Applying Business Intelligence for Knowledge Generation in Tourism Destinations – A Case Study from Sweden

Fuchs, Matthias; Höpken, Wolfram; Lexhagen, Maria

doi:10.1007/978-3-658-06660-4_11

Cited by 11 publications

(8 citation statements)

References 31 publications

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“…Fuchs et al, (2014) have concluded that Big Data tools can provide real-time knowledge on how tourists are behaving in the online environment in relation with a destination. This means that the Big Data tools can provide knowledge which will help the destination's decision makers to better understand what the expectations are and needs of potential tourists of a destination, while the same outcome was noted from Fuchs et al, (2015).…”

Section: The Current Trends On Big Data In the Tourist Industrymentioning

confidence: 92%

“…Overall, Big Data can have a major contribution as a tool which will provide the necessary data for taking critical decisions. The trend is that Big Data analytics can be used as a knowledge creation tool and the use of business intelligence approaches (Fuchs et al, 2014(Fuchs et al, , 2015Marine-Roig & Clav, 2015;Höpken et al, 2015), or for the creation of databases Stylos et al, 2021). The most important trend, however, is that Big Data Analytics can become a useful strategic tool (Varelas et al, 2020;Miah et al, 2017;Li et al, 2018;Li et al, 2020).…”

Section: The Current Trends On Big Data In the Tourist Industrymentioning

confidence: 99%

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The Use of Big Data in Tourism: Current Trends and Directions for Future Research

Belias

Malik

Rossidis

et al. 2021

Acad. J. Interdiscip. Stud.

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The aim of this research is to examine the new landscape that is taking shape in the tourism economy, due to the adoption of technological innovations. The technologies and systems used to make the most of the resulting interdisciplinary and multilingual big data, the methods by which heterogeneous elements from different sources are incorporated to bring new knowledge, and the modern services that are ultimately implemented and provided to the public, as well as the people involved in this process, and those who benefit from the new services are among the issues analyzed. The work is not limited to a simple mapping of space but concludes with an evaluation of the systems already implemented and the various methods of analysis and exploitation of large-scale data for this industry, which is based on detailed research of the current literature to identify the potential gaps for future research. This is a literature review. The authors have identified the content of this research on well-known online databases which include scholar, google and Scopus. They used the appropriate keywords such as “big data” & “tourism innovation” to reach the publications used in this research, also given attention on using recent papers which are derived from reliable journals. The research has concluded that the use of big data in the tourism sector is a rising trend. Big data creates expectations for a better understanding of tourism demand and the adjustment of supply by tourism companies to meet the demands of tourists and the profitable activity of tourism businesses. There is a need to examine how can big data help the hotels to deal with Covid-19 pandemic, which can be a topic for future research. Big data is one of the most recent trends on innovation, Information Communication Technologies (ICT) and knowledge management. Hence, there is a need to gather and analyze the existing publications which concern the tourist industry. The originality of this research stems from making an analysis of the current situation as well as it is a bridge to the future by making suggestions on how future research can be shaped. The key limitation of this research resides on that it focuses on the existing literature and used secondary data. However, it gives directions for the future research which can take place with primary data collection. Received: 3 July 2021 / Accepted: 19 August 2021 / Published: 5 September 2021

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Section: The Current Trends On Big Data In the Tourist Industrymentioning

confidence: 92%

Section: The Current Trends On Big Data In the Tourist Industrymentioning

confidence: 99%

The Use of Big Data in Tourism: Current Trends and Directions for Future Research

Belias

Malik

Rossidis

et al. 2021

Acad. J. Interdiscip. Stud.

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“…The system obtains knowledge from tourist destination indicators that measure destination performance and tourist experience, gaining new knowledge from the customer-based destination process. Further research development was carried out with the Åre Destination Management Information System (DMIS-Åre) [27], aimed at studying tourist habits. Research using BI tools integrated with tourism destinations, web monitors, web mining, and web analytics aims [28], to determine tourist habits.…”

Section: Related Workmentioning

confidence: 99%

Modeling of a Tourism Group Decision Support System using Risk Analysis based Knowledge Base

Sugiartawan¹,

Hartati²,

Musdholifah³

2020

IJACSA

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The increasing number of tourist destination becomes the main factor for export earning, job vacancy, business development, and infrastructure. The problem that occurs is the difference in regional income (GDP) that is quite significant in each region. Thus, it is necessary for the government to make a decision or policy in increasing tourist visits, mainly in Bali. In this case, choosing the most efficient decision from a number of decisions is for the government, tourists, community leaders, academics, and entrepreneurs in the tourism sector, especially in Bali. It is important to have a modeling decision support group (GDSS). GDSS modeling by integrating a knowledge-based (KB) risk analysis can determine decisions, extract information, and identify problems in the tourism sector especially, tourism objects in each region, more specifically. Problem identification in risk analysis modeling is determining decisions in handling risks and finding solutions from alternative tourism decisions that are potentially enlarged and knowledge gained from each decision-maker (DM). The process of identifying knowledge starts with comparing the assessment criteria on each tourism object and knowledge of tourism decision-makers. The results of GDSS modeling are subsequently integrated into knowledge-based risk analysis so that a decision is obtained in the form of an impact or risk and solution or recommendation in developing the specified tourism object. The purpose of combining the result is to understand the impacts or risks that may arise, and recommendations recommended so that the impacts or risks can be avoided.

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“…Obviously, this development is of particular interest to HSN, since it offers a route to provide lifelong learning support to a large sector of the industry and links in to the formal qualification system they provide. Work in this area has already been undertaken in agriculture in New Zealand [54] and in tourism in Sweden [55], but the approach taken in this project is novel, focusing on the development of the dashboard as a virtual learning environment.…”

Section: B Integrating With the Sakai Vlementioning

confidence: 99%

Integrating Online Learning into Workplace Information Systems – Supporting the Goal of Lifelong Learning

MacKinnon

Bacon

Graven

et al. 2017

Proceedings of the 15th LACCEI International Multi-Conference for Engineering, Education, and Technology: “Global Partnership F

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Applying Business Intelligence for Knowledge Generation in Tourism Destinations – A Case Study from Sweden

Cited by 11 publications

References 31 publications

The Use of Big Data in Tourism: Current Trends and Directions for Future Research

The Use of Big Data in Tourism: Current Trends and Directions for Future Research

Modeling of a Tourism Group Decision Support System using Risk Analysis based Knowledge Base

Integrating Online Learning into Workplace Information Systems – Supporting the Goal of Lifelong Learning

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