Now showing items 2-7 of 7

    • Is Voluntary Profiling Welfare Enhancing? 

      Koh, Byungwan; Raghunathan, Srinivasan; Nault, Barrie R.
      Although consumer profiling advocates tout benefits from personalization, consumer advocacy groups oppose profiling in online markets because of concerns about privacy and price discrimination. Policies such as optout or ...
    • Is Voluntary Profiling Welfare Enhancing? 

      Koh, Byungwan; Raghunathan, Srinivasan; Nault, Barrie R.
      Although consumer profiling advocates tout benefits from personalization, consumer advocacy groups oppose profiling in online markets because of concerns about privacy and price discrimination. Policies such as optout or ...
    • Is Voluntary Profiling Welfare Enhancing? 

      Koh, Byungwan; Raghunathan, Srinivasan; Nault, Barrie R.
      Although consumer profiling advocates tout benefits from personalization, consumer advocacy groups oppose profiling in online markets because of concerns about privacy and price discrimination. Policies such as optout or ...
    • Outsourcing Information Security: Contracting Issues and Security Implications 

      Cezar, Asunur; Cavusoglu, Huseyin; Raghunathan, Srinivasan
      A unique challenge in information security outsourcing is that neither the outsourcing firm nor the managed security service provider (MSSP) perfectly observes the outcome, the occurrence of a security breach, of prevention ...
    • Platform or Wholesale? a Strategic Tool for Online Retailers to Benefit from Third-Party Information 

      Kwark, Young; Chen, Jianqing; Raghunathan, Srinivasan
      Online retailing is dominated by a channel structure in which a retailer either buys products from competing manufacturers and resells to consumers (wholesale scheme) or lets manufacturers sell directly to consumers on its ...
    • When Algorithmic Predictions Use Human-Generated Data: A Bias-Aware Classification Algorithm for Breast Cancer Diagnosis 

      Ahsen, M. E.; Ayvaci, Mehmet Ulvi Saygi; Raghunathan, Srinivasan (INFORMS: Institute for Operations Research and the Management Sciences, 2018-12-20)
      When algorithms use data generated by human beings, they inherit the errors stemming from human biases, which likely diminishes their performance. We examine the design and value of a bias-aware linear classification ...