A Juggler's manifesto: elevating creativity to stay productive amid uncertainty
Bibliographic record
Abstract
Purpose The Industry 4.0 environment is characterized by fast data, vertically and horizontally interconnected systems, and human–machine interfaces. In the middle stands the manager, whose sustained performance is critical to the organization's success. Business disturbances—such as supply chain disruptions during the pandemic—can quickly test the manager's resiliency. While creativity and flexibility are critical for success in these situations, these skills are often not promoted directly. This paper will discuss strategies for enhancing managers' creativity and resiliency and give suggestions for improving professional development training and post-secondary business education. Design/methodology/approach A synthesis of the literature in business and psychology provides a foundation for creating a conceptual model incorporating strategies to promote managerial creativity and resiliency. While the model focuses on managerial performance under adverse conditions, the tenets of the model also apply during times of relative stability. Findings Findings based on a synthesis of the literature on creativity in business and psychology provide the foundation for a conceptual model to identify potential elements in training and curriculum design to further managers' creativity and resiliency. This model recommends clear, actionable training and program-level curriculum design suggestions for improved managerial performance. Originality/value This paper identifies a conceptual model to enhance managerial creativity leading to increased resiliency through professional development programs and suggestions for educators in post-secondary business education. This model provides tools for managers to deal with adverse and rapidly changing conditions flexibly, promoting employee productivity and satisfaction.
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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 | 0.022 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.023 |
| Scholarly communication | 0.020 | 0.010 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.008 | 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".