Prioritization of technology commercialization success factors using fuzzy best worst method.

• Main Authors: Olawore, Ayodeji Sulaiman, Wong, Kuan Yew, Ma'aram, Azanizawati, Sutopo, Wahyudi
• Format: Article
• Language: English
• Published: Elsevier B.V. 2023
• Subjects: TJ Mechanical engineering and machinery
• Online Access: http://eprints.utm.my/106995/1/KuanYewWong2023_PrioritizationofTechnologyCommercializationSuccessFactors.pdf; http://eprints.utm.my/106995/; http://dx.doi.org/10.1016/j.joitmc.2023.100096
• Institution: Universiti Teknologi Malaysia

The existence of intense competition in the global market has pressurized organizations to increasingly survive. Many universities strive to create technologies because of their enormous influence on the universities’ development and values. However, the success of achieving this task is arduous due to several commercialization-related difficulties assessing the potential of technology commercialization (TC) in universities. The purpose of this study is to identify and prioritize the factors that influence the development and commercialization of software products in universities. The criteria were identified via a literature review and Delphi study. Fuzzy best worst method (BWM) was applied to prioritize the criteria. The outcomes of fuzzy BWM were validated with fuzzy analytic hierarchy process via comparative analysis. The results of the fuzzy BWM revealed the orders of priority of the criteria from the most important to the least important as entrepreneurial capability, funding, R&D capability, networking, marketing, distinctive features, intellectual property protection, motivational aids, and knowledge-sharing culture. However, the comparative analysis of the outcomes for the 2 multi-criteria decision-making methods indicated that the rankings of the criteria are the same except for the changes between the sixth and seventh criteria. The difference in subjectivities among the experts based on their diverse backgrounds could be a factor contributing to this disparity. This research contributes to existing research by identifying and prioritizing the criteria that influence the performance of TC which have not been previously addressed by using fuzzy BWM. Fuzzy BWM was established to be more consistent and efficient in the decision-making process.