طراحی مدل نقشه شناختی فازی عوامل مؤثر بر عملکرد طرح‌های توسعۀ خوشه کسب و کار ایران
محورهای موضوعی : مدیریت صنعتیمحمد دهقان بنادکی 1 , سید حیدر میرفخرالدینی 2 , سید حبیب الله میرغفوری 3 , سید محمود زنجیرچی 4
1 - دانشجوی دکتری گروه مدیریت صنعتی، دانشکده اقتصاد، مدیریت و حسابداری ،دانشگاه یزد، ایران
2 - استاد گروه مدیریت صنعتی،دانشکده اقتصاد، مدیریت و حسابداری، دانشگاه یزد، ایران
3 - دانشیار گروه مدیریت صنعتی،دانشکده اقتصاد، مدیریت و حسابداری، دانشگاه یزد، ایران
4 - دانشیار گروه مدیریت صنعتی،دانشکده اقتصاد، مدیریت و حسابداری، دانشگاه یزد، ایران
کلید واژه: تأمین مالی, نقشه شناختی فازی, تحلیل تم, خوشه کسبوکار, افزایش اقدامات مشترک,
چکیده مقاله :
مدل توسعۀ خوشه‌های کسبوکار، شیوه نوینی است که در دو دهه اخیر به منظور توسعه و توانمندسازی بنگاه‌های کوچک و متوسط به‌کار گرفته شده است. بسیاری از کشورها اعم از توسعه یافته و در حال توسعه از این مدل توسعه‌ای برای دستیابی به اهداف توسعۀ منطقه‌ای و ارتقای سطح کیفی و کمی کسب و کارهای مزیت‌دار خود که در یک منطقه جغرافیایی مستقر می‌باشند بهره گرفته‌اند. در این پژوهش به بررسی عوامل مؤثر بر عملکرد طرح‌های توسعۀ خوشه کسب و کار در ایران با استفاده از تکنیک تحلیل تم و تکنیک نقشۀ شناختی فازی پرداخته شده است. روش گردآوری اطلاعات به صورت مصاحبه و پرسشنامه و دورۀ زمانی پژوهش از سال ۱۳۹۷ تا سال ۱۴۰۰ و در گسترۀ کشور ایران تمامی استان‌هایی که طرح‌های توسعۀ خوشه کسب و کار در آنها اجرا گردیده می باشد. بر اساس مدل نقشه شناختی فازی به‌دست آمده، مهمترین عامل اثرگذار بر عملکرد طرح‌های توسعۀ خوشه کسب و کار، تأمین مالی طرح‌ها و مهمترین عامل اثرپذیر، افزایش اقدامات مشترک می‌باشد. در انتها نیز، بر اساس شناسایی مهمترین عوامل اثرگذار و اثرپذیر، سناریو‌های رو به عقب و رو به جلوی مرتبط با عملکرد طرح‌های توسعۀ خوشه کسب و کار مورد بررسی قرار گرفت.
The business cluster development model is a new method that has been used in the last two decades to develop and empower small and medium enterprises. Many countries, both developed and developing, have taken advantage of this development model to achieve regional development goals and improve the quality and quantity of their advantageous businesses located in a geographical area. In this research, the factors influencing the performance of business cluster development in Iran have been investigated using theme analysis and fuzzy cognitive map techniques. The data was collected using open interview tools and questionnaires. The present research encompasses the period from 2018 to 2021 and focuses on Iran, specifically examining all provinces where business cluster development plans have been enacted. Based on the model obtained from the fuzzy cognitive map method, the most important factor that affects the performance of business cluster development initiatives is financing of the plans and projects. On the other hand, the most crucial factor for effectiveness is the augmentation of collaborative efforts. Finally, based on the identification of the most important affected and influencing factors, backward and forward scenarios related to the performance of business cluster development plans were examined.
Broersma, L. (2001). The role of services in innovative clusters. Paper within the framework of the Research Programme Structural Information; Provision on Innovation in Services (SIID) for the Ministry of Economic Affairs, Directorate for General Technology Policy.
Chen, M.K., Wu, S.W., Huang, Y.P., Chang, F.J. (2022). The Key Success Factors for the Operation of SME Cluster Business Ecosystem. Sustainability, 14, 1-14.doi:10.3390/su14148236.
Ceglie, G., Dini, M. (1999). Sme Cluster and Network Development in Developing Countries: The Experience of UNIDO. Vienna: (IPC/PSD) of UNIDO, 1-28.
