behavioral model of consumer spatial decision making - martin cadwallader

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A Behavioral Model of Consumer Spatial Decision MakingAuthor(s): Martin CadwalladerSource: Economic Geography, Vol. 51, No. 4 (Oct., 1975), pp. 339-349Published by: Clark UniversityStable URL: http://www.jstor.org/stable/142918 .Accessed: 27/10/2014 02:37

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A BEHAVIORAL MODEL OF CONSUMER SPATIAL DECISION MAKING*

MARTIN CADWALLADER University of Wisconsin

During the past few years there has been a growing effort to explain behavior in space through the identification of behavioral postulates that operate at the level of the individual decision maker [11; 19].1 This strategy is commonly re- ferred to as the behavioral approach. The behavioral approach is based on the premise that there are regularities in spatial behavior, and that these regularities are invariant across different spatial structures. That is, it is assumed that there are some innate rules of spatial behavior, regardless of structure [16]. Con- sequently, it is argued, we need to identify these basic behavioral postulates, in contrast to our present strategy of merely explaining behavior in terms of the structure in which it is operating [37, p. 392].

In general, behavioral geographers have attempted to replace the behavior- ally unsatisfactory concept of "economic man" with a more realistic counterpart [11, p. 70]. Following Simon, it has been suggested that a more realistic model of man will combine the principles of satis- ficing behavior and bounded rationality [40; 41].

t I would like to thank William Clark and Robert Sack for their comments on an earlier draft of this paper.

1 This paper follows Rushton's distinction between spatial behavior and behavior in space. "The essential feature of a useful postulate is that it should describe the rules by which alternative locations are evaluated and choices con- sequently made. This procedure we may call 'spatial behavior,' reserving the term 'behavior in space' for the description of the actual spatial choices made in a particular system" [37, p. 392].

The rejection of "economic man" as a viable descriptive model of man appears eminently reasonable, as complete rationality with respect to the real world is but one of a range of possible behav- iors. However, as Harvey has pointed out, care must be taken to replace it with something that is amenable to operation- alization and is theoretically useful [24]. As yet, due to the difficulties involved in determining aspiration levels, the sat- isficer concept has proven very difficult to operationalize [45].

By comparison, the principle of bounded rationality appears to be more promising. It takes into account man's simplified and distorted view of reality, thus attempting to explain behavior in space in terms of the individual's perception of that space. This line of rea- soning is not to deny that "economic man" has been a very powerful tool in the normative context, but simply to em- phasize that the behavioral approach is concerned with identifying regularities in actual, not optimal, behavior.

The principle of bounded rationality is closely related to the concept of a "behavioral environment."2 This concept refers to the environment as it is perceived. A fundamental axiom of the be-

2 Kirk made the following distinction: "The Behavioural Environment is thus a psycho- physical field in which phenomenal facts are arranged into patterns of structures (gestalten) and acquire values in cultural contexts. It is the environment in which rational behaviour be- gins and decisions are taken which may or may not be translated into overt action in the Phe- nomenal Environment" [29, p. 366].


ECONOMIC GEOGRAPHY

havioral approach is that an individual's behavior is based upon his perception of the environment, and not upon the environment as it actually exists.

Research on environmental perception, to ascertain the nature of this "behavioral environment," has proceeded apace [39; 46]. Most of this work has fallen into one of two categories, char- acterized by Cox as designative and appraisive aspects of space perception.3 Some of the pioneering work on designative perceptions was undertaken by Lynch and his followers [10; 21; 33]. More recent work in this area has been concerned with cognitive distance [1; 5]. Research into the appraisive perception of space has included work concerning environmental attitudes, especially the perception of environmental hazards [38], and studies of residential preference surfaces [7; 20; 28].

One of the weaknesses of research on environmental perception, however, lies in the fact that an individual's perception of the environment has never been explicitly related to his subsequent behavior within that environment, although it is always assumed that such a relationship does exist [34].4 Until it can be shown that an individual's behavior in space is closely related to his perception of the environment, the "behavioral environment" cannot take its place as a cornerstone of a general theory of spatial behavior. It is in this respect that the research reported here is of some con- sequence, as it seeks to explicitly sub- stantiate the thesis that behavior in space

3 Cox defined these two terms as follows. "Designative perceptions are those perceptions that we have of the attributes of places and which are devoid of all evaluation of those attributes." . . . "Appraisive perceptions, on the other hand, are those value judgments that we have of places" [9, p. 101].

