Exploring the Structural Characteristics of Consumers' Knowledge

ABSTRACT - The knowledge structures of more and less knowledgeable subjects were examined in terms of three characteristics: dimensionality, articulation, and abstraction. Compared to the less knowledgeable subjects, the knowledge structures of the more experienced, more knowledgeable subjects had more dimensions, were more articulated, and contained more concrete level knowledge. The two groups also differed in the content of their knowledge dimensions. Suggestions are offered for future research on consumers knowledge structures.

Citation:

Beth Walker, Richard Celsi, and Jerry Olson (1987) ,"Exploring the Structural Characteristics of Consumers' Knowledge", in NA - Advances in Consumer Research Volume 14, eds. Melanie Wallendorf and Paul Anderson, Provo, UT : Association for Consumer Research, Pages: 17-21.

Advances in Consumer Research Volume 14, 1987      Pages 17-21

EXPLORING THE STRUCTURAL CHARACTERISTICS OF CONSUMERS' KNOWLEDGE

Beth Walker, Pennsylvania State University

Richard Celsi, University of South Carolina

Jerry Olson, Pennsylvania State University

[Financial support for this research was provided by the Binder Faculty Fellowship and Grant No. 5901-0410-8-0151-0 from the Science and Education Administration of the U.S. Department of Agriculture to the third author.]

[The authors are doctoral candidate in Marketing, Assistant Professor of Marketing, and Professor of Marketing and Binder Faculty Fellow, respectively.]

ABSTRACT -

The knowledge structures of more and less knowledgeable subjects were examined in terms of three characteristics: dimensionality, articulation, and abstraction. Compared to the less knowledgeable subjects, the knowledge structures of the more experienced, more knowledgeable subjects had more dimensions, were more articulated, and contained more concrete level knowledge. The two groups also differed in the content of their knowledge dimensions. Suggestions are offered for future research on consumers knowledge structures.

INTRODUCTION

By the late 1970's, consumer researchers had begun to recognize that consumers' stored knowledge in memory strongly influences their cognitive processes (Bettman 1979; Olson 1978.) Prior knowledge has been shown to affect speed of pattern recognition (Chase and Simon 1973), recall (Arkes and Freedman 1984; Reder and Anderson 1980), information acquisition (Chiesi, Spilich and Voss 1979), information search (Brucks 1985; Biehal 1983; Johnson and Russo 1984; Punj and Staelin 1983; Srull 1983), information processing strategies (Sujan 1985; Fiske, Kinder and Larter 1983), and memory organization (Sujan 1985; Alba and Hasher 1983; Fiske et al. 1983; Chi, Feltovich and Glaser 1981). In addition, more knowledgeable individuals are able to make finer discriminations between objects within a domain, as well as combine information into larger meaning units or chunks (Showers and Cantor 1985; Markus, Smith and Moreland 1985). In sum, this literature suggests that more knowledgeable "experts" have greater amounts of knowledge and better-organized structures of knowledge than do less knowledgeable individuals or novices. These more complex knowledge structures, in turn, produce more accurate and efficient information processing (Showers and Cantor 1985).

Rather than measure the content and structure of subjects' knowledge, however, most researchers have merely assumed that certain subjects have different types and amounts of knowledge in their memories because of variations in the type and amount of their past experiences. Therefore, subjects are often blocked on their past experience--more vs. less--as a surrogate for the extensiveness or complexity of their domain specific knowledge.

Next, researchers predict the information processing consequences of these presumed differences in knowledge structures for these more and less experienced subjects (cf. Markus and Sentis 1982; Markus et al. 1985; Sujan 1985). Then, any observed differences in processing outcomes between the two groups are attributed to differences in their stored knowledge. However, researchers seldom take direct measures of the content and organization of the knowledge structures that presumably cause the observed effects. In fact, subjects' actual knowledge has been directly measured in only a few studies (see Chi et al. 1981; Conover 1982; Dacin and Mitchell 1986; Hirschman and Douglas 1981; Kanwar, Olson and Sims 1981).

