J. Wesley Hutchinson

• Tong Lu, Eric Bradlow, J. Wesley Hutchinson, Binge Consumption of Online Content.
• Evan Weingarten and J. Wesley Hutchinson (Forthcoming), Does Ease Mediate the Ease-of-Retrieval Effect? A Meta-Analysis. Abstract

  A wealth of literature suggests individuals use feelings in addition to facts as sources of information for judgment. This paper focuses on a manipulation in which participants list either a few or many examples of a given type, and then make a judgment. Instead of using the number of arguments or evidence strength, participants are hypothesized to use the subjective ease of generating examples as the primary input to judgment. This result is commonly called the ease-of-retrieval effect, and the feeling of ease is typically assumed to mediate the effect. We use meta-analytic methods across 142 papers, 263 studies, and 582 effect sizes to assess the robustness of the ease-of-retrieval effect, and whether or not the effect is mediated by subjective ease. On average, the standard few/many manipulation exhibits a medium-sized effect. In experimental conditions designed to replicate the standard effect, about one third to one half of the total effect is mediated by subjective ease. This supports the standard explanation, but suggests that other mediators are present. Further, we find evidence of publication bias that reduces the standard effect by up to one-third. We also find that (1) moderator manipulations that differ from the standard manipulation lead to smaller, often reversed effects that are not as strongly mediated by ease, (2) several manipulations of theory-based moderators (e.g., polarized attitudes, misattribution) yield strong theory-consistent effects, (3) method-based moderators have little or no effects on the results, and (4) the mediation results are robust with respect to assumptions about error structure.

• Tong Lu and J. Wesley Hutchinson, Split-Second Decisions During Online Information Search: Static vs. Dynamic Decision Thresholds for Making the First Click.
• Robert Meyer and J. Wesley Hutchinson (2016), (When) Are We Dynamically Optimal? A Psychological Field Guide for Marketing Modelers, .
• Evan Weingarten and J. Wesley Hutchinson (Work In Progress), The Effects of Cognitive and Perceptual Salience on Product Valuations.
• J. Wesley Hutchinson, Tong Lu, Evan Weingarten, “Visual Attention in Consumer Settings”. In International Handbook of Consumer Psychology, edited by Cathrine Janssen-Boyd and Magdalena Zawisza, (:, 2016)
• J. Wesley Hutchinson, Joseph W. Alba, Eric Eisenstein (2011), Heuristics and Biases in Data-Based Decision Making: The Effects of Experience, Training, and Graphical Data Displays, Journal of Marketing Research. Abstract

  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.

• J. Wesley Hutchinson, Gal Zauberman, Robert Meyer (2010), On the Interpretation of Temporal Inflation Parameters in Stochastic Models of Judgment and Choice, Marketing Science, 29 (1), pp. 133-139. Abstract

  The implications of Salisbury and Feinberg’s (2010) paper [Salisbury, L. C, R M. Feinberg. 2010. Alleviating the constant stochastic variance assumption in decision research: Theory, measurement, and experimental test. Marketing Sci. 29(1) 1-17] for the process of model development and testing in the field of intertemporal choice analysis is explored. Although supporting the overall thrust of Salisbury and Feinberg’s critique of previous empirical work in the area, we also see their paper as illustrating the dangers of drawing strong inferences about the behavioral interpretation of statistical model parameters without seeking convergent empirical evidence. In particular, we are skeptical about the extent to which the reported effects of temporal distance on the estimated scale parameter, ?_c, are uniquely, or even primarily, due to unobserved error inflation that reflects consumer’s uncertainty about future utility. This interpretation is brought into question by several lines of reasoning. Conceptually, we note that "uncertainty" is different from "error" and that, for choice data, the error inflation model is mathematically identical to a model in which the scale parameter is a deterministic function of the temporal discount rate. Empirically, a reanalysis of data from previously published experiments does not consistently support temporal error inflation, temporal convergence of choice shares, or the scale parameter as an explanation of variety seeking in choice sequences. In our opinion, the cumulative results of research on intertemporal choice require models in which the attributes of choice alternatives are differentially discounted over time. Despite these findings, we advocate that choice researchers should indeed follow Salisbury and Feinberg’s advice to not assume that error variances will be unaffected by experimental manipulations, and such effects should be explicitly modeled. We also agree that uncovering effects on error variance is just the first step, and the ultimate goal should be to rigorously explain the reasons for such effects.

• Xiaoyan Deng and J. Wesley Hutchinson (Working), Just Do It Yourself: Does Self-Design Work and, If So, Why?.
• Yanliu Huang and J. Wesley Hutchinson (Working), There Is More to Planned Purchases than Knowing What You Want: Dynamic Planning and Learning in Multi-Store Price Search Task.
• All Research from J. Wesley Hutchinson »

Past Courses

• MKTG3500 - Special Topics

  CONSUMER NEUROSCIENCE: How can studying the brain improve our understanding of consumer behavior? While neuroscience made tremendous strides throughout the 20th century, rarely were meaningful applications developed outside of medicine. Recently, however, breakthroughs in measurement and computation have accelerated brain science and created a dizzying array of opportunities in business and technology. Currently, applications to marketing research and product development are experiencing explosive growth that has been met with both excitement and skepticism. This mini-course provides an overview of the neuroscience behind and the potential for these developments. Topics will range from well-known and widely used applications, such as eye-tracking measures in the lab and field, to emerging methods and measures, such as mobile technologies, face-reading algorithms, and neural predictors of marketing response. The course will also discuss applications in branding and product development, including wearable physiological devices and apps, sensory branding for foods and fragrances, pharmaceuticals and medical devices, and neuroscience-based products designed to enhance cognitive functions. These applications stem from many subfields of cognitive neuroscience, including attention, emotion, memory, and decision making. This course is self-contained and has no prerequisites. However, students with some background in business, economics, psychology, and/or neuroscience are likely to find the material covered in this course complementary to their existing knowledge.

