Achieving cultural resonance: Four strategies toward rallying support for entrepreneurial endeavors
Bibliographic record
Abstract
Abstract Research Summary We theorize the strategies that entrepreneurial actors employ to instill their endeavors with culturally resonant meanings and rally the support of key audiences (investors, analysts, or customers). In extant cultural entrepreneurship research, endeavors are assumed to achieve resonance and gain support when actors deploy the culture they share with their targeted audiences. But what if actors and audiences hold cultural repertoires that poorly overlap? We consider actors' efforts to “mobilize” and “enrich” the repertoires of both parties. Specifically, we introduce a typology identifying four strategies: anchoring, retooling, channeling, and seeding. Viewing culture as an engine of stability and change, we contend that each strategy addresses a distinct tension that actors must skillfully balance. We develop propositions to explain how and when actors manage these tensions. Managerial Summary Entrepreneurs must explain their endeavors in terms that audiences (investors, analysts, or customers) will understand and value. We know that entrepreneurs do so by telling stories and performing other symbolic actions, or by revising their stories and actions. However, prior insights assume a preexisting fit between what entrepreneurs and audiences value. How is this fit created? We identify four strategies by which entrepreneurs leverage a preexisting fit, and foster greater fit. We explain how entrepreneurs leverage a preexisting fit by presenting endeavors in familiar terms, and guiding audiences' interpretations. We explain how entrepreneurs foster greater fit by learning what audiences value, and educating audiences about their endeavors' value. Considering the inherent tension that each strategy entails, we explain how and when entrepreneurs use these strategies.
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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 | 0.008 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.016 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.003 |
| 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".