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Abstract

We develop and test an optimization model for maximizing response rates for online marketing research survey panels. The model consists of (1) a decision tree predictive model that classifies panelists into "states" and forecasts the response rate for panelists in each state and (2) a linear program that specifies how many panelists should be solicited from each state to maximize response rate. The model is forward looking in that it optimizes over a finite horizon during which S studies are to be fielded. It takes into account the desired number of responses for each study, the likely migration pattern of panelists between states as they are invited and respond or do not respond, as well as demographic requirements. The model is implemented using a rolling horizon whereby the optimal solution for S successive studies is derived and implemented for the first study. Then, as results are observed, an optimal solution is derived for the next S studies, and the solution is implemented for the first of these studies, etc. The procedure is field tested and shown to increase response rates significantly compared to the heuristic currently being used by panel management. Further analysis suggests that the improvement was due to the predictive model and that a "greedy algorithm" would have done equally well in the field test. However, further Monte Carlo simulations suggest circumstances under which the model would outperform the greedy algorithm.
MANAGEMENT SCIENCE
Vol. 55, No. 5, May 2009, pp. iv–vi
issn 0025-1909 eissn 1526-5501 09 5505 00iv
informs®
doi 10.1287/mnsc.1090.1039
© 2009 INFORMS
Management Insights
Blockbuster Culture’s Next Rise or Fall: The Impact
of Recommender Systems on Sales Diversity (p. 697)
Daniel Fleder, Kartik Hosanagar
The last ten years have seen an extraordinary increase
in the number of products available. This trend is
part of the “long tail” phenomenon, and many believe
that it could amount to a cultural shift from hit
to niche goods. A difficulty that arises, however, is
how consumers will find their ideal, niche products
among myriad choices. Recommender systems are
one solution. These systems use data on purchases
and user profiles to identify which products are best
suited to each user. Although recommenders have
been assumed to diversify choice, we show why some
systems may do the opposite. Recommenders can
create self-reinforcing cycles in which popular items
are recommended more, recommended items are pur-
chased more, purchased items are recommended even
more, and so on. These cycles reduce diversity. Con-
sequently, consumers and niche producers may be
underserved if there exist better product matches out-
side of the hits, and retailers may find that they offer
the right assortment but their recommender system
may be promoting a narrow range of products. We
recommend that managers consider design modifica-
tions to ensure that their recommender system limits
these popularity effects and promotes exploration.
On the Value of Commitment and Availability
Guarantees When Selling to Strategic
Consumers (p. 713)
Xuanming Su, Fuqiang Zhang
Product availability plays an important role in attract-
ing consumer demand. Despite technological and
managerial advances, industry evidence shows that
stockouts are a common phenomenon and prod-
uct availability remains a key issue in marketing
and operations. In environments in which consumers
make choices based on product availability, we pro-
pose two strategies that firms can use to improve
profits. First, firms can make upfront commitments
to consumers that at least a certain quantity will
be stocked. Second, firms can provide availability
guarantees to compensate consumers in the event of
stockouts. Interestingly, firms may have an incentive
to overcompensate consumers during stockouts. To
attain maximum possible profits, we show that firms
need to use both strategies in conjunction.
An Optimal Contact Model for Maximizing Online
Panel Response Rates (p. 727)
Scott A. Neslin, Thomas P. Novak, Kenneth R. Baker,
Donna L. Hoffman
This paper develops and field tests a model for maxi-
mizing response rates for online survey research pan-
els. The model includes several important features:
(i) it accounts for the “state” of each panelist (e.g., pre-
vious response rate); (ii) it can plan for several studies
at a time; (iii) it recognizes that current decisions may
influence future response rates; (iv) it allows the user
to stipulate the desired sample size and demographic
makeup for the sample; and (v) it anticipates growth
in the panel. In a field test conducted for four stud-
ies, the model yields an average response rate of 43%
per study, compared to 25% for the heuristic currently
used by an online panel’s manager and 14% for ran-
dom selection. These results suggest that managers
could use the model to improve upon current prac-
tice either by obtaining the same sample size while
soliciting fewer panelists (thus avoiding panelist
“burnout”) or by increasing the sample size while
soliciting the same number of panelists (thus provid-
ing smaller standard errors and hence more accurate
results).
Contagion of Wishful Thinking in Markets (p. 738)
Nicholas Seybert, Robert Bloomfield
How does one person’s behavior affect the decisions
of others when all parties are making risky decisions
to increase their wealth? We show that investors in
a stock market have a tendency to engage in “wish-
ful betting,” where they invest or bet as if desirable
outcomes are unreasonably likely. When one investor
engages in wishful betting and purchases additional
shares of stock (as if he believes shares are under-
valued), other investors may fail to adjust for this
bias, thereby leading them to hold unreasonably opti-
mistic beliefs, which we term “wishful thinking.”
Wishful thinking could occur in a variety of con-
texts, including managerial decisions influenced by
competitor behavior, and individual career path deci-
sions influenced by peer behavior. The results of our
studies suggest that people should be cautious when
interpreting others’ behavior; otherwise, they may
unwittingly sacrifice their own wealth when making
investments.
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Management Insights
Management Science 55(5), pp. iv–vi, © 2009 INFORMS v
Quasi-Robust Multiagent Contracts (p. 752)
Anil Arya, Joel Demski, Jonathan Glover, Pierre Liang
Incentive contracting has been a much-studied topic
in information economics. However, it has been noted
that the theoretically derived optimal contracts are
often at odds with observed practice in that they
are highly fine-tuned to the details of the envi-
ronment. Many of these theoretically optimal con-
tracts would perform quite poorly if the environment
were even slightly different from that assumed. This
paper attempts to contribute to the emerging the-
ory of robust contracts in the hope that such con-
tracts will help us better understand observed prac-
tice. A distinguishing feature of our approach is
that we assume a manager must choose the design
of a contract mechanism before some key informa-
tion is known about the environment. We use our
method to provide insights into an auction that has
to be designed for a variety of bidders and bidder
correlations.
