Virtual Seminar: Professor Xueming Luo - Temple University, USA

Seminar brought to you by Marketing and International Business (MIB).

Abstract: Although artificial intelligence (AI can galvanize humans, whether—and how—to best design and deploy the human-AI coexistence is poorly understood. We address this issue in the context of using human managers and AI to evaluate employee behavior and provide recommendations, in a multi-field experiment approach. In Experiment 1, we find that despite providing more recommendations to employees, the human-AI coexistence has a lower impact on employee performance than either human-alone or AI-alone. That is, the human-AI coexistence actually backfires. Furthermore, such detrimental impact is amplified for disadvantaged employees who have lower prior performance. In exploring the underlying mechanisms, we find that after controlling for information overload, algorithm aversion, and other alternative explanations, the human-AI coexistence induces a higher level of recommendation inconsistency, which then hampers employees’ learning and performance. To solve these perils of the human-AI coexistence, we conduct Experiment 2 using an improved coexistence design, in which human managers play an active role by first reviewing and comparing their own and data-driven AI recommendations and then removing the inconsistent ones before sending them to employees. Results support that the improved human-AI coexistence design effectively reduces recommendation inconsistency and improves employee performance and that such improvement is asymmetrically greater for the disadvantaged employees. Overall, our findings provide a more nuanced understanding of the coexistence of human managers with AI algorithms and how to improve it.

Bio: Xueming Luo is the Charles Gilliland Distinguished Chair Professor of Marketing, Professor of Strategy and MIS, and Founder/ Director of the Global Institute for Artificial Intelligence & Business Analytics (formerly known as Global Center for Big Data and Mobile Analytics) in the Fox School of Business at Temple University. His research focuses on integrating artificial intelligence, 5G/AR/VR metaverse technologies, big data machine learning, and field experiments to model, explain, and optimize customer behaviors, company strategies, platform designs, and creator & sharing economy. He leverages text/audio/image/video data and develops AI/ML algorithmic solutions with casual inference and behavioral insights for digital marketing, personalized recommendations, promotions, competitive pricing, omnichannel, and social media networks. Xueming has worked with leading global companies in mobile communications, banking, e-commerce, health care, education, pharmaceutical, and petroleum industries. His research has been featured by most top-ranking journals in Marketing, Strategy, Information Systems, and Management (e.g., Management Science, Marketing Science, SMJ, JM, JMR, MIS Quarterly), as well as popular trade presses such as the Harvard Business Review, MIT Sloan Management Review, Wall Street Journal, ScienceDaily, Financial Times, and others. For further details please see here.