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Building on a small body of work, the authors' study aims to investigate some important antecedents of online review characteristics in the Chinese restaurant industry.

Using a data set of restaurant reviews collected from a most popular review platform in China, the authors conduct a series of analyses to examine the influence of travel experience and travel distance on travelers' review characteristics in terms of review rating and media richness. The moderating effect of restaurant price on the influence is also investigated.

Travelers with a longer travel distance and more travel experience tend to provide higher and lower online ratings, respectively, which can be explained by the construal level theory (CLT) and the expectation-confirmation theory (ECT), respectively. Furthermore, these strong feelings can then induce travelers to post enriched reviews with more pictures, more words and more affective words to release consumption tension. Besides, restaurant price can moderate these relationships.

Distinguished from most studies which mainly focus on the consequences of online review characteristics or antecedents of review helpfulness, the authors pay attention to the effects of travelers' individual differences in terms of travel distance and travel experience on travelers' online reviewing behavior. In addition to review rating, the authors also focus on media richness in terms of visual and textual information. The authors' research findings can benefit restaurant consumers and managers for their online word-of-mouth utilization and management.

This paper aims to investigate the effects of low-viscosity and ultralow-viscosity engine oils on the comprehensive friction and fuel economy of turbocharged gasoline direct injection (TGDI) through simulation analysis and experiments.

Numerical analysis models of friction loss for reciprocating, crankshaft and valve train are established. Based on the FAST, the friction loss of 24 specific parts of a TGDI engine was analyzed. Finally, the engine test bench was built, which was used to test the mechanical loss, external characteristics and universal characteristics.

Compared with the baseline oil, lower viscosity lubricating oil can reduce the friction loss of nine components to varying degrees. When the viscosity decreases, the friction distribution ratio of reciprocating, crankshaft and balance shaft will gradually decrease. The proportion of reciprocating when using 0W12 is reduced by 4%. Tests have shown that ultralow viscosity engine oil reduces torque loss by up to 15.74% (2,000 rpm, full throttle), but its fuel consumption rate becomes higher in low-speed and high-torque conditions.

This work helps to understand the effect of lubricating oil characteristics on the comprehensive friction performance of the engine.

This study aims to develop a data fusion method for powder-bed fusion (PBF) process monitoring based on process image information. The data fusion method can help improve process condition identification performance, which can provide guidance for further PBF process monitoring and control system development.

Design of reliable process monitoring systems is an essential approach to solve PBF built quality. A data fusion framework based on support vector machine (SVM), convolutional neural network (CNN) and Dempster-Shafer (D-S) evidence theory are proposed in the study. The process images which include the information of melt pool, plume and spatters were acquired by a high-speed camera. The features were extracted based on an appropriate image processing method. The three feature vectors corresponding to the three objects, respectively, were used as the inputs of SVM classifiers for process condition identification. Moreover, raw images were also used as the input of a CNN classifier for process condition identification. Then, the information fusion of the three SVM classifiers and the CNN classifier by an improved D-S evidence theory was studied.

The results demonstrate that the sensitivity of information sources is different for different condition identification. The feature fusion based on D-S evidence theory can improve the classification performance, with feature fusion and classifier fusion, the accuracy of condition identification is improved more than 20%.

An improved D-S evidence theory is proposed for PBF process data fusion monitoring, which is promising for the development of reliable PBF process monitoring systems.

Anhui Winall Hi-Tech Seed Co., Ltd., a high-tech seed enterprise integrating crop seed research, production, processing and marketing at home and abroad, is the first seed company listed on GEM in China. Its main business is research and development, breeding and marketing of seeds of hybrid rice, edible rape, cotton, melon and vegetable, with hybrid rice as its leading product. In terms of business model, Winall Hi-tech is engaged in procurement, production, sales and promotion of modified varieties and after-sales service. However, Winall Hi-tech also has to face a few potential problems.

During the Coronavirus crisis (COVID-19) that started in 2019 and at the extensive quarantine regulations, educational institutions, companies, and individuals have reacted by shifting their teaching and learning activities to virtual spaces. Yet, although the use of online learning has increased, it has not been able to achieve the long-promised transformative effect. The COVID-19 crisis has the potential to boost online education overall or at least enable better preparation of the system for the next crisis. Ultimately, to make a digital transformation sustainable, appropriate skills are required. In this study, we adapt the dynamic capabilities foundations creating a theoretical approach to explain how educational institutions have responded to the changing environmental conditions during the COVID-19 pandemic.

The purpose of this study is to develop a deep learning framework for additive manufacturing (AM), that can detect different defect types without being trained on specific defect data sets and can be applied for real-time process control.

This study develops an explainable artificial intelligence (AI) framework, a zero-bias deep neural network (DNN) model for real-time defect detection during the AM process. In this method, the last dense layer of the DNN is replaced by two consecutive parts, a regular dense layer denoted (L1) for dimensional reduction, and a similarity matching layer (L2) for equal weight and non-biased cosine similarity matching. Grayscale images of 3D printed samples acquired during printing were used as the input to the zero-bias DNN.

