Beyond “Hero-based” Management: Revisiting HRM Practices for Managing Collective Expertise
Bibliographic record
Abstract
While expert knowledge is a crucial resource for large science-based companies, management of the specific population of experts remains a sensitive issue for the HRM. In order to recognize and retain these employees, companies traditionally implement a dual ladder—a career management tool that proposes an alternative technical career track to the managerial one, thus allowing recognition of an expert status in the organization. However, multiple studies have demonstrated that the implementation of a dual ladder does not bring the expected results. While previous research has investigated the individual aspirations of experts as possible reasons for their dissatisfaction with this managerial tool, we show the importance of the collective dimension of expertise and claim that the latter is insufficiently supported by HRM practices. Drawing on a case study in a large multinational firm, we explore the consequences of individualized practices on expert work and discuss the role of HRM in dealing with so-called “hero-based” management. The findings show that individualized practices could endanger the learning and innovation capacities of the firm and compromise processes such as decision making and problem solving. It could also jeopardize the continuity of expertise from a long-term perspective as younger generations refuse to align with a “hero-based” culture. Despite such a strategic challenge, HR managers experience difficulties in reinforcing the collective dimension of expertise. This opens up new perspectives for the HRM function that could lead the management of experts towards new horizons by supporting the fragile equilibrium between “agency” and “communion” in expertise processes.
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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 | 0.021 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.022 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".