In sum, almost all of the-expert/novice research has focused on the "cognitive consequences" produced by subjects' knowledge. Most researchers have ignored the mediating knowledge structure. Thus, we have only a limited understanding of the specific characteristics of knowledge structures that may be responsible for the observed processing differences between more and less knowledgeable subjects. In addition, little attention has been directed toward developing general theoretical explanations for these knowledge effects. Compared to novices, experts are said to have more "well-developed, extensive, efficient, or enriched" knowledge structures. But the specific characteristics of an extensive or efficient knowledge structure have not been specified. A first step in doing so is to carefully describe the differing content and organization of knowledge structures possessed by relative experts and novices.

In this study, we build on the research of Kanwar, Olson and Sims (1981) and Dacin and Mitchell (1986). We describe how three characteristics of knowledge structures--dimensionality, articulation, and abstraction --differ for more and less knowledgeable subjects. These aspects of knowledge may be responsible for some of the processing consequences reported in the "expert/novice" research literature. This type of descriptive data should be useful in developing a theory of consumers' knowledge, especially the structural characteristics of knowledge that may underlie expertise effects.

CHARACTERISTICS OF KNOWLEDGE STRUCTURES

Several researchers (Bieri 1955, 1966; Conover 1981; Hirschman and Douglas 1981; Kanwar, Olson and Sims 1981; Scott 1963, 1967, 1974) have described characteristics of knowledge structures, including the three basic features examined here: dimensionality, articulation, and abstraction. In this section, we briefly describe these constructs and discuss the relevant literature concerning their effects.

Dimensionality

Dimensionality refers to the number of unique (different) attributes or concepts in a person's knowledge structure for a particular domain (Kanwar, Olson and Sims 1981; Scott 1963). The dimensions people use in thinking about a domain are acquired through their experience with the domain. Because experts or more knowledgeable people have had greater experience in a domain, they should be able to describe objects in that domain in terms of more dimensions whan novices or less knowledgeable individuals can.

The expertise literature generally supports the proposition that the knowledge structures of more knowledgeable individuals have greater dimensionality. For instance, Scott (1967) suggested that more dimensional or cognitively complex individuals are able to construe social behavior in terms of zany dimensions. Mitchell and Chi (1986) found that experts had much more knowledge about a domain than did novices. Markus, Smith and Moreland (1985) observed that experts were able to categorize and interpret an object in many different ways. Finally, Showers and Cantor (1985) noted that highly knowledgeable individuals were quite flexible in how they processed information within a domain, presumably because of the greater dimensionality of their knowledge structures.

Articulation

A dimension is more articulated if a person can make more (finer) discriminations along that dimension. For example, if we assume that a Likert scale represents a knowledge dimension in memory, the number of divisions or categories on that scale--perhaps three, five or seven points--is analogous to the level of articulation. The articulation of an entire knowledge structure is indicated by the average number of reliable distinctions that a person makes across the salient dimensions in memory (Bieri 1966; Kanwar, Olson and Sims 1981; Scott 1966). Due to their greater experience in applying dimensions to objects in a domain, more knowledgeable individuals should be able to make finer distinctions between stimuli in terms of their salient dimensions. In contrast, less experienced individuals are likely to possess less articulated knowledge structures.

Experts' ability to make finer discriminations among objects than novices has been supported by the growing literature on expertise. According to Murphy and Medin (1985), experts differ from novices primarily in their ability to make finer distinctions between things. This difference between experts and novices was first recognized by Rosch et al. (1976) when studying the categorization processes. Markus et al. (1985) also distinguished experts by their ability to divide information "into exceedingly small units," whereas novices tend to use more general and common categories. Markus, Hamill and Sentis (1979) suggest that the differences between schematics and aschematics in self-categorization tasks are due to differences in the relative articulation of their knowledge structures. In sum, the ability of more knowledgeable persons to make fine discriminations between things, remember more detailed information, and process new information more efficiently seems due in part to their more articulated knowledge structures.

