Predecision Processes in Consumer Choice: Effects of Prior Knowledge on Aspects of Decision Structuring

Eloise Coupey, Virginia Polytechnic Institute & State University
Onur Bodur, Virginia Polytechnic Institute & State University
David Brinberg, Virginia Polytechnic Institute & State University
ABSTRACT - Although much attention has been directed to the processes consumers use to make decisions with presented information displays, less research has been focused on the processes by which consumers build decisions; that is, the predecision processes that determine what information will be included in a decision.
[ to cite ]:
Eloise Coupey, Onur Bodur, and David Brinberg (1998) ,"Predecision Processes in Consumer Choice: Effects of Prior Knowledge on Aspects of Decision Structuring", in NA - Advances in Consumer Research Volume 25, eds. Joseph W. Alba & J. Wesley Hutchinson, Provo, UT : Association for Consumer Research, Pages: 226-232.

Advances in Consumer Research Volume 25, 1998      Pages 226-232


Eloise Coupey, Virginia Polytechnic Institute & State University

Onur Bodur, Virginia Polytechnic Institute & State University

David Brinberg, Virginia Polytechnic Institute & State University


Although much attention has been directed to the processes consumers use to make decisions with presented information displays, less research has been focused on the processes by which consumers build decisions; that is, the predecision processes that determine what information will be included in a decision.

We empirically examine the effect of the content of knowledge (i.e., method and attribute) and confidence in that knowledge on the information consumers acquire and include when they make a choice among birth control methods. Study results are described with analyses that simultaneously capture effects of information recall and acquisition on the probability of including a piece of information into the decision representation.

Consumers have available mny sources of information that can be used to make decisions about product purchases. Marketers and researchers have placed importance on understanding how consumers make decisions using information from memory and from external sources (Biehal and Chakravarti 1986). A popular focus has been on the strategies consumers use for evaluating information, given characteristics of information displays (Coupey 1994; Kleinmuntz and Schkade 1995). For example, research on the effects of format (Bettman and Kakkar 1977) and on the type of information provided (Maheshwaran and Sternthal 1990) can be used to guide the development of marketing communications that serve as the information base with which consumers make product choices.

Although much attention has been directed to the processes consumers use to make decisions with presented information displays, relatively little emphasis has been given to the processes by which consumers build decisions; that is, the predecision processes that guide the determination of what information will be included in a decision. Interactive technologies, such as the World Wide Web, provide consumers with flexibility in the sources, formats, and amounts of information available to make purchase decisions. As a result, it is necessary for marketers to better understand how consumers construct the representations from which they make decisions among brands and products. In addition, policy makers can improve consumers’ decisions by developing strategies to increase the quality (as in terms of adequacy and completeness) of the decision representation, typically with a goal of increasing isomorphism between the representation and the decision situation (Gettys 1983).

The research we describe in this paper contributes to knowledge of decision processes in several ways. We offer a conceptual description of predecision processes that integrates cognitive and decision theoretic perspectives. Within the predecision stage we separate information acquired to make a decision from information included in a decision. We also describe techniques and measures that effectively capture and reflect structuring processes.

The predecision processes used to create decision representations are described as aspects of decision making that are conceptually distinct from the processes used to define decision goals or to evaluate options within a decision representation. To study decision structuring, we examine the effects of two dimensions of knowledge: confidence in knowledge (i.e., overconfident or underconfident), and content of knowledge (i.e., alternative or attribute) on the acquisition and inclusion of information into decision representations.


Predecision Processes and Constructive Processing

Researchers have tended to depict decision making as a series of stages, in which the overarching objective is to move from an initial state to a desired state (Bettman 1979a; Newell and Simon 1972). For example, a consumer faced with the need to prevent conception may articulate a goal of choosing an acceptable method of birth control, and then complete the steps necessary to arrive at the decision that the Pill is the right method for her.

