This study offers a new perspective on the depth-versus-breadth debate in innovation strategy by modeling inventive search within dynamic collective knowledge systems and underscoring the importance of timing for technological impact. Using frontier machine learning to project patent citation networks in hyperbolic space, we analyze 4.9 million U.S. patents to examine how search strategies give rise to distinct temporal patterns of impact accumulation. We find that inventions based on deep search, which relies on a specialized understanding of the complex structure of recombination, drive higher short-term impact through early adoption within specialized communities, but face diminishing returns as innovations become “locked-in” with limited diffusion potential. Conversely, when inventions are grounded in broad search that spans disparate domains, they encounter initial resistance but achieve wider diffusion and greater long-term impact by reaching cognitively diverse audiences. Individual inventions require both depth and breadth for stable impact. Organizations can strategically balance approaches across multiple inventions: using depth to build reliable technological infrastructure while pursuing breadth to expand applications. We advance innovation theory by demonstrating how deep and broad search strategies distinctly shape the timing and trajectory of technological impact, and how individual inventors and organizations can leverage these mechanisms to balance exploitation and exploration.

This paper investigates the mechanisms underlying scientific stratification in the era of transition from elite to mass science. Existing scholarship has largely examined scientific stratification through the Matthew effect framework at the individual, institutional, and lineage levels, but this theoretical lens has grown limited in today's academic landscape, where mass, team-based, and lab-centered research has become the dominant mode of knowledge production. As scientists increasingly share institutional and lineage backgrounds, considerable variation within these units remains unexplained. We propose a new framework that integrates concepts and methodological tools from demography into the social study of science. Drawing on the parallel between biological families and scholarly lineages as fundamental units of reproduction, we adapt the concept of birth order to examine how the sequence of doctoral students within a lineage shapes their career trajectories. Using data on more than one million U.S. doctoral graduates, our analysis shows that, much like in biological families, later students systematically perform worse than earlier ones across multiple dimensions of academic achievement, both short and long term. Examining the underlying mechanisms, we find that later students receive less cognitive stimulation from mature scholars and instead more from peers, and specialize in narrower intellectual domains as senior siblings occupy adjacent territories. These factors constrain their intellectual development as independent scholars. By introducing a demographic framework into the study of science, this paper offers a new perspective on scientific stratification and demonstrates how demographic concepts can be fruitfully extended to analyze broader social and epistemic systems.

Startups face a classic dilemma in innovation strategy: should they pursue cumulative, low-risk improvements or disruptive, high-risk breakthroughs? The Henderson and Clark framework suggests that architectural innovation, which reconfigures existing economic modules in novel ways, tends to be disruptive and risky for established organizations, but the success of this strategy for entrepreneurs remains less well understood, largely based on methodological constraints. Building on a complex-economics perspective and advanced computational models, we distinguish architectural innovation from modular innovation, which incrementally updates economic modules, and modular invention, which forges new ones, within the entrepreneurship context. Then we examine how each strategy influences startup performance. We analyze 298,915 U.S. venture-funded start-ups from 1976 to 2020, embedding company descriptions within a dynamic semantic space constructed from business and patent discourse to measure innovation structure across the entire economy. Event history models show that architectural innovation is associated with successful IPOs and high-value acquisitions, while both modular innovation and invention are linked to increased risk of failure. By comparing the outcomes of architectural and modular innovation and invention, this paper reveals that what is typically seen as the riskiest form of innovation may, for startups, represent the safest route to success. These findings shed new light on the exploration-exploitation trade-off in organizational learning, with implications for entrepreneurial strategy and innovation policy.

Existing studies of innovation emphasize the power of social structures to shape innovation capacity. Emerging machine learning approaches, however, enable us to model innovators' personal perspectives and interpersonal innovation opportunities as a function of their prior experience. We theorize and then quantify subjective perspectives and their interaction based on innovator positions within the geometric space of concepts inscribed by dynamic machine-learned language representations. Using data on millions of scientists, inventors, screenplay writers, entrepreneurs, and Wikipedia contributors across their respective creative domains, here we show that measured subjective perspectives predict which ideas individuals and groups will creatively attend to and successfully combine in the future. Across all cases and time periods we examine, when perspective diversity is decomposed as the difference between collaborators' perspectives on their creation, and background diversity as the difference between their experiences, the former consistently anticipates creative achievement while the latter portends its opposite. We analyze a natural experiment and simulate creative collaborations between AI agents designed with various perspective and background diversity, which support our observational findings. We explore mechanisms underlying these findings and identify how successful collaborators leverage common language to weave together diverse experiences obtained through trajectories of prior work. These perspectives converge and provoke one another to innovate. We examine the significance of these findings for team formation and research policy.

Entrepreneurial firms often hedge against uncertainty by building on prior scientific and technological knowledge. Such firms can usefully be represented as complex systems, which apply and combine technologies to solve business problems or create opportunities. In the emergence of complex systems, modularity is critical for the survival and evolution of systems robust to exploratory failure. Here we build a theory of entrepreneurship as one of second-order invention whereby the most successful new ventures assemble novel combinations of modular inventions, already successfully applied, rather than discovering novel technologies or applying them to solve novel problems. Such ventures limit their risk of failure to higher-order system assembly, rather than lower-order discovery and application. We evaluate this with new ventures documented in Crunchbase and VentureXpert over 45 years, classifying description words as techniques or business areas. We build a semantic space with dynamic neural word embedding models constructed from 108 business newspapers and magazines, which allow us to precisely measure what techniques and business areas are characterized as close or distant to the business press and public. Using event history models, we demonstrate that new ventures are much more likely to achieve successful IPOs and high-priced acquisitions if they combine diverse, applied innovations.

