Journal of Retailing 2001

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Experimental choice analysis of shopping strategies

Peter T.L. Popkowski Leszczyc1, Harry Timmermansa,2,*aEuropean Institute of Retailing and Services Studies, Eindhoven University of Technology, PO Box 513,

Mail Station 20, 5600 MB Eindhoven, The Netherlands

Received 18 August 1995; accepted 14 June 2001

Abstract

Recent changes in retail structure have created additional ways for consumers to organize their

shopping trips. This study examines the prevalence of different shopping strategies and the impact of

managerial decisions related to pricing, promotions, service, and assortment on the choice of shopping

strategy. A conjoint choice model is developed to address these questions. The model differs from

most previous conjoint choice models in retailing in that it incorporates the similarity of competing

strategies and allows one to test whether consumer choices of shopping strategy are dependent oncontextual variables such as weekday vs. weekend vs. month-end shopping. © 2001 by New York

University. All rights reserved.

Keywords: Shopping strategies; Retail formats; Format strategies; Conjoint choice experiment; Cross-effects

universal logit model

1. Introduction

The retail industry in recent decades has witnessed the emergence of several, striking newretail formats. Traditional convenience and specialty stores, strip malls, and regional mallsnow face competition from box stores, power centers, and factory outlets. In addition, new

* Corresponding author.1 University of Alberta.2 Prof. Peter T.L. Popkowski Leszczyc is an Associate Professor of Marketing at the University of Alberta,

Edmonton. When conducting this project, Prof. Harry Timmermans was Carthy Foundation Chair of Marketing

and Director of the Canadian Institute of Retailing and Services Studies at the University of Alberta. He is also

chaired professor of Urban Planning and Director of the European Institute of Retailing and Services Studies at

the Eindhoven University of Technology, Eindhoven, The Netherlands.

Pergamon

Journal of Retailing 77 (2001) 493–509

0022-4359/01/$ – see front matter © 2001 by New York University. All rights reserved.

PI I : S0 0 2 2 - 4 3 5 9 ( 0 1 ) 0 0 0 5 4 - 9



specialty and department stores have entered the market or repositioned themselves by

emphasizing discounts, customer quality, service, and/or selection. As a result, consumers

now have their choice among many more types of retail outlets. At one end of the retail

spectrum, there is a plethora of small-scale retail operations offering personalized service,

convenient locations, and high quality products. At the other, there are the increasingly larger

megastores and hypermarkets offering one-stop convenience and lower prices, but often at

the cost of less service and less convenient locations. For these large stores, this range of

shopper options raises the question of how to assess competition from small stores; for small

stores, it raises the problem of how to develop ways to coexist with the large, general-

assortment retailers.

The answer to strategic positioning questions, and the likely effects of managerial

decisions, depends heavily upon shopping strategies that consumers employ to select upon

the available options. In light of the current and prospective retail formats, and related

challenges to managerial decision-making, a key research question becomes how consumers

organize their shopping trips when faced with the enlarged set of retail format alternatives.

Which shopping strategies are prevalent? Do consumers typically organize their shopping

behavior in terms of single-stop trips to large-scale retail operations, in terms of multistop

trips to specialty and convenience stores, or in terms of some hybrid strategy in which their

shopping at large scale retail operations is complemented by trips to smaller specialty stores?

Also, what is the effect of management’s strategic decisions related to pricing, promotion,

merchandising, and service on the choice of a particular shopping strategy? And, finally, to

what extent is the opportunity for consumers to apply a specific shopping strategy dependent

upon the competitive retail structure in that market?

An examination of the retailing literature suggests that this issue has not been widely

addressed other than in terms of descriptive analysis. At conferences, several authors have

reported the results of empirical studies that investigated (changes in) consumer shopping

behavior after the coming of new retail formats in a particular market. In the larger cities of

two smaller market areas in Minnesota, for example, Brennan (1998) found that the entrance

of larger discounters curtailed a negative trend of shopping. Consumers now shopped at these

discounters somewhat more frequently than at specialty stores; primarily because of low

prices, but perhaps more surprisingly also because of the large, merchandise selection.

Shopping at extant variety stores had been primarily to buy specialty items individually.

