Caiting Dong, Xiang Li and Xinzhi Chang
Based on the strategy and new institutional economic literature, this study aims to explore how different levels of supplier concentration (SC) will be characterized by…
Abstract
Purpose
Based on the strategy and new institutional economic literature, this study aims to explore how different levels of supplier concentration (SC) will be characterized by differences in switching cost and coordinated adaptation in an ecosystem, thereby shaping its research and development (R&D) intensity, innovation performance and innovation efficiency.
Design/methodology/approach
This study adopted a set of panel data of Chinese listed firms in the Growth Enterprise Board and their top five suppliers from 2012 to 2016. A Tobit model is used to test the hypotheses.
Findings
The study finds that SC has an inverted U-shape effect on R&D intensity. This finding implies that firms are more likely to invest in R&D when SC is intermediate level. While it has a U-shape relationship between SC and innovation output, both lower SC and higher SC are more efficient in innovation because of their advantage in low switching cost and better coordinative adaptability, respectively.
Originality/value
The study complements the innovation ecosystem literature by using SC to represent the structure of the interdependence between firms and suppliers in an ecosystem, then examining the correlation between SC and firms’ innovation investment and output, respectively. Second, combining strategy and new institutional economic literature, the non-linear effects of SC on firms’ innovation are found.
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Keywords
Jin-Hai He, Yu-Tao Pang, Xinzhi Dang and Wan-Cheng Yuan
The purpose of the study is to investigate and reveal this relationship of various engineering demand parameters (EDPs) of this structural type and intensity measures (IMs) under…
Abstract
Purpose
The purpose of the study is to investigate and reveal this relationship of various engineering demand parameters (EDPs) of this structural type and intensity measures (IMs) under intra-plate earthquakes.
Design/methodology/approach
The nonlinear finite element model used was calibrated first to the existing results of the shaking table test to verify the modeling technique.
Findings
This paper investigated the relationship between intensity measures and various engineering demand parameters of cable-stayed bridges using intra-plate earthquakes. The correlation analysis and Pearson coefficient are used to study the correlation between EDPs and IMs. The results showed that peak ground velocity (PGV)/peak ground acceleration, peak ground displacement and root-mean-square of displacement showed weak correlation with IMs. PGV, sustained maximum velocity, a peak value of spectral velocity, A95 parameter, Housner intensity and spectral acceleration at the fundamental period, the spectral velocity at the fundamental period and spectral displacement at the fundamental period were determined to be better predictors for various EDPs.
Originality/value
This paper investigated the correlation between the intensity measures of intra-plate earthquakes with the seismic responses of a typical long-span cable-stayed bridge in China. The nonlinear finite element model used was calibrated to the existing results of the shaking table test to verify the modeling technique. In total, 104 selected ground motions were applied to the calibrated model, and the responses of various components of the bridge were obtained. This study proposed PGV as the optimal IM.
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Xinzhi Zhu, Shuo Yang, Jingyi Lin, Yi-Ming Wei and Weigang Zhao
Electricity demand forecasting has always been a key issue, and inaccurate forecasts may mislead policymakers. To accurately predict China’s electricity demand up to 2030, this…
Abstract
Purpose
Electricity demand forecasting has always been a key issue, and inaccurate forecasts may mislead policymakers. To accurately predict China’s electricity demand up to 2030, this paper aims to establish a cross-validation-based linear model selection system, which can consider many factors to avoid missing useful information and select the best model according to estimated out-of-sample forecast performances.
Design/methodology/approach
With the nine identified influencing factors of electricity demand, this system first determines the parameters in four alternative fitting procedures, where for each procedure a lot of cross-validation is performed and the most frequently selected value is determined. Then, through comparing the out-of-sample performances of the traditional multiple linear regression and the four selected alternative fitting procedures, the best model is selected in view of forecast accuracy and stability and used for forecasting under four scenarios. Besides the baseline scenario, this paper investigates lower and higher economic growth and higher consumption share.
Findings
The results show the following: China will consume 7,120.49 TWh, 9,080.38 TWh and 11,649.73 TWh of electricity in 2020, 2025 and 2030, respectively; there is hardly any possibility of decoupling between economic development level and electricity demand; and shifting China from an investment-driven economy to a consumption-driven economy is greatly beneficial to save electricity.
Originality/value
Following insights are obtained: reasonable infrastructure construction plans should be made for increasing electricity demand; increasing electricity demand further challenges China’s greenhouse gas reduction target; and the fact of increasing electricity demand should be taken into account for China’s prompting electrification policies.