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The purpose of this study is to provide an overview of the research conducted on hospitality and tourism articles published in Q1 category journals from 1990 to 2023. This study also aims to measure the topic prevalence in selected journals throughout the years, their change over time and similarities of journals.

Latent dirichlet allocation algorithm is used as a topic modeling method to identify and analyze topics in hospitality and tourism research over the past 30 years.

The results of the study indicate that hospitality and tourism research has recently focused on topics such as employee behavior, customer satisfaction, online reviews, medical tourism and tourist experience. However, the results also indicate a negative trend in topics such as hotel management, sustainability, profession, economic growth and tourist destination.

This study can be used to examine the evolution of research patterns over time, find hot and cold themes and uncover untapped or understudied areas. This can aid academics in their investigations and practitioners in making sound strategic decisions.

This study contributes to the existing literature by providing a new approach and comprehensive analysis of hospitality and tourism research topics. It delineates an overview of the progression of hospitality and tourism research over the past 30 years, identifies the trending topics and explores the potential impacts that these identified topics may have on future studies.

In this study, the effects are investigated of brand and product storytelling on customer assessments of perceived attitude, quality, experience, recommendation and purchase intention based on narrative transportation theory within the domain of winery e-commerce.

In the research, two distinct constructs are introduced for brand and product storytelling, and multivariate analysis of variance is applied to analyze data collected from 391 respondents from the UK.

It is indicated in the study that embedding storytelling into elements, such as age verification screens on e-commerce sites, notably improves evaluations for both brands and products. Product storytelling broadly boosts customer appraisals in every evaluated aspect, while brand storytelling shows effectiveness in more limited cases. This suggests that narratives focused on products may have a wider appeal in enhancing the online customer experience.

This research underscores the strategic significance of balancing the focus between brand and product in crafting storytelling narratives for e-commerce contexts. It offers new insights into tailoring storytelling to meet individual consumer needs online, enriching the existing body of literature on storytelling’s application in digital commerce. Importantly, the study provides actionable guidance for wineries and other businesses aiming to enhance their online customer engagement through targeted narrative strategies.

The purpose of this study is to examine how existing supply chain finance challenges confronting SMEs are affected by the emergence of smart fintech providers. In so doing the paper aims at uncovering critical role of fintech service provision in SCF and associated mechanisms that affect the SCF partners.

An in-depth case study approach has been applied in this study. The overall design is informed by a 5-stage-based case study approach developed in operation management, including the literature review and research question, followed by case selection and instrument development, the data gathering, the analysis and findings and dissemination.

The study shows that fintech service provider is capable of offering different digital technologies adapted to specific needs while concomitantly orchestrating the information flow across the partners. Key mechanisms that influence the establishment of trust-based relationships among the SCF partners, and related service processes and value creation based on the platform system architecture are explained.

Several practical implications for digital platform management and other key digital SCF partners are identified.

This paper contributes a novel perspective on the importance of digital trust in SCF and also contributes to the existing literature by filling up a gap with a new and fine-grained understanding of the role of fintech companies in SCF.

Considering the relevance of understanding what influences environmentally sustainable consumer choices, the present study aims to examine and synthesize the key determinants factors from literature and outline a new conceptual framework for explaining green purchasing behaviors (GPBs).

A bibliometric analysis was conducted on 161 articles extracted from Web of Science and Scopus databases, which were systematically evaluated and reviewed, and represent the current GPB knowledge base. Content analysis, science mapping and bibliometric analysis techniques were applied to uncover the major theories and constructs from the state-of-the-art.

The evolving debate between altruistic and self-interest consumer motivations reveals challenges for rational-based theories, as most empirical applications are not focused on buying behaviors, but instead either on pro-environmental (non-buying) activities or on buying intentions. From the subset of leading contributions and emerging topics, nine thematic clusters are unveiled in this investigation, which were combined to create the new PSICHE framework with the purpose of predicting GPB: (P)roduct-related factors, (S)ocial influences, (I)ndividual factors, (C)oncerns about the environment, (H)abits and (E)motions.

