Employees’ Trust in Artificial Intelligence in Companies: The Case of Energy and Chemical Industries in Poland

Abstract

The use of artificial intelligence (AI) in companies is advancing rapidly. Consequently, multidisciplinary research on AI in business has developed dramatically during the last decade, moving from the focus on technological objectives towards an interest in human users’ perspective. In this article, we investigate the notion of employees’ trust in AI at the workplace (in the company), following a human-centered approach that considers AI integration in business from the employees’ perspective, taking into account the elements that facilitate human trust in AI. While employees’ trust in AI at the workplace seems critical, so far, few studies have systematically investigated its determinants. Therefore, this study is an attempt to fill the existing research gap. The research objective of the article is to examine links between employees’ trust in AI in the company and three other latent variables (general trust in technology, intra-organizational trust, and individual competence trust). A quantitative study conducted on a sample of 428 employees from companies of the energy and chemical industries in Poland allowed the hypotheses to be verified. The hypotheses were tested using structural equation modeling (SEM). The results indicate the existence of a positive relationship between general trust in technology and employees’ trust in AI in the company as well as between intra-organizational trust and employees’ trust in AI in the company in the surveyed firms.

The Impact of Artificial Intelligence on Firm Performance: An Application of the Resource-Based View to e-Commerce Firms

“…We operated a cross-sectional survey to experiment with the theoretical model ( Akram et al, 2020 , 2021 ; Chen and Siau, 2020 ; Dubey et al, 2020 ; Haftor et al, 2021 ; Łapińska et al, 2021 ), and the online questionnaire was designed utilizing WJX.cn (an online questionnaire tool; Payne et al, 2021 ; Yao et al, 2021 ). This study conducted a targeted target audience survey through e-commerce associations, WeChat communities of e-commerce entrepreneurs ( Chaudhuri et al, 2021 ).…”

“…Structural equation modeling (SEM) has the advantage of examining the interrelationships among multiple independent variables and one or more dependent variables ( Belhadi et al, 2021 ; Łapińska et al, 2021 ). As an exploratory study, the most appropriate approach for this paper would be the PLS-SEM ( Chatterjee et al, 2021b ).…”

“…Combined with RBV, we define AIC of e-commerce firms as the ability of firms to build, integrate, and utilize AI-based related resources ( Łapińska et al, 2021 ; Mikalef and Gupta, 2021 ). Effective and efficient implementation of AI in firms, on the other hand, requires significant infrastructural resources (tangible resources; Bag et al, 2021c ; Chatterjee et al, 2021a ), which includes financial support ( Łapińska et al, 2021 ), data ( Herhausen et al, 2020 ; Hwang and Kim, 2021 ), hardware devices and software ( Zhang et al, 2020 ), and technical support ( Rahman et al, 2021 ). The majority of the businesses across E-commerce companies are throughout the Internet, and thus they have a natural advantage in acquiring data resources ( Wang and Fan, 2021 ).…”

“…Firm creativity is a necessary antecedent that influences firms to compose strategic thinking and decisions ( Dixit et al, 2021 ). Artificial intelligence management (AIM) refers to a firm support management system assisting the implementation of artificial intelligence ( Bag et al, 2021a ; Łapińska et al, 2021 ). Such initiatives depend heavily on the commitment of firms to implement AI technologies ( Haesevoets et al, 2021 ).…”

“…The rapid evolution of artificial intelligence (AI) brings enterprises more business opportunities ( Hughes et al, 2020 ; Obschonka and Audretsch, 2020 ; Shareef et al, 2021 ). Artificial intelligence is the machines (programs) that operates in the simulation of human intelligence ( Łapińska et al, 2021 ) in technologies, such as machine learning, data mining, natural language processing, image recognition, etc. ( Khalid, 2020 ).…”