Abstraction

Abstraction may be the most important characteristic of knowledge structures that differentiates experts from novices. Abstraction refers to the level of inclusiveness of the salient concepts or dimensions in a knowledge structure. More abstract concepts are "bigger," "broader," more general, and less representative of tangible "reality." In contrast, less abstract, more concrete concepts are "smaller," more "narrow," more specific, and more directly representative of physical objects in the environment.

The development of knowledge structures containing abstract concepts was described by Hayes-Roth (1977). According to her unitization theory, a knowledge structure begins as a few, weakly associated concepts. With experience, more concepts are acquired and sets of concepts become more closely associated with one another. Thus, they tend to be activated together. As experience grows further, the person begins to form more abstract representations of these sets of interrelated concepts. For instance, the concepts "nutritious," "vitamins," and "protein" might be recoded to form a more abstract memory concept, "good for you." As knowledge structures become more abstract, concrete knowledge is organized or chunked into fewer, but more general, abstract concepts. In general, then, we would expect increasing levels of experience or knowledge to produce more abstract knowledge structures.

Many findings in the expert/novice literature suggest that experts have more abstract knowledge than novices. Markus et al. (1985) suggest that experts tend to chunk or group knowledge to form more abstract dimensions. Consequently, more experienced people should be able to integrate new information with previously acquired knowledge more easily than novices. Research on the categorization processes of experts and novices has demonstrated that "experts" possess knowledge at several interrelated levels of abstraction. Their abstract knowledge allows them to organize seemingly different stimuli by some general principles or features (Murphy and Wright 1984). On the other hand, "novices" tend to categorize objects based on less abstract, more sensory-level features. Other researchers have suggested that, compared to novices, experts use fewer, more inclusive, more abstract concepts (Chi et al. 1981) and have knowledge concepts that subsume more information (Fiske, Kindler and Larter 1983).

However, knowledge research also suggests that more knowledgeable individuals have substantial concrete knowledge, as well as more abstract knowledge (Showers et al. 1985; Markus et al. 1985). For example, Conover (1982) found that more experienced subjects used many more concrete or specific attributes when describing a domain than did less familiar subjects.

This idea that more knowledgeable individuals have more concrete as well as more abstract knowledge is consistent with the purpose of the abstraction process discussed by Rosch (1975). Abstract knowledge structures develop out of the need for cognitive economy. Because experienced consumers have relatively more knowledge dimensions, they have a greater need to form abstract categories to organize this information and increase their processing efficiency. In essence, experts have more abstract knowledge because they have a larger base of concrete, specific knowledge. Thus, the knowledge structures of more experienced individuals are likely to contain more concrete knowledge as well as more abstract knowledge.

Levels of Abstraction. Several researchers have developed conceptual schemes that classify consumers' product knowledge in terms of its level of abstraction (Cohen 1979; Geistfeld, et al. 1977; Gutman 1982; Hirschman 1980; Lancaster 1976; Myers and Shocker 1979). Means-end chains provide the most theoretically elaborate conceptualization of the abstraction of consumers' product knowledge (Gutman 1979 1982; Reynolds and Gutman 1984; Olson and Reynolds 1983). The means-end chain model includes six levels ranging from concrete attribute knowledge to the abstract level of self knowledge:

FIGURE

Concrete attributes such as size or color are relatively direct, unidimensional representations of physical, tangible product characteristics. Abstract attributes such as style or quality are further removed from physical characteristics and tend to subsume several concrete attributes. Product-related concepts at higher levels of abstraction reflect the functional consequences (often tangible) of product e, such as lose weight or save money. Psychological or social consequences, such as feel good or attract attention, constitute even more abstract meanings associated with the product. Finally, at the highest levels of abstraction, consumers can represent a product in terms of the values and basic needs that are be achieved through its use, such as self-esteem or happiness. Note that these valued end-states are several levels more abstract than the level of physical product attributes. The abstraction coding scheme used in this research is based on this means-ends conceptualization of knowledge.

