大数据交易现状与定价问题研究

发布时间：2018-05-04 20:45

  本文选题：大数据交易 + 现状　； 参考：《价格月刊》2017年12期

【摘要】：大数据具有数量巨大、信息多样以及实时更新等特征,这会使其价值具有很大的不确定性、稀缺性和多样性。如何进行大数据分类、科学定价以及如何进行实质性交易都是大数据发展中面临的现实问题。效用价格理论可以明确大数据交易的最高定价,而成本价格理论则指出了大数据的最低定价线。我国大数据交易实践表明,多数大数据交易平台及其交易实践都还处于初级阶段,整个大数据交易规模也还比较小,主要原因就在于当前的大数据交易缺乏行业性的科学定价。应根据大数据的特征,采取预处理定价、大数据拍卖定价、大数据协商定价和反馈性定价等定价策略,不断扩大大数据交易规模,加快大数据行业发展步伐。
[Abstract]:Big data has the characteristics of large quantity, diverse information and real time updating, which will make its value have great uncertainty, scarcity and diversity. How to classify big data, how to price scientifically and how to carry out substantial transaction are all the realistic problems in big data's development. The utility price theory can define the maximum price of big data's transaction, while the cost price theory points out the minimum pricing line of big data. The trading practice of big data in China shows that the majority of big data trading platform and its trading practice are still in the primary stage, and the scale of the entire big data transaction is still relatively small, which is mainly due to the lack of scientific pricing in the current big data transaction. According to the characteristics of big data, we should adopt pricing strategies such as pretreatment pricing, auctioneering pricing with big data, and pricing by way of consultation and feedback pricing, so as to expand the scale of big data trade and accelerate the pace of the development of big data industry.
【作者单位】： 吉林工程技术师范学院;
【基金】：吉林省教育科学“十二五”规划课题“基于工作过程导向的应用型本科院校课程改革研究”(编号:GH14417)
【分类号】：F49
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本文编号：1844585