Individual Differences in the Workplace: Conceptual and Methodological Innovations
Bibliographic record
Abstract
Individual differences are central to management science, underpinning talent management, organizational behavior research, and diversity initiatives. Recent advances have transformed these constructs from static, between-person traits into multifaceted, dynamic constructs. This symposium highlights the untapped potential of individual difference research. It aims to inspire scholars to advance their conceptualization, measurement, and modelling of individual difference to enhance the theoretical depth and practical impact of their own work. The symposium features four papers exploring personality, passion, and racial identity. Each paper significantly advances the understanding of their respective individual difference. Together, they challenge traditional approaches and present actionable opportunities around configuration effects, multilevel modelling, contextual moderation, and perspective-conscious measurement. We aim for all attendees to leave with fresh perspectives and inspiration for their own research. The Big Bad Five: Clusters of Counterproductivity and Associated Personality Profiles Author: Michael P. Wilmot; University of Arkansas Author: Brenton M. Wiernik; University of South Florida Author: Deniz S Ones; University of Minnesota Passionate to a Fault? A Multilevel Perspective on the Relationship Between Passion for Work and Job Performance Author: Kai Krautter; Harvard University Author: Jon Michael Jachimowicz; Harvard Business School Fitting In, Burning Out: The Hidden Pressures and Costs of Identity Shifting Among Black Employees in Predominantly White Workplaces Author: Alice Danbi Choe; Author: Camellia Bryan; Author: David Zweig; University of Toronto Thinking Aloud about Personality: A Qualitative Comparison of Self- and Informant-Reports Author: Andrew Perossa; Author: Brian S. Connelly; University of Toronto Author: Anne Wiedenroth; - Author: Adamo Sgrignuoli; -
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".