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Record W2754406787 · doi:10.5430/ijba.v8n6p11

Training U.S. Managers for Distant Shores

2017· article· en· W2754406787 on OpenAlexvenueno aff
Yezdi H. Godiwalla

Bibliographic record

VenueInternational Journal of Business Administration · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicInternational Student and Expatriate Challenges
Canadian institutionsnot available
Fundersnot available
KeywordsFlexibility (engineering)Adaptation (eye)EmpathyTraining (meteorology)BusinessPublic relationsPsychologyPersonal developmentAnxietyMarketingSocial psychologyPolitical scienceManagementEconomics

Abstract

fetched live from OpenAlex

Proper pre-departure training and post-arrival mentoring of US managers who are assigned for distant and culturally and operationally different countries are vital for their success in their foreign assignment. Training them for foreign assignments is vital because they will be overwhelmed by an onslaught of diverse challenges of their tasks and unfamiliar operating and cultural situations, all of which will confound even the most capable domestic manager. Supervisory and decision making situations will be different from the home country situations with which they are so used to working before they left for the foreign shores. Specifically, they must cope and better manage their personally challenging issues, which are their own personal anxiety and stress arising out of unfamiliar situations that defy the cause-effect logic they were used to in their home countries, the foreign country’s unfamiliar environment causing perceived environmental uncertainty, their own personal flexibility and adaptation, communicating and leading with empathy in host country cultures, and self-efficacy and their own sustained drive for continuously working long hours to accomplish their own personal career goals and the foreign subsidiary’s objectives.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.656
Threshold uncertainty score0.609

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.000
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 head0.109
GPT teacher head0.416
Teacher spread0.307 · 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 designTheoretical or conceptual
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

Citations0
Published2017
Admission routes1
Has abstractyes

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