The Relationship Between Knowledge and Search: It Depends

ABSTRACT - While consumer behavior researchers have discussed the relationship between knowledge and external search behavior extensively, little consensus has emerged regarding the direction and form of that relationship, and little discussion has been offered as to why different relationships emerge. We review the literature in an attempt to reconcile these varying findings, concluding that the knowledge-search relationship is moderated by a number of factors, including the particular dimensions of knowledge and search under consideration. Further, we present a preliminary discussion of how characteristics of the task environment likely moderate the effects of knowledge on search. Research directions are also considered.


Carol A. Fiske, Lisa A. Luebbehusen, Anthony D. Miyazaki, and Joel E. Urbany (1994) ,"The Relationship Between Knowledge and Search: It Depends", in NA - Advances in Consumer Research Volume 21, eds. Chris T. Allen and Deborah Roedder John, Provo, UT : Association for Consumer Research, Pages: 43-50.

Advances in Consumer Research Volume 21, 1994      Pages 43-50


Carol A. Fiske, University of South Carolina

Lisa A. Luebbehusen, University of South Carolina

Anthony D. Miyazaki, University of South Carolina

Joel E. Urbany, University of South Carolina

[The authors (listed in alphabetical order) would like to thank Paul Miniard, Mike Barone, and two anonymous reviewers for constructive comments on earlier drafts.]


While consumer behavior researchers have discussed the relationship between knowledge and external search behavior extensively, little consensus has emerged regarding the direction and form of that relationship, and little discussion has been offered as to why different relationships emerge. We review the literature in an attempt to reconcile these varying findings, concluding that the knowledge-search relationship is moderated by a number of factors, including the particular dimensions of knowledge and search under consideration. Further, we present a preliminary discussion of how characteristics of the task environment likely moderate the effects of knowledge on search. Research directions are also considered.

Three of the top ten most frequently cited articles in the Journal of Consumer Research between 1974 and 1989 (Brucks 1985; Bettman and Park 1980; Johnson and Russo 1984) address the impact of consumer knowledge on search behavior and information processing, suggesting substantial interest in the topic (cf. Cote, Leong, and Cote 1991). There exists an empirical inconsistency in this literature, however, that merits a close look. While many studies have found positive effects of knowledge on search (e.g., Brucks 1985; Srinivasan and Ratchford 1991), several others have found negative effects (see the summary in Beatty and Smith 1987), and still others suggest an inverted-U relationship (cf. Bettman and Park 1980; Johnson and Russo 1984). We explore this inconsistency with the aim of providing an account for different findings in light of the many different operationalizations of the knowledge and search constructs. We conclude that, while general directional (i.e., positive or negative) predictions are typically made regarding the effects of knowledge on search behavior, the nature of this relationship appears to depend significantly upon the conceptual and operational definitions of knowledge and search, as well as a host of what can be called task or market characteristics (Moore and Lehmann 1980).

We begin by briefly reviewing conceptual accounts of the potential effects of knowledge on external search. We then discuss some general conclusions from the empirical literature and develop a preliminary model describing how knowledge-search effects may be moderated. [Note that a formal meta-analysis of this literature would be difficult, if not impossible, to interpret given the noncomparability of measures used across studies (see Table).]


Before examining the literature, it is useful to distinguish between two components of knowledge which appear in discussions of the knowledge-search relationship. Punj and Staelin (1983) discuss a dimension of knowledge that pertains to the quantity of directly relevant brand information held in memory (i.e., the elements of the "relevant alternative by attribute" matrix; p. 368). Although they refer to this dimension of knowledge as Usable Prior Information or Usable Prior Knowledge, it may be more appropriate to refer to it as Brand Knowledge (BK), since it is concerned with direct information about brands (or alternatives) currently available in the market of interest (see Brucks' [1986] reference to "brand facts" and "personal product usage").

