About

Sanjiv Erat is an Associate Professor of Innovation, Technology, and Operations at the Rady School of Management. His research interests include new product development and technology management. Prior to joining the Rady School, he taught operations management to undergraduates at Georgia Tech. He received his Ph.D. in operations management from Georgia Institute of Technology. Earlier in his career, Erat spent time as a software design engineer for Microsoft and Texas Instruments.

Research topics

• Computer Science
• Economics
• Microeconomics
• Business
• Marketing
• Operations research
• World Wide Web
• Mathematics
• Mathematical economics
• Labour economics
• Data science
• Industrial organization

Selected publications

• Efficiency and Equity in an Experimental Repeated Entry Game

  SSRN Electronic Journal · 2026-01-01

  preprintOpen access1st authorCorresponding
  PublisherDOI

• EXPRESS: Pay More, Use More: Consumer Bias and Demand Management for Digital Services

  Production and Operations Management · 2026-05-15

  article

  Consumers often purchase access to a digital service by paying an upfront fee, and then consume the service over a period of time. In this article, we examine the implications of such temporal separation of purchase and consumption on a user’s consumption choices, and on the firm’s optimal demand management strategy. Relying on behavioral economics and consumer behavior literature, we develop a formal micro-founded model of a user’s decision calculus, and use it to derive the implied demand function and thus analyze the firm’s optimal decisions. In contrast to the classical recommendation to pursue admission control through higher prices as a means to manage demand for the digital service, we find that when mental accounting bias is a key driver of consumer choices, it might be optimal for a firm to pursue consumption control through lower prices. These results are robust when quality is endogenized, capacity is constrained, subscription duration is finite, and in the presence of a two-part tariff. We translate our findings into a conceptual framework for digital service management that characterizes the optimal demand management strategy along two key dimensions: the strength of the consumer bias and the cost of servicing demand. When these factors are significant, firms need to employ a combination of admission control and consumption control so as to manage and maintain profitability.

  PublisherDOI

• Designing Knowledge-Driven Innovation Contests

  Management Science · 2025-05-23

  articleSenior author

  Innovation contests incentivize the participants’ to exert effort toward combining (recombining) their existing knowledge to create solutions. In the current work, we consider the case of serial contests, where effort to create solutions for earlier contests can also expand the participant’s knowledge, which can then be valuable in future contests. We develop a novel framework that explicitly includes the generation and utilization of knowledge by participants in knowledge-driven serial innovation contests, and we analyze the implications of this framework for optimal incentive design. Analysis of our model reveals that the efforts expended by participants in a contest can depend on future rewards, especially when learning emerges as a “side effect” of execution effort (i.e., learning while doing). In fact, participants will exert effort in an earlier contest even when its associated reward is zero. In contrast, when explicit knowledge generation effort is feasible (i.e., learning before doing), the contest designer should increase the reward for the earlier contest to prevent participants from postponing their learning. Our model demonstrates that whether one should assign higher reward to the earlier or later contest depends on the mode of learning, the participant pool’s ex ante knowledge, and the transferability of learning from one contest to the next. This paper was accepted by Ashish Arora, entrepreneurship and innovation. Supplemental Material: The online appendices are available at https://doi.org/10.1287/mnsc.2022.03369 .

  PublisherDOI

• Momentum Equilibria in Participation on Platforms: Implications for Inequity

  SSRN Electronic Journal · 2024-01-01

  articleOpen access1st authorCorresponding
  PublisherDOI

• Spillovers on Crowdsourcing Platforms

  SSRN Electronic Journal · 2024-01-01

  preprintOpen accessSenior author
  PublisherDOI

• Efficiency and Equity in Repeated Entry Games

  SSRN Electronic Journal · 2024-01-01

  preprintOpen access1st authorCorresponding
  PublisherDOI

• Optimal Prototyping with Noisy Measurements

  Manufacturing & Service Operations Management · 2024-11-26 · 1 citations

  articleSenior author

  Problem definition: Prototyping and testing are an integral part of almost any new product development process, helping firms navigate the inherent uncertainties of creating new products. Recent developments in rapid prototyping, including technologies that enable cheaper low-fidelity tests, have opened up the possibilities for firms in reconfiguring their product development processes. Firms can, by choosing the level of evaluation fidelity, alter the traditional cost-quality trade-offs inherent in sequential prototyping. Methodology/results: The current article formulates a general model of sequential search where firms can proceed by obtaining noisy low-fidelity evaluations of their prototypes. Our results demonstrate that the imperfect fidelity of evaluations alters the firm’s optimal experimentation, with the starkest difference being that it may make it optimal for the firm to select and launch a prototype that did not yield the best evaluation. In addition, our analysis of optimal measurement technology reveals that the focal firm should demand the most precise measurements when their ex-ante uncertainty is moderate (not too high or low). We also consider extensions analyzing how the optimal choice of evaluation fidelity is affected by the number of available prototypes, by operational flexibility (to dynamically change measurement technology), and by the ability to outsource evaluations to an experimentation platform. Managerial implications: We develop managerial insights for how the optimal choice of fidelity and the optimal length of the evaluation cycle should be planned depending on the evaluation costs and the firm’s ex-ante uncertainty. The resulting framework offers guidance to product and software development firms to successfully leverage imperfect fidelity experiments. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2024.1133 .

  PublisherDOI

• Dueling Contests and Platform’s Coordinating Role

  Management Science · 2024 · 12 citations

  • Computer Science
  • Business
  • Computer Science

  Crowdsourcing platforms typically take a passive approach, and they let the competing firms freely design their own contests and allow every solver to self-select and join any of the concurrently running contests. In a model of competing noise-driven contests, we show that the duopoly prize allocation has fewer (but larger) prizes compared with a monopolist contest designer. We also find that contests with firm-chosen budgets and solvers’ endogenous participation create coordination inefficiencies. Thus, platform policies that constrain the competing firms from freely choosing their budgets and offer solvers non-enforceable recommendations toward specific noise-driven contests strictly enhance total welfare. Extending our framework to include arbitrarily correlated ability-driven contests, we highlight the critical role of inter-contest dependence on the efficacy of a platform’s interventions. Specifically, platform nudges to improve solver-contest (mis)matches are welfare enhancing only when the contests are sufficiently related, and allowing solvers to self-sort is appropriate otherwise. This paper was accepted by Gabriel Weintraub, revenue management and market analytics. Supplemental Material: The online appendix is available at https://doi.org/10.1287/mnsc.2021.03973 .

  PublisherDOI

• Designing Data Science Contests: The Role of Training vs Test Splits

  SSRN Electronic Journal · 2024-01-01

  preprintOpen accessSenior author
  PublisherDOI

• Optimal Prototyping on Experimentation Platforms

  SSRN Electronic Journal · 2023-01-01

  articleOpen accessSenior author
  PublisherDOI

Frequent coauthors

• Sreekumar R. Bhaskaran

  Southern Methodist University

  5 shared

• Konstantinos I. Stouras

  5 shared

• Vish Krishnan

  4 shared

• Uri Gneezy

  4 shared

• Jeeva Somasundaram

  3 shared

• Stylianos Kavadias

  3 shared

• Kenneth C. Lichtendahl

  Google (United States)

  3 shared

• Lakshminarayana Nittala

  3 shared