Journal of Hospitality and Tourism Technology最新文献

{"title":"The uneven impact of the COVID-19 pandemic on domestic tourist flows: what does mobile phone data tell us?","authors":"A. Condeço-Melhorado, J. García-Palomares, Javier Gutiérrez","doi":"10.1108/jhtt-04-2023-0103","DOIUrl":"https://doi.org/10.1108/jhtt-04-2023-0103","url":null,"abstract":"Purpose\u0000The COVID-19 pandemic has significantly impacted global tourism, with international travel bearing the burden of restrictions. Domestic tourism has also faced substantial challenges. This paper aims to analyse the impact of the COVID-19 pandemic on domestic tourism in Spain, focusing on travel from Madrid (the country’s capital) to other tourist destinations.\u0000\u0000Design/methodology/approach\u0000Mobile phone data has been used to study the evolution of tourist trips over the summers of 2019, 2020 and 2021. Regression models are used to explain the number of visitors at destinations.\u0000\u0000Findings\u0000The pandemic not only caused a drastic drop in tourist flows but also disrupted the overall pattern of the domestic flow system. Winning destinations were typically areas in proximity to Madrid and less densely populated destinations, while urban destinations were major losers. The preferences of domestic tourists varied notably by income group, but the decrease in trip volumes showed only marginal differences.\u0000\u0000Originality/value\u0000The paper demonstrates the potential of mobile phone data analysis to study the uneven impact of external shocks, such as the COVID-19 pandemic, on tourist destinations. This approach considers spatial resilience heterogeneity within regions or provinces. By incorporating income information, the analysis introduces a social dimension to highly detailed spatial data, surpassing traditional studies conducted at the regional or national levels.\u0000","PeriodicalId":51611,"journal":{"name":"Journal of Hospitality and Tourism Technology","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140674712","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Robotic safety and hygiene attributes: visitors’ intention to receive robot-delivered hospitality services","authors":"M. Parvez, K. Eluwole, T. Lasisi","doi":"10.1108/jhtt-10-2023-0307","DOIUrl":"https://doi.org/10.1108/jhtt-10-2023-0307","url":null,"abstract":"Purpose\u0000This study aims to investigate tourists’ intentions to use hotel service robots with a focus on safety and hygiene. It examines the impact of perceived safety, health awareness and service assurance on consumer engagement and robot usage.\u0000\u0000Design/methodology/approach\u0000Survey data from 275 participants with experience in robotic service were analyzed using structural equation modeling (SEM). The study used purposive sampling and collected data via the Prolific platform, using SEM and SmartPLS Ver. 3.0 for analysis.\u0000\u0000Findings\u0000Results indicate customers prioritize safety and hygiene, valuing effective service responses and cleanliness. Perceived robotic safety and service assurance positively influence personal engagement, with a preference for service robots among female guests.\u0000\u0000Research limitations/implications\u0000While emphasizing the importance of safety and service assurance in hotel robotics, the study acknowledges limitations in personalization and conclusive use of service robots.\u0000\u0000Originality/value\u0000This research contributes to understanding the role of perceived safety in service robot usage, highlighting the significance of user trust and comfort in human–robot interactions. It also explores the novel connection between service assurance and service robots, offering insights into robotic performance reliability in user-centric contexts.\u0000","PeriodicalId":51611,"journal":{"name":"Journal of Hospitality and Tourism Technology","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140700693","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Embracing the ChatGPT revolution: unlocking new horizons for tourism","authors":"Ji Shi, Minwoo Lee, V. G. Girish, Guangyu Xiao, Choong-Ki Lee","doi":"10.1108/jhtt-07-2023-0203","DOIUrl":"https://doi.org/10.1108/jhtt-07-2023-0203","url":null,"abstract":"\u0000Purpose\u0000This study aims to investigate tourists’ attitudes and intentions regarding the usage of Chat Generative Pre-trained Transformer (ChatGPT) for accessing tourism information. Furthermore, by integrating the perceived risks associated with ChatGPT and the theory of planned behavior (TPB), this research examines the impact of three types of perceived risks, such as privacy risk, accuracy risk and overreliance risk, on tourists’ behavioral intention.\u0000\u0000\u0000Design/methodology/approach\u0000Data were gathered for this study by using two online survey platforms, thus resulting in a sample of 536 respondents. The online survey questionnaire assessed tourists’ perceived risks, attitude, subjective norm, perceived behavioral control, behavioral intention and demographic information related to their usage of ChatGPT.\u0000\u0000\u0000Findings\u0000The structural equation modeling analysis revealed that tourists express concerns about the associated risks of using ChatGPT to search for tourism information, specifically privacy risk, accuracy risk and overreliance risk. It was found that perceived risks significantly influence tourists’ attitude and intention toward the usage of ChatGPT, which is consistent with the hypotheses proposed in previous literature regarding tourists’ perceived risks of ChatGPT.