Consumer Knowledge Structures: Networks and Frames


Robert Lawson (1998) ,"Consumer Knowledge Structures: Networks and Frames", in NA - Advances in Consumer Research Volume 25, eds. Joseph W. Alba & J. Wesley Hutchinson, Provo, UT : Association for Consumer Research, Pages: 334-340.

Advances in Consumer Research Volume 25, 1998      Pages 334-340


Robert Lawson, William Paterson University

Understanding the content and structure of human knowledge is an ongoing challenge to cognitive scientists. Marketers’ understanding of what people know about products, while motivated by more practical matters, depends upon basic scientific advances. The purpose of this paper is to review the progress that has been made toward arriving at a scientific understanding of consumer knowledge structures as well as marketers’ applications of this knowledge. Following this critical review, I argue for a frame structure (Barsalou 1992) approach to describing the content and organization of consumer knowledge.

Although basic scientific research on knowledge organization has produced a steady stream of work beginning with Collins and Quillian’s (1969) computer model of semantic memory, interest in developing consumer knowledge structures primarily has been confined to a relatively brief period in the late 1970’s and early 1980’s, although more recent significant advances have been made (Keller 1993; Mitchell and Dacin 1996). Consistent with the prevailing scientific conceptualization of semantic memory in those years, writers in the consumer behavior field adopted an associative network structure to describe consumers’ knowledge of products. Such structures typically focus on the target product, either a brand or a product category, and show linkages to various other concepts. The widespread acceptance of the associative network is noted by its universal adoption in consumer behavior textbooks (e.g., Peter and Olson, 1996).

However, continuing advances in the cognitive sciences, such as those associated with frame theory or neural networks, have signalled that the notion of an associative network will not be the last word on knowledge structures. In addition, while consumer associative networks are displayed, and are declared to be centrally important to marketers, little has been done to clarify why this should be true. In addition to providing a review of the consumer knowledge structure lierature, the purpose of this paper is to develop an alternative framework that will both reflect the state of the art in knowledge structures and be useful to marketers.

In particular, I will argue that the associative network construct presents an inadequate framework for describing what consumers know about products insofar as it fails to convey the basic underlying frame structure of knowledge. This failure implies additional problems with the network approach, including an overreliance on nonconscious spreading activation as the primary retrieval process, a lack of constraint on useful content, and a research methodology which is inherently unreliable. While I do not claim to have answers to these problems, I outline a new approach to consumer knowledge, based upon frame theory, which does attempt to deal with these issues. I begin by offering a brief review of the consumer knowledge structure literature, and a more detailed critique of these issues before describing a frame theory approach.


Content of Consumer Knowledge Structures. Ever since the early work on semantic memory structures (e.g., Collins and Quillian, 1969), there has been nearly universal agreement that the content of a knowledge structure for a target concept consists of other concepts that are associated with it. The target concepts that have interested researchers in consumer behavior are consumers’ representations of product categories and, especially, brands. The standard depiction of a consumer knowledge structure found in marketing textbooks shows a network of concepts that are linked to each other without any restrictions placed upon membership to the network. Possible types of associations include the target concept’s characteristics, related products, product uses, attitudes and other summary evaluations, purchase-related associations including store and price information, and second-hand memories from such sources as advertisements and word-of-mouth. While all these types of associations may exist, they cannot be equally important either to the consumer or to the marketer. With few exceptions, there has been little attempt on the part of researchers to determine importance by placing restrictions on the content of consumers’ knowledge.

Earlier work on content emphasized the presence of consumer evaluations of product characteristics in addition to the characteristics themselves. For example, Olson (1978; Marks and Olson 1981) describes knowledge structures as containing factual knowledge, evaluations, affect, purchase criteria, and decision rules. In a protocol analysis study, Russo and Johnson (1980) develop a five-level classification scheme based upon presumed stages in a brand choice process. In perhaps the most integrative attempt to develop a taxonomy of consumer knowledge, Brucks (1986) used eight categories which included product-level aspects, brand-level aspects, evaluations, usage situations, and acquisition procedures. The taxonomy itself was partly theory-driven, insofar as it was inspired by Bloom’s taxonomy, and partly data-driven, insofar as the actual categories relied upon protocol analysis for their inclusion. A factor analysis suggested that three factors were operative: knowledge of product attributes, knowledge centered around situational usage that would distinguish experts from novices, and personal knowledge.

