In the hearts and minds of employees: A model of pre-adoptive appraisal toward artificial intelligence in organizations

Yi-Te Chiu, Yu-Qian Zhu and Jacqueline Corbett

International Journal of Information Management, 2021, vol. 60, issue C

Abstract: Organizations face increasing pressure to implement artificial intelligence (AI) within a variety of business processes and functions. Many perceived benefits surround AI, but a considerable amount of trepidation also exists because of the potential of AI to replace human employees and dehumanize work. Questions regarding the future of work in the age of AI are particularly salient in pre-adoption organizations, before employees have the opportunity to gain direct experience with AI. To cope with this potentially stressful situation, employees engage in cognitive appraisal processes based on their own knowledge and personal use of AI. These pre-adoptive appraisals of AI influence both affective and cognitive attitudes, which in turn trigger behavioral responses that influence an organization’s ability to leverage AI successfully. Our survey of 363 Taiwanese employees shows that perceptions of AI’s operational and cognitive capabilities are positively related to affective and cognitive attitudes toward AI, while concerns regarding AI have a negative relationship with affective attitude only. Interaction effects of employee knowledge and affective attitude are also observed. This work’s main contribution lies in the development of an empirically-tested model of the potential impact of AI on organizations from an employee perspective in the pre-adoption phase. These results have practical implications for how organizations prepare for the arrival of this transformative technology.

Keywords: Affective attitude; Artificial intelligence; Cognitive appraisal; Cognitive attitude; Employees; Pre-adoption; Subjective knowledge; Turnover (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (5)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ininma:v:60:y:2021:i:c:s0268401221000724

DOI: 10.1016/j.ijinfomgt.2021.102379

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