Strategic Design Responsiveness: An Empirical Analysis of US Retail Store Networks

This study uses a service operations management (SOM) strategy lens to investigate chain store retailers' strategic design responsiveness (SDR)—a term that captures the degree to which retailers dynamically coordinate investments in human and structural capital with the complexity of their service and product offerings. Labor force and physical capital are respectively used as proxies for investments in human capital and structural capital, whereas gross margins are proxies for product/service offering complexity. Consequently, SDR broadly reflects three salient complementary choices of SOM design strategy. We test the effects of “brick and mortar” chain store retailers' SDR on current and future firm performance using publically available panel data collected from Compustat and the University of Michigan American Customer Satisfaction Index databases for the period 1996–2011. We find that retailers that fail to keep pace with investments in both structural and human capital exhibit short‐term financial benefits, but have worse ongoing operational performance. These findings corroborate the importance of managers strategically maintaining the complementarity of design‐related choices for improving and maintaining business performance.     

Looks‐Based Hiring and Wrongful Discrimination

Popular clothing retailer Abercrombie and Fitch (A&F) is well‐known for hiring attractive store sales clerks. While the economic benefits of this hiring practice for the company are undeniable, many commentators contend that it constitutes wrongful discrimination against unattractive job seekers. In this article, I explore the ethics of A&F‐style lookism and challenge two common perspectives on this issue. I argue that on one hand, looks‐based hiring cannot be defended based on its economic benefits alone, as race‐based hiring also can be profitable in some circumstances. At the same time, I reject arguments that looks‐based hiring is not “job relevant” given its economic impact in many contexts. Through a comparison between race‐ and looks‐based hiring, I conclude that at least for businesses that are relevantly similar to A&F—firms for which lookism produces clear economic benefits—looks‐based hiring is permissible.     

Inference in Semi‐Parametric Dynamic Models for Repeated Count Data

This paper deals with a longitudinal semi‐parametric regression model in a generalised linear model setup for repeated count data collected from a large number of independent individuals. To accommodate the longitudinal correlations, we consider a dynamic model for repeated counts which has decaying auto‐correlations as the time lag increases between the repeated responses. The semi‐parametric regression function involved in the model contains a specified regression function in some suitable time‐dependent covariates and a non‐parametric function in some other time‐dependent covariates. As far as the inference is concerned, because the non‐parametric function is of secondary interest, we estimate this function consistently using the independence assumption‐based well‐known quasi‐likelihood approach. Next, the proposed longitudinal correlation structure and the estimate of the non‐parametric function are used to develop a semi‐parametric generalised quasi‐likelihood approach for consistent and efficient estimation of the regression effects in the parametric regression function. The finite sample performance of the proposed estimation approach is examined through an intensive simulation study based on both large and small samples. Both balanced and unbalanced cluster sizes are incorporated in the simulation study. The asymptotic performances of the estimators are given. The estimation methodology is illustrated by reanalysing the well‐known health care utilisation data consisting of counts of yearly visits to a physician by 180 individuals for four years and several important primary and secondary covariates.     

Diagnostics in elliptical regression models with stochastic restrictions applied to econometrics

We propose an influence diagnostic methodology for linear regression models with stochastic restrictions and errors following elliptically contoured distributions. We study how a perturbation may impact on the mixed estimation procedure of parameters in the model. Normal curvatures and slopes for assessing influence under usual schemes are derived, including perturbations of case-weight, response variable, and explanatory variable. Simulations are conducted to evaluate the performance of the proposed methodology. An example with real-world economy data is presented as an illustration.     

Health care risks and access within the community of Michigan over-the-road truckers

This mixed methods study explored health care access problems faced by long-distance truck drivers. Thirty trucking professionals were interviewed at truck-stops in Michigan about health status and health care access. Thirty of the 88 drivers approached participated. The most common illnesses they reported concerned the eye, back, excessive stress at work and painful joints or arthritis. Most respondents had a family doctor; for those who did not, cost was a major contributing factor. More than one third stated their health is "not excellent" because of poor accessibility to healthcare on the road. Almost unanimously, drivers desired to have access to health care clinics in truck-stop areas and described particular access problems; the solution may be as simple as making the existing health care system more amenable to those driving large truck rigs.     

