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Purpose -Human resources have become a key issue in relation to the strong competition between service firms. Therefore, the purpose of this paper is to explore the relationship between high-performance human resource management (HRM) within this field to firm performance, making a useful attempt to explore the "black box" of enterprise human resources management effect on firm performance. Design/methodology/approach -In order to validate the relationship between high-performance HRM and firm performance, Chinese service industry samples were collected. Structural equation modeling and regression are adopted to estimate the direct effect of high-performance HRM on firm performance and the mediating role of innovation. Findings -The results show that the impacts of high-performance HRM on firm performance are significant. Moreover, innovation plays a partial mediating role between them. Training, work analysis and employee participation has a significantly positive impact on firm performance, while effects of profit sharing, employee development and performance evaluation on enterprise performance is not significant. The results strongly support the hypothesis that innovation holds intermediary variables between high-performance HRM and firm performance. Practical implications -Studying the relationship between high-performance HRM and firm performance can help Chinese enterprises more reasonable and effective learning foreign advanced management ideas and methods. And then can help Chinese enterprises to establish a high-performance HRM system that is suitable for Chinese enterprises; the research can help enterprises to identify meaningful practice of human resources management, outstanding keys, and perfect the HRM system of enterprises; research on innovation and innovative thinking is conducive to develop employees' innovation motive, promote employee' innovative behavior, and improve firm performance. Originality/value -This paper takes innovation as a mediating variable into the model and studies the intermediary role of innovation.

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.

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To manage supply chain efficiently, e-business organizations need to understand their sales effectively. Previous research has shown that product review plays an important role in influencing sales performance, especially review volume and rating. However, limited attention has been paid to understand how other factors moderate the effect of product review on online sales. This study aims to confirm the importance of review volume and rating on improving sales performance, and further examine the moderating roles of product category, answered questions, discount and review usefulness in such relationships. By analyzing 2,939 records of data extracted from Amazon.com using a big data architecture, it is found that review volume and rating have stronger influence on sales rank for search product than for experience product. Also, review usefulness significantly moderates the effects of review volume and rating on product sales rank. In addition, the relationship between review volume and sales rank is significantly moderated by both answered questions and discount. However, answered questions and discount do not have significant moderation effect on the relationship between review rating and sales rank. The findings expand previous literature by confirming important interactions between customer review features and other factors, and the findings provide practical guidelines to manage e-businesses. This study also explains a big data architecture and illustrates the use of big data technologies in testing theoretical framework.