Commitment Dynamics in the Contemporary Workplace: Exploring the Implications of Changes and Crises
Bibliographic record
Abstract
Commitment remains a central focus in management and organizational behavior research due to its impact on critical employee outcomes as well as organizational effectiveness. In an ever-changing world characterized by new ways of working, new technologies, new forms of organizations and workspaces, increasing need for flexibility and mobility, organizations rely on their employees to adapt to contextual transformations. However, such changes not only threaten identity and attachment to the workplace, but they also affect employees very differently. Not surprisingly, recent research has consistently worked with a more nuanced understanding of workplace commitments by recognizing that employees may experience commitment to various commitment targets, that these commitments are dynamic, and that the context is the most proximal explanation for the observed trajectories, including commitment synergies and conflicts. Despite these advances, gaps remain in understanding how commitments change, interrelate, and compete under varying conditions. Nor have we accumulated enough evidence on how various contextual shocks may undermine the maintenance or the juggling of multiple commitments. Notably, the COVID-19 pandemic served as a catalyst for modifying work practices and the interaction patterns between employees, as well as reshaping conceptualizations of organizations and careers. With many organizations rethinking how to preserve and enhance employees’ well- being and performance, the purpose of this symposium is to improve our understanding of the complex and dynamic nature of workplace commitments in the contemporary workplace. Dynamics, Conflicts, and Synergies of Commitment in Periods of Uncertainty Author: Felipe Teixeira Genta Maragni; University of Sao Paulo Author: Ana Carolina De Aguiar Rodrigues; University of São Paulo Author: S. Arzu Wasti; Sabanci University Threat-response in Commitment Systems Author: Leon Hupkens; Vrije Universiteit Amsterdam Author: Tim Vantilborgh; Vrije Universiteit Brussel The Stronger they Stand, the Harder they Fall: Nurses’ Loss of Commitment to Professional Identity Author: Frances Jorgensen; Royal Roads University Author: Adelle Bish; North Carolina Agricultural and Technical State University Author: Mette Strange Noesgaard; Aalborg University The Dawn of Career Commitment: A Natural Experiment with Early-Career Students Author: Lucas dos Santos-Costa; University of São Paulo Author: Yvonne Van Rossenberg; Radboud University Nijmegen Come What May: Nature of Commitment Profiles During Organizational Changes and External Shocks Author: MD Oliur Rahman Tarek; University of Klagenfurt Author: Heiko Breitsohl; University of Klagenfurt Author: S. Arzu Wasti; Sabanci 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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".