Corporate Purpose and Strategy: A Microfoundational Perspective
Bibliographic record
Abstract
This symposium aims to showcase novel insights of the growing body of research that examines corporate purpose through a microfoundational, stakeholder-driven lens. We selected papers that explore the role of a shared corporate purpose that goes beyond short-term profit generation on stakeholder resource provision. The first paper outlines the theoretical foundations of the relationship between a corporate purpose, stakeholder outcomes and firm performance, and the rest of the papers provide empirical examinations of how employee resource provision is impacted by what employees know about the purpose of their organization. The Value of Organizational Purpose Author: Witold Jerzy Henisz; U. of Pennsylvania Corporate Social Responsiveness and Employee Outcomes: The “All-Or-Nothing” Conundrum Author: Anna Szerb; INSEAD Social Responsibility Orientation and Employer Advantages Across the Employee Lifecycle Author: J. Daniel Kim; The Wharton School, U. of Pennsylvania Author: Matthew Lee; Harvard Kennedy School Theory and Experimental Evidence of Stakeholder Responses to CEO Political Activism Author: Tommaso Bondi; Cornell SC Johnson College of Business Author: Vanessa Burbano; Columbia Business School Author: Fabrizio Dell'Acqua; Harvard Business School
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.039 |
| Scholarly communication | 0.016 | 0.012 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".