Assessing Engineering Resilience for Systems with Multiple Performance Measures

Recently, efforts to model and assess a system's resilience to disruptions due to environmental and adversarial threats have increased substantially. Researchers have investigated resilience in many disciplines, including sociology, psychology, computer networks, and engineering systems, to name a few. When assessing engineering system resilience, the resilience assessment typically considers a single performance measure, a disruption, a loss of performance, the time required to recover, or a combination of these elements. We define and use a resilient engineered system definition that separates system resilience into platform and mission resilience. Most complex systems have multiple performance measures; this research proposes using multiple objective decision analysis to assess system resilience for systems with multiple performance measures using two distinct methods. The first method quantifies platform resilience and includes resilience and other “ilities” directly in the value hierarchy, while the second method quantifies mission resilience and uses the “ilities” in the calculation of the expected mission performance for every performance measure in the value hierarchy. We illustrate the mission resilience method using a transportation systems‐of‐systems network with varying levels of resilience due to the level of connectivity and autonomy of the vehicles and platform resilience by using a notional military example. Our analysis found that it is necessary to quantify performance in context with specific mission(s) and scenario(s) under specific threat(s) and then use modeling and simulation to help determine the resilience of a system for a given set of conditions. The example demonstrates how incorporating system mission resilience can improve performance for some performance measures while negatively affecting others.     

Investigating the Task Dependency of Viewing Time Effects

The viewing time method (VT) is an indirect measure of sexual interest in which participants are usually asked to rate the sexual attractiveness of target stimuli while their response latencies are unobtrusively measured. Higher response latencies for a certain group of depicted individuals indicate sexual interest in that group. Contrary to the general assumption that the picture content accounts for this effect by eliciting affect- or attention-based response delays, we hypothesized that the attractiveness rating task might be responsible for the VT effect. To test this hypothesis, we used two different tasks. Our heterosexual and homosexual male participants (N = 50) were instructed to rate the attractiveness or the sex of the depicted individuals. As expected, VT effects only emerged in attractiveness rating trials. Based on these findings, we conclude that VT effects are task dependent and are unlikely to be caused by affective or attentional processes (at least when participants are instructed to rate the attractiveness of target stimuli). We argue that rating tasks in VT measures cause participants to use affect independent response strategies. These response strategies seem to undermine stimulus-driven processes (like increased attention directed toward salient stimuli) which were thought to cause VT effects according to previous hypotheses.     

An international study of ethics

This study presents the results of a survey of graduate students from three cultural backgrounds. English, German, and American business students responded to an eleven-item survey designed to assess one's beliefs about what is necessary to succeed in graduate business programs. The results indicate that the attributes which students believe are required to succeed differ among cultures, with the greatest differences occurring between the American and European respondents.     

Stochastic orders and non-Gaussian risk factor models

The main results of this paper are monotonicity statements about the risk measures value-at-risk (VaR) and tail value-at-risk (TVaR) with respect to the parameters of single and multi risk factor models, which are standard models for the quantification of credit and insurance risk. In the context of single risk factor models, non-Gaussian distributed latent risk factors are allowed. It is shown that the TVaR increases with increasing claim amounts, probabilities of claims and correlations, whereas the VaR is in general not monotone in the correlation parameters. To compare the aggregated risks arising from single and multi risk factor models, the usual stochastic order and the increasing convex order are used in this paper, since these stochastic orders can be interpreted as being induced by the VaR-concept and the TVaR-concept, respectively. To derive monotonicity statements about these risk measures, properties of several further stochastic orders are used and their relation to the usual stochastic order and to the increasing convex order are applied.     

Undercounting Medicaid Enrollment in Maryland: Testing the Accuracy of the Current Population Survey

The U.S. Census Bureau’s Current Population Survey is the most commonly cited source for estimates of the insurance status of Americans, but there are concerns that the data may undercount participation in public programs such as Medicaid. Such a Medicaid undercount may result from survey respondents not acknowledging Medicaid coverage because they are unaware that they are enrolled in Medicaid, because they have not recently received health services, due to the stigma associated with receiving public assistance programs, or due to simple recall bias. This paper estimates the extent to which the Current Population Survey undercounts Medicaid participation in a single mid-Atlantic state, Maryland. We administered the Current Population Survey questionnaire to a random selection of known Medicaid participants. We find evidence that the Current Population Survey significantly undercounts Medicaid participation in Maryland and that much of the undercount could be corrected if the survey better identified the Maryland Medicaid program. We also find that recall bias may contribute to the undercount as well. There was no indication that stigma contributed to any undercount. Though not an aim of this study, we find that the Medicaid undercount may contribute to an over count of the number of uninsured.

