Evaluating the efficiency of municipalities in the presence of unobserved heterogeneity

Journal of Productivity Analysis - The paper applies some of the latest advances of probabilistic approach to account directly for unobserved heterogeneity in the estimation of efficiency measures...     

Explicit filters and supply chain design

Due to the complexity of present day supply chains it is important to select the simplest supply chain scheduling decision support system (DSS) which will determine and place orders satisfactorily. We propose to use a generic design framework, termed the explicit filter methodology, to achieve this objective. In doing so we compare the explicit filter approach to the implicit filter approach utilised in previous OR research the latter focusing on minimising a cost function. Although the eventual results may well be similar with both approaches it is much clearer to the designer, both why and how, an ordering system will reduce the Bullwhip effect via the explicit filter approach. The “explicit filter” approach produces a range of DSS designs corresponding to best practice. These may be “mixed and matched” to generate a number of competitive delivery pipelines to suit the specific business scenario.     

The Multilevel Approach to Repeated Measures for Complete and Incomplete Data

Repeated measurements often are analyzed by multivariate analysis of variance (MANOVA). An alternative approach is provided by multilevel analysis, also called the hierarchical linear model (HLM), which makes use of random coefficient models. This paper is a tutorial which indicates that the HLM can be specified in many different ways, corresponding to different sets of assumptions about the covariance matrix of the repeated measurements. The possible assumptions range from the very restrictive compound symmetry model to the unrestricted multivariate model. Thus, the HLM can be used to steer a useful middle road between the two traditional methods for analyzing repeated measurements. Another important advantage of the multilevel approach to analyzing repeated measures is the fact that it can be easily used also if the data are incomplete. Thus it provides a way to achieve a fully multivariate analysis of repeated measures with incomplete data. This revised version was published online in June 2006 with corrections to the Cover Date.     

The derived demand for faculty research

Estimation of the demands for many services supplied by government and charitable organizations are hampered by two practices common to the supply of these services. The suppliers often employ non‐price rationing of these services, and they price discriminate. The supply of college training, for example, is rationed on the basis of academic ability as well as willingness to pay, and more able students are typically quoted lower net prices (associated with scholarships) than less able students. This paper suggests a method for dealing with both practices in the analysis of cross‐sectional data. This methodology is used to investigate the question of why university faculty members are expected to do research as well as teach. One answer supported by our empirical work is that the customers (e.g., the students) demand it. Thus, controlling for price and non‐academic features, better students will choose to attend a university where more scholarly research is performed. Moreover, our empirical findings also support a strong negative link between faculty time devoted to teaching and the supply of research. Copyright © 2003 John Wiley & Sons, Ltd.     

Topics in nonparametric comparative statics and stability

This paper presents the analysis of the Jacobian matrix of a small, macroeconomic model to determine the robustness of the model's comparative statics. The values of the model's coefficients are assessed with respect to seven alternative estimation strategies such as ordinary least squares or two-stage least squares. For each alternative version, the invertibility and stability of the Jacobian matrix is studied. The model is robust to the degree that these characteristics are shared by any matrix with the same sign pattern or other similar nonparametric conditions on its entries. One way to address the relative success of the different estimation strategies is through the robustness of the comparative statics of the resulting model.     

Churn analysis for an Iranian mobile operator

To survive in the challenging environment of a global market, organizations must recognize and analyze customer attitudes. To be competitive, organizations must recognize and forecast customer preferences and behaviors to maximize customer retention before their rivals do so. This research identifies factors that affect customer churn, the single most valuable of an organization's assets. One year's data from call log files relating to 3150 customers were selected randomly from an Iranian mobile operator call-center database. Binomial Logistic Regression was the method of analysis used in this research. The results of this research indicate that a customer's dissatisfaction, their amount of service usage and certain demographic characteristics have the most influence on their decision to remain or churn. The results also imply that customer status (active or inactive status) mediates the relationship between churn and the cause of churn. The Iranian government's current plan to privatize the telecommunications industry without deregulation leads to a non-square competition environment. Deregulation in favor of delegating more authorities of customer care is necessary in order to develop a square private competition environment in the Iranian mobile telecommunications industry.     

The Role of Future‐Market Focus in the Early Stages of NPD across Varying Levels of Innovativeness

The concept of future‐market focus (FMF) arose out of the debate about firm size and incumbency in the face of radical or disruptive innovations, and has been demonstrated to have a positive correlation with radical innovation (RI) success. This study examines the relationship between FMF and the processes used in the early stages of NPD for four types of innovation projects: incremental innovations, technological breakthroughs, market breakthroughs, and radical innovations. We found that the future‐market focus of a project team can influence the early stage processes used in a new product innovation project, and does so differentially across levels of innovativeness. In particular, the concept generation process, understanding of market needs, and screening decision criteria are different for low‐ versus high‐FMF projects and there are differences based on the level of innovation. In addition, we found that radical innovation projects rated low in FMF are markedly different than the radical innovation projects described in prior studies.