Inferential Belief Formation in the Cue Utilization Process

Jerry C. Olson, Associate Professor of Marketing, Pennsylvania State University
ABSTRACT - The extant research on cue utilization is briefly reviewed. Generally, this research has ignored inferential processes by which beliefs are formed about concepts not present in the immediate environment. A model of the cue utilization process is presented which explicitly includes the inferential belief formation process. Memory schemata are proposed as the basic memory structure which enables inferences or attributions to be made. A discussion of how research on the inference process could be conducted follows, and the paper concludes by identifying several research issues that could be examined from the conceptual perspective provided by the model.
[ to cite ]:
Jerry C. Olson (1978) ,"Inferential Belief Formation in the Cue Utilization Process", in NA - Advances in Consumer Research Volume 05, eds. Kent Hunt, Ann Abor, MI : Association for Consumer Research, Pages: 706-713.

Advances in Consumer Research Volume 5, 1978      Pages 706-713

INFERENTIAL BELIEF FORMATION IN THE CUE UTILIZATION PROCESS

Jerry C. Olson, Associate Professor of Marketing, Pennsylvania State University

ABSTRACT -

The extant research on cue utilization is briefly reviewed. Generally, this research has ignored inferential processes by which beliefs are formed about concepts not present in the immediate environment. A model of the cue utilization process is presented which explicitly includes the inferential belief formation process. Memory schemata are proposed as the basic memory structure which enables inferences or attributions to be made. A discussion of how research on the inference process could be conducted follows, and the paper concludes by identifying several research issues that could be examined from the conceptual perspective provided by the model.

INTRODUCTION

How do people acquire and use information about their environment to make various types of judgement choices? What factors influence these processes? Issues regarding the cue utilization process have fascinated psychologists for many years. More recently, consumer researchers have become quite interested in how consumers use informational cues to accomplish various tasks. This paper briefly reviews the types of cue utilization studies and summarizes the typical approach taken. It is then suggested that most of the cue utilization research has ignored the potentially important process of inferential belief formation. A flow-chart model is presented which illustrates how inferential processes might be included in the cue utilization process. The paper concludes by briefly discussing several research topics which could be investigated from the perspective provided by the proffered model.

Overview of Cue Utilization Research

Define somewhat loosely, the cue utilization process may be considered to encompass the cognitive processes involved in obtaining information from the external environment and in using the information (in a cognitive, information processing sense) to produce a particular behavior, e.g., an evaluation or a choice. Defined at this broad general level, the cue utilization process is involved in virtually all information processing phenomena.

Given this broad perspective, then, a wide variety of research areas can be seen as focussing on aspects of the cue utilization process. Included are research areas such as impression formation (Asch, 1946; Wyer, 1974; Zanna and Hamilton, 1977),decision theory and judgment formation (Slovic, Fischhoff, and Lichtenstein, 1977; Kahneman and Tversky, 1973), integration theory (Anderson, 1971), attribution theory (Frieze and Weiner, 1971; Fishbein and Ajzen, 1975), syllogistic relationships among beliefs (Henninger and Wyer, 1975; McGuire, 1960), or social inference (Gollob, Rossman and Abelson, 1973; Wyer, 1975). Although there appears to be relatively little cross-communication between these fields, each research area deals, in the broad sense suggested above, with how people use informational cues. That is, the investigator in each research area is interested in how people select and comprehend environmental information and possibly use the information in forming the response requested (usually an evaluative judgment or overt choice behavior).

In the consumer research literature, cue utilization research is not quite so compartmentalized, although studies in virtually all of the above-mentioned research traditions have been published. Essentially, just three major research areas have evolved. The largest part of consumer-oriented research is focused on informational effects on consumers' product judgments, usually of quality (see Olson, 1972), and particularly on the effects of price cues on such judgments (Monroe, 1973; Olson, 1977). Another rapidly developing research area involves study of consumer information acquisition behavior by Jacoby and his colleagues (cf. Jacoby, Chestnut, Weigl, and Fisher, 1976). A third research area which involves cue utilization processes seeks to identify the combinatorial "rules" used by consumers to integrate or combine various items of information in order to accomplish some task (cf. Bettman, 1970; Wright, 1975; Scott and Wright, 1976; Sheth and Raju, 1974).

