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Record W4404067380 · doi:10.1080/16184742.2024.2400135

Organizational culture and organizational change in non-profit sport organizations: a processual approach

2024· article· en· W4404067380 on OpenAlexaffabout
Ashley Thompson, Milena M. Parent

Bibliographic record

VenueEuropean Sport Management Quarterly · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicNonprofit Sector and Volunteering
Canadian institutionsUniversity of OttawaBrock University
Fundersnot available
KeywordsOrganizational cultureOrganizational changeOrganizational commitmentBusinessOrganizational studiesProfit (economics)Knowledge managementPublic relationsMarketingSociologyPolitical scienceEconomicsMicroeconomicsComputer science

Abstract

fetched live from OpenAlex

Research question The purpose of this study was to explore the interplay between organizational culture and organizational change in non-profit sport organizations.Research methods Five Canadian national sport organizations (NSOs) participated in a multiple case study. Forty-nine semi-structured interviews with NSO staff and Board members and 151 documents were collected and analyzed thematically.Results The results highlight the close and chaotic relationship between culture and change – namely, how change can trigger the formation of divisive sub-cultures where conflict ensues, further constraining the change process. Moreover, while it may not be possible to control culture change, the findings demonstrate how change leaders can leverage workshops, facilitators, and turnover to help facilitate the implementation of culture changes.Implications The study contributes to existing understandings of organizational culture and change beyond examining culture change. In doing so, it shows the complex and chaotic interplay between culture and change, where change can trigger divisive cultures, which in turn, further constrain change.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.393
Threshold uncertainty score0.846

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.012
GPT teacher head0.259
Teacher spread0.247 · 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 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

Citations3
Published2024
Admission routes2
Has abstractyes

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