Knowledge that Transforms

Prior research has shown that a large fraction of tenured university faculty in the U.S. and other countries were trained at a small number of highly prestigious universities. The question remains whether this concentration is due to competitive advantages held by candidates from these universities, or whether it reflects the larger output of early-career researchers aspiring to faculty positions by these universities. To address this question, we analyze data covering the full population of doctoral graduates in Germany since the 1960s. Similar to studies of the U.S. system of higher education, we observe a strong concentration of professors trained at a small number of universities, with the top five universities accounting for 17.9% of all appointed university professors. However, we find no systematic evidence indicating that the prestige of the doctoral degree-granting university systematically affects individuals' odds of being appointed to professorships, as prestigious universities train disproportionate numbers of doctoral graduates. Despite increasing stratification tendencies within the German system of higher education, our results also do not indicate that the importance of the degree-granting university for the academic careers of its doctoral graduates has increased over the past 50 years. While doctoral graduates from top-tier traditional universities and top-tier technical universities appear to be more likely to secure faculty positions at universities of the same category, this pattern reflects a large share of doctoral graduates returning to their degree-granting university after initial appointments elsewhere. • Tenured faculty concentrate among a small number of PhD-granting universities in Germany • Concentration in faculty appointments tracks concentration in the output of PhDs • No evidence of increasing hiring premium for graduates from highly prestigious universities • Preferential hiring within prestige groups reflects return mobility • Odds of appointment have declined strongly across cohorts since 1960s

We analyze benefits and limitations of information sharing among product market rivals via a smart-contract-enabled blockchain. Firms face uncertain demand but obtain informative signals that can be reported to a blockchain, which ensures immutable recording but does not guarantee truth-telling. Truthful reporting is incentivized by requiring firms to post collateral (“stake”), which is forfeited if the report is deemed untruthful ex-post. We derive (a) conditions under which truthful reporting arises in equilibrium and (b) the necessary amount of collateral that ensures truthful reporting, and examine their comparative statics with respect to ex-ante demand uncertainty, signal precision, and competitive interaction among firms. While the collateral is returned to firms whose production decision is consistent with their report, financially constrained firms may struggle to afford the required stake. Thus, they are potentially unable to join the blockchain. We investigate conditions under which it is beneficial for firms to partially subsidize their rivals’ collateral costs. Finally, we examine welfare implications of information sharing via blockchain, and we show that such information sharing is welfare improving.

In October 2020, the United States Patent and Trademark Office (USPTO) introduced an automated process for routing new patent applications, in part to increase examiner specialization. In this research note, we first assess whether the USPTO achieved this objective using a difference-in-differences (DiD) research design on a sample of applications assigned to examiners between 2018 and 2021. The new routing system increased technical overlap between examiner work histories and newly assigned cases by 57 percent, indicating a substantial increase in examiner specialization across the agency. In addition, we show that automated routing — largely unknown in the academic literature — effectively eliminated quasi-random assignment of applications to examiners. Given the importance of quasi-random assignment for empirical research, we discuss our findings within this context and potential impact on future research.

This study examines how disruptive sustainable business models (DSBMs) drive sustainability transitions across micro, meso , and macro levels of socio-technical systems. Analyzing 29 case companies, the research identifies five key drivers of DSBM development and scaling: technological experimentation, entrepreneurial initiative, user-driven innovation, industry collaboration, and regulatory support. These drivers interact with seven critical factors: industry dynamics, organizational structure, geographical context, innovation ecosystems, regulatory environment, policy coherence, and market readiness that shape how DSBMs evolve from niche innovations to regime-level change. Drawing on and extending the Multi-Level Perspective (MLP) framework, the study demonstrates how firms actively couple business model innovation with institutional change across levels. The findings highlight that DSBMs achieve large-scale impact by leveraging policy mixes that destabilize unsustainable practices while supporting innovation scaling. The role of adaptive governance frameworks and cross-sectoral policy coherence is crucial for aligning DSBMs with global sustainability goals, such as the Paris Agreement. Moreover, the study offers a novel contribution by conceptualizing DSBMs as recursive and cross-scalar instruments of transition agency. This research contributes to the literature on sustainability transitions by integrating business model innovation more explicitly into MLP theory and providing evidence of how firm-level agency interacts with structural change. • Disruptive sustainable business models operate across niche, regime, and landscape levels. • Business model innovation drives sustainability transitions beyond technological change. • Policy fragmentation constrains the scaling of disruptive sustainable business models. • Adaptive governance enables firms to challenge and reconfigure unsustainable regimes. • Evidence from 29 firms reveals sector-specific transition pathways.

