Pricing Method for Big Data Knowledge Based on Two-part Tariff Pricing Scheme

Abstract

Nowadays big data knowledge is being bought and sold online for market research, new product development, or other business decisions, especially when customer demands and consumer preferences knowledge for new product development are needed. Previous studies have introduced two commonly used pricing schemes for big data knowledge transactions (e.g., cloud services): Subscription pricing and pay-per-use pricing from a big data knowledge provider’s standpoint. However, few studies to date have investigated a two-part tariff pricing scheme for big data knowledge transactions, albeit this pricing scheme may increasingly attract the big data knowledge providers in this hyper-competitive market. Also, little research has been done from the perspective of the knowledge recipient firm which is an important and integral part of big data knowledge transactions. This study constructs a two-part tariff pricing decision model for big data knowledge transactions from the perspective of the knowledge recipient firms. The model is a more generalized pricing scheme decision model and can be used to compare the profitability of three pricing schemes: Subscription pricing, pay-per-use pricing, and two-part tariff pricing. It shows that the influence of free knowledge on new product development performance of knowledge recipient firms cannot be ignored, and the pay-per-use pricing scheme is the best solution for knowledge recipient firms.