Bargaining over Data and Analytics: Sellers, Buyers and Consultants

Jyotishka Ray et al.

Information Systems Research2026https://doi.org/10.1287/isre.2023.0404article
FT50UTD24AJG 4*ABDC A*
Weight
0.50

What the paper says

Most firms routinely gather vast amounts of data as part of doing business. Monetizing this proprietary data is an increasingly attractive revenue stream for many of these firms. Two fundamental decisions need to be made when deciding to sell, the first involving a choice between selling exclusively to a single buyer or nonexclusively to many, and the second related to what exactly should be sold—just the data or a data product that bundles data with analytics services. As firms often resort to bargaining to arrive at sale agreements, we analyze these decisions through the lens of a bargaining framework. When the buyer needs help from a third-party consultant, she too needs to make a decision—between negotiating separately with the seller and the consultant, and with both of them simultaneously. We find that there are situations where sellers can benefit from bundling the data with analytics services even when their analytics capabilities are weak relative to those of external consultants. Simultaneous negotiations enable buyers to extract more of the consultant’s contribution, making them preferable to buyers when consultants add substantial value. Broadly, this study provides a road map for structuring contracts to firms considering the sale of their proprietary data.

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https://doi.org/https://doi.org/10.1287/isre.2023.0404

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@article{jyotishka2026,
  title        = {{Bargaining over Data and Analytics: Sellers, Buyers and Consultants}},
  author       = {Jyotishka Ray et al.},
  journal      = {Information Systems Research},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1287/isre.2023.0404},
}

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

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