Heuristic Coordination of Decentralized Inventory Systems Using Induced Backorder Costs

In this paper, we investigate a one‐warehouse multiple‐retailer system, where the inventory control decisions are coordinated using a near optimal induced backorder cost, β*. All installations use continuous review installation‐stock (R, Q) policies. The analysis builds on an approximation model where the stochastic warehouse delays are replaced by their correct averages. The contributions include insights as to how β* is influenced by system parameters, and the determination of simple closed form β* estimates. The latter offering a practical means to achieve coordinated control of large size systems.     

Who Reaps the Benefits,Who Bears the Risks? Comparative Optimism,Comparative Utility,and Regulatory Preferences for Mobile Phone Technology

Although the issue of risk target (e.g., self, others, children) is widely acknowledged in risk perception research, its importance appears underappreciated. To date, most research has been satisfied with demonstrating comparative optimism, i.e., lower perceived risk for the self than others, and exploring its moderators, such as perceived controllability and personal exposure. Much less research has investigated how the issue of target may affect benefit perceptions or key outcomes such as stated preferences for hazard regulation. The current research investigated these issues using data from a public survey of attitudes toward mobile phone technology (N= 1,320). First, results demonstrated comparative optimism for this hazard, and also found moderating effects of both controllability and personal exposure. Second, there was evidence of comparative utility, i.e., users believed that the benefits from mobile phone technology are greater for the self than others. Third, and most important for policy, preferences for handset regulation were best predicted by perceptions of the risks to others but perceived benefits for the self. Results suggest a closer awareness of target can improve prediction of stated preferences for hazard regulation and that it would be profitable for future research to pay more attention to the issue of target for both risk and benefit perceptions.     

Consumer Awareness, Understanding and Usage of Unit Pricing

The debate continues over whether prescribed quantities or unit pricing offers the best method to enable consumers to make value-for-money comparisons. In theory, the 'informed' consumer should benefit from being given unit-price information to identify optimum purchases; in practice, however, it would appear that many consumers do not use the data. Although unit pricing can reduce the level of confusion caused by large product ranges and large numbers of unique size–price combinations, results of a survey of over 1000 people showed that: 31% of the sample did not understand how unit pricing was meant to help them compare products; 35% could not be bothered to look at unit prices and 28% stated that unit pricing was too difficult to use. Those least likely to look at unit prices are women, the least educated and consumers aged 18–34. The research investigated why consumers do not use unit-price information and found: some consumers do not possess the cognitive ability to process the information and feel unit pricing is too complicated to use; some products are not comparable, which makes unit prices misleading; many shops do not provide unit-price information; unit-price information was felt unnecessary when evaluating products with few or no alternative sizes or brands; unit-price comparisons take too much time; consumers use simpler strategies for getting value for money, e.g. volume discount heuristic, own brands, special offers, x% free, reward points, etc. The paper explores policy and retailer implications for unit pricing and examines recommendations for government.     

Severance Payments and Firm–specific Human Capital

What effect does employment protection through severance payments have on the behaviour of employed workers? We analyse this issue within a stochastic two–period framework where workers decide on human capital investments and find two competing effects: severance payments imply higher job security that fosters human capital formation. At the same time, a lay–off is perceived by the workers to be a weaker penalty if severance payments are provided. This incentive lowers their optimal amount of firm–specific investments. Which effect prevails on balance depends on the distribution of investment returns among firm and workers. For strong positive reactions, employment protection is also in the interests of the firm.     

The meaning of empowerment: the interdisciplinary etymology of a new management concept

Empowerment has become a widely used management term in the last decade or so, though, in practical terms, it shares the ambiguity of its predecessors in the HRM tradition. This paper sets out to unravel the web of meaning surrounding empowerment to show what a contested concept it is, and hence why its application in organizational settings is fraught with misunderstanding and tension. It does so by taking an approach that contributes to the examination of HRM discourse and management rhetoric. To demonstrate the ambiguity of empowerment as a concept, the paper reviews the various ways in which the term has been used across non-management disciplines (women, minority groups, education, community care, politics), culminating with a review of the use of empowerment in contemporary management theory. The paper concludes that organizations and managers have chosen to coin a phrase which is open to different, sometimes contradictory, meanings and which, when applied, evokes both subjective attitudes and objective behaviour, means different things in varying contexts, and is affected fundamentally by individual differences in perception and experience. Unless organizations offer clear operational definitions when using empowerment, instead of purely acquiescing to a vague and seductive version of the concept, they are abdicating responsibility for the unpredictable consequences that result.     

Interval estimation for log-linear models with one variable subject to non-ignorable non-response

Summary.  Log-linear models for multiway contingency tables where one variable is subject to non-ignorable non-response will often yield boundary solutions, with the probability of non-respondents being classified in some cells of the table estimated as 0. The paper considers the effect of this non-standard behaviour on two methods of interval estimation based on the distribution of the maximum likelihood estimator. The first method relies on the estimator being approximately normally distributed with variance equal to the inverse of the information matrix. It is shown that the information matrix is singular for boundary solutions, but intervals can be calculated after a simple transformation. For the second method, based on the bootstrap, asymptotic results suggest that the coverage properties may be poor for boundary solutions. Both methods are compared with profile likelihood intervals in a simulation study based on data from the British General Election Panel Study. The results of this study indicate that all three methods perform poorly for a parameter of the non-response model, whereas they all perform well for a parameter of the margin model, irrespective of whether or not there is a boundary solution.     

Efficient likelihood-free Bayesian Computation for?household epidemics

Considerable progress has been made in applying Markov chain Monte Carlo (MCMC) methods to the analysis of epidemic data. However, this likelihood based method can be inefficient due to the limited data available concerning an epidemic outbreak. This paper considers an alternative approach to studying epidemic data using Approximate Bayesian Computation (ABC) methodology. ABC is a simulation-based technique for obtaining an approximate sample from the posterior distribution of the parameters of the model and in an epidemic context is very easy to implement. A new approach to ABC is introduced which generates a set of values from the (approximate) posterior distribution of the parameters during each simulation rather than a single value. This is based upon coupling simulations with different sets of parameters and we call the resulting algorithm coupled ABC. The new methodology is used to analyse final size data for epidemics amongst communities partitioned into households. It is shown that for the epidemic data sets coupled ABC is more efficient than ABC and MCMC-ABC.     

Robust variable selection with application to quality of life research

A large database containing socioeconomic data from 60 communities in Austria and Germany has been built, stemming from 18,000 citizens’ responses to a survey, together with data from official statistical institutes about these communities. This paper describes a procedure for extracting a small set of explanatory variables to explain response variables such as the cognition of quality of life. For better interpretability, the set of explanatory variables needs to be very small and the dependencies among the selected variables need to be low. Due to possible inhomogeneities within the data set, it is further required that the solution is robust to outliers and deviating points. In order to achieve these goals, a robust model selection method, combined with a strategy to reduce the number of selected predictor variables to a necessary minimum, is developed. In addition, this context-sensitive method is applied to obtain responsible factors describing quality of life in communities.