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Record W3006395612 · doi:10.1177/1069031x19898767

Digital Environment in Global Markets: Cross-Cultural Implications for Evolving Customer Journeys

2020· article· en· W3006395612 on OpenAlexaff
Hyoryung Nam, P.K. Kannan

Bibliographic record

VenueJournal of International Marketing · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCustomer Service Quality and Loyalty
Canadian institutionsSmiths Detection (Canada)
Fundersnot available
KeywordsExtant taxonMultinational corporationBusinessMarketingCustomer engagementCustomer to customerMarket segmentationCustomer retentionIndustrial organizationComputer scienceService (business)Social mediaService quality

Abstract

fetched live from OpenAlex

Digital technologies and digital media are changing the environments in which firms interact with customers. However, the evolution of digital organizational forms, customer technology use, and the nature of customer journeys differ significantly across global markets. Drawing on observations of customer journeys across different international markets, the authors propose a framework to explain the observed differences in terms of the cross-cultural and socioeconomic factors that influence customer journeys. The authors put forth several propositions built on logical extensions of the extant research findings and identify areas for future academic research. In addition, they outline the managerial implications arising from the application of the framework for multinational firms seeking to market their products and services across global markets.

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)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0030.006
Scholarly communication0.0100.005
Open science0.0000.005
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.025
GPT teacher head0.294
Teacher spread0.269 · 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 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

Citations116
Published2020
Admission routes1
Has abstractyes

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