Eric M. Eisenstein Assistant Professor Fox School of Business Temple University 527 Alter Hall Philadelphia, PA 19122-6083 Phone: 215-204-7039 E-mail: eric dot eisenstein --at-- temple . edu Webpage: Click to go to Eisenstein's official Fox School page Curriculum Vitae Eisenstein's resumé is available as an Adobe Acrobat PDF document Schedule a meeting with me:

Research Interests
(1) Managerial and consumer decision-making in high-motivation environments; (2) Decision-support systems, decision aids, and debiasing methods; (3) Learning and the development of expertise in managerial and consumer environments.

Substantive interests in marketing strategy, marketing and public policy, and marketing resource allocation.

Research

Eisenstein, Eric M. (2008), "Identity Theft: An Exploratory Study with Implications for Marketers," Journal of Business Research, November.

Identity theft is the fastest growing crime in America, and millions of people become victims each year. Furthermore, identity theft costs corporations over $20 billion per year, and consumers are forced to spend over $2 billion and 100 million hours of time to deal with the aftermath. This paper uses a system dynamics model to explore policy options dealing with identity theft and to provide implications for marketers. The results indicate that the current approach to combating identity theft will not work. However, inexpensive security freezes could be effective, because they result in a nonlinear reduction in identity theft that is similar to the “herd immunity” seen in epidemiology. Thus, identity theft can be addressed by protecting just a fraction of the total population.

Eisenstein, Eric M. and J. Wesley Hutchinson (2006), “Action Based Learning: Goals and Attention in the Acquisition of Market Knowledge,” Journal of Marketing Research, May.

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.

Hutchinson, J. Wesley, Joseph W. Alba, and Eric M. Eisenstein (Forthcoming), "Numerical Inferences: The Effects of Prior Beliefs, Framing, and Graphic Presentation on Budget Allocation Decisions,"Journal of Marketing Research.

Managers use numerical data as the basis for many decisions. The experimental findings reported here support several conclusions about biases that occur in budget allocation decisions based on numerical data. First, graphical presentations do not reduce the types of heuristic-based biases previously reported for tabular data and, in some cases, exacerbate these biases. Second, the effects of graphical format are large compared to the effects of prior beliefs and semantic frame identified in previous research. Third, the biases are resistant to classic debiasing methods, such as monetary incentives and explicit training. Fourth, equally strong biases are found for marketing managers and undergraduate students, suggesting that real-world experience does not reduce heuristic-based biases (even though managers’ verbal descriptions of their decision processes are more normative). Finally, allocation decisions reveals considerable heterogeneity, and modeling this heterogeneity yields new insights about two distinct sources of the observed effects, namely, heuristic selection and strength of bias.

Hutchinson, J. Wesley, Eric M. Eisenstein, and Joseph W. Alba (2008), "Consumer Learning and Expertise," Springer, Germany. (email for a copy)

This book integrates two related fields of study, learning and expertise, as they have been applied to consumer behavior. The first part of the book focuses on two central hypotheses that are seldom explicitly endorsed or rejected. In the normal course of everyday life, consumers become increasingly familiar with the products and service that they use. Possibly, over time people learn from these experiences and gain true expertise in a variety of product domains. Thus, the first hypothesis that increased familiarity leads to increased expertise: learning from experience (H1). Second, it seems reasonable that as expertise increases, people become more efficient consumers: increased consumer welfare (H2). The authors analyses reveal that these hypotheses are often, but not always supported, and sometimes opposite results obtain. The remaining parts of the book provide systematic reviews of the theories, methods, and applications that have been prominent in research on consumer learning and expertise.

Eisenstein, Eric M. and Stephen J. Hoch (2005), “Intuitive Compounding: Framing, Temporal Perspective, and Expertise,” (Under review at the Journal of Consumer Research).

A proper understanding of compound interest is essential for good financial planning. In three experiments, we demonstrate that most people estimate compound interest by anchoring on simple interest and insufficiently adjust upward. This results in large prediction errors, particularly when the timeframe is long or when the interest rate is high. Expert individuals use a different strategy, often referred to as the Rule of 72, that is much more accurate. Regardless of strategy, accuracy is asymmetric. Prospective predictions are easier than retrospective estimates. Finally, we demonstrate that it is possible to substantially improve people’s accuracy by using a short training procedure, which has little cost of use.

Cited in the Wall Street Journal, 16 January 2008, Personal Finance Section, p. D1, "If you don't know your math, you'll end up taking a bath."

Book Chapters

Eisenstein, Eric M. and Leonard M. Lodish (2002), "Precisely Worthwhile or Vaguely Worthless: Are Marketing Decision Support and Intelligent Systems 'Worth It'?," Handbook of Marketing, Barton Weitz and Robin Wensley (eds.), Sage Publications, London.

Our goal in this chapter is to review the marketing decision support system (MDSS) literature so as to provide maximal guidance to researchers and practitioners on how best to improve marketing decision-making using decision support systems. In order to achieve this goal, we lay out a taxonomy of decision support systems, create an integrative framework showing the drivers that maximally aid successful implementation, and propose future research that will help to resolve the inconclusive results in the literature. Throughout the chapter, we also attempt to reunite the divided decision support system literature by examining the assumptions underlying different research traditions in a broad, integrative context.

Hutchinson, J. Wesley, and Eric M. Eisenstein (2008), "Consumer Learning and Expertise," The Handbook of Consumer Psychology, Haugtvedt, Herr, and Kardes (eds.).

Consumer learning has been a central construct in models of consumer behavior since at least the 1960s (e.g., Howard and Sheth 1969; Massy, Montgomery, and Morrison 1970). Research on consumer knowledge and expertise is more recent (e.g., Bettman and Park 1980; Brucks 1985; Alba and Hutchinson 1987). In cognitive psychology, the topics of learning and expertise are more or less separate domains, or perhaps more accurately, expertise is subfield that focuses on the highest levels of learning, where learning has occurred naturally over many years rather than in the laboratory as the results of experimental procedures (e.g., Chi, Glaser and Farr 1988; Shanteau 1992). In consumer research, the topics have been more closely related and generally involve comparisons of more knowledgeable and less knowledgeable consumers without requiring that the more knowledgeable consumers be experts in the sense of representing the highest attainable levels of knowledge (e.g., grand masters in chess, professional judges of agricultural products, medical doctors, meteorologists, etc.). This focus on “relative” rather than “absolute” expertise is natural because many (arguably most) important problems in consumer behavior involve the very earliest stages of naturalistic learning (e.g., the adoption of innovations, transitions from trial to repeat purchases, differences between light and heavy users, etc.). Thus, in this chapter we emphasize the integration of learning and expertise and focus on the effects of relative differences in consumer knowledge across individuals.