Emotion regulation in management: Harnessing the potential of NeuroIS tools
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it