article

Market Valuation of Artificial Intelligence Implementation Announcements

Authors:

Cheng-Kui (Tony) Huang,

Yu-Tsung LeeAuthors Info & Claims

ACM SIGMIS Database: the DATABASE for Advances in Information Systems, Volume 54, Issue 2

Pages 9 - 28

https://doi.org/10.1145/3595863.3595866

Published: 01 May 2023 Publication History

Abstract

With the advent of artificial intelligence (AI), more and more enterprises have begun developing AI to promote their own competitive advantage and increase their business value. In order to reveal the value-added results of AI implementation, this study adopts an event study methodology to capture the abnormal returns resulting from the announcement of AI implementation. Based on the empirical results of this study, in response to the announcement of AI implementation, we find significant positive abnormal returns on the event day. With respect to the grouping analysis, the results show significant differences in average cumulative abnormal returns between announcements with detailed information and those without detailed information. In addition, our findings present significant differences in average cumulative abnormal returns between IT-companies and non-IT companies on the event day. Ultimately, according to the content analysis, only one characteristic, the frequency of negative words, is modestly and negatively correlated with average cumulative abnormal returns.

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Cited By

Kanchibhotla CKota KSrinivas PChannappa SKumar CKatta S(2024)Innovations In Ai and Deep Learning for Scalable Network Data Processing2024 International Conference on Intelligent Computing and Emerging Communication Technologies (ICEC)10.1109/ICEC59683.2024.10837414(1-6)Online publication date: 23-Nov-2024
https://doi.org/10.1109/ICEC59683.2024.10837414
Moro-Visconti RCruz Rambaud SLópez Pascual J(2023)Artificial intelligence-driven scalability and its impact on the sustainability and valuation of traditional firmsHumanities and Social Sciences Communications10.1057/s41599-023-02214-810:1Online publication date: 8-Nov-2023
https://doi.org/10.1057/s41599-023-02214-8

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Published In

cover image ACM SIGMIS Database: the DATABASE for Advances in Information Systems

ACM SIGMIS Database: the DATABASE for Advances in Information Systems Volume 54, Issue 2

May 2023

106 pages

ISSN:0095-0033

EISSN:1532-0936

DOI:10.1145/3595863

Editors:
K.D. Joshi
University of Nevada - Reno, Renvo, NV
,
Nancy Deng
California State University, Dominguez Hills, Carson, CA
,
Heidi Seward
Baylor University, Waco, TX
,
Mina Jafarijoo
Stockton University, Galloway, NJ

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Copyright © 2023 Copyright is held by the owner/author(s).

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

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Published: 01 May 2023

Published in SIGMIS Volume 54, Issue 2

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Cited By

Kanchibhotla CKota KSrinivas PChannappa SKumar CKatta S(2024)Innovations In Ai and Deep Learning for Scalable Network Data Processing2024 International Conference on Intelligent Computing and Emerging Communication Technologies (ICEC)10.1109/ICEC59683.2024.10837414(1-6)Online publication date: 23-Nov-2024
https://doi.org/10.1109/ICEC59683.2024.10837414
Moro-Visconti RCruz Rambaud SLópez Pascual J(2023)Artificial intelligence-driven scalability and its impact on the sustainability and valuation of traditional firmsHumanities and Social Sciences Communications10.1057/s41599-023-02214-810:1Online publication date: 8-Nov-2023
https://doi.org/10.1057/s41599-023-02214-8

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