Multiple Sourcing and Procurement Process
Selection with Bidding Events (p. 763)
Tunay I. Tunca, Qiong Wu
We study the process selection problem of an indus-
trial buyer who employs online reverse auctions for
procurement. We compare two types of procurement
processes: (1) simple reverse auctions (“single-stage”
processes) and (2) processes where the buyer makes
additional price-quantity adjustments with the win-
ning suppliers after the auction (“two-stage” pro-
cesses). If there is a large number of bidding sup-
pliers and production is not scalable (i.e., capacity is
rigid), then we find that single-stage procurement is
preferred. However, the two-stage process tends to be
relatively more attractive as the number of bidders
decreases or as capacity becomes more scalable (i.e.,
an increase in quantity does not generate a consider-
able increase in the per-unit cost).
Information Sharing and Order Variability Control
Under a Generalized Demand Model (p. 781)
Li Chen, Hau L. Lee
Information sharing between partners is one means
to improve supply chain performance, e.g., through
mitigation of the bullwhip effect. However, when
a retailer shares point-of-sales data, the supplier
can fully exploit this information only if the sup-
plier also has knowledge of the characteristics of
the demand process and the retailer’s order policy.
How can a supplier realize the value of informa-
tion sharing when such knowledge is lacking? Our
paper shows that this can be achieved by having the
retailer share its projections of future orders and their
revisions.
A Generalized Approach to Portfolio Optimization:
Improving Performance by Constraining Portfolio
Norms (p. 798)
Victor DeMiguel, Lorenzo Garlappi,
Francisco J. Nogales, Raman Uppal
We provide a general framework for finding portfo-
lios that perform well out-of-sample in the presence
of estimation error. This framework relies on solv-
ing the traditional minimum-variance problem but
subject to the additional constraint that the norm
of the portfolio-weight vector be smaller than a
given threshold. We show that several established
approaches in the literature are actually special cases
of our framework. We use five data sets to compare
the out-of-sample performance of our method with
10 known strategies and find that our method often
yields a higher Sharpe ratio.
Optimal Policies and Approximations for a Bayesian
Linear Regression Inventory Model (p. 813)
Katy S. Azoury, Julia Miyaoka
We consider the inventory management problem
when demand is estimated using a regression model.
We assume the regression parameters are unknown,
and a Bayesian approach is used to update the dis-
tribution on the regression parameters as new infor-
mation becomes available. Within our framework we
identify the optimal inventory policy. However, it is
computationally complex to implement, so we pro-
pose heuristic policies and demonstrate that they
yield near-optimal performance.
Information Market-Based Decision Fusion (p. 827)
Johan Perols, Kaushal Chari, Manish Agrawal
In many decision-making scenarios, such as fraud
detection and bankruptcy prediction, the decisions of
multiple human experts and/or software are fused to
determine the overall decision. This paper provides
an innovative approach for decision fusion based on
information markets. Our computational results indi-
cated that our approach is superior to other existing
approaches. This information market-based method
can help organizations lower costs and facilitate new
decision-making systems that combine the expertise
of humans and software.
Private Network EDI vs. Internet Electronic
Markets: A Direct Comparison of Fulfillment
Performance (p. 843)
Yuliang Yao, Martin Dresner, Jonathan Palmer
How can purchasing organizations decrease cycle
times and improve order fulfillment? The key to
these performance improvements may lie in the sup-
ply chain technology used for transaction exchanges.
Using a data set comprised of 2.8 million transactions
Management Insights
vi Management Science 55(5), pp. iv–vi, © 2009 INFORMS
placed through the U.S. government’s Federal Sup-
ply Services, we provide a direct comparison between
private network electronic data interchange (EDI) sys-
tems and Internet-based electronic market systems.
We find that when purchasers use the Internet-based
electronic market, cycle times are reduced by two
days and complete orders fulfilled are increased by
two percentage points, compared to the competing
EDI system.
Loss Functions in Option Valuation: A Framework
for Selection (p. 853)
Dennis Bams, Thorsten Lehnert, Christian C. P. Wolff
We investigate the importance of different loss func-
tions when estimating and evaluating option pricing
models. Our analysis shows that it is important to take
into account parameter uncertainty because this leads
to uncertainty in the predicted option price. We find
strong evidence to support the idea that the absolute
pricing error criterion may serve as a general-purpose
loss function in option valuation applications. At the
same time, we provide a first yardstick to evaluate the
adequacy of the loss function. This is accomplished
through a data-driven method to deliver not just a
point estimate of the pricing error, but a distribution.
Additive Utility in Prospect Theory (p. 863)
Han Bleichrodt, Ulrich Schmidt, Horst Zank
Decision making in managerial environments where
alternatives consist of risky multiple-attribute out-
comes is becoming a very difficult task because of
the complex way in which individuals evaluate risks.
Utility measurement tools have been developed for
single-attribute outcomes but those tools may be
inappropriate for multiple-attribute outcomes if loss
aversion is attribute specific. For example, when indi-
viduals evaluate job offers, the effect of loss aversion
relating to a drop in salary income may be of different
magnitude than the effect of loss aversion relating to
how onerous the job is. We show how utility measure-
ment can be improved if loss aversion is understood
as an attribute-specific feature, and we provide new
decision models that improve the empirical toolbox
of decision makers and managers.
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