4 Studies of environmental hazards have had some success in showing that the perception of the hazard influences adaptive precautions.

can be better understood in terms of the "behavioral environment" than in terms of the environment as it objectively exists.

This goal is accomplished by an investigation of consumer spatial behavior. First, the predictive capacity of cognitive distance, as regards consumer behavior, is compared with that of real distance.5 Second, a behavioral model of consumer behavior is constructed and tested. Its successful formulation lends weight to the idea that the "behavioral environment" is a valuable analytical concept.

Of direct interest to the present investigation, is the work that has been done with respect to the decision making process of the individual consumer [25; 26]. In this context it has been widely suggested that the gravity model provides an adequate description of consumer behavior [27; 35]. In the gravity model the likelihood of patronizing a particular store is a decreasing function of the distance to that store, and an increasing function of store size. This kind of statement, however, does not represent a satisfying explanation, in the sense that it fails to identify the underlying behavioral processes. It is now felt that such processes might be uncovered through the construction of models that involve search and learning behavior [4; 18]. This most recent research has not been content with merely describing overt behavior, but has been concerned with what Rushton has termed basic postulates of spatial behavior [37]. The research reported on here attempts to identify one such postulate. It is de- signed to provide evidence that cognitive distance is a better predicter of shopping patterns than real distance.

5 Cognitive distance refers to distance as it is represented in an individual's "behavioral environment." That is, cognitive distance is a measure of how far an individual thinks two places are apart.

340


CONSUMER DECISION MAKING

DATA

The data for this research were col- lected, by questionnaires, from fifty- three households located in west Los Angeles. The questions were answered by that member of the family who was normally responsible for the grocelry shopping, and the sample size of fifty- three represents a complete census, as there was no attenuation due to incom- plete questionnaires.

The objective was to obtain a group of consumers who live within three blocks of each other, and so possess almost identical opportunity sets in terms of the available supermarkets. There were two reasons why this particular group of consumers was chosen. First, they live in an area where there are five very accessible supermarkets, thus con- fronting the consumer with an identifi- able choice situation. Second, the consumers are comparable in texrms of socioeconomic status, as they could all be de- scribed as lower middle-class. This homogeneity was felt to be advantage- ous, as the study is not concerned with the influence of socioeconomic differences.

COGNITIVE DISTANCE AND CONSUMER SPATIAL BEHAVIOR

The initial problem was to investigate the spatial rationality of consumer behavior, in an attempt to show that more consumers think they patronize the closest supermarket than actually do. In other words, the hypothesis is that consumers are more rational with respect to cognitive distance than they are with respect to real distance.6

In order to test the hypothesis of spa- 6 This hypothesis is of some theoretical sig-

nificance, as it is at odds with classical central place theory. Christaller's original formulation of central place theory is rather confusing concerning the question of distance minimization, as distance is generally discussed in terms of economic distance rather than straight-line distance [6].

tial rationality, each consumer was asked to estimate the distance from his home to the five supermarkets. The distances were estimated using time estimates and the method of direct magnitude estimation, which provides a cognitively scaled distance estimate.7 The time estimates provide a measure of cognitive time distance, and the method of direct magnitude estimation provides a measure of cognitive mileage distance. The shortest road distances to the supermarkets varied between 0.43 (0.69 km.) and 1.75 miles (2.8 km.) (Table 1). The data were analyzed to determine the proportion of consumers who thought they were using the closest supermarket, as opposed to the proportion of constumers who were actually using the closest supermarket. To accomplish this the consumers were also asked to indicate which supermarkets they most often patronized. It is significant that they were not asked to identify which one they had used on their last shopping expedition, as that might have represented only a minor fluctuation in their overall shopping strategy.

The results indicate that the hypothesis is well founded (Table 2). That is, for this particular group of consumers, more people think they go to the near- est supermarket than actually do. The evidence suggests that consumers are in- tendedly rational with respect to dis-

7 The method of direct magnitude estimation is a technique that has been used by psycholo- gists to investigate loudness, in order to estab- lish a scale of subjective magnitude [23; 36; 43]. The construction of such a scale is out- lined by Corso [8], and has been previously used in the context of cognitive distance by Cadwallader [5].

