MétaCan
Menu
Back to cohort
Record W2117522003 · doi:10.1002/tie.21611

Outcomes and Benefits of a Managerial Global Mind‐set: An Exploratory Study with Senior Executives in North America and India

2014· article· en· W2117522003 on OpenAlexaboutno aff
Subramaniam Ananthram, Alan Nankervis

Bibliographic record

VenueThunderbird International Business Review · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicInternational Student and Expatriate Challenges
Canadian institutionsnot available
Fundersnot available
KeywordsMultinational corporationGlobalizationExploratory researchSet (abstract data type)Global LeadershipHuman resource managementWork (physics)Human resourcesPublic relationsBusinessSenior managementManagementMarketingPolitical scienceSociologyEconomicsSocial scienceComputer scienceFinanceEngineeringLaw

Abstract

fetched live from OpenAlex

Globalization and its associated challenges are compelling managers in multinational corporations to develop appropriate skill sets. An emerging body of research in international management has suggested that meeting this challenge requires the cultivation and development of a managerial global mind‐set. There has been limited empirical work on the outcomes and benefits of a managerial global mind‐set and consequently this article attempts to fill that gap. Based on semistructured interviews with a total of 56 senior executives of multinational corporations in North America (United States and Canada) and India, this study identifies five outcomes of a global mind‐set with benefits for managers and their organizations. The findings have theoretical implications, which are discussed along with their practical applications for multinational corporations, their senior executives, and human resource professionals, with respect to identifying management development programs that assist in the cultivation and nurturing of a global mind‐set. © 2014 Wiley Periodicals, Inc.

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)0.003
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation 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: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.002
Scholarly communication0.0030.001
Open science0.0010.003
Research integrity0.0010.001
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 head0.036
GPT teacher head0.332
Teacher spread0.296 · 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 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

Citations24
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueThunderbird International Business ReviewSame topicInternational Student and Expatriate ChallengesFrench-language works237,207