Bibliographic record
Abstract
In corporate governance today, there is a lot of emphasis on structural reform. Individual aspects of the board have increasingly been the focus of policy reform and shareholder activism. Specific attributes of board structure like the separation of the posts of chairperson and the CEO, percentage of outside directors on the board, etc., have become important considerations in the quest for effective corporate governance. In contrast, taking a process view of corporate governance, this article examines the utility of shareholder value analysis for corporate governance. In value-based management, shareholder value maximization is set as the objective of the firm. The adoption of this objective as the goal of the firm can promote effective corporate governance in three ways. First, it provides the necessary ‘pre-commitment’ between shareholders and managers regarding the goal of the firm. Second, it necessitates a greater flow of firm-specific information and the disaggregation of financial information. In corporate governance a clear identification of the goal of the firm and firm-specific information is important because of the incomplete contracts between shareholders and managers. Substantive flows of firm-specific information are needed to bridge the gaps in the incomplete contracts. Shareholder value management techniques, by providing this information in a non-agency context can provide a valuable input towards effective corporate governance. Finally, the goal of shareholder wealth maximization ensures a closer interdependence between strategy formation and the setting of operational objectives for managerial decisions. We illustrate some of these points with examples from the Lloyds–TSB experience of implementing shareholder value analysis and the findings of a study on investor communication.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".