Characterizations of the steps between the initial state and the desired state differ by resarcher. Despite these differences, however, researchers exhibit consistency in their depiction of decisions as the results of processes completed in stages. For example, Bettman (1979) suggests a goal hierarchy, in which a set of subgoals constitutes a plan for reaching an end goal. Huber (1989) proposes a set of subprocesses, effected by a set of operators, which move the decision through from an initial to a desired state. The primary unresolved issue is how the decisions reach the point at which they can be processed with a goal of making a choice; that is, how decisions are constructed to enable evaluation.

Consumer researchers have examined characteristics of information search to better understand decision construction. Bettman (1979b) addresses the effects of brand-based versus attribute-based search on evaluation, and Brucks (1985) examines the effect of prior knowledge on the amount and type of information acquired. One aspect of predecision processing which has received little attention, however, is the extent to which information that is acquired is actually used in the decision representation that serves as the basis for making a choice. For example, a consumer may conduct an extensive search for information, but some information may be discarded as new goals or criteria for brands are developed (Hayes-Roth 1982). Of particular interest are the factors that may lead to systematic differences in information acquisition and information use as components of predecision structuring.

One factor that may lead to differences in acquisition and inclusion is the availability and accessibility of information - from internal and from external sources. In the following sections, we propose that differences between the information that is acquired and the information that is included in a decision representation may be due to content of knowledge and confidence in that knowledge.

Dimensions of Knowledge

Confidence in Knowledge: Perceived and Actual Knowledge

Knowledge can be described in terms of the information consumers accurately possess, relative to the information consumers believe that they have. In the former instance, knowledge has been described as 'actual’ (e.g., Radecki and Jaccard 1996) or 'objective’ (e.g., Brucks 1985), while in the latter instance, knowledge is 'perceived,’ or 'subjective.’ Actual knowledge can be assessed by performance on a knowledge test, while perceived knowledge can be measured using consumers’ self-reported assessments of knowledge (Mitchell and Dacin 1996) and their reported confidence in their knowledge (Radecki and Jaccard 1996). While perceptions of knowledge should be related to actual knowledge, the correspondence between the types is seldom high.

Differences between actual and perceived knowledge have been observed by researchers in numerous experimental settings (Park, Mothersbaugh, and Feick 1994). A growing literature on perceived and actual knowledge indicates that judgments are often guided more by what people think they know than by what they actually know (Radecki and Jaccard 1996; Coupey and Narayanan 1996). This view is echoed implicitly in the conclusions of behavioral decision theorists who find that decision representations are typically incomplete; that is, people are overconfident that they have specified all possible options and outcomes in developing a problem representation (Gettys 1983).

The ability to measure confidence relative to some objective standard provides researchers with a method for characterizing the discrepancy between actual knowledge and perceived knowledge (c.f., Arkes, Christensen, Lai and Blumer 1987). For example, a consumer who believes that she has more knowledge about aproduct category than she really does (i.e., perceived knowledge exceeds actual knowledge) is overconfident. Conversely, a consumer whose actual knowledge exceeds his perceived knowledge may be described as underconfident.

Content of Knowledge: Method and Attribute Knowledge

Knowledge may also be characterized as method-based (i.e., option) or attribute-based knowledge. Method-based knowledge reflects an array of attribute values-the overall description of the performance of the brand on each attribute-and is unique to a particular method (option). Knowing all of the values of one brand does not provide a consumer with information that is necessarily diagnostic for discriminating relative brand quality or for gauging the importance of a particular attribute in predicting the performance of a brand. Attribute-based knowledge provide consumers with information about range, typical and average values and does allow the consumer to contrast the value of various options on each attribute.

We propose that different predecision processes may be triggered as a function of the confidence and content of knowledge just described, and by the interaction of these knowledge types. That is, information acquisition and inclusion can be viewed as responses to variations in amounts and combinations of knowledge content and confidence.

Theoretical Mechanisms for Acquisition and Inclusion

Consumers may search for information from external sources for several reasons. For example, they may acquire information because it can reduce uncertainty about the ability of a product to meet a need, or reach a desired state. This form of acquisition reduces uncertainty about what is not known, by adding to the set of what is known.

