Ordinal preference measures have a number of advantages relative to cardinal measures in the estimation of individual level multi-attribute utility functions. This paper: (a) outlines a theoretical foundation for estimating a cardinal scaled utility function from ordinal preference data, in...

Firms periodically alter the characteristics of their brands in hope of increasing their popularity and profitability. This paper develops and illustrates a methodology to evaluate these multi-dimensional repositioning strategies. We start by forwarding a procedure to estimate the multiattribute...

Marketers routinely make use of stated consumer preferences and the relative attribute importance weights implied by these preferences when making decisions on issues such as advertising messages and product design. Using this information as a basis for managerial decision-making is risky,...

Marketers routinely make use of stated consumer preferences and the relative attribute-importance weights implied by these preferences when making decisions on issues such as advertising messages and product design. Using this information as a basis for managerial decision making is risky,...

In recent years there has been a growing stream of literature in marketing and economics that models consumers as Bayesian learners. Such learning behavior is often embedded within a discrete choice framework that is then calibrated on scanner panel data. At the same time, it is now accepted...

Human Rights and Global Development Goals -- Principles and Practice, Human Rights and Development -- Challenging Inequalities -- Health Systems -- Access to Productive Assets: Labor -- Access to Productive Assets: Land -- Politics and Accountability: Implementing the SDGs.

Open innovation platforms that enable organizations to crowdsource ideation to parties external to the firm are proliferating. In many cases, the platforms use open contests that allow the free exchange of ideas with the goal of improving the ideation process. In open contests, participants...

This paper proposes a post-model selection inference procedure, called targeted undersmoothing, designed to construct uniformly valid confidence sets for functionals of sparse high-dimensional models, including dense functionals that may depend on many or all elements of the high-dimensional...