| 1. |
- Gretzel, Ulrike, et al.
(författare)
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e-Tourism beyond COVID-19 : a call for transformative research
- 2020
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Ingår i: Information Technology & Tourism. - : Springer Science and Business Media LLC. - 1098-3058 .- 1943-4294. ; 22, s. 187-203
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Tidskriftsartikel (refereegranskat)abstract
- This viewpoint article argues that the impacts of the novel coronavirus COVID-19 call for transformative e-Tourism research. We are at a crossroads where one road takes us to e-Tourism as it was before the crisis, whereas the other holds the potential to transform e-Tourism. To realize this potential, e-Tourism research needs to challenge existing paradigms and critically evaluate its ontological and epistemological foundations. In light of the paramount importance to rethink contemporary science, growth, and technology paradigms, we present six pillars to guide scholars in their efforts to transform e-Tourism through their research, including historicity, reflexivity, equity, transparency, plurality, and creativity. We conclude the paper with a call to the e-Tourism research community to embrace transformative research.
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| 2. |
- Hoepken, Wolfram, et al.
(författare)
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Big data analytics for tourism destinations.
- 2018. - 4
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Ingår i: Encyclopedia of Information Science and Technology<em> </em>. - : IGI Global. ; , s. 349-363
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Bokkapitel (refereegranskat)abstract
- The objective of this chapter is to address the above deficiencies in tourism by presenting the concept of the tourism knowledge destination – a specific knowledge management architecture that supports value creation through enhanced supplier interaction and decision making. Information from heterogeneous data sources categorized into explicit feedback (e.g. tourist surveys, user ratings) and implicit information traces (navigation, transaction and tracking data) is extracted by applying semantic mapping, wrappers or text mining (Lau et al., 2005). Extracted data are stored in a central data warehouse enabling a destination-wide and all-stakeholder-encompassing data analysis approach. By using machine learning techniques interesting patterns are detected and knowledge is generated in the form of validated models (e.g. decision trees, neural networks, association rules, clustering models). These models, together with the underlying data (in the case of exploratory data analysis) are interactively visualized and made accessible to destination stakeholders.
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| 3. |
- Keil, Dimitri, et al.
(författare)
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Optimizing user interface design and interaction paths for destination management information system
- 2017
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Ingår i: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). - Cham : Springer International Publishing. - 9783319586403 ; , s. 473-487
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Konferensbidrag (refereegranskat)abstract
- Destination Management Organizations (DMO) being the central units in destination management within European destinations face increasing pressure due to effects of globalization. At the same time, effects of digitalization combined with methods summarized by the umbrella term of Business Intelligence create opportunities to tackle these challenges. Höpken et al. (2011) described how destinations can evolve to so-called knowledge destinations. With the help of a Destination Management Information System (DMIS) managers of DMOs as well as its various stakeholders are provided with holistic decision support when working on strategic development of the destination. The objective of this study is to conceptualize a novel DMIS user interface and evaluate its usability. The study (1) defines different analysis perspectives and corresponding performance indicators enabling a powerful decision support for destination managers and tourism stakeholders, (2) defines interaction paths along different abstraction levels to support drill-down analyses, and (3) evaluates the usability and understandability of the DMIS interface in the south-western Swedish destination Halland.
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