Nonstore versus store retailing. A situationally based market structure assessment

KENNETH C. GEHRT THOMAS N. I N G R A M VINCE H O W E

Nonstore versus Store Retailing A Situationally Based Market Structure Assess men t KENNETH C. GEHRT i s an assistant professor of marketing at the University of Maine He earned his DBA in marketing from the University of Kentucky His research interests include international marketing, health-care marketing. and nonstore shopping analysis, a topic which he has previously explored in JDM, both as sole author and in collaboration with the present coauthors. Dr Gehrt has also published articles in OtherjOUrnalS THOMAS N INGRAM has a PhD from Georgia State University and holds the Sales and Marketing Executives of Memphis Chair in Sales Excellence at the Fogelman College of Business and Economics, Memphis State University, where he i s a professor of marketing The Sales and Marketing Executives International. a professional organization. named him their “Marketing Educator of the Year” for 1990. He has coauthored two books and contributed widely tojournals. VINCE HOWE is an assistant professor of marketing at the University of North Carolina at Wilmington. Previously a marketing manager in the automotive industry, Dr Howe has also taught at the Universities of Kentucky and Georgia, and has contributed to several academic publications. His research interests are marketing research, channels, and nonprofit marketing.

KENNETH C GEHRT

THOMAS N INGRAM

VINCE HOWE

ABSTRACT The impact of situational factors has typically been investigated in the context of goods marketing. Very few studies have investigated the impact of situational factors o n retailing. This study demonstrates the importance of situational influence o n retail marketing b y delineating a situationally defined competitive market structure for store and nonstore retailers. This is done by 1 ) characterizing ten sporting goods retailers with respect to pertinent situational contingencies, and 2) clustering the retailers in terms of the similarity/substitutability of their situational characterizations. The general marketing implications of the procedure and the specific retailing implications of the findings are discussed. 0 1991 John Wiley 8r Sons, lnc. and Direct Marketing Educational Foundation, Inc. CCC 0892-0591/91/02044-10$04.00

CC

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INTRODUCTION

Retail growth in recent years has been prolific in the nonstore sector. Nonstore retailing consists largely of exchanges transacted via mail and telephone, but also includes exchanges transacted via party-selling, door-to-door selling, at-work selling, and vending machines (1 1,20,38). It is highly similar to direct marketing, but does not include business-to-business transactions. Recent estimates show nonstore retail sales increasing 50 percent faster than store sales (41).This growth is consistent with predictions regarding retail evolution (18,23) which suggest vast growth potential in the area of nonstore retailing. Thus, the nature of competition in the retail marketplace has been substantially altered and will probably continue to change. Because successful retailing concepts can be undermined so quickly (3,22), it is essential that nonstore retailers understand the competitive dynamics of the marketplace. Despite substantial progress in the investigation of store patronage, however, nonstore patronage remains a relatively neglected topic. Cox and Rich’s (12) study was the first significant empirical investigation of nonstore patronage behavior. Today, the nonstore patronage literature consists of only a handful of studies. The research has typically attempted to explain nonstore patronage proclivities on the basis of demographic profiles (9,14,19,34,36,43) or on the basis of nonstore retailer attributes (1,42).A comparison of the studies reveals that their findings often conflict. The research has largely excluded other potentially important predictor variables. PURPOSE OF T H E STUDY

The present study offers several contributions to the nonstore patronage literature. Past research is extended by incorporating situational factors (4,5,8,30) as determinants of nonstore patronage. This is consistent with several studies (29,30,44,45) which recognize the possibility that inter-retailer competition may be affected by situational contingencies. The study uses situational factors to characterize nonstore retailers with respect to their appropriateness in various patronage situations. Furthermore, multiple store and nonstore retailers are analyzed in the context of the situationally defined

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marketplace. This provides a more comprehensive delineation of retail competition than past nonstore research which has often utilized one store and one nonstore alternative (9,12,16,34,43). SITUATIONAL INFLUENCE