METHODS

Research Objective

The purpose of this research was to describe the differences in knowledge structures between more and less knowledgeable consumers. In particular, we were interested in differences in the dimensionality, articulation and abstraction of their knowledge structures for nutrition.

Subjects

Subjects were 40 undergraduate students at a large university, selected on a convenience basis. Subjects volunteered to participate and were paid $4.00 for the approximately one hour of their time required to produce the data discussed here.

Because we were interested in the nutrition knowledge structures of more and less experienced individuals, we selected 20 senior nutrition majors (more knowledgeable or "expert" subjects) and 20 senior non-nutrition majors (less knowledgeable or "novice" subjects). Each nutrition major had taken from 4 to 6 advanced nutrition courses, while none of the other majors had taken a nutrition course. Of course, like most adults in the U.S. society, the non-nutrition majors did have some knowledge about nutrition. The key difference between the groups was the amount of their formal classroom training concerning nutrition.

Procedures and Measures

Kelly's (1955) repertory grid task, as modified by Kanwar, Olson, and Sims (1981), was used to provide relatively direct measures of dimensionality, articulation, and abstraction. The forty subjects were interviewed individually by a trained interviewer. Each subject was shown ten sets of three food concepts, one triad at a time. For example, eggs, cheese, and peanut butter comprised one triad; yogurt, ice cream and milk formed another; sugar cookies, pretzels, and potato chips comprised another triad. For each triad, the subJect was asked to describe "all the ways in which two of these foods are similar, and different from the third." After a subject had elicited all the dimensions he/she could think of, the next triad was presented, and so on.

Dimensionality. Our focus in this research was on nutrition knowledge structures. Therefore, we first separated the nutrition-related concepts from the non-nutrition concepts. Then, we computed a measure of dimensionality by counting the total number of unique nutrition concepts elicited by each subject across all ten triads. That is, a unique nutrition dimension was counted only once, even if it was mentioned several times by a subject.

Articulation. Following the triad elicitation task, the interviewer asked the subject to rate each elicited nutrition concept In terms of Its Importance to them (l to 5 scale; unimportant to very important). To keep the interview at a reasonable length, the four most important nutrition concepts were selected for the next step. For each dimension, subjects were told to sort a set of food concepts (the 30 foods used in the triads--eggs, cheese, yogurt, pretzels, etc.) into piles so that the foods in each pile were similar with respect to that nutrition dimension and different from the foods In the other piles. They were told to use as many or as few piles as they wanted. We computed the average number of levels/piles across the four dimensions as a measure of articulation.

Abstraction. The abstractness of subJects' knowledge structures was measured by classifying the unique nutrition dimensions according to the six levels of the means-end chain conceptualization of knowledge discussed above. The two judges who coded the unique nutrition concepts agreed on 86% of their classifications. Discrepancies were resolved by a third judge.

Across both experience groups, we found that most (91%) of the nutrition concepts were in the three least abstract levels--concrete attributes (31%), abstract attributes (50%), and functional consequences (10%). [Conover (1982) also reported obtaining more concrete level knowledge using a different version of the repertory grid procedure.] The remaining concepts (9%) were psychosocial consequences, instrumental values, or uncodable thoughts. No terminal values were elicited from subjects. In sum, concepts at the most abstract levels of the means-end chain were not produced by the triad procedure. Therefore, our analysis of the relative abstractness of the knowledge structures for more and less knowledgeable subjects is limited to comparisons at the least abstract levels of knowledge--concrete attributes, abstract attributes, and functional consequences.

RESULTS

The purpose in this research was to describe and compare the knowledge structures of more and less knowledgeable individuals in terms of three characteristics: dimensionality, articulation, and abstraction.