Amid accelerating socio-ecological crises—climate breakdown, mass extinctions and widening inequalities—there is an urgent need for transformative approaches that address root causes rather than symptoms. Disruptive sustainability seeks such transformations; however, it remains theoretically underdeveloped regarding how radically different knowledge systems can work together across the life-cycle of technologies. Drawing on science and technology studies and decolonial scholarship, we build a decolonial typology of knowledge integration in ‘pluriversal technologies’—technologies co-designed, co-produced and co-owned across epistemic communities. We identify four modes: extractive appropriation, parallel operation, adaptive integration and transformative integration. We demonstrate how each appears along the design, production and ownership dimensions of a technology's life-cycle. This study makes three contributions. First, by demonstrating that a single technology can occupy different integration modes across its life-cycle, it exposes the dimensional unevenness that often derails ostensibly collaborative initiatives. Second, it moves beyond binary treatments of integration to a graded, four-mode framework. Third, it identifies catalytic mechanisms—such as trust building, co-learning, shared governance, recognition and reparation—that enable initiatives to shift between modes. This typology enriches research on disruptive sustainability by clarifying how diverse knowledge systems can collaborate or clash and by mapping routes toward more just, sustainable and effective innovation. • Decolonial typology of knowledge integration in pluriversal technologies • Four modes: extractive, parallel, adaptive and transformative • Integration varies across design, production and ownership phases • Identifies mechanisms enabling shifts between integration modes • Moves sustainability transitions beyond participatory/not-participatory framings

Increasingly complex societal challenges require more transdisciplinary collaboration between researchers and practitioners. Previous studies measured and categorized motivations driving researchers to choose projects involving practitioners and provided empirical evidence for the existence of the ‘puzzle-ribbon‑gold’ typology. However, neither well-known decision factors such as research funding and societal impact nor preferences on the terms of collaboration have yet been integrated in any researcher typology. Here, we use a discrete choice experiment to elicit the preferences of 862 researchers employed at German public research institutes (PRIs) over research project alternatives that vary in six attributes (funding volume, academic success, societal impact, knowledge transfer, knowledge co-production, and practitioner type), of which most had three levels (low, medium, high). We determine that scientists in our sample, on average, attach positive value to engaging with practitioners. However, their preferences are not homogeneous. We find that PRI researchers can be categorized in three latent classes: ‘changemakers’, who prioritise creating societal impact via public-sector knowledge co-production; ‘fundraisers’, who focus on acquiring financial resources by transferring knowledge to the private sector; and ‘scholars’, who concentrate on academic success and avoid practitioners. Thus, we derive inductively a researcher typology that accounts for so far neglected but demonstrably decision-relevant aspects, that is impact, funding, collaboration form, and partner type. Interestingly, various observable individual and contextual variables do not offer much additional information in terms of explaining preference heterogeneity. We conclude by discussing policy instruments to foster academic engagement as well as future research directions. • Many scientists inherently value to collaborate with practitioners. • A choice experiment and latent class analysis measure researcher preferences and identify a researcher typology. • Researchers may be categorized into impact-seeking changemakers, knowledge-transferring fundraisers, and reclusive scholars. • Organisational context explains small parts of researcher preference heterogeneity, but most of it is unobserved.

Open collaboration is increasingly used to advance scientific discovery, yet sustaining participation remains difficult when leadership is informal and problems are open-ended. We study projects from Polymath—a large-scale open collaborative initiative in which mathematicians jointly work on unsolved mathematical problems under conditions of extreme epistemic and social uncertainty. Drawing on public online discussion data from four Polymath projects and using computerized text analysis, we examine how leaders' emotional expressions facilitate continued participation. We find that leaders' emotional expressions are associated with higher crowd participation. This effect does not happen through emotional contagion but through cognitive inference. Participants interpret leaders' emotions as informational cues that help them contribute despite radical uncertainty. The relationship weakens when leaders' emotions are framed as questions rather than affirmative statements, suggesting that clarity in emotional signaling matters for sustaining participation. These findings show how emergent epistemic leadership operates as leaders shape collective inquiry not through formal coordination but by signaling meaning and direction through emotions.