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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.

This study attempts to identify and explicate the unique segmentation of the increasingly growing virtual reality (VR) user market based on the user experience. Consequently, it collects five hundred forty-five online survey questionnaires through the Prolific platform and deploys cluster analysis to identify mutually exclusive groups of VR users. The research variable, user experience, contains 16 indicators explained by four dimensions. As a result, this study is able to unveil three mutually exclusive markets which are labeled as (1) beginner, (2) aficionado, and (3) utilitarian. The unique features of these three groups are further compared based on their VR tour behaviors. In the conclusion section, it offers managerial implications for devising novel marketing strategies.

Kaiping Diaolou in Guangdong Province, China, is a UNESCO World Heritage site that is often used as a film location. This study aims to investigate the antecedents of film-induced heritage conservation behaviour in tourists visiting Kaiping Diaolou. The conceptual premises of stimulus-organism-response theory were used to provide additional insight into the impact of film- and heritage-induced images, and tourists’ perceived authenticity, destination attachment and heritage conservation intention.

The authors tested the hypotheses using covariance-based structural equation modelling by using the data collected from the 391 valid questionnaires.

The empirical results reveal that both film- and heritage-induced images have a direct impact on tourists’ heritage conservation intention. Furthermore, perceived authenticity and destination attachment fully mediate the relationship between destination image and conservation intention, while the serial multiple mediator effect in the heritage destination image model is not significant.

The findings contribute to the understanding of tourists’ heritage conservation intention through the lens of destination image, perceived authenticity and destination attachment. The study’s findings enrich the literature on film and heritage tourism regarding destination image construction and heritage conservation and have implications for the sustainable development of heritage tourism and heritage conservation, as well as the marketing of heritage sites.

位于中国广东省的开平碉楼是联合国教科文组织认证的世界文化遗产, 并经常被用作电影拍摄地。本研究调查了影响开平碉楼影视旅游游客的遗产保护行为的前因。本研究采用刺激-有机体-反应 (SOR) 理论的概念, 探究电影和遗产所诱发的目的地形象、游客的感知真实性和目的地依恋, 对游客遗产保护意图的影响。

本研究共回收 391 份有效问卷, 并使用基于协方差的结构方程模型来检验研究假设。

实证结果表明, 电影和遗产诱导的目的地形象都直接影响游客的遗产保护意愿。此外, 游客的感知真实性和目的地依恋完全中介了目的地形象与保护意愿之间的关系。但是在遗产目的地形象的模型中, 感知真实性和目的地依恋的串行多重中介效应不显着。

研究结果有助于通过目的地形象、感知真实性和目的地依恋来理解游客的遗产保护意图。本研究丰富了关于目的地形象建设和遗产保护的文献, 并对遗产旅游和遗产保护的可持续发展以及遗产地的营销产生了积极影响。

Based on a stimulus–organism–response theory, the purpose of this study is to empirically examine structural equation model linking personal involvement, positive emotions, tourist satisfaction and destination loyalty among sport tourists to a mega sport event. Moreover, moderating effects of place attachment on the relation between the aforementioned variables were investigated.

The validities and reliabilities of the measures were investigated through confirmatory factor analysis, Cronbach's alpha and correlation analysis. A structural equation modelling with maximum likelihood estimation was tested to analyze the relationships among the research variables using 383 participants.

The results revealed positive associations among stimulus (personal involvement), organism (positive emotions) and response (tourist satisfaction and destination loyalty). Moreover, the authors found moderating effect of place attachment on the relationships between personal involvement and positive emotions, personal involvement and tourist satisfaction and tourist satisfaction and destination loyalty.

This study holds the potential to aid destination managers in acquiring a more profound comprehension of how personal involvement contributes to elicit positive emotions, keep tourists satisfied and build destination loyalty as well as demonstrating the moderating roles of place attachment. However, generalizing the findings to alternative contexts presents a formidable challenge. Enhancing the applicability of these findings could be achieved through prospective research endeavors that explore visitors in diverse cities spanning various continents.

The study contributed to the literature by providing empirical evidence that personal involvement evokes positive emotions while also plays significant role in improving satisfaction and loyalty. Given the importance of experiences in sport tourism, this study also confirmed the role of positive emotions on tourist satisfaction and destination loyalty. Additionally, this study examined the moderating effect of place attachment, which has not been investigated in sport tourism context.