TABLE 1 SHORTEST TRAVEL DISTANCE TO EACH SUPERM4IARKET

Supermarket A Supermarket B Supermarket C

Supermarket D Supermarket E

0.55 mls. (0.88 km.) 0.43 mls. (0.69 km.) 1.60 mls. (2.56 km.) 1.75 mis. (2.8 km.) 0.80 mls. (1.28 km.)

341


ECONOMIC GEOGRAPHY TABLE 2

THE SPATIAL RATIONALITY OF CONSUMER BEHAVIOR

Patronizing closest in terms of real distance

cognitive scaled distance

cognitive time distance

Percent Number 53 28

70 37

72 38

tance. On a more general level, the results pose an interesting problem of in- terpretation. Consumers might be choos- ing between stores on the basis of a priori distance estimates. Alternatively, the distance distortions might be attrib- utable to some kind of cognitive dissonance process that occurs after a store has been chosen. The latter explanation appears to be the more reasonable of the two. In general, cognitive dissonance occurs after any decision in which an individual has chosen between two fair- ly attractive alternatives. The individual tends to reduce the dissonance by exag- gerating the attractive features of the chosen alternative and the unattractive features of the rejected alternative [14]. In the context of cognitive distance and consumer behavior, this would suggest that after patronizing a particular store the consumer will begin to rationalize his decision, and imagine that the chosen store is relatively closer than the alternatives.8

A MODEL OF CONSUMER SPATIAL BEHAVIOR

This part of the research involves the construction and testing of a behavioral model of consumer decision making. For our purposes decision making is defined as the cognitive process of selecting from among alternatives, and in this study the alternatives are represented by the five supermarkets. Around evelry household

8 In order to truly understand a situation of this nature we would need to determine how an individual's distance estimate changes over time. It should also be noted that the argument concerning cognitive dissonance applies equally well to the attractiveness component of the model to be discussed below.

or group of households there exists an objectively defined opportunity set. That is, within a certain radius around every household, there is a finite number of stores. The problem then becomes one of analyzing how the household chooses between the alternatives presented by the opportunity set. The aim is to con- struct a model that is capable of pre- dicting the proportion of consumers who will choose each of the five alternatives. If this is successfully accomplished, it suggests that some of the major variables involved in the decision making process have been identified.

The conceptual form of the model postulates that a prospective consumer sorts his information about each store, in order to form judgments about their relative attractiveness and accessibility. The interplay between these two factors, attractiveness and distance, is then translated into an overt response. After a store has been patronized, there will be a feedback effect as regards the amount, and nature, of the information that the consumer possesses concerning his opportunities. This new information will cause him to reassess his judgments regarding the relative merits of each store. In this way the decision making process keeps repeating itself, and the stores are continually re-evaluated in the light of new information.

This model can be regarded as a dy- namic process-response model [31]. Ini- tially, in the search stage, there will be major fluctuations in the response pattern, as the consumer tries to acquaint himself with his system of opportunities. Eventually, however, a more consistent response pattern will develop [18]. Any changes in the system of opportunities, such as the appearance of a new store, are likely to result in renewed search behavior.

The model suggests that the proportion of consumers patronizing a particular store will depend upon the interplay between attractiveness, distance, and in-

342



formation. Mathematically this can be expressed as

P- f(A, Di, I) (1) Where Pi is the proportion of con-

sumers patronizing store i;

Ati is some measure of the attractiveness of store i;

Di is some measure of the distance to store i from the consumers' homes;

I is some measure of the amount of information generated by store i.

More specifically, it is postulated that the proportion of consumers patronizing any particular store increases with increasing attractiveness, and decreases with increasing distance. This trade-off between distance and attractiveness operates in conjunction with an information variable. The stores that are knowvn to exist by more consumers will tend to attract more trade; a consumer cannot patronize a store that is outside his field of information. Mathematically this can be expressed as

/ Ai Pi - Ii (2)

D\ i

It is noteworthy that this kind of formulation is very different from that con- tained in central place theory, where it is assumed that consumer behavior is solely distance minimizing, and that the

attributes of the stores are identical. It is, however, similar to a gravity model formulation, with the addition of the information variable. The major difference between this model and the conventional gravity model lies in the specification of the mass and distance variables. In the present model these variables are measured subjectively by the consumers, whereas in the more conventional forms of the gravity model the mass is measured according to some objective cri- terion, such as retail floor space, and distance is measured in terms of real distance. Also, the conventional models generally specify the distance variable in exponential form. This is not done in the present model, however, as the in- tention is to show how the distance variable may be most suitably defined, rather than to search for the best exponent. In this context it should be noted that the exponent attached to the distance variable may well vary according to the particular pattern of spatial alternatives under investigation. However, the most appropriate definition of distance, either cognitive time distance, cognitive mileage distance, or real distance, is likely to remain the same, irrespective of any particular configuration of spatial opportunities.

