From a conceptual perspective based on Luhmann's social systems theory, we examine how digital technologies – notably those of artificial intelligence (AI) – can reframe work-related meaning processing from which workers and organizations perceive work meaningfulness.
From a Luhmannian perspective, this research first advances a conceptual framework that considers (1) work as communication that can belong to a series of communication systems and that organizes structural couplings between these systems at the individual, organizational and societal levels and (2) digital technologies as mediums that intermediate work-related communication systems and codify work-related structural couplings. It next connects the framework with the empirical issues identified from an exploratory analysis of 137 existing studies to elucidate how digital mediums can reframe work in terms of meaning processing for workers as well as for organizations.
Our analysis stipulates that (1) digital mediums, doubly intermediating work-related meaning processing notably between workers and organizations, draw on and, in parallel, engender codifying references that normalize, formalize, signify and interconnect work-related communication and structure couplings; (2) the amplification of codifying references could stimulate self-referencing processes by forming coding communication technical systems that reframe different work-related meaning processing systems, which include psychic systems (e.g. workers) and social systems (e.g. organizations) and (3) the structural intermediateness afforded by digital mediums between these systems could affect the ways in which work meaningfulness is individually, organizationally and societally perceived.
This research provides a conceptual prism based on Luhmann's social systems theory to address the timely preoccupation related to the implications of AI for work. Its contributions can be mobilized, for example, to study from a systemic view (1) how to understand digitally reframed work and work meaningfulness, (2) how to specify and, if necessary, converge digitally meaningful work, psychically meaningful work and societally meaningful work, (3) how to preserve workers' meaning processing in the context of working with AI and (4) how to capture the societal effects of working with coding communication by considering the interconnection between multilevel work-related systems.
