Recent, Ongoing, and Future Research Projects
My research addresses new and emerging forms of coordination where financial incentives are impossible or insufficient to achieve the desired outcomes. A significant area of my work is in volunteering and prosocial behavior, where I have use econometrics, machine learning, and analytical modelling to study the behavioral foundations of in-person and online volunteering. I am particularly interested in online volunteering, such as ‘citizen science’, where operations management techniques offer the potential to expand the scale and improve the efficiency of projects pushing the frontier of natural science.
Ongoing projects include studies of new forms of coordination in grocery supply chains to use real-time information sharing to reduce food waste, the impact of groups allocation and gender diversity on the uptake of voluntary healthcare initiatives, and the management of novel innovation projects, where delegated search cannot be effectively incentivized with only financial bonuses.
You can find more about these projects here.
Citations: 126
H-Index: 5
Published & Working Papers
Hold Me Accountable: Anonymity and Prosocial Behavior
Published at M&SOM (May 2025)
With Claire Senot (Tulane University). This paper focuses on the lever of accountability and how it can used to motivate prosocial behavior in a hierarchical setting. This paper uses a quasi-experimental method to investigate how service providers can use the names of their consumers to create an implicit incentive to engage in prosocial behavior by relying on social context. We study the case where a service provider removed the names of participants from test kits in a viral testing program. The effect was a surprising 21% reduction in participation, negative moderated by the size of the groups participants were randomly assigned to. We confirm the robustness of this result using difference-in-differences, regression discontinuity, and augmented local linear models. We examine further moderating effects and offer guidance to service providers can use accountability as an implicit lever to encourage prosocial behavior, both in volunteering settings and businesses.
Presented at: POMS 2023, INFORMS 2023, University of Toronto, Wharton Empirical Workshop 2024, POMS 2024.
Media Attention:
Cultures for Innovation
Major Revision at Management Science (January 2026)
With Jeremy Hutchison-Krupat (University of Cambridge). We examine how an organization can align its strategic objectives with its innovation culture. Where innovation culture varies according to the targets that are set, the autonomy that exists, and the tolerance management has for failed experiments. Recognizing that organizational culture varies, we build a model of a relational contract where a principal delegates the execution of a derivative and a novel innovation to an expert agent. Given a set of cultural traits, we determine the level of novelty that a principal could pursue. We then analyze how the principal may use their ability to influence culture in pursuit of the novel innovation that forms their strategic objective. We confirm that tolerance for failure can be beneficial to enable novel initiatives, but our results also provide an alternative, cautionary view of tolerance for failure: pursuit of the most novel innovations may require tolerating less failure. We verify that setting targets to constrain the amount of derivative innovation in the portfolio may increase the rate of novel innovation and reduce the likelihood that projects are terminated. We further extend our analysis to examine benefits of target setting and the principal's decision over cultural traits. For managers, we make specific recommendations to map organizational culture to successful innovation.
Presented at: INFORMS 2024, POMS 2025, University College Dublin, McGill University, INFORMS 2025
Raising the Bar: Motivating Contributors in AI-Assisted Crowdsourcing
Major Revision at Management Science (September, 2026)
With Setareh Farahjollahzadeh (McGill University, Desautels Faculty of Management). We study how AI-generated benchmarks in annotation tasks affect human contributors’ performance and retention on crowdsourcing platforms. These benchmarks are set as minimum performance requirements to ensure that human-generated annotations are valuable for subsequent model training. However, higher minimum requirements may also increase effort costs and fatigue, potentially reducing future performance and reducing contributor retention. Using data from a large-scale natural experiment in crowdsourced DNA annotation within the popular online game Borderlands 3, we find that higher minimum requirements in prior tasks lead to, surprisingly, greater discretionary performance in subsequent tasks, measured as performance above the required minimum; it also increases contributor retention. We further show that higher past requirements induce contributors to exert more trial-and-error effort and time to meet the minimum requirement in subsequent tasks. Our empirical findings and behavioral model support the interpretation that higher minimum requirements in prior tasks increase contributors’ intrinsic value of effort, motivating them to exert greater effort in subsequent tasks. This mechanism is consistent with the psychological theory of learned industriousness, which suggests that repeated associations between effort and reward can increase intrinsic motivation. Consistent with this mechanism, we show that the effect persists even when the major reward is removed. Our results further indicate that platforms must continue to reinforce the effort–reward association; otherwise, intrinsic motivation and subsequent discretionary performance will attenuate over time. Overall, our findings provide guidance for task sequencing and for managing motivation, work quality, and retention on crowdsourcing platforms.
