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Record W4400663262 · doi:10.1108/imr-09-2023-0252

Understanding SMEs’ internationalization through digital platforms: the role of knowledge sharing and consumer education

2024· article· en· W4400663262 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInternational Marketing Review · 2024
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInternational Business and FDI
Canadian institutionsMacEwan University
Fundersnot available
KeywordsInternationalizationBusinessContext (archaeology)OriginalityKnowledge sharingMarketingGuanxiKnowledge managementValue (mathematics)Co-creationQualitative researchChina

Abstract

fetched live from OpenAlex

Purpose The aim of this study is to understand how digital platforms and context-specific characteristics of China – such as swift guanxi – affect opportunities for small and medium enterprises (SMEs) entering this market. Design/methodology/approach This study adopts a qualitative approach based on a multiple-case study of Italian SMEs in the wine industry that have activated international activities in China. Primary data consist of 32 interviews with SMEs’ managers, local consumers and other stakeholders involved in firm internationalization. Findings The findings of this study highlight that in SMEs’ internationalization, the process of knowledge/learning on digital platforms takes place in a bidirectional way thanks to the interactions among multiple stakeholders, which activate consumer education and knowledge sharing. Originality/value While previous research has emphasized firms' knowledge acquisition in the internationalization process, this study incorporates both the consumer perspective and the firm perspective, along with considering interactions with various stakeholders.

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.

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: none
Teacher disagreement score0.949
Threshold uncertainty score0.490

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 communication0.0010.002
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 head0.044
GPT teacher head0.290
Teacher spread0.247 · 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