Unlocking operational efficiency: how AI human capital investment enhances data processing efficiency
Unlocking operational efficiency: how AI human capital investment enhances data processing efficiency
This study examines the impact of firms’ investment in artificial intelligence (AI) human capital on their data processing efficiency. Using the proportion of AI-related employees as a measure of AI human capital investment for US firms from 2005 to 2020, the findings reveal that higher levels of AI human capital significantly reduce the time that firms require to announce their earnings. However, AI tends to be more effective in assisting firms with the processing of routine rather than complex information. Additionally, beyond enhancing the speed of data processing, AI also contributes to greater accuracy in firms’ data compilation and disclosure
Liu, Yongda
e81fbd22-a33f-4458-a646-09c196b0fe02
Zhang, Zhuang
df7b9fa8-04fd-4085-b74d-c9c1506b974e
4 January 2025
Liu, Yongda
e81fbd22-a33f-4458-a646-09c196b0fe02
Zhang, Zhuang
df7b9fa8-04fd-4085-b74d-c9c1506b974e
Liu, Yongda and Zhang, Zhuang
(2025)
Unlocking operational efficiency: how AI human capital investment enhances data processing efficiency.
Economics Letters, 247.
(doi:10.1016/j.econlet.2024.112147).
Abstract
This study examines the impact of firms’ investment in artificial intelligence (AI) human capital on their data processing efficiency. Using the proportion of AI-related employees as a measure of AI human capital investment for US firms from 2005 to 2020, the findings reveal that higher levels of AI human capital significantly reduce the time that firms require to announce their earnings. However, AI tends to be more effective in assisting firms with the processing of routine rather than complex information. Additionally, beyond enhancing the speed of data processing, AI also contributes to greater accuracy in firms’ data compilation and disclosure
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Accepted/In Press date: 24 December 2024
e-pub ahead of print date: 28 December 2024
Published date: 4 January 2025
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Local EPrints ID: 498672
URI: http://eprints.soton.ac.uk/id/eprint/498672
ISSN: 0165-1765
PURE UUID: 6bc53ac0-2a38-4430-9a7c-e5684f6278b2
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Date deposited: 25 Feb 2025 17:43
Last modified: 26 Feb 2025 02:53
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Yongda Liu
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