An Extended Expectancy Model of Consumer Comparison Processes

Jacob Jacoby, Purdue University
Jerry C. Olson, Pennsylvania State University
[ to cite ]:
Jacob Jacoby and Jerry C. Olson (1974) ,"An Extended Expectancy Model of Consumer Comparison Processes", in NA - Advances in Consumer Research Volume 01, eds. Scott Ward and Peter Wright, Ann Abor, MI : Association for Consumer Research, Pages: 319-333.

Advances in Consumer Research Volume 1, 1974    Pages 319-333

AN EXTENDED EXPECTANCY MODEL OF CONSUMER COMPARISON PROCESSES

Jacob Jacoby, Purdue University

Jerry C. Olson, Pennsylvania State University

[The cooperation of the Procter & Gamble Company in carrying out various facets of this investigation is gratefully acknowledged. Also warmly appreciated are the substantive contributions made to this investigation by S. D. Shores and G. T. Davis.]

[Dr. Jacoby is an Associate Professor, Department of Psychological Sciences, Purdue University, West Lafayette, Indiana 47907.]

[Dr. Olson is an Assistant Professor, Department of Marketing, Pennsylvania State University, University Park, Pennsylvania.]

The subject of psychological motivation involves a consideration of two basic questions: (1) What is it that arouses an individual and causes him to initiate a sequence of behavioral acts? (2) Given arousal, how does an individual select one behavioral alternative (or course of action)-from among all such alternatives (or courses of action) available to him? Generally speaking the arousal or "content" theories of motivation seek to answer the first question while the direction or "process" theories address themselves to the latter (cf. Campbell, Dunnette, Lawler, & Weick, 1970).

If one's purpose is to understand consumer choice behavior, in which the individual typically selects one out of a set of alternatives, then examination of the process theories should prove more fruitful. Of the available process approaches, what has come to be known as "expectancy" theory seems to possess the greatest general utility for improving our understanding of the motivational factors involved in consumer choice behavior.

Considerable interest has recently been manifested in utilizing expectancy models to partially explain consumer choice behavior (e.g. Bither & Miller, 1969; Cohen & Ahtola, 1971; Hansen, 1969; Howard & Sheth, 1969;Kaplan 1971; Reza & MacLachlan, 1971; Schendel, Wilkie, & McCann, 1971; Sheth & Talarzyk, 1972; Talarzyk & Bass, 1969), although the operationalizations have not always been faithful to the conceptualizations (cf. Bither & Shuart, 1972; Cohen, Fishbein, & Ahtola, 1972; Kaplan, 1971). The source for the models used in these studies is usually attributed to the social psychological work of either Fishbein (1967) or Rosenberg (1956, 1960), although,as documented elsewhere, (Jacoby, in press), current expectancy theory encompasses more than just these formulations and reflects substantial contributions from the personality, learning theory, and industrial-organizational subdomains of psychology.

Whether one applies the label of attitudes (Fishbein; Rosenberg) or motivation (e.g., Peak, 1955; Vroom, 1964) to the conceptual underpinnings, and disregarding differences in terminology, the basic models are dynamically and computationally equivalent (although conceptually somewhat different) and can be considered to contain the following four core concepts: alternatives, outcomes, valence (perceived importance or desirability), and expectancy [The terminology used here is most similar to the Vroom (1964) motivational force model.] (see Figure 1). A typical situation to which the model might apply is one in which an individual (or group, e.g., a family) is in the process of making a choice from among several comparable alternatives (such as brands in a product class, e.g., Crest, Colgate, Gleem, Ultra Bright, etc.). Associated with each alternative is a set of outcomes, that is, events which either will or will not occur as a function of having selected that alternative. In general, the outcomes associated with each of the alternatives (brands) in a product class overlap considerably so that it is possible to generate a single list of outcomes appropriate for the entire group of alternatives (e.g.,fresh breath, whiter teeth, cleaner teeth, decay prevention, and sex appeal for toothpaste). Each outcome varies in terms of its valence (i.e., perceived importance desirability, or want-satisfying ability) for the decision maker. Finally, associated with each alternative-outcome pair is an expectancy, or subjective probability estimate regarding the likelihood that selecting the alternative will lead to that specific outcome.

