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Record W7117769198 · doi:10.54097/47r8wn58

Leveraging Kidulting for Brand Growth: A Case Study of Jellycat’S Emotional Marketing

2025· article· W7117769198 on OpenAlexaff
Binwen Zhang

Bibliographic record

VenueAcademic journal of management and social sciences · 2025
Typearticle
Language
FieldBusiness, Management and Accounting
TopicConsumer Retail Behavior Studies
Canadian institutionsEarl Haig Secondary School
Fundersnot available
KeywordsCompensation (psychology)Core (optical fiber)Consumer behaviourTourismEmotional behaviorAffect (linguistics)

Abstract

fetched live from OpenAlex

With the global spread of the “Kidulting” trend—where adults seek childhood-like comfort to alleviate life pressure—and the rise of emotional consumption, plush toys have shifted from children’s items to adult emotional companions. This paper uses the case study analysis method, outlining how jELLYCAT, a UK’s high-end plush brand, aligns with Kidulting psychology to drive success. jELLYCAT’s brand background includes its positioning shift to “all age groups” and strengths in design, quality, and sustainability. This study discusses the underlying reasons for the brand’s success. The brand’s success drivers include capturing adult emotional needs (childlike and compensation psychology), scenario-based/limited-edition products, in-store “healing” services, and multi-channel marketing. In addition, this study identifies challenges like high prices, counterfeits, and competition, and proposes solutions (AR/VR experiences, customizable accessories). The study concludes that jELLYCAT’s core strength lies in transforming products into “emotional partners”, offering a model for brands leveraging Kidulting trends in the emotional economy.

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)0.008
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.591
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0000.001
Open science0.0010.001
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 head0.063
GPT teacher head0.331
Teacher spread0.267 · 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 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
Published2025
Admission routes1
Has abstractyes

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