Knowledge that Transforms

The sovereign right of self-governance for Native nations has been both contentious and unevenly applied throughout its deeply rooted history in the United States. As a vital aspect of tribal sovereignty, this research explores the outcomes of entrepreneurial self-determination of Native American Tribes. Using a mixed methods approach, our study leans on qualitative interviews of 18 tribal leaders to catalyze a quantitative analysis consisting of data from 161 Native American Tribes collected from the US Federal Register, the National Indian Gaming Commission, and from a Freedom of Information Act request of the US Department of Interior. Data are used to better understand the role that entrepreneurial self-determination plays in Native American Tribes and its effects on economic and cultural sovereignty. What we find is that the institution of sovereignty itself is a site of innovation, where tribal leaders are not only defending but innovating it, reinterpreting what it means and how it functions through modern entrepreneurial mechanisms. The research contributes to innovation policy and institutional theory by theorizing sovereignty as a contested institution that is reshaped by entrepreneurial self-determination under conditions of institutional multiplicity.

Interdisciplinary scientific fields emerge at the intersections of existing disciplines, driving innovations that are well-suited to address grand societal challenges. While the literature generally recognizes the role of individual researchers in advancing these fields, less is known about how the researchers' positions impact the emergence of an interdisciplinary scientific field. Using synthetic biology as a case, this study analyzes publication and research grant data to explore how researchers contribute to and shape these evolutionary processes. We conceptualize four types of researchers as drivers in the formation of interdisciplinary fields: (1) impactful, (2) innovation-oriented, (3) socially-central, and (4) interdisciplinary researchers. Employing a dynamic partitioning approach based on personal characteristics, and leveraging findings from a random forest regression analysis, we examine how distinct groups and features of researchers contribute to the evolution of synthetic biology. Our findings indicate that researchers with a background in biological sciences have a higher citation impact on the field and highlight the critical role of early- and mid-career researchers in shaping the field's early innovation potential, as measured by patent citations. Furthermore, our analysis shows that interdisciplinary collaborations are–unsurprisingly–important for an emerging interdisciplinary field, as indicated by researchers' social centrality. However, researchers with more distant academic backgrounds relative to the emerging interdisciplinary scientific field tend to be more interdisciplinary than those with a higher proximity. These insights advance our understanding of how researchers' positions shape emerging scientific fields, offering insights into which researchers contribute to field formation and enhancing our knowledge of the micro-level dynamics of interdisciplinary scientific field formation. • Micro-level investigation of interdisciplinary field formation • Conceptualization of positions of researchers in interdisciplinary fields • Researchers with close and distant background impact field formation. • Early- and mid-career researchers impact scientific innovation potential. • Early interdisciplinary exposure may raise the chance of researchers joining a new field.

Sustained and inclusive economic development is the product of mature firms, commonly referred to as scale-ups. While some studies suggest equity investors enable firm growth, others argue equity-backed startups are more likely to be acquired or relocate to more developed regions. To address this ambiguity, we examine the Israeli high-tech sector, where a growing number of startups are scaling up locally. Building on existing literature and exploratory interviews with industry members, we focus on how investors' business strategies and founders' characteristics shape startups' ability to scale up locally. We argue that local scale-up is most likely in cases where equity funds act as patient capital and invest in startups founded by experienced entrepreneurs or founders who are highly embedded within the local eco-system. Exponential competing risk models of the growth outcomes of 5689 Israeli high-tech firms established between 2005 and 2020 largely confirm our claims. We discuss how our results inform understanding of equity investors' impact on scale-up and public policies that can support more inclusive innovation ecosystems. • Inclusive economic development is often the product of mature and profitable firms, commonly referred to as scale-ups. • Funding from patient equity investors (PEIs) increases the likelihood of local scale-up. • Funding from PEIs also increases the likelihood that startups will relocate to other ecosystems. • Funding from PEIs is more likely to result in local scale-up if startup founders are highly embedded in their ecosystem. • Funding from PEIs is more likely to result in local scale-up if startup founders have high entrepreneurial experience.

Despite the growing importance of the digital sector, research on economic complexity and its implications continues to rely mostly on administrative records—e.g. data on exports, patents, and employment—that have blind spots when it comes to the digital economy. In this paper we use data on the geography of programming languages used in open-source software to extend economic complexity ideas to the digital economy. We estimate a country's software economic complexity index (ECI software ) and show that it complements the ability of measures of complexity based on trade, patents, and research to account for international differences in GDP per capita, income inequality, and emissions. We also show that open-source software follows the principle of relatedness, meaning that a country's entries and exits in programming languages are partly explained by its current pattern of specialization. Together, these findings help extend economic complexity ideas and their policy implications to the digital economy. • We measure countries' software economic complexity using GitHub data • Software economic complexity predicts GDP, income inequality and emissions • We show that countries diversify into related programming languages over time • We extend economic complexity methods and their policy implications to the digital sector

A first-order design task in blockchain-based decentralized autonomous organizations is to ensure that malicious actors are sanctioned. We show that, when voters act strategically and the system is insufficiently decentralized, payoff-matching bribes undermine the sanctioning of malicious actors under conventional governance. Our framework formalizes DAO voting mechanisms and lets us identify those that mitigate the problem. Stochastic voting decouples a tokenholder’s influence from the voting behavior of others. Thus, bribery-proofness can be restored in the presence of sufficiently centralized governance tokenholders. Alternatively, masked voting increases resilience against bribery. Our work contributes to the broader debate on the merits and pitfalls of decentralization and highlights the need to align governance mechanisms with the degree of decentralization in blockchain networks.

