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Because of the COVID-19, the digital transformation of global hospitality and tourism speeds up. This paper aims to provide comprehensive frame of the digital transformation for further hospitality and tourism research.

Through conducting a critical review of the impact of COVID-19, the current situation about the application of digital technology and digital transformation in hospitality and travel, this study used a qualitative approach to present the viewpoints.

This research presents a theoretical research framework for the hospitality and tourism about digital transformation, including possible directions, contexts and methods. It highlights the importance of digital transformation, and further proposing specific research topics.

This research brings valuable implications and guidance for future research from the aspects of key research streams, research context and methodological approaches in hospitality and tourism about digital transformation.

This paper supplies existing critical reviewed research through paying attention to the digital transformation approach in hospitality and tourism, providing research guidance technically to the industry of hotels and travel.

Corporate social responsibility (CSR) is significant in the financial market. Despite plenty of existing research on CSR, few studies have quantified the fine-grained aspects of CSR and examined how diverse CSR aspects are associated with firms' trade credit. Based on the released CSR reports, this paper strives to measure the CSR fulfillment of firms and examine the relationships between CSR and trade credit in terms of textual features presented in these reports.

This research proposes a natural language processing-based framework to extract the overall readability and the sentiment of fine-grained aspects from CSR reports, which can signal the performance of firms' CSR in diverse aspects. Furthermore, this paper explores how the textual features are associated with trade credit through partial dependence plots (PDPs), and PDPs can generate both linear and nonlinear relationships.

The study’s results reveal that the overall readability of the reports is positively associated with trade credit, while the performance of the fine-grained CSR aspects mentioned in the CSR reports matters differently. The performance of the environment has a positive impact on trade credit; the performance of creditors, suppliers and information disclosure, shows a U-shaped influence on trade credit; while the performance of the government and customers is negatively associated with trade credit.

This study expands the scope of research on CSR and trade credit by investigating fine-grained aspects covered in CSR reports. It also offers some managerial implications in the allocation of CSR resources and the presentation of CSR reports.

This paper aims to reveal the factors patients consider when choosing a doctor for consultation on an online medical consultation (OMC) platform and how these factors influence doctors' consultation volumes.

In Study 1, influencing factors reflected as service features were identified by applying a feature extraction method to physician reviews, and the importance of each feature was determined based on word frequencies and the PageRank algorithm. Sentiment analysis was used to analyze patient satisfaction with each service feature. In Study 2, regression models were used to analyze the relationships between the service features obtained from Study 1 and the doctor's consultation volume.

The study identified 14 service features of patients' concerns and found that patients mostly care about features such as trust, phraseology, overall service experience, word of mouth and personality traits, all of which describe a doctor's soft skills. These service features affect patients' trust in doctors, which, in turn, affects doctors' consultation volumes.

This research is important as it informs doctors about the features they should improve, to increase their consultation volume on OMC platforms. Furthermore, it not only enriches current trust-related research in the field of OMC, which has a certain reference significance for subsequent research on establishing trust in online doctor–patient relationships, but it also provides a reference for research concerning the antecedents of trust in general.

The purpose of this paper is to propose a framework for describing and evaluating the representativeness of a small set of search results extracted from the original results: this is deemed desirable in information retrieval in enterprise information systems.

The paper proposes a combined measure, namely RF_β, to evaluate the extracted small set in terms of the notions of coverage and redundancy. Data experiments were conducted on three different extraction strategies to evaluate the representativeness, i.e. coverage and redundancy.

Both from intuitive and experimental perspectives, the proposed coverage measure, redundancy measure and RF_β measure could effectively evaluate the representativeness.

The search results, e.g. in the form of documents and texts, are modeled using a vector space model and cosine similarity. Semantic models and linguistic models could be further introduced into this research to improve the proposed measures.

With the rapidly growing need for information retrieval in enterprise information systems, the representativeness of search results become more desirable and important for search engine users. The well‐designed representativeness measures will help them achieve satisfactory results.

The originality of the paper lies in the definition of representativeness of a small set of search results extracted from the original results. This focuses on the two aspects of coverage rate and redundancy rate both from intuitive and experimental perspectives.

This study aims to investigate the determinants of herd behavior in online hotel service evaluations, focusing on the cultural and geographic distance characteristics of customers.

On the basis of 381,462 TripAdvisor reviews of hotels in the USA written by more than 100,000 customers from 92 countries, this study uses the empirical analysis to explore the collective roles of cultural distance, geographic distance and hospitality experience on herd behavior in online hotel ratings.

Cultural and geographic distances between customers and product and service locations positively affect herding and these two effects are substitutable. The hospitality experience of customers attenuates the impacts of distances on herding. These results are robust for multiple hotel service ratings.

Findings help hotels understand perceptual biases of customers on hotel services under the social influence and consequently develop effective marketing strategies to boost hotel revenues and increase profitability.

