Multi Attribute Analysis

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Journal of the Academy of Marketing Science

Summer 1977, Vol. 5, No. 3, 281-294

A Multi-Attribute Approach To

Understanding Shopping Behavior

Ronald L. Vaughn, Ph.D. and Behram J. HansoUa, Ph.D.

Bradley University

INTRODUCTION

Since the 1920's, there has been a continuing interest in building and testing

formal models of consumer spatial behavior. With the opening in 1950 of

Northgate, a regional shopping center in Seattle, a retailing innovation was

launched that rapidly diffused to other metropolitan suburbs. As the environment

of retailing continues to be a dynamic one due to spatial mobility, geographical

population redistribution and changes in other consumer and competitive factors,

the retailer is faced with the continuing challenge of trying to understand and/or

predict consumer spatial behavior in order to survive and prosper.

Some researchers have approached this retail problem by trying Reilly's formula,

substituting square footage of selling space and driving time for population and

distance. Generally, ho~vever, the results were mixed and unsatisfactory. Other

researchers have followed the lead of Huff who formulated a spatial model based on

Luce's choice axiom (Huff, 1962; and Luce, 1959). Huffs probabilistic approach to

modeling consumer spatial behavior states that the probability of a consumer

choosing a given shopping center is equal to the ratio of the utility of that center to

the combined utilities of alternative shopping centers and has typically been


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operationalized using such proxy variables for perceived utility and disutility as

shopping center square footage and distance, respectively. Even recent extensions

of the Huff model typically use surrogate indicators for estimating center attraction

or utility and cost or disutility (Bucklin, 1971; Hilliard, Vaughn, and Reynolds,

1975; and, Stanley and Sewall, 1976).

281 VAU(iHN AND HANSO'|'IA 282

SOME PROBLEMS

Despite some improvements in predicting consumer spatial behavior, several

limitations remain, including: appropriate parameter estimation procedures,

popt.lation heterogeneity, shopping center interdependence, appropriate measures

for what attracts an individual to a given shopping center, and missing variables

from the current approaches to modeling spatial behavior (See Vaughn, 1975, for a

more complete discussion of these issues). Only the latter two issues are examined in

this paper.

Recent theoretical developments by Luce (1959) on individual choice bahavior,

by the decision theorists, Baumol and lde (1956), and by Lancaster's (1971) new

utility approach to economics all seem to offer promising frameworks for

investigating those variables which determine individual shopping center choice.

Further, recent methodological developments such as conjoint measurement and

the revised least squares approach by Nakanishi and Cooper (1974) seem to present

the real possibility of a potential contribution to the theory and measurement of

consumer spatial behavior. Thus it would seem, that a number of approaches are

available to make the theory of consumer spatial behavior more isomorphic with


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reality, hopefully diminishing a common weakness of gravitational models, the

lack of an explicit theory to explain the workings of gravity models.

This paper attempts to expand spatial choice explanations by using the more

explicit behavioral approach offered by imagery research or multi-attribute models,

rather than just the "economic man" who is theoretically assumed to weigh the

utilities and disutilities of each choice alternative. The approach used in this paper

raises a number of interesting questions. Can a shopping center's relative

attractiveness be estimated by measuring it along salient image dimensions? If so,

which attributes are most important? Do determinant attributes exist? How is the

information about a center structurally organized, i.e., rather than just describing a

center as very beautiful or clean, how is information about a shopping center coded

and grouped into cognitive categories? Do some types of shopping centers have a

less well-defined image? if so, why? Studying these relatively new issues to consumer

spatial behavior should provide insight as to how spatial opportunities are

evaluated with respect to each other. While the objective of this paper is more

limited in scope than the usual gravitational objective of predicting consumer

spatial behavior, this study will serve as a prerequisite to developing new predictive

models.

METHODOLOGY

A questionnaire was designed to determine the relative importance of

characteristics that may influence a person's choice of a shopping center. Thirteen 283 A

MULTI-ATTRIBUTE APPROACH TO

UNDERSTANDING SHOPPING BEHAVIOR


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shopping center selection attributes were developed by interviewing about 3,000

Peoria, Illinois shoppers (as part of another study) and a review of the relevant

literature (Applebaum and Kaylin, 1974; Downs, 1970; Jonassen, 1955; and

Middleton and Vaughn, 1974). In the data collection, each subject was first asked to

indicate his/her frequency of visits and overall perference of each center.

Respondents were then asked to indicate the importance of each shopping center

selection criterion along a seven-point scale. Finally, subjects used a seven-point

scale to evaluate the degree of favorability of each of four different shopping centers

on thirteen different dimensions.

