consumer involvement in retail store positioning

Consumer Involvement in Retail Store Positioning

Raj Arora, Ph.D. Bradley University

INTRODUCTION

An area of great importance in the field of retailing deals with strategic positioning of retail stores. The retailer's strategic objective is to develop an integrated marketing program encompassing a wide range of marketing elements to create a market position within the dynamic competitive environment. To establish a market position, the retailer strives to develop a store image which is analyti- cally determined and designed to appeal to the store's target customers. The store's image is the product of a set of attributes that are salient in determining customer patronage.

Various approaches to measuring retail consumers' perceptions have been discussed in the literature, including such methods as semantic differential scaling (McDougall, 1974-75; Menezes and Elbert, 1979), Stapel scaling (Menezes and Elbert, 1979) and, more recently, multi-dimensional scaling (Doyle and Fenwick, 1974-75; Green, 1975; Green and Rao, 1972; Singson, 1975).

Development of a store's position requires measurement of customer perceived salient dimensions to gauge the store's progress toward achievement of the desired position. After evaluation of the perceived images held by relevant customer segments, manage-

�9 Academy of Marketing Science, Journal of tile Academy ot Marketiny, Science Spring, 1982, Vol. 10, No. 2, 109-124

0092-0703/82/1002--0109 $2.00

109

110 CONSUMER INVOLVEMENT IN RETAIL STORE POSITIONING

ment may then seek to reinforce the current image or to induce change via communication, the merchandising mix, etc. It is to these change methods that the concept of involvement is particularly relevant (Freedman, 1964; Hoveland, 1957).

INVOLVEMENT AND RETAIL STRATEGY

What is important in retail strategy formulation is the effect of involvement on the relationship between discrepant communication and attitude change. The theory of involvement states that for a person who is highly involved, if the anchor (a person's initial position) is near, the message will be perceived to be more similar to the recipient's own position than it actually is--assimilation effect. Analogously, if the anchor is very far away, the message is perceived to be more discrepant from the recipient's view than it actually is--contrast effect (Freedman, 1964; Sheriff and Hovland, 1964). Thus, regardless of the level of discrepancy, a highly involved consumer is more difficult to persuade. Also implied in the concept of involvement is the notion that those who are highly involved in certain issues have more extensive concerns about them than those low in involvement. Translated in the area of retailing, this notion suggests that those high in involvement will be concerned about a greater number of attributes, the high involvement individuals will also attach a greater degree of importance to each of these attributes (Rothschild and Houston, 1977).

INVOLVEMENT

No clear definition of involvement exists. The term has been used loosely in the field of Social Psychology and Consumer Behavior. Festinger (1957) defines involvement as concern with the issue. Freedman (1964) defines involvement as concern about, interest in, or commitment to a particular position on an issue, Howard and Sheth (1969) refer to "degree of involvement" as another label for the variable's importance. According to Sheriff and Cantril (1947) a subject is said to be involved when the social object is in the subject's ego domain. An individual is said to be "involved" when ego attitudes are present.

ARORA 111

Rokeach (1968-69) posits that the individual's value system is comprised of values which are ranked in order of importance. Rokeach also found a relationship between terminal values and involvement. Ostram and Brock (1968) propose a structure to measure the degree of involvement. Their system is based on first determining the relationship between the object's attributes and the values (terminal) associated with it, e.g., the styling of a particular make of automobile with the value of being recognized as a styling leader. Secondly, how central is this value "recognized as a leader." For Howard and Sheth, Rokeach and the early works of Sherif, it appears then, that involvement refers to the importance attached to the given object.

In their later work Sherif, Sherif and Nebergall (1965) again em- phasize the "importance" as a measure of involvement, but add another component, "commitment to a stand on an issue." This commitment component advances the concept of involvement mak- ing explicit the notions of latitude of acceptance, latitude of rejection and latitude of non-commitment.

