Bibliographic record
Abstract
ABSTRACT With the advocacy on corporate social responsibility (CSR), it is common for firms to integrate profit objectives with social responsibilities, such as with an aim to boost consumer welfare. We focus on a socially responsible firm that is concerned with its profit as well as consumer surplus and examine four different types of pro‐social behavior by the firm: optimizing a weighted average of the expected profit and consumer surplus (referred to as the mixed‐objective model), negotiating with pro‐social executives (referred to as the Nash bargaining), charitable donations after profit maximization (referred to as the donation), and ensuring the portion of consumer surplus to be a given fraction of the social welfare (referred to as the fairness model). Our results show that under all behaviors, there is a more substantial boost to consumer surplus at the expense of a slight decrease in profit when consumer surplus consideration (referred to as the CSC level) is lower. Among those four behaviors, while maintaining the same profit level, a donation is not the most consumer‐surplus‐enhancing pro‐social behavior among those four behaviors, when the overhead cost is sufficiently high or when a high enough profit level needs to be maintained. This finding challenges Milton Friedman's advocacy that socially responsible businesses should indirectly fulfill their societal duties by first focusing on profit maximization and then redistributing the generated profit for social causes. Our results imply and quantify the managerial insight that in balancing consumer surplus against profit loss, a little commitment to consumers can go a long way. We also shed light on when the firm should choose a decentralized pro‐social behavior, such as donations, and when it should incorporate consumer surplus consideration into operational decisions for consumer surplus enhancement.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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 teacher head, 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".