Using Social Marketing to Tackle Compulsive Buying
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Background: The present paper focuses on compulsive buying, outlining the need to tackle this phenomenon using a social marketing approach, for the wellbeing of the affected individuals, their families and contacts, and for the health of our society at large. Focus of the Article: This conceptual development article is centered on behavior change and social marketing strategies that can address compulsive buying. Research Questions: How can social marketers help in curbing compulsive buying? What conceptual components and practical guidelines can be used in marketing programs for addressing compulsive shopping? Program Design/Approach: The platform developed herein outlines segmentation, targeting, product, price, place and promotional strategies recommended based on theoretical elements across disciplines. Importance to the Social Marketing Field: To date, compulsive buying has largely been ignored in the social marketing field, despite its relevance and prevalence. This paper provides a framework that can be employed in developing social marketing programs. Method: The proposed platform was created by bridging the literatures on compulsive buying and social marketing, identifying useful theoretical elements (e.g., the potential of the Thranstheoretical model), adapting and customizing these elements to provide actionable insights for intervention programs. The toolkit used for tackling other addictions was taken into account and integrated into the current development. Future Research: This paper offers an initial framework for social marketing efforts aimed at compulsive buying. It hopes to inspire significantly more work in this area to explore the potential of other theories and approaches to foster behavioral change for the better.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it