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Wine tourism is gaining increasing popularity among Chinese tourists, making it necessary to thoroughly examine tourist behavior. While online reviews posted by wine tourists have been extensively studied from the perspectives of destinations and wineries, the perspective of the tourists themselves has been overlooked. To address this gap, this study aims to identify significant attributes intrinsic to the tourism experiences of Chinese wine tourists by adopting a text-mining approach from a tourist-centric perspective.

The authors use topic modeling to extract these attributes, calculate topic intensity to understand tourists’ attention distribution across these attributes and conduct topical sentiment analysis to evaluate tourists’ satisfaction levels with each attribute. The authors perform importance-performance analyses (IPAs) using topic intensity and sentiment scores. Furthermore, the authors conduct semistructured in-depth interviews with Chinese wine tourists to gain insights into the underlying reasons behind the key findings.

The study identifies eleven attributes for domestic wine tourists and seven attributes for outbound wine tourists. From the reviews of both domestic and outbound tourists, three common attributes have been identified: “scenic view”, “wine tasting and purchase” and “wine knowledge”.

According to the results of the IPAs, there is a pressing need for enhancements in the wine tasting and purchasing experience at domestic wine attractions. Additionally, managers of domestic wine attractions should continue to prioritize the positive aspects of the family trip experience and scenic views. On the other hand, for outbound wine attractions, it is crucial for managers to maintain their efforts in providing opportunities for wine knowledge acquisition, ensuring scenic views and upholding the reputation of wine regions.

First, this study breaks new ground by adopting a tourist-centric perspective to extract significant attributes from real wine tourism reviews. Second, the authors conduct a comparative analysis between Chinese wine tourists who travel domestically and those who travel abroad. The third novel aspect of this study is the application of IPA based on textual review data in the context of wine tourism. Fourth, by integrating topic modeling with qualitative interviews, the authors use a mixed-method approach to gain deeper insights into the experiences of Chinese wine tourists.

The purpose of this paper is to identify electronic word-of-mouth (eWOM) customers from customer reviews. Thus, firms can precisely leverage eWOM customers to increase their product sales.

This research proposed a framework to analyze the content of consumer-generated product reviews. Specific algorithms were used to identify potential eWOM reviewers, and then an evaluation method was used to validate the relationship between product sales and the eWOM reviewers identified by the authors’ proposed method.

The results corroborate that online product reviews that are made by the eWOM customers identified by the authors’ proposed method are more related to product sales than customer reviews that are made by non-eWOM customers and that the predictive power of the reviews generated by eWOM customers are significantly higher than the reviews generated by non-eWOM customers.

The proposed method is useful in the data set, which is based on one type of products. However, for other products, the validity must be tested. Previous eWOM customers may have no significant influence on product sales in the future. Therefore, the proposed method should be tested in the new market environment.

By combining the method with the previous customer segmentation method, a new framework of customer segmentation is proposed to help firms understand customers’ value specifically.

This study is the first to identify eWOM customers from online reviews and to evaluate the relationship between reviewers and product sales.

This study aims to develop a process model to facilitate enterprises’ co-creating value with consumers through social media.

Drawing on the concepts of internet-based co-creation and collective action theory, this study outlines a five-stage model (Interact-Engage-Propose-Act-Realize, IEPAR) of utilizing social media to co-create with consumers, enriches the model through in-depth interviews with industry experts and briefly illustrates how it can be applied in practice using a service firm case.

This study clarifies the co-creation process in the social media environment. For each of the process’s five stages, the objectives to be accomplished by the social media operator and the means to complete the objectives are illustrated.

This study illustrated the proposed model with a representative service firm. Future study may refine the model by gathering additional data from real implementations to improve its effectiveness in practice.

This study suggests how an enterprise can construct a consumer co-creation platform from a managerial perspective. The proposed model can serve as a reference that enterprises can implement to increase customer value through co-creation using social media.

Enterprises have begun to notice the power of serving as a platform for co-creating value with consumers. However, it is seldom related to literature. The proposed model of the co-creation process in the social media environment can supplement past research.

In the sharing economy (SE), consumer misbehaviour is an operational challenge for platforms due to its negative outcomes. The psychological mechanism behind consumer misbehaviour remains unclear. As such, this research aims to investigate how consumers' sharing motivations affect their misbehaviours in the SE.

Drawing on motivated cognition theory, the authors establish a research model explaining the effects of consumers' sharing motivations on their cost–benefit analysis of misbehaviour and misbehaviour intention. A scenario-based online experiment is used to test the research hypotheses.

The results indicate that consumers' extrinsic and intrinsic motivations to share have different impacts on their perceived benefits and costs of potential misbehaviour, thereby influencing their misbehaviour intention.

This study reveals consumers' psychological mechanism underlying their misbehaviours in the SE and provides operational implications for platforms to help them effectively reduce consumer misbehaviours through preventive measures.

The purpose of this paper is to develop the multi-objective optimization approach and its numerical implementation to synthesise the model-base control for the part curing at autoclave processing, which supplies the stability and uniformity of the structure and mechanical properties of the material within the cured composite part.

The approach includes conversion of the cured part and mold geometry from their computer-aided design (CAD) to computer-aided engineering (CAE) representation, a finite element (FE) formulation of the coupled forward heat transfer/thermal kinetic problem with the parameters of prepreg, which should be determined by the thermal analysis, and, finally, a mapping of the area of 4D design space (thermal control parameters) to 2D objective space, whose coordinates are the maximum deviations of degree of cure and temperature within the cured part calculated at each call of the FE model.

