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Demographics and patronage motives of supercenter shoppers in the United States Jason M. Carpenter Department of Retailing, University of South Carolina, Columbia, South Carolina, USA

Demographics and patronage motives 5 Received March 2006 Revised July 2006 Accepted 28 July 2006

Abstract Purpose – This paper seeks to provide an updated, general understanding of supercenter shopping behavior in the USA. Design/methodology/approach – The study employs a sample generated from Retail Forward panel data to assess the impact of demographic variables, including gender, age, ethnicity, education, income, marital status, and household size, on supercenter shopping frequency across four product categories (apparel, health and beauty, home furnishings, and consumer electronics). Descriptive and inferential statistical techniques (regression, ANOVA) are used to evaluate the data. Findings – The paper identifies demographic groups who frequent supercenters and examines patronage motives as drivers of supercenter shopping behavior. Research limitations/implications – Generalizations of the findings of this study to markets outside the USA are limited due to the differences in consumers and retail formats available in various countries. Future research could compare shopping behavior within large formats across international markets. Practical implications – This research provides supercenter retailers who operate within the USA with specific knowledge of the patronage motives driving cross-category shopping in supercenters (e.g. value, one-stop shopping convenience, brands, product assortment) and identifies the demographic characteristics of cross-category shoppers. The results suggest marketing strategy implications for supercenter operators in the US market. As competition in the sector continues to evolve and consumer demographics change within the US market, understanding cross-category shopping will be critical to retailer performance in the industry. Originality/value – This study uses demographics and patronage motives as a framework for profiling cross-category shoppers in the US supercenters. The paper is unique because there are few similar empirical studies which focus on consumer behavior within supercenters. Keywords Consumer behaviour, Demographics, Retail trade, United States of America Paper type Research paper

Introduction Over the past decade, the proliferation of the supercenter format has brought about marked changes in the retail landscape of the USA. Supercenters are large format stores that offer grocery products in combination with general merchandise, successfully capturing substantial market share from traditional grocery stores as well as mass merchandisers (Graff, 2006; Arnold and Luthra, 2000; Seiders et al., 2000; Morganosky, 1997). Supercenters often appeal to lower income, large households because of their ability to offer low prices. In addition, time-pressed consumers are attracted to the format because of its one-stop shopping convenience. However, recent reports in the trade literature indicate several interesting changes in the makeup of the

International Journal of Retail & Distribution Management Vol. 36 No. 1, 2008 pp. 5-16 q Emerald Group Publishing Limited 0959-0552 DOI 10.1108/09590550810846965

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supercenter customer base including a decline in sales to lower-income households and growth in sales to affluent consumers, Hispanics, and African-Americans (Progressive Grocer, 2005) as well as significant growth in supercenter patronage among males (Duff, 2003). A recent study by Datamonitor (2005) predicts that the global supercenter/ hypermarket segment will grow by approximately 25 percent between 2004 and 2009. In light of the considerable growth of the supercenter format and the aforementioned changes in consumer patronage, it is crucial for supercenter retailers to reexamine the composition of the customer base and develop appropriate marketing strategies to reach various segments. Understanding consumer shopping behavior within the format will be critical to the success of retailers operating in the industry. The findings of numerous studies focusing on growth in the supercenter category are reported in the trade and academic literatures (Fernie et al., 2006; Graff, 2006; Datamonitor, 2005; Duff, 2005; Promo, 2005; Chain Store Age, 2003; Morganosky, 1997). In addition, topics such as market entry effects of supercenters on traditional retailers (Fernie et al., 2006; Arnold and Luthra, 2000; Seiders et al., 2000), competition among supercenters (Graff, 2006) and consumer experiences in large retail formats (Morganosky and Cude, 2000) are examined in the academic literature. However, few recent academic studies have attempted to identify consumer groups that frequent supercenters and examine the motivations behind their shopping behavior within the format. Considering the explosive growth of the supercenter format, the shifts in consumer patronage of supercenters, and the lack of academic literature related to supercenter shopping behavior, the purpose of this research is to provide an updated general understanding of supercenter shopping behavior within the USA. In particular, the study seeks to identify groups of consumers who shop in supercenters for grocery and general merchandise products and to examine their motivations. By identifying the demographic characteristics and patronage motives of supercenter shoppers, this research will provide a current view of the nature of supercenter shopping in the USA. Findings of this study will assist academics and practitioners to better understand shoppers’ attitudes and behavior in regard to the supercenter format. Review of the literature Demographics and choice of retail format Previous research reveals a connection between demographic characteristics and patronage of retail formats, suggesting that individual characteristics of consumers influence their shopping behavior. Tigert et al. (1992) find that warehouse club members represent an upscale market compared to the general population. Stone (1995) compares the demographic profiles of supermarket shoppers and warehouse club shoppers, finding that warehouse club members are younger, more educated, and have higher incomes. Arnold (1997) finds significant differences between the demographic profiles (e.g. age, education, household size) of large-format department store shoppers as compared to non-shoppers. Few recent academic studies focus specifically on the demographic characteristics of supercenter shoppers. Fox et al. (2004) examine the effect of demographics on retail patronage behavior across grocery stores, mass merchandisers, and drug stores. Findings from the study indicate weak relationships between household size, income, level of education and store choice. The findings of recent studies in the trade literature suggest a

