Cross-boundary AI Innovation as Recombinant Search in Heterogeneous Landscapes: A Network Analysis of Computer Science and Autonomous Vehicle Fields (2009-2020)

38 Pages Posted: 7 Feb 2024

See all articles by Kaige Gao

Kaige Gao

Case Western Reserve University - Weatherhead School of Management

Dongyeob Kim

Case Western Reserve University - Weatherhead School of Management

Zhewei Zhang

University of Warwick - Warwick Business School

Youngjin Yoo

Independent; Case Western Reserve University - Weatherhead School of Management

Date Written: January 11, 2024

Abstract

AI has rapidly penetrated various industries, hailed as a universal problem-solving tool. Scholars have studied AI innovation across the contexts of their development and implementation. As general-purpose technology, however, AI innovations need to first jump across its disciplinary boundaries before they can subsequently become useful as applications. To unpack how such jumps are made, we conceptualize cross-boundary AI innovation as an outcome of recombinant search in heterogeneous innovation landscapes that are, in turn, comprised of a set of interconnected epistemic objects. We take a dynamic network view as an analytical perspective and identify two structural attributes: structural embeddedness and junctional embeddedness, which represent its popularity and role as a bridge, respectively. To assess their impact on the likelihood of a jump by an epistemic object, we test our theory using a data set of AI-related journal and conference articles from both Computer Science and Autonomous Vehicle fields in the period from 2009 to 2020. Our results show that junctional embeddedness has a positive impact on an epistemic object’s jump particularly in the early periods of time, while the effect of structural embeddedness varies over the periods.

Keywords: AI/ML, Diffusion of Innovation, Network Analysis, Recombinant Search

JEL Classification: O31, O33, M15

Suggested Citation

Gao, Kaige and Kim, Dongyeob and Zhang, Zhewei and Yoo, Youngjin, Cross-boundary AI Innovation as Recombinant Search in Heterogeneous Landscapes: A Network Analysis of Computer Science and Autonomous Vehicle Fields (2009-2020) (January 11, 2024). Available at SSRN: https://ssrn.com/abstract=4692169 or http://dx.doi.org/10.2139/ssrn.4692169

Kaige Gao

Case Western Reserve University - Weatherhead School of Management ( email )

10900 Euclid Ave.
Cleveland, OH 44106-7235
United States

Dongyeob Kim (Contact Author)

Case Western Reserve University - Weatherhead School of Management ( email )

Zhewei Zhang

University of Warwick - Warwick Business School ( email )

Coventry CV4 7AL
United Kingdom

Youngjin Yoo

Independent

Case Western Reserve University - Weatherhead School of Management ( email )

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