摘要
以建筑企业员工对精益建设技术的采纳意愿为研究对象,通过调查问卷获得精益建设技术采纳意愿影响因素的数据,并利用粗糙集筛选出其中的重要影响因素;然后以技术接受模型为基础,借助系统动力学工具构建影响因素与采纳意愿的仿真模型,利用BP神经网络结合MIV算法来确定模型中的参数,并通过计算机仿真对比分析这些影响因素在不同技术成熟度下对采纳意愿的影响程度,探析影响因素与采纳意愿之间的作用规律。研究结果表明,这些影响因素对员工采纳意愿作用明显,其中精益建设技术感知有用性最显著,其次是精益建设技术感知易用性,而精益建设技术感知愉悦性的作用相对较弱。最后,就提高员工对精益技术的采纳意愿提出建议。
Taking the employees' adoption intention of lean construction technology in construction enterprise as the research object, this paper obtains the survey data on influence factors of adoption intention for lean construction technology from the questionnaire and recognizes the important influence factors by making use of rough set. Based on the technology acceptance model, the paper creates a simulation model which describes the relevance between the factors and adoption intention by system dynamics. Then, it uses the method of BP neural network combining with MIV algorithm to calculate the model parameters. With the help of computer simulation, the paper comparatively analyzes the effect of the influence factors on adoption intention at different levels of technology maturity and explores the interaction rules between influence factors and adoption intention. The research result shows that the employees' adoption intention is obviously affected by the influence factors. Among them, the first one is the perceived usefulness of lean construction technology; the second one is the perceived ease of use ; and the third one is the perceived pleasure. Finally, some suggestions are given to improve the em- ployees' adoption intention of lean technology.
出处
《科技管理研究》
CSSCI
北大核心
2014年第22期172-177,182,共7页
Science and Technology Management Research
基金
国家自然科学基金项目"精益建设(LC)技术采纳行为与决策模型研究"(71171140)
天津市社科规划项目"天津市低碳生态城市建设的战略对策研究"(TJGL12-026)
关键词
精益建设技术
采纳意愿
粗糙集
BP神经网络
系统动力学
lean construction technology
adoption intention
rough set
BP neural network
system dynamics