Psychological and technical drivers of anthropomorphism in artificial intelligence tools using fractal fuzzy analysis across healthcare, finance and marketing sectors

The aim of this study is to identify the most critical factors affecting the development of anthropomorphism in artificial intelligence tools and to reveal the differences between these factors across sectors. In this study, three separate fuzzy multi-criteria decision-making models are developed for the healthcare, finance, and marketing sectors. To consider the demographic characteristics of the experts, a Euclidean distance-based expert weighting method is applied. The updated SIWEC technique is used to calculate the criteria weights. Furthermore, fractal-based Sierpinski Triangle fuzzy sets are adapted to the proposed model to model uncertainty more flexibly and precisely. The main contributions of the proposed model to the literature can be summarized in four dimensions: (1) developing new fractal-based fuzzy sets that offer an alternative modeling structure to existing triangular, trapezoidal, spherical, and type-2 sets; (2) enabling the development of sector-specific strategies by conducting comparative analyses across three different sectors; (3) obtaining more realistic factor weights with the updated SIWEC technique. The findings denote that the most important factor in the health sector is the perception of reliability (0.141), in the finance sector, the perception of reliability (0.177) and ethical compliance (0.172), and in the marketing sector, behavioral consistency (0.119) comes to the fore.