Preserving Consumer Autonomy in an Interactive Informational Environment Toward Development of a Consumer Decision Aid Model

ABSTRACT - The growing information industry has not yet widely incorporated a decision aid model for the consumer as part of an interactive data base system. Several models have been developed or discussed which would provide the consumer with more information and, to varying degrees, make recommendations concerning product purchase. This paper presents some of the issues involved in developing an integrative consumer aid model as well as some of its limitations.


Donna J. Hill (1989) ,"Preserving Consumer Autonomy in an Interactive Informational Environment Toward Development of a Consumer Decision Aid Model", in NA - Advances in Consumer Research Volume 16, eds. Thomas K. Srull, Provo, UT : Association for Consumer Research, Pages: 144-151.

Advances in Consumer Research Volume 16, 1989      Pages 144-151


Donna J. Hill, Indiana University

Maryon F. King, Southern Illinois University


The growing information industry has not yet widely incorporated a decision aid model for the consumer as part of an interactive data base system. Several models have been developed or discussed which would provide the consumer with more information and, to varying degrees, make recommendations concerning product purchase. This paper presents some of the issues involved in developing an integrative consumer aid model as well as some of its limitations.


Consumer behavior research has focused a great deal of attention and effort upon understanding how consumers make decisions. Attitude modeling, multiattribute judgments and brand choice models have been extensively discussed and researched in the marketing literature. Much less discussion and research has centered on normative consumer choice models (Sheth 1980). Yet in the growing consumer information industry, the implications of such models for consumers, manufacturers, retailers and sales techniques should be of concern to the marketing discipline.

Increasing consumer affluence combined with advances in new product technology have resulted in the production and distribution of more technically complex products. One side effect of the rapid introduction of such products is the advent of the well-documented information vacuum (Maynes 1979; Scherhorn 1985; Grunert 1984). In response to the consumer need created by this vacuum a growing consumer information industry is emerging. This information industry consists not only of such well known institutions as Consumers Union and government regulatory agencies, but also includes numerous advice publications such as Changing Times, Money, and self-help shopping guides. As Naisbitt (1984) has so aptly noted, "the information society is no longer an idea --- it is a reality."

A recent development in information provision technology is the introduction of the interactive Videotex system, which allows users lo directly access information from a central data base and display it in their homes or businesses, or in other locations such as libraries. One such application in the United States is Comp-U-Store, which offers a shopping and browsing service in which the customer specifies the type of product, brand preference (if any), and desirable features; the system then displays a list of the products that satisfy these parameters.

Given the increasing acceptance of personal computers and related developments in decision support systems (see Bonczek, Holsapple and Whinston 1984; Sprague and Carlson 1982; Sprague and Watson 1986), the opportunity exists for operationalizing systems that facilitate decision making in complex, ill-structured, multi-criteria, situations, perhaps as a part of a such a videotex system. The methodology consists of integrating data management, model handling, and user interface facilities into a uniform environment in which the database component constitutes the cornerstone of the system (Jelassi, Haug, and Swamidass 1986).

The purpose of this paper is not to build an explicit interactive consumer aid model per se, but rather to review attempts to build such models and to present the framework connecting a number of relevant issues concerning their development and use First, the evolution of consumer decision aid models will be examined with a description of four existing models. Next, the concepts and steps involved in the development of an integrative consumer aid model will be presented. This will be followed by a discussion of the limitations associated with development of such models.


The objective of consumer decision aid models is to provide the "right" information so that consumers can make "better" decisions based upon the specification of the "ideal" amount of a characteristic that the best product might provide (Bettman 1975). One of two perspectives can be adopted when specifying product characteristics and their weights. First, the scores and weights of the various product characteristics could be provided by external sources (for example Consumer's Reports recommendations or the FDA's nutritional requirements). The second perspective is based on the premise of consumer autonomy and incorporates the consumer's own specification of the "ideal" levels of certain product characteristics. The former approach has been called "policy normative" (Bettman 1975) and is based on the premise that the intent of such models should be "educating" the consumer to make "better" purchase decisions, whereas the latter is more appropriately referred to as "processing normative" and is intended to aid the consumer in processing the appropriate purchasing information. In practice, however, the distinction is not always clear; both orientations could be seen as a type of policy. The focus of this paper is on a type of hybrid model where the scores or levels of the various product characteristics are provided by an outside testing agency such as Consumers Union and the product attribute importance weights are provided by the individual consumer.

