ABSTRACT - Consumer belief change in response to incongruent information is considered from a schematic processing perspective. The features of a schema are identified, and the ways these features and change is discussed. Furthermore, it is argued that the type of information on which the schema is based, the disconfirmability of the schema, and the organization of incongruent informAtion also affect the ways schemas change.


Jennifer Crocker (1984) ,"", in NA - Advances in Consumer Research Volume 11, eds. Thomas C. Kinnear, Provo, UT : Association for Consumer Research, Pages: 472-477.

Advances in Consumer Research Volume 11, 1984      Pages 472-477


Jennifer Crocker, Northwestern University


Consumer belief change in response to incongruent information is considered from a schematic processing perspective. The features of a schema are identified, and the ways these features and change is discussed. Furthermore, it is argued that the type of information on which the schema is based, the disconfirmability of the schema, and the organization of incongruent informAtion also affect the ways schemas change.


Consumers have a wealth of knowledge and beliefs about products and the types of people who use them that influence their decisions about whether to purchase those products. Although we joke about the notions that "real men don't eat quiche" or "real women don't pump gas," beliefs like these guide our behavior as consumers. Researchers in cognitive and social psychology have, in recent years, recognized the major role that beliefs such as these play in the way we process information, and make inferences and judgments (c... Cantor & Mischel, 1977; 1979; Fiske & Linville, 1980; Hastie, 1981; Markus, 1977; Minsky, 1975; Rumelhart & Ortony, 1978; Schank & Abelson, 1977; Taylor & Crocker, 1981). These beliefs are represented in cognitive structures called schemas. A schema is an abstract or generic knowledge structure, stored in memory, that specifies the de fining features and relevant attributes of some stimulus domain, and the interrelations among those attributes (cf. Fiske & Linville, 1980; Hastie, 1981; Rumelhart & Ortony, 1978; Taylor & Crocker, 1981).

Most research and theorizing about schemas has emphasized their ability to resist change. A remarkable number of studies have demonstrated the ways that schemas can bias and distort the encoding, representation, and retrieval of schema-relevant information, as well as inferences about schema-relevant material (see Crocker & Park, 1983; & Fiske & Taylor, in press; Hastie, 1981; Nisbett & Ross, 1980; and Taylor and Crocker, 1981 for reviews). The ability of schemas to resist change is usually quite functional to the perceiver. Stable schemas lend a sense of order, structure, and coherence to stimuli that would otherwise be complex, unpredictable, and often overwhelming. If our cognitive representations shifted in response to each piece of information that was not exactly consistent, then this sense of order and predictability would be lost.

On the other hand, it is clear that schemas are dysfunctional to the perceiver if they are completely resistant to change. Sometimes people acquire schemas that are incorrect, either because they were exposed to unrepresentative instances, because they were taught an incorrect schema, or because reality has changed since the schema was learned. T]sing an incorrect schema as a basis for decisions and behavior can lead to a variety of negative consequences, including inefficiency, inaccuracy, and sometimes altering reality to fit an inaccurate schema (Crocker, Fiske & Taylor, 1984; Taylor & Crocker, 1981).

Given the high costs of having an incorrect schema, it is fortunate that schemas can and do accommodate in response to new information. Although previous research and theorizing have emphasized the "theory-driven" quality of schematic processing (i.e., the distortion of information to fit the theory or schema), it is also the case that social information processing can be "data-driven." That is, schemas are et least somewhat responsive to the characteristics of the stimuli that they process (Neisser, 1975; Rumelhart & Norman, 1978; Rumelhart & Ortony, 1978). Simply stated, people learn things, and one form that learning takes is the modification of existing knowledge structures in response to incongruent information

In this paper, I will apply the schematic approach to information processing to the problem of belief change in response to incongruent information. By incongruent, I mean information that is improbable given the schema (cf. Hastie, 1981). That is, when the schema specifies that some stimulus configuration is unlikely to occur in an instantiation of the schema, that stimulus is incongruent with the schema. For example, one's schema for "real men" may specify that they do not eat quiche. An instance of a "real man" eating quiche would be clearly incongruent with the schema.


