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Measurement of Perceived Organizational Readiness for Change in the Public Sector

2008· article· en· W2058387788 on OpenAlexaffabout
Inta Cinite, Linda Duxbury, Christopher D. Higgins

Bibliographic record

VenueBritish Journal of Management · 2008
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicJob Satisfaction and Organizational Behavior
Canadian institutionsWestern UniversityCarleton University
Fundersnot available
KeywordsOperationalizationStructural equation modelingPsychologyPublic sectorCompetence (human resources)Organizational changeOrganizational commitmentSample (material)Perceived organizational supportSocial psychologyChange management (ITSM)Applied psychologyPublic relationsBusinessMarketingPolitical scienceComputer science

Abstract

fetched live from OpenAlex

Using the act frequency approach we developed and operationalized two constructs: perceived organizational readiness for change and perceived organizational unreadiness for change. Using a sample drawn from five Canadian public sector organizations, it was found that perceived readiness for change can be conceptualized with three sub‐constructs: commitment of senior managers to the change, competence of change agents, and support of the immediate manager. Perceived unreadiness for change had two sub‐constructs: poor communication of change and adverse impact of change on work. Using structural equation modelling techniques, the measurement scales of all these constructs were tested for reliability and validity using job stress and perceived organizational support as outcome variables.

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)0.005
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.029
Threshold uncertainty score0.058

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.018
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.054
GPT teacher head0.238
Teacher spread0.184 · 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 designObservational
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

Citations155
Published2008
Admission routes2
Has abstractyes

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