Design and Analysis of Exit Strategies from Recession in Active Companies in the Automotive Parts Manufacturing Industry Using the Fuzzy Cognitive Map Approach (Case Study: Northwest Iran)

The purpose of this study is to design and analyze a model for exit strategies from recession in companies operating in the automotive parts manufacturing industry. This study is of a developmental-applied nature and follows a mixed-methods approach (qualitative and quantitative). The qualitative section is based on a multi-grounded theory approach to develop a qualitative model of dimensions, combining empirical exploratory findings (interviews) and theoretical exploratory findings (meta-synthesis of scientific documents). Quantitative analyses were conducted using the fuzzy cognitive map approach and clustering of companies based on k-means partitioning. The validity of the qualitative findings was confirmed using content validity for constructs and the opinions of external experts. The quantitative research population consisted of two groups: an expert panel of ۱۸ individuals and managers and directors of automotive parts manufacturing companies located in northwest Iran. The expert panel was selected through purposive sampling, while the sample of companies, comprising ۳۶۰ firms, was selected using stratified random sampling based on provincial distribution. The qualitative findings identified a model of exit strategies from recession for automotive parts manufacturers within a framework of ۶۳ key concepts, ۱۱ subcategories, and ۳ major categories. The quantitative findings resulted in the development of a relational model comprising one sender node, two receiver nodes, and eight central nodes. Company clustering identified three major clusters of firms in response to recession exit strategies. The overall analysis of corporate exit strategies from recession indicated that automotive parts manufacturers should adopt a long-term plan focused primarily on transformation-oriented strategies to achieve a suitable level of competitiveness and competitive advantage in the business environment. The purpose of this study is to design and analyze a model for exit strategies from recession in companies operating in the automotive parts manufacturing industry. This study is of a developmental-applied nature and follows a mixed-methods approach (qualitative and quantitative). The qualitative section is based on a multi-grounded theory approach to develop a qualitative model of dimensions, combining empirical exploratory findings (interviews) and theoretical exploratory findings (meta-synthesis of scientific documents). Quantitative analyses were conducted using the fuzzy cognitive map approach and clustering of companies based on k-means partitioning. The validity of the qualitative findings was confirmed using content validity for constructs and the opinions of external experts. The quantitative research population consisted of two groups: an expert panel of ۱۸ individuals and managers and directors of automotive parts manufacturing companies located in northwest Iran. The expert panel was selected through purposive sampling, while the sample of companies, comprising ۳۶۰ firms, was selected using stratified random sampling based on provincial distribution. The qualitative findings identified a model of exit strategies from recession for automotive parts manufacturers within a framework of ۶۳ key concepts, ۱۱ subcategories, and ۳ major categories. The quantitative findings resulted in the development of a relational model comprising one sender node, two receiver nodes, and eight central nodes. Company clustering identified three major clusters of firms in response to recession exit strategies. The overall analysis of corporate exit strategies from recession indicated that automotive parts manufacturers should adopt a long-term plan focused primarily on transformation-oriented strategies to achieve a suitable level of competitiveness and competitive advantage in the business environment.

کلیدواژه ها:

Supply chain management strategies ، recession ، automotive component manufacturing industry ، ، clustering