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Exploring the Heterogeneous Knowledge Spillover Effects from University AI Research on the Creation and Performance of AI Start-Ups

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Exploring the Heterogeneous Knowledge Spillover Effects from University AI Research on the Creation and Performance of AI Start-Ups Exploring the Heterogeneous Knowledge Spillover Effects from University AI Research on the Creation and Performance of AI Start-ups Qi Wang Ke-Wei Huang National University of Singapore National University of Singapore [email protected] [email protected] Abstract This paper investigates whether and which type of AI academic research at universities can create regional knowledge spillover effects that improve the quantity and quality of AI start-ups. Using data of AI start-ups from Crunchbase.com and AI conference publications from CSRankings.com, we find that knowledge spillovers from university AI research indeed contribute to the creation and VC financing performance of local AI start-ups at the MSA level in the United States. Moreover, we find significant heterogeneous effects of knowledge spillovers in different AI subfields. The knowledge spillovers from research published in machine learning conferences, including only ICML and NIPS, have the strongest effects on the creation and performance of AI start-ups. In addition, we find evidence that impactful conferences exhibit stronger spillover effect. At the same time, surprisingly, our results show that knowledge spillovers from theoretical AI research have stronger effects on the creation of start-ups. Keywords: Artificial intelligence, Knowledge Spillovers, Entrepreneurship, Venture Capital, Information Technology 1. Introduction The last two decades have witnessed the rapid advances in artificial intelligence (AI). Recognizing the vital role of AI, more than 17 countries (e.g., the United States, Germany, and China) have implemented policies for promoting AI development at universities. This kind of policy is designed based on the assumption that AI research at universities has positive societal or economic benefits. Academic literature has shown that scientific research in universities has positive spillover effect to the industry R&D because of the public good nature of knowledge (Jaffe 1989). Commercialization of academic research is achieved either by established firms or start-ups. Since only few countries have large established companies with AI R&D capabilities, most governments focused on policy tools to cultivate AI entrepreneurship. At the same time, it is well-known that the start-up failure rate is more than 90%. Therefore, an important question that naturally arises is whether university AI research has positive effect on the creation and survival of AI start-ups. Literature has documented that the accumulation of university knowledge is a key driver of economic growth (Belenzon and Schankerman 2013). Given the importance of university knowledge spillovers, prior literature has focused on the effects of localized knowledge spillovers from universities on regional entrepreneurship. A consistent finding is that university research positively affect local venture creation drawing upon the Knowledge Spillover Theory of Entrepreneurship (Audretsch et al. 2005). However, these studies examined broad spectrum of traditional disciplines (Fritsch and Aamoucke 2017; Jaffe 1989), while AI research has not been the focus of this stream of literature partly due to its nascence. More importantly, the extant literature has provided a limited understanding of which type of AI research has stronger knowledge spillover effects on nearby entrepreneurship. This paper attempts to fill the gaps in the entrepreneurship and innovation literature in the following ways. First, AI is considered as a “general purpose technology” that can drive innovation across sectors (Perrault et al. 2019). In other words, AI is pervasive and can be widely applied in a range of industries. Therefore, AI knowledge is expected to provide unprecedented entrepreneurial opportunities. Although the governments have established the important role of AI research in national AI strategies, there is limited empirical evidence on the relationship between knowledge spillovers from university AI research and entrepreneurship. Second, AI spillover effects deserve special attention also because AI is a broad and interdisciplinary area with many subfields. Existing literature cannot provide information about several important policy questions regarding which type of academic research could create stronger spillover effects. For example, which filed of AI research has the strongest knowledge spillover effects? Whether applied AI research can produce stronger spillover effects than theoretical AI research? A recent study by Akcigit et al. (2021) have shown that over-subsidizing applied research can accentuate the dynamic misallocation in the