IDEAS home Printed from https://ideas.repec.org/a/gam/jsoctx/v12y2022i2p50-d772108.html
   My bibliography  Save this article

What Factors Make Online Travel Reviews Credible? The Consumers’ Credibility Perception-CONCEPT Model

Author

Listed:
  • Tiziana Guzzo

    (Institute for Research on Population and Social Policies, National Research Council, 00185 Rome, Italy)

  • Fernando Ferri

    (Institute for Research on Population and Social Policies, National Research Council, 00185 Rome, Italy)

  • Patrizia Grifoni

    (Institute for Research on Population and Social Policies, National Research Council, 00185 Rome, Italy)

Abstract

Online reviews have become a fundamental element in searching for and buying a tourism service. In particular, in the phase of post-pandemic caused by the COVID-19, social media are important channels of inspiration of dreams and encouragement to begin envisioning future trips. However, the growing trend of fake reviews is becoming a big issue for consumers. This study proposes and empirically validates a new model that enables predicting consumers’ Credibility Perception of Online Reviews (CPOR) related to tourism, considering all integrated factors of the communication process. A survey was carried out via a structured questionnaire. In particular, 615 answers from Italian travel groups were collected, and correlation and regression analyses were conducted. Results show that the website brand, advisor’s expertise, reviews’ sidedness and consistency, and consumer experience are significant predictors of CPOR. Website usability and reputation are instead weak predictors. This study provides the design and test of a credibility model, contributing to the theoretical and empirical advancement of the literature and enhancing the knowledge on consumer behavior.

