Organizational Neuroscience

Abstract

Organizational neuroscience—a novel scholarly domain using neuroscience to inform
management and organizational research, and vice versa—is flourishing. Still missing,
however, is a comprehensive coverage of organizational neuroscience as a self-standing
scientific field. A foundational account of the potential that neuroscience holds to advance
management and organizational research is currently a gap. The gap can be addressed with a review of the main methods, systematizing the existing scholarly literature in the field
including entrepreneurship, strategic management, and organizational behavior, among
others.

Full text

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Organizational Neuroscience
Sebastiano Massaro, Surrey Business School, University of SurreyandDorotea Baljević,
University of Technology Sydney
https://doi.org/10.1093/acrefore/9780190224851.013.324
Published online: 28 January 2022
Summary
Organizational neuroscience—a novel scholarly domain using neuroscience to inform
management and organizational research, and vice versa—is flourishing. Still missing,
however, is a comprehensive coverage of organizational neuroscience as a self-standing
scientific field. A foundational account of the potential that neuroscience holds to advance
management and organizational research is currently a gap. The gap can be addressed with a
review of the main methods, systematizing the existing scholarly literature in the field
including entrepreneurship, strategic management, and organizational behavior, among
others.
Keywords: neuroscience, emotions, cognition, entrepreneurship, organizational behavior, strategic
management, methods
Subjects: Business Policy and Strategy, Entrepreneurship, Ethics, Organizational Behavior, Research
Methods
Introduction
In the early 21st century, neuroscience gained traction in the management and organizational
disciplines, leading to the emergence of organizational neuroscience. Together with a growing
number of scientific articles, academic journals’ dedicated Special Issues, and conference
workshops, the epitome of this scholarly momentum is the creation of the Organizational
Neuroscience Interest Group <http://www.aom.org/neu> (NEU) within the Academy of
Management (AOM), the leading professional body of management scholars and practitioners
(Beugré et al., 2019).
Different views have been offered on what constitutes organizational neuroscience thus far.
Becker and colleagues (2011) presented organizational neuroscience as a “paradigm or
interpretive framework,” (Becker et al., 2011, p. 937) and as a “deliberate and judicious
approach to spanning the divide between neuroscience and organizational science” (Becker &
Cropanzano, 2010, p. 1055). Conversely, Lee et al. (2012, p. 804) chose the term
“organizational cognitive neuroscience” to capture “the cognitive neuroscientific study of
organizational behaviour.” Aiming to settle the discrepancy between these views and provide
Sebastiano Massaro, Surrey Business School, University of SurreyandDorotea Baljević,
University of Technology Sydney

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a more complete conceptualization, organizational neuroscience is here defined as the
scientific field that uses neuroscience insights, methods, and theory to inform and advance
management and organizational research, and vice versa.
This definition is closely aligned with NEU’s institutional vision and mission, and, importantly,
it puts forward organizational neuroscience as a self-standing discipline alongside more
established scholarly areas in both neuroscience and management. Indeed, this notation seeks
to offer a “big umbrella” approach to the use of neuroscience in management, while
respecting idiosyncrasies and identities of each departing domain. This definition allows
convergence under the same arena of the various features of social, cognitive, affective, and
decision neuroscience, together with possible declinations of “neuroentrepreneurship,”
“neurostrategy,” and the like, beyond organizational behavior alone. This approach provides a
pragmatic, parsimonious, and shared framework to increase inclusion of organizational
neuroscience among the management scholarly community at large, as well as providing a
common ground to foster cross-collaboration among different domains in neuroscience,
management, and organization studies.
Despite an overall enthusiasm for organizational neuroscience, the field has not been immune
to skepticism and critiques. Some early works have provided critical analyses on possible
theoretical and practical issues surrounding this field (e.g., Lindebaum, 2015). Yet, as the field
matures, it can learn how similar challenges were addressed by cognate disciplines—the likes
of neuromarketing and neuroeconomics. The intent here is to pacify these worries while
avoiding either extreme, “neuro-hopes” or “neuro-hypes” (Ashkanasy et al., 2014; Jack et al.,
2019). To achieve this goal, it is paramount to begin the present coverage of organizational
neuroscience by answering the question: Why does management research need neuroscience?
Notwithstanding potentially epistemological chasms that might strike some readers when
facing this question, management and neuroscience have more in common than what might
appear at a first look. Over their histories, despite using different theoretical, methodological,
contextual, and analytical tools, both disciplines have advanced toward identifying
architectures, levels, and components of human psychology and behavior and toward
describing their organization and interrelations with the environment. In other words, both
fields are allies in the common quests of natural and social sciences: understanding behavior.
Thus, neuroscience can complement management research with information on the
functioning of the nervous system as a whole—composed of the central (i.e., brain and spinal
cord) and peripheral (and, in particular, autonomic) systems—and its connections with
psychological, social, cultural, and organizational niches. Indeed, organizational neuroscience
does not provide a reductionist approach whereby organizational constructs can eventually be
replaced by mere descriptions of neurobiological processes. Instead, it aims to integrate
management research with a mechanistic, physiologically informed understanding of mental
processes and behaviors, as they take place within organizational life or are relevant to it. In
other words, armed with neuroscience insights and methods, management researchers can
seek to generate more comprehensive theoretical and practical frameworks that build on
objective physiological data and computations. As will be discussed, the type of information
that neuroscience and its methods provide is indeed that of quantifiable, continuous data and

