The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Analysis of the perceived value of online tourism reviews: Influence of readability and reviewer characteristics
Open accessAbstract
Online reviews provide additional product information to reduce uncertainty. Hence, consumers often rely on online reviews to form purchase decisions. However, an explosion of online reviews brings the problem of information overload to individuals. Identifying reviews containing valuable information from large numbers of reviews becomes increasingly important to both consumers and companies, especially for experience products, such as attractions. Several online review platforms provide a function for readers to rate a review as “helpful” when it contains valuable information. Different from consumers, companies want to detect potential valuable reviews before they are rated to avoid or promote their negative or positive influence, respectively. Using online attraction review data retrieved from TripAdvisor, we conduct a two-level empirical analysis to explore factors that affect the value of reviews. We introduc a negative binomial regression model at a review level to explore the effects of the actual reviews. Subsequently, we apply a Tobit regression model at the reviewer level to investigate the effects of reviewer characteristics inferred from properties of historical rating distribution. The empirical analysis results indicate that both text readability and reviewer characteristics affect the perceived value of reviews. These findings have direct implications for attraction managers in their improved identification of potential valuable reviews.
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Cites
Prospect Theory: An Analysis of Decision under Risk
Kahneman, D., & Tversky, A. (1979) · Econometrica · doi:10.2307/1914185 · Cited by 48,524
The Effect of Word of Mouth on Sales: Online Book Reviews
Chevalier, J. A., & Mayzlin, D. (2006) · Journal of Marketing Research · doi:10.1509/jmkr.43.3.345 · Cited by 6,003
The Psychological Meaning of Words: LIWC and Computerized Text Analysis Methods
Tausczik, Y. R., & Pennebaker, J. W. (2010) · Journal of Language and Social Psychology · doi:10.1177/0261927x09351676 · Cited by 5,774
Tried and tested: The impact of online hotel reviews on consumer consideration
Vermeulen, I. E., & Seegers, D. (2009) · Tourism Management · doi:10.1016/j.tourman.2008.04.008 · Cited by 1,311
Research works printed but not linked: 24
- Ahluwalia, 1997, Special session summary: the effects of negative information in the political and marketing arenas: exceptions to the negativity effect, Advances in Consumer Research, 24, 222
- Basuroy, 2003, How critical are critical reviews? the box office effects of film critics, star power, and budgets, Journal of Marketing, 67, 103, 10.1509/jmkg.67.4.103.18692 · DOI · Paper page
- Burtch, 2013, An empirical examination of the antecedents and consequences of contribution patterns in crowd-funded markets, Information Systems Research, 24, 499, 10.1287/isre.1120.0468 · DOI · Paper page
- Cenfetelli, 2011, Identifying and testing the inhibitors of technology usage intentions, Information Systems Research, 22, 808, 10.1287/isre.1100.0295 · DOI · Paper page
- Connors, 2011, Is it the review or the reviewer? A multi-method approach to determine the antecedents of online review helpfulness, 1
- Duan, 2008, Do online reviews matter? – an empirical investigation of panel data, Decision Support Systems, 45, 1007, 10.1016/j.dss.2008.04.001 · DOI · Paper page
- Forman, 2008, Examining the relationship between reviews and sales: the role of reviewer identity disclosure in electronic markets, Information Systems Research, 19, 291, 10.1287/isre.1080.0193 · DOI · Paper page
- Ghose, 2011, Estimating the helpfulness and economic impact of product reviews: mining text and reviewer characteristics, IEEE Transactions on Knowledge and Data Engineering, 23, 1496, 10.1109/TKDE.2010.188 · DOI · Paper page