The shopping centers used in this study were selected on these bases: must be

located within the Peoria retail trade area; must not contain less than 125,000 square

feet of retail spa~e; and, must contain at least three different types of stores. Further,

all neighborhood centers, ribbon developments, and crossroad centers were

considered to be tertiary retail shopping facilities and they did not suit the purpose

of this study. Given these criteria, the following shopping centers qualified for this

design:

i. The Peoria Central Business District (CBD)--with well over 1,000,000 square

feet (depending on how the area is defined) of retail space;

2. Northwoods Mall (NW)--a large regional shopping center with about

750,000 square feet;

3. Sheridan Village (SV)--while it has about 350,000 square feet, its character-

istics seem to fall in between the two categories of a small regional center and

a large community-level center; and,


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4. Madison Park (MP)--a large neighborhood center of about 125,000 square

feet.

These centers are the primary retailing areas in Peoria.

The questionnaire was mailed to a random sample of 1,000 residences drawn

from the Peoria telephone directory, with 238 questionnaires being completed and

returned. The low response rate (24%) was a result of the combined factors of

difficulty with the constant sum preference scale, a lengthy questionnaire, and no

incentives provided for the respondent. However, respondents were contrasted with

the local population on the basis of census data and were found to be only slightly

skewed toward the upper socio-economic strata.

Relative Importance o! Shopping Center Selection Criteria

The first stage of the analysis was to determine the relative importance of the

various shopping center choice criteria (see Table 1). The variable means range from

a low of 3.80 for popularity with family and friends to a high of 6.312 for quality of

VAUGHN AND HANSOTIA 284

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merchandise. The rank of the choice criteria supports an earlier British study which

found that retail establishment factors (such as service quality) were more

important than shopping center structure and function factors (such as visual

appearance) in influencing shopping center choice (Downs, 1970). Table I indicates

that the average individual in the Peoria area puts a premium on the quality of the

merchandise, followed by variety of merchandise, availability of parking, helpful

and courteous sales assistants, convenience of days and hours open, and finally,

distance from home. These results suggest that gravitational modeling components

should be reconsidered. Distance, the most common disutility operationalization,

ranks only sixth. Further, the first five ranking variables suggest that typical

operationalizations of the utility component in the gravity models, e.g., mass or

retail square footage, are somewhat naive. While variety may closely parallel square

footage, this is not necessarily true with the quality of merchandise nor with helpful

and courteous sales assistants. The variables ranking third and fifth, availability of

parking and convenience of days and hours open, suggest other inconsistencies with


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the square footage 0perationalization. A prior test of two gravitational models in

the Peoria area noted that model predictions were poorer for the Central Business

District (CBD) than for a variety of other types of shopping areas. This study

suggested that square footage seemed to be a relatively poor proxy variable for the

CBD since the hours of operation were much shorter and since the CBD's ratio of

parking space to square footage was about one-third that of alternative shopping

areas (Vaughn, 1975). The fact that the present analysis found availability of

parking and convenience of days and hours open to be relatively important lends

additional credibility to this assertation. Finally, the fact that distance from work

had the second smallest mean, 3.994, indicates that most individuals do not tend to

shop in the vicinity of their work.

DETERMINANT ATTRIBUTES

The second stage of the analysis was to assess the principal shopping center

decision variables and their relative determinance in shopping center selection

decisions. Determinant attribute analysis should help to understand those features

which move the consumer to action, i.e., to make him prefer a particular shopping

area, actually shop there, recommend it to friends, etc., (Myers and Alpert, 1968).

Attribute determinance was estimated by evaluating (I) the importance associated

with a particular shopping center selection criterion (see column labeled

"Importance Rank" in Table I) and (2) the degree to which competing shopping

centers were preceived to differ in terms of that selection criterion (see column

labeled "Deviations Rank" in Table I). By averaging the two columns which rank

VAUGHN AND HANSOTIA 286


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order each attribute on each of the two criteria for attribute determinance, an

overall determinancy index was constructed. This index was then ranked for ease in

interpretation (see last column of Table 1). Several attributes stand out as relatively

determinant: Availability of parking, quality of merchandise, variety of

merchandise, distance from home, atmosphere of shopping area, and, possibly,

convenience of days and hours open. It should be noted that this overall ranking is

highly dependent on the diverse nature of the small sample of shopping areas

studied, e.g., availability of parking would have dropped several places in rank

order if the CBD with its unique parking problem was eliminated and, likewise, for

