Leveraging board expertise: strategy mapping as teaching tool
Bibliographic record
Abstract
Purpose As a result of the many governance failures in the past decade, new legislation, increased regulation, and best practices have been adopted by boards in an effort to improve corporate governance. Unfortunately, not all of the changes, such as increasing the number of external directors, have favorably impacted the quality of board governance. While having the majority of external directors on a board increases the board's independence from the CEO, these external directors lack inside directors' understanding of the firm's operations, customers and business model. The board members' lack of understanding presents a key challenge to CEOs, as their tenures depend on keeping their boards informed about the firm's business model. If CEOs are to succeed in this new governance climate, they need to find a way to effectively explain the business model to external directors in order to educate them, access their competencies, and ensure their long term support. The purpose of this paper is to examine the role of the strategy map to communicate the firm's business model to the board. Design/methodology/approach The paper used the authors' experiences, a review of the literature, and a case study as a basis for making recommendations presented in the article. Findings Outside directors may struggle to understand the firm's business model. While some may argue this is not the CEO's problem as it is the board's role to govern and management's job to manage, the authors argue it is an important issue for CEOs for two reasons: First, if the board does not understand the impact of changes to a firm's business model then CEOs are not fully leveraging their boards' expertise. Second, if CEOs do not keep the board adequately informed about the business model it hinders, rather than helps CEOs from building open and transparent relationships with their boards. By ensuring that directors receive the right information about the organization's business model and then have the opportunity to have a constructive dialog regarding the quality of the business model, CEOs can build trusting relationships with their boards and thus ensure they succeed over the longer term. Originality/value Recent governance failures have demonstrated a need for better communication between boards and CEOs. This is one of the first papers to examine the role of the strategy map to communicate the firm's business model to the board.
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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 | 0.008 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".