Research

Publications

"Algorithm-Augmented Work and Domain Experience: The Countervailing Forces of Ability and Aversion" with Raj Choudhury. 2022. Organization Science 33(1), 149-169.

**"Best PhD Student Paper" at SMS conference 2020

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Past research offers mixed perspectives on whether domain experience helps or hurts algorithm-augmented worker performance. Reconciling these perspectives, we theorize that intermediate levels of domain experience are optimal for algorithm-augmented performance, due to the interplay between two countervailing forces—ability and aversion. Although domain experience can increase performance via increased ability to complement algorithmic advice (e.g., identifying inaccurate predictions), it can also decrease performance via increased aversion to accurate algorithmic advice. Because ability developed through learning by doing increases at a decreasing rate, and algorithmic aversion is more prevalent among experts, we theorize that algorithm-augmented performance will first rise with increasing domain experience, then fall. We test this by exploiting a within-subjects experiment in which corporate information technology support workers were assigned to resolve problems both manually and using an algorithmic tool. We confirm that the difference between performance with the algorithmic tool versus without the tool was characterized by an inverted U-shape over the range of domain experience. Only workers with moderate domain experience did significantly better using the algorithm than resolving tickets manually. These findings highlight that, even if greater domain experience increases workers’ ability to complement algorithms, domain experience can also trigger other mechanisms that overcome the positive ability effect and inhibit performance. Additional analyses and participant interviews suggest that, even though the highest experience workers had the greatest ability to complement the algorithmic tool, they rejected its advice because they felt greater accountability for possible unintended consequences of accepting algorithmic advice.

"A Spanner in the Works: Category-Spanning Entrants and Audience Valuation of Incumbents" with Rory McDonald. 2021. Strategy Science.

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Previous work has examined how audiences evaluate category-spanning organizations, but little is known about how their entrance affects evaluations of other, proximate organizations. We posit that the emergence of category-spanning entrants signals the advent of an altered future state—and seeds doubt about incumbents’ prospects in a reordered industry-categorization scheme. We test this hypothesis by treating announcements of funding for startups as an information shock to investors evaluating incumbent financial service providers between 2010 and 2017—a period marked by atypical category combinations at FinTech startups. We find that announcements by startups that embodied unusual combinations of categories resulted in lower cumulative average returns for incumbents, both in absolute terms and in comparison with typical startups. Our theory and results contribute to research on categorization in markets and to theories of disruptive innovation and industry evolution.

"Machine Learning for Pattern Discovery in Management Research" with Raj Choudhury and Michael Endres. 2021. Strategic Management Journal 42(1), 30-57.

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Supervised machine learning (ML) methods are a powerful toolkit for discovering robust patterns in quantitative data. The patterns identified by ML could be used for exploratory inductive or abductive research, or for post hoc analysis of regression results to detect patterns that may have gone unnoticed. However, ML models should not be treated as the result of a deductive causal test. To demonstrate the application of ML for pattern discovery, we implement ML algorithms to study employee turnover at a large technology company. We interpret the relationships between variables using partial dependence plots, which uncover surprising nonlinear and interdependent patterns between variables that may have gone unnoticed using traditional methods. To guide readers evaluating ML for pattern discovery, we provide guidance for evaluating model performance, highlight human decisions in the process, and warn of common misinterpretation pitfalls. The Supporting Information section provides code and data to implement the algorithms demonstrated in this article

"From Local Modification to Global Innovation: How Research Units in Emerging Economies Innovate for the World" with Shad Morris, James Oldroyd, Daniel Chng, and Jian Han. 2023. Journal of International Business Studies.

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More and more companies are turning to emerging markets as sources of global innovation to help transform business and society. However, building innovation capabilities in emerging markets is still elusive for most companies. To understand how some companies are successfully building these capabilities, we examined workers within R&D units in China across six foreign multinational corporations. In contrast with prior literature that emphasizes a structural view of who the workers interacted with to innovate, our inductive analysis highlights a behavioral view of how R&D unit personnel interact during the problem and solution search process. We identified two key behaviors associated with the problem and solution search: (1) observing customers in their everyday context, and (2) uncovering general knowledge principles from internal experts. Respectively, these behaviors helped R&D workers to question assumptions about existing products as they relate to customers and to apply useful principles from expert knowledge rather than copying solution templates. Our findings offer an alternative path to building global innovation capabilities in markets where structural constraints exist for the company.

