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https://doi.org/10.1177/01492063231226136
Journal of Management
Vol. XX No. X, Month XXXX 1 –25
DOI: 10.1177/01492063231226136
© The Author(s) 2024
Article reuse guidelines:
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1
A Scientific Method for Startups
Teppo Felin
Utah State University
University of Oxford
Alfonso Gambardella
Bocconi University
Elena Novelli
Bayes Business School, City, University of London
Todd Zenger
University of Utah
Recent scholarship has sought to develop a “scientific method” for startups. In this paper we
contrast two approaches: lean startup and the theory-based view of startups. The lean startup
movement has served an important function in calling for a normative and scientific approach
to startups and venture creation. The theory-based view shares this agenda. But there are dif-
ferences in the underlying theoretical mechanisms and practical prescriptions suggested by
each approach. We highlight these differences and their implications for both research and
practice. For example, we contrast lean startup’s emphasis on bounded rationality and entre-
preneur–customer information asymmetry with the theory-based view’s emphasis on generative
rationality and belief asymmetry. The theory-based view focuses on contrarian beliefs, associ-
ated problem formulation, and the development of a startup-specific causal logic for experimen-
tation, resource acquisition, and problem solving. The right mix of entrepreneurial actions is
contingent and startup-specific—guided by a startup’s unique theory. After pointing out differ-
ences between the lean and theory-based view of startups, we discuss opportunities for partial
reconciliation, as well as opportunities for empirically comparing perspectives. Overall, we
emphasize that a scientific method for startups needs to recognize the importance of contingent,
Acknowledgements: Alfonso Gambardella acknowledges financial support from the Italian Ministry for Education,
project: “Entrepreneurs As Scientists: When and How Start-ups Benefit from A Scientific Approach to Decision
Making,” call PRIN 2017, Prot. 2017PM7R52, CUP J44I20000220001. Teppo Felin and Elena Novelli acknowl-
edge financial support from the UK Department of Business Energy and Industrial Strategy - Innovate UK, project:
104754, “A Scientific Approach to SMEs Productivity”.
Corresponding author: Elena Novelli, Bayes Business School, City, University of London, 106 Bunhill Row,
London, EC1Y8TZ, UK.
E-mail: elena.novelli.1@city.ac.uk
1226136JOMXXX10.1177/01492063231226136Journal of Management /Felin et al. / A Scientific Method for Startups
research-article2024
2 Journal of Management / Month XXXX
discriminating alignment between entrepreneurial theories and the actions they prescribe
(including different types of experimentation and validation, search, and forms of organization).
Keywords: entrepreneurship theory; macro topics, knowledge management; entrepreneurial/
new venture strategy
Introduction: Entrepreneurs as Scientists
Lean startup—as developed by Blank (2013), Blank and Dorf (2012), Ries (2011), and
Osterwalder and Pigneur (2010)—has had a significant influence on how entrepreneurs
approach startups and new ventures. Lean startup has also begun to shape how academics
study and teach entrepreneurship (e.g., Leatherbee & Katila, 2020; Shepherd & Gruber,
2021), offering a framework and practical tool that also captures some of the insights of
longer-standing academic literatures—including the literatures on technology evolution,
organizational learning, product development, and strategic management (Contigiani &
Levinthal, 2019).
We strongly endorse the normative message of lean startup, namely, that entrepreneurs
can optimize their odds of success when they adopt a “scientific approach to the creation of
startups” (Ries, 2011). The idea that entrepreneurs should act like scientists—and utilize the
scientific method—is the central premise of the theory-based view (Felin & Zenger, 2009,
2016, 2017; Felin, Gambardella, & Zenger, 2021; Zellweger & Zenger, 2023). This approach
has been formally modeled (Ehrig & Schmidt, 2022; Camuffo, Gambardella, & Pignataro,
2023a) and empirically tested through randomized control trials (e.g., Agarwal et al., 2023;
Camuffo, Cordova, Gambardella, & Spina, 2020; Novelli & Spina, 2024). The theory-based
view of startups concurs with lean startup’s emphasis on the scientific method and its focus
on practical action and hypothesis-driven experimentation. Both lean startup and the theory-
based view can be seen as part of an important normative movement within strategy and
entrepreneurship that seeks to identify practical “interventions,” treatments and forms of
training that might enable startups and companies to be more scientific and evidence-based
about their decision making (e.g., Chatterji, Delecourt, Hasan, & Koning, 2019; Heshmati
& Csaszar, 2023; Kotha, Vissa, Lin, & Corboz, 2023). But we argue that these interventions
need to be theory-guided, both at the level of their scientific investigation and at the level of
startups and firms themselves.
In this article, we take Blank and Eckhardt’s (2023) recent contribution as a reference
point and offer contrasts between the theory-based view of startups and lean startup. We do
so particularly in terms of the central mechanisms and the prescriptive “method” suggested
by each approach. While some of these differences have briefly been discussed before (Felin,
Gambardella, Stern, & Zenger, 2020), we go well beyond this work by addressing the central
and novel points raised by Blank and Eckhardt (2023) in their target article. We recognize
that lean startup has been further developed since its original conception—adding new
frameworks and tools (e.g., Shepherd & Gruber, 2021), empirical tests (e.g., Burnell,
Stevenson, & Fisher, 2023; Leatherbee & Katila, 2020), and links to adjacent disciplines
(e.g., Ramoglou, Zyglidopoulos, & Papadopoulou, 2023). Some of these extensions suggest
Felin et al. / A Scientific Method for Startups 3
that the gap between the lean and theory-based approach to startups might be narrowing.
However, the need to point to differences is made evident by Blank and Eckhardt’s (2023: 4)
suggestion that the theory-based view is “consistent” with lean startup—a conclusion we do
not fully endorse. While both approaches can broadly be “viewed as an application of the
scientific method to entrepreneurship” (Blank & Eckhardt, 2023: 2), the specific mecha-
nisms, assumptions, interventions, and practical prescriptions for startups and entrepreneurs
are substantially different. We carefully point out these differences and offer possible com-
plementary future directions that could further the development of a “scientific method” for
startups. Some of the differences between the theory-based and lean view of startups can be
reconciled by a “contingent” approach to entrepreneurship—an approach that recognizes the
contextual and situational factors that shape which method or practice should be utilized
(when and why), depending on the startup-specific theories held by an entrepreneur. We
conclude by pointing out the need for those architecting startups to pursue a discriminating
alignment between the type of theory entrepreneurs seek to explore and the downstream
actions or choices related to different forms of experimentation and organization.
Lean Versus Theory-Based Startup
Blank and Eckhardt (2023) offer an extensive summary of lean startup, including a dis-
cussion of key concepts and tools such as Business Model Canvas and Market Opportunity
Navigator (cf. Gruber & Tal, 2017, 2024; Osterwalder & Pigneur, 2010). Their paper pro-
vides a highly useful articulation of the current state of the lean startup approach, including
links to recent developments (building on Shepherd & Gruber, 2021; cf. McGrath &
Macmillan, 2000). Blank and Eckhardt’s paper also points to links between lean startup and
adjacent theories and approaches, such as effectuation, bricolage, and discovery-creation. To
their credit, their paper is inclusive and far-ranging. However, given space considerations, in
this paper we focus largely on contrasting lean startup’s core assumptions and arguments
with those of the theory-based view of startups.
Lean Startup: Foundations and Model Assumptions
As argued by Blank and Eckhardt (2023), the “core” premise of lean startup is that entre-
preneurs and startups need to forego excessive planning and quickly engage with customers,
for example by developing a minimum viable product. While attention is given to other
stakeholders through tools like Business Model Canvas, Blank and Eckhardt specifically
emphasize the need to “[reduce] information asymmetries between entrepreneurs and cus-
tomers” (2023: 5, italics added). The assumption behind this information asymmetry is that
customers have vital information or knowledge that a startup needs to somehow elicit, access,
or incorporate into their nascent product, service, or value offering. The sooner the entrepre-
neur engages with the customer, the quicker this information asymmetry can be reduced. In
short, lean startup’s primary emphasis is on “early and frequent customer feedback”—“quick
rounds of experimentation and feedback”—to enable startups to “continually learn from cus-
tomers” (Blank, 2013: 5-7). Notice that, according to lean startup, this asymmetry of infor-
mation is one-sided, where the key information and knowledge is held by the customer and
needs to be accessed by the startup.
