Search results

Despite the growing interest in IJVs and their governance, systematic research is limited on the board of directors and their roles in international joint ventures in emerging markets. In this study, we draw from corporate governance research that suggests that the levels of control and collaboration by boards are influenced by organizational complexity. While joint ventures possess several similarities compared to unitary firms, they also have unique sources of complexity given the fact that two or more international partners collaborate within JVs under an incomplete contract. Based on a sample of 114 IJVs, we argue and show four separate conditions that influence the functions that boards undertake as well as how control and collaboration as two separate functions are interrelated. Our findings address calls for research to open the black box of what boards actually do as well as to bring corporate governance theory to new organizational forms such as joint ventures.

The authors examine the impact of CEO generalist experience on firm performance. Using 522 listed firms in Indonesia for the period 2010–2018, the authors find that the generalist CEO is negatively associated with firm performance. Generalist CEOs tend to experience ambiguity in adjustments in the new environment. In order to decrease the impact of a generalist CEO, our empirical evidence finds that CEO tenure does not significantly moderate the association between the two. This is because generalist CEOs with longer tenure tend to avoid changing strategies, and therefore the negative impact of CEO generalist is not altered. The results of this study provide suggestions for the firm in the developing country to appoint a CEO with generalist experience.

Modelling and forecasting interval-valued time series (ITS) have received increasing attention in statistics and econometrics. An interval-valued observation contains more information than a point-valued observation in the same time period. The previous literature has mainly considered modelling and forecasting a univariate ITS. However, few works attempt to model a vector process of ITS. In this paper, we propose an interval-valued vector autoregressive moving average (IVARMA) model to capture the cross-dependence dynamics within an ITS vector system. A minimum-distance estimation method is developed to estimate the parameters of an IVARMA model, and consistency, asymptotic normality and asymptotic efficiency of the proposed estimator are established. A two-stage minimum-distance estimator is shown to be asymptotically most efficient among the class of minimum-distance estimators. Simulation studies show that the two-stage estimator indeed outperforms other minimum-distance estimators for various data-generating processes considered.

A growing body of research with contributions from different parts of the world documents accounts and analyses of negative behaviors by persons in leadership positions. Researchers today are acknowledging and paying increasing attention to the consequences of leadership that is characterized as being destructive. The chapter outlines organizational outcomes of destructive leadership and aims to emphasize the person–situation interaction in explaining these organizational phenomena. Both the direct outcomes that result from poor decision-making and the indirect effects that emerge as a consequence of the destructive leader's negative impact on the followers are discussed.

A detailed description will be provided of all the classification algorithms that have been widely used in the domain of medical science. The foundation will be laid by giving a comprehensive overview pertaining to the background and history of the classification algorithms. This will be followed by an extensive discussion regarding various techniques of classification algorithm in machine learning (ML) hence concluding with their relevant applications in data analysis in medical science and health care. To begin with, the initials of this chapter will deal with the basic fundamentals required for a profound understanding of the classification techniques in ML which will comprise of the underlying differences between Unsupervised and Supervised Learning followed by the basic terminologies of classification and its history. Further, it will include the types of classification algorithms ranging from linear classifiers like Logistic Regression, Naïve Bayes to Nearest Neighbour, Support Vector Machine, Tree-based Classifiers, and Neural Networks, and their respective mathematics. Ensemble algorithms such as Majority Voting, Boosting, Bagging, Stacking will also be discussed at great length along with their relevant applications. Furthermore, this chapter will also incorporate comprehensive elucidation regarding the areas of application of such classification algorithms in the field of biomedicine and health care and their contribution to decision-making systems and predictive analysis. To conclude, this chapter will devote highly in the field of research and development as it will provide a thorough insight to the classification algorithms and their relevant applications used in the cases of the healthcare development sector.

This chapter presents a novel human arm gesture tracking and recognition technique based on fuzzy logic and nonlinear Kalman filtering with applications in crane guidance. A Kinect visual sensor and a Myo armband sensor are jointly utilised to perform data fusion to provide more accurate and reliable information on Euler angles, angular velocity, linear acceleration and electromyography data in real time. Dynamic equations for arm gesture movement are formulated with Newton–Euler equations based on Denavit–Hartenberg parameters. Nonlinear Kalman filtering techniques, including the extended Kalman filter and the unscented Kalman filter, are applied in order to perform reliable sensor fusion, and their tracking accuracies are compared. A Sugeno-type fuzzy inference system is proposed for arm gesture recognition. Hardware experiments have shown the efficacy of the proposed method for crane guidance applications.

Recent years have brought significant advances in research on behavioral ethics. However, research on ethical decision making is still in a nascent stage. Our objective in this paper is twofold: First, we argue that the practice of mindfulness may have significant positive effects on ethical decision making in organizations. More specifically, we will discuss the benefits of “reperceiving” – a meta-mechanism in the practice of mindfulness for ethical decision making and we provide an overview of mindfulness research pertaining to ethical decision making. Subsequently, we explore areas in which neuroscience research may inform research on ethics in organizations. We conclude that both neuroscience and mindfulness offer considerable promise to the field of ethical decision making.

In the past few decades, service offshoring industry has been developing globally, and in the past few years its dynamic growth has been observed in Central and Eastern Europe (CEE). Intensive process migrations have been influenced by the primary goal of minimizing operational expenses, at the same time improving service efficiency and gaining access to wide talent pools. The chapter summarizes current state and key development trends of the offshore service delivery sector in the CEE region. Moreover, it aims at contributing to the understanding of corporate culture at the CEE service delivery organizations, which has been the dominant factor influencing their effectiveness and quality of delivery. The exploratory, pilot study conducted at the sample of three organizations from three countries in the region was supposed to examine the organizational culture at CEE nearshore delivery centres, by applying analysis based on seven factors, broken into 26 cultural artefacts. With minor exceptions, the results highlight positive perception of key organizational culture's elements across employees (e.g. communication, goal setting, team work, reward orientation, innovativeness standards). The only negatively assessed element is the performance standards, including their clear definition, coaching for personal improvement of employees and emphasizing good performance of the organizations. The quantitative study is limited to only three sample companies from three countries, therefore extending the research scope is recommended, as well as exploring the link of national culture elements to the employees' perception of organizational relations and culture.

Internet + and Electronic Business in China is a comprehensive resource that provides insight and analysis into E-commerce in China and how it has revolutionized and continues to revolutionize business and society. Split into four distinct sections, the book first lays out the theoretical foundations and fundamental concepts of E-Business before moving on to look at internet+ innovation models and their applications in different industries such as agriculture, finance and commerce. The book then provides a comprehensive analysis of E-business platforms and their applications in China before finishing with four comprehensive case studies of major E-business projects, providing readers with successful examples of implementing E-Business entrepreneurship projects.

Internet + and Electronic Business in China is a comprehensive resource that provides insights and analysis into how E-commerce has revolutionized and continues to revolutionize business and society in China.