Search results

This paper aims to discuss the evolution of tourism data and critically debates future perspective for producers and users of tourism data.

This paper provides a perspective on tourism data based on selected literature.

Industry developments, technological changes and novel methodologies have influenced tourism data sources. Closer attention to new data collection methods and novel analytics is required.

A considerate and integrated system of tourism data (statistics, indicators, and big data) shall remain a priority for scholars and practitioners alike.

The thoughtful merging of tourists’ digital traces with industry data, the competences of data analysts and the theoretical strengths of tourism scholars will result in a redesign of the tourism data landscape.

This perspective article provides a brief overview of the development and challenges related to the future use of tourism statistics, indicators and big data.

The paper offers a novel vision of tourism data by combining three different but complementary aspects of tourism data.

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 research aims to explore the potential of big data technology for sustainable management and investigate its impact on tourism. Its goal is to obtain meaningful results related to sustainable tourism to understand better how big data technology plays a role in decision-making by looking at it through the lens of various studies.

A systematic review, which is a qualitative method, was used in this study. The analysis was conducted using secondary data from the Web of Science Core Collections databases.

Big data technology has many economic benefits for businesses, but it also has managerial benefits such as forecasting, decision-making and tracking human and machine behaviour. Furthermore, big data technology offers sustainability benefits such as resource efficiency, preventive quality systems, carbon reduction and environmentally friendly production.

Big data's capabilities enable businesses to make more informed business decisions, improve overall business performance and contribute to achieving various SDGs. Big data, which aids in developing smart and sustainable tourism in the tourism sector, assists tourism managers in making economically, socially and environmentally sound decisions.

This chapter provides an overview of the status of applied econometric research in hospitality and tourism management and outlines the econometric toolsets available for quantitative researchers using empirical data from the field. Basic econometric models, cross-sectional models, time-series models, and panel data models are reviewed first, followed by an evaluation of relevant applications. Next, econometric modeling topics that are germane to hospitality and tourism research are discussed, including endogeneity, multi-equation modeling, causal inference modeling, and spatial econometrics. Furthermore, major feasibility issues for applied researchers are examined based on the literature. Lastly, recommendations are offered to promote applied econometric research in hospitality and tourism management.

This paper aims to compare the forecasting performance of different models with and without big data predictors from search engines and social media.

Using daily tourist arrival data to Mount Longhu, China in 2018 and 2019, the authors estimated ARMA, ARMAX, Markov-switching auto-regression (MSAR), lasso model, elastic net model and post-lasso and post-elastic net models to conduct one- to seven-days-ahead forecasting. Search engine data and social media data from WeChat, Douyin and Weibo were incorporated to improve forecasting accuracy.

Results show that search engine data can substantially reduce forecasting error, whereas social media data has very limited value. Compared to the ARMAX/MSAR model without big data predictors, the corresponding post-lasso model reduced forecasting error by 39.29% based on mean square percentage error, 33.95% based on root mean square percentage error, 46.96% based on root mean squared error and 45.67% based on mean absolute scaled error.

Results highlight the importance of incorporating big data predictors into daily demand forecasting for tourism attractions.

This study represents a pioneering attempt to apply the regularized regression (e.g. lasso model and elastic net) in tourism forecasting and to explore various daily big data indicators across platforms as predictors.

This paper aims to conduct a review of the literature published, between 2006 and 2018, that used search engine data on tourism and hospitality research, namely, Google Insights for Search and Google Trends. More specifically, it intends to identify the purpose and context of the data use, ascertaining the main findings and reviewing the methodological approaches.

A systematic literature review of Scopus indexed research has been carried out. Given the novelty of search engine data use in tourism and hospitality research and the relatively low number of search results in Scopus, other databases were used to broaden the scope of analysis, namely, EBSCO and Google Scholar. The papers selected were subjected to content and statistical analyses.

Google Trends data use in tourism and hospitality research has increased significantly from 2012 to 2017, mainly for tourism forecasting/nowcasting; knowing the interest of users’ searches for tourist attractions or destinations; showing the relationship between the official tourism statistics and the search volume index of Google Trends; and estimating the effect of one event on tourism demand. The categories and search terms used vary with the purpose of the study; however, they mostly focus on the travel category and use the country as the search term.

Google Trends has been increasingly used in research publications in tourism and hospitality, but the range of its applications and methods used has not yet been reviewed. Therefore, a systematic review of the existing literature increases awareness of its potential uses in tourism and hospitality research and facilitates a better understanding of its strengths and weaknesses as a research tool.

本文回顾2006年至2018年发表文献使用酒店旅游相关的搜索引擎数据, 即Google Insights for Search 以及Google Trends。确切地说, 本文旨在研究数据使用目的和背景, 归纳主要研究成果和研究方法。

本文采用Scopus索引, 由于旅游酒店领域使用搜索引擎数据的文献较少, Scopus搜索结果样本量较低, 本文扩展到其他数据库, 即EBSCO以及Google Scholar。选定的样本文献采用文本分析和统计分析法。

旅游酒店领域中对Google Trends数据使用的增加主要集中在2012年到2017年, 主要研究领域有（1）旅游预测/即时预报；（2）了解用户搜索旅游景点或目的地的需求；（3）官方旅游数据和Google Trends搜索量索引之间的关系；以及（4）评估大事件对旅游需求的影响。文献归类和搜索名词根据研究目的而不同。然而, 大多数文章使用‘旅游’归类以及使用国家作为搜索关键词。

Google Trends在酒店旅游领域研究中的使用逐渐增加, 但是据作者所知, 其应用的范畴和方法仍处在起步阶段。因此, 对现有文献的系统回顾可以提高对其在旅游酒店领域中应用的认知, 并且本文结果使其作为研究工具的优劣分析更深理解。

Google Trends, Google insights for search, 搜索引擎数据, 旅游酒店研究, 系统文献回顾

This study aims to conduct a systematic review and critically analyze the sentiment analysis literature in hospitality and tourism from methodological (data sets and analyzes) and thematic (topics, theories, key constructs and their relationships) perspectives.

