Neha Sharma

Assistant Professor of Operations, Information and Decisions

Contact Information

Primary Email:
nehash@wharton.upenn.edu

office Address:
3730 Walnut Street
550 Jon M. Huntsman Hall
Philadelphia, PA 19104

Research Interests: Online Marketplaces | Knowledge Sharing Communities | Urban Mobility in Emerging Economies | Energy Markets |

Links: CV

Overview

Neha Sharma is an Assistant Professor of Operations, Information, and Decisions at The Wharton School. Her research examines the design and governance of online platforms, with applications to urban mobility, energy markets, and knowledge communities. Methodologically, she combines analytical modeling (game theory, stochastic models) with data-driven and statistical-learning approaches to identify incentive mechanisms that improve market efficiency and social welfare. Current projects study how platform rules structure online communities, how strategic suppliers game reservation systems, how information design coordinates human–tech operations (e.g., cloud kitchens), and how large language models reshape knowledge-sharing networks.

Her work has been recognized and selected as finalist for IBM Best Student Paper, IBM Service Science Best Cluster Paper, and won best student presentation in INFORMS. Previously, She completed my PhD in Operations Research at The Kellogg School, Northwestern University and her in M.S. in Statistics in 2023. She also teaches undergraduate elective, OIDD 2200: Operations Management Analytics in Spring.

Research

Neha Sharma (Working), What remains after LLMs: technical knowledge moves from hubs to niches. Abstract
Large language models (LLMs) are reshaping how people seek and produce technical knowledge. To evaluate the nature of knowledge where human expertise remains essential, we find a natural testbed in the largest technical Question and Answer community, StackOverflow. Each question asked in the community carries tags that mark the knowledge domains it draws upon. We term the unique combinations of these knowledge domains (i.e., unique sets of tags) a “specialization” that characterizes the skill set needed to answer that question. Following ChatGPT’s public release in November 2022, total question volume on StackOverflow fell sharply—a widely noticed trend. Yet beneath this decline, we discovered a notable trend: the proportion of novel questions that require stitching together knowledge domains in unprecedented ways rose significantly. More interestingly, we find that this surge in novelty emerges mainly from new recombinations of existing domains rather than new domains introduced by LLMs. This shift has also resulted in the reorganization of the community’s knowledge structure. In particular, modeling the knowledge in the community as a network of co-occurring domains, we find that popular knowledge domains, i.e., historically well-discussed domains, have weakened significantly, as activity migrates toward niche domains. Consequently, the community network becomes more fragmented with a weaker core. Our findings suggest that LLMs substitute for standardized queries, while novel problems that require creative integration of knowledge domains still need human expertise. Such selective substitution of questions has profound implications for the sustainability of online knowledge communities and for the reliability of training data that future AI systems will depend on.

Related
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Neha Sharma, Sumanta Singha, Milind Sohoni, Achal Bassamboo (Under Revision), The Empty Promise: How Strategic Suppliers Could Undermine Reservation Systems. Abstract
Peer-to-peer (P2P) reservation platforms often struggle to manage advance-booking customers. Because supply is self-scheduled, hosts’ and customers’ incentives may be misaligned. When platforms set high prices, the asset owners (hosts) would commit their assets early—i.e., list in advance; however, it may deter advance-booking customers from reserving. Conversely, setting lower prices would discourage hosts from listing early. Moreover, a host’s decision to list also depends on matching probability, which is governed by the platform’s design and search frictions— that is, its matching efficiency. We examine when and why advance-booking customers face a supply shortfall and how the platform’s matching efficiency contributes to this outcome. We develop a two-period game-theoretic model in which the platform sets prices dynamically, and hosts choose when to list their assets. The model incorporates endogenous matching probabilities to capture real-world search frictions. To validate our theoretical insights, we analyze data from a major car-sharing platform, focusing on host listing decisions and their impact on service availability. We characterize the hosts’ optimal listing strategies in response to the platform’s prices and identify three possible outcomes: no commitment, partial commitment, and full commitment. We show that when the demand imbalance between advance-booking and just-in-time customers exceeds a threshold, the platform prefers to induce a breakdown in the advance-booking market. Counterintuitively, this threshold decreases as the platform’s matching efficiency increases, meaning more efficient platforms are more prone to such breakdowns. Peer-to-peer reservation platforms should be cautious when choosing revenue-maximizing prices during severe demand imbalances, as it can disrupt advance-booking markets. Beyond pricing, a platform designer can use matching efficiency as a strategic lever to shape outcomes. By improving matching for early listers—through visibility boosts or recommendation algorithms—platforms can better align host incentives, reduce breakdowns, and enhance service across customer segments.

