E-Tourism Curriculum

Fuchs, Matthias; Höpken, Wolfram

doi:10.1007/978-3-030-05324-6_107-1

Cited by 5 publications

(3 citation statements)

References 59 publications

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“…What is more, Dolnicar (2020) critically notes that too many variables are often used in clustering and therefore factor analysis should be performed in advance. This typically results in a massive loss of information since the dependencies between the individual variables and the distances between them vanish.…”

Section: Discussionmentioning

confidence: 99%

“…A study by Zins (2008) revealed that approximately 5% of tourism studies are related to the topic of market segmentation, and Dolnicar (2006) argues that the way segmentation approaches are carried out has remained fundamentally consistent over time. Moreover, though clustering approaches have dominated marketing and tourism literature (Dolnicar, 2008, 2020; Dolnicar & Lazarevski, 2009), D’Urso et al (2016) question the use of classical algorithms—mainly because of the dissimilarities between modern and postmodern tourist behaviors—and push for new approaches.…”

Section: Introductionmentioning

confidence: 99%

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Vectorize Me! A Proposed Machine Learning Approach for Segmenting the Multi-optional Tourist

Egger

2023

Journal of Travel Research

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Contemporary consumer behavior is characterized by its multidimensionality and complexity, which, at the same time, pushes traditional segmentation approaches to their limits. In response, this methodological study proposes a multistage machine learning-based segmentation process using semiotic-semantic community detection. This innovative method was conducted exemplarily and evaluated on a representative sample of 1,101 German travelers. The main contribution of this study lies in the novel use of word vectors, which result from assigning a semiotic meaning to travel-type images. Thus, high-dimensional data could be used during the segmentation process, overcoming several classical segmentation problems. By using semantic similarities, tourists could be grouped and represented in their multidimensionality. From a theoretical perspective, this study was inspired by postmodern tourism practices in order to better understand the (oftentimes) hybrid and multilayered behaviors of tourists. To make this innovative approach reproducible, recommendations for implementation and all necessary data have been provided.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Vectorize Me! A Proposed Machine Learning Approach for Segmenting the Multi-optional Tourist

Egger

2023

Journal of Travel Research

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“…Education: There is minimal exposure to travel tech and startup culture in tourism education, from high school to university (Fuchs and Höpken 2020). A quick look at a typical syllabus of an average BA degree in Hospitality Management would most likely reveal no units on tech entrepreneurship in travel, not to mention something a bit more advanced as coding or UX design.…”

Section: The Catastrophe and The Opportunity: No One Is Immune Againsmentioning

confidence: 99%

Collaborating against COVID-19: bridging travel and travel tech

Mizrachi

Gretzel

2020

Inf Technol Tourism

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The distinction between “traditional” travel businesses and travel tech companies has remained steady for years due to knowledge, resource and financial reasons, leading them to operate in separate bubbles. The massive damages caused by COVID-19 for both sides of the industry pose a unique opportunity for them to ditch the old transactional working model, and to seek for more strategic collaborations in order to weather the storm. This viewpoint article discusses the business potential behind such collaborations in the short and long runs—from improving immediate safety perceptions to building technological foundations for the travel industry of tomorrow. It argues that local government mediation is needed for it to succeed, in formats such as funded hackathons or incubation programs that are targeted at addressing COVID-19 and its unique challenges.

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eLearning for Tourism During COVID-19 - Learning from Students’ Perspectives. A Pilot Study

Hasenzahl

Ghezili

Cantoni

2022

Information and Communication Technologies in Tourism 2022

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This paper shows a first analysis of the experiences and challenges of studying tourism during the times of the COVID-19 pandemic. 14 tourism students from two higher education institutions in Europe participated in three focus group discussions. One generation of these students started their education in presence and had to shift online with the start of the pandemic, while the other generation started their education knowing that lessons would be mainly online. Authors used qualitative content analysis to analyze the participants’ statements. As a result of the analysis, several themes emerged, and students contextualized eLearning as an education method for a future without COVID-19.

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E-Tourism Curriculum

Cited by 5 publications

References 59 publications

Vectorize Me! A Proposed Machine Learning Approach for Segmenting the Multi-optional Tourist

Vectorize Me! A Proposed Machine Learning Approach for Segmenting the Multi-optional Tourist

Collaborating against COVID-19: bridging travel and travel tech

eLearning for Tourism During COVID-19 - Learning from Students’ Perspectives. A Pilot Study

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