Purpose – This study aims to examine the potential impact of external complementary resources on inbound open innovation and whether transformative capacity acts as a mediator in the process. If small and medium enterprises (SMEs) are to successfully implement inbound open innovation, they require injections of external complementary resources. Design/methodology/approach – Structural equation modeling was used to conduct confirmatory factor analysis to evaluate measurement model, while ordinary least squares regression analysis was used to test the hypotheses; research data are collected using surveys of 200 Taiwanese-owned SMEs in mainland China. Findings – Empirical results indicate that when SMEs seek to implement inbound open innovation, technological complementarity is the most important factor in resource complementarity. In addition, transformative capacity has a significant mediating role on the relationship between resource complementarity and inbound open innovation. Originality/value – The contribution of this paper lies in explaining the role played by transformative capacity in the process of inbound open innovation for SMEs through empirical analysis.
Medical facilities help to ensure a higher quality of life and improve social welfare. The spatial accessibility determines the allocation fairness and efficiency of medical facilities. It also provides information about medical services that residents can share. Although critical, scholars often overlooked the level of medical facilities, the composition of integrated transportation networks, and the size of service catchment in the literature on accessibility. This study aims to fill this research gap by considering the integrated transportation network, population scale, travel impedance between medical facilities and residential areas, and the impact of medical facilities’ levels on residents’ medical choices. An improved potential model was constructed to analyze the spatial accessibility of medical facilities in Changning District of Shanghai, China. Interpolation analysis was conducted to reveal the spatial accessibility pattern. Cluster and outlier analysis and Getis-Ord Gi* analysis were applied for the cluster analysis. Results show that the spatial accessibility of medical facilities is quite different in different residential areas of Changning District, Shanghai. Among them, the spatial accessibility of medical facilities is relatively high in Hongqiao subdistrict, Xinjing Town, and part of Xinhua Road subdistrict. In addition, residents have overall better access to secondary hospitals than to primary and tertiary hospitals in the study area. This study provides a spatial decision support system for urban planners and policymakers regarding improving the accessibility of healthcare facilities. It extends the literature on spatial planning of public facilities and could facilitate scientific decision making.
Regional energy development and approaches are significant for China’s overall economic and social development. The GDP in Zhejiang province surpassed 6 trillion yuan in 2019, and its energy consumption reached 200 million tons of standard coal (tce), both of which are at the forefront of China. In order to explore the main factors of the increase in energy consumption in Zhejiang and provide essential references for energy saving and other provinces, this paper analyzes the total energy consumption and industrial sectors on the basis of the logarithmic mean divisia index (LMDI) model. Study results show that the economy’s scale is the most crucial factor affecting Zhejiang’s energy consumption, with a significant growth effect. In 2015, the scale effect increased energy consumption to the highest value of 14 million tce and then reduced it to 13 million tce in 2019. The impact of the population on energy consumption increased by 10 million tce from 2010 to 2019. Energy intensity reduces energy consumption by between 0.05 and 0.15 billion tce per year, which is the main factor in reducing energy consumption. The energy structure generally plays a weak positive role due to the different energy types. The decomposition of the energy consumption per unit of value added in the industrial sector showed that the intensity and structural effect primarily reduce energy consumption, for example, the metal smelting and rolling, textile printing and paper, electric power, heating, and other industries. According to the results, enterprises should enhance the intelligence and efficiency of dispatch management and emergency responses. Zhejiang should also accelerate an international oil and gas trading center and resource allocation base to reach its carbon-neutrality goal.
This paper selects the panel data on 31 provinces (cities) in China, from 2007 to 2017, and uses Stochastic Frontier Analysis Model to measure the efficiency of technical efficiency and influencing factors of agricultural production in China. The research shows that from 2007 to 2017, China’s agricultural average production technology efficiency has steadily improved, but there is still much room for development. The agricultural production technology efficiency is greatly different in different part of China. The eastern region has the highest efficiency, and the central and western regions are lower than the national average. Labor input, fertilizer application, and diesel fuel are factors that directly affect the technical efficiency of agricultural output. Time trend and urbanization level indirectly affect the agricultural production technology efficiency, and the impact is positive.
This paper applies the logarithmic mean divisia index to explore the decomposition and decoupling of sectoral carbon emissions across four provinces in the Yangtze River Delta (YRD). The results show that (1) the YRD experienced a significant upward trend in sectoral carbon emissions, among which Anhui and Jiangsu provinces experienced the largest increase in carbon emissions. (2) Anhui and Jiangsu showed expansive coupling or weak decoupling in the “10th Five-Year Plan (FYP)”, and Shanghai and Zhejiang indicated weak decoupling and strong decoupling in the “11th FYP”, “12th FYP”, and “13th FYP”. These observations show that the YRD has been transforming its economic growth patterns and increasingly concentrating on the green economy. (3) Shanghai was the most progressive in decoupling. An inspection of decoupling indicators reveals that declining energy intensity contributed most to decoupling, whereas the population scale was less important. In addition, the industry structure and energy structure varied among the provinces.
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