Modeling behavioral intention of using health-related WeChat official accounts through ELM and SCT factors using the PLS-SEM approach
Health-related WeChat Official Accounts are widely used in China. However, limited research has explored how cognitive processing and psychological beliefs influence user behavior on these platforms. Previous studies have rarely examined the cognitive and psychological mechanisms that underlie users...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Research
2025
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| Online Access: | http://psasir.upm.edu.my/id/eprint/120378/ http://psasir.upm.edu.my/id/eprint/120378/1/120378.pdf |
| Summary: | Health-related WeChat Official Accounts are widely used in China. However, limited research has explored how cognitive processing and psychological beliefs influence user behavior on these platforms. Previous studies have rarely examined the cognitive and psychological mechanisms that underlie users’ behavioral intention in this context. To address this gap, this study proposes a psychologically grounded dual-pathway model that integrates the Elaboration Likelihood Model (ELM) and Social Cognitive Theory (SCT) to explain how individual information processing and self-efficacy jointly shape user behavioral intention. Data were collected through an online survey (n = 434) conducted from April 11 to May 9, 2024. PLS-SEM was applied using SmartPLS 4.1. The results show that both central and peripheral processing routes significantly influence self-efficacy, which in turn mediates their effects on behavioral intention. Gender moderates the peripheral pathway, with female users more responsive to credibility cues. However, user experience did not have a significant moderating effect. This study extends the application of ELM and SCT in digital health communication by clarifying how different processing routes influence user behavior via self-efficacy. It offers practical insights for healthcare institutions, government health departments, and nonprofit organizations seeking to improve user engagement and satisfaction with WOAs. |
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