Inertia and Incentives: Bridging Organizational Economics and Organizational Theory
Bibliographic record
Abstract
Organizational theorists have long acknowledged the importance of the formal and informal incentives facing a firm''s employees, stressing that the political economy of a firm plays a major role in shaping organizational life and firm behavior.Yet the detailed study of incentive systems has traditionally been left in the hands of (organizational) economists, with most organizational theorists focusing their attention on critical problems in culture, network structure, framing and so on in essence, the social context in which economics and incentive systems are embedded.We argue that this separation of domains is problematic.The economics literature, for example, is unable to explain why organizations should find it difficult to change incentive structures in the face of environmental change, while the organizational literature focuses heavily on the role of inertia as sources of organizational rigidity.Drawing on recent research on incentives in organizational economics and on cognition in organizational theory, we build a framework for the analysis of incentives that highlights the ways in which incentives and cognition while being analytically distinct concepts are phenomenologically deeply intertwined.We suggest that incentives and cognition coevolve so that organizational competencies or routines are as much about building knowledge of "what should be rewarded" as they are about "what should be done."
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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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.017 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".