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This study investigated the travel intentions (TIs) of Chinese travelers and their utilization of virtual tourism technology during the Zero COVID-19 policy period by using a stimulus-organism-response (SOR) model. The study specifically examines the interplay between knowledge of policy, perception of risk, TI, usage of virtual tourism technology (UVTT) and the mediating role of personal values.

The data were collected from 333 Chinese travelers through an online questionnaire, and structural equation modeling (SEM) was employed to test the proposed hypotheses.

The study suggests that knowledge of policy and risk perception increase changes in personal values (PVs), which, in turn, affect Chinese travelers' TIs and the UVTT, with PVs playing a mediating role. Risk perception has a positive effect on the UVTT.

This study highlights the positive impact of tourism policy knowledge and risk awareness on individual values as a stimulus. Stakeholders need to implement industry-specific policies that are in line with scientific developments. Tourism managers should prioritize understanding the psychological reactions of tourists in crises and provide support to mitigate negative emotions. Anticipating changes in PVs is crucial, as instability affects tourists' behavior. The findings of the study also provide valuable insights for technology designers and underscore the substitutability of virtual technologies in improving the tourism experience.

This study is the first to examine the mediating role of PVs in the relationship between knowledge of policy, tourism risk perception (TRP), TI and the UVTT based on the SOR model. The insights gained from this analysis can assist policymakers and tourism managers in understanding the psychological changes of tourists, thereby facilitating the development of appropriate tourism planning.

This study aims to explore the most salient contributors to residents' support for tourism. As such, the authors measure residents' fear and risk of coronavirus disease 2019 (COVID-19) and residents' animosity towards tourists as predictors of attitudinal, intentional and behavioural support for tourism in China, Malaysia, Kazakhstan and Iran.

An online survey of 1,318 respondents across four countries was conducted, with data analysed using structural equation modelling.

This study shows that residents' perceptions about tourism development vary in different countries based on the impact of COVID-19, even though some factors, such as perceived risk and fear of COVID-19, have a similar effect on residents' attitudes and intentions to support tourism.

Only residents from four countries participated due to the collaborative effort of researchers from these specific countries.

Insight into residents' perceptions and responses to COVID-19 can aid policymakers and managers in developing effective crisis recovery strategies.

The data from this study can serve as a foundation for future research to examine residents' attitudes and support towards tourism during the post-COVID-19 period.

Unlocking the unrevealing of residents' perceptions and coping mechanisms towards tourists during the pandemic, this research shines a light on their crucial role in the revival of the tourism industry. With an exclusive focus on residents' attitudes and behaviours, this study stands out amongst the few that delve into this crucial aspect of post-pandemic recovery.

The development of digital inclusive finance appears to be able to solve the difficulty of traditional finance, which cannot completely cover agriculture and farmers and provides better financial services and products to Chinese farmers. Thus, it improves the farmers' enthusiasm for agricultural production. The purpose of this paper is to clarify whether this goal is indeed being achieved.

This paper theoretically analyzes the mechanism that influences the effect of digital inclusive finance on rural households' agricultural production decisions and conducts an empirical study based on a sample from the Chinese family database (CFD).

First, the development of digital financial inclusion in general can encourage rural households to reduce agricultural production. Second, the negative effect of digital inclusive finance on households' agricultural output is realized by widening the gap between the efficiency of non-agricultural economic activities and the efficiency of agricultural production. The wider the gap is, the lower the enthusiasm of households for agricultural production. Third, the mediating effect of “digital financial inclusion – difference in efficiency – agricultural output” has a significant negative effect on households with low agricultural production efficiency, but not households with high agricultural production efficiency. Digital inclusive finance has no significant effect on the difference in efficiency between the two economic activities of high-efficiency households, but a greater difference in efficiency between the two economic activities corresponds to higher enthusiasm of households for agricultural production.

To the best of our knowledge, this paper is the first to analyze the impact of digital financial inclusion on Chinese farmers' agricultural production. The findings of this study can provide policy-related insights to help local governments promote the development of digital finance in China's agricultural economy.