Keller (1993) would include far more in his conceptualization of consumer brand knowledge. He uses an abstractness dimension to distinguish among attributes, benefits, and attitudes. Attributes are further classified as either product- or non-product-related in which the latter category includes knowledge of price, packaging, usage situation, and typical user. Benefits are classified as functional, symbolic or experiential, following Park, Jaworski and MacInnes’ (1986) distinction. Brand attitudes are viewed as overall summary evaluations of the brand. I addition, Keller includes the measurement categories of favorability, strength, and uniqueness of the associations, as well as memorial access to the structure through brand recall and brand recognition. Keller’s consumer knowledge structure is especially intriguing because it includes both what a consumer might hold as his or her own personal knowledge, and what a marketer might consider in trying to make sense of consumers’ knowledge, such as memory processes and measurement dimensions. Recent research by Lefkoff-Hagius and Mason (1993) and Wright and Lynch (1995) provide support for the psychological reality of the central distinction among attributes, benefits, and attitudes.

Despite some rather minor differences among theorists concerning exactly what type of associations comprise the content of a consumer knowledge structure, the history of thinking on this issue has shown overwhelming agreement on the notion that the product feature is the foundation of the entire structure. These features are seen as being linked to the target concept, or to other features, with the entire set of features and links forming an associative network. Mitchell (1982) was the first consumer behavior theorist to make the distinction between this type of structure, and what he called a "subdivided" memory structure, in which the associated features are first organized into categories. For example, instead of associating "good gas mileage" directly with the target automobile, this feature is linked to a "performance" node, which is then linked to the target. Of course, this distinction concerns not only the content of the knowledge structure, but the organization of it as well.

Organization of Consumer Knowledge Structures. It is difficult to consider the content of a knowledge structure as an issue that is separate from its organization. As discussed above, the predominant conceptualization of consumer knowledge organization is that of the associative network. In this type of organizational structure, it is widely accepted that once a concept node is activated, that the activation automatically spreads through pathways to neighboring nodes. Evidence for spreading activation in associative networks comes from priming studies in which prior exposure to a concept results in enhanced recognition of associated concepts under speeded or degraded stimulus conditions (e.g., Neely 1977). With regard to consumer knowledge structures, with the notable exception of the recent study by Grunert (1996), I know of no research which has sought a more detailed understanding of either the organization or the automatic activation processes of associative networks. Instead, consumer behavior researchers seem to have adopted the associative network as the optimal structure for holding the full range of knowledge based upon evidence aimed only at automatic processes.

Another structural aspect of the associative network that has been borrowed from cognitive psychology is the notion of the hierarchical organization. Based upon Rosch’s et al, (1976) concepts of basic, superordinate, and subordinate levels of associations for natural object categories, consumer researchers sought to establish similar distinctions for product categories. Sujan and Dekleva (1987) argued for a hierarchy in which product types are at the basic level, with product classes being superordinate and brands being subordinate. Meyers-Levy and Tybout (1989) present a similar hierarchy except that instead of brands representing the subordinant level, more detailed descriptions of basic level product types do. Although such hierarchies seem to be well-established in researchers’ models, their roles in consumer knowledge structures need to be clarified.

The type of knowledge structure in which hierarchies and other conceptual relations among associations is most likely to develop is the frame structure. Recent work by cognitive psychologist Lawrence Barsalou (1992) has produced a clear view of the properties of frames, a structure which he contrasts with feature lists. The primary distinction is that feature lists are simply collections of characteristic, whereas in frames, those characteristics are organized as values of more abstract categories called attributes. For example, for the product tennis racket, the characteristic of "4 1/2-inch grip" would be a feature in a feature list structure, but would be a value of the more abstract attribute "grip size" in a frame structure. The major shortcoming of the feature list structure is its lack of organization: the characteristics that are added to the list are whatever happens to come to mind. In this view, associative networks are fundamentally unorganized lists of features. By specifying what attributes to include, a frame structure promises to provide the "framework" upon which to organize and hang what a consumer knows about a product. As discussed above, Mitchell (1982), in describing network and subdivided consumer memory structures, anticipated this important distinction. Also Barsalou and Hutchinson (1987) present evidence of this basic structure at work using protocol data in which respondents planned vacations. In the next major section of this paper, I will present a case for developing the frame structural approach.

