Yunlong Duan, Kun Wang, Hong Chang, Wenjing Liu and Changwen Xie
This paper aims to investigate the following issues: the mechanisms through which different types of top management team’s social capital influence the innovation quality of…
Abstract
Purpose
This paper aims to investigate the following issues: the mechanisms through which different types of top management team’s social capital influence the innovation quality of high-tech firms, and the moderating effect of organizational knowledge utilization on the relationship between top management team’s social capital and innovation quality in high-tech firms.
Design/methodology/approach
This study categorizes top management team’s social capital into political, business and academic dimensions, investigating their impact on innovation quality in high-tech firms. Furthermore, a research model is developed with organizational knowledge utilization as the moderating variable. Data from Chinese high-tech firms between 2010 and 2019 are collected as samples for analysis.
Findings
The innovation quality of high-tech firms shows an inverted U-shaped trend as the top management team’s political capital and business capital increase. The top management team’s academic capital has a significantly positive correlation with the innovation quality of high-tech firms. Moreover, organizational knowledge utilization plays a significant moderating role in the relationship between the top management team’s social capital and innovation quality in high-tech firms.
Originality/value
This study explores the relationship among different dimensions of top management team’s social capital, innovation quality and organizational knowledge utilization. It holds significant theoretical value in enriching and refining the interactions between top management team’s social capital, knowledge management theory and innovation management theory. In addition, it offers important practical implications for firms to rationally approach top management team’s social capital, emphasize top management team configuration management and establish a comprehensive and efficient organizational knowledge utilization mechanism.
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Tu Hongsheng, Huang Changwen and Guo Chenye
Currently, the existing similar simulation is still limited in the following aspects: un-rotatable laboratory devices, the difficulty in the pavement on steep seams and great…
Abstract
Purpose
Currently, the existing similar simulation is still limited in the following aspects: un-rotatable laboratory devices, the difficulty in the pavement on steep seams and great error of the experimental data.
Design/methodology/approach
To address above-mentioned problems, this study combined theoretical analysis and numerical simulation and developed a rotatable experimental system for similar simulation on steep coal seam mining on the premise of ensuring experimental safety.
Findings
The present experimental system mainly consists of the model support, the rotation system and the bearing system. By taking into account the experimental requirements and actual laboratory space, the sizes of the model support and the bearing system were determined. Considering the requirements in space limit and rotation stability, the rotation mode of vertical sliding on the left side and the horizontal sliding on the lower side was designed.
Originality/value
Using programmable logic controller automatic angle control technology, the rotation angle, velocity and displacement of the model can be automatically adjusted and controlled so as to achieve safe rotation and precise control. Finally, the calculation method of the mass of the required similar materials for paving the coal strata at different inclination angles and in different horizons was analyzed, and the related mass proportion calculation software was developed.
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Tamoor Khan, Jiangtao Qiu, Ameen Banjar, Riad Alharbey, Ahmed Omar Alzahrani and Rashid Mehmood
The purpose of this paper is to assess the impacts on production of five fruit crops from 1961 to 2018 of energy use, CO2 emissions, farming areas and the labor force in China.
Abstract
Purpose
The purpose of this paper is to assess the impacts on production of five fruit crops from 1961 to 2018 of energy use, CO2 emissions, farming areas and the labor force in China.
Design/methodology/approach
This analysis applied the autoregressive distributed lag-bound testing (ARDL) approach, Granger causality method and Johansen co-integration test to predict long-term co-integration and relation between variables. Four machine learning methods are used for prediction of the accuracy of climate effect on fruit production.
Findings
The Johansen test findings have shown that the fruit crop growth, energy use, CO2 emissions, harvested land and labor force have a long-term co-integration relation. The outcome of the long-term use of CO2 emission and rural population has a negative influence on fruit crops. The energy consumption, harvested area, total fruit yield and agriculture labor force have a positive influence on six fruit crops. The long-run relationships reveal that a 1% increase in rural population and CO2 will decrease fruit crop production by −0.59 and −1.97. The energy consumption, fruit harvested area, total fruit yield and agriculture labor force will increase fruit crop production by 0.17%, 1.52%, 1.80% and 4.33%, respectively. Furthermore, uni-directional causality is correlated with the growth of fruit crops and energy consumption. Also, the results indicate that the bi-directional causality impact varies from CO2 emissions to agricultural areas to fruit crops.
Originality/value
This study also fills the literature gap in implementing ARDL for agricultural fruits of China, used machine learning methods to examine the impact of climate change and to explore this important issue.