How to implement a new strategy without disrupting your organization.
Bibliographic record
Abstract
Throughout most of modern busi ness history, corporations have attempted to unlock value by matching their structures to their strategies: Centralization by function. Decentralization by product category or geographic region. Matrix organizations that attempt both at once. Virtual organizations. Networked organizations. Velcro organizations. But none of these approaches has worked very well. Restructuring churn is expensive, and new structures often create new organizational problems that are as troublesome as the ones they try to solve. It takes time for employees to adapt to them, they create legacy systems that refuse to die, and a great deal of tacit knowledge gets lost in the process. Given the costs and difficulties involved in finding structural ways to unlock value, it's fair to raise the question: Is structural change the right tool for the job? The answer is usually no, Kaplan and Norton contend. It's far less disruptive to choose an organizational design that works without major conflicts and then design a customized strategic system to align that structure to the strategy. A management system based on the balanced scorecard framework is the best way to align strategy and structure, the authors suggest. Managers can use the tools of the framework to drive their unit's performance: strategy maps to define and communicate the company's value proposition and the scorecard to implement and monitor the strategy. In this article, the originators of the balanced scorecard describe how two hugely different organizations--DuPont and the Royal Canadian Mounted Police-used corporate scorecards and strategy maps organized around strategic themes to realize the enormous value that their portfolios of assets, people, and skills represented. As a result, they did not have to endure a painful series of changes that simply replaced one rigid structure with another.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.023 | 0.030 |
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".