The importance of and need for any type of approach has been the focus of much debate (e.g., see Scherhorn 1985; Hjorth-Anderson 1984). Does the consumer need such a model and what are the public policy implications that emerge when such models are implemented? If one holds that information is readily available in a usable and low cast form and that the consumer is generally able to make rational (logically consistent) purchase decisions based on this information, then the conclusion is that consumer aid models of any type should be conceded marginal significance at best. However, the evidence indicates that these are not appropriate assumptions. The existence of informationally imperfect markets characterized by unjustified price dispersion (extensive price dispersion unexplained by utility-conferring properties of the product) is well documented (Sproles 1977; 1986; Dardis and Gieser 1980; Maynes and Assum 1982). The existence of these inefficient markets, in turn, is attributed to the fact that consumers consistently fail to obtain and utilize relevant information while manufacturers refuse to provide information through advertising in a rationally usable form.

Furthermore, evidence suggests that even when consumers are presented with relevant information, information processing biases often result in selection of a suboptimal alternative. Hogarth (1980) notes that the bulk of processing biases cited in the literature result from (1) task variables such as time pressures, amount and complexity of information, (2) inability or unwillingness to expend mental effort and (3) inconsistency in applying a judgmental rule. A related problem often faced by the consumer is his or her inability to articulate and/or select "appropriate" product attributes as criteria in their decision processes. Jacquet-Lagreze and Shakun (1984) conclude that the mitigating effects of such task factors suggests that the buying decision process could be much more efficient if it was augmented by some type of consumer decision support system.

Before entering into a discussion of the issues and concepts involved in building a consumer aid model, it would be useful to briefly describe four efforts at model building in this area. One major contribution in this area was the development of a product quality model by Maynes (1976). The explicit purpose of Maynes' model is to help the consumer determine the extent to which a specimen possesses the service characteristics he or she desires (1976, p. 542). Maynes defines quality as a weighted average of characteristics which comprise the product's ability to provide satisfaction. In Maynes model both the characteristic weights (desirability) and their scores (utility obtained from that variety) are assigned by the individual consumer. Once quality is determined in this manner, the purchase decision is facilitated by building a Price-Quality Chart from which the consumer can choose a product that would allocate his income so that each dollar spent yields approximately the same utility, or quality (i.e., buy the desired quality at the lowest possible price).

A second approach to enhancement of consumer decision making is offered by Thorelli (1974). Thorelli suggests a two-pronged approach which (1) involves the establishment and maintenance of minimum requirement levels of various product characteristics by some regulatory agency, and (2) requires firms to indicate the quantity of these characteristics on the product's label. The consumer is then able to make informed decisions which will presumably result in better product choices.

A different approach is taken by Geistfeld's (1977) TEA (technical efficiency approach) model which attempts to aid the consumer in determining, for a given set of characteristics, which brands are too expensive. The model focuses on the way in which changes in brand prices affect the ability of a given brand to provide characteristics efficiently. Characteristic values are provided by objective measures such as calories and protein per pound. After the product set has been reduced by the model to only efficient varieties, other criteria such as personal preferences and threshold levels, presumably provided by the consumer, are used to determine the final variety to be purchased.

One of the primary goals of the approach developed by Jacquet-Lagreze and Shakum (1984) is to enable the consumer to learn in an efficient way what his/her preferences and goals are relative to currently available products. The model involves four phases (with feedbacks among them): (1) selecting criteria and an admissible set of alternative products, (2) searching for consistency between the decision maker's holistic preference and an analytical model of it, (3) assessing a compromise preference model, and (4) evaluating the alternative products using the compromise preference model. An example of a commercial model that uses these steps is PREFCALC (Euro-Decision 1983), a micro-computer software package intended to help decision makers assess their preferences in a multi-criteria situation.


The preceding models represent substantial contributions toward enhancing the ability of consumers to make better product choices. However, each of these models contain only a subset of those elements which must be included if a comprehensive consumer aid model is to be developed. For example, Maynes relies upon the consumer to specify the set of desirable product attributes (thus attempting to preserve consumer autonomy), while Thorelli focuses on insuring the availability of relevant information as well as external expertise. The major advantage of the step-wise model building approach proposed below is that it attempts to systematically integrate all of the major elements identified in previous research (e.g., information availability, consumer autonomy, and the availability/input of expert information) in order to develop a comprehensive consumer decision making aid.

For the model discussed in this paper, two assumptions are made: (I) the decision maker has explicit values or goals and is to decide how to make the best choice among available alternatives, and (2) the decision maker will choose to behave in a logically consistent manner, that is, choose to maximize expected utility of desired characteristics in a particular product category. The steps involved in structuring a decision problem and the corresponding steps in the model development are outlined in Exhibit 1. These steps and their interrelationships are now briefly considered.