By uncovering the multiple intervening factors in GPB decision processes, this study will assist practitioners and academics to move forward on how to foster more sustainable consumer behaviors.

The present study provides readers a summary of an unprecedentedly broad collection of papers, from which the key themes are categorized, the domain's intellectual structure is captured and an actionable framework for enhancing the understanding GPB is proposed. Four new thrust areas and a set of future research questions are included.

This study aims to address these challenges by enhancing the resistance of Ag-based pastes to corrosion and sulfurization, thereby improving their performance and weatherability in high-power and high-frequency electronic applications.

This study investigates the influence of Sn doping in W-doped Ag paste to enhance resistance against electrochemical corrosion and sulfurization. A systematic examination was conducted using transient liquid phase sintering and solid–liquid inter-diffusion techniques to understand the microstructural and electrochemical properties.

This study found that Sn addition in W-doped Ag paste significantly improves its resistance to electrochemical corrosion and sulfurization. The sintering process at 600°C led to the formation of an Ag₂WO₄ phase at the grain boundaries, which, along with the presence of Sn, effectively inhibited the growth of Ag₂WO₄ grains. The 0.5% Sn-doped samples exhibited optimal anti-corrosion properties, demonstrating a longer grain boundary length and a passivation effect that significantly reduced the corrosion rate. No Ag₂S phase was detected in the weatherability tests, confirming the enhanced durability of the doped samples.

The findings of this study highlight the potential of Sn-doped Ag-W composites as a promising material for electronic components, particularly in environments prone to sulfurization and corrosion. By improving the anti-corrosion properties and reducing the grain size, this study offers a new approach to extending the lifespan and reliability of electronic devices, making a significant contribution to the development of advanced materials for high-power and high-frequency applications.

Trust plays a crucial role in overcoming uncertainty and reducing risks. Uncovering the trust mechanism in the sharing economy may enable sharing platforms to design more effective marketing strategies. However, existing studies have inconsistent conclusions on the trust mechanism in the sharing economy. Therefore, this study aims to investigate the antecedents and consequences of different dimensions of trust (trust in platform and trust in peers) in the sharing economy.

First, we conducted a meta-analysis of 57 related articles. We tested 13 antecedents of trust in platform (e.g. economic benefits, enjoyment, and information quality) and eight antecedents of trust in peers (e.g. offline service quality and providers’ reputation), as well as their consequences. Then, we conducted subgroup analyses to test the moderating effects of economic development level (Developed vs Developing), gender (Female-dominant vs Male-dominant), platform type (Accommodation vs Transportation), role type (Obtainers vs Providers), and uncertainty avoidance (Strong vs Weak).

The results confirm that all antecedents and consequences significantly affect trust in platform or peers to varying degrees. Moreover, trust in platform greatly enhances trust in peers. Besides, the results of the moderating effect analyses demonstrate the variability of antecedents and consequences of trust under different subgroups.

This paper provides a clear and holistic view of the trust mechanism in the sharing economy from an object-based trust perspective. The findings may offer insights into trust-building in the sharing economy.

The purpose of this study is to investigate and demonstrate the advancements achieved in the field of chest X-ray image captioning through the utilization of dynamic convolutional encoder–decoder networks (DyCNN). Typical convolutional neural networks (CNNs) are unable to capture both local and global contextual information effectively and apply a uniform operation to all pixels in an image. To address this, we propose an innovative approach that integrates a dynamic convolution operation at the encoder stage, improving image encoding quality and disease detection. In addition, a decoder based on the gated recurrent unit (GRU) is used for language modeling, and an attention network is incorporated to enhance consistency. This novel combination allows for improved feature extraction, mimicking the expertise of radiologists by selectively focusing on important areas and producing coherent captions with valuable clinical information.

