Effects of Fear and Humor Appeals in Public Service Announcements (PSAs) on Intentions to Purchase Medications via Social Media.

The increasing prevalence of online purchase of medications, specifically via social media platforms, poses significant health risks due to high chances of such medications being substandard and falsified (SF). The current study uses a 2 (persuasive appeal: fear vs. humor) x 3 (message repetition) mixed factorial experiment to investigate the effectiveness of persuasive appeals (on intentions to purchase medications online via social media referrals, mediated by psychological reactance (threat to freedom and anger), attitudes toward the public service announcements (PSAs), and viral behavioral intentions. ANOVA results showed the superiority of humor appeals compared to fear appeals in (1) reducing psychological reactance, (2) igniting favorable responses to the PSA, and (3) marginally reducing the intentions to purchase medications vial social media despite lower online engagement intentions (viral behavioral intentions). Pre-existing risk perceptions moderated these differences. A moderated serial mediation model, conducted using PROCESS models, was examined to assess the mechanism by which persuasive appeals and risk perceptions interact in influencing purchase intentions. Findings are discussed theoretically in regard to extending the psychological reactance model within the digital environment and practically in terms of public health, brand protection, and law enforcement recommendations.

Seeing through the forest: The gaze path to purchase.

Eye tracking studies have analyzed the relationship between visual attention to point of purchase marketing elements (price, signage, etc.) and purchase intention. Our study is the first to investigate the relationship between the gaze sequence in which consumers view a display (including gaze aversion away from products) and the influence of consumer (top down) characteristics on product choice. We conducted an in-lab 3 (display size: large, moderate, small) X 2 (price: sale, non-sale) within-subject experiment with 92 persons. After viewing the displays, subjects completed an online survey to provide demographic data, self-reported and actual product knowledge, and past purchase information. We employed a random forest machine learning approach via R software to analyze all possible three-unit subsequences of gaze fixations. Models comparing multiclass F1-macro score and F1-micro score of product choice were analyzed. Gaze sequence models that included gaze aversion more accurately predicted product choice in a lab setting for more complex displays. Inclusion of consumer characteristics generally improved model predictive F1-macro and F1-micro scores for less complex displays with fewer plant sizes Consumer attributes that helped improve model prediction performance were product expertise, ethnicity, and previous plant purchases.