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Theory-Based Decisions: Foundations and Introduction

December 2024
Strategy Science 9(4):297-310

DOI:10.1287/stsc.2024.intro.v9.n4

Authors:

Teppo Felin

University of Oxford

Alfonso Gambardella

Bocconi University

Todd R. Zenger

University of Utah

Theories are foundational for decision making. We point to a normative agenda that emphasizes the need for decision makers—including managers, firms, entrepreneurs, and startups—to develop their own theories. Our argument is that decision makers should be like scientists and use the scientific method—theories, causal reasoning, and experimentation—to problem solve and generate new data and value. In this paper and special issue introduction, we discuss the foundations of the theory-based view (TBV), particularly as it relates to forward-looking reasoning and decision making under uncertainty. Rather than handing prescriptive theories to others, the goal of the TBV is to provide the scaffolding for economic actors themselves to develop their own, idiosyncratic theories. Theories are built on heterogeneous beliefs and causal reasoning that enable the generation of unique paths to value. We discuss the implications of this argument for key theories within strategy and economics, along with highlighting research opportunities at the nexus of the TBV and topics such as learning, entrepreneurship, finance, ownership and leadership. We also introduce the special issue papers and discuss their contributions to the TBV. Funding: We appreciate ION Management Science Laboratory at Bocconi for funding the special issue conference and related activities. A. Gambardella also acknowledges support from the European Research Council (ERC) under the European Union’s Horizon 2020 Research and Innovation Programme [Grant 101021061].

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Theory-Based Decisions: Foundations and Introduction

Teppo Felin,

a,b,

* Alfonso Gambardella,

Todd Zenger

Department of Strategy and Entrepreneurship, Huntsman School of Business, Utah State University, Logan, Utah 84322;

Department of

Strategy, Innovation and Management, Saïd Business School, University of Oxford, Oxford OX1 1HP, United Kingdom;

Department of

Management and Technology & ION Management Science Laboratory, SDA Bocconi, Bocconi University, 20136 Milan, Italy;

Department of

Entrepreneurship and Strategy & ION Management Science Laboratory, David Eccles School of Business, University of Utah, Salt Lake City,

Utah 84122

*Corresponding author

Contact: teppo.felin@usu.edu, https://orcid.org/0000-0003-2044-0145 (TF); alfonso.gambardella@unibocconi.it,

https://orcid.org/0000-0002-8714-5813 (AG); todd.zenger@utah.edu, https://orcid.org/0000-0002-9830-4066 (TZ)

https://doi.org/10.1287/stsc.2024.intro.v9.n4

Abstract. Theories are foundational for decision making. We point to a normative agenda that

emphasizes the need for decision makers—including managers, ﬁrms, entrepreneurs, and

startups—to develop their own theories. Our argument is that decision makers should be like

scientists and use the scientiﬁc method—theories, causal reasoning, and experimentation—to

problem solve and generate new data and value. In this paper and special issue introduction,

we discuss the foundations of the theory-based view (TBV), particularly as it relates to forward-

looking reasoning and decision making under uncertainty. Rather than handing prescriptive

theories to others, the goal of the TBV is to provide the scaffolding for economic actors them-

selves to develop their own, idiosyncratic theories. Theories are built on heterogeneous beliefs

and causal reasoning that enable the generation of unique paths to value. We discuss the impli-

cations of this argument for key theories within strategy and economics, along with highlight-

ing research opportunities at the nexus of the TBV and topics such as learning,

entrepreneurship, ﬁnance, ownership and leadership. We also introduce the special issue

papers and discuss their contributions to the TBV.

Funding: We appreciate ION Management Science Laboratory at Bocconi for funding the special issue

conference and related activities. A. Gambardella also acknowledges support from the European

Research Council (ERC) under the European Union’s Horizon 2020 Research and Innovation Pro-

gramme [Grant 101021061].

Keywords:theory-based view •decision making •strategy •entrepreneurship •causal reasoning •theories •governance

Introduction

The theory-based view (TBV) begins with the premise

that unique beliefs and theories guide economic actors.

To develop and test their beliefs and theories, economic

actors—entrepreneurs, managers, innovators, startups,

and ﬁrms—should act like scientists (Felin and Zenger

2009, 2017; Camuffo et al. 2020). The TBV provides nor-

mative guidance about how to utilize the scientiﬁc

method, speciﬁcally emphasizing the roles of causal

reasoning, problem framing and solving, and experi-

mentation in the generation of economic value. Theories

are practical instruments that enable economic actors to

craft unique causal paths for value creation. Instead of

the traditional academic approach of prescribing deci-

sions, strategies, or universal theories to managers or

entrepreneurs, the TBV provides economic actors with

the scaffolding—tools and processes—they need to

develop their own theory of value, which then guides

their unique actions and decisions.

In this introduction to the Strategy Science special

issue, we ﬁrst offer an overview of the TBV. We speciﬁ-

cally discuss the unique and differentiating attributes of

the TBV, focusing on cognition, causal reasoning and its

normative focus on actor-speciﬁc theories. Thereafter, we

highlight the implications of the TBV for key theories

within the ﬁeld of strategy (like the resource-based view

and positioning school) and also discuss opportunities for

future research in domains such as learning, entrepreneur-

ship, ﬁnance, and governance and ownership. We then

introduce the special issue papers and point to the respec-

tive contributions that these papers make to the TBV.

TBV: An Overview

Theorizing is traditionally thought of as a cognitive

activity performed by scientists. But the TBV builds on

the idea that all human cognition—whether it involves

scientists, infants, entrepreneurs or executives—involves

theorizing and experimentation (Gopnik et al. 1999, Felin

and Zenger 2009).

The TBV sees theorizing as central for any decision

maker. Perhaps the most ambitious claim of the TBV is

that everything within the domain of economic decision

making is theory driven. Theories shape what economic

actors perceive and see, what they search for, how or

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STRATEGY SCIENCE

Vol. 9, No. 4, December 2024, pp. 297–310

ISSN 2333-2050 (print), ISSN 2333-2077 (online) https://pubsonline.informs.org/journal/stsc

whether they learn, what experiments they engage in,

and how they govern. Theories shape the engagement

of actors with the world, whether the theory is implicit

or explicit, shallow or profound, based on tight or weak

logic. Theories can of course be tested and experimen-

ted on to assess whether they are plausible. But the TBV

seeks to make theories and the theorizing of economic

actors more explicit, rational and productive, enabling

decision makers to compose better theories that are

more unique and pragmatically useful in guiding deci-

sion making.

A central premise of the TBV is that economic actors—

much like scientists—can be taught to be more effective

in composing theories and utilizing the scientiﬁc method

(Felin et al. 2024). Although humans have a natural

capacity for theorizing, this ability can be enhanced and

emphasized. Thus, the agenda of the TBV is normative.

Accordingly, theory-based treatments and interventions

can be compared with other treatments, and their efﬁ-

cacy tested in terms of the decisions and outcomes they

yield. Existing empirical work—largely with random-

ized controlled trials (RCTs)—provides strong support

for the theory-based approach (Camuffo et al. 2020),

including a number of large-scale replications (Agarwal

et al. 2023, Camuffo et al. 2024b, Coali et al. 2024, Novelli

and Spina 2024).

Given the importance of theories in guiding strategic

decision making, we turn now to an overview of the

TBV. We emphasize the unique aspects of theory-based

reasoning, and where appropriate draw links to related

areas and perspectives.

