Shapeholders: managing them as allies, partners and significant constituents
Bibliographic record
Abstract
Purpose The authors review the concept of building relationships with Shapeholders,: a broad group of players that have no financial stake in the company yet can substantively influence it. The process for doing this is the subject of a new book by Mark Kennedy, Shapeholders: Business success in the age of social activism. Design/methodology/approach The authors examine Mark Kennedy’s framework for managing the firm’s shapeholders, a model composed of seven basic steps (7A’s): Align with a purpose, Anticipate, Assess, Avert, Acquiesce, Advance common interests, and Assemble to win. Findings Managing corporate reputation in alliance with enlightened shapeholders is a potential defense against self-aggrandizing schemes to wantonly maximize shareholder value in the short run. Practical implications Managing shapeholders is part of the messy democratic process that works when power is apportioned fairly among those affected by a firm’s decisions, and this process underpins the winning business models of true market leaders. Social implications Stakeholders previously discredited as mere “mosquitos” have gained new power, particularly when their legitimate concerns and unfair treatment resonate with the interests of a significant segment of the public and influential shapeholders. Originality/value Shapeholders can create enormous opportunities for smart managers capable of effectively engaging with them.
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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.022 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.003 |
| 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".