Several rounds of banking reforms in China have aimed to increase the competitive condition and further enhance stability in the Chinese banking sector, while the joint effects of competition and risk-taking behaviour on the profitability in the banking sector have not been studied well enough so far in the literature. The current study contributes to the empirical literature by testing the impacts of risk and competition on profitability in the Chinese banking industry (state-owned, joint-stock and city commercial banks) over the period 2003–2011 under a one-step Generalized Method of Moments (GMM) system estimator. The results do not show any robust finding with regards to the impacts of competition and risk on bank profitability, while it is found that Chinese bank profitability is affected by taxation, overhead cost, labour productivity and inflation. The study provides policy implications to the Chinese banking industry and different ownership types of Chinese commercial banks

Purpose: The purpose of this paper is to evaluate the determinants of bank profitability in China. It examines the effects of inflation on bank profitability, while controlling for comprehensive bank‐specific and industry‐specific variables.Design/methodology/approach: The sample comprises a total of 101 banks (five state‐owned banks, 12 joint‐stock commercial banks and 84 city commercial banks). The period under consideration extends from 2003‐2009. The two step generalized methods of moments (GMM) estimators are applied.Findings: Empirical results exhibit that there is a positive relationship between bank profitability, cost efficiency, banking sector development, stock market development and inflation in China. The authors report that low profitability can be explained by higher volume of non‐traditional activity and higher taxation. Moreover, the authors confirm that there is a competitive environment in the Chinese banking industry. Furthermore, the authors propose policy actions that should be taken to improve bank profitability in China.Research limitations/implications: Further research can be conducted by investigating the profitability of numerous branches of all national banks and its determinants.Practical implications: The findings of the current study have considerable policy relevance. First, Chinese banks should emphasize the improvement of labour management and training skills, the purpose of which is to increase their productivity and boost the profitability. Furthermore, the government should gradually continue to open the banking and stock market, as the development of the financial sector is helpful in increasing the banks' profitability in China.Originality/value: Particular emphasis will be placed on the investigation into the effect of inflation on bank profitability while controlling for most comprehensive internal and external factors.

How to improve the predictive accuracy of box office revenue with social media data is a big challenge and is particularly important for movie distributors and cinema operators. In this research, we find that microblogging UGC (MUGC) is a significant predictor of box office revenue and has stronger predictive power than UGC on Douban! Movies (DUGC) based on our examination of 60 movies released in China in 2012. To increase the attendance rate of movies, cinema operators can consider previous valence and volume of MUGC before scheduling the current film screenings because these messages can quickly predict the future box office revenue of a movie. Besides, we find that the volume of enterprise microblogs (i.e., MGC) can predict both box office revenue and MUGC, indicating that movie distributors should optimize their online media strategy by shifting more resources to utilizing enterprise microblogging. Although rebroadcasting volume from microblogging platforms does not predict box office revenue directly, it can indirectly predict it via MGC. Accordingly, compared with third-party platforms, rebroadcasting as one of the key distinct functions of microblogging platforms also shows its usefulness in box office revenue prediction. Overall, metrics from microblogging platforms are more effective in predicting box office revenue than those from third-party platforms.

Online peer-to-peer (P2P) lending, one of the most successful technology-enabled initiatives in the fintech revolution, has drastically changed the way individual investors and borrowers meet and transact. While prior research has found herding among investors at the listing level, such social behavior has been underexplored at a macro, platform level. In this study, we attempt to fill this gap by examining whether subsequent investors follow their predecessors’ actions when choosing which platform to invest, and if so, how various platform attributes and regulations moderate herding behavior. We collected a novel data set from leading platforms in a large P2P lending market. Our baseline analysis reveals that herding exists at the platform level. Using a multilevel model, we further identify several interesting moderators: the investor’s herding behavior is accentuated by platforms’ market share and the cumulative amount funded, but attenuated by their time in operation. Finally, we find that government regulatory events dampen the magnitude of the herding effect, suggesting that more information disclosure and stricter operation standards reduce the value of observational learning. The results from our analysis provide implications for P2P lending investors, platform designers, and policymakers.

We investigate a retailer’s and a supplier’s incentive to facilitate information disclosure, i.e., consumer learning of their true product valuation, under two popular supply chain contracts, i.e., the agency pricing model and the wholesale pricing model. Our results show that when a product has medium or high dispersion in its consumers’ true valuation distribution and the degree of information disclosure before facilitation is moderate, two parties might have opposing interests as to more information disclosure. Specifically, in the agency pricing model, the revenue sharing mechanism leads the supplier to benefit, but the retailer to suffer, from more information disclosure. In the wholesale model, potential misalignment of interests as to more information disclosure disappears if the demand is linear. Double marginalization absorbs influence of two parties’ marginal cost discrepancy and eventually tunes the two parties’ margin proportional to each other. If the demand is log-concave and derived from common valuation distributions such as normal or logistic distributions, misalignment reappears in the wholesale model, but interestingly, the retailer benefits and the supplier suffers from more disclosure, which is opposite to the misalignment result in the agency model under the same log-concave demand. Our results suggest that information disclosure facilitation has a different interplay with the revenue sharing mechanism in the agency model than with double marginalization in the wholesale model. The online appendix is available at https://doi.org/10.1287/isre.2017.0770 .