Big data-enabled solutions framework to overcoming the barriers to circular economy initiatives in healthcare sector

Kazancoglu, Yigit, Sağnak, Muhittin, Lafcı, Çisem, Luthra, Sunil, Kumar, Anil and Taçoğlu, Caner (2021) Big data-enabled solutions framework to overcoming the barriers to circular economy initiatives in healthcare sector. International Journal of Environmental Research and Public Health, 18 (14). pp. 1-21. ISSN 1660-4601

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Abstract / Description

Ever-changing conditions and emerging new challenges affect the ability of the healthcare sector to survive with the current system, and to maintain its processes effectively. In the healthcare sec-tor, the conservation of the natural resources is being obstructed by insufficient infrastructure for managing residual waste resulting from single-use medical materials, increased energy use, and its environmental burden. In this context, circularity and sustainability concepts have become essential in healthcare to meliorate the sector’s negative impacts on the environment. The main aim of this study is to identify the barriers related to circular economy (CE) in the healthcare sec-tor, apply big data analytics in healthcare, and provide solutions to these barriers. The contribution of this research is the detailed examination of the current healthcare literature about CE adaptation, and a proposal for a big data-enabled solutions framework to barriers to circularity, using fuzzy Best-Worst Method (BWM) and fuzzy VIKOR. Based on the findings, managerial, policy, and theoretical implementations are recommended to support sustainable development initiatives in the healthcare sector.

Item Type:	Article
Uncontrolled Keywords:	circular economy; sustainable development; healthcare sector; big data tools; barriers; fuzzy best-worst; fuzzy VIKOR; waste issues
Subjects:	600 Technology > 650 Management & auxiliary services
Department:	Guildhall School of Business and Law
Depositing User:	Anil Kumar
Date Deposited:	12 Jul 2021 08:38
Last Modified:	20 Jul 2021 10:51
URI:	https://repository.londonmet.ac.uk/id/eprint/6846

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