Step 1

To develop such a model, a definition of product is needed to delineate the set of items from which the alternative will be chosen. As an example, Lancaster's (1971) concept of a product grouping starts with the idea that it is the characteristics of a product rather then the product itself which provide consumer satisfaction. According to Lancaster's definition, goods which yield the same characteristics can be defined as members of a "generic goods" class -and analyzed as brands in an industry. Lancaster asserts that the relevant characteristics should be defined not in terms of people's reactions to the good, but rather in terms of objective measurement (such as calorie content or watts per channel).

Another definition of product is provided by Maynes (1976, p. 53): "the set of goods which, for some maximum outlay, will serve the same general purpose in the judgment of the purchasing consumer.' The maximum outlay specification in the definition serves to make for more meaningful product groupings. Thus a Mercedes-Benz sedan, due to the -price, would not be classified as a compact sedan even though in terms of size and a number of other characteristics it would qualify.

Another possibility is to let the consumer determine the consideration set, perhaps based on prior experiences, which he specifies as a subset of choice alternatives from a menu of alternatives which the model builder has determined meet the criteria for a product category. This last method of delineating "product" is the most attractive in terms of consumer understanding and appeal, but might lead to a less than optimal decision. The issue involved here is the extent to which the model builder believes education and normative model building need to go hand in hand. For instance, suppose an alternative initially deleted from consideration by the consumer is the most optimal. The model could be built to handle this by incorporating a function which points out to the consumer the superiority of an eliminated alterative, and asks the consumer to consider it as a feasible option.

In whatever manner the product grouping is specified, in order to make logical comparisons, different specimens within a product class should have similar characteristics. The exact product delineation should depend upon the model's sponsor and the user's purpose. For instance, if the model were part of a local independent information system then products should be delimited by local geographic availability. On the other hand, if the model is part of a nationwide TV shopping system then it could include all brands of that product available through that service. Finally, if the decision aid was located at the point-of-purchase (e.g., in a mall) then it would include all products meeting the criterion available at that shopping location. It should be made clear to the user exactly how this product grouping was determined since the correctness of the decision is limited by the product grouping included in the model.

Step 2

The second step involves determination and measurement of the product attributes or characteristics to include in the data base. The identification of a complete set of relevant characteristics poses a problem. Unless prompted, research indicates that consumers tend to use relatively few of the a!tributes available to them (Jacoby, Szybillo and Busato-Schach 1977). Due to the technical complexity of many products, consumers may be unaware of some of a product's characteristics. Furthermore, relevant characteristics differ from commodity to commodity. Again, if an "important" characteristic is omitted the result may be a suboptimal purchase decision. It would appear, however, that such characteristics may be identified by substantive experts. For example, Thorelli and Thorelli (1977) have suggested a number of generic characteristics as deserving of attention. In the Jacquet-Lagreze and Shakum (1984) model the decision support system indicates a list of characteristics frequently considered to be important, and requires the decision maker to specify the subset of characteristics to be used.

After identifying the relevant characteristics each must now be measured in commensurable units. The measure of a characteristic must be on a cardinal scale rather than an ordinal scale. One must be able to say whether brand X has n times as much of a characteristic as brand Y. Again substantive experts are needed, for example test engineers for physical products and professionals trained in their respective fields for services. Indeed, most evaluations of characteristics will require a high degree of subjective expert judgment. However, in order to insure credibility, defining and measuring produces characteristics should be carried out by an independent testing organization such as Consumer's Union. This point should be made clear to the consumer/user of the model.

Step 3

The object of this step is to determine the consumer's attitudes toward the relative importance of the characteristics. Importance weights should be provided by each individual consumer through the interactive system. The weights should be assigned by the consumer or transformed by the computer program so that they sum to one, quantifying them in the form of probabilities. Setting an importance weight at zero in effect excludes it from consideration. Under this method, the relevant characteristics have been identified by independent testers and presented to the user; however, the consumer can still exercise autonomy by deciding whether to include the attribute in the model and personal preference as to its importance. In order to make the task easier for the user, it is possible to do this in a series of steps beginning with ranking of the characteristics in terms of importance, then translating the rankings to ratings (Edwards 1977). Also, in many situations, assigning equal weights to the characteristics can be satisfactory (Einhorn and McCoach 1977) and should be included as an option of the model. The interactive system could also be designed with the option of using either a rank ordering or a direct assignment of weights. A different approach for assessing preferences is taken by PREFCALC (Euro-Decision 1983; Jelassi et al. 1986), it includes two different methods: (1) a direct method that asks the decision maker to provide an importance rating for each characteristic (as discussed previously) in order to estimate an overall analytical preference; and (2) an indirect method that asks for the decision maker's holistic rank ordering of selected products/alternatives. If he decision maker provides the relative importance of each characteristic directly, then the system proposes a utility function of the preferences by displaying the relative weight of each' selected characteristic. If the decision maker decides to rank order the products/alternatives, then the system checks if the rank order is consistent with a computed additive utility function. Both methods can also be used interactively to check for consistency between the two. This enables the decision maker to discover hidden characteristics which perhaps better express the underlying values, tastes or needs of the decision maker.



