In this paper, we study the drivers of customer satisfaction for financial services. We discuss a full Bayesian analysis based on data collected from customers of a leading financial services company. Our approach allows us to explicitly accommodate missing data and enables quantitative...

The nonparametric Bayesian shape-constrained bioassay estimator of Ramgopal et al. [Ramgopal, P., Laud, P., Smith, A.F.M., 1993. Nonparametric Bayesian bioassay with prior constraints on the shape of the potency curve. Biometrika 80 (3), 489-498.] is compared with the shape constrained maximum...

For the classical least-squares model it is often the case that shape or order restrictions are appropriate for the regression function, in the form of a set of linear inequality constraints imposed on the parameters. It is generally understood that the hypothesis test using the constrained...

An unbiased test for the appropriateness of the simple linear regression model is presented. The null hypothesis is that the underlying regression function is indeed a line, and the alternative is that it is convex. The exact distribution for a likelihood ratio test statistic is that of a...

We study the key determinants of customer satisfaction with software products. Our analysis, based upon a large sample of over 2,500 customer responses, suggests that capability and usability are the critical drivers of overall customer satisfaction. We also find that the importance of seven key...

The information technology (IT) industry is characterized by rapid innovation and intense competition. To survive, IT firms must develop high quality software products on time and at low cost. A key issue is whether high levels of quality can be achieved without adversely impacting cycle time...

Managers, typically, are unaware of the significant impact their decisions could have on the random mechanism driving a data generating process. Here, a new parametric Bayesian technique is introduced that would allow managers to obtain an estimate of the impact of their decisions on the...

This paper develops an estimation strategy for and then applies a spatial autoregressive multinomial probit model to account for both spatial clustering and cross-alternative correlation. Estimation is achieved using Bayesian techniques with Gibbs and the generalized direct sampling (GDS). The...

Several studies incorporating estimated volatilities into option pricing formulas have appeared in the literature. However, the models described in these studies tend to perform quite poorly in out-of-sample tests. In particular, significant departures from the observed prices can be seen for...