Predicting e-Commerce for Sustainable Accommodations Behavior Intention Using Machine Learning Approaches: The Case of National Capital Region, Philippines
2026 17th International Conference on E-Education, E-Business, E-Management and E-Learning (IC4e), (2026), pp. 994-999
Lovely Juliana H. Abalos
a
,
Anthony Basil G. Garcia
a
,
Naomi R. Garmay
a
,
Ryujin Ace D. Medrano
a
,
Alexander A. Hernandez
a
,
Joferson L. Bombasi
b
,
Armando D. Del Mundo
b
,
Gima B. Montecillo
c
a College of Computer Studies and Multimedia Arts, FEU Institute of Technology, Manila, Philippines
b College of Computer Studies and Multimedia Arts, FEU Alabang, Muntinlupa, Philippines
c College of Computing Studies, Pamantasan ng Cabuyao, Laguna, Philippines
Abstract: Travelers are choosing eco-friendly accommodations, and the attention is drawn to sustainable tourism. Nevertheless, there is a gap between travelers' proenvironmental intentions on sustainable accommodation options using e-commerce platform, and it should be understood what influences decisions for eco-tourism. This study aims to explain ecommerce for sustainable accommodation adoption intention. The data used were from survey responses collected from travelers in the National Capital Region (NCR) of the Philippines to predict adoption intention. The performance of Support Vector Machine and Decision Tree in predicting sustainable accommodation adoption intention are found accurate and reliable. Awareness of consequences (AC), ascription of responsibility (AR), personal norms (PN) with attitude (AT), subjective norms (SN), perceived behavioral control (PBC) are key influencing factors, and all of these under the Theory of Planned Behavior (TPB). Finally, the Norm Activation Model (NAM) explains the role of trust (TR) and perceived value (PV) in the model. It shows knowledge of the intention behavior gap, and hence for the need of AI driven recommendations and policy incentives for the promotion of sustainable tourism. Overall, this study indicates the potential of e-commerce for sustainable accommodations behavior Intention using predictive models. Research and practical implications are presented.