Despite the specific focus of most of this research, it is clear that the cue utilization process is involved in virtually all behavioral phenomena. That is, nearly any behavior will require processing of informational cues selected from the external environment. Thus, the process of cue utilization is of broad importance. However, for purposes of this paper, our attention is restricted to those controlled situations in which the subject is given a particular task to accomplish that requires cue processing, as in an experimental study of cue utilization behavior.

Summary of Cue Utilization Research

Much of the cue utilization research in marketing must be characterized as simple. Many studies involved presenting consumer subjects with task environments containing only one relevant cue (e.g., Leavitt, 1954; Peterson, 1970). More recent quality perception studies have tended to provide more cue stimuli and, thus, have created somewhat more complex and ecologically valid task environments (see Olson, 1977). Consumers have been provided with two cues (Enis and Stafford, 1969; Valenzi and Andrews, 1971), three cues (Szybillo and Jacoby, 1974; Wheatly and Chiu, 1977) and four cues (Jacoby, Olson, and Haddock, 1971) in an environment with the task to evaluate the quality of one or several alternative brands. The research on cue weighting in the integration process has used task environments containing even greater numbers of cues, as many as 10 or 11 (see review by Scott and Wright, 1976). In most of these cue utilization studies, the consumer's task has been to give evaluative Judgments of the product (e.g., menu appeal, Green, Wind, and Jain, 1972). Thus the typical dependent variable has traditionally been a rather simple evaluative scale such as good-bad, like-dislike, or high quality-low quality. Essentially then, many of these studies may also be considered as examining the attitude formation process.

A SIMPLE MODEL OF THE CUE UTILIZATION PROCESS

In sum, the cue utilization process can be considered to involve two basic processes--(a) the acquisition of cues and (b) the integration of the information derived from the cues to form the desired response (Olson and Jacoby, 1972). Actually, however, this view is highly simplified and, in fact, tends to hide several complex information handling sub-processes that may occur upon exposure to stimulus cues in a task context. Current interest in the cognitive states and processes involved in information processing (cf. Jacoby and Olson, 1977) suggests that several hierarchically-arranged stages are involved. Figure 1 presents a simple model of some of the stages involved in cue utilization. This model should be considered only as a convenient illustrative scheme to communicate some of the complexities involved. Most of the extant research on cue utilization can be described within the confines of this model.

FIGURE 1

A SIMPLIFIED MODEL OF THE CUE UTILIZATION PROCESS

Briefly, presentation of a task goal (Which brand do you think is of highest quality?) and a task environment (four brands of shoes with price, brand name, and other informational cues), focuses the attention of the consumer/ subject and initiates the goal-oriented cue utilization process. The initial sensory processing of the environment is probably extremely rapid, especially for familiar task environments, and may not be available to conscious awareness. Therefore, this stage typically goes unrecognized and unreported (Broadbent, 1977). One can think of this preconscious processing as orienting the organism to the general class of stimuli. This will be elaborated later in the paper.

Following, relatively more conscious selection of specific informational cues (dimensions, attributes, etc.) occurs. These specific cues are subjected to further processing, the outcome of which is the comprehension of the selected cues, i.e., the assignment of categories of meaning to each cue. This meaning--now encoded information--may be stored as descriptive beliefs about the object. Descriptive beliefs encode the perceived association between the task concept and specific attributes or dimensions present in the task environment. These beliefs may then be integrated together or combined to accomplish the task, such as rating the quality of each brand.

