Building a hierarchical framework of corporate sustainability transition challenges using the qualitative information approach
Industrial Management & Data Systems
Article publication date: 13 April 2021
Issue publication date: 30 April 2021
This study aims to form a valid measure and hierarchical framework to achieve corporate sustainability transitions (CSTs).
The fuzzy Delphi method (FDM) is applied to validate and eliminate challenges in sustainability transition regarding qualitative information. Fuzzy interpretive structural modeling (FISM) is used to build a hierarchical framework under uncertainties.
This study finds that technology investment, data management, eco-management and sociospatial embedding challenges are the highest hierarchical framework levels and affect CST.
A lack of awareness and knowledge, a lack of commitment, a lack of strategy, tolerance of unsustainable practices, a lack of stakeholder participation and a fragmented market are perceived as the challenges that show the highest driving and dependence power. These challenges serve as a reference for government and construction firms in the transition to sustainable corporate practices.
Unsustainable corporate practices have caused large amounts of energy consumption, resource depletion and environmental impacts. There are challenges in transitioning to corporate sustainability that must be addressed. The most significant challenges that need to be solved to facilitate the transition to corporate sustainability are identified and arranged in a hierarchical model. By identifying the hierarchical relationships among the challenges, a theoretical framework that extends the existing models is developed to assist decision-makers.
This study is partially supported by the Ministry of Science and Technology, Taiwan. 108-2221-E-468 -004 -MY2
Tseng, M.-L., Kurrahman, T., Hanita, A., Lim, M.K. and Negash, Y.T. (2021), "Building a hierarchical framework of corporate sustainability transition challenges using the qualitative information approach", Industrial Management & Data Systems, Vol. 121 No. 5, pp. 1107-1141. https://doi.org/10.1108/IMDS-08-2020-0471
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