Ghalayini, A. M., & Noble, J. S. (1996). The changing basis of performance measurement. International Journal of Operations & Production Management, 16(8), 63-80. doi:10.1108/01443579610125787.
Jetter, A., Zhang, P. (2018). A Framework for Building Integrative Scenarios of Autonomous Vehicle Technology Application and Impacts, using Fuzzy Cognitive Maps (FCM) Portland International Conference on Management of Engineering and Technology (PICMET), 2018, pp. 1-14. doi:10.23919/ PICMET.2018.8481747.
Kosko, B. (1986). Fuzzy cognitive maps. International journal of man-machine studies, 24(1), 65-75. doi:10.1016/S0020-7373(86)80040-2.
Lindqvist, G., Ketels, C., & Sölvell, Ö. (2013). The Cluster Initiative Greenbook 2.0: Ivory Tower Publishers, 1-89.
Maffioli A., Pietrobelli C., & Stucchi R., (2016). The Impact Evaluation of Cluster Development Programs: Methods and Practices, Washington, D.C.: Inter-American Development Bank, 1-203.
Marešová, P., Jašíková, V., & Trousil, M. (2011). Method for evaluating the performance of clusters in the Czech Republic. In Proceedings of the International Conference on Urban Sustainability, Cultural Sustainability, Green Development, Green Structures and Clean Cars, USCUDAR, Prague, Czech Republic, pp. 30-35.
Markusen, A. (1996). Sticky Places in Slippery Space: A Typology of Industrial Districts. Economic Geography, 72(3), 293-313. doi:10.2307/144402.
Murali, B., & Banerjee, S. (2011). Fostering Responsible Behaviour in MSMEs Clusters. https://fmc.org.in/wp-content.
Skokan, K., & Zotyková, L. (2015). Evaluation of Business Cluster Performance During its Lifecycle. Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, 62(6), 1395-1405. doi:10.11118/ actaun201462061395.
Soleymani, G., Azizmohammadlou, H., & Vahdat, S. (2015). Identified Business Clusters in Iran. Tehran. Aein-e-Mahmoud Publication, 1-316. (In Persian).
Sölvell, Ö. (2003). Clusters: Balancing Evolutionary and Constructive Forces: Ivory Tower, 1-137.
Sölvell, Ö., Lindquist, G., & Ketels, C. (2008). The Cluster Initiative Greenbook. Ivory Tower AB: Stockholm, 1-89.
Talebpoor, A.R., Ahmadi. S. (2009). Intelligent evaluation of fuzzy recognition map. Journal of Business Management, 8(30), 9-28.
Yoon, S., Nadvi, K. (2018). Industrial clusters and industrial ecology: Building “eco- collective efficiency in a South Korean cluster. Geoforum, No. 90, pp. 159-173. doi:10.1016/j.geoforum.2018.01.013.
Braun, V., Clarke, V. & Weate, P. (2016). Using thematic analysis in sport and exercise research. In B. Smith & A. C. Sparkes (Eds.), Routledge handbook of qualitative research in sport and exercise (pp. 191-205).
Arthurs, D., Cassidy, E., Davis, C., & Wolfe, D. (2009). Indicators to support innovation cluster policy. Int. J. Technology Management Int. J. Technology Management, 464, 263-279. doi:10.1504/IJTM.2009.023376.
Cesar Ribeiro Carpinetti, L., Cardoza Galdámez, E. and Cecilio Gerolamo, M. (2008), "A measurement system for managing performance of industrial clusters: A conceptual model and research cases", International Journal of Productivity and Performance Management, Vol.57 No.5, pp.405-419. doi:10.1108/17410400810881854.
Glullani. A., & Pletrobelli, C. (2016). Social Network Analysis Methodologies for the Evaluation of Cluster Development Programs. Inter-American Development Bank, 1-43.
Klofsten, M., Bienkowska, D., Laur, I., & Sölvell, I. (2015). Success factors in cluster initiative management: Mapping out the ‘big five’. Industry and Higher Education, 29(1), 65-77. doi:10.5367/ihe.2015.0237.
_||_Broersma, L. (2001). The role of services in innovative clusters. Paper within the framework of the Research Programme Structural Information; Provision on Innovation in Services (SIID) for the Ministry of Economic Affairs, Directorate for General Technology Policy.
