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Infectious diseases are a global public health problem, which requires timely and effective responses. This study proposes a novel model that contributes to the development of such responses. First, the problem scenario features of infectious disease emergency scenarios are extracted, and the problem scenario is structurally described. A Markov model is adopted to analyze the scenario evolution of the infectious disease outbreaks. Then, a dynamic case-based reasoning model is built. Different matching algorithms are designed for crisp symbols, crisp numbers, interval numbers, and fuzzy linguistic variables. The similarity between the target scenario and various historical scenarios is calculated. Finally, an optimized dynamic emergency decision guide is provided. An experiment is conducted to test the validity and feasibility of the proposed method. The results suggest that the model can realistically simulate the process of infectious disease outbreaks and quickly match the recorded scenarios to generate effective and real-time responses.

This study explores the possibility of using a machine learning approach to analysing social media big data for tourism demand forecasting. We demonstrate how to extract the main topics discussed on Twitter and calculate the mean sentiment score for each topic as the proxy of the general attitudes towards those topics, which are then used for predicting tourist arrivals. We choose Sydney, Australia as the case for testing the performance and validity of our proposed forecasting framework. The study reveals key topics discussed in social media that can be used to predict tourist arrivals in Sydney. The study has both theoretical implications for tourist behavioural research and practical implications for destination marketing.

The study of flow behaviour of water-sand mixtures in fractured rocks is of great necessity to understand the producing mechanism and prevention of water inrush and sand gushing accidents. A self-developed seepage test system is used in this paper to conduct laboratory experiments in order to study the influence of the particle size distribution, the void ratio, and the initial mass of Aeolian sand on the flow behavior. It is concluded that the water flow velocity is insensitive to the initial mass of the Aeolian sand but increases with the power exponent in the Talbot formula and the specimen height. The outflow of the Aeolian sand increases with the power exponent in the Talbot formula, the specimen height, and the initial mass of the Aeolian sand. Besides, the outflow of the Aeolian sand changes exponentially with the water flow velocity. Finally, it is found that the fractured specimen has a maximum sand filtration capacity beyond which the outflow of the Aeolian sand significantly increases with the initial mass of the Aeolian sand.

Understanding tourist movements provides insights for destination planning, service design and marketing. The key challenge is to develop a tool that can capture the value in the tourist mobility data. This study presents a new heuristic approach that combines adaptive spatial clustering with frequent pattern mining to improve the performance and efficiency of trajectory data analytics. The aim is to fully leverage the semantic information in the tourist data and the duration that tourists stay at an attraction. Anonymous mobile positioning data from 741 tourists to one of China's leading destinations are used to illustrate the application of the new analytical approach. The results reveal a four-level destination spatial structure ranging from core to peripheral areas. The findings provide practical implications for facilitating intra-destination cooperation and optimizing destination resource allocation and service design.

Traveling in a group brings various social and environmental benefits, yet members might have different (and sometime conflicting) preferences. In this study, a tour recommendation framework is proposed that receives a set of must-visit and preferred points of interest from each tourist and forms multi-day tours that cover all must-visit points. Furthermore, the framework attempts to maximize fairness among group members. This ensures all members are motivated to participate in the group tour. While generating the itinerary, the framework also guarantees that a threshold on the commuted distance, time, and monetary budget is met on each day. The benefits of this approach are the maximization of social wellbeing and minimization of energy consumption. The advantages of the proposed framework are confirmed via a test using a Foursquare dataset of two major cities of New York and Tokyo and a user study.

Consumers increasingly use eWOM to make decisions about various products and services. However, few studies have investigated how different visual and verbal eWOM cues affect the intention and decision to visit tourist destinations and their attractions. The current study fills this gap by drawing on Dual Coding Theory and investigating the influence of verbal and visual eWOM cues on consumers’ intention and behavior. The findings of a field study and an experimental study revealed that eWOM mainly affects tourists’ intentions and decisions through visual cues. Specifically, popularity heuristics, performance visual heuristics, and user-generated pictures affect tourists’ intention and decision to visit a destination and its attractions. Interestingly, information quality did not affect tourists’ decisions. The study offers important theoretical and managerial implications.