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models related to physiological substrates underlining certain mental processes and
behaviors. These data are less prone to biases because they are commonly acquired beyond
participants’ awareness of the target process and/or construct investigated (Massaro et al.,
2020).
This article aims to leverage this understanding while catering to the needs of upholding the
scientific standing of both mainstream neuroscience and management. Thus, it aims to
educate readers on the benefits and limitations of organizational neuroscience while
mobilizing neuroscience knowledge into actionable avenues for advancing management
theory and practice. The encyclopedic flavor of this work assists in providing such a blueprint,
one that will offer not only foundational knowledge to novice researchers but also an updated
compendium to more seasoned scholars.
In this article, the current, core methodology pertaining to organizational neuroscience is
described, with particular emphasis on brain imaging methods. A discussion of existing
structures to organize such methods and highlight core considerations, benefits, and
limitations of the related techniques follows. Next, the state-of-the-art knowledge in
organizational neuroscience is reviewed. This effort substantially differs from existing reviews
of organizational neuroscience (cf., Waldman et al., 2017). Moreover, the intention here is not
to repurpose knowledge exposed in neuroscience publications into a sort of conceptual
management narrative. Instead, this article specifically focuses on reviewing neuroscience-
related research published in acknowledged management and organizational scholarly
journals. As such, the retrieved body of knowledge is systematized under traditional
disciplinary domains—linked to strategic management, organizational behavior,
entrepreneurship, and so forth—while analyzing key findings and considerations toward
further enriching the potential of organizational neuroscience. In closing, avenues for future
research and recommendations are suggested. Altogether this article aims to contribute to
the growth of organizational neuroscience by offering a comprehensive outline of this novel,
interdisciplinary scholarly field.
The Methods
The rise of organizational neuroscience is certainly corroborated by the advancements in
neuroscience techniques that occurred in the late 20th century. Brain imaging methods in
particular have comfortably acquired a secure reputation to contribute to organizational
research (Murray & Antonakis, 2019). At the same time, these techniques require specialized
expertise, often involving a steep learning curve for business school scholars (Waldman,
2013).
Given that detailed coverage of the multitude of neuroscience techniques that can assist
management and organizational research would require a textbook length, here, the aim is to
provide broad introductory coverage, while directing readers to more nuanced references
whenever appropriate (see Table 1). Thus, most of the methods are presented under the
paradigm of functional neuroscience and neuroimaging, the latter referring to such