Information can also reduce uncertainty by confirming what a consumer thinks s/he knows; that is, the internal knowledge is isomorphic with externally available information. Decision theorists have examined the effects of confirmation bias in the context of hypothesis generation and testing (Klayman and Ha 1987). In acquisition processing, this confirming behavior is a search for consistency between prior knowledge and an information display. Consistency checking is, in effect, an attempt to create a match between perceived and actual knowledge, as possessed by the consumer.

To illustrate the potential effects of knowledge on search, consider that overconfidence leads to reduced hypothesis generation (Gettys 1983); that is people tend to construct sets with fewer options, or possible states of the world, than are possible. Extrapolating this finding to consumer decision making suggests that consumers who believe they know more than they actually know will tend to acquire and include less information from an external display than consumers who feel they know less than a test of actual knowledge indicates. This effect may be more pronounced for attributes than for brands, because attribute knowledge may be more generally influential than brand knowledge in the evaluation of brands. Although they do not address the role of perceived versus actual knowledge, Brucks and Shurr (1990) present data which suggest that increased knowledge about attributes (e.g., knowing the possible range of values) decreases information search relative to bargaining. In addition, Maheswaran and Sternthal (1990) find that experts tend primarily to use attribute information, while novices use attribute information in conjunction with benefit information: a more specific form of evaluative information (e.g., "This widget is extra-sturdily constructed for years of use"). These findings suggest that in processing for choice, consumers search for and use only the information that is diagnostic (i.e., that reduces uncertainty), and that the determination of what may be diagnostic is influenced by perceptions of what is known.

Reseach Propositions

The preceding discussion may be summarized as follows. Three main stages can be discriminated in predecision processes: 1) problem recognition/definition, 2) information acquisition, and 3) information inclusion in the decision representation. Further, the discussion suggests three propositions that can be examined empirically:

Proposition 1: The predecision stages of acquisition and inclusion are conceptually distinct, and they can be differentiated by the processes and behaviors that consumers exhibit when making product/brand decisions.

Proposition 2: Two general dimensions of knowledge may affect the predecision processes of information acquisition and inclusion:

a) the consumer’s level of confidence in his or her knowledge influences the acquisition and inclusion of information, and

b) the content of prior knowledge influences the acquisition and inclusion of information.

Proposition 3: Different types of knowledge effect predecision processes by altering/ influencing consumer perceptions of information utility. For example, overconfidence may lead the consumer to treat method and attribute information as having the same utility whereas underconfidence may lead the consumer to treat this information differently.

We conducted a study to assess the extent to which information included in a decision representation for choice was influenced by prior knowledge and newly acquired information, given different perceptions of prior knowledge (i.e., over- and underconfidence) for different types of knowledge (i.e., method and for attribute information).


Subjects and Procedure

Thirty-eight subjects completed a study designed to assess the effects of the content of knowledge and confidence in that knowledge on information acquisition and inclusion in a choice task among methods of birth control. Subjects were students enrolled in an undergraduate marketing course and received extra credit for participation. The study was conducted in two sessions, with the sessions separated by at least one day and not more than one week. Each session took an average of forty-five minutes to complete. In the first session, we obtained information about subjects’ recall of birth control methods and attributes, and we assessed subjects’ objective and subjective knowledge of the category. In the second session, we obtained information about subjects’ information search and how they structured the decision to make a choice among methods.

Subjects were told that they were participating in an experiment to study consumer decision making. Information from subjects was obtained from a pencil and paper recall task, from a computer-based proces tracing system, and from notes subjects generated as they made their decisions. In the first session, subjects were asked to use pencil and paper to create a matrix that contained the methods of birth control with which they were familiar, any attributes and the value of each method on each attribute. After the recall task, subjects completed a questionnaire with multiple choice and true/false questions. The questionnaire was designed to assess subjects’ objective and subjective knowledge of the product category, including method and attribute information. This information was obtained in a group setting.

