Bayesian analysis of the two‐part model with endogeneity: application to health care expenditure

This paper studies the effect of managed care on medical expenditure using a model in which the insurance status is assumed to be endogenous. Insurance plan choice is modeled through the multinomial probit model. The medical expenditure variable, the outcome of interest, has a significant proportion of zeros that are handled using the two‐part model, extended to handle endogenous insurance. The estimation approach is Bayesian, based on the Gibbs Sampler. The model is applied to a sample of 20 460 individuals obtained from the Medical Expenditure Panel Survey. The results provide substantial evidence of selectivity. Copyright © 2006 John Wiley & Sons, Ltd.     

Using variety-seeking-based segmentation to study promotional response

The link between variety seeking and promotional response has been of interest to marketing researchers for quite some time. By segmenting consumers according to their variety-seeking needs, researchers have established some interesting results regarding the variation in promotional response across segments. However, the sole basis for segmentation thus far has been unidimensional (e.g., high-and low-variety seekers). This research discusses a unique way to segment consumers based not only on the usual extent (or mean) of variety seeking but also on the intensity (or consistency) of variety-seeking behavior, a new segmentation criterion. The authors conduct an empirical study to test the two-dimensional segmentation scheme and investigate differences in response to a variety of promotions across the segments. The inclusion of the intensity aspect of variety seeking as an additional basis for segmentation has a significant impact on promotional response and offers substantially richer managerial interpretation. Minakshi Trivedi is an associate professor of marketing at the School of Management, State University of New York at Buffalo. Her research interests lie in modeling purchase behavior, and game theory. She has published in various journals in cludingMarketing Science, Management Science, and theJournal of Business & Economic Statistics.     

Size Effects in the Pricing of Corporate Bonds

Large orders for corporate bonds get preferential treatment unlike large orders for stocks on the NYSE. A structural explanation, namely, that the corporate bond market is dealer‐dominated, has been offered for the favorable pricing. In this paper, we offer an additional explanation, namely, that the improved pricing for large orders is due to the net impact such orders have on a market maker's costs. Using a data sample that is substantially free of timing mismatch, we support our assertion by sorting the sample into ‘brokered’ trades, which are trades where the dealer merely crosses buy and sell orders and ‘inventoried’ trades, where the dealer trades out of his inventory. We find that large orders raise information costs, but lower inventory costs for ‘inventoried’ trades. The net result is a smaller price advantage than received by large orders on ‘brokered’ trades which are not subject to these costs.     

Designing e-government services: Key service attributes and citizens’ preference structures

Advances in Internet technologies have led to the popularity of technology-based self-services, with the design of such services becoming increasingly important. Using technology-based services in the public sector as the setting, we identified the key service attributes driving adoption and use of transactional e-government services, and citizens’ preference structures across these attributes. After identifying four key attributes, i.e., usability, computer resource requirement, technical support provision and security provision, we conducted a Web-based survey and a conjoint experiment among 2465 citizens. In a two-stage Web-based survey, citizens reported their perceptions about a smartcard technology for transactional e-government services before use, and their use and satisfaction 4 months later. Results showed that the key attributes (noted above) influenced citizens’ intentions, subsequent use and satisfaction. In the conjoint experiment, citizens reported their preferences for key service attributes for two transactional e-government services. Further, a cluster analysis uncovered four distinct citizen segments, i.e., balanced, usability-focused, risk-conscious and resource-conservative, that can inform efforts in designing e-government services. A post hoc analysis confirmed the appropriateness of the market segmentation in understanding citizens’ adoption and use of transactional e-government services.     

Bayesian analysis of the ordered probit model with endogenous selection

This paper presents a Bayesian analysis of an ordered probit model with endogenous selection. The model can be applied when analyzing ordered outcomes that depend on endogenous covariates that are discrete choice indicators modeled by a multinomial probit model. The model is illustrated by analyzing the effects of different types of medical insurance plans on the level of hospital utilization, allowing for potential endogeneity of insurance status. The estimation is performed using the Markov chain Monte Carlo (MCMC) methods to approximate the posterior distribution of the parameters in the model.     

A stochastic model for predicting discharges: applications for achieving occupancy goals in hospitals.

In this paper a model based upon probability theory has been developed for predicting patient discharges from hospital units. Also, a study performed to obtain patients' length-of-stay distributions has been described. The paper includes a discussion on occupancy goals for hospital units and an application of the model in attaining those goals. Due to the availability of large computer facilities in many hospitals it is feasible to incorporate the model into a hospital's management information system to provide valuable input for various management functions such as decision making, planning and formulation of policy.     

Regional and Categorical Patterns in Consumer Behavior: Revealing Trends

We offer a method of analysis that allows for an “unbundling” of the data to a disaggregate household level, and then “rebundling” it in a manner designed to identify patterns and relationships which are otherwise masked. Applying the method in the context of ‘healthy’ products and using census block group level data, we study consumption over several categories in two locations. The analyses show that studies involving geographically dispersed data must test for, and take into account when required, conceptually sound spatial effects in order to accurately assess impact. We also show that while both location and the product category have a significant impact on the proportion of healthy products purchased, the degree to which consumers choose healthier alternatives is a function of the category as well as the location. Finally, we provide preliminary evidence from survey data that supports the variations we find, and further explores attitudinal differences as well. There are rich implications for retailers in that new products introduced to benefit from popular trends (such as ‘healthy’ alternatives) may not succeed for all categories or locations. Retailers would benefit from understanding the spatial, demographic and attitudinal effects that play into consumption behavior, and such effects can be better understood when studying choice at the category and region level. Finally, public policies aimed at promoting healthier purchasing habits may have greater impact if special attention is given to specific categories and regions.     

Investigating the Factors That Make a Fashion App Successful: The Moderating Role of Personalization

Online fashion retailers promote use of fashion apps among their consumers. However, dissatisfaction in using these apps is one of the reasons consumers uninstall them. This research studies the influence of information quality, service quality, and system quality on app satisfaction. Further, the influence of app satisfaction on purchase intentions is also studied. In addition, the moderating role of personalization among the three quality dimensions and app satisfaction is observed. Primary data were collected from 268 respondents. The results established system quality as the most significant factor influencing app satisfaction. Further, personalizing the app increased the influence of the three quality dimensions on app satisfaction.