Measurement of Consumer Knowledge Structures. Obviously, the issue of how best to measure consumer knowledge about products depends upon one’s conceptualization of consumer knowledge. Early interest in this issue centered around the possibility of obtaining surrogate measures as opposed to more direct assessment. Consumers’ actual experience with using a product (e.g., Bettman and Park 1980) or subjective assessment of their own knowledge (e.g., Cole, Gaeth, and Singh 1986) have been used for the purpose of gauging how much consumers know about a product rather than the details of what they know.

There have been only a few attempts to measure knowledge structures, per se. Following the theory of access through spreading activation in associative networks, Olson and his colleagues (Olson and Muderrisoglu 1979; Marks and Olson 1981; Kanwar, Olson and Sims 1981) have developed the "free elicitation" technique. Basically, subjects are given a probe with the instruction to tell all the characteristics that come to mind. In the multiple free elicitation technique, the responses that are elicited serve as probes for the next level, and so on. While this technique produces a network of associations, it is clear that they are highly idiosyncratic, and become increasingly so the greater the number of levels of elicitation. The second technique employed to measure knowledge structures has been protocol analysis. Although similar to free elicitation in that subjects supply open-ended responses to product probes, it differs in that the statements are categorized according to some predetermined set of categories. Knowledge structures created by Russo and Johnson (1980) and Brucks (1986) are examples of this technique.

Finally, in what has been the most complete and detailed study of a consumer knowledge structure to date, Mitchell and Dacin (1996) combined a variety of measurement techniques. In comparing expert and novice knowledge structures of motorcycles, they collected data on usage experience using a questionnaire, and associated concepts for the product class, different product types, brands, models, components, and performance attributes using first-, second-, and third-level memory probes. Additional tasks and measures were used in a second session to provide data on knowledge organization, along with a vocabulary quiz and attribute-performance task. Finally, choices and reasons for choice were assessed in a third data collection session. Associative networks for subjects who differed on the "subjective/objective knowledge" and "friends owning motorcycles" factors were qualitatively different. It is interesting to note that the data collection tasks in this study were planned to reveal what consumers did or did not know about specific product characteristics, rather than the typical approach of encouraging less constrained responses.


Clearly, two decades of research and theory development on the structure of consumer knowledge (see Table 1 for summary) has settled on the conclusion that the associative network is the best description of what resides in consumers’ heads. Despite this consensus, the evidence and reasoning leading to this conclusion is not compelling. Consider the following points:

(1) Reliability of Evidence for Structural Features. The basic structural features of an associative network are the concept nodes and the linkages which connect them to each other. While previous studies have used techniques such as free elicitation and protocol analysis to flesh out these connections as they exist at one point in time, no research has been directed toward the crucial issue of the structure’s stability over time. If the associative network that has been measured and identified yesterday is essentially the same one that emerges today and tomorrow, then we have evidence that what is being measured is a construct with some ability to endure beyond momentary random distractions. Barsalou (1993) reports that both between-individual and within-individual measures of the stability of associations generated to concepts is (at best) moderate. While it would be relatively easy to compute such reliabilities in the context of assessing consumer knowledge structures, such studies have not been conducted. To the extent that flexibility, rather than stability, of knowledge structures is revealed, then such evidence would argue against the particular structure being evaluated.



(2) Nonconscious Retrieval Processes. Associative network theory, complete with its spreading activation process, was originally conceptualized as an organizational structure that was designed to support automatic or nonconscious retrieval processes (Anderson 1983). Supportive evidence came from priming studies, in which a prior presentation of a concept automatically made easier the subsequent processing of associated concepts. While such automatic processes are important in building a complete account of consumer information processing, a basic understanding of what consumers know about products and how they retrieve knowledge must also accommodate consciously controlled search mechanisms. Although the associative network structure allows for controlled retrieval processes (see Grunert, 1996), the structure itself would have to be endowed with more organizational features, such as labeled links or a natural hierarchy of concepts, in order to provide a meaningful basis for searching.

In other words, while the spreading activation process that is embedded in an associative network structure is useful for explaining how retrieval can be automatic, nonconscious, and rapid, there has been little theoretical development aimed at explaining how retrieval can be more thoughtful and complete. What is needed is an approach to consumer knowledge that can allow for a more complete view of how consumers access their knowledge.

(3) Lack of Constraints on Content. The associative network structure allows for the entry of any concept as long it "comes to mind". This allows for extremely idiosyncratic and unusual concepts to join the network. The use of higher order free elicitation techniques, in which associations are given to associations, quickly compounds the problem. With the notable exception of Mitchell and Dacin’s (1996) study, accounts of consumer knowledge have not attempted to constrain the content of the network.

