ExeAnalyzer : A deep generative adversarial network for multimodal online impression analysis and startup funding prediction

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with host publication)peer-review

2 Scopus Citations
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Detail(s)

Original languageEnglish
Title of host publicationProceedings of the 54th Annual Hawaii International Conference on System Sciences
EditorsTung X. Bui
PublisherHawaii International Conference on System Sciences (HICSS)
Pages2501-2510
ISBN (Print)9780998133140
Publication statusPublished - Jan 2021

Publication series

NameProceedings of the Annual Hawaii International Conference on System Sciences
ISSN (Print)1530-1605
ISSN (Electronic)2572-6862

Conference

Title54th Hawaii International Conference on System Sciences (HICSS 2021)
PlaceUnited States
CityKauai
Period4 - 8 January 2021

Abstract

With the rise of equity crowdfunding platforms, entrepreneurs' online impressions are of great importance to startups' initial funding success. Guided by the design science research methodology, one contribution of our research is to design a novel Generative Adversarial Network, namely ExeAnalyzer, to analyze CEOs' online impressions by using multimodal data collected from social media platforms. More specifically, ExeAnalyzer can detect CEOs' first impressions, personalities, and other sociometric attributes. Based on a dataset of 7,806 startups extracted from AngelList, another contribution of our research is the empirical analysis of the relationship between CEOs' online impressions and startups' funding successes. Our empirical analysis shows that CEOs' impression of dominance is negatively related to startups' funding performance, while the social desirability of CEOs is positively associated with startups' funding success. Our empirical study also confirms that the impression features extracted by ExeAnalyzer have significant predictive power on startups' funding performance.

Citation Format(s)

ExeAnalyzer: A deep generative adversarial network for multimodal online impression analysis and startup funding prediction. / Yang, Kai; Lau, Raymond Y.K.
Proceedings of the 54th Annual Hawaii International Conference on System Sciences. ed. / Tung X. Bui. Hawaii International Conference on System Sciences (HICSS), 2021. p. 2501-2510 (Proceedings of the Annual Hawaii International Conference on System Sciences).

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with host publication)peer-review