Construct and Object Complexity in Cognitive Structures: Some Concepual Issues

ABSTRACT - Morphological properties of cognitive structure such as complexity have been receiving increasing attention in buyer behavior research. Frequently a conceptual framework and assessment procedure have been transferred from psychology and applied with little or no modification. This paper in addressing cognitive complexity or differentiation, which centers on dimensionality of cognitive structure, poses several conceptual and methodological questions that arise in examining cognitive space in buyer behavior contexts. Included among these is the issue of object complexity and its interface with construct complexity.


Richard M. Durand and Zarrel V. Lambert (1983) ,"Construct and Object Complexity in Cognitive Structures: Some Concepual Issues", in NA - Advances in Consumer Research Volume 10, eds. Richard P. Bagozzi and Alice M. Tybout, Ann Abor, MI : Association for Consumer Research, Pages: 559-564.

Advances in Consumer Research Volume 10, 1983      Pages 559-564


Richard M. Durand, Auburn University

Zarrel V. Lambert, Auburn University


Morphological properties of cognitive structure such as complexity have been receiving increasing attention in buyer behavior research. Frequently a conceptual framework and assessment procedure have been transferred from psychology and applied with little or no modification. This paper in addressing cognitive complexity or differentiation, which centers on dimensionality of cognitive structure, poses several conceptual and methodological questions that arise in examining cognitive space in buyer behavior contexts. Included among these is the issue of object complexity and its interface with construct complexity.


Recent research within marketing has begun focusing on the structure of cognitions within a cognitive domain, thus going beyond the content of cognitions which is exemplified by the ExV component of multiattribute attitude models. Structure as addressed in this paper refers primarily to the number of dimensions present in cognitive space and the pattern of relationships among these dimensions (Streufert and Streufert 1978, p.16). Interest in structure has been growing because theorists in psychology indicate that it influences information processing tasks such as (1) filtering and selecting information, (2) moderating and organizing information received from external sources, along with (3) facilitating adaptive responses to new and varying situations (e.R., Bieri 1971; Schroder. Driver and Streufert 1967).

Most of the over thirty published marketing-related studies examining various properties of cognitive structure have borrowed, knowingly or unknowingly, from psychology a conceptual and/or methodological framework which typically embodied its own peculiarities. Few studies have dealt with important conceptual and/or methodological issues that arise in applying these frameworks, which had been developed in other settings, to buyer behavior research questions; exceptions include work by Kanwar, Olson and Sims (1981) and Olson and Muderrisoglu (1979).


This paper's purpose is to focus attention on selected conceptual and methodological questions inherent in buyer behavior studies of cognitive complexity, an aspect of structure which is also frequently termed differentiation. Resolution of these issues in early stages of the expanding research stream in marketing may lessen the likelihood that a number of studies reporting inconsistent and contradictory findings will be conducted before potential conceptual and methodological artifacts become evident. Issues relating to complexity are particularly timely and relevant because alluring assessment procedures are readily available in psychology literature and complexity is becoming one of the more widely examined properties of structure in buyer behavior literature. It should be noted, however, that numerous additional properties of structure and attendant assessment methods can be found in recent psychology literature (e.g., Adams-Webber 1979; Fransella and Bannister 1977; Goldstein and Blackman 1978; Scott, Osgood and Peterson 1979; Streufert and Streufert 1978).


Examination of various issues may be facilitated by briefly reviewing cognitive complexity because there appears, at least superficially, to be variation in existing conceptual and operational definitions. Complexity or differentiation in essence refers to a person's capacity to construe an attitude object in a multidimensional manner. The more complex or differentiated the cognitive structure the greater the number of separate constructs at an individual's disposal which enables a greater variety of objects within the cognitive domain to be meaningfully discriminated (Adams-Webber 1979, p. 11). This basic notion is central to the conceptual frameworks underlying most of the work in marketing, those of Bieri (1955; Bieri et al. 1966) and Crockett (1965).

Bieri's framework is predicated on Kelly's (1955) personal construct theory which postulates that individuals are actively involved in a process of interpretation and reinterpretation of their environment through the use of personal constructs. These personal constructs, which serve to discriminate among objects (e.g., mother, father, boss), are idiosyncratic in nature and form an organized system. Bieri argued that "inasmuch as constructs represent differential perceptions or discriminations of the environment" (1955, p. 263), the greater the differential perceptions among objects in a domain the more differentiated a cognitive or construct system.