Step 4

Step 4 involves the application of a decision rule. The outcome of this step is the generation of a list of all alternatives by decreasing utility value. While a variety of decision rules are available. conceptually, the most straightforward strategy is the linear compensatory model. The overall worth of each alternative brand is calculated by summing each alternative's scores of the characteristics as weighted by the importance weights determined in step 3. The normative rule is to choose the alternative having the highest value. Under a set of not-too restrictive assumptions, this is quite a good choice model. First, all the information concerning the alternatives is explicitly considered. Second, the decision maker has assigned weights to each dimension which reflect the extent to which he or she is willing to 'trade off one characteristic against another. Third, the linear-compensatory model has been shown to have good predictive robustness (Dawes and Corrigan 1974).

Apart from problems of measurement (and hence commensurability), the principal issue concerning the appropriateness of this model for choice is the extent to which the characteristics are independent of each other. Two forms of lack of independence are relevant. The first occurs when two characteristics are highly correlated (Curry and Faulds 1986). If both are included as relevant characteristics then adding the weighted dimensions of the linear model will involve double-counting and be inconsistent with the scheme for weighting the dimensions. Isolating the few attributes of the physical product that correlate near zero will convey the most information to the consumer. Second, there may be lack of independence in the sense that a combination of characteristics is more or less valuable to the decision maker than their weighted sum. However, the linear model described is not able to incorporate such interactions.

There is one departure from the additive approach that could easily be handled at this point in the model. The linear compensatory model could be preceded by (or combined with) a conjunctive rule in which the decision maker sets certain cut-off points on the characteristics such that any alternative that falls below a cut-off is eliminated. For example, the model might include a threshold level for certain safety features, or for other product characteristics which are particularly important to the consumer. Use of the conjunctive rule before calculation of the expected values would tend to reduce the choice set.

Step 5

Step 5 involves integrating product cost as a factor into the decision model. There are several ways to deal with the cost function, depending upon the objective and data bank of the model. The most straightforward method (and the one supported by the PREFCALC system <Euro-Decision 1983>) is to simply include price as one of the characteristic variables. In this way the consumer can set maximum and minimum prices through the conjunctive step.

An alternative approach would be for the model to present the product value scores along with their associated prices. This method permits the consumer to compare quality among alternative brands for a particular price. Immediately eliminated would be dominated alternatives (those specimens that offer less value at a higher price than another alternative). The remaining alternatives would be ranked by value and price. Then moving from top to bottom the consumer could make an evaluation as to whether the improvement in value offered by successive alternatives is worth an increase in cost. To help make this determination, the model could include a program which would implement the general utility-maximizing rule: spend your income so that, at the margin, each penny spent on purchases yields the same increment of utility (value)

A final method, recommended by Thorelli a,nd Thorelli (1977, is to divide the brands (models) into price classes and then rank them within each class. This approach would allow the consumer to consider the trade-offs in moving up or down in price product categories. This last approach is highly recommended since in terms of both ease of user comprehension as well as its similarity to brand processing normally carried--out by the consumer.

Step 6

Since subjective weights are involved, the consumer may want to conduct a sensitivity analysis to observe the extent to which the decision is sensitive to changes in value weights or price selection (assuming a price cutoff approach was used). This could easily be incorporated with a loop or series of loops in the model. In fact, the sensitivity analysis, like the interactive preference elicitation procedure, would tend to facilitate the learning process for the consumer.

Step 7

Ordering and/or selection of location is the final step in the model. If the model is sponsored by a shop-at-home service, then completion of the transaction would consist of the consumer answering a series of questions such as quantity ordered, size, address for delivery, and method of payment. A local information system might merely generate a listing of stores in the area where the chosen alternative is available, perhaps including the current price offered at each location


A model such as the one described above offers great promise as a decision aid. It directly addresses the three sources of processing bias identified by Hogarth. However there are several limitations which should be considered when building and marketing such a model. First, the model does not specifically define the product category, nor make interproduct comparisons, such as comparing a motorcycle to the family automobile, or public transportation versus the bicycle.

