Examining 50 years of social marketing through a bibliometric and science mapping analysis
Bibliographic record
Abstract
Purpose Through a bibliometric analysis and scientific mapping, this study aims to examine research in the field of social marketing over the past 50 years and to propose a future research agenda. Design/methodology/approach A bibliometric analysis based on keyword co-occurrences is used to analyze 1,492 social marketing articles published from 1971 to 2020. The articles were extracted from the Web of Science and Scopus databases. SciMAT software was used, which provides a strategic diagram of topics, clusters, networks and relationships, allowing for the identification and assessment of relational connections among social marketing topics. Findings The results show that advertising, fear and children were some of the driving themes of social marketing over the past 50 years. In addition, the analysis identifies four promising areas for future research: consumption, intervention, strategy and analytical perspectives. Research limitations/implications This analysis can serve as a reference guide for future research in the field of social marketing. This study focused on quantitative analysis. An in-depth qualitative analysis would be a valuable future extension. Originality/value This research offers a unique systematic analysis of the progression of social marketing scholarship and provides a guide for future research related to social marketing. Importantly, this work suggests crucial issues that have not yet been sufficiently developed.
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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 | 0.015 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.198 | 0.211 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".