Exploring Mistakes and Failures in Social Marketing: The Inside Story
Bibliographic record
Abstract
Background: Social marketing successes are relatively well-documented, but mistakes and failures in the field are not. When mistakes and failures are reported, they are usually on an ad hoc basis, as opposed to a systematic gathering of evidence. This paper is the second half of a two-part research study that aims to understand the perceptions of social marketing professionals with regard to mistakes and failures in the field. Focus: This article is related to research and evaluation of the social marketing field. Research Question: What are the perceptions of the social marketing community regarding mistakes and failures in the field? Importance to the field: A greater understanding of mistakes and failures in the social marketing field will assist practitioners to assess their own shortcomings, address causes of mistakes and failures, and improve program outcomes. Method: This research is qualitative and exploratory, with a constructivist, grounded theory methodology. Surveys were completed by 100 social marketing community members. Survey data was analyzed and coded using SPSS software and Microsoft Excel. Results: According to the analyzed survey data, the social marketing community believes that inadequate research, poor strategy development, and mismanagement of stakeholders are the most common mistakes made by social marketers. Further, weak evaluation and monitoring is considered to be the “least well-managed” program element. Poor strategy development, external influences, and poorly designed program and behavioral objectives are considered to be the primary reasons for social marketing program failure. Recommendations for research or practice: Future research may explore the extent to which external influences lead to social marketing program success or failure, particularly in comparison to mistakes made by social marketers. Additionally, practitioners should be aware of and develop strategies to mitigate common mistakes and failures in order to improve program outcomes. Limitations: The 100 social marketing professionals who responded to the survey are not representative of the global social marketing community. Further, responses were based on self-report rather than direct observation, which may make them more susceptible to bias.
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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 | 0.027 | 0.061 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.018 | 0.031 |
| Scholarly communication | 0.014 | 0.025 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.003 | 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".