The affective commitment of newcomers in hybrid work contexts: A study on enhancing and inhibiting factors and the mediating role of newcomer adjustment
Bibliographic record
Abstract
This study focuses on one of the most impacted human aspects of digital transformation in contemporary organizations: the development of the affective commitment of newcomers in hybrid work contexts. Specifically, this study addresses a research gap related to the factors that influence the affective commitment of newcomers in hybrid work contexts. First, it investigates the role of two drawbacks of the remote component of hybrid work contexts inhibiting affective commitment: workplace social isolation and technostress. Second, it explores the role of two factors that were previously investigated in in-presence contexts and proved to enhance affective commitment: perceived organizational support and perceived supervisor support. Moreover, this study considers the possible mediating role of newcomer adjustment, intended as a proximal outcome of successful onboarding and an antecedent of newcomer affective commitment. In order to examine enhancing and inhibiting factors and the mediating role of newcomer adjustment, a quantitative study was carried out involving newcomers who began to work in their current organization after January 2021 and who still do remote work at least 1 day a week. Results confirm the inhibiting role of workplace social isolation and the enhancing role of perceived organizational support and perceived supervisor support on affective commitment in hybrid work contexts. Furthermore, they support the mediating role of newcomer adjustment in the relationship between workplace social isolation and affective commitment. While contributing to theory advancement in understanding newcomer affective commitment in current hybrid work contexts, these results also suggest important managerial implications in the field of human resources management, specifically the need to pay greater attention to strategies devoted to increasing newcomers' perception of organizational and supervisor support.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".