Knowledge that Transforms

The absorptive capacity construct derives its functions, dimensions, and internal relationships from the implicit assumption that humans are the only learning agents underlying knowledge absorption. I posit that the emergence of artificial intelligence (AI) challenges this assumption and requires a re-examination of the construct. Through a theory-building abductive study combining conceptual mapping with qualitative interviews, this work investigates the complex relationship between AI and knowledge absorption. It theorizes new dimensions of absorptive capacity pertaining to AI-driven processes and analyzes the interactions between these new dimensions and the traditional facets of the construct. Ultimately, the paper seeks to reaffirm the importance of absorptive capacity in the age of AI and develop a nuanced understanding of AI's role in reshaping the construct's antecedents, mechanisms, and implications.

Innovation is cumulative and interdependent: successful inventions build on prior knowledge within technological fields and may also affect success across related ones. Yet these dimensions are often studied separately in the innovation literature. This paper asks whether patent success across technological categories can be represented within a single dynamic framework that jointly captures within-category reinforcement, cross-category spillovers, and a set of aggregate regularities observed in patent data. To address this question, we propose a model of interacting reinforced Bernoulli processes in which the probability of success in a given category depends on past successes both within that category and across other categories. The framework yields joint predictions for success probabilities, cumulative successes, relative success shares, and cross-category dependence. We implement the model using granted US patent families from GLOBAL PATSTAT (1980–2018), defining category-specific success through a cohort-normalized forward-citation index. The empirical analysis shows that successful innovations continue to accumulate, but less than proportionally to the growth in patent opportunities, while technological categories remain interdependent without becoming homogeneous. Under a mean-field restriction, the model-based inferential exercise points to a positive but non-maximal interaction across technological categories.

In this paper, we examine the role of hype in the discursive construction of emerging quantum technologies. Our findings reveal that, as these technologies transition from academia to industry, they are the object of ‘hyping’ in which the discourses of risk and responsibility figure prominently. We find that hype – a collective vision around which attention, excitement, and expectations escalate – emerges from a process of discursively constructing emerging technologies as risk management solutions to highly salient geopolitical and economic risks to state actors, while acknowledging that they may pose less salient, novel social risks to citizens and social groups within a state. We also find that the former construction overwhelms the latter, which has important implications for what actions associated with developing these emerging technologies are considered responsible. As a result, and building on the sociology of expectations, we develop a discursive model showing how hyping mobilizes risk and responsibility to shape the meanings attached to emerging technologies and, in turn, the expectations that form around them – with important implications for the direction and pace of technological development.

This paper evaluates existing approaches to identifying artificial intelligence (AI)-related patents and introduces a novel, scalable framework for improving classification performance. Motivated by growing reliance on patent data in innovation research, we assess widely used methods, including patent class-based approaches and the USPTO’s Artificial Intelligence Patent Dataset (AIPD), with an independent, human-expert-annotated ground-truth dataset. We document substantial performance limitations in existing approaches, particularly in terms of precision and generalizability. To address these challenges, we develop a CPC-informed, iterative positive-unlabeled (PU) learning framework for constructing high-quality training data. Our approach integrates hierarchical patent classification with data-driven refinement procedures to reduce label noise and improve representativeness. Using this refined dataset, we train a range of machine learning, deep learning, and transformer-based models. Our results show that models trained within our framework significantly outperform existing methods, including AIPD, achieving improvements over AIPD in F1 scores of approximately 18–21% on the same benchmark dataset. These gains are primarily driven by enhanced precision without sacrificing recall, highlighting the central role of training data quality in classification performance. We further demonstrate the empirical value of improved AI patent identification through two applications, showing that the release of ChatGPT increased both the market valuation of AI patents and firms’ allocation of innovative effort toward AI technologies. To support future research, we release our training data, source code, and patent-level predictions, with ongoing updates to reflect the evolving nature of AI innovation.

This study examines the effects of geopolitical tensions and technology-related sanctions on academic research, with a focus on the U.S. Entity List's inclusion of Chinese academic institutions. Using a comprehensive dataset of academic publications from the Open Academic Graph, we employ a stacked event study design to estimate the causal effects of the Entity List sanctions on academic research productivity. Our findings reveal that Entity List sanctions reduce the quantity but improve the quality of research output among affected Chinese researchers. Mechanism analyses, supplemented by semi-structured interviews, show that these changes are driven by research diversification and collaboration network reconfiguration. Sanctioned researchers shift toward new topics and form partnerships with non-listed Chinese institutions, which serve as intermediaries that restore access to restricted resources and expose researchers to novel knowledge. Notably, we find no significant decline in collaboration with U.S.-based researchers, suggesting that sanctions reconfigure rather than sever international networks. Theoretically, this study extends the disruption-as-catalyst logic to geopolitically imposed resource constraints, showing that sanctions can trigger productive diversification. By demonstrating that sanctions affect research quantity and quality in opposing directions through specific behavioral mechanisms, this study also reconciles inconsistent findings in prior literature and advances our understanding of how geopolitical tensions reshape the global academic research landscape.