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Learning in retail entry

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  • Yang, Nathan

Abstract

Retailers may face uncertainty about the profitability of local markets, which provide opportunities for learning when making entry decisions. To quantify these informational benefits, I develop an empirical framework for studying dynamic retail entry with uncertainty and learning (from others). Using novel data about fast food chains, I estimate the model with a forward simulation estimation approach augmented with particle filtering as a way to flexibly account for unobserved firm beliefs about market profitability. The estimates confirm the presence of uncertainty and learning. Most importantly, simulations using the estimated model demonstrate that learning from others may indeed help mitigate some of the uncertainty.

Suggested Citation

  • Yang, Nathan, 2020. "Learning in retail entry," International Journal of Research in Marketing, Elsevier, vol. 37(2), pages 336-355.
    • Handle: RePEc:eee:ijrema:v:37:y:2020:i:2:p:336-355
    DOI: 10.1016/j.ijresmar.2019.09.005
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    Citations

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    Cited by:

    1. Dmitrii Tereshchenko, 2022. "Competition among Russian Grocery Stores: Database on St. Petersburg, 2017–2021," HSE Working papers WP BRP 258/EC/2022, National Research University Higher School of Economics.
    2. Robert Clark & Yiran Gong, 2021. "Why Do Some New Products Fail? Evidence from the Entry and Exit of Vanilla Coke," Working Paper 1475, Economics Department, Queen's University.
    3. Aghaie, Sina & Javadinia, Amir & Mirahmad, Hooman & Janani, Saeed, 2022. "How incumbents’ response strategy impacts rivals’ market exit timing?," Journal of Business Research, Elsevier, vol. 146(C), pages 251-263.
    4. Maier, Erik & Bornschein, Rico & Manss, Rico & Hesse, Damian, 2023. "Financial consequences of adding bricks to clicks," International Journal of Research in Marketing, Elsevier, vol. 40(3), pages 609-628.
    5. Christian Peukert & Imke Reimers, 2022. "Digitization, Prediction, and Market Efficiency: Evidence from Book Publishing Deals," Management Science, INFORMS, vol. 68(9), pages 6907-6924, September.

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