Designing and integrating composite networks for monitoring multivariate gaussian pollution fields

Networks of ambient monitoring stations are used to monitor environmental pollution fields such as those for acid rain and air pollution. Such stations provide regular measurements of pollutant concentrations. The networks are established for a variety of purposes at various times so often several stations measuring different subsets of pollutant concentrations can be found in compact geographical regions. The problem of statistically combining these disparate information sources into a single 'network' then arises. Capitalizing on the efficiencies so achieved can then lead to the secondary problem of extending this network. The subject of this paper is a set of 31 air pollution monitoring stations in southern Ontario. Each of these regularly measures a particular subset of ionic sulphate, sulphite, nitrite and ozone. However, this subset varies from station to station. For example only two stations measure all four. Some measure just one. We describe a Bayesian framework for integrating the measurements of these stations to yield a spatial predictive distribution for unmonitored sites and unmeasured concentrations at existing stations. Furthermore we show how this network can be extended by using an entropy maximization criterion. The methods assume that the multivariate response field being measured has a joint Gaussian distribution conditional on its mean and covariance function. A conjugate prior is used for these parameters, some of its hyperparameters being fitted empirically.     

Decision Making in `Random in a Broad Sense' Environments

It is shown that the uncertainty connected with a `random in a broad sense' (not necessarily stochastic) event always has some `statistical regularity' (SR) in the form of a family of finite-additive probability distributions. The specific principle of guaranteed result in decision making is introduced. It is shown that observing this principle of guaranteed result leads to determine the one optimality criterion corresponding to a decision system with a given `statistical regularity'.     

Bayesian prediction of waiting times in stochastic models

The authors show how saddlepoint techniques lead to highly accurate approximations for Bayesian predictive densities and cumulative distribution functions in stochastic model settings where the prior is tractable, but not necessarily the likelihood or the predictand distribution. They consider more specifically models involving predictions associated with waiting times for semi‐Markov processes whose distributions are indexed by an unknown parameter θ. Bayesian prediction for such processes when they are not stationary is also addressed and the inverse‐Gaussian based saddlepoint approximation of Wood, Booth & Butler (1993) is shown to accurately deal with the nonstationarity whereas the normal‐based Lugannani & Rice (1980) approximation cannot, Their methods are illustrated by predicting various waiting times associated with M/M/q and M/G/1 queues. They also discuss modifications to the matrix renewal theory needed for computing the moment generating functions that are used in the saddlepoint methods.     

A semi-parametric method for estimating the beta coefficients of the hidden two-sided asset return jumps

ABSTRACT

We introduce a new methodology for estimating the parameters of a two-sided jump model, which aims at decomposing the daily stock return evolution into (unobservable) positive and negative jumps as well as Brownian noise. The parameters of interest are the jump beta coefficients which measure the influence of the market jumps on the stock returns, and are latent components. For this purpose, at first we use the Variance Gamma (VG) distribution which is frequently used in modeling financial time series and leads to the revelation of the hidden market jumps' distributions. Then, our method is based on the central moments of the stock returns for estimating the parameters of the model. It is proved that the proposed method provides always a solution in terms of the jump beta coefficients. We thus achieve a semi-parametric fit to the empirical data. The methodology itself serves as a criterion to test the fit of any sets of parameters to the empirical returns. The analysis is applied to NASDAQ and Google returns during the 2006–2008 period.     

Urban ecosystem Services in Latin America: mismatch between global concepts and regional realities?

Urban Ecosystems - Latin America and the Caribbean (LAC) is one of the most urbanized and biologically diverse regions in the world but is often characterized by weak environmental governance and...