Responsible executive leadership
Bibliographic record
Abstract
Purpose – The purpose of this article is to develop a moral identity perspective on Barnard's conceptualization of executive responsibility. Design/methodology/approach – The paper uses a prospective study design, as an alternative to a transitional grounded approach, to develop a theory-based framework to compare textual patterns in Barnard's writings. By using Barnard's conceptualization of executive responsibility within the identity control theoretical framework, the paper analyzes the challenges of executive moral identification. Findings – The paper develops a theory-based, yet practical, typology of moral identification of responsible executive leaders. Research limitations/implications – Although this proposed typology appears rather parsimonious, it is recognized that issues of moral behavior are certainly complex, and therefore should be addressed in a requisite manner in future model developments. Originality/value – The paper posits that Barnard's conceptualization provides a useful channel to address the critical domain at the intersection of responsible executive leadership, identity, and ethics relative to the issues of CSR, diversity management, gender equity, and community involvement. The paper considers the typology of moral identification to be an operative conduit for subsequent empirical research and practical guidance for executive leadership development.
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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".