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Web Footprints of Firms: Using Online Isomorphism for Competitor Identification

Author

Listed:
  • Gautam Pant

    (Department of Management Sciences, University of Iowa, Iowa City, Iowa 52242)

  • Olivia R. L. Sheng

    (Department of Operations and Information Systems, University of Utah, Salt Lake City, Utah 84112)

Abstract

Competitive isomorphism refers to the phenomenon of competing firms becoming similar as they mimic each other under common market forces. With the growing presence of firms as well as their consumers and suppliers on the Web, we discover a parallel phenomenon of online isomorphism wherein the Web footprints of competing firms are found to overlap. We propose new online metrics based on the content, in-links, and out-links of firms' websites to measure the presence of online isomorphism as well as uncover its utility in predicting competitor relationships. Through rigorous analysis involving more than 2,600 firms, we find that predictive models for competitor identification based on online metrics are largely superior to those using offline data such as Standard Industrial Classification codes and market values of firms. In addition, combining online and offline metrics can boost the predictive performance. We also find that such models are valuable for identifying nuances of competitor relationships such as asymmetry and the role of industrial divisions. Furthermore, the suggested predictive models can effectively rank firms in an industrial division by their likelihood of being competitors to a focal firm as well as identify new future competitors, thus adding to a portfolio of evidence indicating their utility for managers and analysts.

Suggested Citation

  • Gautam Pant & Olivia R. L. Sheng, 2015. "Web Footprints of Firms: Using Online Isomorphism for Competitor Identification," Information Systems Research, INFORMS, vol. 26(1), pages 188-209, March.
    • Handle: RePEc:inm:orisre:v:26:y:2015:i:1:p:188-209
    DOI: 10.1287/isre.2014.0563
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    References listed on IDEAS

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    Cited by:

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    7. Liu, Yezheng & Qian, Yang & Jiang, Yuanchun & Shang, Jennifer, 2020. "Using favorite data to analyze asymmetric competition: Machine learning models," European Journal of Operational Research, Elsevier, vol. 287(2), pages 600-615.

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