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Big Data Knowledge Pricing Schemes for Knowledge Recipient Firms

Chuanrong Wu1,*, Haotian Cui1, Zhi Lu2, Xiaoming Yang3, Mark E. McMurtrey4
1 School of Economy and Management, Changsha University of Science & Technology, Changsha, China
2 Peter B. Gustavson School of Business, University of Victoria, Canada
3 College of Business Administration, University of Nebraska, Omaha, 68182, USA
4 College of Business, University of Central Arkansas, 72035, USA
* Corresponding Author: Chuanrong Wu. Email:

Computers, Materials & Continua 2021, 69(3), 3275-3287. https://doi.org/10.32604/cmc.2021.019969

Received 02 May 2021; Accepted 03 June 2021; Issue published 24 August 2021

Abstract

Big data knowledge, such as customer demands and consumer preferences, is among the crucial external knowledge that firms need for new product development in the big data environment. Prior research has focused on the profit of big data knowledge providers rather than the profit and pricing schemes of knowledge recipients. This research addresses this theoretical gap and uses theoretical and numerical analysis to compare the profitability of two pricing schemes commonly used by knowledge recipients: subscription pricing and pay-per-use pricing. We find that: (1) the subscription price of big data knowledge has no effect on the optimal time of knowledge transaction in the same pricing scheme, but the usage ratio of the big data knowledge affects the optimal time of knowledge transaction, and the smaller the usage ratio of big data knowledge the earlier the big data knowledge transaction conducts; (2) big data knowledge with a higher update rate can bring greater profits to the firm both in subscription pricing scheme and pay-per-use pricing scheme; (3) a knowledge recipient will choose the knowledge that can bring a higher market share growth rate regardless of what price scheme it adopts, and firms can choose more efficient knowledge in the pay-per-use pricing scheme by adjusting the usage ratio of knowledge usage according to their economic conditions. The model and findings in this paper can help knowledge recipient firms select optimal pricing method and enhance future new product development performance.

Keywords

Big data knowledge; knowledge transfer; subscription pricing; pay-per-use pricing; new product development performance

Cite This Article

C. Wu, H. Cui, Z. Lu, X. Yang and M. E. McMurtrey, "Big data knowledge pricing schemes for knowledge recipient firms," Computers, Materials & Continua, vol. 69, no.3, pp. 3275–3287, 2021.



This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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