About

I study how the criteria used to evaluate technological innovations emerge and change.

I am on the 2026–2027 academic job market.

Technological evolution depends on both the innovations firms generate and the selection pressures that determine which of those innovations advance. In many settings, selection operates through evaluators — regulators, standard-setting bodies, certification agencies — who rely on criteria that specify which performance dimensions they will assess. These criteria orient firm technological search: innovations that do not perform well under existing criteria can be penalized, delayed, or misunderstood, so firms direct effort toward dimensions evaluators already recognize. My research examines the reverse direction of influence: firms searching under existing criteria can develop knowledge that feeds back into those criteria, allowing selection itself to evolve.

My dissertation builds a theory of evaluative evolution: which firms develop the knowledge to propose new evaluation criteria, when evaluators adopt those proposals, and how rivals respond. Firms compete not only by developing innovations, but by helping to define performance.

I study these questions in the pharmaceutical industry, where firms propose clinical endpoints — the prespecified outcomes on which the FDA assesses drug performance — and where the wrong criteria can mean approved drugs that don't actually help. Evaluation criteria are at once legal rules, statistical measurements, and objects of competition; my training in law, biostatistics, and strategic management lets me treat them as all three. To study criteria change at scale, I build custom AI/LLM research pipelines — including retrieval-augmented systems that outperform state-of-the-art general-purpose models on domain-specific tasks. I have been invited to present my research at leading pharmaceutical firms.

Research Interests

  • Innovation Strategy
  • Evaluation of Innovations
  • Organizational Learning
  • Pharmaceutical Industry
  • Strategic Management of Intellectual Property

Methods

  • Causal Inference / Econometrics
  • Natural Language Processing
  • LLM/RAG Pipeline Design
  • Mathematical Modeling
  • Medical Concept Classification Systems
♦

Research

Dissertation: Evaluative Evolution

The dissertation investigates how evaluation criteria change through a linked sequence of firm proposals, evaluator adoption, and competitive response. Evaluators do not directly observe how innovations perform in development and use, so when criteria need to change, the knowledge to change them is more likely to come from the firms being evaluated than from the evaluators assessing them. The first study asks which innovating firms propose new evaluation criteria, and why. The second asks what evaluators do with these proposals. A proposal puts the evaluator in a bind: it carries information the evaluator lacks together with the firm's interest in criteria that favor its own innovation, and the evaluator must weigh the two with its credibility on the call. The third turns to rivals. Adoption poses a choice: compete on the new criterion — conceding a dimension the proposing firm likely leads on — or refuse it and forfeit direct comparison.

Together, the three studies identify one mechanism through which selection evolves: firm search can originate proposed changes in evaluation criteria; evaluator adoption can validate those proposals; and competitive response can redirect the technological search of other firms. They open a broader research program on how evaluative knowledge is distributed across innovating firms, evaluators, and rivals — and on settings beyond pharmaceuticals where specialized evaluators stand between complex innovations and users and evaluation criteria are explicit enough for firms to target: medical devices, financial regulation, environmental standards.

Job Market Paper

Competing by Defining Performance: Firm Technological Search and the Evolution of Evaluation Criteria

Yunxiang Bai

Presented at the CCC Doctoral Conference, Bocconi University, 2026

Firms do not only adapt to the criteria by which their innovations are judged — some propose what those criteria should become. Interest in favorable evaluation is widespread and so cannot explain which firms propose new criteria — what varies is the knowledge required to make a proposal. Firms accumulate this knowledge at two stages of an innovation's life — through the technological search that produces it and through the downstream use that follows. I test this with clinical endpoints proposed in U.S. late-stage drug trials from 2008 to 2025 — a setting where mandatory preregistration makes proposed criteria publicly observable.

Existing research shows how selection shapes organizational adaptation and technological evolution: evaluators determine which innovations advance, and their criteria orient firm search by making some performance dimensions more consequential than others. This study examines the reverse direction of influence: selection can also evolve as evaluation criteria change. Firms search under existing evaluation criteria, yet their innovations may reveal performance implications those criteria do not assess. I ask which innovating firms can translate such experience into proposals for new evaluation criteria, and why. I argue that firms draw on two sources of knowledge. Firms following more novel technological trajectories are more likely to develop innovations whose performance implications existing criteria do not capture. Firms with more diverse downstream use knowledge are more likely to observe which uncaptured performance implications matter across users, time horizons, and use conditions. Using clinical endpoints proposed in U.S. pharmaceutical late-stage trials from 2008 to 2025, I test these propositions in a firm–therapeutic area–year panel of 11,825 active observations. This study advances research on technological evolution by showing how firm technological search can feed back into evaluation, identifying one mechanism through which selection evolves.