Some organizations remain adaptable across centuries while others struggle to evolve and ultimately fade into irrelevance. Only a handful of theories can explain this extraordinary adaptability. We test two competing theoretical perspectives in imprinting research: Liability of aging suggests that older organizations are at greater risk of disruption by modern technologies, but older organizations can also repurpose their imprinted structures and processes to their advantage in a process termed exaptation. To resolve this contradiction, we analyze Catholic religious orders and their monasteries. They represent the oldest extant organizations, were founded in various historical eras, and are facing contemporary challenges posed by digitalization. Our quantitative and qualitative findings indicate that the orders with historically decentralized imprints show higher adaptability in embracing digital innovation. Our results confirm that long-term adaptability is increased in organizations whose imprinted decentralized logics provide a propensity for exaptation. However, these long-standing organizations are also more wary of the negative effects of digital disruption and appear to shield their organizational core more strongly. We contribute to imprinting research by shedding light on the intricate relationship between historically imprinted organizational logics and contemporary organizational practice and highlight the often-underappreciated importance of exaptation for long-term adaptability. • Imprinting theory implies that aging organizations are less able to react to changes. • However, structures adapted for initial conditions may be repurposed. • We test both these assumptions in the organizational logics of Catholic orders. • Catholic orders were founded in diverse eras and face challenges with digitalization. • Historically decentralized organizational logics adapt best to digital innovation.

This paper investigates whether a causal, theory-driven decision framework, such as the Entrepreneurs-as-Scientists ( E -a-S) framework, influences how entrepreneurs reconfigure their entrepreneurial teams. We conducted a randomized controlled trial on 132 early-stage startups that participated in a pre-incubation program. During the pre-incubation program, we randomly assigned the early-stage startups to either a treatment group, which learned how to apply the E -a-S framework to make decisions, or a control group, which learned the same tools and skills as the treatment group, but without the E -a-S framework. For startups in both groups, we tracked the composition of the initial entrepreneurial team and its subsequent reconfigurations over 64 weeks. Employing a question-driven approach, we investigated whether E -a-S entrepreneurial teams evolved differently from those in the control group. Our analysis reveals that the entrepreneurs who learned the E -a-S framework composed their entrepreneurial teams differently from the other entrepreneurs: entrepreneurs who learned an E-a-S framework were more likely to use a resource-seeking strategy to compose their teams than those in the control group. Specifically, E -a-S entrepreneurs decreased the proportion of team members with technical backgrounds, while selectively adding individuals with managerial and industry-specific experience. These findings demonstrate that E-a-S not only shapes venture pivots and terminations but also influences the formation of entrepreneurial teams. The implications of the results are discussed for research, entrepreneurs, and investors. • E -a-S entrepreneurs reconfigure their entrepreneurial teams differently compared to other entrepreneurs. • E-a-S entrepreneurs rely more on a resource-seeking strategy than non-E-a-S entrepreneurs when reconfiguring their teams. • E-a-S entrepreneurs do not differ from non-E-a-S in using an interpersonal attraction strategy when reconfiguring teams. • Randomized control trial experiment: Analysis based on 132 early-stage startups.

This work investigates the role of green foreign direct investments (FDIs) and local skill composition for regional technological diversification in green domains. We conduct the analysis on 287 US Metropolitan Statistical Areas (MSAs) observed from 2003 to 2018. Our results show that diversification in green technological domains is more likely to take place in MSAs with higher volumes of green FDIs and higher intensity of abstract skills. Moreover, we find that the local endowment of abstract and routine skills moderates the impact of green FDIs, activating compensation and reinforcing mechanisms, respectively. The findings of this work provide novel insights for the academic debate on the determinants of green technological diversification and for the design of an effective policy toolbox to sustain the regional green transition.

Science-based startups, which develop technologies at the frontier of scientific knowledge, play a crucial role in innovation ecosystems. However, despite their potential for groundbreaking innovation, these startups may face frictions in securing venture capital (VC) funding. This paper investigates whether science-based startups systematically take longer to secure VC funding compared to startups that are less rooted in science. We develop a formal model that highlights a misalignment between scientists, who often prioritize technological advancement, and VCs, who seek market validation. This misalignment is particularly relevant in early funding rounds, where startups have stronger outside options. Drawing on PitchBook data for startups founded between 1990 and 2015, we find that science-based startups often struggle to attract timely investment, which may limit their ability to scale and commercialize new technologies. This is reflected in a negative correlation between longer times to VC funding and subsequent startup performance. • Science-based startups face delays in securing VC funding, which negatively correlates with startup performance. • Scientists prioritize academic recognition, potentially creating an agency problem when seeking VC investment. • Misalignment between researchers and investors is particularly relevant in early funding rounds. • An inverted U-shaped relationship exists between a startup’s scientific orientation and its likelihood of securing early VC funding.

Why do some scientific fields benefit far more from AI than others? To answer this question, we study the diffusion and effects of AI across more than 170 scientific fields between 2005 and 2023, drawing on a dataset of over 80 million scientific publications, and assessing its contribution to knowledge creation using indicators of novelty and impact. We find that, on average, the use of AI is associated with more novel and highly cited research, and that these effects are strengthened with recent technological advances (namely, transformers and large language models). However, these benefits are far from uniform. The contribution of AI, especially in terms of novelty, intensifies with its penetration within a field, that is, the extent to which AI is widely adopted and embedded in research practices, and with the “roughness” of the underlying knowledge space, meaning the degree to which ideas are fragmented and combinatorially complex. • AI positively contributes to scientific novelty and impact. • Effects strengthened with recent AI advances. • Effects are uneven across 170+ fields. • The contribution of AI intensifies with its penetration within a field. • Strongest gains occur in fragmented (“rough”) knowledge spaces.