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Record W2999983458 · doi:10.1080/08961530.2019.1710737

Country Image Effects in the Era of Protectionism

2020· article· en· W2999983458 on OpenAlexaffabout
Francis Charette, Alain d’Astous

Bibliographic record

VenueJournal of International Consumer Marketing · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicConsumer Behavior in Brand Consumption and Identification
Canadian institutionsHEC Montréal
Fundersnot available
KeywordsProtectionismContext (archaeology)PerceptionLegitimacyBusinessSituational ethicsPolitical scienceInternational tradeMarketingPoliticsEconomicsPsychologySocial psychologyGeography

Abstract

fetched live from OpenAlex

The research presented in this article examines the effects of protectionist measures on the perceptions that consumers of a targeted country form of the country having adopted these measures as well of other countries not directly involved in the conflict. An experiment was conducted in which 148 Canadian adult consumers were informed that Germany had announced its decision to impose import tariffs on Canadian products. Two types of protectionist measures were considered, either initiated by Germany (i.e., proactive context) or as a response to similar measures adopted by Canada (i.e., reactive context). Consumers’ perceptions concerned the quality of products made in Germany, its image as a travel destination, and as a place to live. These perceptions were also obtained for three European countries differing with respect to their psychic distance with Germany; Austria (shortest distance), France (medium distance), and Spain (greatest distance). The results show that consumers’ perceptions depend on the type of protectionist measures adopted (proactive versus reactive), primarily among consumers interested in international economic news. Reactive protectionist measures were perceived as more legitimate than proactive measures and, in turn, legitimacy had a negative effect on the degree to which consumers developed situational political animosity toward Germany and, consequently, more negative country perceptions. These results were shown to spill over other countries as a function of their psychic distance with Germany. Implications for firms involved in global trading and for governments are derived from these results.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.264
Threshold uncertainty score0.286

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 studies0.0000.000
Scholarly communication0.0000.001
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 head0.016
GPT teacher head0.249
Teacher spread0.233 · 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 designObservational
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

Citations4
Published2020
Admission routes2
Has abstractyes

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