Experimental choice analysis of shopping strategies

Pergamon Journal of Retailing 77 (2001) 493 509 Experimental choice analysis of shopping strategies Peter T.L. Popkowski Leszczyc 1, Harry Timmermans a,2, * a European 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 on contextual 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 new retail formats. Traditional convenience and specialty stores, strip malls, and regional malls now 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. 0022-4359/01/$ see front matter 2001 by New York University. All rights reserved. PII: S0022-4359(01)00054-9

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

P.T.L. Popkowski Leszczyc, H. Timmermans / Journal of Retailing 77 (2001) 493 509 495 To that end, recent developments in experimental (conjoint) choice analysis have been used to analyze the basis for the selection of shopping strategies. As such, this study complements the studies on modeling multistop, multipurpose behavior based on revealed shopping choice behavior (Horowitz, 1980; Kitamura, 1984; O Kelly, 1981; Borgers and Timmermans, 1986; Arentze et al., 1993; Bacon, 1995; Dellaert et al., 1997, 1998). Because conjoint analysis is derived from consumer responses under (quasi-) laboratory situations, it permits the researcher to present scenarios that reflect potential retail management decisions not currently observed in the market place and to study their impact on consumer choice behavior. The analysis used in the present study also differs from the more commonly used approach in that it allows the examination of context and cross-effects in addition to the typically tested attribute effects (Anderson, 1990; Oppewal and Timmermans, 1991; Anderson 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 be followed 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 shopping trips when having to purchase two bundles of goods: general merchandise and drug store related products. Assume that consumers are faced with a retail environment where these goods can be bought at smaller specialty or convenience stores, and at larger combination stores. Characteristic of the first kind of retail establishment is that only one of the two bundles can be bought at a single store, whereas the term combination store is used here to 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 wider classification. The primary purpose of this study is exploratory, as we wish to evaluate the potential of our methodology for theoretical analysis of consumer choice strategies. Under the outlined circumstances, consumers can choose among four alternative shopping strategies; a term which is used here to indicate how consumers organize their shopping behavior 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 drug store located within. This strategy is likely to be one of pure convenience. Y multistop specialty/convenience store shopping strategy: consumers choose to buy their bundles of goods in smaller specialty/convenience stores only during a multistop shopping trip. This strategy is likely to be one of better service or wider selection.

496 P.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 larger store 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 typical of 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 multiformat competitive 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. Each attributes was varied in terms of three levels. Fig. 1 lists these strategy-specific 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 orthogonal fractional factorial design of the 3 23 full factorial design involving 81 fixed choice-sets. This design allows the estimation of all main effects plus some selected interactions. Each choice set thus consists of the four possible shopping strategies that vary in terms of the selected 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

P.T.L. Popkowski Leszczyc, H. Timmermans / Journal of Retailing 77 (2001) 493 509 497 Fig. 1. The attributes and their levels. when the trip is made. To this effect, a distinction was made between a weekday, a weekend day, 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 varied independently 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 zero within statistical error. Significant cross-effects allow one to draw conclusions with respect to the question of whether the shopping strategies are complementary or competitive. 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 nonstudents to complete the questionnaire. Students received credits for their participation in the research projects, provided they did a good job. Respondents were to be selected at random. The aim of 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

