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Evaluating the information quality of Web sites: A methodology based on fuzzy computing with words

Author

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  • Enrique Herrera‐Viedma
  • Gabriella Pasi
  • Antonio G. Lopez‐Herrera
  • Carlos Porcel

Abstract

An evaluation methodology based on fuzzy computing with words aimed at measuring the information quality of Web sites containing documents is presented. This methodology is qualitative and user oriented because it generates linguistic recommendations on the information quality of the content‐based Web sites based on users' perceptions. It is composed of two main components, an evaluation scheme to analyze the information quality of Web sites and a measurement method to generate the linguistic recommendations. The evaluation scheme is based on both technical criteria related to the Web site structure and criteria related to the content of information on the Web sites. It is user driven because the chosen criteria are easily understandable by the users, in such a way that Web visitors can assess them by means of linguistic evaluation judgments. The measurement method is user centered because it generates linguistic recommendations of the Web sites based on the visitors' linguistic evaluation judgments. To combine the linguistic evaluation judgments we introduce two new majority guided linguistic aggregation operators, the Majority guided Linguistic Induced Ordered Weighted Averaging (MLIOWA) and weighted MLIOWA operators, which generate the linguistic recommendations according to the majority of the evaluation judgments provided by different visitors. The use of this methodology could improve tasks such as information filtering and evaluation on the World Wide Web.

Suggested Citation

  • Enrique Herrera‐Viedma & Gabriella Pasi & Antonio G. Lopez‐Herrera & Carlos Porcel, 2006. "Evaluating the information quality of Web sites: A methodology based on fuzzy computing with words," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 57(4), pages 538-549, February.
    • Handle: RePEc:bla:jamist:v:57:y:2006:i:4:p:538-549
    DOI: 10.1002/asi.20308
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    Cited by:

    1. Christian Koch & Michael Hartmann, 2023. "Importance of the perceived quality of touchpoints for customer journey analysis – evidence from the B2B sector," Electronic Commerce Research, Springer, vol. 23(3), pages 1515-1538, September.
    2. Hee-Min Lee & Jee-Won Kang & Young Namkung, 2021. "Instagram Users’ Information Acceptance Process for Food-Content," Sustainability, MDPI, vol. 13(5), pages 1-15, March.
    3. Heradio, Rubén & Cabrerizo, Francisco Javier & Fernández-Amorós, David & Herrera, Manuel & Herrera-Viedma, Enrique, 2013. "A fuzzy linguistic model to evaluate the quality of Library 2.0 functionalities," International Journal of Information Management, Elsevier, vol. 33(4), pages 642-654.
    4. Chuu, Shian-Jong, 2011. "Interactive group decision-making using a fuzzy linguistic approach for evaluating the flexibility in a supply chain," European Journal of Operational Research, Elsevier, vol. 213(1), pages 279-289, August.
    5. Baabdullah, Abdullah M., 2024. "Generative conversational AI agent for managerial practices: The role of IQ dimensions, novelty seeking and ethical concerns," Technological Forecasting and Social Change, Elsevier, vol. 198(C).
    6. Runxi Zeng & Siting Guo & Richard Evans, 2024. "Intentional news avoidance on short-form video platforms: a moderated mediation model of psychological reactance and relative entertainment motivation," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-9, December.
    7. Gao Jingzu & Long Siyu & Wu Mengling & Qing Yang & Abdullah Al Mamun & Naeem Hayat, 2024. "Sustainable entrepreneurship through customer satisfaction and reuse intention of online food delivery applications: insights from China," Journal of Innovation and Entrepreneurship, Springer, vol. 13(1), pages 1-22, December.
    8. Markovich, Amiram & Efrat, Kalanit & Raban, Daphne R. & Souchon, Anne L., 2019. "Competitive intelligence embeddedness: Drivers and performance consequences," European Management Journal, Elsevier, vol. 37(6), pages 708-718.

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