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The purpose of this study is to explore the potential contribution of the metaverse to improve training and development as a function of human resource development (HRD) perspective. The authors explore the benefits and challenges of the metaverse and introduce cases of companies using the metaverse in training.

A narrative literature review was conducted to collect information on the metaverse in training. The authors reviewed peer- and non-peer-reviewed articles, book chapters, white papers, corporate websites and blogs and business magazines.

A total of 75 articles were reviewed, including 14 cases, which were summarized to demonstrate how companies are applying metaverse technology in training contexts. For a more in-depth review, three cases were selected and summarized in terms of context, process and outcomes.

The metaverse is an emergent topic in HRD. It has the potential to revolutionize the functions of training and development through the combination of advanced technologies, including virtual reality, augmented reality and mixed reality. This article is the foundational attempt to provide a comprehensive summary of existing literature and case studies that highlight the potential of the metaverse in training within the context of HRD.

This study aims to examine the combinations of internal and external knowledge flows between research and development (R&D) incumbents and start-ups in the context of open innovation. While there is a growing body of knowledge that has examined how, in a knowledge economy, a firm’s knowledge and innovation activities are closely linked, there is no systematic review available of the key antecedents, perspectives, phenomenon and outcomes of knowledge spillovers.

The authors have conducted dual-stage research. First, the authors conducted a systematic review of literature (97 research articles) by following the theories–contexts–methods framework and the antecedent-phenomenon-outcomes logic. The authors identified the key theories, contexts, methods, antecedents, phenomenon and outcomes of knowledge spillovers between R&D-driven incumbents and start-ups in the open innovation context. In the second stage, the findings of stage one were leveraged to advance a nomological network that depicts the strength of the relationship between the observable constructs that emerged from the review.

The findings demonstrate how knowledge spillovers can help incumbent organisations and start-ups to achieve improved innovation capabilities, R&D capacity, competitive advantage and the creation of knowledge ecosystems leading to improved firm performance. This study has important implications for practitioners and managers – it provides managers with important antecedents of knowledge spillover (knowledge capacities and knowledge types), which directly impact the R&D intensity and digitalisation driving open innovation. The emerging network showed that the antecedents of knowledge spillovers have a direct relationship with the creation of a knowledge ecosystem orchestrated by incumbents and that there is a very strong influence of knowledge capacities and knowledge types on the selection of external knowledge partners/sources.

This study has important implications for practitioners and managers. In particular, it provides managers with important antecedents of knowledge spillover (knowledge capacities and knowledge types), which directly impact the R&D intensity and digitalisation driving open innovation. This will enable managers to take important decisions about what knowledge capacities are required to achieve innovation outcomes. The findings suggest that managers of incumbent firms should be cautious when deciding to invest in knowledge sourcing from external partners. This choice may be driven by the absorptive capacity of the incumbent firm, market competition, protection of intellectual property and public policy supporting innovation and entrepreneurship.

Identification of the key antecedents, phenomenon and outcomes of knowledge spillovers between R&D-driven incumbents and start-ups in the open innovation context. The findings from Stage 1 helped us to advance a nomological network in Stage 2, which identifies the strength and influence of the various observable constructs (identified from the review) on each other. No prior study, to the best of the authors’ knowledge, has advanced a nomological network in the context of knowledge spillovers between R&D-driven incumbents and start-ups in the open innovation context.