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Even after establishing their business successfully, many business owners get demotivated, and it leads to unwillingness to grow. This study aims to propose a comprehensive model that represents interrelationships among various personal factors affecting “unwillingness to grow.”

The personal factors for unwillingness to grow were identified by extant literature, and expert interviews were conducted to establish the contextual relationships among these factors. The interrelationships among the filtered variables have been done using interpretive structural modeling (ISM) and MICMAC analysis was done to determine the importance of each factor in influencing “unwillingness to grow.”

In total, 30 personal attributes were identified from previous literature, out of which 15 were selected for the final study. The result identifies 7 variables having a strong impact on “unwillingness to grow.” These attributes are “absence of strong network,” “lack of vision,” “lack of proactiveness,” “reluctance to involve external consultants,” “absence of/small founding team,” “lack of ambition” and “improper attitude.”

The research attempts to create a bricolage of all the important personal factors affecting “unwillingness to grow.” Previous researches have used few attributes, but with the help of ISM, a graphical modeling technique, it became possible to draw interrelationship between 15 attributes. Further, with the help of MICMAC, the importance of each attribute was determined.

Blockchain technology (BCT) can play a vital role in manufacturing industries by providing visibility and real-time transparency. With BCT adoption, manufacturers can achieve higher productivity, better quality, flexibility and cost-effectiveness. The current study aims to prioritize the performance metrics and ranking of enablers that may influence the adoption of BCT in manufacturing industries through a hybrid framework.

Through an extensive literature review, 4 major criteria with 26 enablers were identified. Pythagorean fuzzy analytical hierarchy process (AHP) method was used to compute the weights of the enablers and the Pythagorean fuzzy combined compromise solution (Co-Co-So) method was used to prioritize the 17-performance metrics. Sensitivity analysis was then carried out to check the robustness of the developed framework.

According to the results, data security enablers were the most significant among the major criteria, followed by technology-oriented enablers, sustainability and human resources and quality-related enablers. Further, the ranking of performance metrics shows that data hacking complaints per year, data storage capacity and number of advanced technologies available for BCT are the top three important performance metrics. Framework robustness was confirmed by sensitivity analysis.

The developed framework will contribute to understanding and simplifying the BCT implementation process in manufacturing industries to a significant level. Practitioners and managers may use the developed framework to facilitate BCT adoption and evaluate the performance of the manufacturing system.

This study can be considered as the first attempt to the best of the author’s knowledge as no such hybrid framework combining enablers and performance indicators was developed earlier.