Cognition: From Information Processing to Belief

Heterogeneity

In terms of cognition, the TBV emphasizes heteroge-

neous beliefs—a sharp departure from traditional mod-

els of cognition in economics and strategy. Existing

models are grounded in information processing and

bounded rationality (Simon 1956, Kahneman 2003).

They emphasize human cognition as constrained by

computational limits. The central emphasis is on

how the mind’s capacity to process information is

ﬁnite, leading to biases and bounded decision making.

Bounded rationality has been a central pillar for large

swaths of the judgment and decision-making literature

across economics and management (for a review, see

Chater et al. (2018)). The core concern in this literature is

on how decision makers rationally handle information

asymmetries and mitigate cognitive biases by proces-

sing more information, thereby becoming more rational

within their local environments (Arrow 1986). The

emphasis is on the quantity of information and the abil-

ity of economic actors to appropriately process it.

This work assumes that differences in economic per-

formance arise from the ability to overcome cognitive

constraints and biases through enhanced information

processing. This literature is also intimately tied to artiﬁ-

cial intelligence (AI) (Simon 1990), with cognition viewed

as an exercise in increasing the amount and accuracy of in-

formation considered before decision making.

In contrast, the TBV shifts the focus from information

processing to the generative aspects of cognition, espe-

cially in situations where decisions are not simply about

the accurate processing of given information or data.

Rather than emphasize the cognitive limits of human

information processing, the TBV highlights the capacity

of economic actors to generate novel beliefs. Belief het-

erogeneity and belief asymmetry, essentially, provide

the raw material for new choice options. Although

belief asymmetries—where beliefs might be contrary to

existing data and evidence—are traditionally seen as

biases (Benabou and Tirole 2016, Pinker 2021), in the

TBV they are a central starting point for the generation

of value. That is, contrarian beliefs enable economic

actors to see value that others may not be able to see.

The TBV does not question that humans have cogni-

tive and computational limits when it comes to decision

making, but the perspective’s focus is elsewhere.

The focus of the TBV is the human capacity to

“endogenously” generate information, knowledge and

options that previously were not salient or obvious to

others. Here the idea of heterogeneous beliefs is funda-

mental. Asymmetric or (in some form) contrarian

beliefs are the cognitive raw material of theories and

novel, economically useful knowledge. From the per-

spective of the TBV, the very notion of “information” is

a black box that demands further speciﬁcation. What

becomes uniquely salient as relevant information is

driven by the ex ante beliefs of economic actors. That is,

beliefs condition the salience and value of information.

To illustrate this view of cognition, consider how it

reshapes the notion of “search” in economics and strat-

egy. Traditional models conceptualize search as initiated

by a process of representing an environment, either par-

tially or exhaustively (Simon 1955, Kauffman 1993,

Gavetti and Levinthal 2000). In these models, search is

largely about processing information to evaluate a prede-

ﬁned array of options, with scholars often urging a shift

from “local search” to more expansive “global search”

where untapped possibilities may be found. The focus is

further on the efﬁciency of information gathering, as

ﬁrms differ in how quickly and effectively they process

data about potential resources (Makadok and Barney

2001; also see Lippman and Rumelt (2003)). Also, search

is about reﬁning the ability to capture and represent the

environment accurately, assessing the value of resources

based on the information at hand.

In contrast, the TBV shifts the focus away from search

as information processing or representation of an environ-

ment. Instead, the TBV emphasizes the endogenous crea-

tion of salience for speciﬁc things in the environment,

including latent sources of value. The emphasis is on how

Felin, Gambardella, and Zenger: Theory-Based Decisions

actors generate the awareness for things to search for, which

are not obvious to others (Felin and Koenderink 2022).

Rather than exhaustively scan the environment for pre-

existing resources, the TBV argues that value itself is gen-

erated through heterogeneous, often contrarian beliefs

and theories that make certain resources or opportunities

salient to some but not others. The environment—or stra-

tegic choices or resources—does not objectively present

itself for evaluation; instead, what is salient in the environ-

ment is shaped and constructed by the heterogeneous

beliefs and theories of the economic actor. Rather than

emphasize bounded rationality, the TBV emphasizes

“generative rationality.” Search, in this sense, is not about

accurately representing or mirroring what is already there,

but about creating salience for something previously

unnoticed or undervalued. This endogenous view of

search, rooted in belief heterogeneity, reﬂects the TBV’s

focus on the generative capacity of cognition, where actors

create rather than discover value (Felin et al. 2023) through

the pragmatic deployment of the scientiﬁc method (Zell-

weger and Zenger 2023).

This generative feature of cognition is achieved as actors

not only think with data and experience (i.e., process infor-

mation) to predict a future state, but think past it to imag-

ine the future, and then causally reason how this future

state might logically be attained (Felin and Holweg 2024).

Admittedly, envisioning the future through causal logic,

rather than predicting it from past data or information

may be an infrequent activity. More frequently, ﬁrms may

simply update goals and set new benchmarks as linear

extensions to existing metrics. But solely updating bench-

marks is an admission to being inside an already existing

performance frontier. The TBV advocates a different path

of imaging a world that does not exist, composing a logical

path to creating it, built with assumptions, subproblems,

and premises, and then gathering data to test the validity

of that causal logic.

Given the TBV’s cognitive emphasis on belief

heterogeneity—rather than mere information processing—

there are useful connections between the TBV and Bayes-

ian reasoning (Zellweger and Zenger 2023, Agrawal et al.

2024, Camuffo et al. 2024a). At the heart of Bayesian

approaches is the idea that decision making under uncer-

tainty can be structured by subjective probabilities, with

“priors” representing the beliefs that decision makers

hold before encountering new evidence (Savage 1972).

These subjective priors align with the TBV’s emphasis on

the importance of heterogeneous beliefs, perhaps offer-

ing a way to formally model how theories and divergent

perspectives shape cognition and decision making. In

both approaches, an actors’ beliefs—rather than an

exhaustive processing of information—drive decisions,

highlighting how beliefs are updated as individuals

respond to new information or evidence.

Although Bayesianism provides a possible way to

formalize belief heterogeneity in the context of the TBV,

there are active conversations about how well it opera-

tionalizes this idea and whether modiﬁcations are

needed (Ehrig and Schmidt 2022, Camuffo et al. 2024a,

Ehrig et al. 2024). A concern is that standard Bayesian

models understate the generative aspects of belief for-

mation emphasized in the TBV, particularly in situa-

tions of deep uncertainty where relevant data are scarce

or nonexistent. Furthermore, the process by which the

relevant evidence is identiﬁed, generated and incorpo-

rated into belief revision is not explicitly discussed

within the Bayesian framework. The intuitive, broad

logic is that a signal consistent with a belief or focal

event (a “good” signal) updates positively the expected

probability of the event, whereas an inconsistent signal

(“bad” signal) updates it negatively. But Bayesian logic

is more restrictive because Bayes’ law has explicit rules

about the posterior distributions of a Bayesian prior

distribution. Bayesian posterior distributions, which

update an initial prior distribution, are simpliﬁed when

we use a conjugate prior—a prior distribution that

matches the form of the posterior distribution produced

by Bayes’ law, combining the probability distributions of

the signal and the event. What this means in practice is

that there are substantial updates and learning that extend

beyond formal Bayesianism (in highly uncertain or com-

plex environments). De facto, this amounts to saying that

not all positive or negative updates of a good or bad signal

are consistent with Bayes’ law, and we can update consis-

tently with the broad logic above even if the update does

not follow Bayes’ law. More importantly, Bayes’ law can-

not deal with unknown events, and thus demands non-

Bayesian logic to be incorporated.

