Competitive Manufacturers Product-Sharing Strategies: P2P Sharing or B2C Sharing?

Abstract

With the rapid development of the sharing economy, many traditional manufacturers gravitate toward the paradigm shift to embrace product/service sharing with B2C (i.e., business-to-consumer) and P2P (i.e., peer-to-peer) sharing models. We develop a duopoly model to investigate the strategic decisions of competing manufacturers regarding three product-sharing strategies: no-sharing (i.e., sales-only), P2P sharing (i.e., sharing products on a P2P platform in addition to sales), and B2C sharing (i.e., renting products with add-ons on a B2C platform in addition to sales). The findings reveal that P2P sharing and B2C sharing play distinct roles in a competitive environment. P2P sharing has a scale effect, incentivizing manufacturers to expand market share and increase profitability. B2C sharing has a price discrimination effect, enabling the high-quality manufacturer to enhance price competitiveness and achieve higher profits. Moreover, the equilibrium outcomes for manufacturers depend on the quality difference and the add-on value. When the add-on value is low and the quality difference is high, both manufacturers prefer the P2P sharing strategy. Otherwise, manufacturers are more inclined to differentiate their sharing strategies, with the low-quality manufacturer favoring P2P sharing and the high-quality manufacturer preferring B2C sharing. Furthermore, compared with manufacturers adopting the no-sharing strategy, the low-quality manufacturer and the high-quality manufacturer are in (i) a lose–win situation when both adopt the P2P strategy; (ii) a win–win situation when manufacturers prefer the differentiated strategy. Notably, irrespective of the sharing strategy adopted, consumer surplus and social welfare are invariably augmented relative to the no-sharing scenario. The relative magnitudes of consumer surplus and social welfare under different equilibrium strategies are influenced by the quality difference and the add-on value. By extending the model, we derive further insights and demonstrate that the main results are robust under some conditions.