Opening the “Black Box” of Algorithmic Management and Control: New Theory and Empirical Directions
Bibliographic record
Abstract
This symposium explores the frontiers of research on algorithmic management while taking stock of existing studies. Algorithmic management systems are transforming organizational design by reshaping coordination, control, and decision-making processes. These systems offer opportunities to enhance efficiency and innovation but also may result in potentially unintended consequences, such as reduced creativity, worker alienation, and diminished trust. Our focus is on examining the mechanisms through which algorithmic systems influence organizational outcomes, highlighting the interaction between human judgment and algorithmic recommendations, and understanding how these dynamics shape the future of work. Featuring a keynote by John Joseph (UCI) and four empirical papers, the symposium investigates how algorithms influence decision-making quality, redefine the nature of work, and reshape innovation dynamics on online platforms. The discussion synthesizes insights from these studies, addressing trade-offs between efficiency and creativity and exploring ways to align algorithmic systems with organizational goals while navigating their limitations. In doing so, we contribute to the STR and TIM divisions. Machine Predictions and Casual Explanations: Evidence from a Field Experiment Author: Xi Kang; Vanderbilt University Author: Hyunjin Kim; INSEAD Thousands of Flowers Have Bloomed: Building a Potager to Study the Future of Work Author: Lindsey Cameron; Author: Audrey Holm; HEC Paris Author: Kevin Woojin Lee; The University of British Columbia Can AI Help Managers Navigate Disruption? Experimental Evidence from Students Playing Strategy Simulations Author: Maelle A Perez; University of Virginia Author: Ryan Allen; University of Washington Author: Mana Heshmati; University of Washington Author: Michael Lenox; University of Virginia Author: Rory Morgan McDonald; Harvard Business School Dropping the F-Bomb: Unintended Consequences of Algorithmic Control on Novelty in User Innovation Author: Jay (Jinwon) Park; University of California Irvine
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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 | 0.016 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.003 | 0.048 |
| Scholarly communication | 0.015 | 0.033 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".