Exploring the knowledge spillovers of a technology in an entrepreneurial ecosystem—The case of artificial intelligence in Sydney

Abstract

New knowledge presents opportunities for commercial value and can hence be a critical asset for entrepreneurial ecosystems (EEs). In particular, general purpose technologies are major drivers of entrepreneurship. Thus, a nuanced understanding on technological knowledge and its spillovers among actors within an EE is warranted. Using knowledge‐spillover‐based strategic entrepreneurship theory, we propose to observe knowledge spillovers through the assessment of the knowledge bases of a technology in an EE. To do so, this article proposes to use three key sources of knowledge: publications reflecting the emerging knowledge base, patents representing the realized knowledge base, and startups showing the experimental knowledge base. This article uses secondary data sources such as Web of Science and applies the method of bibliometrics to illustrate how an assessment is carried out in practice by evaluating the artificial intelligence (AI) knowledge bases in Sydney from 2000 to 2018. The findings are summarized with an illustration of the evolution of the key actors and their activities over time in order to indicate the key strengths and weaknesses in Sydney's AI knowledge among the different bases. Contrary to expectations from the high potential of knowledge spillovers from a general purpose digital technology such as AI, the article shows that apparent knowledge spillovers are yet highly limited in Sydney. Even though Sydney has a strong emerging knowledge base, the realized knowledge base seems weak and the experimental knowledge base is slowly improving. That observation itself verifies the need to take strategic actions to facilitate knowledge spillovers within EEs. After the implications for theory and policy makers are discussed, suggestions for further studies are proposed.

A Digital Tale of Two Cities—Observing the Dynamics of the Artificial Intelligence Ecosystems in Berlin and Sydney

“…Our research sits at the nexus of both the knowledge bases for AI technology, and the related policy frameworks of the knowledge-intensive EE. Following the assessment model developed earlier [18], this paper focuses on the three key knowledge bases to observe the evolution of AI-based EEs: emerging, realised, and experimental. Each knowledge base resides on a key ecosystem agent, namely researchers, inventors, and entrepreneurs.…”

“…To do this, we use a bibliometric analysis of secondary data. Academic publications serve as a proxy for the emerging knowledge, patents as a proxy for realised knowledge, and startup data as a proxy for experimental knowledge [18]. In other words, these three actors' practices are captured through their actions regarding publications, patents, and entrepreneurial activities [38][39][40][41].…”

“…Regarding the knowledge bases, we adapt our previous study's assessment model and collect data from secondary sources regarding academic output, intellectual property (IP), and startup formation. We observe the evolution of three types of knowledge bases: emerging, realised, and experimental [18]. The findings are summarised with an illustration of the evolution of the key actors and their activities over time to indicate the key strengths and weaknesses both in Berlin and Sydney's AI knowledge among the different bases.…”

Ethics and privacy of artificial intelligence: Understandings from bibliometrics

“…), ontologies, communication technologies, and neuroscience 10 . Machine learning, one of the key areas in AI, shares close connections with almost all AI techniques, and thus attracts the most attention in this THT and are connected with all ethical issues, such as fairness, discrimination, liability, frauds, and criminals 11 . It is easy to explain these cases.…”

“…Bibliometrics, which is a set of approaches for analyzing scientific documents (e.g., research articles, patents, and academic proposals), has been widely used as a tool for science, technology and innovation studies [7], such as identifying technological topics [8], discovering latent relationships [9], and predicting potential future trends [10]. Recently, AI has received recognition in bibliometrics as an emerging topic for empirical investigation [11,12]. These investigations either align with the interest in technology management (e.g., using AI as a representative case in digital transformation) or emphasize its role in examining the reliability of the proposed methods.…”