Chemical Leasing Business Models—A Contribution to the Effective Risk Management of Chemical Substances

Chemicals indisputably contribute greatly to the well-being of modern societies. Apart from such benefits, however, chemicals often pose serious threats to human health and the environment when improperly handled. Therefore, the European Commission has proposed a regulatory framework for the Registration, Evaluation and Authorization of Chemicals (REACH) that requires companies using chemicals to gather pertinent information on the properties of these substances. In this article, we argue that the crucial aspect of this information management may be the honesty and accuracy of the transfer of relevant knowledge from the producer of a chemical to its user. This may be particularly true if the application of potentially hazardous chemicals is not part of the user's core competency. Against this background, we maintain that the traditional sales concept provides no incentives for transferring this knowledge. The reason is that increased user knowledge of a chemical's properties may raise the efficiency of its application. That is, excessive and unnecessary usage will be eliminated. This, in turn, would lower the amount of chemicals sold and in competitive markets directly decrease profits of the producer. Through the introduction of chemical leasing business models, we attempt to present a strategy to overcome the incentive structure of classical sales models, which is counterproductive for the transfer of knowledge. By introducing two models (a Model A that differs least and a Model B that differs most from traditional sales concepts), we demonstrate that chemical leasing business models are capable of accomplishing the goal of Registration, Evaluation and Authorization of Chemicals: to effectively manage the risk of chemicals by reducing the total quantity of chemicals used, either by a transfer of applicable knowledge from the lessor to the lessee (Model A) or by efficient application of the chemical by the lessor him/herself (Model B).     

The distribution of cook's d statistic

Cook (1977) proposed a diagnostic to quantify the impact of deleting an observation on the estimated regression coefficients of a General Linear Univariate Model (GLUM). Simulations of models with Gaussian response and predictors demonstrate that his suggestion of comparing the diagnostic to the median of the F for overall regression captures an erratically varying proportion of the values.

We describe the exact distribution of Cook's statistic for a GLUM with Gaussian predictors and response. We also present computational forms, simple approximations, and asymptotic results. A simulation supports the accuracy of the results. The methods allow accurate evaluation of a single value or the maximum value from a regression analysis. The approximations work well for a single value, but less well for the maximum. In contrast, the cut-point suggested by Cook provides widely varying tail probabilities. As with all diagnostics, the data analyst must use scientific judgment in deciding how to treat highlighted observations.     

Quasiextinction Probabilities as a Measure of Impact on Population Growth

A probabilistic language based on stochastic models of population growth is proposed for a standard language to be used in environmental assessment. Environmental impact on a population is measured by the probability of quasiextinction. Density-dependent and independent models are discussed. A review of one-dimensional stochastic population growth models, the implications of environmental autocorrelation, finite versus "infinite" time results, age-structured models, and Monte Carlo simulations are included. The finite time probability of quasiextinction is presented for the logistic model. The sensitivity of the result with respect to the mean growth rate and the amplitude of environmental fluctuations are examined. Stochastic models of population growth form a basis for formulating reasonable criteria for environmental impact estimates.     

Analysis of the National Gambling Impact Study Commission Act

The following article by Keith Whyte represents the first of occasionally reprinted publications that are relevant to important historical events in the field of gambling studies. This article describes the act that led to the formation of the National Gambling Impact Study Commission. This Commission released its findings to the President and Congress of the United States during June of 1999.     

Task-related and Partner-related Selection Criteria in UK International Joint Ventures

This paper identifies partner selection criteria in a sample of UK international joint ventures with Western European, US and Japanese partners. Rankings of selection criteria are derived for the sample using a typology that distinguishes between task-related and partner-related selection criteria. The most important task-related criteria are found to be access to knowledge of local market, access to distribution channels, access to links with major buyers and access to knowledge of local culture. The most important partner-related selection criteria are trust between the top management teams, relatedness of partner's business and reputation. A parsimonious set of selection criteria for the sample is provided by means of factor analysis. Hypotheses are tested on the relationship between the relative importance of selection criteria and a number of characteristics of the sample—partner nationality, industry of the joint ventures, joint-venture purpose, geographical location of the venture, initial approach for joint-venture formation and relative partner size. The greatest variation in the relative importance of selection criteria occurs with the geographic location of the joint venture.     

The Impact of Imperfect Processes on Production Run Times

This paper investigates the interaction between the economics of production and imperfections in the production process. Specifically, this paper is the first to devise a model in an attempt to provide managers with guidelines to choose the appropriate production run times to buffer against both the production of defective items and stoppages occurring due to machine breakdowns. In addition to providing several structural properties of the model, we show that a manager will always incur a cost penalty when (s)he uses the results of two oft‐cited models‐the EMQ (Economic Order/Manufacturing Quantity) and the NR‐E (No‐Resumption, Exponential machine breakdown)‐to determine production run times.     

Normality testing: two new tests using L-moments

Establishing that there is no compelling evidence that some population is not normally distributed is fundamental to many statistical inferences, and numerous approaches to testing the null hypothesis of normality have been proposed. Fundamentally, the power of a test depends on which specific deviation from normality may be presented in a distribution. Knowledge of the potential nature of deviation from normality should reasonably guide the researcher's selection of testing for non-normality. In most settings, little is known aside from the data available for analysis, so that selection of a test based on general applicability is typically necessary. This research proposes and reports the power of two new tests of normality. One of the new tests is a version of the R-test that uses the L-moments, respectively, L-skewness and L-kurtosis and the other test is based on normalizing transformations of L-skewness and L-kurtosis. Both tests have high power relative to alternatives. The test based on normalized transformations, in particular, shows consistently high power and outperforms other normality tests against a variety of distributions.