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Record W1441893441 · doi:10.1080/13527266.2015.1054859

Creating the right customer experience online: The influence of culture

2015· article· en· W1441893441 on OpenAlexaff
Saeed Shobeiri, Ebrahim Mazaheri, Michel Laroche

Bibliographic record

VenueJournal of Marketing Communications · 2015
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicConsumer Behavior in Brand Consumption and Identification
Canadian institutionsLaurentian UniversityConcordia UniversityUniversité de Sherbrooke
Fundersnot available
KeywordsExperiential learningContext (archaeology)MarketingHofstede's cultural dimensions theoryAdvertisingConsumption (sociology)BusinessPsychologySociologySocial psychologyGeographySocial sciencePedagogy

Abstract

fetched live from OpenAlex

The effective role of experiential marketing in the differentiation of brands has been documented in both traditional and online services. The digital context of e-retailing makes it both a suitable platform for experiential consumption and an easily accessible shopping method across cultures. In spite of this, investigation of the impacts of cultural differences on the desire for experiential benefits has been very limited in the e-retailing literature. Using Hofstede's (2001) cultural dimensions, this article studies how two student samples of North American and Chinese customers react differently to the experiential values offered on the websites of e-retailing services. The findings support our hypotheses and suggest that offering experiential values on a company's website is more influential for North American than for Chinese customers. More specifically, the influences of experiential values on site involvement and customers' patronage intentions are stronger for North Americans than for Chinese visitors. On the other hand, the impacts of site involvement on site attitudes and the influence of site attitudes on patronage intentions are stronger for Chinese compared with North American customers.

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)0.003
metaresearch head score (Gemma)0.002
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 score0.207
Threshold uncertainty score0.343

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
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 science0.0010.000
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 head0.048
GPT teacher head0.313
Teacher spread0.265 · 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

Citations70
Published2015
Admission routes1
Has abstractyes

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