About

Param Vir Singh is the Associate Dean of Research at the Tepper School of Business and holds the title of Carnegie Bosch Professor of Business Technologies and Marketing. His role involves leading research initiatives within the school, contributing to the development of business technologies and marketing strategies. His academic and professional focus is aligned with the strategic vision of the Tepper School, which emphasizes the intersection of business, technology, and analytics. Singh's work supports the school's mission to shape the future of business education through innovative research and thought leadership.

Research topics

• Computer Science
• Artificial Intelligence
• Machine Learning
• Business
• Sociology
• Political Science
• Economics
• Computer Security
• Data science
• Algorithm
• Geography
• Mathematics
• Marketing
• Law
• Finance
• Engineering
• Art
• Advertising
• Visual arts
• World Wide Web
• Microeconomics

Selected publications

• Algorithmic Lending, Competition, and Strategic Provision of Preapproval Tools

  Marketing Science · 2025-08-01

  articleSenior author

  This paper theoretically studies financial lenders’ strategic decisions in providing preapproval checking tools, and shows such decisions could be asymmetric.

  PublisherDOI

• Learning in Human-AI Collaboration

  SSRN Electronic Journal · 2025-01-01

  preprintOpen accessSenior author
  PublisherDOI

• Innocent Algorithms, Guilty Inputs: How Strategic Reporting Drives Tacit Collusion

  SSRN Electronic Journal · 2025-01-01

  preprintOpen accessSenior author
  PublisherDOI

• Inventing with Machines: Generative AI and the Evolving Landscape of IS Research

  Information Systems Research · 2025-11-21 · 7 citations

  article

  Generative artificial intelligence (AI) is not merely changing how information systems (IS) research gets done—it is reshaping what research can be. We stand at a pivotal moment where machines can help generate hypotheses, synthesize vast literatures, and identify patterns that would take human researchers months to uncover. Yet, this unprecedented capability presents equally unprecedented risks to scholarly integrity. Because the field is uniquely positioned to understand sociotechnical transformations, IS research faces an extraordinary opportunity to pioneer “inventing with machines” while preserving the human insight and oversight that gives scholarship, as currently defined, its meaning. This transformation demands more than tool adoption. It requires a reimagination of scholarly infrastructure, norms, and practice. However, this transformation of research tooling creates a dangerous paradox: Powerful AI tools are now accessible to researchers who lack the technical literacy to understand and use them responsibly, threatening everything from citation accuracy to theoretical validity. Yet within this paradox lies the potential for revolutionary advances in how we craft our future as scholars. Informed by the sociotechnical perspective, we argue that the path forward requires coordinated community action that goes far beyond individual skill development. The IS community must lead the development of specialized AI tools that consider our theoretical traditions, create educational frameworks that preserve scholarly values while embracing computational capabilities, and pioneer review processes that harness AI’s analytical power without ceding human control, at least, in the short run. Success will determine not only the future of IS scholarship but our field’s capacity to guide other disciplines through this fundamental transformation of academic practice. The era of human-AI collaboration in research has already begun. How we govern and guide it will define the next generation of scholarly discovery.

  PublisherDOI

• Unequal Impact of Zestimate on the Housing Market

  Marketing Science · 2025-07-17 · 1 citations

  article

  Despite lower accuracy in poor neighborhoods, Zillow’s automated home value estimates provide greater benefits to these housing markets by reducing more uncertainty.

  PublisherDOI

• Personalization, Consumer Search, and Algorithmic Pricing

  Marketing Science · 2025-05-07 · 6 citations

  article

  This paper shows that personalized product rankings, although improving search relevance, can unintentionally enable AI pricing algorithms to raise prices and reduce consumer welfare.

  PublisherDOI

• Cloud Metadata and Interoperability

  Oxford University Press eBooks · 2024-02-22 · 6 citations

  book-chapter1st authorCorresponding

  Abstract This chapter explores the challenges of cloud platform interoperability and makes a case for cloud platform metadata as a next frontier opportunity for open innovation and open source software (OSS) tooling. Cloud platforms that are dominant, as well as their potential disruptors, have an open innovation opportunity with respect to improved interoperability and metadata tooling, and this chapter explains why. This opportunity is huge and therefore the current dominant players may be disrupted by up-and-coming players that are leaner and nimbler. At the heart of every modern cloud application and cloud platforms are data models. These data models define the metadata (data about data) and the structure of the underlying database. The data modeling tools market is highly fragmented, making it difficult to compare models across apps and cloud platforms, so data modelers and architects are often reduced to using spreadsheets for this task, and thereby lose the richness of data modeling. This chapter looks at data models, tooling, and open data standards as a means of fostering data interoperability within and between cloud platforms.

  PublisherDOI

• Characteristics of Additive Manufacturing Process

  2024-09-20

  otherSenior author

  Because of its unique qualities, additive manufacturing (AM) methods are a family of netformed fabrication techniques that are widely utilized and embraced. AM techniques have recently shown themselves as deserving of contributing to the manufacturing revolution. These processes promise several benefits, such as, but not limited to, realizing previously unheard-of levels of design freedom, conserving properties, and reducing waste rather than just components, among others. The characteristics of additive manufacturing process, generation of layer information, stereolithography, laser sintering, and layer laminate manufacturing are discussed in this chapter.

  PublisherDOI

• Do Lower-Quality Images Lead to Higher Demand on Airbnb?

  SSRN Electronic Journal · 2023-01-01 · 1 citations

  articleOpen access
  PublisherDOI

• Unequal Impact of Zestimate on the Housing Market

  SSRN Electronic Journal · 2023-01-01 · 7 citations

  articleOpen access
  PublisherDOI

Frequent coauthors

• Yan Huang

  22 shared

• Shunyuan Zhang

  16 shared

• Kannan Srinivasan

  Carnegie Mellon University

  15 shared

• Tridas Mukhopadhyay

  Carnegie Mellon University

  12 shared

• Anindya Ghose

  New York University

  11 shared

• Runshan Fu

  9 shared

• Nachiketa Sahoo

  Boston University

  9 shared

• Elina H. Hwang

  University of Washington

  8 shared