The emphasis on discrimination among events or objects by Bieri differs from Crockett's conceptual definition which centers on the number of attributes or constructs in a cognitive domain. The emphasis on number of constructs rather than discrimination is consistent with the approaches of Fishbein and Ajzen (1975, p. 99) and Zajonc (1960). This conceptual difference in terms of emphasis has important implications in examining and understanding buyer behavior. For example, constructs identified and enumerated following Crockett's free elicitation procedure may be disregarded using Bieri's approach unless there is evidence that they discriminate among the particular set of objects covered by the analysis.

Before addressing issues associated with grid and free elicitation assessment procedures, two of the most frequently used methods, relationships between cognitive complexity, personality, and knowledge and memory structures might be noted parenthetically. Although viewed from a personality perspective by some (e.g., Bieri 1971; Bieri et al. 1566), differentiation typically has been treated conceptually and operationally as domain specific rather than as a broadly generalized characteristic (e.g., Bieri 1971; Bieri et al. 1966). Furthermore, empirical evidence suggests relatively small shared variance across domains (e.g., Durand and Lambert 1976; Scott, Osgood and Peterson 1979, pp. 145147; Tan and Dolich 1980; Tan and Lim 1982).

While not attempting to provoke a debate at this juncture, one might observe that many conceptual differences between construct theory and psychological literature pertaining to knowledge and memory structure appear to be more semantic than substantive in nature. In fact, some of the same assessment procedures are utilized by researchers in both areas. Some of these similarities will become apparent in the following sections.

Issues Related to Grid Assessment Procedures

Grid procedures, which have been the most widely used measurement approach, are based on Bieri's (1955; Bieri et al. 1966) method which is a modification of the Kelly Role Construct Repertory Test (1955). Kelly's approach was to "elicit a representative sample of those constructs upon which an individual customarily relies to interpret and predict the behavior of significant people in his life, and to assess the way in which he relates these constructs to one another" (Adams-Webber 1979, p. 20). He operationalized this method by presenting subjects with triads of role persons and asking them to identify ways (i.e., personal constructs) in which two of.the three persons were identical and different from the third. Bieri, by contrast, provided subjects with a grid consisting of predetermined sets of bipolar constructs and role persons. Then subjects rated each person on each construct using a +3 to -3 scale.

Following Bieri's approach, an index of complexity or differentiation is computed by summing the number of identical construct ratings assigned to a role person first across constructs and then across the role-persons (Bieri et al. 1966, pp. 189-199). This index is treated as an inverse measure of complexity because larger numbers of identical ratings are considered indicative of less construct discrimination among objects (i.e., nondiscriminating constructs are presumed not to be part of the dimensionality of cognitive structure).

In addition to the fundamental issue of whether constructs must discriminate among objects to sufficiently measure dimensions of structure, questions pertaining to the influence of salience are inherent in most marketing studies, as well as those in psychology, that have utilized the Bieri procedure. In marketing, researchers typically have altered the Bieri grid by replacing (1) role persons with brands in a product category and (2) interpersonal constructs with product attributes (e.g., Durand 1978; Durand and Gur-Arie 1979; Mazis 1973; Menasco 1976). There seem to be two implicit assumptions when attributes (i.e., constructs) in the grid are provided rather than elicited from subjects: (1) salience of the attributes is invariant across subjects; or (2) salience does not affect the measure of discrimination. Suppose, for example, that in a product class ten attributes are salient for each of two subjects, and the attributes provided to them in the grid include five salient and five nonsalient ones for one subject but all ten salient attributes for the other subject. Will the total number of identical ratings and hence the measure of cognitive complexity differ for the two subjects despite the identical dimensionality of their cognitive spaces (each consisting of ten although different salient attributes)? If an affirmative answer comes to mind, some supporting albeit indirect evidence can be found in the psychology literature (e.g., Adams-Webber 1979, pp. 23-27).

