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Record W1528576321

Challenges and Strategies of French Luxury Goods Marketing in Advanced Emerging Countries: A Case Study of Chinese Luxury Consumers

2010· article· en· W1528576321 on OpenAlexaff
Sandra Saidi, Muhammad Mohiuddin, Zhan Su, Elie Chrysostome

Bibliographic record

VenueSSRN Electronic Journal · 2010
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicConsumer Behavior in Brand Consumption and Identification
Canadian institutionsUniversité LavalThompson Rivers University
Fundersnot available
KeywordsMarketingStrategistLuxury goodsBusinessPurchasingProduct (mathematics)Emerging marketsAdvertisingConsumer behaviourCommerce
DOInot available

Abstract

fetched live from OpenAlex

Changing economic epi-centre from the west towards the east is accompanying the luxury goods markets and the emerging Chinese market is becoming impossible to overlook for the luxury goods brands. Potential purchasers buying process of conspicuous and hedonic values enabled Luxury goods differ from the buyers of usual consumer products. In consequence, Taste, motivations and factors of luxury goods purchasing process differs from the buying process of usual consumer products. Adding to this already complex buying process of luxury brands, the novice Chinese luxury buyer’s and potential aspirer’s mode of luxury purchasing process further complicate the marketing and management strategist strategy formulation process. In many count, Chinese luxury buyers, High-net-worth-individuals (HNWIs), are different from their western counterparts. The Confucian background, socialist political system, frugal-value oriented collective society and above all the fastest economic rise and rapidly changing societal pre-existent values make this new comer’s luxury buying attributes very challenging to the strategist. Thus the rapid economic growth, public policy and cultural factors have made Chinese affluent customers different from their peers in the west as well as in the east (i.e Japan and India). On the other hand, subjective and complex concept of luxury is primarily built on consumer perceptions, as the meaning of luxury is determined by personal and interpersonal motives (Vigneron & Johnson 2004). Due to different interactions on a personal and social level, consumers will develop different perceptions of luxury brands. Thus luxury product can be considered as a multi-dimensional object which was studied and analyzed in academic research by economist (/management), sociologist as well as marketing experts. Marketing theories on luxury goods consumption and marketing emphasized on the relationships among luxury, income and culture. This study tries to focus on the distinctiveness of Chinese consumers and how to target them effectively. Based on systematic literature review, content analysis of academic and professional publications and partial empirical data, luxury marketers need to include Chinese social, economic and cultural factors while formulating marketing strategy in this rising market. Targeting the right wealthy consumers, appropriate Marketing tools, promoting local content, building long term co-operations with local firms and service providers are very important for this market. Introducing programs and policies for brand awareness and luxury taste (i.e Luxury Knowledge) among the Chinese HNWIs are very important for success in long term. Gaining experiences in luxury products retailing in an emerging market like China can also open the flood gate of opportunity in the future to new emerging countries in Asia and elsewhere.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.116
Threshold uncertainty score0.230

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0060.002
Scholarly communication0.0030.002
Open science0.0010.002
Research integrity0.0020.001
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 head0.014
GPT teacher head0.277
Teacher spread0.263 · 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 designQualitative
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

Citations4
Published2010
Admission routes1
Has abstractyes

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