Evaluation Commercialization Challenges and Resolutions in SMEs Using ML-FCM (Case study: Sanat Prozheh Toos)

Journal of Systems Thinking in Practice

دوره 2، شماره 1 - شماره پیاپی 4، خرداد 2023، صفحه 39-55   

نوع مقاله: Research Article

شناسه دیجیتال (DOI): 10.22067/jstinp.2023.80127.1026

نویسندگان

Vahideh Bafandegan Emroozi¹؛ Ali Faezian^{* 2}؛ Kambiz Seffati³؛ Hossein Ebrahimi⁴؛ Babak Dadakhani³

¹Department of Management, Faculty of Economics and Administrative sciences, Ferdowsi University Of Mashhad, Mashhad, Iran.

²Food Machineries Department, Research Institute of Food Science and Technology, Mashhad, Iran.

³South Pars Fifth Refinery, Asalouye, Iran.

⁴South Pars seventh Refinery, Asalouye, Iran.

چکیده

Commercialization of innovative products in small and medium-sized enterprises (SMEs) faces many challenges. In this study, the factors related to the existing challenges and resolutions are identified with the help of the multi-layer fuzzy cognitive mapping (ML-FCM) method. The most effective criterion is introduced by examining the centrality. Also, the challenges and the existing resolutions to overcome these challenges are specified, and the most effective ones are determined. The present study addresses the practical experience of Sanat Prozheh Toos Company, which operates in the design and production of mechanical noise pollution control equipment (e.g., Silencers). The data is collected based on the organization's documents and experts' opinions. Research findings confirm that among the challenges of commercialization of innovative products associated with the case study, management challenge has the highest degree of effectiveness and centrality; moreover, among the ways of overcoming these challenges, organizational integration has the highest degree of centrality. Thus, the findings provide policy and management suggestions for SMEs policymakers and managers in commercializing advanced technologies.

کلیدواژه‌ها

Small and medium-sized enterprises (SMEs)؛ Silencer؛ Noise pollution؛ Commercialization؛ Multi-layer fuzzy cognitive mapping method (ML-FCM)

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