Assessing the Impact of AI on Smart Waste Management Framework for Sustainable Eco-Tourism Development
DOI:
https://doi.org/10.54536/ajth.v3i1.5596Keywords:
Artificial Intelligence, Behavioral Intention, Smart Waste Management, Structural Equation Modeling, Sustainable Eco-Tourism, Technology Acceptance ModelAbstract
The swift progression of Artificial Intelligence (AI) technologies has engendered novel prospects for the enhancement of environmental sustainability, particularly within the domain of eco-tourism. This research endeavors to explore the function of AI-based Smart Waste Management (SWM) systems in fostering Sustainable Eco-Tourism Development (SET) in Malaysia. Grounded in the Technology Acceptance Model (TAM) and augmented by the construct of Impact of Artificial Intelligence (IAI), the investigation aimed to evaluate how principal perception-based variables influence stakeholders’ intention to embrace smart waste management solutions and the subsequent ramifications on sustainability outcomes. A meticulously structured questionnaire was disseminated to 630 participants drawn from a variety of stakeholder groups, encompassing tourists, local inhabitants, eco-tourism operators, and governmental representatives across pivotal eco-tourism locales in Malaysia. We conducted an examination of the data with the help of Partial Least Squares Structural Equation Modeling (PLS-SEM) to verify the accuracy of both the measurement and structural models. Results showed that Perceived Usefulness (PU) is the key factor influencing the intention to embrace smart waste management technologies, while Perceived Ease of Use (PEOU) and the direct effect of IAI on intention lacked evidence. Nonetheless, IAI exhibited a noteworthy positive influence on Sustainable Eco-Tourism Development, and the behavioral intention towards SWM emerged as the predominant catalyst for sustainability outcomes. The results provide both theoretical and practical ramifications. From a theoretical perspective, the study extends the TAM framework into an environmental and tourism-specific context, underscoring the mediating role of intention. From a practical standpoint, it posits that enhancing stakeholder cognizance of AI’s utility and demonstrating environmental outcomes can facilitate greater adoption. The research concludes with recommendations for technology training, policy integration, and stakeholder engagement to amplify AI-driven sustainability solutions within the eco-tourism sector.
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