How does AI surpassing humans influence public innovativeness? A multi-method empirical study
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
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Detail(s)
Original language | English |
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Number of pages | 18 |
Journal / Publication | Behaviour & Information Technology |
Online published | 8 Feb 2024 |
Publication status | Online published - 8 Feb 2024 |
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Abstract
As AI develops, it has become more capable and efficient than humans. We investigate how AI surpassing humans in capabilities and performance (hereafter AI surpassing humans) influences public innovativeness. Based on the research of personal innovativeness motivation and expectation value theory, we propose that AI surpassing humans enhances public innovativeness. A multi-method empirical study proves our hypothesis. In Study 1, we perform textual analysis on 1.2 million tweets. We reveal that people mention more words about innovation for AI surpassing humans. In Study 2, a controlled experiment proves the main and mediating effect. Theoretically, we contribute to the research on the impact of AI and personal innovativeness. This is the first to investigate how AI surpassing humans influences public innovativeness. Meanwhile, we fill this gap in how AI impacts public innovativeness. In addition, our findings have practical implications for government and policymakers, companies, and the public. Especially for government and policymakers, we suggest they need to pay more attention to the potentially overlooked impact of AI outperforming humans on public innovativeness compared with other common techniques. Meanwhile, we point out that encouraging the public to keep human intellectual superiority may attenuate the effect of AI surpassing humans. © 2024 Informa UK Limited, trading as Taylor & Francis Group.
Research Area(s)
- Artificial intelligence, goal, expectation value theory, public innovativeness
Citation Format(s)
How does AI surpassing humans influence public innovativeness? A multi-method empirical study. / Ma, Yongchao Martin; Deng, Zhongzhun.
In: Behaviour & Information Technology, 08.02.2024.
In: Behaviour & Information Technology, 08.02.2024.
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review