• What tourists gaze on varies across the three groups. • North American tourists prefer to gaze on water scenes. • European tourists have a preference for the scenes of foliage and sky. • Asian tourists like to present themselves in front of traditional buildings. • The three groups also show differences in places of interest.

The fast development of machine learning and artificial intelligence has led to a great improvement of the smart tourism recommendation system, however many problems associated with the choice of transport modes in city tourism have yet to be solved. This research attempts to address this issue by proposing a model of customized day itineraries with consideration of transport mode choice. With improved particle swarm optimization and differential evolution algorithm, a nondominated sorting heuristic approach was devised. A case study was carried out in Chengdu, China to examine the performance of our approach. The results show that compared with extant methods, our approach achieves better performance. In addition, our approach can create more sensible, multifarious, and customized itineraries than previous methods. Tourism organizations and mobile map app providers could integrate our proposed model into their existing smart service systems, as part of their e-business or digital strategy for enhancing tourist experience.

This study extends tourism research by examining whether residents' perceptions of tourism development drives their participation in value co-creation with tourists. Moreover, we investigate the subsequent impact of this value co-creation activity on residents' subjective wellbeing. Drawing upon self-determination and social exchange theories, we proposed an integrated theoretical model and tested it using data collected from residents in four major Chinese cities. The results indicate that residents’ participation in value co-creation with tourists has a positive effect on their subjective wellbeing. Furthermore, their perceptions of tourism development benefits positively influence their value co-creation with tourists, whereas the perceived costs of tourism have a negative effect. Finally, we found that support for tourism development is positively related to participation in value co-creation with tourists.

There is scant knowledge about how public opinion climate of risk influences tourist behavior in times of a safety crisis at the destination. In this study, we examined the potential moderation effect of public opinion climate on the relationships between tourist risk perception, destination image, and tourist satisfaction. We further distinguished tourist pre-travel and post-travel risk perceptions and proposed a path model linking pre-travel risk's effect on tourist satisfaction with post-travel risk perception and destination image as the serial multiple mediators. Data were collected from an on-site survey of tourists who visited China's Xinjiang area. The results indicate public opinion climate has a significant moderation effect above and beyond the effects of both pre-travel and post-travel risk perceptions on destination image and tourist satisfaction.

This study examines the key survival factors in the exhibition industry. Secondary data were collected from 656 exhibitions held in China from 1981 to 2019. The results of Kaplan-Meier analysis show that the probability of failure is significantly lower in large, first-tier cities such as Shanghai than in smaller cities. The survival probability of machinery exhibitions is significantly higher than that of automobile and motorcycle exhibitions. The results of semi-parametric regression with an extended Cox model reveal five key survival factors, namely, exhibition history, start-up size, trade association connection, relevant industry clusters, and public transportation. However, the influence of exhibition history diminishes with time. The findings provide important managerial implications for both the exhibition industry and hosting cities.

Ganoderma lucidum (G. lucidum, Lingzhi) is a well-known Chinese traditional medicine to improve health and to treat numerous diseases for over 2000 years in Asian countries. G. lucidum has the abundant chemical components such as triterpenes and polysaccharides, which have various biological activities including anti-oxidation, anti-inflammation, anti-liver disorders, anti-tumor growth and metastasis, etc. Recently, many lines of studies have elucidated the therapeutic effects of G. lucidum and its extractions on various acute kidney injury (AKI) and chronic kidney disease (CKD) pathogenesis, including autosomal dominant polycystic kidney disease, diabetic nephropathy, renal proximal tubular cell oxidative damage and fibrotic process, renal ischemia reperfusion injury, cisplatin-induced renal injury, adriamycin-induced nephropathy, chronic proteinuric renal diseases, etc. Clinical researches also showed potent anti-renal disease bioactivities of G. lucidum. In this chapter, we review experimental and clinical researches and provide comprehensive insights into the renoprotective effects of G. lucidum. In recent years, renal diseases have gradually aroused attention on account of their booming prevalence worldwide and lack of effective therapies. Although the complicated pathogenesis of kidney diseases, such as acute kidney injury (AKI) and chronic kidney diseases (CKD) have been intensively studied. The morbidity and mortality of AKI and CKD still rise continuously. Thanks to the conventional experience and the multi-target characteristics, natural products have been increasingly recognized as an alternative source for treating renal diseases.

