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This study aims to chart the impact of technological advancements on tourism from the post–Second World War era to the present and forecast their influence until 2050. It assesses how technologies have reshaped travel experiences and operations, with a focus on upcoming innovations such as the metaverse, Web 3.0 and AI, and their implications for sustainable and ethical tourism.

This study uses a hybrid approach, combining historical analysis and future projections. It analyzes archival data, industry reports and academic literature.

This study identifies crucial technological milestones that have significantly impacted tourism, including the rise of commercial aviation, the internet and AI. Future trends suggest emerging technologies will further transform the sector. Challenges in sustainability, ethics and inclusivity are highlighted as critical considerations for future development.

This paper offers a unique longitudinal perspective on technology’s influence on tourism, bridging past trends with future projections.

本研究采取混合方法, 融合历史分析与未来趋势预测。研究分析了丰富的档案数据、行业报告以及学术文献。

旨在勾勒从二战后至今技术进步对旅游业的影响, 并展望至2050年的潜在影响。本研究着重评估技术如何重塑旅游体验和运作, 特别是对元宇宙、网络3.0和人工智能等即将到来的创新技术及其对可持续和伦理旅游的意义。

识别了旅游业中关键的技术里程碑, 包括商业航空、互联网和人工智能的崛起。研究指出, 未来趋势显示新兴技术将继续深刻改变旅游业。同时强调, 可持续性、伦理和包容性是未来发展中不可忽视的关键要素。

本文从独特的纵向视角出发, 深入探讨了技术对旅游业的历史与未来影响, 将过去发展趋势与未来展望紧密结合。

Este estudio emplea un enfoque híbrido que combina el análisis histórico y las proyecciones de futuro. Analiza datos de archivo, informes del sector y bibliografía académica.

La investigación pretende trazar el impacto de los avances tecnológicos en el turismo desde la era posterior a la Segunda Guerra Mundial hasta la actualidad y prever su influencia hasta 2050. Evalúa cómo las tecnologías han reconfigurado las experiencias y las operaciones de viaje, centrándose en las próximas innovaciones como el Metaverso, la Web 3.0 y la IA, y sus implicaciones para un turismo sostenible y ético.

El estudio identifica hitos tecnológicos cruciales que han tenido un impacto significativo en el turismo, como el auge de la aviación comercial, Internet y la IA. Las tendencias futuras sugieren que las tecnologías emergentes transformarán aún más el sector. Los retos en sostenibilidad, ética e inclusividad se destacan como consideraciones críticas para el desarrollo futuro.

Este artículo ofrece una perspectiva longitudinal única sobre la influencia de la tecnología en el turismo, tendiendo un puente entre las tendencias pasadas y las proyecciones futuras.

Heritage tourism is an increasingly popular form of tourism that allows individuals to connect with the past and immerse themselves in cultural and historical narratives. Hence, the purpose of this study is to explore the intricate relationships among vicarious nostalgia (VNOS), memorable tourism experiences (MTEXs) and their collective influence on tourists’ behavioral intentions (BINTs). Additionally, this study examines the moderating effect of social return (SN) in the context of heritage tourism.

Data were gathered using a self-administered questionnaire from 259 tourists visiting heritage sites in Jaipur. The proposed model was tested using structural equation modeling.

The results confirmed that VNOS had a significant positive impact on BINT in the context of heritage tourism. The causal relationship between VNOS and BINT was fully mediated by MTEX. The results further verified that the presence of SN strengthens the association between MTEXs and BINT.

This research will guide the firms associated with heritage tourism to target specific cohorts interested in heritage tourism. Policymakers may find it easier to create unique offerings and packages that appeal to visitors interested in historical sites and produce memorable travel experiences. One key implication is to create “social media friendly spaces” at different locations of the sites. To increase tourism, managers may use the findings from this research to create plans for the ethical promotion and protection of cultural and natural heritage sites.

Overall, this research advances the understanding of the role of VNOS in heritage tourism by elucidating its cognitive and emotional aspects and their subsequent influence on the memorability of tourist experiences and BINT s. Additionally, by considering the moderating effect of SN, this study provides a comprehensive view of how these factors collectively shape tourists’ decisions and actions in the context of heritage destinations. This research has been conducted in the heritage city of Jaipur (North-Western India), which, surprisingly – despite its popularity as a heritage tourism site – has not been sufficiently explored in the scholarly research.

This study aims to test the complaints of tourists who visit five-star hotels in Antalya, Turkey, on those same hotels’ websites.

In the study, the data were collected with qualitative methods but analyzed with the mixed analysis method. In this context, the authors collected 1,012 comments on the website between 2016 and 2019.

According to the results of the study, the most intense complaints were found to be concentrated in categories such as ambience, food and staff.

First of all, it is thought that it will make an important contribution to the literature, since different methodologies are adopted in the study. In addition, online shares, evaluations and comments produce positive or negative results for the destination or business in question. It is necessary to closely monitor such activities in electronic environments, as they may have negative consequences, thus revealing the need to take corrective or preventive measures. For this reason, the research is important in terms of not having such a large-scale study in the literature and contributing to the hospitality industry.

This research paper aims to investigate the predictors and outcomes of immersive technology adoption in tourism.

PLS-SEM is used for data collected from tourists visiting the UAE and Egypt to examine predictors and consequences of adoption.

It is revealed that perceived ease of use, enjoyment, immersion, usefulness and attitude towards technology predict immersive technology adoption. It is also revealed that the adoption affects tourists’ perceived value and engagement, which, in turn, affects tourists’ satisfaction and loyalty.

The study has integrated a research model that combines both antecedents and consequences of immersive technology adoption where few empirical investigations were revealed to draw conclusions on this research area. Also, missing relations have been included and tested in the research model.