decline in supercenter sales to lower-income households and growth in sales to higher income consumers, Hispanics, and African-Americans (Progressive Grocer, 2005). In addition, Duff (2003) reports increased supercenter patronage among male shoppers. Patronage motives and choice of retail format Pricing, product assortment, and customer services are thought to be important factors in determining patronage of store formats (Arnold, 1997; Sparks, 1995). Seiders et al. (2000) compare supercenter shoppers with traditional supermarket shoppers, reporting that supercenter shoppers identify low prices and range of product assortment as the primary reasons for their patronage of the format. Hansen and Solgaard (2004) identify product assortment as the most influential patronage motive across discount stores, hypermarkets and conventional supermarkets. Similarly, Brown (2004) identifies low prices, product assortment, and quick checkout as influential patronage motives among loyal supercenter shoppers. Studies published in the trade literature reflect similar results, identifying product assortment, availability, convenience, and pricing as significant drivers of format choice (Chain Store Age, 2004; Taylor, 2003). Methodology Data for the study are drawn from Retail Forward’s ShopperScape database (www. retailforward.com) for December 2005. Retail Forward collects shopping data from an online panel of consumers each month, focusing on shopping behavior in a variety of retail formats and product categories. The panel includes nearly one million households and nearly three million individuals in the USA. Consumers are recruited for participation through more than twenty recruiting partners including large web portals, specialized web communities, web aggregators, and internet advertising firms subject to regular recruiting and purging cycles. Data are weighted by key demographic characteristics based on US Census data. Survey respondents are the selfdesignated primary shopper in their household and earn points for participation which can be exchanged for cash, prizes, or charity donations as incentives. The average monthly response rate for the survey is approximately 46 percent. No panelist is permitted to participate in more than two surveys per month. A key objective of the current study is to capture a group of shoppers who engage in various levels of shopping within the supercenter format. The ShopperScape survey identifies shoppers who purchase groceries at supercenters in the USA and probes respondents regarding their shopping frequency in additional categories within this format (e.g. apparel, health and beauty, home furnishings, and consumer electronics). According to Retail Forward’s December 2005 database (N ¼ 5,092), a total of 2,404 respondents (47.2 percent) report shopping in supercenters for grocery products. Among these 2,404 respondents, a random sample of 500 respondents is extracted using SPSS and constitutes the final sample for the analyses. Measurement Supercenter shopping frequency is measured using the question “how often do you shop in each of the following departments (e.g., apparel, health and beauty, home furnishings, and consumer electronics) when shopping a supercenter for groceries?” Respondents are classified into one of three groups based on whether they indicate shopping in a department on every trip to the supercenter (frequent shoppers), more