Any discussion of schema change raises the issue of what elements or characteristics of schemas could be altered in response to incongruent information. Answering this question depends on describing what a schema is, and what the components of a schema are. When we have been explicit about these components, then we can specify how they change in response of incongruent information.

Schemas have variables

A schema specifies the relevant attributes of some stimulus domain. Rumelhart and Norman (1978) call those attributes "variables." A schema for breakfast cereals, for example, might include the attributes taste, nutrition, and cost. Each of these is a variable in the sense that members of the category "breakfast cereals" will vary on each of these dimensions. Any particular instantiation of the schema will provide specific values for the variable. Schemas differ in their complexity (i.e., the number of uncorrelated variables they have), and perceivers differ in the complexity of the schemas they have for the same stimulus domain (Linville, 1982a; 1982b; Linville & Jones, 1980).

One way that schemas can change, then, is that variables can be added to or dropped from the schema. This type of change might occur either through exposure to instances that vary along some previously ignored dimension, or if new instances fail to vary along a previously used dimension. Alternatively, exposure to such new data might simply lead to a change in the strength of association between a given variable and a schema (Anderson, Kline & Beasley, 1979).

Variables have default values

Perhaps the most widely agreed upon feature of schemas is that there are "expected" or "default" values associated with the schema's variables (Minsky, 1975; Rumelhart & Ortony, 1978; Rumelhart & Norman, 1978; Schank & Abelson, 1977; Taylor & Crocker, 1981). When a schema is incompletely instantiated, the schema fills in missing information with default values. For example, the default values for breakfast cereals might be that they taste pretty good, are moderately nutritious, and cost about a dollar a box. If you told me you ate cereal for breakfast, I would assume the cereal you ate had those characteristics.

When information that is incongruent with the schema is encountered, one aspect of the schema that can change is the default value for a variable. Default values may, however, be rather difficult to change. A single instance that contains a non-default value (e.g., an expensive cereal that costs $3.50 a box) does not directly contradict the default value, and missing or ambiguous information may actually strengthen confidence in the default value, because the individual simply assumes that the instance fits the default values. As a result, default values are one aspect of a schema that tends to resist change.

Variables have constraint values

Another feature of schemas is constraint values; that is, variables have some range of possible values that they can assume. The constraint values are based on the probability that an instance of the schema will have a particular value on an attribute (Fried & Holyoak, in press). For example, the constraint values for the cost of breakfast cereals may vary from seventy-five cents to $3.00 a box, with the highest probability at $1.25 a box. These constraint values are used to identify instances of the category. That is, if an instance falls outside the constraint values, it may not be a member of the category. For example, a breakfast cereal priced at $17.50 a box is either a mistake, or it is some exotic food that we would not categorize as a breakfast cereal.

Repeated exposure to instances that fall outside the constraint values may change that particular constraint. In fact, this may be the most common type of change in a schema. When instances that do not fit the constraint values are encountered, the default values may be unaffected, but the constraint values may shift to acknowledge more diversity or variability within the category.

Variables have interrelations

Schemas also contain information about how variables are related to one another. Specifically, variables may be correlated with one another (Medin, Altom, Edelson & Freko, 1982; Rosch, 1978) or they may be causally related (Read, 1983; Schank & Abelson, 1977). Schemas for events, or scripts, also include information about the temporal relations among variables (Schank & Abelson, 1977). Correlated variables might specify, for example, that taste in breakfast cereals is negatively related to nutritional value. The correlations specified by the schema need not be accurate, however (Crocker, 1981). Causal relationships between variables specify which variables are causes, and which are effects. For example, in breakfast cereals, the schema might specify that cost is causally related to the amount of advertising done by the manufacturer.