Nevertheless, the TBV’s emphasis on forward-looking

beliefs certainly has some parallels with Bayesianism

and offers a plausible mechanism for formally modeling

aspects of this process, perhaps in amended form

(Ehrig and Foss 2022, Zellweger and Zenger 2022b).

In both frameworks, the diversity and heterogeneity

of forward-looking beliefs are fundamental starting

points for generating value in uncertain environments.

But a central emphasis from the perspective of the TBV

is placed on the causal reasoning and experimentation

that enables the generation of new evidence (Felin and

Holweg 2024).

From Data-Driven Decision Making to Forward-

Looking Causal Reasoning

Existing theories of decision making place a signiﬁcant

emphasis on rationality, speciﬁcally deﬁned as deci-

sions driven by existing data and evidence (for a review,

see Pinker (2021)). Thus, economists emphasize how

decision makers are frequently “inattentive” to the right

data or to sample sizes (Gabaix 2019) or how they

engage in biases in probabilistic reasoning (Benjamin

2019). Behavioral biases and ﬂaws in judgment and

information processing are said to pervade human

Felin, Gambardella, and Zenger: Theory-Based Decisions

cognition (Thaler 2016, Chater 2018, Kahneman et al. 2021).

For example, Benabou and Tirole (2016, pp. 142–148) dis-

cuss how humans engage in “information avoidance”

and are “resistant to many forms of evidence.”

Of course, when the right data are available, eco-

nomic actors should rationally update their beliefs. But

as suggested by our discussion above, the TBV is more

interested in decision making where data or evidence is

not yet available, or where existing evidence is con-

tested. The TBV is focused on asymmetric beliefs and

their role in triggering what might be called forward-

looking causal reasoning. From the perspective of the

TBV, when data and evidence for a belief or some hypo-

thetical future state are absent (or contested), economic

actors can engage in actions that generate the requisite

new data. In the TBV economic actors “intervene” in

their surroundings to generate new knowledge and evi-

dence, rather than passively “read” or scan their envir-

onments. Causal reasoning enables them to delineate a

path—a set of actions, steps or experiments—that help

them create the conditions for the realization of beliefs

that previously appeared unwarranted and lacking in

evidence.

The focus on causal reasoning in the TBV builds on and

extends Pearl’s existing work on causal inference (Pearl

and MacKenzie 2018; also see Heckman and Pinto (2024)),

particularly the emphasis on counterfactual reasoning and

interventions that go beyond merely analyzing existing

data. These causal models allow decision makers to under-

stand the relationship between variables by imagining

hypothetical interventions and observing their potential

effects. However, Pearl’s work largely emphasizes causal

reasoning on the part of scientists and researchers—using

data to understand past events. To illustrate, Pearl’s most

frequent example is about determining whether smoking

causes cancer, which highlights how causal reasoning can

be applied retrospectively to analyze data. TBV builds on

and extends this framework to forward-looking decision

making and causal reasoning by all decision makers, not

just scientists or researchers. Emphasis is placed on

forward-looking decision making by asking what should

be done—how might we “intervene”—to achieve desired

future outcomes. The TBV is not only concerned with

understanding what has already happened but also in

determining what could or needs to happen for a particu-

lar belief or outcome to be realized. In this forward-looking

approach, the emphasis shifts to exploring what interven-

tions, experiments, or conditions are necessary and sufﬁ-

cient to bring about some presently implausible sounding

future state.

To offer an example of how the TBV’s emphasis on

causal reasoning provides a unique lens on central ques-

tions in strategy, consider its implications for “re-

combination” as an explanation for technological novelty

and value creation (Schumpeter 1934, Weitzman 1998,

Fleming and Sorenson 2004, Bresnahan 2012, Koppl et al.

2023). The central premise of recombination is that new

technologies and innovations arise from novel combina-

tions of existing components. Although this idea has been

widely accepted, much of the literature lacks an explana-

tion for how or why speciﬁc combinations are selected

from an almost indeﬁnite set of possibilities. If countless

recombinations are possible—and searching through

them is costly—then what drives economic actors to

choose and pursue particular combinations over others?

The TBV offers a cognitive mechanism that explains

how certain components and combinations become

salient in the minds of economic actors. At the core of

this process is the identiﬁcation and formulation of a

problem, which directs actors’ attention to speciﬁc com-

ponents that are relevant for solving it. Rather than

searching through random combinations, economic

actors are far more focused. They use problem solving

and causal reasoning to identify which elements might

be pragmatically useful with the selected problem. If

existing solutions or components cannot be identiﬁed,

decision makers may also create or modify components

to develop solutions. Through active theorizing, actors

recognize underappreciated possibilities, leverage their

asymmetric beliefs to envision how interventions—

such as recombining or altering existing technologies—

could generate value. In this way, the TBV not only

explains how certain combinations come into focus but

also how individual components, even those requiring

modiﬁcation, become salient as part of a broader pro-

cess of problem solving and value creation.

Consider the distinction between data-driven decision

making and theory-guided causal reasoning in a context

like mergers and acquisitions. Data-driven strategists

might examine performance correlations with existing

events or cases and from these make a conjecture about

the attractiveness of acquisition targets. Firms might

assess whether to embark on an acquisition by looking at

the performance of cases that are as similar as possible to

theirs. Or an opportunity for a merger might be analyzed

on the basis of various ﬁnancial metrics. Variables like

“experience”—the number of times an activity has been

performed—traditionally also play an important role in

varied types of corporate transactions. But from the nor-

mative perspective of the TBV, any corporate action

should be guided by a theory and associated causal rea-

soning. Theory-less decisions lead to needlessly costly

mistakes and generic strategies, while theory-guided

causal reasoning enables economic actors to identify and

build unique, ﬁrm-speciﬁc sources of value.

Our point is not that some ﬁrms only observe correla-

tions and make predictions, whereas others theorize

and test. Most likely, ﬁrms will vary in the emphasis

they give to each and may even match their use to the

problems and opportunities they face. But our more

general point is that the more ﬁrms engage in theory

development and causal reasoning, the more likely they

Felin, Gambardella, and Zenger: Theory-Based Decisions

will be innovative, push the frontier, and enjoy competi-

tive advantages. The extent to which ﬁrms engage more

with one or the other then depends on their attitudes

toward risk and uncertainty, their time horizons, their

ability to theorize, and their awareness of the beneﬁts

and consequences of these processes for different cir-

cumstances. As noted, the aim of the TBV is to be nor-

mative, providing guidance as to how to deploy a

scientiﬁc, theory-guided approach to decision making.

What Is Strategy? One-Size-Fits-All vs. Idiosyn-

cratic Theories and Decisions

A central premise of the TBV is that there is no one-size-

ﬁts-all theory, tool, or set of practices for creating

economic value. Theories should be unique and idio-

syncratic, built from forward-looking causal logic to

enable the realization of future states. This need for

economic actors to develop their own unique theories

has interesting implications for the theories we as

academics produce. Theories of decision making and

strategy tend to—explicitly or implicitly—suggest one-

size-ﬁts-all normative prescriptions. For example, the

resource-based view says that ﬁrms should “look

inside” to ﬁnd valuable resources, as buying resources

is not plausible due to the efﬁciency of factor markets

(Barney 1986, 1995; Denrell et al. 2003; Maritan and

Peteraf 2011). The vast literature on open innovation

argues that openness is the key to value creation and

innovation (Chesbrough 2003, Laursen and Salter 2006).