To influence consumer perceptions, firms often manipulate online product reviews on their own websites or third-party forums by anonymously adding positive reviews, deleting unfavorable reviews or offering rewards to encourage favorable reviews. This study aims to investigate consumer perceptions of online review deceptions and how these perceptions influence their subsequent purchase behavior. In particular, consumers’ awareness, suspicion and detection are studied and specific manipulation tactics are evaluated.

Both qualitative and quantitative studies are relied upon to understand consumer perceptions of online review deceptions. In-depth interviews with 16 experienced online shoppers were conducted to collect the illustrative accounts concerning consumer awareness of online review deceptions, their suspicion, detection and evaluation of different manipulation tactics. A survey of 199 consumers was then followed to validate and corroborate the findings from the qualitative study and generalize the interview results onto the general public.

The results from in-depth interviews suggest that consumers take a negative view toward online review deceptions, but the degree of negativity varies across different manipulation tactics. Moreover, different types of manipulations vary in terms of perceived deceptiveness, ease of detection and unethicality, as well as their effect on consumer purchase intention and perceived helpfulness of online product reviews. The findings from the survey further confirmed the qualitative findings.

The findings have a number of meaningful managerial implications for industry associations and policymakers on whether and how to regulate online review deceptions.

This study applies and extends information manipulation theory and deception detection literature to an online context to increase the richness of the relevant theories. It is among the first to empirically investigate online review deceptions from a consumer’s perspective, as opposed to a firm’s perspective as previous studies have done.

The mobile medical consultation (MMC) service is growing rapidly, but not all consumers are always willing to actively engage with it. To address this issue, based on IT identity theory, this study explores the underlying mechanism of how two types of platform-related consumer experience influence MMC platform identity, in turn, result in consumer negatively-valenced engagement in MMC.

The data was collected from 400 consumers with the experience of MMC and analyzed by the partial least square (PLS) method.

The findings unfold that these two distinct consumer experience, servicescape experience (i.e. perceived telepresence and perceived platform surveillance) and service search experience (i.e. perceived diagnosticity and perceived serendipity), are associated with MMC platform identity and consumer negatively valenced engagement with MMC.

Research on consumer negatively-valenced engagement in the field of MMC is still in a nascent stage. The study identifies consumer experience in accordance with the unique context of the MMC platform and fills the research gap on the role of IT identity in consumer negatively valenced engagement.

Continuous knowledge contribution behaviors (CKCB) are critical for the healthy development of online medical communities (OMCs). However, it is unclear that if and how contributors' prior actions and the responses they received from the community influence the nature of their future contributions. Drawing upon the Information Systems Continuance theory and Service Feedback theory, the purpose of the study is to examine the impact of knowledge contribution performance (KCP) on doctors' CKCB. Evaluation of social motivation, financial incentive and the moderating influence of expertise level (EL) provided further insight into the pathways that motivate various forms of CKCB.

In order to better understand the CKCB of physicians in OMCs, the authors divided it into two categories: A_CKCB (active CKCB) and P_CKCB (passive CKCB). Information Systems Continuance theory and Service Feedback theory are adapted and integrated with empirical findings from previous research on OMCs to develop a model of CKCB. This study used ordinary least squares (OLS) regression to test hypotheses in the preexisting research model based on data collected from a Chinese OMC platform.

The results show that KCP helps develop several facets of CKCB. According to the findings, doctors' CKCB improved dramatically after receiving feedback from A_CKCB and P_CKCB, but feedback from peers did not promote CKCB. This study found that financial rewards only have a significant positive effect on P_CKCB, and that the level of expertise has a negative effect on the effect. The findings also demonstrated that doctors' level of expertise moderates the relationship between fA_CKCB (a comprehensive evaluation of doctors' A_CKCB) and A_CKCB.

Future studies should look at the role of self-efficacy as a mediator and attitudes as a moderator in the link between KCP and various forms of CKCB. This will help authors figure out how important KCP is for physicians' CKCB. And future research should use more than one way to gather data to prove the above roles.

This study makes a significant contribution to understanding the association between CKCB and KCP by highlighting the significance of distinguishing between the various forms of CKCB and their underlying causes.

This research has advanced both the theory and practice of OMCs' user management by illuminating the central role of KCP in this context.