\u0000\u0000\u0000Research limitations/implications\u0000This work is a preliminary empirical study that assesses tourists’ behavioral intention toward the use of ChatGPT in the field of tourism. Previous research has remained at the hypothetical level, speculating about the impact of ChatGPT on the tourism industry. This study investigates the behavioral intention of tourists who have used ChatGPT to search for travel information. Furthermore, this study provides evidence based on the outcome of this research and offers theoretical foundations for the sustainable development of generative AI in the tourism domain. This study has limitations in that it primarily focused on exploring the risks associated with ChatGPT and did not extensively investigate its range of benefits.\u0000\u0000\u0000Practical implications\u0000First, to address privacy concerns that pose significant challenges for chatbots various measures, such as data encryption, secure storage and obtaining user consent, are crucial. Second, despite concerns and uncertainties, the introduction of ChatGPT holds promising prospects for the tourism industry. By offering personalized recommendations and enhancing operational efficiency, ChatGPT has the potential to revolutionize travel experiences. Finally, recognizing the potential of ChatGPT in enhancing customer service and operational efficiency is crucial for tourism enterprises.\u0000\u0000\u0000Social implications\u0000Recognizing the potential of ChatGPT in enhancing customer service and operational efficiency is crucial for tourism enterprises. As their interest in adopting ChatGPT grows, increased investments and resources will be dedicated to developing and implementing ChatGPT solutions. This enhancement may involve cr","PeriodicalId":51611,"journal":{"name":"Journal of Hospitality and Tourism Technology","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140718541","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Examining Turkish travellers’ non-immersive virtual heritage tour experiences through stimulus–organism–response model","authors":"Hande Akyurt Kurnaz, O. Kahraman, Alper Kurnaz, O. Atsız","doi":"10.1108/jhtt-10-2023-0323","DOIUrl":"https://doi.org/10.1108/jhtt-10-2023-0323","url":null,"abstract":"\u0000Purpose\u0000This study aims to examine how travellers’ non-immersive virtual heritage authenticity, sense of presence and virtual tour satisfaction stimulate their behavioural intentions (continuance and travel intention) within the stimulus–organism–response model.\u0000\u0000\u0000Design/methodology/approach\u0000A questionnaire was designed to survey Turkish travellers (n = 275) participating in a virtual tour. A structural equation modelling method was used to estimate the model and test the research hypotheses.\u0000\u0000\u0000Findings\u0000Research findings revealed that four out of six hypotheses were supported. Based on the study outputs, authenticity and sense of presence impact overall travellers’ satisfaction. Furthermore, satisfaction influences continuance intention and travel intention.\u0000\u0000\u0000Originality/value\u0000The study presents a pioneering effort to investigate tourists’ non-immersive virtual heritage tour experiences in a developing destination context through a theoretical framework.\u0000","PeriodicalId":51611,"journal":{"name":"Journal of Hospitality and Tourism Technology","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140367327","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Influence of social media communication on consumer purchase decisions: do luxury hotels value perceived brand authenticity, prestige, and familiarity?","authors":"Wang Qing, A. Safeer, Muhammad Saqib Khan","doi":"10.1108/jhtt-09-2023-0282","DOIUrl":"https://doi.org/10.1108/jhtt-09-2023-0282","url":null,"abstract":"\u0000Purpose\u0000This paper aims to examine the influence of social media communications, particularly firm-generated content (FGC) and consumer-generated content (CGC) on predicting consumer purchase decisions (CPD) through the lens of perceived brand authenticity (PBA). This paper also investigates the moderating influence of brand prestige (BP) and brand familiarity in the luxury hotel sector.\u0000\u0000\u0000Design/methodology/approach\u0000This study collected data from 390 consumers who were regularly using social media platforms, traveled frequently and stayed in luxury hotels. Following stringent data filtering, 371 responses were analyzed via structural equation modeling.\u0000\u0000\u0000Findings\u0000The findings indicate that FGC and CGC significantly strengthened PBA. However, CGC was the effective driver that directly influenced CPD. Likewise, PBA directly and indirectly substantially impacted CPD. Finally, BP’s direct and moderating effects significantly influenced CPD in the luxury hotel sector.\u0000\u0000\u0000Originality/value\u0000This novel study contributes to signaling theory, social media communications and branding literature in the luxury hotel sector.\u0000","PeriodicalId":51611,"journal":{"name":"Journal of Hospitality and Tourism Technology","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140220981","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Aligning restaurants and artificial intelligence computing of food delivery service with product development","authors":"Shu-Hua Wu, Edward C.S. Ku","doi":"10.1108/jhtt-10-2023-0322","DOIUrl":"https://doi.org/10.1108/jhtt-10-2023-0322","url":null,"abstract":"Purpose\u0000This study aims to analyze how restaurants' collaboration with mobile food delivery applications (MFDAs) affects product development efficiency and argues that technological capabilities moderate relational ties impact the joint decision-making and development efficiency of restaurant products.