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This paper aims to understand how the Covid-19 pandemic has changed consumers’ perceptions of outdoor consumption categories, such as retail shopping, eating out, public events and travel and how these perceptions may impact businesses in these domains in the long term. Further, this research aims to understand demographic effects on outdoor consumption inhibition during the current pandemic and discuss how businesses can use these insights to rebrand their offerings and evolve after the pandemic.

Data collected by CivicScience, a survey-based consumer intelligence research platform, during April–July 2020 forms the basis of the preliminary analysis, where the chi-square test has been used to examine significant differences in consumer attitudes between different age groups, income groups and genders. Further, a social media analysis of conversations around outdoor consumption activities is undertaken to understand the rationale behind these demographics-based attitude differences.

Results lend varying degrees of support to the hypothesized consumer attitudes toward outdoor consumption activities during the Covid-19 pandemic. As the pandemic wore on, older (vs younger), female (vs male) consumers and lower (vs higher) income-group consumers had reportedly higher inhibition toward different outdoor activities. Older individuals were significantly less likely to shop, dine and attend public events than younger individuals. Lower-income consumers were significantly less likely to dine and travel than higher-income consumer consumers. Female consumers were significantly less likely to shop and travel than male consumers. Social media scan of conversations suggests that differences in perceived health and financial risks may have resulted in demographics-based differences in outdoor consumption activities.

This study contributes to the literature by understanding demographic differences in consumer participation in outdoor activities. One limitation is that due to the time-sensitive nature of the pandemic research, further studies could not be conducted to understand the implications of other variables, beyond demographics that influence consumer behavior during a crisis. A future research direction is to understand how other psychological variables or traits, influence health and financial risk-taking behavior during a similar crisis.

The principal contribution of the present research is that it tests the risk-taking theory in the context of outdoor consumption during the Covid-19 pandemic. The present research has implications for businesses as they continue to evolve during and post Covid-19.

The COVID-19 pandemic is considered a “once in a century” public health shock that, at the time of writing, continues to have a profound impact on global tourism and New Zealand. The paper aims to assess how consumer behaviour trends changed using a trends analysis framework.

Positioning the paper in the prognosis–prediction paradigm from futures studies and using a trend analysis approach, the authors forecasted a series of tourist trends at the beginning of COVID-19 based upon a multitude of sources trends. Then, 12 months later, they reported on the accuracy of these forecasts.

The matrix identifies 15 trends based upon consumer behaviour changes, which are either dominant, slowed, advanced or arrested. The prognosis was largely correct, which was supported by evidence gathered 12 months later.

The paper uses a series of different data sources to reflect on the initial forecasts. To some, this may be an issue of rigor, but the authors argue that through triangulation, credibility and validity are increased.

First, the evaluation matrix allows users to make sense of COVID-19 based upon the concepts of dominant, slowed, advanced or arrested trends. Second, the matrix allows users to evaluate changes and movement of trends. Third, the trends featured in this paper could be generalisable to several different circumstances associated with simple identity. Fourth, this paper has tested the ability to predict trends in an uncertain environment within the context of the ontological paradigm of prognosis and prediction of futures states.

The main goal of this study is to employ unsupervised (lexicon-based) learning approaches to identify readers' emotional dimensions and thumbs-up empathy reactions to reviews of online travel agency apps based on appraisal and stimulus–organism–response (SOR) theories.

Using the Google Play Scraper, we gathered a total of 402,431 reviews from the Google Play Store for two travel agency apps, Tripadvisor and Booking.com. Following the filtering and cleaning of user reviews, we used lexicon-based unsupervised machine learning algorithms to investigate the associations between various emotional dimensions of reviews and review readers' thumbs-up reactions.

The study's findings reveal that the sentiment of different sorts of reviews has a substantial influence on review readers' emotional experiences, causing them to give the app a thumbs up review. Furthermore, readers' thumbs-up responses to the text reviews differed depending on the eight emotional aspects of the reviews.

The results of this research can be applied in the development of online travel agency apps. The findings suggest that app developers can enhance users' emotional experiences by considering the sentiment and emotional aspects of reviews in their design and implementation. Additionally, the results can be used by travel agencies to improve their online reputation and attract more customers by providing a positive user experience.

The findings of this research have the potential to have a significant impact on society by providing insights into the emotional experiences of users when they engage with online travel agency apps. The study highlights the importance of considering the emotional aspect of user reviews, which can help app developers to create more user-friendly and empathetic products.

