A New Multi-Attribute Decision-Making Framework for Policy-Makers by Using Interval-Valued Triangular Fuzzy Numbers
Volume 32, Issue 3 (2021), pp. 583–618
Ayoub Mohammadian
Jalil Heidary Dahooie
Ali Reza Qorbani
Edmundas Kazimieras Zavadskas
Zenonas Turskis
Ali Reza Qorbani
Edmundas Kazimieras Zavadskas
Zenonas Turskis
Pub. online: 15 July 2021
Type: Research Article
Open Access
Open Access
Received
1 October 2020
1 October 2020
Accepted
1 March 2021
1 March 2021
Published
15 July 2021
15 July 2021
Abstract
Policy-makers are often hesitant to invest in unproven solutions because of a lack of the decision-making framework for managing innovations as a portfolio of investments that balances risk and return, especially in the field of developing new technologies. This study provides a new portfolio matrix for decision making of policy-makers to identify IoT applications in the agriculture sector for future investment based on two dimensions of sustainable development as a return and IoT challenge as a risk using a novel MADM approach. To this end, the identified applications of IoT in the agriculture sector fall into eight areas using the meta-synthesis method. The authors extracted a set of criteria from the literature. Later, the fuzzy Delphi method helped finalise it. The authors extended the SWARA method with interval-valued triangular fuzzy numbers (IVTFN SWARA) and used it to the weighting of the characteristics. Then, the alternatives were rated using the Additive Ratio Assessment (ARAS) method based on interval-valued triangular fuzzy numbers (IVTFN ARAS). Finally, decision-makers evaluated the results of ratings based on two dimensions of sustainability and IoT challenge by developing a framework for decision-making. Results of this paper show that policy-makers can manage IOT innovations in a disciplined way that balances risk and return by a portfolio approach, simultaneously the proposed framework can be used to determine and prioritise the areas of IoT application in the agriculture sector.
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Biographies
Mohammadian Ayoub
A. Mohammadian is a Dr. Assistant professor of information technology (IT) management at the University of Tehran. He earned his B.S. in Business Administration and his M.A. and PhD in IT management from the University of Tehran. In addition to teaching, he is the founder of a platform for university-industry collaborations. He was honoured with the award for his contributions to the national e-government project in Iran’s commerce ministry. He is the author of more than 40 scholarly articles in academic journals and conferences on digital business models, digital strategy and digital innovations. His recent research focuses on the internet of things business value and challenges.
Heidary Dahooie Jalil
J. Heidary Dahooie is an associate professor at the Department of Industrial Management, University of Tehran, Tehran, Iran. He has received a BSc degree in Industrial Engineering from the Sharif University of Technology and MSc and PhD degrees from the Department of Industrial Engineering of the Amirkabir University of Technology. His research and teaching interests include technology and knowledge management, multi-criteria decision-making, data mining and business process management. He has published articles in various indexed journals, including Technological Forecasting and Social Change, Journal of Knowledge Management, Expert Systems with Applications, Neural Computing & Applications, and International Journal of Fuzzy Systems.
Qorbani Ali Reza
A.R. Qorbani is an MSc graduate in IT management from the University of Tehran and received his bachelor’s degree in industrial management from the International University of Imam Khomeini. MSc. Student of Information Technology (IT) Management, Faculty of Management, University of Tehran, Tehran, Iran. He is currently working as a researcher in technology management, technology assessment, multi-criteria decision-making, fuzzy logic, literature review and text mining at the University of Tehran.
Zavadskas Edmundas Kazimieras
E.K. Zavadskas, PhD, DSc, Dr. habil., Dr. H. C. multi, Prof. Chief researcher of Institute of Sustainable Construction, Faculty of Civil Engineering, Vilnius Gediminas Technical University, Lithuania. PhD in building structures (1973). Dr. Sc. (1987) in building technology and management. Dr. Habil (1993). Founder of Vilnius Gediminas Technical University (1990). A member of the Lithuanian Academy of Science; a member of several foreign Academies of Sciences; an Honorary doctor from Poznan, Saint-Petersburg, and Kyiv universities. A member of international organizations; a member of steering and program committees at many international conferences; chairman of EURO Working Group ORSDCE; associate editor, guest editor, or editorial board member for 40 international journals (Computer-Aided Civil and Infrastructure Engineering, Automation in Construction, Informatica, International Journal of Information Technology and Decision Making, Archives of Civil and Mechanical Engineering, International Journal of Fuzzy Systems, Symmetry, Sustainability, Applied Intelligence, Energy, Entropy and other); the author and co-author of more than 600 papers and many monographs in Lithuanian, English, German and Russian. Founding editor of journals Technological and Economic Development of Economy, Journal of Civil Engineering and Management, International Journal of Strategic Property Management. He was a highly cited researcher in 2014, 2018, 2019, and 2020. Research interests: multi-criteria decision-making, civil engineering, sustainable development, fuzzy multi-criteria decision making.
Turskis Zenonas
Z. Turskis is prof. Dr. of technical sciences, professor at the Department of Construction Management and Real Estate, a chief research fellow at the Laboratory of Operational Research, director of Research Institute of Sustainable Construction, Vilnius Gediminas Technical University, Lithuania. Research interests: construction technology and management, decision-making theory, computer-aided automation in design, and expert systems. He is the author of more than 165 research papers referred to in the Web of Science database. He was a highly cited researcher in 2019 and 2020.
