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Centering Workers in Voice Research: Emerging Frontiers in Worker Voice

2023· article· en· W4385221259 on OpenAlexaboutno aff
Arrow Minster, Yaminette Díaz‐Linhart, Ariel C. Avgar, Patricia Satterstrom, Dongwoo Park, Duanyi Yang, Tingting Zhang, Thomas A. Kochan, Jenna E. Myers

Bibliographic record

VenueAcademy of Management Proceedings · 2023
Typearticle
Languageen
FieldArts and Humanities
TopicLanguage, Discourse, Communication Strategies
Canadian institutionsnot available
Fundersnot available
KeywordsEmployee voiceS VoiceVoice therapySpeech recognitionBusinessPsychologyAudiologyComputer scienceMedicineSocial psychology

Abstract

fetched live from OpenAlex

Since Hirschman’s Exit, Voice, and Loyalty (1970), worker voice was largely seen as an extension of Hirschman’s conceptualization as a mean to “change, rather than escape from an objectionable state of affairs.” Many organizational scholars conceptualize voice as “the communication of ideas, suggestions, concerns, problems, or opinions about work-related issues, with the intent to bring about improvement or change” (Morrison, 2023). In the management literature, voice is usually framed as a prosocial behavior (Organ, Podsakoff, & MacKenzie, 2006) intended to benefit the organizations (Detert & Burris, 2007; Morrison & Milliken, 2000; Van Dyne, Ang, & Botero 2003). Much of the management research is preoccupied with macro, meso, and micro-level antecedents of voice as well as the consequences of voice for individuals, teams, and the organization. Voice is of interest to scholars and practitioners alike because cultivating worker voice may lead to improvements in efficiency (Kim, MacDuffie, & Pil 2010), quality (Litwin & Eaton 2018), turnover (Batt, Colvin & Keefe, 2002), and safety (Li, Liao, Tangirala, & Firth, 2017). Although management scholars have developed rich theories on worker voice, a sizable voice gap exists in American workplaces today. Many workers today just don’t have much clout when it comes to the things that matter most to them on the job. A survey of American workers found that a majority of American workers report having less influence at work than they believe they should have on benefits, compensation, promotion, job security, respect shown to employees, protection from abuses, and new technologies (Kochan, Yang, Kimball, & Kelly, 2019). To close the voice gap, it requires us to develop and evaluate multi-option systems of worker voice in contrast to both labor law and prevailing managerial practices. In this symposium, we challenge the traditional conceptualization of worker voice and explore how different group of workers exercise voice in their everyday lives and how their voice can shape their job quality and working conditions. In keeping with the theme of the annual meeting—putting the worker front and center—our symposium raises the question of how and when workers voice in service of their specific needs. We explore this question by centering the problems that workers raise about organizations, be they material, emotional, and/or moral issues. Recent work has documented worker participation can aid in organizational problem-solving (Satterstrom, Kerrissey, & DiBenigno, 2021), but scholars know less about how and why specific problems get identified, socialized, and addressed by workers themselves. Set against a pervasive norm not to complain or talk about issues, it is crucial for contemporary research to amplify the organizational issues that workers experience and to explain how and why such problems are ignored or solved. The missing worker voice in job quality: Developing a conceptual framework and survey instrument Author: Yaminette Diaz-Linhart; Massachusetts Institute of Technology Author: Arrow Minster; Massachusetts Institute of Technology Author: Dongwoo Park; ILR at Cornell Author: Duanyi Yang; Massachusetts Institute of Technology Author: Thomas A. Kochan; Massachusetts Institute of Technology Partners on the frontline: When and how frontline managers aid in solving workers' problems Author: Arrow Minster; Massachusetts Institute of Technology When and how technology developers can facilitate worker voice Author: Jenna E. Myers; U. Of Toronto-Ind Rel Lbr Voice without Representation: Worker Voice in China’s Networked Public Sphere Author: Duanyi Yang; Massachusetts Institute of Technology Author: Tingting Zhang; U. of Illinois at Urbana-Champaign

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)0.002
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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.438
Threshold uncertainty score0.674

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.127
GPT teacher head0.361
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 teacher head, 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

Citations0
Published2023
Admission routes1
Has abstractyes

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