This study demonstrates that the approach is capable of successfully detecting multiple types of defects such as cracks, stringing and warping with high accuracy without any prior training on defective data sets, with an accuracy of 99.5%.

Once the model is set up, the computational time for anomaly detection is lower than the speed of image acquisition indicating the potential for real-time process control. It can also be used to minimize manual processing in AI-enabled AM.

To the best of the authors’ knowledge, this is the first study to use zero-bias DNN, an explainable AI approach for defect detection in AM.

“Competition intensity” is a factor in addressing competitiveness. The understanding on competition intensity is prerequisite to the formulation of industrial competition policies as well as firms’ competition strategies. In the construction context, whereas competition intensity can be measured using a number of traditional approaches (e.g. competitor number, concentration), the measurement is often criticized for poor efficiency. The purpose of this paper is to propose a new model for measuring competition intensity in light of the theory of discriminant analysis.

The proposed model is composed of predictor variables concerned with market operation as well as criterion variables that classify markets into a few predefined groups based on the values of competition intensity. Empirical data of China's local construction markets were collected to verify the proposed model.

The research findings indicate that the model can offset the drawbacks of traditional measures in the construction market.

It is recommended using the proposed model to predict the competition trend of construction market especially when data for the traditional approaches are poor or not readily available.

The proposed model is a development of the literature in examining competition intensity.

Applying brand names to international markets remains a challenge to multinational corporations. Consumers’ sociolinguistic backgrounds shape their responses to brand names. This paper uses a sociolinguistic approach as a conceptual framework in understanding brand naming and translating in the Chinese market. The approach promotes that sociolinguistics a) recognizes linguistic competence, b) advances symbolic values imbedded in linguistic forms, and c) renders attached social valence to cultural scrutiny. Three brand‐naming cases in China are presented for discussion, which may benefit multinational corporations on brand decisions involving Chinese consumers.

This study aims to discuss the state-of-the-art digital factory (DF) development combining digital twins (DTs), sensing devices, laser additive manufacturing (LAM) and subtractive manufacturing (SM) processes. The current shortcomings and outlook of the DF also have been highlighted. A DF is a state-of-the-art manufacturing facility that uses innovative technologies, including automation, artificial intelligence (AI), the Internet of Things, additive manufacturing (AM), SM, hybrid manufacturing (HM), sensors for real-time feedback and control, and a DT, to streamline and improve manufacturing operations.

This study presents a novel perspective on DF development using laser-based AM, SM, sensors and DTs. Recent developments in laser-based AM, SM, sensors and DTs have been compiled. This study has been developed using systematic reviews and meta-analyses (PRISMA) guidelines, discussing literature on the DTs for laser-based AM, particularly laser powder bed fusion and direct energy deposition, in-situ monitoring and control equipment, SM and HM. The principal goal of this study is to highlight the aspects of DF and its development using existing techniques.

A comprehensive literature review finds a substantial lack of complete techniques that incorporate cyber-physical systems, advanced data analytics, AI, standardized interoperability, human–machine cooperation and scalable adaptability. The suggested DF effectively fills this void by integrating cyber-physical system components, including DT, AM, SM and sensors into the manufacturing process. Using sophisticated data analytics and AI algorithms, the DF facilitates real-time data analysis, predictive maintenance, quality control and optimal resource allocation. In addition, the suggested DF ensures interoperability between diverse devices and systems by emphasizing standardized communication protocols and interfaces. The modular and adaptable architecture of the DF enables scalability and adaptation, allowing for rapid reaction to market conditions.

Based on the need of DF, this review presents a comprehensive approach to DF development using DTs, sensing devices, LAM and SM processes and provides current progress in this domain.

The purpose of this paper is intended to create a model to measure quality of service, using fuzzy linguistics to analyze the quality of service of medical tourism in Taiwan so as to find the direction for improvement of service quality in medical tourism.

The study developed fuzzy questionnaires based on the characteristics of medical tourism quality of service in Taiwan. Questionnaires were delivered and recovered from February to April 2014, using random sampling according to the proportion of medical tourism companies in each region, and 150 effective samples were obtained. The critical quality of service level is found through the fuzzy gap analysis using questionnaires examining expectations and perceptions of customers, as the direction for continuous improvement.

From the study, the primary five critical service items that improve the quality of service for medical tourism in Taiwan include, in order: the capability of the service provider to provide committed medical tourism services reliably and accurately, facility service providers in conjunction with the services provided, the cordial and polite attitude of the service provider eliciting a sense of trust from the customer, professional ability of medical (nursing) personnel in hospital and reliability of service provider.

The contribution of this study is to create a fuzzy gap analysis to assess the performance of medical tourism service quality, identify key quality characteristics and provide a direction for improvement and development for medical tourism service quality in Taiwan.