By Hong Yu
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).
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). 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). Nevertheless, misconceptions about Chinese shoppers are prevalent (Cavender, 2010) and few studies focus on Chinese consumer behavior in a shopping mall environment (Li et al., 2003). 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). 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), shopping value (Babin, Darden, and Griffin, 1994), mall activities (Bloch, Ridgway, and Dawson, 1994), 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).
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) 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.