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 pr...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.展开更多
A decision model of knowledge transfer is presented on the basis of the characteristics of knowledge transfer in a big data environment.This model can determine the weight of knowledge transferred from another enterpr...A decision model of knowledge transfer is presented on the basis of the characteristics of knowledge transfer in a big data environment.This model can determine the weight of knowledge transferred from another enterprise or from a big data provider.Numerous simulation experiments are implemented to test the efficiency of the optimization model.Simulation experiment results show that when increasing the weight of knowledge from big data knowledge provider,the total discount expectation of profits will increase,and the transfer cost will be reduced.The calculated results are in accordance with the actual economic situation.The optimization model can provide useful decision support for enterprises in a big data environment.展开更多
基金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).
文摘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.
基金supported by NSFC(Grant No.71373032)the Natural Science Foundation of Hunan Province(Grant No.12JJ4073)+3 种基金the Scientific Research Fund of Hunan Provincial Education Department(Grant No.11C0029)the Educational Economy and Financial Research Base of Hunan Province(Grant No.13JCJA2)the Project of China Scholarship Council for Overseas Studies(201208430233201508430121)
文摘A decision model of knowledge transfer is presented on the basis of the characteristics of knowledge transfer in a big data environment.This model can determine the weight of knowledge transferred from another enterprise or from a big data provider.Numerous simulation experiments are implemented to test the efficiency of the optimization model.Simulation experiment results show that when increasing the weight of knowledge from big data knowledge provider,the total discount expectation of profits will increase,and the transfer cost will be reduced.The calculated results are in accordance with the actual economic situation.The optimization model can provide useful decision support for enterprises in a big data environment.
