Search results

We present a simulation designed to capture the impact of both formal authority ties and informal socialization ties on the performance of an organization adapting to a turbulent world. We present a summary of three key models that informed our approach and then outline and describe the operation of our resulting simulation. Using an experiment that manipulated both the authority network structure and the stress the organization placed on socialization, we show inefficient authority structures harm performance, and also that socialization has a strong and nonlinear impact on peak organizational performance and on the performance of top management. We also present a case study, instantiating the general model with the specific context of a real-world organization. Finally, our integrated multimodel suggests that companies should pursue different strategies in hiring key strategic actors than they do for other actors.

This paper proposes a new class of estimators for the autoregressive coefficient of a dynamic panel data model with random individual effects and nonstationary initial condition. The new estimators we introduce are weighted averages of the well-known first difference (FD) GMM/IV estimator and the pooled ordinary least squares (POLS) estimator. The proposed procedure seeks to exploit the differing strengths of the FD GMM/IV estimator relative to the pooled OLS estimator. In particular, the latter is inconsistent in the stationary case but is consistent and asymptotically normal with a faster rate of convergence than the former when the underlying panel autoregressive process has a unit root. By averaging the two estimators in an appropriate way, we are able to construct a class of estimators which are consistent and asymptotically standard normal, when suitably standardized, in both the stationary and the unit root case. The results of our simulation study also show that our proposed estimator has favorable finite sample properties when compared to a number of existing estimators.

Traditional clinical psychological practices have often been adapted for the context of executive coaching. Cognitive behavioral therapy (CBT) in particular is the most scientifically supported psychological modality. CBT like other practices has been used in coaching as cognitive behavioral coaching but rarely discussed more explicitly for the executive population. Here, we offer a specific adaptation – cognitive behavioral executive coaching (CBEC) – and suggest that it presents a flexible structure that can meet the multiple agendas that are framed for executive coaching. Additionally, the core features of CBT and CBEC in particular satisfy the major needs of executives in coaching arrangements. We conclude by demonstrating a CBEC process model for coaching the high-performing executive.

The authors propose a semiparametric approach for testing independence between two infinite-order cointegrated vector autoregressive series (IVAR(∞)). The procedures considered can be viewed as extensions of classical methods proposed by Haugh (1976, JASA) and Hong (1996b, Biometrika) for testing independence between stationary univariate time series. The tests are based on the residuals of long autoregressions, hence allowing for computational simplicity, weak assumptions on the form of the underlying process, and a direct interpretation of the results in terms of innovations (or shocks). The test statistics are standardized versions of the sum of weighted squares of residual cross-correlation matrices. The weights depend on a kernel function and a truncation parameter. Multivariate portmanteau statistics can be viewed as a special case of our procedure based on the truncated uniform kernel. The asymptotic distributions of the test statistics under the null hypothesis are derived, and consistency is established against fixed alternatives of serial cross-correlation of unknown form. A simulation study is presented which indicates that the proposed tests have good size and power properties in finite samples.

Purpose – Intersections are hazardous locations and to improve their safety we need to understand the factors contributing to crashes at these locations and provide evidence-based recommendations to reduce them. This chapter provides a summary of the findings on infrastructure-related factors contributing to crashes at urban and rural intersections and some discussions on the implications and potential countermeasures.

Approach – A review of the literature on intersection crashes was performed to identify the infrastructure-related crash-contributing factors. Some discussions on the implications and potential countermeasures are then provided.

Findings – The factors contributing to road crashes are diverse and complex. While the safety effects of a few factors (e.g., exposure and speed) are relatively consistent, many factors have different impacts on crash frequency and severity (e.g., types of intersection) and different impacts on urban and rural intersections (e.g., bus stops).

Research Implications – More studies are needed on developing a stronger theoretical or conceptual foundation on the effects of roadway designs and traffic controls on different dimensions of safety (e.g., exposure, frequency, severity, etc.), types of crashes (e.g., head-on, rear-end, etc.) or road users involved (e.g., drivers, pedestrians, cyclists, etc.).

