Managers’ and Employees’ Judgment and Decision Making: New Theoretical Developments
Bibliographic record
Abstract
The quality of managers’ and employees’ decision making is one of the most important antecedents of organizations’ efficiency — if employees and managers make suboptimal decisions, their company’s performance would suffer (Ceschi, Demerouti, Sartori, & Weller, 2017; Dane & Pratt, 2007; Dean Jr & Sharfman, 1996; Schwenk, 1995). This symposium brings together groundbreaking research on judgment and decision making that advances scholarship in managerial decision making and cognition, human resources, and organizational behavior. Four presentations will illustrate how people’s judgment and decision making biases lead managers and employees to make suboptimal decisions in diverse organizational contexts, such as overtime work, bonus allocation, job selection, and pay disparity. Further, the research discussed in this symposium not only highlights how decision making biases lead people to make irrational decisions, but also identifies interventions to rectify people’s biases, thereby making the current findings useful to both management researchers and practitioners. Inverse Demand Curves in the Workplace: Asking Employees to Work Longer When They are Unproductive Presenter: Siran Zhan; U. of New South Wales Presenter: Krishna Savani; Nanyang Technological U. Equity Bias in Bonus Allocation to Teams: Giving Too Much to Larger but Unproductive Teams Presenter: Yun Bai; Xi'an Jiaotong U. Presenter: Krishna Savani; Nanyang Technological U. Should You Join a Large Team or a Small Team? Role of Employee Qualifications on Team Selection Presenter: Zhiyu Feng; Nanyang Technological U. Presenter: Krishna Savani; Nanyang Technological U. Does the Cause of Inequality Influence Perceptions of Fairness? Presenter: Simone Tang; Cornell U. Presenter: Kin Fai Ellick Wong; Hong Kong U. of Science and Technology
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.018 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| 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".