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Record W4407285188 · doi:10.33423/ajm.v25i1.7514

The Impacts of Organizational Changes on Work Engagement and Quiet Quitting

2025· article· en· W4407285188 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.

Bibliographic record

VenueAmerican Journal of Management · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicEmployee Performance and Management
Canadian institutionsWycliffe College
Fundersnot available
KeywordsQUIETWork (physics)Work engagementPsychologyBusinessKnowledge managementComputer scienceEngineeringPhysics

Abstract

fetched live from OpenAlex

Change has become a constant theme in the world where all organizations face new challenges and opportunities that require them to constantly adapt and evolve. The COVID-19 pandemic highlighted the importance of organizational agility and adaptability in the face of challenges and uncertainties. A recent Gallup poll revealed that most workers in the U.S. workforce are either quiet quitting or highly disengaged. This study aims to investigate the relationship between various organizational changes and the likelihood of work engagement, quiet quitting, and high disengagement. Drawing on a survey on 252 employees in various companies, we find that organizational changes including higher demand for competence, improved results monitoring, enhanced informal communication, and job redesign increased the likelihood of work engagement relative to quiet quitting and high disengagement. Furthermore, organizational and job characteristics such as perceived organizational support and job autonomy moderated the relationship between organizational changes and the likelihood of work engagement, quiet quitting, and high disengagement. Practical implications and suggestions for future research are discussed.

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)0.002
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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.773
Threshold uncertainty score0.317

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 head0.013
GPT teacher head0.298
Teacher spread0.285 · 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