Qualitative thematic review and quantitative systematic review were performed on 70 papers obtained from hospitality and tourism categories of two databases, namely, Web of Science and Scopus.

A total of 5 topics and 27 sub-topics were identified and the major theme is market intelligence. Sentiment variables were investigated not only as independent but also as dependent variables. The customer rating is the most investigated dependent variable, whereas moderators and mediators were rarely tested. Most reviewed studies did not use theory. The findings from the methodological review show that analysis of big data was rare. Moreover, testing the performance of sentiment analyzes was uncommon, and only one paper tested the performance of aspect/feature extraction.

This study extends prior review studies by providing a comprehensive view of how knowledge and methodologies of sentiment analysis have developed. The identified themes and key constructs serve as a solid base for future knowledge advancement. Future research directions on sentiment analysis are also provided.

To the best of the authors’ knowledge, this study is the first comprehensive methodological and thematic review of sentiment analysis in hospitality and tourism. Based on the identified findings, the authors propose several directions for future research.

Dynamic, volatile, and time-sensitive industries, such as tourism, travel and hospitality require agility and market intelligence to create value and achieve competitive advantage. The aim of the current study is to examine the influence of big data (BD) on the performance of service organizations and to probe for a deeper understanding of implementing BD, based on available technologies.

An ethnographic study was conducted following an abductive approach. A primary qualitative research scheme was used with 35 information technology and database professionals participating in five online focus groups of seven participants each. Analytical themes were developed simultaneously with the literature being revisited throughout the study to ultimately create sets of common themes and dimensions.

BD can help organizations build agility, especially within dynamic industries, to better predict customer behavioral patterns and make tailor-made propositions from the BD. An integrated BD-specific framework is proposed to address value according to the dimensions of need, value, time and utility.

Little research exists on the key drivers of BD use for dynamic, real-time and agile businesses. This research adds to the developing literature on BD applications to support organizational decision-making and business performance in the tourism industry.

This study responds to scholars’ recent calls for more empirical research with contextual understanding of the use of BD to add value in marketing intelligence within business ecosystems. It delineates factors contributing to BD value creation and explores the impacts on the respective service encounters.

This paper aims to examine the extent to which Business Intelligence and Big Data feature within academic research in hospitality and tourism published until 2016, by identifying research gaps and future developments and designing an agenda for future research.

The study consists of a systematic quantitative literature review of academic articles indexed on the Scopus and Web of Science databases. The articles were reviewed based on the following features: research topic; conceptual and theoretical characterization; sources of data; type of data and size; data collection methods; data analysis techniques; and data reporting and visualization.

Findings indicate an increase in hospitality and tourism management literature applying analytical techniques to large quantities of data. However, this research field is fairly fragmented in scope and limited in methodologies and displays several gaps. A conceptual framework that helps to identify critical business problems and links the domains of business intelligence and big data to tourism and hospitality management and development is missing. Moreover, epistemological dilemmas and consequences for theory development of big data-driven knowledge are still a terra incognita. Last, despite calls for more integration of management and data science, cross-disciplinary collaborations with computer and data scientists are rather episodic and related to specific types of work and research.

This work is based on academic articles published before 2017; hence, scientific outputs published after the moment of writing have not been included. A rich research agenda is designed.

This study contributes to explore in depth and systematically to what extent hospitality and tourism scholars are aware of and working intendedly on business intelligence and big data. To the best of the authors’ knowledge, it is the first systematic literature review within hospitality and tourism research dealing with business intelligence and big data.

Several review articles have been published within the Artificial Intelligence (AI) literature that have explored a range of applications within the tourism and hospitality sectors. However, how efficiently the applied AI methods and algorithms have performed with respect to the type of applications and the multimodal sets of data domains have not yet been reviewed. Therefore, this paper aims to review and analyse the established AI methods in hospitality/tourism, ranging from data modelling for demand forecasting, tourism destination and behaviour pattern to enhanced customer service and experience.

The approach was to systematically review the relationship between AI methods and hospitality/tourism through a comprehensive literature review of papers published between 2010 and 2021. In total, 146 articles were identified and then critically analysed through content analysis into themes, including “AI methods” and “AI applications”.

The review discovered new knowledge in identifying AI methods concerning the settings and available multimodal data sets in hospitality and tourism. Moreover, AI applications fostering the tourism/hospitality industries were identified. It also proposes novel personalised AI modelling development for smart tourism platforms to precisely predict tourism choice behaviour patterns.

This review paper offers researchers and practitioners a broad understanding of the proper selection of AI methods that can potentially improve decision-making and decision-support in the tourism/hospitality industries.

This paper contributes to the tourism/hospitality literature with an interdisciplinary approach that reflects on theoretical/practical developments for data collection, data analysis and data modelling using AI-driven technology.