In consumer behavior applications, however, the use of commitment is still ambiguous. Does commitment refer to a particular position on an attribute of an object as used by Sherif (1965) or commitment for a brand in a product class as used by Jacoby and Kyner (1973)?

Rothschild and Houston (1977) introduce the concept of a consumer involvement matrix. The degree of involvement is reflected by the ratio of the vertical (representing the size of the consumer's set of evaluative criteria) to the horizontal (ranges of acceptable attribute levels) dimension. Reviewing the literature, we find that involvement refers to:

1. ego involving attitudes,

2. the degree of importance attached to a given object/attribute and,

3. the dependence of the effects of persuasive communication on the level of individual's involvement.

While the issue of involvement and its implications in attitude change via persuasive communications have been discussed at length (Hovland, 1975; Krugman, 1966), very few direct applications in the field of consumer behavior have resulted (Hupfer and Gardner, 1971; Lastovicka and Gardner, 1978; 1979).


Wilkinson, (1975) tests the hypothesis that the cognitive structure of the high-involved is more complex than that of the low-involved. The groups for the study were car club members and dog breeders, chosen for their intense involvement in their relative domain. The results failed to support the hypothesis, indicating that although the groups may differ in their level of involvement, the structures used to represent the perceptions of objects are constant across groups.

In a recent study Lastovicka and Gardner (1978; 1979) test the hypothesis that a unidimensional model of car model similarities will produce a better representation of car model perceptions for low-involved consumers than for high-involved consumers. Young's Torsca (1968) multi-dimensional scaling (MDS) program resulted in one dimensional stresses of approximately .01 and. 14 for low and high involvement groups respectively. This leads them to conclude in favor of the hypothesis.

The concept of involvement and its impact on changing consumer's perceptions of the stores should be explicitly noted in formulat- ing or changing strategy. This paper focuses on these issues. Speci- fically, we propose to use the level of involvement as a moderator variable and determine, using MDS and related analysis, if any differences exist in perceptions of retail stores among those with different levels of involvement.

RESEARCH OBJECTIVES

The MDS techniques may reveal two types of differences between the two groups. The difference may be "structural," i.e., in the number of dimension used to evaluate the stores, and/or it may be "spatial," referring to the position of the stimuli on these dimensions (Moinpour et. al., 1976). Since the highly involved individuals are expected to attach a greater degree of importance to a larger number of stores' attributes, we expect to observe both the structural and spatial differences in the perceptual maps of high- and low-involved individuals. The implicit hypothesis here is that the number of dimensions of the MDS solution will be greater for the high involvement group. Or, analogously, the "stress" for the same number of dimensions will be larger for the high involvement group.

ARORA 113

METHOD

Choice of Stores (stimulus set)

It would be useful for a manager to use information relating to both "intertype" competition and "intratype" competition, the former referring to competition between and the latter referring to different types of stores, such as traditional vs. discount competition within similar stores. Another consideration is to include those stores that are familiar to most of the respondents, so as not to result in missing data which may be a problem in data analysis.

With this view, eight stores located in Stockton, California were selected as follows:

Department Stores

J.C. Penney Sears Montgomery Ward Macy's Weinstock's

Discount Stores Specialty Stores

KMart Mervyn's nemco

These eight stores resulted in twenty-eight pairwise comparisons. Respondents were asked to provide three different types of input. First, respondents were asked to rate the similarity (dissimilarity) of each pair of stores on a scale from one to nine. Similarity implies that the stores within the pair are likely to be substituted for shopping purposes (Green and Rao, 1972).

The second input was to rank each of the eight stores in order of preference with 1 indicating the most preferred and 8 as the least preferred store.

