Big Data Knowledge Pricing Schemes for Knowledge Recipient Firms

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dc.contributor.author Wu, Chuanrong
dc.contributor.author Cui, Haotian
dc.contributor.author Lu, Zhi
dc.contributor.author Yang, Xiaoming
dc.contributor.author McMurtrey, Mark E.
dc.date.accessioned 2021-10-08T21:21:47Z
dc.date.available 2021-10-08T21:21:47Z
dc.date.copyright 2021 en_US
dc.date.issued 2021
dc.identifier.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 en_US
dc.identifier.uri https://doi.org/10.32604/cmc.2021.019969
dc.identifier.uri http://hdl.handle.net/1828/13452
dc.description.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. en_US
dc.description.sponsorship This research was funded by the National Natural Science Foundation of China, Grant Number (71704016), the Key Scientific Research Fund of Hunan Provincial Education Department of China, Grant Number (19A006), and the Enterprise Strategic Management and Investment Decision Research Base of Hunan Province, Grant Number (19qyzd03). en_US
dc.language.iso en en_US
dc.publisher Computers, Materials & Continua en_US
dc.subject Big data knowledge en_US
dc.subject knowledge transfer en_US
dc.subject subscription pricing en_US
dc.subject pay-per-use pricing en_US
dc.subject new product development performance en_US
dc.title Big Data Knowledge Pricing Schemes for Knowledge Recipient Firms en_US
dc.type Article en_US
dc.description.scholarlevel Faculty en_US
dc.description.reviewstatus Reviewed en_US

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