Exploring the impact of human-centred AI on firms’ social and operational performance: A large language model approach.
The main highlights are the development of the HER framework for identifying human-centred AI, and the use of large language models to measure human-centred AI from patent data.
The paper asks two main research questions:
1. Can human-centred AI improve firms’ social and operational performance?
2. What boundary conditions, specifically CSR committees and industrial AI exposure, shape the relationship between human-centred AI and firms’ social and operational performance?
This relevant to Business and Management studies because it examines how firms implement human-centred artificial intelligence (HCAI) during the Industry 5.0 transition. It contributes to this discipline by clarifying the concept of HCAI through the Human-centric design, Ethical management, and Responsible implications (HER) framework, linking HCAI with firms’ social and operational performance, and extending situated AI theory by incorporating organisational and contextual conditions, including corporate social responsibility (CSR) committees, industrial AI exposure, and workforce diversity.
The paper has potential practical impact for firms, managers, and policymakers involved in AI adoption. For firms, the HER framework provides guidance for identifying and implementing HCAI during the Industry 5.0 transition, helping organisations consider human-centric design, ethical management, and responsible implications when introducing AI into operations. For managers, the paper highlights that the value of HCAI is influenced not only by the technology itself but also by organisational conditions. In particular, firms may strengthen CSR committees to better align HCAI with social objectives and pay attention to workforce diversity, which the study identifies as an important mechanism for generating social value. For policymakers and industry stakeholders, the paper suggests that industry-level AI environments influence the effectiveness of HCAI, indicating that support for AI-related resources, technologies, and industrial development may encourage more effective adoption of HCAI.
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