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Record W3212689514 · doi:10.1016/j.chbr.2021.100152

When everything is urgent! Mail use and employee well-being

2021· article· en· W3212689514 on OpenAlexaff
Andre Lanctot, Linda Duxbury

Bibliographic record

VenueComputers in Human Behavior Reports · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicWork-Family Balance Challenges
Canadian institutionsCarleton University
Fundersnot available
KeywordsInformation overloadPsychologyStructural equation modelingControl (management)Path analysis (statistics)Sample (material)Social psychologyComputer scienceWorld Wide Web

Abstract

fetched live from OpenAlex

Karasek's (1979) demand-control (JDC) model of stress was used to theoretically justify the following hypothesis regarding the relationship between employee email use and well-being: (1) the demands imposed on an employee by their use of email (i.e., email volume, hours spent per week in email, the perceived importance and importance/urgency of the email they send/receive) will predict their perceptions of email overload, (2) perceptions of email overload will predict employee well-being, and (3) work control will moderate the relationships between email demands and email overload. Partial least squares structural equation modelling was used to test the paths in the model using a sample of 1491 knowledge workers. Our analysis confirmed that all four conceptualizations of email demands included in our model predicted email overload and that email overload was a significant predictor of perceived stress. Control over work moderated the path between ‘Important and Urgent’ email and email overload, providing partial support for Karasek's buffer hypothesis. This study contributes to the well-being literature by demonstrating that: (1) email overload is a distinct type of role overload, and (2) email-related strain can be mitigated by giving employees more control over their work.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score0.941

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.046
GPT teacher head0.314
Teacher spread0.268 · 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; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations10
Published2021
Admission routes1
Has abstractyes

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