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.
The expansion of advanced technologies is intensifying demand for energy, materials, and efficiency of production systems, with semiconductors exemplifying the tension between rapid technological progress and resource sustainability, and the development of circular innovations. Yet it remains unclear whether circular innovations in this sector tend to be genuinely disruptive. This study examines the disruptiveness of circular (vs. non-circular) innovations and analyses the role of inventor network structures. The empirical analysis relies on the patents of 299 European Semiconductor Equipment Manufacturing (SEM) companies retrieved from the ORBIS IP database for the period 2013–2022. First, we operationalize disruptiveness by adopting a citation-based measure to reveal whether subsequent inventions build upon or bypass a focal patent's prior art. Then, co-inventor networks are constructed, and inventor centrality and cohesion measures are aggregated to the patent level to link inventor network structure to disruptive outcomes over time. Our findings show that circular patents exhibit, on average, higher disruptiveness than non-circular patents, and that inventor network structures characterized by more peripheral and high local cohesive configurations align with disruptive circular outcomes. The effect of the network antecedents differs systematically between circular and non-circular innovations, indicating distinct pathways to disruption. The study contributes to the innovation literature by integrating a structural disruptiveness measure with inventor network analytics, offering one of the first patent-level comparisons of circular versus non-circular disruptiveness in the semiconductor industry, and by deriving implications for organizing collaboration portfolios and policy instruments that foster circular transitions.
In the pursuit of innovation, diverse actors engage in frequent interactions. Although Artificial Intelligence (AI) is becoming an active participant in innovation search, less is understood about how its distinct search capabilities complement those of human actors. We explore the complementarities that emerge in human–AI search and identify three relational dialectics that arise in these interactions. The first contrasts human-centered search, which draws on internal organizational knowledge and relational networks, with AI's boundary-spanning search across distant and unfamiliar domains and its ability to synthesize dispersed internal knowledge. The second juxtaposes human contextualizing, which brings relevance and situational meaning, with AI de-contextualizing, which removes assumptions and exposes abstract, pattern-based connections. The third contrasts human judging, involving evaluation and persuasion, with AI envisaging, which generates and visualizes alternative possibilities. We argue that these dialectical complementarities bring about expanded rationality and mitigate biases in the search process, thereby enhancing the potential for innovation.
We study the 2013 U.S. federal government shutdown and its impact on federally funded research in Antarctica. Although the shutdown lasted only 16 days, it coincided with the start of the Antarctic summer field season, a narrow window for data collection that relies on geographically unique, time-sensitive, and difficult-to-substitute research inputs. Using scientist-level data with a difference-in-differences design and exploiting the timing of the shutdown, we document an 11% decline in the number of byline-weighted publications among affected researchers, as well as altered collaboration patterns, which we corroborate with qualitative survey evidence. These findings show that even brief disruptions can have lasting effects on science when they block access to specialized, time-sensitive, and hard-to-replace research inputs.
Radical innovation and strategic leadership research share a foundational challenge of theorizing how firms make choices about an uncertain future that shape their competitiveness, survival, and long-term performance. Yet, each field has developed a distinct explanatory signature. Radical innovation research has followed the technology, tracing how the architecture and trajectories of technological discontinuities determine organizational fates. Strategic leadership research has followed the leader(s), tracing how the cognition, composition, and interactions of those at the organizational apex shape organizational choices. We argue that this productive division of labor is being jointly frustrated by four shifts in contemporary organizing: structurally, innovation is migrating from firms to ecosystems; technologically, AI is transforming both the creative process and the cognitive conditions of leadership; relationally, permeable boundaries are redistributing influence across actors; and culturally, evaluative criteria are expanding from performance to purpose. We identify the foundational assumptions these shifts unsettle and propose four research frontiers organized around where, how, with whom, and why radical innovation happens. We conclude with key reflections from three foundational scholars on the future of the field.
At the heart of scientific discovery are researchers who identify ideas worthy of inquiry. In performing research tasks, researchers increasingly use generative artificial intelligence (AI) technologies. While this technology offers the promise of increased efficiency across the research process, little is known about how generative AI assists researchers with the generation of initial research ideas—one of the most fundamental tasks in science—and their willingness to adopt it for such purpose. In a pre-registered randomized online experiment with 310 researchers and a follow-up ideation study with 28 researchers, we show how one's research experience plays a role in receiving generative AI suggestions when formulating new research ideas. Experience negatively moderates both the incorporation of generative AI suggestions as well as its effect on perceived novelty and impact of the researcher's idea. We also find that generative AI may be reshaping research ideation by adding a layer of non-human exchange that precedes feedback from human experts. By focusing on the critical moment at which research ideas spark, we provide an initial understanding of how researchers respond to generative AI at a time when the paradigm of human knowledge production may be shifting from a human-only to a joint human-AI model.
