Model-Based Decision Support in Manufacturing and Service Networks

In this paper, we sketch some of the challenges that should be addressed in future research efforts for model-based decision support in manufacturing and service networks. This includes integration issues, taking into account the autonomy of the decision-making entities in face of information asymmetry, the modeling of preferences of the decision makers, efficiently determining robust solutions, i.e. solutions that are insensitive with respect to changes in the problem data, and a reduction of the time needed for model building and usage. The problem solution cycle includes problem analysis, the design of appropriate algorithms and their performance assessment. We are interested in a prototypical integration of the proposed methods within application systems, which can be followed up with field tests of the extended application systems. We argue that the described research agenda requires the interdisciplinary collaboration of business and information systems engineering researchers with colleagues from management science, computer science, and operations research. In addition, we present some exemplifying, illustrative examples of relevant research results.     

Consumer perception of packaging: An eye‐tracking study of gluten‐free cookies

The growing interest in gluten‐free products makes it crucial to understand the mechanism of consumers' perception of gluten‐free claims. Due to the limited research measuring the influence of the type (verbal/nonverbal), number and location of gluten‐free statements on product labels on visual attention and consumers' purchase decisions, the main aim of this research was to evaluate the perception of gluten‐free claims and the Crossed Grain symbol on cookie packages of both consumers who are on a gluten‐free diet and those who are not. To that end, a questionnaire survey (600 respondents) and an eye‐tracking study (67 respondents) were carried out. The results showed that the respondents are aware of the basic rules of gluten‐free products' labelling, but 32% of those on the diet claimed that the products were not labelled properly. The analysis proved (p < .05) the significant relation between the number of gluten‐free claims and consumers' purchase intention. The respondents paid more attention to verbal gluten‐free claims than to nonverbal ones on packaging (p < .05). In the case of the Crossed Grain symbol, addition of verbal statement strengthened the information and decreased the respondents' level of uncertainty about a given product, which is important, especially for people newly adopting the diet. No difference in the visual attention to gluten‐free statements between the followers and non‐followers of the diet was found (p > .05), which allowed us to conclude that the perception of gluten‐free claims is an automatic process. These results suggest that designing product labels by considering the information architecture concerning gluten may help strengthen consumers' attitudes towards gluten‐free products and impact buying behaviour.     

Corporate governance performance ratings with machine learning

We use machine learning with a cross-sectional research design to predict governance controversies and to develop a measure of the governance component of the environmental, social, governance (ESG) metrics. Based on comprehensive governance data from 2,517 companies over a period of 10 years and investigating nine machine-learning algorithms, we find that governance controversies can be predicted with high predictive performance. Our proposed governance rating methodology has two unique advantages compared with traditional ESG ratings: it rates companies' compliance with governance responsibilities and it has predictive validity. Our study demonstrates a solution to what is likely the greatest challenge for the finance industry today: how to assess a company's sustainability with validity and accuracy. Prior to this study, the ESG rating industry and the literature have not provided evidence that widely adopted governance ratings are valid. This study describes the only methodology for developing governance performance ratings based on companies' compliance with governance responsibilities and for which there is evidence of predictive validity.     

Storing power: market structure matters

We assess how firms' incentives to operate and invest in energy storage depend on the market structure. For this purpose, we characterize equilibrium market outcomes allowing for market power in storage and/or production, as well as for vertical integration between storage and production. Market power reduces overall efficiency through two channels: It induces an inefficient use of the storage facilities, and it distorts investment incentives. The worst outcome for consumers and total welfare occurs under vertical integration. The results are key to understanding how to regulate energy storage, an issue which is critical for the deployment of renewable energies.     

Ethnic-specific infant care practices and infant mortality in late Imperial Russia

The Russian Empire had the highest infant mortality rate in Europe at the beginning of the twentieth century. Using a variety of official statistical sources and qualitative evidence, this paper documents uniquely high infant mortality among ethnic Russians. In contrast, among other ethnic groups of the empire, infant mortality rates did not exceed those of the European countries by much. The evidence suggests that the explanation for the Russian infant mortality pattern was ethnic-specific infant care practices, such as the early introduction of solid food, which increased the incidence of lethal gastrointestinal diseases. Our findings highlight the importance of traditional infant feeding practices in mortality in pre-industrial societies.