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Record W2158928204 · doi:10.1177/0018726709104543

Supervisory approaches and paradoxes in managing telecommuting implementation

2009· article· en· W2158928204 on OpenAlexaff
Brenda A. Lautsch, Ellen Ernst Kossek, Susan Eaton

Bibliographic record

VenueHuman Relations · 2009
Typearticle
Languageen
FieldSocial Sciences
TopicWork-Family Balance Challenges
Canadian institutionsSimon Fraser University
FundersMichigan State University
KeywordsTelecommutingWork (physics)Public relationsBusinessSupervisorPsychologyManagementPolitical scienceEconomicsEngineering

Abstract

fetched live from OpenAlex

Voluntary telecommuting is an increasingly prevalent flexible work practice, typically offered to assist employees with managing work— family demands. Most organizations with telecommuting policies rely on supervisor discretion regarding policy access and implementation in their department. Although supervisors' approaches have implications for telecommuters and their non-telecommuting co-workers, few studies integrate these stakeholder perspectives. Drawing on surveys and interviews with 90 dyads of supervisors and subordinates, some of whom were telecommuters and some of whom were not, we examine effective managerial approaches regarding telecommuting implementation. First, supervisors should stay in close contact with telecommuters, but this contact should emphasize sharing information rather than close monitoring of work schedules. Telecommuters supervised with an information-sharing approach were more likely to report lower work—family conflict, increased performance, and were more likely to help co-workers. Second, supervisors should encourage telecommuting employees to separate work and family boundaries, which is related to lower work—family conflict. However, supervisors face a paradox as a separation approach can negatively affect workgroup relations: telecommuters who are encouraged to create boundaries between work and family were less likely to extend themselves in crunch times or after hours to help their colleagues. Non-telecommuters' workload and work—family conflict may increase as a result.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.020
metaresearch head score (Gemma)0.046
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score0.106

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0200.046
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0060.007
Scholarly communication0.0050.004
Open science0.0020.006
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.112
GPT teacher head0.346
Teacher spread0.234 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

Citations268
Published2009
Admission routes1
Has abstractyes

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