The following sections describe how each part of the model can be operationalized, and then the output from the model is compared with observed behavior. This will give an indication of the predictive capacity of the model. In this initial investigation only the static form of the model is tested, as the col- lected data preclude any analysis of the feedback effect.

TABLE 3

THE ATTRACTIVENESS IMATRIX

Checkout service

Prices

Quality of goods Range of goods

SUPERMARKETS A B C D E

4.62 5.39 5.13 5.71 4.78

3.34 6.00 5.33 6.00 3.82

6.57 5.24 5.33 5.86 5.33

6.74 5.16 5.40 5.29 4.77

TABLE 4

THE WEIGHTING VECTOR

Checkout service

Prices

Quality of goods Range of goods

0.13

0.32

0.32

0.23

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ECONOMIC GEOGRAPHY

STORE ATTRACTIVENESS

Store attractiveness is evaluated by the consumers themselves. In recent years there has been increased concern with how stores are imaged by consumers [2; 32]. Within this context multi- dimensional scaling and the semantic differential have been used to identify the major underlying dimensions of these images [3; 12; 42]. These studies identify the major attributes of stores, and describe how these attributes group to- gether in multi-dimensional space.

In the present study supermarket attractiveness is measured across four variables: (1) speed of checkout service; (2) range of goods sold; (3) quantity of goods sold; and (4) prices. These variables were chosen on the basis of a pilot study, in which consumers were asked to list those factors which they considered most important when selecting a supermarket. The most often men- tioned items were then included in the final questionnaire.

Each supermarket was rated by the consumers with respect to each of the four variables. This was accomplished by using a seven-point rating scale go- ing from very unsatisfactory to very satisfactory. The resulting scale values for each store on each variable were then aggregated, using the median scale values, into a four by five attractiveness matrix (Table 3). This matrix indicates, for example, that the average level of satisfaction with supermarket B, with respect to prices, is 6.0. It is noteworthy that in some cases there are relatively small disparities between the stores with respect to a particular variable. For ex-

TABLE 5

TIlE ATTRACTIVENESS VECTOR

Supermarket A

Supermarket B

Supermarket C

Supermarket D

Supermarket E

5.322

5.485

5.321

5.754 4.646

ample, the highest value for the checkout service is 5.71 and the lowest value is 4.62. There are two possible reasons for this. First, is could be that the differences between the supermarkets as regards the checkout service are not sufficiently large to warrant any significant differentiation. Second, it could be related to the methodology. As already noted, the subjects were provided with a seven-point scale, and the tendency is to choose the middle points on the scale. The effect of this is magnified when the median values are used, as is the case here.

The consumers were also asked to rank the variables in order of their importance in selecting a supermarket. This ranking produced a row weighting vector, the values of which have been standardized so that they add to one (Table 4). This vector shows, for example, that the prices and quality of goods sold are judged to be of equal importance.9 The attractiveness matrix was then pre-multiplied by this row weighting vector. In effect this means that the scores on each variable, and the resulting aggregate attractiveness of each store, are adjusted by the importance consumers attach to each variable. The resulting row vector describes the relative attractiveness of each supermarket (Table 5).

This vector indicates that supermarket D is perceived to be the most attractive supermarket. It is significant that, in order for the variables to be additive, they are assumed to be independent. In general this seems to be reasonable. For example, there is no reason to suppose that the range of goods sold is related to the quality of goods sold. The only excep- tions to this assumption might be the quality of goods sold and prices, where

9 The weighting vector indicates the relative importance of the different variables as regards the consumers as a whole, rather than repre- senting the differences between individuals.