Measure of Involvement

The last information dealt with the importance of the stores' attributes in patronage decisions regarding the store. The measure of involvement for this research was the one used by Hupfer and Gardner. The degree of involvement in retail stores was inferred from the degree of importance attached to various attributes of the store. Respondents were asked to rate the importance of the stores' attributes in their choice process on a scale from I (not important at all) to 9 (very important). The attributes were those that have been


found to be useful in previous studies on retail store image/strategy (Hansen and Deutscher, 1977-78). These are:

Known brands High quality

Wide Selection Low price

Lay away Helpful people

Easy return Friends shop there

Clean Store Fast Checkout

Attractive decor Easy to reach

Easy to park Easy to charge

Sample

Data used for analysis were collected by personal interviews via systematic sampling at the Weber's Mall, where all the stores ex- cept, KMart, Gemco, and Mervyn's are located. Both male and female adults (21 years of age or older) were interviewed in approxi- mate proportion to their presence during interviewing days. Since the s tudy was exploratory in nature, no attempt was made for the sample to be representative of the population characteristics; however, care was taken to conduct interviews on a cross-section of days of the week as well as different times of the day to ensure a more representative shopper sample. A total of 209 eligible respondents were approached, 192 agreed to answer the questionnaire, resulting in 190 usable questionnaires. The number of male and female respondents was 51 and 139 respectively. In order to counterbalance the potential bias resulting from the order in which stores were compared, the order of the store pairs was reversed for half the sample.

ANALYSIS AND RESULTS

The first step in the analysis is to determine the level of involvement of the respondents. The degree of involvement is reflected by the total summated scores of the respondents on the importance of the stores' attributes. The greater the importance of the attributes to

ARORA 115

an individual, the higher the overall summated score of attributes and, thus, the more involved is the individual in these attributes. The mean summated score of all respondents is used to classify the respondents as high or low in involvement. Those with overall scores greater than or equal to the mean are classified as high in involvement and those with overall scores less than the mean score are classified as low in involvement. This resulted in 105 respondents classified as high in involvement and 85 as low in involvement. 2

Perceived Positioning of Stores

The similarity, dissimilarity data are scaled by Kruskal's MDS- CAL V Program (1969). The resulting perceptual maps of retail stores are shown in Figures A and B. Figure A shows the stores in two dimensions as perceived by those high in involvement, and Figure B shows these stores for low involvement individuals.

The number of dimensions chosen to represent the stores' position is arrived at by taking into consideration the fit (stress) and interpretability (Green, 1975). The common procedure is to choose the smallest number of dimensions for which the stress is satisfactorily small. The relationship between stress and goodness of fit based on Kruskal's revised formula in MDS is as follows:

Stress Goodness

0% Perfect 5 Excellent 10 Good 20 Fair 40 Poor

The stress corresponding to the number of dimensions for each group of respondents is shown in Table 1. The iterative solution procedure of the MDSCAL V algorithm may result in a local minimum. To guard against the possibility, the data were scaled with the use of different, internally generated, starting configurations as well as with user supplied initial configurations. The results were identical.


FIGURE A

PERCEPTUAL POSITIONING OF STORES - HIGH INVOLVEMENT

Axis 2

0 Penney

0 Sears

0 Ward

Ideal

0 Mervyn's

0 Macy's

0 Weinstock

~xis I

0 Gemco

0 K-Mart

Joint Space Perceptions and Preferences

The preference rankings of various stores by the two groups are analyzed using PREMAP Program. PREMAP relates the preference data to a multidimensional solution via a hierarchy of models. The results in this paper are based on phase 3 of the program. Given a

A R O R A 117

FIGURE B

PERCEPTUAL POSITIONING OF STORES - LOW INVOLVEMENT

Axis 2

0 Penney Sears 0

0 Ward

0 Gemco

Ideal

0 Mervyn's

0 K-Mart

Macy's 0 0

Weinstock

Axis 1

stimulus configuration of 8 stores in 2 dimensional space and a set of preference scales, the program finds an ideal point in the given stimulus space such that the euclidean distances from each store to


the ideal point are monotonically related to the preferences ex- pressed by the group, (Chang and Carroll, 1972). The resulting ideal points of high and low involvement groups are shown in figures A and B respectively.

