Charting New Terrain in Work Design: A Study of Hybrid Work Characteristics
Bibliographic record
Abstract
Abstract Research on work design to date has focused on work characteristics associated primarily with one of three domains—task, social, or contextual. The present paper introduces a new concept—hybrid work characteristics—that refer to work characteristics which are not fully captured within any one of the three domains but possess features from more than one domain. We identify boundarylessness, multitasking, non‐work‐related interruptions, and demand for constant learning as hybrid work characteristics in the modern work environment. Furthermore, we theorise that boundarylessness, multitasking, and demand for constant learning carry both enriching and depleting potential, but non‐work‐related interruptions have only depleting potential. In our study, we developed instruments to assess the four work characteristics and tested their relationship with jobholders’ job satisfaction, occupational commitment, emotional exhaustion, and somatic health symptoms, through three independent studies (a total of 968 employees across a wide range of jobs). The results demonstrated convergent, predictive, and discriminant validity for the newly developed scales, and showed partial support for the prediction that boundarylessness and multitasking are beneficial as well as detrimental for jobholders and consistent support for the depleting potential inherent in non‐work‐related interruptions. We conclude with a discussion of how our exploration of hybrid work characteristics contributes to research on work design and management practices.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".