Sponsorship in focus: a typology of sponsorship contexts and research agenda
Bibliographic record
Abstract
Purpose Sponsorship has become an important marketing activity. However, research on the topic treats the sponsorship context, characterized according to the type of sponsored property and the social role of these properties, as a stable characteristic or as a dichotomous characteristic within empirical studies. Therefore, the authors outline a multi-level typology of the different types of sponsorship contexts to account for traditional types of sponsorship as well as emerging themes such as online sponsorship. The authors then propose an agenda for future research. Design/methodology/approach The authors conduct a general review of the sponsorship literature to synthesize established sponsorship types with newly emerging themes to develop a multi-level typology of sponsorship contexts and a research agenda. Findings The authors’ conceptual analysis revealed a typology of sponsorship contexts that captures both general and specific types of sports sponsorship, prosocial cause sponsorship, culture and community sponsorship, and media and programming content sponsorship. Research limitations/implications The authors’ typology provides an organizing framework for future research focussing on different sponsorship contexts. However, the emergent categories still require further empirical testing. Therefore, the authors develop a set of questions to guide future research on the topic. Practical implications The authors’ typology outlines the different sponsorship contexts that should be considered by organizations that engage in sponsorship-linked marketing. Originality/value This paper provides a multi-level categorization of sponsorship contexts that integrates both traditional categories and newly emerging categories to better inform future research on situational differences in sponsorship.
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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 | 0.018 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.006 | 0.012 |
| Scholarly communication | 0.012 | 0.016 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| 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".