These interrelations among variables may also change in response to incongruent information. For example, through exposure to new instances, the consumer may decide that taste and nutritional value are not negatively correlated, after all. The ease or difficulty of achieving this type of change is difficult to specify, but it seems likely that change in these interrelations will often involve changing the consumer's causal understanding of the category.

Schemas have vertical and horizontal structure

Schemas include information that has two types of structure. Some schemas may subsume others in relations of super- and sub-ordinance. At the most superordinate level is a generic concept (e.g., food) that has variables (e.g., taste, cost, nutrition) and default values. At this superordinate level, the constraint values associated with the variables may accept a wide range of values. A schema with vertical structure will have more subordinate levels within it, and the number of levels may vary from schema to schema (Cantor & Mischel, 1977; Rosch, 1978; Schank & Abelson, 1977).For our breakfast cereal example, the levels from most to least superordinate might be: food, breakfast food, cereal, processed cereal, sweetened processed cereal, honey-nut Cheerios. A consumer who is a big eater of breakfast cereals might have more levels of super- and subordinance within his schema than a person who never eats breakfast cereal.

One way that schemas can change is by developing more vertical structure, or more subordinate categories. This type of change is likely to occur in conjunction with change in the horizontal structure of the category. Horizontal structure refers to the number of subcategories included at any given level of subordinance. One way schemas respond to incongruent information is to create a more differentiated structure by adding subcategories at a more specific level of abstraction. For example, one's breakfast cereal schema might differentiate between the subcategories natural cereals and processed cereals, at the next level of subordinance. Alternatively, the schema could change by subtracting subcategories, if some of the existing subcategories were never used.

Schemas include particular instantiations of the schema

The most subordinate level of the schema consists of specific instances or exemplars of the schema. Generally, the instances that are stored with the schema will be "good examples" of the schema, with instantiations of variables that are probably quite close to the default values for the schemas (e.g., Rosch, 19785. For consumer items, these instances are specific brands of the products. For example, the breakfast cereal schema might include the instances, honey-nut Cheerios, Post raisin bran, and granola.

One type of change that can occur in a schema is change in the instances that are stored with a schema. If new information indicates that an instance of the schema has attributes that are incongruent with the schema, the instance may be dropped from the schema. r or example, in the breakfast cereal schema, one attribute might be that they taste good with milk. If a cereal was discovered not to taste good with milk, it might be recategorized as a different type of food, such as a snack. A second type of change is adding new instances to the schema.

Thus, there are several elements of a schema that can change in response to incongruent information. Variables can be added to or deleted from the schema, the default values associated with variables can change, the vertical and horizontal structure of the schema can change, the relations between variables can change, and instances associated with the schema may be dropped or added. Which of these features actually will change in response to incongruent information depends on both the characteristics of the schema and the characteristics of the new information. In the next section of this paper, I will argue that the type of information on which the schema is based has a number of implications for how the schema will later respond to incongruent information. Then I will provide examples of characteristics of schemas and characteristics of data that influence the nature of schema change. Additional factors are reviewed elsewhere (Crocker, Fiske & Taylor, 1984).


Most current research and theorizing about schemas ignores the issue of where those schemas come from. When the acquisition of schemas is considered, research typically assumes that schemas are abstracted from experience with members of the category--that is, they are instance-based. Of course, experience with instances is an important source of the information in schemas, but there is another, equally important source that is often ignored--that is, we may learn generalizations about the category. Thus, some schemas may be learned without any exposure to instances of the category. Instead, we are told or taught what members of the category are typically like. Some of these abstractions are learned as part of the socialization process. For example, children are taught that boys play with some toys and girls play with different toys. Another important source of abstraction-based schemas for consumers is advertising. Of course, some schemas may be based on a combination of exposure to instances and learned abstractions. It may be possible to order consumers' schemas on a continuum from entirely instance-based to entirely abstraction-based. Instance and abstraction-based schemas should tend to differ in their response to incongruent information for a number of reasons.

