Analysis of Factors Influencing Behavioral Intention Regarding AI Technology for Quality Inspection Using the Technology Acceptance Model at PT XYZ

Abstract

The development of Artificial Intelligence (AI) technology provides significant opportunities for the manufacturing industry to improve the efficiency and accuracy of quality inspection processes. The implementation of AI in quality inspection is expected to reduce human error and accelerate decision-making. However, the successful adoption of this technology depends largely on user acceptance and intention to use it within the work environment. Many digital transformation initiatives fail to achieve optimal outcomes due to employees’ limited perceptions of technological benefits and ease of use. Therefore, this research is important for understanding the factors influencing user acceptance of AI-based quality inspection systems, enabling companies to develop more effective implementation strategies. This study applied the Technology Acceptance Model (TAM) by incorporating the variables of perceived usefulness (PU), perceived ease of use (PEU), perceived enjoyment (PE), perceived cyber risk (PCR), perceived innovativeness in IT (PIIT), self-efficacy (SE), and behavioral intention (BI). The research employed a quantitative approach through questionnaire distribution to employees at PT XYZ who were directly involved in quality inspection activities. The results of the study using the PLS-SEM method showed that the perceived usefulness (PU) variable had the strongest significant effect on behavioral intention (BI). Therefore, strategies to improve technology acceptance should prioritize variables with substantial impacts, particularly by providing training and enhancing employee understanding of the benefits of AI in supporting work performance.