4 Journal of Management / Month XXXX
Consistent with the concept of information asymmetry, lean startup builds on the idea of
bounded rationality. As discussed by Blank and Eckhardt, entrepreneurs “are imperfect deci-
sion makers who suffer from biases in decision-making” (2023: 6). The argument is that
bounded rationality is reduced or lessened if startups use the lean method—again, by garner-
ing information and knowledge through various forms of customer interaction and valida-
tion. Lean startup recognizes that entrepreneurs cannot somehow access “all” customers, but
they need to satisfice by securing frequent, “good enough data” from them (Blank, 2013).
Customer interaction and feedback is meant to offer much-needed, ongoing scientific valida-
tion and evidence to ensure the venture is moving in the right direction—rather than wasting
resources.
Several questions emerge from the emphasis that lean startup puts on information asym-
metry between customers (or even other stakeholders) and the startup, as well as the strong
emphasis placed on bounded rationality as an underlying assumption. For example, is cus-
tomer interaction indeed the best way to validate a startup’s product idea, value offering, or
strategy, relative to many other alternatives? Can customer interaction reliably offer a signal
about what a startup should do? Is the emphasis on bounded rationality the right way to think
about entrepreneurial cognition and startup learning? We discuss these questions in turn.
While customer feedback undoubtedly can be useful in some situations, there are several
problems with focusing on customer feedback as the central mechanism for learning and
validation. The immediate, practical problem with customer feedback is that it is likely to be
extremely heterogeneous. One customer might like a particular product feature while another
might not. Feedback might be highly idiosyncratic depending on the customers the startup
happens to sample and interact with, and efforts to avoid the problem of idiosyncratic feed-
back—for example by sampling an even larger set of customers—only compound the prob-
lem. Customers might offer indefinite thoughts on how a particular product should evolve
and what features ought to be added, improved, or completely removed. Lean startup offers
no coherent mechanism for arbitrating between all this information, to recognize which bits
of information might actually validate an idea or product and which might lead a startup
astray. Interestingly, this problem was recognized in early work related to business models.
As noted by Osterwalder and Pigneur, “another challenge lies in knowing which customers
to heed and which customers to ignore” (2010: 129). To foreshadow our argument, we think
theories are fundamental to the process of knowing who to listen to (or which customers or
stakeholders to even solicit feedback from). In short, with heterogeneous customer feedback,
it is hard to separate the signal from the noise.1
Of course, in principle, there is nothing wrong with sampling and interacting with custom-
ers. As we discuss below, in some situations, the right form of customer interaction and
experimentation can be useful; however, our central point here is that customer interaction is
not a panacea for validation, and there is no clear reason to make information asymmetry
between customers and startups—and the bounded rationality of the latter—the central prob-
lem that needs to be solved.
Customer feedback is but one of many tools and forms of experimentation and intermedi-
ate validation that a startup can use to guide its actions. When it comes to startup activity,
there are no one-size-fits-all tools. Of course, whether customers buy a product is the ulti-
mate market test and (eventual) source of validation. But it is not clear why customers might
have better information than startup founders themselves when developing the startup’s
Felin et al. / A Scientific Method for Startups 5
product or value offering that is offered to customers. Customers might not even have a
proper awareness of their own needs. Thus, much-needed validation might come from prob-
lem solving, experimentation, and exploration that do not initially involve customers at all.
This could involve searching for a critical technology, exploring key assumptions, or con-
versing with potential resource providers. Validation might also come through efforts to elicit
the engagement and buy-in of co-founders, early employees, and investors—actors who have
far more riding on the possible success of the startup than customers. In fact, in some cases,
founders and early employees are essentially customers themselves. They can be seen as lead
users whose opinions and tastes shape how a product offering or technology evolves (as
historically has been the case with Apple). These employees create the products they would
like to see exist, rather than asking customers what they think is needed.
Importantly, customer feedback is of less value in situations where startups seek to develop
radically discontinuous, novel product offerings and new sources of value. Customers might
offer useful, incremental improvements on products that they are already habitually aware of
and familiar with, but novel product offerings often demand more than casual responses to a
mocked-up product. As we will discuss, the most valuable product and business ideas ema-
nate from theories involving “what-if” forms of causal logic, that is, what if the following
assumptions are true or the following problems can be solved? Obtaining quick customer
feedback on such forms of novelty requires customers to imagine and embrace the underly-
ing causal logic, which may be extraordinarily difficult to achieve without first demonstrat-
ing the accuracy of assumptions or the solvability of subproblems. This is aptly captured by
Henry Ford’s famous quip: “If I had asked customers what they wanted, they would have said
a faster horse.” It is also not clear that Henry Ford would have generated highly useful feed-
back from a rapidly developed crude prototype of the Model T.
Bounded rationality—an idea closely linked to information asymmetry—forms a second
central assumption of lean startup, as discussed by Blank and Eckhardt (2023: 6). Boundedly
rational models of search and decision-making essentially build on the idea of an information
asymmetry between the searching actor and the environment (Simon, 1956). Searching
actors cannot process or compute information omnisciently, and they therefore need to selec-
tively sample and satisfice. In the context of lean startup, this sampling and information
gathering is done by interacting with customers and by soliciting feedback on minimum
viable products.
Lean startup’s focus on bounded rationality is aptly captured by Leatherbee and Katila in
their work. They emphasize how “bounded rationality—finite information, finite minds, and
finite time—makes young firms imperfect decision-makers” (2020: 571). Essentially, start-
ups need to access information, advice, and feedback from customers to “mitigate” against
bounded rationality. The logic of mitigating against bounded rationality—by seeking exter-
nal advice and feedback (or “opening the aperture”)—has been discussed more broadly in
entrepreneurship, in contexts such as incubators and entrepreneurial strategy (e.g., Cohen,
Bingham, & Hallen, 2019; Miller, O’Mahony, & Cohen, 2024). Bounded rationality is also
the underlying assumption of the literature in entrepreneurship that highlights the role of
heuristics and information processing in uncertain environments (e.g., Artinger, Petersen,
Gigerenzer, & Weibler, 2015). Bounded rationality of course is a central concept not just in
entrepreneurship but also in organization economics, management, and strategy more broadly
(e.g., Puranam, Stieglitz, Osman, & Pillutla, 2015).
6 Journal of Management / Month XXXX
When applied to startups and entrepreneurs, however, the concept of bounded rationality—
particularly when operationalized as the one-sided information asymmetry between startups
and customers—comes with some unhelpful baggage, in terms of what is assumed about
human cognitive capacities and the organism-environment relationship. The focus on informa-
tion processing—and associated bounded rationality—places emphasis on the cognitive task of
seeing or “reading” the environment correctly (Chater et al., 2018). This makes entrepreneurial
judgment and decision-making into a computational or representational task where the relevant
data is “out there”—in the environment (for example, information held by customers)—and
needs to somehow be appropriately mirrored, sampled, or processed. Applied to lean startup,
the idea here is that entrepreneurs should focus on quickly learning from their environments—
customers and other stakeholders—and apply these lessons to their products and strategy.
However, from a theory-based perspective, entrepreneurs do not want to accurately mirror
their environments in the sense suggested by the idea of information processing.
Entrepreneurial decision-making necessarily aspires to be generative. Startups are essen-
tially trying to render something true that currently is untrue. Startups are seeking to create
and essentially present sources of value rather than represent their environments. This creates
a mismatch with the focus on bounded rationality and information processing. The idea of
bounded rationality is focused on a representation of environments (in whole or in part;
Chater et al., 2018), and it is usually applied to tasks with an objective answer, as is illus-
trated by popular experiments where subjects are asked to identify which of two cities has a
larger population (Gigerenzer & Goldstein, 1996; for a review, see Felin & Koenderink,
2022). Search tasks like this, however, hardly capture the essence of entrepreneurial deci-
sion-making, which is focused on forward-looking beliefs and novelty. In entrepreneurial
decision-making—unlike situations where bounded rationality is the relevant constraint—
there is no “lookup table” for the right answer. Yet, lean startup essentially treats customer
feedback as a form of lookup table for validated truth. In the uncertain environments which
characterize most startup activity, however, there is no such table—and even if there were,
the lookup table would only match current realities rather than the future ones that entrepre-
neurs are attempting to create.
Another problem with anchoring on bounded rationality in entrepreneurial decision-mak-
ing—specifically in terms of the focus on human bias and error—is readily evident in a par-
ticular comment made by Blank and Eckhardt. They argue that “with appropriate training
and discipline, agents can at best become boundedly rational decision agents” (Blank &
Eckhardt, 2023, emphasis added). Lean startup essentially positions itself as a method for
mitigating against human mistakes and errors by the entrepreneur (cf. Kahneman, 2011).
