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Are Remote and Non-Remote Workers Different? Exploring the Impact of Trust, Work Experience and Connectivity on Performance Outcomes

2002· book-chapter· en· W108349436 on OpenAlexaff
D. Sandy Staples

Bibliographic record

VenueAdvances in end user computing series/Advances in end user computing (AEUC) book series · 2002
Typebook-chapter
Languageen
FieldPsychology
TopicTechnostress in Professional Settings
Canadian institutionsQueen's University
Fundersnot available
KeywordsCompetence (human resources)PerceptionWork (physics)Job satisfactionBusinessInterpersonal communicationPsychologyInformation technologyAffect (linguistics)Knowledge managementPublic relationsSocial psychologyComputer scienceEngineeringPolitical science

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesMeta-epidemiology (narrow)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.252
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0000.003
Open science0.0010.001
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.029
GPT teacher head0.319
Teacher spread0.290 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2002
Admission routes1
Has abstractyes

Explore more

Same venueAdvances in end user computing series/Advances in end user computing (AEUC) book seriesSame topicTechnostress in Professional SettingsFrench-language works237,207