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than half, but not all trips to the supercenter (occasional shoppers), or less than half of all trips (infrequent shoppers). To capture patronage motives, respondents categorized as frequent supercenter shoppers within each department are then asked to answer the question “what motivates you to shop (department name) regularly?” The format of this question is “check all that apply” and respondents are free to respond using up to five patronage motives including value (price for quality), brands offered, one-stop shopping convenience (ability to purchase a wide variety of products in a single store), fast and easy shopping experience (speed and ease of the shopping experience), and product assortment. Demographic information is also collected (e.g. gender, age, ethnicity, income, education, marital status, and household size). Analysis A combination of descriptive and inferential statistical techniques is used to analyze the effects of demographics (independent variables) on supercenter shopping frequency (dependent variable). t-Tests are used to examine differences between males and females, while linear regression is used to examine the effect of the continuous demographic variables on supercenter shopping including age, income, education level, and household size. Stepwise regression models are fit for each of the three levels of shopping frequency (frequent, occasional, and infrequent) using a minimum inclusion a of 0.05. Significance tests and b estimates are evaluated to assess the magnitude and direction of the effect(s) of the continuous demographic variables on shopping frequency. One way analysis of variance (ANOVA) is used to examine the effect of ethnicity and marital status on each level of supercenter shopping frequency. Significant ANOVA models are further investigated using post-hoc testing (Tukey’s Honestly Significant Difference statistic) to describe specific differences among the demographic variables and each of the three levels of the dependent variable. In addition, Levene’s test for homogeneity of variance is evaluated for each of the ANOVA models as well as for the t-tests. Since, the questions used to assess patronage motives for supercenter shopping are dichotomous in nature and respondents are allowed to list up to five motives per question, the SPSS multiple response command is used to analyze the data. Results Sample characteristics Sample characteristics are analyzed for respondents’ gender, age, ethnicity, income, education, marital status, and household size. Comparison to the US Census Bureau (2000) indicates a larger percentage of females in the sample as compared to the population (Table I). In addition, the sample distribution for the 35þ age groups is heavier than that of the population. Examination of additional variables including ethnicity, income, education, and marital status indicates correspondence for the most part to the US census figures. Minor exceptions to this correspondence indicate over-representation of Hispanics in the sample, slight under-representation of those in the lowest income group, slight over-representation of those in the highest income group, and differences in marital status. Table I provides a comparison of the sample characteristics with US Census Bureau (2000).

Variable

Level

Frequency

Percent

US Census percent

Gender

Male Female Total 18-24 25-34 35-44 45-54 55-64 65 þ Total Median Caucasian/White African American/Black Asian/Pacific Islander Native American Hispanic Other Total Less than $25,000 $25,000-$50,000 $50,001-$100,000 . $100,000 Total No high school degree High school graduate Some college Two year degree Four year degree Graduate/professional degree Total Single, never married Married Separated, divorced, widowed Total

78 422 500 17 68 113 119 79 104 500 51.7 years 407 51 15 7 13 1 494 174 151 131 44 500 13 123 187 58 84 32 497 68 293 139 500 2.70 (mean)

15.6 84.4 100 3.4 13.6 22.6 23.8 15.8 20.8 100

49.1 50.9 100 13.9a 14.2 16 13.4 8.6 12.4 71.3 35.3 years 70 12.3 3 0.8 11.5 2.4 100 28.6 29.3 29.7 12.3 100 19.6 28.6 21 6.3 15.5 9 100 27.1 54.4 18.5 100 2.59 (mean)

Age

Ethnicity

Income (annual)

Education

Marital status

Avg. household size

81.4 10.2 3 1.4 2.6 0.2 98.8b 34.8 30.2 26.2 8.8 100 2.6 24.6 37.4 11.6 16.8 6.4 99.4b 13.6 58.6 27.8 100

Notes: aUS Census data includes ages 15-19 in this category, but the sample includes those 18 and older; bmissing values resulted in less than 100 percent response for variable

Shopping frequency In the apparel category, 58 respondents are frequent shoppers (11.6 percent), 127 are occasional (25.4 percent), and 315 are infrequent (63 percent). For health and beauty products, 207 respondents are frequent (40.4 percent) shoppers, 154 are occasional (30.8 percent), and 139 are infrequent (27.8 percent). A total of 46 respondents are frequent shoppers for home furnishings (9.2 percent), 67 are occasional (13.4 percent), and 387 are infrequent (77.4 percent). For consumer electronics, 56 shoppers are frequent (11.2 percent), 79 are occasional (15.8 percent), and 365 are infrequent (73 percent).