Error-avoidance in decision-making is, of course, important, but by focusing on error-avoid-
ance and bounded rationality—which provides the central logic for why lean startups should
quickly validate ideas, products, and value offerings with customers—one is likely to only
consider conservative options (including ones that can be more immediately validated),
rather than options that go beyond the incremental. The very mechanism of pushing for early
interaction with customers reinforces this conservatism. As a new lean startup tool to combat
this tendency, the Market Opportunity Navigator invites a “more distant or global search for
where to play” (Shepherd & Gruber, 2021: 971).
The emphasis of the theory-based view of startups is different from lean startup. This is
not to say that lean startup is completely wrong, but simply to point out that there are substan-
tive differences in what is prescribed to entrepreneurs. As we discuss below, the theory-based
Felin et al. / A Scientific Method for Startups 7
view argues that the most valuable forms of entrepreneurship emerge from contrarian beliefs
and theories involving what-if forms of causal logic—logic that requires entrepreneurs and
those evaluating what they propose, to essentially imagine an unseen state of the world, one
in which a currently unsolved problem is solved. In many cases, rapid customer feedback is
not the optimal place to start developing or testing such a theory. With such novel forms of
entrepreneurship, the adage that “you cannot observe the counterfactual” has particular
meaning. With these most valuable forms of entrepreneurship, you simply cannot observe the
relevant facts or evidence, or even elicit them from customers or other stakeholders.
Theory-Based Startup: Different Foundations and Model Assumptions
The theory-based view of startups begins with different foundations and underlying
assumptions from those of lean startup. The theory-based view of startups sees information,
knowledge, and rationality through a very different lens. It sees humans—including eco-
nomic actors like entrepreneurs—as generative agents rather than boundedly rational infor-
mation processors, a critical distinction (Felin, Koenderink, & Krueger, 2017; also see Chater
et al., 2018). Generative rationality means that rationality is not about asymmetric informa-
tion processing—that is, the processing of data from customers, other stakeholders, or the
environment—rather, rationality is highly proactive, shaped, and directed by the economic
actor itself. The overly abstract notion of an environment, as traditionally understood in man-
agement, is not a meaningful construct within the theory-based view, nor is the idea of infor-
mation asymmetry, as traditionally understood. Rather, the theory-based view emphasizes
the role that beliefs, hypotheses, and theories play in directing awareness and attention
toward highly specific, possible things in one’s surroundings (again, rather than the computa-
tion of information somehow received from the outside).
The central premise of the theory-based view is that humans do not strictly (or directly)
learn from the environment. Rather, observation and learning are necessarily theory-laden. It
is only when armed with a theory that something in the environment becomes salient and
meaningful. Humans learn as their theories and hypotheses direct their perception, attention,
and awareness toward specific things. Humans are endowed with a natural capacity for theo-
rizing and hypothesizing about their surroundings, and it is this activity that is behind the
emergence of novelty. Thus, entrepreneurs with different theories learn different things from
the same environment (or customers, for that matter). Environments and environmental
learning are therefore theory-specific. This mirrors the process of learning and knowledge
acquisition in human development (Gopnik, Meltzoff, & Kuhl, 1999; Spelke, Breinlinger,
Macomber, & Jacobson, 1992), evolutionary biology (Felin & Kauffman, 2023), as well as
science (Popper, 1969). Environments “teem” with possible things that an agent might focus
on and become aware of. But much of this remains latent, outside awareness (Felin &
Koenderink, 2022). Things—any type of data or information—only become salient or visible
in light of the hypotheses and theories that agents possess. This logic is aptly captured by
Einstein who argued that “whether you can observe a thing or not depends on the theory
which you use. It is the theory which decides what can be observed” (Polanyi, 1974: 64).
This is the central starting point of the theory-based view.
This emphasis on theory might at first glance be seen as broadly consistent with lean
startup. In fact, in the target article Blank and Eckhardt (2023) emphasize the importance of
theory. Citing some of our recent work (specifically Felin, Gambardella, Stern, & Zenger,
8 Journal of Management / Month XXXX
2020), Blank and Eckhardt argue that “an element that scholars often overlook is that the lean
startup is theory-driven and customer tested, as the theory of a potential business is devel-
oped before customer testing occurs” (2023: 7).
The emphasis on first developing a theory is welcomed by us.2 However, while the empha-
sis on theory is welcome, we suggest there is work that remains in composing this integra-
tion. While perhaps an accidental oversight, the word “theory” or “hypothesis”—or any
derivation of either word—is not even mentioned by Blank and Eckhardt in their table, which
lists 24 different “key concepts and constructs” for lean startup (2023: 9-10). The authors
certainly do discuss theories and theorizing in other parts of their article, but we think this
omission from the summary of lean startup may simply highlight how hard it is to reconcile
the idea of proactive theorizing with Blank and Eckhardt’s heavy emphasis on bounded ratio-
nality and a one-way information asymmetry between entrepreneurs and customers (and the
need for the former to learn from the latter). If information asymmetry between entrepreneurs
and customers is indeed the central problem—as they argue—then lean startup is logically
consistent in placing its primary emphasis on reducing that asymmetry by “[favoring] rapid
information gathering” (Blank & Eckhardt, 2023: 2).
The theory-based view does not make information asymmetry between entrepreneurs and
customers (or other stakeholders)—or even the cognitive boundedness of entrepreneurs—its
centerpiece. This is because relevant information is not necessarily held by customers (although
it can be). Rather, the theories that entrepreneurs develop can be seen as having informational
content themselves—thus, if anything, the asymmetry might in fact run in the other direction
where startups need to educate customers rather than the other way around. Importantly, how-
ever, information and associated insights are theory-dependent. Put differently, theories encap-
sulate knowledge. Theories guide entrepreneurs to look for and observe specific things. The
central assumption behind this approach is that all humans—including scientists and economic
actors like entrepreneurs—engage in a quasi- or proto-scientific activity of hypothesizing and
theorizing when engaging with their surroundings. Granted, just like in science, this process is
not without its errors (Zellweger & Zenger, 2023). Critically, however, entrepreneurial theories
might in fact go against existing data, information and even scientific (or customer) opinion and
lead to—as pointed out by the Einstein quote above—the identification of novel data and infor-
mation. Lean startup’s emphasis on “rapid information gathering” from customers (Blank &
Eckhardt, 2023: 2) might lead to the premature invalidation of the most valuable theories.
To further contrast lean and the theory-based view of startups, while lean startup focuses
on the asymmetry between entrepreneurs and customers in terms of information, the theory-
based view focuses instead on heterogeneity and asymmetry in beliefs. Contrarian, discrep-
ant or unique beliefs are the raw material of hypotheses and theories (Felin et al., 2021).
Startups can be seen as a unique point of view, conjecture, or hypothesis about the future.
Contrarian beliefs enable startups to see the world differently and to “hack” seemingly effi-
cient, strategic factor markets (cf. Barney, 1986; Felin, Kauffman, & Zenger, 2023).
Contrarian or divergent beliefs represent a point of view that by definition is not widely
shared—which is the source of their value—and precisely because of their uniqueness, those
holding such beliefs may find it hard to secure funding or other forms of intermediate valida-
tion (from customers or other stakeholders; Benner & Zenger, 2016).
One way that this idea of a startup-specific “point of view” manifests itself specifically is
in how it sees the process of search. To offer a contrast, the aforementioned Market
Opportunity Navigator—a tool that is part of lean startup—is a framework that enables
Felin et al. / A Scientific Method for Startups 9
startups to engage in “distant or global search.” The goal of distant or global search is to find
and “identify a portfolio of market opportunities,” assess their “relative attractiveness” and
to “choose the most promising option” (Shepherd & Gruber, 2021: 971-973; building on the
work of McGrath & Macmillan, 2000). This form of general or global search—delineating
options, comparing them, and choosing the best one—is certainly valuable and offers a plau-
sible tool for startups to identify valuable opportunities. However, the theory-based approach
to search is quite different. Search within the theory-based view is seen as a highly targeted
process, where contrarian beliefs and theories provide startups with a “search image” that
enables the recognition of value that is not evident to others (Felin et al., 2023). This might
sound like a mere semantic distinction, but the distinction is in fact quite fundamental.
Namely, with distant or global search there is a focus on information processing, that is, a
focus on listing and amassing promising options or opportunities, comparing them, and
choosing the best one (Shepherd & Gruber, 2021). The theory-based view, on the other hand,
emphasizes that the salience or recognition of a valuable option is theory-dependent in the
first place. Thus, the theory-based view does not focus on traditional forms of search (for
example, on landscapes or other types of environments) but sees the process as a far more
targeted one—a process of searching-for rather than searching-through. The distinction
between global (or local-distant) versus theory-specific search has not only been discussed in
the context of value creation (Felin et al., 2023), but it also has foundations in the cognitive
sciences and research in the field of perception (see Chater et al., 2018).
