THE EFFECT OF CONFIRMATION ON CONTINUED USE INTENTION OF THE ANT FOREST USERS: THE MEDIATING EFFECT OF PERCEIVED VALUE AND SATISFACTION
DOI:
https://doi.org/10.52152/Keywords:
Ant Forest,continued use intention, perceived value, satisfaction, Expectation Confirmation ModelAbstract
In the face of global climate challenges, digital platforms like Ant Forest have emerged as innovative tools to promote sustainable behavior through gamification and user engagement. Despite Ant Forest’s wide adoption, limited research has explored the psychological mechanisms that sustain users’ continued engagement. Grounded in the Expectation Confirmation Model (ECM), this study investigates how confirmation influences continued use intention through the mediating effects of perceived value and satisfaction. Perceived value is conceptualized as a multidimensional construct encompassing perceived usefulness, ease of use, enjoyment, and interactivity. Data were collected from 544 active Ant Forest users through an online questionnaire, and analyzed using Partial Least Squares Structural Equation Modeling. Results reveal that confirmation significantly influences both perceived value and satisfaction, which in turn strongly predict continued use intention. A serial mediation pathway was also identified, in which confirmation affects continued use intention via perceived value and subsequently satisfaction. These findings extend the ECM by integrating emotional and experiential constructs into a digital sustainability context. The study contributes theoretically by demonstrating how cognitive (confirmation and value) and affective (satisfaction) evaluations jointly drive long-term engagement. Practically, it offers design insights for digital environmental platforms seeking to enhance user retention through gamification, interactivity, and value communication. Policymakers and developers are encouraged to support and refine such platforms as behavioral tools in national sustainability agendas. Future research should examine longitudinal dynamics, behavioral data, and cross-cultural differences to further validate and refine the model.
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