Utilization of a grid consisting of a set of objects and attributes, even if the attributes are elicited from subjects, commonly rests on another implicit assumption. Summing identical ratings to measure complexity assumes that the dimensionality of the cognitive space is invariant for objects in that domain; i.e., the identity and number of salient attributes are the same for all objects in the grid. Evidence from psychology suggests that this is not always the case (e.g., Burgoyne and Pietrushka 1979; Crockett 1965; Deaux and Farris 1975). If the identity and/or number of dimensions vary, what are the implications for understanding cognitive structure and its influence on. information processing such as memory organization and retrieval? Furthermore, ail the objects (e.g., brands) presented in a grid in most instances are implicitly assumed to be salient r or all subjects; i.e., in the evoked sets of all subjects. If salience of the objects varies across subjects (i.e., all objects are not in the evoked sets of all subjects), will the grid measures of complexity be confounded?Similarly, what are the effects if the objects presented in the grid fail to constitute an adequate representation of those in the domains of all the subjects?

One departure from the Bieri grid approach has been to allow the identity of the attributes and objects to vary among the subjects while maintaining an invariant number of attributes and objects across all subjects (Tan and Dolich 1980, 1981). In this modification, attributes were elicited from each subject who then used these particular attributes to rate ten of their most familiar brands. While mitigating perhaps some of the potential difficulties cited earlier, does the elicitation of an invariant number of attributes, such as ten in the above studies, open the door for demand characteristics that may result in salience variation among attributes? Similarly, does an invariant number of objects, ten in the above instances, lead to variation in brand familiarity or brand salience across subjects? If attribute and/or object salience affects grid complexity indexes and varies among subjects, then the complexity measures become confounded. If the numbers of attributes and objects are permitted to vary across subjects to overcome salience issues, can the total number of identical ratings for each subject be transformed to a base or scale common to all subjects?

It would appear that use of conventional and modified grids as described above involves retrieval of information from a subject's memory. If this is the case, a number of issues arise pertaining to the potential effects of differences in the wording, format, and other aspects of grids to activate memory (e.g., Mitchell 1981). In other words, can substantial differences in research findings result from variations in the effectiveness of grid elements to activate memory?

Another grid modification has been to replace interpersonal roles with brands (objects) from multiple product classes that fit various descriptions such as a brand which one likes (Malhotra 1982). Because such brands appear to come from multiple cognitive domains, there is a question of whether the resulting complexity measure reflects an "average" cognitive structure that is representative of any single domain or structure.

Issues Related to Free Elicitation Procedures

Crockett's (1965) elicitation procedure, which is one of those most often cited in psychology literature but not used directly in marketing heretofore, has subjects identify persons fitting several roles and then write an impression or each individual. The number of different constructs evident in these descriptions/impressions is treated as a measure of complexity. Several other free elicitation procedures have been suggested and used (e.g., Fishbein and Ajzen 1975, p. 99; Kanwar, Olson and Sims 1981; Marks and Olson 1981; Olson and Muderrisoglu 1979; Zajonc 1960). These include multiple topic memory probes (product category, product/purchase situations, brand name) with the same subjects and layers of probes in which elicitations are subsequently employed as probes. Treating the total number of unique elicitations as the measure of complexity is common to these procedures. However, one variation has been to analyze the number of elicitations obtained by each layer of probes as separate measures of complexity (Hirschman 1980). Another, which constitutes a major conceptual departure from Kelly's position that cognitive structures are idiosyncratic, has been to tally the number of elicitations from a multiple person decision making unit such as husband and wife (Crosby and Taylor 1981).

Use of layered probes would seem to again pose questions pertaining to salience. From the perspective of buyer behavior, for example, what are the relevant dimensions of cognitive structure in terms of their identity and number, those dimensions that are obviously salient or all those that a person can possibly mention when subjected to constant probing?

Multiple topic and/or layered probes call into question the definition or boundaries of a cognitive domain. What constitutes domain boundaries, and how does a researcher determine if the probes utilized have cut across boundaries and evoked elicitations from multiple domains? For example, the associative network model of memory suggests-that multiple topic probes including the use of elicitations as subsequent probes may activate linkages between domains and hence result in elicitations from multiple domains. The likelihood of such activation may be higher when a subject's structure is more complex; i.e., consists of a relatively larger number or dimensions. Unless recognized, multiple domain elicitations can confound measures of complexity or differentiation.

How do the boundaries of cognitive domains in terms of construct theory compare to those of schemas, frames, and scripts in memory structure terms? Do cognitive domains cut across boundaries of the above memory structure elements? Is previously reported evidence indicating very limited generalizability of cognitive complexity across domains a function of poor boundary specifications?