Another reason that asymmetric, heterogeneous beliefs are emphasized by the theory-
based view—over one-sided information asymmetry and bounded rationality—is because
valuable beliefs may initially appear delusional to others—not just to customers, but also to
other market actors or potential stakeholders, like investors. Beliefs that may turn out to be
true (eventually), may go against existing data, evidence, and understandings, as is readily
evident in the history of science. In fact, the more breakthrough or revolutionary the theory,
the more likely it is to go against existing data and therefore lack access to immediate valida-
tion. To illustrate, Galileo had a contrarian and (at the time) unorthodox belief that the Earth
orbited around the sun. The data, observations, and scientific consensus at the time were all
against Galileo’s theory (Wootton, 2010). Existing scientific observations, data, and facts
invalidated him. Therefore, he resorted to alternative sources of validation and evidence for
his contrarian belief—new sources of data and experimentation illuminated by the theory.
Eventually Galileo was proven correct. Startups similarly may possess contrarian beliefs and
be in pursuit of realities that presently lack validation, data, and evidence. Startups of course
are not providing validation or evidence for the laws of nature, but, rather, for the possible
value of future products, strategies, and sources of value. This requires startups and firms to
develop their own, underlying causal logic for “intervening” in the world and uniquely creat-
ing value (Felin & Zenger, 2017; cf. Heckman & Pinto, 2023; Pearl & Mackenzie, 2018).
If—as we suggest—customers (or even existing data) are not a reliable source of valida-
tion for a startup, then what is? The theory-based view recognizes any number of different
mechanisms and intermediate sources of experimentation and validation for the realization
of value. Notice that the mechanisms of validation advocated by lean startup—various forms
of customer interaction and feedback—are but one of many ways for a startup to be more
evidence-based and scientific. The choice of mechanism and scientific method depends on
what a startup seeks to do and the type of theory the startup hopes to realize (Wuebker,
Zenger, & Felin, 2023). The method of validation is theory-dependent. The theory-based
10 Journal of Management / Month XXXX
view sees the realization of a contrarian belief about value as a process of problem formula-
tion and problem solving. Intermediate “validation” (of a sort)—and the eventual realization
of a value offering—here comes from searching for and finding a solution to a problem
(Hsieh, Nickerson, & Zenger, 2007) or solutions to a structured set of subproblems (Felin
et al., 2021) that, if collectively solved, solve the larger problem. That is, a startup’s contrar-
ian or discrepant belief provides the impetus for carefully thinking about and formulating the
set of assumptions that must be true, or the set of subproblems that must be solved in order
to make a belief true. Once formulated, startups can then search for feasible solutions to these
subproblems, or seek out evidence to validate assumptions. Failure to validate an assumption
or solve a subproblem prompt early pivots—pivots that, when possible, preserve the remain-
der of the theory (Ehrig & Schmidt, 2022). Importantly, these early pivots can occur long
before customer feedback on a complete solution is possible.
To offer a practical example of this process, consider Steve Jobs’s contrarian belief of the
mid to late 1970s that computers would be a mass-market product—a belief that led Steve Jobs
and Apple to engage in a process of problem formulation and problem solving. The contrarian
belief was central for initiating the process of value creation. At this point in time, it was by no
means obvious that personal computers would become a mass-market product, as existing
applications of computing were focused on industrial and research settings or large-scale, spe-
cialized office applications. Even the first microcomputer, the Altair 8800, sold less than
10,000 units globally, which certainly did not suggest a basis for widespread consumer demand.
The data at the time seemed to suggest that Jobs’s belief in the possibility of personal computers
was wrong, if not delusional. Undeterred, Jobs’s contrarian belief led to the formulation of a
theory and the articulation of central subproblems that stood in the way of solving the broader
problem of rendering personal computers a mass consumer product. These subproblems
included that computer use at the time required highly specialized skills, that computers were
prohibitively expensive, that computer interfaces were hard if not impossible for lay people to
interact with, that computers lacked aesthetic appeal and that the extant applications had no
resonance with the average consumer. Once formulated, such problems enabled Jobs and Apple
to search in a very direct way, to be guided toward and recognize subproblem solutions that
enabled the development of a persuasive final product—the personal computer.3 We suspect
that an early effort to quickly roll out a clunky minimum viable product would merely have
frustrated consumers and producers, rather than provide productive feedback.
We recognize that Blank and Eckhardt discuss various opportunities to advance and
strengthen the lean startup approach—from its original conceptualizations—and specifically
highlight the need to include “improvements to theorizing” (2023: 15-16). They argue that
Osterwalder and Pigneur’s Business Model Canvas (BMC) “provides a way of building a
complete, falsifiable theory of a business that helps the entrepreneur avoid omitting an activity
essential to new business formation” (Blank & Eckhardt, 2023: 16). We concur that the BMC
indeed features many important issues that a startup should consider: key partners, activities,
resources, cost structure, value propositions, customer relationships, channels, revenue
streams, and customer segments. As we discuss next, however, we see hypotheses and theories
as something that originates from contrarian beliefs about how to solve problems, rather than
an exercise in mapping business models across categories like key partners or cost structure.
In our minds, the elements featured in BMC represent important downstream questions to
consider once a contrarian view and theory of value has been articulated. Specifically, a theory
enables the formulation of a problem and subproblems and guides the subsequent search for
Felin et al. / A Scientific Method for Startups 11
solutions to these problems. Many of the formulated problems can then in fact be addressed
by considering BMC-related elements like key partners or resources—but it is the overall
theory that enables the startup to recognize and see any of these possibilities (for example, in
terms of how/which key partners might help or what particular resources might be needed).
Beyond theory, Blank and Eckhardt also recognize the importance of the construct of a
problem and, particularly, what they call “problem testing.” However, problem testing,
according to Blank and Eckhardt, “starts with ethnographic interviews” of customers and
others that might have insights into various aspects of the BMC (2023: 7, emphasis added).
From the perspective of the theory-based view, problems are not “tested” per se (although
certainly some aspects might be). Rather, startups should first formulate a problem and rel-
evant subproblems, compose a theory, and then engage in a process of solving subproblems
by acquiring relevant resources, finding relevant technologies, or partnering with particular
stakeholder or actors. We discuss the logic behind this argument next, and link it to the practi-
cal tool—called Value Lab—that originates from the theory-based view.
Practical Framework and Examples: Lean Versus Theory-Based
Approach
Since both the theory-based view and lean startup are normative, it is important to delineate the
“steps” and advice that each approach respectively offers for startups and entrepreneurs. In many
ways, lean startup’s great virtue is that it has offered a set of practical tools for startups (Shepherd
& Gruber, 2021). This research is in line with extant work that has sought to specify different
types of “interventions,” treatments, and normative prescriptions that might enable startups and
companies to be more effective in their decision-making (e.g., Chatterji et al., 2019; Heshmati &
Csaszar, 2023; Kotha et al., 2023; Morris, Carlos, Kistruck, Lount, & Thomas, 2023). This work
is in stark contrast to much academic research in entrepreneurship, which focuses on empirical
description or theoretical abstraction, and therefore tends to be less accessible and useful to prac-
titioners. The theory-based approach shares the desire to offer a normative framework for inter-
vening in the world—even a pragmatic tool to help entrepreneurs be more effective (Felin et al.,
2021). The theory-based view asks startups to envision how they might counterfactually “inter-
vene” in the world—emphasizing causal analysis and causal inference (Frisch, 2013; Heckman &
Pinto, 2023; Pearl & Mackenzie, 2018)4—and further asks startups to develop their own, unique,
forward-looking “causal logic” for how to create value. The more general premise of the theory-
based view is that theories inherently are (or should be) practical or pragmatic. Any intervention
made by startups should be theory-guided. Thus, we strongly concur with Lewin who argued that
“there is nothing so practical as a good theory” (1943: 118).
Value Lab as Practical Tool: Causal Logic for Theory Building and Testing
Blank and Eckhardt (2023) discuss and highlight some of the key practical frameworks of
lean startup in their article, such as the Market Opportunity Navigator and Business Model
Canvas. To offer a contrast to these frameworks, we discuss below a practical framework
based on the theory-based view, called the Value Lab (see Figure 1, building on Felin et al.,
2021). Contrasting the prescriptions of lean startup and the theory-based view is useful as it
highlights what is practically emphasized and normatively suggested to entrepreneurs.