Several points about the cue utilization process are made obvious by Figure 1. First, the cue utilization process is probably highly selective of certain cues. Interestingly very few studies have examined factors which may influence the cue selection process (cv. Cox, 1962; Olson, 1972). Second, the outcome of the integration sub-process is highly dependent upon the precise encoded informational form of the selected cues, i.e., the specific beliefs about the stimulus concept (Olson, in press). That is, environmental cues may be transformed during encoding and assigned a meaning that may or may not be similar to the physical form of the cue. For instance, a price of 294 may be encoded as "cheap," rather than "294," and integrated with other product information in the "cheap" form (Jacoby and Olson, 1977). Third, certain complex informational cues may not be encodable in a meaningful way by a perceiver (e.g., nutritional cues, Jacoby, Chestnut and Silberman, 1977), and thus may have little effect on the task response. For the most part, these three points have not been clearly recognized nor explicitly considered in previous cue utilization research.

INFERENTIAL BELIEF FORMATION

A fourth implication, implicit in Figure 1, involves the formation of inferential beliefs, a process that has generally been ignored in cue utilization research. That is, it is possible that in processing the given cues from a task environment, one might develop beliefs about other aspects of the task stimuli not represented by environmental cues. This is the process of inferential belief formation (see Fishbein and Ajzen, 1975). The importance of this process is that both the descriptive beliefs (directly related to environmental cues) and the inferential beliefs (inferred from the environmental cues) will, according to the basic expectancy-value theory, both influence global evaluation of attitude toward the concept of interest (cf. Fishbein and Ajzen, 1975; Lutz, 1975).

Although as yet not explicitly studied in the consumer behavior literature, there have been several empirical demonstrations of inferential belief information. For instance, Mazis and Adkinson (1976) found that exposure to a corrective ad about a mouthwash's inability to block colds and sore throats also changed beliefs regarding the brand's ability to "kill germs." Informational cues regarding killing germs were not present in the task environment and thus the latter beliefs must have been formed via some inferential process. Lutz (1975) found that a persuasive message regarding a single attribute of detergent affected the strengths of other beliefs about the product. As a final example, Mitchell and Olson (1977) found that an ad containing only a symbolic visual image and the brand name created stronger beliefs about the symbolically-communicated product attribute than an explicit verbal claim about the same attribute. In this case, the beliefs were inferred from a pictorial cue. Fishbein and Ajzen (1976) describe many other studies from the social psychology literature that also demonstrate the formation of inferential beliefs.

In sum, it is clear that people may form beliefs about the task object/concept that are not represented by cues in the task environment. This phenomenon is termed inferential belief formation. The next task, then, is to develop a theoretically-based model of the cue utilization process which can logically account for such phenomena.

FIGURE 2

CONCEPTUAL MODEL OF THE CUE UTILIZATION PROCESS, INCLUDING THE INFERENTIAL BELIEF FORMATION PROCESS

INFERENTIAL BELIEF FORMATION IN THE CUE UTILIZATION PROCESS

It is obvious that an explanation for inferential belief formation requires a consideration of previously learned information upon which one can logically base, or from which one can derive, inferences. That is, an adequate model of the cue utilization process must account for the influence of memory on the inferential belief formation process. Fortunately, the cognitive psychology field has developed numerous memory concepts and models. Several of these ideas appear useful in developing such a more theoretically complete model of cue utilization process--one that includes the possibility of inferential belief formation (cf. Olson, 1977).

