Value creation and value appropriation in public and nonprofit organizations
Bibliographic record
Abstract
Research Summary In recent years, strategy scholarship expanded its scope beyond the realm of private firms. Despite notable advances, the field still lacks theoretical and empirical frameworks for fully understanding how public and nonprofit organizations interact with private firms to create and appropriate value. By recognizing the inherent complexity of interactions between organizations with different purposes and the existing challenges for designing effective governance arrangements, we assess how recent scholarship addresses some dilemmas related to both private and public value creation. Based on the extant literature and on some novel aspects raised by the articles in this issue, we also propose a framework to advance strategy research in the field. We emphasize the importance of stakeholder management capabilities among public, private, and nonprofit organizations in pursuit of enhanced public value and continuous support from appreciative stakeholders. Managerial Summary Despite abundant examples of governance arrangements involving public, private, and nonprofit organizations (e.g., public‐private partnerships and alliances involving NGOs, firms, multilateral organizations, public contracting, and so on), the strategic management field has only recently given attention to value creation and value appropriation beyond the scope of private organizations. Here we connect strategic management and public management to identify relevant dimensions that shape value‐creating strategies and underpinning outcomes in public‐private‐nonprofit interactions. We highlight that public value arises from private interests and that the dynamic of value creation and value appropriation in activities involving the public interest can be influenced by not only resource endowments and organizational capabilities but also by the way organizations address and manage stakeholder expectations.
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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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.016 |
| Scholarly communication | 0.015 | 0.013 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".