Measurement of organizational investments in social capital: The service employee perspective

This paper describes the development and validation of a measure of organizational investments in social capital (OISC). The scale development process is carried out over three stages (item generation, scale purification, scale validation), with two separate data collection phases involving a total of 735 working adults from multiple and diverse service-related workplace settings. As such, the data provide evidence for the face, content, discriminant, convergent and nomological validity, dimensionality and reliability of the OISC measure. The OISC measure is a concise, unidimensional scale that has the potential for significant usage in the development and testing of theory, as well as practical application in retail and other service provision contexts.     

Managers’ Moral Decision-Making Patterns Over Time: A Multidimensional Approach

Taking multidimensional ethics scale approach, this article describes an empirical survey of top managers’ moral decision-making patterns and their change from 1994 to 2004 during morally problematic situations in the Finnish context. The survey questionnaire consisted of four moral dilemmas and a multidimensional scale with six ethical dimensions: justice, deontology, relativism, utilitarianism, egoism and female ethics. The managers evaluated their decision-making in the problems using the multidimensional ethics scale. Altogether 880 questionnaires were analysed statistically. It is concluded that relying on the utilitarian principles is a core ethical evaluation criterion amongst top business managers in Finland. This study proves that managers’ moral decision-making patterns change over time. According to the results of this research, managers’ moral decision-making became more multidimensional during the study period. The change is explained by (1) the inclusion of female ethics items in the scale which allows managers to show more diversity in their decision- making, (2) the change in the Finnish economic context from depression to economic prosperity and growth during the study period, which is conducive to the spread of post materialist values, such as the importance of social relations and (3) the increasing public discussion of the importance ethical issues in business.     

Empirical Bayes analysis of log-linear models for a generalized finite stationary Markov chain

This article presents the empirical Bayes method for estimation of the transition probabilities of a generalized finite stationary Markov chain whose ith state is a multi-way contingency table. We use a log-linear model to describe the relationship between factors in each state. The prior knowledge about the main effects and interactions will be described by a conjugate prior. Following the Bayesian paradigm, the Bayes and empirical Bayes estimators relative to various loss functions are obtained. These procedures are illustrated by a real example. Finally, asymptotic normality of the empirical Bayes estimators are established.     

Introducing Environmental Variables in Nonparametric Frontier Models: a Probabilistic Approach

This paper proposes a general formulation of a nonparametric frontier model introducing external environmental factors that might influence the production process but are neither inputs nor outputs under the control of the producer. A representation is proposed in terms of a probabilistic model which defines the data generating process. Our approach extends the basic ideas from Cazals et al. (2002) to the full multivariate case. We introduce the concepts of conditional efficiency measure and of conditional efficiency measure of order-m. Afterwards we suggest a practical way for computing the nonparametric estimators. Finally, a simple methodology to investigate the influence of these external factors on the production process is proposed. Numerical illustrations through some simulated examples and through a real data set on Mutual Funds show the usefulness of the approach.JEL Classification: C13, C14, D20     

“Panelizing” Repeated Cross Sections

This paper considers the implementation of a non-stationary, heterogeneous Markov model for the analysis of binary dependent variables in a time series of repeated cross-sectional (RCS) surveys. The model offers the opportunity to estimate entry and exit transition probabilities and to examine the effects of time-constant and time-varying covariates on the hazards. We show how maximum likelihood estimates of the parameters can be obtained by Fishers method-of-scoring and how to estimate both fixed and time-varying covariate effects. The model is exemplified with an analysis of the labor force participation decision of Dutch and West German women using ISSP (and other) data from 10 annual Dutch surveys conducted between 1987 and 1996 and 7 annual West German surveys conducted between 1988 and 1994. Some open problems concerning the application of the model are discussed.     

Dynamic and Comparative Static Analysis of Imperfectly Competitive International Fishery

A dynamic system for change of fish stock is formulated assuming that two asymmetric rival countries harvest fish from a common fishing ground and sell it in an imperfectly competitive market. The existence of a unique stable steady state is examined based on the dynamic system. Comparative static analysis is conducted in relation to changes in national fishing management policies to explore, among other things, rent-shifting from one country to another.     

Approximate maximum likelihood estimation for stochastic differential equations with random effects in the drift and the diffusion

Consider N independent stochastic processes \((X_i(t), t\in [0,T])\), \(i=1,\ldots , N\), defined by a stochastic differential equation with random effects where the drift term depends linearly on a random vector \(\Phi _i\) and the diffusion coefficient depends on another linear random effect \(\Psi _i\). For these effects, we consider a joint parametric distribution. We propose and study two approximate likelihoods for estimating the parameters of this joint distribution based on discrete observations of the processes on a fixed time interval. Consistent and \(\sqrt{N}\)-asymptotically Gaussian estimators are obtained when both the number of individuals and the number of observations per individual tend to infinity. The estimation methods are investigated on simulated data and show good performances.     

Broadband Demand Analysis for Network Building

An increase of broadband demand is forecasted by transitional methods that consider the effect of this increase through many factors, such as customer requirement diversification, and new service introduction and deployment under competition. Broadband demand forecasting has become important for closing the digital divide, promoting regional developments, and constructing networks economically; therefore, a demand forecast model that considers the mechanisms of market structure is necessary. In this paper, a demand analysis method for broadband access combining macro- and micro-data mining is proposed, and the service choice behaviour of customers is introduced as a customer model not only to express the macro trend of market structure, but also to consider area marketing. The proposed method can estimate the potential demand, determine the point at which broadband demand growth peaks in a specified area, and support a decision for ultra high-speed broadband access facility installation.