Middle management involvement in resource allocation: The evolution of automated teller machines and bank branches in India
Bibliographic record
Abstract
Research Summary Managers at multiple levels of a firm influence resource allocation but most research focuses on senior rather than middle managers. We study involvement of middle managers in decision making, focusing on how rewards and controls shape resource allocation. We argue that higher income growth uncertainty (rewards) and lower monitoring (controls) increase resource allocation most strongly when middle managers are more involved in decisions. We test the arguments for ATM and bank branch allocations in Indian banks from 2011 to 2014. We assess causal mechanisms by comparing more and less favorable conditions for allocation, as well as considering a poststudy exogenous shock. The results suggest that the rewards and controls have different associations with resource allocation depending on the involvement of senior and middle managers. Managerial Summary The study examines how rewards and controls shape resource allocation decisions by middle managers, focusing on rewards arising from uncertainty about employee income and controls based on monitoring. The work suggests that rewards and controls that influence resource allocation by one level of managers may have less effect for another level. Hence, a firm's plans for resource deployment need to include rewards and controls that are relevant for both senior and middle managers.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".