Predicting Corporate Venture Capital Investment

Ruiyun Xu; Hailiang Chen; J. Leon Zhao

Predicting Corporate Venture Capital Investment

Ruiyun Xu
, Hailiang Chen
, J. Leon Zhao

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

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 and difficult due to the large number of potential targets and the high uncertainty arising from an investment deal. In this study, we adopt the design science approach and develop a prediction model to support CVC investment decisions by identifying a list of potential investees from a large pool of portfolio companies for a CVC investor. We develop five key features using data science techniques including business proximity, wisdom of crowds in CVC investments, strategic alignment, status differential, and geographic proximity. To evaluate the performance of the proposed model, we plan to conduct experiments on the CrunchBase dataset.

Original language	English
Title of host publication	38th International Conference on Information Systems (ICIS 2017)
Subtitle of host publication	Transforming Society with Digital Innovation
Publisher	Association for Information Systems
Pages	5841-5849
Volume	8
ISBN (Print)	9781510853690
Publication status	Published - 12 Dec 2017
Event	38th International Conference on Information Systems (ICIS 2017): Transforming Society with Digital Innovation - COEX, Convention and Exhibition Center, Seoul, Korea, Republic of Duration: 10 Dec 2017 → 13 Dec 2017 http://icis2017.aisnet.org/ http://icis2017.aisnet.org/wp-content/uploads/2017/12/ICIS2017_ProgramBook_1209.pdf

Conference

Conference	38th International Conference on Information Systems (ICIS 2017)
Place	Korea, Republic of
City	Seoul
Period	10/12/17 → 13/12/17
Internet address	http://icis2017.aisnet.org/ http://icis2017.aisnet.org/wp-content/uploads/2017/12/ICIS2017_ProgramBook_1209.pdf

Research Keywords

Corporate venture capital (CVC)
CVC investment network
Data science
Prediction model

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@inproceedings{bab10200613e4782ad59d32e17cbdb14,

title = "Predicting Corporate Venture Capital Investment",

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 and difficult due to the large number of potential targets and the high uncertainty arising from an investment deal. In this study, we adopt the design science approach and develop a prediction model to support CVC investment decisions by identifying a list of potential investees from a large pool of portfolio companies for a CVC investor. We develop five key features using data science techniques including business proximity, wisdom of crowds in CVC investments, strategic alignment, status differential, and geographic proximity. To evaluate the performance of the proposed model, we plan to conduct experiments on the CrunchBase dataset.",

keywords = "Corporate venture capital (CVC), CVC investment network, Data science, Prediction model",

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

year = "2017",

month = dec,

day = "12",

language = "English",

isbn = "9781510853690",

volume = "8",

pages = "5841--5849",

booktitle = "38th International Conference on Information Systems (ICIS 2017)",

publisher = "Association for Information Systems",

address = "United States",

note = "38th International Conference on Information Systems (ICIS 2017) : Transforming Society with Digital Innovation ; Conference date: 10-12-2017 Through 13-12-2017",

url = "http://icis2017.aisnet.org/, http://icis2017.aisnet.org/wp-content/uploads/2017/12/ICIS2017\_ProgramBook\_1209.pdf",

}

Predicting Corporate Venture Capital Investment. / Xu, Ruiyun ; Chen, Hailiang; Zhao, J. Leon.
38th International Conference on Information Systems (ICIS 2017): Transforming Society with Digital Innovation. Vol. 8 Association for Information Systems, 2017. p. 5841-5849.

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

TY - GEN

T1 - Predicting Corporate Venture Capital Investment

AU - Xu, Ruiyun

AU - Chen, Hailiang

AU - Zhao, J. Leon

PY - 2017/12/12

Y1 - 2017/12/12

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 and difficult due to the large number of potential targets and the high uncertainty arising from an investment deal. In this study, we adopt the design science approach and develop a prediction model to support CVC investment decisions by identifying a list of potential investees from a large pool of portfolio companies for a CVC investor. We develop five key features using data science techniques including business proximity, wisdom of crowds in CVC investments, strategic alignment, status differential, and geographic proximity. To evaluate the performance of the proposed model, we plan to conduct experiments on the CrunchBase dataset.

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 and difficult due to the large number of potential targets and the high uncertainty arising from an investment deal. In this study, we adopt the design science approach and develop a prediction model to support CVC investment decisions by identifying a list of potential investees from a large pool of portfolio companies for a CVC investor. We develop five key features using data science techniques including business proximity, wisdom of crowds in CVC investments, strategic alignment, status differential, and geographic proximity. To evaluate the performance of the proposed model, we plan to conduct experiments on the CrunchBase dataset.

KW - Corporate venture capital (CVC)

KW - CVC investment network

KW - Data science

KW - Prediction model

UR - https://www.scopus.com/pages/publications/85041702317

UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-85041702317&origin=recordpage

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

AN - SCOPUS:85041702317

SN - 9781510853690

VL - 8

SP - 5841

EP - 5849

BT - 38th International Conference on Information Systems (ICIS 2017)

PB - Association for Information Systems

T2 - 38th International Conference on Information Systems (ICIS 2017)

Y2 - 10 December 2017 through 13 December 2017

ER -

Predicting Corporate Venture Capital Investment

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