Mutual gains? labor and management evaluate their employee involvement programs

This study examines the impact of employee involvement programs on social, psychological, production, and economic issues. The findings are based on a diverse group of 236 EI programs in Pennsylvania, where data were gathered from both management and labor in the same workplace. The majority of the programs are based on a quality circle model; few contain innovative pay systems; and less than one third could be classified as intensive. Management indicates that these programs have a positive impact on a variety of factors, especially social and psychological effects. Unions are less enthusiastic about EI but are generally positive or neutral, with few indicating any negative effects. Despite these generally positive reports by both labor and management, direct comparison yields little agreement between labor and management on the effects of EI. Our results question the assumption of “mutual gains,” i.e., that the effects of EI programs are clear, public, and shared equally by both labor and management. Alternative explanations are discussed. This research was conducted with grants from the Pennsylvania Department of Labor and Industry and the Pennsylvania MILRITE Committee. The views expressed, however, are solely those of the authors.     

The Family Rule Concept in the Theory of Family Therapy**

This paper is an exploration of the usefulness of the concept of a family rule in systems thinking. Wittgenstein's analysis of rule conforming behaviour is considered alongside the early contributions of Jackson, Haley and others. Confusion has resulted from using the word “rule” in both its everyday sense and as a theoretical construct. A parallel confusion shows Itself in the activity of reframing where the selection of an alternative context is often theoretically based while the client receives an explanation in the language of everyday intentional actions.     

Statistical quantifiers in observational calculi: An application in GUHA-methods

GUHA or general unary hypotheses automaton aims at generating automatically all relevant statements and generalizations about a field of research, for example, a chosen empirical field of social research. For that reason the concept of observational calculi and their relation to the theoretical level of social research is discussed. A statistical generalization of the observational calculus, statistical inference rules and ten generalized statistical quantifiers are defined and discussed. After this formal outline of the theory of GUHA methods, some applications of statistical quantifiers in GUHA methods are discussed. Finally, a bibliography of GUHA methods concludes the paper.     

Ordering,Pricing, and Lead‐Time Quotation Under Lead‐Time and Demand Uncertainty

In this article, we study the newsvendor problem with endogenous setting of price and quoted lead‐time. This problem can be observed in situations where a firm orders semi‐finished product prior to the selling season and customizes the product in response to customer orders during the selling season. The total demand during the selling season and the lead‐time required for customization are uncertain. The demand for the product depends not only on the selling price but also on the quoted lead‐time. To set the quoted lead‐time, the firm has to carefully balance the benefit of increasing demand as the quoted lead‐time is reduced against the cost of increased tardiness. Our model enables the firm to determine the optimal selling price, quoted lead‐time, and order quantity simultaneously, and provides a new set of insights to managers.     

Performance of Variable Selection Methods in Regression Using Variations of the Bayesian Information Criterion

The Bayesian information criterion (BIC) is widely used for variable selection. We focus on the regression setting for which variations of the BIC have been proposed. A version that includes the Fisher Information matrix of the predictor variables performed best in one published study. In this article, we extend the evaluation, introduce a performance measure involving how closely posterior probabilities are approximated, and conclude that the version that includes the Fisher Information often favors regression models having more predictors, depending on the scale and correlation structure of the predictor matrix. In the image analysis application that we describe, we therefore prefer the standard BIC approximation because of its relative simplicity and competitive performance at approximating the true posterior probabilities.     

Stochastic Algorithms for the Estimation of an Optimal Solution of a LP Problem. Convergence and Central Limit Theorem

In this article, we consider a Linear Programming (LP) problem with unknown objective function. We introduce a class of stochastic algorithms to estimate an optimal solution of the LP problem. The almost sure convergence and the speed of convergence of these algorithms are analyzed. We also prove a central limit theorem for the estimation errors of the algorithms.     

Errors in variables: consistent adjusted least squares (cals) estimation

A single equation errors-in-variables model is considered. Exact restrictions on the parameters in the model are assumed to be available such that the model is just-identified. A Consistent Adjusted Least Squares (CALS) estimator for this model is proposed and its asymptotic distribution is given. Special cases are given as illustrations. CALS is identical to the Method of Moments (MM), and to Maximum Likelihood (ML) under the structural interpretation. Under the functional interpretation it is identical to ML in cases where the latter method is consistent.