Toward a moral approach to stakeholder management: insights from the inclusion of marginalized stakeholders in the operations of social enterprises
Bibliographic record
Abstract
Purpose Previous literature on sustainable supply chain management has largely adopted an instrumental view of stakeholder management and has focused on understanding the effect of powerful stakeholders who have a more decisive influence on an organization's supply chain decisions. Social enterprises have emerged as organizations that often aim to create impact by integrating marginalized stakeholders into their operations and supply chains. This study examines the trade-offs that social enterprises experience due to their moral stance toward stakeholder engagement, evidenced in their commitment to serving marginalized stakeholders, as well as the responses adopted to these trade-offs. Design/methodology/approach The study follows a theory elaboration approach through a multiple case study design. The authors draw on insights from stakeholder theory and use the empirical insights to expand current constructs and relationships in a novel empirical context. Based on an in-depth analysis of primary and secondary qualitative data on ten social enterprises, the authors examine how these organizations integrate marginalized stakeholders into various roles in their operations. Findings When integrating marginalized customers, suppliers and employees, social enterprises face affordability, reliability and efficiency trade-offs. Each trade-off represents conflicts between the organization's needs and the needs of marginalized stakeholders. In response to these trade-offs, social enterprises choose to internalize the costs through slack creation or vertical integration or externalize the costs to stakeholders. The ability to externalize is contingent on the growth orientation of the organization and the presence of like-minded B2B (Business-to-Business) customers. These responses reflect whether organizations accept the trade-offs at the expense of one or more stakeholders or if they avoid the trade-offs and find mutually beneficial solutions. Originality/value Building on the empirical insights, the authors elaborate on stakeholder theory with a focus on the integration of marginalized stakeholders by emphasizing a moral justification for stakeholder engagement, identifying the nature of the underlying trade-offs which can arise when various stakeholder needs are in conflict and examining the contingencies affecting organizational responses to these trade-offs.
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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 | 0.020 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.017 | 0.053 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.004 | 0.007 |
| 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".