Staying Engaged During the Remote Work Revolution: An Integrated Job Crafting Perspective
Bibliographic record
Abstract
Hybrid and remote workers now comprise nearly one-third of the working population in the U.S. and Canada (Barrero et al., 2021; StatCan, 2021), while employee engagement has dropped to its lowest point in a decade (Harter, 2023). It is now more crucial than ever to identify valuable strategies for individuals and organizations to increase engagement at work. Job crafting is a bottom-up approach to work design (Chen, 2022a, 2022b; Donaldson et al., 2021; Tims et al., 2012; Wrzesniewski & Dutton, 2001), extensively studied as a proactive employee behavior associated with increased engagement among other positive work outcomes (Lichtenthaler & Fischbach, 2019; Mukherjee & Dhar, 2022; Tims et al., 2012). However, job crafting can also be a “double-edged sword” (Harju et al., 2021), with promotion-focused (boundary expansion) behaviors contributing to engagement while prevention-focused (boundary reduction) behaviors detracting from engagement (Lichtenthaler & Fischbach, 2019). This dissertation is one of the first to investigate work engagement in the remote work context from an integrated promotion- and prevention-focused job crafting perspective (Tims et al., 2022). A sample of (n = 433) hybrid and remote workers were recruited for this cross-sectional study using CloudResearch Connect. Structural Equation Modeling (SEM) was utilized to determine whether promotion- and prevention-focused job crafting mediated the relationship between remote work resources/demands and work engagement. Hierarchical regression was run to understand the moderating role of perceived job crafting success on the relationship between job crafting and work engagement in gain cycles and loss spirals. Study findings supported the mediating role of promotion-focused job crafting on the relationship between remote work resources, demands, and engagement. Participants with high remote work resources and demands were found to engage in promotion-focused job crafting, while those with only high demands resorted to prevention-focused job crafting. Perceived job crafting success positively moderated the relationship between prevention-focused job crafting and work engagement. In conclusion, organizations can increase work engagement and the formation of gain cycles by providing adequate remote work resources, such as increased visibility and social support, to encourage promotion-focused job crafting. At the same time, hindering remote work demands, such as professional isolation and technology overload, should be minimized to avoid the preponderance of prevention-focused job crafting behaviors associated with decreased engagement. Managers can help employees break out of self-sabotaging loss spirals by facilitating short-term reductions in work boundaries and offering additional resources to offset hindering demands. Additional insights based on the study findings are provided for individuals and organizations navigating the sea of changes brought about by the remote work modality.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".