FIGURE 1

THE TRADITIONAL EXPECTANCY MODEL

Parenthetically, the elements of the basic expectancy model are capable of providing operationalizations for the components of perceived risk as this concept has been applied to consumer decision processes (cf. Bauer, 1960; Cox, 1967). Specifically, expectancy provides an operationalization of the uncertainty component, while outcome valence or importance operationalizes the consequences component of perceived risk.

Both the valences and the expectancies may be assessed using standard scaling techniques. Multiplying the outcome valence by the expectancy rating for each alternative-outcome pairing and then summing these products across all outcomes yields a numerical index for each alternative (see Formula 1, Figure 1) which supposedly represents the individual's motivational force (MF) or attitude (A) toward each alternative (cf. Rosenberg, 1956; Vroom, 1964, respectively). The model's prediction is that the alternative with the higher MF or A will be selected.

Results from studies applying the basic expectancy model (as derived from Rosenberg or Fishbein) to consumer behavior, while encouraging, are hardly exceptional. While improper and crude operationalizations of the variables involved are sometimes suggested as reasons for these poor findings (Bither & Shuart, 1972; Cohen, Fishbein, & Ahtola, 1972), it is also possible that the general expectancy formulation, as currently stated and used by consumer researchers, fails to account for a large enough slice of reality to enable it to be anything other than weakly predictive of purchase behavior. Indeed, the Rosenberg and Fishbein models were developed in order to provide more accurate measures of attitudes and were not intended to be related directly to behavior. In his more recent work, Fishbein (Ajzen & Fishbein, 1969; Ajzen & Fishbein, 1970; Ajzen & Fishbein, 1972) incorporates the notion of "intention" as a class of factors which intervene between attitudes and behavior, and he contends that intentions are more accurate than attitudes as predictors of behavior. Recent research in the consumer realm tends to support this contention (e.g., O'Brien, 1971).

In contrast, the expectancy models developed by the organizational psychologists are more directly concerned with predicting specific work-related behaviors (see reviews by Campbell & Pritchard, in press; Heneman & Schwab 1972; House & Wahba, 1972; Miner, 1973; Mitchell & Biglan, 1971). In general, the more recently developed of these models tends to be conceptualized (and operationalized) somewhat differently and encompass more aspects of reality than are included in the attitudinally-based expectancy models currently used by consumer researchers. The purpose of this paper is to describe one of the most recent "extended" expectancy models to appear in the industrial-organizational psychology literature and present the results of an investigation which attempted to apply this new model to the consumer context.

Briefly, in addition to considering the outcomes derived from a set of alternatives, Jacoby's (Jacoby, in press) extended model incorporates information sources, past experience, and other inputs into the comparison process. Three core constructs--inputs, significance, and likelihood--are added to the four original concepts, resulting in the extended expectancy model depicted in Figure 2 [The full model contains additional extensions of an abstract nature which need not be described here.]. Examples of inputs appropriate for the toothpaste purchasing situation described earlier would include: a dentist's recommendation; information acquired from advertising; word-of-mouth influence; one's past experience with each of the alternatives; the price of each alternative, etc. Such considerations, while not desirable outcomes as this notion as been conceptualized by traditional expectancy modelers, also enter into the decision process and may affect the subsequent direction of behavior. The significance of an input represents its perceived importance to the decision maker, while the likelihoods represent the perceived influence that an input has upon the choice of specific alternatives. Thus, significances and likelihoods are related to inputs in a manner analogous to that in which desirabilities and expectancies are related to outcomes. The composite extended expectancy model is expressed by Formula 2.

Formula 2:  EQUATION

where:

MF Aa = the motivational force of alternative a

SIi = the significance of input i

LIiAa = the likelihood that input i will lead to selecting alternative a

DOk = the desirability of outcome k

EAaOk = the expectancy that selecting alternative a will result in obtaining outcome k

n = the number of inputs

m = number of outcomes

W0, W1 = regression-determined weights

The push-pull dichotomy frequently used to categorize motivational theories is relevant here. Outcomes are the goals or functional consequences which result from selecting and using one alternative over another. They are the pull components and attract behavior. Inputs, on the other hand, are those factors which lead to or push an individual to select one out of a set of alternatives.

