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This paper examines the effect of managerial interlocking networks (MINs) on firm risk spillover by using a sample of Chinese A-share listed firms.

Applying the complex network approach, we build managerial interlocking networks (MINs) and leverage degree centrality to quantify a manager’s network position. To gauge firm risk spillover, we utilize the conditional autoregressive value at risk (CAViaR) model to compute the value-at-risk. Subsequently, we employ ordinary least squares to investigate the influence of MINs on firm risk spillover.

Our research uncovers a direct correlation between a firm risk spillover and the status of network positions within managerial interlocking networks; namely, the more central the position, the greater the risk spillover. This increase is believed to be due to central firms in MINs having greater connectedness and influence. This fosters a similarity in decision-making across different firms through interfirm managerial communication, thus amplifying the risk spillover. Economic policy uncertainty (EPU) and Guanxi culture furtherly intensify the effects of MINs. Additional analysis reveals that the impact of MINs on the firm risk spillover is significantly noticeable in non-state-owned enterprises, while good corporate governance diminishes the risk spillover prompted by MINs.

Our findings offer fresh insights into the interfirm risk outcome associated with MINs and extend practical guidelines for attenuating firm risk spillover with a view toward mitigating systemic risk.

Online reviews serve as valuable sources of information, reflecting tourists’ attentions, preferences and sentiments. However, although the existing research has demonstrated that incorporating online review data can enhance the performance of tourism demand forecasting models, the reliability of online review data and consumers’ decision-making process have not been given adequate attention. To address the aforementioned problem, the purpose of this study is to forecast tourism demand using online review data derived from the analysis of review helpfulness.

The authors propose a novel “identification-first, forecasting-second” framework. This framework prioritizes the identification of helpful reviews through a comprehensive analysis of review helpfulness, followed by the integration of helpful online review data into the forecasting system. Using the SARIMAX model with helpful online review data sourced from TripAdvisor, this study forecasts tourist arrivals in Hong Kong during the period from August 2012 to June 2019. The SNAÏVE/SARIMA model was used as the benchmark model. Additionally, artificial intelligence models including long short-term memory, back propagation neural network, extreme learning machine and random forest models were used to assess the robustness of the results.

The results demonstrate that online review data are subject to noise and bias, which can adversely affect the accuracy of predictions when used directly. However, by identifying helpful online reviews beforehand and incorporating them into the forecasting process, a notable enhancement in predictive performance can be realized.

First, to the best of the authors’ knowledge, this study is one of the first to focus on the data issue of online reviews on tourism arrivals forecasting. Second, this study pioneers the integration of the consumer decision-making process into the domain of tourism demand forecasting, marking one of the earliest endeavors in this area. Third, this study makes a novel attempt to identify helpful online reviews based on reviews helpfulness analysis.