The “real‐world” challenges of managers: implications for management education
Bibliographic record
Abstract
Purpose This study aims to examine the day‐to‐day problems that managers face in trying to be effective and the resources and solutions that they access as a means of dealing with these challenges. Design/methodology/approach This research summarizes the results of 185 interviews with managers employed at a broad range of levels and employers. Findings The managers were concerned with managing employee conflict, addressing employee performance and attitude issues, and finding employees who do not present these problems. Helpful advice came from the people around them rather than from print sources. The managers were unlikely to view consultants as sources of help. Research limitations/implications In identifying relevant teaching from a managerial perspective, this study suggests that curriculum designers pay more attention to teamwork and generic skill sets. Future research should examine the implications of pedagogical approaches on the practice of management, employ a random sample of a larger group of managers in order to cross‐validate this study's results, and, given that there may be a disparity between managers' perceptions and practice, a follow‐up study is needed that examines day‐to‐day managerial practice. Practical implications This study proposes that management researchers and teachers may need to do a “Mintzbergian turn” and examine how much time they spend directly addressing the issues that are important to managers on a day‐to‐day basis. This study suggests that management educators may need to move beyond presenting managerial issues as a series of “topics” to be recalled by students at a later date. Rather, there is a need to consider what issues real managers grapple with and how they go about doing so. Originality/value This study's open‐ended research approach permitted the examination of what was most important to managers from their perspective.
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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 | 0.014 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.010 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".