Purpose This paper aims to show the interaction effects between clusters and cluster-specific attributes and the industrial internet of things (IoT) knowledge of a firm on the innovativeness of firms. Cluster theory and the concept of key enabling technologies are linked to test their effect on a firm’s incremental and radical knowledge generation. Design/methodology/approach Quantitative approach at the firm-level. By combining several data sources (e.g. ORBIS, PATSTAT and German subsidy catalogue) the paper relies on a unique database encompassing 8,347 firms in Germany. Ordinary least squares (OLS)-regression techniques are used for data analysis. Findings Industrial IoT is an important driver of radical patents, mediated positively by firm size. For incremental knowledge, a substitution effect occurs between a cluster and IoT effects, which is bigger for larger firms and dependent on cluster attributes and firms’ outside connections. Research limitations/implications The paper opens up new research paths considering long-term disruptive effects of the industrial IoT compared to short-term effects on the innovativeness of firms within clusters. Additionally, it enables further research enriching the discussion about cluster attributes and how these affect ongoing processes. Practical implications Linking cluster theory and policy with Industry 4.0 raises awareness for being considerate in terms of funding and scrutinising one-size-fits-all approaches. Originality/value Connecting the concepts of a cluster and advanced manufacturing technologies as a proxy for industrial IoT, specifically focussing on both radical and incremental innovations is a new approach. Especially, taking into account the interaction effects between cluster attributes and the influence of industrial IoT on the innovativeness of firms.

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Artificial intelligence (AI) is often seen as a key technology for future economic growth. However, its concrete effects on the emergence of radical innovations and the associated socio-economic impacts, through increasing divergence between smaller and larger firms, have not yet been systematically researched. This paper addresses this by investigating the extent to which AI-related knowledge influences the emergence of radical innovations and differentiates between SMEs and large firms. Based on a unique dataset of European firms combining firm-level data with patent data, we find a nuanced influence from AI. While AI applications assert a positive influence, AI techniques negatively influence the emergence of radical innovations. Being an SME significantly moderates these effects. Larger firms gain from AI applications, whereas SMEs gain from AI techniques. Therefore, AI knowledge in itself is not a general answer to increase the likelihood of creating radical innovation. Instead, a more differentiated view on AI is needed.

Artificial intelligence (AI) is seen as a key technology for future economic growth. It is labelled as a general-purpose technology, as well as an invention of a method for inventing. Thus, AI is perceived to generate technological opportunities and through these, innovations, and productivity growth. The leapfrogging hypothesis suggests that latecomer firms can use these opportunities to catch up. The aim of this paper is to provide insight into this catch-up process of latecomer firms through integrating AI into their knowledge portfolio and thereby creating new technological trajectories. The moderating effect of firm size is also analysed. Combining firm-level data with patent data, a regression at the firm level is conducted. Evidence is found that smaller firms experience productivity growth from AI when operating at the productivity frontier, indicating the opposite of the leapfrogging hypothesis. However, there is evidence for the positive impact of AI on firm innovation, which is higher for latecomer firms that are larger in size. In general, we find a diverging pattern of the influence of AI on productivity and innovation growth, indicating the need for a finer grained analysis that takes indirect effects - that also could explain the observed productivity paradox - into account.