Research Policy最新文献

Modern scientific research evokes ecological imagery and metaphors, given that it is global, interdependent, and diverse. Ecological network structures—like matrices of species inhabiting islands across an archipelago—can be reordered to form nested patterns. These patterns describe the overall health of ecosystems, place species on a spectrum between being described as generalists (foxes) or specialists (hedgehogs), and which of these interactions might appear or disappear. Using the number of citations universities receive for work published in a particular subfield taken from over 66 million scientific publications in OpenAlex, we construct and analyze yearly nested ecological networks of a dozen academic fields between 1990 and 2017. We find increasingly nested structures across fields infer future acknowledgment in different subfields. We argue that this framework can inform policy on scientific research and university funding and evaluation.

Research has rarely studied how innovators conduct knowledge search in response to an increased risk that their original ideas may be imitated (i.e., duplicative imitation threat). We address this gap by focusing on a duplicative imitation threat common to digital platforms, which allows for the entry of pirated software at a low cost with rapid distribution and presents a significant appropriability risk to the original software developers. We treat the jailbreak of Apple's iOS 7 that enabled Apple users to install pirated apps as an exogenous shock that increases such a threat, and adopt a quasi-experiment design in our study. Our empirical analysis shows that after the jailbreak, iOS app developers increase their search depth and reduce their search scope compared to Android app developers. Our findings imply that innovators' adjustment in their knowledge search is contingent upon specific characteristics of the imitation threat they face.

Transitioning toward more sustainable livelihoods requires tackling complex challenges in innovative ways. Positioned at the intersection of innovation and transition studies, transdisciplinary research (TD) has surfaced as a method to confront sustainability challenges by integrating various scientific disciplines and engaging non-academic stakeholders. Currently, there is a growing call from both policy makers and research funders to assess the effects of TD research, especially its societal impacts. However, TD research typically involves local projects where the context may substantially shape the effects of the research conducted. In this paper we explore how understanding the context factors of a TD project contributes to the understanding of its effects. We built a working model, based on different established concepts from policy studies to conceptualize context and effects of TD project. We applied the model to a comparative case study of two regional sub-projects of one large TD project on circular bioeconomy for sustainable agriculture in Sub Saharan Africa. The core concept and design were the same for both subprojects, but they achieved different effects. We studied the differences in context factors to explain this variation. We found that a strongly developed public and private sector and access to a variety of action resources such as political support or laws, supported the implementation of innovations as well as dialogue with policy. Nevertheless, a strong public sector can also hinder a project's success, if the interest of the government is not in line with the interests of the project. Further, we found that TD projects may achieve learning and social effects by tackling certain gaps in action resources. Our findings highlight that taking the context of a project into account is key to understanding the scope of action and possibilities of a project. Thus, the context should be considered not only when planning but also - and especially - when evaluating a TD project.

Frakes and Wasserman (2020) finds evidence that United States Patent and Trademark Office (USPTO) patent examiners disproportionately receive credit for examination activities at the end of production periods and interprets this behavior as systemic examiner procrastination. Using newly assembled micro data on the timing of USPTO examiner activities, our results show examiner work effort more closely resembles a uniform distribution over the production period, indicating consistent workflow and not procrastination. The assembled data better track the precise timing of examiner work activities than the data used in Frakes and Wasserman (2020), explaining the differences in our results. While Frakes and Wasserman (2020) conduct a thorough analysis of the markers and consequences of “end-loading”, its use of an inadequate proxy for examination activity (i.e., Office action count dates) leads to incorrect conclusions about the examination process (i.e., widespread examiner procrastination).

Using panel data on 251,511 patent inventors matched with voter registration records containing partisan affiliation, we provide the first large-scale look into the partisanship of American inventors. We document that the modal inventor is Republican and that the partisan composition of inventors has changed in ways that are not reflective of partisan affiliation trends amongst the broader population. We then show that the partisan affiliation of inventors is associated with technological invention related to guns and climate change, two issue areas associated with partisan divide. These findings suggest that inventor partisanship may have implications for the direction of inventive activity.

Venture capital (VC) in most economies outside the U.S. is still developing and relies heavily on foreign investment. This raises concerns that startups are migrating out of these countries as a consequence. Using a novel large-scale dataset of startup headquarter (HQ) location histories across a heterogeneous set of 17 countries, we document novel facts on startup relocation across countries. First, international startup relocation is relatively common: about 6 % of startups move across borders, representing 17 % of the total startup value created. Second, by far, most relocation (85 %) is directed to the US. Third, relocation leads to the majority of startups' workforce ending up in the foreign country. Fourth, foreign VC investment, particularly from the US, is strongly associated with relocation, with the effect implying that one in ten US investments leads to relocation. This foreign VC effect is robust to matching, panel data, and instrumental variable analyses, and is more pronounced when startup financing conditions are poor. These findings shed light on the global movement of startups and suggest that the outflow can be addressed by improving startups' local financing conditions.