• MKTG8500 - Special Topics

  CONSUMER NEUROSCIENCE: How can studying the brain improve our understanding of consumer behavior? While neuroscience made tremendous strides throughout the 20th century, rarely were meaningful applications developed outside of medicine. Recently, however, breakthroughs in measurement and computation have accelerated brain science and created a dizzying array of opportunities in business and technology. Currently, applications to marketing research and product development are experiencing explosive growth that has been met with both excitement and skepticism. This mini-course provides an overview of the neuroscience behind and the potential for these developments. Topics will range from well-known and widely used applications, such as eye-tracking measures in the lab and field, to emerging methods and measures, such as mobile technologies, face-reading algorithms, and neural predictors of marketing response. The course will also discuss applications in branding and product development, including wearable physiological devices and apps, sensory branding for foods and fragrances, pharmaceuticals and medical devices, and neuroscience-based products designed to enhance cognitive functions. These applications stem from many subfields of cognitive neuroscience, including attention, emotion, memory, and decision making. This course is self-contained and has no prerequisites. However, students with some background in business, economics, psychology, and/or neuroscience are likely to find the material covered in this course complementary to their existing knowledge.

• MKTG9420 - Research Methods Mktg A

  This course provides an introduction to the fundamental methodological issues that arise in experimental and quasi-experimental research. Illustrative examples are drawn from the behavioral sciences with a focus on the behavior of consumers and managers. Topics that are covered include: the development of research ideas; data collection and reliable measurement procedures; threats to validity; control procedures and experimental designs; and data analysis. Emphasis is placed on attaining a working knowledge of the use of regression methods for non-experimental and quasi-experimental data and analysis of variance methods for experimental data. The primary deliverable for this course is a meta-analysis of a research problem of the students choosing that investigates the effects of research methods on empirical results.

• MKTG9950 - Dissertation

  Dissertation

• MKTG9990 - Independent Study

  Requires written permission of instructor and the department graduate adviser.

• Society for Consumer Psychology Distinguished Scientific Contribution Award, 2010
• Finalist, H. Paul Root Award, Best Paper in Journal of Marketing, 2009 Description

  2009 MSI/H. Paul Root Award Finalist (Journal of Marketing article that made the most significant contribution to further the practice of marketing) for “Does In-Store Marketing Work? Effects of the Number and Position of Shelf Facings on Brand Attention and Evaluation at the Point of Purchase,” co-authored with Pierre Chandon, Eric Bradlow, and Scott H. Young.

• Sheth Foundation/Journal of Consumer Research Long-Term Contribution Award, 2008 Description

  2008 Sheth Foundation/Journal of Consumer Research Long-Term Contribution Award for “Dimensions of Consumer Expertise,” co-authored with Joseph W. Alba

• The ACR Working Paper Best Poster Award, 2007 Description

  For “A Three Factor Model of Consumer Preference for Self-Designed Products,” with Xiayan Deng

• Best Article Award for the Journal of Consumer Research, 2004 Description

  For “Knowledge Calibration: What Consumers Know and What They Think They Know,” coauthored with Joseph W. Alba

• President, Association of Consumer Research, 2002-2003, 2002
• O'Dell Award Finalist, 1999 Description

  For best article in the Journal of Marketing Research 1992-1994, for “Finding Choice Alternatives in Memory: Probability Models of Brand Name Recall,” co-authored with Kalyan Raman and Murali Mantrala

• Senior Fellow, Wharton Financial Institution Center, 1996 Description

  1995-1996

• AMA Advanced Research Techniques Forum Special Session Selection, 1995 Description

  For “Finding Choice Alternatives in Memory: Probability Models of Brand Name Recall,” co-authored with Kalyan Raman and Murali Mantrala

• J. C. Penney Term Professorship, 1992 Description

  1992-1994

• Sternberg Foundation/Maison Blanche Faculty Fellowship, 1992 Description

  1991-92

• Outstanding Article, Journal of Consumer Research, 1990 Description

  For “Dimensions of Consumer Expertise,” co-authored with Joseph W. Alba

• National Science Foundation Graduate Fellow, 1977 Description

  1977-1980, Stanford

Knowledge @ Wharton

• How Dynamic Electricity Pricing Can Improve Market Efficiency, Knowledge @ Wharton - 11/12/2024
• Is Algorithmic Management Too Controlling?, Knowledge @ Wharton - 11/12/2024
• The Hidden Cost of AI Energy Consumption, Knowledge @ Wharton - 11/12/2024
• Charitable and Political Giving | Pinar Yildirim, Knowledge @ Wharton - 11/12/2024
• The Future of Banking, Knowledge @ Wharton - 11/11/2024
• Election Statistics, Knowledge @ Wharton - 11/06/2024
• Robots Are Taking Over Low-skilled Jobs — and Changing Votes, Knowledge @ Wharton - 11/05/2024
• Crypto Politics: Liberals’ and Conservatives’ Confidence in Cryptocurrencies, Knowledge @ Wharton - 11/05/2024
• Leadership and Elections | Mike Useem, Knowledge @ Wharton - 11/05/2024
• Automation Is Harming Low-skilled Workers and Possibly Their Voting Preferences, Knowledge @ Wharton - 11/01/2024