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Record W4386689227 · doi:10.54254/2754-1169/9/20230390

The Marketing Position and Strategy of IKEA in China under New Retail Background

2023· article· en· W4386689227 on OpenAlexaff
Jingwen Tan

Bibliographic record

VenueAdvances in Economics Management and Political Sciences · 2023
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicConsumer Retail Behavior Studies
Canadian institutionsThe Scarborough HospitalUniversity of Toronto
Fundersnot available
KeywordsMarketingPublicityBusinessDiversification (marketing strategy)Marketing strategyPromotion (chess)ChinaCompetition (biology)Position (finance)Product (mathematics)InformatizationGlobalizationAdvertisingEconomicsEngineering

Abstract

fetched live from OpenAlex

With the development of economic globalization and technological informatization, the home furnishing industry is facing the transformation from traditional retail to a new retail model. Based on literature analysis and case studies, this paper discusses the concept of new retail and its difference from the traditional retail model and analyzes the challenges faced by IKEA in the Chinese market, such as slowing sales and traffic growth, late e-commerce layout, and fierce competition. Using the STP model and the 4P model, we analyze IKEA's marketing strategy in the Chinese market and suggest improvements to the product, promotion, and publicity strategies. It will promote IKEA's development and innovation, and bring thoughts and strategic value to the Chinese home furnishing industry in the new retail era. Through the analysis of IKEA's marketing strategy, it can be determined that in the new era, home marketing should start from product diversification and networking to win consumers with more appropriate prices and convenient channels.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.041
Threshold uncertainty score0.082

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0020.002
Open science0.0000.001
Research integrity0.0000.000
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 head0.031
GPT teacher head0.283
Teacher spread0.252 · 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 designNot applicable
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
Published2023
Admission routes1
Has abstractyes

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