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Popularity signals in trial-offer markets with social influence and position bias

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  • Maldonado, Felipe
  • Van Hentenryck, Pascal
  • Berbeglia, Gerardo
  • Berbeglia, Franco

Abstract

This paper considers trial-offer markets where consumer preferences are modelled by a multinomial logit with social influence and position bias. The social signal for a product is given by its current market share raised to power r (or, equivalently, the number of purchases raised to the power of r). The paper shows that, when r is strictly between 0 and 1, and a static position assignment (e.g., a quality ranking) is used, the market converges to a unique equilibrium where the market shares depend only on product quality, not their initial appeals or the early dynamics. When r is greater than 1, the market becomes unpredictable. In many cases, the market goes to a monopoly for some product: which product becomes a monopoly depends on the initial conditions of the market. These theoretical results are complemented by an agent-based simulation which indicates that convergence is fast when r is between 0 and 1, and that the quality ranking dominates the well-known popularity ranking in terms of market efficiency. These results shed a new light on the role of social influence which is often blamed for unpredictability, inequalities, and inefficiencies in markets. In contrast, this paper shows that, with a proper social signal and position assignment for the products, the market becomes predictable, and inequalities and inefficiencies can be controlled appropriately.

Suggested Citation

  • Maldonado, Felipe & Van Hentenryck, Pascal & Berbeglia, Gerardo & Berbeglia, Franco, 2018. "Popularity signals in trial-offer markets with social influence and position bias," European Journal of Operational Research, Elsevier, vol. 266(2), pages 775-793.
  • Handle: RePEc:eee:ejores:v:266:y:2018:i:2:p:775-793
    DOI: 10.1016/j.ejor.2017.10.056
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    References listed on IDEAS

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    1. Yuan, Xuchuan & Hwarng, H. Brian, 2016. "Stability and chaos in demand-based pricing under social interactions," European Journal of Operational Research, Elsevier, vol. 253(2), pages 472-488.
    2. Stummer, Christian & Kiesling, Elmar & Günther, Markus & Vetschera, Rudolf, 2015. "Innovation diffusion of repeat purchase products in a competitive market: An agent-based simulation approach," European Journal of Operational Research, Elsevier, vol. 245(1), pages 157-167.
    3. Catherine Tucker & Juanjuan Zhang, 2011. "How Does Popularity Information Affect Choices? A Field Experiment," Management Science, INFORMS, vol. 57(5), pages 828-842, May.
    4. Andrew Lim & Brian Rodrigues & Xingwen Zhang, 2004. "Metaheuristics with Local Search Techniques for Retail Shelf-Space Optimization," Management Science, INFORMS, vol. 50(1), pages 117-131, January.
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    Cited by:

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    2. Rui Chen & Hai Jiang, 2020. "Assortment optimization with position effects under the nested logit model," Naval Research Logistics (NRL), John Wiley & Sons, vol. 67(1), pages 21-33, February.
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    5. Berbeglia, Franco & Berbeglia, Gerardo & Van Hentenryck, Pascal, 2021. "Market segmentation in online platforms," European Journal of Operational Research, Elsevier, vol. 295(3), pages 1025-1041.

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