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Record W4294866203 · doi:10.2991/aebmr.k.220307.111

The Marketing Model of Chinese Warehouse Retailers under the New Retail Background

2022· article· en· W4294866203 on OpenAlexaff
Fei Yi, Weiye Zhang, Zhao Zhang

Bibliographic record

VenueAdvances in economics, business and management research/Advances in Economics, Business and Management Research · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicE-commerce and Technology Innovations
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsWarehouseBusinessMarketingData warehouseComputer scienceAdvertisingDatabase

Abstract

fetched live from OpenAlex

New retailing refers to companies relying on the Internet to upgrade and transform the production, circulation, and sales processes of commodities through the use of advanced technology such as big data and artificial intelligence, thereby reshaping the business structure and ecosystem, and providing online services. It is a new retail model that integrates offline experience and modern logistics deeply. New media marketing is an increasingly vigorous marketing model, and more and more industries have begun to participate in it, including warehouse supermarkets. This article will start from this point and discuss how warehouse supermarkets can use new media to market under the background of new retail. This study found that warehouse supermarkets can increase their awareness and sales in the short term through influencers, but this approach does not provide the company with long-term sustainability. To avoid the possible negative effects of influencer marketing, the company inevitably needs to establish brand image and customer loyalty. Underlying the rapid development of China's e-commerce, it can be possible to conduct more research in the future on how to conduct new media marketing while also establishing a brand image faster and ensuring customer retention.

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)0.010
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
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 score0.609
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.004
Science and technology studies0.0020.002
Scholarly communication0.0010.004
Open science0.0020.006
Research integrity0.0000.001
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 head0.051
GPT teacher head0.314
Teacher spread0.262 · 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.

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

Citations0
Published2022
Admission routes1
Has abstractyes

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