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Recovery seems to be one of the most important mechanisms explaining the relationship between acute stress reactions and chronic health complaints (Geurts & Sonnentag, 2006). Moreover, insufficient recovery may be the linking mechanism that turns daily stress experiences into chronic stress. Given this role recovery has in the stress process, it is important to ask in which contexts and under what circumstances recovery takes place.

Changing work/family dynamics and economic developments have made it more likely that an employee might work with a family member or spouse. Such working relationships offer a unique perspective by which to understand the work/family interface; however, relatively little research has explored the implications of working with family for employee stress and well-being. In this chapter, we review the existing research concerning stress associated with working with family. We integrate this research into broader demand/resource perspectives on employee stress and well-being, highlighting the manner in which working with family provides unique demands and resources through differences in work–family linking mechanisms. We conclude with suggestions for future research that might enhance our understanding of the work/family interface by considering the dynamics of working with family.

This study compares the performance of two types of clustering methods, time-based and non-time-based clustering, in the identification of river discharge patterns at the Johor River basin during the northeast monsoon season. Time-based clustering is represented by employing dynamic time warping (DTW) dissimilarity measure, whereas non-time-based clustering is represented by employing Euclidean dissimilarity measure in analysing the Johor River discharge data. In addition, we combine each of these clustering methods with a frequency domain representation of the discharge data using Discrete Fourier Transform (DFT) to see if such transformation affects the clustering results. The clustering quality from the hierarchical data structures of the identified river discharge patterns for each of the methods is measured by the Cophenetic Correlation Coefficient (CPCC). The results from the time-based clustering using DTW based on DFT transformation show a higher CPCC value as compared to that of non-time-based clustering methods.

Addressing occupational stress and fostering employee wellness helps meet a host of organizational stakeholder expectations including high quality of work life (employees), reasonable return on investment (investors), increased productivity (management), and competitiveness (owners). Despite being dynamic in nature, stress and wellness are often studied using a static perspective. One reason for the scarcity of dynamic empirical research is the limited knowledge and use of the tools available to assess change over time. To address this limitation, four tools used to assess change and dynamics of occupational stress and well-being are described: growth models, latent change score models, spectral analysis, and computational modeling. First, we begin by discussing growth curve models and then transition to latent change score models. We then expand into spectral analysis, a tool used to determine cycles of ups and downs that repeat regularly. Last, computational modeling is discussed, where computers and simulations are used to understand a dynamic process. For each tool, we give examples of how they have been used, make recommendations for future use, and provide readers with suggestions and references for how to complete analyses in software and programs, most of which are freely available (i.e., R, Vensim).

Recently, star variables and the post-crisis nature of cyclical fluctuations have attracted a great deal of interest. In this chapter, the authors investigate different methods of assessing business cycles (BCs) for the European Union in general and the euro area in particular. First, the authors conduct a Monte Carlo (MC) experiment using a broad spectrum of univariate trend-cycle decomposition methods. The simulation aims to examine the ability of the analysed methods to find the observed simulated cycle with structural properties similar to actual macroeconomic data. For the simulation, the authors used the structural model’s parameters calibrated to the euro area’s real gross domestic product (GDP) and unemployment rate. The simulation outcomes indicate the sufficient composition of the suite of models (SoM) consisting of popular Hodrick–Prescott, Christiano–Fitzgerald and structural trend-cycle-seasonal filters, then used for the real application. The authors find that: (i) there is a high level of model uncertainty in comparing the estimates; (ii) growth rate (acceleration) cycles have often the worst performances, but they could be useful as early-warning predictors of turning points in growth and BCs; and (iii) the best-performing MC approaches provide a reasonable combination as the SoM. When swings last less time and/or are smaller, it is easier to pick a good alternative method to the suite to capture the BC for real GDP. Second, the authors estimate the BCs for real GDP and unemployment data varying from 1995Q1 to 2020Q4 (GDP) or 2020Q3 (unemployment), ending up with 28 cycles per country. This analysis also confirms that the BCs of euro area members are quite synchronized with the aggregate euro area. Some major differences can be found, however, especially in the case of periphery and new member states, with the latter improving in terms of coherency after the global financial crisis. The German cycles are among the cyclical movements least synchronized with the aggregate euro area.

The discrete Fourier transform (dft) of a fractional process is studied. An exact representation of the dft is given in terms of the component data, leading to the frequency domain form of the model for a fractional process. This representation is particularly useful in analyzing the asymptotic behavior of the dft and periodogram in the nonstationary case when the memory parameter d≥12. Various asymptotic approximations are established including some new hypergeometric function representations that are of independent interest. It is shown that smoothed periodogram spectral estimates remain consistent for frequencies away from the origin in the nonstationary case provided the memory parameter d < 1. When d = 1, the spectral estimates are inconsistent and converge weakly to random variates. Applications of the theory to log periodogram regression and local Whittle estimation of the memory parameter are discussed and some modified versions of these procedures are suggested for nonstationary cases.

The author develops and extends the asymptotic F- and t-test theory in linear regression models where the regressors could be deterministic trends, unit-root processes, near-unit-root processes, among others. The author considers both the exogenous case where the regressors and the regression error are independent and the endogenous case where they are correlated. In the former case, the author designs a new set of basis functions that are invariant to the parameter estimation uncertainty and uses them to construct a new series long-run variance estimator. The author shows that the F-test version of the Wald statistic and the t-statistic are asymptotically F and t distributed, respectively. In the latter case, the author shows that the asymptotic F and t theory is still possible, but one has to develop it in a pseudo-frequency domain. The F and t approximations are more accurate than the more commonly used chi-squared and normal approximations. The resulting F and t tests are also easy to implement – they can be implemented in exactly the same way as the F and t tests in a classical normal linear regression.

The logic of economic inquiry requires two distinct research programs. One program treats economic life in terms of invariant formal categories across time and place. The other program treats the continual generation of novelty and turbulence through time and human interaction. These programs are not commensurable: one cannot be reduced to the other. The former program must be conveyed by a theory of equilibrium; the latter program requires a process-based theory of emergent phenomena. Roy Weintraub articulated a neo-Walrasian research program in his General Equilibrium Analysis, and here I sketch a complementary neo-Mengerian program. In presenting this sketch, I also explain that needless analytical confusion and antagonism can result from a failure to recognize that economic analysis requires two distinct research programs. As a historical side-bar, Carl Menger probably recognized this situation, as evidenced by his correspondence with Léon Walras.

As a company that has continuously achieved business innovation, Apple in the United States has successfully applied strategic knowledge creation to produce a series of products that integrate various digital devices as well as diverse contents and applications, such as the iPod, iPhone, and iPad, based on a corporate vision of a digital hub concept. At the same time, the redefining of corporate boundaries that expanded Apple’s business in a horizontal direction from the Macintosh PC business to the delivery of music, smartphones, and tablets is also an indication of the evolution of a corporate vision involving Apple’s strategic transformation. This chapter presents the strategic and creative processes that enabled practitioners, including the late Steve Jobs, to demonstrate “strategic innovation capability” by “holistic leadership” at every level of management at Apple and successfully achieve a business ecosystem strategy through “creative collaboration” across diverse boundaries within and outside the company.