A Hybrid Fuzzy AHP–Entropy–TOPSIS and SEM Framework for Adoption and Impact Assessment of Intelligent Decision Support Systems in Business Management

Abstract

Intelligent Decision Support Systems (IDSS) integrate artificial intelligence, machine learning, and advanced analytics to enhance organisational decision-making. While IDSS adoption has grown considerably in recent years, a unified framework addressing both adoption drivers and measurable performance outcomes across multiple business sectors remains lacking. To address this gap, this study introduces the Multi-Criteria Adoption Assessment Model (MCAAM). The framework integrates fuzzy Analytical Hierarchy Process (AHP), Shannon entropy weighting, TOPSIS, and covariance-based structural equation modelling (CB-SEM) to evaluate adoption determinants, organisational ranking, and causal relationships. Unlike existing approaches such as TAM and UTAUT, MCAAM integrates subjective expert judgment with objective empirical data within a single evaluation structure. Survey data were collected from 387 business organisations across India, Southeast Asia, and the Middle East, spanning manufacturing (n = 112), finance (n = 98), healthcare (n = 89), and supply chain (n = 88) sectors during March–September 2024. Findings indicate that the strongest predictors of adoption are trust in AI-driven outcomes (β = 0.412, p < 0.001) and data infrastructure maturity (β = 0.387, p < 0.001). Organisations implementing IDSS achieved a 34.7% improvement in decision accuracy and a 28.3% reduction in decision latency. Limitations include the cross-sectional design, geographic concentration, and reliance on self-reported data, which may affect generalisability. Future research should incorporate longitudinal designs, broader geographic coverage, and integration of generative AI capabilities.