Global sustainability challenges demand large, rapid and systemic transformation of the systems that underpin modern life. Disruption is increasingly discussed as a trigger of these transformations, but the drivers of disruption have remained unclear. While much of the existing literature focuses on technological innovation as a potential driver, limited although growing attention has been paid to non-technological sources. This paper explores business model innovation as a non-technological driver of disruptive sustainability. We define disruptive sustainability as far-reaching changes in more than one dimension of socio-technical systems, triggered by a high-intensity effect, towards realising environmental, social and economic goals. We conduct a theorizing review of academic literature on the last mile in low-income and lower-middle income countries. We develop an explanatory model of how interdependencies between business model innovation components drive disruptive sustainability by re-defining the problem-solution nexus at different depths. Our contributions are threefold. First, we expand the conceptualization of disruptive sustainability by unpacking the crucial aspect of depth of change. We suggest that disruptions can trigger shallow, medium or deep change within and across dimensions of socio-technical systems. Second, we explain how the interdependencies between different business model innovation components drive disruptive sustainability by changing the problem-solution nexus at different depths. Third, based on these contributions, we develop a tool which guides stakeholders in driving disruptive sustainability in three distinct steps linked to different degrees of depth.
Schumpeterian arguments of “creative destruction” predict that innovation is countercyclical. However, empirical findings demonstrate the opposite. We apply corporate finance principles to innovation economics and propose a “hurdle-rate theory of inventive procyclicality.” In our 1977–2018 sample of U.S. firms, macroeconomic episodes of high equity risk premia (ERP) hinder innovation because many R&D projects fail corporate budgeting decisions when the aggregate discount rate is high. Using the staggered variation in state-level R&D tax credits, we conduct a difference-in-differences analysis to establish a causal link between the ERP and patent value. In line with our hurdle-rate theory, we demonstrate that high-ERP episodes negatively impact firms that focus on exploratory search more than their exploitative counterparts. Evidence consistently suggests that the hurdle rate effect is less pronounced in firms with financial slack, weak product market competition, and high institutional investor ownership.
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 label-refinement framework inspired by positive-unlabeled (PU) learning to construct 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 with only a modest reduction in 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 a novel dataset of patent-level predictions (AIPat), with ongoing updates to reflect the evolving nature of AI innovation.
We investigate the impact of equity crowdfunding exemptions on new venture creation. We further explore whether the effect varies across industries with different levels of external finance dependence, and whether this heterogeneity is influenced by regional industrial structures. Empirical evidence shows that, while the average effect of crowdfunding exemptions on new venture entry is not statistically significant, the impact is positive and significant in industries with higher external finance dependence. Moreover, this positive effect is further amplified in regions characterized by greater related variety. Complementary analyses show that the increase in new venture creation does not appear to be accompanied by a decline in entry quality. These findings highlight the importance of firm-level financial constraints and regional industrial context in shaping the effectiveness of crowdfunding policies.
This study investigates how technologies emerge to sustain technological sovereignty in response to exogenous shocks. We focus on the efforts of the Finnish state to overcome the shortage of mineral oil-based lubricants during the Second World War and identify tar oil as a central technology in this endeavor. By analyzing the emergence and decline of the Finnish tar oil industry, we elaborate on the life cycle and key mechanisms of temporary technologies as well as their role in maintaining technological sovereignty. These findings lead to a definition of temporary technology as a distinct form of technology evolution. Our findings contribute to the technology evolution literature by theorizing a form of technology that has a limited long-term impact, making it suitable for addressing temporary shocks without altering long-term technology evolution. For the technological sovereignty literature, we extend understanding of how sovereignty can be maintained in the face of unanticipated exogenous shocks and how that involves cooperation between the state and non-state actors.
Grant peer review (GPR) is a cornerstone of modern research funding, yet the process by which reviewers integrate multiple criteria into a single overall evaluation—known as commensuration—remains poorly understood. We study the implicit weights that reviewers assign to PI qualifications, research quality, and societal relevance, using a unique dataset of 2105 review reports for 586 proposals submitted to the Swedish Foundation for Strategic Research (2011–2017), complemented by manual coding of comments and interviews with screeners and funding officers. Commensuration influences roughly 29% of final scores. The research quality criterion dominates evaluations, receiving about twice the weight of relevance. This commensuration pattern is surprisingly stable across pools of reviewers from different fields and geographic areas and irrespective of the reviewers' profile and their presumed ability to assess relevance. PI qualifications generally play a minor role, tempering concerns about pervasive reputational advantages, and tend to be used more when evaluators feel less competent, hinting that reputation is used as a fallback consideration when the reviewer lacks confidence. Overall, the results suggest that commensuration may systematically contribute to prioritizing technical merit over relevance even beyond the intentions of the funders.
This study investigates the “stars effect” of recruiting overseas scholars as deans and its impact on academic output in China from 2001 to 2019. We find that appointing a returnee dean increases a department's English publications by 40% annually. This positive effect applies to both top-tier and non-top-tier journals, without crowding out Chinese publications. The magnitude of the effect correlates with the dean's international connections and the ranks of the destination and source institutions. Returnee deans enhance output through knowledge spillovers, expanded networks, and increased overseas personnel, but not additional research grants. Our findings demonstrate the positive role and extensive influence of power-granted talent initiatives in developing regions.