498 P.T.L. Popkowski Leszczyc, H. Timmermans / Journal of Retailing 77 (2001) 493 509 responsible for daily shopping. They were told that the researchers might check whether the correct procedure was followed and telephone numbers of respondents were requested for possible call-back. Although we did not find any evidence of students not taking this task seriously or not strictly following our guidelines, it is still best to consider the data as a convenience sample. Given the purpose of the study, this is not an issue. We had no intentions of generalizing the results to the Edmonton population. As explained earlier, our primary interest was to examine the nature of shopping strategies and to explore the appropriateness of the conjoint choice model for such an analysis. The average age of the sample was 32.8 years with an average household size 3.1; 55% of the respondents were married, and 67% of the interviewed respondents were women. The vast majority of the respondents were employed. In addition to these demographics, respondents were also invited to report their actual behavior. Multistop behavior to specialty stores accounts for the highest proportion of their shopping behavior. 2.4. Experimental task Each respondent was shown a subset of 27 out of the 81 choice sets plus four trial sets that were not used in the analysis. To minimize the influence of potential order effects, we created 18 different randomized versions of the experimental design. Both the order of the choice sets and that of the attributes were randomized across versions. Respondents were asked to choose the strategy they liked best from each of the 27 choice sets, given its corresponding attribute profile and the specified context (day of the week/month). The four trial choice sets were used to familiarize the respondents with the experimental task. Students were told to make sure that respondents asked questions of explanation at this stage of the interview. Students were requested to explain the task in detail to the respondents if there was any indication that respondents did not fully comprehend what they were being asked. Students themselves completed the task in class and were trained intensively in preparation for the interviewing sessions. Questionnaire and choice set layout, and the wording used to describe the attribute profile and explain the experimental task were piloted before putting the instrument in the field. 3. Analysis and results 3.1. Analysis The choices observed for each of the 4 shopping strategies in each choice set were aggregated across respondents. This resulted in 81 5 405 choice frequencies, providing the relative frequency with which respondents chose the four shopping strategies, and the base alternative of no choice for each of the 81 choice sets. The choice frequencies constitute the observations of the dependent variable of the choice model. Effect coding was used to represent the selected attribute levels. This implies that two indicator variables were used for each three-level attribute. The first level of each

P.T.L. Popkowski Leszczyc, H. Timmermans / Journal of Retailing 77 (2001) 493 509 499 three-level attribute was coded as (1,0), the second level as (0,1), whereas the third level was coded as (-1,-1). Consequently, the intercept of the utility function can be interpreted as the mean utility of the shopping strategies; the coefficients of the choice model can be interpreted as the incremental contribution of the corresponding attribute level to the average utility of the shopping strategy. Dummy-coding was used to identify the four shopping strategies. The universal logit model (McFadden, Train, and Tyne, 1977) was used to estimate choice probabilities. This choice model may be expressed as: where, P j exp V j j exp V j V j 0 k l jkl X jkl j j k l jj kl X j kl j P j is the probability that shopping strategy j will be chosen; V j is the structural utility of shopping strategy j; X jkl is an indicator variable representing the l-th level of the k-th attribute associated with shopping strategy j; jkl is the effect of level l of attribute k of strategy j on the utility of j; is the cross-effect of the l-th level of the k-th attribute of strategy j on the utility of strategy j. (j j ) jj kl The parameters of this model were estimated using iteratively, reweighted least squares analysis. Overall, the goodness-of-fit of the assumed universal logit model was good, as indicated by a value of McFadden s rho-square of 0.24 and a significant improvement of the estimated choice model over the null model. 3.2. Results The attribute effects related to the various shopping strategies are shown in Table 1. As indicated by the alternative-specific constants, this table shows that sample respondents preferred the multistop specialty/convenience store shopping strategy B closely followed by the hybrid/specialty drug store shopping strategy C. The one-store shopping strategy A was less popular among the sample respondents. At first glance, this finding may seem surprising given the success of major new retailers such as Wal-Mart. A few circumstances should however be kept in mind when interpreting these results. 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 should realize that the utility constant reflects the average for the attribute values varied in the experiment, and thus depends on the selected attribute levels. A closer inspection of Table 1 indicates that the prevalence of the specialty/convenience store shopping strategy depends on the extent to which the specialty stores can successfully effectuate lower prices for general

500 P.T.L. Popkowski Leszczyc, H. Timmermans / Journal of Retailing 77 (2001) 493 509 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 drugs 0.341 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 drugs 0.077 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 GM 0.258 0.286 0.108 ( 6.708) ( 7.459) ( 2.752) 0% higher on GM 0% higher on drugs 0% higher on GM 0.087 0.024 0.044 ( 2.318) ( 0.650) ( 1.132) Assortment 3 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/hour ( 1.943) 0.254 ( 4.994) Travel time 15 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 line 3 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 GM 0.526 0.230 0.368 (14.820) (6.369) (9.657) 10% discount overall 10% discount on drugs 10% discount on GM 0.328 0.023 0.067 (9.107) (1.929) (1.691) 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 estimated attribute 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

P.T.L. Popkowski Leszczyc, H. Timmermans / Journal of Retailing 77 (2001) 493 509 501 Fig. 2. Significant main effects of price. 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 shopping strategy. The significant results of Table 1 are summarized in Figs. 2 6. Fig. 2, shows the Fig. 3. Significant main effects of assortment.

