TY - JOUR
T1 - Free range startups? Market scope, academic founders, and the role of general knowledge in AI
AU - Chattopadhyay, Shinjinee
AU - Honoré, Florence
AU - Won, Shinjae
N1 - Breadth of market applicability This definition is in line with prior scholarly work. For instance, Mosakowski ( 1991 ) explains as follows: \u201CVertical markets refer to specific industrial niches for computer products. The breadth of these niches varies considerably, ranging from large niches such as the health\u2010care industry to narrow niches such as the automobile repair industry (p. 119).\u201D Compared to industry sectors, verticals often signify applicability across various industries. For example, within Fintech, one of the verticals, one can find a product that bridges commercial lending applications, insurance products, and financial platforms. PitchBook's list of verticals as a measure of market applicability has gained legitimacy and traction outside of academia as well. National Science Foundation (NSF) refers to the same list of verticals provided by PitchBook within the report prepared by the National Science Board, titled \u201C.\u201D The prominent investment financial services company Morningstar, which is worth approximately $12 billion in market cap, recently introduced indices based on PitchBook's verticals measure, demonstrating that firms are staking future revenue on PitchBook's measure of industry verticals. We provide more information on how PitchBook assigns verticals to firms as well as the complete list of verticals within our sample in Section 5 in the Appendix.
PY - 2024
Y1 - 2024
N2 - Research Summary: High-tech startups develop technologies, the market applicability of which can vary widely, enabling startups to target a range of market segments. Using a question-driven approach to contrast startups with and without academic founders, we investigate the difference in the market applicability between the two groups on a sample of 988 startups in the artificial intelligence (AI) field. Our findings reveal that academics' pursuit of basic research drives the creation of general knowledge, which in turn leads to wider market applicability. With fewer requirements for complementary downstream assets in the AI ecosystem, academics can more easily translate their general ideas to market applications and locate downstream in the value chain. Our findings highlight the role of problem-formulation and -solving in startups and of academic startups within AI. Managerial Summary: Using a sample of 988 startups in the Artificial Intelligence field, we find that startups with at least one academic on their founding team are associated with a higher number of verticals (potential market segments for the technology the startups developed) compared to startups without any academics. Teams with academic founders produce more general publications and patents than others, which drives the association with more verticals. Academics formulate and solve more general problems relative to non-academics, leading to the creation of more general products that are applicable to a broader range of verticals. With fewer requirements for complementary downstream assets in the AI ecosystem, academics can more easily translate their general ideas to market applications and locate downstream in the value chain.
AB - Research Summary: High-tech startups develop technologies, the market applicability of which can vary widely, enabling startups to target a range of market segments. Using a question-driven approach to contrast startups with and without academic founders, we investigate the difference in the market applicability between the two groups on a sample of 988 startups in the artificial intelligence (AI) field. Our findings reveal that academics' pursuit of basic research drives the creation of general knowledge, which in turn leads to wider market applicability. With fewer requirements for complementary downstream assets in the AI ecosystem, academics can more easily translate their general ideas to market applications and locate downstream in the value chain. Our findings highlight the role of problem-formulation and -solving in startups and of academic startups within AI. Managerial Summary: Using a sample of 988 startups in the Artificial Intelligence field, we find that startups with at least one academic on their founding team are associated with a higher number of verticals (potential market segments for the technology the startups developed) compared to startups without any academics. Teams with academic founders produce more general publications and patents than others, which drives the association with more verticals. Academics formulate and solve more general problems relative to non-academics, leading to the creation of more general products that are applicable to a broader range of verticals. With fewer requirements for complementary downstream assets in the AI ecosystem, academics can more easily translate their general ideas to market applications and locate downstream in the value chain.
KW - academic entrepreneur
KW - entrepreneurial team
KW - generality
KW - innovation
KW - market opportunities
KW - problem-solving perspective
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U2 - 10.1002/smj.3685
DO - 10.1002/smj.3685
M3 - Article
AN - SCOPUS:85212229081
SN - 0143-2095
JO - Strategic Management Journal
JF - Strategic Management Journal
ER -