The realization that human behavior depends on situational factors as well as individual factors has a theoretical foundation in Lewin’s (26) field theory. Field theory asserts that each individual views his or her physical/social predicament somewhat differently. This subjective conceptualization of the world is called “life-space” and is a result of 1) individual traits, and 2 ) environmental or situational factors. Dickson (17) contends that utility functions and consumption behavior vary as a result of differences in life-space. Thus individual traits should not be the sole source of explanatory variables for buyer behavior. Situational factors must also be examined. Situational factors have proven to be effective predictors in studies investigating store choice (29,30,47), product choice (6,28,40,45), and brand choice (5,37,48) (Table 1). They have not yet been explored in the context of nonstore retailing. Situational influences include all of those factors particular to a time and place which do not follow from knowledge of personal and stimulus attributes and which have systematic effect on current behavior ( 4 ) . Specifically, situational factors are likely to include physical, social, temporal, and task definition dimensions ( 5 ) . Consideration of situational factors may help to provide a more complete understanding of nonstore patronage behavior. Early nonstore research implicitly assumed that individual traits, invariant across situations, determine a person’s perceptions and behavior regarding nonstore patronage. More recent research has at least acknowledged that the investigations are situationally grounded (9,42). MODELING INTER-RETAILER COMPETITIVE MARKET Structure o n a Situational Bask Inter-product and inter-retailer competition have often been operationalized on the basis of product

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TABLE 1 Research on Situational Influence Influence on Product Choice Sandell (40)

Findings: Preference for drinks varies with situation. Situation: Various appropriate scenarios with unequal amount of information.

Belk (4)

Findings: Usage of meat and snack products varies with situation. Situation: Various appropriate scenarios w i t h unequal amount of information.

tutz, Kakkar 128)

Findings: Usage of snack products varies with situation. Situation: Various appropriate scenarios w i t h unequal amount of information.

Srivastava, Leone, Shocker (45)

Findings: Delineation of competitive market structure for banking services on basis of situational factors. Situation: In/out-of-town; expected/unexpected; large/small dollar amount; retail credit available/unavailable.

Influence o n Brand Choice Srivastava, Shocker, Day (44)

Findings Development of parsimonious situational taxonony

Warshaw (48)

Findings Situationally conditioned purchase intentions for soft drinks more accurately predict actual purchase than unconditional purchase intentions Situation Purchase locations (three alternatives), number of brands purchased (one/ more than one)

Rosen, Sheffett (37)

Findings Usage o f generic/non generic brands varies with situation Situation Formal/informal dinner. guest/no guest attending

Influence on Store Choice Miller, Ginter (30)

Findings: Store preference. store patronage. frequency. store attribute importance varies w i t h situation. Situation: Various appropriate scenarios with unequal amounts of information.

Vincent, Zikmund (47)

Findings: Perceived risk varies with situation. Situation: Personal use/gift.

Mattson (291

Findings: Store preference, store attribute importance varies with situation. Situation: Personal use/gift: time pressure/no time pressure.

and store attributes (22,24,32,33,39).Products and retailers which have similar attributes are assumed to be competitive. However, this narrowly defined, product-oriented conceptualization is primarily applicable to inter-brand level competition (2,45). Competitive threats from other/new product forms are somewhat difficult to detect. A more broadly conceived, consumer-oriented definition of the market could account for competitive threats which transcend a single product form. One such conceptualization is a situationally defined market structure in which the market is analyzed in terms of situational contingencies which affect the consumer’s purchase decision. It has been argued that when several products are perceived to b e useful in a particular usage situation, they are substitutable and, consequently, competitive prod-

46 JOURNAL OF DIRECT MARKETING

ucts (7,17,45).Thus, by characterizing products or retailers with respect to pertinent situational contingencies, it is possible to determine whether those products or retailers are competitive. METHODS Operationalization of Situational Factors and Retailers

