Abstract

PurposeWith the rapid advancement of technology, companies use new technologies to produce their products and services to maintain a competitive advantage. As companies alone cannot research and develop their technologies, they should use knowledge sources outside the organization that may exist throughout the world; hence, organizations need technology transfer. Because the success rate of technology transfer projects is low, the need to accurately assess and investigate the critical success factors of technology transfer projects is felt. In this regard, this study aims to identify and prioritize the critical success factors in technology transfer projects.Design/methodology/approachIn this research, 56 critical success factor (CSF) were extracted from the context of the articles and were adjusted using experts’ opinions in different phases, as well as the fuzzy-Delphi approach. Finally, 15 factors were categorized in the form of steps of the technology transfer model: STAGE-GATE. In the next step, the set of criteria needed to prioritize CFSs was extracted from the literature and finalized with the help of the experts. Then, how each of the CSF influences the identified criteria was scored according to the organization’s export opinions. Finally, the priority of each key success factor was calculated using the additive ratio assessment (ARAS) method.FindingsThe results obtained for prioritization of the critical success factors show that experience in technology transfer in the transferee company, the existence of experienced technology transfer managers, sufficient organizational infrastructure and documenting project problems, achievements and experiences are four critical success factors of the technology transfer projects. Considering the long-term and short-term specific goals of the technology transfer process and the choice of technology in line with the company’s commercial strategy are also the critical success factors with the next priorities.Originality/valueThe combination of ARAS and step-wise weight assessment ratio analysis methods for identifying and prioritizing managerial decisions in the high-tech industries is a value of this research. Also, a combination of novel multi-attribute decision-making methods by the older framework of new product development is another contribution of this research.

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