Memory Schemata

Perhaps the most useful concept for the purpose of this paper is that of memory schemata. Norman and Bobrow (1975; Bobrow and Norman, 1975) developed this idea of highly organized substructures of knowledge stored in semantic memory. [Tulving (1972) suggested a distinction between episodic memory (memory for past events in our lives) and semantic memory (stored knowledge about the world). It would seem that inferential processes about products are more likely to be influenced by semantic than episodic memory. This is a particularly so given the perspective of focusing on specific product characteristics followed by most product perception or multi-attribute research. It would seem that semantic memory, in which is stored the meanings linked to the concept via its associated attribute/characteristics, is likely to influence the formation of inferential beliefs.] Schemata for products or brands would contain previously learned knowledge about the concept, plus, the interrelationships between these items of knowledge, stored in an organized logical framework. Interestingly, schemata may also contain "rules" for responding to the stimulus concept--e.g., a learned tendency to favorably evaluate a high price for a particular product. In this sense, schemata are "active" cognitive units which can influence behavior rather "directly," once activated. A schema may be activated simply by exposure to the stimulus representing to concept-con-text combination. Thus, schemata tend to be highly context specific. One might find different knowledge and different "rules" stored in the schemata for (a) toothpaste to use in the morning and (b) toothpaste to use before a social engagement.

Figure 2 represents an initial attempt to model how schemata may be integrated into the cue utilization process in a way that may explicate the inferential belief formation process. In Figure 2, the process begins with an experimental setting in which a subject is given a specific task (goal) and an informational cue environment within which to accomplish the task. Consider the previous example of evaluating the quality of four pairs of men's dress shoes given a set of cues about the shoes. The early attentive and sensory processing will activate (make available for conscious processing) the relevant schema, in this case the schema for men's dress shoes. This, of course, presumes that the subject has acquired a schema for men's dress shoes. If the consumer has not developed a special knowledge structure for dress shoes, the task environment might activate a more general schema for men's shoes.

Once activated, the schema "controls" the processes of cue selection and cue comprehension or encoding. For instance, if a relevant component of the schema for men's dress shoes is leather soles, a desired feature, then the consumer may "be directed by the schema" to select cues regarding the composition of the soles for the alternative brands. Once a cue is selected, the encoding/comprehension process also proceeds "in light of" the activated, hopefully relevant schema. For instance, a price of $49.00 may be encoded as "about average" given one consumer's schema for dress shoes. Another consumer may interpret the same price as "excessively excessive"-due to a difference schema for dress shoes. Moreover, the same $49.00 price might be encoded differently by the same consumer when associated with casual sports shoes than with dress shoes if a different schema has been activated. Clearly, how we encode incoming informational cues, i.e., the meaning we assign to cues, is a function of our past experiences (cf. Fishbein and Ajzen, 1975). The schemata concept can be seen as a theoretical representation of the cognitive structure created by those past experiences.

Now, as a result of selecting and encoding some or all of the informational cues in the task environment, beliefs are formed. Those directly related to environmental cues are termed descriptive beliefs to distinguish them from inferential beliefs. It may be that no inferential beliefs are formed. For example, in the case where one desires whole wheat bread, one may acquire and encode only those cues related to the grains used in manufacturing the bread and select the first whole wheat alternative found. Other beliefs about the breads may not be formed. However, in most situations it seems likely that inferential beliefs will be formed. In fact, for cases involving activation of well-developed schemata, the inferential process may proceed without much conscious analytical thinking, apparently automatically.

The inferential process itself, although still not completely explicated, can be likened to an attributional process in which incoming encoded information from the task environment is compared/fitted into the established knowledge structure of the schema and inferences drawn about other concepts not present in the immediate environment.

Both descriptive beliefs and inferential beliefs (if any) form the cognitive content upon which the integration process operates. As noted above, the schema may also provide the combinatorial "rules" for integrating the information. The resultant cognitive state created by integrating the information is then "passed" to a "response generator process" which converts the cognitive state into the appropriate response called for by the experimental task--e.g., a check mark on a 7-point product quality scale.

