Analyzing User Acceptance of AI Based Water Quality Monitoring through the UTAUT2 Framework

Qurotul  Aini; Marviola  Hardini; Adam  Faturahman; Dwi  Apriliasari; Alexander Chou  Kiboy

Authors

Qurotul Aini Kristen Satya Wacana University Author https://orcid.org/0000-0002-7546-5721
Marviola Hardini Sundara Group Author https://orcid.org/0000-0003-3336-2131
Adam Faturahman Bosindo Author https://orcid.org/0000-0001-9727-9092
Dwi Apriliasari University of Raharja Author https://orcid.org/0000-0001-5597-0475
Alexander Chou Kiboy Ijiis Incorporation Author https://orcid.org/0009-0006-7046-8639

Keywords:

Water Quality System, Artificial Intelligence, UTAUT2, PLS-SEM, SmartPLS

Abstract

This research explores the factors influencing user acceptance and utilization of AI-enhanced water quality systems by applying the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) framework. AI technology has become increasingly important in improving water quality management by analyzing real-time data from multiple sources, helping to enhance environmental sustainability and public health. This study aims to identify and analyze the factors that affect user behavioral intention toward adopting AI-based water quality systems, particularly focusing on perceived usefulness, ease of use, social norms, and motivation. Data were collected from 357 respondents who had used AI-based water quality systems for at least three months and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) with SmartPLS software. The findings indicate that perceived ease of use, social norms, and motivation have a significant influence on behavioral intention to adopt the systems, while perceived usefulness shows no significant effect. These results validate the UTAUT2 model applicability to AI-driven environmental technologies and provide practical guidance for developers and policymakers to enhance user engagement, affordability, and usability of AI-based water quality systems to promote sustainable environmental management and public well-being.

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Analyzing User Acceptance of AI Based Water Quality Monitoring through the UTAUT2 Framework

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