Chen, M.K., Wu, S.W., Huang, Y.P., Chang, F.J. (2022). The Key Success Factors for the Operation of SME Cluster Business Ecosystem. Sustainability, 14, 1-14.doi:10.3390/su14148236.
Ceglie, G., Dini, M. (1999). Sme Cluster and Network Development in Developing Countries: The Experience of UNIDO. Vienna: (IPC/PSD) of UNIDO, 1-28.
Ghalayini, A. M., & Noble, J. S. (1996). The changing basis of performance measurement. International Journal of Operations & Production Management, 16(8), 63-80. doi:10.1108/01443579610125787.
Jetter, A., Zhang, P. (2018). A Framework for Building Integrative Scenarios of Autonomous Vehicle Technology Application and Impacts, using Fuzzy Cognitive Maps (FCM) Portland International Conference on Management of Engineering and Technology (PICMET), 2018, pp. 1-14. doi:10.23919/ PICMET.2018.8481747.
Kosko, B. (1986). Fuzzy cognitive maps. International journal of man-machine studies, 24(1), 65-75. doi:10.1016/S0020-7373(86)80040-2.
Lindqvist, G., Ketels, C., & Sölvell, Ö. (2013). The Cluster Initiative Greenbook 2.0: Ivory Tower Publishers, 1-89.
Maffioli A., Pietrobelli C., & Stucchi R., (2016). The Impact Evaluation of Cluster Development Programs: Methods and Practices, Washington, D.C.: Inter-American Development Bank, 1-203.
Marešová, P., Jašíková, V., & Trousil, M. (2011). Method for evaluating the performance of clusters in the Czech Republic. In Proceedings of the International Conference on Urban Sustainability, Cultural Sustainability, Green Development, Green Structures and Clean Cars, USCUDAR, Prague, Czech Republic, pp. 30-35.
Markusen, A. (1996). Sticky Places in Slippery Space: A Typology of Industrial Districts. Economic Geography, 72(3), 293-313. doi:10.2307/144402.
Murali, B., & Banerjee, S. (2011). Fostering Responsible Behaviour in MSMEs Clusters. https://fmc.org.in/wp-content.
Skokan, K., & Zotyková, L. (2015). Evaluation of Business Cluster Performance During its Lifecycle. Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, 62(6), 1395-1405. doi:10.11118/ actaun201462061395.
Soleymani, G., Azizmohammadlou, H., & Vahdat, S. (2015). Identified Business Clusters in Iran. Tehran. Aein-e-Mahmoud Publication, 1-316. (In Persian).
Sölvell, Ö. (2003). Clusters: Balancing Evolutionary and Constructive Forces: Ivory Tower, 1-137.
Sölvell, Ö., Lindquist, G., & Ketels, C. (2008). The Cluster Initiative Greenbook. Ivory Tower AB: Stockholm, 1-89.
Talebpoor, A.R., Ahmadi. S. (2009). Intelligent evaluation of fuzzy recognition map. Journal of Business Management, 8(30), 9-28.
Yoon, S., Nadvi, K. (2018). Industrial clusters and industrial ecology: Building “eco- collective efficiency in a South Korean cluster. Geoforum, No. 90, pp. 159-173. doi:10.1016/j.geoforum.2018.01.013.
Braun, V., Clarke, V. & Weate, P. (2016). Using thematic analysis in sport and exercise research. In B. Smith & A. C. Sparkes (Eds.), Routledge handbook of qualitative research in sport and exercise (pp. 191-205).
Arthurs, D., Cassidy, E., Davis, C., & Wolfe, D. (2009). Indicators to support innovation cluster policy. Int. J. Technology Management Int. J. Technology Management, 464, 263-279. doi:10.1504/IJTM.2009.023376.
Cesar Ribeiro Carpinetti, L., Cardoza Galdámez, E. and Cecilio Gerolamo, M. (2008), "A measurement system for managing performance of industrial clusters: A conceptual model and research cases", International Journal of Productivity and Performance Management, Vol.57 No.5, pp.405-419. doi:10.1108/17410400810881854.
Glullani. A., & Pletrobelli, C. (2016). Social Network Analysis Methodologies for the Evaluation of Cluster Development Programs. Inter-American Development Bank, 1-43.
Klofsten, M., Bienkowska, D., Laur, I., & Sölvell, I. (2015). Success factors in cluster initiative management: Mapping out the ‘big five’. Industry and Higher Education, 29(1), 65-77. doi:10.5367/ihe.2015.0237.
