Toxic Decision Processes: A Study of Emotion and Organizational Decision Making
Bibliographic record
Abstract
This paper addresses the role of emotion in organizational decision making. Grounding our research in the decision process literature, we introduce the concept of “toxic decision processes”: organizational decision processes that generate widespread negative emotion in an organization through the recursive interplay of members' actions and negative emotions. We draw on a longitudinal, qualitative analysis of six toxic decision processes to develop a model that describes the three phases—inertia, detonation, and containment—through which these processes unfold. Each phase is characterized by distinctive sets of interactions among decision makers and other organizational members, and by emotions such as anxiety, fear, shame, anger, and embarrassment, that shape and are shaped by these interactions. We show that toxic decision processes are triggered by issues that are sensitive, ambiguous, and nonurgent and identify several mechanisms that connect actors' emotions and actions, over time creating a toxic decision process that leads to the cumulative buildup and diffusion of toxicity. These mechanisms include the construction of a “danger zone” around the issue that is avoided by all parties, the spread of negative emotion through processes of empathetic transmission and emotional contagion, and the suppression of widespread negative emotion that leads to the development of a volatile emotional context for future decision making. This study has important implications for the decision process literature, revealing how the different lenses through which decision making is usually viewed are connected by the emotionality that runs through each of them.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".