User Satisfaction of Artificial Intelligence Air Quality Detection: UTAUT2 Approach

Abstract

The Artificial Intelligence (AI)-based air quality detection application is a technology that can assist the public in monitoring the air conditions around them. This application provides information on pollution levels, health implications, and recommendations for appropriate actions based on air quality. However, the usage of this application is still constrained by various factors that influence user satisfaction. This study aims to examine the impact of elements within the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) model on user satisfaction with AI-based air quality detection applications. The UTAUT2 model comprises 9 constructs. This research employs an online survey method with a sample of 150 respondents who have used AI-based air quality detection applications. Data were analyzed using the PLS-SEM (Partial Least Square Structural Equation Modeling) technique using SmartPLS4. The research findings indicate that only Performance Expectancy and Behavioral Intention significantly influence usage intention and behavior of the application. These findings highlight the critical role of user intention and performance expectations in determining usage behavior and user satisfaction. The practical implications and theoretical of this study, including recommendations for application developers and future researchers, are further discussed in this research.