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Cited by:

  1. Dirk Bergemann & Marco Ottaviani, 2021. "Information Markets and Nonmarkets," Cowles Foundation Discussion Papers 2296, Cowles Foundation for Research in Economics, Yale University.
  2. Omid Rafieian, 2023. "Optimizing User Engagement Through Adaptive Ad Sequencing," Marketing Science, INFORMS, vol. 42(5), pages 910-933, September.
  3. Hsing Kenneth Cheng & D. Daniel Sokol & Xinyu Zang, 2024. "The rise of empirical online platform research in the new millennium," Journal of Economics & Management Strategy, Wiley Blackwell, vol. 33(2), pages 416-451, March.
  4. Lalit Jain & Zhaoqi Li & Erfan Loghmani & Blake Mason & Hema Yoganarasimhan, 2024. "Effective Adaptive Exploration of Prices and Promotions in Choice-Based Demand Models," Marketing Science, INFORMS, vol. 43(5), pages 1002-1030, September.
  5. Shengjun Mao & Sanjeev Dewan & Yi-Jen (Ian) Ho, 2023. "Personalized Ranking at a Mobile App Distribution Platform," Information Systems Research, INFORMS, vol. 34(3), pages 811-827, September.
  6. Schaefer, Maximilian & Sapi, Geza, 2023. "Complementarities in learning from data: Insights from general search," Information Economics and Policy, Elsevier, vol. 65(C).
  7. Ma, Xuejiao & Che, Tianqi & Jiang, Qichuan, 2025. "A three-stage prediction model for firm default risk: An integration of text sentiment analysis," Omega, Elsevier, vol. 131(C).
  8. Cloarec, Julien, 2020. "The personalization–privacy paradox in the attention economy," Technological Forecasting and Social Change, Elsevier, vol. 161(C).
  9. Tino Werner, 2022. "Elicitability of Instance and Object Ranking," Decision Analysis, INFORMS, vol. 19(2), pages 123-140, June.
  10. Herhausen, Dennis & Bernritter, Stefan F. & Ngai, Eric W.T. & Kumar, Ajay & Delen, Dursun, 2024. "Machine learning in marketing: Recent progress and future research directions," Journal of Business Research, Elsevier, vol. 170(C).
  11. Chenshuo Sun & Panagiotis Adamopoulos & Anindya Ghose & Xueming Luo, 2022. "Predicting Stages in Omnichannel Path to Purchase: A Deep Learning Model," Information Systems Research, INFORMS, vol. 33(2), pages 429-445, June.
  12. Hema Yoganarasimhan & Ebrahim Barzegary & Abhishek Pani, 2020. "Design and Evaluation of Personalized Free Trials," Papers 2006.13420, arXiv.org.
  13. Florian Peiseler & Alexander Rasch & Shiva Shekhar, 2022. "Imperfect information, algorithmic price discrimination, and collusion," Scandinavian Journal of Economics, Wiley Blackwell, vol. 124(2), pages 516-549, April.
  14. Omid Rafieian & Hema Yoganarasimhan, 2021. "Targeting and Privacy in Mobile Advertising," Marketing Science, INFORMS, vol. 40(2), pages 193-218, March.
  15. Ludovica Cesareo & Claudia Townsend & Eugene Pavlov, 2023. "Hideous but worth it: Distinctive ugliness as a signal of luxury," Journal of the Academy of Marketing Science, Springer, vol. 51(3), pages 636-657, May.
  16. Tsan‐Ming Choi & Subodha Kumar & Xiaohang Yue & Hau‐Ling Chan, 2022. "Disruptive Technologies and Operations Management in the Industry 4.0 Era and Beyond," Production and Operations Management, Production and Operations Management Society, vol. 31(1), pages 9-31, January.
  17. Ali Goli & Anja Lambrecht & Hema Yoganarasimhan, 2024. "A Bias Correction Approach for Interference in Ranking Experiments," Marketing Science, INFORMS, vol. 43(3), pages 590-614, May.
  18. Tino Werner, 2023. "Quantitative robustness of instance ranking problems," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 75(2), pages 335-368, April.
  19. Yang, Hui & Li, Dan & Hu, Peng, 2024. "Decoding algorithm fatigue: The role of algorithmic literacy, information cocoons, and algorithmic opacity," Technology in Society, Elsevier, vol. 79(C).
  20. van Giffen, Benjamin & Herhausen, Dennis & Fahse, Tobias, 2022. "Overcoming the pitfalls and perils of algorithms: A classification of machine learning biases and mitigation methods," Journal of Business Research, Elsevier, vol. 144(C), pages 93-106.
  21. Josué Martínez-Garmendia, 2024. "Machine learning for product choice prediction," Journal of Marketing Analytics, Palgrave Macmillan, vol. 12(3), pages 656-667, September.
  22. Lohmann, Paul M & Gsottbauer, Elisabeth & Farrington, James & Human, Steve & Reisch, Lucia A, 2024. "Choice architecture promotes sustainable choices in online food-delivery apps," LSE Research Online Documents on Economics 125835, London School of Economics and Political Science, LSE Library.
  23. Zhang, Ruchuan & Gao, Weiyan & Chen, Shanshan & Zhou, Li & Li, Aijun, 2024. "Dose digital transformation contribute to improving financing efficiency? Evidence and implications for energy enterprises in China," Energy, Elsevier, vol. 300(C).
  24. Alantari, Huwail J. & Currim, Imran S. & Deng, Yiting & Singh, Sameer, 2022. "An empirical comparison of machine learning methods for text-based sentiment analysis of online consumer reviews," International Journal of Research in Marketing, Elsevier, vol. 39(1), pages 1-19.
  25. Carballa-Smichowski, Bruno & Lefouili, Yassine & Mantovani, Andrea & Reggiani, Carlo, 2025. "Data Sharing or Analytics Sharing?," TSE Working Papers 25-1615, Toulouse School of Economics (TSE).
  26. Hema Yoganarasimhan & Ebrahim Barzegary & Abhishek Pani, 2023. "Design and Evaluation of Optimal Free Trials," Management Science, INFORMS, vol. 69(6), pages 3220-3240, June.
  27. Brei, Vinicius Andrade, 2020. "Machine Learning in Marketing: Overview, Learning Strategies, Applications, and Future Developments," Foundations and Trends(R) in Marketing, now publishers, vol. 14(3), pages 173-236, August.
  28. Jens Foerderer, 2023. "Should we trust web-scraped data?," Papers 2308.02231, arXiv.org.
  29. Reuter-Oppermann, Melanie & Wolff, Clemens & Pumplun, Luisa, 2021. "Next Frontiers in Emergency Medical Services in Germany: Identifying Gaps between Academia and Practice," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 124665, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
  30. S. B. Avdasheva & O. S. Khomik & V. S. Chesnokov & V. A. Khlyupina, 2025. "Impact of the Scale Effect of Recommendation Systems on Competition in Digital Platform Sectors," Studies on Russian Economic Development, Springer, vol. 36(3), pages 388-395, June.
  31. Hanyao Gao & Gang Kou & Haiming Liang & Hengjie Zhang & Xiangrui Chao & Cong-Cong Li & Yucheng Dong, 2024. "Machine learning in business and finance: a literature review and research opportunities," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 10(1), pages 1-35, December.
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