Environmental Entrepreneurship and Interorganizational Arrangements: <scp>A</scp> Model of Social‐benefit Market Creation
Bibliographic record
Abstract
Research summary S ocial‐benefit markets, such as those for carbon trading, are becoming increasingly popular for combating complex social and environmental problems. However, their unique characteristics pose substantial challenges to market creation and require novel entrepreneurial approaches. Integrating the entrepreneurship literature with that of management information systems, we conceptualize social‐benefit markets as a new type of interorganizational arrangement and develop a model of social‐benefit market creation. First, we argue that a core entrepreneurial collective, comprising a plurality of actors from government, business, and social movements, is essential. Second, we elaborate a six‐phase process through which the interests of entrepreneurs are aligned and inscribed in a market artifact and the market is formed. The model is illustrated with reference to the W estern C limate I nitiative's carbon market creation efforts. Managerial summary C arbon markets have become a popular strategy for reducing greenhouse gas emissions, with similar market‐based solutions being proposed for other social and environmental challenges. We refer to these new structures as social‐benefit markets. Social‐benefit market creation is a complex undertaking that will require novel entrepreneurial approaches and new interorganizational information systems. In an effort to reduce some of this complexity, we propose a model to explain how entrepreneurs from government, business, and social movements must work collectively to build social‐benefit markets. We further elaborate a six‐phase process through which entrepreneurs are able to align their diverse interests and create a stable market artifact. For managers from all sectors, our work offers actionable guidance for forming collective ventures that deliver real social benefits. Copyright © 2017 Strategic Management Society
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".