Online-to-offline (O2O) Platforms, Proprietary Platforms and Firm Performance

This paper studies the impact of O2O (Online-to-offline) platforms on the performance of participating firms. Specifically, the study uses a sample of 3406 cinemas to investigate the moderating effect of O2O platforms, particularly proprietary platforms, on the relationship between cinemas’ resources and their performance. Resources are measured in two dimensions. One dimension is physical resources, measured by the number of seats, while the other dimension is intangible resources, measured by the operating duration of a cinema. The findings show that both O2O platforms and proprietary platforms have significantly positive impact on the relationship between the number of seats and firm performance. O2O platforms negatively moderate the relationship between duration and firm performance, while proprietary platforms positively moderate the relationship. Implications of the findings are discussed

The Effect of Artificial Intelligence (AI) on Firm Labor Structure

This paper aims to study the effect of AI on firm labor structure. Using a unique panel data of over 1300 publicly listed companies in China from 2007 to 2018, we study the effect of AI on firms’ labor composition measured by labor force’s education levels. We further compare the effect of AI on firms in the manufacturing sector to the effect on firms in the service sector. Our analysis generates two major findings. First, the use of AI leads to a larger labor demand increase for jobs requiring lower education levels than those requiring higher education levels. Second, the effect is stronger in the service sector than in the manufacturing sector. These findings contradict predictions of the “skill-biased technological change” (SJTB) and U-shaped “job polarization” effects proposed in the prior literature. We propose that “technology-enabled deskilling” effect is driving the effect of AI on labor structure

Imprint of the stochastic nature of photon emission by electrons on the proton energy spectra in the laser-plasma interaction

The impact of stochasticity effects (SEs) in photon emissions on the proton energy spectra during laser-plasma interaction is theoretically investigated in the quantum radiation-dominated regime, which may facilitate SEs experimental observation. We calculate the photon emissions quantum mechanically and the plasma dynamics semiclassically via two-dimensional particle-in-cell simulations. An ultrarelativistic plasma generated and driven by an ultraintense laser pulse head-on collides with another strong laser pulse, which decelerates the electrons due to radiation-reaction effect and results in a significant compression of the proton energy spectra because of the charge separation force. In the considered regime the SEs are demonstrated in the shift of the mean energy of the protons up to hundreds of MeV. This effect is robust with respect to the laser and target parameters and measurable in soon available strong laser facilities

Model for Dynamic Multiple of CPPI Strategy

Focusing on the parameter “Multiple” of CPPI strategy, this study proposes a dynamic setting model of multiple for gap risk management purpose. First, CPPI gap risk is measured as the probability that the value loss of active asset exceeds its allowed maximum drop determined by a given multiple setting. Moreover, according to the statistical estimation using SV-EVT approach, a dynamic choice of multiple is detailed as a function of time-varying asset volatility, expected loss, and the possibility of occurrence of extreme events in the active asset returns illustrated empirically on Shanghai composite index data. This study not only enriches the literature of dynamic proportion portfolio insurance, but also provides a practical reference for CPPI investors to choose a moderate risky exposure achieving gap risk management, which promotes CPPI’s application in emerging capital market