IT and Innovation: How did the Internet affect firms’ reliance on science? (available upon request)
(job market paper)
This paper examines how the Internet facilitates the utilization of science in industrial innovation. I find that the Internet enables firms to discover “hidden gems”- commercializable yet under-recognized scientific findings in less prestigious journals, from early-career scientists, with fewer academic citations, and with higher forward patent citations. I compiled a database that contains 541,568 patent citations to scientific papers from 3,651 public firm-locations between 1992 and 2000, and identified the staggered adoption of basic Internet at these firms. Using a difference-in-differences framework, I show that access to the Internet at firm-locations is associated with a 9.3% increase in the likelihood of citing scientific papers; and up to 13.2% increase in citing the "hidden gem" papers. These findings suggest that IT reshapes the process of firm sourcing knowledge in innovation. By reducing search cost, IT enables firms to have equal access to previously less noticeable scientific knowledge and thus discover their commercial value. The results shed light on how IT reinforces the link between science and innovation.
Cornell Innovation, Entrepreneurship, and Technology Brownbag Workshop (Ithaca, Sep 2021)
Cornell Strategy and Business Economics Workshop (Ithaca, Oct 2021)
Information Systems Student Presentations Over the Cloud Workshop (ISPOC) (Nov, 2021)
We investigate how worker mobility influences the adoption of a new general-purpose technology (GPT). Using data from over 153,000 establishments between 2010 and 2018, we observe establishment decisions to adopt machine learning. Taking advantage of state-level changes to the enforceability of noncompete agreements as an exogenous shock to worker mobility, we find that changes that facilitate worker movements are associated with a significant decline in the likelihood of adoption. Moreover, the magnitude of establishment response depends upon characteristics of the establishment and the location in which it resides, in particular, establishment size and number of large establishments in the same industry-location. These results are consistent with the view that increases in worker mobility lead to greater risks for establishments that are contemplating adoption of a new GPT that involves significant downstream innovation.
Workshop on Information Systems and Economics (WISE) (Virtual, Dec 2020)
Temple-CMU-NYU 2020 Conference on Artificial Intelligence, Machine Learning, and Business
Analytics (Virtual, Dec 2020)
ISB 2nd AI & Strategy Consortium (Virtual, Jan 2021)
Wharton Innovation Doctoral Symposium (WINDS) (Virtual, Feb 2021)
the 19th ZEW Conference on the Economics of Information and Communication Technologies (Virtual, June 2021)
AOM Symposium- Machine Learning, Artificial Intelligence, and Strategy: Emerging Research on
the Importance of Complements (Virtual, Aug 2021)
2021 NBER Economics of Artificial Intelligence Conference (Virtual, Sep 2021)
The impact of high-speed railway (HSR) expansion on entrepreneurial firm dynamics: Evidence from China
This paper investigates the impact of China’s high-speed railway (HSR) expansion on its entrepreneurial activities using firm registration data between 2011 to 2015. I find that connecting to HSR benefits mega-cities, while has lead a reduction in new firm entry in smaller cities. To address the non-random railway station placement problem, I constructed an instrumental variable of a hypothetical HSR station network that subjects to global construction cost minimization. I also adopted a market access approach similar to Donaldson (2018), where I calculated the impact of HSR on each city by capturing the changes in all its market access using a reduced-form expression derived from general equilibrium trade theory. I demonstrate that non-connection-induced market access significantly increases firm entry in mega cities by 2%, while decreases firm entry in Tier-3 and Tier-4 cities by up to 5%.
Work in Progress
Project summary: we explore the knowledge creation in digital platforms. Built on a dataset that includes 18,523 Instagram influencers and 804,397 brand-mentioning posts, we identified the dynamic network among those creators. We then propose a computational framework using computer vision approach to quantify the creativity of postings by category. The research goal is to understand the knowledge flows across social networks and the effects of network position of creators on knowledge creation in social media.
How does industry-academic cooperation affect the commercialization of AI?
Project summary: The number of AI-related publications has been exploding in the past few years, yet little is known on how these AI research have contributed to industrial innovation. This paper explores the effects of corporate-university network on the commercialization of AI research. I firstly identify the AI-related papers, including journal articles, conference papers, and unpublished online manuscripts, that have been cited in granted patents up to 2020 using a hybrid of rule-based and machine learning method, as an extension to the Patent-citation-to-science dataset (Marx and Fuegi 2020). I then build the coauthorship network using disambiguated firm inventor name and paper author name, which includes both the direct link and indirect link.
Robot adoption in hospital industry: Evidence from China
Project summary: I collected data of the adoption of AI rehabilitation robot for stroke treatment among 476 hospitals in China with more than 200,000 patient visit records between 2014 and 2020. The research goal is to provide insights for adoption decision at the hospital level, and to understand patients’ learning process to use AI treatment at individual level.