Modelling method effects as individual causal effects

Summary.  Method effects often occur when different methods are used for measuring the same construct. We present a new approach for modelling this kind of phenomenon, consisting of a definition of method effects and a first model, the method effect model , that can be used for data analysis. This model may be applied to multitrait–multimethod data or to longitudinal data where the same construct is measured with at least two methods at all occasions. In this new approach, the definition of the method effects is based on the theory of individual causal effects by Neyman and Rubin. Method effects are accordingly conceptualized as the individual effects of applying measurement method j instead of k . They are modelled as latent difference scores in structural equation models. A reference method needs to be chosen against which all other methods are compared. The model fit is invariant to the choice of the reference method. The model allows the estimation of the average of the individual method effects, their variance, their correlation with the traits (and other latent variables) and the correlation of different method effects among each other. Furthermore, since the definition of the method effects is in line with the theory of causality, the method effects may (under certain conditions) be interpreted as causal effects of the method. The method effect model is compared with traditional multitrait–multimethod models. An example illustrates the application of the model to longitudinal data analysing the effect of negatively (such as 'feel bad') as compared with positively formulated items (such as 'feel good') measuring mood states.     

Produktdiversifizierung und Unternehmenserfolg: Nutzung der Forschungsdatenzentren der Statistischen ?mter

Zusammenfassung: Im Rahmen seiner industrie?konomischen Forschung beabsichtigt das DIW Berlin das Verhalten von Unternehmen auf den Produktm?rkten intensiver zu analysieren. In einem ersten Schritt soll der Zusammenhang zwischen Produktdiversifizierung, der Einführung neuer Produkte und dem Erfolg von Unternehmen im verarbeitenden Gewerbe untersucht werden. Dies soll mit Hilfe einer ?konometrischen Panelanalyse auf der Grundlage von Mikrodaten von Unternehmen erfolgen. Statt des in der Arbeitsmarktforschung üblichen Employer-Employee Datensatzes wird für diesen Zweck ein Producer-Product Datensatz ben?tigt. Erstmals sollen diese Mikrodaten im Rahmen einer Panelanalyse durch die Forschung ausgewertet werden. Dabei werden zwei getrennt durchgeführte Erhebungen, die Kostenstrukturerhebung und die Produktionserhebung, für einzelne Unternehmen zusammengefasst. M?glich wird dieses Forschungsvorhaben durch die Nutzung des auf Empfehlung der Kommission zur Verbesserung der informationellen Infrastruktur neu eingerichteten Forschungsdatenzentrums (FDZ). Das vorliegende Papier berichtet über das Forschungskonzept und die bisher erfolgten Schritte bei der Nutzung der Forschungsdatenzentren des Bundes und der L?nder am regionalen Standort Berlin.

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Summary: For a better understanding of the strategies of firms on their product markets, the DIW Berlin intends to intensify the analysis on the relationship between product diversification, including the introduction of new products and the success of firms. It is planned to do this by applying econometric panel methods on micro level data for German manufacturing enterprises. For this objective, it is necessary to create a producer-product database. This affords the merging of two different datasets of the system of surveys in manufacturing for Germany. The cost structure survey supplies information on the operating surplus of individual firms, while the production survey supplies the information on the goods produced by these firms. Conducting such an ambitious project has now been made possible in using the newly installed Research Data Centres (FDZ) for Germany. This paper gives an overview on the research project and on first experiences with the use of the local Research Data Centre in Berlin.

     

Nature and role of customer satisfaction in the solution business

The paper focuses on the solution business. Here we argue that the strategy can only lead a company to perform better if it encompasses a direct and positive effect on satisfaction with a supplier’s consulting capabilities. Hence we introduce the concept of consulting satisfaction, identify antecedents and analyze its consequences. To do so, we apply a mixed-methods approach. Content analysis from open interviews with company professionals yields a list of antecedents of consulting satisfaction used to devise a set of hypotheses. The latter was translated into a questionnaire based measurement instrument in order to first collect and then analyze data from a larger sample survey. Since three of the antecedents could not be supported by our study, possible explanations are presented. With our approach we tap new dimensions of solutions research. First, we inaugurate research on buying behavior within the field of solutions. Second, we stress the role of capabilities in this field and extend service–profit chain thinking. And third, we present consulting satisfaction as a lever to translate the S-D logic of marketing into more practical applications. To our knowledge this is a novel insight and can help industry decision makers better prepare for the solution business.     

Organizational cloud service adoption: a scientometric and content-based literature analysis

This paper takes a scientometric research approach and undertakes a categorized literature analysis to provide a comprehensive and systemic overview of the current status of research on cloud services and their adoption by organizations. We review 52 journals and proceedings of the information systems field to identify and systematically categorize 36 articles on the topic. Our content-based analysis shows that the scarce theoretical and empirical work on organizational cloud service adoption has developed and explored factors that directly or indirectly drive organizations to adopt or inhibit them from adopting cloud services from different perspectives and dimensions. We classify these driving and inhibiting determinants based on the technology organization environment framework Tornatzky and Fleischer (The process of technological innovation. Lexington Books, Lexington, 1990) to provide a structured summary. Based on our analysis and recent cloud evolution in practice, we identify five new research areas, including theoretical and practical phenomena describing determinants and consequences of cloud service adoption in organizations: information technology (IT) related public threat appeals, trust in the technology versus trust in the vendor, bring your own cloud, transformation of organizational identity, and intelligent clusters of IT initiatives. For each phenomenon we provide relevant research questions, potential theoretical foundations, and methodologies to guide future research endeavors in this field.