Cultivating organizational performance through the performance measurement systems: Role of psychological empowerment and creativity.

Interactive performance measurement systems (PMSs) play a critical role in shaping individual behavior and performance. To identify the underlying mechanism of how PMSs enhance organizational performance, a proposed model was constructed to investigate psychological empowerment and employee creativity as possible mediating variables. Based on a sample of 211 managers from Chinese organizations, a partial least squares structural equation modelling (PLS-SEM) approach was used to examine the mediating effect presented in the aim. Interactive use of PMS has a positive and direct impact on psychological empowerment. Psychological empowerment positively influences creativity, which, in turn, positively influences organizational performance. The findings also show that psychological empowerment and creativity mediate the impacts of interactive use of PMS on organizational performance. Our study highlights the role of PMSs, and how to use them interactively in turbulent environments. Particularly, we demonstrate that interactive use of PMS is important for facilitating a manager's sense of psychological empowerment and fostering creativity, which, in turn, contributes to better performance and greater competitive advantages.

Impact persistence of stock market risks in commodity markets: Evidence from China.

The risk spillover among financial markets has been noticeably investigated in a burgeoning number of literature. Given those doctrines, we scrutinize the impact persistence of volatility spillover and illiquidity spillover of Chinese commodity markets in this paper. Based on the sample from 2010 to 2020, we reveal that there is a cross-market spillover of volatility and illiquidity in China and also, interactions between volatility and illiquidity in different financial markets are pronounced. More importantly, we demonstrate that different commodity markets have different responsiveness to stock market shocks, which embeds their market characteristics. Specifically, we discover that the majority of the traders in gold market might be hedger and therefore gold market is more sensitive to stock market illiquidity shock and thus the shock impact in persistent. On the other hand, agricultural markets like corn and soybean markets might be dominated by investors and thus those markets respond to the stock market volatility shocks and the shock impact in persistent over 10 periods given the first period of risk shock happening. In fact, different Chinese commodity markets' responsiveness towards Chinese stock market risk shocks indicates the stock market risk impact persistence in Chinese commodity markets. This result can help policymakers to understand the policy propagation effect according to this risk spillover channel and risk impact persistence mechanism in China.

Liquidity effects on oil volatility forecasting: From fintech perspective.

Fin-tech is an emerging field, inspiring revolutionary innovations in the financial field. It may initiate the evolutionary episode of the financial research, where volatility forecasting is a crucial topic in finance. For forecasting volatility, GARCH model is a prevailing model, however, further improvement of the GARCH model is still challenging. In this paper, we demonstrate how Fintech can play a part in volatility forecasting by employing a metaheuristic procedure called Genetic Programming. On the basis, we are able to develop a new volatility forecasting model, which can beat GARCH family models (including GARCH, IGARCH and TGARCH models) in a significant way. Since genetic programming is an evolutionary algorithm based on the principles of natural selection, this innovative work will be a breakthrough point in the financial area. The innovation of this paper demonstrates how GP technology can be applied in the financial field, attempting to explore the volatility forecasting area from the combination of new technology and finance, known as fintech. More importantly, when the formula of volatility forecasting is unknown as we introduce a new factor, namely, the liquidity factor, we unveil that how GP method can be helpful in determining the specific volatility forecasting model format. We thereby exhibit the liquidity effects on volatility forecasting filed from the fintech perspective.