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This study investigates the role of environmental, social and governance (ESG) policies in enhancing the resilience of Chinese firms during the COVID-19 crisis. By analyzing data from over 3,069 publicly listed companies, the research aims to elucidate the impact of robust ESG practices on stock market performance and operational outcomes during economic disruptions.

Using a dataset comprising ESG scores and financial performance metrics of Chinese firms, we conduct an empirical analysis to assess the correlation between ESG practices and corporate resilience during the COVID-19 pandemic. The study focuses on evaluating the individual contributions of the social and governance components to overall firm performance.

The analysis reveals that firms with higher ESG scores, especially in social and governance aspects, exhibit superior stock market performance and operational outcomes during the pandemic. Companies with strong governance mechanisms demonstrate more pronounced benefits, including better long-term sales growth and return on equity (ROE). The findings highlight the critical role of ESG policies in ensuring corporate stability and competitive advantage during crises.

This article provides a comprehensive overview of the impact of corporate ESG ratings on corporate trust and offers a detailed discussion on the protective role of ESG/CSR on firm value during crises, thus providing an original literature contribution.

This study examines the influence of CEO characteristics on firm innovation within the Australian market, using R&D expenditure as a proxy for innovation. The aim is to analyze how factors such as CEO gender, educational background and dual roles (CEO-chairman) impact firms' R&D investment across various industries.

Panel and Tobit regression models are employed to assess the relationship between CEO characteristics and R&D expenditure. The study controls for endogeneity and applies firm-level control variables to ensure robustness, examining CEO traits like gender, educational qualifications and CEO-chairman duality.

The study reveals that CEO gender and educational level significantly impact firm innovation, particularly R&D expenditure, compared to other characteristics like CEO-chairman duality. Female CEOs and those with PhD degrees are associated with higher R&D spending, with variations across industries such as basic materials and healthcare.

The study is limited by its focus on Australian firms and the time span of 2006–2016. Additionally, mixed results for CEO-chairman duality and CEO location may reduce the generalizability of the findings across all industries on the ASX.

The findings highlight the importance of gender diversity and CEO education in driving firm innovation. Companies aiming to enhance competitiveness and performance through R&D activities, especially in industry-specific contexts, should consider these CEO characteristics.

This study provides novel insights by analyzing the impact of CEO characteristics, such as gender and education level, on firm innovation in the underexplored Australian market. By using R&D expenditure as a proxy for innovation and employing both panel and Tobit regression models, it highlights the significance of CEO traits, particularly in specific industries. The findings emphasize the stronger influence of CEO gender and educational level compared to CEO-chairman duality and location, offering valuable implications for gender diversity and industry-specific innovation strategies in enhancing firm competitiveness.

As environmental protection and sustainable development become more widely recognized, greater emphasis has been placed on the significance of green supplier selection (GSS), which can support businesses both upstream and downstream in enhancing their environmental performance while preserving their strategic competitiveness. Therefore, this paper aims to propose a new framework to study GSS.

Firstly, this paper establishes a GSS evaluation criteria system including product competitiveness, green performance, quality of service and enterprise social responsibility. Secondly, based on the spherical fuzzy sets (SFSs), the Average Induction Ordered Weighted Averaging Operator-Criteria Importance Through Inter Criteria Correlation (AIOWA-CRITIC) method is used to determine the subjective and objective weights and the combination of weights are determined by game theory. In addition, the GSS framework is constructed by the Cumulative Prospect Theory-Technique for Order Preference by Similarity to Ideal Solution (CPT-TOPSIS) method. Finally, the validity and robustness of the framework is verified through sensitivity comparative and ablation analysis.

The results show that Y3 is the most promising green supplier in China. This study provides a feasible guidance for GSS, which is important for the greening process of the whole supply chain.

Under spherical fuzzy sets, AIOWA and CRITIC are used to determine weights of indicators. CPT and TOPSIS are combined to construct a decision model, considering the ambiguity and uncertainty of information and the risk attitudes of decision-makers.

The success of public–private partnership (PPP) projects is highly correlated to the successful management of risks encountered during the operation phase. PPP projects are especially exposed to risk due to the long operation period over which revenues need to be generated to recoup substantial initial investment and operational running costs. Despite the critical impact of risk exposure, limited research has been specifically undertaken on the matter of operational risk management. This study seeks to address this oversight by identifying and evaluating operational risk management strategies for PPPs.