The current study is the first to evaluate the impact of users' thumbs-up empathetic reactions on user evaluations of online travel agency applications using unsupervised (lexicon-based) learning methodologies.

In recent years, electronic word-of-mouth (e-WOM) concerning travel products reflected in online review information has become an important reference for tourists to make their product purchase decisions, while for travel service providers (TSPs), monitoring and improving the e-WOM of their travel products is always an important task. Therefore, based on the online review information, how to capture e-WOM of travel products and find out specific ways to improve the e-WOM is a noteworthy research problem. The purpose of this paper is to develop a method for capturing and analyzing e-WOM toward travel products based on sentiment analysis and stochastic dominance.

Specifically, online review information of travel products is first crawled and preprocessed. Second, sentiment strengths of online review information toward travel products concerning each feature are judged. Then, the matrix of structured online review information toward travel products is formed. Further, the matrix of e-WOM comparisons between any two travel products is constructed, and e-WOM ranking concerning each travel product is determined. Finally, trade-off chart models are constructed to conduct the e-WOM improvement analyses concerning the travel products.

An empirical study based on the online review information toward six travel products crawled from the Tuniu.com website is given to illustrate the use of the proposed method.

The proposed method can not only realize the real-time e-WOM monitoring to travel products but also be useful for TSPs to improve the e-WOM of their travel products.

This paper aims to explore the image of travel destinations after the visit by analysing sentiment orientation of the online reviews, and how this orientation, as well as other electronic word of mouth (eWOM)’s credibility sources, can affect the perceived helpfulness of shared opinions measured through the helpfulness score.

Tourist destinations are increasingly affected by travel-related information shared through the Web. More and more people first check the previous travel experiences of other people to build their own destination image and to help them in their choice of destination. This paper analyses the shared opinions related to the city of Barcelona in a well-known eWOM website. The reviewers’ opinion and the credibility sources of eWOM are extracted from the web using a webscraper, while the sentiment score to analyse the discourse orientation (positive vs negative) is calculated using computer-based sentiment analysis techniques.

Online reviews’ users are reluctant to provide extreme polar opinions (very negative, very positive) to any travel subcategory (hotel, restaurant, attractions and night-life) of a tourist destination. The results obtained also reveal that eWOM’s perceived helpfulness grows with the expertise of the reviewer. However, the helpfulness score given to the reviews posted is not influenced by the sentiment orientation of the author’s opinion.

This research is limited to the case study of Ciao, which is a well-known consumer platform, and the city of Barcelona, which is a top touristic destination. However, the approach proposed can be easily extended to other similar consumer platforms and cities using the same methodology.

Understanding the information posted in the media environment is a major concern in the field of marketing destination planning. Positive and negative eWOM offers potential consumers a clear picture on the tourist destination, and this information can be used by Destination Marketing Organisations to meet customers’ needs and expectations. The perceived helpfulness of reviews analysed in this paper can also help practitioners and scholars to understand those factors that make reviews more trustable.

From a methodological point of view, the main contribution of this research is the utilisation of an unstructured approach to the measurement of the destination image based on the sentiment analysis of shared opinions. From a theoretical point of view, the study relates the post-visit destination image with the pre-visit image formation process, using the sentiment orientation of the former and the perceived helpfulness of the latter.

The purpose of this study is to interpret the travel motivations of travelers in India amidst the coronavirus pandemic. It takes into consideration the “push and pull motivational forces” of travelers, translating their emotional insights and behavioral intentions based on these forces.

The research design consists of a mixed-mode approach of study inclusive of semi-directive interviews of 231 participants from India. The statistical computing language R has been used to conduct sentiment analysis.

The results obtained show that “fear” and “sadness” are the prevalent emotions among the travelers due to the coronavirus outbreak based on the results of sentiment analysis. While, positive emotions of “trust” and “joy” were missing comparatively. These emotions along with other traveling constraints amidst coronavirus acted as the intrinsic push factors and extrinsic pull factors affecting their travel motivations.

The study throws light on the practical and theoretical implications of such travel motivations, emotions affecting these motivations, leading to travelers' behavioral intentions. The work adds to the limited works of tourism literature amidst the coronavirus outbreak.