Three focus group interview sessions were conducted to generate a list of pertinent situational factors as well as a list of pertinent retailers. In order to tap a wide range of experience with nonstore shopping, the focus groups were comprised of individuals from three varied sources. The first group consisted of seven members of a recreational club;

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the second group was comprised of 10 members of a church group; and the third group of 10 individuals included business people and business faculty. Group participants were asked to identify situations in which the purchase of sporting goods from a catalog would be appropriate or inappropriate. Participants were next asked to develop an extended list of retail outlets which they might find suitable to patronize, given the list of situations. Finally, the participants were asked for additional situational factors which might have bearing on retailer patronage decisions as suggested by the expanded list of retailers. The procedure prevented the discussion from stagnating and encouraged consideration of a broadly defined set of situations and retailers. Upon review of the recorded transcripts, 10 retailers and two situational factors were selected. The patronage situation factors include 1) four task factors, and 2) two product factors. The task and product factors were combined, fully crossed, to yield eight situational scenarios (see Table 2 ) . The four nonstore and six store retailers that were elicited are also shown in Table 2. The questionnaire would ask respondents to rate the appropriateness of the 10 retailers in each of the eight situational scenarios using a five-point scale (1 = highly inappropriate, 5 = highly appropriate). Selection of appropriateness as the dependent variable is explained below.

TABLE 2

Situational Scenarios Warmup/for Yourself for Workouts 10/01 You have decided to purchase a warmup suit for yourself for workout sessions. Warmup/for Yourself for competition (0/1 ] You have decided to purchase a warmup suit for yourself for an upcoming athletic event in which you will be competing. Warmup/Gift ( 0 / 2 ) You have decided to purchase a warmup suit to be given to someone as a gift. Warmup/Team (0/3) You and a group of your friends/your team have decided to purchase warmup suits. Shoes/for Yourself for Workouts ( I / O ) You have decided to purchase a pair of athletic shoes for yourself for workout sessions. Shoes/for Yourself for Competition [ 1 / 1 I You have decided to purchase a pair of athletic shoes for yourself for an upcoming athletic event in which you will be competing. Shoes/Gift ( 1 /21 You have decided to purchase a pair of athletic shoes to be given to someone as a gift. Shoes/Team I I /31 You and a group of your friends/your team have decided to purchase athletic shoes.

Selection of Dependent Variable

The existing nonstore research has relied largely on self-reported, past patronage frequency as a dependent measure (9,12,13,16,19,27,34,36).Because of the number of retailer X situation combinations which subjects had to respond to in this study, selfreport behavioral measures would have put excessive demands on respondents’ recall of past patronage behavior. A perceptual dependent measure was used to circumvent recall problems. A perceptual measure also provides strategic insight regarding the competitive market structure (15). Rather than describing the market as it formerly existed, a perceptual measure can provide insight regarding future market structure ( 4 9 ) . Measures including preference (28,40), appropriateness (7,15,45,46),and likelihood of purchase (37,481 have been used successfully in the extant situational research. This study utilized retailer appropriateness as the dependent variable. Appropriateness was utilized by Srivastava, Leone, and

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Store/Nonstore Retailers Discount store. Department store. Factory outlet. General sporting goods store. Specialty sporting goods store. Outdoors store. General sporting goods catalog Specialty sporting goods store. Outdoors store. Direct magazine advertising.

Shocker ( 4 5 ) and Srivastava, Alpert, and Shocker (44) in a context similar to this study. Pretest

A pretest of the questionnaire was conducted in or-

der to identify potential problems with instrument

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47

ambiguities. A convenience sample of 47 upper-division business students was utilized. The respondents had little problem assigning retailer appropriateness ratings. In the final data collection, presentation was counter-balanced to avoid potential sequencing effects (25). Sample

