Few studies have explored how emotional expressions regarding various aspects affect review helpfulness despite having emotionally charged online reviews of various topics or aspects. The ultimate purpose of this study is to provide a comprehensive understanding of how different emotional expressions in online reviews influence their perceived helpfulness, specifically focusing on the moderating effect of restaurant type. To achieve this, we developed and empirically verified a research model using 85,117 online reviews collected from Yelp.com based on cue utilization theory. Specifically, it investigates the moderating effect of restaurant type on the relationship between emotional expression and review helpfulness in five aspects of online restaurant reviews (location, food, price, service, and ambiance). A BERT-based ABSA was performed to measure the emotional expression of each aspect of online restaurant reviews accurately, and a negative binomial regression model was used for the regression analysis. Emotional expressions regarding food and ambiance affected review helpfulness negatively. By contrast, services, prices, and locations affected review helpfulness positively. Negative reviews of food and ambiance were more helpful in luxury restaurants than in casual dining restaurants. In addition, a positive price evaluation had a more significant effect on review helpfulness for luxury restaurants than for casual-dining restaurants. Positive evaluations of service and location aspects significantly influence review helpfulness more in luxury restaurants than in casual-dining restaurants. Therefore, this study’s results can help restaurant practitioners to identify useful restaurant attributes and reduce information overload when potential consumers search for information.

Purpose: This study investigates the impact of Chinese senior leaders’ diplomatic visits on outward and inward foreign direct investment (OFDI and IFDI, respectively). Additionally, it aims to discern the differential impacts of diplomatic visits on FDI across different Chinese administrations.Design/methodology/approach: We utilize data about the diplomatic visits of Chinese senior leaders to 146 host countries. The dataset was divided into two leadership eras: the tenure of President Hu Jintao and Prime Minister Wen Jiabao from 2003 to 2012 and the period from 2013 to 2021 under President Xi Jinping and Prime Minister Li Keqiang. We employ generalized least squares, lagged effect, and two-stage least squares methods to estimate the econometric model. This analytical framework assesses the influence of high-level diplomatic visits on FDI flows, addressing potential time-serial and endogeneity concerns.Findings: The findings reveal that senior leaders’ diplomatic visits significantly boost Chinese OFDI. The effect on IFDI in China is not statistically significant. The administrations of Hu and Xi played positive roles in promoting OFDI. However, only Hu’s administration showed a statistically significant positive relationship with IFDI. The result suggests a potential increase in IFDI between China and host countries within two to three years following diplomatic visits.Originality/value: This study contributes to the understanding of how Chinese senior leaders’ diplomatic activities affect foreign direct investment. It provides a deeper understanding of Chinese senior leaders’ diplomatic efforts to compensate for investment across two administration periods. It offers insights into the potential influence of diplomatic efforts on FDI, enriching the understanding of diplomacy’s role in international business contexts.

Online review texts serve as a valuable source of auxiliary information for addressing the data sparsity problem in recommender systems. These reviews often reflect user preferences across multiple item attributes and can be effectively incorporated into recommendation models to enhance both the accuracy and interpretability of recommendations. Review-based recommendation approaches can be broadly classified into implicit and explicit methods. Implicit methods leverage deep learning techniques to extract latent semantic representations from review texts but generally lack interpretability due to limited transparency in the training process. In contrast, explicit methods rely on hand-crafted features derived from domain knowledge, which offer high explanatory capability but typically capture only shallow information. Integrating the complementary strengths of these two approaches presents a promising direction for improving recommendation performance. However, previous research exploring this integration remains limited. In this study, we propose a novel recommendation model that jointly considers implicit and explicit representations derived from review texts. To this end, we incorporate a self-attention mechanism to emphasize important features from each representation type and utilize Bidirectional Encoder Representations from Transformers (BERT) to capture rich contextual information embedded in the reviews. We evaluate the performance of the proposed model through extensive experiments using three real-world datasets. The experimental results demonstrate that our model outperforms several baseline models, confirming its effectiveness in generating accurate and explainable recommendations.

Diplomatic Engagement and Economic Influence: The Interplay of Chinese OFDI and High-Level Visits-Outward Foreign Direct Investment, Economic Diplomacy, High-Level Visits, Foreign Policy, Simultaneous Equation Model