Preis-Promotion-Framing – Ein Überblick zum Stand der Forschung

This article reviews published literature and provides an overview on present empirical evidence about the framing of price promotions. Concerning explicit price reductions (discounts, coupons, rebates), the effects of advertised reference prices and different frames of price discounts (relative versus absolute price discount, objective versus tensile price claims) on consumers’ reactions are discussed. Furthermore, the article examines empirical results on implicit price reductions (volume packs, free extra products) as well as on sale signs and restrictions (e.g. purchase limits, time limits) as popular ways of framing a deal. The key findings point out that framing a price promotion in different ways can affect consumers’ perceptions and evaluations of the deal. Based on the reviewed literature, the concluding section provides managerial implications and an agenda for further research.     

Median problems with positive and negative weights on cycles and cacti

This paper deals with facility location problems on graphs with positive and negative vertex weights. We consider two different objective functions: In the first one (MWD) vertices with positive weight are assigned to the closest facility, whereas vertices with negative weight are assigned to the farthest facility. In the second one (WMD) all the vertices are assigned to the nearest facility. For the MWD model it is shown that there exists a finite set of points in the graph which contains the locations of facilities in an optimal solution. Furthermore, algorithms for both models for the 2-median problem on a cycle are developed. The algorithm for the MWD model runs in linear time, whereas the algorithm for the WMD model has a time complexity of  O(n²)\mathcal{O}(n^{2}) .     

A Hybrid Geometric Phase II/III Clinical Trial Design based on Treatment Failure Time and Toxicity

The problem of comparing several experimental treatments to a standard arises frequently in medical research. Various multi-stage randomized phase II/III designs have been proposed that select one or more promising experimental treatments and compare them to the standard while controlling overall Type I and Type II error rates. This paper addresses phase II/III settings where the joint goals are to increase the average time to treatment failure and control the probability of toxicity while accounting for patient heterogeneity. We are motivated by the desire to construct a feasible design for a trial of four chemotherapy combinations for treating a family of rare pediatric brain tumors. We present a hybrid two-stage design based on two-dimensional treatment effect parameters. A targeted parameter set is constructed from elicited parameter pairs considered to be equally desirable. Bayesian regression models for failure time and the probability of toxicity as functions of treatment and prognostic covariates are used to define two-dimensional covariate-adjusted treatment effect parameter sets. Decisions at each stage of the trial are based on the ratio of posterior probabilities of the alternative and null covariate-adjusted parameter sets. Design parameters are chosen to minimize expected sample size subject to frequentist error constraints. The design is illustrated by application to the brain tumor trial.     

Ausgelernt? Professor/innen im Prozess der Professionalisierung

Do university teachers have finished to learn, when they are hired for a position as full professor? That seems to be false, if you look at knowledge enrichment by research as learning. For Teaching and management it seems more justified. Factually full professors very seldom participate at corresponding continuing education or consulting. Learning takes place mostly on the side as learning by doing. Professionalization in a full sense however is only realized, if competences are developed also in these fields. As corresponding learning can be supported by continuous education and consulting is a very important task of academic personnel development.     

Mood in Daily Contexts: Relationship With Risk in Early Adolescence

Disturbances in affect have been linked to problem behavior in adolescence and future psychopathology, but little is known about how such disturbances manifest themselves in everyday contexts. This study investigated daily mood in Dutch 7th graders, aged 11–14. Cluster analysis of problem measures distinguished high-risk (n=25) and low-risk (n=106) subgroups. Participants completed experience-sampling reports of mood, social context, and location nine times daily for 5 days. Multilevel regression analyses of four mood measures confirmed higher anxiety and depressed mood in the high-risk group. Moods varied by location and social context, with significant differences between groups in two specific social contexts. First, when with family, low-risk adolescents felt less depressed than when alone, whereas high-risk adolescents felt more depressed. Second, high-risk adolescents showed more pronounced anxiety in social situations outside the network of family and friends. These findings point to everyday social contexts in which young adolescents with emotional and behavioral problems appear to be particularly vulnerable.     

OUTSOURCING WHEN INVESTMENTS ARE SPECIFIC AND INTERRELATED

Using the universe of large Canadian manufacturing firms in 1988 and 1996, we investigate to what extent outsourcing patterns concord with the predictions of a simple property rights model. The unique availability of disaggregate information on outputs as well as inputs permits the construction of a detailed measure of vertical integration. We rely on five measures of technological intensity to proxy for investments that are likely to be specific to a buyer–seller relationship. A theoretical model that allows for varying degrees of investment specificity and interrelatedness—externalities between buyer and supplier investments—guides the analysis. Property rights predictions on the link between investment intensities and optimal ownership are strongly supported, but only for transactions with low interrelatedness. High specificity and low risk of appropriation strengthen the predictions in the model and in the data.     

A tutorial on adaptive MCMC

We review adaptive Markov chain Monte Carlo algorithms (MCMC) as a mean to optimise their performance. Using simple toy examples we review their theoretical underpinnings, and in particular show why adaptive MCMC algorithms might fail when some fundamental properties are not satisfied. This leads to guidelines concerning the design of correct algorithms. We then review criteria and the useful framework of stochastic approximation, which allows one to systematically optimise generally used criteria, but also analyse the properties of adaptive MCMC algorithms. We then propose a series of novel adaptive algorithms which prove to be robust and reliable in practice. These algorithms are applied to artificial and high dimensional scenarios, but also to the classic mine disaster dataset inference problem.     

On the reciprocity of connections in weighted and unweighted networks

Bivariate Bernoulli and bivariate geometric distributions are proposed to model reciprocity in weighted and unweighted networks. Sampling properties of commonly used reciprocity measures are studied, under both presence and absence of reciprocity. Extensions to situations where link parameters depend on outgoing and incoming nodes are investigated. Results are illustrated with a data set, on 50 anesthesia providers at a hospital, that describes the number of times an anesthesia provider starting an operation transfers his/her responsibilities to another anesthesia provider.