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Record W3183887799 · doi:10.5465/ambpp.2021.81

Work-From-Home Adjustment in the COVID-19 Pandemic: The Role of Psychological Climate for Face Time

2021· article· en· W3183887799 on OpenAlexaff
Marie‐Colombe Afota, Y Savard, Ariane Ollier‐Malaterre, Emmanuelle Léon

Bibliographic record

VenueAcademy of Management Proceedings · 2021
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicJob Satisfaction and Organizational Behavior
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsPsychologyExpatriateContext (archaeology)Social psychologyCLARITYMandatePerceptionWork (physics)Political science

Abstract

fetched live from OpenAlex

The COVID-19 pandemic has led many organizations to mandate large-scale full-time work-from-home (WFH), shattering the dichotomy between remote workers and those who are physically in the office. Emerging findings on full-time WFH have not yet brought clarity on the causes and mechanisms of employees’ well-being and productivity in this context that contrasts with prior-pandemic work. Drawing on the transactional theory of stress (Lazarus & Folkman, 1984), we argue that employees who perceive their organization’s psychological climate as emphasizing face time may appraise full-time WFH as a threat and, as a result, perceive higher availability expectations on the part of their organization. We further argue that this relationship will be stronger in the US than in Europe, where emploment protection is higher. In turn, perceived expectations of extended availability predict WFH adjustment, a multi-dimensional affective, behavioral, and cognitive construct we borrow from expatriate adjustment’s literature to guide future research on WFH. In a two-wave study on an organizational sample of 532 employees working in the US and in Europe, we find that a psychological climate for face time hampers employees’ adjustment to WFH through increased perceptions of availability expectations, and that this process is exacerbated in the US.

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 score0.227
Threshold uncertainty score0.419

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.045
GPT teacher head0.309
Teacher spread0.264 · 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

Citations4
Published2021
Admission routes1
Has abstractyes

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