Variability of the category members.

First, instance-based schemas should include information about the diversity of instances within the category, whereas abstraction-based schemas include less or no information about how members of the category differ from one another. That is, abstraction-based schemas nave narrower constraint values than instance-based schemas. This is because instances Of a category are not all identical on most variables, but generalizations that we learn about the category need not include information about within-category variability. As a result, we tend to believe that members of abstraction-based categories are more homogeneous than they actually are. For example, if we learned that motorcycles are dangerous from being told that abstraction by our parents when we wanted to own one, we are less likely to realize that motorcycles differ in how dangerous they are, than if we learned about motorcycles from experience with instances. (Of course, learning from abstractions has the advantage, in this case, that one can survive the learning experience). The implication of the differences in constraint values for instance- and abstraction-based schemas is that abstraction-based schemas may be changed more by a single incongruent instance, because that instance is more discrepant from what is expected.

Consensus about the schema

a second implication of the instance- vs. abstraction-based distinction is that abstraction-based schemas are more likely to be widely shared and agreed upon. When they are part of one's cultural socialization, or communicated through the mass media, members of the culture will share the same schemas. When schemas are based on instances, however, the schemas that individuals have will vary according to the particular instances that they have been exposed to. Those instances may or may not be representative of the category. To the extent that instances of the category are actually variable, and consumers are exposed to different sets of instances, there will be less agreement among people in their schemas for the category. When there is widespread agreement in people's schemas, it may be more difficult to change them, because those schemas will be supported in interpersonal interactions.

Relationship to other schemas

Furthermore, to the extent that abstraction-based schemas are part of one's cultural socialization, they may be tied to the values and ideology of the culture, and consequently have more affect associated with them. For example, the notion that men don't use hairspray is linked to a wider set of affect-laden schemas about appropriate sex roles in our culture, whereas the belief that hairspray makes hair feel like cardboard, derived from experience with instances, is not part of a wider set of affect-laden beliefs. Consequently, it may be easier to change people's belief that hairspray makes hair feel unnatural than it is to change their belief that men do not wear hairspray.

Content of the schema

A final implication of the instance- vs. abstraction-based distinction concerns the content of the schema. When schemas are based on experience with instances of the category, the types of attributes that the consumer will associate with members of the category are attributes that can be observed or easily inferred. For example, based on experience with instances, a consumer might develop a schema for frozen dinners that they taste soggy, that they are easy to prepare, etc. When the schema is based on learning from abstractions, however, the attributes that are associated with the category are not limited to observable or inferable attributes. For example, one might learn from socialization processes that frozen dinners are not served by good mothers. This is not an attribute of frozen dinners that can be easily inferred from experience with the product. When a schema includes attributes that are not observable or inferable from experience with instances, then the schema will not change from exposure to instances that do not fit the schema.


Considering the implications of the type of information on which the schema is based makes it clear that not all schemas are the same, and they do not all change in similar ways under similar circumstances. The issue of schema change is complicated by the fact that schema differ in important ways, that cause them to respond to incongruent information differently. As I have hinted above, one way that schemas differ is that some schemas are more easily disconfirmed than others. In this section, I will explore this characteristic of schemas, and its implications for schema change. There are two types of disconfirmability that a schema may have. A schema may be logically disconfirmable or not, and it may be practically disconfirmable or not. These two kinds of disconfirmability emphasize different issues in the disconfirmation process.

Logical disconfirmability

A logically disconfirmable schema specifies what stimuli should not occur given the schema. For example, birds do not have teeth, honest people do not cheat, and Consumer Reports tells the truth, are all examples of logically disconfirmable schemas. A logically undisconfirmable schema is less specific about what should not occur. For example, the belief that a product is unpredictable is very vague about what an instance of the product should not do.

