2022/05/20 by E. S. Vorm, Eric S. Vorm, David J. Y. Combs +1 · 2 citations
Business, Management and Accounting · Decision Sciences · Social Sciences · #Big Data and Business Intelligence #Ethics and Social Impacts of AI #Technology Adoption and User Behaviour
paper · doi:10.1080/10447318.2022.2070107
openalex publication_date 2022/05/20 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/17
Intelligent systems such as technologies related to artificial intelligence, robotics, machine learning, etc. open new insights into data and expand the concept of work in myriad domains. These technologies, while potentially useful, face high barriers to widespread adoption and acceptance by industries and citizens alike. The complexity and multi-dimensionality inherent in intelligent systems often renders traditional validation efforts (e.g., traceability analysis) impossible. In addition, contexts where predictions or computer-generated recommendations have real-world consequences, such as in medical prognosis, financial investing, or military applications introduce new risks and a host of moral and ethical concerns that can further hinder the widespread adoption of intelligent systems. Naturally, such reluctance by would-be users limits the potential of intelligent systems to solve real-world problems. This article reviews the challenges to technology acceptance through the lens of system transparency and user trust, and extends the Technology Acceptance Model (TAM) structure with issues germane to intelligent systems. We examine several prospective transparency frameworks that could be adopted and used by Human-Computer Interaction (HCI) practitioners involved in systems development. Our intention is to assist practitioners in the design of more transparent systems with a specific eye towards enhancing trust and acceptance in intelligent systems. Further, as a result of our review, we suggest that the well-known TAM should be expanded in the context of intelligent systems to include trust and transparency as key elements of the model. Finally, we conclude with a research agenda that might offer empirical evidence showing how transparency might enhance acceptance and use of intelligent systems.