502 P.T.L. Popkowski Leszczyc, H. Timmermans / Journal of Retailing 77 (2001) 493 509 Fig. 4. Significant main effects of travel time. significant main effects for different price levels, and indicates that respondents are less likely to select strategies A, C, and D, when prices for the respective categories (for items not purchased in a specialty store) are only five percentage lower. The first three effects 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 less likely to select shopping strategies B, C, and D, when prices are ten percentage more expensive 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 expectations for better assortments at specialty stores. As indicated by Fig. 4, lower travel time Fig. 5. Significant main effects for length of checkout line.

P.T.L. Popkowski Leszczyc, H. Timmermans / Journal of Retailing 77 (2001) 493 509 503 Fig. 6. Significant main effects of promotion. 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 promotions, 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 significance. 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 the one-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

504 P.T.L. Popkowski Leszczyc, H. Timmermans / Journal of Retailing 77 (2001) 493 509 Fig. 7. Significant cross effects on one-stop shopping strategy. 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

P.T.L. Popkowski Leszczyc, H. Timmermans / Journal of Retailing 77 (2001) 493 509 505 Fig. 8. Significant cross effects on strategy B. strategies, and due to prices that are only 5% lower. Increased assortment has a negative effect for both strategies, but only with respect to the other multistop strategies. Different from strategy B, a positive effect is observed for strategies C and D when there is a $1.00 parking 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

506 P.T.L. Popkowski Leszczyc, H. Timmermans / Journal of Retailing 77 (2001) 493 509 Fig. 9. Significant cross effects on strategy C. strategies C and D. However, strategy C is more impacted by competitive actions than strategy D. Finally, both strategies are only negatively impacted by 20% discounts for strategy B.

P.T.L. Popkowski Leszczyc, H. Timmermans / Journal of Retailing 77 (2001) 493 509 507 4. Conclusion and discussion Fig. 10. Significant cross effects on strategy D. Modern retail systems may be evolving towards an increasing dichotomy where small specialty and convenience stores try both to compete and coexist with increasingly larger stores. In light of this pattern of competition, it is surprising to find a lack of attention in the

508 P.T.L. Popkowski Leszczyc, H. Timmermans / Journal of Retailing 77 (2001) 493 509 retailing literature on how consumers organize their daily shopping trips, especially when faced with the problem where to buy bundles of goods which can be bought both at the larger combination stores (one-stop shopping) and at the specialty stores. To gain more insight into this question, we constructed a conjoint choice experiment and applied this to a convenience sample of shoppers in the Edmonton market. The problem was conceptualized by allowing the respondents to choose between four ways of organizing their shopping trip (shopping strategies), which were varied in terms of potential marketing decisions of retailers, and manipulated for the day of the week/month when the shopping trip was 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 be chosen. In terms of managerial implications, the results of this study suggest that consumers prefer to shop at specialty stores for the products studied. A single-stop trip to a combination store was 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-effects indicate the degree of similarity between shopping strategies. Managers can use the information represented by these effects to attract customers from other 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 and convenience (shorter check-out lines), while managers of specialty stores can most effectively compete by providing a better assortment. Hence, while assortment is important for both specialty and combination stores, specialty stores can more effectively attract customers from their competition by offering widening their typically limited selections. Combination stores will be less effective in attracting customers from competition by offering more assortment; offering lower prices is superior. Specialty stores gain most from using price promotions (a strong main effect), and also attract consumers from other multistop shopping strategies but not from the combination stores. Note that many of these findings can only be obtained from cross-effects we introduced in our model. Acknowledgments The authors would like to thank G. Natesan, a former Ph.D. candidate at the University of Alberta for coordinating the field work, inputting the data, and for his assistance in running the analyses. The authors would also like to thank the editor, L. P. Bucklin, and three anonymous reviewers for their insightful critical comments on an earlier draft of this paper and their suggestions for improvement. They considerably improved the organization and readability of the paper. References Anderson, D.A. (1990). A review of statistical designs for estimating cross effects in choice modelling experiments, paper presented at the Banff Invitational Symposium on Consumer Decision Making and Choice Behavior, May 10 15, Banff, Alberta, Canada.

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