations for parent selection, crossover, and mutation, iterating until the optimal solution was found. The output helped identify the most effective outsourcing providers, aided managerial decision-making, and enhanced organizational performance. Findings: In this study, using the roulette wheel method and based on available data, the selection process among six contractors was carried out. The results show that among the six Knowledge Management outsourcing providers, the Future Development Consulting Company, with the highest score, was chosen as the best option. Other companies ranked in the following order: Dana Knowledge Management Consulting Group, Inotex Iran, Nadak Engineering Consulting, Persica, and Rasa Consulting. Regarding the evaluation of various factors: Managerial capabilities: Future Development Consulting Company received the highest score of 90. Adequacy of skilled staff: Both Future Development Consulting Company and Dana Knowledge Management Consulting Group scored 90. Information technology facilities and capabilities: Future Development Consulting Company and Dana Knowledge Management Consulting Group scored 80 each. Number of completed Knowledge Management projects: Future Development Consulting Company received the highest score of 90. Technology readiness: Future Development Consulting Company scored 90, the highest. Project completion time: Future Development Consulting Company scored 90, the highest. These results align with previous studies, including those by Lin et al. (2016) and Olajomok (2018), who used genetic algorithms to solve similar issues and confirmed the positive impact of outsourcing on company performance. The results also align with research by Büyüközan and Ersoy (2012), Majidi (2016), and Radfar (2010) regarding factors influencing outsourcing. Furthermore, the findings are consistent with studies by Minooie et al. (2010), Oakville et al. (2004), Lee (2014), Leo et al. (2014), and Fanni et al. (2005) on selecting effective outsourcing factors. The research also matches findings by Gabi et al. (2004) and Chen and Wang (2009) regarding components such as price, cost, quality, delivery time, and technological capabilities. The genetic algorithm, used as an intelligent tool in this study, allowed for the analysis and optimization of multiple criteria, including managerial capabilities, skilled staff, IT facilities, number of completed projects, technology readiness, and project completion time. This framework, combining fuzzy methods and genetic algorithms, offers a systematic approach for assessing and selecting Knowledge Management outsourcing providers, helping managers make better decisions based on precise, scientific criteria. The findings indicate that the use of genetic algorithms not only provides more accurate data analysis but also reduces the complexity of decision-making and enhances its precision. This framework is applicable not only in Knowledge Management but also in other outsourcing areas, allowing managers to benefit from it in diverse fields. Research limitations/implications: The limitations of this study include the small number of informed experts consulted, which may limit the generalizability of the results to other contractors. Additionally, due to the inherent characteristics of metaheuristic algorithms, there is no guarantee that genetic algorithms will always lead to the optimal solution. These algorithms typically perform well but generally settle for a satisfactory solution based on the specific conditions of each problem. Furthermore, the selection criteria were chosen in a general manner and are not specific to any particular industry. Practical implications: To enhance the selection process of Knowledge Management providers, it is recommended to use multi-criteria decision-making techniques, which can offer a more comprehensive analysis of the influencing factors. Additionally, organizations should personalize the selection criteria based on their specific characteristics to better align with their needs and improve performance. This tailored approach could lead to more effective and efficient decision-making. Future research should also explore additional dimensions of Knowledge Management outsourcing, as this study was limited by the length of the questionnaire. Expanding the scope to include more factors may provide a deeper understanding and have a significant impact on the provider selection outcomes. These recommendations aim to optimize the decision-making process and provide valuable insights for both researchers and managers in the field. Originality/value: This study utilizes genetic algorithms and a fuzzy approach to analyze and evaluate Knowledge Management outsourcing providers, an area that has received limited attention in the existing literature. | ||