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techniques able to “visualize the functionality” of the nervous system (Massaro, 2016).
Readers are also introduced to the potential of computational methods—while not yet used in
management research, theoretical and computational neuroscience offer important benefits
for the administrative sciences as a whole (Churchland & Sejnowski, 1992). For example,
consider “neural networks” (Dayan & Abbott, 2003), computing systems inspired by biological
neural networks of the brain, which are a mainstay behind many machine learning
implementations (Abraham, 2002).
Conversely, methods on nonhumans (e.g., primates or other research animals), requiring
invasive procedures (e.g., intracranial recording, positron emission tomography or PET), and
benchwork techniques (e.g., DNA extractions) are not discussed, given their complex
ramifications and limited application among organizational scholars (Massaro, 2016). As such,
this article does not describe methods related to neuropharmacology and neurogenetics, two
areas generally focusing on the role of neuropeptides (e.g., hormones) and genes in the
formation and functioning of the nervous system and their associations to psychology and
behavior, respectively. The methods in these areas vary widely, can span from molecular
biology (e.g., Polymerase Chain Reaction [PCR]) to electrophysiology techniques (e.g., patch
clamp), and often require raw data acquisition involving anything from blood work to
administration of exogenous substances to research participants. While the needed
benchwork to extract data appears to be impractical for business scholars, an increasing
number of public data sets containing information on genomics and neural biomarkers of
large samples of populations are now available to scholars (e.g., UK Biobank), reducing the
barrier to entry. Similarly, salivary kits to collect participants’ samples for genetic or hormonal
testing are becoming more accessible; companies often offer one-stop shopping services that
cover everything from the delivery of the kit to the provision of data in “readable” file formats
(e.g., *.csv). Moreover, fields such as human resources and occupational research are well-
placed to leverage neuropharmacology and genetics, given their accessibility to occupational
health practices, which are often equipped to perform biological assessments as part of
occupational screening routines.
Methods Classifications
There are different types of systems for the methodology of organizational neuroscience. The
most common is technical resolution. This classification proposes that methods can be
categorized according to a matrix based on each technique’s distinct resolutions: the ability of
a given tool to discriminate between points in space (i.e., spatial resolution) and time (i.e.,
temporal resolution) (Menon et al., 1998). Concretely, spatial resolution defines to what
granularity an object in an image (e.g., a given brain area) can be resolved: the higher the
resolution the better the quality of the image. Similarly, temporal resolution refers to the
ability to obtain data in relation to time. Thus, in lay terms, spatial resolution refers to the
capability that a brain imaging technique has to inform exactly which area(s) of the brain is
engaged, while temporal resolution describes its ability to precisely tell when the activation
occurred. This concept is important, because with high-resolution tools it is possible to resolve
cortical circuits in vivo (Felleman & Van Essen, 1991; Goense et al., 2016).

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Another viewpoint, summarized in Table 2, indicates that organizational neuroscience
methods can be classified into three functional categories according to their underlying
rationale (Massaro, 2017). Thus, association methods, such as electroencephalography or
functional Magnetic Resonance Imaging (fMRI), generally involve the “manipulation” of a
mental state, the aligned recording of neural activity, and a correlation between the two
(hence the association). Necessity tests disrupt neural activity to identify the role of a specific
mental function, often offering additional interpretations beyond the simpler identification of
neural correlates. Finally, sufficiency methods alter levels of neural activity and investigate
whether they result in a specific behavior or mental state.

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Table 1. Main Methods Used in Organizational Neuroscience Publications (2005–2020)
Method Measures/
Assessments
Resolutions Main Strengths Main Weaknesses Examples of
Applications
Actual or Potential
Use in Organizational
Neuroscience
Research
Functional magnetic
resonance imaging (fMRI)
Indirect measure
of neural activity
via magnetic
properties of
hemoglobin that
measures
changes in
oxygenated
blood flow to
brain regions
(BOLD signal)
Spatial: up
to about 1
mm
Temporal:
about 5 s
Strong spatial
localization
Rise in
popularity has
seen access to
greater
comparative
research and
continual
refinement of
techniques for
measurement
and analysis
High
operating
costs
Not portable
Results are
not always
easily
translatable
to biological
constructs
Complex
techniques
for cleaning,
processing,
and
analyzing
data
Identify
“activated”
brain regions
during a task
Assess
connectivity of
regions of
brain at rest,
during activity,
and after
activity
Assess neural
basis of
individual
differences
“Compared to
personal-oriented non-
inspirational messages,
collective-oriented
inspirational messages
will lead to increased
activation in the (a)
pars opercularis and
(b) inferior parietal
lobule, regardless of
whether they originate
from an in-group or an
out-group
leader.” (Molenberghs
et al., 2017)
3

