Richer forms of online reviews such as videos or follow‐on reviews convey ‘additional information and can attract consumers’ attention. However, prior studies focused mostly on the relationship between aggregated online reviews and sales. This paper investigates the impact of online review richness (i.e., reviews containing videos or follow‐on reviews) on sales. Leveraging attribute substitution theory, we conjecture that online review richness can provide heuristic cues in the online shopping environment to help consumers make better purchase decisions. Using data from http://jd.com, we found that reviews containing either videos or follow‐on reviews positively affect sales. In addition, different product types can also serve as heuristic cues to replace target cues, which can further affect how different forms of online review richness affect sales. We found that the impact of online review richness on sales is stronger for utilitarian products than for hedonic products, and stronger for negatively commented products than for positively commented products. Moreover, we conducted two online experiments and confirmed that the causal relationship is from online review richness to sales. The research findings offer practical implications for online retailers and constitute one of the first steps toward a better understanding of the relationship between online review richness and sales.

There is complicated correlations in mechanical system. By using the advantages of copula function to solve the related issues, this paper proposes the mechanical system reliability model based on copula function. And makes a detailed research for the serial and parallel mechanical system model and gets their reliability function respectively. Finally, the application research is carried out for serial mechanical system reliability model to prove its validity by example.Using Copula theory to make mechanical system reliability modeling and its expectation, studying the distribution of the random variables (marginal distribution) of the mechanical product' life and associated structure of variables separately, can reduce the difficulty of multivariate probabilistic modeling and analysis to make the modeling and analysis process more clearly.

This study examines the relationship and risk spillover between Bitcoin, crude oil, and six traditional markets (the US stock, Chinese stock, gold, bond, currency, and real estate markets) from 2019 to 2020, during which the coronavirus disease 2019 (COVID-19) outbreak occurred as well. We first discuss the static relationship between Bitcoin and these markets using a quantile-on-quantile model and examine the dynamic relationship using a time-varying copula model. A conditional value-at-risk model is subsequently used to estimate the risk spillover between the markets studied. The empirical results reveal that the relationship between these markets is always time-varying, and the COVID-19 outbreak has revealed such changes in the relationship between Bitcoin and other traditional financial markets. The risk of all single markets has enhanced because of the pandemic. Further, the risk spillover of these markets has also changed dramatically since the COVID-19 outbreak during which the Bitcoin market has played an important role and exerted a significant impact on the crude oil market, and the four other markets (US stock, gold, Chinese stock, and real estate markets). Overall, our findings indicate that investors and policymakers need to be made aware of the risk spillover between Bitcoin, crude oil, and other traditional markets and that flexible hedge strategies and policies should be implemented in response to the challenges and economic recession observed following the COVID-19 outbreak.

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This paper selects the data of China's A-shares from 2001 to 2020 to study the premium effect of low-cost stocks in China's A-share market from different sectors using multiple regression analysis. The research results show that there is a premium effect of low-cost stocks in each sector of the A-share market. The increase in the shareholding ratio of institutional investors and the attention of analysts can reduce the premium effect of low-cost stocks. JEL classification numbers: F832.5 Keywords: Low-priced stocks, Premium effect, Excess return, Financial anomalies.