How has Remote Work Self-Efficacy Changed After a Quarter Century?
Bibliographic record
Abstract
The purpose of this research was to replicate and extend Staples et al.’s (1999) study to determine if their findings are consistent relative to modern remote work models to provide contextually relevant suggestions for managers to enact policies that leverage remote work to benefit their employees and organization. The study consisted of assessing the mechanisms that lead remote workers to experience enhanced performance and improved well-being. In addition to the model illustrated in Staple et al.’s (1999) study, I also examined whether technology industries moderate the relationships between antecedents of remote work self-efficacy, and if remote work intensity, the degree an employee works from home, moderates the relationships between remote work self-efficacy and outcomes. Through a combination of using Prolific and convenience sampling, I obtained 434 valid responses. I then used SPSS to conduct regression analysis to test hypotheses. The results in general confirm Staples et al.’s (1999) findings. I found that modelling best practices by manager, IT experience and training, and general computer self-efficacy were positively associated with remote work self-efficacy; while computer anxiety had a negative association. Furthermore, remote work self-efficacy had positive associations with remote work performance, job satisfaction, affective commitment, ability to cope, and a negative association with job stress. In terms of moderation effects, there is a stronger, positive relationship between general computer self-efficacy and remote work self-efficacy for employees working in technology industries than those working in non-technology industries. Theoretical, practical contributions, and future research directions are discussed.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".