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Record W4410538724 · doi:10.1145/3710915

Remote Workplace Interactions and Extraversion: A Field Study on Wellbeing and Productivity Among Knowledge Workers

2025· article· en· W4410538724 on OpenAlexaff
Anastasia Ruvimova, Alexander Lill, Lauren Howe, Elaine M. Huang, Gail C. Murphy, Thomas Fritz

Bibliographic record

VenueProceedings of the ACM on Human-Computer Interaction · 2025
Typearticle
Languageen
FieldPsychology
TopicTechnostress in Professional Settings
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsExtraversion and introversionProductivityPersonalityField (mathematics)PsychologyBig Five personality traitsKnowledge managementSocial psychologyApplied psychologyComputer science

Abstract

fetched live from OpenAlex

Since the COVID-19 Pandemic, the knowledge workplace has seen a dramatic transition from collocated-first to hybrid or fully-remote arrangements, the implications of which are yet to be fully understood. One of the biggest unknowns is how remote team communication impacts the individual worker, especially in consideration of personality type. The aim of this study is to investigate the effects of remote workplace interactions on productivity and wellbeing, and how these effects are moderated by extraversion. The study lasted for 2-3 months and involved 60 knowledge workers. The data was analyzed using a combination of quantitative and qualitative methods. We present novel findings on how remote communication affects individuals differently depending on the type of interaction, interaction agent, and personality of the individual, showing that the impact of communication on workers is far from straightforward. We contextualize these findings with an in-depth analysis of communication patterns and experiences in the remote workplace, adding to existing literature. Finally, we present suggestions for a more individualized communication approach in industry and future research.

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)0.000
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 score0.463
Threshold uncertainty score0.746

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
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 head0.038
GPT teacher head0.387
Teacher spread0.349 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

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