Content constraints are important to consider for both theoretical and practical reasons. From a theoretical perspective, the general finding that associations appear unconstrained may only reflect the basic unreliability of extant methods used to sample associations, rather than deeper structural characteristics (see Barsalou, 1993). Even if the individual consumer’s knowledge actually is "chaotic", aggregate descriptions of knowledge may still be very oderly, with idiosyncratic associations "smoothed" out.

(4) Minimal Organization. Finally, other than the hierarchical structure (Meyers-Levy and Tybout 1989), and Keller’s (1993) abstraction-based categories, there have been no statements of the associative network approach that have provided a basis for organizing the associations. Rather, the assumption seems to have been that associations emerge out of haphazard experience as opposed to conceptual necessity. Unfortunately, this strongly empirical approach has left the consumer in a chaotic tangle in which factors such as recency and frequency drive retrieval.

Perhaps the basic question concerns what organizational categories to include. It appears that the bulk of the research reviewed is biased toward methods which are designed to elicit raw feature lists rather than categorized exemplars. Such an approach will not yield highly organized networks. The inclusion of categories inherently is organizing. The basic empirical issue centers around whether or not consumers carry these categories, and, if so, use them to organize their knowledge, much like Mitchell (1982) suggested, and Barsalou and Hutchinson (1987) observed. This extra layer of associations, which in turn may have different "values" associated with them, is the crucial link in developing as an alternative to the associative network the more organized frame structure.


According to Barsalou (1992), there are three basic structural features of frames: attribute-value sets, structural invariants, and constraints. Instead of conceptualizing knowledge as consisting of haphazard associations, the frame structure requires that all characterisics of a concept be described at two levels: attribute and value. An attribute, defined as a concept which describes an aspect of at least some members of a category, is at a rather abstract level of description because it must be able to take on particular values. Thus, in describing the target product concept of "men’s dress shoes", instead of simply linking to the node, "uncomfortable", as might be the case in the associative network, the frame structure depicted in Figure 1 requires the specification of the attribute "comfort", and then possible values such as "comfortable" and "uncomfortable". Similarly, "style" is an attribute of the target concept, which has associated with it the more detailed attributes of "laces", "tassle", and "narrow toe". Each of these attributes can take on "yes" or "no" values. It is important to note that "brand" is conceptualized as an attribute of a product-level concept, and that the specific brand names are values.

As shown in Figure 2, the brand names (Florscheim, Gucci), in turn, serve as target concepts which are partially described by the same set of attribute-value associations that are specified at the "brand" level. Thus, the attribute-value structural feature has the effect of organizing the consumer’s knowledge in a way that allows comparisons to be made. For example, when the consumer is shopping for shoes, he may mentally compare and contrast expectations from Sears, Walmart, and a catalog retailer regarding the attributes of price, convenience, being able to try them on, and sales assistance.

Within the frame some relations between attributes describe normative truths and are, therefore, relatively constant. Barsalou (1992) refers to these relations as structural invariants. For product frames in general, in considering the "purchase-related" attribute, the notion of "price" is conceptually necessary, as is "image" to the attribute of "brand". These relations represent structural invariants, which have the effect of constraining which categories of concepts are permitted as associations.

In addition, values of frame attributesact to constrain each other. For example, the more durable the shoe, the more likely it is to be expensive; or, the more narrow the toe, the lower the comfort level. Constraints such as these, which may or may not be factual, lead to various preferences or determinations on the part of the consumer. For example, I will not buy shoes for myself by catalog because of my belief that there is a high probability that what will be delivered will not fit correctly, based on my experience with having to try on several (if not many) pairs before I find one that is comfortable. This belief is based on the constraint between "comfortable" and "trying on".

An optimization is a particularly interesting form of constraint which has far-reaching implications for our understanding of consumer decision making processes. Optimizations are attempts to make selections within the knowledge structure that are instrumental to achieving the consumer’s goals. Figure 1 shows the agent with the goal of buying moderately-priced, comfortable shoes that are not wingtips. Thus, as a type of style, wingtips are eliminated from the selection process, the attribute of narrow toes is negatively constrained, Sears is preferred over Walmart or purchasing by catalog, and Florscheim is preferred over Gucci. Had the agent had a different set of goals, like buying an expensive, Italian shoe, then a different set of optimization constraints would emerge. Given that the consumer is not a pure novice with regard to a particular product category, consumer decision making can be viewed as flowing from the knowledge structure in a goal-driven manner, rather than as an on-line process of feature comparison among brands. This, and other implications of consumer knowledge frames are discussed next.


