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Construal Level Theory in Organizational Research

2017· article· en· W2339117071 on OpenAlexaff
Batia M. Wiesenfeld, Jean-Nicolas Reyt, Joel Brockner, Yaacov Trope

Bibliographic record

VenueAnnual Review of Organizational Psychology and Organizational Behavior · 2017
Typearticle
Languageen
FieldPsychology
TopicCultural Differences and Values
Canadian institutionsMcGill University
Fundersnot available
KeywordsConstrual level theoryLeverage (statistics)PsychologyIndustrial and organizational psychologyOrganizational behaviorCognitionOrganizational theoryOrganizational studiesContext (archaeology)Knowledge managementSocial psychologyOrganizational commitmentComputer scienceManagementArtificial intelligence

Abstract

fetched live from OpenAlex

Construal level theory (CLT) offers a rich and rigorous conceptual model of how the context shapes mental representations and subsequent outcomes. The theory has generated new understanding of cognitions and behaviors such as prediction, evaluation, and decision making in the fields of psychology and consumer behavior. Recently, management and organizational scholars have begun to leverage CLT to derive novel insights regarding organizational phenomena. This article describes CLT and its theoretical underpinnings, provides a focused and integrated review of organizational research incorporating CLT, and offers an agenda for future work in which CLT opens the door to new avenues of inquiry in organizational research and reinvigorates scholarly interest in cognition in organizations.

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)0.011
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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.011
Threshold uncertainty score0.058

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.018
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0060.010
Science and technology studies0.0020.020
Scholarly communication0.0070.009
Open science0.0020.004
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.0040.001

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 head0.184
GPT teacher head0.499
Teacher spread0.315 · 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 designTheoretical or conceptual
Domainnot available
GenreReview

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

Citations226
Published2017
Admission routes1
Has abstractyes

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