This study aims to systematically review the literature in the fields of liquidity, informational efficiency and default risk. The authors outline the key research streams and provide possible pathways for future research.

The study adopts bibliographic mapping to identify the most influential studies in the research fields of liquidity, informational efficiency and default risk from 1984 to 2021.

The study identifies four key research themes that include efficiency and transparency of markets; corporate yield spreads; market interactions: bonds, stocks and cryptocurrencies; and corporate governance. By assessing publications published from 2018 to 2021, the authors also document seven key emerging research trends: cross markets, managerial learning and corporate governance, state ownership and government subsidies, international evidence, machine learning (FinTech approaches), environmental themes and financial crisis. Drawing on these emerging trends, the authors highlight the opportunities for future research.

Keyword searches have limitations since some studies might be overlooked if they do not match the specified search criteria, even though their relevance to the topic is under investigation. Adopt the R project to expand this review by incorporating more literature from other databases, such as the Scopus database could be a possible solution.

The four key research streams contribute to a comprehensive understanding of liquidity, informational efficiency and default risk. The emerging trends integrate existing knowledge and leave the chance for innovative research to expand the research frontier.

This study fulfills the systematic literature review streams in the fields of liquidity, informational efficiency and default risk, and provides fruitful opportunities for future research.

Since the development of the COVID-19 vaccinations, questions surrounding race have been prominent in the literature on vaccine uptake. Early in the vaccine rollout, public health officials were concerned with the relatively lower rates of uptake among certain racial/ethnic minority groups. We suggest that this may also be patterned by racial/ethnic residential segregation, which previous work has demonstrated to be an important factor for both health and access to health care.

In this study, we examine county-level vaccination rates, racial/ethnic composition, and residential segregation across the U.S. We compile data from several sources, including the American Community Survey (ACS) and Centers for Disease Control (CDC) measured at the county level.

We find that just looking at the associations between racial/ethnic composition and vaccination rates, both percent Black and percent White are significant and negative, meaning that higher percentages of these groups in a county are associated with lower vaccination rates, whereas the opposite is the case for percent Latino. When we factor in segregation, as measured by the index of dissimilarity, the patterns change somewhat. Dissimilarity itself was not significant in the models across all groups, but when interacted with race/ethnic composition, it moderates the association. For both percent Black and percent White, the interaction with the Black-White dissimilarity index is significant and negative, meaning that it deepens the negative association between composition and the vaccination rate.

The analysis is only limited to county-level measures of racial/ethnic composition and vaccination rates, so we are unable to see at the individual-level who is getting vaccinated.

We find that segregation moderates the association between racial/ethnic composition and vaccination rates, suggesting that local race relations in a county helps contextualize the compositional effects of race/ethnicity.

The purpose of this paper is to bridge the gap between the existing theoretical and empirical studies by examining the asymmetric return–volume relationship. Indeed, the authors aim to shed light on the return–volume linkages for French-listed small and medium-sized enterprises (SMEs) compared to blue chips across different market regimes.

This study includes both large capitalizations included in the CAC 40 index and listed SMEs included in the Euronext Growth All Share index. The Markov-switching (MS) approach is applied to understand the asymmetric relationship between trading volume and stock returns. The study investigates also the causal impact between stock returns and trading volume using regime-dependent Granger causality tests.

Asymmetric contemporaneous and lagged relationships between stock returns and trading volume are found for both large capitalizations and listed SMEs. However, the causality investigation reveals some differences between large capitalizations and SMEs. Indeed, causal relationships depend on market conditions and the size of the market.

This paper explains the asymmetric return–volume relationship for both large capitalizations and listed SMEs by incorporating several psychological biases, such as the disposition effect, investor overconfidence and self-attribution bias. Future research needs to deepen the analysis especially for SMEs as most of the literature focuses on large capitalizations.

This empirical study has fundamental implications for portfolio management. The findings provide a deeper understanding of how trading activity impact current returns and vice versa. The authors’ results constitute an important input to build and control trading strategies.

This paper fills the literature gap on the asymmetric return–volume relationship across different regimes. To the best of the authors’ knowledge, the present study is the first empirical attempt to test the asymmetric return–volume relationship for listed SMEs by using an accurate MS framework.

