J. Wesley Hutchinson

Dimensions of Consumer Expertise

The purpose of this article is to review basic empirical results from the psychological literature in a way that provides a useful foundation for research on consumer knowledge. A conceptual organization for this diverse literature is provided by two fundamental distinctions. First, consumer expertise is distinguished from product-related experience. Second, five distinct aspects, or dimensions, of expertise are identified: cognitive effort, cognitive structure, analysis, elaboration, and memory. Improvements in the first two dimensions are shown to have general beneficial effects on the latter three. Analysis, elaboration, and memory are shown to have more specific interrelationships. The empirical findings related to each dimension are reviewed and, on the basis of those findings, specific research hypotheses about the effects of expertise on consumer behavior are suggested.

Knowledge Calibration: What Consumers Know and What They Think They Know

Consumer knowledge is seldom complete or errorless. Therefore, the self-assessed validity of knowledge and consequent knowledge calibration (i.e., the correspondence between self-assessed and actual validity) is an important issue for the study of consumer decision making. In this article we describe methods and models used in calibration research. We then review a wide variety of empirical results indicating that high levels of calibration are achieved rarely, moderate levels that include some degree of systematic bias are the norm, and confidence and accuracy are sometimes completely uncorrelated. Finally, we examine the explanations of miscalibration and offer suggestions for future research. Copyright 2000 by the University of Chicago.

Non-Conscious Influences on Consumer Choice

While consumer choice research has dedicated considerable research attention to aspects of choice that are deliberative and conscious, only limited attention has been paid to aspects of choice that occur outside of conscious awareness. We review relevant research that suggests that consumer choice is a mix of conscious and nonconscious influences, and argue that the degree to which nonconscious influences affect choice is much greater than many choice researchers believe. Across a series of research domains, these influences are found to include stimulus that are not consciously perceived by the consumer, nonconscious downstream effects of a consciously perceived stimuli or thought process, and decision processes that occur entirely outside of awareness.

Ignoring Irrelevant Information: Situational Determinants of Consumer Learning

Three experiments examined the effects of situational factors on the ability to learn simple rules for classifying products and estimating prices. In each experiment, multiattribute information about stereo speakers was presented to subjects in a training phase. However, only one attribute was diagnostic. Analytic processing (i.e., the ability to isolate the diagnostic attribute in a subsequent test of product knowledge) was measured. Results showed that analytic processing varied significantly as a function of memory load, processing goals. type of information search, and the relative perceptual salience of product attributes. Surprisingly little holistic (i.e., multiattribute) processing was observed among nonanalytic subjects. Most of these subjects relied on a small subset of attributes, often placing heavy emphasis on a single nondiagnostic attribute. Copyright 1991 by the University of Chicago.

Nearest neighbor analysis of psychological spaces.

Geometric models impose an upper bound on the number of points that can share the same nearest neighbor. A much more restrictive bound is implied by the assumption that the data points represent a sample from some continuous distribution in a multidimensional Euclidean space. The analysis of 100 data sets shows that most perceptual data satisfy the geometric-statistical bound whereas many conceptual data sets exceed it. The most striking discrepancies between the data and their multidimensional representations arise in semantic fields when the stimulus set includes a focal element (e.g., a superordinate category) that is the nearest neighbor of many of its instances. Theoretical and methodological implications of nearest neighbor analysis are discussed.

Unobserved Heterogeneity as an Alternative Explanation for 'Reversal' Effects in Behavioral Research

Behavioral researchers use analysis of variance (ANOVA) tests of differences between treatment means or chi-square tests of differences between proportions to provide support for empirical hypotheses about consumer behavior. These tests are typically conducted on data from “between-subjects” experiments in which participants were randomly assigned to conditions. We show that, despite using internally valid experimental designs such as this, aggregation biases can arise in which the theoretically critical pattern holds in the aggregate even though it holds for no (or few) individuals. First, we show that crossover interactions – often taken as strong evidence of moderating variables – can arise from the aggregation of two or more segments that do not exhibit such interactions when considered separately. Second, we show that certain context effects that have been reported for choice problems can result from the aggregation of two (or more) segments that do not exhibit these effects when considered separately. Given these threats to the conclusions drawn from experimental results, we describe the conditions under which observed heterogeneity can be ruled out as an alternative explanation based on one or more of the following: a priori considerations, derived properties, diagnostic statistics, and the results of latent class modelling. When these tests cannot rule out explanations based on unobserved heterogeneity, this is a serious problem for theorists who assume implicitly that the same theoretical principle works equally for everyone, but for random error. The empirical data patterns revealed by our diagnostics can expose the weakness in the theory but not fix it. It remains for the researcher to do further work to understand the underlying constructs that drive heterogeneity effects and to revise theory accordingly.

Netscal: A network scaling algorithm for nonsymmetric proximity data

A simple property of networks is used as the basis for a scaling algorithm that represents nonsymmetric proximities as network distances. The algorithm determines which vertices are directly connected by an arc and estimates the length of each arc. Network distance, defined as the minimum pathlength between vertices, is assumed to be a generalized power function of the data. The derived network structure, however, is invariant across monotonic transformations of the data. A Monte Carlo simulation and applications to eight sets of proximity data support the practical utility of the algorithm.

An Assessment of Basic Computer Proficiency Among Active Internet Users: Test Construction, Calibration, Antecedents and Consequences

The purpose of this article is to describe our efforts to create a test of basic computer proficiency, examine its properties using parametric test scoring methods, and identify some antecedents and consequences that accompany differences in performance. We also consider how much insight people have into their level of knowledge by examining the relationship between our tested measure of computer knowledge and self-rated knowledge scores collected at the same time. This research also adds to the large body of existing empirical work on computer literacy in the student population, by looking at computer literacy in a more general sample of the Internet-using population. A further purpose of this research, as a result, is to make our dataset available for future research.

Heuristics and Biases in Data-Based Decision Making: Effects of Experience, Training, and Graphical Data Displays

Managers use numerical data as the basis for many decisions. This research investigates how data on prior advertising expenditures and sales outcomes are used in budget allocation decisions and attempts to answer three important questions about data-based inferences. First, do biases exist that are strong enough to lead to seriously suboptimal decisions? Second, do graphical data displays, real-world experience, or explicit training reduce any observed biases? Third, are the observed biases well explained by a relatively small set of natural heuristics that managers use when making data-based allocation decisions? The results suggest answers of yes, no, and yes, respectively. The authors identify three broad classes of heuristics: difference-based (which assess causation by comparing adjacent changes in expenditures to changes in sales), trend-based (which assess causation by comparing overall trends in expenditures and sales), and exemplar-based (which emulate the allocation pattern of the observations with the highest sales). All three heuristics create biases in some situations. Overall, exemplar-based heuristics were used most frequently and induced the greatest biasing of the three (sometimes allocating the most to an advertising medium that was uncorrelated with sales). Difference-based heuristics were used less frequently but generated the most extreme allocations. Trend-based heuristics were used the least.

Action-Based Learning: Goals and Attention in the Acquisition of Market Knowledge

In this article, the authors examine the costs and benefits of action-based learning (i.e., learning that occurs as a by-product of making repeated decisions with outcome feedback). The authors report the results of three experiments that investigate the effects of different decision goals on what is learned and how transferable that learning is across related decision tasks. Contrary to popular wisdom, compared with traditional learning, experiential learning is likely to be a risky proposition because it can be either accurate and efficient or errorful and biased.