Data Mining in Tourism Data Analysis: Inbound Visitors to Japan

Shapoval, Valeriya; Wang, Morgan C.; Hara, Tadayuki; Shioya, Hideo

doi:10.1177/0047287517696960

Cited by 36 publications

(24 citation statements)

References 64 publications

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“…Kim, Timothy, and Hwang (2011) used decision trees to analyze the factors affecting the intention to visit South Korea by Japanese tourists, based on 300 surveys. Shapoval et al (2018) applied decision trees to explain motivations of repeat visitors to Japan based on 4,000 surveys. Both these publications used data sets whose sizes are considered relatively small.…”

Section: Methodsmentioning

confidence: 99%

The Influence of Animosity, Ethnocentric Tendencies, and National Attachment on Tourists’ Decision-Making Processes during International Conflicts

Степченкова

Dai

Kirilenko

et al. 2019

Journal of Travel Research

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This study expands the empirical evidence amassed to date concerning tourists’ decision-making processes in a situation of conflict between a country of origin’s market and country of destination. It uses the constructs of animosity, national attachment, consumer ethnocentrism, country image, and perceptions of the state of bilateral relations in order to examine three intent to visit scenarios. To obtain the robust results, the study uses two methods of analysis from distinctly different repertoires of techniques, such as traditional statistical and data-mining approaches, and applies them to each scenario. The results consistently indicate a high level of influence of general animosity and ethnocentric tendencies in addition to country image and bilateral relation variables in tourists’ decision making. The situational context of the study is the conflict between China, the country of origin, and South Korea, the country of destination, over the Terminal High Altitude Area Defense (THAAD) system.

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Section: Methodsmentioning

confidence: 99%

The Influence of Animosity, Ethnocentric Tendencies, and National Attachment on Tourists’ Decision-Making Processes during International Conflicts

Степченкова

Dai

Kirilenko

et al. 2019

Journal of Travel Research

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“…A decision tree (DT) is a simple yet effective data mining modeling technique that creates a tree-like model where each node represents a decision using the input features until a leaf with a prediction is reached. Shapoval et al (2018) adopted a DT to analyze secondary data on 4,000 tourists obtained from the Japan Tourism Agency about tourists visiting Japan. Features such as food and nationality were found relevant to explain tourist satisfaction.…”

Section: Data Mining and Tourismmentioning

confidence: 99%

Evaluating a guest satisfaction model through data mining

Moro

Esmerado

Ramos

et al. 2019

IJCHM

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Purpose This paper aims to propose a data mining approach to evaluate a conceptual model in tourism, encompassing a large data set characterized by dimensions grounded on existing literature. Design/methodology/approach The approach is tested using a guest satisfaction model encompassing nine dimensions. A large data set of 84 k online reviews and 31 features was collected from TripAdvisor. The review score granted was considered a proxy of guest satisfaction and was defined as the target feature to model. A sequence of data understanding and preparation tasks led to a tuned set of 60k reviews and 29 input features which were used for training the data mining model. Finally, the data-based sensitivity analysis was adopted to understand which dimensions most influence guest satisfaction. Findings Previous user’s experience with the online platform, individual preferences, and hotel prestige were the most relevant dimensions concerning guests’ satisfaction. On the opposite, homogeneous characteristics among the Las Vegas hotels such as the hotel size were found of little relevance to satisfaction. Originality/value This study intends to set a baseline for an easier adoption of data mining to evaluate conceptual models through a scalable approach, helping to bridge between theory and practice, especially relevant when dealing with Big Data sources such as the social media. Thus, the steps undertaken during the study are detailed to facilitate replication to other models.

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“…Li and Sun [24] used support vectors to predict firm failure using financial and non-financial data, while Chen and Wang [25] used the support vector technique to forecast the demand. Pantano et al [26] used tourist attraction characteristics and the random forest method to predict tourist response, while Shapoval et al [27] used inbound visitors numbers and the decision tree technique to develop effective destination marketing.…”

Section: Literature Reviewmentioning

confidence: 99%

Forecasting Hotel Prices in Selected Middle East and North Africa Region (MENA) Cities with New Forecasting Tools

Shehhi¹,

Karathanasopoulos²

2018

TEL

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The purpose of this paper is to understand the potential of traditional and non-traditional statistical techniques to predict dynamic hotel room prices. Four forecast models were employed: the simple moving average, the autoregressive integrated moving average (ARIMA), the radial basis function (RBF), and the support vector machine (SVM). This research is based on an empirical study of data obtained from the company Smith Travel Research (STR). The economic predictors were obtained from other reliable sources such as the World Bank and the World Tourism Organization. This study agreed with existing literature on the ability of machine learning to predict hotel room prices precisely. Given the complexity of the hotel industry, the effect of external economic predictors was tested in the model. The challenge lay in dealing with the mixed frequencies observed in the collected data. This research is designed to add an innovative approach to the existing literature on machine learning in the hotel industry in the Middle East and North Africa (MENA) region. Some of the machine learning techniques used in this study constitute a contribution to the research conducted in this region. This creates a bridge between many academic disciplines such as computer science, economics, and marketing. Small hotel operators should benefit from this research when setting strategies as well as in using the model to set their relative room prices.

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Data Mining in Tourism Data Analysis: Inbound Visitors to Japan

Cited by 36 publications

References 64 publications

The Influence of Animosity, Ethnocentric Tendencies, and National Attachment on Tourists’ Decision-Making Processes during International Conflicts

The Influence of Animosity, Ethnocentric Tendencies, and National Attachment on Tourists’ Decision-Making Processes during International Conflicts

Evaluating a guest satisfaction model through data mining

Forecasting Hotel Prices in Selected Middle East and North Africa Region (MENA) Cities with New Forecasting Tools

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