Artificial Intelligence in Tourism: A Review and Bibliometrics Research

Kirtil, İsmail Gökay; Aşkun, Volkan

doi:10.30519/ahtr.801690

Cited by 41 publications

(16 citation statements)

References 88 publications

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“…However, the above two classic models also have shortcomings, especially in handling nonlinear and complex data (Kirtil and As kun, 2021). Time series models struggle to sharply capture the data's turning points; thus, prediction performance has a substantial negative effect on data instability (Zheng et al, 2020).…”

Section: Literature Review 21 Forecasting Model Of Hotel Demandmentioning

confidence: 99%

Daily hotel demand forecasting with spatiotemporal features

Huang

Zheng

2022

IJCHM

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Purpose Given the importance of spatial effects in improving the accuracy of hotel demand forecasting, this study aims to introduce price and online rating, two critical factors influencing hotel demand, as external variables into the model, and capture the spatial and temporal correlation of hotel demand within the region. Design/methodology/approach For high practical implications, the authors conduct the case study in Xiamen, China, where the hotel industry is prosperous. Based on the daily demand data of 118 hotels before and during the COVID-19 period (from January to June 2019 and from January to June 2021), the authors evaluate the prediction performance of the proposed innovative model, that is, a deep learning-based model, incorporating graph convolutional networks (GCN) and gated recurrent units. Findings The proposed model simultaneously predicts the daily demand of multiple hotels. It effectively captures the spatial-temporal characteristics of hotel demand. In addition, the features, price and online rating of competing hotels can further improve predictive performance. Meanwhile, the robustness of the model is verified by comparing the forecasting results for different periods (during and before the COVID-19 period). Practical implications From a long-term management perspective, long-term observation of market competitors’ rankings and price changes can facilitate timely adjustment of corresponding management measures, especially attention to extremely critical factors affecting forecast demand, such as price. While from a short-term operational perspective, short-term demand forecasting can greatly improve hotel operational efficiency, such as optimizing resource allocation and dynamically adjusting prices. The proposed model not only achieves short-term demand forecasting, but also greatly improves the forecasting accuracy by considering factors related to competitors in the same region. Originality/value The originalities of the study are as follows. First, this study represents a pioneering attempt to incorporate demand, price and online rating of other hotels into the forecasting model. Second, integrated deep learning models based on GCN and gated recurrent unit complement existing predictive models using historical data in a methodological sense.

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Section: Literature Review 21 Forecasting Model Of Hotel Demandmentioning

confidence: 99%

Daily hotel demand forecasting with spatiotemporal features

Huang

Zheng

2022

IJCHM

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“…First, an initial search was performed using all journals available on the Scimago Journal & Country Rank (SJR) under the subject area of Tourism, Leisure, and Hospitality Management. Similar step was taken by Güzeller & Çeliker (2018) and Kirtil & Aşkun (2021) through a bibliometric analysis search, where SJR was considered as one of the sources when conducting the search. This initial step yielded a total of 123 journals.…”

Section: Literature Searchmentioning

confidence: 99%

A Meta-Analysis of the Antecedents and Consequences of Delight in Tourism and Hospitality Research

Terrah

Liu

Nanu

2022

Advances in Hospitality and Tourism Research (AHTR)

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Customer delight is a concept that has recently gained momentum in consumer behavior, marketing, and hospitality literature. This study investigated the antecedents and subsequent of delight in a hospitality context. A meta-analysis was conducted, including 22 articles and producing 141 independent effect sizes. Results revealed a strong relationship between experiential context (service quality, physical environment, and authenticity), positive emotions, and surprise as antecedents of delight, with behavioral intentions, satisfaction, and loyalty as consequences of delight. Furthermore, gender was tested as a moderator, yielding a strong relationship between surprise and loyalty. Practical and theoretical implications are discussed as well as future research perspectives.

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“…Dignum (2019) argued that most AI applications in the tourism sector are focused on improving customer service and engagement. However, Kirtil and Aşkun (2021) mentioned that AI applications had been used in tourism to estimate hotel occupancy and tourism demand since the late 1990s. Moreover, the availability of hotel and travel information, modelling tourist decisions, assessing consumption statistics, data mining, and voice and image recognition are some ways AI has been applied in tourism (Nam et al, 2020).…”

Section: Ai In Tourismmentioning

confidence: 99%

Factors Affecting the Likelihood of Using Artificial Intelligence in Tourism Businesses: A Case of Egypt.

Ragab¹,

Ezzat²

2022

International Journal of Heritage, Tourism and Hospitality

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Tourism is being shaped by new consumer demands known as "digital natives", who expect technology such as Artificial Intelligence (AI). However, AI in tourism is still in its infancy compared to other sectors such as banking, healthcare, etc. Thus, governments and businesses need to develop AI policies and strategies to keep tourism services competitive. The objective of this research is to understand the factors that determine the likelihood of using AI in tourism businesses in Egypt. A measurement model is developed by combining the technology, organization, and environment context model and the benefits, organizational readiness, and external pressure model. The findings reveal that perceived functional benefits, strategic benefits, technological competence, and perceived risks are significant predictors of the likelihood of using AI in tourism businesses. The paper concludes that the public and private tourism sectors should enhance awareness of AI technologies implementation for a more prosperous, productive, and competitive tourism sector in Egypt.

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Artificial Intelligence in Tourism: A Review and Bibliometrics Research

Cited by 41 publications

References 88 publications

Daily hotel demand forecasting with spatiotemporal features

Daily hotel demand forecasting with spatiotemporal features

A Meta-Analysis of the Antecedents and Consequences of Delight in Tourism and Hospitality Research

Factors Affecting the Likelihood of Using Artificial Intelligence in Tourism Businesses: A Case of Egypt.

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