Suggested Citation

  • Tiziana Guzzo & Fernando Ferri & Patrizia Grifoni, 2022. "What Factors Make Online Travel Reviews Credible? The Consumers’ Credibility Perception-CONCEPT Model," Societies, MDPI, vol. 12(2), pages 1-16, March.
    • Handle: RePEc:gam:jsoctx:v:12:y:2022:i:2:p:50-:d:772108
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2075-4698/12/2/50/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2075-4698/12/2/50/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Sullivan, Yulia W. & Kim, Dan J., 2018. "Assessing the effects of consumers’ product evaluations and trust on repurchase intention in e-commerce environments," International Journal of Information Management, Elsevier, vol. 39(C), pages 199-219.
    2. Patrizia Grifoni & Fernando Ferri & Tiziana Guzzo, 2017. "CREMOR: CREdibility Model on Online Reviews-How people Consider Online Reviews Believable," International Business Research, Canadian Center of Science and Education, vol. 10(7), pages 56-66, July.
    3. Filieri, Raffaele, 2016. "What makes an online consumer review trustworthy?," Annals of Tourism Research, Elsevier, vol. 58(C), pages 46-64.
    4. Rezaei, Sajad, 2015. "Segmenting consumer decision-making styles (CDMS) toward marketing practice: A partial least squares (PLS) path modeling approach," Journal of Retailing and Consumer Services, Elsevier, vol. 22(C), pages 1-15.
    5. Hung, Kam & Law, Rob, 2011. "An overview of Internet-based surveys in hospitality and tourism journals," Tourism Management, Elsevier, vol. 32(4), pages 717-724.
    6. Hee Lye Park & Zheng Xiang & Bharath Josiam & Haejung Maria Kim, 2013. "Personal Profile Information as Cues of Credibility in Online Travel Reviews," Springer Books, in: Lorenzo Cantoni & Zheng (Phil) Xiang (ed.), Information and Communication Technologies in Tourism 2013, edition 127, pages 230-241, Springer.
    7. Soo Young Rieh, 2002. "Judgment of information quality and cognitive authority in the Web," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 53(2), pages 145-161.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Harrison-Walker, L. Jean & Jiang, Ying, 2023. "Suspicion of online product reviews as fake: Cues and consequences," Journal of Business Research, Elsevier, vol. 160(C).
    2. Cai, Ya-Jun & Lo, Chris K.Y., 2020. "Omni-channel management in the new retailing era: A systematic review and future research agenda," International Journal of Production Economics, Elsevier, vol. 229(C).
    3. Affifa Sardar & Amir Manzoor & Khurram Adeel Shaikh & Liaqat Ali, 2021. "An Empirical Examination of the Impact of eWom Information on Young Consumers’ Online Purchase Intention: Mediating Role of eWom Information Adoption," SAGE Open, , vol. 11(4), pages 21582440211, October.
    4. Maggioni, Isabella & Sands, Sean & Kachouie, Reza & Tsarenko, Yelena, 2019. "Shopping for well-being: The role of consumer decision-making styles," Journal of Business Research, Elsevier, vol. 105(C), pages 21-32.
    5. Cheah, Jun-Hwa & Lim, Xin-Jean & Ting, Hiram & Liu, Yide & Quach, Sara, 2022. "Are privacy concerns still relevant? Revisiting consumer behaviour in omnichannel retailing," Journal of Retailing and Consumer Services, Elsevier, vol. 65(C).
    6. Kim, Jooyoung & Ahn, Sun Joo (Grace) & Kwon, Eun Sook & Reid, Leonard N., 2017. "TV advertising engagement as a state of immersion and presence," Journal of Business Research, Elsevier, vol. 76(C), pages 67-76.
    7. Josef Zelenka & Tracy Azubuike & Martina Pásková, 2021. "Trust Model for Online Reviews of Tourism Services and Evaluation of Destinations," Administrative Sciences, MDPI, vol. 11(2), pages 1-21, March.
    8. Sunyoung Hlee & Hanna Lee & Chulmo Koo, 2018. "Hospitality and Tourism Online Review Research: A Systematic Analysis and Heuristic-Systematic Model," Sustainability, MDPI, vol. 10(4), pages 1-27, April.
    9. Daniel A. Sanchez-Loor & Wei-Shiun Chang, 2023. "Experimental study of the effects of structural assurance, personal experiences, and product reviews on repurchase behavior in e-commerce platforms," Electronic Commerce Research, Springer, vol. 23(3), pages 1971-2010, September.
    10. Chen, Ke & Chen, Jianxun & Zhan, Wu & Sharma, Piyush, 2020. "When in Rome! Complaint contagion effect in multi-actor service ecosystems," Journal of Business Research, Elsevier, vol. 121(C), pages 628-641.
    11. Agrawal, Shiv Ratan & Mittal, Divya, 2022. "Optimizing customer engagement content strategy in retail and E-tail: Available on online product review videos," Journal of Retailing and Consumer Services, Elsevier, vol. 67(C).
    12. Raoofpanah, Iman & Zamudio, César & Groening, Christopher, 2023. "Review reader segmentation based on the heterogeneous impacts of review and reviewer attributes on review helpfulness: A study involving ZIP code data," Journal of Retailing and Consumer Services, Elsevier, vol. 72(C).
    13. Chakraborty, Debarun & Paul, Justin, 2023. "Healthcare apps’ purchase intention: A consumption values perspective," Technovation, Elsevier, vol. 120(C).
    14. Cindy Helinski & Gerhard Schewe, 2022. "The Influence of Consumer Preferences and Perceived Benefits in the Context of B2C Fashion Renting Intentions of Young Women," Sustainability, MDPI, vol. 14(15), pages 1-25, August.
    15. Ketron, Seth, 2017. "Investigating the effect of quality of grammar and mechanics (QGAM) in online reviews: The mediating role of reviewer crediblity," Journal of Business Research, Elsevier, vol. 81(C), pages 51-59.
    16. Javornik, Ana & Filieri, Raffaele & Gumann, Ralph, 2020. "“Don't Forget that Others Are Watching, Too!” The Effect of Conversational Human Voice and Reply Length on Observers' Perceptions of Complaint Handling in Social Media," Journal of Interactive Marketing, Elsevier, vol. 50(C), pages 100-119.
    17. Raffaele Filieri & Elisabetta Raguseo & Claudio Vitari, 2018. "What moderates the influence of extremely negative ratings? The role of review and reviewer characteristics," Post-Print halshs-01923196, HAL.
    18. Zhen-Song Chen & Sheng Wu & Kannan Govindan & Xian-Jia Wang & Kwai-Sang Chin & Luis Martíınez, 2022. "Optimal pricing decision in a multi-channel supply chain with a revenue-sharing contract," Annals of Operations Research, Springer, vol. 318(1), pages 67-102, November.
    19. Xiuping Zhang & Jaewon Choi, 2022. "The Importance of Social Influencer-Generated Contents for User Cognition and Emotional Attachment: An Information Relevance Perspective," Sustainability, MDPI, vol. 14(11), pages 1-18, May.
    20. Chatterjee, Swagato & Goyal, Divesh & Prakash, Atul & Sharma, Jiwan, 2021. "Exploring healthcare/health-product ecommerce satisfaction: A text mining and machine learning application," Journal of Business Research, Elsevier, vol. 131(C), pages 815-825.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsoctx:v:12:y:2022:i:2:p:50-:d:772108. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.