Senior tourist is a salient segment of the tourism sector. This segment reflects a robust ageing population with discretionary income and an appetite for tourism activities. However, to date, there has been a paucity of empirical insight on how the combination of intrinsic and extrinsic motivations may influence senior tourists’ connectedness and booking intentions towards home-sharing accommodation. Thus, this study aims to investigate how senior tourists’ curiosity and social interaction may influence their connectedness towards Airbnb and subsequently booking intention.

A conceptual model was developed and tested using partial least squares structural equation modelling to analyse data collected from a sample of 195 senior tourists in Malaysia.

The results showed that intrinsic (curiosity) and extrinsic (social interaction) motivations positively influence senior tourists’ connectedness towards platform accommodation, which in turn positively affects the outcome variable. Furthermore, this study found that a sense of connectedness is crucial in linking motivators and booking intentions.

This research was carried out in Malaysia; therefore, cross-national studies are encouraged to establish whether the findings described in this study can be extrapolated to other cultures/countries.

From a practitioner’s perspective, this study reinforces the need to address and understand senior tourists’ curiosity and how it may invoke their connectedness and behavioural actions towards the Airbnb platform. More importantly, this study gives home-sharing practitioners practical leverage on how combined intrinsic and extrinsic motivations may deduce senior tourists’ booking intentions.

The study contributes to the literature on senior tourism and the home-sharing sector by demonstrating the role of curiosity and social interaction in shaping senior tourists’ connectedness towards Airbnb and behavioural intentions.

The tourist industry has to adopt a big data-driven foresight approach to enhance decision-making in a post-COVID international landscape still marked by significant uncertainty and in which some megatrends have the potential to reshape society in the next decades. This paper, considering the opportunity offered by the application of the quantitative analysis on internet new data sources, proposes a prediction method using Google Trends data based on an estimated transfer function model.

The paper uses the time-series methods to model and predict Google Trends data. A transfer function model is used to transform the prediction of Google Trends data into predictions of tourist arrivals. It predicts the United States tourism demand in Italy.

The results highlight the potential expressed by the use of big data-driven foresight approach. Applying a transfer function model on internet search data, timely forecasts of tourism flows are obtained. The two scenarios emerged can be used in tourism stakeholders’ decision-making process. In a future perspective, the methodological path could be applied to other tourism origin markets, to other internet search engine or other socioeconomic and environmental contexts.

The study raises awareness of foresight literacy in the tourism sector. Secondly, it complements the research on tourism demand forecasting by evaluating the performance of quantitative forecasting techniques on new data sources. Thirdly, it is the first paper that makes the United States arrival predictions in Italy. Finally, the findings provide immediate valuable information to tourism stakeholders that could be used to make decisions.

This paper reports the results of research that examines the interrelationships between efficacy of sustainability values (SV) and pro-sustainable behaviors of potential tourists. A partially mediated model is postulated and tested to help explain additional error variance in predicting consumers’ destination choice decisions in tourism, hence voiding a critical research gap. Coined as the “environmentally intellectualist behavior,” a new mediator variable is tested to explain additional error variance in human-value models.

The study is based on data collected from two representative samples of potential tourists from the USA and Canada. Data analyses include exploratory and confirmatory factor analyses that were used to examine the underlying domain structures of SV, followed by a predictive model using structural equation modeling.

The study findings suggest that values are salient factors that underlie pro-sustainable tourism and travel behavior. Moreover, the results confirm the existence of a higher-order sustainability construct. The study contributes original insights to the field by demonstrating that there are direct and indirect positive relationships between SV, environmental behaviors and decisions of consumers who take a pro-sustainable stance when traveling.

By modeling values as antecedents to attitudes and testing interrelationships between SV and the mediator variables coined as the environmentally intellectual behavior, the authors developed and tested a predictive model to explain destination- and product choice decisions. The model tested herein advances the value theory in two fundamental ways: first, this study demonstrates that SV can be modeled as higher-order factors. Second, values are antecedents to attitude and other variables, therefore must be included in consumer behavior models. Finally, the culture or origin of tourists matters when examining the impact of values on tourists’ choice decisions. Political actions and environmental attitudes can be modeled as mediators to explain additional error variance.

Online reviews serve as valuable sources of information, reflecting tourists’ attentions, preferences and sentiments. However, although the existing research has demonstrated that incorporating online review data can enhance the performance of tourism demand forecasting models, the reliability of online review data and consumers’ decision-making process have not been given adequate attention. To address the aforementioned problem, the purpose of this study is to forecast tourism demand using online review data derived from the analysis of review helpfulness.

The authors propose a novel “identification-first, forecasting-second” framework. This framework prioritizes the identification of helpful reviews through a comprehensive analysis of review helpfulness, followed by the integration of helpful online review data into the forecasting system. Using the SARIMAX model with helpful online review data sourced from TripAdvisor, this study forecasts tourist arrivals in Hong Kong during the period from August 2012 to June 2019. The SNAÏVE/SARIMA model was used as the benchmark model. Additionally, artificial intelligence models including long short-term memory, back propagation neural network, extreme learning machine and random forest models were used to assess the robustness of the results.

The results demonstrate that online review data are subject to noise and bias, which can adversely affect the accuracy of predictions when used directly. However, by identifying helpful online reviews beforehand and incorporating them into the forecasting process, a notable enhancement in predictive performance can be realized.

First, to the best of the authors’ knowledge, this study is one of the first to focus on the data issue of online reviews on tourism arrivals forecasting. Second, this study pioneers the integration of the consumer decision-making process into the domain of tourism demand forecasting, marking one of the earliest endeavors in this area. Third, this study makes a novel attempt to identify helpful online reviews based on reviews helpfulness analysis.