In entrepreneurship, a tool like lean startup’s minimum

viable product is said to be critically important (Blank

and Eckhardt 2024).

The problem is that most academic theories in strat-

egy and entrepreneurship draw conclusions from retro-

spective data, estimated from a common model, to

empirically report average effects, essentially highlight-

ing strategies that worked in the past—on average (Lei-

blein et al. 2018). But strategy is a forward-looking

exercise that demands economic actors build their own

unique models about what might drive performance.

The problem is that estimates from one-size-ﬁts-all the-

ories simply provide a mirror on the past that necessar-

ily delimits the range of plausible approaches for

creating value. They leave little room for contrarian

approaches that go against existing theories and frame-

works. To illustrate, while openness was being lauded

as a “universal” strategy for creating value, Apple

maintained a closed ecosystem and achieved unprece-

dented success by focusing on integrated hardware and

software. Similarly, at a time when Porter’s Five Forces

identiﬁed industries like retail or airlines as wildly unat-

tractive (due to low proﬁtability and intense competi-

tion), companies like WalMart and Southwest Airlines

deﬁed these predictions. Tesla, in an era dominated by

outsourcing, pursues vertical integration, keeping pro-

duction and innovation in-house to maintain control

over its supply chain and accelerate breakthroughs in

electric vehicles. Contrarian strategies that run counter

to existing theories and instead rely on ﬁrms’ own idio-

syncratic theories and causal paths can lead to signiﬁ-

cant value creation.

The TBV is a one-size-ﬁts-all theory only in the meta-

theoretical sense that every ﬁrm needs to have their

own theory. But what that theory is—what it implies for

resources, positioning, the actions that need to be taken,

or the extent to which the ﬁrm should be open or closed,

or how it should govern itself—is highly idiosyncratic.

A ﬁrm’s theory offers unique guidance for downstream

decision making related to these issues, as well as a

wealth of other practical matters such as what assets to

secure or combine, what type(s) of experiments to run,

how to think about human capital, and the right forms

of organizational governance (Wuebker et al. 2023).

Overall, a ﬁrm’s strategic theory is essentially an experi-

ment in value creation.

Applying the TBV and Future Agenda

As a normative cognition- and action-oriented approach

to exploring any uncertain future, the TBV’s aspirational

reach and ambition is substantial. We highlight here a

range of topics core to the strategy and entrepreneurship

literature around that we believe the TBV has much to

contribute. For each core theory or topic, we highlight

valuable insights or alternative framings that the TBV

provides, and discuss important, unanswered questions.

This also represents an implicit future agenda, as signiﬁ-

cant work in each of these domains remains to be done.

We begin with a discussion of the TBV’s connection to

strategy’s two most predominant theories, before turn-

ing to the topics of learning, valuation, entrepreneurship,

and leadership.

Theories as the Origin of Resources

The underlying logic of the resource-based view rests on

the assumption that competitive advantage either stems

from (a) superior information about resource value that

allows resource procurement at a discount (Barney 1986)

or from (b) the unique possession of valuable resources

that are difﬁcult to imitate (Barney 1991). However,

because the ﬁeld has generally assumed that factor mar-

kets for resources are informationally efﬁcient—that is,

prices reﬂect all available information—securing dis-

counted resources has been seen as unlikely (Denrell et al.

2003). Therefore, the ﬁeld’s focus has largely been on

“looking inside” (Barney 1995) for unique resources that

are already possessed by an organization.

The TBV begins from an alternative premise, namely,

that “resources are an epiphenomenon of the theories

that animate them” (2017, p. 259). In this way, the TBV

solves a central puzzle related to resource-based argu-

ments in strategy. Namely, idiosyncratic theories offer a

Felin, Gambardella, and Zenger: Theory-Based Decisions

key mechanism for seeing value amongst the “a vast

reservoir of unpriced resources and resource

combinations” (Lippman and Rumelt 2003, p. 1085;

Felin et al. 2023). Resource-based arguments emphasize

search (which can be costly), whereas the emphasis in

the TBV is placed on generating awareness of unseen

states and potential problem solutions through theories

(Felin and Zenger 2017, Ehrig and Zenger 2024). Fur-

thermore, although the superior expectations that

enable discovering resources in factor markets may

reﬂect information differences (Makadok and Barney

2001), the TBV recognizes that actors with equivalent

information, but different theories can routinely identify

and fabricate valuable resources (Felin and Zenger

2017, Ehrig and Zenger 2024). The rational expectations

assumption that undergirds the RBV assumes that mar-

kets are efﬁcient, there are no $500 bills on the sidewalk.

Essentially, all actors use the same model to interpret

information; or as put by Thomas Sargent, “there is a

communism of models” (Evans and Honkapohja 2005,

p. 566). The TBV recognizes that divergent actors com-

pose divergent models through which they perceive,

process information, reason causally and evaluate

resources. These actor-speciﬁc models animate other-

wise inert resources and reveal previously unseen paths

to value creation and rents.

Notably, with the TBV, the unit of analysis shifts from

resources to a ﬁrm-speciﬁc theory and the problem it

seeks to solve. This focus elevates the importance of

classifying theories, as differences in theories can then

be linked to differences in sources of rents. Thus, Rin-

dova and Martins (2024) classify theories as analytic,

reconﬁgurative, and projective, whereas Wuebker et al.

(2023) classify theories based on the types of rents they

seek: arbitrage rents, recombinative or Schumpeterian

rents, and cospecialization or Williamsonian rents.

There remains valuable theoretical work to be done in

mapping theories to the formation or acquisition of

resources and rents. We see considerable opportunity

for further formal work linking concepts from the TBV

to the value-added literature stream (Brandenburger

and Stuart 1996, MacDonald and Ryall 2004, Bryan et al.

2022), such as the paper by Ehrig and Zenger (2024).

Theories expand the state spaces visible to managers

and entrepreneurs (Camuffo et al. 2024a) and funda-

mentally alter both the paths available for learning and

bargaining leverage that actors possess. We also see

additional opportunities to further classify theory types

or forms of cognition and map these to rents and

resources. An empirical agenda of equal importance

might directly explore the origins of resources—

whether they reﬂect serendipitous endowments or cog-

nitive creations.

Research might also apply the TBV to work on

resource fungibility and redeployment (Helfat and

Eisenhardt 2004, Sakhartov and Folta 2015, Levinthal

and Wu 2024). Although this research ties resource rede-

ployment to certain “intrinsic resource characteristics”

(Anand et al. 2016), the speciﬁc mechanisms behind how a

new use for a resource—or novel resource combination—

is made salient has not been studied. Theory-based cogni-

tion and reasoning provides a plausible mechanism for

explaining why and how some actors are able to “see”

novel uses for resources (even if these resources are

owned by others).

Theories as Causal Paths to Positions

The TBV also carries important implications for strat-

egy’s positioning perspective, as initially advanced by

Michael Porter (1985, 1991). In contrast to the RBV’s

common focus on internal resources, in this perspective

ﬁrms achieve competitive advantage through strategic

positioning, selecting positions based on an analysis of

industry factors that shape proﬁtability. From a norma-

tive perspective, the task is to select from among rather

generic strategy types that vary in focus from broad to

narrow and in their emphasis either from cost or differ-

entiation. The perspective also highlights the concept of

ﬁt among the choices and activities that support these

positions (Ghemawat and Levinthal 2008, Porter and

Siggelkow 2008).

