Corporate Social Responsibility and Horizontal Differentiation in Imperfect Competitive Markets with Global Warming Effects
Bibliographic record
Abstract
Abstract: This study develops a model of corporate social responsibility (CSR) behavior that is compatible with both the mutual goals of profit maximization and the reduction of global warming effects. The theory developed in this paper indicates that, under perfect competitive and unregulated markets, firms must take an innovative entrepreneurship approach to reduce global warming externalities, and, consequently, respond to the demands of stakeholders to behave in a CSR fashion. In this setting, managers and firms find incentives to pursue strategies leading to horizontal differentiation when a segment of the market has strong revealed consumption preferences for environmentally friendly products, and when consumers derive a consumption disutility from products creating global warming effects. To achieve these goals, firms using a safe technology also incur certification/labeling costs in order to gain market power. That is, this study demonstrates that, in unregulated competitive markets, efforts to clearly identify sources of global warming effects require innovative entrepreneurship thinking above and beyond government regulatory efforts. Thus, firms behaving in a CSR fashion may achieve monopolistic power, and therefore positive profits. In sum, our model demonstrates that CSR is compatible with the triple bottom line of economic, social and environmental performance.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".