An Integrated Shannon-PAF Method on Gray Numbers to Rank Technology Transfer Strategies

Abstract

The selection of an appropriate technology transfer (TT) method is a complex multidimensional problem, which involves a multitude of situational qualitative and quantitative criteria. Despite multiple classifications and effective practices, expert opinion is still essential for every case. The complexity and dependence of TT method selection on human judgment have led to an increase in the application and integration of Multi-Criteria Group Decision-Making (MCGDM) methods, as well as fuzzy and gray systems theories, to address uncertainties related to data collection and selection of TT methods and criteria. The present study contributes to this trend by presenting a novel integrated Shannon-Projection Attribute Function (PAF) method, based on three-parameter interval gray numbers and describes its application in TT methods for the building industry. To calculate the weight of the assessment criteria, selected based on the literature review and Delphi panel, Shannon entropy can reduce uncertainties associated with the weighting criteria. Furthermore, three-parameter interval gray numbers can reduce uncertainties related to expert appraisal. In this study, we used the PAF method to rank TT methods. Also, we presented a brief analysis of the method application in the building industry. The results showed that reverse engineering and import of capital goods and machinery are the best TT methods, respectively.

Keywords:

• Shannon Entropy Method
• Paf Method
• Gray Numbers
• Technology Transfer

EMJ Focus Areas:

• Technology Management
• Project Management
• Technology Selection

Additional information

Notes on contributors

Sirous Amirghodsi

Sirous Amirghodsi is a PhD candidate of technology management at the Progress Engineering Department of Iran University of Science and Technology (IUST). He was recognized as one of the premier researchers in 2019. He earned his MBA degree as a top-ranking student from IUST in 2014 and his BS degree in civil engineering from Khajeh Nasir Toosi University in 1995. His research interests include technology transfer, multi-criteria decision making, and knowledge management of engineering projects.

Ali Bonyadi Naeini

Ali Bonyadi Naeini is a faculty member of the Progress Engineering Department of Iran University of Science and Technology. He has taught several courses in fields of marketing management, neuromarketing, and management of technology and innovation. He has also presented several conference papers and scholarly journal papers on quantitative decision-making techniques, such as DEA, DEMATEL, and ISM, in addition to writing two books in the field of neuromarketing.

Behrooz Roozbehani

Behrooz Roozbehani is the chief editor of the American Journal of Oil and Chemical Technologies. In addition to being a research associate of Texas Rice University, he is also an adjunct professor of Houston Community College. He was the head of Research and Development Department of Iran Petroleum University of Technology for 15 years. He is the designer and manufacturer of several pilot plants and has published his books in the USA and Iran.