Although critiques of this perspective focus on its

static nature and neglect of internal resources, the

theory-based critique is that it fails to articulate the

causal path to generating these positions or activity sys-

tems that yield competitive advantages. The focus of

existing work is on simply describing positions and the

sets of choices, activities, and assets that support

them. This approach effectively highlights what grants

Southwest or Walmart its advantage, perhaps noting

Southwest’s remarkable ﬁt among activity choices or

Walmart’s scale. And it provides a set of tools that help

describe these valuable states (or positions) already

achieved in an industry or illuminate those still avail-

able. But importantly, neither ﬁrm started from these

positions. What the emphasis on positions or activities

fails to provide is a mechanism or tool to guide the pro-

cess of achieving these positions.

Unique activities and strategic positions are an out-

come for the TBV. The TBV asks economic actors to

identify contrarian or asymmetric beliefs about the

future and to formulate the problems that stand in the

way of realizing these future states. It is the process of

reasoning and problem solving that enables economic

actors to delineate their unique causal path to valuable

positions and the relevant activities. But much work

remains to be done here to build the normative tools

that will help managers select targeted future states (as

distinct problems to solve), build the causal logic to

achieve them, and then test and update these theories,

ideally building sufﬁcient conﬁdence in their usefulness

to warrant their pursuit and funding. This suggests a

Felin, Gambardella, and Zenger: Theory-Based Decisions

robust research agenda linking the positioning school

and competitive strategy more broadly.

Learning with Theories

Learning is a central path to value creation for both

entrepreneurs and strategists, with simple experience,

repeated practice, failure, and various forms of experi-

mentation seen as the central mechanisms of learning

(Leatherbee and Katila 2020, Pillai et al. 2020). These

processes lead to accumulating knowledge, often

empirically captured as learning curves, where experi-

ence in a task reduces cost or improves productivity

across time. These “well-known learning mechanisms”

are also the central foundation of the literature on

dynamic capabilities (Eisenhardt and Martin 2000, p.

1105).

But “learning” is not so straightforward (Levinthal

and March 1993), as the plausible sources from which

one might learn are highly varied and extremely hetero-

geneous. This is particularly evident in entrepreneur-

ship. From whom or what should an entrepreneur or

startup learn from: other entrepreneurs, customers,

experts, experience, or “the environment”? For exam-

ple, lean startup argues the answer is customers and

therefore advises deploying minimum viable products

(MVPs), which enable “validated” learning (Shepherd

and Gruber 2021, Blank and Eckhardt 2024). Others

emphasize learning from the “advice from peers” or

“learning from mentors” (Chatterji et al. 2019, Cohen

et al. 2019a, b). But peers, mentors, other founders, and

investors all come with different advice. How should a

startup decide which advice is correct, which to ignore,

and which to learn from? For those deploying lean

startup, which customers should be listened to? The

problem is that using lean startup might lead decision

makers to learn trivial things—reaching conclusions

that are seemingly “validated” by customers but lead-

ing to incremental learning (Felin et al. 2024). In all,

among the cacophony of heterogeneous advice and

plausible sources of learning, economic actors need to

avoid learning the wrong things.

The TBV advocates a completely different approach

to learning. Learning is theory dependent. Theory-

driven learning is more about validating the underlying

causal path to value rather than gaining social validation

or learning from others’ advice. Social validation and

advice can of course be vitally important, though it

plays a different role within the TBV. But given that

future states are inherently difﬁcult to validate in the

ﬁrst place, the emphasis is on the causal reasoning itself.

Perhaps a counterintuitive and provocative claim of the

TBV is that while focal economic actors—like startups

and ﬁrms—can certainly learn from others, it might be

that others should just as readily learn from these eco-

nomic actors. The learning literature has been overly

focused on exogenous sources of learning (including

peers, others, experts, and the environment), whereas

learning might also be endogenous to the theories of

economic actors. If this is the case, then a central issue is

the ability of economic actors to “persuade” others

about the plausibility of their theory and causal path

(Ehrig and Schmidt 2022, Adner and Levinthal 2024).

Of course, the method the TBV advocates for eco-

nomic actors is simply the scientiﬁc method that we

advocate for and use ourselves. Theories guide the

experiments and evidence gathering that we take up

for validation. Rather than relying on simple trial and

error, theories deﬁne hypotheses and premises to test.

Through the scientiﬁc method, organizations test

hypotheses in practice and gather data and feedback

to reﬁne their understanding of what pragmatically

works. From this perspective, learning is an ongoing,

iterative process of developing and testing theories to

continuously improve decision making and organiza-

tional performance.

Many of the papers in the special issue directly con-

nect to learning, highlighting ways in which a theory-

based, scientiﬁc approach shapes and often accelerates

the process of learning (Adner and Levinthal 2024,

Camuffo et al. 2024a, Chavda et al. 2024, Ehrig and

Zenger 2024, Hannah and Ott 2024, Sorenson 2024, Val-

entine et al. 2024). We see vast opportunities for contin-

ued theoretical and empirical progress, as future work

either integrates theory-based learning with traditional

approaches to learning or provides important contrasts.

In addition, there is important work to do within the

TBV to better integrate Bayesian learning and experi-

mentation with concepts of belief revision (Ehrig and

Schmidt 2022). The RCT work that has already emerged

under the theory-based umbrella tests the comparative

usefulness of two approaches to learning in an entrepre-

neurial setting. But the implications for learning extend

far beyond entrepreneurship. Learning is central to per-

formance in innovation, in organizational design, in

strategy, and at all levels of an organization, and explor-

ing the nature and usefulness of theory-based learning

in these contexts remains a signiﬁcant opportunity.

Financing and Evaluating Theories

The process of valuing companies, especially early stage

ventures, remains a particularly challenging task.

Venture capitalists and private equity of course exist for

precisely this reason—they are in the business of evalu-

ating, valuing and investing in companies which

“otherwise have difﬁculty attracting ﬁnancing” (Gom-

pers and Lerner 2001, p. 145; Lerner and Leamon 2023).

Much of this literature remains relatively descriptive,

focusing on ﬁnancing aspects such as how venture capi-

talists structure contracts to manage uncertainty (Ewens

et al. 2022), cycles in venture capital investment (Jane-

way et al. 2021), and the critical role venture capital

plays in fostering innovation (Lerner and Nanda 2020).

Felin, Gambardella, and Zenger: Theory-Based Decisions

Other work has asked the important question of

whether valuations should prioritize the management

team (the “jockey”) or the business idea and execution

(the “horse”) in their decision making (Kaplan et al.

2009), generally concluding with an answer of both

(Gompers et al. 2020).

But, of course, evaluating the team is much easier

than evaluating the business idea itself. There is enor-

mous uncertainty involved in building a valuation

model that captures the potential prospects for and

payoffs of a new venture. Much of the uncertainty

surrounds the theory that undergirds the venture,

including the validity of its assumptions and the sound-

ness of the logic that connects them. Inherent to the val-

uation task is an information asymmetry problem

(Akerlof 1970). Founders seek to persuade investors of

the soundness of their theory, whereas investors seek to

evaluate it. Of course, as with any information asymme-

try problem, there is the potential for an unfavorable

“market for lemons” outcome to result. In this case, the

quality of a venture’s theory may be difﬁcult to commu-

nicate, particularly because high quality theories are

also unique and contrarian. Therefore, to elevate valua-

tion, founders may choose to diminish the uniqueness

of their theories, thereby lowering uncertainty and

enabling easier evaluation of their ventures (Litov et al.