\u0000\u0000Design/methodology/approach\u0000A product development efficiency model was formulated using a resource-based view and real options theory. In all, 472 samples were collected from restaurants collaborating with MFDAs, and partial least squares structural equation modeling was applied to the proposed model.\u0000\u0000Findings\u0000The findings of this study indicate three factors are critical to the product development efficiency between restaurants and MFDAs; restaurants must develop a strong connection with the latter to ensure meals are consistently served promptly. Developers of MFDAs should use artificial intelligence analysis, such as order records of different genders and ages or various consumption attributes, to collaborate with restaurants.\u0000\u0000Originality/value\u0000To the best of the authors’ knowledge, this study is one of the few that considers the role of MFDAs as a product strategy for restaurant operations, and the factors the authors found can enhance restaurants’ product development efficiency. Second, as strategic artificial intelligence adaptation changes, collaborating firms and restaurants use such applications for product development to help consumers identify products.\u0000","PeriodicalId":51611,"journal":{"name":"Journal of Hospitality and Tourism Technology","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140221470","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Contribution of innovation studies to the intellectual structure of the hospitality and tourism literature","authors":"H. Arıcı, Mehmet Ali Köseoglu, Cagdas Aydin, Ceren Aydin, Levent Altinay","doi":"10.1108/jhtt-12-2022-0347","DOIUrl":"https://doi.org/10.1108/jhtt-12-2022-0347","url":null,"abstract":"\u0000Purpose\u0000This study aims to identify the role of innovation research in formulating the intellectual structure of the hospitality and tourism literature by performing a bibliometric analysis.\u0000\u0000\u0000Design/methodology/approach\u0000In total, 6,255 journal articles on innovation were gathered from Scopus and analyzed using co-citation, bibliographic coupling and thematic content analyses. The most influential articles were also carefully read to reveal a nomological network of innovation research in hospitality and tourism scholarship.\u0000\u0000\u0000Findings\u0000Co-citation analysis reveals that there are six significant clusters in the field of innovation research. Various philosophical underpinnings might be used in different circumstances, with actor-network and Schumpeterian theory playing significant roles. A review of current works using bibliographic coupling reveals five interesting emerging research areas and makes numerous recommendations for when to conduct more studies. A review of influential articles displayed differences between the co-citation and bibliographic coupling analysis findings and produced a framework for further investigation of the knowledge field.\u0000\u0000\u0000Originality/value\u0000This paper is among the first integrative reviews on innovation research in hospitality and tourism by quantitatively reviewing published articles and qualitatively reviewing the content of the most influential studies.\u0000","PeriodicalId":51611,"journal":{"name":"Journal of Hospitality and Tourism Technology","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140425476","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"What if ChatGPT generates quantitative research data? A case study in tourism","authors":"Serhat Adem Sop, Doğa Kurçer","doi":"10.1108/jhtt-08-2023-0237","DOIUrl":"https://doi.org/10.1108/jhtt-08-2023-0237","url":null,"abstract":"\u0000Purpose\u0000This study aims to explore whether Chat Generative Pre-training Transformer (ChatGPT) can produce quantitative data sets for researchers who could behave unethically through data fabrication.\u0000\u0000\u0000Design/methodology/approach\u0000A two-stage case study related to the field of tourism was conducted, and ChatGPT (v.3.5.) was asked to respond to the first questionnaire on behalf of 400 participants and the second on behalf of 800 participants. The artificial intelligence (AI)-generated data sets’ quality was statistically tested via descriptive statistics, correlation analysis, exploratory factor analysis, confirmatory factor analysis and Harman's single-factor test.\u0000\u0000\u0000Findings\u0000The results revealed that ChatGPT could respond to the questionnaires as the number of participants at the desired sample size level and could present the generated data sets in a table format ready for analysis. It was also observed that ChatGPT's responses were systematical, and it created a statistically ideal data set. However, it was noted that the data produced high correlations among the observed variables, the measurement model did not achieve sufficient goodness of fit and the issue of common method bias emerged. The conclusion reached is that ChatGPT does not or cannot yet generate data of suitable quality for advanced-level statistical analyses.