Demographics and patronage motives 9

Table I. Sample characteristics as compared to US Census Bureau (2000) data

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Consumer demographics and supercenter shopping t-Tests are used to examine the effect of gender on shopping frequency across four product categories (apparel, health and beauty, home furnishings, consumer electronics). Results indicate significant differences between males and females in frequency for health and beauty products (t ¼ 2 4.369, p ¼ 0.000, mean difference, 2 0.435) and consumer electronics (t ¼ 2.371, p , 0.020, mean difference ¼ 0.231) (Table II). Levene’s test for equality of variances is non-significant for the health and beauty category, but is significant for consumer electronics (F ¼ 19.389, p ¼ 0.000) in which case the t-statistic for non-equal variances is interpreted. The effects of the continuous demographic variables including age, income, level of education and household size on shopping frequency in each of the four categories is examined using stepwise regression. The overall regression model for apparel yields a significant statistic (F ¼ 8.711, p , 0.000) with age (b ¼ 2 0.059, t ¼ 2 2.591, p , 0.010), income (b ¼ 2 0.075, t ¼ 2 2.313, p , 0.021), and household size (b ¼ 0.068, t ¼ 3.062, p , 0.002) as significant predictors (Tables III and IV). The regression model for health and beauty shopping is non-significant. The regression model for home furnishings is significant (F ¼ 3.864, p ¼ 0.050) with age generating a significant effect (b ¼ 2 0.39, t ¼ 2 1.966, p ¼ 0.050). The regression model for the consumer electronics category is also significant (F ¼ 7.454, p ¼ 0.001) with two predictors including age (b ¼ 2 0.069, t ¼ 2 3.285, p ¼ 0.001) and education (b ¼ 2 0.056, t ¼ 2 2.335, p , 0.020). One-way analysis of variance is used to examine the effects of ethnicity and marital status on shopping frequency in the four product categories. Levene tests for homogeneity of variances for ethnicity and marital status across all four product categories produce non-significant results. ANOVA models for ethnicity and marital status in all product categories also yield non-significant results (Tables V and VI). Patronage motives by product category Value and one-stop shopping convenience are the commonly cited patronage motives across all product categories. Brands appear to be important patronage motives in the health and beauty and consumer electronics categories, but not as important in the apparel and home furnishings categories. Conversely, product assortment and fast/easy shopping are less frequently cited by health and beauty shoppers but appear to be important in the apparel and home furnishings categories (Table VII).

Format

Table II. Effect of gender on supercenter shopping frequency

Levene’s test for equality of variances F Sig.

Apparel 0.166 Health and beauty 0.193 Home furn 0.047 Electronics 19.389

t

df

t-Tests for equality of means Significance (two-tailed) Mean difference

0.684a 2 0.870 498 0.661a 2 4.369 498 0.828a 0.039 498 0.000b 2.371c 95.704

0.385 0.000 * * 0.970 0.020 *

2 0.075 2 0.435 0.003 0.231

Notes: aNon-significant Levene statistic indicates unequal variances between gender groups; significant Levene statistic assumes equal variances between gender groups for Warehouse Club; c equal variances not assumed. *a , 0.05; * *a , 0.01 b

