Applying the lens of entrepreneurial psychology, this paper examines vicarious learning as an approach that entrepreneurs can use to overcome external uncertainty of overseas investments by accumulating international know-how and experience through interlocking director connections with other experienced companies. Through the analysis of a sample of Chinese companies, our findings suggest that entrepreneurs obtaining foreign experience from interlocking partners can significantly promote their firm's international growth when investing in the same country, and that this positive effect is significant in relation to both first-degree and second-degree connections. We further find that, if an entrepreneur makes a connection with an interlocking partner in the same industry, it enhances their knowledge absorption, thereby providing a positive moderating effect, while investing in a country with a strong degree of openness weakens the effect of knowledge application, and thus plays a negative moderating role. This study makes practical and theoretical contributions by exploring specific vicarious learning means for entrepreneurs to enhance their firm's international competitiveness, and also identifying three different learning processes.
Summary
With the advanced development of smart devices and network technique, Internet of Things has seen a large number of popular applications, among which, smart agriculture is a good example. The sensor nodes collect some parameters in the greenhouse, and send them to the control center. Then the control center can conduct some operations according to the analysis of the collected parameters. In this paper, we discuss how to efficiently aggregate and collect data with features of privacy protection in smart agricultural system. We propose an effective and scalable framework. The genetic algorithm is used to obtain the optimized data collection route for the agricultural system. The use of unmanned aerial vehicle also greatly improves the communication efficiency of resource‐constrained sensors in the system, which further increases the use time of the entire agricultural system. The experimental analysis shows that our framework has good efficiency and enjoys good scalability.
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