2012, Benner and Zenger 2016). This bias toward easy

evaluation leads to more incremental value creation.

Thus, this information asymmetry not only affects

the ﬂow of capital but also inﬂuences the very strate-

gies ﬁrms are willing to pursue, potentially leading to

suboptimal outcomes for both the entrepreneur and

the VC.

In the TBV this asymmetry runs beyond pure infor-

mation. It is not merely that the founder and ﬁnancier

possess different information, but rather that they pos-

sess different beliefs (Felin et al. 2024). In the TBV, the

asymmetry arises not merely from differences in avail-

able data, but from fundamental differences in how

investors and entrepreneurs understand the venture’s

theory of value. For example, entrepreneurs may have

a contrarian belief about a causal path to some future

state and they seek funding to pursue relevant experi-

ments that test this causal path. The central valuation

challenge is not really about information asymmetry

or even whether these experiments will overcome it,

but rather awareness asymmetry regarding the under-

lying causal logic and strategic narrative. This places a

premium on the ability of entrepreneurs to persuade

funders that their causal path is plausible—a task that

becomes more challenging the more novel and com-

plex the underlying theory. We see enormous opportu-

nity to draw on the emerging TBV to build and

empirically test normative solutions to evaluating and

valuing theories in contexts such as entrepreneurship

and ﬁnance.

Entrepreneurs as Theorists

The TBV has made its deepest inroads into entrepre-

neurship (Felin and Zenger 2009, Camuffo et al. 2024b,

Coali et al. 2024). Early-stage entrepreneurs are a natu-

ral context for studying, analyzing and empirically test-

ing the TBV (Camuffo et al. 2020). Entrepreneurs, at

early stages, face rampant uncertainty. And the TBV is a

normative approach that asks entrepreneurs and start-

ups to utilize the scientiﬁc method to develop their own

theory and path to value creation (Felin et al. 2024).

Some critiques of the TBV argue its link to the scien-

tiﬁc method makes it poorly suited for understanding

the uncertain process of entrepreneurship. As the argu-

ment goes, entrepreneurs aim to create uncertain

futures, whereas scientists seek to explain the present or

uncover truths (Sergeeva et al. 2022). Similar logic is

often used to place the TBV and its application of the sci-

entiﬁc method (Zellweger and Zenger 2023) within the

discovery side of the creation versus discovery debate

(Alvarez et al. 2024). Although the creation-discovery

distinction is valuable, the pragmatist orientation of the

TBV and the scientiﬁc method ﬁnds it less central to its

agenda (see Zellweger and Zenger (2022a) for a more

complete discussion). As the pragmatist philosopher

Richard Rorty argues, scientists themselves “are better

thought of as solving puzzles than as gradually disclos-

ing the true nature of things” (2007, p. 77). The TBV

simply sees entrepreneurs, like scientists, as problem

ﬁnders and solvers (Nickerson and Zenger 2004, Felin

et al. 2021), who pragmatically “ﬁx” beliefs, which “if

they work out well …count as discoveries” (Williams

2009, p. xxviii). Economic actors acting as pragmatist

scientists simply have less need for this the made-

versus-found distinction (Zellweger and Zenger 2022a;

see also Dewey (1916)).

The TBV is most interested in the practical agenda of

enabling startups and economic actors to create value

through the development of unique theories and causal

paths. Randomized control trials—thus far, largely with

entrepreneurs and small ﬁrms—have offered unique

insights into this process. For instance, research shows

that entrepreneurs who adopt a theory-based, scientiﬁc

approach terminate underperforming ventures earlier,

thereby avoiding the unnecessary waste of resources.

They also perform better conditional on their survival

(Camuffo et al. 2020, 2024b). These ﬁrms are also more

likely to avoid “false positives”—bad ventures or

ideas—because the approach fosters skepticism and

doubt, leading economic actors to more carefully assess

their decisions and steer clear of poorly performing pro-

jects while still allowing them to exploit potentially

valuable new ideas (avoid false negatives). A recent

study by Coali et al. (2024) demonstrates that adopting

a scientiﬁc approach to decision making leads to stricter

project selection, with entrepreneurs terminating low-

potential projects earlier. Although this might suggest

Felin, Gambardella, and Zenger: Theory-Based Decisions

an increased likelihood of false negatives (discarding

potentially viable projects), the evidence shows that

these entrepreneurs are more accurate in identifying

which projects to terminate. Over the long term, the sci-

entiﬁc approach results in better economic outcomes, as

resources are reallocated to more promising ventures

that receive more external funding and survive longer.

A signiﬁcant research opportunity lies in comparing

how various treatments and interventions derived from

alternate entrepreneurship theories fare and compete

against each other (Felin et al. 2024). That is, we should

allow varied entrepreneurial approaches to put forward

their normative implications and treatments and then

compare their efﬁcacy. Some recent empirical work

within the TBV has started this process. For example,

Gambardella and Messinese (2024) study 308 early

stage entrepreneurs in Italy and show that theory-

driven entrepreneurs perform better on average than

entrepreneurs who follow a design-based approach

that puts actions before theories, although for more

innovative projects the ideal approach is a combination

of the two. Agarwal et al. (2023) run an RCT with Tanza-

nian entrepreneurs that compares a theory- and

evidence-based approach with an approach that focuses

only on the collection of evidence and ﬁnd that the com-

bination of theory- and evidence-based generates

higher performance. In all, we see a need to better under-

stand how and why theory-based approaches elevate

performance, as well as further theoretical and empirical

work comparing varied treatments and approaches. This

might include “horse races” between various treatments

as well as studies of their interrelation.

Ownership and Strategic Leadership

The TBV offers a unique perspective on ownership.

Ownership is not merely a governance solution, but

something that privileges one economic actor’s theory

over another. Consequently, setting aside the substan-

tial issues of effective persuasion and related politics,

competition for ownership is fundamentally a competi-

tion over theories. In this regard, the TBV relates to the

recent literature on ownership competence. Decisions

by the owner-leader are driven by a ﬁrm’s unique the-

ory about how to create value, with a ﬁrm-speciﬁc the-

ory providing overarching guidance about what to own

(matching competence), how to own (governance com-

petence), and when to own (timing competence) (Foss

et al. 2021). As this literature describes, ownership is not

just about aligning incentives, as traditional agency the-

ory suggests, but about exercising judgment and apply-

ing a theory of value creation to make decisions about

assets and their use, combination and modiﬁcation.

Of course, owner-leaders also compete based on their

capacity to learn through a scientiﬁc, theory-based

approach to decision making (Camuffo et al. 2023).

Concentrated ownership that enables the pursuit of

rather contrarian theories may be of particular impor-

tance in uncertain environments where opinions about

how to proceed are most varied. This dynamic may

explain why concentrated ownership often outper-

forms more dispersed ownership models, particularly

in environments where innovation and strategic ﬂexi-

bility are critical.

We see greater need for work that explores the

linkages between ownership, leadership and types of

theories. As noted, some theories demand tacit under-

standing and expertise that are particularly difﬁcult

to share without costly investments to persuade and

build understanding (both cognitive and in terms of

resources). These unique theories make decision dele-

gation particularly difﬁcult and elevate the need for

owners to also be strategists (Camuffo et al. 2023). These

owners may then recruit teams which share similar

values and understanding of the underlying theory—

an additional topic worth empirical inquiry.

