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Although it is widely acknowledged that health care delivery systems are complex adaptive systems, there are gaps in understanding the application of systems engineering approaches to systems analysis and redesign in the health care domain. Commonly employed methods, such as statistical analysis of risk factors and outcomes, are simply not adequate to robustly characterize all system requirements and facilitate reliable design of complex care delivery systems. This is especially apparent in institutional-level systems, such as patient safety programs that must mitigate the risk of infections and other complications that can occur in virtually any setting providing direct and indirect patient care. The case example presented here illustrates the application of various system engineering methods to identify requirements and intervention candidates for a critical patient safety problem known as failure to rescue. Detailed descriptions of the analysis methods and their application are presented along with specific analysis artifacts related to the failure to rescue case study. Given the prevalence of complex systems in health care, this practical and effective approach provides an important example of how systems engineering methods can effectively address the shortcomings in current health care analysis and design, where complex systems are increasingly prevalent.

The contemporary workplace is characterized by transience: Organizational members frequently turn over and careers span multiple organizations. Consequently, workplace friendships that were once close become less close and intimate, that is they become peripheral and can deteriorate. While research has examined the benefits for employees who move on to new opportunities, less clear is how stayers, or employees who remain behind in the work setting, are affected. To understand stayers’ experiences and how they manage, we draw on theories of belongingess and to offer a three-part episodic process model, which explains how stayers’ engagement in the task and social domains are influenced. In doing so, we (1) present a dynamic view of the deterioration of dyadic relationships, highlighting how workplace relationships can change over time; (2) discuss both the depth and breadth of emotions involved for stayers; and (3) integrate a positive organizational scholarship perspective by considering both strength of friendships with other present coworkers and coping approaches of stayers as important boundary conditions, which can facilitate their recovery process. We draw attention to the broader implications of our theorizing for research on relationships and emotions, and practical implications for management.

Asynchronous current account trends between North and South of the Euro Area were accompanied by significant appreciations of real exchange rate originating in the strong shifts in consumer prices and unit labor costs in the periphery economies relative to the core countries of the Euro Area. The issue is whether the real exchange rate is a significant driver of persisting current account imbalances in the Euro Area considering that, according to some authors, differences in domestic demand are more important than is often realized. In the paper we examine relative importance of real exchange rate and demand shocks according to the current account adjustments in the Euro Area member countries. Our results indicate that while the prices and costs related determinants of external competitiveness affected current account adjustments primarily during the pre-crisis period, demand drivers shaped current account balances mainly during the crisis period.

Purpose – The purpose of this study is to develop a numerical framework to predict the time-dependent probability of failure of a bridge subjected to multiple vehicle impacts. Specially, this study focuses on investigating the inter-relationship between changes in life-cycle parameters (e.g., damage size caused by vehicle impact, loss of initial structural capacity, and threshold intervention) and bridges probability of failure.

Design/Methodology/Approach – The numerical procedure using MATLAB program is developed to compute the probability failure of a bridge. First, the importance and characteristics of life-cycle analysis is described. Then, model for damage accumulation and life cycle as a result of heavy vehicle impacts is discussed. Finally, the probability of failure of a bridge subjected to vehicle impacts as a result of change in life-cycle parameters is presented.

Findings – The results of study show that damage size caused by both vehicle impacts and loss of initial structural capacity have a great impact on the long-term safety of bridges. In addition, the probability of failure of a bridge under different threshold limits indicates that the structural intervention (e.g., repair or maintenance) should be undertaken to extend the service life of a bridge.

Research Limitations/Implications – The damage sizes caused by heavy vehicle impacts are based on simple assumptions. It is suggested that there would be a further study to estimate the magnitude of bridge damage as a result of vehicle impact using the full-scale impact test or computational simulation.

Practical Implications – This will allow much better predictions for residual life of bridges which could potentially be used to support decisions on health and maintenance of bridges.

Originality/Value – The life-cycle performance for assessing the time-dependent probability of failure of bridges subjected to multiple vehicle impact has not been fully discussed so far.