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Method Measures/
Assessments
Resolutions Main Strengths Main Weaknesses Examples of
Applications
Actual or Potential
Use in Organizational
Neuroscience
Research
Electroencephalogram
(EEG)
Direct measure
of neuronal
activity via
electrodes
placed on scalp
that measure
electrical
changes caused
by neuronal
firing
Spatial: up
to 10–20
mm
Temporal:
ms
Strong
temporal
resolution
Affordable
Portable
equipment
Allows for
realistic
interaction
between
subjects
Longest
established
method
Sensitive to
other
sources of
electrical
current (e.g.,
muscular
activity).
Detected
signals are
not wholly
spatially
independent
Test the timing
of cognitive
processes.
Test
hypotheses
related to
known and
reliable ERP
signatures
(e.g., N400,
P300)
Test
hypotheses
relating to
high-frequency
neuronal
oscillations
(e.g., alpha,
gamma)
“Can a discriminant
function of power
spectral analysis
variables be defined to
classify, with accuracy,
those leaders
exhibiting
transformational
leadership behaviours
from those who do
not?” (Waldman et al.,
2011)

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Method Measures/
Assessments
Resolutions Main Strengths Main Weaknesses Examples of
Applications
Actual or Potential
Use in Organizational
Neuroscience
Research
Magnetoencephalography
(MEG)
Direct measure
of neural activity
via
magnetometers
near scalp that
measure
perturbations of
magnetic fields
caused by
neuronal firing
Spatial: up
to about 3
mm
Temporal:
ms
Strong spatial
and temporal
resolutions
More reliable
and accurate
than EEG
High
operating
costs
Not portable
Very
sensitive to
external
noise
Complex and
time-
consuming
techniques
for cleaning
and
analyzing
data
Test the timing
of cognitive
processes,
neuronal
oscillations,
and
connectivity
between
regions
When
combined with
EEG, can
detect more
signals
“Analyze the role of
specific brain regions in
perceptions of fairness
in organizational
settings” (Beugré,
2009)
Functional near-infrared
spectroscopy (fNIRS)
Indirect measure
of neural activity
via changes in
near-infrared
light absorption
Spatial:
about10–20
mm
Temporal:
about 5 s
Better
temporal
resolution than
fMRI
Lower spatial
resolution
than fMRI
Test
hypotheses for
tasks that are
not optimally
suited for fMRI
“To investigate the
processes underlying
moral behavior, this
research aimed to
investigate

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Method Measures/
Assessments
Resolutions Main Strengths Main Weaknesses Examples of
Applications
Actual or Potential
Use in Organizational
Neuroscience
Research
of hemoglobin
that measures
changes in
oxygenated
blood flow to
brain regions
Low operating
costs (after
initial
purchase)
relatively to
fMRI
Portable
Participant
does not need
to remain
stationary
Allows for
realistic
interaction
between
subjects
Can only
detect
activity on
the cortical
surface
paradigms
(e.g., requiring
movement or
face-to-face
social
interaction)
Longitudinal
studies, given
its relatively
low cost
neurophysiological and
behavioral correlates
of decision-making in
moral
contexts.” (Balconi &
Fronda, 2020)
Transcranial magnetic
stimulation (TMS)
Direct measure
of causal
relationship
between neural
activity and a
Spatial:
accurate to
about 5–10
mm
Good temporal
resolution
Portable
It can only
influence
cortical
Test whether a
particular
brain region
might be
required for
“Evaluate whether the
ventromedial
prefrontal cortex
(VMPF) does mediate

