文献类型：期刊文献

中文题名：A SYSTEM OPTIMIZATION MODEL OF ADOPTION OF A NEW INFRASTRUCTURE WITH MULTI-RESOURCE AND MULTI-DEMAND SITES

英文题名：A SYSTEM OPTIMIZATION MODEL OF ADOPTION OF A NEW INFRASTRUCTURE WITH MULTI-RESOURCE AND MULTI-DEMAND SITES

作者：Yaru Zhang[1];Huayi Chen[1];Tieju Ma[1,2]

机构：[1]School of Business, East China University of Science and Technology, Shanghai 200237, China;[2]International Institute for Applied System Analysis, Sehlosplatz 1 A-2361 Laxenburg, Austria

年份：2016

卷号：25

期号：1

起止页码：62

中文期刊名：Journal of Systems Science and Systems Engineering

外文期刊名：系统科学与系统工程学报（英文版）

收录：Scopus;CSCD:【CSCD2015_2016】；

语种：英文

中文关键词：System optimization model;technology adoption;new infrastructure

外文关键词：System optimization model, technology adoption, new infrastructure

摘要：This study develops a conceptual system optimization model of adoption of a new infrastructure technology with multiple resource sites and multiple demand sites. With the model, this paper analyzes how the distance, spillover effect, demand, initial investment cost, and learning rate influence the adoption of the new infrastructure technology and presents optimization results of the model in different scenarios. The main findings of the study are： from the perspective of system optimization, （1） different distances among different resource-demand pairs will result in different adoption time of a new infrastructure; （2） technological spillover among different resource-demand pairs will accelerate the adoption of a new infrastructuxe; （3） it is hard to say that higher demand will pull faster adoption of a new infrastructure, and the optimal time of adopting of a new infrastructure is very sensitive to its technological learning rate.
This study develops a conceptual system optimization model of adoption of a new infrastructure technology with multiple resource sites and multiple demand sites. With the model, this paper analyzes how the distance, spillover effect, demand, initial investment cost, and learning rate influence the adoption of the new infrastructure technology and presents optimization results of the model in different scenarios. The main findings of the study are： from the perspective of system optimization, （1） different distances among different resource-demand pairs will result in different adoption time of a new infrastructure; （2） technological spillover among different resource-demand pairs will accelerate the adoption of a new infrastructuxe; （3） it is hard to say that higher demand will pull faster adoption of a new infrastructure, and the optimal time of adopting of a new infrastructure is very sensitive to its technological learning rate.