12 Journal of Management / Month XXXX
At a high level, Value Lab invites entrepreneurs and their collaborators to engage in three
conversations to develop their theories and underlying causal logic for value creation. The
first is a conversation about beliefs. Here entrepreneurs are pushed to articulate what they
believe—specifically, what they believe that is in some form distinct, different, or contrarian
from what others believe in relation to a space they seek to enter or a problem they seek to
solve. Beliefs are the essential “raw material” of hypotheses and theories. The reason valu-
able beliefs need to be distinct, contrarian, or discrepant is because this enables startups to
attend to potential sources of value that are not evident to others. Beliefs that are contrar-
ian—somehow unique and different—enable entrepreneurs to “hack” competitive factor
markets and create value (Barney, 1986; Felin et al., 2023). After all, value creation happens
in a competitive context where obvious sources of value are likely to be competed away, thus
placing a premium on unique and different ways of seeing the world.
As highlighted by Value Lab (see the first column), one way to elicit contrarian or hetero-
geneous beliefs is to first articulate the common beliefs or “orthodoxies” that others hold.
These are deeply held beliefs or unquestioned assumptions about such things as customer
taste or behavior, technology, or any number of other domains: supply chains or structure,
governance, the evolution of markets, or future societal trends. Articulating the commonly
held beliefs within an industry or market space can help entrepreneurs consider and sharpen
what is truly unique or different about what they believe. To offer some brief examples, Steve
Jobs famously held the unique belief that personal computers could become a mass market
Figure 1
Value Lab
Source. Adapted from Felin, Gambardella, and Zenger (2021: 70).
Felin et al. / A Scientific Method for Startups 13
product; Howard Schultz believed coffee could be sold at a substantial premium; and, in the
1970s, the management of Luxottica—now the world’s largest eyewear conglomerate—
believed eyewear could be transformed into a fashion item.
Of course, contrarian beliefs are just “talk” unless they lead to some form of practical
problem solving and action. Therefore, the second conversation (see the second column of
Figure 1) invites entrepreneurs to transform their unique beliefs into well framed problems
that need to be solved (cf. Baer, Dirks, & Nickerson, 2013). Put differently, problems can be
seen as the obstacles that stand in the way of realizing the contrarian or heterogeneous belief
of the startup. Value creation in the theory-based view is fundamentally about finding, for-
mulating, and solving problems (Nickerson & Zenger, 2004)—a process that is initiated by
contrarian or heterogeneous beliefs (Felin et al., 2021). This enables the firm to develop a
unique causal logic for how to create value. This involves formulating and solving problems
unseen by others or solving widely recognized problems in new and novel ways. Again, this
conversation involves more than restating a contrarian belief as a problem, but rather demands
articulating the central obstacles that stand in the way of making a contrarian belief true.
An alternative framing asks, what must be true—or made to be true—for the entrepreneur
to solve the central problem at hand. Often the factors that must be made true are a set of
subproblems that need to be solved. To illustrate, Airbnb’s initial contrarian belief was that
vacant rooms or apartments could be utilized as “hotel” accommodations—a belief that ini-
tially was seen as ludicrous (Felin & Zenger, 2017). The core problem for Airbnb was to
broker safe, easy, and reliable access to the idle capacity found in privately owned housing.
To solve this problem, the founders needed to address several key subproblems: develop an
efficient and accessible matching mechanism (matching those seeking accommodation with
those willing to offer it), facilitate secure payment, develop trust between complete strangers,
and develop an efficient and effective vehicle for onboarding and listing properties that accu-
rately signal the level of quality.
Notice that the process of assembling value is, more often than not, multistage, where dif-
ferent aspects of the theory are tested through different means. Put differently, composing
value demands that different actions are used to solve different subproblems which collec-
tively solve some larger problem. Thus, there is no one-size-fits-all approach to how a startup
might solve problems or validate a particular solution. Rather, what the startup needs to do is
theory-dependent (Wuebker et al., 2023). For example, Airbnb founders—as suggested by
lean startup—in fact created what some might term a minimum viable product by renting out
their own apartment (Gallagher, 2017).5 Other aspects of their theory were addressed through
different means, for example, by searching for subproblem solutions—like how to promote
trust among strangers—which they solved by incorporating an eBay-like rating system.
Thus, the eventual test of a theory, and the resulting product or service offering, emerges
from different experiments, tests, and solutions linked to individual subproblems with the
overall causal logic providing the glue that integrates across subproblems and assembles the
actions and resulting value. To offer other examples: for Jobs and Apple, the core problem of
generating a mass market personal computer required solving problems related to elegance,
ease of use, and reliability; for Luxottica, launching eyeglasses as fashion items required
developing a competence in fashion design, composing an ability to market eyeglasses in
different countries, and developing a capacity to access and control their retail distribution
(Camuffo, 2003). In all, in Column 2 of Value Lab (Figure 1), the aim is to structure the larger
14 Journal of Management / Month XXXX
problem by articulating the set of subproblems that the entrepreneur believes must be solved
to solve the larger problem. The problem and constellation of subproblems—and their over-
all causal structure—then becomes both the scaffolding around which a theory is built, and
the guidance for actions to test various components of the theory.
The third conversation invites entrepreneurs to transform this articulation of an overarching
problem with subsidiary subproblems into an expression of the firm’s theory of value. This
expression seeks to capture the overall causal reasoning and structure of how value will be cre-
ated—representing an exercise in causal logic (Pearl, 2009; Pearl & MacKenzie, 2018; also see
Heckman & Pinto, 2023). The startup essentially is asked to think about how they might practi-
cally “intervene” in the world to create the conditions that enable the creation of the contrarian
value that they foresee. Value Lab pushes the startup to create a logical causal diagram that goes
from startup-specific beliefs to associated problems (and subproblems) and associated actions
(including various forms of experimentation). As highlighted at the bottom of Column 2 in Figure
1, the overall logic of the theory can be summarized as a causal if-then statement that captures the
overarching problem and subproblems. To illustrate, this might take the following form in the
context of a company like Airbnb: “Airbnb believes that it can broker safe, reliable access to pri-
vate hotel capacity, if it can generate trust between strangers renting and offering private hotel
space, offer secure payment, and provide an effective vehicle for onboarding new properties while
accurately signaling the quality of properties.” Clearly this expression is not necessarily a version
crafted for public consumption, but it lays out what Airbnb believes it needs to make true to solve
the problems it seeks to resolve, and thereby compose novel value.
The first two columns of Value Lab—focused on contrarian beliefs and problem solving
(and establishing an underlying causal logic of the theory—offer the central foundations of
the theory-based view and thus provide a useful contrast with lean startup. Economic value
from the theory-based perspective originates from contrarian beliefs—and their pursuit along
with associated problem framing and solving—while lean startup primarily emphasizes the
rapid feedback from customers. In the third column of Value Lab, entrepreneurs are invited
to consider alternative actions to take—actions that test, experiment, and explore solutions to
the set of subproblems that must be resolved to solve the larger problem and generate the
value that the entrepreneur foresees. This may involve conversations with customers, but
also conversations with potential suppliers, resource providers, or other stakeholders
(Wuebker et al., 2023). This process also involves identifying resources or technologies that
need to be acquired for the hypothesized value to be created, where the theory guides startups
to see and recognize solutions to the problems that have been formulated. As we discuss
below, part of what the theory-based view of startups reveals is a way to accelerate learning
about a theory even before obtaining customer feedback, by effectively matching entrepre-
neurial actions—including experiments—to the theories entrepreneurs propose. In all, the
unique, startup-specific mix of actions (see Column 3 of Figure 1)—types of experiments,
identification and securing of resources, and search for solutions—is guided by the cognitive
work and theorizing that is done by addressing the previous two columns.
Discriminating Alignment Versus One-Size-Fits-All
The theory-based view of startups is a form of “meta”-theory that does not prescribe or
emphasize any one way of validation, experimentation, team building, or governance. Rather,
the theory-based view—and a tool like Value Lab—provides entrepreneurs with the
Felin et al. / A Scientific Method for Startups 15
scaffolding to come up with their own theory and startup-specific causal logic, and then to
align or “match” the right activities and practices to validate and compose value with that
theory (Wuebker et al., 2023). The theory-based view thus takes a page from transaction cost
economics (Williamson, 1998)6 and argues that entrepreneurial actions (including experi-
ments) should be contingent on the type of theory and value that an entrepreneur envisions
and explores. Our focus on contingency is broadly echoed by Zahra who argues that entre-
preneurship research has “overlooked the importance of the contextual variables that stimu-
late, shape, and define the entrepreneurial act” (2008: 243). In our case, these contextual
variables have to do with the heterogeneous beliefs and theories of startups and how different
forms of experimentation, testing, and acting enable their realization and the creation of
value.