This study investigated two major trends shaping contemporary technological progress: the growing complexity of innovation and the increasing reliance on government support for private research and development (R&D). We analyzed United States patent data from 1981 to 2016 using structural vector autoregressions and uncovered an indirect interplay between these trends. Our findings showed that government incentives and support played a crucial role in spurring private-sector innovation. This government-fueled innovation, in turn, paved the way for advancements in more intricate and sophisticated technological areas.

Our study sheds light on the dual role of the United States' innovation policy over the past four decades; the policy has not only accelerated technological advancement but also steered it toward increasingly complex domains. While this trend presents opportunities for economic growth and technological breakthroughs, it also poses challenges, including the potential for further escalating R&D costs. This research has significant implications for policymakers and industry leaders, suggesting a need for a balanced approach to fostering innovation while considering the long-term economic and technological landscape.

Research agendas in science are fundamentally important to the generation of new knowledge and innovation. Yet, there remains a lack of scholarly attention and poor understanding on how science teams engage with research agendas in ways that influence their development. New insights are needed to better understand the factors that contribute to research agenda development and adaptation. In this paper, we draw on the framing perspective to explore how research agendas are framed in science teams over time. Research agendas can be understood as collective action frames within science teams that mobilize, guide, and coordinate the transformation of innovative but abstract science aspirations into something more concrete. Our research utilises a longitudinal case study analysis of two science teams over seven years (2016–2022). Our findings provide several new insights. First, we detail two ways in which research agendas are framed. Through centralised framing, research agendas are embodied and dictated by a visionary science team leader. In contrast, through decentralised framing, team leadership is weakly enacted and multiple team members discuss and deliberate the composition and direction of the research agenda. Second, we show centralised and decentralised approaches to framing enable and constrain the reframing and transformation of research agendas. Third, we demonstrate centralised and decentralised framing of research agendas are respectively stabilised by passive and active team learning environments across three areas: research agenda responsibility and accountability, nature of autonomy, and leadership development pathways. Finally, we theorise that, to enhance spillover, leaders who centralise framing of the research agenda need to balance between the benefits of reframing efficiency, and enabling greater team interaction and opportunities for S&T human capital development. On the other hand, when framing of research agendas is decentralised, team leaders need to balance between the benefits of team collaboration and leader development, and path dependent decision making. These insights lead to propositions that offer implications for theory and practice.

There is a growing trend for high-status original equipment manufacturers (OEMs) such as premium electronics manufacturers and premium carmakers to create and capture value through digital extensions of their products. However, these incumbents face disruptive threats from platforms offering substitutes for these digital extensions. The literature suggests that coopetition—the interplay of cooperation and competition—is a viable strategic response to this threat. However, we have a limited understanding of how high-status OEMs coopete with platforms to maintain their digital extensions' edge over time. We address this gap through a longitudinal case study of InnoCar, a premium European carmaker whose digital extensions—car-specific digital services that enhance the driving experience, such as real-time navigation and infotainment—were challenged by Google and Apple. In response, InnoCar pursued what we call the slipstream strategy, which consists of two phases with varying intensities of cooperation and competition. A high-status OEM first increases its cooperation with platforms at the expense of competition in order to establish shared demand-related complementary assets. Second, it focuses on competing with platforms on the quality of its digital extensions while keeping cooperation to a minimum. We develop a conceptual framework that specifies the slipstream strategy and provide boundary conditions for its application. Our paper contributes to research on coopetition with platforms.

Interdisciplinary scientific fields, such as synthetic biology, bioinformatics, and human brain science, often emerge at the intersection of existing scientific disciplines. This fundamental process is described in the literature streams of ‘science convergence’ and the ‘evolution of new scientific fields’. However, despite their empirical relevance and the potential for science convergence to accelerate the evolution of these new fields, the two concepts have been developed separately up to this point. In this study, we therefore investigate the interplay between the two concepts by first conducting a systematic review of the literature on science convergence to examine its underlying dynamics. We then integrate the concept of science convergence into the current understanding of the evolutionary process of new scientific fields, leading to a new theoretical conceptualization and typology of the different pathways in the evolution of interdisciplinary scientific fields. The pathways exhibit varying levels of interdisciplinary research activities at different stages of the evolutionary process. We apply this typology to cases of synthetic biology, bioinformatics, and human brain science, illustrating how science convergence and an early emphasis on interdisciplinary research activities drive the evolutionary process of a new scientific field. In essence, our typology and its related proxies enable policymakers and other actors to understand how science convergence gives rise to new interdisciplinary scientific fields.