Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/45476
Title: Investigating the predictors of adolescent learners’ continuance intention to engage with a gamified e-learning platform about traffic safety in Vietnam
Authors: LE, Hoang Nam 
CUENEN, Ariane 
TRINH, Tu Anh
JANSSENS, Davy 
WETS, Geert 
KHATTAK, Wisal 
BRIJS, Kris 
Issue Date: 2025
Publisher: Elsevier
Source: Transportation Research Part F: Traffic Psychology and Behaviour, 109 (February) , p. 1229 -1245
Abstract: Introduction: Although e-learning systems are seen as a key part of learning and training activities, many learners stop using them after a period of initial engagement. Prior studies provided insights into e-learning participation, but in the context of traffic safety, little research has been done on learners’ intention to continue using (gamified) e-learning. Objectives: This study aims to investigate whether the following predictors of learners’ continuance intention towards a gamified e-learning platform: satisfaction, attitude, interface design, learning content, learning management and gamification. In addition, the study investigates whether satisfaction mediates the effects of these predictors. Method: A sample of 322 Vietnamese adolescents participated in a study where a gamified e-learning platform was implemented in a sample of high school students. Results: Satisfaction, gamification, interface design and learning content were found to be important predictors of continuance intention. Moreover, satisfaction was a significant mediator of the effects generated by gamification, interface design and learning content on continuance intention. Practical implications: The findings of this study offer useful insights on the successful implementation of a gamified e-learning platform about traffic safety.
Keywords: Continuance intention;E-learning;Gamification;Traffic safety education;Vietnam
Document URI: http://hdl.handle.net/1942/45476
ISSN: 1369-8478
e-ISSN: 1873-5517
DOI: https://doi.org/10.1016/j.trf.2025.01.032
Rights: © 2025 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.
Category: A1
Type: Journal Contribution
Appears in Collections:Research publications

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