0.050

0.010

0.008

0.029

0.100

0.088

0.171

R2

0.224

R

0.025

0.006

0.002

0.045

Adjusted R 2

0.671

0.632

0.819

0.680

Std. error of estimate

6.719 222.645 229.364

1.544 197.860 199.404

3.309 330.112 333.421

12.090 228.075 240.165

Sum of squares

2 494 496

1 495 496

4 492 496

3 493 496

df

3.360 0.451

1.544 0.400

0.827 0.671

4.030 0.463

Mean square

Sig. 0.000 * * *

0.296

0.050 *

0.001 *

F 8.711

1.233

3.864

7.454

Notes: aPredictors: constant, age, income, and household size; bpredictors: constant and age; cpredictors: constant, age, and education; *a , 0.05; * *a , 0.01; * * *a , 0.001

Apparel a Regression Residual Total Health and beauty Regression Residual Total Home furnishings b Regression Residual Total Consumer Electronics c Regression Residual Total

Model/dependent variable

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Table III. Summary regression models for effect of demographic variables on supercenter shopping frequency

IJRDM 36,1 Model/predictor variable

12

Table IV. Predictor effects and b estimates for demographic variables on supercenter shopping frequency

Apparel Constant Age Income Household size Home furnishings Constant Age Consumer electronics Constant Age Education

Health and beauty Home furn Consumer electronics

Apparel Health and beauty Home Furn Table VI. ANOVA models for marital status and supercenter shopping frequency

b

Standardized coefficients T

Sig.

1.693 2 0.059 2 0.75 0.068

0.137 0.023 0.032 0.022

2 0.121 2 0.105 0.148

12.354 22.591 22.313 3.062

0.000 0.010 * 0.021 * 0.002 *

1.470 2 0.039

0.083 0.020

2 0.088

17.617 21.966

0.000 0.050 *

1.845 2 0.069 2 0.056

0.125 0.021 0.024

2 0.147 2 0.104

14.733 23.285 22.335

0.000 0.001 * 0.020 *

Notes: *a , 0.05; * *a , 0.01; * * *a , 0.001

Apparel

Table V. ANOVA models for ethnicity and supercenter shopping frequency

Unstandardized coefficients B Std. error

Consumer electronics

Between groups Within groups Total Between groups Within groups Total Between groups Within groups Total Between groups Within groups Total

Between groups Within groups Total Between groups Within groups Total Between groups Within groups Total Between groups Within groups Total

Sum of squares

df

Mean square

F

Sig.

5.058 234.392 239.449 2.422 330.217 332.640 1.778 198.045 199.824 1.052 228.099 229.152

5 488 493 5 488 493 5 488 493 5 488 493

1.012 0.480

2.106

0.063

0.484 0.677

0.716

0.612

0.356 0.406

0.876

0.497

0.210 0.467

0.450

0.813

Sum of squares

df

Mean square

F

Sig.

2.007 238.895 240.902 0.324 336.428 336.752 0.797 199.641 200.438 1.491 228.547 230.038

2 497 499 2 497 499 2 497 499 2 497 499

1.003 0.481

2.088

0.125

0.162 0.677

0.240

0.787

0.399 0.402

0.992

0.371

0.746 0.460

1.622

0.199

Responses Apparel motives Value One-stop convenience Assortment Fast, easy shopping Brands Total Health and beauty motives Value One-stop convenience Brands Fast, easy shopping Assortment Total Home furnishings motives Value One-stop convenience Fast, easy shopping Assortment Brands Total Consumer electronics motives Value Brands One-stop convenience Fast, easy shopping Assortment Total