A second dimension of knowledge described by Punj and Staelin (1983) is Prior Memory Structure, which refers to "the consumer's knowledge of the buying process as well as knowledge associated with [the product category] in general" (p. 368). To distinguish this more general knowledge construct from specific brand knowledge, we label it Product Category Knowledge (PCK). [Note that PCK contains components of both declarative knowledge and procedual knowledge (cf. Anderson 1976). Our sole purpose here is to distinguidh specific knowledge about brands from all other kinds of knowledge, as Punj and Staelin (1983) intended.] Thus, consumers high in PCK know more about important attributes and their interrelationships, know what questions to ask, can disregard irrelevant information, are better able to comprehend new information, and can process information faster (Alba 1983; Alba and Hutchinson 1987; Bettman 1979; Brucks 1985, 1986; Chi 1981; Punj and Staelin 1983). There are two reasons for distinguishing BK from PCK. First, the two constructs may have different effects on search behavior. Second, while BK and PCK likely develop in tandem over time, there are many situations in which existing PCK is relevant to a search problem, yet BK is not (e.g., when a consumer moves to a new market or several new brands have been introduced since the last purchase). Below, we briefly review the different explanations of the knowledge-search relationship and then consider how the distinction between BK and PCK provides further insight into the empirical results.

Positive Relationship

The positive knowledge-search relationship reflects a facilitating effect of knowledge: more knowledgeable consumers have better developed cognitive structures in place which improve their efficiency in (i.e., lower their cost of) gathering and processing new information (cf. Alba and Hutchinson 1987; Chi 1981; Punj and Srinivasan 1989). In addition, such consumers have more cognitive resources which can be devoted to search, enhanced abilities to encode new information, and knowledge of what questions to ask in the search process (Brucks 1985). A number of studies have identified primarily positive effects of knowledge on search behavior and tend to focus on PCK rather than on BK (Brucks 1985; Duncan and Olshavsky 1982; Jacoby, Chestnut, and Fisher 1978; Srinivasan and Ratchford 1991; Urbany, Dickson, and Wilkie 1989).

Negative Relationship

Brand knowledge (i.e., information about specific brands or alternatives) provides the most straightforward account of a negative knowledge-search relationship. Punj and Staelin (1983) propose that if both internal and external brand knowledge are useful in helping a consumer make a choice, then "the more information obtained prior to active search, the less the need for external search, and vice versa" (p. 368). Evidence consistent with this de-motivating effect is provided by a number of researchers (Bettman and Park 1980; Kiel and Layton 1981; Moore and Lehmann 1980; Punj and Staelin 1983; see also Green, Mitchell and Staelin 1977). [The de-motivating effect of prior brand knowledge is contingent on the consumers' (re)entrance to a market which has not changed a great deal (i.e., the BK is not obsolete).] A second general explanation for a negative knowledge-search relationship is a selective search effect, driven by PCK (cf. Brucks 1985). This suggests that consumers high in PCK can identify and focus their search on important attributes and appropriate alternatives relatively quickly (Brucks 1985; Claxton, Fry, and Portis 1974; Johnson and Russo 1984; Newman and Staelin 1972; Srinivasan and Ratchford 1991).



The Inverted-U

Combinations of the previously described effects could account for an inverted-U relationship. It is reasonable to suggest that the leftmost portion of the inverted-U curve reflects a segment of consumers who effectively limit their search because they know too little to even begin the process of search and, potentially, in "blissful ignorance," apply heuristics that do not require extensive information about brand alternatives (cf. Olshavsky and Granbois 1979; Park and Lessig 1981; Urbany et al. 1989). This heuristic effect is consistent with the inverted-U relationship proposed recently by Rao and Sieben (1992), which suggests that both novices and experts will limit their search of intrinsic information, but for different reasons: experts because they know extrinsic cues are sufficiently diagnostic in judging performance (assuming that these cues are, in fact, diagnostic), and novices because they have difficulty understanding the intrinsic information. [Intrinsic cues are associated with actual physical and/or performance attributes of a product, while extrinsic cues are product related attributes apart from the physical product (Rao and Monroe 1988).] Although low-knowledge consumers possess little brand knowledge (and would presumably be motivated to acquire some to aid in the decision process), they lack the necessary general product information (PCK) that facilitates learning the brand information. Further, confidence in using particular heuristics (e.g., buy at Sears, or judge quality using price) may mitigate uncertainty associated with a lack of knowledge (Urbany et al. 1989).