Dimensionality

Dimensionality was measured by the total number of unique nutrition concepts elicited by the ten triads of food concepts. As we expected, the knowledge structures of more knowledgeable subjects had greater dimensionality (more unique nutrition concepts) than those of the less knowledgeable subjects (30.9 versus 22.2 unique nutrition concepts per subject; F 1/38 = 11.35, p < .01)

Articulation

Articulation was measured by the average number of discriminable levels per nutrition concept produced during the free sort task. Although the more knowledgeable subjects averaged somewhat more categories per nutrition concept than did the less knowledgeable subjects (3.98 vs. 3.45 categories), the difference was not statistically significant (F1/38 = 2.30, p = NS)

Abstraction

The abstractness of subjects' nutrition knowledge structures was measured in terms of the six levels of the means-end chain model, as described above. The two experience groups differed only in terms of the concrete attributes elicited. More knowledgeable subjects produced substantially more concrete attribute dimensions than did less knowledgeable subjects (X - 11.90 versus 4.65; F1/38 - 42.29; g < .01). Although in the expected direction, differences between higher and lower knowledge subjects in abstract attributes (14.00 versus 12.65) and functional consequences (3.44 versus 2.68) were not statistically significant (n's > .05).

Differences in Salient Knowledge Dimensions

Table 1 presents the nutrition dimensions most frequently identified as important by the more and less experienced subjects. The more knowledgeable subjects tended to use somewhat technical knowledge dimensions in distinguishing between food concepts, whereas the less knowledgeable subjects tended to use general, rather abstract attributes or functional consequences. For instance, the largest differences between groups emerged on two dimensions, "good for you" and "nutrition content." Fifteen out of 20 (75%) of the less knowledgeable subjects said that "good for you" was an important concept, whereas only 15% of the more knowledgeable subjects mentioned "good for you" as important. Conversely, 18 of the 20 higher knowledge subjects (90%) mentioned "nutrition content or value," "nutrient content," or "nutrient density" as being important. Only 9 out of 20 (45%) of the lower knowledge subjects elicited these concepts. Higher knowledge subjects also mentioned fat and fat content and vitamins more frequently than did the lower knowledge subjects. As one would expect, these data indicate that the more knowledgeable subjects (several formal nutrition courses) think about foods in terms of specific, nutrition-related concepts. In contrast, less knowledgeable subjects tend to use more general, abstract concepts.

Table 1 also shows that several knowledge dimensions are shared by the more and less experienced subjects. lt is possible, though, that the meanings of these shared dimensions say be quite different for the two groups of subjects. For example, both more and less knowledgeable subjects may mention that "nutrition content" is an important dimension of knowledge. However, more knowledgeable subjects might know that "nutrition content" is important because it helps build and maintain strong bones and tissue, provides energy, maintains health, and allows the person to enjoy and live a long life. In contrast, less knowledgeable subjects may believe that "nutrition content" is important, but know little about its consequences. To really understand the meaning of salient dimensions, we need to identify the other concepts with which it is associated. Measuring such chains of associated meanings (means-end chains) requires a more directed interview procedure like laddering (cf. Gutman 1982: Olson and Reynolds 1983).

TABLE 1

NUMBER OF SUBJECTS MENTIONING SPECIFIC NUTRITION CONCEPTS

DISCUSSION AND FUTURE RESEARCH

In this research, we compared more and less knowledgeable (more and less experienced) subjects in terms of three characteristics of their knowledge structures--dimensionality, articulation, and abstractness.

Dimensionality

As expected, we found that the knowledge structures of more knowledgeable subjects were more dimensional than those of less knowledgeable subjects. These results are consistent with a number of studies described earlier, including Conover's (1982) findings, as well as predictions made by Kanwar et al. (1981) and Hirschman et al. (1981) that more knowledgeable subjects activate and use more dimensions than less knowledgeable subjects do.

Articulation

We also found a slight, but not statistically significant, tendency for more knowledgeable subjects to have more articulated knowledge structures than less knowledgeable subjects. Several researchers (cf. Bieri 1963; Scott 1974) have indicated that more "cognitively complex" individuals should have more articulated knowledge than less cognitively complex individuals. Likewise, more knowledgeable subjects about nutrition would be expected to have more articulated knowledge structures than less knowledgeable subJects.