| کلیدواژه‌ها [English] | ||
| Knowledge Management Outsourcing, Genetic Algorithm, Contractor Selection | ||
| مراجع | ||
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Akhavan Kharazian, M., Shahbazi, M. M., & Fatehi, M. (2017). Performance Evaluation of Knowledge Workers at R&D department in Outsourcing Conditions. Research in Production and Operations Management, 8(1), 139-156. https://doi.org/10.22108/jpom.2017.21550 (In Persian) Ahn, G., & Hur, S. (2020). Efficient genetic algorithm for feature selection for early time series classification. Computers & Industrial Engineering, 142, 106345. https://doi.org/10.1016/j.cie.2019.106345 Alder, D. (2003). Making the HR outsourcing decision. MIT Sloan Management Review, 45(1), 53-60. Babai Meybodi, H., & Emadi, S. H. (2017). Outsourcing services: Reasons, advantages, and disadvantages. National Conference on New Research in Management and Accounting, Isfahan, Iran*. Retrieved from https://civilica.com/doc/761400 (In Persian) Bolat, T., & Yilmaz, Ö. (2009). The relationship between outsourcing and organizational performance: Is it myth or reality for the hotel sector? International Journal of Contemporary Hospitality Management, 21 (1), 7-23. https://doi.org/10.1108/09596110910930151 Büyüközkan, G., & Çifçi, G. (2012). A novel hybrid MCDM approach based on fuzzy DEMATEL, fuzzy ANP and fuzzy TOPSIS to evaluate green suppliers. Expert Systems with Applications, 39(3), https://doi.org/3000-3011. 10.1016/j.eswa.2011.08.162 Chen, L., Xu, R., & Huang, Z. (2021). A genetic algorithm for supplier selection and procurement outsourcing. *International Journal of Production Economics, 231*, 107877. https://doi.org/10.1016/j.ijpe.2020.107877 Chen, L. Y., & Wang, T.-C. (2009). Optimizing partners’ choice in IS/IT outsourcing projects: The strategic decision of fuzzy VIKOR. International Journal of Production Economics, 120(1), 233–242. https://doi.org/10.1016/j.ijpe.2008.07.022 Delen, G. P., Peters, R. J., Verhoef, C., & Van Vlijmen, S. F. M. (2019). Foundations for measuring IT-outsourcing success and failure. Journal of Systems and Software, 156, 113-125. Fadaei, M., Eshghi, H., Almodaresi, S. A., & Ourani, B. (2018). Investigating factors influencing outsourcing in transportation organizations of cities comparable to Shahin Shahr. (In Persian) Feeny, D., Lacity, M., & Willcocks, L. P. (2005). Taking the measure of outsourcing providers. Sloan Management Review, 46 (3), 41-48. Gabbay, J., & Le May, A. (2004). Evidence based guidelines or collectively constructed “mindlines”? Ethnographic study of knowledge management in primary care. BMJ, 329(7473), 1013. Habibi, A., Sarafrazi, A., & Izadyar, S. (2014). Delphi technique theoretical framework in qualitative research. The International Journal of Engineering and Science, 3(4), 8-13. Hosseini Dehshiri, S. J., & Heidari Dehooie, J. (2019). Using grey number theory in multi-criteria decision-making methods to evaluate the risk of outsourcing information technology projects. Journal Name, 7(28), 167–198. (In Persian) https://doi.org/10.22054/ims.2019.10243 Johnson, M., Chen, H., & Wang, Y. (2023). Managing outsourcing risks and uncertainties in the supply chain: A genetic algorithm approach. *Expert Systems with Applications, 227*, 120145. https://doi.org/10.1016/j.eswa.2023.120145 Jensen, P. D. Ø., & Pedersen, T. (2012). Offshoring and international competitiveness: Antecedents of offshoring advanced tasks. Journal of the Academy of Marketing Science, 40(2), 313-328. Kohli, P. P. S., Zargar, S., Arora, S., & Gupta, P. (2019). Stock prediction using machine learning algorithms. In Applications of Artificial Intelligence Techniques in Engineering: SIGMA 2018, Volume 1 (pp. 405-414). Springer Singapore. https://doi.org/10.1007/978-981-13-1819-1_38 