The DOI matches its Crossref record; 40 of the 40 references it cites were found in a registry or an index.
User generated content: the use of blogs for tourism organisations and tourism consumers
Open accessAbstract
As the World Wide Web has developed considerable bargaining power has been transferred from suppliers to consumers; there is a real need to improve market intelligence and market research for private and public tourism organisations and facilitate timely consumer decision making. This article explores the development of user generated content and specifically the use of web logs or blogs. Tourism organisations cannot afford to ignore the development of user generated content, peer-to-peer web applications and virtual communities. A recent survey found that consumers trusted more websites with reviews than professional guides and travel agencies and far from being an irrelevance, blogs are often perceived to be more credible and trustworthy than traditional marketing communications. But there is a problem: given the sheer number of possibly relevant travel blogs there is a need to locate, extract and interpret blog content and this has proven so far to be time consuming, exhausting and costly, thus negating the relative value of the information obtained. A way forward may be the use of artificial intelligence and “opinion mining” or a blog visualisation system.
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“Whilst personal advice often ranks as the most influential source of pre-trip decision making, the overall credibility of blogs and online social media compared to that of traditional WOM is relatively high (Akehurst, 2009).”
From Sentiment Analysis in Tourism: Capitalizing on Big Data (2019) · cites this paper as Akehurst, 2009
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Metadata
- Authors
- Akehurst, G.
- Affiliations
- Erasmus University Rotterdam (NL)
- Corresponding author
- Akehurst, Gary
- Venue
- Service Business
- Published
- 2008-12-11
- Volume
- 3
- Issue
- 1
- Pages
- 51-61
- ISSN
- 1862-8508 (ISSN-L)
- 1862-8516
- OpenAlex
- W2069664012
- Cited by
- 422
- References
- 40
- Citations by year
- 2026: 14 · 2025: 9 · 2024: 17 · 2023: 21 · 2022: 25 · 2021: 40 · 2020: 36 · 2019: 38 · 2018: 27 · 2017: 37 · 2016: 26 · 2015: 40 · 2014: 29 · 2013: 20 · 2012: 19
- FWCI
- 27.78
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by-nc
- Locations
- Service Business (publishedVersion, cc-by-nc)
- RePEc: Research Papers in Economics (submittedVersion)
- Topics
- Social Sciences › Business, Management and Accounting › Marketing › Digital Marketing and Social Media
- Social Sciences › Business, Management and Accounting › Tourism, Leisure and Hospitality Management › Diverse Aspects of Tourism Research
- Social Sciences › Library and Information Sciences › Web and Library Services
- Keywords
- user-generated content, blogs, tourism organizations, tourism consumers, online reviews, consumer trust, market intelligence, online communities, sentiment analysis, artificial intelligence, purchase decision
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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