Research analyzing online travelers’ reviews has boomed over the past years, but it lacks efficient methodologies that can provide useful end-user value within time and budget. This study aims to contribute to the field by developing and testing a new methodology for sentiment analysis that surpasses the standard dictionary-based method by creating two hotel-specific word lexicons.

Big data of hotel customer reviews posted on the TripAdvisor platform were collected and appropriately prepared for conducting a binary sentiment analysis by developing a novel bag-of-words weighted approach. The latter provides a transparent and replicable procedure to prepare, create and assess lexicons for sentiment analysis. This approach resulted in two lexicons (a weighted lexicon, L1 and a manually selected lexicon, L2), which were tested and validated by applying classification accuracy metrics to the TripAdvisor big data. Two popular methodologies (a public dictionary-based method and a complex machine-learning algorithm) were used for comparing the accuracy metrics of the study’s approach for creating the two lexicons.

The results of the accuracy metrics confirmed that the study’s methodology significantly outperforms the dictionary-based method in comparison to the machine-learning algorithm method. The findings also provide evidence that the study’s methodology is generalizable for predicting users’ sentiment.

The study developed and validated a methodology for generating reliable lexicons that can be used for big data analysis aiming to understand and predict customers’ sentiment. The L2 hotel dictionary generated by the study provides a reliable method and a useful tool for analyzing guests’ feedback and enabling managers to understand, anticipate and re-actively respond to customers’ attitudes and changes. The study also proposed a simplified methodology for understanding the sentiment of each user, which, in turn, can be used for conducting comparisons aiming to detect and understand guests’ sentiment changes across time, as well as across users based on their profiles and experiences.

This study contributes to the field by proposing and testing a new methodology for conducting sentiment analysis that addresses previous methodological limitations, as well as the contextual specificities of the tourism industry. Based on the paper’s literature review, this is the first research study using a bag-of-words approach for conducting a sentiment analysis and creating a field-specific lexicon.

论可推广性的情感分析法以创建酒店字典：以TripAdvisor酒店评论为样本的大数据分析

对于在线游客评论的研究在过去的几年中与日俱增, 但是仍缺乏有效方法能在有限的时间喝预算内提供终端用户价值。本论文开发并测试了一套情感分析的新方法, 创建两套酒店相关的词库, 此方法超越了标准词典式分析法。

研究样本为TripAdvisor酒店客户评论的大数据, 通过开发崭新的有配重的词库法, 来开展两极式情感分析。这个崭新的具有配重的词库法能够呈现透明化和可复制的程序, 准备、创建、并检验情感分析的词条。这个方法用到了两种词典（有配重的词典L1和手动选择的词典L2）, 本论文通过对TripAdvisor大数据进行使用词类划分精准度, 来检测和验证这两种词典。本论文采用两种热门方法（公共词典法和复杂机器学习算法）来对比词典的准确度。

精确度对比结果证实了本论文的方法, 相较于机器学习算法, 显著地超越了以字典为基础的方法。研究结果还表明, 本论文的方法可以就预测用户情感趋势进行推广。

本论文开发并验证了一项方法, 这种方法通过创建可信的词典进行大数据分析, 以判定用户情感。本论文创建的L2酒店词库对分析客人反馈是可靠有用的工具, 这个词库还能帮助酒店经理了解、预测、以及积极相应客人的态度和改变。本论文还提出了一项可以了解每个用户情感的简易方法, 这项方法可以通过对比的方式来检测和了解客人不同时间的情感变化, 以及根据其不同背景和经历的不同用户之间的变化。

本论文提出并检测了一项新方法, 这项情感分析方法可以解决之前方法的局限并立脚于旅游行业。基于文献综述, 本论文是首篇研究, 使用词库法来进行情感分析和创建特别领域词典的方式。

The main objective of this paper is to investigate whether the investors' behavior under optimistic (pessimistic) conditions has an impact on risk transmission between the Chinese stock and bond markets and the sector indices mainly during the COVID-19 pandemic.

This study uses a new measure of the investor's sentiment based on Google trend to construct a Chinese investor's sentiment index and a quantile causal approach to examine the causal relationship between googling investor's sentiment and the Chinese stock and bond markets as well as the sector indices. On the other hand, the network connectedness is used to estimate the spillover effect on the investor's sentiment and index returns. To check the robustness of the study results, the authors employed the Chinese VIX, as another measure of the investor's sentiment using daily data from May 2019 to December 2020.

