Date of Award

2020

Degree Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Graduate Group

Operations & Information Management

First Advisor

Lynn Wu

Abstract

Given the recent proliferation of digitization and increase in data availability, this dissertation, comprised of three essays, examines the influence of modern information technologies, data-driven analytics and artificial intelligence (AI), on facilitating innovation. In spite of the growing expectation that technological improvements can accelerate the development of innovation, a collective set of evidence suggested considerable slowdown of innovation productivity in recent years: innovative ideas are getting harder to find (Bloom et al. 2020). Through large-scale empirical analyses, this dissertation studies under what conditions data analytics and AI-related technologies can facilitate innovation in firms and whether they can mitigate the recent innovation decline. In the first essay, we investigate the extent to which the rise of data analytics can reduce declines in firms’ innovative performance after an initial public offerings (IPO) event. It shows that through the increased use of data analytics, post-IPO firms can mitigate substantial reductions in some types of innovation: those innovations that involve new combinations of existing knowledge and that leverage existing knowledge in new problem domains. The second and third essays examine the effect of AI on one of the most complex innovation processes - drug innovation in the global pharmaceutical industry. Despite substantial research and development (R&D) spending by pharmaceutical firms, drug innovation has slowed down in terms of finding new drugs with new chemical properties. In the second essay, we first differentiate the impact of AI on different stages of drug innovation process. We find that AI can primarily support the early compound discovery and pre-clinical trials stages, but AI is less useful in later stages when human engagements, judgements, and organizational decisions are essential. Furthermore, we examine the conditions on when AI is most effective in aiding drug discovery. We find that AI has an advantage in discovering drugs whose mechanism of impact on a disease is known and drugs at the medium level of chemical novelty. In the third essay, we explore how AI can affect the value of R&D alliances, characterized by exchange and share of information among firms in the pharmaceutical industry. We find that drug innovation at the intermediate level of novelty can be best achieved through the complementarity between firms’ investments in AI and R&D alliances. Overall, this dissertation demonstrates the capabilities that analytics and AI can provide to partially alleviate the decline in innovation, and in particular, accelerate the development of certain types of innovation.

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