Does standardization matter to make AI start-ups attractive to venture capital? Evidence from China

Research output: Contribution to conferencePaperResearchpeer-review

Abstract

The Artificial intelligence (AI) industry is emerging. During the past few years, we have witnessed a global booming of AI start-ups with a wide range of technologies and business models, making the AI industry into a fertile ground for business creation, investment, and technology development. Meanwhile, the AI industry is featured with high of technology and market uncertainty, making it highly risky for investment. A crucial factor of industry structure that matters for venture capital (VC) investment is industry standards, which affect technology diffusion and the way in which start-ups can benefit from technological change.
Given the surging amount of VC investment into the AI industry, a gap needs to be filled in by researching how VC makes investment decisions, while AI standardization is still in its early phase. The theoretical foundation of this study is the expanded conceptualization of the knowledge spillover theory of entrepreneurship (KSTE), which links contextual factors to the creation and growth of entrepreneurship. This study addresses the following question: how does the level of standardization in AI technological fields influences the attractiveness of AI start-ups to VC investment in the Chinese context? To answer this question, we develop measures for standardization in AI technological fields in China, and investigate the impact of standardization level on VC investment to AI startups.
The data on VC investment in China includes a sample of 629 Chinese startups in the AI industry and their VC capital received during 1996-2018. The measures on standardization in AI technological fields are established based on the "White paper on AI Standardization" published in 2018 by the National Standardization Administration Council. We use different regression models to test the impact of standardization on the total amount of VC and how many rounds of VC each startup received. The results show that: (1) AI startups in the technological fields that are of high levels of standardization received higher volume of VC investment than those that are of low levels of standardization; (2) AI startups in the technological fields that are of high levels of standardization were more likely to receive more rounds of VC investment than those are of low levels of standardization, but this effect is limited to the Chinese standards and not applied to the international standards.
Original languageEnglish
Publication date2019
Number of pages24
Publication statusPublished - 2019
EventR&D Management Conference 2019 - Polytechnique Paris and HEC Paris, Paris, France
Duration: 17 Jun 201921 Jun 2019

Conference

ConferenceR&D Management Conference 2019
Location Polytechnique Paris and HEC Paris
CountryFrance
CityParis
Period17/06/201921/06/2019

Cite this

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title = "Does standardization matter to make AI start-ups attractive to venture capital? Evidence from China",
abstract = "The Artificial intelligence (AI) industry is emerging. During the past few years, we have witnessed a global booming of AI start-ups with a wide range of technologies and business models, making the AI industry into a fertile ground for business creation, investment, and technology development. Meanwhile, the AI industry is featured with high of technology and market uncertainty, making it highly risky for investment. A crucial factor of industry structure that matters for venture capital (VC) investment is industry standards, which affect technology diffusion and the way in which start-ups can benefit from technological change.Given the surging amount of VC investment into the AI industry, a gap needs to be filled in by researching how VC makes investment decisions, while AI standardization is still in its early phase. The theoretical foundation of this study is the expanded conceptualization of the knowledge spillover theory of entrepreneurship (KSTE), which links contextual factors to the creation and growth of entrepreneurship. This study addresses the following question: how does the level of standardization in AI technological fields influences the attractiveness of AI start-ups to VC investment in the Chinese context? To answer this question, we develop measures for standardization in AI technological fields in China, and investigate the impact of standardization level on VC investment to AI startups. The data on VC investment in China includes a sample of 629 Chinese startups in the AI industry and their VC capital received during 1996-2018. The measures on standardization in AI technological fields are established based on the {"}White paper on AI Standardization{"} published in 2018 by the National Standardization Administration Council. We use different regression models to test the impact of standardization on the total amount of VC and how many rounds of VC each startup received. The results show that: (1) AI startups in the technological fields that are of high levels of standardization received higher volume of VC investment than those that are of low levels of standardization; (2) AI startups in the technological fields that are of high levels of standardization were more likely to receive more rounds of VC investment than those are of low levels of standardization, but this effect is limited to the Chinese standards and not applied to the international standards.",
author = "Jason Li-Ying and Jieyu Zhou",
year = "2019",
language = "English",
note = "R&D Management Conference 2019 ; Conference date: 17-06-2019 Through 21-06-2019",

}

Li-Ying, J & Zhou, J 2019, 'Does standardization matter to make AI start-ups attractive to venture capital? Evidence from China' Paper presented at R&D Management Conference 2019, Paris, France, 17/06/2019 - 21/06/2019, .

