Expanded strategy simulations: developing better managers
Bibliographic record
Abstract
Purpose The purpose of this paper is to explore the use of simulations in strategy teaching. The authors’ conceptualization is built upon the benefits and limitations of simulations by establishing a link between the skills required to be a competent manager and the capacity of simulations to develop them. Design/methodology/approach Using deductive theory building, the authors pinpoint the shortcomings of simulations, and offer a framework categorizing managerial skill development using simulations to teach strategic management. Findings The authors propose a new perspective on the use of simulations to teach strategic management by elaborating on their effectiveness in developing soft skills related to social issues often overlooked in simulations’ learning outcomes. The framework provides propositions concerning the ability of simulations to develop both soft (societal and human) and hard skills (technical and conceptual) needed by managers. Research limitations/implications Literature shows that computer‐based platforms significantly increase the learning process. While such tools are widely used in teaching hard skills for decision making, they are relatively absent from teaching soft skills for decision making. Future studies should empirically explore the extent to which computer‐based platforms help cultivate soft skills. Practical implications Simulations are one of the most praised learning tools by management students. MBA administrators and strategy instructors would benefit from improved simulations that take into account the social environment surrounding managers. Expanded simulations, then, might lead to better preparation of management candidates for their tasks. In addition, simulation developers may find guidance in the authors’ conceptualizations to construct more effective teaching aids. Originality/value Contrary to the mainstream literature that focuses on hard‐skill development through simulations, this study calls attention to simulations’ capacity to foster the soft‐skills required to be a competent manager.
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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 | 0.007 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".