When Does It (Not) Pay to Be Good? Interplay Between Stakeholder and Competitive Strategies
Bibliographic record
Abstract
Using the instrumental stakeholder theory lens, we examine how generic competitive strategies influence the link between stakeholder management (SM) and firm financial performance. We develop a framework that highlights the synergistic effects of a differentiation strategy on SM but also the trade-offs between a cost leadership strategy and SM in their consequences for financial performance. We test our theoretical mechanism further by distinguishing between primary and secondary stakeholders, who differ in their degree of firm specificity and instrumentality. We propose that for firms pursuing a low-cost competitive advantage, secondary SM intensifies the trade-offs between SM and financial performance when compared with primary SM, whereas both primary and secondary SM are likely to improve financial performance for differentiators. Empirical analyses using a panel data set of S&P 500 firms over a 15-year period (2005–2019) and a series of robustness tests support our predictions. Our findings highlight important boundary conditions for SM's impact on firms’ financial performance and highlight not only “when SM pays” but also “when SM may not pay.”
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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 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".