SmartCVC: A novel startup selection method for corporate venture capital

Ruiyun Xu; Hailiang Chen; J. Leon Zhao

SmartCVC: A novel startup selection method for corporate venture capital

Department of Information Systems

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

1 Citation (Scopus)

Abstract

Corporate venture capital (CVC) has been growing rapidly in the past decades. As a critical first step for effective CVC investment, the selection of appropriate portfolio companies is challenging due to the large number of potential targets and the difficulty in evaluating the fit between CVC and different startups. In this study, we tackle this business problem through data science and develop an innovative prediction model, smartCVC, to recommend a short list of highly promising startups to a given CVC investor. To assess the strategic alignment and fit between a CVC investor and a startup, we adopt topic modeling techniques to analyze the business descriptions of both parties and construct a first-order topical similarity measure. We also construct two second-order topical similarity features from the existing CVC-portfolio investment network to characterize how similar a startup's past CVC investors are to a given CVC investor and how similar a CVC investor's past portfolios are to a given startup. Computational experiments on the CrunchBase dataset suggest that the proposed method achieves a significantly better performance than the baseline method.

Original language	English
Title of host publication	International Conference on Information Systems 2018 Proceedings
Publisher	Association for Information Systems
ISBN (Print)	9780996683173
Publication status	Published - Dec 2018
Event	39th International Conference on Information Systems (ICIS 2018) - San Francisco Marriott Marquis, San Francisco, United States Duration: 13 Dec 2018 → 16 Dec 2018 https://icis2018.aisconferences.org/ https://archives.aisconferences.org/icis2018/

Publication series

Name	International Conference on Information Systems

Conference

Conference	39th International Conference on Information Systems (ICIS 2018)
Country/Territory	United States
City	San Francisco
Period	13/12/18 → 16/12/18
Internet address	https://icis2018.aisconferences.org/ https://archives.aisconferences.org/icis2018/

Bibliographical note

Full text of this publication does not contain sufficient affiliation information. With consent from the author(s) concerned, the Research Unit(s) information for this record is based on the existing academic department affiliation of the author(s).

Research Keywords

Corporate venture capital (CVC)
CVC investment network
Data science
Predictive analytics
Target selection

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title = "SmartCVC: A novel startup selection method for corporate venture capital",

abstract = "Corporate venture capital (CVC) has been growing rapidly in the past decades. As a critical first step for effective CVC investment, the selection of appropriate portfolio companies is challenging due to the large number of potential targets and the difficulty in evaluating the fit between CVC and different startups. In this study, we tackle this business problem through data science and develop an innovative prediction model, smartCVC, to recommend a short list of highly promising startups to a given CVC investor. To assess the strategic alignment and fit between a CVC investor and a startup, we adopt topic modeling techniques to analyze the business descriptions of both parties and construct a first-order topical similarity measure. We also construct two second-order topical similarity features from the existing CVC-portfolio investment network to characterize how similar a startup's past CVC investors are to a given CVC investor and how similar a CVC investor's past portfolios are to a given startup. Computational experiments on the CrunchBase dataset suggest that the proposed method achieves a significantly better performance than the baseline method.",

keywords = "Corporate venture capital (CVC), CVC investment network, Data science, Predictive analytics, Target selection",

author = "Ruiyun Xu and Hailiang Chen and {Leon Zhao}, J.",

note = "Full text of this publication does not contain sufficient affiliation information. With consent from the author(s) concerned, the Research Unit(s) information for this record is based on the existing academic department affiliation of the author(s). ; 39th International Conference on Information Systems (ICIS 2018) ; Conference date: 13-12-2018 Through 16-12-2018",

year = "2018",

month = dec,

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isbn = "9780996683173",

series = "International Conference on Information Systems ",

publisher = "Association for Information Systems",

booktitle = "International Conference on Information Systems 2018 Proceedings",

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}

Xu, R , Chen, H & Leon Zhao, J 2018, SmartCVC: A novel startup selection method for corporate venture capital. in International Conference on Information Systems 2018 Proceedings. International Conference on Information Systems , Association for Information Systems, 39th International Conference on Information Systems (ICIS 2018), San Francisco, United States, 13/12/18. <https://aisel.aisnet.org/icis2018/datascience/Presentations/19/>

SmartCVC: A novel startup selection method for corporate venture capital. / Xu, Ruiyun ; Chen, Hailiang; Leon Zhao, J.
International Conference on Information Systems 2018 Proceedings. Association for Information Systems, 2018. (International Conference on Information Systems ).

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

TY - GEN

T1 - SmartCVC

T2 - 39th International Conference on Information Systems (ICIS 2018)

AU - Xu, Ruiyun

AU - Chen, Hailiang

AU - Leon Zhao, J.

N1 - Full text of this publication does not contain sufficient affiliation information. With consent from the author(s) concerned, the Research Unit(s) information for this record is based on the existing academic department affiliation of the author(s).

PY - 2018/12

Y1 - 2018/12

N2 - Corporate venture capital (CVC) has been growing rapidly in the past decades. As a critical first step for effective CVC investment, the selection of appropriate portfolio companies is challenging due to the large number of potential targets and the difficulty in evaluating the fit between CVC and different startups. In this study, we tackle this business problem through data science and develop an innovative prediction model, smartCVC, to recommend a short list of highly promising startups to a given CVC investor. To assess the strategic alignment and fit between a CVC investor and a startup, we adopt topic modeling techniques to analyze the business descriptions of both parties and construct a first-order topical similarity measure. We also construct two second-order topical similarity features from the existing CVC-portfolio investment network to characterize how similar a startup's past CVC investors are to a given CVC investor and how similar a CVC investor's past portfolios are to a given startup. Computational experiments on the CrunchBase dataset suggest that the proposed method achieves a significantly better performance than the baseline method.

AB - Corporate venture capital (CVC) has been growing rapidly in the past decades. As a critical first step for effective CVC investment, the selection of appropriate portfolio companies is challenging due to the large number of potential targets and the difficulty in evaluating the fit between CVC and different startups. In this study, we tackle this business problem through data science and develop an innovative prediction model, smartCVC, to recommend a short list of highly promising startups to a given CVC investor. To assess the strategic alignment and fit between a CVC investor and a startup, we adopt topic modeling techniques to analyze the business descriptions of both parties and construct a first-order topical similarity measure. We also construct two second-order topical similarity features from the existing CVC-portfolio investment network to characterize how similar a startup's past CVC investors are to a given CVC investor and how similar a CVC investor's past portfolios are to a given startup. Computational experiments on the CrunchBase dataset suggest that the proposed method achieves a significantly better performance than the baseline method.

KW - Corporate venture capital (CVC)

KW - CVC investment network

KW - Data science

KW - Predictive analytics

KW - Target selection

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M3 - RGC 32 - Refereed conference paper (with host publication)

SN - 9780996683173

T3 - International Conference on Information Systems

BT - International Conference on Information Systems 2018 Proceedings

PB - Association for Information Systems

Y2 - 13 December 2018 through 16 December 2018

ER -

SmartCVC: A novel startup selection method for corporate venture capital

Abstract

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Research Keywords

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