Our restricted focus on only the four most important dimensions for each subject may have partially caused the small differences in articulation we observed. Since our lower knowledge subjects did have some nutrition knowledge, their four most important dimensions might have been about as well articulated as those of the higher knowledge subjects. Perhaps if we had examined a wider range of more and less important dimensions, we might have found larger differences in articulation. Future research should take pains to select subjects who evidence even greater differences in experience and/or expertise.

Abstraction

Compared to the lower knowledge subjects, we found that the higher knowledge subjects elicited more concrete knowledge (the three lower levels of the means-end chain model). However, these differences were statistically reliable only for the concrete attribute level. These results are consistent with much of the expertise literature described earlier. Note, however, our finding that subjects elicited few abstract concepts does not necessarily indicate that they had no abstract knowledge in memory. Rather, these data may reflect the tendency of the triad elicitation task to activate relatively concrete knowledge. Other procedures such as laddering (Gutman 1982) may be necessary to produce more abstract levels of knowledge in order to differentiate between the knowledge structures of more and less experienced subjects.

We also found that abstract attributes were the most frequently mentioned level of knowledge (50% of all elicited dimensions) for both the more and less knowledgeable subjects. This suggests that abstract attributes may be the "basic" category level shared by most people (cf. Rosch 1975 1978). These data further imply that consumers' begin to develop their knowledge structures at the abstract attribute level. For instance, knowledge at the abstract attribute level concept may become linked to several more concrete and specific attributes. These connections to concrete level knowledge give "extra" meaning to the abstract attributes. Our results indirectly support this notion as the more knowledgeable subjects elicited more concrete attributes than did the less knowledgeable subjects. A similar finding was obtained by Conover (1982) and was predicted by Geistfeld et al. (1977).

Knowledge structures can also develop in the opposite direction. As consumers gain experience and acquire more knowledge, their need to organize this knowledge into efficient and economical meaning units also increases. As suggested by Hayes-Roth's (1977) unitization theory, concrete concepts are recoded into fewer abstract concepts, thereby effectively reducing the number of salient dimensions that need to be activated from memory. Unfortunately, the triad procedure produced very few abstract dimensions (psychosocial consequences and values). Therefore, we are unable to explore whether more and less knowledgeable subjects differ in their more abstract knowledge.

Content of Knowledge

Finally, we described some differences in the actual dimensions used by more and less knowledgeable subjects. In general, the more experienced subjects tended to use more technical dimensions, and the less knowledgeable subjects tended to use more general concepts. However, many of concepts used by the two groups were similar. Future research should give greater attention to differences in the actual knowledge content of more and less experienced subjects.

Effects of Measurement

In all research, the measurement procedures affect the type of data that are obtained. When measuring knowledge structures, the effects of measurement seem especially obvious. For instance, in the present study, the triad elicitation procedure seems to elicit relatively concrete levels of knowledge. Future research should use other methods--such as free elicitation (Olson and Muderrisoglu 1979; Dacin and Mitchell 1986), laddering (Gutman 1982), or knowledge questionnaires--in combination with the triad task to measure the content and organization of knowledge structures. For instance, free elicitation could be combined with the triad procedure to activate a wider range of content in consumers' knowledge structures (cf. Kanwar, Olson and Sims 1981). Then laddering could be used to reveal the linkages and semantic associations between concepts at different levels of abstraction. These richer and deeper descriptions of knowledge might reveal differences between more and less experienced consumers. Again, future research should study subjects with extreme differences in experience and knowledge to maximize observed differences in measured knowledge structures. Finally, specific purchase or usage situations could be incorporated into all of these procedures to explore how situational contexts affect the activation of consumers' knowledge.

SUMMARY

We found that more and less knowledgeable subjects differed somewhat in the dimensionality, articulation, and abstraction of their knowledge structures. We are encouraged by these results. They provide evidence that aspects of peoples' knowledge structures can be measured relatively directly. Future research should strive to improve the approach used here and combine it with alternative methods for directly measuring consumers' knowledge.

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