Of course, logical disconfirmability is not an all-or-none attribute of a schema. Rather, schemas vary along a continuum of how specific they are about what should not occur given the schema. Although no research has yet investigated the disconfirmability of schemas, work by Reeder and Brewer (1979), which identifies different types of trait concepts, is consistent with the notion that some schemas are more logically disconfirmable than others. Their work might be extended to schemas in gene ra 1.

Practical disconfirmability

The second type of disconfirmability that a schema may have is practical disconfirmability. That is, independent of logical issues, how likely is it that the consumer will have the opportunity to obtain incongruent information? For example, a product might be advertised as preferred by experts, and most consumers would never have an opportunity to observe disconfirming information. If the product was advertised as convenient or good-tasting, however, that attribute would be practically disconfirmable, because consumers would have the opportunitY to observe disconfirmations.

These two types of disconfirmability suggest that consumer's schemas might not change if the schema is logically disconfirmable. Schemas that are logically disconfirmable, however, will change in response to incongruent information, and schemas that are practically disconfirmable will have frequent opportunities for disconfirmation.

Characteristics of data influence schema change: Organization of incongruent information

Another factor in the process of schema change is the nature of the new information. There are several characteristics of incongruent information that influence how the schema will charge, including the degree of discrepancy between the schema and the incongruent information, the ambiguity of the incongruent information, its organization, and, sometimes, its memorability (Crocker, Fiske & Taylor, 1984).

The organization of the incongruent information provides an interesting example of how characteristics of incongruent information influence schema change. A fixed number of disconfirming events may be concentrated in a few instances of the category, or they may be dispersed across many members of the category. For example, if a schema specifies that foreign sports cars are fast, mechanically unreliable, and expensive, then disconfirmations could be organized such that a few foreign sports cars are slow, mechanically reliable and cheap, or the disconfirmations could be dispersed such that some foreign sports cars are slow (but unreliable and expensive), others are reliable (but fast and expensive) and still others are cheap (but fast and unreliable). These different ways that incongruent information can be dispersed across instances of the category has implications for how the schema will change. In our own research, we have found that when a fixed number of disconfirmations are concentrated within a few instances, those instances are seen as exceptions, or atypical members of the category, and the schema changes less than when the disconfirmations are dispersed across many members of the category (Crocker & Weber, 1983; Weber & Crocker, 1983). The slow, reliable and cheap sports cars are likely to be regarded as odd instances of sports cars, and will have relatively little effect on our impressions of sports cars in general. However t when some sports cars are cheap, some are reliable, and some are slow, we may alter our default values for the typical sports car on these dimensions.

The Complex Nature of Belief Change: Real Men Do Eat Quiche

I have argued that there are many features of a schema that could change in response to incongruent information, and that factors such as the type of information on which the schema is based, the characteristics of the schema, and the characteristics of the incongruent information all determine how the schema will respond to incongruent information. The conclusion to be drawn from all of this is that belief change is, at best, a very complex process, that is best understood in terms of multiway interactions rather than main effects. In this section, I will illustrate how this approach can be applied to changing consumer beliefs through an example--changing the belief that real men don't eat quiche.

According to the schematic approach to belief change, the first thing we need to do is assess the consumer's current schema. In this example, the consumer's quiche schema would be a subordinate schema to the food schema, and it might specify that quiche is a lunch food, with ingredients of eggs and cheese, that is goes well with salad and white wine, and that it is a feminine food-that is, real men don't eat it. The "real men don't eat quiche" schema is probably based on two types of information--generalizations, which may be part of our socialization about what boys do and what girls do, and observations of instances of people eating quiche. The schema probably has very narrow constraint values on the masculine/feminine variable. That is, the likelihood of a real man eating quiche is very low. Because this belief is clearly part of our cultural wisdom, there is probably wide agreement among people about the belief. Furthermore, the belief is tied to cultural values and norms regarding desirability of being a "real man", and the "real man" schema. With respect to disconfirmability, the "feminine" attribute of the schema is not directly observable, but refers to an underlying disposition of the consumer that must be inferred from observable characteristics. As a result, although the schema is logically disconfirmable, in a practical sense it may be hard to disconfirm. When the consumer sees a man eating quiche, the fact that he is eating quiche may be evidence that he is not a "real" man.

