Quant Marketing 1: Information Processing & Demand
(A2025-125935)

AUTHORS

Arash Laghaie, Nova School of Business and Economics; Rafael P. Greminger, University College London, School of Management; Sergey Turlo, Goethe University Frankfurt; Fan Zhang, Nova SBE; Shrabastee Banerjee, Tilburg University; Jack Tong, Nanyang Technological University

ABSTRACT

This quantitative marketing session on “Information Processing and Demand” focuses on demand estimation when there are information frictions and when acquiring information is costly. The session includes four papers that try to explore how to estimate demand when integrating attribute information is costly, how to estimate choice models robust to consumer search, how Q&As influence consumer demand, and how popular search genres affect mobile shopping. In the first paper, Demand Estimation with Costly Attribute Information Integration, by Sergey Turlo, Matteo Fina, Johannes Kasinger, Arash Laghaie, and Thomas Otter, the authors explore the implications of ignoring costly information integration in multiattribute choice both in designing experiments and in estimating demand. The authors propose a new discrete choice experiment (DCE) design that accounts for choice occasions when processing some attributes is costly and show that failing to account for costly information processing when modeling demand in this DCE data can lead to biased preference and demand estimates. They use a rational inattention discrete choice model to account for hard to process attributes. The second paper, A Method to Estimate Discrete Choice Models that is Robust to Consumer Search, by Jason Abaluck, Giovanni Compiani, and Fan Zhang, tries to find sufficient conditions under which choice data alone suffices to recover preferences even if consumers are partially informed. In their model, they assume that if consumers search, they do so in decreasing order of expected utility. They show that if this condition is satisfied, along with a few additional restrictions, there is a function of choice probabilities which recovers preferences whether consumers are fully or partially informed. In the third paper, When More Is Too Much: Effect of Interacting Information Signals on Consumer Ratings, by Roshini Sudhaharan and Shrabastee Banerjee, the authors investigate how Q&As influence consumer decision-making alongside reviews on online platforms, particularly for highly subjective product categories like books. They study whether the complementary effect of Q&A holds in contexts characterized by greater product uncertainty. They find that Q&A adoption leads to an average decrease in star ratings and a reduction in review volume. The fourth paper, Priming in Search: A Large-Scale Field Experiment on the Impact of Popular Search Genres in Mobile Shopping, by Shuang Zheng, Siliang (Jack) Tong, Sihan Fang, Anand Gopal, and Xianneng Li implements a large-scale field experiment within the food delivery context to investigate how access to the popular ranking search aid (PRSA) affects consumer mobile shopping. It finds that, despite the lowered search costs in the query generation stage, the increase in exploratory searches results in higher search costs during the product exploration stage. The paper offers theoretical contributions and managerial implications for mobile commerce operations.