Time Optimization of Multiple Knowledge Transfers in the Big Data Environment

• 作者: Chuanrong Wu Evgeniya Zapevalova Feng Li Yingwu Chen
• 来源期刊: 计算机、材料和连续体(英文)
• 学科: 经济
• 关键词: BIG data KNOWLEDGE TRANSFER time optimization DEP simulation EXPERIMENT
• 年，卷（期）: 2018,（3）
• 页码范围: 269-285
• 页数: 17页
• 分类号: F42

摘要

In the big data environment, enterprises must constantly assimilate big dataknowledge and private knowledge by multiple knowledge transfers to maintain theircompetitive advantage. The optimal time of knowledge transfer is one of the mostimportant aspects to improve knowledge transfer efficiency. Based on the analysis of thecomplex characteristics of knowledge transfer in the big data environment, multipleknowledge transfers can be divided into two categories. One is the simultaneous transferof various types of knowledge, and the other one is multiple knowledge transfers atdifferent time points. Taking into consideration the influential factors, such as theknowledge type, knowledge structure, knowledge absorptive capacity, knowledge updaterate, discount rate, market share, profit contributions of each type of knowledge, transfercosts, product life cycle and so on, time optimization models of multiple knowledgetransfers in the big data environment are presented by maximizing the total discountedexpected profits (DEPs) of an enterprise. Some simulation experiments have beenperformed to verify the validity of the models, and the models can help enterprisesdetermine the optimal time of multiple knowledge transfer in the big data environment.