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科学家作为发明人:离群创新的来源、新颖性及扩散
University scientists as inventors: The sources, novelty and diffusion of outlier innovations
在加快实现科技自立自强的关键时期,离群创新作为突破性技术创新的关键来源,对提升原始创新能力、引领产业变革发展具有战略意义。尽管科学家在创新活动中扮演着关键角色,但关于科学家作为发明人的创新行为,特别是其离群创新的研究仍然不足。本文基于1993—2018年的中国专利数据,采用技术分类组合构建技术地形空间(technological landscape)以识别离群创新,并运用Logit和Negative Binomial回归分析科学家离群创新的来源、新颖性及扩散。研究发现:1)相比一般发明人,具有研究型大学科学家背景的发明人在搜索和生成离群创新方面表现出显著优势;2)在创新产出的特征维度上,科学家发明人所发现的离群创新表现出更高程度的技术新颖性;3)尽管科学家发明人在搜索和生成离群创新方面具有优势,但其创新成果在后续扩散过程中面临更大挑战,获得其他发明人认可的难度相对更高。本文的研究结论拓展了离群创新相关理论,深化了已有文献对科学与技术创新关系的认知,并为促进基础研究向创新成果转化提供了理论支撑,对我国加快实现高水平科技自立自强具有重要启示。
As nations strive for scientific and technological autonomy, outlier innovations have emerged as a critical driver of breakthrough technologies and industrial transformation. While scientists are key contributors to innovation, their role as inventors—particularly in generating outlier innovations—remains understudied. This study examined university scientists' outlier innovations by using Chinese patent data from 1993 to 2018. By constructing a technological landscape through patent classification analysis and employing Logit and Negative Binomial regression models, we investigated the emergence, characteristics, and diffusion patterns of scientists' outlier innovations. Our research made three theoretical contributions: first, we demonstrated how scientific knowledge and research expertise facilitate the discovery of outlier innovations within the technological landscape; second, we revealed how the fundamental nature of scientific research shapes the novelty of these innovations; and third, we identified the barriers in translating scientific breakthroughs into technological applications. Our findings indicated that university scientists are more likely to generate outlier innovations compared to other inventors, and their innovations demonstrate higher novelty but face greater challenges in gaining widespread adoption. These insights have advanced our understanding of outlier innovations and the science-technology interface, while offering valuable implications for bridging the gap between basic research and technological innovation, particularly for China's pursuit of high-level scientific and technological self-reliance.
科学家创新 / 离群创新 / 科学技术关联性 / 技术新颖性
scientist innovation / outlier innovation / science technology link / innovation novelty
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Are lone inventors more or less likely to invent breakthroughs? Recent research has attempted to resolve this question by considering the variance of creative outcome distributions. It has implicitly assumed a symmetric thickening or thinning of both tails, i.e., that a greater probability of breakthroughs comes at the cost of a greater probability of failures. In contrast, we propose that collaboration can have opposite effects at the two extremes: it reduces the probability of very poor outcomes—because of more rigorous selection processes—while simultaneously increasing the probability of extremely successful outcomes—because of greater recombinant opportunity in creative search. Analysis of over half a million patented inventions supports these arguments: Individuals working alone, especially those without affiliation to organizations, are less likely to achieve breakthroughs and more likely to invent particularly poor outcomes. Quantile regressions demonstrate that the effect is more than an upward mean shift. We find partial mediation of the effect of collaboration on extreme outcomes by the diversity of technical experience of team members and by the size of team members' external collaboration networks. Supporting our meta-argument for the importance of examining each tail of the distribution separately, experience diversity helps trim poor outcomes significantly more than it helps create breakthroughs, relative to the effect of external networks.
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A simple model is developed to explore the interrelationship between processes of organizational level change and population selection forces. A critical property of the model is that the effect on organizational fitness of the various attributes that constitute an organization's form is interactive. As a result of these interaction effects, the fitness landscape is “rugged.” An organization's form at founding has a persistent effect on its future form when there are multiple peaks in the fitness landscape, since the particular peak that an organization discovers is influenced by its starting position in the space of alternative organizational forms. Selection pressures influence the distribution of the organizational forms that emerge from the process of local adaptation. The ability of established organizations to respond to changing environments is importantly conditioned by the extent to which elements of organizational form interact in their effect on organizational fitness. Tightly coupled organizations are subject to high rates of failure in changing environments. Furthermore, successful “reorientations” are strongly associated with survival for tightly coupled organizations, but not for more loosely coupled organizations that are able to engage in effective local adaptation.
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Analyzing data on utility patents from 1975 to 2002 in the careers of 35,400 collaborative inventors, this study examines the influence of brokered versus cohesive collaborative social structures on an individual's creativity. We test the hypothesis that brokerage—direct ties to collaborators who themselves do not have direct ties to each other—leads to greater collaborative creativity. We then test interaction hypotheses on the marginal benefits of cohesion, when collaborators have independent ties between themselves that do not include the individual. We identify the moderators of brokerage and argue for contingent benefits, based on the interaction of structure with the attributes, career experiences, and extended networks of individuals and their collaborators. Using a social definition of creative success, we also trace the development of creative ideas from their generation through future use by others. We test the hypothesis that brokered ideas are less likely to be used in future creative efforts. The results illustrate how collaborative brokerage can aid in the generation of an idea but then hamper its diffusion and use by others.
