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This study aims to establish a model for rapid and accurate emotion recognition in restaurant online reviews, thus advancing the literature and providing practical insights into electronic word-of-mouth management for the industry.

This study elaborates a hybrid model that integrates deep learning (DL) and a sentiment lexicon (SL) and compares it to five other models, including SL, random forest (RF), naïve Bayes, support vector machine (SVM) and a DL model, for the task of emotion recognition in restaurant online reviews. These models are trained and tested using 652,348 online reviews from 548 restaurants.

The hybrid approach performs well for valence-based emotion and discrete emotion recognition and is highly applicable for mining online reviews in a restaurant setting. The performances of SL and RF are inferior when it comes to recognizing discrete emotions. The DL method and SVM can perform satisfactorily in the valence-based emotion recognition.

These findings provide methodological and theoretical implications; thus, they advance the current state of knowledge on emotion recognition in restaurant online reviews. The results also provide practical insights into intelligent service quality monitoring and electronic word-of-mouth management for the industry.

This study proposes a superior model for emotion recognition in restaurant online reviews. The methodological framework and steps are elucidated in detail for future research and practical application. This study also details the performances of other commonly used models to support the selection of methods in research and practical applications.

This paper aims to promote urban–rural synergy in carbon reduction and achieve the dual carbon goal, reconstruct the low-carbon urban–rural spatial pattern and explore planning strategies for carbon mitigation in urban agglomerations.

The authors propose the idea of land governance zoning based on low-carbon scenario simulation, using the Beijing–Tianjin–Hebei (BTH) urban agglomeration as the empirical research area. Specifically, the authors analyze its spatiotemporal evolution characteristics of carbon balance over the past two decades and simulate the land use pattern under the scenario of low-carbon emission in 2030. Furthermore, the authors create spatial zoning rules combined with land use transition characteristics to classify the urban agglomeration into carbon sink restoration zone, carbon sink protection zone, carbon control development zone and carbon transition agriculture zone and put forward corresponding targeted governance principals.

The study findings classify the BTH urban agglomeration into carbon sink restoration zone, carbon sink protection zone, carbon control development zone and carbon transition agriculture zone, which account for 28.1%, 17.2%, 20.1% and 34.6% of the total area, respectively. The carbon sink restoration zone and carbon sink protection zone are mainly distributed in the northern and western parts and Bohai Rim region. The carbon transition agriculture zone and carbon control development zone are mainly distributed in the southeastern plain and Zhangjiakou.

The authors suggest restoring and rebuilding ecosystems mainly in the northwest and east parts to increase the number of carbon sinks and the stability of the ecosystem. Besides, measures should be taken to promote collaborative emission reduction work between cities and optimize industrial and energy structures within cities such as Beijing, Langfang, Tianjin and Baoding. Furthermore, the authors recommend promoting sustainable intensification of agriculture and carefully balance between both economic development and ecological protection in Zhangjiakou and plain area.

The authors propose a zoning method based on the optimization of land use towards low-carbon development by combining “top-down” and “bottom-up” strategies and provide targeted governance suggestions for each region. This study provides policy implications to implement the regional low-carbon economic transition under the “double carbon” target in urban agglomerations in China.

Prior research demonstrated that China's Outward FDI (OFDI) is aimed at sustaining long-term economic growth by promoting industrialization and technological upgrading in the country. However, empirical evidence on the effectiveness of this strategy remains scarce. This study intends to fill this gap by exploiting endogenous changes in industrial productivity stemming from OFDI to examine if China's new strategy to spur OFDI is economically beneficial for the industries involved.

The authors employed the two-step system-GMM and pooled mean group approaches on a panel dataset of 18 Chinese industries over the 2004–2017 period. The industrial sectors are further classified into the state dominated and non-state dominated ones to evaluate whether the productivity growth impact of OFDI varies by the level of ownership structure. Besides, the dataset is further decomposed into the ex ante and ex-post BRI era to test if this initiative has altered the underlying relationship.

