Emotion regulation in management: Harnessing the potential of NeuroIS tools
Bibliographic record
Abstract
Management decisions are taken by human beings, not by robots. Consequently, management decisions, and of course also the respective managers, are affected by emotions. Thus, they rely on accurate emotional processing. Research on decision making has shown that individuals with high emotion regulation capabilities perform better in taking effective decisions. Managers perpetually have to take rapid decisions in fast-paced environments, between the poles of diverse interests and motives of colleagues, customers, partners, and rivals. Sophisticated management is the key to any business. Therefore, we argue that IS research should build on the advances in cognitive neuroscience and harness the potential of NeuroIS tools in the field of management support. In this paper, we propose a conceptual framework and taxonomy for how NeuroIS tools may support managers in taking effective decisions by firstly improving their emotion regulation capabilities and, secondly, providing them with real-time feedback and decision support based on physiological measurements. Based on the framework, we outline a specific application for how emotions can affect decision making in the dynamic process of negotiations and for how NeuroIS research can contribute to a better understanding of the underlying visceral processes.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".