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The standard method to estimate a stochastic frontier (SF) model is the maximum likelihood (ML) approach with the distribution assumptions of a symmetric two-sided stochastic error v and a one-sided inefficiency random component u. When v or u has a nonstandard distribution, such as v follows a generalized t distribution or u has a χ2 distribution, the likelihood function can be complicated or untractable. This chapter introduces using indirect inference to estimate the SF models, where only least squares estimation is used. There is no need to derive the density or likelihood function, thus it is easier to handle a model with complicated distributions in practice. The author examines the finite sample performance of the proposed estimator and also compare it with the standard ML estimator as well as the maximum simulated likelihood (MSL) estimator using Monte Carlo simulations. The author found that the indirect inference estimator performs quite well in finite samples.

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.

The protective aspects of relationships for health have been extensively studied. Here, we assess whether different dimensions of partnership status at the time of a child’s birth are associated with better self-assessed health later in mid-life. Data are from three countries with different social welfare policies relating to union status and parenting: the US, the UK, and Norway. Results indicate that women who were partnered at first birth had better health at midlife in all three countries than women who were unpartnered. The analysis indicates no differences in the mid-life health of Norwegian women who were married or cohabiting at birth, whereas for US and UK women, being married at the birth of a first child is more beneficial for mid-life health than bearing the child in a cohabiting union. In the US, women who are least likely to marry do not demonstrate better mid-life health if they had wed relative to cohabiting. In the UK, in contrast, the women least likely to be married at the birth experience better returns if they marry. These findings highlight the importance of paying closer attention to heterogeneous treatment effects as they relate to childbearing, relationship status, and mid-life health.

Sustainable tourism will be an appropriate strategy to be promoted during the post COVID-19 pandemic, as this is a turning point for the tourism industry to grab the unique chance to have a true reset by focussing on achieving long-term sustainability and a shift from a ‘me to we’ economy. To support sustainable tourism and foster future success in the tourism industry, the process of integrating green knowledge and knowledge management can begin with entrepreneurial education in higher education institutions (HEIs). However, empirical research on university students' green entrepreneurial intention in sustainable tourism has not been exhaustively studied. Additionally, there is a need to further explore knowledge management process and entrepreneurial learning in HEIs. Hence, the aim of this study is to analyze knowledge management as a technique to explore the green entrepreneurial intention of students in HEIs in sustaining Malaysia's tourism post COVID-19 pandemic. Exploratory research with quantitative analysis was conducted through partial least squares structural equation modelling (PLS-SEM). The findings reveal that there is a positive and significant relationship between green entrepreneurial knowledge and green entrepreneurial intention in sustainable tourism among university business students. Additionally, knowledge revision and conceptual change positively and significantly influence green entrepreneurial knowledge and green entrepreneurial intention in sustainable tourism. However, knowledge application has no impact on green entrepreneurial knowledge and green entrepreneurial intention. The results of this study also reveal that green entrepreneurial knowledge does not have a mediation effect on green entrepreneurial intention. The present work contributes by going beyond the study of entrepreneurial intention, as the research focusses on interconnection among these three major areas: knowledge management, sustainable tourism, and entrepreneurship education post COVID-19 pandemic. Hence, the combination of these diverse aspects in this study provides insights to educators and policy makers to investigate the importance of green entrepreneurial knowledge and benefits of knowledge management that can be integrated into entrepreneurship education for current and future sustainable tourism development.

School violence can cause or exacerbate individual and collective trauma. Trauma-informed school approaches offer schools and educators guidance for how to respond. In this chapter, we provide an overview of trauma-informed school approaches and their contributions to healing individual and collective trauma. We begin this chapter by addressing the complex intersection of disability and trauma, and the unique implications of school-based violence for students with disabilities and their teachers. We then define trauma-informed care, describe current short- and long-term trauma-informed school approaches, and explain the aims of these approaches at individual and collective levels. Next, we locate trauma-informed responses to school violence in a context of systemic trauma and share considerations for disrupting the systemic conditions that perpetuate trauma and school violence. We discuss critiques of the trauma-informed care movement and conclude with recommendations for scholars pursuing research in this area.

The development of information technologies has led to a considerable transformation in human resource management from conventional or commonly known as personnel management to modern one. Data mining technology, which has been widely used in several applications, including those that function on the web, includes clustering algorithms as a key component. Web intelligence is a recent academic field that calls for sophisticated analytics and machine learning techniques to facilitate information discovery, particularly on the web. Human resource data gathered from the web are typically enormous, highly complex, dynamic, and unstructured. Traditional clustering methods need to be upgraded because they are ineffective. Standard clustering algorithms are enhanced and expanded with optimization capabilities to address this difficulty by swarm intelligence, a subset of nature-inspired computing. We collect the initial raw human resource data and preprocess the data wherein data cleaning, data normalization, and data integration takes place. The proposed K-C-means-data driven cuckoo bat optimization algorithm (KCM-DCBOA) is used for clustering of the human resource data. The feature extraction is done using principal component analysis (PCA) and the classification of human resource data is done using support vector machine (SVM). Other approaches from the literature were contrasted with the suggested approach. According to the experimental findings, the suggested technique has extremely promising features in terms of the quality of clustering and execution time.

In this study, workplace spirituality is discussed in the context of remote work and the COVID-19 pandemic. First, the focus is on the changes in the meaning and function of employees in the organization during the evolutionary process of industrialization and management paradigms. Afterward, conceptual frameworks for spirituality and workplace spirituality are presented, and in the last section, academic studies that deal with workplace spirituality with distance or hybrid work arrangements are included. Early studies offer insights and recommendations on conceptualizing, developing, and managing workplace spirituality. They all emphasize that spirituality is necessary for remote work as a tool to overcome stress and mental health problems and increase employee well-being. Unfortunately, the remote work and workplace spirituality literature is relatively narrow and needs to be expanded.

Strategic decisions leaders make involving organizational changes such as mergers and acquisitions (M&A), divestitures, and downsizing, which can influence and/or interact with other organizational factors. For example, within the context of M&A, changes impact financial performance, firm behaviors, and organizational culture. In addition, strategic decisions for these types of change can also interrelate with other more intrapersonal factors, including both leaders’ and employees’ health and well-being. Employee stress, also referred to as “merger syndrome,” outlines individual negative impacts of the changes including, but not limited to, cynicism and distrust, change wariness, and burnout, all accumulating to psychological effects including increases in detachment to work, stress, and sick leave. In this chapter, the authors outline the different impacts M&A phases have on stress and well-being and how they interrelate with the strategic decisions leaders make. The authors also outline future research opportunities and practical implications for how leaders and employees could better manage future major changes such as M&A activities.