TITLE:
Artificial Intelligence Implementation in Manufacturing Management: A Comprehensive Framework for Thailand’s Eastern Industrial Region
AUTHORS:
Thanakit Ouanhlee
KEYWORDS:
Artificial Intelligence, Manufacturing Management, Technology Adoption, Emerging Economies, Thailand, Eastern Economic Corridor, Implementation Framework, Industry 4.0, Mixed-Methods Research
JOURNAL NAME:
American Journal of Industrial and Business Management,
Vol.16 No.1,
January
15,
2026
ABSTRACT: Purpose: This study investigates the implementation of artificial intelligence (AI) in manufacturing management in Thailand’s Eastern Industrial Region, addressing significant knowledge gaps regarding technology adoption in emerging economies. The research develops and validates a comprehensive framework integrating the Technology-Organization-Environment (TOE) framework, Resource-Based View (RBV), and Technology Acceptance Model (TAM) to examine multidimensional factors influencing AI adoption, implementation challenges, performance outcomes, and future trajectories. Methodology: An explanatory sequential mixed-methods design was employed, combining quantitative survey data from 1150 manufacturing companies across twelve sectors with qualitative insights from 115 stakeholder interviews. The study achieved a 68.5% response rate, with respondents representing small (26.2%), medium (24.1%), and large (49.7%) enterprises. Ten hypotheses organized across technological, performance, organizational, and environmental dimensions were tested using correlation analysis, t-tests, ANOVA, and chi-square analysis. Findings: The findings reveal an 80.6% AI adoption rate across the surveyed population. Organizations addressing all four framework dimensions—technological infrastructure, organizational readiness, workforce capabilities, and external environment—achieved 76.8% implementation success compared to 32.4% for limited approaches. Hypothesis testing confirmed nine of ten hypotheses. Implementation challenges span workforce (71.3%), technical (67.8%), and organizational (58.9%) dimensions, with skills shortages (84.2%) and data quality issues (72.4%) as primary barriers. Quality improvements emerged as the strongest benefit dimension (53.4% positive, mean 2.56), with average defect reductions of 47.3%. Financial validation confirms 68.4% achieved positive ROI, averaging 147% for comprehensive implementations. Future trajectories indicate that 77.6% of organizations plan investment increases, while workforce expectations show 45.7% anticipating job creation versus 23.4% expecting reductions. Practical Implications: Evidence-based implementation guidelines demonstrate that phased approaches achieve 72.4% success, versus 43.8% for immediate deployment; comprehensive training is associated with 71.4% higher success rates; and hands-on training reaches 82.7% effectiveness, versus 34.2% for lecture-based approaches. These findings enable manufacturing organizations to prioritize high-impact practices and establish realistic performance expectations. Theoretical Implications: This research contributes to technology adoption theory by empirically validating an integrated TOE-RBV-TAM framework, demonstrating that multidimensional approaches significantly outperform single-dimensional strategies. The synergistic effects observed (52.3% versus 28.7% improvement for multi-application implementations) provide empirical support for resource complementarity concepts within RBV. Originality/Value: This study addresses the underrepresentation of Southeast Asian manufacturing contexts in AI implementation research. The comprehensive framework and evidence-based guidelines provide contextually appropriate guidance for emerging economy manufacturers, supporting Thailand’s Industry 4.0 transformation objectives and informing policy frameworks for workforce development and technological advancement.