344



one might expect some interdepen- dence.10

DISTANCE

The next step in the model construction is to operationalize the concept of distance. This is accomplished by using three different measures of distance; cognitive scaled distance, cognitive time distance, and real distance. This means that the trade-off between store attractiveness and distance can be expressed in three different ways.

Ai CSDi '

Ai ATiT (4) CTD, At Di (5)

Where Ai is the attractiveness of store i;

CSDi is the cognitive scaled distance to store i;

10 The construction of this attractiveness in- adex is based on the work of Kotler, who was concerned with the problem of brand loyal- ties [30]. Kotler developed what he called a competitive marketing mix matrix, which sum- marized the average market perception of three different brands across eight different dimensions of competition. Golledge later applied Kotler's model to shopping centers, but the distance variable was not separated out from the other variables, such as prices and product quality [17].

TABLE 6

THE COGNITIVE AND REAL DISTANCES TO EACH SUPERMARKET

CSD CTD RD Supelrmarket A 100 5.18mins. 0.55mls. (0.88km.) Supermarket B 100 5.26mins. 0.43mls. (0.69km.) Supermarket C 258 10.48mins. 1.60mls. (2.56km.) Supermarket D 289 10.22mins. 1.75mls. (2.8 km.) Supermarket E 192 7.42mins. 0.80mls. (1.28km.)

CTDL is the cognitive time distance to store i;

RDi is the real distance to store i.

The median value for each distance measure, to each supermarket, was computed (Table 6). It is evident that there is some variation in the distance measures as regards the ordering of alternatives. For example, supermarket D is perceived to be closer than supermarket C in terms of cognitive time distance, but farther away in terms of cognitive scaled distance. This difference is not surprising, as the two variables are measuring two different kinds of cognitive distance. In this situation the aim is to identify which of these distance measures should be inserted in models of consumer behavior.

INFORMATION

Having operationalized the concepts of attractiveness and distance, it remains only to specify how the levels of information will be measured. In the present study this is accomplished very simply. Information is regarded as a dichoto- mous variable; a consumer is either aware of a particular store, or he is un- aware of that store. Following from this, the level of information associated with each store is measured by the proportion of consumers who are aware of that store (Table 7). This vector indicates, for example, that whereas all the consumers are aware of supermarket A, only sixty percent are aware of supermnarket D. It is obvious that these disparities in

TABLE 7

THE INFORMATION VECTOR

Supermarket A

Supermarket B

Supermarket C

Supermarket D

Supermnarket E

1.00

0.97

0.68

0.60

0.74

345


ECONOMIC GEOGRAPHY

levels of information must be built into any worthwhile model, as without this constraint the attractiveness matrix is grossly misleading.

THE MODEL

The final step is to combine the measures of attractiveness, distance, and information into the final model. Because of the three different distance measures there are three different formulations for the proportion of consumers patronizing a store, Pi.11

/ A( Pi - - I (6) \ CSDi

r A()

CTDi Ii

/ Ai Pi -- i (8)

\ RDi

The results obtained from equations (6), (7), and (8) can be compared with the actual proportion of consumers patronizing each supermarket by using the index of dissimilarity (Table 8) .12 They can

N N N D Z Xi/ 2 X-Yj/ 2 Yi .100

i=l i=l i=l

2

Where Xi is the observed proportion of consumers patronizing supermarket i;

Y1 is the predicted proportion of consumers patronizing supermarket i;

N is the number of supermarkets. For examples of the use of this index see Duncan and Duncan [13], and Wheeler [44].

11 This formulation of the model assumes that the influence of the explanatory variables is additive. This is obviously not entirely cor- rect. However, the intercorrelations between these variables are very weak.

12 The index of dissimilarity is computed as follows:

also be compared with the predicted proportion of consumers patronizing each supermarket as calculated from a classical gravity model formulation, in which the variables are measured in purely objective terms, i.e., the objective attractiveness of a store, OAi. For this purpose the following equation was used.13

OAi :RD (9) RDi

From the results it is evident that the predictive capacity of the model is very high (Table 8). When using equation (6) three of the five predicted values are exactly equal to their observed counterparts. Also, the use of the cognitive distance measures gives better results than when using real distance. When using cognitive scaled distance the index of dissimilarity is three, whereas when using real distance it is eight. It can also be seen that the purely objective measures, equation (9), provide the least satisfactory predictions.

