It is vital to promote and optimize the technological innovation efficiency of new energy vehicle (NEV) enterprises for the green transformation of China’s automobile industry. However, China’s NEV enterprises still have problems such as insufficient research of technology and unreasonable innovative resource allocation. To improve the technological innovation efficiency of China’s NEV enterprises, the NEVs’ technological innovation process is divided into two stages: the research and development (R&D) stage and the achievement transformation stage in this research. Combining Tobit regression with data envelopment analysis (DEA), an evaluation framework of technological innovation efficiency of the NEV enterprises is constructed. Then, the innovation efficiency of 23 NEV listed enterprises from 2013 to 2018 is analyzed. The result reveals three findings. First, the overall technological innovation efficiency of NEV enterprises is low. Second, enterprises’ R&D efficiency is generally higher than the achievement transformation efficiency. Third, according to two-stage efficiency, 23 NEV enterprises are divided into four categories. For different types of enterprises, targeted guidance to improve innovation efficiency is proposed. This research provides a theoretical and practical basis for improving the innovation efficiency of NEV enterprises.

Failure mode and effects analysis (FMEA) is a widely used tool of risk assessment to identify and eliminate failures of products and systems. However, the conventional FMEA has some defects, such as the same importance of risk factors, utilizing crisp numbers to evaluate failures without considering vagueness. Although fuzzy methods are used to improve the conventional FMEA, the fuzzy FMEA requires much priori information (e.g., fuzzy membership function), which cannot flexibly reflect the changes of a decision maker's preference. In addition, most of the previous methods suppose that decision makers are totally rational without considering their psychological factors. Actually, FMEA members’ judgments on the failure modes are often influenced by their bounded rationality. To solve those problems, an integrated FMEA model is proposed in this paper, which integrates the strength of variable precision rough set theory in handling vagueness and the merit of TODIM (an acronym in Portuguese of Interactive and Multicriteria Decision Making) approach in manipulating bounded rationality of decision makers. Finally, the proposed method is validated with a case study of a steam valve system to demonstrate its effectiveness and efficiency.

Purpose: This paper aims to investigate the impact of sources of information on trading behavior by analyzing the influence of investor personality in Chinese futures market. Design/methodology/approach: The authors adopted the Big Five personality framework and examined the survey results of individual investors (n = 333) in Chinese futures market. Personality traits of futures investors were measured by the NEO-Five Factor Inventory (Costa and McCrae, 1989) which is a shortened version of revised NEO personality inventory of the Big Five model (Costa and McCrae, 1992). Confirmatory factor analysis was conducted to assess the fitness of model. Structural equation modeling was used to evaluate the moderating influence of investor personality traits on the association between source of information and trading behavior. Findings: The results confirm the previous findings that the sources of information used by investors as a foundation of their financial choices have a significant impact on trading frequency. The authors also provide an empirical evidence that investor personality traits moderate the relationship between sources of information and trading behavior. Financial advice from professionals is likely to increase trading frequency in investors with neuroticism and openness personality traits, and to reduce trading frequency in conscientious and extravert investors. Similarly, financial information acquired via word-of-mouth communication results in more trading in extravert and agreeable investors. Finally, information acquisition from specialized press causes more adjustment of conscientious investors’ portfolios. Theoretical explanations, implications and recommendations for future research are discussed. Originality/value: This study combines information search and behavioral finance literature to demonstrate that the impact of various sources of market information on asset allocation decisions is influenced by investor personality. No previous study has been conducted yet to explain variations in the impact of sources of information on trading behavior by the Big Five personality traits and this paper seeks to fill this gap in Chinese futures market.

Apart from promoting social-economic development and increasing social employment, the real estate industry in China has also brought up problems such as high energy consumption and high emissions. Scholars now focus more on energy conservation, emission reduction and sustainable development of real estate companies in their current research. The data used by this paper are three-year panel data from 2015 to 2018, with observations from 15 representative real estate companies. CO₂ and green credit index are introduced as the undesirable output and the green output of real estate companies respectively. First, with the DEA model and the Malmquist index model, this paper evaluates the green productivity of real estate companies statically and dynamically. The Tobit model is then employed by the author to analyze factors that may affect green productivity. Our results indicate that (1) the green productivities of 15 Chinese real estate companies have improved by various degrees. The average green productivity rises from 0.701 in 2015 to 0.849 in 2018, indicating that the energy utilization rate of enterprises has gradually increased. From the calculation and decomposition of the Malmquist total factor productivity index, we know that technological progress is vital in improving the green productivity of real estate companies. (2) As for the influencing factors, the green productivity is positively related to factors such as policy compliance indicator P, environmental responsibility commitment indicator R, indicator of green innovation capability I, and indicator of green development information disclosure M. The asset-liability ratio on the contrary has a negative impact on green productivity. It’s worth to point out that the green innovation index and green productivity is significantly correlated and the correlation coefficient can be up to 0.636, which implies that the key to improving green productivity is to increase research and development investment.

Previous studies have pointed out that Industry-University-Research Institution (IUR) collaborative innovation is an important means to ensure the sustainable development of regional innovation, and there may be spillover effects among different regional innovation systems. However, the impact of regional spatial correlation and IUR collaborative innovation synergy degree on regional innovation performance is not that clear. Based on the panel data of 31 regions in China from 2006 to 2015, we construct static and dynamic spatial econometrics models to analysis the relationships among regional innovation performance, IUR collaborative innovation and spatial correlation. The research results show that there are significant positive spillover effects among different regions, indicating that the dynamic flows of innovation elements among regions is conducive to improve the regional innovation performance. In addition, IUR collaboration innovation also has a positive impact on regional innovation performance: the current period of IUR synergy degree has a negative impact, while the lagged one has a positive impact. It means that it will take a while for IUR collaborative innovation to be effective and it will have far-reaching contributions to long-term improvements rather than short-term benefits in social development. The results are significant for both static and dynamic spatial econometrics models. The conclusions of this paper have important policy significance to fully understand the coordination of innovative elements and promote the sustainable development of regional innovation systems.

With the socio-economic development in China, the central government pays more and more attention to innovations. Universities, research institutes, and industries (URI) are the main bodies of innovation. Therefore, in order to perform sustainable development of innovation, it is essential for Chinese policy makers to better understand the relationship among URI innovations. In this paper, indicators for evaluating URI innovations are collected, which are classified into three subsystems. Utilizing regional data from 2009–2015 in China, the entropy method is subsequently used to examine the contribution of indicators to the relative innovation subsystems. Then, a coupling coordination degree model is developed based on the physical concept “coupling” to investigate the relationship of URI innovation subsystems in China. The results reveal that in the period of study: (1) research and development (R&D) input and output indicators are both important to the regional innovation levels of URI, namely, they are critical for improving the coupling coordination degree among the three innovation subsystems; (2) There is an increasing trend of the coupling coordination of URI innovation system in China; however, the gap between regions/provinces becomes larger due to different innovative resource endowments and socio-economic development conditions. The results shown in this paper can provide important implications for policy makers to obtain the sustainable development of URI innovation subsystems in China.