The results provide robust evidence that China's OFDI through reverse spillover effects promotes productivity growth in the domestic industries, and such productivity gains are greater for the non-state dominated industries, and the OFDI in the BRI era. The findings suggest that OFDI can act as a catch-up strategy to release excess capacity and acquire technology and smart business practices.

This study is the first attempt to highlight the reverse productivity spillovers associated with OFDI at the industrial level. The study's findings guide the government officials and the practitioners of foreign investment to better understand the implications of their investment projects in terms of technology improvements and to optimize market opportunities.

The Belt and Road Initiative (BRI) is the most comprehensive and substantial international cooperation platform, creating a new market influenced by economic and political factors. In this paper, the authors aim to examine whether and how the BRI impacts the Chinese enterprises' corporate environmental responsibility (CER).

Based on China's listed firms' database from 2011 to 2018, the authors use the PSM-DID method, an econometrics method combined with propensity score matching (PSM) and difference-in-differences (DID), to conduct causal inference between the BRI and Chinese enterprises' CER and conduct a series of robustness analyses. Moreover, the authors explore the mechanisms underlying the main effect from both market and non-market perspectives.

The results suggest that the BRI significantly increases Chinese enterprises' CER. Further analyses show that market competition and government support are two possible mechanisms through which the BRI has an effect on the enterprises' CER.

The research study supplements existing work on the environmental effects of the BRI at a microlevel and adds to the literature on the drivers of CER. The findings offer valuable insights into governments and scholars by demonstrating that CER is a crucial tool for Chinese enterprises to gain a competitive advantage in the increasingly competitive markets along the BRI.

This study aims to explore the degree of Chinese consumers' trust and confidence in the Chinese dairy products supply chain and the relationships between trust and overall confidence in dairy products safety and quality.

This study collected data from 1,278 respondents by field survey from five provinces of China. The data were analyzed using ordered logit model.

This study shows the following results: (1) Chinese consumer confidence in domestic dairy products and trust in actors of the dairy chain are at a moderate-to-low level. (2) Government regulators are considered to take the most responsibility, with both an optimism-enhancing and a pessimism-reducing effect (the former effect is greater), while perceived trust in dairy farmers and retailers has little effect. (3) Perceived care has both an optimism-enhancing and a pessimism-reducing effect, and the former effect is stronger. Competence and openness have an optimism-enhancing effect and a pessimism-reducing effect, respectively. (4) The importance of the three dimensions of trust related to optimism-increasing and pessimism-reduction is limited, except in the case of government regulators.

This study contributes to a better understanding of consumer trust in food safety and also help demonstrate to the actors and institutions involved in the dairy supply chain the best way to improve the performance of their duties to meet the consumers' needs for safe and quality dairy products.

In the digital age, emerging technologies have affected every industry. Information and communications technology and digital technologies have transformed traditional supply chains into smart and more resilient ones, enabling effective management of challenges. Given the importance of the two topics, namely sustainable supply chain management and Industry 4.0 in supply chain management, on the one hand, and the dearth of theoretical research performed in this area on the other, this study aims to propose a conceptual model on a sustainable digital supply chain management in manufacturing companies.

This study utilized a qualitative approach. First, an in-depth review of the relevant literature was done. Then, following a multi-grounded theory methodology, relevant data were gathered by reviewing 92 papers and conducting nine semi-structured interviews with industry experts. These data were analyzed using the MAXQDA software.

A total of 41 concepts, ten sub-components and three main components (dimensions) were extracted, and the proposed conceptual model was presented. Finally, based on this conceptual model, three propositions were suggested.

Considering that the present study was performed in the context of Iranian manufacturing companies, caution should be exercised in relation to the generalizability of the obtained results. Also, due to the problems in the digital technology infrastructure and the limited use of these technologies by manufacturing companies (emphasized by the interviewees), this study focused on the theoretical dimension of using digital technologies by these companies.

The proposed comprehensive model can help academicians as well as practitioners to focus better and explore the variables and constructs of the model, paving the way toward successful implementation of digital technologies in the manufacturing supply chain.

To the best knowledge of the authors, this study is among the first of its kind which presents a holistic and comprehensive digital supply chain model aimed at guiding companies to consider sustainability from all the main dimensions and their relevant indicators.