N

Percent

42 28 25 25 16 136

30.9 20.6 18.4 18.4 11.8 100.0

128 121 86 49 46 430

29.8 28.1 20.0 11.4 10.7 100.0

16 12 8 7 6 49

32.7 24.5 16.3 14.3 12.2 100.0

22 13 12 7 5 59

37.3 22.0 20.3 11.9 8.5 100.0

Conclusions and discussion The t-tests for gender indicate that females engage in supercenter shopping for health and beauty products more often than males, while males engage in shopping for consumer electronics more often than females. Supercenter operators should take note of this finding and use gender-appropriate marketing strategies in each department in order to attract shoppers and stimulate sales. The regression models examining continuous demographic variables (age, income, education, and household size) are significant for apparel, home furnishings, and consumer electronics. For apparel, age and income demonstrate inverse relationships to shopping frequency while household size shows a direct relationship. Therefore, it appears that younger, lower-income, larger households are the most likely to be frequent shoppers for apparel at supercenters. Age is the single significant predictor (inverse relationship) in the regression model for home furnishings. This finding suggests that younger respondents are the most likely to shop in supercenters for these types of products. Given these findings, supercenter operators may choose to gear primary marketing efforts toward younger, lower income households in the apparel category. Likewise, marketing and merchandising efforts in the home furnishings category should be geared toward younger consumers.

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Table VII. Patronage motives by product category

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For consumer electronics, both age and education demonstrate inverse relationships to supercenter shopping frequency. It appears that younger, less educated respondents are likely to shop for electronics. Similar to the apparel and home furnishings categories, supercenter operators find their primary market for consumer electronics products consisting of younger consumers and should tailor marketing strategies appropriately. The ANOVA models for the fixed factor demographic variables (ethnicity and marital status) produce non-significant results. This finding suggests that the ethnicity and marital status variables may not be effective bases of segmentation among supercenter shoppers. Taken together, these findings suggest that several demographic variables (e.g. age, income, education, and household size) may be useful in predicting supercenter shopping frequency in some product categories. The age variable appears particularly promising, demonstrating significance in three out of the four product categories examined in the study. It appears that in general, younger consumers take advantage of the convenience provided by supercenters. Examination of the consumer patronage motivations across the four product categories reflects the importance of value to frequent supercenter shoppers. Supercenter operators should take note of this finding and continue to provide the highest quality levels possible while maintaining low prices. The convenience of one-stop shopping is also mentioned frequently in three out of four product categories (apparel, health and beauty, home furnishings). This finding suggests that frequent supercenter shoppers place importance on the ability to take care of as many shopping needs as possible while visiting the same store. Supercenters should therefore continue the focus on offering a wide variety of products across many categories. The selection of brands offered is frequently mentioned among consumer electronics shoppers. Marketers should pay special attention to this finding and include appropriate name-brand electronic products in their assortment. Likewise, brands appear to be significant for health and beauty products shoppers as compared to other product categories. Therefore, health and beauty products assortments should include appropriate name-brands within the selection. Interestingly, brands appear less important to frequent shoppers in apparel and home furnishings. Instead, these shoppers appear to focus on product assortment and a fast/easy shopping experience. This finding may suggest that recognized brands are not as important to supercenter apparel and home furnishings shoppers, thus providing an opportunity for supercenter retailers to capitalize on gross margin by inserting a larger percentage of store brands in these categories. Overall, the findings provide interesting perspectives on the shopping behavior of supercenter patrons in the USA. Supercenters appear to appeal to younger, lower-income consumers with large households, which may explain the recent shifts in supercenter marketing strategies toward increasing patronage among other consumer demographic groups. It appears that shoppers who take the fullest advantage of supercenters do so because of the value provided in terms of the price/quality trade off and because of the convenience of one-stop shopping provided by the inclusion of various product categories within the same store. The relative importance of name brands within the health and beauty/consumer electronics categories as compared to the apparel/home furnishings categories also provides interesting insight in terms of directives for the development of in-house brands. Again, this finding suggests that supercenter operators should determine the relative brand equity of store brands to national brands and develop appropriate strategies to capitalize on store brand opportunities within key categories.