Practical Implications – Transport engineers need to be aware that some treatments may have different effects on different crash types and road users involved. Even though the overall safety may be improved by the treatments designed, they need to consider and mitigate any unintended consequences to satisfy the Pareto improvement principle and the social equity criterion.

Motivated by empirical features that characterize cryptocurrency volatility data, the authors develop a forecasting strategy that can account for both model uncertainty and heteroskedasticity of unknown form. The theoretical investigation establishes the asymptotic optimality of the proposed heteroskedastic model averaging heterogeneous autoregressive (H-MAHAR) estimator under mild conditions. The authors additionally examine the convergence rate of the estimated weights of the proposed H-MAHAR estimator. This analysis sheds new light on the asymptotic properties of the least squares model averaging estimator under alternative complicated data generating processes (DGPs). To examine the performance of the H-MAHAR estimator, the authors conduct an out-of-sample forecasting application involving 22 different cryptocurrency assets. The results emphasize the importance of accounting for both model uncertainty and heteroskedasticity in practice.

Human beings spend their daily lives within the range of the atmospheric boundary layer, where airflow is affected by friction from Earth's surface. The airflow in this area is generally called wind. Strong wind occasionally causes severe damage to infrastructures and people because of its aerodynamic effects, but even weak and moderate winds can have serious environmental impacts on human society such as those seen with air-pollution problems and thermal effects.

Students with emotional and behavioral disorders (EBD) are one of the most underserved populations in today's schools (Kauffman, Mock, & Simpson, 2011). Many of these students also have additional disabilities in conjunction with an EBD identification, such as Learning Disabilities (LD), Attention Deficit Disorder (ADD), Attention Deficit Hyperactivity Disorder (ADHD), Oppositional Defiant Disorder (ODD), or Obsessive Compulsive Disorder (OCD), among other psychiatric disorders (Henley, Ramsey, & Algozzine, 2009; Kauffman, 2005).

Because the identification of EBD examines behaviors that tend to be more subjective in nature than other disabilities and because these pervasive behaviors are manifested in a variety of forms, EBD is one of the most misidentified disability categories (Skiba, Poloni-Staudinger, Gallini, Simmons, & Feggins-Azziz, 2006). For students with EBD, the behavior(s) they exhibit contribute to learning difficulties in multiple academic and functional areas. This chapter provides in-depth information on the common characteristics and behavioral dimensions of this population. Additionally, the in-school performance and long-term outcomes of students with EBD are discussed.

The assumption that a set of observed variables is a function of an underlying common factor plus some error has dominated measurement in marketing and the social sciences in general for decades. This view of measurement comes with assumptions, which, however, are rarely discussed in research. In this article, we question the legitimacy of several of these assumptions, arguing that (1) the common factor model is rarely correct in the population, (2) the common factor does not correspond to the quantity the researcher intends to measure, and (3) the measurement error does not fully capture the uncertainty associated with measurement. Our discussions call for a fundamental rethinking of measurement in the social sciences. Adapting an uncertainty-centric approach to measurement, which has become the norm in in the physical sciences, offers a means to address the limitations of current measurement practice in marketing.

The observed increase in the atmospheric concentration of greenhouse gases since the industrial period, due to human activities, is very likely causing the warming of the climate system. Anthropogenic warming and rising sea levels will continue for centuries due to the time scales associated with climate processes and feedbacks. Even if greenhouse gas concentrations were to be stabilized, different types of adaptation measures are needed to cope with the inevitable change. At the same time mitigation measures aiming at decreasing greenhouse gas emissions and enhancing carbon sinks must be taken in order to reduce the potential extent of global warming. This chapter covers the main aspects of the current understanding of the physical basis of climate change, including the directly measured observations and estimated projections for the 21st century. Causes and effects of climate change are also addressed. Finally, the main uncertainties of climate projections and a few general considerations on the different ways to respond to the climate change issue are discussed.