The present modeling and optimization approach to the cure process control of the prepreg with thermosetting resin, as well as the means of visualizing optimization results, allow providing insight into complex curing phenomena, estimating the best achievable quality indicators of manufactured composite parts, finding satisfactory parameters of the control law and deciding considering all manufacturing constraints.

The research can be effectively used to optimize the cure process control for a wide class of polymeric composite parts, even with a complex geometry, but it requires the exact conversion of the geometry of the modeled part from the CAD to CAE environment, which implies the need for excluding all topological imperfections of original CAD model to eliminate the possible formation of void elements and other reasons that do not allow the correct FE meshing. Because thermal, rheological and kinetics parameters, which include the governing equations of cure process, depend on the reinforcing fibers, and especially on the resin properties, the thermal testing for the new modeled prepreg needs to be performed.

Computer implementation of the proposed approach and numerical method for model-based optimal control synthesis for composite part cure process can be used in aircraft, rotorcraft, ship and automotive technologies at the design of manufacturing process of the large composite parts with complex shape.

This will allow much better quality for large-scale composite parts, excluding very expensive, time-, energy- and material-consuming multiple cure process testing.

This is first time the problem of optimal control synthesis for curing the large-scale composite parts of complex shape was solved.

Seasonal fluctuation interference often affects the relational analysis of economic time series. The main purpose of this paper is to propose a new grey relational model for relational analysis of seasonal time series and apply it to identify and eliminate the influence of seasonal fluctuation of retail sales of consumer goods in China.

First, the whole quarterly time series is divided into four groups by data grouping method. Each group only contains the time series data in the same quarter. Then, the new series of four-quarters are used to establish the grey correlation model and calculate its correlation coefficient. Finally, the correlation degree of factors in each group of data was calculated and sorted to determine its importance.

The data grouping method can effectively reflect the correlation between time series in different quarters and eliminate the influence of seasonal fluctuation.

In this paper, the main factors influencing the quarterly fluctuations of retail sales of consumer goods in China are explored by using the grouped grey correlation model. The results show that the main factors are different from quarter to quarter: in the first quarter, the main factors are money supply, tax and per capita disposable income of rural residents. In the second quarter are money supply, fiscal expenditure and tax. In the third quarter are money supply, fiscal expenditure and per capita disposable income of rural residents. In the fourth quarter are money supply, fiscal expenditure and tax.

This paper successfully realizes the application of grey relational model in quarterly time series and extends the applicable scope of grey relational model.

Building on the social leverage model (SLM), this study aims to examine the influence of event-related attributes on residents' perceived social impact of a major sport event, as mediated by event involvement. It also investigates the moderating effect of event rights holders' credibility on the relationship between event involvement and perceived social impact.

Using a two-wave, time-lagged survey, data were collected from 220 residents of a Super Bowl host city. Hypotheses were tested using structural equation modeling (SEM).

High celebratory atmosphere, social camaraderie and social responsibility as perceived before the event were associated with residents' perceptions of the social impact of the Super Bowl. Moreover, the association between social camaraderie and perceived social impact was mediated by event involvement. When appraising the rights holder as credible, involved residents reported an increased level of perceived social impact.

This study contributes to research on the SLM by demonstrating its application among indirect participants of major sport events. Additionally, it suggests the imperative role of rights holders' credibility in promoting the perceived social impact among involved residents.

This study tends to investigate how the outbreak of the coronavirus disease 2019 (COVID-19) pandemic has affected banks' contribution to systemic risk. In addition, the authors examine whether the impact of the pandemic may vary across advanced/emerging economies, and with banks with differed characteristics.

The authors construct the bank-specific conditional value at risk (CoVaR) and marginal expected shortfall (MES) to measure their contribution to systemic risk and define the outbreak of the COVID-19 pandemic by the timing when countries report more than 100 confirmed cases. The authors use the approach of difference-in-differences to assess the impact of the COVID-19 pandemic on banks' contribution to systemic risk. This sample comprises monthly panel data of around 900 listed commercial banks in 39 advanced and emerging economies.

The authors find that, firstly, the COVID-19 pandemic increased banks' contribution to systemic risk significantly around the world. Secondly, the impact of the COVID-19 virus was more pronounced in developed countries than in emerging economies. Finally, banks with a larger size and higher loan-to-deposit ratio are more greatly affected by the COVID-19 pandemic, while a higher capitalization for banks is insufficient to shelter them from the adverse impact of such pandemic.

The authors assess the impact of the COVID-19 pandemic on banks' contribution to systemic risk. Using the conditional value at risk (marginal expected shortfall) of banks as the measure, this study’s results suggest that banks' contribution to systemic risk increases by around 25% (48%) amid the COVID-19 pandemic. This study’s findings may shed some light on the potential policies that financial regulators may employ to ameliorate the adverse outcomes of the ongoing pandemic.

This paper aims to use artificial neural network (ANN) methods to predict stock price crashes in the Chinese equity market.

Three ANN models are developed and compared with the logistic regression model.

Results from this study conclude that the ANN approaches outperform the traditional logistic regression model, with fewer hidden layers in the ANN model having superior performance compared to the ANNs with multiple hidden layers. Results from the ANN approach also reveal that foreign institutional ownership, financial leverage, weekly average return and market-to-book ratio are the important variables when predicting stock price crashes, consistent with results from the traditional logistic model.

First, the ANN framework has been used in this study to forecast the stock price crashes and compared to the traditional logistic model in the world’s largest emerging market China. Second, the receiver operating characteristics curves and the area under the ROC curve have been used to evaluate the forecasting performance between the ANNs and the traditional approaches, in addition to some traditional performance evaluation methods.