MP on quality of merchandise. However, some insight can still be gained. This

approach shows distance from home to be more closely associated to shopping

center selection than implied in the prior section; it is tied for the fourth rank with

atmosphere of the shopping area. On the other hand, helpful and courteous sales

assistance, despite its relatively high importance rank (fourth), dropped to seventh

place on the overall determinancy index rank due to the small perceived variation

among the centers. To a lesser extent, the same was true of quality of merchandise,

variety of merchandise, and convenienc~of days and hours open, for each of these

was one place (in rank order) higher on their importance rank than they were on

their determinant attribute rank. This analysis also shows inconsistencies with

traditional gravitational models, as distance still does not seem to be a dominant

factor in influencing shopping center selection. Similary, it seems intuitively

inconsistent for utility to be approximated by mass or square footage when other

variables which do not seem closely parallel have been shown to be relatively


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X 3 0.20206 0.66020**

X 4 0.44164 0.28241

X 5 0.46159 0.43353

X6 0.38106 0.50181

X 7 0.64466* 0.39190

X 8 0.68585* 0.23295

X 9 0.59900 0.41215

XIO 0.35144 0.50256

XII 0.36086 0.66321"*

XI2 0.10320 0.48192

XI3 0.40442 0.52760

*Variable useful in defining factor i.

**Variable useful in defining factor 2.

factor has high loadings on variables XI, X2, X7, and X8, which are quality of

merchandise, variety of merchandise, atmosphere of shopping area, and availability

of sale items (specials). The second factor has high loadings on variables three and

eleven, namely availability of parking and amount of walking required. Note, the

first factor deals with variables which can largely be controlled by management

once the center is established, as three of the variables are related to merchandise

and a fourth (which has the lowest loadings of the four) to the atmosphere of the

shopping area. This complex dimension we could term the "merchandise-

atmosphere" index. The second factor is loaded high on the structural properties of

availability of parking and amount of walking required, which are relatively outside


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the control of management after the shopping area has been established. We could

call this a "convenience" index. VAUGHN AND HANSOTIA 288

Northwoods Mall

Two factors were extracted which jointly accounted for 51 percent of the variance

(convergence was obtained in six iterations). The matrix of factor loadings after the

varimax rotation is given in Table 3. Factor 1 has high loadings on variables

TABLE 3

Factor Loadings for Northwoods Mall

Variables Factor 1 Factor 2

X 1 0.71170* 0.19327

X 2 0.61608* 0.26355

X 3 0.34536 0.41189

X 4 0.39410 0.31162

X 5 0.50496 0.27346

X 6 0.13955 0.63210**

X 7 0.53081 0.26355

X 8 0.57217 0.29273

X 9 0.70371* 0.23644

XI0 0.29032 0.68433**

XII 0.38155 0.63383**

XI2 0.26718 0.54473

XI3 0.45415 0.40733

*Variable useful in labeling factor i.


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**Variable useful in labeling factor 2.

X l, X2, and X9 which are quality of merchandise, variety of merchandise, and ease

of shopping comparisons. This seems to be a "merchandise" index (atmosphere of

shopping area which was heavier loaded for the CBD has a moderate loading for

Northwoods Mall). The second factor has high loadings on variables X6, X I0, and

X I I which are distance from home, pedestrian congestion, and amount of walking

required. Hence, it could also be labeled as a "convenience" index.

Sheridan Village

Analysis for this center resulted in two factors being extracted (convergence was

obtained in six iterations) accounting for 58 percent of the total variance. The

matrix of factor loadings after varimax rotation is presented in Table 4. The first

factor loaded on variables X6, XI I, and XI3 which are distance from home,

amount of walking required, and popularity with family or friends. As there is no

unifying thread in these variables (nor in the next several largest factor loadings), we

shall just label it as a "complex dimension" index. As the second factor is loaded

high on X 1, X2, and X7, namely quality of merchandise, variety of merchandise.

and atmosphere of shopping area, we will call this a "merchandise-atmosphere"

index. 289 A MULTi-ATTRIBUTE APPROACH TO


TABLE 4

Factor Loadings for Sheridan Village

Variables Factor i Factor 2

X 1 0.30235 0.66931**


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X 2 0.30453 0.73648**

X 3 0.56897 0.30296

X 4 0.45304 0,36988

X 5 0.52141 0.39839

X 6 0.64818* 0.22619

X 7 0.31410 0.74277**

X 8 0.35174 0.58727

X 9 0.37969 0.59093

XIO 0.57606 0.37264

XII 0.73314* 0.34411

XI2 0.54775 0.29745

XI3 0.62341* 0.45274

*Variable useful in labeling factor i.

