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Record W4409204430 · doi:10.1111/1748-8583.12601

Worker Voice and Mutual Gains From Remote Performance Management: Evidence From Digitalized Services in North America and Germany

2025· article· en· W4409204430 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueHuman Resource Management Journal · 2025
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Management and Leadership
Canadian institutionsMcMaster University
FundersMcMaster UniversityArts Research Board, McMaster UniversityAmerican Federation of Labor and Congress of Industrial Organizations
KeywordsBusinessTelecommunicationsComputer science

Abstract

fetched live from OpenAlex

ABSTRACT The expansion of remote working arrangements has required managers to adjust their approach to managing performance, as they transition from in‐person to technology‐mediated tools and practices. Past research has identified negative worker impacts associated with intensified digital monitoring and discipline‐based coaching. However, few studies have investigated the antecedents of more worker‐friendly arrangements. This paper examines the role of collective worker voice in shaping remote work performance management choices, based on a comparative study of telecommunications call centers in Canada, the United States, and Germany. Findings suggest that strong collective voice, especially when backed by institutional power, fosters a balanced approach to remote performance management by constraining the intensity of electronic performance monitoring and use of disciplinary practices, as well as by supporting more developmental coaching.

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.

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 categoriesMeta-epidemiology (narrow), Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.332
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0020.002
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 head0.031
GPT teacher head0.242
Teacher spread0.210 · 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