Davidson and Rummel (1998) concluded that the entrance of Wal-Mart in Maine attracted

new shoppers and increased total retail sales. Finn and Timmermans (1996), analyzing the

impact of Wal-Mart in the Edmonton market, concluded that consumers who patronized this

discount department store were more involved in cross-shopping than consumers who

patronized a traditional department store.

The present study seeks to complement the existing literature by addressing:

(i) the prevalence of competing shopping strategies,

(ii) the effect of managerial decisions on the choice of shopping strategy, and

(iii) the extent to which shopping strategies are context-sensitive, especially in terms of

when the shopping trip is made.

494 P.T.L. Popkowski Leszczyc, H. Timmermans / Journal of Retailing 77 (2001) 493–509



To that end, recent developments in experimental (conjoint) choice analysis have beenused to analyze the basis for the selection of shopping strategies. As such, this studycomplements the studies on modeling multistop, multipurpose behavior based on revealedshopping choice behavior (Horowitz, 1980; Kitamura, 1984; O’Kelly, 1981; Borgers andTimmermans, 1986; Arentze et al., 1993; Bacon, 1995; Dellaert et al., 1997, 1998). Becauseconjoint analysis is derived from consumer responses under (quasi-) laboratory situations, itpermits the researcher to present scenarios that reflect potential retail management decisionsnot currently observed in the market place and to study their impact on consumer choicebehavior. The analysis used in the present study also differs from the more commonly usedapproach in that it allows the examination of context and cross-effects in addition to thetypically tested attribute effects (Anderson, 1990; Oppewal and Timmermans, 1991; Ander-

son and Wiley, 1992).We organize the remaining sections as follows: We first formally introduce the research

problem and research questions. Then, we present the research methodology used to befollowed by a discussion of the analyses and the main findings. Finally, we draw conclusions,discuss managerial implications and avenues of future research.

2. Methodology

2.1. The choice problem and conceptual considerations

Consider a consumer faced with the problem of how to organize his/her weekly shoppingtrips when having to purchase two bundles of goods: general merchandise and drug storerelated products. Assume that consumers are faced with a retail environment where thesegoods can be bought at smaller specialty or convenience stores, and at larger “combinationstores.” Characteristic of the first kind of retail establishment is that only one of the twobundles can be bought at a single store, whereas the term “combination store” is used hereto indicate that the store has two departments, where both bundles can be bought. The latter

are more representative of discount box stores or the more traditional department stores.Of course, this classification into two kinds of stores represents a simplification, but as we

will discuss later, the present approach can be easily generalized to include a widerclassification. The primary purpose of this study is exploratory, as we wish to evaluate thepotential of our methodology for theoretical analysis of consumer choice strategies.

Under the outlined circumstances, consumers can choose among four alternative shoppingstrategies; a term which is used here to indicate how consumers organize their shoppingbehavior in terms of the kind of stores they choose to buy different bundles of goods from.

Y one store shopping strategy: consumers choose to buy their bundles of goods at a

single larger, “combination” store, which sells general merchandise and has a drugstore located within. This strategy is likely to be one of pure convenience.

Y multistop specialty/convenience store shopping strategy: consumers choose to buytheir bundles of goods in smaller specialty/convenience stores only during a multistopshopping trip. This strategy is likely to be one of better service or wider selection.

495P.T.L. Popkowski Leszczyc, H. Timmermans / Journal of Retailing 77 (2001) 493–509



Y hybrid specialty/drug store shopping strategy: consumers choose to buy their drugs in

a smaller specialty/convenience store, and their general merchandise in a larger store.Y hybrid specialty/general merchandise shopping strategy: consumers choose to buy

their general merchandise in a smaller specialty/convenience store and their drugs in a

larger store.

We assume that different retail formats, competing for the same market, will implement

strategic and marketing decisions to induce consumers to choose a shopping strategy such

that they will patronize the retail format of interest. Larger stores will try to convince

consumers to become more engaged in one-store shopping. Smaller stores will try to

emphasize the unique features of specialty/convenience stores, stimulating consumers to

adopt a specialty/convenience store shopping strategy, or seek coexistence with the largerstore hoping for hybrid shopping strategies. In this study, we assume that retail managers will

influence consumer shopping strategies using price, parking, promotion, travel time, length

of check-out lines, and assortment retail profile. Note that some of these attributes are of a

more strategic nature, and cannot be easily changed in the short run. Some may be typicalof the retail format, while other attributes are more related to day-to-day operations of the

specific retail format.

