Rethinking Uncertainty: Advances in Research on Uncertainty at Work
Bibliographic record
Abstract
Uncertainty, unpredictability, and ambiguity are inherent features of modern organizations. From increasingly diverse workforces and complex job demands to rapid technological advances, organizations today operate in environments characterized by constant change. These challenges are further compounded by global health crises, intergroup conflicts, and climate change, underscoring the pervasive nature of uncertainty in both individual experiences and organizational dynamics (Bridoux et al., 2024; Carnevale & Hatak, 2020; George et al., 2016). As a consequence, uncertainty continues to be a central focus of organization and management research (Jauch & Kraft, 1986; Grote, 2009). Traditionally, organizational and management research has portrayed uncertainty as a negative force––an obstacle to be minimized, avoided, or controlled (FeldmanHall & Shenhav, 2019; Kramer, 1999). Yet, emerging research takes a more nuanced view, challenging this long-standing premise (Griffin & Grote, 2020; Smith & Lewis, 2011). This growing body of research acknowledges that while uncertainty can be challenging, it also holds the potential to inspire innovation, exploration, and personal growth. Rather than treating uncertainty solely as an aversive condition, this perspective repositions it as a dynamic force that can be regulated and leveraged for positive outcomes. This symposium offers new perspectives on uncertainty, featuring four presentations that explore its multifaceted nature. Together, they challenge conventional thinking and indicate new ways to conceptualize and study uncertainty. By doing so, they demonstrate how embracing uncertainty can enhance decision-making, drive proactivity, and foster collaboration in increasingly unpredictable environments. Attendees will leave this session equipped with actionable insights to rethink uncertainty at work: not only as a problem to eliminate, but as a potential catalyst for transformation and growth. We thereby hope to contribute to the understanding of an emerging shift in organizational and management research––from uncertainty reduction to uncertainty regulation. Knowledge Under Uncertainty: Justification Practices and Implications for Management Author: Justin Weinhardt; University of Calgary Author: Reiner August Schaefer; University of Southampton Knowing Enough to Be Dangerous: How Certainty about AI Representations Disrupts Expertise Relations Author: Paul Leonardi; Author: Virginia Leavell; University of Cambridge Shifting Perspectives: Feedback Seeking Through the Lens of Uncertainty Regulation Author: Alina Gerlach; ETH Zürich Author: Gudela Grote; ETH Zürich Debiasing with Emotions: Ambivalence Increases Bias Awareness and Positive Affect Towards Outgroups Author: Naomi Beth Rothman; Lehigh University Author: Gordon Moskowitz; Lehigh University
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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 | 0.014 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.006 | 0.026 |
| Scholarly communication | 0.018 | 0.028 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.007 | 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".