Issues Related to Protocol and Importance Measures of Complexity

Measuring complexity by tallying the number of dimensions utilized by subjects in ranking preferences as evident in protocol descriptions (Wilson and Tan 1977) is somewhat analogous to Crockett's procedure. In addition to the questions raised earlier, there is the issue or whether "the number of dimensions used" in expressing preference rankings constitutes the complete set of dimensions that forms a domain in one's cognitive structure.

Treating the number of attributes rated highly important by subjects as a measure of complexity (Park 1976; Park and Schaninger 1976; Park and Sheth 1975) also raises the issues associated with salience. Do "highly important" attributes constitute the dimensions, in terms of identity and number, that are utilized in one's construing process?

Issues Related to Construct Comprehensiveness

As cited earlier in relation to grid procedures, it has been assumed implicitly in most marketing studies that the same attributes, constructs or dimensions, depending on terminology, are equally relevant in construing all objects within a cognitive domain. Kelly (1955) in his original work, as noted earlier, as well as Smith and Medin (1981) more recently suggest this assumption is invalid. According to Kelly (1955, pp. 477-479) distinctions need to be drawn between comprehensive constructs that subsume a relatively wide variety of objects and incidental constructs that subsume a small number of objects. In the terminology of Smith and Medin (1981 p. 150), there is no reason the feature of one exemplar such as a brand should necessarily be the feature of another brand in the product class. Empirical evidence found inside and outside marketing literature suggests the notion that constructs vary across objects which seem to be in the same domain (e.g., O'Keefe and Delia 1978; Olson and Muderrisoglu 1979). For example, the mean number of elicited attributes per brand varied by approximately 14% over two brands in each of three Product classes (Olson and Muderrisoglu 19795.

If variation in construct comprehensiveness is ignored, what is the impact on measures of complexity and subsequent interpretations of statistical analyses? One obvious result is that the complexity measure is in essence a mean value for the objects studied and may be atypical for several or even most of the objects.

Issues Related to Object Complexity

Conventional measures of complexity focus on the number of dimensions utilized in construing objects in the domain being studied, with the measurement procedure holding the number of objects invariant across subjects. To illustrate the simplification that results from invariance in the number of objects, suppose a grid or elicitation procedure is employed with ten specified objects, the traditional number in grids. The resulting complexity measure or score will fall on a line that runs perpendicular to the object or X axis and parallel to the construct or Y axis as shown in Figure A. In short, the measurement methodology permits observed complexity to vary only on a single axis, that for constructs. Is this restriction conceptually defensible in buyer behavior research?



In the marketplace, buyers may be aware of and/or consider less than the total number of objects actually present in a domain such as a product class. Hence, literature on buyer decision making processes have considered both the number of objects (brands) and number of attributes to be significant variables (e.g., Bettman 1979; Howard and Sheth 1969).

Relaxing the conventional restriction and allowing the number of objects in the domain to vary across subjects, however, poses several conceptual and methodological issues in examining complexity. For example, how does the cognitive complexity of a consumer who evaluates a few objects on many attributes compare overall to the complexity of a consumer who evaluates many objects on a few constructs (e.g., Bettman 1979, p. 59)? Many combinations of construct and object complexity, of course, may exist between these two extremes (e.g., P1 to P4 in Figure A). How then should overall complexity be defined and measured? For instance, is a person who utilizes four constructs in judging two objects, P3 in Figure A, more, less or equally as complex as one who applies two constructs to four objects, P4 in Figure A?

There is also a question whether construct and object complexity are related, say, inversely? Some might infer from studies of information processing capabilities that consumers will tend to be less complex with respect to either constructs or objects if they are comparatively complex with regard to the other.

If construct and object complexity are correlated, does one exert a causal effect on the other? Are there other variables that underlie and influence both?

Some of these issues were explored utilizing data collected from 484 adult subjects, age early 20's to mid 80's. The subjects were asked to select a health and hospital insurance policy, that they believed would satisfy their own needs, from among thirty experimental policies identified by number only to preclude referral from known companies. In making selections, subjects were instructed to request as much or as little of the available information on nine policy attributes (e.g., premiums, waiting period, pre-existing conditions, renewability provisions) as they desired and for as many policies as they wanted. The procedure permitted subjects to acquire information by attributes or by policy (hereafter called brands), or in any mixed order with equal ease, according to their own choosing. Pertinent data for complexity issues were the number of attributes and brands for which information was requested. It might be noted parenthetically that no one wanted information on more than eight attributes or for more than thirteen policies.

