Big Data Knowledge Pricing Schemes for Knowledge Recipient Firms
Date
2021
Authors
Wu, Chuanrong
Cui, Haotian
Lu, Zhi
Yang, Xiaoming
McMurtrey, Mark E.
Journal Title
Journal ISSN
Volume Title
Publisher
Computers, Materials & Continua
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.
Description
Keywords
Big data knowledge, knowledge transfer, subscription pricing, pay-per-use pricing, new product development performance
Citation
Wu, C., Cui, H., Lu, Z., Yang, X., & McMurtrey, M. E. (2021). Big data knowledge pricing schemes for knowledge recipient firms. Computers, Materials & Continua, 69(3), 3275-3287. https://doi.org/10.32604/cmc.2021.019969