Measurement of Individual Varied Behavior ACRoss Product Classes - Results and Applcations

Moshe Handelsman, The University of Santa Clara
ABSTRACT - A product-specific measure of varied purchase behavior at the individual level serves to describe the sample population's varied behavior. The distribution of this measure is computed for three frequently purchased product categories. A comparison across them provides some useful managerial applications.
[ to cite ]:
Moshe Handelsman (1983) ,"Measurement of Individual Varied Behavior ACRoss Product Classes - Results and Applcations", in NA - Advances in Consumer Research Volume 10, eds. Richard P. Bagozzi and Alice M. Tybout, Ann Abor, MI : Association for Consumer Research, Pages: 80-83.

Advances in Consumer Research Volume 10, 1983      Pages 80-83


Moshe Handelsman, The University of Santa Clara

[The author expresses his appreciation to Edgar A. Pessemier who supervised the dissertation on which this paper is based.]


A product-specific measure of varied purchase behavior at the individual level serves to describe the sample population's varied behavior. The distribution of this measure is computed for three frequently purchased product categories. A comparison across them provides some useful managerial applications.


Varied behavior and its causes have been thoroughly research by psychologists. Recently, the varied purchase behavior topic has been investigated by marketing researchers. On the other hand, the managerial applications of consumer variety seeking have not been given much attention. The target of this paper is to discuss several applications of purchase behavior measurement and to report findings in this area.

The following part describes prior research and measurements of varied consumer behavior, including the specific measure used in this research. The next section discusses the question under consideration -- the managerial implications of varied buying behavior in different product classes -- and sets formal hypotheses. A description of the data base and methodology follows, and the paper concLudes with research results and applications.


The roots of varied purchase behavior are multiple (Pessemier 1981) and the relative contribution of each of the stochastic elements versus the explicable elements has been debated (Bass 1974, Pessemier 1978).

Psychologists (Dashiell 1995, Dember 1956 and Berlyne 1950) have modeled and reported observations of variety seeking and suggested that it is as an innate drive. Subsequently, several psychologists have advanced by adapting a new concept -- the Optimal Level of Stimulation (OLS) -- this concept replaced the concept of Drive Reduction, which assumed that the optimal level of stimulation is the minimum, thus, people are motivated toward minimizing stimulation (Leuba 1955, Berlyne 1954, 1955, 1960, Fiske and Maddi 1961, Driver and Streufert 1964). Venkatesan (1973), Pessemier and McAlister (1981) and Raju (1981) thoroughly summarized and compared these theories, thus, there is no point in presenting them here.

Prior work in the marketing area was done by Jeuland (1978) who formalized the concept of varied-utility within his model of Variety Seeking, "... the utility of a given brand is a function of the past experience that the consumer has had with it..." More experience with a brand decreases its utility, thus leading to variety seeking.

A problem in Jeuland's (1978) model was observed by Hagerty (1980). Hagerty raised the question of the impact the experience with an item has on another item which is similar to the first one. Intuitively, and also from the perspective of cumulative attribute levels, such an experience should lower the relative variable utility of the similar, not purchased, item. Jeuland's model does not Provide a solution to this problem.

The topic of satiation at a given time in conjunction with attribute balancing was dealt by Farquhar and Rao (1976) who proposed a model for evaluating subsets of items where the-choice criterion is based on the balance among the subset's items attributes. McAlister (1979) has developed this model by adding individual ideal-points for each attribute. She tested it in two environments: one, when all of the multiple-item subset is consumed; the second, when only one item is consumed.

A second source of satiation is consumption in successive periods. Howard and Sheth (1969) suggested that in product classes where the risk is not very high, consumers tend to become bored with brands they have preferred for a relatively long period and they look for something new. Some support to this theory can be found in Brickman and D'Amato (1975), who observed now subjects satisfied their familiarity and variety preferences by playing novel musical selections over forty trials. This is also in agreement with Jeuland's (15,8) model.

Most of the prior varied behavior measurements in marketing used borrowed measures, e.g., to assess the OLS level, a few measures-have been constructed and employed (Mehrabian and Russell 1974, Goodwin 1980, Raju 1980). A few marketing-specific measures were generated by Laurent (1978), Pessemier (1981) and Givon (1981).

Handelsman (1982) has suggested and validated a different [The VBM shares some common attributes with one of the measures suggested by Pessemier (1981).] product specific varied behavior measure (VBM) at the individual level. The VBM combines two major elements into one index. The first element is the structural variety embedded in the product map of a product category as perceived by the individual. The second element is the temporal variety as expressed by the purchase behavior along time. The first element is measured by the perceived normalized interbrand distance from the product map. The second element employs Jeuland's (1978.) model to account for the temporal utility of varied behavior. The VBM falls in the interval between 0 and 1. where 0 represents the variety avoider.


Taking into consideration the various motives underlying buying behavior in each product category, it seems plausible to assume that varied behavior differs from one product class to another. Without going into details of the aforementioned motives. it is also plausible to assume that the market adapts itself to the variety needed by providing a wider assortment in product classes where the consumer body requires it. Thus. one may expect to find a stronger VBM distribution's skewness toward the lower varied behavior pole in product classes characterized by a narrower assortment.