By way of contrast, lean startup tends to push toward one-size-fits-all solutions, at the
expense of a more contingent perspective. Lean startup’s strong emphasis on customer vali-
dation—due to information asymmetry between startup and customer—and the associated
prescription of MVPs provides but one example (Blank & Eckhardt, 2023). Other examples
can be highlighted. For example, lean startup argues that the idea that startups should engage
in “stealth mode” has been made obsolete by the power of quick and transparent customer
interaction. As put by Blank, “the lean startup methodology makes [stealth mode] obsolete
because it holds that in most industries customer feedback matters more than secrecy and that
constant feedback yields better results than cadenced unveilings” (2013: 6). We disagree.
From a theory-based perspective, whether a startup should engage in secrecy or not—or any
other practice (including the development of an MVP)—is dependent on the nature of the
product or value offering that the startup is envisioning. Stealth and secrecy, in some situa-
tions, can be vitally important to the ultimate success of a startup, and therefore critical to
maintain as a theory is explored and realized (Wuebker et al., 2023; also see Bryan, Ryall, &
Schipper, 2022).
The prescriptions of the theory-based view—which experiments to conduct, or which
actions to take—are contingent. To illustrate the contingent actions prescribed by the theory-
based view, we might return to Value Lab (Figure 1). Specifically, the third column points
toward various types of actions that a firm might take to validate, experiment with, execute,
and realize various aspects of their theory of value. In other words, once a contrarian or dis-
crepant belief has been developed (see Column 1) a problem (with subproblems) identified,
and a theory composed, then startups can engage in a structured process of experimentation,
resource identification, or acquisition, focused on solving the problem and subproblems. For
example, a multitude of validation methods might be utilized in the realization of a given
theory of value. The fashion eye glass firm Luxottica engaged in various forms of preliminary
experimentation and actions—before interacting with customers—by observing the success
of specific market players (essentially vicariously learning) and acquiring them (Camuffo
et al., 2023a). The learning and activities of Luxottica were driven by the firm’s overall theory
about “fashionable” glasses and the downstream problems—many of them related to vertical
integration and different forms of licensing arrangements—which they needed to solve to cre-
ate value from that theory.
The difference between a lean versus theory-based approach to startups is that the latter
does not prescribe a primary method of validation, experimentation, or entrepreneurial
action. This contrasts with lean startup. Lean startup argues that “while other methods of
experimentation are not explicitly excluded, the primary methods of testing business theory
16 Journal of Management / Month XXXX
in the lean startup” are focused on three ways of interacting with customers, namely: the “use
of interviews with potential customers and experts, product testing with an MVP, and cus-
tomer surveys (Blank & Eckhardt, 2023: 18, emphasis added). The primary methods for
testing a theory from a theory-based perspective are more far-ranging and depend on the
nature of the theory itself, specifically the subproblems that need to be solved, or the prem-
ises that need to be made true. From a theory-based perspective there is no primary method
of experimentation, but rather a multitude of methods, including talking with potential sup-
pliers, analyzing relevant technology, thought experimentation, persuading various stake-
holders, searching for subproblem solutions (perhaps in other industries), and of course
eventually obtaining customer feedback. From a theory-based perspective, the method of
experimentation that is utilized depends on what the startup hopes to accomplish and the
nature of the subproblems the startup needs to solve.
The problem is that rapidly developed customer-oriented MVPs only cover—and provide
seeming validation for—a small and (often) incremental set of products that startups could
feasibly create. In terms of creating significant value, startup products and value offerings are
more likely to reflect theories involving multiplicative or combinatorial “packages” or bun-
dles of features and unresolved subproblems that cannot meaningfully be validated by cus-
tomers all at once upfront. The imagined end product often results from a “multi-step”
process and overall causal structure that involves formulating problems and subproblems,
then searching for solutions, engaging in experimentation, and acquiring the relevant solu-
tions and resources. Some technology solutions might be readily incorporated off-the-shelf,
while others require further development and integration. Some aspects of the product or
value offering might be validated by a sequence of experiments, for example through A/B
testing (aspects that lend themselves to comparing more desirable features: like what color a
product should be) or some other form of interaction with customers or other stakeholders.
An entrepreneur’s theory guides the orchestration of an overall process of value creation,
including the mix of activities and types of experiments that the startup should engage in.
Thus, with many startup products and value offerings—particularly ones that are truly dis-
ruptive and not merely incremental—there is no immediate MVP or prototype that can be
created to enable quick feedback or easy customer validation. In some cases, this might be
possible—particularly for a specific aspect of a startup’s overall theory—but, in many cases,
customers may in fact provide misleading signals rather than useful validation, particularly
for products that they simply cannot (yet) imagine using.
All that said, lean startup’s emphasis on the need for startups to “learn” is certainly echoed
from a theory-based perspective. However, the mechanisms of learning from a theory-based
view include a larger menu of options. Rather than jumping by default to quickly develop and
test an MVP (or a sequence of MVPs) and thereafter calibrating product market fit, here the
learning exercise—as pointed to in the last column of Value Lab—typically involves testing
assumptions, searching for subproblem solutions, and evaluating relevant technology or
resources that might enable solving critical subproblems. In this sense initial experimentation,
search, and learning is not about product market fit, but about determining whether a path to
substantiating the contrarian belief—and a path to solving the corresponding problems—is
feasible. Again, some aspects of the startup’s value offering might be tested with an MVP,
amongst a host of other forms of experimentation, solution search, and resource acquisition.
The central point here is that startups need to appropriately “match” their actions with the
type of theory they are pursuing, rather than relying on one-size-fits-all solutions. Here we
Felin et al. / A Scientific Method for Startups 17
might think of the entrepreneur as a Coasean (Coase, 1937) “entrepreneur-co-ordinator” who
judges what activities to pursue and how and with whom to pursue them. The theory-based
view similarly argues that these various activities and practices—whether to engage in them
or not, and how—depend on the type of theory the entrepreneur is pursuing. In some
instances, targeted feedback from (some) customers might indeed offer a valuable informa-
tional signal about a particular aspect of a prospective product or value offering. In other
instances, however, customers might merely lead a startup astray. This type of discriminant
nuance is essential. In all, the real power of generating a well formulated theory through a
tool like Value Lab lies in accelerating the pace at which an entrepreneur learns about a the-
ory’s value. A theory provides the roadmap for actions that accelerate learning. In this effort,
the theory-based view is not wedded to any particular action or form of experimentation—
like the need to focus on immediate customer validation. Of course, these approaches are not
ruled out, but their use depends on the nature of the theory a startup is pursuing.
Pivots, Structured Theories, and Revised Beliefs
As emphasized by Blank and Eckhardt (2023), lean startup highlights not just learning
from customers but also the need for startups to pivot. A pivot is broadly defined as a change
in the direction, strategy, product or value offering of a startup or firm (also see Kirtley &
O’Mahony, 2023, Burnell et al., 2023, and Leatherbee & Katila, 2020). Lean startup argues
that if early and frequent interactions with customers do not offer validation for a particular
product or strategy, then startups need to learn and change, that is, pivot toward something
else. The central idea is that faster failure leads to faster pivots—a quicker shift to a more
productive path. As put by Blank, startups “that ultimately succeed go quickly from failure to
failure, all while adapting, iterating on, and improving their initial ideas as they continually
learn from customers” (2013: 5, emphasis added).
From a theory-based perspective, learning, changing, and pivoting are also important;
however, the central question for lean startup is, how should a startup decide what to pivot
toward (or what aspect of the value offering to change, and how)? What does a startup learn
from the process of interacting with customers? Might a startup have learned the wrong
things from a particular customer interaction? Or, what should happen if a startup’s MVP
does not receive validation from customers? When responding with a pivot, should the focus
be on changes in the customer segment targeted, in the product attributes or mix, in the pric-
ing, distribution, or perhaps the entire business model? Without a theory, a startup is left to
the whims of customer feedback or aimless trial and error. From a theory-based perspective,
any feedback is informed by a startup-specific theory, thereby providing greater precision for
when and what to pivot toward.7
A virtue of the theory-based view of startups is that it provides greater precision around
what motivates (or should motivate) an entrepreneur’s decision to pivot, including an early
pivot before a minimum viable product can even be composed. In the theory-based view,
early pivots are motivated by an observation that a subproblem is unsolvable or a critical
assumption is false. By contrast, lean startup focuses on pivots stemming from failure to
achieve product market fit. While the theory-based view acknowledges this important source
of pivots, the need to change a theory may become salient long before obtaining product
market feedback, because, for example, the entrepreneur realizes that some of the subprob-
lems are unsolvable or some of the assumptions are unsupported. In an important sense, a
18 Journal of Management / Month XXXX
well composed theory permits even faster pivoting—pivoting in advance of obtaining market
feedback on a product offering or a full MVP. Well-developed theories also enable more
informed pivots—or, put differently, more informed revisions to beliefs. By exploring spe-
cific assumptions or seeking out solutions to critical subproblems, entrepreneurs examine the
causal links or assumptions of their theories (Ehrig & Schmidt, 2022). This form of testing
may occur in different ways for different aspects of a product or value offering. For example,
Steve Jobs explored possible solutions to the subproblem of ease-of-use and eventually
encountered the graphical user interface. Airbnb founders sought out solutions to elevating
trust between strangers or arranging for secure payments, and found a useful approach in
how eBay and other companies had dealt with similar problems. Luxottica explored different
solutions for getting control of the retail network. The identification of these solutions—that
is, what made these solutions salient to the entrepreneurs—was only possible given the initial
contrarian belief and the formulation of a core problem which motivated the search for these
solutions.
