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Record W4211011527 · doi:10.1177/0149206320929088

Organizational Adaptation

2020· article· en· W4211011527 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of Management · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInnovation and Knowledge Management
Canadian institutionsWestern University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsAdaptation (eye)Congruence (geometry)Knowledge managementResource (disambiguation)PsychologyComputer scienceProcess managementSociologyManagement scienceBusinessSocial psychology

Abstract

fetched live from OpenAlex

Organizational adaptation is equivocal. On the one hand, the concept is ubiquitous in management research and acts as the glue binding together the central issues of organizational change, performance, and survival. On the other hand, it lurks around in various guises (e.g., "fit," "alignment," "congruence," and "strategic change") studied from multiple theoretical streams (e.g., behavioral, resource based, and institutional) and at different levels of analysis (e.g., organization and industry levels). In a novel approach to reviewing 443 adaptation articles that leverages both computational and hand-coded analysis, we produce an interactive visual of the themes most studied by adaptation scholars. We inductively draw out a definition of adaptation as intentional decision making undertaken by organizational members, leading to observable actions that aim to reduce the distance between an organization and its economic and institutional environments. We then review the literature across three main areas of inquiry and six theoretical perspectives that surfaced from our analysis and identify 11 difficulties that have hampered adaptation research in the past 50 years. Our review suggests ways to address these difficulties to enable future research to develop and cumulate.

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)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.975
Threshold uncertainty score0.782

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0010.001

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 head0.029
GPT teacher head0.211
Teacher spread0.181 · 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