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Method Measures/
Assessments
Resolutions Main Strengths Main Weaknesses Examples of
Applications
Actual or Potential
Use in Organizational
Neuroscience
Research
task by affecting
neuronal firing
(i.e., activating
or suppressing of
particular brain
regions using
electromagnetic
fields)
Temporal:
N/A
Can allow for
causal claims
due to direct
stimulation
Can both
increase and
decrease
neuronal
excitability
Noninvasive
means of
stimulating the
brain
surfaces
beneath the
skull
Impact of
machine
noise and
potential
pain can
affect
participants
Limited
coverage
(researcher
must select
regions to
test)
specific
cognition and
behavior
Test and
resolve
concerns
about reverse
inference.
preferential
judgement.” (Senior et
al., 2011)
Transcranial direct-
current stimulation
(TdCS)
Similar to TMS
but uses a
current applied
between
electrodes
Spatial:
accurate to
about 10–20
cm
Can indicate
causal claims
due to direct
stimulation
Portable
Less spatial
and temporal
resolution
than TMS
Test whether a
particular
brain region
might be
required for
“we hypothesized that
cathodal (inhibitory)
transcranial direct
current stimulation
(tDCS) will facilitate
performance in a

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Method Measures/
Assessments
Resolutions Main Strengths Main Weaknesses Examples of
Applications
Actual or Potential
Use in Organizational
Neuroscience
Research
Temporal:
N/A
Well tolerated
by participants
Can both
increase and
decrease
neuronal
excitability
Noninvasive
means of
stimulating the
brain
Side-effects
(e.g., stinging
sensation,
headaches)
last longer
than with
TMS
Technique
continues to
develop
Limited
coverage
(researcher
must select
regions to
test)
specific
cognition and
behavior
flexible use generation
task.” (Chrysikou et al.,
2013)
Heart rate variability
(HRV)
Indirect measure
of neural activity
via assesses
beat-to-beat
changes in the
heart rate over
Spatial: N/A Low entry
costs
Portable
Access to real-
time data
Sensitive to
pathological
and
physiological
factors (e.g.,
respiration)
ANS inference
on behaviors,
emotions, and
mood
“investigating, in
healthy subjects, the
associations between
acute mental stress
and short/ultra-short

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Method Measures/
Assessments
Resolutions Main Strengths Main Weaknesses Examples of
Applications
Actual or Potential
Use in Organizational
Neuroscience
Research
time as a proxy
for autonomic
nervous system
(ANS) activity
and its effects on
behavior
Temporal:
milliseconds
(sampling
rate of 500–
1,000 Hz)
Availability of
off-the-shelf
wearable
devices
Sex, age,
ethnicity,
and physical
activity can
cause wide
variance
among
subjects
Varying
degree of
quality and
accuracy of
devices
“Well-being”
correlates
term HRV features in
time, frequency, and
non-linear domains.”
(Castaldo et al., 2017)
Electrodermal activity
(EDA)
Indirect measure
of neural activity
via changes in
electrical
conductance of
the skin as a
proxy for ANS
activity
Spatial: N/A
Temporal:
seconds to
minutes
Portable
Participants do
not always
need to remain
stationary
Sex, age,
ethnicity,
and physical
activity can
cause wide
variance
among
subjects
Inference to
emotion-
related
sympathetic
activity
“how the chances of
winning and bet size
affected choice
behaviour and
psychophysiological
arousal” (Studer &
Clark, 2011)

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•
• •
•
Method Measures/
Assessments
Resolutions Main Strengths Main Weaknesses Examples of
Applications
Actual or Potential
Use in Organizational
Neuroscience
Research
Varying
degree of
quality and
accuracy of
devices
Inference to
behavior,
emotions,
attention, and
mood
Understanding
how
sympathetic
bodily arousal
relates to
regional brain
activity
Eye tracking Indirect measure
of mental states
and processes
through
recording and
analyzing eye
movements and
gaze patterns
Spatial:
0.5°–2°
(VOG; less
for EOG)
Temporal:
milliseconds
(EOG;
milliseconds
Often portable
Participant
does not need
to remain
stationary
Usually
requires a
head piece,
which can
limit
activities of
the study
Inference to
attention (pop-
out effect, top-
down vs.
bottom-up
control)
Inference on
behavior and
cognition—not
the direct
“Whether differences in
social value orientation
are reflected in specific
patterns of information
search that can be
predicted across
tasks.” (Fiedler et al.,
2013)