Description
ISB Interview Series
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Neha Sharma, Gad Allon, Achal Bassamboo (Under Review), Structuring Online Communities. Abstract
Online Question and Answer communities were started to supplement customer support services. In contrast to conventional customer support, users in online communities can post questions, and other users with more experience or knowledge can answer these questions. Generally, questions answered get rewards and visibility in the community, while the askers gain knowledge if their questions get answered. We study how users decide to join, leave, and participate in these communities. We link the user participation decisions to the underlying network structure of the community. Finally, we explore the levers a community designer can use to balance user participation level and the community’s efficiency in providing answers to users’ questions. We model the community as a multistage stochastic game where all the users have different skill levels. We find the stationary equilibrium of this game and theoretically show that only a core-periphery network structure can emerge in such communities. This network structure has been empirically observed in most online communities. Furthermore, we find that increasing the cost of asking questions in the community improves the proportion of askers that get answers to their questions. This results in higher user satisfaction. However, a higher asking cost lowers the participation level in the community. This trade-off between participation and community efficiency results in non-monotonicity in the number of users in the community with the participation cost. The paper explores the cost of asking a question as a lever that can be used by communities to control the number and knowledge type of users in the community. The communities typically operationalize higher asking costs by either directly penalizing question asking activity or setting up stricter guidelines for questions to be answered. We find that increasing the cost of asking is not always bad for the community. In fact, a higher asking cost improves user satisfaction which can lead to an increase in the number of users in the community despite higher asking cost. We also discuss how the existence of low knowledge users in the community (and not necessarily the high knowledge users) is essential to the survival of such communities.

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Neha Sharma, Sripad Devalkar, Milind Sohoni (2020), Payment for Results: Funding Non-Profit Operations, Production and Operations Management. https://onlinelibrary.wiley.com/doi/abs/10.1111/poms.13336 Abstract
Payment for results (PfR) funding approach, where donors reimburse the non-profit organization (NPO) based on outcomes, is being increasingly adopted in the non-profit sector. However, there is also concern expressed by many voluntary organizations that such a funding approach puts an undue financial burden on small NPOs and could actually be detrimental to social welfare. In this study, we build a theoretical framework to analyze PfR funding mechanisms. We use a sequential game to model the interaction between the donor and the NPO, with the donor as the first mover. This model captures how PfR funding is typically implemented in practice using social impact bonds (SIB), wherein social investors provide the upfront funding needed by the NPO to implement the project. The donor provides funding, based on the actual benefit delivered, at the end of the project and the investors are paid back using these funds. We find that higher targets set by the donor do not necessarily translate to higher expected utility or expected benefit delivered under PfR. When comparing the performance of PfR and traditional funding (TF) mechanisms, we find that the donor typically has a higher expected utility under the PfR mechanism when the probability of a negative outcome shock is either high or low, and is better off using the TF approach otherwise. When the donor’s opportunity cost of funding the project is high, the donor is better off using a PfR mechanism when her belief about the NPO having low efficiency is sufficiently high. Interestingly, we find that for a large range of parameter values there is a mismatch between the approach that gives a higher expected utility to the donor and the approach that maximizes the expected social benefit delivered. Our model and analysis suggest that the optimal funding approach, and the optimal target set under PfR, depend on the NPO’s financing cost from social investors and project outcome uncertainty.

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Teaching

Instructor – OIDD 2200: Operations Management Analytics

Term: Spring
This course introduces basic concepts of operations management and application of the same in business practice today. We will examine the theoretical foundations of operations management and how these principles or models can be employed in both tactical and strategic decision making. Topics covered in detail are forecasting techniques, planning under deterministic and uncertain demand, operations planning and scheduling, queuing theory, service operations management, newsvendor models, risk pooling strategies in firms, capacity and revenue management, and supply chain coordination. We will conclude by discussing how supply chains evolve under technological change.

Guest Lecturer – OIDD 9010: PhD Seminar

Term: Spring

Past Courses

OIDD2200 - Operations Management Analytic
This course introduces basic concepts of operations management and application of the same in business practice today. We will examine the theoretical foundations of operations management and how these principles or models can be employed in both tactical and strategic decision making. Topics covered in detail are forecasting techniques, planning under deterministic and uncertain demand, operations planning and scheduling, queuing theory, service operations management, newsvendor models, risk pooling strategies in firms, capacity and revenue management, and supply chain coordination. We will conclude by discussing how supply chains evolve under technological change.