2.2. The experiment

The central question we address is: Which shopping strategies are most competitive,and which (co-) existence strategies can retailers develop when faced with multiformatcompetitive conditions? We examine this question by developing a conjoint choice

experiment (Green and Srinivasan 1978; Timmermans 1984; Louviere 1988; Oppewal,

Louviere, and Timmermans, 1994). In particular, we developed a conjoint choice

experiment (Louviere and Woodworth, 1983) that allowed us to examine the impact of managerial strategies on the choice of shopping strategy, the influence of context on the

choice process, and to assess the competition among shopping strategies. We assumed

that the attributes reflecting strategic and managerial decisions have a specific impact on

the various shopping strategies.Five attributes were selected for each of the four shopping strategies. The price variable

required a specification of levels separately for general merchandise and drugs, requiring

three additional attributes. Thus, we examined the effects of 4 5 3 23 attributes. Eachattributes was varied in terms of three levels. Fig. 1 lists these strategy-speci fic attributes and

their levels. Attribute levels were selected to reflect the actual shopping situation in the

market where the data were collected.Combinations of these attribute levels (profiles) were created by constructing an orthog-

onal fractional factorial design of the 323 full factorial design involving 81 fixed choice-sets.

This design allows the estimation of all main effects plus some selected interactions. Eachchoice set thus consists of the four possible shopping strategies that vary in terms of theselected attributes, plus a base alternative, defined as ’none of these.

In addition, the day of the week was varied in the experiment as a context variable. This

allowed us to test whether a particular shopping strategy depends on the day of the week




when the trip is made. To this effect, a distinction was made between a weekday, a weekendday, and a month-end day. The design strategy suggested in Oppewal and Timmermans(1991) was used to examine these context-effects. That is, ‘day of the week ’ was variedindependently as part of the experimental design.

Finally, the design allows the examination of cross-effects (Anderson and Wiley,1992), which can be interpreted in terms of the similarity between shopping strategies.

If the shopping strategies are independent, then the cross-effects will be equal to zerowithin statistical error. Significant cross-effects allow one to draw conclusions withrespect to the question of whether the shopping strategies are complementary or com-petitive. This information may be valuable for retailers to decide on strategies of coexistence.

2.3. The sample

A sample of 405 respondents was drawn in the city of Edmonton, Canada in early 1996.

Students enrolled in three different marketing classes were asked to find three nonstudentsto complete the questionnaire. Students received credits for their participation in the researchprojects, provided they did a good job. Respondents were to be selected at random. The aimof the study and the importance of a good sampling scheme were explained at length.Students were requested to interview the person in the selected households primarily

Fig. 1. The attributes and their levels.




three-level attribute was coded as (1,0), the second level as (0,1), whereas the third levelwas coded as (-1,-1). Consequently, the intercept of the utility function can be inter-preted as the mean utility of the shopping strategies; the coef ficients of the choice modelcan be interpreted as the incremental contribution of the corresponding attribute level tothe average utility of the shopping strategy. Dummy-coding was used to identify the fourshopping strategies.

The universal logit model (McFadden, Train, and Tyne, 1977) was used to estimate choiceprobabilities. This choice model may be expressed as:

PjexpV j

j

expV j

V j 0 k ljklX jkl

jjk l jjklX jkl j

where,

Pj

is the probability that shopping strategy j will be chosen;V j is the structural utility of shopping strategy j;

Xjkl is an indicator variable representing the l-th level of the k -th attribute associatedwith shopping strategy j;

jkl is the effect of level l of attribute k of strategy j on the utility of j;

jjkl is the cross-effect of the l-th level of the k -th attribute of strategy j on the utilityof strategy j. (j j)

The parameters of this model were estimated using iteratively, reweighted least squaresanalysis. Overall, the goodness-of-fit of the assumed universal logit model was good, asindicated by a value of McFadden’s rho-square of 0.24 and a significant improvement of theestimated choice model over the null model.

3.2. Results

The attribute effects related to the various shopping strategies are shown in Table 1. Asindicated by the alternative-specific constants, this table shows that sample respondentspreferred the multistop specialty/convenience store shopping strategy B closely followed bythe hybrid/specialty drug store shopping strategy C.

