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A Primer on Herding Cats: 25 Years of Qualitative Studies on Change Recipients' Reactions

2015· article· en· W2597962836 on OpenAlexaff
Steffi Siegert, Marc Pilon, Neil Cruickshank, P. Hope, Linda Duxbury

Bibliographic record

VenueAcademy of Management Proceedings · 2015
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicOrganizational Learning and Leadership
Canadian institutionsCarleton UniversityLaurentian University
Fundersnot available
KeywordsHerdingOrganizational changeContext (archaeology)Qualitative researchPsychologyIdentity (music)Public relationsSocial psychologySociologyPolitical scienceSocial scienceGeography

Abstract

fetched live from OpenAlex

This paper offers a meta-analysis of qualitative studies of individuals’ reactions to organizational change published in high impact factor journals. Through an inductive review of articles published between 1990 and 2014, the authors provide insights into the types of studies conducted, the research methods employed, the objectives stated, and the change consequences reported, at both an individual and organizational level. Additionally, this paper provides a roadmap for readers who wish to undertake a similar initiative. On the basis of their review the authors conclude that most of the change initiatives were planned and initiated from the top-down. In terms of the consequences of change, one interesting observation is that the ratio of adverse to beneficial consequences is reversed between the organization and the individual. Plainly put, the papers included in this review support the idea that organizations gain benefits from change while recipients suffer. Another observation from the findings is that nearly half of the studies looked at recipients’ reactions beyond the purely organizational context and considered their effect on the whole person, whether it be their identity, their sense of self- worth, or their relationships with others. The authors conclude by proposing directions for future research and implications for practitioners.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.852
Threshold uncertainty score0.663

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 science0.0000.000
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 head0.280
GPT teacher head0.381
Teacher spread0.101 · 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 designTheoretical or conceptual
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
Published2015
Admission routes1
Has abstractyes

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