Guidelines for sponsorship signaling within socially complex markets
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
Organizations use sponsorships to influence various marketing, financial, and public relations outcomes. However, sponsorship communications occur in socially complex markets where messages diffuse faster. Messages are also more widely accessible to and influenced by various audiences that can be supportive, neutral, skeptical, or decisively antagonistic. These conditions require managers to adopt more nuanced and holistically integrated ways of making their messages acceptable and engaging for a wide variety of audiences, while also being more robust to scrutiny. The paper addresses this challenge by drawing on signaling theory to present a process model and guidelines for managing sponsorships within socially complex markets. Specifically, it outlines how different message content and sponsorship characteristics combine to influence signal reception, market responses, and feedback. The model is then merged with research on sponsorship authenticity to guide managerial application. Initially, sponsors establish the signal content and primary target audiences through selecting sponsee partners with whom they have authentic fit (Guideline 1). Sponsors can then develop specific characteristics of commitment, observability, and credibility (Guidelines 2 - 4). Finally, sponsors should conduct pre-launch and post-launch assessments to adapt to how the sponsorship is received by various audiences and subgroups on an ongoing basis (Guideline 5).
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 | 0.006 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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