Does standardization matter to make AI start-ups attractive to venture capital? Evidence from China. / Li-Ying, Jason; Zhou, Jieyu .

2019. Paper presented at R&D Management Conference 2019, Paris, France.

Research output: Contribution to conferencePaperResearchpeer-review

TY - CONF

T1 - Does standardization matter to make AI start-ups attractive to venture capital? Evidence from China

AU - Li-Ying, Jason

AU - Zhou, Jieyu

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N2 - The Artificial intelligence (AI) industry is emerging. During the past few years, we have witnessed a global booming of AI start-ups with a wide range of technologies and business models, making the AI industry into a fertile ground for business creation, investment, and technology development. Meanwhile, the AI industry is featured with high of technology and market uncertainty, making it highly risky for investment. A crucial factor of industry structure that matters for venture capital (VC) investment is industry standards, which affect technology diffusion and the way in which start-ups can benefit from technological change.Given the surging amount of VC investment into the AI industry, a gap needs to be filled in by researching how VC makes investment decisions, while AI standardization is still in its early phase. The theoretical foundation of this study is the expanded conceptualization of the knowledge spillover theory of entrepreneurship (KSTE), which links contextual factors to the creation and growth of entrepreneurship. This study addresses the following question: how does the level of standardization in AI technological fields influences the attractiveness of AI start-ups to VC investment in the Chinese context? To answer this question, we develop measures for standardization in AI technological fields in China, and investigate the impact of standardization level on VC investment to AI startups. The data on VC investment in China includes a sample of 629 Chinese startups in the AI industry and their VC capital received during 1996-2018. The measures on standardization in AI technological fields are established based on the "White paper on AI Standardization" published in 2018 by the National Standardization Administration Council. We use different regression models to test the impact of standardization on the total amount of VC and how many rounds of VC each startup received. The results show that: (1) AI startups in the technological fields that are of high levels of standardization received higher volume of VC investment than those that are of low levels of standardization; (2) AI startups in the technological fields that are of high levels of standardization were more likely to receive more rounds of VC investment than those are of low levels of standardization, but this effect is limited to the Chinese standards and not applied to the international standards.

AB - The Artificial intelligence (AI) industry is emerging. During the past few years, we have witnessed a global booming of AI start-ups with a wide range of technologies and business models, making the AI industry into a fertile ground for business creation, investment, and technology development. Meanwhile, the AI industry is featured with high of technology and market uncertainty, making it highly risky for investment. A crucial factor of industry structure that matters for venture capital (VC) investment is industry standards, which affect technology diffusion and the way in which start-ups can benefit from technological change.Given the surging amount of VC investment into the AI industry, a gap needs to be filled in by researching how VC makes investment decisions, while AI standardization is still in its early phase. The theoretical foundation of this study is the expanded conceptualization of the knowledge spillover theory of entrepreneurship (KSTE), which links contextual factors to the creation and growth of entrepreneurship. This study addresses the following question: how does the level of standardization in AI technological fields influences the attractiveness of AI start-ups to VC investment in the Chinese context? To answer this question, we develop measures for standardization in AI technological fields in China, and investigate the impact of standardization level on VC investment to AI startups. The data on VC investment in China includes a sample of 629 Chinese startups in the AI industry and their VC capital received during 1996-2018. The measures on standardization in AI technological fields are established based on the "White paper on AI Standardization" published in 2018 by the National Standardization Administration Council. We use different regression models to test the impact of standardization on the total amount of VC and how many rounds of VC each startup received. The results show that: (1) AI startups in the technological fields that are of high levels of standardization received higher volume of VC investment than those that are of low levels of standardization; (2) AI startups in the technological fields that are of high levels of standardization were more likely to receive more rounds of VC investment than those are of low levels of standardization, but this effect is limited to the Chinese standards and not applied to the international standards.

M3 - Paper

ER -