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Method Measures/
Assessments
Resolutions Main Strengths Main Weaknesses Examples of
Applications
Actual or Potential
Use in Organizational
Neuroscience
Research
to tens of
milliseconds
for VOG)
Varying
degree of
quality and
accuracy of
devices
relationship
with brain
systems
Computational
Techniques/Modeling
Mathematical
models,
theoretical
analysis, and
abstractions of
the brain to
understand the
principles that
govern the
development,
structure,
physiology, and
cognitive
abilities of the
brain
Spatial: N/A
Temporal:
N/A
Identification
of scale
interactions
and dynamics
in neural
structures
provides a
framework for
understanding
the principles
that govern
how neural
systems work
Access to
latent
variables that
cannot be
Complex
techniques
for
computation
Theoretical
method for
investigating
the function
and
mechanism of
the nervous
system
Test
hypotheses
that can be
directly
verified by
current or
future
biological
experiments
“Compare two, or
more, seemingly
disparate cognitive
neuroscience models
to determine whether
they share functional
similarity.” (Ashby &
Heile, 2011)

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Method Measures/
Assessments
Resolutions Main Strengths Main Weaknesses Examples of
Applications
Actual or Potential
Use in Organizational
Neuroscience
Research
directly
observed from
behavior

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Table 2. Functional Categories of Organizational Neuroscience Methods
Type of Test Method
Association Functional Magnetic Resonance Imaging
Electroencephalography
Heart Rate Variability
Electrodermal Activity
Functional Near-Infrared Spectroscopy
Magnetoencephalography
Neurogenetics
Necessity Transcranial Direct Current Stimulation (cathodal)
Transcranial Magnetic Stimulation
Neuropharmacology
Sufficiency Transcranial Direct Current Stimulation (anodal)
Neuropharmacology (e.g., neurotransmitter loading)
Future research in organizational neuroscience will need to combine these perspectives into
an integrated approach. Such an approach will have to both support multimodal assessments
(e.g., convergence of multiple methods with different resolutions) as well as offer predictive
analytic capabilities (e.g., move from correlational inferences to causal claims). As Bandettini
(2015, p. 1) remarks: “as new methods for data acquisition are developed, fundamentally new
questions about the brain may be asked (…) and new methods, tailored to the specific
acquisition method and with the specific questions or applications in mind, are developed.”
Functional Magnetic Resonance Imaging
Functional Magnetic Resonance Imaging is one of the most fascinating and widely used
imaging techniques in neuroscience research (Soares et al., 2016). Its principles were exposed
in 1990 (Ogawa et al., 1990); fMRI leverages the magnetic properties of blood hemoglobin to
infer functional activity in the brain. Thus, fMRI provides an indirect measure of the brain’s