Working Papers

"Methodological Pluralism and Innovation in Data-Driven Organizations" with Rory McDonald. Revise and Resubmit at Administrative Science Quarterly

** "Best Paper" at Strategy Science Conference 2022
** "Best Paper" at Wharton Innovation Doctoral Symposium 2022

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Are data-driven organizations more likely to produce commercially successful new-product innovations? This question evokes polarized viewpoints: some argue that reliance on data helps innovation performance because it aligns new products with observable customer demand; others argue that such reliance harms innovation by allocating resources toward merely incremental innovations, thereby missing the big hits that drive commercial success. We argue that the impact of data on innovation depends on the methodological pluralism of the organization—the extent to which different types of analyses are used. Specifically, we hypothesize that qualitative analysis positively moderates the effect of quantitative analysis on innovation performance. Using data on 3,500 product launches and text from employee résumés at 61 large consumer product firms, we provide evidence that increased quantitative analysis negatively impacts innovation performance when qualitative analysis is low, but positively impacts innovation performance when qualitative analysis is high. Interestingly, the organizations that produced the greatest number of big hits (top 5%) used the most quantitative analysis—but they were pluralistic enough to use qualitative analysis liberally as well. Additional analyses establish how methodological pluralism impacts product novelty, explore the effect of methodological pluralism within (rather than between) organizational members, establish market uncertainty as a boundary condition, and investigate the antecedents of data-driven decision-making in organizations.

"Market Size Inversion: How Diffusion Dynamics Invert Market Size Expectations for Novel Products"

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This paper seeks to explain the “market size inversion” puzzle: the observation that many breakthrough products achieve success despite low initial expectations, while other products commonly fail despite high projected demand. I explain the puzzle as a function of social diffusion processes. It presents model in which relatively novel products are more ambiguous to evaluate, so potential adopters rely more on neighbors' endorsements in their adoption decision. Therefore, a larger portion of demand does not exist until after the product diffuses, so initial market size estimates for relatively novel products will be downward biased. The model also implies what I call a “market size trap”: because novel products appear to have lower market demand than non-novel products with larger anticipated market sizes, firms that rely too much on quantitative estimates of market size will launch fewer highly novel or breakthrough products. I empirically validate the model using sales and attribute data from 1,600 consumer product launches, combined with measures of firms' use of quantitative market sizing analysis. The paper provides a demand-side framework to complement the existing competition-based strategy explanation for the same puzzle—that breakthroughs are often surprising because if they were not, the opportunity would have been competed away.

"The Limits of Experimentation for Product Innovation in Homogenous User Communities" with Rory McDonald and Rob Bremner.

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This study investigates how firms experiment with user communities to drive market growth for new products. Prior research has largely focused on the benefits of leveraging user communities as a resource for experimentation and feedback in product development. This paper posits a boundary condition: reaping these benefits is contingent on the degree to which the user community accurately represents demand in the broader market. Using longitudinal and interview data on experimental PC game development, we find that adapting games in response to early user-community feedback increases games’ growth in the market, on average. But this effect is reversed when the feedback comes from a homogenous user community that is concentrated in a narrow market segment. Such homogenous communities can produce signals of market demand that, when incorporated into the game, diminish its appeal to potential customers outside the community.

Other Works in Progress

"Data-driven Decision-making and Organizational Hierarchy" with Kramer Quist.

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This study develops and empirically tests a formal model for how organizational hierarchy affects demand for data-driven decision-making. The model shows that although data can substitute for hierarchy by establishing a framework for consensus, hierarchy also increases demand for data because hierarchies require legible and commensurable results. We empirically validate the model using data from employee profiles on a career networking website. We use job titles to measure the span of control across levels of hierarchy in 61 consumer product organizations, and job descriptions to measure the prevalence of data-driven decision-making.

"Sequencing Entrepreneurial Scaling" with Aticus Peterson

"Roll the dice: Observable endowments that predict who joins the Unicorn Club" with Suresh Kotha, Ben Hallen, Sung Ho Park, and Joseph Shin