Data and methods for identifying artificial intelligence-related patents
This paper evaluates existing approaches to identifying artificial intelligence (AI)-related patents and introduces a novel, scalable framework to improve classification performance. Motivated by the growing reliance on patent data in innovation research, we assess widely used methods and document their substantial performance limitations. To address these challenges, we develop a CPC-informed label-refinement framework inspired by positive-unlabeled (PU) learning to construct high-quality training data.
Understanding Firms’ AI Efforts and Their Economic Impact
Our research asks a central question for businesses investing heavily in artificial intelligence: Is AI actually making firms more productive, and if so, how? Using 15 years of data on AI-skilled employment at U.S. public firms, we find that firms increasing their AI investments experienced significantly faster productivity growth from 2018 to 2024—roughly one percentage point of additional productivity growth per year for a one-standard-deviation increase in AI investment. Importantly, these gains do not appear immediately; they build gradually over several years.
From Hypothesis Testing Towards Inference to Best Explanation: A PEEBI Testimonial Structure for Abductive Studies in Strategy
Though scholars employ abduction, there is no agreed-upon structure to reporting their findings. Moreover, the traditional hypo-deductive reasoning structure does not align with the epistemology of abduction.
First-Party Content Production in a Competitive Media Market
Streaming platforms are pouring money into original content, but whether it pays off depends on two things: how much their content already overlaps with competitors and how flexible their pricing is. When prices are fixed (e.g., standard subscription tiers), platforms are more likely to invest in originals—especially if competitors offer similar libraries—because originals help differentiate. But when platforms can easily adjust prices, heavy content overlap actually reduces the incentive to invest in originals, since pricing can be used instead to compete.
If We Build It, We Will Come: Strategies for Developing Academic Institutions and the Evolution of Career Choices by Top Talent During Japan’s Industrialization
Modern day economies rely on academia—with its focus on generating new knowledge and training future work forces—as a critical complement to industry in contributing to endogenous growth. How well academia performs this role, however, depends on its ability to recruit and retain talented faculty who have lucrative alternative options in industry; moreover, such allocation of talent in academia vs. industry is conditioned by path-dependencies in the evolution of these sectors.
Three Strategic Bets on AI’s Future
This paper examines competition in the consumer AI assistant market using worldwide iOS and Android app-store data from seven major AI assistants from May 2023 through December 2025. Rather than finding a winner-take-all market, we show that major product launches tend to coincide with growth in the overall category, with little evidence of direct cannibalization across leading models. In other words, the “AI war” appears less zero-sum than commonly assumed.
Evolution of Ride Services: From Ride- Hailing to Autonomous Vehicles
In recent years the ride service industry has been evolving rapidly, driven by disruptive technologies such as mobile apps, AI, and autonomous vehicles (AVs). While platform-based decentralized ride hailing companies have gained significant market share, vertically-integrated robotaxi services using emerging AVs are starting to enter the market. In this paper, we aim to provide insights about the evolution and the future of ride services studying these two competing business approaches.