The notion of functional consequences is critical in distinguishing between inputs and outcomes. Outcomes refer to the functional aspects of the alternatives in the product set; they usually are the primary purpose for buying and/or using the product. Inputs, on the other hand, are those considerations other than perceived functional consequences which occur prior to the decision and which influence the direction of the behavioral act. To illustrate, we may purchase Brand A toothpaste in order to clean and whiten teeth (both outcomes) and because it is the most inexpensive brand available (an input). However, while input considerations (such as low price for a given brand) may influence our decision, they can in no way be considered outcomes in the same sense as are preventing cavities or cleaning teeth. Stated somewhat differently, one doesn't buy toothpaste (as a product) to save money, but to clean, whiten, and protect one's teeth.

FIGURE 2

AN EXTENDED INPUT-OUTCOME EXPECTANCY MODEL

As described in Jacoby (in press), it is the inputs (especially personal experience with the product) which primarily determine the initial perceived expectancies and outcome desirabilities. Moreover, changes in expectancies and desirabilities will subsequently feed back to affect input significance and likelihoods. Thus, the present extended expectancy model represents an attempt to describe statically something which is a dynamic process. At best, it is a reasonable, but not completely accurate, approximation of the consumer comparison process.

It should be emphasized that the expectancy formulation presented here is not intended to encompass the entire consumer decision process from problem recognition to post-purchase behavior (cf. Engel, Kollat, & Blackwell, 1968). Rather, it is a limited domain model which focuses on the alternative comparison and evaluation processes that may precede purchase.

The primary purpose of the present investigation was to determine whether this organizationally based extended expectancy model could contribute appreciably to understanding consumer comparison processes above and beyond that provided by a more traditional model. Assuming it did, a second purpose was to determine which of the two components, outcomes or inputs, tended to contribute the most.

METHOD

Subjects

The subjects were 149 housewives interviewed at a central location testing site in St. Louis during April, 1971.

Product Studied

A frequently purchased paper product was selected for study. Ten product-related outcomes obtained from consumer interviews (e.g., "softness") and four appropriate inputs (e.g., "friends' and neighbors' recommendations") were examined.

Instrument

A questionnaire served to measure the four necessary elements (input significance, likelihood, outcome desirability, and expectancy) of the extended model and to collect the criterion data (i.e., overall opinion of the five major brands, amount of each brand purchased during the preceding year, and intention to buy each brand in the future). To counterbalance and partially control for biases which might have been introduced as a function of the position in which specific items appeared in the questionnaire, four different versions were constructed by placing items in different orders. The number of respondents for versions 1 through 4 were 39, 38, 34, and 38, respectively.

Input significance and outcomes desirability, while conceptually different, may be assessed using the same operational procedures. In the present study, both significance and desirability measures were obtained by asking the housewives to rate each of the inputs and outcomes on a four-point scale ranging from "extremely important", through "very important" and "fairly important", to "not too important".

Likelihoods for the four inputs were assessed by asking each housewife to indicate how likely it was that she would buy each of the brands if the specific input in question were the only thing she had to consider in making her purchase. These data were collected using a five-point scale ranging from "definitely would buy" through "probably would buy," "probably would not buy", to "definitely would not buy," with the mid-point "don't know whether or not would buy."

Expectancy was operationalized by asking the housewives to indicate how good they thought each brand currentlY was relative to each of the ten outcomes, rather than how they expected it to perform in the future. The reasoning here was twofold: (1) it is probably easier and psychologically more comfortable for most people to describe a current state rather than to indicate what they expect an object or event will be like in the future; and (2) probably the best indication of what the consumer thinks a product will be like in the future is what she thinks it is like now, assuming no new information is added to the system. This scale ranged from "one of the best", through "very good", "fair", and "poor", with a "no opinion" option. The "no opinion" alternative was coded as zero and, when entered into Formula 2, caused that brand-outcome pair to drop from the model.

RESULTS

Inasmuch as one form contained several respondent errors and another was only partially completed, all analyses are based on 147 of the 149 respondents.