In this study, we have presented a new report generation approach that utilizes dynamic convolution applied Resnet-101 (DyCNN) as an encoder (Verelst and Tuytelaars, 2019) and GRU as a decoder (Dey and Salemt, 2017; Pan et al., 2020), along with an attention network (see Figure 1). This integration innovatively extends the capabilities of image encoding and sequential caption generation, representing a shift from conventional CNN architectures. With its ability to dynamically adapt receptive fields, the DyCNN excels at capturing features of varying scales within the CXR images. This dynamic adaptability significantly enhances the granularity of feature extraction, enabling precise representation of localized abnormalities and structural intricacies. By incorporating this flexibility into the encoding process, our model can distil meaningful and contextually rich features from the radiographic data. While the attention mechanism enables the model to selectively focus on different regions of the image during caption generation. The attention mechanism enhances the report generation process by allowing the model to assign different importance weights to different regions of the image, mimicking human perception. In parallel, the GRU-based decoder adds a critical dimension to the process by ensuring a smooth, sequential generation of captions.

The findings of this study highlight the significant advancements achieved in chest X-ray image captioning through the utilization of dynamic convolutional encoder–decoder networks (DyCNN). Experiments conducted using the IU-Chest X-ray datasets showed that the proposed model outperformed other state-of-the-art approaches. The model achieved notable scores, including a BLEU_1 score of 0.591, a BLEU_2 score of 0.347, a BLEU_3 score of 0.277 and a BLEU_4 score of 0.155. These results highlight the efficiency and efficacy of the model in producing precise radiology reports, enhancing image interpretation and clinical decision-making.

This work is the first of its kind, which employs DyCNN as an encoder to extract features from CXR images. In addition, GRU as the decoder for language modeling was utilized and the attention mechanisms into the model architecture were incorporated.

This study aims to identify key contributors, research themes, research gaps, and future directions in hospitality innovation by conducting bibliometric and content analyses of peer-reviewed articles in this field.

A bibliometric analysis was conducted using VOSviewer software on 2,698 peer-reviewed English-language articles retrieved from the Web of Science database, published between 1995 and 2023. Key contributors were identified based on publication volume, citation, and co-citation analysis. Co-occurrence analysis of index keywords and content analysis of influential articles were used to identify research themes.

The study identified four distinct research themes in hospitality innovation: (1) digital technology adoption primarily among customers, (2) innovation management within hospitality firms, focusing on knowledge management and eco-innovation, (3) service innovation primarily among employees, and (4) business model innovation involving multiple stakeholders. Additionally, the study determined key contributors, highlighted research gaps, and provided suggestions for future research directions.

This study contributes to the existing literature by providing a systematic and in-depth review of hospitality innovation research. It identifies key contributors, research themes, and potential gaps for future research, offering valuable insights for both industry practitioners and scholars.

Many developing countries have encountered frequent pollution accidents during their rapid development, while the previously weak environmental insurance systems could seriously undermine the progress of sustainable development. Some developing countries like China has initiated and strengthened environmental pollution liability insurance, so how effective this system would be in resolving enterprises environmental risks need to be further revealed.

This research identifies the possible consequence that compulsory environmental pollution liability insurance pilot (CEPLIP) policy would bring to the risk-taking capacity of heavy-polluting corporations of China by the Differences-in-Differences (DID) approach.

The result supports the implementation of CEPLIP policy in increasing corporate risk-taking capacity. Furthermore, the CEPLIP policy can promote the corporate’s risk-taking capacity by reducing financial distress constraints and enhancing trade credit, supporting its dual role of “fallback effect” as well as “external supervision effect” of environmental insurance. As a result of heterogeneity test, the policy is more pronounced in enterprise samples with mature life cycle stage or lower industrial concentration degree. Similarly, it is more significant in enterprise samples owned better environmental management capabilities or greater strategic deviance.

This paper verifies the effectiveness of the CEPLIP policy by strengthening its supervision mechanism and restraining opportunistic behavior tendency and provides implications for alleviating increasing environmental risk pressure and building more sustainable environmental protection management systems.