We tested the plausibility of a cognitive-emotional model to understand the effects of messages framed in terms of gain, non-loss, non-gain, and loss, and related to the health consequences of red/processed meat consumption. A total of 544 Italian participants reported their attitude toward reduced red/processed meat consumption and intention to eat red/processed meat. One week later, participants were randomly assigned to four different message conditions: gain messages focused on the positive health outcomes associated with low meat consumption; non-loss messages focused (...) on the avoided negative health outcomes associated with low meat consumption; non-gain messages focused on the missed positive health outcomes associated with high meat consumption; loss messages focused on the negative health outcomes associated with high meat consumption. After reading the messages, participants answered a series of questions regarding their emotional and cognitive reactions to the messages, their evaluation of the messages, and again their attitude and intention toward red/processed meat consumption. Comparing different multivariate linear models under the Bayesian approach, we selected the model with the highest plausibility conditioned to observed data. In this model, message-induced fear influenced systematic processing, which in turn positively influenced message evaluation and attitude, leading to reduced intention to consume red/processed meat. Vice versa, message-induced anger reduced systematic processing, which in turn negatively influenced message evaluation, and led to no effect on attitude and intention. The comparison among message conditions showed that gain and non-loss messages activated integrated emotional and cognitive processing of the health recommendation, while loss and non-gain messages mainly activated emotional shortcuts toward attitude and intention. Overall, these results advance our comprehension of the effects of message framing on receivers' attitudes and intentions. (shrink)

We tested the plausibility of a persuasion model to understand the effects of messages framed in terms of gain, non-loss, loss, and non-gain, and related to the physical, mental and social consequences of doing physical activity at home during the lockdown restrictions. 272 Italian participants responded to a questionnaire on their attitude and intention at Time 1, frequency of past behavior, and self-efficacy related to exercising at home. Then, participants were randomly assigned to four different message conditions: gain messages focused (...) on the positive outcomes associated with doing physical activity at home; non-loss messages focused on the avoided negative outcomes associated with doing physical activity at home; loss messages focused on the negative outcomes associated with not doing physical activity at home; non-gain messages focused on the missed positive outcomes associated with not doing physical activity at home. After reading the messages, participants answered a series of questions regarding their perception of threat and fear, their evaluation of the messages, and their attitude and intention toward exercising at home at Time 2. Using multigroup structural equation modeling, we compared message conditions, and tested whether the effects of the messages on attitude and intention at Time 2 were mediated by message-induced threat, message-induced fear, and message evaluation. Results showed that the perception of the messages as not threatening was the key point to activate a positive evaluation of the recommendation. The highest persuasive effect was observed in the case of the non-loss frame, which did not threaten the receivers, triggered a moderated fear and, in turn, activated a positive evaluation of the recommendation, as well as higher attitude and intention to do home-based physical activity at Time 2. Overall, these results advance our comprehension of the effects of message framing on receivers' attitudes and intentions toward home-based physical activity. (shrink)

Although the study of the affective components involved in predicting physical activity is spreading faster and faster, there is a lack of studies testing their role when promoting physical activity through message interventions. In the present study, we considered these components by focusing on how anticipated affective reactions and emotional processing of the messages influence receivers’ affective attitude toward physical activity, concurrent behavior, and future intention. A sample of 250 participants was involved in an intervention relying on prefactual messages promoting (...) physical activity. All messages were sent through a research app and were focused on the expected consequences of exercising. Four experimental conditions involving messages differing as to their outcome sensitivity framing were compared to a control condition. Results showed that reading gain and non-gain messages enhanced the positive affective attitude toward physical activity, compared to control. Enhanced affective attitude after the intervention increased, in turn, self-reported physical activity and future intention. Interestingly, gain messages were even more persuasive for people with a low level of positive anticipated affective reactions. Furthermore, their effectiveness was especially attributable to the elicitation of hope in receivers. Discussion focuses on the advantages of considering affective components and their implications when promoting physical activity. (shrink)

Effective recommendations on healthy food choice need to be personalized and sent out on a large scale. In this paper, we present a model of automatic message selection tailored on the characteristics of the recipient and focused on the reduction of red meat consumption. This model is obtained through the collaboration between social psychologists and artificial intelligence experts. Starting from selected psychosocial models on food choices and the framing effects of recommendation messages, we involved a sample of Italian participants in (...) an experiment in which they: filled out a first questionnaire, which was aimed at detecting the psychosocial antecedents of the intention to eat red/processed meat; read messages differing as to the framing of the hypothetical consequences of reducing versus not reducing red/processed meat consumption; filled out a second questionnaire, which was aimed at detecting participants’ reaction to the messages, as well as any changes in their intention to consume red/processed meat. Data collected were then employed to learn both the structure and the parameters of a Graphical Causal Model based on a Dynamic Bayesian Network, aimed to predicting the potential effects of message delivery from the observation of the psychosocial antecedents. Such probabilistic predictor is intended as the basis for developing automated interactions strategies using Deep Reinforcement Learning techniques. Discussion focuses on how to develop automatic interaction strategies able to foster mindful eating, thanks to considering the psychosocial characteristics of the people involved; sending messages tailored on these characteristics; adapting interaction strategies according to people’s reactions. (shrink)