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Record W4365518847 · doi:10.54254/2754-1169/4/20221085

Marketing Strategy Research in the Furniture: Case Study from IKEA

2023· article· en· W4365518847 on OpenAlexaff
Jiali Ding

Bibliographic record

VenueAdvances in Economics Management and Political Sciences · 2023
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicConsumer Retail Behavior Studies
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMarketingExperiential learningMarketing strategyMarketing mixProduct (mathematics)Marketing researchPromotion (chess)BusinessWork (physics)AdvertisingSociologyEngineeringPolitical science

Abstract

fetched live from OpenAlex

IKEA furniture is assembled like a giant Lego block, and foods such as hot dogs and ice cream cones are both delicious and inexpensive. On the market, there is no comparable store. It is noteworthy that IKEA is the first place that most people think of when they need furniture, but there are no other options available. This raises the research question of why IKEA’s marketing strategy is successful. IKEA’s success can be measured in three ways: the 4ps strategy, experiential marketing, and scene marketing. IKEA’s structure comprises the 4p’s: product, price, promotion, and placement. The success of IKEA’s marketing strategy doesn’t only depend on the 4P theory. In addition, the scene marketing strategy enables individuals to immerse themselves in the scenarios that IKEA creates for its customers, heightening their senses, touching, seeing, and so on. Then, using the experiential marketing strategy, IKEA lets people try out the benefits of its products. As a consequence, IKEA’s sales increased, and it became prosperous. Ikea’s success depends on this marketing. These strategies would not have made IKEA as successful if used alone. These marketing strategies work together to make IKEA profitable.

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)0.004
metaresearch head score (Gemma)0.000
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 score0.143
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
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 head0.117
GPT teacher head0.383
Teacher spread0.266 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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