Conflict inside and outside: Social comparisons and attention shifts in multidivisional firms
Bibliographic record
Abstract
Research summary: Behavioral Theory highlights the crucial role of social comparisons in attention allocation in adaptive aspirations. Yet, both the specification of social reference points and the dynamics of attention allocation have received little scholarly examination. We address performance feedback from two social reference points relative to divisions in multidivisional firms: economic reference point and political reference point. Comparing divisional performance with the two reference points can give consistent or inconsistent feedback, which has important consequences for the dynamics of attention allocation in adaptive aspirations. We find consistent feedback leads to more attention to own experience, while inconsistent feedback results in more attention to the social reference point the focal division underperforms. Results reveal that political reference point plays an important role in determining managerial attention allocation . Managerial summary: This article is based on how goal‐based performance of divisions relative to both their relevant external market rivals and sister divisions in multidivisional firms influences corporate resource allocation. As a result, various combinations of performance against the two groups of peers drive the reallocation of divisional management attention. We show that specific attention shifts occur on average as a function of the focal division's performance relative to the marketplace performance and that of sister divisions . Copyright © 2016 John Wiley & Sons, Ltd.
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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 | 0.003 | 0.022 |
| 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 | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".