| Abstract |
Globally, industries are focusing on green habits, with world leaders demanding net zero carbon; clean energy is considered an attractive and viable option. The Internet of things (IoT) is an emerging technology with potential opportunities in the clean energy domain for quality improvement in production and management. Earlier studies on IoTs show evidence that direct adoption of such digital technology is an ordeal and incurs adoption barriers that must be prioritized for effective management. Motivated by the claim, in this paper, the authors attempt to prioritize the diverse adoption barriers with the support of the newly proposed Fermatean fuzzy-based decision framework. Initially, qualitative rating information is collected via questionnaires on barriers and criteria from the circular economy (CE). Later, these are converted to Fermatean fuzzy numbers used by integrated approaches for decision processes. A regret scheme is put forward for determining CE criteria importance, and the barriers are prioritized by using a novel ranking algorithm that incorporates the WASPAS formulation and experts' personal choices during rank estimation. The applicability of the developed framework is testified via a case example. Sensitivity analysis and comparison reveal the merits and limitations of the developed decision model. Results show that labor/workforce skill insufficiency, an ineffective framework for performance, a technology divide, insufficient legislation and control, and lack of time for training and skill practice are the top five barriers that hinder IoT adoption, based on the rating data. Additionally, the criteria such as cost cutting via a reuse scheme, resource circularity, emission control, and scaling profit with green habits are the top four criteria for their relative importance values. From these inferences, the respective authorities in the clean energy sector could effectively plan their strategies for addressing these barriers to promote IoT adoption in the clean energy sector. |
