- Managerial and consumer decision-making in high-motivation environments.
- Decision-support systems, decision aids, and debiasing methods.
- 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
Rosenthal, Edward C. and Eric M. Eisenstein (2016), ÒA Rescheduling and Cost Allocation Mechanism for Delayed ArrivalsÓ, Computers and Operations Research, 66, February, pp. 20-28.
Chressanthis, George A., Eric M. Eisenstein, and Patrick Barbro (2015), ÒWhat makes more, better? An exploratory study on the effects of firm-level commercial operations attributes on pharmaceutical business performanceÓ, Journal of Medical Marketing, 15 (1-2), pp. 10-25.
Vallen, Beth, Lauren Block, and Eric M. Eisenstein (2014), ÒThe Liberating Effect of Lateness: How Missed Temporal Deadlines Increase Purchase IntentÓ, Journal of Consumer Marketing, 31, 5, pp. 360-370.
Ý Awarded the Outstanding Paper Award for 2014 by the editorial board of the Journal of Consumer Marketing
Eisenstein, Eric M. (2013). ÒConsumer ExpertiseÓ In Jagdish N. Sheth and Naresh K. Malhotra (Ed.), Wiley International Encyclopedia of Marketing: Consumer Behavior (vol. 3). New York: Wiley.
Wilcox, Kieth, Lauren Block and Eric M. Eisenstein (2011), ÒLeave Home Without It? The Effects of Credit Card Debt and Available Credit on SpendingÓ, Journal of Marketing Research, 48, pp. 60-78.
Hutchinson, J. Wesley, Joseph W. Alba, and Eric M. Eisenstein (2010), ÒManagerial Inferences: The Effects of Graphical Formats on Data-Based Decision Making,Ó Journal of Marketing Research, 47, 4, pp. 627-642.
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, 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.