In order to analyze the text complexity of Chinese and foreign academic English writings, the artificial neural network (ANN) under deep learning (DL) is applied to the study of text complexity. Firstly, the research status and existing problems of text complexity are introduced based on DL. Secondly, based on Back Propagation Neural Network (BPNN) algorithm, analyzation is made on the text complexity of Chinese and foreign academic English writings. And the research establishes a BPNN syntactic complexity evaluation system. Thirdly, MATLAB2013b is used for simulation analysis of the model. The proposed model algorithm BPANN is compared with other classical algorithms, and the weight value of each index and the model training effect are further analyzed by statistical methods. Finally, L2 Syntactic Complexity Analyzer (L2SCA) is used to calculate the syntactic complexity of the two libraries, and Mann–Whitney U test is used to compare the syntactic complexity of Chinese English learners and native English speakers. The experimental results show that compared with the shallow neural network, the deep neural network algorithm has more hidden layers and richer features, and better performance of feature extraction. BPNN algorithm shows excellent performance in the training process, and the actual output value is very close to the expected value. Meantime, the error of sample test is analyzed, and it is found that the evaluation error of BPNN algorithm is less than 1.8%, of high accuracy. However, there are significant differences in grammatical complexity among students with different English writing proficiency. Some measurement methods cannot effectively reflect the types and characteristics of written language, or may have a negative relationship with writing quality. In addition, the research also finds that the measurement of syntactic complexity is more sensitive to the language ability of writing. Therefore, BPNN algorithm can effectively analyze the text complexity of academic English writing. The results of the research provide reference for improving the evaluation system of text complexity of academic paper writing.

In order to analyze the text complexity of Chinese and foreign academic English writings, the artificial neural network (ANN) under deep learning (DL) is applied to the study of text complexity. Firstly, the research status and existing problems of text complexity are introduced based on DL. Secondly, based on Back Propagation Neural Network (BPNN) algorithm, analyzation is made on the text complexity of Chinese and foreign academic English writings. And the research establishes a BPNN syntactic complexity evaluation system. Thirdly, MATLAB2013b is used for simulation analysis of the model. The proposed model algorithm BPANN is compared with other classical algorithms, and the weight value of each index and the model training effect are further analyzed by statistical methods. Finally, L2 Syntactic Complexity Analyzer (L2SCA) is used to calculate the syntactic complexity of the two libraries, and Mann–Whitney U test is used to compare the syntactic complexity of Chinese English learners and native English speakers. The experimental results show that compared with the shallow neural network, the deep neural network algorithm has more hidden layers and richer features, and better performance of feature extraction. BPNN algorithm shows excellent performance in the training process, and the actual output value is very close to the expected value. Meantime, the error of sample test is analyzed, and it is found that the evaluation error of BPNN algorithm is less than 1.8%, of high accuracy. However, there are significant differences in grammatical complexity among students with different English writing proficiency. Some measurement methods cannot effectively reflect the types and characteristics of written language, or may have a negative relationship with writing quality. In addition, the research also finds that the measurement of syntactic complexity is more sensitive to the language ability of writing. Therefore, BPNN algorithm can effectively analyze the text complexity of academic English writing. The results of the research provide reference for improving the evaluation system of text complexity of academic paper writing.

In order to analyze the text complexity of Chinese and foreign academic English writings, the artificial neural network (ANN) under deep learning (DL) is applied to the study of text complexity. Firstly, the research status and existing problems of text complexity are introduced based on DL. Secondly, based on Back Propagation Neural Network (BPNN) algorithm, analyzation is made on the text complexity of Chinese and foreign academic English writings. And the research establishes a BPNN syntactic complexity evaluation system. Thirdly, MATLAB2013b is used for simulation analysis of the model. The proposed model algorithm BPANN is compared with other classical algorithms, and the weight value of each index and the model training effect are further analyzed by statistical methods. Finally, L2 Syntactic Complexity Analyzer (L2SCA) is used to calculate the syntactic complexity of the two libraries, and Mann–Whitney U test is used to compare the syntactic complexity of Chinese English learners and native English speakers. The experimental results show that compared with the shallow neural network, the deep neural network algorithm has more hidden layers and richer features, and better performance of feature extraction. BPNN algorithm shows excellent performance in the training process, and the actual output value is very close to the expected value. Meantime, the error of sample test is analyzed, and it is found that the evaluation error of BPNN algorithm is less than 1.8%, of high accuracy. However, there are significant differences in grammatical complexity among students with different English writing proficiency. Some measurement methods cannot effectively reflect the types and characteristics of written language, or may have a negative relationship with writing quality. In addition, the research also finds that the measurement of syntactic complexity is more sensitive to the language ability of writing. Therefore, BPNN algorithm can effectively analyze the text complexity of academic English writing. The results of the research provide reference for improving the evaluation system of text complexity of academic paper writing.