**Variable useful in labeling factor 2.

Madison Park

In this case, three factors were extracted, together they accounted for 71 percent

of the total variance (convergence was obtained in 17 iterations). Reported in Table

5 is the matrix of factor loadings after the varimax rotation was performed. Factor l

had high loadings on variables XI, X2, X7, and X9 which are quality of

merchandise, variety of merchandise, atmosphere of shopping area, and ease of

shopping comparisons. This again turns out to be a "merchandise-atmosphere"

index, with variable X9, ease of shopping comparions, replacing variable X8,

availability of sale items (see CBD factor analysis). The second factor has high


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loadings on variables X3, XS, and Xll, which are availability of parking,

convenience of days and hours open, and amount of walking required. Note that an

additional variable, convenience of days and hours open~ has been included to the

second factor of the CBD. This factor may still be classified as a "convenience"

index. As the third factor has high loadings on variables X6 and X I2, i.e., distance

from home and distance from work, we can label it as a "distance" index. VAUGHN AND

HANSOTIA 290

TABLE 5

Factor Loadlngs for ~adiaon Park

Variables Factor I Factor 2 Factor 3

Xl 0.75365* 0.29799 0.28820

x 2 0.80852* 0.24602 0.25548

X 3 0.21577 0.80254~* 0.05485

](4 0.48700 0.40844 0.18645

x 5 0.34041 0.70590~ 0.29065

X 6 0.28775 0.19070 0.77936~

x 7 0.71047~ 0.30641 0.26589

X8 0.59904 0.28849 0.13106

X9 0.63916~ 0.41028 0.25467

XIO 0.35652 0.55883 0.24369

XI1 0.25726 0.72884*e 0.27028

X12 0.18939 0.28401 0.64560~*

XI3 0.53660 0.07528 0.51505


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9 useful in labeling factor 1.


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9 ~Vars useful in labeling factor 2.


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9 **Variable useful in labeling factor 3.

Summary of Factor Analysla

Several patterns evolved in the factor analytic results. First, factor analysis of

those shopping centers which were smaller in square footage had a higher degr~ of

the total variance explained than the larger centers, i.e., from the smallest size center

to the largest, the variation explained was 71, 58, 51, and 54 percent. While a

number of explanations for this apparent pattern may exist, one possibility is that

the individual can better generalize the image of a small, less complex shopping

center. Another possible explanation is that the smaller center's typical emphasis is

on convenience goods as opposed to shopping goods and the numerous periodic

shopping trips associated with convenience goods results in a high amount of

information (for those frequenting the center) or a well-defined image of that center.

It was somewhat surprising that the CBD had more variance explained than NW, as

it was believed that the rather nebulous definition of the CBD and a number of

streets serving as dividing lines would make it very difficult to generalize. However,

two other points should be considered as they may have some influence on the

amount of variance explained. First, Northwoods Mall (NW) is less than three

years old and the CBD has been getting almost daily publicity concerning stores

entering and leaving the downtown area, progress on providing more parking, and

the development of a downtown shopping mail Second, a merchandise or 291

TABLE 6

Summary of Factor Analysis

CBD--Two factors accounted for 54% of the total variance.


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I. Merchandlse-Atmosphere Index described by:

a. Quality of merchandise

b. Variety of merchandise

c. Atmosphere of shopping area

d. Availability of sale items

2. Convenience Index described by:

a. Availability of parking

b. Amount of walking required

NW- -Two factors accounted for 51% of the total variance.

1. Merchandise Index described by:



c. Ease of shopping comparisons


a. Pedestrian congestion

b. Amount of walking required

c. Distance from work

SV--Two factors accounted for 58% of the total variance.

1. Complex Index described by:

a. Distance from home

b." Amount of walking required

c. Popularlty with family or friends

2. Merchandlse-Atmosphere Index described by:


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c. Atmosphere of shopping area

MP--Three factors accounted for 71% of the total variance.

1. Merchandise-Atmosphere Index described by:



c. Atmosphere of shopping center

d. Ease of shopping comparisons


a. Availability of parking

b. Convenience of days and hours open

c. Amount of walking requlred

3. Distance Index described by:

a. Distance from home A MULTI-ATTRIBUTE APPROACH TO


292

merchandise-atmosphere underlying structure was found to exist in the case of each

center. This factor was expected in light of gravitational literature which has

explained the utility of a center using square footage as a surrogate. However, this

merchandise dimension was more complex than traditional gravitational models

seem to imply, as merchandise quality, merchandise variety, atmosphere of

shopping area, availability of sale items, and ease of shopping comparisions were all


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component parts of this underlying dimension. Third, distance from home was

apparent only in the smaller shopping centers as an underlying dimension. Further,

when it did appear as a heavily loaded factor (for SV and MP), it was combined

with other factors: in one case, MP, it was combined with amount of walking

required and, unexplainably, popularity with family or friends. Fourth, a

convenience dimension was discovered for three of the shopping centers (CBD,

NW, and MP) that seemed to be concerned with getting quickly in and out of the

shopping center.

