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Record W2131959316 · doi:10.1177/1470595811413101

The effect of self-construals on perceptions of organizational events

2011· article· en· W2131959316 on OpenAlexaff
Andre Pekerti, Catherine T. Kwantes

Bibliographic record

VenueInternational Journal of Cross Cultural Management · 2011
Typearticle
Languageen
FieldPsychology
TopicCultural Differences and Values
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsSalientSocial psychologyFatalismPsychologyConstrualsPerceptionMultinational corporationSelf construalInterdependenceAbusive supervisionOrganizational cultureConstrual level theoryBusinessPolitical sciencePublic relations

Abstract

fetched live from OpenAlex

This empirical research examines the effect of culture on the way people perceive and assign causes to events in organizations. It explores the idea that attributional biases and errors are moderated by a person’s culture. Results supported proposed hypotheses; they showed that Indonesians, New Zealanders, and Canadians perceived their interdependent self-construal as salient, moderately salient, and least salient, respectively. Furthermore, self-construals moderated and mediated people’s perceptions of organizational events. High-interdependents attributed negative organizational events to factors that are external, less controllable, thus had a more fatalistic outlook compared to Moderate-interdependents and Low-interdependents, respectively. In contrast, Low-interdependents attributed positive organizational events to more internal and stable factors compared to Moderate- and High-interdependents, respectively; they also perceived positive events as being controllable and caused by their own actions compared to High-interdependents. Implications for management practices in multinational organizations are discussed.

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)0.000
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 score0.249
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.038
GPT teacher head0.390
Teacher spread0.352 · 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

Citations15
Published2011
Admission routes1
Has abstractyes

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