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Purpose The ecosystem perspective on innovation and business has emerged as the secret sauce of innovative organizations. While its theoretical foundations are premised on innovation system literature, the broad adoption of the ecosystem concept has resulted in conceptual ambiguity. The purpose of this paper is to tackle the ambiguous use of innovation ecosystem terminology and structure a conceptual frame for the field, identifying definitions of an innovation ecosystem and how the concept has been established in previous literature. Design/methodology/approach This paper examines the ambiguous use of terminology by reviewing the literature with bibliometric coupling and co-citation analysis by which thematic differences in ecosystem literature were identified. The study gathered the scientific publications from Thomson Reuters Web of Sciences Core Collection (n=4,681) from 1990 to 2015. Findings Six major bibliometrically coupled clusters were identified, of which the three largest clusters are innovation system studies, regional innovation studies and technological innovation studies. In addition, further analysis shows an emerging cluster that is focused on ecosystems, having its roots in eight seminal papers. This ecosystem research cluster includes seven sub-clusters, such as innovation ecosystem studies, business ecosystem studies and studies focusing on ecosystem development. Research limitations/implications The authors’ approach highlights how a lot of recent ecosystem studies actually belong to previous, well-developed research streams. However, there is also a separate, emergent research stream that includes the innovation and ecosystem studies. As a research implication, the paper concludes by suggesting the research agenda for further studies. Originality/value Even though literature on innovation systems and ecosystems is extensive literature, no studies have captured the emergence of the ecosystems approach and its relation with the systems of innovation literature.

The delineation of coordinates is fundamental for the cartography of science, and accurate and credible classification of scientific knowledge presents a persistent challenge in this regard. We present a map of Finnish science based on unsupervised-learning classification, and discuss the advantages and disadvantages of this approach vis-à-vis those generated by human reasoning. We conclude that from theoretical and practical perspectives there exist several challenges for human reasoningbased classification frameworks of scientific knowledge, as they typically try to fit new-to-the-world knowledge into historical models of scientific knowledge, and cannot easily be deployed for new large-scale data sets. Automated classification schemes, in contrast, generate classification models only from the available text corpus, thereby identifying credibly novel bodies of knowledge. They also lend themselves to versatile large-scale data analysis, and enable a range of Big Data possibilities. However, we also argue that it is neither possible nor fruitful to declare one or another method a superior approach in terms of realism to classify scientific knowledge, and we believe that the merits of each approach are dependent on the practical objectives of analysis.

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