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Web data as academic and business quality estimates: A comparison of three data sources

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  • Liwen Vaughan
  • Rongbin Yang

Abstract

Earlier studies found that web hyperlink data contain various types of information, ranging from academic to political, that can be used to analyze a variety of social phenomena. Specifically, the numbers of inlinks to academic websites are associated with academic performance, while the counts of inlinks to company websites correlate with business variables. However, the scarcity of sources from which to collect inlink data in recent years has required us to seek new data sources. The recent demise of the inlink search function of Yahoo! made this need more pressing. Different alternative variables or data sources have been proposed. This study compared three types of web data to determine which are better as academic and business quality estimates, and what are the relationships among the three data sources. The study found that Alexa inlink and Google URL citation data can replace Yahoo! inlink data and that the former is better than the latter. Alexa is even better than Yahoo!, which has been the main data source in recent years. The unique nature of Alexa data could explain its relative advantages over other data sources.

Suggested Citation

  • Liwen Vaughan & Rongbin Yang, 2012. "Web data as academic and business quality estimates: A comparison of three data sources," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 63(10), pages 1960-1972, October.
    • Handle: RePEc:bla:jamist:v:63:y:2012:i:10:p:1960-1972
    DOI: 10.1002/asi.22659
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    Cited by:

    1. Krzysztof BORODAKO & Jadwiga BERBEKA & Michał RUDNICKI & Mariusz ŠAPCZYŃSKI, 2021. "Online Visibility and Knowledge-Intensive Business Services Performance: The Scope of Interrelatedness," Journal of Emerging Trends in Marketing and Management, The Bucharest University of Economic Studies, vol. 1(1), pages 157-173, August.
    2. David Gunnarsson Lorentzen, 2014. "Webometrics benefitting from web mining? An investigation of methods and applications of two research fields," Scientometrics, Springer;Akadémiai Kiadó, vol. 99(2), pages 409-445, May.
    3. Enrique Orduna-Malea & Selenay Aytac, 2015. "Revealing the online network between university and industry: the case of Turkey," Scientometrics, Springer;Akadémiai Kiadó, vol. 105(3), pages 1849-1866, December.
    4. Stine Labes & Nicolai Hanner & Ruediger Zarnekow, 2017. "Successful Business Model Types of Cloud Providers," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 59(4), pages 223-233, August.
    5. Liwen Vaughan, 2016. "Uncovering information from social media hyperlinks: An investigation of twitter," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 67(5), pages 1105-1120, May.
    6. Pardeep Sud & Mike Thelwall, 2014. "Linked title mentions: a new automated link search candidate," Scientometrics, Springer;Akadémiai Kiadó, vol. 101(3), pages 1831-1849, December.
    7. Vaughan, Liwen & Yang, Rongbin, 2013. "Web traffic and organization performance measures: Relationships and data sources examined," Journal of Informetrics, Elsevier, vol. 7(3), pages 699-711.

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