Ciro Donald Esposito

Ciro Donald Esposito

Assistant Professor of Data Entrepreneurship at JADS, Tilburg University

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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.

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Posts

Future Blog Post

less than 1 minute read

Published:

This post will show up by default. To disable scheduling of future posts, edit config.yml and set future: false.

Blog Post number 4

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 3

less than 1 minute read

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Blog Post number 2

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 1

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

portfolio

publications

Getting off to a good start: emerging academic fields and early-stage equity financing

Published in Small Business Economics, 2023

Prior studies show that access to academic knowledge plays a crucial role in new venture financing. We extend this research by shifting the focus from the access to academic knowledge to the developmental state of the academic field, where the academic knowledge is generated. Using natural language processing (NLP), we clustered peer-reviewed academic knowledge from Scopus into various fields. We then analyzed a sample of 341 new biotech ventures from Crunchbase to determine if increased past activity by (1) academics and (2) early-stage venture investors in a particular academic field is associated with the early-stage equity financing of new ventures associated with that field. We found that new ventures associated with academic fields for which academic activity has grown in the past receive more early-stage equity capital. However, contrary to our expectations, we also revealed that when a particular academic field shows greater early-stage venture investments in the past, the amount of early-stage equity capital received by subsequent ventures associated with the same academic field decreases. This suggests that while emerging academic fields signal the presence of business opportunities with high reward potential, past increase in the number of investments by peer early-stage investors associated with a particular academic field signals the opposite.

Recommended citation: Esposito, C. D., Szatmari, B., Sitruk, J. M. C., & Wijnberg, N. M. (2024). "Getting off to a good start: emerging academic fields and early-stage equity financing." Small Business Economics 62, 1591-1613. https://doi.org/10.1007/s11187-023-00816-9

Evaluation of unsupervised static topic models’ emergence detection ability

Published in PeerJ Computer Science, 2025

Detecting emerging topics is crucial for understanding research trends, technological advancements, and shifts in public discourse. While unsupervised topic modeling techniques such as Latent Dirichlet allocation (LDA), BERTopic, and CoWords clustering are widely used for topic extraction, their ability to retrospectively detect emerging topics without relying on ground truth labels has not been systematically compared. This gap largely stems from the lack of a dedicated evaluation metric for measuring emergence detection. In this study, we introduce a quantitative evaluation metric to assess the effectiveness of topic models in detecting emerging topics. We evaluate three topic modeling approaches using both qualitative analysis and our proposed emergence detection metric. Our results indicate that, qualitatively, CoWords identifies emerging topics earlier than LDA and BERTopics. Quantitatively, our evaluation metric demonstrates that LDA achieves an average F1 score of 80.6% in emergence detection, outperforming BERTopic by 24.0%. These findings highlight the strengths and limitations of different topic models for emergence detection, while our proposed metric provides a robust framework for future benchmarking in this area.

Recommended citation: Li, X., Esposito, C. D., Groth, P., Sitruk, J. , Szatmari, B., & Wijnberg, N. (2025). "Evaluation of unsupervised static topic models’ emergence detection ability." PeerJ Computer Science 11:e2875. https://doi.org/10.7717/peerj-cs.2875

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