Detecting Senior Executives' Personalities for Predicting Corporate Behaviors : An Attention-based Deep Learning Approach
Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review
Author(s)
Related Research Unit(s)
Detail(s)
Original language | English |
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Title of host publication | 40th International Conference on Information Systems, ICIS 2019 |
Publisher | Association for Information Systems |
Number of pages | 17 |
ISBN (print) | 9780996683197 |
Publication status | Published - Dec 2019 |
Publication series
Name | International Conference on Information Systems, ICIS |
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Conference
Title | 40th International Conference on Information Systems (ICIS 2019) |
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Location | Internationales Congress Center München (ICM) |
Place | Germany |
City | Munich |
Period | 15 - 18 December 2019 |
Link(s)
Document Link | Links
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Link to Scopus | https://www.scopus.com/record/display.uri?eid=2-s2.0-85093077326&origin=recordpage |
Permanent Link | https://scholars.cityu.edu.hk/en/publications/publication(f5c7b23a-82e6-4c04-ae8c-ec37addfc974).html |
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.
Research Area(s)
- Corporate Behaviors, Design Science, Machine Learning, Personality Detection
Citation Format(s)
Detecting Senior Executives' Personalities for Predicting Corporate Behaviors: An Attention-based Deep Learning Approach. / Yang, Kai; Lau, Raymond Y.K.
40th International Conference on Information Systems, ICIS 2019. Association for Information Systems, 2019. (International Conference on Information Systems, ICIS).
40th International Conference on Information Systems, ICIS 2019. Association for Information Systems, 2019. (International Conference on Information Systems, ICIS).
Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review