It is important to consider that both brand knowledge and product category knowledge are acquired through experience with the product category, and are thus integrally related in most circumstances. Therefore, another explanation for an inverted-U relationship would be that the effects of PCK which produce the upward slope (facilitating effects such as lower search costs, greater capacity for search, and knowing what questions to ask) are overridden by the negative effects of PCK (selective search and informed use of heuristics), as well as the de-motivating effects of BK, by the time consumers reach some moderate level of overall knowledge. Of course, even when BK is essentially zero, the opposing effects of PCK may still elicit a curvilinear relationship between knowledge and search (Brucks 1985).

Moving to the downward slope of the inverted U, experts are more likely to be knowledgeable about the relationship between surrogate cues (whether extrinsic or intrinsic) and brand performance in a market. To the extent that surrogate cues are truly correlated with performance, highly knowledgeable consumers will attend more to those extrinsic cues relative to searching for intrinsic brand information, e.g., specific feature evaluations (Rao and Monroe 1988; Rao and Sieben 1992; see also Park and Lessig 1981; Srinivasan and Ratchford 1991). This would presumably reduce the amount of search for intrinsic brand information. This heuristic effect may also be applied to the case where consumers know (or believe they know) correlations among intrinsic attributes, and as a result only need to search a certain subset of attributes (e.g., materials used or type of construction).


To assess in more depth the relationships described above, we attempted to enumerate all the studies which have specifically examined the knowledge-search relationship since Katona and Mueller's (1955) classic work. Twenty-five papers were identified in the major journals and conference proceedings and were categorized according to the knowledge construct assessed (PCK vs. BK vs. mixed), measure of search behavior (search time vs. activities/outcomes), and direction of the knowledge-search relationship observed. Our objective in the review was to identify commonalities among studies which had identified positive, negative, or inverted-U relationships. (A table summarizing the studies is available from the authors upon request.) The following conclusions were reached.

1. Studies obtaining positive knowledge-search effects tend to use measures which appear to capture product category knowledge. Four of the five studies cited earlier as identifying positive effects of knowledge on search, while varying in methodology, have in common knowledge measures that appear to predominantly capture general product category knowledge, and not specific brand facts. Brucks (1985) took both objective and subjective measures of prior knowledge, but placed subjects in a purchase task in an unfamiliar marketplace (i.e., no brand names), which eliminated brand knowledge as an influence on search, and found that more knowledgeable subjects obtained more attribute information in complex purchase situations. Srinivasan and Ratchford's (1991) measures of (non-brand specific) subjective knowledge also positively affected search, although the effect was modeled to operate through perceived benefits of search. The knowledge measures used by Duncan and Olshavsky (1982) and two of three used by Urbany et al. (1989) addressed general, rather than brand-specific knowledge (e.g., ability to judge, knowledge of important attributes). Further, the product categories examined in those studies (automobiles, televisions, and appliances) are purchased infrequently, suggesting that brand knowledge may need replenishing in new purchase situations.

2. Studies obtaining negative knowledge-search effects tend to use measures of some type of purchase "experience." Srinivasan and Ratchford (1991) illustrate these findings well, as they find search-reducing effects of both positive past experience (satisfaction) and amount of general experience (number of previous purchases). Similar effects of past experience have been found (Bennett and Mandell 1969; Green et al. 1977; Katona and Mueller 1955; Moore and Lehmann 1980; Punj and Srinivasan 1989; Punj and Staelin 1983; see also Reilly and Conover 1983). A consistent theme throughout most of these studies is that satisfaction with previous purchases demotivates search by focusing the consumer's attention on known brands (see, e.g., Kiel and Layton 1981; Newman and Staelin 1971, 1972; Srinivasan and Ratchford 1991). Clearly, satisfied consumers may have a store of brand knowledge which is sufficient to discourage search, while dissatisfied consumers have greater motivation to seek new alternatives (see also Punj and Staelin 1983).

