Making mobile health information advice persuasive: An elaboration likelihood model perspective

Jinjin Song, Yan Li, Xitong Guo, Kathy Ning Shen, Xiaofeng Ju

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)

Abstract

As m-health apps become more popular, users can access more mobile health information (MHI) through these platforms. Yet one preeminent question among both researchers and practitioners is how to bridge the gap between simply providing MHI and persuading users to buy into the MHI for health self-management. To solve this challenge, this study extends the elaboration likelihood model to explore how to make MHI advice persuasive by identifying the important central and peripheral cues of MHI under individual difference. The proposed research model was validated through a survey. The results confirm that (1) both information matching and platform credibility, as central and peripheral cues, respectively, have significant positive effects on attitudes toward MHI, but only information matching could directly affect health behavior changes; (2) health concern significantly moderates the link between information matching and cognitive attitude and only marginally moderates the link between platform credibility and attitudes. Theoretical and practical implications are also discussed.

Original languageEnglish
Article number287573
JournalJournal of Organizational and End User Computing
Volume34
Issue number4
DOIs
Publication statusPublished - Jul 1 2022

Keywords

  • Elaboration Likelihood Model
  • Health Behavior Changes
  • Health Concern
  • Health Information Matching
  • Mobile Health Information
  • Platform Credibility

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Computer Science Applications
  • Strategy and Management

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