Second, many product purchases involve group decisions. However, the model described above does not explicitly incorporate group weights. A group decision model would require incorporation of three specific characteristics: a jointly acceptable database of underlying facts, jointly acceptable definitions of alternatives/products, and mutually understood definitions of characteristics and preferences (Jarke and Jelassi 1986).

Third, for products in which the price is highly volatile, negotiable or for which trade-ins are allowed, the model would be less useful. Fourth, attributes such as pre-sale and point-of-purchase service (location, opening hours, parking facilities, display, credit, etc.) would be difficult, although feasible, to incorporate into such a model.

Fifth, the market into which the product category falls will to a large extent determine the model's utility. Maynes (1976, p.75) has described four types of markets which incorporate a consumer oriented concept of information: (1) quality variable, informationally imperfect; (2) quality variable, informationally near-perfect; (3) quality uniform, informationally imperfect; and (4) quality uniform, informationally perfect (information being defined in terms of quality-price correlation.) Obviously the model would be most useful for products falling into the first category (such as washing machines or cameras), and less useful for those in category four (such as gasoline or shares of General Motors stock).

Sixth, the decision rule is a linear model and as such does not take into consideration diminishing returns. (For example, going from .3 to .5 on fuel mileage is assumed to be equal to going from .8 to 1.0.) Seventh, unlike the Consumer Inquirer Program (see Appendix F of Thorelli and Thorelli 1976) designed at Indiana University in 1971, this model does not hold consumer education as one of its major goals (although this may be an indirect benefit). even though the program would tailor recommendations to personal needs and preferences.

Finally, such a model focuses on characteristics which are amenable to quantification and standardization. It ignores factors such as style, shape, and other esthetic elements. For instance, it does not make sense to say for the characteristic of shoe fit that one pair of shoes fits twice as well an another pair --- only that it fits better. For services, this means that such important techniques as courtesy, understanding, trust and communication are not included in the characteristic set. Thus the model may indicate a brand or service provider as having the best overall quality, but be completely unacceptable to the individual consumer in terms of "appearance", psychosocial characteristics or, in the case of a service provider, level of individualized attention. Hjorth-Andersen (1984) points out that weighted sum quality scores may be appropriate only in cases where customers are likely to have homogeneous preference structures .

Maynes (1976) indicates that this problem can at least be partially mitigated by having characteristic scores determined by categories of users. For instance, teen-agers may have a different perception of convenience than do middle age consumers buying the same product. Thorelli and Thorelli (1976) suggest that testing organizations include a frank and open discussion of psychosocial aspects along with recommendations. This last suggestion could easily be incorporated into an interactive system along with a pictorial representation of the product which would help the consumer make subjective evaluations. Jacquet-Lagreze and Shakun (1984) suggest dividing subjective criteria into two types: those which use objective characteristics (e.g. classifying the product through test of durability, strength, component costs, structural integrity, and performance), and those which use subjective characteristics which will require the consumer to make a personal evaluation of each alterative (e.g. the shape of a car could be evaluated on a scale as unacceptable, ordinary, attractive, or outstanding). Another method for including esthetic elements would be use of ratings from a consumer panel.

Overall, these generalized model limitations may not be as important to the model builder who is designing a model for application in a particular situation as is developing a clear definition of what the model can and cannot do, and the communication of its specific functions to the user. Probably he single most important criterion is the maintenance of a current data base. Price, feature quality, and availability of products must be continually updated. Additionally, the credibility of the testing agency must be recognized. The usefulness of the data and services offered must be apparent. This, in turn, will build confidence and efficiency in its use, two factors recognized as important to diffusion of models.


In the midst of a growing consumer information industry an opportunity exists for the development of an interactive normative consumer model. Although several models have been developed, none have received widespread application. This paper attempts to review these methods and identify some of the relevant issues concerning their development and implementation. An approach is adopted in which the focus is on maintaining consumer autonomy while capitalizing upon the expertise of professional testing agencies. A simple linear compensatory model was offered as a good choice rule. However, even though such a model would be useful in overcoming processing biases, many obstacles presently hinder adoption and implementation. Ultimately, consumer utilization of information systems, like other products, will depend on how the service is priced, promoted and distributed (Capon and Lutz 1979). Clearly there is considerable need for research to identify characteristics of information systems which are desired by the consumer, and to determine how these characteristics can be transformed into a viable system.


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Donna J. Hill, Indiana University


NA - Advances in Consumer Research Volume 16 | 1989

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