The Individual Differences and Competencies of Hybrid Workers: A Systematic Review
Bibliographic record
Abstract
Despite the significant increase in employees working away from the traditional office (i.e., hybrid work), our understanding of the individual differences that support hybrid workers’ success remains fragmented, limiting theoretical development and practical applications. To remedy this gap in the literature, we conducted a systematic review of the individual differences and capabilities (i.e., traits, abilities, skills) related to hybrid work outcomes (e.g., job performance, job satisfaction). We identified 53 studies published in 49 published and unpublished sources. Then, to gain an understanding of common clusters of individual differences and capabilities, we inductively grouped the extracted study variables to identify three trait and ability clusters (e.g., Big Five Personality, other personality constructs, abilities) and four higher-order competency clusters (e.g., self-management, interpersonal-focused, leadership-specific, technology-based). To aid in practical applications and future research directions, we connected these trait and competency clusters to two computer-mediated work theories (e.g., media richness theory, social presence theory) and two organizational behavior theories (e.g., trait activation theory, role theory).
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 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 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.013 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".