Zobrazeno 1 - 10
of 42
pro vyhledávání: '"Maria Lexhagen"'
Publikováno v:
Matkailututkimus, Vol 10, Iss 1 (2014)
Externí odkaz:
https://doaj.org/article/2489afbcdbfb4d19aa2821149465b47f
Autor:
Peter Fredman, Tuija Sievänen, Frank S. Jensen, Vegard Gundersen, Sandra Wall-Reinius, Maria Lexhagen, Christine Lundberg, Klas Sandell, Odd Inge Vistad, Daniel Wolf-Watz
Publikováno v:
The Routledge Handbook of Nature Based Tourism Development ISBN: 9781003230748
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8b042b48b970a30febdda4355581ad09
https://doi.org/10.4324/9781003230748-12
https://doi.org/10.4324/9781003230748-12
Publikováno v:
Journal of Travel Research. 60:998-1017
Because of high fluctuations of tourism demand, accurate predictions of tourist arrivals are of high importance for tourism organizations. The study at hand presents an approach to enhance autoregressive prediction models by including travelers’ we
Publikováno v:
Journal of Hospitality and Tourism Technology. 11:69-82
PurposeThe purpose of this study is to analyse the suitability of photo-sharing platforms, such as Flickr, to extract relevant knowledge on tourists’ spatial movement and point of interest (POI) visitation behaviour and compare the most prominent c
Publikováno v:
Journal of Travel Research
Popular culture tourism encompasses a range of expressive practices that attract fans traveling to destinations associated with their fandom pursuit. However, scholarship on this multifaceted phenomenon is today over-fragmented and obscured by separa
This volume considers world-making as the intersection of the fan pilgrimage experience and the responses of destinations. It critically examines the emerging field of popular culture tourism and its close connection with fan studies and placemaking.
Publikováno v:
Journal of Destination Marketing & Management. 23:100690
Publikováno v:
Information Technology & Tourism. 21:45-62
Accurate forecasting of tourism demand is of utmost relevance for the success of tourism businesses. This paper presents a novel approach that extends autoregressive forecasting models by considering travellers’ web search behaviour as additional i