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Record W2731576791 · doi:10.1002/job.2208

Making sense of organizational change: Is hindsight really 20/20?

2017· article· en· W2731576791 on OpenAlexafffund
Laura Gover, Linda Duxbury

Bibliographic record

VenueJournal of Organizational Behavior · 2017
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicManagement and Organizational Studies
Canadian institutionsCarleton UniversityVancouver Island University
FundersNIH Clinical CenterCanadian Institutes of Health Research
KeywordsSensemakingHindsight biasPsychologyContext (archaeology)Construct (python library)NarrativeCognitionSocial psychologyOrganizational changeQualitative propertyPerceptionApplied psychologyKnowledge managementPublic relationsPolitical scienceComputer science

Abstract

fetched live from OpenAlex

Summary This qualitative study explores the conceptual links between 2 different approaches to managerial cognition, sensemaking and cognitive bias, in the context of organizational change. A longitudinal case study utilizing both real‐time assessments and retrospective sensemaking data from interviews with 26 hospital employees at 3 points in time was undertaken. Patterns related to individuals' retrospective accounts and real‐time assessments were identified and used to construct 4 prototypical narratives. Data analysis revealed that organizational change was not a markedly negative experience for most informants, which is contrary to the prevailing theme in the literature. This and other findings are discussed in terms of sensemaking and cognitive bias. This study makes 2 contributions to our understanding of how individual's experience and make sense of organizational change over time as (a) little is known about how the process of change unfolds over time at the individual level and (b) extant research has not investigated the extent to which individuals' retrospective sensemaking about organizational change reflects or diverges from their real‐time assessments over the course of the change. More broadly, the study provides insights and focused advice for management researchers regarding the use of retrospective data to understand individuals' perceptions of situations that have already occurred.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.086
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.002
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.053
GPT teacher head0.286
Teacher spread0.233 · 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.

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

Citations28
Published2017
Admission routes2
Has abstractyes

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