@Article{iasc.2020.014961,
AUTHOR = {Chuanrong Wu, Huayi Yin, Xiaoming Yang, Zhi Lu, Mark E. McMurtrey},
TITLE = {Pricing Method for Big Data Knowledge Based on a Two-Part Tariff Pricing Scheme},
JOURNAL = {Intelligent Automation \& Soft Computing},
VOLUME = {26},
YEAR = {2020},
NUMBER = {5},
PAGES = {1173--1184},
URL = {http://www.techscience.com/iasc/v26n5/40833},
ISSN = {2326-005X},
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.},
DOI = {10.32604/iasc.2020.014961}
}