An empirical investigation of trust in AI in a Chinese petrochemical enterprise based on institutional theory

Abstract Despite its considerable potential in the manufacturing industry, the application of artificial intelligence (AI) in the industry still faces the challenge of insufficient trust. Since AI is a black box with operations that ordinary users have difficulty understanding, users in organization...

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Autores principales:	Jia Li, Yiwen Zhou, Junping Yao, Xuan Liu
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/421d28692dad43a9b3b9df4161a87387
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spelling	oai:doaj.org-article:421d28692dad43a9b3b9df4161a873872021-12-02T14:33:51ZAn empirical investigation of trust in AI in a Chinese petrochemical enterprise based on institutional theory10.1038/s41598-021-92904-72045-2322https://doaj.org/article/421d28692dad43a9b3b9df4161a873872021-06-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-92904-7https://doaj.org/toc/2045-2322Abstract Despite its considerable potential in the manufacturing industry, the application of artificial intelligence (AI) in the industry still faces the challenge of insufficient trust. Since AI is a black box with operations that ordinary users have difficulty understanding, users in organizations rely on institutional cues to make decisions about their trust in AI. Therefore, this study investigates trust in AI in the manufacturing industry from an institutional perspective. We identify three institutional dimensions from institutional theory and conceptualize them as management commitment (regulative dimension at the organizational level), authoritarian leadership (normative dimension at the group level), and trust in the AI promoter (cognitive dimension at the individual level). We hypothesize that all three institutional dimensions have positive effects on trust in AI. In addition, we propose hypotheses regarding the moderating effects of AI self-efficacy on these three institutional dimensions. A survey was conducted in a large petrochemical enterprise in eastern China just after the company had launched an AI-based diagnostics system for fault detection and isolation in process equipment service. The results indicate that management commitment, authoritarian leadership, and trust in the AI promoter are all positively related to trust in AI. Moreover, the effect of management commitment and trust in the AI promoter are strengthened when users have high AI self-efficacy. The findings of this study provide suggestions for academics and managers with respect to promoting users’ trust in AI in the manufacturing industry.Jia LiYiwen ZhouJunping YaoXuan LiuNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Jia Li Yiwen Zhou Junping Yao Xuan Liu An empirical investigation of trust in AI in a Chinese petrochemical enterprise based on institutional theory
description	Abstract Despite its considerable potential in the manufacturing industry, the application of artificial intelligence (AI) in the industry still faces the challenge of insufficient trust. Since AI is a black box with operations that ordinary users have difficulty understanding, users in organizations rely on institutional cues to make decisions about their trust in AI. Therefore, this study investigates trust in AI in the manufacturing industry from an institutional perspective. We identify three institutional dimensions from institutional theory and conceptualize them as management commitment (regulative dimension at the organizational level), authoritarian leadership (normative dimension at the group level), and trust in the AI promoter (cognitive dimension at the individual level). We hypothesize that all three institutional dimensions have positive effects on trust in AI. In addition, we propose hypotheses regarding the moderating effects of AI self-efficacy on these three institutional dimensions. A survey was conducted in a large petrochemical enterprise in eastern China just after the company had launched an AI-based diagnostics system for fault detection and isolation in process equipment service. The results indicate that management commitment, authoritarian leadership, and trust in the AI promoter are all positively related to trust in AI. Moreover, the effect of management commitment and trust in the AI promoter are strengthened when users have high AI self-efficacy. The findings of this study provide suggestions for academics and managers with respect to promoting users’ trust in AI in the manufacturing industry.
format	article
author	Jia Li Yiwen Zhou Junping Yao Xuan Liu
author_facet	Jia Li Yiwen Zhou Junping Yao Xuan Liu
author_sort	Jia Li
title	An empirical investigation of trust in AI in a Chinese petrochemical enterprise based on institutional theory
title_short	An empirical investigation of trust in AI in a Chinese petrochemical enterprise based on institutional theory
title_full	An empirical investigation of trust in AI in a Chinese petrochemical enterprise based on institutional theory
title_fullStr	An empirical investigation of trust in AI in a Chinese petrochemical enterprise based on institutional theory
title_full_unstemmed	An empirical investigation of trust in AI in a Chinese petrochemical enterprise based on institutional theory
title_sort	empirical investigation of trust in ai in a chinese petrochemical enterprise based on institutional theory
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/421d28692dad43a9b3b9df4161a87387
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An empirical investigation of trust in AI in a Chinese petrochemical enterprise based on institutional theory

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