Shoppers at a specialty sporting goods store in a major midwestern city were asked to complete the questionnaire while they were in the store. Only individuals who qualified as heads of household were allowed to complete the questionnaire. The questionnaires were distributed o n a face-to-face basis as shoppers prepared to leave the store. In order to maintain anonymity with respect to their retailer ratings, as well as to the demographic information, respondents were instructed to deposit the questionnaire in a slotted box upon completion. The survey method resulted in the collection of 271 usable questionnaires, a response rate of 88 percent. Reliability A subsequent mail questionnaire was used to assess

the test-retest reliability of the store-exit survey instrument. Subjects were asked to participate in the follow-up study before they deposited their onpremises questionnaires. This was done until 70 subjects had volunteered. Coding of corresponding on-premises and mail questionnaires required that test-retest respondents relinquish anonymity. Fifty-one of the 70 retest questionnaires were returned. Retailer ratings from the on-premises questionnaire were regressed against ratings from corresponding mail questionnaires. Regression analysis yielded an acceptable reliability coefficient of .79 (10,251. DATA ANALYSIS

The delineation of a situationally defined competitive market structure is essentially a two-step process. First, the 10 retailers are situationally characterized with respect to the two situational factors. This is accomplished by using dummy variable regression analysis to develop situational beta weights for each retailer. Situationally competitive retailers are then identified in terms of the similarity of their situational

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beta weights. Cluster analysis is used to group retailers whose situational beta weights are most similar and, consequently, most competitive. Situational Characterization of Retailers/ D u m m y Regression

Retailers were situationally characterized by first preparing the appropriateness ratings data by summing across individuals. This yielded an 8 (situation) X 10 (retailer) matrix of ratings. Situational characterization of retailers was completed by subjecting the matrix to dummy variable multiple regression. The coding of the situational scenarios is shown in Table 2. Ten separate regression models were estimated, o n e for each retailer. The situational beta weights are utilized because this study is not concerned with each retailer’s current market position which would be reflected by the y-intercept of unstandardized regression coefficients ( 4 5 ) . The results of the multiple regression procedure are shown in Table 3. The adjusted R 2 measures which are well above .80 indicate a very strong relationship between the situation and retailer ratings. All of the models are significant at 5.10. Identification of Competitive Retailers/ Cluster Analysis

Once the retailers were characterized, the SPSSx algorithm CLUSTER was used to group objects (retailers) in an iterative manner which maximizes the prediction of the accountable variance (beta weights rather than raw scores) between objects on some relevant predictor variable (situational beta weights). Squared Euclidean distance and average linkage between groups were used to calculate clusters. The agglomerative algorithm begins by treating each of the 10 retailers as a separate cluster. The pair of retailers with the most similar situational beta weights is clustered first. The next step could either 1) group two single retailers, or 2) group a single retailer with the previously clustered pair (31). The algorithm provides a measure of the distance between retailers clustered at each stage which was used as a clustering stopping rule. The distance measure will generally increase stage by stage. A substantial increase in the measme would indicate that the retailers being clustered are in-

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TABLE 3

Situational Beta Weights and Adjusted R2

Direct advertising Specialty store

Personal Competition

Gift Giving

-.83

-.2 1

-.60

.77

,846

.I9

.87b

-.I0

.9Ob

-.94

-.23

.I5

-.23

.93b

-.80

-.I9

.39

-.26

.97a

Outdoors catalog Department store

-.33

-.34

Adjusted R2

-. 17

-.92

.39

Group Purchase

-.39

Factory outlet

General catalog

-.93

-.24

.I5

Outdoors store

-.90

-.22

.25

General store

J =

Shoes

.48

.35

.I 1 -.I6

99a ,972

-.I0

-.24

,952

Discount store

-.9 1

-.29

.03

-.33

.9 1 b

Specialty catalog

-.97

-.I0

-.2 I

-.94

.92b

significant at 5 .01, b = significant at < 0 5

creasingly dissimilar and that the predictive power of the model is rapidly decreasing (21). Interpretation of the solution is accomplished by comparing the beta weights of retailers (included in Figure 1) within a single cluster (competitive retailers). The similarities of their beta weights reveal the manner in which they are situationally competitive. Interpretation can also be aided by comparing the beta weights of retailers between clusters (noncompetitive retailers). The dissimilarities of their beta weights reveal the manner in which they are situationally distinctive. Competitive retailers may be competitive because their four situational betas are generally similar. Their competitiveness could also be the result of a single, highly similar situational beta weight. Comparison of noncompetitive retailers will generally reveal disparate situational betas. Findings of Hierarchical Clustering A substantial increase in the clustering distance