Limitations and future research Generalizations of the findings of this study to markets outside the USA are limited due to the differences in consumers and retail formats available in various countries. Future research could compare shopping behavior within large formats across international markets. Future studies could also continue to identify key demographic predictors of supercenter shopping and improve the accuracy of prediction. In addition, lifestyle or psychographic factors could be investigated for their effect on supercenter shopping behavior. The findings also suggest that product category may play an important role in the decision to shop in supercenters. Future research could examine additional product categories to further investigate this effect. Research to further explore the relative importance of store brands across supercenter product categories could also provide interesting and actionable insights for the development of retail brand strategies. References Arnold, S. (1997), “Shopping habits at Kingston department sores: wave III: three years after Wal-Mart’s entry into Canada”, Report No. 3, Queen’s University School of Business, Kingston, July. Arnold, S. and Luthra, M. (2000), “Market entry effects of large format retailers: a stakeholder analysis”, International Journal of Retail & Distribution Management, Vol. 28 Nos 4/5, pp. 139-54. Brown, J. (2004), “Determinants of loyalty to grocery store type”, Journal of Food Products Marketing, Vol. 10 No. 3, pp. 1-11. Chain Store Age (2003), “Retailing 2010: a point of view”, Chain Store Age, August, pp. 14A-15A. Chain Store Age (2004), “Age doesn’t affect buying patterns”, Chain Store Age, June, p. 33. Datamonitor (2005), “Global hypermarkets and supercenters industry profile”, Reference code: 0199-2291, available at: www.datamonitor.com (accessed May). Duff, M. (2003), “Core supercenter shopper more ‘he’ than meets the eye”, DSN Retailing Today, Vol. 42 No. 11, p. 5. Duff, M. (2005), “Food and GM synergies producing advantages”, Food Retailing Today, May, pp. F2-F3. Fernie, J., Hahn, B., Gerhard, U., Pioch, E. and Arnold, S. (2006), “The impact of Wal-Mart’s entry into the German and UK grocery markets”, Agribusiness, Vol. 22 No. 2, pp. 247-66. Fox, E., Montgomery, A. and Lodish, L. (2004), “Consumer shopping and spending across retail formats”, Journal of Business, Vol. 77 No. 2, pp. S25-S60. Graff, T. (2006), “Unequal competition among chains of supercenters: Kmart, Target, and Wal-Mart”, The Professional Geographer, Vol. 58 No. 1, pp. 54-64. Hansen, T. and Solgaard, H. (2004), New Perspectives on Retailing and Store Patronage Behavior, Kluwer Academic Publishers, Boston, MA. Morganosky, M. (1997), “Retail market structure change: implications for retailers and consumers”, International Journal of Retail & Distribution Management, Vol. 25 No. 8, pp. 269-74. Morganosky, M. and Cude, B. (2000), “Large format retailing in the US: a consumer experience perspective”, Journal of Retailing & Consumer Services, Vol. 7 No. 2, pp. 215-22. Progressive Grocer (2005), “Study reveals changing customer mix at Wal-Mart”, Progressive Grocer, Vol. 84 No. 17, p. 12.

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Promo (2005), “Traffic zooms for supercenters”, Promo, Vol. 19 No. 4, p. 17. Seiders, K., Simonides, C. and Tigert, D. (2000), “The impact of supercenters on traditional food retailers in four markets”, International Journal of Retail & Distribution Management, Vol. 28 Nos 4/5, pp. 181-93. Sparks, L. (1995), “Customer service in retailing”, in Akehrst, G. and Nicholas, A. (Eds), Retail Marketing, Frank Call, London. Stone, K. (1995), Competing with the Retail Giants: How to Survive in the New Retail Landscape, Wiley, New York, NY. Taylor, R. (2003), “Top of mind: saving America’s grocers”, Brandweek, Vol. 44 No. 18, pp. 22-3. Tigert, D., Arnold, S. and Cotter, T. (1992), “Warehouse/membership clubs in North America: are they retail? And who is at risk? An 11-city study of household and business shoppers”, Babson College Retailing Research Reports, Report No. 6, April. US Census Bureau (2000), Census of the United States, available at: www.census.gov Further reading Dunne, P. and Lusch, R. (2005), Retailing, 5th ed., South-Western Publishing, Mason, OH. Edelson, S. (2005), “Mass stores go class: upgrading apparel areas to attract new shoppers”, Women’s Wear Daily, Vol. 189 No. 124, pp. 1-2. Corresponding author Jason M. Carpenter can be contacted at: [email protected]

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