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Guided by social exchange theory and signaling theory, this chapter investigates the relationship between individual perceptions of high-performance work systems (HPWS), union instrumentality, and employees’ turnover intention. The results obtained from a multilevel and multisource sample of more than 1,300 employees in 37 multinational corporation based in China show that, in contrast to our hypothesis, union instrumentality is not directly related to turnover intention; rather, the results from the post hoc mediation analysis show that union instrumentality is indirectly and negatively related to turnover intention through affective organizational commitment. Consistent with our hypothesis, the results of our analysis show that union instrumentality serves as an important contingent factor in the relationship between HPWS and employee turnover intention. The relationship between HPWS and turnover intention becomes positive when employee union instrumentality is low.

The chapter analyses Chinese and Russian hybrid warfare and their strategies. Although still under debate, it is increasingly recognised that both countries integrate and apply hybrid warfare in their strategic thinking. In doing so, they are able to increase their sphere of influence, promote authoritarian regimes and weaken democracies. It is therefore vital that we better understand their strategies in order to identify and bring to light the processes that encourage today’s rise of nationalism and populism, withdrawal from international organisations and an overall distrust in the global institutional order. Approaching the puzzle from current weaknesses in the EU and the United States, the chapter proposes a framework to analyse Chinese and Russian hybrid warfare. The chapter demonstrates that Western liberal democracies are not only unprepared for these new forms of warfare, but appear unwilling to take the necessary measures. In doing so, these countries leave the door open for (further) destabilisation and a risk of increased domestic polarisation.

This chapter investigates the environmental behaviours of three, four- and five-star hotels in Azuay (Ecuador). The methodology involved a quantitative research that measured the relationship between environmental responsibility (Gallardo, Sanchez, & Corchuelo, 2013) and stakeholder engagement (Kostova & Roth, 2002; Llamas-Sanchez, García-Morales, & Martin-Tapia, 2013; Vargas-Sánchez & Riquel-Ligero, 2012). The main findings suggest that the managers of the hotels implement environmental practices as they reduce waste, gas emissions and recycle materials. These practices are aligned with Ecuador’s extant legislation and regulations. In conclusion, this contribution implies that the hotels’ managers ought to communicate about their environmental responsibility with their stakeholders, including the employees, suppliers and customers.

This chapter discusses key training challenges that organizations need to confront with the objective of building a robust human resource management system. Given the dynamics of the current business environment, training and development has become an indispensable function in global organizations. Building an effective human capital that contributes to continual organizational growth has become the established norm to survive in a competitive business landscape. However, the training and development function is often rendered ineffective, on account of various bottlenecks existing in the organization. Addressing these bottlenecks is quintessential in ensuring the creation of a performance-driven human capital. The goal of this chapter is to draw attention to the training impediments that hinder organizational growth and to diagnose the underlying causes for the same. This chapter concludes with recommendations that organizational decision-makers can leverage in their quest to strengthen the human capital, by utilizing their training and development infrastructure optimally.

Managing diverse talents has become a necessary part of the human resource management of contemporary organizations. The growing diversity of organizations' workforce makes companies reassess their conventional HRM approaches. State-owned enterprises get the increasing attention of talent management scholars since state firms enthusiastically compete for talents. These companies have some particularities that distinguish them from private firms, and there is a need to analyse the existing research on the HRM in state companies which has the potential to add a missing part to the puzzle of managing diverse talents. We study the major topics in the literature on human resource management and talent management in state-owned enterprises, the key findings researchers provide and the gaps in the literature that need to be covered and the resulting research directions for future studies.

Machine learning (ML) has recently gained momentum as a method for measurement in strategy research. Yet, little guidance exists regarding how to appropriately apply the method for this purpose in our discipline. We address this by offering a guide to the application of ML in strategy research, with a particular emphasis on data handling practices that should improve our ability to accurately measure our constructs of interest using ML techniques. We offer a brief overview of ML methodologies that can be used for measurement before describing key challenges that exist when applying those methods for this purpose in strategy research (i.e., sample sizes, data noise, and construct complexity). We then outline a theory-driven approach to help scholars overcome these challenges and improve data handling and the subsequent application of ML techniques in strategy research. We demonstrate the efficacy of our approach by applying it to create a linguistic measure of CEOs' motivational needs in a sample of S&P 500 firms. We conclude by describing steps scholars can take after creating ML-based measures to continue to improve the application of ML in strategy research.

This chapter explores the significance of blockchain technology in protecting data for intelligent applications across various industries. Blockchain is a distributed ledger that ensures the immutability and security of transactions. Given the increasing need for security measures in industries, understanding blockchain technology is crucial for preparing for its future applications.

This chapter aims to examine the use of blockchain technology across industries and presents a compilation of existing and upcoming blockchain technologies for intelligent applications. The methodology involves reviewing research to understand the security needs of different industries and providing an overview of methods used to enhance multi-institutional and multidisciplinary research in areas like the financial system, smart grid, and transportation system.

The findings highlight the benefits of blockchain networks in providing transparency, trust, and security for industries. The Responsible Sourcing Blockchain Network (RSBN) is an example that utilizes blockchain's decentralized ledger to track sustainable sourcing from mine to final product. This information can be shared with auditors, corporate governance organizations, and customers.

The practical implications of this chapter are significant, serving as a valuable resource for industries concerned with identity privacy, traceability, immutability, transparency, auditability, and security. Understanding and implementing blockchain technology can address the growing need for secure and intelligent applications, ensuring data protection and enhancing trust in various sectors.

In recent years, a great number of top conferences and workshops on artificial intelligence (AI) were held in China, showing Chinese AI plays an important role in the world. Meanwhile, Chinese government announced an ambitious scheme, “New Generation Artificial Intelligence Development Plan,” for the country to become a world leader in AI technologies by 2030. The AI research in China has covered various aspects, ranging from chips to algorithms. This chapter attempts to give an overview of the recent advances of AI research and development in China, as well as some perspectives on the future development of AI in China.

Industrial Revolution 4.0 (IR 4.0) has caused revolutionary changes in various industries of South Asia, including financial services. Financial inclusion has been recognized as an important driver of economic growth. The combination of financial inclusion and the industrial revolution offers exceptional opportunities for business. The present chapter delves into the significance of financial inclusion within the framework of IR 4.0 in Asia and its potential to stimulate growth, innovation, and societal influence. It includes the discourse regarding challenges and opportunities for business in a new era of financial inclusion and the industrial revolution. Based on a thorough discussion, we give practical insights and best practices for businesses aiming to maximize the opportunities offered by financial inclusion in the era of IR 4.0. This chapter provides an in-depth understanding of Asia’s expanding financial inclusion landscape and empowers companies with the information and tools needed to prosper in this dynamic market.