Exploring Value‐Based Management Sophistication: The Role of Potential Economic Benefits and Institutional Influence
Bibliographic record
Abstract
ABSTRACT The complexity of value‐based management (VBM) is often not captured in empirical research. In particular, potential differences in the extent of VBM implementation are not considered. Firms are predominantly classified dichotomously into either VBM “adopters” or “non‐adopters.” In this study, we aim to fill this gap by introducing a framework to assess differences in the extent of VBM implementation (VBM‐sophistication) based on publicly available data. This approach enables us to study determinants of VBM‐sophistication based on a hand‐collected data set comprising 2,683 firm‐year observations from 16 European countries between 2005 and 2014. Specifically, we investigate (i) whether potential economic benefits associated with VBM implementation lead to a higher level of VBM‐sophistication, and (ii) if this relation is influenced by extra‐organizational institutions (e.g., industry norms). Our results indicate that companies exhibit higher VBM‐sophistication if certain firm characteristics that increase the potential economic benefits of VBM are present. Moreover, our study provides evidence that this effect is enhanced by extra‐organizational institutions that pressure and support firms in realizing the potential benefits of higher VBM‐sophistication.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".