Drawing upon self-determination theory, this study investigates the direct and indirect impact of health-promoting leadership on employee engagement via workplace relational civility and explores the moderating effect of employability on these factors.

The authors collected a total of 723 matched and valid responses from nurses in Guangxi, China. Data regarding health-promoting leadership, workplace relational civility, employability and employee engagement were gathered using a survey administered in two waves, 1 week apart. The authors utilised structural equation modelling and linear regression to test the model.

This study reveals that health-promoting leadership has both direct and indirect positive effects on nurses' engagement through workplace relational civility. Furthermore, the authors found that employability negatively moderates the impact of workplace relational civility on nurses' engagement but does not moderate the impact of health-promoting leadership on nurses' engagement.

This is one of the few studies that have examined the effects of health-promoting leadership within the nursing industry. The authors confirm the importance of health-promoting leadership and workplace relationship civility on employee engagement. In addition, this study demonstrates the moderating role of employability in employment relationships.

In any time and space and under any circumstance, we find peasants are never passive actors in their livelihoods and rural development. Instead, they always create space for manoeuvre in order to make changes. This chapter analyses the innovative actions taken by the majority of rural inhabitants in rural areas during the overwhelming modernization process, so as to affirm that peasants are the main actors of rural development. It is they who have shaped the transformation of rural societies and the history. Through the analysis, this chapter concludes that rural development is not an objective, a blueprint nor a design. It is not the to-be-developed rear field in modernization. It is not the babysitter for cities, nor a rehearsal place for bureaucrats to testify their random thoughts. Rural development is what peasants do. The path they have chosen reveals scenery so different from modernization. If we regard development as a social change, or a cross with influential meanings, we could understand rural development as peasants’ victories over their predicament. Villages accommodate not only peasants, but without peasants villages would surely vanish. In this sense, the most important part in rural development or rural change is peasants – their conditions and their feelings.

The purpose of this paper is to develop an early fire-alarm raising system based on video processing, and combine it with the omnidirectional projecting system. It not only gives alarm immediately in early fire so that people can be able to strive for more time to escape from the spot, but also solves problem of discontinued screen which was presented fire scene.

The smoke detection system is made by image processing. The flowchart of smoke detection is improved, which the method of background updating can filter out the moving objects that only stay for a short time in the image; and avoids these objects being determined to be the background. Moreover, the authors extract the flickering area to separate the non-smoke and smoke from the candidate of smoke regions. Finally, the image processing is applied in omnidirectional projecting system, then presented the 360-degree fire scene.

The results show that the smoke detection system can accurately detect the smoke and mark its location, then combining it with the omnidirectional projecting system, although the resolution of omnidirectional projecting system is not enough, it can present a continued screen and location of smoke on the 360-degree cylindrical screen.

This paper develops the smoke detection based on a improved method of image processing, and the control center staff can see the 360-degree fire scene via omnidirectional projecting system, so shorten the time to find the source of smoke.

The prediction of stock market (SM) indices is a fascinating task. An in-depth analysis in this field can provide valuable information to investors, traders and policy makers in attractive SMs. This article aims to apply a correlation feature selection model to identify important technical indicators (TIs), which are combined with multiple deep learning (DL) algorithms for forecasting SM indices.

The methodology involves using a correlation feature selection model to select the most relevant features. These features are then used to predict the fluctuations of six markets using various DL algorithms, and the results are compared with predictions made using all features by using a range of performance measures.

The experimental results show that the combination of TIs selected through correlation and Artificial Neural Network (ANN) provides good results in the MADEX market. The combination of selected indicators and Convolutional Neural Network (CNN) in the NASDAQ 100 market outperforms all other combinations of variables and models. In other markets, the combination of all variables with ANN provides the best results.

This article makes several significant contributions, including the use of a correlation feature selection model to select pertinent variables, comparison between multiple DL algorithms (ANN, CNN and Long-Short-Term Memory (LSTM)), combining selected variables with algorithms to improve predictions, evaluation of the suggested model on six datasets (MASI, MADEX, FTSE 100, SP500, NASDAQ 100 and EGX 30) and application of various performance measures (Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error(RMSE), Mean Squared Logarithmic Error (MSLE) and Root Mean Squared Logarithmic Error (RMSLE)).