All the equations have comparatively small values for the index of dissimilarity, given that the index has a range of from zero to one hundred. At first glance this would suggest that all four formu-

13 Note that exponents have not been attached to the distance variables in any of these equations, as the pulrpose is to identify the most appropriate way of measuring distance.

TABLE 8

OBSERVED AND PREDICTED BEHAVIOR

Observed Predicted

(6) (7) (8) (9) Supermarket A 32% 35% 32% 32% 40% Supermarket B 35% 35% 32% 41% 31%

Supermarket C 13% 10% 11% 7% 9%

Supermarket D 8% 8% 11% 6% 7%

Supermarket E 12% 12% 14% 14% 13%

D* = 3 D* = 5 D* = 8 D* = 9

* Where D is the index of dissimilarity.

346



lations work almost equally well. How- ever, if we knew nothing about the underlying decision making process, our best prediction would be that an equal proportion of consumers will patronize each supermarket. That is, twenty percent of the consumers will patronize each of the five supermarkets. If this prediction is compared to the observed behavior, the index of dissimilarity is twenty-seven. Thus, twenty-seven is a more plausible extreme value for the index, in this particular context, than one hundred.14 Viewed in this light, the results suggest that the predictive power of equations (6) and (7) is genuinely superior to that of equations (8) and (9).

In general, then, these results substan- tiate the claim that consumer spatial behavior can be better understood in terms of subjectively measured variables than in terms of their more objective counterparts. It is pertinent to bear in mind, however, that, although the model shows an encouraging predictive capacity, full explanation has yet to be reached. The model is based on the premise that the subjectively distorted environment is a better predicter of human behavior than the objective environment. However, it does not go so far as to explain why the environment is subjectively distorted in the way that it is.

CONCLUSION

This paper has suggested that the demonstration of the link between an individual's perception of the environment, and his overt behavior within that environment, is of the utmost importance to the behavioral approach. It is only by explicitly addressing ourselves to the nature of the relationship between percep-

14 This argument can only be made because the values for observed behavior represent the behavior of the entire statistical population, rather than a random sample drawn from that population. In the latter situation, the value of twenty-seven would only represent a sample value drawn from a population of such values.

tion and behavior that we will better understand how we can transform physical space into the appropriate cognitive space, and so be able to analyze decision making in terms of the cognitive space in which it is being undertaken. The research reported here provides some evidence, through the construction of a behavioral model, that such a relationship does exist, at least within the context of consumer spatial behavior.

In the model of consumer behavior, despite the good predictive capacity, there is still room for a number of im- provements. First, there could be more flexibility in the choice of variables making up the attractiveness index, thus al- lowing for the fact that the attributes used to discriminate between alternatives are unlikely to remain invariant across different socioeconomic groups. Second, the model should be sufficiently robust to predict shopping patterns regardless of the spatial distribution of opportunities. To accomplish this, without having to approach consumers be- forehand, appropriate surrogates for the subjectively scaled variables must be found. This will be relatively easy once we have identified the functional rela- tionships between the subjective variables and their objective counterparts.15 Finally, the present model must be ex- tended to take account of search and learning processes [15; 22].

15 For example, it has already been suggested that the relationship between cognitive distance and real distance is curvilinear [1]. However, it has also been shown that the nature of the relationship between these two variables varies according to how cognitive distance is measured [5].

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Article Contentsp. [339]p. 340p. 341p. 342p. 343p. 344p. 345p. 346p. 347p. 348p. 349

Issue Table of ContentsEconomic Geography, Vol. 51, No. 4 (Oct., 1975), pp. 305-392Front MatterA Tale of Two Cities: Flood History and the Prophetic Past of Rapid City, South Dakota [pp. 305-320]Capital Flows and the Developing Urban Hierarchy: State Bank Capital in Wisconsin, 1854-1895 [pp. 321-338]A Behavioral Model of Consumer Spatial Decision Making [pp. 339-349]A New Look at the Minimum Requirements Approach to Regional Economic Analysis [pp. 350-356]Distance, Direction, and Entropy in the Evolution of a Settlement Pattern [pp. 357-365]Factorial Ecology and Factor Invariance: An Investigation [pp. 366-382]BooksReview: untitled [pp. 383-385]Review: untitled [p. 385]Review: untitled [pp. 385-388]Review: untitled [pp. 388-389]

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