Title: Optimisation model of price changes after knowledge transfer in the big data environment

Authors: Chuanrong Wu; Evgeniya Zapevalova; Deming Zeng

Addresses: School of Economy and Management, Changsha University of Science and Technology, No. 960 Wanjiali South Road, Changsha, Hunan, 410114, China ' School of Economy and Management, Changsha University of Science and Technology, No. 960 Wanjiali South Road, Changsha, Hunan, 410114, China ' School of Business Administration, Hunan University, No. 1 Lushan South Road, Changsha, Hunan, 410082, China

Abstract: Big data knowledge and private knowledge are the two dominant types of knowledge that an enterprise needs for new product innovation in a big data environment. Big data knowledge can help enterprises to make decisions, trim costs and lift sales. Private knowledge is usually the core patent knowledge, which can help to enhance the quality of products. The decline in product cost and the increased quality of a new product after knowledge transfer may lead to pricing decisions with respect to an enterprise's new product. By considering the changes in product costs, market share and the profits after knowledge transfer caused by price changes to a new product, an optimisation model of price changes after knowledge transfer in the big data environment is presented. It can enable pricing decisions about new products for enterprises in the big data environment.

Keywords: big data; knowledge transfer; optimisation model; price change; price decision.

DOI: 10.1504/IJCSE.2019.100234

International Journal of Computational Science and Engineering, 2019 Vol.19 No.2, pp.284 - 292

Received: 30 Aug 2017
Accepted: 26 Mar 2018
Published online: 20 Jun 2019 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article