This study explores whether manager mobility can influence syndications between private equity (PE) firms by constructing coupling network models. Using data from China’s private equity market from 1993 to 2017, we found that driving forces, resistant forces, and network structure play significant roles in determining resource flows between PE firms. Specifically, driving forces indicate that managers moving from domestic and foreign PE firms to state-owned PE firms are more likely to induce syndications. Furthermore, if the manager is promoted when changing jobs, mobility is likely to enhance the flow of resources. Resistant forces indicate that increased geographical distance reduces syndications. As for the influence of structure, if managers leave PE firms with higher status, they are more likely to induce syndications. This study contributes to the coupling network literature by providing a clarified three-factor framework. By exploring the characteristic of managers in state-owned private equity firms, we specified the syndication theory in China. This study can help private equity firms hire valuable managers and expand syndication networks in practice.

Covid-19 has impacted the U.S. economy and business organizations in multiple ways, yet its influence on company fundamentals and risk structures have not been fully elucidated. In this paper, we apply LDA, a mainstream topic model, to analyze the risk factor section from SEC filings (10-K and 10-Q), and describe risk structure change over the past two years. The results show that Covid-19 has transformed the risk structures U.S. companies face in the short run, exerting excessive stress on international interactions, operations, and supply chains. However, this shock has been waning since the second quarter of 2020. Our model shows that risk structure change (measured by topic distribution) from Covid-19 is a significant predictor of lower performance, but smaller companies tend to be stricken harder.

ABSTRACTThis study investigates how venture capital firms (VCs) choose syndication partners. Exponential random graph models of Chinese VC syndication networks from 2006 to 2013 show that the homophily mechanism does not always determine VCs’ partner selection. In selecting partners, VCs have to strike a balance between reducing uncertainty and mobilizing heterogeneous resources. Therefore, decisions about partners depend on institutional uncertainty and VCs’ investment preferences. While VCs that focus on traditional business in an immature market are more likely to form homogeneous syndications, their peers that prefer to invest in innovative companies and that can rely on a stable market tend to syndicate with heterogeneous partners.

Innovation or the creation and diffusion of new material, social and cultural things in society has been widely studied in sociology and across the social sciences, with investigations sufficiently diverse and dispersed to make them unnavigable. This complexity results from innovation's importance for society, but also the fundamental paradox underlying innovation science: When innovation becomes predictable, it ceases to be an engine of novelty and change. Here we review innovation studies and show that innovations emerge from contexts of discord and disorder, breaches in the structure of prior success, through a process we term destructive creation. This often leads to a complementary process of creative destruction whereby local structures protect and channel the diffusion of successful innovations, rendering alternatives obsolete. We find that social scientists naturally focus far more on how social and cultural contexts influence material innovations than the converse. We highlight computational tools that open new possibilities for the analysis of novel content and context in interaction, and show how this brings us empirically toward the broader range of possibilities that complex systems and science studies have theorized-and science fiction has imagined-the social, cultural and material structures of innovation conditioning each other's change through cycles of disruption and development.

The composition of venture capital syndication has great inpact on the development of the portfolio company.This paper examines the impact of VC heterogeneity on the hazard rate of high-tech company's IPO or high-price M&A.Based on the survival analysis, it shows that VC syndications which are more diversified in industry and geography scope tend to be more beneficial to the high-tech company, whereas similar background in ownership type leads to desirable outcome.Further analysis reveals subtler variations of the heterogeneity effect across different rounds.The heterogeneity effect tends to be greater for the high-tech firms which have received many rounds of investment.

中国风险投资机构在何种逻辑下进行联合投资？以社会网络理论中的关系嵌入理论与结构嵌入理论,而不是资源依赖视角以及交易成本理论的逻辑,来解释中国风险投资(Venture Capital,以下简称VC)机构的联合投资选择不失为有效途径.研究发现,中国VC机构在进行联合投资行为时,特别强调了长期社会关系和声望在这一领域中的重要性.研究者以关系嵌入与结构嵌入的视角,利用2000-2013年清科数据库的VC联合投资数据,建立QAP模型,对其建立行为动机进行了解释,验证得知两家VC过去共同投资的经历越多、越稳定,他们之间的关系距离越近、共同朋友越多、越占据优势网络位置,将来联合投资的可能性越高.

China is experiencing urbanization and modernization at the largest scale in human history. An army of over 280 million "peasant workers" are an integral part of this great transformation to modernity. Drawing on different data sources, including national representative random samples and the authors' first-hand survey data, we provide systematic comparisons between the older and the younger generation of peasant workers and between young peasant entrepreneurs in cities and young returnee entrepreneurs. We found that, compared with the older generation, the younger generation of peasant workers is better educated, holds more stable jobs, has a higher income, lives a happier life and is more optimistic about the future. Our analysis of the two types of peasant entrepreneurs shows that they are the elites among peasant workers, running successful business and making handsome profits. We also note that discrimination and institutional obstacles, especially the hukou system, remain to be overcome in the peasant workers' transition to modernity.