In all, however, it is difficult to determine whether some measures of purchase experience have captured satisfaction, brand knowledge, product category knowledge, or some aspect of procedural knowledge. Further, it is not entirely clear why some measures of experience (e.g., number of cars previously purchased) reduce search when they may be highly correlated with measures of PCK (which, under some circumstances, appears to affect search positively; Srinivasan and Ratchford 1991). Finally, Beatty and Smith's (1987) results do not fit the pattern described here, as they find consistent negative effects of knowledge on search using what appear to be general PCK measures similar to those discussed in the previous section.

3. Studies obtaining inverted-U effects find varying attention to intrinsic information. The primary commonality across the three studies which clearly identify inverted-U effects (Bettman and Park 1980; Johnson and Russo 1984; Rao and Sieben 1992) is that in each study low knowledge consumers apparently processed information regarding intrinsic attributes less than moderate knowledge consumers and relied more on extrinsic or surrogate cues (e.g., brand name, place, price) in decision or judgment tasks. Presumably, the costs of assessing intrinsic attribute information are relatively high for such consumers. At the other extreme, high knowledge subjects rely more on existing BK or use extrinsic cues because they simply hold more information in memory and/or are more efficient in identifying diagnostic extrinsic information. Moderately knowledgeable consumers are better able than low knowledge and more motivated than high knowledge consumers to search intrinsic information (cf. Bettman and Park 1980).

4. Measurement approaches are quite diverse. The Table presents a categorization of the wide variety of approaches that have been used to measure knowledge and search in this literature. We classified the knowledge measures on the basis of objectivity (cf. Brucks 1985) as well as the component of knowledge captured (product category knowledge vs. brand knowledge vs. mixed). In general, measures which directly tested consumers' knowledge of the product category were classified as objective, while those that were more indirect, capturing consumers' beliefs about how much they know, or surrogates of knowledge such as purchase experience, were classified as subjective. Classifying PCK and BK was sometimes quite difficult, necessitating the "mixed" category (e.g., Rao and Sieben 1992). Measures were classified as capturing PCK if they appeared to assess knowledge of the product category in general, and not knowledge about brands. A relatively large number of measures fit in the PCK group, while only a few capture brand knowledge exclusively. [Since product category knowledge and brand knowledge may be highly correlated in certain instances, it is likely that even measures which appear to capture one component still capture the other to some degree.]

Like the knowledge measures, the search measures were also classified on the basis of objectivity (where objective refers primarily to researcher-controlled measurement and subjective refers to participant reports of search activities). In addition, we distinguished among time-based, activity-based, and indexed measures of search. As can be seen, the search measures are also very diverse, with most studies relying on self-reported measures of search. Note that time-based measures potentially confound the impact of knowledge on search since more-experienced consumers may be able to search more efficiently and therefore obtain more information in less time than less-experienced consumers. Similarly, an index loses information since an experienced consumer may search differently across these activities than an inexperienced consumer, yet yield the same index score. (For example, experience may lead to searching more brands on fewer, more diagnostic attributes vs. fewer brands and more attributes.) Due to the diversity of search measures used, compounded by the multiple operationalizations of knowledge, few generalizations can be made concerning the effects of employing particular types of search measures.


While the patterns described in the previous section provide some insight, it becomes apparent that our inability to discern a more consistent set of findings is a function of tremendous variation in the types of measures used. We contend that the effect of knowledge on search behavior depends upon the particular components of knowledge and search that are studied. In addition, a number of factors associated with the market or task environment (e.g., number/complexity of alternatives; Beatty and Smith 1987; Moore and Lehmann 1980; Payne 1982) will likely moderate the relationship between knowledge and search, an important consideration which has not been raised in the literature. The remainder of the paper provides an overview of these issues and consideration of research directions.

