Innovation Management Journal

Innovation Management Journal

Designing a Model for the Factors Affecting the Development and Growth of Innovative Footwear and Leather Industrial Clusters Using Structural-Interpretive Modeling Technique (Case study of active industries in the shoe and leather field in Tabriz).

Document Type : Original Article

Authors
1 Ph.D. Student in Department of Management, Tabriz Branch, Islamic Azad University, Tabriz, Iran
2 Associate Prof., Department of Management, Tabriz Branch, Islamic Azad University, Tabriz, Iran
3 Associate Prof., Department of Management, Tabriz Branch, Islamic Azad University, Tabriz, Iran,
Abstract
For over a decade, industrial development based on innovative clusters has been considered as a novel strategy by planners and policymakers in both industrialized and developing countries. Therefore, the main objective of this research is to identify and rank the relationships between the components of the factors affecting the growth and development of innovative footwear and leather industrial clusters using the Structural-Interpretive Modeling (ISM) technique in the active footwear and leather industries of Tabriz. This study is descriptive-survey in nature, applied in terms of objectives, and cross-sectional in terms of time. To determine the level and prioritize the identified factors, the opinions of 25 experts and specialists were utilized. The sample size for this research was determined based on theoretical saturation, according to the research objectives. Moreover, the sampling method employed in this study is purposive. Data were collected through a researcher-designed questionnaire, and for analyzing the collected data, the Structural-Interpretive Modeling (ISM) technique was applied. The results of this study indicated that the government factor, which ranks at the seventh level, is the most influential factor on the development and growth of innovative footwear and leather industrial clusters in Tabriz. Finally, using the MICMAC technique, the components were analyzed based on their driving power and dependence.
Keywords
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  • Receive Date 17 November 2024
  • Revise Date 29 November 2024
  • Accept Date 02 December 2024