Raghunathan, Srinivasan

Permanent URI for this collectionhttps://hdl.handle.net/10735.1/3644

Srinivasan Raghunathan was awarded an Asbel Smith Professorship in 2017. His current research interests include: 1) economics of IT security and 2) information sharing in supply chains.

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Now showing 1 - 7 of 7
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    Digitization of Music: Consumer Adoption Amidst Piracy, Unbundling, and Rebundling
    (University of Minnesota, 2019-03-01) Koh, B.; Hann, I. -H; Raghunathan, Srinivasan; Raghunathan, Srinivasan
    Digital music formats and the Internet as a distribution mechanism have fundamentally disrupted the music industry by altering the way music is packaged, distributed, and consumed. This disruptive innovation has come in two stages. First, it enabled music to be purchased as an individual song (digital single) or as an album (digital album) or to be enjoyed without paying for it (unlicensed digital music). More recently, music has become available as a streaming service (streaming music). Prior to these innovations, music was primarily distributed as an album using a physical medium such as the CD. Building on multi-generation diffusion models, we identify and quantify different types of concurrent demand migration in the music industry such as generational substitution, unbundling, and piracy effects in the first stage and streaming effects in the subsequent stage. Measuring the relative contributions of factors that drive each of these different migration types, we find that the introduction of licensed digital downloads (digital single and digital album) has weakened the piracy effect. Since the introduction of licensed digital downloads, the piracy effect on the demand for CDs has decreased about 15 percent every year. At the same time, unbundling, rather than piracy, has become the dominant factor in the decline of industry revenue. More recently, streaming music services such as Pandora have moved demand from digital albums to streaming music. However, demand has not yet migrated from digital singles to streaming music. The introduction of streaming music has further weakened the piracy effect by about 7 percent every year. ©2019 University of Minnesota. All rights reserved.
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    When Algorithmic Predictions Use Human-Generated Data: A Bias-Aware Classification Algorithm for Breast Cancer Diagnosis
    (INFORMS: Institute for Operations Research and the Management Sciences, 2018-12-20) Ahsen, M. E.; Ayvaci, Mehmet Ulvi Saygi; Raghunathan, Srinivasan; 0000-0001-6997-1639 (Ayvaci, MUS); Ayvaci, Mehmet Ulvi Saygi; Raghunathan, Srinivasan
    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 algorithm that accounts for bias in input data, using breast cancer diagnosis as our specific setting. In this context, a referring physician makes a follow-up recommendation to a patient based on two inputs: the patient's clinical-risk information and the radiologist's mammogram assessment. Critically, the radiologist's assessment could be biased by the clinical-risk information, which in turn can negatively affect the referring physician's performance. Thus, a bias-aware algorithm has the potential to be of significant value if integrated into a clinical decision support system used by the referring physician. We develop and show that a bias-aware algorithm can eliminate the adverse impact of bias if the error in the mammogram assessment due to radiologist's bias has no variance. On the other hand, in the presence of error variance, the adverse impact of bias can be mitigated, but not eliminated, by the bias-aware algorithm. The bias-aware algorithm assigns less (more) weight to the clinicalrisk information (radiologist's mammogram assessment) when the mean error increases (decreases), but the reverse happens when the error variance increases. Using point estimates obtained from mammography practice and the medical literature, we show that the bias-aware algorithm can significantly improve the expected patient life years or the accuracy of decisions based on mammography. © 2019, INFORMS.
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    Is Voluntary Profiling Welfare Enhancing?
    (MIS Research Center) Koh, Byungwan; Raghunathan, Srinivasan; Nault, Barrie R.; Raghunathan, Srinivasan
    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 opt-in that provide consumers the option to voluntarily participate in profiling are the favored compromise. We compare voluntary profiling to no profiling and show that voluntary profiling leads to some counterintuitive results. Consumers that do not participate in profiling and some that participate are worse off under voluntary profiling. Neither social welfare nor aggregate consumer surplus is necessarily higher under voluntary profiling; even when voluntary profiling leads to an increase in social welfare, it may come at the expense of consumer surplus. If the seller cannot price discriminate and charge only a uniform price for everyone or the seller can only charge different prices based on the consumer's participation status, then aggregate consumer surplus under voluntary profiling is higher and a reduction in privacy cost has a positive impact on all consumers as well as the seller. However, when personalized pricing is possible, reducing privacy cost alone may reduce aggregate consumer surplus. The primary reason for these results is that voluntary profiling allows the seller to identify high valuation consumers that have no incentive to participate and set a higher price for them (compared to no profiling) while simultaneously benefititng from the profile information of low valuation consumers that participate. However, a positive privacy cost mitigates the participation incentives of even low valuation consumers and hence sellers' ability to engage in price discrimination.
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    Is Voluntary Profiling Welfare Enhancing?
