DETERMINANTS OF AI AND MACHINE LEARNING TECHNOLOGY ACCEPTANCE IN BUSINESS DECISION-MAKING AMONG THAI SMES: EVIDENCE FROM CHACHOENGSAO PROVINCE

Abstract

This research examines factors affecting the acceptance of artificial intelligence (AI) and machine learning (ML) technologies in business decision-making among small and medium enterprises (SMEs) in Thailand. Using quantitative methodology, data were collected from 367 SMEs in Chachoengsao Province through structured questionnaires. The study employed multiple regression analysis to identify key determinants of technology acceptance. The findings reveal that only 24.8% of SMEs currently utilize AI/ML technologies, with AI chatbots being the most popular application. The regression model (R² = 0.742) demonstrates that perceived usefulness emerges as the strongest positive predictor (β = 0.242), while security concerns represent the most significant negative factor (β = -0.218). Other influential factors include top management support (β = 0.207), technology knowledge and skills (β = 0.189), and perceived ease of use (β = 0.176). Significant differences were observed across business characteristics, with medium enterprises showing higher acceptance rates than micro and small enterprises. Service businesses demonstrated the highest technology acceptance compared to manufacturing, trade, and agriculture sectors. The main barriers identified include lack of knowledge and skills (73.6%), budget constraints (68.4%), and data security concerns (61.2%). The study contributes to understanding AI/ML adoption patterns among Thai SMEs and provides insights for developing effective policies and strategies to promote technology acceptance in business decision-making processes within the SME sector.