The 2019 novel coronavirus (COVID-19) outbreak is projected to have adverse consequences on the global tourism and hospitality industry. This paper aims to examine how the outbreak may alter Chinese tourists’ lifestyle choices, travel behaviour and tourism preferences in the short and long term.,This paper is based on the synthesis of news broadcasted by several media outlets to be supported by an overview of the related literature on tourism marketing, tourism management and tourist behaviour. The authors’ experiences investigating trends in tourism and hospitality at the local and international level have also contributed to the study.,This paper predicts that COVID-19 will likely affect Chinese travellers’ consumption patterns, such as the growing popularity of free and independent travel, luxury trips and health and wellness tourism. New forms of tourism including slow tourism and smart tourism may also drive future tourism activities. Such changes are likely to force businesses to reconsider their service designs and distribution channels.,While Chinese and other potential visitors rethink how they travel, professionals, too, should reflect upon how to bring positive or negative changes to the tourism industry following this pandemic. Subsequent research should also consider how to mitigate the effects of similar public health crises in the future.,Recommendations for industry practitioners and policymakers focus on tailoring travel arrangements to tourists’ backgrounds. The suggestions may help to alleviate outbreak-related stress, offer travellers newly enriching experiences and partially mitigate the effects of COVID-19 on the tourism and hospitality industry. These recommendations can also apply more broadly to global tourist markets.,The COVID-19 outbreak has already brought significant impacts to nearly every society and industry. Tourism scholars and practitioners should carefully consider this tragedy and how it may inform industry and social practices. This and other public health crises represent sterling opportunities to view the industry holistically in terms of its effects on the environment, climate and travellers themselves.,This paper presumably represents a frontier study, critically examining the possible impacts of COVID-19 on Chinese travellers’ consumption patterns and how the tourism and hospitality industry may respond to such changes in the future.,预计2019年新型冠状病毒（COVID-19）爆发将对全球旅游和酒店业产生不利影响。本文预测, COVID-19可能会影响中国旅客的消费方式, 例如自由行, 豪华旅行以及健康与养生旅游的普及。包括慢速旅游和智能旅游在内的新型旅游形式也可能推动未来的旅游活动。这种变化可能促使企业重新考虑其服务设计和分销渠道。针对行业从业者和政策制定者的建议着重于根据游客的背景和需求量身定制旅行安排。我们的建议可能有助于减轻与疫情暴发导致的压力, 为旅行者提供全新的丰富体验, 并从一定程度减轻COVID-19对旅游业和酒店业的影响。这些建议还可以更广泛地应用于全球旅游市场。,COVID-19, 生活方式 旅游行为 灾后 集体主义倾向 中国,El impacto del COVID-19 se preve importante en la actividad turistica global y en la industria de la hosteleria. Este articulo predice que el COVID-19 afectara con probabilidad alta los patrones de consumo de los viajeros chinos, incluyendo los cada vez mas populares viajes comprados directamente por los consumidores chinos, en el segmento del lujo, y el turismo de salud y belleza. Nuevas formas de turismo, incluyendo el slow tourism, y el turismo responsable pueden convertirse en importantes tendencias de futuro igualmente. Dichos cambios llevaran con cierta seguridad a la industria a reconsiderar y adaptar su oferta de servicios en este sector, en particular su diseno y los canales de distribucion utilizados. Las recomendaciones para la industria y los responsables de la politica turistica se alinean con una mayor proximidad de la oferta a los gustos cambiantes del consumidor. Dichas acciones ayudaran a reducir fricciones y fallos en la definicion del negocio turistico, ofreciendo a los viajeros nuevas y enriquecedoras experiencias, asi como podran mitigar en parte los efectos adversos estimados del COVID-19, los cuales son relevantes a dia de hoy. Asi mismo, dichas recomendaciones son aplicables de una manera mas global a la industria del turismo y la hospitalidad en el mercado mundial.,COVID-19, Estilo de vida, Conducta del viajero, Post-desastre, Orientacion collectivista, China

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COVID-19: potential effects on Chinese citizens’ lifestyle and travel

Using deep learning and visual analytics to explore hotel reviews and responses

A machine learning approach to cluster destination image on Instagram

In reaction to the growing attention paid to big data and artificial intelligence in hospitality and tourism research, we systematically reviewed 270 relevant studies to identify topical themes and...

A look back and a leap forward: a review and synthesis of big data and artificial intelligence literature in hospitality and tourism

Color and engagement in touristic Instagram pictures: A machine learning approach

Discovering the tourists' behaviors and perceptions in a tourism destination by analyzing photos' visual content with a computer deep learning model: The case of Beijing

With the popularity of social media, it is possible to use UGC (user-generated content) for tourist behavior study. However, current UGC-based tourism research is still text-centric; the visual con...

How are Tourists Different? - Reading Geo-tagged Photos through a Deep Learning Model

Responsible tourism is an essential issue of considerable concern to multiple stakeholders. Few studies focus on how specific academic courses increase future practitioners’ responsibility in tourism. This study employs nature-based experiential learning to inspire and enhance tourism students’ awareness of and capacity for responsible tourism. We apply mixed methods (i.e. pretest–posttest, observations and reflection memos) to justify the overall learning process. Findings demonstrate that the undergraduates’ responsible tourism-associated perception and capacity are improved. A nature-based experiential learning framework is proposed to provide insights into high-quality education for responsible tourism.

Nature-based experiential learning as a framework for preparing responsible tourism practitioners

\nPurpose\nThis study aims to conduct an empirical investigation of differing perceptions of nine types of urban space and nine visual elements among tourists in destination using a computer vision (CV) approach.\n\n\nDesign/methodology/approach\nThe data for this study was extracted from YFCC 100 M dataset. Nine types of urban space in Beijing were initially identified using a scene recognition model. Subsequently, a semantic segmentation model was applied, which yielded substantial evidence relating to nine visual elements that were used to elicit differing perceptions among tourists from different continents.\n\n\nFindings\nTourists from three continents had different perceptions about corridors, old buildings, overlooks and traffic spaces, reflecting their cultural convention. Asians, Europeans and North Americans diversely gazed at the landscape element of buildings, foliage, sky and people in urban space. All those provided evidence to contribute to the tourist gaze theory's construction.\n\n\nOriginality/value\nThis study firstly depicted how tourists perceive the tourism symbol of urban space. The novel approach of employing two CV models offer methodological insights to tourism research relevant to visual perception.\n

Tourists’ perceptions of urban space: a computer vision approach

With a two-stage analysis framework, this paper investigates how information communication technology can improve the sustainable performance of smart tourism destinations. It analyzes the impact of online attention (in the form of activity on a search engine) and the level of the digital economy on sustainable performance of smart tourism destinations, measured using an advanced data envelopment analysis model. The empirical results from 50 key smart tourism destinations in China show that: (1) the average value of smart destination performance from 2008 to 2019 is 0.74; (2) both online attention and digital economy have significant positive impacts on sustainable performance; (3) digital economy positively moderates the impact of online attention on sustainable performance; (4) undesirable outputs (air pollution) and tourist satisfaction are two factors that can be considered in measuring the sustainable performance of a destination. 

Chunlin Li

Papers

Discovering the tourists' behaviors and perceptions in a tourism destination by analyzing photos' visual content with a computer deep learning model: The case of Beijing

How are Tourists Different? - Reading Geo-tagged Photos through a Deep Learning Model

Nature-based experiential learning as a framework for preparing responsible tourism practitioners

Tourists’ perceptions of urban space: a computer vision approach

Measurement and determinants of smart destinations’ sustainable performance: a two-stage analysis using DEA-Tobit model