Time: Friday, 17 March 2023, 10:30–12:00
Room: LW420
Abstract
Increasingly, artificial intelligence (AI) serves as a frontline operator, while humans perform backend operations. Despite growing firms in the gig economy employing a business model with AI as the default service provider, the literature is limited regarding the impact of such a model on employee income. To address such limitations, this research proposes an AI-first service framework, which asserts that AI initially attempts to solve tasks, but upon unsatisfactory service outcome, customers pay a fee for employee assistance with such tasks. To empirically investigate our proposed framework, we partner with an AI-first learning app, where AI and tutors are the default and on-request service providers, respectively. We use tutor-level observational data and find that as AI, the default service provider, becomes more effective, tutor income is mediated by changes in task volume and margin, but that the pathways differ depending on employee expertise. Findings from our granular data show that, as AI effectiveness increases, tasks that are difficult due to their broad coverage across topics are passed on to tutors such that low-expertise (vs. high-expertise) tutors accept fewer tasks. We also conduct a field experiment at the customer level to show that customers are willing to pay for extra assistance from tutors, despite receiving free service from AI, for tasks that are difficult due to in-depth knowledge required within a topic. We discuss the theoretical implications of our findings and practical ramifications to effectively manage and develop human competency in the era of an AI-driven economy.
Bio
Sang-Pil Han is an Associate Professor of Information Systems in the W. P. Carey School of Business at Arizona State University. His research focuses on artificial intelligence, digital platforms, and business analytics. His research has been published in top-tier academic journals such as Management Science, Management Information Systems Quarterly, Information Systems Research and Journal of Marketing, and featured in Harvard Business Review and BBC News. He has received grants from the Marketing Science Institute and Wharton Interactive Media Initiative, the NET Institute, the Wharton Customer Analytics Initiative, the Korea Research Foundation, the Hong Kong General Research Fund, as well as private companies. At ASU, he was a Co-Faculty Director for the Master of Science in Business Analytics program. He served as an Associate Editor at Information Systems Research. He advises a variety of organizations, including tech startups like Mathpresso, a leading AI-powered education platform, and RoundIn, an online golf learning platform, as well as non-profits like Simple Steps, a 501c3 organization that assists female immigrant talent in achieving their professional goals. In his spare time, he enjoys playing golf with his wife and two daughters.