    (MIS Research Center) Koh, Byungwan; Raghunathan, Srinivasan; Nault, Barrie R.; Raghunathan, Srinivasan
    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 opt-in that provide consumers the option to voluntarily participate in profiling are the favored compromise. We compare voluntary profiling to no profiling and show that voluntary profiling leads to some counterintuitive results. Consumers that do not participate in profiling and some that participate are worse off under voluntary profiling. Neither social welfare nor aggregate consumer surplus is necessarily higher under voluntary profiling; even when voluntary profiling leads to an increase in social welfare, it may come at the expense of consumer surplus. If the seller cannot price discriminate and charge only a uniform price for everyone or the seller can only charge different prices based on the consumer's participation status, then aggregate consumer surplus under voluntary profiling is higher and a reduction in privacy cost has a positive impact on all consumers as well as the seller. However, when personalized pricing is possible, reducing privacy cost alone may reduce aggregate consumer surplus. The primary reason for these results is that voluntary profiling allows the seller to identify high valuation consumers that have no incentive to participate and set a higher price for them (compared to no profiling) while simultaneously benefititng from the profile information of low valuation consumers that participate. However, a positive privacy cost mitigates the participation incentives of even low valuation consumers and hence sellers' ability to engage in price discrimination.
  • Item
    Is Voluntary Profiling Welfare Enhancing?
    (MIS Research Center) Koh, Byungwan; Raghunathan, Srinivasan; Nault, Barrie R.; Raghunathan, Srinivasan
    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 opt-in that provide consumers the option to voluntarily participate in profiling are the favored compromise. We compare voluntary profiling to no profiling and show that voluntary profiling leads to some counterintuitive results. Consumers that do not participate in profiling and some that participate are worse off under voluntary profiling. Neither social welfare nor aggregate consumer surplus is necessarily higher under voluntary profiling; even when voluntary profiling leads to an increase in social welfare, it may come at the expense of consumer surplus. If the seller cannot price discriminate and charge only a uniform price for everyone or the seller can only charge different prices based on the consumer's participation status, then aggregate consumer surplus under voluntary profiling is higher and a reduction in privacy cost has a positive impact on all consumers as well as the seller. However, when personalized pricing is possible, reducing privacy cost alone may reduce aggregate consumer surplus. The primary reason for these results is that voluntary profiling allows the seller to identify high valuation consumers that have no incentive to participate and set a higher price for them (compared to no profiling) while simultaneously benefititng from the profile information of low valuation consumers that participate. However, a positive privacy cost mitigates the participation incentives of even low valuation consumers and hence sellers' ability to engage in price discrimination.
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    Platform or Wholesale? a Strategic Tool for Online Retailers to Benefit from Third-Party Information
    Kwark, Young; Chen, Jianqing; Raghunathan, Srinivasan; 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 platform for a commission (platform scheme). Easy access to publicly available third-party information such as product reviews that facilitate consumers' purchase decisions is another distinctive and ubiquitous characteristic of online retailing. We show that retailers can use the upstream pricing scheme, wholesale or platform, as a strategic tool to benefit from third-party information. Information on the quality dimension homogenizes consumers' perceived utility differences between competing products and increases the upstream competition, which benefits the wholesale-based retailer but hurts the platform-based retailer. Information on the fit dimension, in constrast, heterogenizes consumers' estimated fits to the products and softens the upstream competition, which hurts the wholesale-based retailer but benefits the platform-based retailer. Consequently, when the precision of the third-party information is high (low), a retailer can benefit from third-party information by adopting the wholesale (platform) scheme if the quality dimension plays a dominant role and by adopting the platform (wholesale) scheme if the fit dimension is dominant. Furthermore, the effect of precision improvement on the retailer's profit depends on the pricing-scheme choice and the relative importance of quality and fit attributes in consumers' evaluations of products. For instance, when the fit dimension is dominant, increasing the precision can hurt the wholesale-based retailer but benefit the platform-based retailer.
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    Outsourcing Information Security: Contracting Issues and Security Implications
    (INFORMS) 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 effort. Detection of security breaches often requires specialized effort. The current practice is to outsource both prevention and detection to the same MSSP. Some security experts have advocated outsourcing prevention and detection to different MSSPs. We show that the former outsourcing contract leads to a significant disincentive to provide detection effort. The latter contract alleviates this problem but introduces misalignment of incentives between the firm and the MSSPs and eliminates the advantages offered by complementarity between prevention and detection functions, which may lead to a worse outcome than the current contract. We propose a new contract that is superior to these two on various dimensions.

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