This study examines the strategic significance of B2B corporate brand image of global logistics corporate brand providers in China offering international express parcel/postage delivery services. Drawing on dual-process theory the study revealed the processing of industrial corporate brand image by managers corresponds to System 1 processing (immediate, effortless and non-conscious), and the conceptualization of corporate brand attributes equates to System 2 processing (considered, deliberate and cerebral). However, in selecting an industrial corporate brand managers accorded greater importance to industrial corporate brand image and to System 1 processing. The study also found that a positive industrial corporate brand image of logistics brands has a favorable impact apropos premium pricing and brand retention. As such, the research reveals how the selection of an industrial corporate brand by managers is materially shaped by corporate brand image and, moreover, the process of selection is more nuanced and complicated that has hitherto been realized.

Participation in business events such as meetings, conventions and exhibitions is costly, and building a high-performance team is vital. This study examines the key influential factors of team performance from the perspective of the “temporary organization”. Using a sample of 516 individuals employed by exhibitors attending trade shows, we demonstrate that “servant leadership” and “swift trust” are the two crucial factors in improving an exhibitor team’s performance. Specifically, two dimensions of servant leadership, namely conceptual skills and commitment to the growth of people, contribute directly to team performance. Swift trust not only has a positive direct influence on team performance but is also a partial mediator between servant leadership and team performance. The results have implications for managers seeking to create a high-performance temporary team.

Bridging the technology divide remains one of the contemporary business issues, yet limited scholarly attention has been devoted to exploring the gap between regional developing nations and their effects on can be bridged. The study examined the state of technological progress in Africa by applying an innovative two-step total factor productivity (TFP) analysis. We first estimated the TFP for 21 African countries with reference to South Africa (regional leader) and the United States (US)(international leader). We then applied the Phillips and Sul (2007) panel convergence tests. We found three convergence clubs with TFP levels relative to South Africa, where only most developed African countries have been able to catch-up. In the case of TFP levels relative to the US, we found two convergence clubs and only a couple of countries have managed to make short-lived progress of catching-up. Our analysis has implications in terms of capitalising on new technology for new businesses to flourish and accelerate the pace of change leading to the weeding-out of inefficient firms.

A series of plane-strain physical model experiments are carried out to study the spatiotemporal evolution rule of rocks fracture surrounding gob-side roadway, which is subjected to the pressure induced by the mining process. The digital photogrammetry technology and large deformation analysis method are applied to measure the deformation and fracture of surrounding rocks. The experimental results indicate that the deformation and fracture of coal pillars are the cause to the instability and failure of the surrounding rocks. The spatiotemporal evolution rule of the rock deformation and fracture surrounding gob-side roadway is obtained. The coal pillar and the roof near coal pillar should be strengthened in support design. The engineering application results also can provide a useful guide that the combined support with wire meshes, beam, anchor bolt and cable is an effective method. Keywords: Model experiment, Digital photogrammetry, Gob-side roadway, Spatiotemporal evolution rule, Stability control and combined support

Post-visit attitude towards products or brands associated with destination countries is an important outcome variable overlooked in the tourism literature. Drawing upon associative network theory of memory, this study aims to contribute to the extant literature by testing tourist post-visit attitude as an outcome variable of tourism experience, in addition to destination loyalty. A conceptual model was developed and tested through a survey of Chinese tourists who have recently visited Britain. Results suggest that both tourist satisfaction of destination attributes and overall satisfaction influence post-visit product attitude. Tourism researchers could thus adopt a wider perspective to explore the influence of international tourist experiences by conceptualizing the destination country as a destination for tourism, investment, education and immigration, as well as a political partner. This paper concludes with a discussion of destination managerial and policy implications.

This study aims to expand tourism value co-creation to include resident-tourist social interactions. Specifically, we aim to empirically verify the effects of residents' life satisfaction and their perceived benefits and costs of tourism development on their value co-creation with tourists. An online questionnaire survey was conducted with a sample of 380 urban residents in China. Partial Least Square structural equation modelling was adopted to analyze the data. The results show that residents' perceived economic and social-cultural benefits of tourism development have positive effects on both value co-creation and life satisfaction, while perceived costs have negative effects. Life satisfaction influences value co-creation. This study contributes to the literature by conceptualizing tourism value co-creation between tourists and residents and empirically investigating residents' participation in value co-creation in the tourism context. Moreover, we introduced broaden-and-build theory to analyze a tourism phenomenon for the first time. This study also provides important managerial implications.