Finally, there are interesting unexplored questions

around how to motivate and incentivize the exploration

of a theory. Incentives are effective when contracts are

anchored to easily observed inputs or outputs. But in

the case of exploring theories, correct inputs or outputs

may be difﬁcult to specify, in part because learning

that a theory does not work may be as valuable as learn-

ing that one does. Another testable hypothesis is that

recruiting teams that believe the owner’s theory may be

more important to performance than crafting the correct

incentives.

Special Issue Papers

The papers in this special issue highlight both the

expansiveness and the foundational nature of the TBV

as a general perspective on value creation. In brieﬂy

summarizing the papers, we divide them into two cate-

gories: (a) papers focused on the theory formation pro-

cesses and (b) papers focused on learning, competing,

and persuading with theories.

Theory Formation Processes

One stream of papers tackles the important question of

where theories come from and how they are developed.

These papers highlight the role of cognition, imagina-

tion and analogy, and introduce various theory devel-

opment practices. Two of these papers also frame a

timely, yet foundational debate about the role that AI

versus human cognition may play in generating theo-

ries that underlie valuable strategies.

Rindova and Martins (2024) argue that the process of

developing a theory starts with a problem and its repre-

sentation (Nickerson and Zenger 2004, Felin and Zenger

2017). They propose that the nature of the problem

determines the type of theory required. For instance,

Felin, Gambardella, and Zenger: Theory-Based Decisions

problems where current knowledge appears sufﬁcient

for a solution call for analytic theories based on deduc-

tive reasoning and hypothesis testing. By contrast, pro-

blems where current knowledge is deemed insufﬁcient

or inadequate demand constructive theories that deﬁne

“steps or movements of transformation” (Shackle 1972,

p. 51, as quoted by Rindova and Martins (2024)). Within

this constructive category, they further distinguish

between “reconﬁguration theories”—theories that seek

to reorganize or recombine existing knowledge—and

projective theories that propose entirely novel new

states of the world. The paper thus highlights three dis-

tinct paths to theory formation, each linked to speciﬁc

problem types.

Carroll and Sorensen (2024) begin with the simple

observation that while causal logic, as advocated by the

TBV, is the optimal method for composing theories of

value, developing causal logic “is a difﬁcult and foreign

way to think” and is not the way “most people naturally

discuss strategy” (page xxx). Although the TBV is nor-

mative, admittedly what it asks of managers, execu-

tives, and entrepreneurs is difﬁcult. Using different

forms of logic “requires disciplined systematic thinking

with detailed attention to unstated assumptions, logical

fallacies, and the like” (page xxx). They argue that such

an effort may “stymie rather than stimulate efforts to

discuss and debate strategy.” Carroll and Sorensen

highlight analogy as a tool that can ease theory develop-

ment and elevates its persuasive capacity. The paper

proposes methods to strengthen analogical reasoning in

strategy, including decomposing analogies into features

or premises, considering both positive and negative

analogies, evaluating both horizontal and vertical rela-

tionships in analogies, and assessing implicit back-

ground knowledge implicit in analogies.

Hannah and Ott (2024) begin with TBV’s fundamen-

tal premise that valuable theories are novel and contrar-

ian and formed from mere fragments of experience and

observation that are then paired with imaginative leaps

to form “novel, cohesive, causal understandings of the

world” (page xxx). But, as they note, prior empirical

work in the TBV has not focused on the practices associ-

ated with theory development and the formation of

strategy. Through extensive interviews and archival

data analysis, these authors provide a deep empirical

examination of nine new ventures engaged in practices

and unique processes of theory formation. They ﬁnd

that ﬁrms move from a spark of a new idea to a well-

developed “actionable theory,” by deploying a distinct

set of theorizing practices, and by generating data to

reﬁne their theories.

Csaszar et al. (2024) explore how AI—particularly large

language models—can impact the strategic decision-

making process within ﬁrms. The authors examine

whether AI could assist or even substitute human deci-

sion makers in generating and evaluating strategies,

presenting empirical evidence from two studies: one with

a leading accelerator program and another from a startup

competition. The results show that AI-generated business

plans and evaluations are comparable to those created by

human entrepreneurs and investors. Importantly, the

authors link their work to the TBV of strategy, considering

how AI might support or challenge core TBV principles,

which emphasize the role of unique, idiosyncratic theo-

ries in guiding strategic decisions. The paper suggests

that while AI could streamline strategy formation, there is

a risk that it might reinforce conventional approaches at

the expense of novelty. Overall, the authors propose a

framework connecting AI use in strategic decision mak-

ing to ﬁrm outcomes and explore how AI might reshape

traditional sources of competitive advantage in strategy,

potentially altering both the application and limits of the

TBV.

Felin and Holweg (2024) criticize the common analogy

between human cognition and computers, arguing that,

although AI relies on backward-looking, data-based

approaches that mirror past patterns, human cognition is

fundamentally different. Human cognition involves

theory-based causal reasoning that is forward-looking,

enabling humans to theorize and experiment in order to

generate new data and knowledge. The authors intro-

duce the idea of “data-belief asymmetries,” where beliefs

often outpace available data and evidence, motivating

individuals to engage in experimentation to create new

knowledge. AI—including related computational and

probabilistic models of cognition—lacks this ability. Felin

and Holweg link their work to the TBV, emphasizing that

in both strategy and cognition, theories guide individuals

and economic actors to intervene and experiment to enable

the generation of new data and novelty. Although AI

excels at prediction (deﬁned as the minimization of sur-

prise based on past data), it cannot replicate the forward-

looking, experimental approach that is essential to human

reasoning and the creation of novelty. As a result, Felin

and Holweg argue that the potential of AI, as currently

instantiated, to replace human decision making in uncer-

tain environments is fundamentally limited.

Learning, Competing, and Persuading

with Theories

A second stream of papers takes up questions related

to how strategic actors learn, compete, and gather

resources when armed with theories. This stream

explicitly links the consequences for action from posses-

sing a theory, as well as the consequences for theory

development from engaging in action. Several papers

explore the role that theories play in learning. The TBV

argues that the possession of a theory should accelerate

learning. Rather than restrict learning to learning by

doing, theories provide a structured and causal path to

learning—revealing assumptions to test (and poten-

tially revise), subproblems to solve, and necessary

Felin, Gambardella, and Zenger: Theory-Based Decisions

conditions to make true. More broadly, this stream also

tackles questions related to experimentation, data gath-

ering, persuasion, and resource access.

Sorenson (2024) explores how the internal structure

of theories—such as their size, complexity, and the con-

ﬁdence actors place in their assumptions—affects the

way individuals and organizations search for solutions

to problems. Rather than relying on random or trial-

and-error methods, the paper highlights how having a

theory can guide exploration and experimentation.

Importantly, the paper highlights that simpler, less con-

ﬁdent theories can lead to more ﬂexible and rapid learn-

ing, while more elaborate theories may restrict the

range of potential solutions but improve efﬁciency and

persistence in complex problem solving. These argu-

ments contribute to the TBV by deepening the under-

standing of how theories, beyond just their predictive

accuracy, shape decision making and strategic search,

aligning with TBV’s emphasis on actors developing

their own theories to guide value creation.