THE INFERENCE PROCESS

Now that the stages represented in Figure 2 have been discussed, let us examine more closely how a memory schema may allow inferences to be made regarding product attributes not represented in the immediate task environment. Part a of Figure 3 illustrates a portion of a memory schema for the generic product category, winter coats. This schema contains a consumer's knowledge regarding coats that are insulated with goose down. This knowledge is stored in a highly organized framework represented by distinct associations between concepts. Thus there are linkages (logical associations, or beliefs) between the concept, down-filled, and the product attributes of weight and warmth. These concepts associated with winter coats and their interrelationships were previously acquired/learned via classical and/or instrumental learning principles (cf. Fishbein, 1967; Olson and Mitchell, 1975) as a function of one's past experiences. In one sense, these associations between the knowledge components of the schema are very much like general product expectations (Olson and Dover, 1976). Now, suppose that this particular subject possessing this schema is exposed to a task environment and instructed to evaluate a particular winter coat. Assume, moreover, that the only cue provided by the environment is that the coat is down-filled. Upon processing (acquiring and encoding) this cue, the subject will form a belief, probably a strong belief, that this brand does indeed possess the attribute, "down-filled" (see Part b of Figure 3). In addition, exposure to the task environment will probably activate the previously-learned schema of winter coats illustrated in Part a of Figure 3. When the descriptive belief that the coat is down-filled is "fitted into" the schema, the previously learned relationships "enable" the consumer to infer certain other qualities of the coat. This inferential belief process is represented in Part c of Figure 3, which illustrates that the two beliefs (represented as subjective probabilities) Pxb and Pba, about the associations between concept X and B and B and A, allow an "inference" to be made about the association between X and A, Pxa. This latter probability is the inferential belief about a product attribute not explicitly present in the task environment.

In other disciplines, notably social psychology, numerous studies have focused on peoples' ability to form inferences and of the factors which influence that ability. Some years ago, McGuire (1960) developed a probability model of the relationships between syllogistically-related beliefs. More recently, Wyer (cf. 1974) has extended this model (Wyer and Goldberg, 1970). Wyer has shown in several studies (cf. Wyer, 1975) that peoples' subjective estimates of some of the associations between concepts in a syllogism predict with considerable accuracy the strength of the other association in the syllogism.

FIGURE 3

THE INFLUENCE OF A MEMORY SCHEMA ON THE INFERENTIAL BELIEF FORMATION

A PROBABILITY MODEL OF THE INFERENCE PROCESS

The McGuire-Wyer model appears useful in explicating how a memory schemata can account for inferential belief formation. Figure 4 illustrates a schema containing three concept, ", B, and C. Also illustrated are some of the logical links or associations between the concepts, stated in probabilistic terms. For example, the subjective estimate of the likelihood of C given " (Pc/a) is .60. The likelihood of C in the absence of " (Pc/a') is only .20. These subjective associations indicate that concept "C" is perceived to be somewhat contingent upon concept ". In contrast, Figure 4 indicates that concept B is not perceived as highly contingent upon the presence of "--note that Pb/a = .70, only .10 less than Pb/a = .80. Further, suppose that exposure to an experimentally controlled task environment creates a strong descriptive belief that concept X possesses concept "---e.g., Pxa = .90. The question of interest involves the belief strengths of the two inferentially-created beliefs, Pxc and Pxb.

FIGURE 4

PROBABILISTIC RELATIONSHIPS BETWEEN THREE CONCEPTS STORED IN A MEMORY SCHEMA.

The McGuire-Wyer Subjective Probability Model of Cognitive Functioning (or Inferential Belief Formation), allows predictions of Pxc and Pxb. The model, based upon conditional probability theory; is as follows:

Pxc = Pxa . Pc/a  + (1-Pxa) . Pca'     (1)

Verbally, the association strength between X and C is equal to the strength of association between X and " times the conditional probability of C given ", plus the probability of no link between X and " times the conditional probability of C given absence of ". This model produces the following prediction of the inferential belief, Pxc:

Pxc = (.90)(.60) + (.10)(.20)

Pxc = .56

Likewise, the inferentially-formed belief regarding the association between concepts X and B can be estimated by the same model:

Pxb = Pxa . Pb/a  + (1-Pxa) . Pb/a'     (2)

Pxb = (.90) (.80) + (.10) (.70)

Pxb = .79

Once estimated, these belief strengths can be compared with direct ratings of the perceived associations. In several studies, Wyer (cf. 1974) reported reasonably strong relationships between model predictions and independent ratings.