measure occurred at stage seven where the distance measure increased by .350 compared to ,045 for the preceding stage (Figure 1).Thus the optimal cluster solution is at stage six. A split-sample analysis was performed to evaluate the stability of the cluster solution and to assure that the obtained results correspond to true perceptual differences rather than method artifacts ( 3 5 ) . The final cluster solution for each half was identical to the solution for the entire sample, providing a measure of cross-validation.

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DISCUSSION

The cluster solution at stage six consists of one seven-retailer cluster with distance coefficient increments not exceeding .059 and three single retailer clusters (see Figure 1). The substantial distance coefficient increments associated with stages seven (.350),eight (.257),and nine (2.653) suggest that the single retailers which would have clustered with other retailers at these stages are perceived as unique entities. Thus the hierarchical clustering procedure reveals some clear distinctions between competitive retailer clusters as well as similarities within clustered groups. The cluster solution can be best understood on the basis of the three single retailer clusters. The respondents apparently perceive these to be so distinctive that the other seven retailers are summarily considered similar. This indicates that the specialty sporting goods store, the general sporting goods store, and direct advertising sporting goods had managed to situationally differentiate themselves from competition. Specialty sporting goods stores are perceived to be the most distinctive retailer, as is evident by the distance coefficient increment for cluster stage nine (2.653). These stores are distinctive in terms of being most appropriate for athletic footwear patronage and personal consumption patronage for use in athletic competition. Specialty sporting goods stores are also second most appropriate for group/

VOLUME 5 NUMBER 2 SPRING 1991 I9

9 3.469 8 7 '6 5 4 3 2 1 0

,816

,559

....

.209 .164 .lo5 ,089 ,039 ,015

Personal Competition

m m

c! @?

.y

y

cn

I.

N

-

m cn cn o

c\! c\! I I

YI

(?

I.

I'

'Optimal solution ~

FIGURE 1

Cluster Analysis Results

team patronage situations and second least appropriate for gift-giving patronage situations. General sporting goods stores which would have clustered in stage eight performed relatively well in situations involving the patronage of athletic footwear for personal use in athletic competition. Although the general sporting goods store was comparatively superior to most of the retailers on this count, it was clearly out-performed by the specialty sporting goods store. Direct response ad retailers which would have clustered in stage seven are distinctive in terms of being least appropriate for gift-giving patronage situations and group/team patronage situations. They would generally be perceived as ap-

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propriate for personal consumption patronage situations. The constituency of the seven-retailer cluster reveals that nonstore retailers compete with other nonstore retailers as well as with store retailers. Thus store/nonstore status does not strictly delineate the competitive retail market structure. Consumers apparently do not view the retail marketplace as a store versus nonstore proposition, contrary to what is proposed by some of the past nonstore literature. The seven-retailer cluster's greatest strength appears to b e in gift-giving patronage situations. The five retailers in the left-hand branch of the cluster have the five highest gift-giving betas. Generally, the procedure represents a means by which retailers can launch situational segmentation efforts. More specifically, the procedure makes it possible for the retailer to identify situational segmentation opportunities for segments that are not being adequately served. In this research, few of the retailers (and none of the nonstore retailers) were perceived as appropriate for athletic footwear patronage. This type of information may be used by retailers to tailor their product assortment, design messages to overcome consumer reluctance to purchase such merchandise, and manipulate other appropriate retailer attributes. The procedure also allows retailers to identify situational segments that are overserved and that consequently may not represent good opportunities. This research reveals that only the gift-giving market has more than two positive situational betas. Each of the other three situational contingencies is served by only two retailers with positive situational betas. The underserved segments represent opportunity if the attributes that appeal to them can be identified and properly manipulated. LIMITATIONS AND FUTURE RESEARCH PRIORITIES

