As part of the increasing efforts toward the prevention and control of motor vehicle pollution, the Chinese government has practiced a range of policies to stimulate the purchase and use of battery electric vehicles (BEVs). Zhejiang Province, a key province in China, has proactively implemented and monitored an environmental protection plan. This study aims to contribute toward streamlining marketing and planning activities to introduce strategic policies that stimulate the purchase and use of BEVs. This study considers the nature of human behavior by extending the theory of planned behavior model to identify its predictors, as well as its non-linear relationship with customers’ purchase intention. To better understand the predictors, a substantial literature review was given to validate the hypotheses. A quantitative study using 382 surveys completed by customers in Zhejiang Province was conducted by integrating a structural equation model (SEM) and a neural network (NN). The initial analysis results from the SEM revealed five factors that have impacted the customers’ purchase intention of BEVs. In the second phase, the normalized importance among those five significant predictors was ranked using the NN. The findings have provided theoretical implications to scholars and academics, and managerial implications to enterprises, and are also helpful for decision makers to implement appropriate policies to promote the purchase intention of BEVs, thereby improving the air quality.

This paper aims to identify indicators of the obstacles that affect the digitalization of real estate enterprises in their operations management, and analyze the influence, hierarchy and relationships of these indicators. The indicators of obstacles that affect the digitalization of real estate enterprise operations management were explored by searching the literature and using the Delphi method and a word cloud diagram. The obstacle indicator system was built according to the upstream, middle and downstream levels of the value chain. The FTA obstacle model was used to analyze the influence of obstacle index, and the DEMATEL–ISM model was used to analyze the hierarchical structure and correlations between indicators. The results are as follows. The biggest barriers in real estate digitalization are in the upstream level, such as the difficulty in adopting new technologies, lack of information technology talent and the high cost of digital software. According to analysis from the DEMATEL–ISM, the difficulty of enterprises to use new technologies has a deep-rooted influence on the digitalization of real estate enterprises. Lack of information technology talent and the high cost of digital software in the upstream level as well as barriers related to a company’s development strategy and investment willingness constitute barriers in the midstream level. These barriers not only affect the external and internal links, but also influence each other. The lack of overall regulation and supervision can explain the reason for these barriers.