\u0000\u0000\u0000Originality/value\u0000This study shows that ChatGPT can provide quantitative data to researchers attempting to fabricate data sets unethically. Therefore, it offers a new and significant argument to the ongoing debates about the unethical use of ChatGPT. Besides, a quantitative data set generated by AI was statistically examined for the first time in this study. The results proved that the data produced by ChatGPT is problematic in certain aspects, shedding light on several points that journal editors should consider during the editorial processes.\u0000","PeriodicalId":51611,"journal":{"name":"Journal of Hospitality and Tourism Technology","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140442345","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Avoiding food waste from restaurant tickets: a big data management tool","authors":"Ismael Gómez-Talal, Lydia González-Serrano, J. Rojo-álvarez, Pilar Talón-Ballestero","doi":"10.1108/jhtt-01-2023-0012","DOIUrl":"https://doi.org/10.1108/jhtt-01-2023-0012","url":null,"abstract":"\u0000Purpose\u0000This study aims to address the global food waste problem in restaurants by analyzing customer sales information provided by restaurant tickets to gain valuable insights into directing sales of perishable products and optimizing product purchases according to customer demand.\u0000\u0000\u0000Design/methodology/approach\u0000A system based on unsupervised machine learning (ML) data models was created to provide a simple and interpretable management tool. This system performs analysis based on two elements: first, it consolidates and visualizes mutual and nontrivial relationships between information features extracted from tickets using multicomponent analysis, bootstrap resampling and ML domain description. Second, it presents statistically relevant relationships in color-coded tables that provide food waste-related recommendations to restaurant managers.\u0000\u0000\u0000Findings\u0000The study identified relationships between products and customer sales in specific months. Other ticket elements have been related, such as products with days, hours or functional areas and products with products (cross-selling). Big data (BD) technology helped analyze restaurant tickets and obtain information on product sales behavior.\u0000\u0000\u0000Research limitations/implications\u0000This study addresses food waste in restaurants using BD and unsupervised ML models. Despite limitations in ticket information and lack of product detail, it opens up research opportunities in relationship analysis, cross-selling, productivity and deep learning applications.\u0000\u0000\u0000Originality/value\u0000The value and originality of this work lie in the application of BD and unsupervised ML technologies to analyze restaurant tickets and obtain information on product sales behavior. Better sales projection can adjust product purchases to customer demand, reducing food waste and optimizing profits.\u0000","PeriodicalId":51611,"journal":{"name":"Journal of Hospitality and Tourism Technology","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139824992","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Avoiding food waste from restaurant tickets: a big data management tool","authors":"Ismael Gómez-Talal, Lydia González-Serrano, J. Rojo-álvarez, Pilar Talón-Ballestero","doi":"10.1108/jhtt-01-2023-0012","DOIUrl":"https://doi.org/10.1108/jhtt-01-2023-0012","url":null,"abstract":"\u0000Purpose\u0000This study aims to address the global food waste problem in restaurants by analyzing customer sales information provided by restaurant tickets to gain valuable insights into directing sales of perishable products and optimizing product purchases according to customer demand.\u0000\u0000\u0000Design/methodology/approach\u0000A system based on unsupervised machine learning (ML) data models was created to provide a simple and interpretable management tool. This system performs analysis based on two elements: first, it consolidates and visualizes mutual and nontrivial relationships between information features extracted from tickets using multicomponent analysis, bootstrap resampling and ML domain description. Second, it presents statistically relevant relationships in color-coded tables that provide food waste-related recommendations to restaurant managers.\u0000\u0000\u0000Findings\u0000The study identified relationships between products and customer sales in specific months. Other ticket elements have been related, such as products with days, hours or functional areas and products with products (cross-selling). Big data (BD) technology helped analyze restaurant tickets and obtain information on product sales behavior.\u0000\u0000\u0000Research limitations/implications\u0000This study addresses food waste in restaurants using BD and unsupervised ML models. Despite limitations in ticket information and lack of product detail, it opens up research opportunities in relationship analysis, cross-selling, productivity and deep learning applications.\u0000\u0000\u0000Originality/value\u0000The value and originality of this work lie in the application of BD and unsupervised ML technologies to analyze restaurant tickets and obtain information on product sales behavior. Better sales projection can adjust product purchases to customer demand, reducing food waste and optimizing profits.\u0000","PeriodicalId":51611,"journal":{"name":"Journal of Hospitality and Tourism Technology","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139885152","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}