Are Remote and Non-Remote Workers Different? Exploring the Impact of Trust, Work Experience and Connectivity on Performance Outcomes
Bibliographic record
Abstract
Information technology (IT) is enabling the creation of virtual organizations and remote work practices. As this practice of employees working remotely from their managers and colleagues grows, so does the importance of making these remote end-users of technology effective members of organizations. This study tested a number of relationships that were suggested in the literature as being relevant in a remote work environment. Interpersonal trust of the employees in their managers was found to be strongly associated with higher self-perceptions of performance, higher job satisfaction and lower job stress. There was weak support for the impact of physical connectivity (i.e., the availability of IT) on job satisfaction, supporting the enabling role of IT. These findings were similar for both remote employees (i.e., those that worked in a different building than their managers) and non-remote employees. However, more frequent communications between the manager and employee was associated with higher levels of interpersonal trust only with the remote workers. Cognition-based trust was also found to be more important than affect-based trust in a remote work environment, suggesting that managers of remote employees should focus on activities that demonstrate competence, responsibility and professionalism.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".