What kind of information would change the "real men don't eat quiche" schema? Because the quiche schema is tied to our schema for what real men are like, a-strategy for changing the quiche schema should use the real man schema, rather than fight it. That is, telling consumers that their beliefs about what makes a "real man" are all wrong is likely to meet with great resistance, since that schema is highly developed and affect-laden. However, one could use the "real man" schema to depict what consumers believe is a real man eating quiche. The type of change this product would depend on the organization of these incongruent instances. To change the "feminine" default value of the quiche schema, disconfirming information should be dispersed across several instances. For example, one ad might show a football player eating quiche as a postgame snack, another ad might claim that quiche tastes good with beer, and a third ad might argue that quiche should be served with potatoes. Each of these ads would challenge only one aspect of the quiche schema at a time. Alternatively, the advertising strategy could try to create two subordinate categories in the quiche schema by convincing consumers that there are really two kinds of quiche: "feminine" quiche, and quiche eaten by real men. The "real man's quiche" might have sausages as an ingredient, and an ad might show a lumberjack eating this quiche with potatoes, and drinking beer. This instance of a person eating quiche would be so disconfirming of 'he original quiche schema, that consumers will set up a new subcategory for it.


Although research and theorizing about schemas has primarily emphasized the ways that schemas resist change, the schematic approach can also be used to understand the ways that beliefs do change. The schema literature provides a theoretically-rich basis for predicting when schemas will and will not change, how they change, what features are likely to change, and what kind of data will prompt change. It is therefore somewhat surprising that little empirical work has actually examined these hypotheses. No doubt one problem that faces the researcher potentially interested in addressing schema change is the problem of how to measure a schema. The term has been used with such vagueness in so much research that many people are confused about what a schema actually is. Applying the schematic approach to belief change will force researchers to clarify their use of the term schema, and apply the concept in their research more carefully. Consequently, research taking the schematic approach to belief change should provide new insights into the nature of schemas, as well as add a new perspective to the issues of attitude change.


Anderson, J.R., Kline, P.J. and Beasley, C.M., Jr. (1979). A general learning theory and its application to schema abstraction. In G.H. Bower (Ed.), The psychology of learning and motivation. New York: Academic Press.

Cantor, N., and Mischel, W. (1977). Traits as prototypes: Effects on recognition memory. Journal of Personality and Social Psychology, 35, 38-48.

Cantor, N., and Mischel, W. (1979). Prototypes in person perception. In L. Berkowitz (Ed.), Advances in experimental social psychology, Vol. 12. New York: Academic Press.

Crocker, J. (1981). Judgment of covariation by social perceivers. Psychological Bulletin, 90, 272-292.

Crocker, J., Fiske, S.T. and Taylor, S.E. (1984). Schematic bases of belief change. In J.R. Eiser (Ed.), Attitudinal Judgment. New York: Springer.

Crocker, J. and Park, B. (1983). The consequences of social stereotypes. Unpublished manuscript, Northwestern University.

Crocker, J. and Weber, R. (1983). Cognitive structure and stereotype change. In R.P. Bagozzi & A.M. Tybout (Eds.), Advances in consumer research, Vol. 10.

Fiske, S.T. and Linville, P.W. (1980). What does the schema concept buy us? Personality and Social Psychology Bulletin, 6, 543-557.

Fiske, S.T. and Taylor, S.E. (in press). Social cognition. Reading, Mass.: Addison-Wesley.

Fried, L.S., and Holyoak, K.J. (in press). Induction of category distributions: A framework for classification learning. Journal of Experimental Psychology: Learning. Memory and Cognition.

