Consumer Adoption of Artificial Intelligence in Air Quality Monitoring: A Comprehensive UTAUT2 Analysis

Abstract

The rapid development of the Internet of Things (IoT), which utilizes cloud architecture and big data, has integrated various aspects of daily life–-such integration results in significant comfort, convenience, and energy savings. The application of these new technologies also benefits artificial intelligence technology in the field of air quality monitoring. Artificial intelligence-based Air Quality Monitoring can analyze historical data on air pollution, weather factors, and other variables to forecast future air quality levels. Artificial intelligencebased prediction models can provide estimates of air pollution fluctuations so that preventive or mitigative measures can be taken quickly. This study aims to investigate the factors that influence the adoption of artificial intelligence technology in air quality monitoring by consumers, using the extended theory of technology acceptance and use (UTAUT 2). UTAUT2 is a strong and appropriate theoretical choice regarding consumer adoption of artificial intelligence in air quality monitoring because UTAUT2’s high predictive capabilities provide advantages in developing a deep understanding of how consumers react to this technology. This theory considers environmental awareness, effort expectation, performance expectation, optimism towards artificial intelligence, habit, facilitating conditions, and social influence. A well-structured face-to-face survey obtained data from consumers living in the Indonesia area. The data were analyzed using structural equation modeling with the partial least squares method. The analysis results show that optimism towards artificial intelligence, environmental awareness, effort expectation, and habit are essential factors influencing consumers’ behavioral intention to adopt new technology in air quality monitoring.

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 15 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.