Interpretive Structural Modeling of Barriers to Knowledge Commercialization

Purpose: The present research aimed to develop an interpretive structural model of the barriers to knowledge commercialization (KC) in Payame Noor University (PNU) of Iran. Method: The present research is an applied research of mixed method types in terms of objectives and it is conducted based on confirmatory factor analysis. Fuzzy Delphi method was used for validation and variable screening, and barriers to KC were prioritized using fuzzy analytical hierarchy process (AHP). Moreover, fuzzy DEMATEL technique and interpretive structural modeling were used for the identification and development of a model for the relationship between variables. The research community was experts of KC at PNU and given the research approach, 30 people were selected among them based on purposive sampling. Findings: Given the amount of effectiveness and affectability of the variables in the interpretive structural model, the variables such as weak legal framework for supporting idea people at the university, inefficiency and ineffectiveness of the rules and regulations for commercialization, lack of regulation for the apportionment of financial gain from commercialization among scholars, lack of skilled and expert human resources, lack of financial resources and facilities for commercialization of research results, and the weakness in the mutual recognition between university and industry, are the most affectable; they are in fact the dependent variables of the model. On the other hand, the variables such as inadequate knowledge of the faculty members, poor fund management in the university, weakness of universities in wealth creation, absence of university entrepreneurial missions, the absence of up-to-date and effective idea banks and databases in the university, and the lack of effective communication between students and industry sector’s activists, are the most effective. Discussion and Conclusion: The attention and reinforcement given to the independent variables in KC can improve the status of the dependent variables and eventually it results in successful KC.