Identifying and analyzing social marketing initiatives using a theory-based approach
Bibliographic record
Abstract
Purpose This paper aims to provide an example of how to review information and social-marketing initiatives using financial well-being as a case point. Design/methodology/approach Literature review and content analysis is used. The audience, channel, message, and evaluation framework is applied. Existent financial well-being initiatives are identified and selected, and further described and analysed in terms of their audience, channel, message and evaluation. The message is further discussed according to the transtheoretical model of change. Findings Most financial well-being campaigns focus on a particular audience, use a multichannel approach to reach their audience, and report some evaluation, consistent with the audience, channel, message and evaluation framework. Message analysis shows that several initiatives address all processes posited by the transtheoretical model of change to trigger behavior change. Potential areas of improvement and boomerang effects are identified. Practical implications Initiatives enhance their effectiveness by using theory, using proper segmentation and channel(s) selection, creating messages based on the audiences’ readiness for change and incorporating evaluation. Originality/value Theoretical and practical insight regarding financial well-being initiatives has been achieved. Campaign designers can inspire from this example to conduct their own research and analysis of existent initiatives as one of the starting points in the process.
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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 | 0.058 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.041 | 0.024 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".