Incumbent Stakeholder Management Performance and New Entry
Bibliographic record
Abstract
Abstract Instrumental stakeholder theory seeks to explain how managing stakeholders effectively can yield competitive advantage for incumbent firms. We extend instrumental stakeholder theory to explain and predict future competition operationalized as new entrepreneurial entries. Our study is among the first to empirically examine the relationships between aggregate stakeholder management performance and the entrepreneurial entries of individuals. Using a combined U.S. dataset from 2003 to 2013 from the Kinder, Lydenberg and Domini (KLD) Index, Compustat, and Kauffman’s Entrepreneurship Survey, we find support for three hypotheses. First, higher levels of stakeholder management performance are related to lower rates of entrepreneurial entry. Second, a curvilinear relationship exists between stakeholder management performance and entrepreneurial entry, where both low and very high stakeholder management performance increase entrepreneurial entry. Third, the greater the variance in stakeholder management performance across stakeholders, the more entrepreneurial entry. Our findings suggest that managing for stakeholders can help to avoid future competition. We add an entrepreneurship lens to the business ethics of stakeholder theory showing how incumbent stakeholder management performance shapes opportunities for entrepreneurs, a largely neglected stakeholder group.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".