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 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.

Dissociation Between Users’ Explicit and Implicit Attitudes Toward Artificial Intelligence: An Experimental Study

“…Some authors argue that helping people overcome the prejudices they have towards AI, by increasing trust in machines, will lead to positive implications for society. A greater acceptance of AI would imply greater use of it [60] in supporting humans when making important decisions (e.g., medical, financial decisions), improving the quality of life (e.g., nutrition, physical exercise, medical screening) and work [61]- [63]. Other authors have raised their concerns, arguing that indiscriminately favoring the acceptance of AI could lead to devastating consequences for society, especially for technologies whose misuse or abuse might turn, in the long term, to be more deleterious than beneficial [64], [65].…”

Assessment of the Relationship Between the AST&D Beneficiaries and the Perceived Benefits and Challenges

“…The institutional theory is the lens through which this study focused. Previous studies attempted to explain the institutional adoption of intervention programs using the institutional framework (Li et al, 2021). According to institutional proponents, organizations and society must strictly adhere to societal expectations of an acceptable practice to benefit from the ongoing support they require for sustenance from their citizenry (Kılıç et The network theory of the benefits weaker countries might obtain from complying with the social rules of stronger nations is embedded in the institutional theory (Robertson et al, 2021).…”

“…Thus, developing countries establishing education intervention funding agents such as TETFUND would derive legitimate benefits from the international education intervention framework's institutional network (Robertson et al, 2021). The institutional theory allows the researcher to comprehend why countries such as Nigeria would emulate and conform to acceptable norms of education intervention practices in the developed countries for their intervention fund agencies due to the perceived benefits of those funds to their beneficiaries and stakeholders (Li et al, 2021). In this regard, normative pressures enable Nigeria to replicate existing foreign intervention models to conform to global education intervention funding standards and share the collective value they bring to beneficiaries.…”

“…The study expands on the institutional framework to argue that the propensity of developing countries to emulate successful and legitimate developed nations by funding higher education initiatives through intervention funding is known as imitative logically equivalent pressures(Li et al, 2021). A region's exposure to bilateral education programs and trade exchanges exposes nations to imitations of best practices in human capital development(Li et al, 2021). (Kılıç et al, 2021; Robertson et al, 2021) also found a connection between sharing of educational resources and the advancement in education.…”

Heterogeneous human–robot task allocation based on artificial trust

“…All trust dimensions can influence the probability that an agent will successfully execute a task. Trust can also be influenced by physical characteristics such as human-likeness of a robot 45 , the type of agent and context 46 , and institutional perspectives 47 . In this paper, we simplify the trust estimate by considering only the dimension of capability, since trust in automation primarily focuses on performance 41 and robot performance is an important and strong contributor to trust in human–robot interaction (HRI) 48 , 49 .…”