economy. Thus, we are interested in the differential effects from theoretical and applied AI research. To address these questions, we conduct a nuanced analysis on AI knowledge spillovers and entrepreneurship, and thus providing significant practical implications for policy makers and entrepreneurs. To fill these gaps, this paper investigates whether and which knowledge spillovers from AI research in universities can benefit nearby AI entrepreneurship in terms of the quantity and quality of AI start-ups at the metropolitan statistical areas (MSA) level in the U.S.. In our study, an AI start-up refers to the start-ups that utilize AI-related technologies to develop products or provide services. We will use AI start-ups to refer to the loosely defined AI-related start-ups. Our results indicate that knowledge spillovers from university AI research indeed contribute to the creation and venture capital (VC) financing performance of local AI start-ups. Moreover, we find significant heterogeneous effects in different AI subfields. For example, research published in ICML and NIPS have the strongest effects on the creation and performance of AI start-ups even those “machine learning” papers are considered more theoretical than applied research published in conferences such as KDD or AAAI. Furthermore, we also find the higher the research impact factor of AI research, the stronger the regional spillover effect in terms of the creation of AI start-ups. Our study adds to existing literature in the following aspects. First, our empirical analyses could be the first study that investigates which type of AI research produces stronger knowledge spillover effects on the regional entrepreneurship by providing a more nuanced analysis than the extant knowledge spillovers literature, which mostly focus on whether general academic research in university has localized spillover effects on entrepreneurial activity. Second, we respond to the call of scholars to examine whether knowledge spillovers from university research has any impact not just on the quantity but also on the quality of new ventures (Rosa and Mohnen 2007). Third, although there is an emerging stream of AI literature studied the impacts of AI on the labor market (Frey and Osborne 2017), there exists limited literature about AI entrepreneurship. Therefore, our study adds to the growing IS literature on economics impact of AI and technology entrepreneurship. 2. Data and measures We draw on several data sources to conduct our analyses. First, information on university-level AI academic publications is collected from CSRankings.com, which covers the ranking of all universities by top CS conference publications. We classified AI fields into six subfields including general AI (AAAI and IJCAI), Computer Vision (CV), Machine Learning (ML), Data Mining (DM), Natural Language Processing (NLP), and the Web and Information Retrieval (IR) based on CSRankings.com. Only very top conferences are counted in the ranking system on this website. For example, only three conferences (CVPR, ICCV, and ECCV) are counted in the CV area. As for the subfields of general AI, only AAAI and IJCAI are counted. Similarly, the field of NLP contains only ACL, EMNLP, and NAACL. Only SIGIR and WWW are included in IR. Since ML includes three conferences, two theoretical-oriented conferences (ICML and NIPS) and more applied data mining conference KDD (Jordan and Mitchell 2015; Yang and Wu 2006). In the meantime, we include ICDE into the DM field because ICDE is considered as the other premier data mining conference. As a result, we have six AI subfields covering 14 conferences (AAAI, IJCAI, CVPR, ECCV, ICCV, ICML, NIPS, KDD, ICDE, ACL, EMNLP, NAACL, SIGIR, and WWW) in our analysis. In total, there are 175 U.S. universities that published at least one paper from 2000 to 2018. Second, we collected the information on AI start-ups from Crunchbase.com. Crunchbase.com includes the basic information of start-ups (such as the founding date, headquarter location, etc.) and VC funding information. We collected employment and unemployment data from the U.S. Bureau of Labor Statistics. In addition, we obtained the number of graduates of all universities from the Survey of Graduate Students and Postdoctorates in Science and Engineering (GSS) in the U.S. ranging from 2000 to 2018. After merging these datasets, our final dataset covers 153 MSAs from 2000 to 2018. Dependent variables. We construct two dependent variables to measure the creation and performance of AI start-ups at the MSA-level. Specifically, we firstly construct the yearly total number of AI start-ups (Firm) at MSA level to capture the creation of AI start-ups. In addition, we use the VC financing performance of start-ups to measure the early-stage performance of AI start-ups. We construct the average amount of VC investment (VCAmount) in millions of the U.S. dollars of all AI start-ups received at each MSA. Independent
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