Why do many otherwise smart CEOs mismanage the reputation asset of their company?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper, examines why CEOs often misunderstand and therefore mismanage the reputations of their companies. The paper describes the way corporate reputations are built, maintained and enhanced and suggests that a good reputation needs several elements: (1) that it be part of the corporate strategy, not just a public relations or advertising slogan; and (2) that it be built from differentiating, sustaining activities of the company. The author couples his own experience with the literature on corporate strategy, noting that reputation is part of the corporate positioning process, which has long been considered the core element in strategy. Fortune magazine’s “Most admired companies” and research conducted by the author are used to highlight the variables of corporate reputation and how perceptions of reputation differ internationally. Using these variables, companies can maintain consistency in their reputation globally, while at the same time allowing regions and countries to customise to meet local needs. The paper argues that companies often fail to achieve their desired reputations because of two primary factors: (1) the failure to identify a clear core competency, relying instead on claims of superiority that have little value to the intended audience; and/or (2) “active inertia”, or continuing to do the same things that made the company successful, despite the fact that these things are no longer relevant to the current situation. Examples of companies that have done a good job at building their corporate reputation and examples of some who have had problems are provided, along with a check list of “warning signs” that a company’s reputation is in trouble, along with some suggested actions.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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 it