Vulnerability theory is the theoretical lens used, with context drawn from Chinese PPP projects. Based on the data collected from expert interviews and questionnaires, 28 operational risk management strategies are identified. A fuzzy synthetic method is employed to analyze the effectiveness of the 28 strategies.

The findings reveal that providing an exit mechanism clause into the contract, establishing a comprehensive performance evaluation mechanism and developing a clear compensation mechanism are the top three effective strategies. This study also reveals that risk mitigation approaches that reduce vulnerability prove more effective than attempts to reduce external threats. Specifically, strategies aimed at managing contract, political, technical and financial risk are the most effective.

The findings of this study extend current knowledge regarding the risk management of PPP projects. They also offer a reference by which practitioners may select effective operational risk management pathways and thereby, galvanize the sustainable development of PPPs.

The purpose of this paper is to explore how GenAI can help companies achieve a higher level of hyper-segmentation and hyper-personalization in the tourism industry, as well as show the importance of this disruptive tool for tourism marketing.

This paper used the Web of Science and Google Scholar databases to provide updated studies and expert authors to explore GenAI in the tourism industry. Analysing hyper-segmentation and hyper-personalization modalities through GenAI and their new challenges for tourists, tourism cities and companies.

Findings reveal that GenAI technology exponentially improves consumers’ segmentation and personalization of products and services, allowing tourism cities and organizations to create tailored content in real-time. That is why the concept of hyper-segmentation is substantially focused on the customer (understood as a segment of one) and his or her preferences, needs, personal motivations and purchase antecedents, and it encourages companies to design tailored products and services with a high level of individual scalability and performance called hyper-personalization, never before seen in the tourism industry. Indeed, contextualizing the experience through GenAI is an important way to enhance personalization.

This paper also contributes to enhancing and bootstrapping the literature on GenAI in the tourism industry because it is a new field of study, and its functional operability is in an incubation stage. Moreover, this viewpoint can facilitate researchers and companies to successfully integrate GenAI into different tourism and travel activities without expecting utopian results. Recently, there have been no studies that tackle hyper-segmentation and hyper-personalization methodologies through GenAI in the tourism industry.

International trade transactions, extracted from customs declarations, include several fields, among which the product description and the product category are the most important. The product category, also referred to as the Harmonised System Code (HS code), serves as a pivotal component for determining tax rates and administrative purposes. A predictive tool designed for product categories or HS codes becomes an important resource aiding traders in their decision to choose a suitable code. This tool is instrumental in preventing misclassification arising from the ambiguities present in product nomenclature, thus mitigating the challenges associated with code interpretation. Moreover, deploying this tool would streamline the validation process for government officers dealing with extensive transactions, optimising their workload and enhancing tax revenue collection within this domain.

This study introduces a methodology focused on the generation of sentence embeddings for trade transactions, employing Sentence BERT (SBERT) framework in conjunction with the Multiple Negative Ranking (MNR) Loss function following a contrastive learning paradigm. The procedure involves the construction of pairwise samples, including anchors and positive transactions. The proposed method is evaluated using two publicly available real-world datasets, specifically the India Import 2016 and United States Import 2018 datasets, to fine-tune the SBERT model. Several configurations involving pooling strategies, loss functions, and training parameters are explored within the experimental setup. The acquired representations serve as inputs for traditional machine learning algorithms employed in predicting the product categories within trade transactions.

Encoding trade transactions utilising SBERT with MNR loss facilitates the creation of enhanced embeddings that exhibit improved representational capacity. These fixed-length embeddings serve as adaptable inputs for training machine learning models, including support vector machine (SVM) and random forest, intended for downstream tasks of HS code classification. Empirical evidence supports the superior performance of our proposed approach compared to fine-tuning transformer-based models in the domain of trade transaction classification.

Our approach generates more representative sentence embeddings by creating the network architectures from scratch with the SBERT framework. Instead of exploiting a data augmentation method generally used in contrastive learning for measuring the similarity between the samples, we arranged positive samples following a supervised paradigm and determined loss through distance learning metrics. This process involves continuous updating of the Siamese or bi-encoder network to produce embeddings derived from commodity transactions. This strategy aims to ensure that similar concepts of transactions within the same class converge closer within the feature embedding space, thereby improving the performance of downstream tasks.

