Trust In AI At Workplaces
The advancement of computing capacities has facilitated artificial intelligence (AI) systems to play an active role in workplace employee management. However, public policymakers remain dubious about the social value of AI systems in motivating individuals and build up trust between human and algorithm (Huang and Rust 2018). Specifically, applying AI for human interactions is debatable as people may doubt the capability of AI and even resist AI applications. Furthermore, there is scant academic evidence about whether and how AI applications will alter individual’s job performance and their acceptance of algorithmic management (Webb et al. 2019). To fill this gap, this research proposal examines the social impact of AI algorithmic management systems to motivate individual performance and drive productivity.
Research Technical Area
- AI and Human Interactions
- Business Studies
- Causal Inference
Benefits to the society
First, we extend extant public policy in understanding factors affecting the value of AI technologies in business management and human and AI interactions. Particularly, we investigate how to improve the acceptance of AI management tool in the workplace and how to use such AI systems to motivate individuals (e.g., frontline workers and individual investors). Second, we theoretically and empirically unravel potential human resistance to AI management systems and propose viable strategies to boost human’ acceptance to AI.
Team’s Principal Investigator
Dr. Siliang (Jack) Tong is an Assistant Professor at the Division of Information Technology and Operation Management, Nanyang Technological University, Singapore. He earned his doctoral degree in Marketing from the Fox School of Business, Temple University in 2020. He is an empirical modeler who is interested in the substantive areas of artificial intelligence, digital platforms, and sharing economy. He is very active in research as his works were awarded as the best paper and finalist in multiple occasions such as AMA conference and ISMS Marketing Science conference. His research is published in Strategic Management Journal, Marketing Science, Journal of Marketing and the Journal of the Academy of Marketing Science. Before joining the academia, he had 6-year management experience in digital marketing and web analytics, and he obtained his MBA degree from the University of Wisconsin-Madison in 2016.
Co Principal Investigator
GOH Kim Huat, College of Business (Nanyang Business School), NTU
Research Focus: Value of IT and analytics in the health-care and financial services industry
LUO Xueming, Fox School Of Business & Mgmt, Temple University
Research Focus: AI and Big data applications