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A useful theoretical lens for understanding innovation in the strategy and entrepreneurship literatures is knowledge recombination. According to a recombination logic, innovations come about by recombining knowledge components, each of which is associated with a core scientific or technological concept. Interactions among a set of recombined components give rise to new meanings and functions that become the basis of an innovation. The singular focus on the components of knowledge underlying an innovation makes knowledge recombination stand out from other theoretical approaches. The rapid growth in research utilizing a recombination logic suggests that the time is ripe for stepping back and assessing its key insights. Therefore, this review provides a framework for a recombination perspective and considers how the literature using a recombination approach has progressed over time, including identification of key features of knowledge components, influences on how components are recombined, and the outcomes of recombination. Finally, a number of new directions for research are proposed.
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To solve a problem, needed information and problem-solving capabilities must be brought together. Often the information used in technical problem solving is costly to acquire, transfer, and use in a new location—is, in our terms, “sticky.” In this paper we explore the impact of information stickiness on the locus of innovation-related problem solving. We find, first, that when sticky information needed by problem solvers is held at one site only, problem solving will be carried out at that locus, other things being equal. Second, when more than one locus of sticky information is called upon by problem solvers, the locus of problem solving may iterate among these sites as problem solving proceeds. When the costs of such iteration are high, then, third, problems that draw upon multiple sites of sticky information will sometimes be “task partitioned” into subproblems that each draw on only one such locus, and/or, fourth, investments will be made to reduce the stickiness of information at some locations.
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In this paper we explore the degree to which patents are representative of the magnitude, direction, and impact of the knowledge spilling out of the university by focusing on the Massachusetts Institute of Technology (MIT), and in particular, on the Departments of Mechanical and Electrical Engineering. Drawing on both qualitative and quantitative data, we show that patenting is a minority activity: a majority of the faculty in our sample never patent, and publication rates far outstrip patenting rates. Most faculty members estimate that patents account for less than 10% of the knowledge that transfers from their labs. Our results also suggest that in two important ways patenting is not representative of the patterns of knowledge generation and transfer from MIT: patent volume does not predict publication volume, and those firms that cite MIT papers are in general not the same firms as those that cite MIT patents. However, patent volume is positively correlated with paper citations, suggesting that patent counts may be reasonable measures of research impact. We close by speculating on the implications of our results for the difficult but important question of whether, in this setting, patenting acts as a substitute or a complement to the process of fundamental research.
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In this paper, we use data from the Carnegie Mellon Survey on industrial R&D to evaluate for the U.S. manufacturing sector the influence of “public”(i.e., university and government R&D lab) research on industrial R&D, the role that public research plays in industrial R&D, and the pathways through which that effect is exercised. We find that public research is critical to industrial R&D in a small number of industries and importantly affects industrial R&D across much of the manufacturing sector. Contrary to the notion that university research largely generates new ideas for industrial R&D projects, the survey responses demonstrate that public research both suggests new R&D projects and contributes to the completion of existing projects in roughly equal measure overall. The results also indicate that the key channels through which university research impacts industrial R&D include published papers and reports, public conferences and meetings, informal information exchange, and consulting. We also finnd that, after controlling for industry, the influence of public research on industrial R&D is disproportionately greater for larger firms as well as start-ups.
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The extent to which new technological knowledge flows across institutional and national boundaries is a question of great importance for public policy and the modeling of economic growth. In this paper we develop a model of the process generating subsequent citations to patents as a lens for viewing knowledge diffusion. We find that the probability of patent citation over time after a patent is granted fits well to a double-exponential function that can be interpreted as the mixture of diffusion and obsolescense functions. The results indicate that diffusion is geographically localized. Controlling for other factors, within-country citations are more numerous and come more quickly than those that cross country boundaries.
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We study rare events data, binary dependent variables with dozens to thousands of times fewer ones (events, such as wars, vetoes, cases of political activism, or epidemiological infections) than zeros (“nonevents”). In many literatures, these variables have proven difficult to explain and predict, a problem that seems to have at least two sources. First, popular statistical procedures, such as logistic regression, can sharply underestimate the probability of rare events. We recommend corrections that outperform existing methods and change the estimates of absolute and relative risks by as much as some estimated effects reported in the literature. Second, commonly used data collection strategies are grossly inefficient for rare events data. The fear of collecting data with too few events has led to data collections with huge numbers of observations but relatively few, and poorly measured, explanatory variables, such as in international conflict data with more than a quarter-million dyads, only a few of which are at war. As it turns out, more efficient sampling designs exist for making valid inferences, such as sampling all available events (e.g., wars) and a tiny fraction of nonevents (peace). This enables scholars to save as much as 99% of their (nonfixed) data collection costs or to collect much more meaningful explanatory variables. We provide methods that link these two results, enabling both types of corrections to work simultaneously, and software that implements the methods developed.
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