Narrow Results

In this research, we discuss three different approaches to generate demand forecasting and pricing decision for mix of national brand and store brand products in the era of big data. We derive the equilibrium wholesale price and retail price for the national brand products, and the equilibrium retail price for the store brand products based on demand forecast under three different information scenarios, including Noninformation Sharing ($N$), Information Sharing ($I$), and Retailer Forecasting ($R$). We comprehensively discuss how information collection, information sharing, forecast accuracy under era of big data affect firms’ prices and profits. Our numerical experiments illustrate and verify our analytical findings and provide further managerial insights and interpretations.

In a framework where both power structure and consumer heterogeneity (brand loyalty and different willingness-to-pay for perceived quality) count, we study six noncooperative games (two Stackerberg and one Nash games with and without a store brand) between a national-brand manufacturer and a store-brand retailer. Our results contribute to the theoretical literature on store brand in four aspects: (a) revealing that the mechanism whereby store brands alleviate double-marginalization problem varies with supply-chain power structures; (b) finding that it is the power manufacturer with a low-loyalty national brand that suffers most from the store brand entry; (c) finding that a category follower retailer is more inclined to introduce its store brand than a power one, and should optimally position its store brand close to the low-loyalty national brand supplied by a power manufacturer; (d) showing that the store brand may lead the manufacturer and the retailer to be trapped in the prisoners’ dilemma.