Modularity, Higher-Order Recombination, and New Venture Success

We analyze the sectoral dynamics of startup venture financing. Based on a dataset of 52000 start-ups and 110000 funding rounds in the United States from 2000 to 2017, and by applying both Principal Component Analysis (PCA) and Tensor Component Analysis (TCA) in sector space, we visualize and measure the evolution of the investment strategies of different classes of investors across sectors and over time. During the past decade, we observe a coherent evolution of early stage i...

Using information theory, we measure innovation systemness as synergy among size-classes, zip-codes, and technological classes (NACE-codes) for 8.5 million American companies. The synergy at the national level is decomposed at the level of states, Core-Based Statistical Areas (CBSA), and Combined Statistical Areas (CSA). We zoom in to the state of California and in more detail to Silicon Valley. Our results do not support the assumption of a national system of innovations in ...

Novelty is not a sufficient condition for innovation. For new ideas and products to succeed, they must be integrated into the collective understanding and existing infrastructure, illustrating how the past determines the future. Here, we develop a comprehensive framework to understand how the structure of accumulated past successes curves the adjacent possible trajectory of future innovations. We observe that certain technological building blocks, upon frequent combination, c...

This study demonstrates that web-search traffic information, in particular, Google Trends data, is a credible novel source of high-quality and easy-to-access data for analyzing technology-based new ventures (TBNVs) growth trajectories. Utilizing the diverse sample of 241 US-based TBNVs, we comparatively analyze the relationship between companies' evolution curves represented by search activity on the one hand and by valuations achieved through rounds of venture investments on...

While the disruptive potential of artificial intelligence (AI) and Big Data has been receiving growing attention and concern in a variety of research and application fields over the last few years, it has not received much scrutiny in contemporary entrepreneurship research so far. Here we present some reflections and a collection of papers on the role of AI and Big Data for this emerging area in the study and application of entrepreneurship research. While being mindful of th...

Mergers and Acquisitions represent important forms of business deals, both because of the volumes involved in the transactions and because of the role of the innovation activity of companies. Nevertheless, Economic Complexity methods have not been applied to the study of this field. By considering the patent activity of about one thousand companies, we develop a method to predict future acquisitions by assuming that companies deal more frequently with technologically related ...

The challenge of raising a creative question exists in recombining different categories of knowledge. However, the impact of recombination remains controversial. Drawing on the theories of knowledge recombination and category spanning, we propose that both the distance of knowledge spanning and the hierarchy of knowledge shape the appeal of questions. Using word embedding models and the data collected from a large online knowledge market (N = 463,545), we find that the impact...

Predicting startup success presents a formidable challenge due to the inherently volatile landscape of the entrepreneurial ecosystem. The advent of extensive databases like Crunchbase jointly with available open data enables the application of machine learning and artificial intelligence for more accurate predictive analytics. This paper focuses on startups at their Series B and Series C investment stages, aiming to predict key success milestones such as achieving an Initial ...

While the application of Artificial Intelligence in Finance has a long tradition, its potential in Entrepreneurship has been intensively explored only recently. In this context, Entrepreneurial Finance is a particularly fertile ground for future Artificial Intelligence proliferation. To support the latter, the study provides a bibliometric review of Artificial Intelligence applications in (1) entrepreneurial finance literature, and (2) corporate finance literature with implic...

Studying acquisitions offers invaluable insights into startup trends, aiding informed investment decisions for businesses. However, the scarcity of studies in this domain prompts our focus on shedding light in this area. Employing Crunchbase data, our study delves into the global network of company acquisitions using diverse network analysis techniques. Our findings unveil an acquisition network characterized by a primarily sparse structure comprising localized dense connecti...