TABLE 1

STRESS FOR VARIOUS DIMENSIONS OF M.D.S. SOLUTION

Total High Low

Dimension Sample Involvement Involvement

4 ,003 .005 .010

3 .005 .006 .009

2 .025 .033 .010

1 .435 ,546 .401

Structural Analysis: The purpose of the structural phase of the analysis is to determine the appropriate number of dimensions. It was hypothesized that the high involvement group attaches a greater degree of importance to a greater number of attributes. Thus the perceptual space is expected to be more complex for the high involvement group than for the low involvement group.

From the stress in Table 1, two dimensional MDS configurations appear to emerge for both the groups. The stress values at a unidimensional level are much too high for each of the groups indicating a poor fit of configuration to the data.

Spatial Analysis: In some cases it becomes necessary to compare the configurations resulting from MDS analysis for the same stimuli but for two different populations. Moinpour et. al. (1976) use root mean square (r m s) correlation for comparing the perceived positioning of various brands of toothpastes. In addition to r m s correlation, it is possible to use correlation coefficient, Tucker's (1951)

ARORA 119

coefficient of congruence (C C), r m s deviation (Harman 1976), and other indices of "Guttman-Lingoes Configurational similarity" (Lingoes, 1967).

An advantage of C C is that there are empirical rules which indicate that a value of .9 or more for C C implies that the two data vectors are similar (Nesselroade and Baltes, 1970). Although the C C and the correlation coefficients are not the same (Harman, 1976, P344), in this study the values of these indices are indentical. Thus the correlation coefficients and r m s deviations are shown in Table 2.

TABLE 2

SIMILARITY INDICES FOR CONFIGUARTIONS IN FIGURES A AND B.

Coeffienl of Congruence Below Main Diagonal.

r m s Deviation Above Main Diagonal.

LI L2 HI H2

LI na .22 na

L2 na na .20

H I ,93 na na

H2 na .97 na

LI, L2 and HI , H2 are the dimensions for low and high involvemenl groups respectively.

na: not application

The available computer program for computing the C C and r m s deviation is FACSIM (Arora and Vaughn, 1981). The r m s deviation index is such that in case of perfect agreement in the data vectors, the value of the index is zero.


DISCUSSION

The analysis focused on structural and spatial differences in the high and low involvement groups. At a Structural level considering the stress values in Table I and the associated goodness of fit, the unidimensional results indicate a poor fit of data to the model. At higher dimensions, the stress values point towards a two dimensional model for each group. This finding is consistent with the findings of Wilkinson (1975).

As regards the spatial analysis of two dimensional configurations, the managerial interest may be in intercompetition and intra- competition. Considering intercompetition, figures A and B reveal that Macy's and Weinstock stores form a cluster and Penney, Sears and Ward form another cluster. Overall the difference in the two groups is very small. However, at intracompetitive level the low involvement group perceives the stores within each of the two clusters to be more similar than the high involvement group. This finding supports the theory in that those high in involvement (by definition those who are more concerned or attach more in~or- tance) do perceive more differences in the stores than those lo~4 in involvement.

The location of the ideal points in relation to the positions of other stores is very similar for the two groups. The high involvement group appears to prefer Penney, Macy's, Mervyn's, Sears, Wein- stock, Ward, Gemco and KMart, whereas the other group prefers Penney's most, followed by Macy's, Ward, Sears, Weinstock, Mervyn's, Gemco and KMart. The order of preferences of stores is identical with the exception of Mervyn's and Ward. The high involvement group prefers Mervyn's to Ward. This finding is reason- able considering that (i) low involvement group did not see much difference between the cluster of stores comprising of Penney's, Sears, Ward, and (ii) Mervyn's is located approximately a mile away from the Webers Mall, where other stores are located. This indicates that only the high involvement group which is concerned about the differences perceives these differences and exhibits them in their preferences.

