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Machine learning (ML) analytical tools are increasingly being considered as an alternative quantitative methodology in management research. This paper proposes a new approach for uncovering the antecedents behind product and product–service innovation (PSI).

The ML approach is novel in the field of innovation antecedents at the country level. A sample of the Equatorian National Survey on Technology and Innovation, consisting of more than 6,000 firms, is used to rank the antecedents of innovation.

The analysis reveals that the antecedents of product and PSI are distinct, yet rooted in the principles of open innovation and competitive priorities.

The analysis is based on a sample of Equatorian firms with the objective of showing how ML techniques are suitable for testing the antecedents of innovation in any other context.

The novel ML approach, in contrast to traditional quantitative analysis of the topic, can consider the full set of antecedent interactions to each of the innovations analyzed.

This paper addresses the growing fragmentation between traditional and digital service innovation (DSI) research and offers a unifying metatheoretical framework.

Grounded in service-dominant (S-D) logic's service ecosystems perspective, this study builds on an institutional and systemic, rather than product-centric and linear, conceptualization of value creation to offer a unifying framework for (digital) service innovation that applies to both physical and digital service provisions.

This paper questions the commonly perpetuated idea that DSI fundamentally changes the nature of innovation. Instead, it highlights resource liquification—the decoupling of information from the technologies that store, transmit, or process this information—as a distinguishing characteristic of DSI. Liquification, however, does not affect the relational and institutional nature of service innovation, which is always characterized by (1) the emergence of novel outcomes, (2) distributed governance and (3) symbiotic design. Instead, liquification makes these three characteristics more salient.

In presenting a cohesive service innovation framework, this study underscores that all innovation processes are rooted in combinatorial evolution. Here, service-providing actors (re)combine technologies (or more generally, institutions) to adapt their value cocreation practices. This research demonstrates that such (re)combinations exhibit emergence, distributed governance and symbiotic design. While these characteristics may initially seem novel and unique to DSI, it reveals that their fundamental mechanisms are not limited to digital service ecosystems. They are, in fact, integral to service innovation across virtual, physical and blended contexts. The study highlights the importance of exercising caution in assuming that the emergence of novel technologies, including digital technologies, necessitates a concurrent rethinking of the fundamental processes of service innovation.