The one-store shopping strategy A was less popular among the sample respondents. Atfirst glance, this finding may seem surprising given the success of major new retailers suchas Wal-Mart. A few circumstances should however be kept in mind when interpreting theseresults. First, Edmonton is an urban market with its downtown area still well-developed.Many neighborhood retail strips and community shopping centers, also offered excellent

opportunities for short shopping trips, from home and from work. Secondly, one shouldrealize that the utility constant reflects the average for the attribute values varied in theexperiment, and thus depends on the selected attribute levels. A closer inspection of Table1 indicates that the prevalence of the specialty/convenience store shopping strategy dependson the extent to which the specialty stores can successfully effectuate lower prices for general




merchandise and drugs. If they can’t, and competitive pressure is high in the real market, then

the utility of this shopping strategy drops from 0.64 to 0.25, making it significantly less

attractive.When we examine the contribution of the attribute levels on the utility and choice

probability of the various shopping strategies, Table 1 demonstrates that all estimatedattribute effects are in readily intuitive directions. The probability of choosing a particular

shopping strategy grows in an increasing, nonlinear way with lower prices and lower parking

Table 1

Attribute effects: different shopping strategies

Parameter Strategy A Strategy B Strategy C Strategy D

Constant 0.007 0.641 0.618 0.5741(0.161) (16.591) (16.088) (15.023)

Price 5% lower overall 10% higher on drugs 5% lower on GM 5% lower on drugs0.341 0.256 0.266 0.456

(6.954) (6.765) (6.846) (10.739)10% lower overall 0% higher on drugs 10% lower on GM 10% lower on drugs0.077 0.036 0.110 0.020

(1.635) (0.982) (3.042) (0.391)10% higher on GM 10% higher on drugs 10% higher on GM0.258 0.286 0.108

(6.708) (7.459) (2.752)0% higher on GM 0% higher on drugs 0% higher on GM0.087 0.024 0.044

(2.318) (0.650) (1.132)Assortment3 brands 0.042 0.099 0.071 0.031

(0.891) (2.675) (1.929) (0.781)5 brands 0.247 0.161 0.238 0.278

(5.453) (4.375) (6.533) (7.275)Parking$0.50/hour 0.093

(1.943)

$1/hour 0.254(4.994)

Travel time15 minutes 0.327 0.327 0.284 0.292

(7.310) (9.107) (7.865) (7.724)30 minutes 0.064 0.056 0.023 0.060

(1.344) (1.511) (0.638) (1.537)Check-out line3 shoppers 0.051 0.154 0.139 0.219

(1.085) (4.206) (3.797) (5.744)5 shoppers 0.027 0.025 0.078 0.133

(0.058) (0.689) (2.137) (3.484)

Promotion 20% discount overall 20% discount on drugs 20% discount on GM0.526 0.230 0.368(14.820) (6.369) (9.657)

10% discount overall 10% discount on drugs 10% discount on GM0.328 0.023 0.067

(9.107) (1.929) (1.691)




fees, better assortment, more national brands, less travel time, and shorter check-out lines.The specific influence and importance of the managerial variables varies by shoppingstrategy.

The significant results of Table 1 are summarized in Figs. 2– 6. Fig. 2, shows the

Fig. 2. Significant main effects of price.

Fig. 3. Significant main effects of assortment.




significant main effects for different price levels, and indicates that respondents are lesslikely to select strategies A, C, and D, when prices for the respective categories (foritems not purchased in a specialty store) are only five percentage lower. The first threeeffects are for items not purchased at specialty stores, while the next five effects are

related to products purchased at a specialty store. In all instances respondents are lesslikely to select shopping strategies B, C, and D, when prices are ten percentage moreexpensive in the specialty store. Providing more assortment has a positive effect (see Fig.3), this is the lowest for shopping strategy B. This may be due to consumers’ expecta-tions for better assortments at specialty stores. As indicated by Fig. 4, lower travel time

Fig. 4. Significant main effects of travel time.

Fig. 5. Significant main effects for length of checkout line.




has a positive effect, which is similar for all shopping strategies. Similarly Fig. 5, shows

that shorter check-out lines have a positive effect for all strategies, with the exception of

strategy A, indicating that length of check-out lines becomes more important when

consumers visit more than one store. Finally, Fig. 6 shows the main effects for promo-

tions, and indicates that a twenty percentage discount has a positive effect in all instances

and is strongest for strategy B.The central question concerns the similarity of shopping strategies. To answer this

question, we tested the cross-effects of the estimated universal logit model for signifi-

cance. We have broken down these effects into the effects for each strategy separately.

