I study the selection side of technological evolution: how
evaluation criteria emerge and change, and how firms participate
in that change. Firms compete not only by developing innovations,
but by helping to define what ‘better’ means.
I am on the 2026–2027 academic job market.
Research on technological evolution shows that firms anticipate
selection when they search for innovations, favoring directions
they expect to be viewed favorably. Yet selection itself can
evolve: firms, in searching under existing criteria, can
produce candidates for new evaluation criteria. My research
examines how that happens — which firms propose new criteria,
and why — and, more broadly, how evaluation criteria come
into being, how firms participate in changing them, and what
happens to competition when they do.
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.
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. To
study criteria change at scale, I build custom AI/LLM research
pipelines, including retrieval-augmented systems for
domain-specific measurement. I have been invited to present my
research at leading pharmaceutical firms.
The through-line is personal as much as intellectual: 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.
Research Interests
Technological Evolution
Innovation Strategy
Evaluation and Selection of Innovations
Organizational Learning
Strategic Management of Intellectual Property
Methods
Causal Inference / Econometrics
Natural Language Processing
LLM/RAG Pipeline Design
Mathematical Modeling
Medical Concept Classification Systems
♦
Research
Behind every innovation that advances — a drug approved, a patent
granted, a model deployed — sits an act of evaluation. My research
asks how the criteria behind those judgments come into being, how
they change, and how firms participate in changing them — because
the possibility of moving the goalposts, not merely scoring well
against them, reshapes what competitive advantage can be built on.
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.
Essay 1 — job market paper: Which
innovating firms propose new evaluation criteria, and why
(featured below).
Essay 2 — work in progress: How evaluators
weigh these proposals. A proposal 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 line.
Essay 3 — work in progress: How rivals
respond when criteria change: compete on the new dimension and
concede ground where the pioneer likely leads, or refuse it and
forfeit direct comparison.
Together, the three studies trace one route by which selection
itself evolves: firm search can originate proposed criteria,
evaluator adoption can validate them, and competitive response
can redirect the technological search of other firms. Two
vantage points underpin this research — the evaluation process
and the knowledge commons below — and the dissertation grew
out of them. It opens a broader research program on how
evaluative knowledge is distributed across innovating firms,
evaluators, and rivals, in settings where specialized evaluators
stand between complex innovations and users: medical devices,
financial regulation, environmental certification.
Job Market Paper
When Selection Itself Evolves: Firm Innovation and the Emergence 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.
Proposing is demanding: a firm must identify an outcome that
existing criteria do not capture, connect it to consequential performance, and formulate it for assessment. Firms accumulate the knowledge
behind such proposals through two forms of search — developing
novel innovations and working across heterogeneous applications.
I test this with clinical endpoints proposed
in U.S. late-stage drug trials from 2008 to 2024 — 3,667
firm–therapeutic area–year observations from 9,064 trials — a
setting where mandatory preregistration makes proposed criteria
publicly observable.
Research on technological evolution shows that firms anticipate
selection when they search for innovations, favoring directions
they expect to be viewed favorably. Yet selection itself might
evolve, as firms themselves come to influence the criteria that
guide it. This study examines that possibility, asking which
innovating firms will propose novel evaluation criteria to the
evaluators. I
study this question in U.S. pharmaceutical development, where
evaluation is federally mandated and firms' proposed criteria
are observable in clinical trial registrations. I argue that a
firm's propensity to propose novel evaluation criteria varies
with how it searches. When firms' search departs more
fundamentally from existing technologies, they can come across
innovations that perform on outcomes existing criteria do not
capture. Experience across selection environments —
applications that differ in what users require — can
instead reveal that a particular outcome matters for a particular
use. Either experience can yield evidence of an omitted outcome
that firms on established paths are unlikely to even consider.
Comparisons of the same firm across therapeutic areas within the
same year support both arguments. The study shows how firms,
through their search, can give rise to new evaluation criteria
— one way in which search shapes selection and selection
itself evolves.
The Evaluation Process
Vision or Delusion? How Evaluation Criteria Sequence Anchors the Assessment of Novelty in Venture Evaluation
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.
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.
Mitigating Nonattendance Using Clinic-Resourced Incentives Can Be Mutually Beneficial: A Contingency Management-Inspired Partially Observable Markov Decision Process Model
I am prepared to teach core strategy; technology and innovation
management or technology strategy; intellectual property
management; nonmarket strategy; and quantitative and research methods.
General Management & Strategy
Instructor of Record
Undergraduate core · UT Austin McCombs · Summer 2024