Hastie, R. (1981). Schematic principles in human memory. In E.T. Higgins, P. Herman, & M. Zanna (Eds.), Social cognition: The Ontario Symposium, Vol. 1 Hillsdale, N.J.: Erlbaum.

Linville, P.W. (1982a). The complexity-extremity effect and age-based stereotyping. Journal of Personality and Social Psychology, 42, 193-211.

Linville, P.W. (1982b). Affective consequences of complexity regarding the self and others. In M.S. Clark & S.T. Fiske (Eds.), Affect and cognition: The 17th annual Carnegie symposium. Hillsdale, N.J.: Erlbaum.

Linville, P.W. and Jones, E.E. (1980). Polarized appraisals of group members. Journal of Personality and Social Psychology, 38, 689-703.

Markus, H. (1977). Self-schemata and processing information about the self. Journal of Personality and Social Psychology, 35, 63-78.

Medin, D.L., Altom, M.W., Edelson, S.M., and Freko, D. (1982). Correlated symptoms and simulated medical classification. Journal of Experimental Psychology: Learning, Memory, and Cognition, 8, 37-50.

Minsky, M. (1975). A framework for representing knowledge. In P.H. Winston (Ed.), The Psychology of computer vision. New York: McGraw Hill, 1975.

Neisser, U. (1976). Cognition and reality: Principles and implications of cognitive psychology. San Francisco: Freeman.

Nisbett, R.E. and Ross, L. Human ingerence: Strategies and shortcomings of social judgment. Englewood N.J.: Prentice-Hall.

Read, S.J. (1983). A knowledge-based model of causal reasoning. Unpublished manuscript, Northwestern University.

Rosch, E. (1978). Principles of categorization. In Rosch, E. & Lloyd, B.B. (Eds.), Cognition and categorization. Hillsdale, N.J.: Erlbaum.

Rumelhart, D.E. and Norman, D.A. (1978). Accretion, tuning, and restructuring: Three modes of learning. In J.W. Cotton & R.L. Klatzky (Eds.), Schematic factors in cognition. Hillsdale, N.J.: Erlbaum.

Rumelhart, D.E. and Ortony, A. (1977). The representation of knowledge in memory. In R.C. Anderson, F.J. Spiro, & W.E. Montague (Eds.), Schooling and the acquisition of Knowledge. Hillsdale, N..J.: Erlbaum.

Schank, R. and Abelson, R. (1977). Scripts, plans, goals and understanding: An inquiry into human knowledge structures. Hillsdale, N.J.: Erlbaum.

Taylor, S.w. and Crocker, J. (1981). Schematic bases of social information processing. In E.T. Higgins, P. Herman, & M. Zanna, (Eds.), Social cognition: The Ontario symposium, Vol. 1. Hillsdale, N.J.: Erlbaum.

Weber, R. and Crocker, J. (1983). Cognitive processes in the revision of stereotypic beliefs. Journal of Personality and Social Psychology, 45, 961-977.



Jennifer Crocker, Northwestern University


NA - Advances in Consumer Research Volume 11 | 1984

Share Proceeding

Featured papers

See More


Reversing the Experiential Advantage: Happiness Leads People to Perceive Purchases as More Experiential than Material

Hyewon Oh, University of Illinois at Urbana-Champaign, USA
Joseph K Goodman, Ohio State University, USA
Incheol Choi, Seoul National University

Read More


The Impact of Implicit Rate of Change on Arousal and Subjective Ratings

James A Mourey, DePaul University, USA
Ryan Elder, Brigham Young University, USA

Read More


The Impact of Product Type on Disposal Intentions

MUSTAFA KARATAŞ, Koc University, Turkey
Rabia BAYER, Koc University, Turkey
Zeynep GURHAN-CANLI, Koc University, Turkey

Read More

Engage with Us

Becoming an Association for Consumer Research member is simple. Membership in ACR is relatively inexpensive, but brings significant benefits to its members.