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International Business in Times of Crisis: What Perspective to Take?

2022· book-chapter· en· W4220909775 on OpenAlexaff
Rob van Tulder, Alain Laurent Verbeke, Lucia Piscitello, Jonas Puck

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldBusiness, Management and Accounting
TopicInternational Business and FDI
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMultinational corporationRelevance (law)International businessPerspective (graphical)Context (archaeology)Corporate governanceBusinessPolitical scienceIndustrial organizationEconomicsManagementComputer scienceGeography

Abstract

fetched live from OpenAlex

Abstract Crises are often studied in international business (IB) research as the external “context” for business strategies, but firms can also be active participants in the unfolding of crises. The study of crises in IB could benefit greatly from studying the role of multinational enterprises (MNEs) as active participants, rather than as mere passive actors, responding to exogenous events. History shows that IB crises typically unfold partially as exogenous processes, and partly as the result of MNE strategies. A multilevel and longitudinal approach to studying crises in IB is clearly necessary. This chapter considers the extent to which smaller events that preceded the present crisis – since 1989 – point to systemic problems in global governance. It also defines five overlapping lenses through which future IB studies can further create relevant insights on how to deal with crises: historic, macro, meso, micro and exogenous. The chapter finally serves as an introduction to the whole Progress in International Business Research volume by indicating the relevance of all parts and chapters that follow.

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)0.002
metaresearch head score (Gemma)0.002
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: Empirical · Consensus signal: none
Teacher disagreement score0.014
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.003
Science and technology studies0.0030.006
Scholarly communication0.0140.013
Open science0.0010.002
Research integrity0.0040.007
Insufficient payload (model declined to judge)0.0060.002

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 head0.017
GPT teacher head0.236
Teacher spread0.220 · 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
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

Citations7
Published2022
Admission routes1
Has abstractyes

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