Three frequently purchased product categories were investigated:

1. Liquid Household Cleaner, where six brands were available.

2. Toothpaste, where eight brands were available.

3. Cake mix, where five brands were available, each of them offering at least three different flavors.

According to the above arguments one may expect the VBM's distribution in the Toothpaste category to be less skewed [Skewness is a statistic which indicates the degree to which a certain distribution approximates the normal, symmetric, bell-shaped distribution. The skewness measure will assume a zero value when the distribution is fully symmetric, a positive value that increases with the number of cases clustering more toward the left pole, and a negative value that decreases with the number of cases clustering more toward the right pole.] toward zero than in the Liquid Household Cleaner category. The Cake-Mix VBM should be the least skewed toward the zero as its available assortment included at least 15 different flavors.

Formally stated, we expected to reject the following hypotheses:

H1: The skewness toward zero of L.H.C.'s VBM > the skewness toward zero of Toothpaste's VBM.

H2: The skewness toward zero of L.H.C.'s VBM > the skewness toward zero of Cake Mix's VBM.

H3: The skewness toward zero of Toothpaste's VBM > the skewness toward zero of Cake Mix's VBM.


The varied behavior measure requires uncommon data: a purchase history and the perceptions of the choice objects (brands) in each product class for each subject. These data are rare because most panel-data suppliers are reluctant to ask their sample population to complete a lengthy questionnaire as well as to complete the required purchase diary. On the other hand, most surveys which collect substantial cross-sectional data about subjects do not collect purchase histories.

The Purdue Consumer Behavior Research Project (PCBRP) [(Burger 1968, Teach 1968 and Tigert 1966)] (1964-65) includes the required data. Here, perceptual data are available along with seven months' purchase diaries. The PCBRP project included 540 housewives from a midwestern town. Among other tasks, each of the participants maintained a purchase diary for a common period of seven months and rated the attributes of the three product categories.

The sample was selected from two distinct populations in two different manners. The non-student sample was a systematic random sample of housewives taken from the telephone directory, and the random student wives' sample was taken only from wives residing in married students' housing.

The attribute ratings and the attitudinal data were collected from the housewives. However, multiple users (i.e., husband/children) influence varied consumption and varied buying behavior. Thus, it is implicit that the wife is regarded as a representative of her household concerning perceptions and attitudes. One may criticize this assumption and point out that discrepancy may exist between the perceptions that were collected and those of other family members that had some influence on the purchase behavior. This problem cannot easily be answered (Davis 1976). Nevertheless, some support to the current method of data collection is found in "ind (1976). Therefore, it seems plausible to monitor the purchase behavior and to collect perceptual data from the housewife.

Two groups were eliminated from the analyses: student wives and wives whose husbands were retired or deceased. The first group was omitted because on the basis of the unstructured group interviews with student wives, it became clear that these housewives did not have well-formed attitudes concerning some of the family activities, shopping behavior, etc., which they were asked about in the questionnaire. The second group was suspected of clouding the analyses due to erroneous diary maintenance, difficulties in rating attributes, etc. In sum, the sample population was not a representative one for a nationwide generalization. However, it may satisfy the current research aims which focus on the different distributions of consumers' varied behavior across-different product classes.


Following are the skewness values for the VBM distribution in each product class:

Liquid Household Cleaner . . . . . .999

Toothpaste . . . . . . . . . . . . . . . . .934

Case Mix . . . . . . . . . . . . . . . . . .511

The distributions are shown in the Appendix.

It is evident that the skewness values agree nicely with the width of the assortment available in the product categories. Thus, hypotheses H1, H) and H3 may be rejected. Furthermore, though the sample population was not fully a representative one, these findings support the potential applicability of VBM to assortment-management issues.

The first application concerns advertising and new-product-introduction strategies. Howard and Sheth (1969) proposed a. rationale for using advertising in highly stable markets because in such markets people get bored with repeat purchases of their preferred brands and are particularly receptive to information about new brands. Therefore, VBM's distributions can serve as an input to advertising decisions concerning established products. When other things are considered equal, a VBM less skewed to the left may indicate a product where advertising will have a stronger impact than in a case where VBM is more skewed to the left. In the former case, it would be plausible to stress the variety element in the advertising vehicle.

The same method may be helpful when a decision concerning introduction of new brands is considered. A launch is expected to flow much easier in product classes where the VBM's distribution is less skewed to the left. A discussion of this topic appears in Givon (1981).

The second application of VBM's distribution resides in the context of segmentation. If a sufficient number of product categories is analyzed in a method similar to the one in this research, one may expect to find a few product clusters where each cluster is internally homogeneous with respect to the individual's VBM level, i.e. the consumer will express a particular pattern of varied purchase behavior across product classes. The latter pattern can be inspected in order to find common attributes that may help in fitting a marketing strategy which will also take into consideration descriptor variables pertinent to the target population. For examples see Raju (1980, p. 280).

Manufacturers/Retailers of nondurable goods are interested in the aggregate buying behavior of the population. Though the VBM is based on individual responses, the distribution of VBM's values across a sample population may be an important source of information to manufacturers/retailers. As discussed above, one expects the market to adjust the assortment width to satisfy consumer need for variety. Nevertheless, it would be safe to assume that the market is not optimal in that there are product classes where the demand for variety is not adequately satisfied, and vice versa. A manufacturer or a retailer may be assisted in selecting the best product class (out or several alternatives) where a new brand will be added or the assortment width will be enlarged, by comparing the VBM's distribution skewness in the categories under consideration. It is suggested that the best alternative is the one where the ratio skewness-to-the-right/assortment-width is the larger. The larger this ratio the less satisfied is the variety need expressed by consumers; thus the probability of success in adding a new brand is higher. This rule applies when ali other factors are equal, especially the consumer's demand heterogeneity which is a dominant cause of assortment.









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