Subjects were grouped according to construct and object complexity based on the number of attributes and brands for which information was requested, using hierarchial cluster analysis. Eleven seemingly meaningful, although somewhat arbitrary, groups emerged as shown in Figure B.



Three groups of subjects, B, C, and D, were arrayed along the construct axis with little variation in object complexity. The mean number of attributes (i.e., constructs) was 2.0, S.7 and 7.6 respectively while the mean number of brands (i.e., objects) was 1.6, 1.5, and 1.6 respectively. In the case of constructs, B appeared to be relatively simple or undifferentiated, and D by contrast was highly complex. All three groups seemed simple in terms of object complexity.

A similar but reverse pattern was evident for object complexity depicted along the horizontal axis. With group B again the anchor, groups A, F, and J reflected increasing object complexity with little variation in construct complexity. The mean number of brands for these four groups was 1.6, 3.5, 6.4, and 19.8 respectively. The number of constructs averaged 2.0, 9.6, 2.1, y and 2.2 respectively.

Other conceivable combinations of construct and object complexity were manifested by the remaining groups whose positions were off the axes. Groups E and G illustrated substantial complexity along one axis with some complexity on the other axis. Considerable complexity in terms of both constructs and objects was evidenced by group I. The highest levels of both construct and object complexity were present in group K, positioned at the opposite extreme on the diagonal from group B, which exemplified the lowest levels of both forms of complexity. Group H was near the midpoint of the diagonal connecting these two extremes.

Regarding the question of association between construct and object complexity, the linear bivariate correlation coefficient had a value of .025 for ungrouped subjects (N = 484, p > .10), which suggested the absence of a linear relationship. The pattern of group positions shown above in Figure B also evidenced little association between construct and object complexity.

The findings from the cluster and correlation analyses implied that consumers may exhibit most combinations of construct and object complexity ranging from low-low to high-high. These results, however, do not answer conceptual and methodological questions pertaining to combinations of complexity between the low-low and high-high extremes; i.e., the complexity manifested by all groups other than B and K in this analysis. Furthermore, little is known about comparative reactions of consumers in nonextreme groups in (1) information processing and (2) response to other phenomena impacted by cognitive structure.

Issues Relating to Situational Variations in Construct and Object Complexity

Despite the documentation of situational influence on the content of cognitions in marketing settings (e.g., Belk 1975; Miller and Ginter 1979), almost no attention has been given to the effects of situations in examining construct and object complexity of buyers, although the potential for situations to impact on properties of cognitive structure has been mentioned (e.g., Nystedt 1981; Pervin 1981). Situational effects typically have been uncontrolled and/or unmeasured because either the research scenario left the situation implicit or no attempt was made to assess the influence of the explicitly posed situation, which might have been chosen more as a result of happenstance than design. A number of fundamental questions about situational effects on the dimensionality of cognitive space and on object complexity in terms of buyer behavior remain to be answered.


When rich conceptual content is found in another discipline, the natural tendency is to apply those frameworks and attendant measurement methods in assessing consumer behavior questions. Cognitive complexity is typical in this regard. This paper has raised issues with respect to cognitive complexity for the purpose of stimulating refinement in the conceptual frameworks and assessment methods with the goal of improving their relevancy in marketing contexts. Early resolution of these and related issues can serve to (1) explain and minimize contradictory and inconsistent findings and (2) reduce interpretational ambiguities resulting from conceptual and methodological artifacts.


Adams-Webber, J.R. (1979), Personal Construct Theory: Concepts and Applications, Great Britain: John Wiley & Sons, Ltd.

Belk, Russell W. (1975), "Situational Variables in Consumer Behavior," Journal of Consumer Research, 2, 157-164.

Bettman, James R. (1979), An Information Processing Theory of Consumer Choice, Reading, MA: Addison. Wesley Publishing Company, Inc.

Bieri, James (1955), "Cognitive Complexity - Simplicity and Predictive Behavior," Journal of Abnormal and Social Psychology, 51, 263-268.