Chavda et al. (2024) explicitly compare how learning

processes differ for entrepreneurs who possess theories

from those who lack theories. They distinguish between

theory-based entrepreneurs or entrepreneurs who

hold abstract understandings of their ideas, and what

they term practice-based entrepreneurs—entrepreneurs

who experiment on particular strategies rather than on

underlying logic or ideas. They develop a formal model

that suggests that theory-based entrepreneurs update

their beliefs about the distribution of strategy outcomes

as they search, while practice-based entrepreneurs main-

tain ﬁxed beliefs about these distributions throughout

the search process. They ﬁnd that theory-based entrepre-

neurs, unlike practice-based entrepreneurs, may con-

tinue searching after ﬁnding a high-value strategy.

Further, theory-based entrepreneurs may revert to a pre-

viously discovered strategy, whereas practice-based

entrepreneurs always execute the last strategy found.

Overall, their model shows theory- and practice-based

entrepreneurs use different search behaviors that lead to

different subjective valuations of entrepreneurial ideas.

Their work bridges prior work on entrepreneurial search

theory and the TBV of strategy and contributes to under-

standing how entrepreneurial mindsets shape opportu-

nity discovery and exploitation.

Adner and Levinthal (2024) highlight two factors that

make the process of scientiﬁc learning with theories par-

ticularly complex. First, many strategic experiments are

simply nonrepeatable. The very act of experimenting

alters the context in which the next experiment will be

performed (Shelef et al. 2024). Second, many experi-

ments must be jointly taken up by multiple actors, who

must all be collectively persuaded that doing so is use-

ful. These two properties pose challenges for learning

and inference. Nonrepeatability of strategic actions

hampers generalizations and learning across contexts,

conditions, and different types of experience. Joint

experiments imply joint actions that require persuasion

and alignment of multiple stakeholders. Their paper

presents a simple two-by-two matrix of strategy con-

texts based on these dimensions and argues that as

ﬁrms grow, they tend to shift toward nonrepeatable

joint action contexts. Theories become more crucial for

prediction and persuasion in complex strategic con-

texts, but the endogenous nature of strategic actions

complicates learning and generalization, and theorizing

in strategy differs from scientiﬁc experimentation due

to these unique challenges.

Valentine et al. (2024) explore how adopting a more

formalized approach to theory formation and experi-

mentation shapes the type and nature of pivots that

occur in the process of entrepreneurial learning. The

study examines how formalization in cognitive pro-

cesses (theorization) and evidence evaluation (experi-

mentation) combine to shape entrepreneurial pivots.

Their paper uses both quantitative analysis of human-

coded and machine-learning coded measures, and qual-

itative case studies from a randomized control trial. A

theory-based treatment increased formalization in both

theorization and experimentation. Valentine et al. ﬁnd

that theorization and experimentation are strongly

complementary in generating focused, radical pivots.

The study contributes to research on theory-driven stra-

tegic decisions and provides practical implications for

entrepreneurs and policymakers. It also demonstrates

the use of AI-generated measures to complement

human coding in strategy research.

Camuffo et al (2024a) develop a framework for

theory-based strategic decision making under uncer-

tainty. The paper represents causal links among the

elements (attributes) of a problem as directed a-cyclical

graphs (DAG, or Bayesian networks). They show

that decision makers should experiment with more

“surprising” theories, as these experiments produce

greater learning. The framework also distinguishes

between experiments to test theories within a given

problem and experiments that test whether decision

makers have chosen the right problem. In so doing, the

paper tackles the type III error problem (model misspe-

ciﬁcation) and takes into account the non-Bayesian

aspect of updating with unknown state spaces. This is

equivalent to the call for starting the strategy formation

process by ﬁrst formulating problems rather than focus-

ing on solutions for given problems (Nickerson and

Zenger 2004, Rumelt 2012). The paper illustrates the

framework using examples from Luxottica and PayPal.

It contributes to the TBV by microfounding the concept

of theory and providing a normative protocol for theory

construction and selection.

Ehrig and Zenger (2024) explore how resource-poor

entrants can secure resources and rents from incum-

bents by competing with their theories and awareness.

Felin, Gambardella, and Zenger: Theory-Based Decisions

They develop a model that combines the TBV with

value capture theory, particularly as extended by Bryan

et al. (2022). Their model distinguishes between three

types of rents that economic actors can generate: aware-

ness rents, conﬁdence rents, and resource rents. Aware-

ness rents arise when entrants have superior awareness

of future states due to novel theories; conﬁdence rents

can occur when actors share awareness but differ in con-

ﬁdence about theories’ probabilities; and resource rents

reﬂect traditional value added from unique and valu-

able resources. The paper employs the Tesla-Daimler

partnership as an extended example to illustrate these

concepts and shows how partial theory revelation can

allow entrants to secure resources while maintaining an

advantage. Overall, the paper challenges assumptions

of factor market efﬁciency in the strategy literature and

calls for rethinking rationality and disclosure in strate-

gic factor markets given competing theories and levels

of awareness.

Conclusion

The TBV offers a framework for understanding how eco-

nomic actors can navigate decision making under uncer-

tainty by developing their own unique and idiosyncratic

theories. By embracing forward-looking causal reason-

ing, experimentation, and problem solving, economic

actors are encouraged to craft causal paths to value

creation. This approach challenges traditional models

and theories—which often are prescribed to decision

makers—by emphasizing the cognitive role of economic

actors themselves in building their own theories. The

papers in this special issue further enrich our under-

standing of the TBV, offering new insights and research

opportunities across key domains like strategy, entrepre-

neurship, and governance.

Endnote

This logic is also captured by John Dewey who saw “science as a

practical art” and further argued that “the entities of science are not

only from the scientist” and that “individuals in every branch of

human endeavor should be experimentalists” (Dewey 1916, pp.

413, 438–442). For the evolutionary roots of human theorizing and

“proto”-scientific reasoning, see Felin and Kauffman (2023, pp.

1382–1387).

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Article

Nov 2024

This paper studies strategic decisions under uncertainty for which past data are not available. It provides microfoundations of the theory-based view of the firm by showing that the strategic problem starts with the selection of theories rather than choosing actions and that theories are selected through experiments. The value of experimenting with theories increases with the number of theories and with their uncertainty. Moreover, uncertainty makes theories superadditive—that is, experimenting with a more uncertain theory increases the benefits of experimenting with other more uncertain theories. The paper also shows that decision makers should experiment with more “surprising” theories because in this case experiments are more informative and enable more learning. A leading example helps to illustrate our concepts throughout the paper. Funding: A. Camuffo and A. Gambardella acknowledge support from the European Research Council (ERC) under the European Union’s Horizon 2020 Research and Innovation Programme [Grant 101021061].

Theory-Based Entrepreneurial Search

Article

Oct 2024

How should Theory-based entrepreneurs search for strategies to implement their ideas? The theory-based view of strategy posits that decision makers hold key conjectures about their path to success and use theory to understand and test beliefs underlying those conjectures. This causal framework also has implications for entrepreneurial search: the process by which entrepreneurs uncover strategies to implement their ideas. In this paper, we develop a Bayesian model where entrepreneurs update their beliefs as they conduct entrepreneurial search. We find several optimal behaviors for Theory-based entrepreneurs such as reverting to a previous strategy after finding a relatively poor strategy and continuing to search after finding a relatively good strategy, which are missing when entrepreneurs lack such a theory-based approach. As our theoretical predictions align with examples of successful entrepreneurs, our findings both provide a method to empirically identify Theory-based entrepreneurs and demonstrate the usefulness of applying the theory-based view to entrepreneurial behavior more generally.

Theory-Based Decisions: Foundations and Introduction

Abstract

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