Bibliographic record
Abstract
This dissertation examines relationships between firm decision makers and stakeholders in light of societal issues. In three sets of studies, I examine different strategic decisions in which stakeholder beliefs and identities are salient. In the first project, I examine why organizational decision makers choose to take sociopolitical stances as part of their business strategies. In an online experiment, I find that higher monetary incentives, cause-individual value alignment, and consequentialist ethics lead to more stance-taking, while credible stakeholder pushback leads to less stance-taking. Liberals tend to view stance-taking as fundamental to the corporation’s social role while conservatives view stance-taking as a means to improve financial performance. These results show that various market and individual-level factors have a nuanced impact on stance-taking decisions. In the second project, my co-authors and I examine how organizational justifications for diversity impact hiring and promotion decisions. In a field experiment with recruiters and a vignette experiment with managers, we find that moral cases for diversity result in greater hiring and promotion of underrepresented minorities than business cases for diversity. In two follow-up studies, we find that a separate set of online participants find moral and legal cases for diversity more effective than the business cases. However, counter to our findings, they predict that managers in our experiments viewed the business cases as more effective than the moral cases. These results show that moral cases may be effective in promoting diversity concerns in personnel decisions and that managers may overestimate the effectiveness of business cases, which could slow efforts to diversify organizations. In the third project, I examine how charitable giving incentives impact group coordination compared to direct monetary payment. I run lab experiments that show that donating participants’ small incomes to a widely admired charity may result in lower group performance compared to using similar-sized monetary incentives. In a follow-on survey, outside observers tend to predict higher coordination rates for those in the charity condition compared to the monetary incentive condition. These results suggest that expectation-setting properties of charitable incentives may impede creation of stable, informal coordination norms in certain settings.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 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 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".