The second section was divided into two main tasks, with which we examined acquisition and inclusion behaviors. In first part of the second session, subjects worked individually at a self-paced, computer-based task. They were asked to acquire any information they felt was necessary about birth control in order to select the best method for them. Following a brief training session to familiarize subjects with the software, subjects were able to acquire information from the computer screen. Options were presented sequentially, mimicking the form in which information is typically provided by health care professionals, in brochures, and via interactive sources. Scrap paper was provided to subjects so that they could make note of information.

After subjects had acquired all of the information they felt they needed, they took a short break while the experimenter prepared the final phase of the data collection. In this phase, we obtained data about information inclusion. Subjects were asked to choose among the methods, using pencil and paper to note the methods they would consider using and the features of these methods. Subjects’ notes constituted the inclusion data. Subjects also recorded their final choices and responded to several manipulation check scales.


The product category of birth control was selected because the set of options can be specified, as well as the set of key attributes typically used to evaluate methods. The ability to specify the complete set of options is important in order to assess the amount of knowledge subjects exhibit, relative to the amount of information they could potentially know or acquire.

Ten methods were used in the study. The methods were described by their performance on each of eight attributes. The set of options and attributes, as well as the tests of actual and perceived knowledge, is based on materials developed and used in Radecki and Jaccard (1996).


The experimental design was a 2x2 between-subjects factorial, in which two dimensions of knowledge were manipulated. The dependent measures to examine the effects of the knowledge manipulations were developed from subjects’ knowledge tests, recall of method information, process tracing data, decision matrices, and scales to assess perceptions of the task and manipulations.

Independent variables. Perceived and actual knowledge were each assessed with questionnaires in the first stage of the study. This elicitation was used as the basis for creating a credible manipulation of what subjects felt they knew, relative to what their tests had indicated they actually knew, about methods of birth control. The manipulation of perceived and actual knowledge was accomplished by telling subjects that they had either more knowledge about the product category (based on their responses to the actual knowledge test) than they believed they did (based on responses to the perceived knowledge assessment), or that they had less knowledge than they believed they had. The feedback was provided to subjects just prior to beginning the acquisition stage of the study in the second session.

The manipulation of perceptions of method versus attribute knowledge was accomplished by altering the feedback statement to emphasize subjects’ knowledge of attributes relative to brands One-half of the subjects were given information about their knowledge for birth control methods, while the other half were given feedback about their knowledge of the attributes on which the methods could be evaluated. Note that the feedback, in terms of the percentages given to the subjects, does not necessarily reflect subjects’ actual performance on the knowledge assessments. The text of the manipulation is reproduced in Table 1.

Dependent variables. The main sets of measures used to assess predecision processing were process tracing and outcome variables. The process variables were obtained from the Mouselab data files and from subjects’ notes. These measures include the number and sequence of box acquisitions, and the number and type of information recalled and/or included in the final decision. The outcome variables include the method chosen and response times. In addition to these measures, manipulation checks were obtained from subjects’ knowledge tests and from scales to assess perceptions of the task and manipulations.


Manipulation Checks

Two sets of analyses were completed to insure that subsequent data interpretation was appropriate. First, we determined whether the assignment of subjects to experimental conditions resulted in non-significant differences between conditions for information accessible in memory, and for perceived and actual knowledge. No significant differences were found, suggesting that the random assignment was successful.

The second set of analyses was completed to test the manipulations of under- and overconfidence and of method versus attribute knowledge. We used a nine-point uni-polar scale that ranged from 1 (not at all believable) to 9 (very believable) to check the manipulation of under- and overconfidence and method-based versus attribute-based knowledge. For the manipulation to be successful, the scale responses should be significant overall, but insignificant by condition. The responses revealed no significant variation by condition. We compared the overall mean to a conservative standard of 5. Analysis indicated that the manipulation was successful; responses did not differ significantly by condition, but the overall mean (x=6.26) differed significantly from the scale midpoint (t(33)=3.83, p<.001).



Analysis of the method/attribute knowledge manipulation provided mixed results. The manipulation of attribute knowledge was successful; a contrast of the pertinent cells revealed that subjects’ perceptions of the number of questions answered correctly about the attributes of birth control differed as a function of whether they were told they knew more [less] about the attributes than they had previously thought they knew (t(30)=2.52, p<.02). A similar analysis for method knowledge indicated that the manipulation exerted no significant effect on subjects’ perceptions of knowledge (t(30)=1.22, p<.23).

