Beyond replacement: human-machine collaboration in the age of AI
Werner H. Kunz, László Sajtos, Carlos Flavián
Abstract
Purpose The purpose is to advance the understanding of human-machine (H-M) collaboration in service industries, conceptualizing a framework that structures the research space and proposing a research agenda to guide future studies on optimizing collaboration dynamics, outcomes and ethical governance. Design/methodology/approach The authors use an artificial intelligence (AI)-based systematic literature based on the SERVSIG Literature Alert database to identify articles related to H-M collaboration. Insights from these papers were analyzed to (1) trace the evolution of H-M collaboration research, (2) formulate an integrative framework spanning foundational resources through outcomes and (3) develop a future research agenda. Findings The paper develops an integrative framework describing the foundation, process, and outcomes of H-M teamwork in service settings. It also introduces a set of new articles from the special issue. Social implications The paper underscores the ethical considerations (e.g. data biases, privacy and transparency) and broader societal concerns (e.g. job displacement and social inequality) and uniquely positions ethics as a cross-cutting theme (product, consumer and societal levels), moving beyond siloed ethical discussions in earlier work. Originality/value Unlike earlier work focusing on either human replacement or narrow task automation, this paper proposes a teamwork perspective, showing how AI and humans are working together and combining capabilities to achieve outcomes together. It maps diverse empirical studies to a comprehensive framework, demonstrating its applicability and enriching theoretical rigor with real-world evidence.