In order to analyze the text complexity of Chinese and foreign academic English writings, the artificial neural network (ANN) under deep learning (DL) is applied to the study of text complexity. Firstly, the research status and existing problems of text complexity are introduced based on DL. Secondly, based on Back Propagation Neural Network (BPNN) algorithm, analyzation is made on the text complexity of Chinese and foreign academic English writings. And the research establishes a BPNN syntactic complexity evaluation system. Thirdly, MATLAB2013b is used for simulation analysis of the model. The proposed model algorithm BPANN is compared with other classical algorithms, and the weight value of each index and the model training effect are further analyzed by statistical methods. Finally, L2 Syntactic Complexity Analyzer (L2SCA) is used to calculate the syntactic complexity of the two libraries, and Mann–Whitney U test is used to compare the syntactic complexity of Chinese English learners and native English speakers. The experimental results show that compared with the shallow neural network, the deep neural network algorithm has more hidden layers and richer features, and better performance of feature extraction. BPNN algorithm shows excellent performance in the training process, and the actual output value is very close to the expected value. Meantime, the error of sample test is analyzed, and it is found that the evaluation error of BPNN algorithm is less than 1.8%, of high accuracy. However, there are significant differences in grammatical complexity among students with different English writing proficiency. Some measurement methods cannot effectively reflect the types and characteristics of written language, or may have a negative relationship with writing quality. In addition, the research also finds that the measurement of syntactic complexity is more sensitive to the language ability of writing. Therefore, BPNN algorithm can effectively analyze the text complexity of academic English writing. The results of the research provide reference for improving the evaluation system of text complexity of academic paper writing.

Using the Stimulus-Organism-Response (SOR) model as a theoretical guide, this study employed a conceptual model involving residents’ place attachment (S) to the destination in which they live and emotional solidarity with tourists (O) as precursors to their involvement in tourism (R). The purpose of this paper is threefold: To determine (1) whether residents’ place attachment explains their emotional solidarity with tourists, (2) if emotional solidarity is an effective predictor of residents’ involvement in tourism planning and development and (3) if emotional solidarity dimensions mediate the relationship between place attachment and involvement.

Data were collected from 378 permanent resident heads of households living in, or adjacent to, central Orlando, using a self-administered survey with a census-guided systematic sampling method. Data were subjected to tests of normality and common method bias, followed by a two-step confirmatory factor analysis and structural equation modeling.

Seven of the 11 proposed model hypotheses were supported, with moderate variances explained in each of the four outcome variables: welcoming nature (R² = 19.3%), emotional closeness (R² = 24.5%), sympathetic understanding (R² = 39.4%) and involvement (R² = 36.8%). Though both place identity and place dependence (as two dimensions of place attachment) were partial mediators, the former proved to be more pronounced.

This study employed non-economic measures—place attachment and emotional solidarity—in determining residents’ involvement in tourism within their community. Such an approach provides fresh insights into how such symbolic constructs can contribute to residents’ positive, actionable involvement in tourism. This research is one of the few that have incorporated emotional solidarity as a construct within the SOR model and the first to examine the indirect effects (through mediation) of emotional solidarity.

This paper aims to determine whether policy uncertainty caused by institutional decay in countries with populist rulers influences the internationalization decision of emerging market firms (EMFs).

The study used binary logit analysis on firms from Latin American countries undertaking cross-border greenfield investment projects.

The results suggest that internationalization decision is demotivated by policy uncertainty generated by populist chief executives and promoted by that of political parties.

This study uses populist rhetoric to describe policy uncertainty due to chief executives and ruling parties, which influences internationalization decision by increasing anticipated transaction costs. This inquiry identifies populism as a variable that influences EMFs to internationalize, while empirically testing the claim of theoretical scholarship that populism reconfigured the sociopolitical and institutional forces that shape the world’s business. This study further advances institutional theory by offering a fresh perspective on the influence of home instead of host-country institutions on the internationalization motivation of firms due to institutional decay caused by populist regimes.