Analyses of Predecision Processes

The objective of the data analysis is to examine the predecision processes that result in a representation used for decision making. Our primary interest is the extent to which the information included in the decision representation is influenced by the information recalled, the information acquired, the type of information (method versus attribute) and level of confidence in the individual’s knowledge.

Proposition 1. If acquisition and inclusion are distinct processes, as we propose, then we should find that different constructs are significant predictors of these two pro-cesses and that each process is not completely determined by the other. We conducted two structural equation analyses to examine the nomological network within which each stage is embedded. Firt, we tested a model in which the recall of information, the person’s level of confidence, the content of knowledge and acquisition of information predict the inclusion of information in a decision. Next, we tested the same model but used inclusion as a predictor variable and acquisition as the endogenous variable. We found that each process was not determined completely by the other process and that the exogenous variables predicted each process differently.

In a second analysis, we fixed the parameter between acquisition and inclusion to one. If the chi-square values in the fixed parameter and the original model differ significantly, then we can conclude that acquisition and inclusion are two different constructs (Pedhazur and Schmelkin 1991). The chi-square values of the two models did differ significantly ((2 (1)=1351.8, p<0.001). Both analyses support our contention that acquisition and inclusion processes are conceptually distinct.

Proposition 2. We tested Proposition 2 with two logistic regression analyses. First, we conducted a logistic analysis in which we regressed information acquisition on the level of confidence, the content of the information (i.e., method and attribute), the interaction of type and content, and the information recalled prior to the manipulations.

In the logistic regression analysis, we used each piece of information potentially available about methods and their attributes as the unit of analysis, rather than indices derived from aggregating subjects’ individual decision behaviors in recall and acquisition. Thus, the data are not independent observations, because each individual may have recalled, acquired or included as many as 110 pieces of information in each stage. These dependencies may affect the significance tests of the logistic regression analysis (e.g., the t-tests or the regression coefficients) by (potentially) inflating the likelihood of a Type I error. We use a conservative alpha-level (.001) to guard against Type I errors.





The unstandardized coefficients in the analysis reflect the effect of each factor on the likelihood of the information being acquired. The chi-square for the overall model was significant ((2(4)=165.9, p<.001) and all predictor variables had a significant effect on information acquisition. Table 2 summarizes the coefficients and their corresponding significance tests.

Main effects tests indicate that when subjects are overconfident, or when their knowledge is attribute-based, subjects are less likely to acquire information. There was also a significant interaction of content with confidence. We explored the interaction by examining the change in slope of knowledge content (method/attribute) at different levels of confidence. When subjects are under-confident about their knowledge, they are slightly more likely ((=-.66) to acquire information if their knowledge is method-based rather than attribute-based. When subjects are overconfident about their knowledge, however, they are more likely ((=.42) to acquire information if knowledge is attribute-based rather than method-based.

In the second logistic regression analysis, we regressed information inclusion on the type of information (i.e., under- and over-confidence), the content of the information (i.e., method and attribute), information acquisition, and the information recalled prior to the manipulations.

All main effects had a positive influence on the inclusion of information. The chi-square for the overall model test was significant ((2(5)=798.4, p<.001). Table 3 summarizes the coefficients and their corresponding significance tests.

Acquiring a piece of information from the external display exerts the greatest influence on the likelihood that the same piece of information will be included in the final decision representation. The impact of the acquisition is more than twice the impact of having retrieved the same piece of information in th recall task. In addition, the content of knowledge (i.e., method or attribute) exerts a greater influence on inclusion than over- or under-confidence.

We assessed the nature of the interaction by examining the change in slope of the method and attribute-based knowledge at different levels of confidence. When subjects are under-confident about their knowledge, a piece of information is more likely to be included in the final representation when the focal knowledge is attribute-based rather than method-based. When subjects are over-confident about their knowledge, however, the likelihood of inclusion is comparable for attribute and method information.