Organization. Through the structural features described above, frames provide for a more organized consumer knowledge structure than was evident in the associative network. However, a crucial theoretical question remains: What attributes should be included in the structure? Since consumer knowledge structures should reflect both what the consumers actually know about the product, and what marketers think they ought to know, I propose that the structure should be a combination of universal marketing attributes, which convey general product characteristics and are to be incorporated in each and every frame, and product-specific attributes, which describe aspects of that particular product. For example, for the men’s dress shoes partial frame in Figure 1, the attributes of performance, purchase-related, and brands are universal attributes, while style is specific to the product category. Of the more specific attributes attached to purchase-related attribute, price, convenience, and assistance are universal, while try on is product-specific. Under brands, for example, image and price are universal, comfort and durability are specific to shoes.

In general, the determination of what universal attributes to include would be driven by one’s conceptualization of a product rather than by consumers’ observed associations. Every consumer of a particular product has some knowledge of its performance characteristics, how it might be acquired, and what particular (brand) alternatives are available. On the other hand, deciding what product-specific attributes to include in the knowledge structure should be more reflective actual consumer associations. For example, comfort and durability were specified as important performance characteristics because consumers cite them as important. To summarize, I argue that the organization of a consumer knowledge structure largely depends upon theoretical decisions regarding what attributes to include, and that it should be a combination of universal marketing andproduct-specific attributes.

Knowledge Frames and Decision Making. A consumer’s knowledge of a product category develops gradually over time. While particular concepts may exhibit considerable flexibility when they are brought to mind from occasion to occasion, the underlying frame-based knowledge structure is stable (Barsalou, 1993). While the sample partial frame shown in Figure 1 highlights some of the major structural features of frames, it does not attempt to depict many other facets of consumer knowledge. Having a well-developed knowledge structure for some product implies the existence of various evaluations, preferences, and other kinds of assessments. In addition to knowing about the various characteristics of men’s dress shoes that are outlined in the frame, the experienced consumer also is likely to have stored brand-based sets of ratings, rankings, preference markers, or avoidance markers. Futrthermore, they should be relatively stable features of the structure. To the extent that these determinations already exist as part of the structure, then the process of brand selection or store selection can be understood more as a matter of knowledge construction that has happened in the past than as a consumer decision making process that is currently happening.

In other words, I am suggesting that a fully articulated knowledge structure already contains within it the determinations that are necessary for the selection of alternatives. The consumer does not have to engage in the traditionally defined decision making processes of information search and "attribute" comparison in order to arrive at a preferred brand alternative because that work already has been done. For example, when I am ready to purchase my next pair of dress shoes sometime in the future, I have already determined that I prefer to buy a pair of black, tassled Florscheims at Sears. In my mind (that is, knowledge structure), that alternative optimizes my various goals. To the extent that these types of preferences are built in to knowledge structures, traditional decision making tasks become superfluous. At the very least, this knowledge structure perspective implies that a useful research agenda on the selection process should focus on what consumers already know about a product category and its brands.





The Role of Goals. Recently, researchers have been attempting to understand what role higher level goals and values play in consumer cognitive processes (Huffman, Ratneshwar and Mick 1997; Lawson 1997). Consumer knowledge frames acknowledge the contribution of goals in two ways. First, as shown in Figure 1, the consumer’s (agent’s) current goals create optimization constraints among the attribute values in the system. These constraints operate to help produce the selections that will satisfy these goals.

Second, and more subtly, deeply-held life goals and values play important roles in developing the knowledge structure itself. They can exert an influence on what an agent can consider as current goals, or can be included in the product’s attributes. For example, in considering a possible frame structure for cigarettes for an anti-smoking activist, the attributes of health-related and advertising strategy would most likely be included. Here, the consumer’s ethical concern would make its way into the knowledge structure for that particular product. Another consumer who is deeply concerned about environmental issues, might include the attribute environment-related as a universal attribute in every product frame, as well as include environmentally-friendly in the agent’s current goals.

In summary, the history of research and theory on consumer knowledge structures has been dominated by the notion of the associative network. However, existing evidence does not unequivocally support that structure. Frame theory, by providing for constraints that lead to a more organized structure, is presented as a promising alternative to associative networks.


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Robert Lawson, William Paterson University


NA - Advances in Consumer Research Volume 25 | 1998

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