Understanding Factors Influencing the Adoption of AI-enhanced Air Quality Systems: A UTAUT Perspective

Integrating artificial intelligence (AI) into air quality monitoring systems has shown promising potential to revolutionize environmental management. This study uses the Unified Theory of Acceptance and Use of Technology (UTAUT) framework in comprehensively analyzing the factors that influence the adoption of artificial intelligence-enhanced air quality systems. This research focuses on Performance Expectations, Effort Expectations, Social Influence, Facilitating Conditions, Awareness of Air Pollution, and their impact on Behavioral Intentions and Use Behavior. Quantitative data were collected through a survey administered to a sample of potential environmental monitoring and management users. 392 collected data will be used to test the relationships proposed by the UTAUT framework. Through SEM analysis using SmartPLS, this study will shed light on the factors that significantly influence the decision to adopt and utilize AI-enhanced air quality systems. This technique enables the assessment of the complex relationships between the above UTAUT variables and the predicted Behavioral Intention to adopt an AI-enhanced air quality system and subsequent Use Behavior. The results of this study have significant implications in both the academic and practical realms on the specific factors that play an essential role in encouraging or hindering the adoption and use of AI-enhanced air quality systems. In conclusion, the results of this study are expected to foster a deeper understanding of the technology adoption process, ultimately encouraging the adoption of AI technologies to effectively manage and improve air quality on a broader scale.