We conducted a structural equation analysis to assess the goodness-of-fit between the underlying models and the observed relations. We used a bootstrapping procedure to estimate standard errors of the parameters and used fit indices that do not rely on tradi-tional significance tests. Both these procedures accommodate the effect of dependencies on the estimation of standard errors and the estimation of model fit.

We found that the estimated standard errors were small (< .001) and the goodness-of-fit indices were consistent with a good model fit for the models contained in Tables 2 and 3 (GFI=1; AGFI=.997; CFI=1; RMSEA=.012, for both models). This analysis is consistent with the logistic regression and provides increased confidence in the robustness of the parameter estimates and the quality of the model.

Proposition 3. We conducted a two-way ANOVA to examine the impact of different levels and types of prior knowledge on consistency checking during acquisition. Consistency checking was determined by whether a specific piece of information (i.e., a specific attribute value for a particular method) recalled by a subject in the first session was also acquired from the displayed matrix in the second session. We did not find a significant main effect of confidence or for content of information, but we did find a significant content of knowledge (method/attribute) by confidence (under/over) interaction (F(1, 3,626)=8.2, p<.01).

The interaction effect indicates that individuals are more likely to do consistency checking when they are under-confident about their method knowledge than when they are under-confident about their attribute knowledge (t(2,308)=3.54, p<.001). When the individuals are over-confident, however, there are no significant differences between their consistency checking behavior in the method and attribute knowledge conditions (t(1318)=-.885, ns).


Our primary objective in this research was to provide insights into the decision processes that precede evaluation. We proposed that predecision processing could be characterized by a series of stages that result in a display used as the basis for evaluation and choice. Two of the stages believed to be important for structuring a decision are information acquisition and inclusion. Although information search has been a focus of much research, little is known about the factors that influence whether acquired information is incorporated into the decision. We suggested that acquisition and inclusion could be discriminated within predecision processing by differences in the behaviors invoked by consumers as a function of the amount of attribute versus method knowledge they possessed and by their confidence in this knowledge.

The results of a study designed to enable examination of predecision processes indicated that the content of knowledge (e.g., attribute and method), as well as perceptions of knowledge (e.g., confidence), exert differential effects on information acquisition and information inclusion in decision structuring for choice. For example, the information accessed by subjects from memory and from an external display was not used in entirety in the final representation for choice. In addition, inormation acquired from an external display tended to be more than twice as likely to be included in the final representation than information that was recalled. This finding underscores the need to consider a stage of predecision processing prior to evaluation that is distinct - both conceptually and behaviorally - from information search.

Our results provide insights into the nature of predecision processes, including the characteristics of distinct stages prior to evaluation. We do not, however, examine in depth the possible explanations for the differences in stage-related behaviors given prior knowledge or perceptions of knowledge. For example, additional research could be directed to the interaction of the external decision context and dimensions of consumer knowledge.

Researchers have noted that external presentations of information can affect the way consumers evaluate information to make decisions (cf., Maheshwaran and Sternthal 1990; Kleinmuntz and Schkade 1994). The effects of context may also be relevant in predecision processing; that is, the external display creates a context within which the consumer structures the decision. This context may affect subdecisions within the structuring process about what information is searched for, and what information is considered appropriate, or diagnostic enough to be included in the representation used for choice.

Information diagnosticity has been conceptualized as the extent to which a piece of information enables the consumer to discriminate between options, such as brands (e.g., Payne 1982). This view suggests that information newly acquired from an external source should be different from that available in memory. Our results suggest, however, that what makes information diagnostic may differ, depending on the predecision stage-acquisition or inclusion. This broader definition, consistent with that proposed by Lynch, Marmorstein and Weigold (1988), suggests that information is diagnostic to the extent that it enables consumers to accomplish decision goals. The increasing availability of interactive decision contexts, such as the World Wide Web, underscores the need for additional research to examine the effects of different forms of context, along with person factors such as knowledge, on the way consumers determine what information to use in decisions.


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