In fact, the authors found a dual causality between the investor's sentiment and the financial market indices in optimistic or pessimistic situations, which indicates that positive and negative financial market returns may have an effect on the Chinese investor's sentiment. In addition, the results indicated that a pessimistic investor's sentiment has a negative impact on the banking, healthcare and utility sectors. In fact, the study results provide a significant peak of connectivity between the investor's sentiment, the stock market and the sector indices during the 2015–2016 and 2019–2020 turmoil periods that coincide respectively with the 2015 recession of the Chinese economy and the COVID-19 pandemic.

This finding suggests that the Chinese googling investor's sentiment is considered as a prominent channel of shock spillovers during the coronavirus crisis, which confirms the behavioral contagion. This study also identifies the contribution of a particular interest for portfolio managers and investors, which helps them to accordingly design their portfolio strategy.

Tourists with different cultural backgrounds think and behave differently. Accurately capturing and correctly understanding cultural differences will help tourist destinations in product/service planning, marketing communication and attracting and retaining tourists. This research employs Hofstede's cultural dimensions theory to analyse the variations in destination image perceptions of Chinese-speaking and English-speaking tourists to Xiamen, a prominent tourist attraction in China.

The evaluation utilizes a two-stage approach, incorporating LDA and BERT-BILSTM models. By leveraging text mining, sentiment analysis and t-tests, this research investigates the variations in tourists' perceptions of Xiamen across different cultures.

The results reveal that cultural disparities significantly impact tourists' perceived image of Xiamen, particularly regarding their preferences for renowned tourist destinations and the factors influencing their travel experience.

This research pioneers applying natural language processing methods and machine learning techniques to affirm the substantial differences in the perceptions of tourist destinations among Chinese-speaking and English-speaking tourists based on Hofstede's cultural theory. The findings furnish theoretical insights for destination marketing organizations to target diverse cultural tourists through precise marketing strategies and illuminate the practical application of Hofstede's cultural theory in tourism and hospitality.

Technological innovations provide huge opportunities to expand and revolutionize the scope of products and services offered. This is particularly true for tourism, which is undergoing significant changes due to the development of new technologies. The level of technology diffusion depends on several factors like the exchange of information among peers, and the attitude and shared perception among the contributors. The aim of the study is to explore the diffusion of technology in tourism with a specific focus on the social media discourse around new technologies. Thus, the paper investigates the level of interest in these new technologies analysing the information exchange occurring between individuals on Twitter in order to explore the influence of reciprocal networking.

To capture the attitudes expressed in the industry, the study analyses the ongoing discourse on Twitter as a proxy for the participants “interest in new technologies. Through a social network analysis of the tweets and retweets conducted over a period of nine months, the research maps the level of information exchange about the diffusion of new technologies. Moreover, the sentiment analysis provides an interesting overview of the individuals” attitudes towards the awareness or the adoption of new technologies.

Our analysis has provided several insights: (1) the information network on blockchain in tourism consists of participants who change very quickly over time (high turnover of accounts); (2) some contributors have an extremely important role in influencing the flow of information in the system (information centralization), they can have a generalist (discussing several topics) or a specialist (focusing on a specific topic) behaviour and this strategic choice influences their network's structure; (3) these central nodes also have an impact on the definition of positive and negative sentiment towards a topic (sentiment influencer).

The paper contributes to the literature on technology diffusion, by focusing on one of the preconditions of diffusion that is the shared positive attitude towards technological innovation. More specifically, we adopt a network-based approach, which is useful to explain the level of information exchange and the public discourse that can impact the shared perception and attitude towards technological innovation. The study also highlights the role of knowledge brokers in influencing this public discourse. Future studies can deepen the association between positive perception, higher levels of information exchange and increasing usage of specific technologies. Our results also suggest further exploring the opportunity to combine social media data and other sources of information to shed more light on the technological innovation diffusion processes.

This paper shows how practitioners can benefit from the analysis of information exchange about new technologies in tourism adopting a network perspective with the aim of understanding the level of influence among contributors. Moreover, the increasing interest in blockchain technology and the potential combination between social media data and other sources of information can offer promising insights.

The present study explores the level of technology diffusion through the analysis of information exchange on social media (Twitter). Furthermore, the dynamics of individual user behaviour offers a better understanding about media effects.

While previous research is focused on the users' perception towards the development of new technologies in tourism, the aim of this study is to investigate the dynamics behind the level of diffusion of information and awareness about these new technologies, which still represents an unexplored area of research.