Bieri, James (1971) "Cognitive Structures in Personality," in Personality Theory and Information Processing, eds. Harold M. Schroder, and Peter Suedfeld, New York: Ronald Press Company, pp. 178-208.

Bieri, James, Briar, A.L., Leaman, S., Miller, R.L., and Tripodi, T. (1966), Clinical and Social Judgment: The Discrimination of Behavioral Information, New York: John Wiley & Sons.

Burgoyne, P.H., and Pietrushka, J. (1979), "Generality of Complexity of Differentiation and Effects of Construct Type, Figure Attractiveness, and Familiarity," Perceptual and Motor Skills, 68, 507-516.

Crockett, Walter H. (1965), "Cognitive Complexity and Impression Formation," in Progress in Experimental Personality Research, Vol. 2, ed. Brendan A. Maher, New York: Academic Press, pp. 74-90.

Crosby, Lawrence A., and Taylor, James R. (1981), "Effects of Consumer Information and Education on Cognition and Choice," Journal of Consumer Research, 8, 43-56.

Deaux, Kay, and Farris, Elizabeth (1975), "Complexity, Extremity, and Affect in Male and Female Judgments," Journal of Personality, 3, 379-389.

Durand, Richard M. (1978), "Cognitive Complexity and the Perception of Attitude Objects: An Examination of Halo Error," Perceptual and Motor Skills, 46, 1235-9.

Durand, Richard M., and Gur-Arie, Oded (1979), "Cognitive Differentiation: A Moderator of Behavioral Intentions," in 1979 Educators' Conference Proceedings, eds. Neil Beckwith, Michael Houston, Robert Mittelstaedt, Kent B. Monroe, and Scott Ward, Chicago: American Marketing Association, pp. 305-308.

Durand, Richard M., and Lambert, Zarrel V. (1976), "Generalizability of Cognitive Differentiation Across Product and Social Domains," Psychological Reports, 39, 665-666.

Fishbein, Martin, and Ajzen, Icek (1975), Belief, Attitude, Intention and Behavior: An Introduction to Theory and Research, Reading, MA: Addison-Wesley Publishing Company, Inc.

Fransella, Fay, and Bannister, Don (1977), A Manual for Repertory Grid Technique, New York: Academic Press.

Goldstein, Kenneth M., and Blackman, Sheldon (1979), Cognitive Style: Five Approaches and Relevant Research, New York: John Wiley & Sons.

Hirschman, Elizabeth C. (1980), "Consumer Modernity, Cognitive Complexity, Creativity and Innovativeness," in Marketing in the 80's: Changes and Challenges, eds. Richard P. Bagozzi, Kenneth L. Bernhardt, Paul S. Busch, David W. Cravens, Joseph F. Hair, Jr., and Carol A. Scott, Chicago: American Marketing Association, pp. 135-139.

Howard, John A., and Sheth, Jagdish N. (1969), The Theory of Buyer Behavior, New York: John Wiley & Sons.

Kanwar, Rajesh, Olson, Jerry C., and Sims, Laura (1981), "Toward Conceptualizing and Measuring Cognitive Structures," in Advances in Consumer Research, Vol. 8, ed. Kent B. Monroe, Ann Arbor, Association for Consumer Research, pp. 122-127.

Kelly, George A. (1955), The Psychology of Personal Constructs, 2 Vols., New York: W.W. Norton & Company, Inc.

Malhotra, Naresh K. (1982), "Information Load and Consumer Decision Making," Journal of Consumer Research, 8, 419-430.

Marks, Larry J., and Olson, Jerry C. (1981), "Toward a Cognitive Structure Conceptualization of Product Familiarity," in Advances in Consumer Research, Vol. 8, ed. Kent 8. Monroe, Ann Arbor: Association for Consumer Research, pp. 145-150.

Mazis, Michael B. (1973), "Cognitive Tuning and Receptivity to Novel Information," Journal of Experimental Social Psychology, 9, 307-319.

Menasco, Michael B. (1976), "Experienced Conflict in Decision-Making As a Function of Level of Cognitive Complexity," Psychological Reports, 39, 923-933.

Miller, Kenneth E., and Ginter, James L. (1979), "An Investigation of Situational Variation in Brand Choice Behavior and Attitude," Journal of Marketing Research, 6, 111-123.