Presented at: University College Dublin, University of Toronto, the Wharton School, Bayes Business School, McGill University, Darden School of Business at the University of Virginia, Judge Business School at the University of Cambridge, Penn State University, Villanova University, Philly Operations and Technology Day, INFORMS 2025, INFORMS 2026
Media Attention:
Minority Figures: Gender Inequality in Risk Taking
Under review at Manufacturing & Service Operations Management (June 2026)
With Claire Senot (Tulane University). Operational performance often depends on individuals disclosing private information that carries interpersonal risk -- such as errors, near-misses, or disease symptoms. Thus, individuals frequently withhold this information, with negative consequences for performance. We examine how group composition -- specifically gender diversity -- affects individuals' willingness to take these risks. Building on social identity theory, we argue that group diversity affects individuals' risk-taking behavior through tokenism and social recategorization. Low diversity leads to status concerns for the minority, while majority members face weaker pressure to disclose unless norms shift. We test this theory in a natural experiment: 5,000 students were randomly assigned to groups during a voluntary viral-screening initiative, where participation had both private costs and stigma risk, yet was required for operational planning. Under-represented gender groups were more likely to participate, and their participation did not induce a spillover effect to the gender majority at low levels of diversity. However, compared to single-gender groups, when the minority share exceeded 40% of the group composition, average group information disclosure increases, driven by an increase in the participation of the majority members. Our results show that gender composition affects the assignment of the interpersonal risks associated with disclosure. We clarify when diversity does (and does not) support operational goals, while also highlighting that the burden may be unevenly distributed in mixed gender groups. We inform the design of team structures and incentive schemes that support equitable, operationally-relevant information sharing.
Presented at: POMS 2025
Traceability and Food Waste in Fresh Produce Supply Chains
Under Review at Management Science (June 2026)
With Javad Nasiry (McGill University) & Elaheh Roshedinejad (University of Toronto). Grocery supply chains account for approximately 42% of global food waste. Policymakers and retailers increasingly view traceability technologies, process improvement, and waste penalties as tools for improving freshness and reducing waste. Yet traceability does not only improve information; it also changes supplier incentives. We study a fresh produce supply chain in which a retailer invests in costly and incomplete batch-level traceability, while a supplier chooses its wholesale price and invests in process conformance, which determines the likelihood that products arrive with high freshness. Consumer demand increases with freshness, and waste arises across the supplier, retailer, and consumer stages. We show that traceability has a non-monotonic effect on upstream process conformance. When conformance is costly, equilibrium freshness is low, and greater traceability induces the supplier to improve conformance. In this region, traceability and process conformance are strategic complements, so traceability improves freshness and can reduce waste. When conformance is less costly, however, equilibrium freshness is already high, and greater traceability can induce the supplier to reduce conformance in order to preserve information rents. In this region, traceability and process conformance are strategic substitutes, and traceability may backfire by lowering freshness and increasing total supply chain food waste. We then study two policy levers: a penalty on retail food waste and a subsidy for traceability investment. We identify conditions under which these policies simultaneously reduce food waste, increase food availability, and improve supply chain profits-a win-win-win for consumers, firms, and the environment. We also derive a threshold on traceability costs below which subsidizing traceability is more effective than increasing waste penalties. Overall, our results show that traceability is not a universal remedy for food waste; its value depends critically on supplier incentives, traceability costs, and consumers' sensitivity to freshness.
Presented at: INFORMS 2024, POMS 2025, McGill University, M&SOM 2026 (Roshedinejad)
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Works in Progress
This section describes several themes in my ongoing work. If you interested in knowing more about the specific work in any area, please email me and we can talk!
Assessing the Capability of Large Language Models to Address Social Media Misinformation on Hereditary Breast Cancer
With Victoria Hayman (MD), Ari Meguerditchian (MD), & Nina Morena (PhD). A major bottleneck in clinical care for cancer patients is the time spent by physicians to address social media misinformation acquired by patients post-diagnosis. While capacity constraints in healthcare mean that patients naturally seek support from readily available information online, only a small proportion of social media content is of high quality and produced by reliable expert sources. With this work, in collaboration with a team of physicians at McGill University Health Centres and researchers at OpenAI, we are designing, implementing, and validating a protocol to assess the quality of social media content instantly via fine-tuned LLMs.
Retention, Learning, and Performance on Citizen Science Platforms
This multi-paper project works with a large team of industry and academic partners to study how the insights and tools of operations management can be applied to voluntary crowdsourcing, such as citizen science platforms.