The 10th e-HRM Conference in Muenster, Germany (June 2026) provided a platform for scholars from around the world to share research and leading-edge insights into electronic/digital human resource management in the midst of another major technological revolution. This special issue highlights key research papers presented at the conference and also invites authors who were unable to attend the conference to submit their related research to this special issue.
Artificial intelligence and robotics may prove as transformative for economic growth and human potential as electrification, mass production, and electronic telecommunications in their eras. The release of ChatGPT in November 2022 highlights the rapid advance of AI technologies, illustrating that such advancement is not necessarily continuous but can occur in sudden bursts. Previously heralded as a tool for automation and increased efficiency, narratives about AI now make a broader claim of a new era of human-AI collaboration in which AI performs routine tasks freeing professionals to take on more value-added work. But this claim is not new. Such promises were made 20 years ago with the introduction of the internet and the expansion of electronic telecommunications, and use of ERP and HRIS technologies.
One goal of the conference is to examine the ways in which AI can advance the science and practice of human resource management, but we also want to remain mindful of the potential limitations and boundaries of AI, such as concerns with inequality and bias, hallucinations, lack of transparency, issues with trust or distrust in the technology and skill erosion. Given these limitations, which HRM processes and workflows can or should be automated, and which augmented? What other technologies might be used along with AI or instead of AI? What can we imagine is next after the big wave of AI?
To advance scholarship and to provide evidence-based, independent guidance, academic researchers have a crucial role to play in influencing how AI technologies are used in organizations to improve outcomes for all stakeholders. It is important therefore to build on the well-developed management literature that already exists. Over the last two decades research on e-HRM, also referred to as digital HRM or d-HRM, shows how a wide range of configurations involving computer hardware, software and electronic networking resources which both enable intended or actual HRM activities not only automates but also transform HRM processes in intended and unintended ways. Digital transformations have also been extensively researched in the traditional HR functions of recruitment, selection, leadership, learning and development, and compensation.
Research in e-HRM has been enriched with such concepts as cloud computing, big data and people analytics, social media, chatbots, gamification, the internet of things, robots, artificial intelligence, machine learning, and other emerging technologies. E-HRM has also evolved to include new forms of organizing, such as industry 4.0 and digital-platform business models. Industry 4.0 redefines the relationship between human workers and machines, and requires a new approach to HRM, or HRM 4.0. Moreover, the emergence of advanced systems, software agents and/or robots which work alongside human workers has resulted in human-robot 'white-collar teams'. The platform or gig economy has created a new type of contingent worker, gig workers who have little or no connection to job providers, who are managed algorithmically through platforms. The digital platform is an important vehicle of their employment relationship, since it creates a new ecosystem for employment/work relations and institutes the use of algorithms/software robots for executing HRM activities. These developments pose new challenges to and opportunities for the e-HRM field, as well as expand and enrich its scope.
With the release of large language models (LLMs) like ChatGPT, Claude, and Gemini, generative AI and AI agents now make communicating with and accessing information from information technology much easier, democratizing insights from HR data and HR analytics. There has been an explosion of AI for HRM start-ups. Despite these impressive advances, the question remains, what is different about AI in the e-HRM context? Do many of the traditional challenges and agendas of e-HRM remain or are there other characteristics that need further study? How is AI different from the preceding electronic technologies and how does this make a difference to the practice of HRM?
AI has three specific material characteristics—constant change, invisibility, and inscrutability—that may challenge traditional approaches to the study of technology and work. Individually these characteristics are not problematic but in combination they challenge applying earlier research frameworks to the study of AI in organizations. The production and use of AI involves a broad swathe of stakeholders in and across organizations whose assumptions and interests influence design, development, implementation, and use. This calls for a broader view, tracing interactions across contexts and expanding analysis upstream from use.
To deliver value for all stakeholders, HR professionals need to be aware of the technology-driven context in which they work and the assumptions about HRM processes and practices that are embedded in the technology. Rather than accepting the dictates of software developers external to the organization, HR leaders need to develop HRM practices that deliver value to their organization and employees. Thus, the aim of much current e-HRM research is to uncover e-HRM outcomes or consequences that lead to an enhanced value of HRM for both organizational members and other stakeholders, eschewing determinative technological pressures. Given that e-HRM aims to create value within and across organizations for targeted employees and management and covers all possible integration mechanisms and content between HRM and Information Technology (IT), this call invites papers dealing with a broad scope of technology application to managing people inside and across organizations.