Detecting Senior Executives' Personalities for Predicting Corporate Behaviors: An Attention-based Deep Learning Approach

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

3 Citations (Scopus)

Abstract

Previous empirical studies reveal that the personalities of senior executives may influence corporate decisions. Grounded in the big-five personality model established in the field of psychology and guided by the design science methodology, the work reported in this paper examines the predictive power of executives' personalities on corporate behaviors. Our main contributions are twofold. First, we design a novel deep learning-based artifact, namely pAttCLSTM which analyzes the senior executives' personalities on a large scale through social media data. In our experiment, pAttCLSTM outperforms other state-of-the-art detectors over two benchmark datasets. Second, empowered by pAttCLSTM, we empirically verify the predictive power of executives' personalities on firms' policy variables by using a dataset including Tweets of 507 executives worked in S&P 1500 companies. To our best knowledge, this is the first successful design of an automatic detector for senior executives' personalities, and hence to examine their impact on firms' behaviors.
Original languageEnglish
Title of host publication40th International Conference on Information Systems, ICIS 2019
PublisherAssociation for Information Systems
Number of pages17
ISBN (Print)9780996683197
Publication statusPublished - Dec 2019
Event40th International Conference on Information Systems (ICIS 2019) - Internationales Congress Center München (ICM), Munich, Germany
Duration: 15 Dec 201918 Dec 2019
https://icis2019.aisconferences.org/
https://aisel.aisnet.org/icis2019/

Publication series

NameInternational Conference on Information Systems, ICIS

Conference

Conference40th International Conference on Information Systems (ICIS 2019)
Country/TerritoryGermany
CityMunich
Period15/12/1918/12/19
Internet address

Research Keywords

  • Corporate Behaviors
  • Design Science
  • Machine Learning
  • Personality Detection

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