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Interorganizational power dependence has become an increasingly important factor for small and medium-sized enterprises (SMEs) to improve product innovation. This paper examines the role of power dependence in SMEs' product innovation trade-offs between exploration and exploitation. The article further studies the mediating effect of supply chain adaptability and the moderating effect of knowledge acquisition on the relationship between power dependence and product innovation.

The study proposes a model to verify the impact of power dependence on SMEs' product innovation trade-offs based on social network theory. Two conceptually independent constructs, “availability of alternatives (ALTRN)” and “restraint in the use of power (RSPTW),” are used to evaluate the power dependence. The model also analyzed how these effects are mediated by supply chain adaptability and moderated by knowledge acquisition. The authors test these relationships using data collected from 224 SMEs in China.

The empirical analysis shows that ALTRN has a more substantial effect on exploration for product innovation, while RSTPW has a more significant impact on exploitation for product innovation. Moreover, empirical data indicate a partial mediating effect by supply chain adaptability between power dependence and product innovation of SMEs. The results also show that knowledge acquisition positively moderates the relationship between ALTRN/RSTPW, supply chain adaptability and product innovation.

Overall, the findings of the study advance the understanding of the roles of power dependence in product innovation for SMEs. In addition, the research also uncovers the impact mechanisms of existing theoretical frameworks and extends the boundaries of the theory.

In China, more than 80,000 people have been diagnosed with COVID-19, and more than 3,000 people have lost their lives. It seems that there will be more deaths since the epidemic is not over. All the Chinese provinces have reported the COVID-19 cases. This chapter aims to explore the trend of COVID-19 treatment efficiency in Chinese provinces using the data released daily by China Center for Disease Control and Prevention. Since China Center for Disease Control and Prevention began to release data daily from January 24 to March 12, we have more than 40 groups of daily data for 31 provinces in China mainland. In the calculation, we take the daily data of each province as a sample and then we have more than 1,200 samples in this study.

We use additive two-stage data envelopment analysis as an efficiency evaluation tool to calculate the COVID-19 treatment efficiency. In our framework, the first stage is to understand the infection rate and the second stage is to evaluate the treatment efficiency. In the first stage for the tth day, we use total population (p) and number of people infected in the previous day (inf _t−1) as the inputs and cumulative number of people infected in the current day (inf _t ) as the output. In the second stage for the tth day, we use cumulative number of people infected in the current day (inf _t ) as the input and cumulative death in the current day (death _t ) and cumulative recovery in the current day (recov _t ) as the outputs. Some techniques on how to deal with undesirable outputs such as inf _t and death _t are employed in this study.

After we have the infection rate and treatment efficiency for the samples more than 1,200, we analyze the COVID-19 treatment efficiency and its development trend from January 24 to March 12 in 34 regions of China from static and dynamic aspects. The results show that, on the whole, the overall efficiency and phased efficiency of COVID-19 treatment efficiency in all regions of China are relatively high, which reflects the key factor for the Chinese government to quickly control the epidemic in the short term. Relatively speaking, the average efficiency value in the infection stage (first stage) is lower than the average efficiency value in the healing stage (second stage), which shows that the focus of anti-epidemic in China should be early detection and prevention rather than treatment process. In terms of trend, the total efficiency of COVID-19 treatment in each region shows a trend of “increasing first and then decreasing.” Our analysis indicates that in the initial stage, the continuous increase of various resources leads to the rise of the total efficiency, while in the later stage, the rapid decline of the number of infected people leads to the decrease of the total efficiency. Based on the results of the efficiency analysis, this study provides corresponding management implications and policy suggestions, hoping to provide some enlightenment and suggestions for the anti-epidemic work of other countries in the severe environment where the epidemic is spreading rapidly.