Intenção de comportamento, produzida por estímulos de Marketing Social, para prevenção da obesidade sob os pontos de vista fisiológico e comportamental
Bibliographic record
Abstract
Studies and applications of Neuroscience in the field of Social Marketing offer new finds on the behavior of target audiences. Ethical applications and the adoption of non-invasive measuring systems with scientific precision encourage exploration of its potential for behavioral analysis through neurophysiological data. This thesis aims to measure which significative effects of emotion and attention adopting a social communication proxy produces in overweight individuals with no food addiction. This classification was chosen due to results from the literature, which state that addicted people have no commitment, while obese people have a low level of emotional awareness and executive functions. To this end, theories were adopted as essential explanations of this phenomenon: Neuroscience of Consumer’s Behavior, Transformative Consumer Research and Social Marketing, Obesity and Overweight, Food Addiction, Planned Behavior Theory, Somatic Markers, Emotion and Feelings, and Attention. Methodologically, the study was divided in three stages and can be described as an exploratory-causal study with a quantitative approach. The first stage consisted of a manipulation check to validate the chosen stimuli (text and audiovisual). The textual stimulus was used as a pre-activation element for the audiovisual stimulus, which, in turn, referred to the stories of Stephen Hochschild, fictitious name and character, being a digital influencer and a researcher professor. The choice of an expert spokesperson, each in his or her area, is due to the repercussion in science of the decline of the authorities. The second stage was directed at selecting participants, considering aspects such as Body Mass Index, level of food addiction, and socio-demographic data. The third stage consisted of the experimental study itself and the data was collected at Tech³Lab, a user experience research lab located in Montreal, Canada, using FaceReader, Eye Tracking, and Galvanic Skin Response as well as data obtained via self-report, through questionnaires. Data analysis was conducted in two stages, the first being through each equipment’s software and the second in R software for statistical analysis. Results indicate both groups had similar positive behavior regarding the intention of behavioral change after exposure to stimuli, exhibiting negative emotions such as rage and disgust in the Digital Influencer group and surprise and neutral expressions in the researcher professor group. Results may be used from the perspective of public policies to determine budget for communication and marketing, which can serve as guidelines to improve or propose new social marketing actions aimed at reducing cost and time. Theoretical contributions include improving the Planned Behavior Theory’s model with the inclusion of physiological measurements based on the theories of Emotion and Attention with results focused on Social Marketing. Methodological implications include simultaneous use of different techniques as well as the empirical dialogue between two areas of expertise: Marketing and Neuroscience.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 source (direct Gemma or distilled Codex), 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".