The Essence of Online Trust: Evaluating a SMART-Based Decision Support System for Car Purchasing
DOI:
https://doi.org/10.24191/jcrinn.v11i2.571Keywords:
Decision Support System, Car Recommendation, Online Trust, Human-Computer Trust Scale, Simple Multi Attribute Rating Techniques, Multi-Criteria Decision MakingAbstract
A decision support system is vital for aiding decision-making processes, particularly in situations involving long-term commitments. Despite its utility, user trust remains a significant concern in the adoption of such systems. This study evaluates user trust in a personalized web-based decision support system that assists consumers in car purchase decisions. The system generates personalized recommendations through the Simple Multi-Attribute Rating Technique (SMART) across five criteria of affordability, safety, fuel efficiency, comfort, and performance. These criteria were identified through literature review and consultations with certified car dealers. A total of 34 participants completed a usability test incorporating the Human-Computer Trust Scale (HCTS). The HCTS questionnaire comprises five dimensions designed to assess users’ perceptions of the system’s ease of understanding, technical proficiency, reliability, emotional connection, and overall trust. The overall trust mean was 4.07, with the highest sub-dimension mean in Personal Attachment (4.13) and the lowest in Faith (4.04). To assess the reliability of the HCTS instrument, Cronbach’s alpha was computed and yielded a value of 0.931, indicating excellent internal consistency among the questionnaire items that measured different aspects of user trust. This high reliability supports the validity of the findings regarding users' perceptions of trust. Overall, the results suggest the prototype fosters both cognitive- and affect-based trust, positioning it as a dependable tool for car purchase deliberations. Future work should widen the recommendation pool to multi-brand and used car inventories and incorporate explainable AI features to strengthen transparency.
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Copyright (c) 2026 Siti Sarah Md Ilyas, Nur Aliah Mufek Zailani, Aznoora Osman, Nadia Abdul Wahab, Norfiza Ibrahim (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
