Abstract
AbstractAI technologies are absolutely changing the rules of the game all around the world. However, the diffusion rate of AI is widely ranging across countries. This study aims to fulfill a research gap regarding multidimensional comprehensive studies which could provide academic information to the policy makers, technology producers, adopters of technology and the workforce. Friction against the use of new technologies has been existing since the beginning of industrial revolution. This study examines the possible factors behind the friction in AI adoption process. The subject of the course in this study is the supply chain resilience which is a keystone in healthcare sector especially after the recent pandemics. Studies promise the efficiency improvements and cost reductions in healthcare when AI technologies are implemented in supply chain management of the industry. This paper proposes a fuzzy Aczel–Alsina-based decision-making model to analyze the factors that enhance the diffusion of AI technologies in healthcare supply chain management. The model is tested for the case of Turkish healthcare industry. Fuzzy decision-making model is used to solve the complexities in unveiling success factors in the implementation and diffusion phases. Results show that among many other factors tested, technology intensity, trialability and government support and policies are the most important AI success factors. The results are discussed to reveal potential policy recommendations.
Publisher
Springer Science and Business Media LLC
Subject
Geometry and Topology,Theoretical Computer Science,Software
Reference94 articles.
1. Abbas M, Asghar MW, Guo Y (2022) Decision-making analysis of minimizing the death rate due to covid-19 by using q-rung orthopair fuzzy soft bonferroni mean operator. J Fuzzy Ext Appl 3(3):231–248
2. Aczel J, Alsina C (1982) Characterization of some classes of quasilinear functions with applications to triangular norms and to synthesizing judgements. Aequ Math 25(1):313–315
3. Adak AK, Kumar D (2023) Spherical distance measurement method for solving MCDM problembs under Pythagorean fuzzy environment. J Fuzzy Exte Appl 4(1):28–39. https://doi.org/10.22105/jfea.2022.351677.1224
4. Ahmad T, Zhang D, Huang C, Zhang H, Dai N, Song Y, Chen H (2021) Artificial intelligence in sustainable energy industry: status Quo, challenges and opportunities. J Clean Prod 289:125834
5. Alavi S, Zeinalnezhad M, Mousavi E (2022) Prioritisation of GPM activities from lean-agile-resilience perspective using fuzzy analytic hierarchy process. J Fuzzy Ext Appl 3(3):263–278
Cited by
3 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献