119,090 research outputs found
Uncovering Hidden Profiles; Managerial Interventions for Discovering Superior Decision Alternatives
A common reason for the use of teams in organizations is the idea that each individual can bring a unique perspective to the decision task; however, research shows that teams often fail to surface and use unique information to evaluate decision alternatives. Under a condition known as the hidden profile, each member uniquely possesses a critical clue needed to uncover the superior solution. Failure to share and adequately evaluate this information will result in poor decision quality. In order to mitigate this team decision-making bias, the present study utilizes experimental research to examine the impact of the devil’s advocacy technique on the decision quality of hidden profile teams. Results show that advocacy groups had higher decision qualities than groups under free discussion; however, advocacy teams also had higher levels of anger and lower levels of individual support for their group’s decision. As a result, while these teams selected the best solution, the presence of a devil’s advocate introduces conditions that may hinder the solution’s implementation. Furthermore, similar experiments with advocacy techniques suggest that the positive effect on decision quality found here is reduced in the presence of stronger hidden profiles
Latent class analysis for segmenting preferences of investment bonds
Market segmentation is a key component of conjoint analysis which addresses consumer
preference heterogeneity. Members in a segment are assumed to be homogenous in their
views and preferences when worthing an item but distinctly heterogenous to members of other
segments. Latent class methodology is one of the several conjoint segmentation procedures
that overcome the limitations of aggregate analysis and a-priori segmentation. The main
benefit of Latent class models is that market segment membership and regression parameters
of each derived segment are estimated simultaneously. The Latent class model presented in
this paper uses mixtures of multivariate conditional normal distributions to analyze rating
data, where the likelihood is maximized using the EM algorithm. The application focuses on
customer preferences for investment bonds described by four attributes; currency, coupon
rate, redemption term and price. A number of demographic variables are used to generate
segments that are accessible and actionable.peer-reviewe
Should Optimal Designers Worry About Consideration?
Consideration set formation using non-compensatory screening rules is a vital
component of real purchasing decisions with decades of experimental validation.
Marketers have recently developed statistical methods that can estimate
quantitative choice models that include consideration set formation via
non-compensatory screening rules. But is capturing consideration within models
of choice important for design? This paper reports on a simulation study of a
vehicle portfolio design when households screen over vehicle body style built
to explore the importance of capturing consideration rules for optimal
designers. We generate synthetic market share data, fit a variety of discrete
choice models to the data, and then optimize design decisions using the
estimated models. Model predictive power, design "error", and profitability
relative to ideal profits are compared as the amount of market data available
increases. We find that even when estimated compensatory models provide
relatively good predictive accuracy, they can lead to sub-optimal design
decisions when the population uses consideration behavior; convergence of
compensatory models to non-compensatory behavior is likely to require
unrealistic amounts of data; and modeling heterogeneity in non-compensatory
screening is more valuable than heterogeneity in compensatory trade-offs. This
supports the claim that designers should carefully identify consideration
behaviors before optimizing product portfolios. We also find that higher model
predictive power does not necessarily imply better design decisions; that is,
different model forms can provide "descriptive" rather than "predictive"
information that is useful for design.Comment: 5 figures, 26 pages. In Press at ASME Journal of Mechanical Design
(as of 3/17/15
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