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Record W4311709847 · doi:10.1057/s41267-022-00574-y

The timing and mode of foreign exit from conflict zones: A behavioral perspective

2022· article· en· W4311709847 on OpenAlexaff
Li Dai, Lorraine Eden, Paul W. Beamish

Bibliographic record

VenueJournal of International Business Studies · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInternational Business and FDI
Canadian institutionsWestern University
Fundersnot available
KeywordsSatisficingSalience (neuroscience)EconomicsMode (computer interface)InterdependencePerspective (graphical)Outcome (game theory)MicroeconomicsMode choiceSample (material)PsychologyComputer scienceCognitive psychologySociologyPolitical science

Abstract

fetched live from OpenAlex

Abstract We examine the timing and mode of firm exits from host-country conflict zones. We argue that timing and mode are interdependent decisions where decision ordering matters, and show that a firm’s prioritizing of either exit timing or mode is dependent on the relative salience of two behavioral stimuli: (1) the firm’s own experience (i.e., its performance shortfall), and (2) the experience of peer firms (i.e., their exits). Using instrumental variables modeling on a sample of 101 Japanese MNE exits from 11 conflict-afflicted countries between 1991 and 2005, we demonstrate that, when mode is prioritized over timing, partial exits tend to occur earlier and whole exits later. However, when timing is prioritized over mode, the decision choices reverse: earlier exits tend to be whole and later exits partial. The outcome of one decision therefore affects that of the other in a unique and predictable manner, such that the ordering of the decisions both produces and precludes strategic choices. Our findings, based on a multidecision problem that has traditionally been treated as a single decision (i.e., foreign exit), delineate expanded boundary conditions for satisficing, as well as reconcile optimizing and satisficing behaviors.

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 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 score0.605
Threshold uncertainty score0.415

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 communication0.0000.001
Open science0.0000.001
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 head0.058
GPT teacher head0.321
Teacher spread0.264 · 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

Citations48
Published2022
Admission routes1
Has abstractyes

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