Revitalizing the Quest for Professionalism in Business and Management: Purpose, Knowledge, Behavior, and Expectation
Bibliographic record
Abstract
One of the biggest challenges of our time is to develop the management discipline into a true profession. In this respect, business schools have been accused for failing to promote better policies and management practices as well as failing to educate students, as prospective managers, about their moral and social responsibilities. This essay outlines a multi-dimensional framework for professionalization, involving the dimensions of purpose, knowledge, behavior, and expectation. Subsequently, this framework is used to define and explore various paths out of the current intellectual stasis of the field of management and business. A key pathway is creating a shared sense of professional purpose and responsibility; another important route involves developing a professional body of knowledge informed by both discovery and validation; third, so-called ‘trading zones’ need to be developed, to offer opportunities for (professionals with) different voices and interests to meet; and the expectations that societal stakeholders have of professional conduct and performance by managers should be raised. Finally, the implications arising from these four pathways for business schools are explored. One of the most challenging implications is the need to improve the alignment between what management professors say they do and what they actually do – as researchers and educators.
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 | 0.027 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.055 |
| Scholarly communication | 0.016 | 0.011 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.001 | 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".