In a valuable extension of the theory-based view, Ehrig and Schmidt (2022) argue that
entrepreneurs should order their assumptions—those things that must be true or must be
made true— based on strength, and then test the weakest premise. When premises or assump-
tions are unsupported, entrepreneurs must revise their beliefs, ideally by replacing the unsup-
ported assumption with an alternative that preserves the remainder of the causal theory. Only
when an alternative cannot be found does the entrepreneur abandon a theory and take up a
major pivot.
Within the framework of Value Lab, we view premises and assumptions as frequently tak-
ing the form of subproblems to be solved, and thereby made true. For instance, Airbnb’s
theory is only as strong as its weakest premise—that is, its ability to solve its most intractable
subproblem. In other words, the theory falls apart if Airbnb cannot find a mechanism to build
trust among strangers who seek to offer or rent private hotel space. Airbnb’s theory hypoth-
esizes a path to solving this subproblem. But if the hypothesized approach fails, Airbnb must
either find an alternative way to resolve it (a sub-pivot of sorts) and thereby make this
assumption true, or Airbnb must revise the theory, finding a new premise or set of premises
that will support the overarching conjecture (Ehrig & Schmidt, 2022). As outlined in Value
Lab, experiments, data gathering, and resource search all focus on solving subproblems, in
support of validating a theory, or facilitating its revision.
Camuffo et al. (2023a) and Camuffo, Gambardella, and Pignataro (2023b) provide a
closely related framing. They argue that entrepreneurship necessarily involves making “low-
frequency high-impact” decisions—decisions that, because they are rare, cannot rely on past
data to guide choice. They argue that theory formation begins with problem framing that
includes defining relevant attributes and the relationships that connect them. For example,
Luxottica realized that it could move into fashion eyewear from its standard business of eye-
wear solutions for vision correction. The theory of the standard business was to focus on
lowering costs, which lowered prices, raised demand, and generated economies of scale—
thereby generating a virtuous cycle of low costs, low prices, and high demand. Since the
product was standard, relations with customers could be delegated to carefully managed
retail stores. However, the idea of transforming eyewear into a fashion item reflected a new
theory. From a potentially wide array of alternative framings about how to create this trans-
formation, Luxottica focused on initiating alliances with fashion brand companies. The
Felin et al. / A Scientific Method for Startups 19
theory was that Luxottica could leverage the competence and brand of these companies
rather than compose their own capability. The theory was that these fashion brands could
apply their craft to a new domain—eyewear—and create truly original styles. In turn, this
implied that Luxottica had to develop direct relations with customers, and this would thus
demand integrating forward into retail. Luxottica tested this theory by monitoring small com-
panies in the fashion glass business and by striking an early alliance with Armani. These
experiments corroborated that there was a potential demand and mass appeal for higher-end
eyewear that was fashionable, and that by building on the style and market of Armani, it
could generate demand for Luxottica’s new products.
Overall, the theory-based view provides a distinctly different approach to learning—one
less reliant on customer feedback and simple product market fit. The theory-based view is
informed by experiments that test assumptions and search for subproblem solutions. Through
this process, startups revise their beliefs as they learn—guided in the varied actions they take
to facilitate learning by a startup-specific theory that points toward testing assumptions,
searching for solutions to problems that have been formulated, or discovering critical
resources.
Corroborating Evidence and Empirical Research Opportunities
The real validation for any normative theory is whether it works. Specifically, does a par-
ticular “treatment”—the advice or set of steps suggested by the theory or approach—actually
enable startups to create more value, to engage in better pivots, and lead to better perfor-
mance outcomes? Thus, next we briefly report on the current and ongoing empirical findings
related to the theory-based view of startups, including one study that also directly compares
lean startup with the theory-based view.
In a randomized control trial (RCT), Camuffo et al. (2020) randomly allocated 116 Italian
startups to a treatment and a control group. Both groups underwent business-related training
(eight sessions, every other week). The treatment group was trained to think scientifically by
asking entrepreneurs to formulate theories and test them. (Note that this study followed the
broad contours suggested by Value Lab, although the study was done prior to the full articula-
tion of the framework.) By contrast, the control group was introduced to standard entrepre-
neurial tools and logic, such as external market analysis. This same design—with the same
treatment and control groups—was replicated with additional RCTs totaling 759 randomly
allocated startups (Camuffo, Gambardella, Messinese, Novelli, Paolucci, & Spina, 2024).
These initial RCTs produced three main findings. First, treated startups were more likely
to terminate the pursuit of their entrepreneurial idea and were more likely to terminate them
earlier. This termination result is intriguing. Treated entrepreneurs recognized earlier, and to
a greater extent, that their ideas were in fact not valuable. This saves entrepreneurs—as well
as investors and other stakeholders—precious resources and time. Anecdotal evidence from
the startups in the training program corroborates this conjecture. Treated entrepreneurs rec-
ognized, based on good logical reasoning, why their ideas were not worth pursuing, and they
recognized it earlier. Second, treated entrepreneurs pivoted once or twice, whereas entrepre-
neurs in the control condition did not pivot at all or pivoted many times. This pivoting result
is consistent with the idea that when entrepreneurs see that their idea does not work, they
know where to pivot, in line with the idea that theory-based entrepreneurs make more
20 Journal of Management / Month XXXX
informed revisions to their beliefs. Conversely, entrepreneurs in the control group were more
inclined not to change their idea, or to pivot rather “indefinitely,” in an aimless search for an
alternative path to creating value. Without an underlying logic—or theory—that explains
why their idea is not successful, they do not see how to remedy it by pivoting to a revised,
better theory. Third, and finally, treated entrepreneurs obtained larger revenues and per-
formed better, conditional on remaining active. This is consistent with the idea that a tighter
theoretical focus can support a superior ability to discard false positives, and that more
informed pivots improve performance results.
Further corroboration has come from the work of Novelli and Spina (2022). Their
study included both new firms as well as more established, small organizations (with less
than 10 employees) in a randomized control trial. Firms in the treatment group were
encouraged to develop a theory with hypotheses that solved a problem. The control group,
on the other hand, was simply exposed to generic strategy frameworks and testing tech-
niques. Treated firms grew more quickly (in terms of revenue) than the control group, but
the effect was more pronounced for more established small firms relative to newer start-
ups. Qualitative evidence suggested that the treated group better understood when some
of their beliefs were unsupported or that some of the problems (or subproblems) could not
be solved, and therefore necessitated a pivot. While not a direct comparison of normative
guidance from the theory-based view versus lean startup, the findings are nonetheless
consistent with the importance of firm-specific theories when exploring and realizing new
and contrarian ideas.
Finally, Agarwal et al. (2023) adopt a more elaborate research design that aims to explore
the impact of a theory-guided approach versus a purely evidence-based approach, more con-
sistent with lean startup. They studied 150 Tanzanian entrepreneurs randomly allocated to
two training programs (six sessions, every other week). In one training program entrepre-
neurs were trained to formulate theories about their business based on causal links (identify-
ing causes and effects) and test them via hypothesis development. In the other training
program entrepreneurs were trained to find evidence for hypotheses, focusing on creating a
minimum viable product and receiving feedback from customers. This study thus offers a
relatively direct test—though preliminary—of the theory-based approach versus lean startup.
