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Profiling Chinese Fashion Shoppers in Beijing: Mall Activities, Shopping Outcome, and Demographics

2011· article· en· W2073441261 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Global Fashion Marketing · 2011
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicConsumer Retail Behavior Studies
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsBeijingProfiling (computer programming)DemographicsShopping mallBusinessAdvertisingChinaMarketingComputer scienceGeographyDemography

Abstract

fetched live from OpenAlex

Abstract China’s burgeoning consumer market has drawn increased attention from the global business community. With the Chinese economy boasting an average growth rate of 9.9% per year since 1981, the country’s retail sales continue to gain momentum. In 2006, China’s retail revenue totalled about $860 billion-the seventh-largest market in the world-and this figure is projected to grow to $2.4 trillion by 2020 (Special Report: Ready for Warfare, 2006). The red-hot Chinese market has attracted global retailers and property developers who are keen to seize this unprecedented opportunity. Foremost among such retail development in China since the late 1980s is the emergence of modern regional and mega shopping centers (Li, Zhou, and Zhuang, 2003 Li, F., Zhou, N. and Zhuang, G. 2003. “Need-driven or pleasure-driven? An empirical study of Chinese consumers’ behavior at shopping malls”. In Proceedings of the Academy of Marketing Studies conference, 69–73. Las Vegas, NV: Allied Academies. [Google Scholar]). While the concept of the shopping mall is quite different from traditional retail practices in China, Chinese people have embraced the convenience of mall shopping (Chen, 2007 Chen, W. 2007. Shoppers embrace convenience of malls. China Daily, 15 [Google Scholar]). During the recent global financial crisis, Chinese consumers’ spending power has become a major driver of the country’s economic growth, even as the developed world’s own economies continue to struggle (Cavender, 2010 Cavender, B. 2010. Misconceptions about Chinese consumers. Interfax China Investment Weekly, Retrieved from ABI/INFORM Global. (March 12) [Google Scholar]). Nevertheless, misconceptions about Chinese shoppers are prevalent (Cavender, 2010 Cavender, B. 2010. Misconceptions about Chinese consumers. Interfax China Investment Weekly, Retrieved from ABI/INFORM Global. (March 12) [Google Scholar]) and few studies focus on Chinese consumer behavior in a shopping mall environment (Li et al., 2003 Li, F., Zhou, N. and Zhuang, G. 2003. “Need-driven or pleasure-driven? An empirical study of Chinese consumers’ behavior at shopping malls”. In Proceedings of the Academy of Marketing Studies conference, 69–73. Las Vegas, NV: Allied Academies. [Google Scholar]). This study intends to fill the gap and to expand the understanding of Chinese mall shoppers. Specifically, the researcher explored segmentation of mall shoppers by fashion orientation, and examined shopping values, mall activities, expenditures, and demographic characteristics across the segments. The researcher used an intercept survey method for data collection in a newly established mega shopping mall in Beijing whose clientele fits middle to upper class profiles. Trained graduate students collected data using a mall intercept survey procedure adapted from Sudman (1980 Sudman, S. 1980. Improving the quality of shopping center sampling. Journal of Marketing Research, 17(2): 423–431. [Crossref], [Web of Science ®] , [Google Scholar]). A total of 296 completed questionnaires were included in the data analysis. The sample consisted of 87 male (29.0%) and 209 female (69.7%) shoppers. About 30% were between the ages of 18–25, 11% were 41–60 years of age, and the rest (57%) were 26–40 years of age. The majority (66.3%) had earned a Bachelor’s degree; 61.3% were employed; and about 4% were retired. The questionnaire included items measuring fashion orientation (Gutman and Mills, 1982 Gutman, J. and Mills, M.K. 1982. Fashion life style, self-concept, shopping orientation, and store patronage: An integrative analysis. Journal of Retailing, 58(2): 64–86. [Web of Science ®] , [Google Scholar]), shopping value (Babin, Darden, and Griffin, 1994 Babin, B., Darden, W.R. and Griffin, M. 1994. Work and/or fun: Measuring hedonic and utilitarian shopping value. Journal of Consumer Research, 20(4): 644–656. [Crossref], [Web of Science ®] , [Google Scholar]), mall activities (Bloch, Ridgway, and Dawson, 1994 Bloch, P.H., Ridgway, N. and Dawson, S.A. 1994. The shopping mall as consumer habitat. Journal of Retailing, 70(1): 23–42. [Crossref], [Web of Science ®] , [Google Scholar]), as well as other demographic information and total customer expenditures during the mall visit. The questionnaire was translated into Chinese and back-translated into English by bilingual experts to ensure validity. Exploratory factor analyses using principle component extraction and varimax rotation were performed on fashion orientation and shopping value. Cluster analysis using fashion orientation as the variable included three steps: Firstly, hierarchical cluster analysis using Ward’s method was conducted; secondly, K-means cluster analysis was performed with the cluster centers from the hierarchical results as the initial seed points; and finally, ANOVA and Chi-square tests were used to compare across the clusters. Factor analysis on fashion orientation resulted in three factors: Fashion Interest and Leadership (alpha=.92); Importance of Being Well-Dressed (alpha=.83); and Anti-Fashion Attitude (alpha=.48). Factor analysis on shopping value scale yielded two dimensions: Hedonic Value (alpha=.81) and Utilitarian Value (alpha=.50). Items with alpha coefficients above 0.70 are considered acceptable in reliability and they were summated into a single score; for those with alpha coefficients lower than 0.70, a single item with the highest factor loading was used to represent the factor dimension in further analyses (Jin and Kim, 2003 Jin, B. and Kim, J-O. 2003. A typology of Korean discount shoppers: Shopping motives, store attributes, and outcomes. International Journal of Service Industry Management, 14(3/4): 396–419. [Google Scholar]). Cluster analysis suggests three clusters: Fashion Leaders (N=74, 26.7%); Independents (N=105, 37.9%); and Uninvolveds (N=98, 35.4%). These groups partially matched Gutman and Mills’s (1982 Gutman, J. and Mills, M.K. 1982. Fashion life style, self-concept, shopping orientation, and store patronage: An integrative analysis. Journal of Retailing, 58(2): 64–86. [Web of Science ®] , [Google Scholar]) findings on clothing fashion lifestyle segments. ANOVA and Chi-square tests show significant group differences in shopping value, mall activities, and the groups’ demographic profiles. Results indicate that the Fashion Leaders and Independents derived a significantly higher level of hedonic value than the Uninvolveds. The Uninvolveds and Fashion Leaders derived a significantly higher level of utilitarian value from shopping at the mall than the Independents. With regards to mall activities, the three groups were similar in consumption of the mall (e.g., walking in the mall for exercise), passing time, and consumption of services, but were significantly different in consumption of products; in particular, the Fashion Leaders and Independents made more unplanned purchases than the Uninvolveds. In terms of demographics, the Fashion Leaders and Independents groups had larger percentages of females, while the Uninvolveds group had nearly equal representation of male and female shoppers. The Fashion Leaders and Independents were relatively younger and included more respondents with Bachelor’s degrees. The Uninvolveds were more likely to be employed or retired. Total expenditure during the mall visit and monthly income levels were not significantly different among the three groups. Based on the findings, implications for mall developers and retailers are discussed.

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.

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationallow
gptno category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationallow
models agreeAgreement compares identical category sets and study designs across arms.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0000.000
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 head0.037
GPT teacher head0.269
Teacher spread0.233 · 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