Conceptual Atlas of the Knowmad Literature: Visual Mapping with VOSviewer

This research paper aims to contribute to the advancement of the knowmad concept understanding in the academic literature. The relevance of the knowmad worker is sustained by their skills and competencies aligned with the pre-announced requirements for competitiveness in the future business environment, where disruptive changes will become the norm. Knowmads are agile, flexible, determined, and overall resourceful to succeed. During COVID-19 pandemics, companies and employees worldwide have been facing one of the most disruptive events of our times that marked the acceleration of an already shifting paradigm: the migration from rigid work arrangements towards flexible ones. In the context where the knowmad worker assets are seen as the solution to a global crisis, and they continue to be seen as significant competencies for the future, we consider that a better understanding of the concept is currently required. This has been achieved in the present research project by conducting a systematic literature review, enhanced by text mining and scientific mapping analysis. Even though the notion of knowmad worker is relatively new in the knowledge management literature being presented by John Moravec only in 2008, novel research instruments are being used as an innovation factor. Considering the unprecedented access to information and advancements in conducting academic research, in the present landscape of the business research domain, new methods are available to structure and examine a body of literature. The text mining and scientific mapping analysis conducted with VOSviewer software version 1.6.16 is allowing us to identify meaningful insights about the knowmad concept, such as the (1) existing research gaps, the (2) future research directions – understood as the peaks and the valleys are defining our knowmad concept atlas – and (3) the research interest trends seen by this topic for the period between 2008 and 2021. To achieve this, a database derived from Web of Science’s core collection has been used, and the text mining based on term co-occurrence analysis contributed to a deeper understanding of current and future global workforce dynamics.