Preference mining techniques for customer behavior analysis

The thesis has studied a number of critical problems in data mining for customer behavior analysis and has proposed novel techniques for better modeling of the customers’ decision making process, more efficient analysis of their travel behavior, and more effective identification of their emerging preference

A big-data analytics method for capturing visitor activities and flows: the case of an island country

© 2019, Springer Science+Business Media, LLC, part of Springer Nature. Understanding how people move from one location to another is important both for smart city planners and destination managers. Big-data generated on social media sites have created opportunities for developing evidence-based insights that can be useful for decision-makers. While previous studies have introduced observational data analysis methods for social media data, there remains a need for method development—specifically for capturing people’s movement flows and behavioural details. This paper reports a study outlining a new analytical method, to explore people’s activities, behavioural, and movement details for people monitoring and planning purposes. Our method utilises online geotagged content uploaded by users from various locations. The effectiveness of the proposed method, which combines content capturing, processing and predicting algorithms, is demonstrated through a case study of the Fiji Islands. The results show good performance compared to other relevant methods and show applicability to national decisions and policies

A big-data analytics method for capturing visitor activities and flows: the case of an island country

© 2019, Springer Science+Business Media, LLC, part of Springer Nature. Understanding how people move from one location to another is important both for smart city planners and destination managers. Big-data generated on social media sites have created opportunities for developing evidence-based insights that can be useful for decision-makers. While previous studies have introduced observational data analysis methods for social media data, there remains a need for method development—specifically for capturing people’s movement flows and behavioural details. This paper reports a study outlining a new analytical method, to explore people’s activities, behavioural, and movement details for people monitoring and planning purposes. Our method utilises online geotagged content uploaded by users from various locations. The effectiveness of the proposed method, which combines content capturing, processing and predicting algorithms, is demonstrated through a case study of the Fiji Islands. The results show good performance compared to other relevant methods and show applicability to national decisions and policies

A Location Analytics Method for the Utilisation of Geotagged Photos in Travel Marketing Decision-Making

Location analytics offers statistical analysis of any geo- or spatial data concerning user location. Such analytics can produce useful insights into the attractions of interest to travellers or visitation patterns of a demographic group. Based on these insights, strategic decision-making by travel marketing agents, such as travel package design, may be improved. In this paper, we develop and evaluate an original method of location analytics to analyse travellers' social media data for improving managerial decision support. The method proposes an architectural framework that combines emerging pattern data mining techniques with image processing to identify and process appropriate data content. The design artefact is evaluated through a focus group and a detailed case study of Australian outbound travellers. The proposed method is generic, and can be applied to other specific locations or demographics to provide analytical outcomes useful for strategic decision support

Incorporating both positive and negative association rules into the analysis of outbound tourism in Hong Kong

This article presents a novel approach to data mining that incorporates both positive and negative association rules into the analysis of outbound travelers. Using datasets collected from three large-scale domestic tourism surveys on Hong Kong residents\u27 outbound pleasure travel, different sets of targeted rules were generated to provide promising information that will allow practitioners and policy makers to better understand the important relationship between condition attributes and target attributes. This article will be of interest to readers who want to understand methods for integrating the latest data mining techniques into tourism research. It will also be of use to marketing managers in destinations to better formulate strategies for receiving outbound travelers from Hong Kong, and possibly elsewhere

A Location Analytics Method for the Utilisation of Geotagged Photos in Travel Marketing Decision-Making

© 2019 World Scientific Publishing Co. Location analytics offers statistical analysis of any geo-or spatial data concerning user location. Such analytics can produce useful insights into the attractions of interest to travellers or visitation patterns of a demographic group. Based on these insights, strategic decision-making by travel marketing agents, such as travel package design, may be improved. In this paper, we develop and evaluate an original method of location analytics to analyse travellers\u27 social media data for improving managerial decision support. The method proposes an architectural framework that combines emerging pattern data mining techniques with image processing to identify and process appropriate data content. The design artefact is evaluated through a focus group and a detailed case study of Australian outbound travellers. The proposed method is generic, and can be applied to other specific locations or demographics to provide analytical outcomes useful for strategic decision support

A Design Framework and AI System for Affective Destination Image Generation to Influence Tourists’ Emotional Response

Affective destination images have received considerable attention from tourism marketing researchers as evidence suggests that affective components in destination images affect tourists’ emotional responses, which in turn influence their behavioural intentions toward the destination. Therefore, tourism practitioners seek solutions to influence the emotional effects of affective destination images for B2C communication. This paper presents a design science research project to develop an AI system to assist practitioners in generating affective destination images that potentially trigger the desired emotional responses of tourists. By leveraging knowledge and techniques from NeuroIS, this paper also proposes a framework of scientific experiments to assess how the generated affective destination images by the AI system affect tourists’ emotional experiences

Improving the resident-tourist relationship in urban hotspots

High volumes of tourists often pose a threat to tourism and decrease the quality of life for local residents, particularly in attractive urban tourism places. Yet, to date only a few solution-oriented studies have attempted to alleviate the overtourism problems and to improve the resident-tourist relationship. This study aims to present potential solutions, based on data analytics. Combining venue-referenced social media data with topic modelling from a case study in Paris, this research reveals both similarities and differences in the temporal and spatial activity patterns of tourists and residents. Results offer strategic support to tourism planners on how to manage over-crowded urban tourism hotspots, which consequently facilitate the improvement of the resident-tourist relationship and improve destination attractiveness in the long run. Results further indicate that the exchange of social media-based information for residents and tourists are part of the practice-based solution for better sustainable tourism planning

A Big Data Analytics Method for Tourist Behaviour Analysis

© 2016 Elsevier B.V. Big data generated across social media sites have created numerous opportunities for bringing more insights to decision-makers. Few studies on big data analytics, however, have demonstrated the support for strategic decision-making. Moreover, a formal method for analysing social media-generated big data for decision support is yet to be developed, particularly in the tourism sector. Using a design science research approach, this study aims to design and evaluate a ‘big data analytics’ method to support strategic decision-making in tourism destination management. Using geotagged photos uploaded by tourists to the photo-sharing social media site, Flickr, the applicability of the method in assisting destination management organisations to analyse and predict tourist behavioural patterns at specific destinations is shown, using Melbourne, Australia, as a representative case. Utility was confirmed using both another destination and directly with stakeholder audiences. The developed artefact demonstrates a method for analysing unstructured big data to enhance strategic decision making within a real problem domain. The proposed method is generic, and its applicability to other big data streams is discussed