Valence means (i.e., significance and desirability ratings) ranged from 3.84 to 1.68 with a median of 3.08 (4-point scale) while standard deviations ranged from 1.22 to .42 with a median of .95. The inputs generally had lower valences than the outcomes. This may be because the ten outcomes included most of the important purposes for which the product is used while the inputs provided did not as comprehensively sample the range of potential inputs. Possibly significant input omissions include "what my husband recommends", "what my mother uses", "amount of advertising exposed to for each brand", "preferences for each brand's advertisements", etc.

Table 1 presents the summary data for the subjects' overall opinion of the five brands, their intention to purchase each of these brands over the next six months, and the number of units of each brand that these subjects estimated they had purchased during the preceding year. The latter measure was transformed to "proportion of total purchases devoted to each brand" for each subject in order to eliminate variance due to a heavy-light usage dimension. All further analyses were conducted using the proportion of purchases measure. Note that the rank order of all five brands was consistent across all four criterion measures.

Table 2 presents the zero-order correlations between the input and outcomes portions of Formula 2 and the three criterion variables. All the coefficients were positive, and the majority were statistically significant beyond D < .01. The outcomes-only analysis generally yielded larger correlation coefficients than did the inputs-only analysis. The three instances in which inputs had higher correlations with a criterion than did outcomes have been underlined.

An interesting hypothesis may be developed from the data presented in Table 2 Note that the rank ordering of the five brands (from A through E) corresponds to the average number of units of each purchased during the preceding year (see Table 1) or the market share of each brand in the present sample. A corresponding monotonic trend in the magnitude of the correlation coefficients is evident, especially in the results of the input-only analysis. Eat is, the correlations between the input component of the model and the criteria generally increased over brands as market share decreased. These data suggest that inputs may have greater influence over consumers who have relatively less experience with a brand, or alternatively, as one's purchasing experience with a product increases, the influence of input factors may decrease, while that of outcomes may increase. Such a speculation coincides with the suggestion (Jacoby, in press) that knowledge regarding outcome desirabilities and expectancies associated with specific brands develops over time as a result of experiences with a product class. In lieu of such "internal information", a consumer may make his brand choice on the basis of "external information" or inputs.

TABLE 1

MEANS AND STANDARD DEVIATIONS FOR THE CRITERION MEASURES  (N = 147)

In order to more precisely identify the relative contributions to criteria prediction provided by the two components of the extended model, each criterion was regressed on the input and outcome components for each brand. De results of these analyses are presented in Table 3 along with a comparison of the prediction power of the extended model as specified in Formula 2 with that of the outcome component alone. Regarding the overall opinion criterion, the outcome component was the major predictor, accounting for virtually all the explained variance on this measure. However, inputs had a stronger predictive effect for the likelihood-of-purchase and proportion-of-purchase measures and actually had larger Beta weights than outcomes in four out of ten instances. For these two purchase-related criteria, note that the input Betas generally increase and the outcome Betas generally decrease as the market share of the brands decreases. As shown in Table 3, this created an increase in predictable variation due to the extended model as brand market share declined and provides another perspective on the hypothesis suggested in the preceding paragraph.

TABLE 2

CORRELATIONS BETWEEN THE INPUT AND OUTCOME COMPONENTS OF THE EXTENDED MODEL AND THREE CRITERION MEASURES

TABLE 3

REGRESSION RESULTS OF APPLYING THE EXTENDED MODEL TO PREDICT THE THREE CRITERIA

Several factors may attenuate the predictive power of a linear combination of variables, one of which is the extent to which the predictor variables are correlated with one another. In the present data, the input and output components of the model are moderately strongly related (r's between input and outcome components = .518, .500, .516, .639, and, 644 for brands A, B, C, D, and E, respectively). Thus, an alternative explanation for the input component's weak predictive power is its nonorthogonality with the outcome component.

DISCUSSION AND CONCLUSIONS

It should be emphasized that the present findings are based on stated past-purchase-behavior and stated intent-to-purchase data collected cross-sectionally. A preferable approach would be to obtain an actual index of past purchase behavior and forecast "future" purchase behavior from longitudinal measures of an individual's past purchases.