Working Papers

Vision or Delusion? How Evaluation Criteria Sequence Anchors the Assessment of Novelty in Venture Evaluation

Yunxiang Bai, Subrina Shen, & Melody Chang

Under review at Strategic Management Journal

Organizations select against novel ventures even when they explicitly seek novelty. The literature diagnoses this as a problem of obscured vision — evaluators fail to see the upside. But evaluators do score both upside potential and feasibility. This study argues that the penalty arises not only from how they see each dimension, but also from the sequence in which they integrate conflicting dimensions into an overall judgment.

Evaluating a novel venture requires reconciling upside potential with feasibility. While prior work has examined evaluators' relative attention to these opposing dimensions, we argue that the sequence of evaluation criteria shapes how evaluators integrate these dimensions into an overall assessment. Analyzing proprietary data from a startup evaluation platform and two pre-registered behavioral experiments, we find that when evaluators are prompted to consider upside potential before feasibility, they prioritize ventures that excel on upside potential while treating uncertain feasibility as a threshold to clear, thereby favoring high-novelty ventures over low-novelty ones. When feasibility is considered first, the anchoring effect reverses, producing a disadvantage for high-novelty ventures. The paper contributes to research on idea evaluation by identifying evaluation criteria sequence as a consequential design lever.

Draft available upon request

On Giants' Shoulders While Keeping Others Off of Yours: Engagement in Science and Firm Generative Appropriability

Francisco Polidoro & Yunxiang Bai

Presented at SMS Annual Conference, Istanbul, 2024

Engaging in public science creates knowledge that rivals can freely use — so does it ultimately help or hurt the publishing firm? The literature has treated this as a single tradeoff, but tracing four decades of knowledge flows reveals that the answer depends on a temporal distinction that prior work has not drawn.

Research on science and innovation highlights how firms' scientific engagement shapes knowledge flows determining who captures returns to innovation. Yet, whether science tilts these flows toward the publishing firm or its rivals has not been directly tested. This study abductively explores this question by tracing patent citation flows for 170 biopharmaceutical firms over four decades. In contrast with existing literature treating the appropriability implications of science as a single tradeoff, this study reveals that the answer depends on temporal perspective: under a retrospective lens, firms sustaining ongoing science capture roughly twice the benefit rivals do, while under a prospective lens, science at invention creates contested opportunities whose firm advantage materializes only at longer horizons. Exploratory evidence suggests science helps firms retrieve knowledge from spillovers.

Draft available upon request

Publications

Mitigating Nonattendance Using Clinic-Resourced Incentives Can Be Mutually Beneficial: A Contingency Management-Inspired Partially Observable Markov Decision Process Model

Yunxiang Bai & Björn P. Berg

Value in Health, 24(8), 1102–1110, 2021

Published version
♦

Teaching

I am prepared to teach courses in strategic management, innovation strategy, and research methods.

General Management & Strategy

Instructor of Record

Undergraduate / Master's · UT Austin McCombs · Summer 2024

Instructor rating: 5.00 / 5.00 · Course rating: 4.89 / 5.00

Biostatistical Literacy

Teaching Assistant

University of Minnesota · 2019–2020

♦

CV

Education

Ph.D. in Management, University of Texas at Austin (Expected 2027)

M.S. in Biostatistics, University of Minnesota (2021)

LL.B., Tsinghua University (2018)

Selected Awards

Outstanding Graduate Research Fellowship (2026–2027)

McCombs Dean's Fellowship (2025–2026)

Cooper Fellowship (2025–2026)

Graduate School Continuing Fellowship (2024–2025)

Conference Presentations

CCC Doctoral Conference, Bocconi University (2026)

Strategic Management Society Annual Conference (2024, 2023)

Download full CV (PDF)
♦

Contact

Email

yunxiang.bai@mccombs.utexas.edu

Office

2110 Speedway
Austin, TX 78712

Profiles

Google Scholar ORCID LinkedIn UT Austin Management Department

I am on the 2026–2027 academic job market.