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Abstract

This study aims to investigate the adoption of Artificial Intelligence (AI) by Micro, Small, and Medium Enterprises (MSMEs) in Indonesia, integrating the Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology (UTAUT). The research examines explicitly how adoption determinants influence Behavioral Intention (BI) and how BI, in turn, drives business performance across key functional areas—marketing, human resources, finance, and operations. A quantitative research design was employed using a cross-sectional survey of 460 MSME owners, managers, and employees from various sectors. Structural Equation Modeling–Partial Least Squares (SEM-PLS) with SmartPLS 4.0 was applied to test the proposed model. Constructs were adapted from established TAM–UTAUT scales and extended with business performance measures. The results confirm that Performance Expectancy, Effort Expectancy, and Facilitating Conditions significantly influence BI, whereas Social Influence does not significantly shape adoption intention. Moreover, BI exerts a significant positive effect on marketing, human resources, financial, and operational performance, and mediates the relationship between adoption determinants and business outcomes. This study extends the TAM–UTAUT framework by empirically linking AI adoption determinants to functional business performance in MSMEs, particularly in a developing economy. The findings highlight the critical role of BI as a mediating mechanism, underscoring that adoption decisions are driven more by perceived value and ease of use than by external social pressures.

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Artificial Intelligence adoption MSMEs TAM UTAUT business performance SEM-PLS

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Relifra, R., Mardiah, A., & Fikriando, E. (2025). AI Adoption and Functional Performance in MSMEs: Evidence Across Marketing, HR, Finance, and Operations. Amkop Management Accounting Review (AMAR), 5(2), 1212–1233. https://doi.org/10.37531/amar.v5i2.3299
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