For the most part, the coefficients obtained in Table 2 are large relative to the coefficients reported by other consumer researchers using attitudinally-based expectancy models. Moreover, the combined input-outcome extension (cf. Table 3) explained a substantial proportion of the variance in the overall opinion and likelihood of purchase criteria--ranging from a low of 20% to a high of 41%--although it was less successful in predicting past purchasing behavior. While one might like to see the extended model explain 60, 80, or ever. 100% of the variance in the criterion measures, common sense suggests that this should not occur. If it did, it would mean that important and expensive elements in the marketing mix not measured here (e.g., frequency of exposure to advertisements for each brand, preferences for these advertisements, special sales and other price reductions, product distribution factors, etc.) would have a negligible effect upon opinions and especially upon likelihood of purchase. Moreover, unreliability in the predictor and criterion measures, [DeLeo (1972), using some of the "cleanest" expectancy measures to be found in the industrial psychology literature, reports median correlation coefficients of only .56, .49, .47, and .39.] as well as inaccurate operationalizations and non-perfect content validity in the choice of inputs and outcomes, all serve to attenuate the predictive validity coefficients. Under these circumstances, being able to explain just a third of the variance is considered encouraging.

The extended expectancy model (in which inputs were incorporated) did, in two out of five cases, contribute to predicting stated likelihood of purchase and proportion of purchases over the predictions of the more traditional expectancy model (i.e., considering only the outcomes). First, since only four inputs were used compared with ten outcomes, it could be argued that the combined predictive power of ten relevant criteria would surely be superior to that obtained using an index based on only four such criteria. Second, a low-risk product (in terms of cost, social visibility, duration of commitment to the product after purchase, etc.) such as was used may not provide the most suitable test for the extended model. It may be that the model is better able to predict attitudes and behavior toward products relatively high in perceived risk (cf. Bauer, 1960; Cox, 1967), since the consumers probably devote more cognitive effort to the alternative evaluation processes for high-risk than for low-risk products. Finally, it seems reasonable to hypothesize that inputs increase in predictive importance for new products or brands, infrequently purchased products or brands, and/or during the first-time or early purchases of a product or brand. Thereafter, as personal experience and familiarity with the outcomes derived from using a product increase and provide a personal base of information, the relative importance of input factors may decrease while that of outcome factors increases. The present data suggest that such an hypothesis has validity and imply that the model might be most fruitfully applied under circumstances which attempted to predict choice behavior with respect to new brands and products.

Despite the fact that the extended model seems to be an improvement in certain instances over the general expectancy model, its use has similar problems. Analogous to the problem of independence between separate attribute value-expectancy cross-products faced by researchers who use a disaggregated expectancy model is the present problem of non-independence between the input and outcome components of the extended model. Perhaps as the operational distinctions between inputs and outcomes become clearer and better-defined, the orthogonality problem will be less serious. A second problem concerns the possibility that the various outcomes (or inputs) may have their individual effects enhanced or reduced via complex interactions with other outcomes (or inputs). In such a case, the simple summation of expectancy-value cross-products (as specified by all expectancy models) would be inappropriate. Future work with a disaggregated expectancy model could include and test interaction terms in their regression models.

Two other issues to which future expectancy model work must be addressed are: (1) what is the reliability associated with each of the expectancy model measures; and (2) does the expectancy model have different validities for different groups (e.g., heavy vs. light users; consumers for whom the product is ego involving and important vs. those for whom the product is not important)?

A final note concerns imprecision in classifying the inputs and outcomes associated with the present model. One of the factors initially classified as an input ("good value for the money") may actually be more accurately classified as a "higher order" outcome (cf. Jacoby, in press). Had this factor been phrased as "the price I pay" there would be little question that it was an input. However, to the extent that the term "value" is a function of many factors other than price (including whether or not the product satisfied the functional requirements for which it was purchased) and also is subjectively and ill-defined at best, one could classify it as an outcome rather than an input. While this problem remains to be resolved in future research, it does not seriously detract from the overall success of the extended input-outcome expectancy theoretic approach to predict current brand attitudes and stated future purchase behavior

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