This is what’s in store for you: How online social learning affects product positioning

Feng Li, Timon C. Du and Ying Wei

Transportation Research Part E: Logistics and Transportation Review, 2023, vol. 179, issue C

Abstract: Customers can share their opinions on the Internet, and this online sharing affects customers’ quality expectations, their purchasing decisions, and firms’ product positioning such as price and quality decisions. In this study, we examine product positioning in monopolistic, competitive, and collusive markets, where companies must address the social learning of customers. We apply a multi-agent model to simulate the learning process and the product positioning of companies as a gaming process via particle swarm optimization. We find that the optimal policies are highly dependent on market status and the level of customers’ ex-ante quality uncertainty. Specifically, in the absence of competition, social learning can help a monopolistic company gain higher profits by offering low quality products at a low (high) price in environments of low (high) uncertainty. However, duopoly competition leads to reduced prices and lower quality designs in general, except for a low degree of uncertainty. Social learning intensifies the competition between companies, and without it, competition is moderated through differentiated price and quality decisions. Under duopoly competition, a company with high uncertainty deviation benefits more from social learning by adopting a low-price-low-quality strategy, outperforming a competitor with low deviation. Finally, while collusion typically leads to higher prices and benefits for companies compared to duopoly competition, it may hurt the companies when the effects of social learning are small.

Keywords: Social learning; Competition; Product positioning; Multi-agent simulation; Particle swarm optimization (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1016/j.tre.2023.103317

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