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Record W3200118087 · doi:10.1080/09585192.2021.1960582

Accommodation, interpersonal justice, and the turnover intentions of employees with disabilities

2023· article· en· W3200118087 on OpenAlexaff
Daniel S. Samosh, Addison Maerz, Matthias Spitzmüller, Stephan Boehm

Bibliographic record

VenueThe International Journal of Human Resource Management · 2023
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicJob Satisfaction and Organizational Behavior
Canadian institutionsQueen's UniversityInstitute for Work & HealthUniversity of Toronto
Fundersnot available
KeywordsWorkgroupAccommodationSocial exchange theoryPsychologyInterpersonal communicationPerceptionSocial psychologyOrganizational justiceWorkforceEconomic JusticeTest (biology)MediationOpenness to experiencePublic relationsOrganizational commitmentSociologyPolitical science

Abstract

fetched live from OpenAlex

The number of employees with disabilities in the workforce is increasing and accommodations are essential to the work of many of these individuals. Prior research has explored perceptions of accommodation requests as well as coworkers’ and managers’ reactions to accommodations; yet, we know little about how employees with disabilities experience their own accommodations. We draw from the disability literature as well as contemporary justice and social exchange theory to develop and subsequently test a multilevel moderated mediation model on this subject. We test our hypotheses with data from 4,083 employees nested in 256 workgroups across two time points. We find support for our prediction that accommodation-focused interpersonal justice influences turnover intentions. The effect of these justice perceptions was mediated by workgroup openness to communication. Further, we find that representation of accommodated employees with disabilities at the workgroup level plays an important role in these relationships. We look beyond the technical aspects of accommodation with this research to highlight the social experience of accommodation as a central driver of employee perceptions and work outcomes.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.867
Threshold uncertainty score0.286

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 communication0.0000.000
Open science0.0010.000
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 head0.021
GPT teacher head0.264
Teacher spread0.243 · 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 designTheoretical or conceptual
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

Citations12
Published2023
Admission routes1
Has abstractyes

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