The Robot Revolution: Managerial and Employment Consequences for Firms
Bibliographic record
Abstract
As a new general-purpose technology, robots have the potential to radically transform employment and organizations. In contrast to prior studies that predict dramatic employment declines, we find that investments in robotics are associated with increases in total firm employment but decreases in the total number of managers. Similarly, we find that robots are associated with an increase in the span of control for supervisors remaining within the organization. We also provide evidence that robot adoption is not motivated by the desire to reduce labor costs but is instead related to improving product and service quality. Our findings are consistent with the notion that robots reduce variance in production processes, diminishing the need for managers to monitor worker activities to ensure production quality. As additional evidence, we also find that robot investments predict improved performance measurement and increased adoption of incentive pay based on individual employee performance. With respect to changes in skill composition within the organization, robots predict decreases in employment for middle-skilled workers but increases in employment for low- and high-skilled workers. We also find that robots predict not only changes in employment but also corresponding adaptations in organizational structure. Robot investments are associated with both centralization and decentralization of decision-making authority depending on the task, but decision rights in either case are reassigned away from the managerial level of the hierarchy. Overall, our results suggest that robots have distinct and profound effects on employment and organizations that require fundamental changes in firm practices and organizational design. This paper was accepted by Lamar Pierce, organizations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.006 |
| 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 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".