In summary, merchandise and convenience seem to be the two underlying

dimensions which consistently appear, but the results of this study can only be

viewed as preliminary pending further study by other researchers.

RESEARCH IMPLICATIONS

To continue to improve our understanding of spatial choice processes, research is

needed on a variety of related issues, including the following:

I. Research is needed to deal with one of the most crucial problems of the

multi-attribute approach, i.e., does the individual actually formulate a general

image of a shopping center or is this image termed by aggregating in some

fashion the individual's perception of several ~key" stores at a given center.

2. For this approach to be practical in the sense that traditional gravitational

models have been frequently used, i.e., as a predictive tool, then research is

needed to test the comparative predictability of the multi-attribute approach.

With this in mind the Nakanishi and Cooper (1974) formulation may be a

useful approach.


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3. Not unlike other behavior, the spatial choice process of the consumer is

probably influenced by situational factors. 3 Fishbein (1975) has made a strong

case for the need for measuring consumer attitudes within a given situation:

If l'm interested in predicting someone's intention to perform a given

behavior in a given situation, i must assess his attitude toward performing

that behavior in that situation and not merely his attitude toward the

behavior per se. Needless to say, my attitude toward "drinking whiskey

first thing in the morning" may be different from my attitude toward

"drinking wlalskey at a party." This is not a trivial point, my attitude

toward buying a given product for a party, may be very different than my

attitude toward buying the same product for my personal use, or as a gift

tot someone else. 293 VAUGHN AND HANSOTIA

In fact, the traditional gravitational model is on the right track (i.e., if

situations are liberally interpreted to mean products) since its distance

parameter is generally estimated for different product categories. As situa-

tional variables are generally important behavioral influences, they should be

considered in evaluating predictability (Vaughn, 1973). Finally, does attribute

salience or determinancy vary with the shopping situation?

4. One of the frequently used theoretical frameworks for gravitational models,

that of Luce (1959), pertains only to single purpose shopping trips. Perhaps

the individuals perception of spatial alternative varies if evaluations are

made in a multi-purpose shopping context, or given that the trip is being

made in conjunction with non-shopping activities.


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5. Finally, does attribute salience or determinancy vary by the type of customer?

Research needs to explore the usefulness of these factors as criteria for

market segmentation and the design of patronage appeals.

FOOTNOTES

tHie, Norman H., et al. Statistical Package for the Social Sciences (SPSS, L New York:

McGraw-Hill, Inc., 1975.

John P. Van de Geer page 147 for the details of this approach.

;A review of the literature on situational influences is available in Vaughn, 1973, and

Woodside and Bearden. 1975.

REFERENCES

Applebaum, William and S.O. Kaylin. Case Studies in Shopping Center Development and

Operation. New York: International Council of Shopping Centers, 1974.

Baumol, William J. and Edward A. lde. "A Variety in Retailing," Management Science, Ill

(I) (October 1956), 93-101.

Bucklin, Louis P. "Trade Area Boundaries: Some Issues in Theory and Methodology,"

Journal of Marketing Research, 8 (February 1970, 30-7.

Downs, Roger M. "The Cognitive Structure of an Urban Sopping Center," Environment

and Behavior, 2 (June 1970), 13-38.

Fishbein, M. "Attitude, Attitude Change, and Behavior, A Theoretical Overview," In P.

Levine (ed.) Attitude Research Bridges the Atlantic. Chicago: American Marketing

Association, 0975), 3-16.

Hilliard, Jimmy E., Ronald L. Vaughn, and Fred D. Reynolds. "A Generalized Utility

Model of Shopping Behavior," Advances in Consumer Research, Volume 2, Association


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for Consumer Research Proceedings, Chicago, November 1974.

Huff, David L. Determination of Intra-Urban Retail Trade Areas. Los Angeles: Division

of Research, Graduate School of Business Administration, University of California, 1962.

Jonassen, C.T. The Shopping Center Versus Downtown. Columbus, Ohio: Bureau of

Business Research, College of Commerce and Administration, 1975.

Lancaster, Kelvin. Consumer Demand: A New Approach. New York: Columbia University

Press, 1971.

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