建筑企业信息技术效率及影响因素研究

王月宁

摘  要

建筑业是社会发展的重要支柱，具有促进经济增长、改善人民生活等重要作用，但目前存在发展方式粗放、劳动力生产率低等问题。随着信息化水平提升，建筑业与信息技术不断结合，对促进建筑业健康发展具有重要作用，因此我国发布众多政策促进建筑业信息化发展。然而目前我国建筑业信息化发展存在很多问题，如建筑企业信息技术投入较少、对信息技术依赖程度低等。因此本研究通过提出建筑企业信息技术效率这一指标，将建筑企业信息技术投资和建筑企业绩效作为投入和产出变量，衡量信息技术投入对建筑企业产出的影响，并且分析建筑企业信息技术效率的影响因素，为提升建筑企业的产出和信息技术效率提供对策建议。

本研究以建筑业上市企业为研究对象，构建了随机前沿分析（SFA）模型分析建筑企业信息技术效率及影响因素，使用数据包络分析（DEA）方法和Tobit模型进行验证。首先探讨了信息技术投入对建筑企业产出的影响，结果表明信息技术硬件和信息技术人员的投入提升建筑企业产出，信息技术软件的投入降低建筑企业产出，2016~2020年建筑企业的信息技术效率均值为43.33%；其次研究了建筑企业信息技术效率的影响因素，结果显示扩大企业规模能够提升建筑企业的信息技术效率，增加研发投入强度、管理费用比例会降低信息技术效率，上市时间短的建筑企业的信息技术效率更高，国有建筑企业的信息技术效率高于非国有建筑企业。

根据研究结果，本研究提出加大建筑企业的资源投入、重视建筑企业的研发投入、加强建筑企业的人才培养、提升建筑企业的管理水平、增加建筑企业的政策扶持等措施，以提升我国建筑企业产出和建筑企业信息技术效率，促进建筑业信息化发展。

关键词：建筑企业；信息化；信息技术效率；影响因素；随机前沿分析

Abstract

Construction industry is an important pillar of social development, which plays an important role in promoting economic growth and improving people's lives. However, there are problems such as extensive development mode and low labor productivity. With the improvement of informatization level, the continuous combination of construction industry and information technology is of great significance to promote the healthy development of construction industry. Therefore, the government published numerous policies to promote the informatization development of construction industry. However, at present, there are many problems in the development of information technology in China's construction industry, such as less information technology investment in construction enterprises and low degree of dependence on information technology. In this paper, by proposing the index of construction enterprise information technology efficiency, the construction enterprise information technology investment and construction enterprise performance were used as the inputs and outputs, measuring the effect of information technology investment on construction enterprise output. And the influential factors of construction enterprise information technology efficiency were analyzed. In order to promote the output and efficiency of information technology of construction enterprise, countermeasures and suggestions were provided.

In this paper, a Stochastic Frontier Analysis (SFA) model for information technology efficiency of construction enterprises was constructed, which was verified by Data Enveloping Analysis (DEA) method and Tobit model. The results show that the input of information technology hardware and information technology personnel improves the output of construction enterprises, while the input of information technology software reduces the output of construction enterprises. From 2016 to 2020, the average efficiency of information technology of construction enterprises was 43.33%. Secondly, to study the influential factors of building enterprise information technology efficiency, according to the results expand the scale of the enterprise can improve efficiency of building enterprise information technology, increasing r&d input intensity of management, will reduce the efficiency of information technology, short time to market of building enterprise information technology efficiency is higher, the state-owned construction enterprise information technology efficiency is higher than non-state-owned construction enterprise; This paper also analyzes the information technology efficiency of construction enterprises of different sizes and types.

According to the research results, this paper puts forward some suggestions to improve the output efficiency and information technology efficiency of construction enterprises in China. Such as improving the construction enterprise resources input, pay attention to the construction enterprise research and development investment, strengthen the construction enterprise personnel training, improve the construction enterprise management level, increase the construction enterprise policy support. These measures can promote the development of construction industry information.

Key words: Construction enterprises; Informatization; Information technology efficiency; Influential factors; Stochastic Frontier Analysis