Despite the exponential growth of smartphone consumption, to date, very few studies have investigated the factors that influence consumers' repurchase intention of smartphone brands. China has become the world's largest consumer markets for smartphones, therefore understanding young Chinese consumers' repurchase intention in the smartphone market is of crucial importance to smartphone companies. A preliminary qualitative study based on 30 face-to-face interviews has led to the development of a new conceptual framework including aesthetic (design appeal), functional (perceived quality), brand value (brand popularity), social (subjective norm) and cultural influences (mianzi). The newly developed framework has been tested through partial least squares structural equation modeling with a sample of 321 young Chinese smartphone users. The results show that young Chinese customer's smartphone repurchase intention is mainly determined by mianzi, perceived quality, brand popularity, and design appeal. Furthermore, findings also highlight that subjective norm, perceived quality and design appeal affect Chinese people's mianzi. Display Omitted This study investigates young Chinese consumers' repurchase intention of smartphone.The study adopts mixed method (interviews+quantitative analysis, PLS-SEM).Develops a new framework including aesthetic, functional, branding, socio-cultural influences.Subjective norm, perceived quality and design appeal influence young Chinese consumers' mianzi.Mianzi, perceived quality, design appeal predict repurchase intentions.

Analyzing carbon emissions is critical for successfully managing sustainable production and consumption. In a dual channel supply chain that includes traditional retailers and online e-tailers, consumer free riding often occurs when consumers enjoy the services provided by a traditional retailer but make purchases at a lower price from an e-tailer. The specific aim of this paper, therefore, is to evaluate the impact of consumer free riding on carbon emissions in a product's life cycle across a dual channel closed loop supply chain and to assess the effect of governmental e-commerce tax on carbon emissions. The study comprises a systematic comparison and numerical analysis of cases in which consumers do or do not free ride. Our results show that although manufacturers may gain economic benefits from consumer free riding behavior, total carbon emissions across the supply chain increase too, and a governmental tax on e-commerce can help reduce consumer free riding and total carbon emissions. Our research also suggests that, a government may have to subsidize the e-tailer in the consideration of maximizing the social welfare.

Consumer voluntary sharing of e-service knowledge in an online community is of great value to both business and consumers. This study develops and tests a research model integrating two personal attributes (consumer innovativeness and subjective knowledge) and two e-service factors (perceived ease of use and usefulness) in predicting consumer voluntary knowledge sharing in an online community. Data were collected using a web-based survey of 364 airline travelers recruited through an online travel community in China. Partial Least Squares Structural Equation Modeling (PLS-SEM) technique was used to analyze the data. The results reveal that personal factors are stronger drivers of knowledge sharing than e-service factors. This study advances consumer knowledge sharing in online communities, and has implications for enhancing a firm's e-service strategies. Examination of the impacts of both personal and e-service factors in knowledge sharing.The impacts of personal attributes are stronger than e-service factors.Subjective knowledge partially mediates innovativeness impact.Perceived usefulness partially mediates perceived ease of the user's impact.

This study integrates the individual psychology constructs (personal innovativeness and subjective knowledge) with the Technology Acceptance Model to develop and test a model of airline passengers’ continuance intention towards online flight check-in services. Predictions were tested with data from a sample of airline passengers in China who have experienced the online check-in service. The findings of this study demonstrate that airline passengers’ innovativeness and subjective knowledge have a direct effect on continuance intention, and an indirect effect through partial mediation of perceived ease of use and perceived usefulness. Theoretical and managerial implications are discussed.

This paper examines the key factors that determine business traveler loyalty toward full-service airlines in China. Based on literature review and panel interview, ten airline attributes under three categories were derived: (a) operational factors: safety, punctuality, and aircraft; (b) competitive factors: frequency of flights, schedule, frequent flyer program, ticket price, and reputation; and (c) attractive factors: in flight food & drinks and in flight staff service. We surveyed 2000 Chinese business travelers on domestic flights, obtaining 462 usable questionnaires. Hierarchical regression analysis reveals that reputation, in-flight service, frequent flyer program, and aircraft have the greatest influence in driving airline loyalty.