Decision Making in Information-Rich Environments: the Role of Information Structure

ABSTRACT - In a world of unlimited data and limited human information processors, the structure of information (i.e., the amount of information provided to consumers) is a key construct. Although the meaning of information is of clear importance to information overload (e.g., Keller and Staelin 1987) and the difficulty in making choices between alternatives (e.g., Shugan 1980), the structure of information has important implications for consumer decision making as well. Because the structure of information can be measured independently from its meaning, structural measures of information allow comparisons between web pages that are seemingly incomparable. To the extent that information structure alone provides insights into consumer decision-making, it is important to use measures that capture the multiple dimensions of information structure. As the interface between firms and consumers becomes increasingly virtual and information-rich, measuring the consumer information environment becomes more and more important.

Citation:

Nicholas Lurie (2002) ,"Decision Making in Information-Rich Environments: the Role of Information Structure", in NA - Advances in Consumer Research Volume 29, eds. Susan M. Broniarczyk and Kent Nakamoto, Valdosta, GA : Association for Consumer Research, Pages: 91-92.

Advances in Consumer Research Volume 29, 2002     Pages 91-92

DECISION MAKING IN INFORMATION-RICH ENVIRONMENTS: THE ROLE OF INFORMATION STRUCTURE

Nicholas Lurie, University of North Carolina

ABSTRACT -

In a world of unlimited data and limited human information processors, the structure of information (i.e., the amount of information provided to consumers) is a key construct. Although the meaning of information is of clear importance to information overload (e.g., Keller and Staelin 1987) and the difficulty in making choices between alternatives (e.g., Shugan 1980), the structure of information has important implications for consumer decision making as well. Because the structure of information can be measured independently from its meaning, structural measures of information allow comparisons between web pages that are seemingly incomparable. To the extent that information structure alone provides insights into consumer decision-making, it is important to use measures that capture the multiple dimensions of information structure. As the interface between firms and consumers becomes increasingly virtual and information-rich, measuring the consumer information environment becomes more and more important.

Traditional approaches to measuring the amount of information provided to consumers (e.g., Jacoby, Speller, and Berning 1974; Jacoby, Speller, and Kohn 1974; Keller and Staelin 1987; Malhotra 1982; Payne 1976; Wright 1975) involve simple counts of the number of alternatives and attributes to which consumers are exposed. These counts are then used to make predictions about the probability that decision makers will be overloaded with information and hence choose "non-optimal" alternatives. Such approaches may ignore important dimensions of information rich environments and, therefore, incorrectly predict information overload.

To see why simple counts may be inadequate for measuring information-rich environments, imagine a consumer using the Internet to gather information about mutual funds. Table 1 shows an example of the type of information that such a consumer may be asked to evaluate.

A traditional approach to measuring the amount of information that the consumer is being asked to process is to count the number of alternatives (in this case six funds) and the number of attributes (in this case four). Such an approach would find no differences between the amount of information associated with the attributes risk, fees, check writing and telephone transfer. At the same time, an examination of table 1 suggests that (all else being equal):

1. There is more information to process about risk (three levels) than fees (two levels)Ci.e., there is more uncertainty with three vs. two attribute levels;

2. There is more information to process about fees (uniformly distributed) than check writing (non-uniformly distributed attribute levels)Ci.e., there is more uncertainty if every attribute level is equally likely;

3. There is no information to process about the attribute telephone transferCi.e., the attribute level occurs with certainty.

If these dimensions affect the amount of information that consumers are being asked to process, and therefore the likelihood that they will be overloaded with information, it is important to adopt an approach that picks up these dimensions. Formal measures of information structure (information theory) developed by Shannon (1949) and extended by Garner (1962) offer a potential alternative to traditional approaches.

Two experimental studies were conducted to examine the role of information structure in consumer decision-making. Study 1 seeks to replicate previous research showing a decline in decision quality when the number of alternatives in a choice set is increased, while also showing that simple counts may lead to inaccurate predictions of overload when other dimensions of information are not taken into account. Study 2 examines how the extent to which consumers correctly perceive structural dimensions of information moderates the effectiveness of structural measures as predictors of information overload.

Results from study 1 support the idea that structural measures of the amount of information in a choice set are better predictors of information overload than simple counts of alternatives. Structural approaches encompass simple counts, thus allowing the prediction and replication of previous approaches in marketing; at the same time, structural approaches account for other dimensions that affect the amount of information in a choice set. This, more thorough, accounting allows structural approaches to predict when the same increase in number of alternatives will not lead to overload. In addition, the structural approach shows that overload may occur, even if there is no change in the number of attributes or alternatives in a choice set. One way this may occur is if the distribution of attribute levels across alternatives becomes more uniform.

Like study 1, study 2 shows that structural measures of information can better predict information than traditional approaches. At the same time, study 2 shows that there may be conditions in which structural approaches do not outperform simple counts in terms of predicting information overload. In study 2, lowering the amount of information through the distribution of attribute levels did not reduce information overload when information was presented for one alternative at a time. This suggests that in novel information environments the format in which information is presented may limit the ability of more complex measures to do a better job of predicting overload. For structural approaches to outperform simple counts, consumers must know or gain awareness of the dimensions that structural approaches identify as important.

For those who study consumer behavior, this research shows the value of measuring information. Just as structural measures enance our ability to predict information overload in diverse information environments, environments that cannot be compared using traditional approaches in marketing, other measures may provide important insights into an increasingly information-based consumer experience. As more and more consumers get their information "online," an understanding of the issues involved in measuring the consumer information environment will be of increasing importance.

TABLE 1

UNDERLYING DATA FOR SIX HYPOTHETICAL FUNDS

REFERENCES

Garner, Wendell R. (1962), Uncertainty and Structure as Psychological Concepts, New York: Wiley.

Jacoby, Jacob, Donald E. Speller, and Carol A. Kohn (1974a), "Brand Choice Behavior as a Function of Information Load," Journal of Marketing Research, 11 (February), 63-69.

Jacoby, Jacob (1974b), "Brand Choice Behavior as a Function of Information LoadCReplication and Extension," Journal of Consumer Research, 1 (June), 33-41.

Keller, Kevin Lane and Richard Staelin (1987), "Effects of Quality and Quantity of Information on Decision Effectiveness," Journal of Consumer Research, 14 (September), 200-213.

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

Payne, John W. (1976), "Task Complexity and Contingent Processing in Decision Making: An Information Search and Protocol Analysis," Organizational Behavior & Human Performance, 16 (August), 366-387.

Shannon, Claude Elwood and Warren Weaver (1949), The Mathematical Theory of Communication, Urbana, IL: University of Illinois Press.

Wright, Peter (1975), "Consumer Choice StrategiesCSimplifying vs. Optimizing," Journal of Marketing Research, 12 (February), 60-67.

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