Organizational approaches to racial inequality have provided contextual insight into a host of traditional stratification outcomes (e.g., hiring, earnings, authority). This chapter extends the organizational approach by drawing on the health-stress framework to explore how organizational context affects experiential and health-related outcomes – discrimination, social support, and psychological distress. Drawing on a sample of Black workers in the United States, we examine the relationship between workplace racial composition and psychological distress, as well as two potential mediators – racial discrimination and workplace social support. Our findings reveal that psychological distress is similar for Black workers in token (<25% Black coworkers), tilted other race (25–49.99% Black coworkers), and tilted same race (50–74.99% Black coworkers) job contexts. Workers in Black-dominated jobs (>75% Black coworkers), however, experience significantly less psychological distress than other compositional thresholds, net of individual, job, and workplace characteristics. This relationship is not explained by either racial discrimination experiences or supervisor and coworker social support. This finding suggests that researchers need to theorize and examine other protective factors stemming from coworker racial similarity.

This chapter explores the traditional and evolving practice of preservation. Traditional preservation practices are traced throughout history. Current practice is described as a foundation from which to understand evolving practices. Traditional preservation has historically focused on tangible assets that take a tangible form we can all see and touch. The traditional practices are compared to the evolving practices of knowledge and intangible resources. The chapter examines why, what, how, when, where we preserve, and who preserves to understand the transition in progress today. The authors make the case that shifting the traditional focus from preserving for the past and evidentiary reasons to preserving for the future to support business challenges is crucial. The authors also tie the need to refocus on historical impediments and challenges to knowledge use and reuse in practical business environments.

Recent U.S. Treasury yields have been constrained to some extent by the zero lower bound (ZLB) on nominal interest rates. Therefore, we compare the performance of a standard affine Gaussian dynamic term structure model (DTSM), which ignores the ZLB, to a shadow-rate DTSM, which respects the ZLB. Near the ZLB, we find notable declines in the forecast accuracy of the standard model, while the shadow-rate model forecasts well. However, 10-year yield term premiums are broadly similar across the two models. Finally, in applying the shadow-rate model, we find no gain from estimating a slightly positive lower bound on U.S. yields.

Management and especially strategy research rely heavily on the use of ratios to measure a variety of firm, industry, and societal characteristics. Most often, these ratios are created simply to control for size effects (i.e., scaling) emanating from differences in the size of firms, industries, populations, or national economies on the variables of interest. In addition, ratios may also hold theoretical meaning apart from that of their components. Despite the popularity of ratios and regardless of their purpose, the use of ratios is not without controversy. In particular, several studies have demonstrated that the use of ratio measures in correlations and multiple regressions can exaggerate relations of interest leading to biased and unstable results. In this chapter, I review the debate surrounding the use of ratio measures, discuss the problems for estimation and inference that are likely to arise when ratios are used in statistical estimation, and provide alternatives to the use of ratio variables that still satisfy the purpose for which ratio measures are often created.

Recently, it has been suggested that macroeconomic forecasts from estimated dynamic stochastic general equilibrium (DSGE) models tend to be more accurate out-of-sample than random walk forecasts or Bayesian vector autoregression (VAR) forecasts. Del Negro and Schorfheide (2013) in particular suggest that the DSGE model forecast should become the benchmark for forecasting horse-races. We compare the real-time forecasting accuracy of the Smets and Wouters (2007) DSGE model with that of several reduced-form time series models. We first demonstrate that none of the forecasting models is efficient. Our second finding is that there is no single best forecasting method. For example, typically simple AR models are most accurate at short horizons and DSGE models are most accurate at long horizons when forecasting output growth, while for inflation forecasts the results are reversed. Moreover, the relative accuracy of all models tends to evolve over time. Third, we show that there is no support to the common practice of using large-scale Bayesian VAR models as the forecast benchmark when evaluating DSGE models. Indeed, low-dimensional unrestricted AR and VAR forecasts may forecast more accurately.