ChatGPT and its variants that use generative artificial intelligence (AI) models have rapidly become a focal point in academic and media discussions about their potential benefits and drawbacks across various sectors of the economy, democracy, society, and environment. It remains unclear whether these technologies result in job displacement or creation, or if they merely shift human labour by generating new, potentially trivial or practically irrelevant, information and decisions. According to the CEO of ChatGPT, the potential impact of this new family of AI technology could be as big as “the printing press”, with significant implications for employment, stakeholder relationships, business models, and academic research, and its full consequences are largely undiscovered and uncertain. The introduction of more advanced and potent generative AI tools in the AI market, following the launch of ChatGPT, has ramped up the “AI arms race”, creating continuing uncertainty for workers, expanding their business applications, while heightening risks related to well‐being, bias, misinformation, context insensitivity, privacy issues, ethical dilemmas, and security. Given these developments, this perspectives editorial offers a collection of perspectives and research pathways to extend HRM scholarship in the realm of generative AI. In doing so, the discussion synthesizes the literature on AI and generative AI, connecting it to various aspects of HRM processes, practices, relationships, and outcomes, thereby contributing to shaping the future of HRM research.

Artificial intelligence (AI) and other AI-based applications are being integrated into firms’ human resource management (HRM) approaches for managing people in domestic and international organisations. The last decade has seen a growth in AI-based applications proliferating the HRM function, triggering an exciting new stream of research on topics such as the social presence of AI and robotics, effects of AI adoption on individual and business level outcomes, and evaluating AI-enabled HRM practices. Adopting these technologies has resulted in how work is organised in local and international firms, noting opportunities for employees and firms’ resource utilisation, decision-making, and problem-solving. However, despite a growing interest in scholarship, research on AI-based technologies for HRM is limited and fragmented. Further research is needed that analyses the role of AI-assisted applications in HRM functions and human-AI interactions in large multinational enterprises diffusing such innovations. In response to these combined issues—the fragmented nature of research and limited extant literature, we present a systematic review on the theme of this special issue and offer a nuanced understating of what is known, yet to be known, and future research directions to frame a future research agenda for international HRM. We develop a conceptual framework that integrates research on AI applications in HRM and offers a cohesive base for future research endeavours. We also develop a set of testable propositions that serve as directions for future research.

Green innovation could become a valuable firm resource for establishing competitive advantage while simultaneously contributing towards sustainable development; in other words, green innovation has the potential to address the dilemma between consuming available resources and preserving them for the future. However, there is a dearth of studies systematically examining the present structure and future scope of research on green innovation as a firm resource. Seeking to explain the sustainable development dilemma of green innovations through the theoretical perspective of the resource‐based view of the firm, we address this gap with a comprehensive bibliometric analysis of 951 relevant articles. The key contributors to the extant literature are recognised with bibliographic coupling, citation analysis and co‐authorship analysis. A co‐citation analysis identifies four major thematic areas of research: green supply chain management, green product design, corporate environmental responsibilities and social sustainability. Further, a dynamic co‐citation analysis tracks the progression of these thematic areas. Content analysis of the thematic areas provides insights into the status of the research domain. This study also contributes to the extant literature by identifying prestigious articles on green innovation as a firm resource, analysing the co‐occurrence of keywords and suggesting future research agendas.

Analyzing multiple data sources from a global information technology (IT) consulting multinational enterprise (MNE), this research unpacks the configuration of a digitalized HR ecosystem of artificial intelligence(AI)‐assisted human resource management (HRM) applications and HR platforms. This study develops a novel theoretical framework mapping the nature and purpose of a digitalized AI‐assisted HR ecosystem for delivering exceptional employee experience (EX), an antecedent to employee engagement (EE). Employing the theoretical lenses of EX, EE, AI‐mediated social exchange, and engagement platforms, this study's overarching aim of this article is to establish how AI‐assisted HRM fits into an organization's ecosystem and, second, how it impacts EX and EE. Our findings show that AI‐assisted applications for HRM enhance EX and, thus, EE. We also see increases in employee productivity and HR function's effectiveness. Implications for research and practice are also discussed.

Purpose: The purpose of this study is to investigate the effects of intrinsic and extrinsic motivation on knowledge sharing and the moderating effects of individual demographics, organizational context and cultural context in that relationship.Design/methodology/approach: This study conducted a meta-analysis of 44 studies involving 14,023 participants to examine the direct and moderating effects of motivation on knowledge sharing.Findings: Results revealed that both extrinsic and intrinsic motivational factors were associated with higher levels of knowledge sharing, while the effect was stronger for intrinsic motivation. Moreover, results revealed that substantial variance was explained by moderating variables. Further investigation revealed that individual characteristics (age, gender), organizational context (organizational setting vs. open system, IT infrastructure) and cultural context (collectivism, uncertainty avoidance, performance orientation, power distance) moderated the motivation and knowledge sharing relationship.Research limitations/implications: As a meta-analysis, this study is confined to variables that have been frequently analyzed in prior research. Future research could further increase our understanding of different types of knowledge sharing and various boundary conditions.Practical implications: Organizations should provide customized incentive systems to specific target groups to align motivation and knowledge sharing. Multinational organizations may consider different motivation schemes across countries to better suit cultural differences.Originality/value: Despite a growing number of studies highlighting the important role of motivation in predicting knowledge sharing, the evidence is mixed. Based on a meta-analysis, this study identified true relationships and identified moderating effects that help explain prior mixed results.