Managing team interdependence to address the Great Resignation
Bibliographic record
Abstract
Purpose Hybrid and virtual work settings offer greater flexibility and autonomy, yet they also have the paradoxical effect of weakening the connection of employees to each other and their identification with the organization. The purpose of this article is to discuss how to manage this paradox effectively. Design/methodology/approach Leveraging structural adaptation theory, the authors discuss hybrid and virtual work as one of five dimensions of team interdependence that collectively determine the tightness of coupling between team members. Findings The authors propose that the introduction of virtual and hybrid work can lead to a lower sense of belonging and identification with the organization that would need to be counteracted by respective increases in team interdependence in one or several of the remaining dimensions of team interdependence. Originality/value The authors apply research on team interdependence to develop a series of practical interventions that can address the Great Resignation. These interventions seek to enhance employees' experiences of belongingness after the shift to virtual and hybrid work. In doing so, the authors provide a toolkit that organizations can leverage to improve their employees' experiences in a post-COVID-19 workplace.
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 | 0.001 | 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) | 0.001 | 0.005 |
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; both teacher heads agree on what is shown here.
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".