Although the two groups exhibit great similarity at the overall (intercompetitive) level, there are some differences at the intracompetitive level. Given that Sears, for example, may be in direct competition with Penney and Ward than other stores, this difference may be important.

ARORA 121

To further evaluate the similarity of configurations of the two groups, simple correlations and r m s deviation are computed using the coordinates of the stimuli on dimension one and on dimension two. Both of these indices confirm the similarity of perceptions of the two groups. Collectively the results do not exhibit "significant" differences in perception or preferences on the basis of the level of involvement. The theory posited earlier (Rothschild and Houston, 1977) stated that the highly involved individuals will attach a greater degree of importance to a larger number of stores' attributes. This the results failed to support. This finding is consistent with the earlier research by Wilkinson.

Based on empirical results, we find very weak support for differences in cognitive structures of the two groups. We recommend further study based on refined measures of involvement in the area and an extension of the research to identify the high involvement group on demographic and other purchase related variables for effective market segmentation strategy.

NOTES

IFinancial assistance of the University ot the Pacitic is acknowledged. The article benefited greatly from the helpful criticisms and suggestions of the reviewers on an earlier draft. A condensed version of this paper based on partial analysis was presented at the South West Marketing Association Meeting in San Antonio, 1979.

2The author is presently engaged in research on validating the measure of involvement. Unfortunately the past research in this area has not addressed the issues of reliability and validity of this concept. Wilkinson assumes that his subject are involved in their respective areas. Lastovicka and Gardner arbitrarily allocate their respondents into low, medium and high involvement groups so as to result in approximately equal sample size in each group. Moreover, their index of involvement is not based on Sherif's own procedure, but is in fact a deviation from the mean, which certainly does not measure involvement. For a good review article on the subject, see, Arora, Raj, (198l), "Consumer Involvement Problems and Issues in Application", Bradlev University, Peoria, Illinois.

REFERENCES

Andreoli, V. and S. Worchel (1978), "Effects of Media, Communicator and Message Position on Attitude Change," Public Opinion Quarterly 42, #1, 59-70.

Arora, R. (1979), "Consumer Involvement in Retail Store Strategy," Proceedings, Southwest Marketing Association, 105-106.

Arora, R. and R. Vaughn (1981), "FACISM: A Program to Compute the SimilariW Indices for Fwo Groups ot Factor Solutkms." Journal ~!t Marketi; G' Research 18 ( \ o \ ember), 4~:,

Bowen, L. and S. Chatfee (1974), "Product Involvement and Pertinent Advertising Appeals ," Iournahsm Quartcrh/5 [ (Winter), 6!3-62 I.


Chang, J. J. and J. D. Carroll (1972), "How to Use PREMAP and PREMAP2-- Programs Which Relate Preference Data to Multidimensional Scaling Solution," Bell Telephone Laboratories, Murray Hill, NJ.

Churchill, G. A., Jr. (1979), "A Paradigm for Developing Better Measures of Market- ing Constructs," Journal of Marketing Research 16 (Feb.), 64-73.

Clarke, K. and R. W. Belk (1979), "The Effects of Product Involvement and Task Definition on Anticipated Consumer Effort," Advances in Consumer Research, 6 ed., W. J. Witkie, Proceedings of the Association for Consumer Research, Ninth An- nual Conference, Miami Beach, FL, 313-318.

Festinger, L. A. (1957), Theory of Cognitive Dissonance, Evanston, [L: Row-Peterson. Freedman, J. L. (1964), "Involvement, Discrepancy, and Change," Journal of Abnor-

mal and Social Psycholo, s'y 60 #3, 290-295. Green, P. (1975), "Marketing Applications of MDS: Assessment and Outlook,"

Journal of Marketing 39, 24-31. , and Vithala R. Rao, (1972), Applied Multidimensional Scaling. Hinsdale,

Illinois: Dryden Press (1972). Hansen, Robert A. and Terry Deutscher, (1977-78), "An Empirical Investigation of

Attribute Importance in Retail Store Selection," Jounlal of Retailing, 53 (Winter), 59-72, 95.