The model presented in the Figure is a preliminary attempt to organize these factors. It is not intended to be a comprehensive model of search, but is instead designed to illustrate the factors which may influence the knowledge-search relationship. First, we represent brand knowledge and product category knowledge separately, acknowledging that they are likely to be correlated (i.e., they often develop simultaneously). Market stability (Moore and Lehmann 1980) is incorporated to recognize the fact that, on the whole, the more stable a market is from one purchase occasion to the next, the more relevant BK and PCK the consumer will have for an upcoming purchase decision. Brand knowledge (the relevant information the consumer has about available brands) will generally reduce search (no matter how search is defined) because it effectively reduces the need for additional information. [There are some interesting complexities regarding brand knowledge that we do not address here. For example, BK may be filled up in different ways - i.e., a consumer may know a little about a lot of brands or vice versa, such that two consumers who have equivalent "amounts" of BK may search very differently. Further, it is also true that a small amount of relevant BK may be sufficient to limit search for two types of consumers: (1) those who are knowledgeable about a brand or brand(s) which they know provide satisfactory performance (cf. Srinivasan and Ratchford 1991), and (2) those who apply simple heuristics to solve the purchase decision problem in spite of limited knowledge about brands (or, perhaps more accurately, because of limited brand and product category knowledge). Generally speaking, though, consumers higher in BK should gather less information in a subsequent purchase.] Product category knowledge (the relevant information the consumer has about the product category) is analogous to Punj and Staelin's (1983) memory structure, and covers most of the dimensions of knowledge defined by Brucks (1986), which tend to be nonbrand-specific in nature. The PCK-Search path has the potential to be generally positive in direction (cf. Alba and Hutchinson 1987), although Brucks (1985) notes logically that negative or curvilinear paths are also possible (thus, "it depends").


The Figure also acknowledges that search may be defined a variety of ways. Economists and managers are likely to be interested in the number of brands/models searched as the key dependent variable (cf. Hauser and Wernerfelt 1990; Stigler 1961), while other researchers tend to be interested in measures relating to the type and amount of information obtained (e.g., Bettman and Park 1980; Brucks 1985), the amount of search "activity" undertaken (Duncan and Olshavsky 1982; Srinivasan and Ratchford 1991), and the consumer's tendency to rely on extrinsic vs. intrinsic cues in search and decision-making (Rao and Sieben 1992). There is no generally "correct" measure of search, and choice of dependent measure appears to be driven by research objectives and/or a desire to be comprehensive. As discussed below, however, the direction of the PCK-Search relationship appears to depend upon how search is defined.

Task Environment and Search Measures

The discussion of how task environment may moderate the knowledge-search relationship focuses on the PCK-search relationship and is initially presented with a focus on a limited number of search measures for reasons of expediency and space. In many cases, variations in the task environment actually reflect variations in the costs and benefits of search (e.g., greater brand differences = greater benefit to search; Stigler 1961). Evidence that the costs and benefits of search may interact in determining search behavior (Axell 1974; Urbany 1986) provides the impetus to consider these potential moderating effects.

Assuming that BK is "empty" enough to create a need for considering external information (i.e., holding BK constant across consumers), [Note that BK may itself moderate the PCK-search relationship. PCK (roughly representing the cost of search) may increase search to the extent that BK is relatively empty, where "empty" means a relatively small amount of BK in memory. In short, PCK may facilitate search when BK is relatively dated, but may have limited impact to the extent that BK is full of relevant information.] the generally positive effect of knowledge proposed by Alba and Hutchinson (1987) is likely moderated by the series of factors listed under task environment in the Figure. The moderating effects, however, are complex and highly dependent upon the search measure used. The most general prediction which can be made is that consumers with greater PCK will better know the outcome they seek, and therefore will tend to search attribute information which is more diagnostic in predicting brand performance. An initial contingency, then, is that the effect of PCK on the number of attributes searched depends upon how many attributes are diagnostic (i.e., how many attributes need to be examined in order to predict brand performance confidently). The larger the number of attributes needed to assess performance, the stronger will be the positive effect of PCK on the number of attributes searched. The relationship is also contingent, however, upon the degree to which the attributes are correlated. The more highly correlated the attributes are (such that information about one attribute predicts values of others), the more likely it is that highly knowledgeable consumers will be able to focus in on a small number of diagnostic attributes to assess performance, and potentially reduce their attribute search relative to less-knowledgeable consumers.

Now, consider the impact of PCK on the number of brands searched. The traditional cost-benefit model suggests that the cost of gathering information about an additional brand (which is generally lower for highly knowledgeable consumers) will be weighed against the predicted benefit of searching that additional brand (i.e., how much additional utility is likely to be gained by searching it). The effect of knowledge on the number of brands searched depends in part upon the degree of perceived differences in brands (where knowledge should more strongly determine search when there are substantial gains to search), the number of brands available (see discussion below), as well as the number of attributes which are diagnostic (e.g., when fewer attributes are diagnostic, more-knowledgeable consumers can zero in on them, which effectively reduces their cost of evaluating a given brand, and potentially increases the number of brands searched).

