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Can Payment-per-Click Induce Improvements in Click Fraud Identification Technologies?

Author

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  • Min Chen

    (School of Business, George Mason University, Fairfax, Virginia 22030)

  • Varghese S. Jacob

    (Naveen Jindal School of Management, University of Texas at Dallas, Richardson, Texas 75080)

  • Suresh Radhakrishnan

    (Naveen Jindal School of Management, University of Texas at Dallas, Richardson, Texas 75080)

  • Young U. Ryu

    (Naveen Jindal School of Management, University of Texas at Dallas, Richardson, Texas 75080)

Abstract

Pay-per-click (PPC) is a common pricing model used to pay for ads on the Web and is open to the possibility for click fraud, where clicks are not from a legitimate user. Identifying click fraud is generally done in a three-stage process: the service provider (SP) first classifies clicks as fraudulent or not, then the advertiser does the same with a different technology, and if there is a disagreement, the SP examines further and his conclusions are considered binding. The advertiser pays for clicks that are identified as valid in the first two stages or confirmed as valid in the last stage. We model the choice of the identification technologies as a double moral hazard problem. We analyze the case where the PPC is incentive compatible to overcome the moral hazard problem, and examine the question of whether the incentive compatible PPC is sufficient to incentivize the two parties to unilaterally make further improvements to their identification technologies and simultaneously increase their profits. We show that when the cost of the third-stage identification technology is large, which is likely to be the case because of its complexity and use of expensive human experts, the incentive compatible PPC does not support unilateral technological improvements. We then examine a setting where the third-stage identification is delegated to a third party and find that this arrangement can induce unilateral improvements to the identification technologies in the first two stages. Collectively our results show that although the PPC model itself may not induce improvements in the first two stages of click fraud identification, a common arrangement espoused of having a third party resolve disagreements helps make PPC support unilateral technological improvements. Accordingly, we show an indirect benefit to the third-party arrangement.

Suggested Citation

  • Min Chen & Varghese S. Jacob & Suresh Radhakrishnan & Young U. Ryu, 2015. "Can Payment-per-Click Induce Improvements in Click Fraud Identification Technologies?," Information Systems Research, INFORMS, vol. 26(4), pages 754-772, December.
  • Handle: RePEc:inm:orisre:v:26:y:2015:i:4:p:754-772
    DOI: 10.1287/isre.2015.0598
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    References listed on IDEAS

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    Cited by:

    1. Brett R Gordon & Kinshuk Jerath & Zsolt Katona & Sridhar Narayanan & Jiwoong Shin & Kenneth C Wilbur, 2019. "Inefficiencies in Digital Advertising Markets," Papers 1912.09012, arXiv.org, revised Feb 2020.
    2. Chen Jin & Luyi Yang & Kartik Hosanagar, 2023. "To Brush or Not to Brush: Product Rankings, Consumer Search, and Fake Orders," Information Systems Research, INFORMS, vol. 34(2), pages 532-552, June.
    3. Cuixia Jiang & Jun Zhu & Qifa Xu, 2022. "Dissecting click farming on the Taobao platform in China via PU learning and weighted logistic regression," Electronic Commerce Research, Springer, vol. 22(1), pages 157-176, March.
    4. Richet, Jean-Loup, 2022. "How cybercriminal communities grow and change: An investigation of ad-fraud communities," Technological Forecasting and Social Change, Elsevier, vol. 174(C).
    5. Chen Jin & Luyi Yang & Kartik Hosanagar, 2019. "To Brush or Not to Brush: Product Rankings, Customer Search, and Fake Orders," Working Papers 19-02, NET Institute.

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