Using this probabilistic approach, one can study the organization of stored knowledge in schemata held in semantic memory, as well as the use of that stored information in the process of inferential belief formation. Briefly, the procedure could be as follows. First, using a relatively non-reactive method such as a free elicitation procedure ("say everything that comes to mind when I say ____________"), identify the concepts stored in semantic memory that are associated with the concept. Then, ask consumers to subjectively rate the strengths of association (perhaps in both directions) between pairs of concepts. These procedures will yield a probabilistic view of the content and structure of the knowledge stored within a memory schema for a particular object concept. With this data, one should be able to predict the inferential beliefs and the strengths of those beliefs created by an experimental message directed at one of thc target concepts within the schema. Certainly, this type of approach should be examined in future research.

OTHER RESEARCH ISSUES

Product Familiarity

The model proposed in Figure 2 has implications for phenomena other than inferential belief formations. For example, the concept of "product familiarity" has frequently been used by consumer researchers as an "explanation'' of some observed data or as a blocking variable in experimental designs. "Product expertise" is another concept commonly used in similar ways. Seldom, however, is either concept precisely defined in clear conceptual terms, and virtually never in terms of the cognitive state associated with each variable.

However, the notion of memory schemata, which possess varying degrees of complexity in terms of amounts of stored knowledge and "interrelatedness" of the stored information, may be used to represent the cognitive state created by degrees of "familiarity" or "expertise" with a product category. It should be noted that the relationship between amount of past experience and complexity of memory schemata is not clear. It may be that familiar, expert customers have in fact, less complex cognitive structures (schemata) than moderately familiar consumers (cf. Hayes-Roth, 1977; Olson and Dover, 1978). Although the degree of complexity is in doubt, it is essentially an empirical question that appears determinable with relative ease. In any case, the schemata of the expert, whatever their complexity, should lead to differential cue utilization behavior compared to a non-expert, and these differences should be predictable once the schemata are measured. This approach to issues of product familiarity/expertise has promise of providing deeper levels of understanding of these important concepts.

Development of Schemata

A variety of important related issues involve the development of memory schemata. We might ask, "What types of product information and experiences lead to the acquisition of well-developed, complex schemata that are useful in reacting to a product in varying circumstances and settings? How much overt, conscious, cognitive analysis is necessary in schemata development? Do low-involvement products have simpler schemata? Does "low involvement processing" lead to non-complex schemata? These questions focus on the fascinating issues involved with the processes of schemata development (cf. Olson, in press) and certainly warrant future research attention.

Activation of Schemata

A final issue to be briefly discussed involves the activation of a schema for conscious processing. A critical question to be answered is, "What cues trigger or activate a schema?" Are purchase context cues or broad purchase goals critical factors in determining the precise schema that is activated? For instance is "buying for self" vs. "buying for a gift" a distinct cue to activating different schemata for products such as ball-point pens or table wine? Or, is the price level a broad cue for activating a particular schemata for certain product categories such as automobiles or men's shirts. Such questions (and many others unstated) are of high relevance to marketers and must be addressed in future research before the schemata concept can be widely useful.

SUMMARY

This paper presents several ideas intended to broaden the approach taken in cue utilization research. It was suggested that inference processes may be involved in the cue utilization process which may create beliefs about concepts not present in the task environment. A model was presented which positioned the inferential belief formation process within the cue utilization process. The concept of memory schemata was proposed as a basis for a viable theoretical explanation for the inference process. The issues raised by this perspective are of interest to consumer researchers for both theoretical and applied reasons. This paper will serve its intended purpose if others are stimulated study the inferential belief formation process, its effects, and the factors which affect it.

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