Managing for stakeholders, stakeholder utility functions, and competitive advantage
Bibliographic record
Abstract
Abstract A firm that manages for stakeholders allocates more resources to satisfy the needs and demands of its legitimate stakeholders than would be necessary to simply retain their willful participation in the firm's productive activities. We explain why this sort of behavior unlocks additional potential for value creation, as well as the conditions that either facilitate or disrupt the value‐creation process. Firms that manage for stakeholders develop trusting relationships with them based on principles of distributional, procedural, and interactional justice. Under these conditions, stakeholders are more likely to share nuanced information regarding their utility functions, thereby increasing the ability of the firm to allocate its resources to areas that will best satisfy them (thus increasing demand for business transactions with the firm). In addition, this information can spur innovation, as well as allow the firm to deal better with changes in the environment. Competitive advantages stemming from a managing‐for‐stakeholders approach are argued to be sustainable because they are associated with path dependence and causal ambiguity. These explanations provide a strong rationale for including stakeholder theory in the discussion of firm competitiveness and performance. Copyright © 2009 John Wiley & Sons, Ltd.
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.
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 | 0.006 | 0.014 |
| 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 | 0.006 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".