Smartphone Users’ Persuasion Knowledge in the Context of Consumer mHealth Apps: Qualitative Study
Bibliographic record
Abstract
BACKGROUND: Persuasion knowledge, commonly referred to as advertising literacy, is a cognitive dimension that embraces recognition of advertising, its source and audience, and understanding of advertisers' persuasive and selling intents as well as tactics. There is little understanding of users' awareness of organizations that develop or sponsor mobile health (mHealth) apps, especially in light of personal data privacy. Persuasion knowledge or recognition of a supporting organization's presence, characteristics, competencies, intents, and persuasion tactics are crucial to investigate because app users have the right to know about entities that support apps and make informed decisions about app usage. The abundance of free consumer mHealth apps, especially those in the area of fitness, often makes it difficult for users to identify apps' dual purposes, which may be related to not only helping the public manage health but also promoting the supporting organization itself and collecting users' information for further consumer targeting by third parties. OBJECTIVE: This study aims to investigate smartphone users' awareness of mHealth apps' affiliations with 3 different types of supporting organizations (commercial, government, and nonprofit); differences in users' persuasion knowledge and mHealth app quality and credibility evaluations related to each of the 3 organization types; and users' coping mechanisms for dealing with personal information management within consumer mHealth apps. METHODS: In-depth semistructured interviews were conducted with 25 smartphone users from a local community in midwestern United States. Interviews were thematically analyzed using inductive and deductive approaches. RESULTS: Participants indicated that their awareness of and interest in mHealth app-supporting organizations were secondary to the app's health management functions. After being probed, participants showed a high level of persuasion knowledge regarding the types of app-supporting organizations and their promotional intents. They thought that commercial companies sponsored mHealth apps mostly as entertainment tools, whereas noncommercial entities sponsored mHealth apps for users' education. They assigned self-promotional motives to commercial organizations; however, they associated commercial mHealth apps with good quality and functioning. Noncommercial entities were perceived as more credible. Participants were concerned about losing control over personal information within mHealth apps supported by different organizations. They used alternative digital identities to protect themselves from privacy invasion and advertising spam. They were willing to trade some personal information for high-quality commercial mHealth apps. There was a sense of fatalism in discussing privacy risks linked to mHealth app usage, and some participants did not perceive the risks to be serious. CONCLUSIONS: The discussion of and recommendations for the safe and ethical use of mHealth apps associated with organizations' promotional strategies and personal data protection are provided to ensure users' awareness of and enhanced control over digitalized personal information flows. The theoretical implications are discussed in the context of the Persuasion Knowledge Model and dual-processing theories.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".