Xueming Luo

Xueming Luo

  • Fox School of Business and Management

    • Marketing

      • Charles E. Gilliland, Jr. Professor in Marketing


Xueming Luo is Charles Gilliland Distinguished Chair Professor of Marketing, Professor of Strategic Management, Professor of Management Information Systems. He is the Founder/Director of the Global Institute for Artificial Intelligence and Business Analytics in the Fox School of Business at Temple University. He is interested in digital mobile marketing, omnichannel customer analytics, and social responsibility with machine learning, artificial intelligence, engineering models, and big data field experiments. His current research focuses on sharing economy platform algorithms, unstructual audio/image/video data, and smart city analytics for personalized recommendations, promotions, competitive pricing, omnichannel, social media networks advertising, and customer equity metrics. His work has been featured by most top ranking journals in Marketing, Strategy, Information Systems, and Management, as well as popular trade press such as the Wall Street Journal, ScienceDaily, Forbes, Financial Times, Harvard Business Review, MIT Sloan Management Review, and others.

Research Interests

  • Large-scale field experiment mobile marketing
  • Customer analytics with machine learning and big data
  • Deep learning for personalized promotions
  • Competitive pricing
  • Omnichannel targeting
  • Social media networking ads
  • Artificial intelligence and recommendation algorithms

Courses Taught




MKTG 9006

Empirical Modeling in Marketing


MKTG 9090

Sem-Sel Topics in Mktg


Selected Publications


  • Sun, C., Adamopoulos, P., Ghose, A., & Luo, X. (2022). Predicting Stages in Omnichannel Path to Purchase: A Deep Learning Model. Information Systems Research, 33(2), 429-445. Institute for Operations Research and the Management Sciences (INFORMS). doi: 10.1287/isre.2021.1071.

  • Zhang, S., Chan, T.Y., Luo, X., & Wang, X. (2022). Time-Inconsistent Preferences and Strategic Self-Control in Digital Content Consumption. Marketing Science, 41(3), 616-636. Institute for Operations Research and the Management Sciences (INFORMS). doi: 10.1287/mksc.2021.1318.

  • Tong, S., Jia, N., Luo, X., & Fang, Z. (2021). The Janus face of artificial intelligence feedback: Deployment versus disclosure effects on employee performance. Strategic Management Journal, 42(9), 1600-1631. doi: 10.1002/smj.3322.

  • Li, J., Luo, X., Lu, X., & Moriguchi, T. (2021). The Double-Edged Effects of E-Commerce Cart Retargeting: Does Retargeting Too Early Backfire? Journal of Marketing, 85(4), 123-140. doi: 10.1177/0022242920959043.

  • Luo, X., Qin, M., Fang, Z., & Qu, Z. (2021). Artificial Intelligence Coaches for Sales Agents: Caveats and Solutions. Journal of Marketing, 85(2), 14-32. doi: 10.1177/0022242920956676.

  • Luo, X., Tong, S., Lin, Z., & Zhang, C. (2021). The Impact of Platform Protection Insurance on Buyers and Sellers in the Sharing Economy: A Natural Experiment. Journal of Marketing, 85(2), 50-69. doi: 10.1177/0022242920962510.

  • Tae, C., Luo, X., & Lin, Z. (2020). Capacity-constrained entrepreneurs and their product portfolio size: The response to a platform design change on a Chinese sharing economy platform. Strategic Entrepreneurship Journal, 14(3), 302-328. doi: 10.1002/sej.1360.

  • Luo, X., Zhang, Y., Zeng, F., & Qu, Z. (2020). Complementarity and cannibalization of offline-TO-ONLINE targeting: A field experiment on omnichannel commerce. MIS Quarterly: Management Information Systems, 44(2), 957-982. doi: 10.25300/MISQ/2020/15630.

  • Tong, S., Luo, X., & Xu, B. (2020). Personalized mobile marketing strategies. Journal of the Academy of Marketing Science, 48(1), 64-78. doi: 10.1007/s11747-019-00693-3.

  • Zhang, Y., Li, B., Luo, X., & Wang, X. (2019). Personalized Mobile Targeting with User Engagement Stages: Combining a Structural Hidden Markov Model and Field Experiment. Information Systems Research, 30(3), 787-804. doi: 10.1287/isre.2018.0831.

  • Phang, C., Luo, X., & Fang, Z. (2019). Mobile Time-Based Targeting: Matching Product-Value Appeal to Time of Day. Journal of Management Information Systems, 36(2), 513-545. doi: 10.1080/07421222.2019.1598696.

  • Fong, N., Zhang, Y., Luo, X., & Wang, X. (2019). Targeted promotions on an E-book platform: Crowding out, heterogeneity, and opportunity costs. Journal of Marketing Research, 56(2), 310-323. doi: 10.1177/0022243718817513.

  • Luo, X., Tong, S., Fang, Z., & Qu, Z. (2019). Frontiers: Machines vs. humans: The impact of artificial intelligence chatbot disclosure on customer purchases. Marketing Science, 38(6), 937-947. doi: 10.1287/mksc.2019.1192.

  • Luo, X., Lu, X., & Li, J. (2019). When and How to Leverage E-commerce Cart Targeting: The relative and moderated effects of scarcity and price incentives with a two-stage field experiment and causal forest optimization. Information Systems Research, 30(4), 1203-1227. doi: 10.1287/isre.2019.0859.

  • Song, Y.(., Phang, C.W.(., Yang, S., & Luo, X. (2018). The Effectiveness of Contextual Competitive Targeting in Conjunction with Promotional Incentives. International Journal of Electronic Commerce, 22(3), 349-385. doi: 10.1080/10864415.2018.1462952.

  • Aspara, J., Klein, J., Luo, X., & Tikkanen, H. (2018). The Dilemma of Service Productivity and Service Innovation: An Empirical Exploration in Financial Services. Journal of Service Research, 21(2), 249-262. doi: 10.1177/1094670517738368.

  • Aspara, J., Wittkowski, K., & Luo, X. (2018). Types of intelligence predict likelihood to get married and stay married: Large-scale empirical evidence for evolutionary theory. Personality and Individual Differences, 122, 1-6. doi: 10.1016/j.paid.2017.09.028.