We Got the Beat: The Role of Music in Management & Organization Theory
Bibliographic record
Abstract
Thirty years ago, the 1995 Academy of Management meeting in Vancouver featured the groundbreaking symposium “Jazz as a Metaphor for Organizing in the 21st Century,” igniting a tradition of exploring music’s relevance to organizational studies. Since then, music has emerged as a compelling context, metaphor, and theoretical lens, offering insights into organizational dynamics, creativity, strategy, and value creation. Yet, skepticism about its utility persists, rooted in outdated views of organization research as purely practical and emotionless. This panel symposium revisits and expands the dialogue between music and management and organization studies, exploring their mutual contributions. Drawing on research into orchestras, the recorded music industry, and music theory, we will highlight how music as a context has illuminated issues like organizational resilience, innovation, and stakeholder dynamics. Further, we will demonstrate how music theory enriches organization theory, addressing concepts such as timing, rhythm, and dissonance to explain competitive anticipation, temporal decision-making, and value alignment. Bringing together scholars from diverse divisions, this symposium seeks to inspire interdisciplinary research that bridges music and organization studies. By challenging traditional boundaries, we aim to advance innovative perspectives on organizing, strategizing, and creating, while emphasizing the unique insights music brings to the complexities of organizations and industries.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.007 | 0.038 |
| Scholarly communication | 0.015 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".