Figs. 7–10 provide a graphical comparison of the cross-effects for the different shopping

strategies. Fig. 7, shows that higher prices by competing strategies or small price differences

increases the utility for a single-stop shopping strategy A. On the other hand, an increased

assortment for multistop strategies (B, C, and D) has a similar negative effect on strategy A.

Reduced travel time in particular to visit two specialty stores, and reduced check-out lines

and 20% discount for the other two hybrid strategies (C and D), decreases the utility of theone-store shopping strategy.

Fig. 8, shows the significant cross-effects on the shopping at specialty stores only strategy

B. This strategy has a higher utility when price are only 5% lower for all the other strategies.

Relative to the other shopping strategies, strategy B is least effected by increased assortment

Fig. 6. Significant main effects of promotion.




for the other strategies. Reduced travel time has a negative effect, but only for the other

multistop strategies.

Figs. 9 and 10 show the significant cross-effects for the two hybrid strategies (C and

D). Utility for these strategies tends to be higher due to higher prices of other shopping

Fig. 7. Significant cross effects on one-stop shopping strategy.




strategies, and due to prices that are only 5% lower. Increased assortment has a negativeeffect for both strategies, but only with respect to the other multistop strategies. Differentfrom strategy B, a positive effect is observed for strategies C and D when there is a $1.00parking fee for shopping strategy A. Similar to the results for the other strategies,reduced travel time, shorter check-out lines have a negative impact on utility for

Fig. 8. Significant cross effects on strategy B.




strategies C and D. However, strategy C is more impacted by competitive actions thanstrategy D. Finally, both strategies are only negatively impacted by 20% discounts forstrategy B.

Fig. 9. Significant cross effects on strategy C.




4. Conclusion and discussion

Modern retail systems may be evolving towards an increasing dichotomy where smallspecialty and convenience stores try both to compete and coexist with increasingly largerstores. In light of this pattern of competition, it is surprising to find a lack of attention in the

Fig. 10. Significant cross effects on strategy D.




retailing literature on how consumers organize their daily shopping trips, especially whenfaced with the problem where to buy bundles of goods which can be bought both at the largercombination stores (one-stop shopping) and at the specialty stores.

To gain more insight into this question, we constructed a conjoint choice experiment andapplied this to a convenience sample of shoppers in the Edmonton market. The problem wasconceptualized by allowing the respondents to choose between four ways of organizing theirshopping trip (shopping strategies), which were varied in terms of potential marketingdecisions of retailers, and manipulated for the day of the week/month when the shopping tripwas made. A context-sensitive universal logit model was used to examine the (competitive)effects of managerial decisions on the probability that a particular shopping strategy will bechosen.

In terms of managerial implications, the results of this study suggest that consumers preferto shop at specialty stores for the products studied. A single-stop trip to a combination storewas the least preferred. Lower prices and travel times, and a larger assortment, however,increase the likelihood that consumers will shop at combination stores. The cross-effectsindicate the degree of similarity between shopping strategies.

Managers can use the information represented by these effects to attract customers fromother stores and identify the strategic decisions likely to have most impact. For example,managers of combination stores can attract more customers by offering lower prices andconvenience (shorter check-out lines), while managers of specialty stores can most effec-

tively compete by providing a better assortment. Hence, while assortment is important forboth specialty and combination stores, specialty stores can more effectively attract customersfrom their competition by offering widening their typically limited selections. Combinationstores will be less effective in attracting customers from competition by offering moreassortment; offering lower prices is superior. Specialty stores gain most from using pricepromotions (a strong main effect), and also attract consumers from other multistop shoppingstrategies but not from the combination stores. Note that many of these findings can only beobtained from cross-effects we introduced in our model.

Acknowledgments

The authors would like to thank G. Natesan, a former Ph.D. candidate at the Universityof Alberta for coordinating the field work, inputting the data, and for his assistance in runningthe analyses. The authors would also like to thank the editor, L. P. Bucklin, and threeanonymous reviewers for their insightful critical comments on an earlier draft of this paperand their suggestions for improvement. They considerably improved the organization andreadability of the paper.

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