Lam, W., & Chua, A. Y. (2009). Knowledge outsourcing: an alternative strategy for knowledge management. Journal of Knowledge Management, 13(3), 28-43. https://doi.org/10.1108/13673270910962851 Li, M., Jin, L., & Wang, J. (2014). A new MCDM method combining QFD with TOPSIS for knowledge management system selection from the user's perspective in intuitionistic fuzzy environment. Applied Soft Computing, 21, 28-37. https://doi.org/10.1016/j.asoc.2014.03.008 Liou, J. J., & Chuang, Y. T. (2010). Developing a hybrid multi-criteria model for selection of outsourcing providers. Expert Systems with Applications, 37(5), 3755-3761. https://doi.org/10.1016/j.eswa.2009.11.048 Majidi Koohbanani, M., Saleh, N., & Pourkiani, M. (2016). The role of knowledge management and strategic knowledge management in organizations. Global Conference on Management, Economics, Accounting, and Humanities at the Beginning of the Third Millennium, Shiraz. Retrieved from https://civilica.com/doc/524913 (In Persian) Market Research Future. (2024). Knowledge process outsourcing market research report - Global forecast till 2034. Market Research Future. Retrieved January 2, 2025, from https://www.marketresearchfuture.com/reports/knowledge-process-outsourcing-market-22570 Mehregan, M. R., Salami, H., & Khajeh, M. (2010). Presenting a multi-criteria decision-making model for business process outsourcing using the analytic network process technique. Development and Transformation Management, 6, 17-31. (In Persian). Minouei, M., Pourzarandi, M. E., & Naderi, N. (2010). Evaluating the status of knowledge management based on the Nonaka-Takeuchi model (Case study: Taliya Company). Management Accounting, 3 (4), 49-58. (In Persian) Mo'tadel, M. R., Afshar Kazemi, M. A., & Dastbaz, S. (2011). Proposing a model to support outsourcing decisions for IT projects using group decision-making methods. Journal of Management Research, 89 (15). (In Persian) Morris, D., Swaminathan, J. M., & Gupta, S. (2022). Outsourcing decision-making in supply chain management: A hybrid genetic algorithm approach. Computers & Operations Research, 139, 105257. https://doi.org/10.1016/j.cor.2021.105257 Naemi, Y., Rostami, M., & Rostami, M. (2018). Investigating the impact of social responsibility and outsourcing on organizational performance: The moderating role of environmental uncertainty (Case study: Mihan Shahr Company, Kermanshah). New Achievements in Humanities Studies, 1 (7). (In Persian) Nematian, M., Motavar, M., & Salehi, B. (2017). Outsourcing: Benefits and risks. Quarterly Journal of Management and Accounting Studies, (International Conference on Management, Economics, and Humanities with an Approach to Resistance Economics, Employment, and Production, Winter), 341–352. (In Persian) Okoli, C., & Pawlowski, S. D. (2004). The Delphi method as a research tool: an example, design considerations and applications. Information & management, 42(1), 15-29. . https://doi.org/10.1016/j.im.2004.01.002 Peters, S., Li, Y., & Kumar, A. (2023). Optimization of IT outsourcing partnerships using genetic algorithms. Journal of Business Research, 150, 743-752. https://doi.org/10.1016/j.jbusres.2022.06.025 Radfar, R., Hosseinzadeh, F., & Khalilou, A. (2010). Measuring customer satisfaction using fuzzy logic (Case study: ATM devices of Bank Saderat Iran in Tehran). Journal of Marketing Management, 8. (In Persian) Saifollahi, N., Ghaffarlou, A., & Sheikh Hassani, N. (2020). Analyzing knowledge sharing among employees in outsourcing supply chain management activities (Case study: Imam Khomeini University of Marine Sciences). Marine Science Education, 7 (23), 1–123. (In Persian) Simon, G., DiNardo, C. D., Takahashi, K., Cascone, T., Powers, C., Stevens, R., & Chin, L. (2019). Applying artificial intelligence to address the knowledge gaps in cancer care. The Oncologist, 24(6), 772. https://doi.org/10.1634/theoncologist Wang, A., Zhang, L., & Zhao, Q. (2022). Genetic algorithm for outsourcing optimization in manufacturing: A case study. Journal of Manufacturing Systems, 61, 104-114. https://doi.org/10.1016/j.jmsy.2021.09.015 | ||
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آمار تعداد مشاهده مقاله: 59 تعداد دریافت فایل اصل مقاله: 51 |
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