Evaluation of barriers to artificial intelligence adoption: grey multi-criteria decision-making

Abstract

Purpose This study aimed to identify and rank the barriers faced by disabled elderly people in China regarding the adoption of artificial intelligence (AI) in their lives using dynamic grey relational analysis (DGRA) and technique for order performance by similarity to ideal solution (TOPSIS). DGRA was employed to assess the strength of relationships between barriers dynamically, while TOPSIS ranked them based on proximity to ideal solutions. Design/methodology/approach Using a cross-sectional study design, primary data were collected from 253 elderly individuals with disabilities in Jiangsu, China. Along with DGRA and TOPSIS, the Kruskal–Wallis test was also conducted to evaluate the differences of identified barriers across age groups. Findings We found that the top three most significant barriers were (1) awareness and training, (2) trust and (3) transparency and explainability in both DGRA and TOPSIS cases. Additionally, the Kruskal–Wallis test revealed that age (=60 years) did not significantly influence these barriers, as the study population consisted of disabled elderly with similar perceptions across age groups. This study is timely because the country’s demographic shift is expected to begin in 2027, and 26% of its population will be 65 years or older by 2050. Originality/value This study is the first of its kind to apply the DGRA, TOPSIS and Kruskal–Wallis tests to identify and rank the barriers to AI adoption for the disabled elderly in China.

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