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Record W4407903549 · doi:10.1080/23322373.2025.2458384

Enhancing international business competence: how cognitive and exposure training approaches matter

2025· article· en· W4407903549 on OpenAlexaff
Christopher Boafo, Utz Dornberger, Nathaniel Boso

Bibliographic record

VenueAfrica Journal of Management · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicInternational Student and Expatriate Challenges
Canadian institutionsUniversity of British Columbia
FundersUniversität LeipzigMinistero dello Sviluppo EconomicoBundesministerium für Wirtschaftliche Zusammenarbeit und EntwicklungDeutscher Akademischer Austauschdienst
KeywordsCompetence (human resources)CognitionPsychologyBusinessCognitive psychologyKnowledge managementComputer scienceNeuroscienceSocial psychology

Abstract

fetched live from OpenAlex

This study draws insights from knowledge acquisition theory to examine how cognitive and exposure training approaches explain differences in international business competence (IBC). Following an interpretive phenomenological approach and in-depth interviews with 23 business school professors and 32 business executives on in-service training programs, the study finds that six major learning processes, which consolidate into cognitive-driven and exposure-driven training approaches, contribute to differences in IBC. The cognitive-driven training approach emphasizes the use of explicit knowledge activities, depth of interaction with internationally sourced academics and professionals, and breadth of international research and study contents to enhance IBC. Exposure-driven training focuses on fostering tacit knowledge activities, diversity of cultural experiences and skills, and participation in international affairs to build IBC. The implications of these findings for knowledge acquisition theory, practice, and policy 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)0.001
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.587
Threshold uncertainty score0.256

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0000.000
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 head0.057
GPT teacher head0.293
Teacher spread0.236 · 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 designQualitative
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

Citations1
Published2025
Admission routes1
Has abstractyes

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