Harman, H. H. (1976), Mode,l Factor Analysis Third Edition, (Chicago: University of Illinois Press).

Harrel, G. D. (1979), "Industrial Product Class Involvement, Confidence in Beliefs, and Attitude-Intent Relationships," in Attitude Research Plays for High Stakes, eds., J. C. Maloney and B. Silverrnan, Chicago: American Marketing Association, 94- 111.

Houston, M. J. and M. L. Rothschild (1977), "A Paradiy, m for Research on Consumer Involvement, "" Working Paper No. 11-77-46, Universi~ of Wisconsin-Madison.

Hoveland, C. I., O. J. Harvey, and M. Sher!f (1957), "'Assimilation and Contrast Effects in Reactions to Communication and Attitude Change," Journal of Abnormal and Social Psychology 54, 257-261.

Howard, J. A. and J. N. Sheth (1969), The Theo~ of Buyer Behavior, New York: John Wiley Press.

Hupfer, N. T. and D. M. Gardner (1971), "Differential Involvement with Products and Issues: An Exploratory Study," Proceedings, Association for Consumer Re- search, 262-270.

Jacoby, J. and D. Kyner (1973), "Brand Loyalty Versus Repeat Purchase Behavior," Journal of Marketing Research 10 (Feb.), 1-9.

Kiesler, C. A. (1971), The Psychology of Conlmitment--Experiments Lmkiny, Behavior to Belie'/, New York: Academic Press, Inc.

Krugman, H. E. (1966), "The Measurement of Advertising Involvement," Public Opinion Quarterly 30, #4, 583-596.

Kruskal, Joseph B. (1964), "Multidimensional Scaling by Optimizing Goodness of Fit to a Non-metric Hypothesis," Psychometrika, 29, (March), 1-27.

Kruskal, J. B. and F. J. Carmone (1969), "How to Use MD-SCAL (Version 5M) and Other Useful Information," Bell Telephone Laboratories, Murray Hill, New Jersey.

Lastovicka, J. L. (1979), "Questioning the Concept of Involvement Defined Product Classes," Advances in Consumer Research, 6, ed., W. J. Wilkie, Proceedings of Association for Consumer Research, Ninth Annual Conference, Miami Beach, FL, 174-179.

ARORA 123

and D. M. Gardner (1979), "Components of Involvement," in Attitude Research Plays for High Stakes, eds., J. C. Maloney and B. Silverman, Chicago, IL:

American Marketing Association, 53-73. and D. M. Gardner (1978), "Low Involvement Versus High Involvement

Cognitive Structures,'" Proceedings, Association for Consumer Research, 5, 87-92. Lingoes, J. C. (1967), "An IBM 7090 Program for Configurational Similarity," Be-

havior Science, 12, 502-503. McDougall, G. H. G. and J. N. Fry (1974-5), "Combining Two Methods of Image

Measurement," Journal of Retailing, 50 (Winter), 53-61. Menezes, D. and N. F. Elbert (1979), "Alternative Semantic Scaling Formats for

Measuring Store Image: An Evaluation," Journal of Marketing Research 16, (February), 80-87.

Moinpour, Reza, James McCullough, and Douglas MacLachlan, (1976), "Time Changes in Perception: A Longitudinal Application of Multidimensional Scaling." Journal of Marketing Research, 13 (August), 245-253.

Myers, J. H. and M. Alpert (1~6~), "'Uetermmant Buying Attitudes: Meaning and Measurement," Journal of Marketing 32 (Oct.), 13-20.