The entrepreneurial firms treated with the theory-based approach attained significantly
higher performance metrics, including higher revenues and higher profits, compared to the
firms in the control condition which received the lean startup treatment (which was included
in the control condition). The RCT also found that when the theory-guided entrepreneurs
choose to pivot, they change more elements at the same time. That is, they adopt a more
holistic approach to the business reflecting a broader, theory-informed perspective of what
they need to do and test, and what they should aim at (and pivot toward). Entrepreneurs in the
purely evidence-based training only changed single elements.
Most of the empirical work within the domain of “entrepreneur as scientist” is relatively
recent. Some of the above RCTs offer early evidence that teaching entrepreneurs to be the-
ory- and science-based improves performance outcomes (above and beyond basic business
training) and leads to better performance as well as more informed experimentation and more
focused pivots (Camuffo et al., 2021). However, Lean startup has of course also received
empirical support from RCTs (Kotha et al., 2023). Our hope is that the varied approaches that
focus on introducing the scientific method to startups can be studied comparatively, side-by-
side, to understand the respective virtues and limitations of each approach.
Felin et al. / A Scientific Method for Startups 21
Various entrepreneurial frameworks—such as the theory-based view, lean startup, effectua-
tion, and discovery-creation—can each offer and put forward their respective treatments and
methods for comparison. Various RCTs and empirical studies have suggested different types of
treatments for startups, highlighting how interventions such as formal advice from peers (e.g.,
Chatterji et al., 2019) and specific types of business training (Kotha et al., 2023; also see
Santamaria, Abolfathi, & Mahmood, 2023) can improve decision making and startup perfor-
mance. While different forms of intervention are feasible, we argue that a theory-based approach
to these interventions—that is, training startups to develop their own theory of value—will yield
the best results. This of course is an empirical question, and thus further work is needed to cor-
roborate this claim. More generally, we hope that future work can design and run explicit “horse
races” between the varied proposed treatments and methods—like the theory-based view and lean
startup (among others)—to discover their relative virtues and comparative implications for startup
performance and value creation. Since intervention-oriented work (like RCTs) are a relatively
new method within the domain of entrepreneurship and strategy, these types of comparisons have
yet to be performed, although this certainly offers an important direction for future work.
In comparing different theories of startups and entrepreneurship, it is important to rec-
ognize the issue of contingency. That is, it might be that the value of different prescriptions
and normative interventions is a function of the types of settings, types of outcomes, and
types of startups that a given theory is focused on. Lean startup’s focus on customers cer-
tainly lends itself to value creation in settings where rapid learning from customers makes
sense; but, in other situations—for example, where products are more complex or require
substantial investment—customer feedback might not be as effective as other forms of
validation. Thus, we see a need to develop contingent arguments that outline different
theory types or forms of value creation, in order to explore which are best matched with
varied types of validation, experimentation, and forms of governance (Wuebker et al.,
2023). Importantly, comparative work like this can begin to establish the respective bound-
aries and contingencies of various approaches to entrepreneurship, delineating when and
why certain approaches work. This type of research would offer extremely valuable
insights and inform what is taught at universities, various training programs, accelerators,
and incubators across the world. Furthermore, it would enable scholars to establish the
boundary conditions of each approach, and enable the development of a more nuanced,
contingent approach to entrepreneurship.
Before concluding, we offer some conciliatory, integrative thoughts. While we have high-
lighted a number of differences between lean startup and the theory-based view, there is
certainly room for a heterogeneity of approaches when it comes to understanding something
as complex as startups, strategy, and value creation. After all, a theory, by definition, cannot
explain everything. Like maps, theories and models aim to provide focused representations
of complex phenomena, rather than fully mirroring reality. Each theory provides a map of
what it sees as important—simplifying and distilling key patterns rather than incorporating
every detail. Different camps and schools of thought—within the domain of entrepreneurship
and strategy—make different things salient, each offering a unique “lens” that focuses aware-
ness and attention on certain phenomena. This is why we think there is power in moving
toward a “contingent” approach with regard to a more scientific approach to startups, where
contingencies and boundaries of different tools and approaches are recognized and appropri-
ately utilized.
22 Journal of Management / Month XXXX
Conclusion
In this paper, we contrast the theory-based view with lean startup, in an effort to point
toward a “scientific method” for entrepreneurship. We laud lean startup for its normative
engagement with entrepreneurial practice and its call for a more scientific approach to startup
activity. The theory-based view shares this agenda. However, while both approaches argue
for a scientific approach to venture creation, they diverge in their underlying mechanisms
and practical guidelines. In this paper we question the strong emphasis that lean startup—as
outlined by Blank and Eckhardt (2023)—places on the information asymmetry between
entrepreneurs and customers, bounded rationality, and the associated emphasis on customer
validation (through MVPs and rapid, frequent feedback from customers). While customer
feedback can be important in some situations, we highlight how it is far from a panacea. By
way of contrast, the theory-based view emphasizes the role that contrarian or heterogeneous
beliefs and theories play in shaping startup-specific experimentation, resource acquisition,
and problem solving. We emphasize the need for discriminating alignment when it comes to
entrepreneurial action, where one-size-fits-all tools yield to a recognition of the importance
of contingently matching different activities, forms of experimentation, and practices with
what entrepreneurs seek to accomplish. Our hope is that further theoretical and empirical
work on the respective similarities and differences across different approaches to entrepre-
neurship will enable scholars to develop normative models that help startups improve their
decision-making and performance outcomes.
ORCID iD
Todd Zenger https://orcid.org/0000-0002-9830-4066
Notes
1. Even if large-scale customer feedback and data is secured or is somehow available, it is unclear how a
startup might (statistically or otherwise) aggregate all this information and use it for scientific validation. Should
startups utilize and focus on the modal, average, or some other form of aggregate customer response? For example,
if many customers say that a particular feature is needed, does this provide the evidence, informational signal, and
scientific validation needed to include that feature? It may or may not. It is easy to mistake frequency with valida-
tion and evidence. It could be that just one customer, amongst dozens or even hundreds, offers a much-needed
insight for the development of the product offering or a certain feature. But there would be no way to identify this
particular customer insight, as startups might naturally focus on more-frequently mentioned points of feedback. In
other words, some mechanism is needed to identify or recognize—amongst a vast set of possible responses—those
insights that might be most valuable. This is why it is critically important to correctly specify the right form of
experimentation and validation upfront.
2. We do not mean to imply that lean startup is “playing catch-up” to the theory-based view. Rather, the
emphasis of each approach simply has been on different issues—which provides the focus of this article. These two
literatures were developed roughly contemporaneously and independently. Early work on the theory-based view was
published in 2009 (Felin & Zenger, 2009), including links to the problem-solving perspective (Nickerson & Zenger,
2004). Ries’s influential and widely-used book Lean Startup was published in 2011. And Steve Blank of course did
important, earlier work on customer development and lean startup.
3. Of course, one of Jobs’s most famous subproblem solutions involved leveraging technology being devel-
oped at Xerox Parc. While the common narrative is that this was a rather serendipitous solution discovery, in truth
Apple engineers were well aware of many details of the technology being developed at Xerox, and Jobs’s visit to
Xerox Parc was preceded by Xerox being granted the right to purchase an equity position in Apple in exchange
Felin et al. / A Scientific Method for Startups 23
for revealing its technology (see https://web.stanford.edu/dept/SUL/sites/mac/parc.html#:~:text=Finally%2C%20
as%20several%20authors%20have,already%20going%20on%20at%20Apple).
4. We recognize that there are extant debates about the right econometric, statistical, and computational
tools for understanding causality (Heckman & Pinto, 2023; Pearl & Mackenzie, 2018). Our emphasis is on the need
for startups to develop their own, firm-specific and unique causal logic for how they imagine creating value—which,
in turn, can then guide their downstream choices for potential measurement, experimentation, and evidence-gather-
ing. We suspect that managerial practice will offer unique insights and tools to also address questions of causality
within the domain of economics and management science.
5. Thanks to one of our editors for pointing this out.
6. The idea of discriminating alignment is aptly captured by Williamson as follows: “transactions, which
differ in their attributes, are aligned with governance structures, which differ in their cost and competence, so to
effect a (mainly) transaction cost economizing result” (1998: 37). The central variable of discriminant alignment
within the theory-based view is focused on heterogeneous beliefs and theories. That is, the theory-based view of
startups starts with the premise of heterogeneity in beliefs or theories and the need to appropriately “match” (or
discriminately align) them with the right forms of experimentation, funding, governance structure, team building
and human capital, and so forth (Wuebker et al., 2023).
7. Precision pertains to the idea that actions have to do with whether particular solutions, experiments, or
resources in fact solve a formulated problem or not. Actions ultimately originate from beliefs which shape the formu-
lation of problems, and if the right solutions cannot be identified, then startups can update their beliefs accordingly.
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