While this study provides an important starting point for situational segmentation research in direct marketing, several limitations should be addressed. First, future research should empirically investigate the causative relationship between retailer attributes and the ability to appeal to various situational segments. Although this article links retailers to their

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situationally defined strengths/niches, it does not delineate the attributes which make this possible. Competitiveness in team patronage situations, for example, may be enhanced by retailer attributes such as 1) quantity discounts, 2 ) monogramming/ silkscreening, and 3) guaranteed availability of necessary quantities, sizes, colors, and quantities. Competitiveness in gift-giving situations may be enhanced by attributes such as the availability of 1) gift-wrapping service, 2 ) notecards enclosed with the gift, and 3) gift-shipping service. Although the aforementioned attributes are largely within the retailer’s control, other attributes that affect situational competitiveness may not be. In many cases, however, apparently uncontrollable attributes may be indirectly manageable. For instance, the reason that nonstore retailers scored poorly in footwear patronage situations was probably because nonstore shoppers are unable to try o n the footwear. Many nonstore retailers have indirectly managed this problem with liberal return policies. Future research should empirically assess the impact of retailer attributes o n situational competitiveness. Such analysis will greatly enhance the managerial implications of situational research by delineating the causative measures that can be taken to appeal to various situational segments. This study examines a limited array of situational factors. Future research should examine additional factors. If additional situational factors are investigated, however, they should be derived from consumer-based elicitation techniques (6,15) to ensure that the situational treatment is valid. It is also important to note that had this study examined an additional two-level situational factor, a full-factorial representation would have doubled the number of cells/questionnaire pages from eight to sixteen. The study has limited generalizability due to the retail product-market which is investigated. The methodologic framework utilized by this research should be tested in other product markets. Sporting goods represents a narrow range of retailing. The profile of a sporting goods shopper may differ from shoppers in other retail product-markets. The nature of situations pertinent to sporting goods shoppers may also differ from other product markets. The sample also limits the research. Because the sample consists of individuals who at least visited and who in some cases patronized a specialty sporting goods store, they are generally predisposed to

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this type of store. If subjects had been drawn from a grdup not closely affiliated with a specific retailer, responses may have differed. Traditionally, segmentation efforts have been based o n demographically defined consumer markets. The opportunity to more fully develop segmentation strategies which are based on situationally defined markets, as illustrated in this study, also appears to be fruitful.

CONCLUSIONS

This research demonstrates that patronage perceptions of various store and nonstore retailers are significantly affected by the nature of the patronage situation. Past nonstore research has been primarily concerned with identifying individual and retailer characteristics which are related to nonstore patronage. Previous research has not investigated perceptual o r behavioral variations attributable to factors outside of the individual or retailer. The study strongly suggests that an explanation of perceptual differences couched solely in terms of individual and retailer characteristics will overlook other important sources of variation. The research represents the first effort to assess the competitive structure of a nonstore retail market. Previous research has generally compared a single store and nonstore retailer. The study delineates the competitive market structure of a group of store and nonstore retailers in terms of their situational similarity/substitutability and provides a methodological framework that can b e useful for future investigations of direct marketing. The study demonstrates that the competitive market does not necessarily consist of store retailers competing against nonstore retailers. The store versus nonstore conceptualization of the marketplace does not reflect the true nature of competition. The research demonstrates that inter-retailer competition is conceived by consumers and can be quantitatively delineated in terms of pertinent situational factors. REFERENCES 1. Akaah, Ismael P. and Korgaonkar, Pradeep K. (1989), “‘l’he Influence of Product, Manufacturer, and Distributor Characteristics on Consumer Interest in Direct Marketing Offerings,” Journal of Direct Marketing, 3 3 (Summer), 27-33.

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