Top Management Teams, Emotional Dynamics and Organizational Change: New Avenues for Research
Bibliographic record
Abstract
The purpose of this symposium is to explore the emotional experience of top management teams (TMTs) in times of change, and to understand how this experience influences TMTs’ strategic actions and the organizations they lead. The symposium brings together four empirical papers that draw on rich, longitudinally collected qualitative data coming from rare, real-time access to the inner thoughts and workings of TMTs. These papers demonstrate that the emotional life of TMTs is emotionally complex and even fraught—both when we might expect it to be so, and when we might not. Our symposium goals are threefold. First, we seek to better document and understand the emotional experience of TMTs as they engage in strategic activities. Second, we seek to better understand how TMT emotions shape TMT actions that in turn affect organizations. Third, through our presentations, the introductory and closing remarks, as well as a Q&A session and audience discussion, we hope to jointly develop new insights into this theoretically and practically important space. The emotional-temporal process of TMT strategic decision-making during industry transitions Author: Timo Olavi Vuori; Aalto U. A systems psychodynamic theory of the emotional structuring of new organizational forms Author: Jennifer Petriglieri; INSEAD How success at leading organizational change can leave TMTs feeling ambivalent about it Author: Emily Truelove; Harvard Business School Resourcing Emotional Energy in the Process of Strategic Decision Making Author: Saouré Kouamé; HEC Montreal & U. of Ottawa
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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 | 0.016 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.005 | 0.023 |
| Scholarly communication | 0.022 | 0.052 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.010 | 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".