- Ghose, 2012, Designing ranking systems for hotels on travel search engines by mining user-generated and crowd sourced content, Marketing Science, 31, 493, 10.1287/mksc.1110.0700 · DOI · Paper page
- Gunning, 1969, The FOG index after twenty years, Journal of Business Communication, 6, 3, 10.1177/002194366900600202 · DOI · Paper page
- Gu, 2012, Research note–the impact of external word-of-mouth sources on retailer sales of high-involvement products, Information Systems Research, 23, 182, 10.1287/isre.1100.0343 · DOI · Paper page
- Kim, 2006, Automatically assessing review helpfulness, 423
- Klare, 1974, Assessing readability, Reading Research Quarterly, 10, 62, 10.2307/747086 · DOI · Paper page
- Korfiatis, 2012, Evaluating content quality and helpfulness of online product reviews: the interplay of review helpfulness vs. review content, Electronic Commerce Research and Applications, 11, 205, 10.1016/j.elerap.2011.10.003 · DOI · Paper page
- Kusumasondjaja, 2012, Credibility of online reviews and initial trust: the roles of reviewer's identity and review valence, Journal of Vacation Marketing, 18, 185, 10.1177/1356766712449365 · DOI · Paper page
- Li, 2008, Self-selection and information role of online product reviews, Information Systems Research, 19, 456, 10.1287/isre.1070.0154 · DOI · Paper page
- Liu, 2007, Low-quality product review detection in opinion summarization, 334
- Liu, 2015, What makes a useful online review? Implication for travel product websites, Tourism Management, 47, 140, 10.1016/j.tourman.2014.09.020 · DOI · Paper page
- Mudambi, 2010, What makes a helpful online review? A study of customer reviews on amazon.com, MIS Quarterly, 34, 185, 10.2307/20721420 · DOI · Paper page
- Nabi, 1999, A cognitive-functional model for the effects of discrete negative emotions on information processing, attitude change, and recall, Communication Theory, 9, 292, 10.1111/j.1468-2885.1999.tb00172.x · DOI · Paper page
- Scheier, 1987, Dispositional optimism a physical well-being: the influence of generalized outcome expectancies on health, Journal of Personality, 55, 169, 10.1111/j.1467-6494.1987.tb00434.x · DOI · Paper page
- Wu, 2011, The influences of negativity and review quality on the helpfulness of online reviews, 1
- Ye, 2009, The impact of online user reviews on hotel room sales, International Journal of Hospitality Management, 28, 180, 10.1016/j.ijhm.2008.06.011 · DOI · Paper page
- Yin, 2014, Anxious or angry effects of discrete emotions on the perceived helpfulness of online reviews, MIS Quarterly, 38, 539, 10.25300/MISQ/2014/38.2.10 · DOI · Paper page
Cited by
Xiang, Z., Du, Q., Ma, Y., & Fan, W. (2017) · Tourism Management · doi:10.1016/j.tourman.2016.10.001 · Cited by 1,051
Sentiment Analysis in Tourism: Capitalizing on Big Data
Alaei, A. R., Becken, S., & Stantic, B. (2019) · Journal of Travel Research · doi:10.1177/0047287517747753 · Cited by 622
Filieri, R., McLeay, F., Tsui, B., & Lin, Z. (2018) · Information & Management · doi:10.1016/j.im.2018.04.010 · Cited by 477
If nearly all Airbnb reviews are positive, does that make them meaningless?
Bridges, J., & Vásquez, C. (2018) · Current Issues in Tourism · doi:10.1080/13683500.2016.1267113 · Cited by 205
El-Said, O. A. (2020) · Tourism Management Perspectives · doi:10.1016/j.tmp.2019.100604 · Cited by 176
Metadata
- Authors
- Fang, B., Ye, Q., Kucukusta, D., & Law, R.
- Venue
- Tourism Management
- Publisher
- Elsevier BV
- Published
- 2016-02
- Volume
- 52
- Pages
- 498-506
- ISSN
- 0261-5177 (ISSN-L)
- OpenAlex
- W1130813113
- Cited by
- 609
- References
- 4
- FWCI
- 104.09
- Type
- journal article
- Language
- en
- Open access
- bronze
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- Funders
- NKBRPC (2014CB340506)
- NSFC (71490724, 71225003)
- Hong Kong Polytechnic University (SB-22)
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