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In this paper, we follow the same logic as in Hausman (1978) to create a testing procedure that checks for the presence of outliers by comparing a regression estimator that is robust to outliers (S-estimator), with another that is more efficient but affected by them. Some simulations are presented to illustrate the good behavior of the test for both its size and its power.

This chapter reconstructs the garbage can model (GCM) of organizational choice as an agent-based model. Subsequently, it modifies the original model by establishing behavioral rules that regulate processes of organizational founding, growth, and disbanding in an artificial garbage can ecology. This population-level GCM reproduces some of the core features of the original GCM. Furthermore, it produces aggregate regularities that are broadly consistent with the historical trajectories followed by actual organizational populations.

A major stumbling block in multivariate discrete data analysis is the problem of evaluating the outcome probabilities that enter the likelihood function. Calculation of these probabilities involves high-dimensional integration, making simulation methods indispensable in both Bayesian and frequentist estimation and model choice. We review several existing probability estimators and then show that a broader perspective on the simulation problem can be afforded by interpreting the outcome probabilities through Bayes’ theorem, leading to the recognition that estimation can alternatively be handled by methods for marginal likelihood computation based on the output of Markov chain Monte Carlo (MCMC) algorithms. These techniques offer stand-alone approaches to simulated likelihood estimation but can also be integrated with traditional estimators. Building on both branches in the literature, we develop new methods for estimating response probabilities and propose an adaptive sampler for producing high-quality draws from multivariate truncated normal distributions. A simulation study illustrates the practical benefits and costs associated with each approach. The methods are employed to estimate the likelihood function of a correlated random effects panel data model of women's labor force participation.

Universities are expected to operate with high efficiency, with ever-growing expectations from a rising number of stakeholders in society. From a theoretical perspective economic science does provide frameworks and methods in order to tackle this, with the cornerstone of defining efficiency as a simple relation of a quantity of inputs toward a quantity of outputs. For the practice of university management and policy this does not answer the crucial questions of which inputs and which outputs to measure, and how to ensure the quality aspect of such management approaches. Higher education research can contribute to answering these questions. This chapter outlines a sector-specific framework for efficiency analysis and management, including suggestions regarding how to implement efficiency-improving measures in university settings.

In this chapter the potential to use water-based Trombe walls to provide heated water for building applications during the summer months is investigated. Design Builder software is used to model a simple single-story building with a south-facing Trombe wall. The effects of using different thermal storage mediums within the Trombe wall on building heating loads during the winter and building cooling loads during the summer are modeled. The amount of thermal energy stored and temperature of water within the thermal storage medium during hot weather conditions were also simulated. On a sunny day on Toronto, Canada, the average temperature of the water in a Trombe wall integrated into a single-story building can reach ∼57°C, which is high enough to provide for the main hot water usages in buildings. Furthermore, the amount of water heated is three times greater than that required in an average household in Canada. The results from this work suggest that water-based Trombe walls have great potential to enhance the flexibility and utility of Trombe walls by providing heated water for building applications during summer months, without compromising performance during winter months.

This chapter suggests a robust Hausman and Taylor (1981), hereafter HT, estimator that deals with the possible presence of outliers. This entails two modifications of the classical HT estimator. The first modification uses the Bramati and Croux (2007) robust Within MS estimator instead of the Within estimator in the first stage of the HT estimator. The second modification uses the robust Wagenvoort and Waldmann (2002) two-stage generalized MS estimator instead of the 2SLS estimator in the second step of the HT estimator. Monte Carlo simulations show that, in the presence of vertical outliers or bad leverage points, the robust HT estimator yields large gains in MSE as compared to its classical Hausman–Taylor counterpart. We illustrate this robust version of the HT estimator using an empirical application.

This chapter discusses the emerging network science approach to the study of complex adaptive systems and applies tools derived from statistical physics to the analysis of tourism destinations. The authors provide a brief history of network science and the characteristics of a network as well as different models such as small world and scale free networks, and dynamic properties such as resilience and information diffusion. The Italian resort island of Elba is used as a case study allowing comparison of the communication network of tourist organizations and the virtual network formed by the websites of these organizations. The study compares the parameters of these networks to networks from the literature and to randomly created networks. The analyses include computer simulations to assess the dynamic properties of these networks. The results indicate that the Elba tourism network has a low degree of collaboration between members. These findings provide a quantitative measure of network performance. In general, the application of network science to the study of social systems offers opportunities for better management of tourism destinations and complex social systems.

In an era of increasing demand for healthcare coupled with decreasing availability of highly skilled healthcare professionals, healthcare administrators are increasingly concerned with how they might recruit and retain talent. Increasingly, they are focusing on compensation strategies to support their recruitment and retention objectives. This article investigates the organizational efficiency and financial performance implications for hospitals of using a hybrid relative wage strategy to compensate their nursing professionals. Considering three types of nursing professionals, registered nurses (RNs), licensed practical nurses (LPNs), and nurse assistants (NAs), we investigated the effectiveness of paying market leading wages to higher skilled nurses and market lagging wages to lower skilled nurses. On the basis of prior utility analyses of the importance of pay practices at particular organizational levels, we hypothesize positive performance consequences as a result of pursuing these relative wage strategies. Using data from 352 short-term stay acute care hospitals in California, we found that a lead pay policy among RNs and a lag pay policies among LPNs and NAs were associated with higher Return on Assets (ROA) (i.e., financial performance) and shorter Average Length of Stay (ALOS) (i.e., organizational efficiency).

The intention of this article is to show possible contributions of the concept of autonomous cooperation to enable complex adaptive logistics systems (CALS) to cope with increasing complexity and dynamics and therefore to increase the systems' information-processing capacity by implementing autopoietic characteristics. In order to reach this target, the concepts of CALS and autopoietic systems will be introduced and connected. The underlying aim is to use the concept of self-organization as one of their essential similarities to lead over to the concept of autonomous cooperation as the most narrow view on self-organizing systems, which is discussed as a possible approach to enable systems to handle an increasing quantity of information. This will be analyzed from both a theoretical and an empirical point of view.

The world has witnessed three major individual revolutions until now. We are in the fourth industrial revolution, and there are technological breakthroughs that have not been seen before. Responding fast to changing consumer expectations in a competitive climate brought on by globalization has become a global reality, requiring enterprises to alter their manufacturing systems. The incorporation of machines that can interact and make decisions into production has altered the manufacturing processes. The application of the Industry 4.0 revolution to manufacturing processes has paved the way for the development of smart factories. Production may be made 24 hours a day in these factories where productivity grows with applications such as the internet of things (IoT), cyber-physical systems, augmented reality and artificial intelligence. All applications utilized in smart factories boost productivity and reduce costs and human error rates. Countries should undergo change in order to adapt to the competitive climate established by Industry 4.0, in which the entire world lives. Many industrialized countries have taken significant strides in this direction, including this process into their national policies. Turkey's ability to adapt to Industry 4.0 technologies in a digitalized competitive environment, as well as swiftly grow smart factory applications in altering production processes, is critical to its global economic standing.