The purpose of this study is to empirically explore the impact of government subsidies for the digital economy on corporate innovation. It aims to determine whether these subsidies promote innovation, and to examine the specific ways in which they inspire corporate innovation.

This study selects Chinese A-share listed companies during the period from 2007 to 2019 as the research object. It employs panel data to empirically examine the impact of government subsidies in the digital economy on corporate innovation.

The findings reveal that government subsidies for the digital economy effectively promote corporate innovation. They significantly increase the number and share of invention patents and improve the quality of corporate innovation. Moreover, it is noted that the positive impact is largely confined to non-state-owned enterprises, small firms and those in highly competitive markets.

The contribution of this paper lies in focusing on government subsidies in the digital economy, which is distinct from the general government subsidies in a broad sense.

In today’s competitive business landscape, organizations are increasingly recognizing the strategic advantage of implementing sustainable practices to gain a competitive edge. This study aims to investigate the effect of green artificial intelligence (AI) on achieving a green competitive advantage, examining the mediating roles of green organizational learning, green product innovation and green process innovation. Additionally, the research explores the moderating role of perceived green climate in the relationship between green AI and these mediating factors.

This research examined companies in Isfahan, Iran, that have varying levels of artificial intelligence adoption within their business processes. The target population consisted of 148 senior managers from these companies. This study uses structural equation modeling to examine the proposed model.

Green AI positively impacted green organizational learning and green process innovation but not green product innovation. In addition, the results showed that green organizational learning, green product innovation and green process innovation had positive effects on green competitive advantage. Finally, the results showed that the perceived green climate did not play a moderating role in the relationship between green AI and these mediating factors.

Organizations should prioritize green AI initiatives, foster a culture of green learning and invest in green innovation to achieve sustainable growth and outpace competitors in the environmentally conscious marketplace.

This study positions itself at the forefront of research on green AI and green competitive advantage. It offers a unique framework by examining the combined effects of green AI, green learning and both product and process innovation on achieving a sustainable competitive advantage.

In the context of increasing concerns about health, nutraceutical restaurants that provide health benefits have emerged in the marketplace. However, customer experiences at these restaurants are poorly understood. This study focused on sensory experiences and examined the underlying mechanism by which they contribute to memorable dining experiences. Grounded in cognitive appraisal theory, this study developed a memorable dining experience model that links sensory stimuli, meaningfulness, novelty, emotions, and behavioral intentions.

Data were collected from 880 Chinese customers who dined at traditional Chinese medicine restaurants and were analyzed via partial least squares structural equation modeling.

The results revealed that sensory stimuli contributed to memorable dining experiences through meaningfulness, novelty, and emotions. Furthermore, memorable dining experiences increased behavioral intentions to spread positive word-of-mouth and revisit intentions. Additionally, customers’ gender moderated the effects of sensory stimuli on meaningfulness and novelty.

The findings of this study can be used to identify important sensory stimuli and their roles in delivering memorable dining experiences in traditional Chinese medicine restaurants. Therefore, this study’s findings contribute to an improved understanding of how to efficiently manage sensory stimuli to stimulate memorable experiences for restaurant patrons.

This study tests the influence of sensory stimuli on the memorable dining experiences of customers in China.

The current study used the Stimuli-Organism-Response (S-O-R) paradigm to analyze value co-creation and customer gratitude influence on hotel guests' online reviews. It also examines the price fairness perception moderating influence on value co-creation and consumer gratitude.

Data were collected from 436 customers using an Internet-based questionnaire. PLS-SEM was utilized to assess hypotheses based on WarpPLS.7 software.

The findings demonstrated that value co-creation (co-production and value in use) significantly impacted customer gratitude and willingness to post positive online reviews. Gratitude positively influenced customers’ willingness to post online reviews. Gratitude mediated the relation between value co-creation and willingness to post positive online reviews. Price fairness perception moderated the relationship between value co-creation dimensions and customer gratitude.

The S-O-R framework underpins this study to measure the effects of co-production and value in use (stimuli) on consumer gratitude (organisms) and willingness to post positive online reviews (response). No prior studies examined this paradigm in an emerging market like Egypt. In addition, the study investigated the fair price fairness perception as a new moderating variable. Theoretical and managerial consequences are addressed.