Mitchell, Andrew A. (1981), "Models of Memory: Implications for Measuring Knowledge Structures," in Advances in Consumer Research, Vol. 9, ed. Andrew A. Mitchell, Ann Arbor: Association for Consumer Research, 45-51.

Nystedt, Lars (1981), " A Model for Studying the Interaction Between the Objective Situation and a Person's Construction of the Situation," in Toward a Psychology of Situations: An Interactional Perspective, ed. David Magnusson, Hillsdale, NJ: Lawrence Erlbaum Associates, Publishers, pp. 375-391.

O'Keefe, Barbara J. and Delia, Jesse G. (1978), "Construct Comprehensiveness and Cognitive Complexity," Perceptual and Motor Skills, 46, 548-550.

Olson, Jerry C., and Muderrisoglu, Aydin (1979), "The Stability of Responses Obtained by Free Elicitation: Implications for Measuring Attribute Salience and Memory Structure," in Advances in Consumer Research, Vol. 6, ed. William L. Wilkie, Ann Arbor: Association for Consumer Research, pp 269-275.

Park, C. Whan (1976), "The Effect of Individual and Situation - Related Factors on Consumer Selection of Judgmental Models," Journal of Marketing Research. 13. 144-151.

Park, C. Whan, and Schaninger, Charles M. (1976), "The Identification of Consumer Judgmental Combination Rules: Statistical Prediction vs. Structured Protocol," in Advances in Consumer Research, Vol. 3, ed. Beverlee B. Anderson, Cincinnati: Association for Consumer Research, pp. 184-190.

Park, C. Whan, and Sheth, Jagdish (1975), "Impact of Prior Familiarity and Cognitive Complexity on Information Processing Rules," Communication Research, 2, 260-266.

Pervin, Lawrence A. (1981), "The Relation of Situations to Behavior," in Toward a Psychology of Situations: An Interactional Perspective, ed. David Magnusson, Hillsdale, NJ: Lawrence Erlbaum Associates, Publishers, pp. 343-360.

Schroder, Harold M., Driver, Michael J., and Streufert, Siegfried (1967), Human Information Processing, New York: Holt, Rinehart & Winston.

Scott, William A., Osgood, D. Wayne, and Peterson, Christopher (1979), Cognitive Structure: Theory and Measurement of Individual Differences, Washington, DC: V. H. Winston & Sons.

Smith, Edward E. and Medin, Douglas L. (1981), Categories and Concepts, Cambridge, MA: Harvard University Press.

Streufert, Siegfried, and Streufert, Susan C. (1978), Behavior in the Complex Environment, Washington, DC: V. H. Winston & Sons.

Tan, Chin Tiong, and Dolich, Ira J. (1980), "Cognitive Structure in Personality: An Investigation of its Generality in Buying Behavior," in Advances in Consumer Research, Vol. 7, ed. Jerry C. Olson, Ann Arbor: Association for Consumer Research, pp. 547-551.

Tan, Chin Tiong, and Dolich, Ira J. (1981), "The Moderating Effects of Cognitive Complexity and Prior Product Familiarity on the Predictive Ability of Selected Multi-Attribute Choice Models for Three Consumer Products," in Advances in Consumer Research, Vol. 8, ed. Kent B. Monroe, Ann Arbor: Association for Consumer Research, pp. 140-144.

Tan, Chin Tiong, and Lin, Hui Hoon (1982), "Cognitive Structure in Buying: Its Generality in Another Culture," in An Assessment of Marketing Thought and Practice, eds. Bruce J. Walker, William O. Bearden, William R. Darden, Patrick E. Murphy, John R. Nevin, Jerry C. Olson, and Barton A. Weitz, Chicago: American Marketing Association, pp. 80-83.

Wilson, David T., and Tan, Chin Tiong (1977), "Dimensional Complexity of Cognitive Structure: A Personality Trait in Decision Making," in Proceedings: American Institute for Decision Sciences, eds. Justin D. Stolen, and James J. Conway, Atlanta: AIDS, pp. 439-444.

Zajonc, Robert B. (1960), "The Process of Cognitive Tuning in Communication," Journal of Abnormal and Social Psychology, 61, 159-167.



Richard M. Durand, Auburn University
Zarrel V. Lambert, Auburn University


NA - Advances in Consumer Research Volume 10 | 1983

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