Analysis of Factors Influencing Artificial Intelligence Adoption Among MSMEs in Batam City Using the TOE and TAM Approaches
DOI:
https://doi.org/10.35870/ijsecs.v6i2.7631Keywords:
Artificial Intelligence, MSMEs, Mixed Methods, TOE Framework, Technology Acceptance Model (TAM), PLS-SEMAbstract
Artificial intelligence (AI) has considerable potential to enhance operational competitiveness, but its adoption among Micro, Small, and Medium Enterprises (MSMEs) remains constrained by structural and perceptual barriers. This study examines the factors influencing AI adoption intention among MSMEs in Batam City using the Technology-Organization-Environment (TOE) framework and Technology Acceptance Model (TAM), with Diffusion of Innovation (DOI) providing additional theoretical support. A sequential explanatory mixed-methods design was employed. Quantitative data were collected from 270 MSME practitioners and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) with SmartPLS 4. The measurement model met the criteria for convergent validity, discriminant validity, and reliability after the removal of the Environmental Pressure construct due to discriminant validity issues. The structural model showed that all six hypotheses retained in the final model were supported. Technology Readiness and Organizational Support had significant positive effects on Perceived Usefulness and Perceived Ease of Use, while Perceived Usefulness was the strongest direct predictor of AI adoption intention. Qualitative interviews with five MSME practitioners further supported the quantitative findings by indicating that practical operational benefits and internal readiness were important considerations in AI adoption. Based on these findings, MSME managers and policymakers in Batam City should emphasize the practical operational benefits of AI and strengthen digital infrastructure and organizational readiness to support adoption.
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