The research contributes to hospitality and online review literature by understanding how cultural and geographic distances shape online hotel service evaluations under the root of the uncertainty of decision-making and the observation of others’ behavior. The research also contributes to the distances in international business literature by deepening the understanding of the substitution and heterogeneity of distance effects. Methodologically, a time-varying and monotonously increasing variable is constructed to depict customers’ hospitality experience. The extensive data volume ensures the generalizability of our results.

This paper aims to automatically derive a 2D parametric model of the main characteristic lines of a car from images, blueprints or hand-made sketches of its side view. Then this model can be used for the further computer-aided design manipulation starting from images of the side view of a car.

The method combines different image edge detection techniques and edge removal processes with optimization techniques according to local and global constraints specific of the single curves to automatically construct a precise parametric model of the main character lines of a car from images. First, process the car image to compute the most important curves and then warp a car template model to match its feature points and curves with the ones detected in the image.

The paper provides method to construct parametric model from an image using maximum cover ratio to the edge points obtained by state-of-the-art edge detection algorithms. A feature points’ organization mechanism produces quadric curves to express feature curves of a product.

The robustness of the presented method depends on the completeness of edge detection results and the accuracy of some key points’ registration result, so if the image is not good, the result cannot be trusted. Only side-view is considered in this paper. Additional limits in the process regard the side view verification: pictures of the front or rear view can be wrongly classified as lateral ones when they contain round lights.

This program enables designers to convert the image to geometric parametric model directly.

The method is applicable to shaded pictures, sketches and blue prints of the side view of a car. It can process a database of car images in a batch mode or a specific picture on user demand. The method classifies the cars to different categories: SUV/Wagon/Hatchback, sedan, city and coupe. The authors obtain good results for every category.

The salinity of the oilfield produced water has a significant effect on steel corrosion. The purpose of this paper is to study the influence of salinity on corrosion behavior of X60 steel and it also provides basic for material selection of gas wells with high salinity.

The weight loss experiment was carried out on steel with high temperature and high pressure autoclave. The surface morphology and composition of corrosion scales were studied by means of scanning electron microscopy, energy dispersive spectroscopy and X-ray diffractometry.

The results show that as salinity increases, the corrosion rate of X60 steel will gradually experience a rapid decline stage and then a slow decline stage. X60 steel is mainly exhibiting uniform corrosion in the first rapid decline stage and pitting corrosion in the second slow decline stage. The increase in salinity reduces gas solubility, which, in turn, changes the morphology and density of the corrosion scales of X60 steel. At low salinity, loose iron oxides generated on the surface of the steel, which poorly protects the substrate. At high salinity, surface of the steel gradually forms protective films. Chloride ions in the saline solution mainly affect the structure of the corrosion scales and initiate pitting corrosion. The increased chloride ions lead to more pitting pits on the surface of steel. The recrystallization of FeCO₃ in pitting pits causes the corrosion scales to bulge.

The investigation determined the critical concentration of pitting corrosion and uniform corrosion of X60 steel, and the new corrosion mechanism model was presented.

The criminalization of online financial fraud is examined by analyzing the existing literature, policies and state statutes within the context of the cybercrime ecosystem. Therefore, this paper aims to investigate online fraud policies within the USA and the prevalence of such incidents to explore the effectiveness of current fraud policies.

This examination explores policies related to online fraud within the USA by defining online financial fraud incidents within the context of the cybercrime ecosystem and analyzing such incidents with routine activities theory to emphasize the current legislative inadequacies with provisional policy recommendations.

This research suggests online financial fraud is not unanimously conceptualized among regulating or criminal institutions. Although federal regulators have governed financial institutions, federal institutions have failed to account for the capabilities of computer-mediated and technological device use (12 USC §1829).

The limited research analyzing the effectiveness of guardianship that prevents or deters internet-mitigated or dependent financial fraud crimes.

Policy recommendations include but are not limited to mandating federal and privatized financial institutions to disclose all fraudulent activity to all stakeholders (e.g. customers and local and federal criminal justice agencies).

This paper provides an innovative approach using a criminological theory and policy framework to examine the prevalence of online fraud and the regulations enacted to counteract such violations.

Discovering the research topics and trends from a large quantity of library electronic references is essential for scientific research. Current research of this kind mainly depends on human justification. The purpose of this paper is to demonstrate how to identify research topics and evolution in trends from library electronic references efficiently and effectively by employing automatic text analysis algorithms.

The authors used the latent Dirichlet allocation (LDA), a probabilistic generative topic model to extract the latent topic from the large quantity of research abstracts. Then, the authors conducted a regression analysis on the document-topic distributions generated by LDA to identify hot and cold topics.

First, this paper discovers 32 significant research topics from the abstracts of 3,737 articles published in the six top accounting journals during the period of 1992-2014. Second, based on the document-topic distributions generated by LDA, the authors identified seven hot topics and six cold topics from the 32 topics.

The topics discovered by LDA are highly consistent with the topics identified by human experts, indicating the validity and effectiveness of the methodology. Therefore, this paper provides novel knowledge to the accounting literature and demonstrates a methodology and process for topic discovery with lower cost and higher efficiency than the current methods.