The Unintended Moral Consequences of Passion, Proactivity, and Information Sharing
Bibliographic record
Abstract
Management scholars routinely advocate for passion, proactivity, and information sharing as ways to increase individual, team, and organizational performance. In this symposium, we gather leading and emerging researchers in the field of behavioral business ethics to consider the moral implications of these practices and recommendations. We contend that management scholars have advocated for these without fully considering their “moral costs.” In a series of papers, we demonstrate that passion, proactivity, and information sharing can have unexpected – and unintended – moral consequences. We explore the influence of these practices and recommendations on moral perceptions and moral decisions, and we consider the consequences of such for individuals, organizations, and societies. Taken together, our papers extend the emerging literature on the systematic side effects of traditional management practices and processes and offer important theoretical and empirical insights into moral decision making in organizations. Blinded by Passion: Perceptions of Passion and Moral Expectations and Evaluations of Others Presenter: Monica Gamez-Djokic; Northwestern Kellogg School of Management Presenter: Maryam Kouchaki; Northwestern Kellogg School of Management Where There is Light, There Must Be Shadow:The Impact of Proactivity on Immoral Behavior and Sleep Presenter: Mona Mensmann; Warwick Business School Presenter: Brian Gunia; Johns Hopkins U. #Hypocrites! The Effect of Conflicting CSR Information From Internal and External Channels Presenter: Lisa Lewin; Rutgers Business School Presenter: Danielle E. Warren; Rutgers U. Does Economics Education Make Us See Honesty as Costly? Presenter: Madeline Ong; Hong Kong U. of Science and Technology Presenter: Julia Lee; U. of Michigan Presenter: Bidhan Parmar; U. of Virginia Deadlined and Deceived: The Unexpected Costs of Revealing Final Deadlines in Negotiations Presenter: Joseph P. Gaspar; Quinnipiac U. Presenter: Redona Methasani; U. of Connecticut
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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 | 0.030 | 0.066 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.043 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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".