Nesselroade, John R., and Paul B~ Baltes (1970), "On a Dilemma of Comparative Factor Analysis: A Study of Factor Matching Based on Random Data," Educational and Psychological Measurement, 30, 935-948.

Newman, L. M. and I. J. Dolich (1979), "An Examination of Ego-Involvement as a Modifier of Attitude Changes Caused from Product Testing," Advances in Consumer Research, 6, ed., W. J. Wilkie, Proceedings of Association for Consumer Research, Ninth Annual Conference, Miami Beach, FL., 180-183.

Rhine, R. J. and W. A. J. Polowhiak (1971), "Attitude Change, Commitment, and Ego Involvement," Journal of Personality and Social Psychology 19, 247-250.

Robertson, T. S. (1976), "Low Commitment Consumer Behavior," Journal of Ad- vertising Research 16 (April), 19-24.

Rokeach, M. (1968), "Role of Values in Public Opinion Research," Public Opinion Quarterly 32 (Winter), 547-560.

Rothschild, M. L. (1979), "Marketing Communications in Nonbusiness Situations of Why It's So Hard to Sell Brotherhood Like Soap," Journal of Marketing 43, #2 (Spring), 11-20.

(1979), "Advertising Strategies for High and Low Involvement Situa- tions," in Attitude Researc Plays for High Stakes, eds., J. C. Maloney and B. Silverman, Chicago: American Marketing Association, 74-93.

and M. J. Housten (1977), "The Consumer Involvement Matrix: Some Preliminary Findings," Educator's Proceedings, American Marketing Association, 95-98.

Sherif, M. and H. Cantril (1947), The Psychological oJEgo Involvement, New York: John Wiley Press.

and C. Hovland (1953), "Judgmental Phenomena and Scale of Attitude Measurement: Placement of Items with Individual Choice of Name of Categories," Journal qf Abnormal and Social Psychology 48, #1, 135-141.

and C. Hovland (1%1), Social Judgment--Assimilation and Contrast Effects in Communicath)n and Attitude Change, New Haven, CT: Yale University Press.

Sherif, C. W., M. Sherif, and R. E. Nebergall (1965), Attitude and Attitude Change: The Social Judgment Involvement Approach, New Haven, CT: Yale University Press.

Singson, Ricardo L., (1975), "Multidimensional Scaling Analysis of Store Image and Shopping Behavior," Journal of Retailing, 51, (Summer), 38, 52, 93.

Swinyard, W. R. and K. Coney (1978), "Promotional Effects on a High-Versus Low-Involvement Electorate," Journal o[Consumer Research 5, No. 1 (June), 41-48.


Tucker, L. R. (1951), "'A Method for Synthesis of Factor Analysts Studies,'" Personnel Research Section Report No. 984. Washington, D.C. Department of the Army.

Tybjee, T. T. (1979), "Refinement of the Involvement Concept: An Advertising Planning Point of View," in Attitude Research Plays for High Stakes, eds., J. C. Maloney and B. Silverman, Chicago: American Marketing Association, 94-111.

(1979), "Response Time, Conflict, and Involvement in Brand Choice,'" Journal of Consumer Research 6 (Dec.), 295-304.

Wilkinson, L. (1975), "Effect of Involvement on Similarity and Preference Structures," unpublished PhD. Dissertation, Yale University.

Young, F. W., (1968), "TORSCA, An IBM Program for Nonmetric Multidimensional Scaling,'" Journal of Marketing Research, 5 (August), 319-321.

ABOUT THE AUTHOR

RAJ ARORA is an Associate Professor of Marketing at Bradley University. He received B.S. and M.S. degrees in Industrial and Systems Engineering from University of Southern California and M.B.A. a n d P h . D , from Claremont Graduate School. He has published in Journal of Advertising Research, Journal of Marketing, Journal of Marketing Research, and in the proceedings of: American Institute of Decision Sciences, Southwest Marketing Association and Midwest Marketing Association.

consumer involvement in retail store positioning

Documents