This paper focuses on estimating the coefficients and average partial effects of observed regressors in nonlinear panel data models with interactive fixed effects, using the common correlated effects framework. The proposed two-step estimation method involves applying principal component analysis to estimate the latent factors based on cross-sectional averages of the regressors in the first step, and jointly estimating the coefficients of the regressors and the factor loadings in the second step. The asymptotic distributions of the proposed estimators are derived under general conditions, assuming that the number of time-series observations is comparable to the number of cross-sectional observations. To correct for asymptotic biases of the estimators, we introduce both analytical and split-panel jackknife methods, and confirm their good performance in finite samples using Monte Carlo simulations. Finally, the proposed method is used to study the arbitrage behaviour of nonfinancial firms across different secur

Firm innovation decision is subject to the influence of financial constraints, but the mechanism through which this impact occurs remains unclear. Using a nationwide representative survey data set of private firms in China in 2016, we first identified the strengthening effect of financial constraints on firm innovation decision, and further demonstrated the influence of corporate governance and risk diversification on such a relationship. The results show that firms tend to increase investment in innovation to improve operations when facing tighter financial constraints. Firms with suboptimal corporate governance and better conditions of risk diversification tend to adopt riskier innovation strategy to cope with tighter financial constraints.

As sustainability reporting and ESG disclosure gain global importance, understanding the factors influencing ESG outcomes becomes crucial for policymakers, investors, and corporate decision-makers. China, a major player in the global economy, has recently taken steps to align its stock exchanges with international ESG reporting standards. In this context, the study examines the individual and joint effects of digital transformation and CEO compensation on ESG performance, considering moderating factors such as firm size, state ownership, and CEO age and gender. The research employs a comprehensive dataset containing 16,205 firm-year observations from 2018 to 2022, combining financial data, ESG ratings, and a matrix of word frequencies related to digital transformation extracted from annual reports. The study adopts a firm-year two-way fixed effect model, utilizing panel data and control variables to address potential endogeneity concerns and unobserved firm heterogeneity. The findings provide evidence support

Non-fungible tokens (NFTs) are increasingly used to safeguard luxury products from counterfeits. Despite their increasing adoption, limited research has investigated how brands should communicate the use of NFTs—a novel and complex concept for consumers to comprehend—to maximize their benefits. This research aims to examine this gap by highlighting that the ease of visualization is critical for effective communication. Study 1A demonstrated that consumers prefer a visualized NFT to a non-visualized one for authenticating a luxury product. Study 1B further demonstrated that consumers place greater trust in a visualized NFT and are willing to pay higher prices for luxury products that utilize it. Study 2 demonstrated that consumers have more favorable attitudes toward a luxury product that features an easy-to-visualize NFT than those with a difficult-to-visualize NFT and that perceived authenticity mediates this effect. Finally, Study 3 demonstrated that the positive impacts of easy-to-visualize NFT cues were m

This study investigates how inaccurate information provided by ChatGPT impacts travelers’ acceptance of recommendations. Six experiments were conducted based on the accessibility-diagnosticity framework. These examined the moderating role of the prominence and type of incorrect information and their effects on decision-making. The results show that participants perceived more accuracy and trustworthiness, leading to stronger intentions to visit when incorrect information was absent. However, there was a decline in their intentions to visit when incorrect information was present and more prominent or in the same domain. This effect diminished when multiple domains were involved or when participants were focused on the initial task. The research highlights that both the prominence and type of incorrect information are boundary conditions and provides insights into AI applications in tourism. Furthermore, it offers practical implications for online travel agencies in terms of user interface and user experience d