Brucks (1985) identified an interesting set of contingencies which illustrates the above arguments. She found that more knowledgeable subjects searched more attributes, but only in a more complex purchase scenario (i.e., where search tended to focus on brands with more attributes diagnostic of performance). Further, she found no effect of knowledge on the number of brands searched. This latter result could have been due (in part) to the fact that only six brands were available in the experimental scenario (three simple, three complex). While we suspect that adding more brands would have moderated the effect of knowledge on the number of brands considered, predicting the precise effect is difficult, as it depends upon the complexity of the purchase situation (e.g., the number of diagnostic attributes). That is, with a large number of brands available, more knowledgeable consumers may have sampled more brands in the simple purchase scenario (because they could easily assess each brand). In the more complex purchase scenario, however, more knowledgeable subjects may have allocated more effort to assessing each of the complex brands and, therefore, may have searched fewer brands than less knowledgeable subjects.

This small sampling of the potential contingencies illustrates the difficulty of specifying interactions. It is conceivable that all the moderating factors can be linked more explicitly to more general theoretical constructs (e.g., ability and motivation), although we have found this to be a difficult exercise as well. The complex nature of the interactions suggests that the most fruitful research approach will be to carefully define the pieces of the puzzle to examine in sequential fashion. It appears that an important thrust of the future work should be the examination of issues using relatively controlled environments where both knowledge and search can be assessed carefully and the task environment controlled or measured. Two methodological approaches are described briefly below.


Controlled Experimentation. Brucks (1985), Moore and Lehmann (1980), and Rao and Sieben (1992) illustrate experimental approaches which allow for the a priori assessment of knowledge and the controlled measurement of search behavior. These studies distinguished more-knowledgeable from less-knowledgeable consumers via pre-search measurements of knowledge. The advantage of this approach is that subjects bring into the experiment their own natural expertiseCas such, observing behavioral differences between different knowledge groups suggests that such differences may be representative of the marketplace. However, a disadvantage is that knowledgeable consumers may differ systematically from less knowledgeable people on other characteristics which influence search behavior (e.g., involvement in the product category). To remedy this, one option is to place subjects in an experimental context in which they learn the information environment from scratch and are tested to assess their levels of knowledge (prior to engaging in a search task). This approach allows knowledge to be manipulated in specific ways and also allows complete control of the information environment to assess potential moderators. Finally, computer-based search tasks allow for objective assessment of search behavior.

In-Market Exploration. Through surveys of recent purchasers of major durables, a greater understanding of the sources used in search and the lower-than-expected search activity for such major purchases has emerged. However, since most of these surveys have occurred after the purchase, the results are potentially confounded by respondent memory, desire to appear rational, post-hoc justification of behavior, and information obtained after the purchase. Assessment of knowledge prior to search becomes particularly muddy, given the level of knowledge acquired through the recent search and purchase process. One approach to better understanding the evolution of search in the marketplace and the impact of knowledge on this process would involve identifying a panel of potential purchasers before or as they begin the search process, administering measures of knowledge prior to search, and following them through the search process. The difficulties inherent in this approach (identification of participants, recruitment into the panel, obtaining unobtrusive measures) most likely explain why it has not been pursued. However, a rigorous in-market exploration can greatly enhance our understanding of the overall search process and the impact of knowledge on this process.


This paper seeks to convey a relatively simple point about a complicated research domainCthat general statements about the effect of knowledge on search behavior are currently not possible. The effect depends upon the component of knowledge and the dimension of search behavior under study, as well as a number of task environment factors (which may themselves interact). It seems fairly certain that extending this literature requires a careful assessment of these contingencies.


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Carol A. Fiske, University of South Carolina
Lisa A. Luebbehusen, University of South Carolina
Anthony D. Miyazaki, University of South Carolina
Joel E. Urbany, University of South Carolina


NA - Advances in Consumer Research Volume 21 | 1994

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