Algorithm as Recommending Source and Persuasive Health Communication: Effects of Source Cues, Language Intensity, and Perceived Issue Involvement

ABSTRACTAlgorithms are now playing significant roles in online health information selection and recommendation. A question arises as to when and why people would be persuaded by the content they recommend. We conducted a 4 (recommending source: algorithm, other users, a friend, the CDC) x 2 (language intensity: high vs. low) experiment to find out. Participants (N = 299) were exposed to a health-related public service announcement embedded in a social media post. The results showed that overall, an algorithm induced a similar level of compliance intention compared with other recommending sources. We also found a significant three-way interaction when comparing the effects of the algorithm and the CDC: for individuals with low issue involvement, the algorithm was less persuasive when paired with a message with high language intensity. In contrast, for high-involvement individuals, the algorithm elicited more fear than the CDC when recommending an assertive message, partially leading to more compliance intention. Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statementThe data that support the findings of this study are available from the corresponding author upon request.Supplementary materialSupplemental data for this article can be accessed online at https://doi.org/10.1080/10410236.2023.2242087Additional informationFundingThe author(s) reported there is no funding associated with the work featured in this article.