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functional activity by measuring changes in oxygenated blood flow to different regions in the
brain, with the assumption being that the regions that are receiving more oxygenated blood
are those more involved in a given experimental/behavioral task or mental process.
The conceptual underpinning behind fMRI is based on evidence that when a brain region, or
network of regions, is engaged in a mental activity, the relative blood flow to that (those)
area(s) increases (Zago et al., 2012). As the working neurons undergo cellular metabolism,
oxygenated hemoglobin is transformed into deoxygenated hemoglobin (D’Esposito et al.,
1999). These states have identifiable magnetic properties that can be appreciated in what is
referred to as the Blood Oxygen Level–Dependent (BOLD) signal, which allows scientists to
infer which brain regions are “activated” during a task. Because fMRI is based on blood flow
changes, its temporal resolution is limited by the hemodynamic response (i.e., how long it
takes the blood flow to reach a certain area), which peaks about 5 seconds after the neurons
begin to fire; the spatial resolution, however, is very high, usually on the order of just a few
millimeters, but also can be less than a millimeter with modern scanners (Glover, 2012).
Given the high magnetic power and sensitivity of the scanners, fMRI studies require carefully
designed settings and procedures. They must be conducted in a magnetically shielded suite in
order to reduce disturbances that could interfere with the data. Similarly, research
participants must lie still in the scanner to avoid disrupting the data acquisition with their
movements. Moreover, the magnet’s strength is extraordinarily high (typically between 1.5
and 3 Tesla [T], but many can be up to 7T), so there must not be any magnetic materials in the
scanner room: participants and researchers must remove metal objects (jewelry, belts, etc.),
and participants may be requested not to have specific types of makeup or tattoos (Callaghan
et al., 2019). Given these caveats, fMRI is overall a highly safe neuroimaging technique for all
involved: it is noninvasive, and it does not involve exposure to any harmful chemicals or
radiation (Huettel et al., 2008). Depending on the nature of the experiment, a full fMRI
protocol can take as little as 30 minutes to as long as a few hours.
Because the magnetic field reaches areas deep below the skull, it is possible to acquire high-
definition images of subcortical areas. It is also good practice to include in a study design
structural (anatomical) brain acquisitions (Faro & Mohamed, 2011) that can provide the basis
for the co-registration process of analysis. These images are composed of small, three-
dimensional “cubes” called voxels, which contain information about the imaged area obtained
from the scanning. In simpler terms, one can think of a voxel as the 3D equivalent of a digital
camera’s pixels.
Specifically designed statistical and analytical programming packages must be used in fMRI
research (see, for a review, Soares et al., 2016). For instance, recording and analyzing fMRI
data requires several pre-processing techniques that are closely aligned to the research
protocol and design used. The most common steps include temporal correction between slices
(i.e., slice timing), spatial correction (to correct for movement of the head in the scanner),
distortion correction due to inhomogeneities of magnetic fields between images, co-

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registration and normalization of the brain in the study to standard reference groups and
anatomical scans, and temporal/spatial filtering (smoothing) to enhance signal-to-noise ratio
(Massaro et al., 2020).
Overall, the use of fMRI has received ongoing positive feedback among multiple scientific
communities, as shown in the popularity of fMRI publications (Logothetis, 2008). While fMRI
is an excellent neuroimaging tool for research, it has received some criticisms, too, which are
often amplified by detractors of organizational neuroscience (e.g., Lindebaum, 2013). For
instance, one provocative study examining a dead salmon showed that without appropriate
analytical steps even that fish could return fMRI-detected “brain activity” (Bennett et al.,
2009). More recently, an analysis of over 40,000 fMRI studies identified issues with spurious
correlations and findings interpretation (Eklund et al., 2016). Yet, many of these concerns are
already known among the fMRI community of researchers, and there are several references
available in the specialized literature to address them (e.g., Lindquist, 2008). For the benefit
of the readers here, it shall be sufficient to remark that because the data obtained from fMRI
have a complex nature and high levels of noise from multiple sources, only accurate statistical
modeling and research design throughout—from sampling size to signal activity computations
—can help overcome possible concerns about the quality of research findings.
Electroencephalography
Electroencephalography (EEG) is a neuroimaging technique with almost 100 years of use (da
Silva, 2013). EEG techniques can be traced back to the late 19th century (Niedermeyer &
Schomer, 2011): The first human EEG recording—often marked as its discovery—is attributed
to the German psychiatrist Hans Berger in 1924 (Berger, 1929).
EEG is a technique in which cortical brain activity is recorded via electrodes placed on a
person’s scalp. These electrodes assess the electrical potentials (i.e., the brain’s electrical
fields) produced by neuronal activity. In contrast to fMRI, which provides an indirect indicator
of brain activity (i.e., blood flow variations), EEG measures actual neural activity. Specifically,
EEG captures postsynaptic hyperpolarization (i.e., inhibitory activity) or depolarization (i.e.,
excitatory activity) of the local population of similarly oriented layers of neurons in the cortex.
After being amplified and aggregated for the local population, these signals are generally
averaged based on a given experimental event, such as the presentation of a stimulus. This
averaged electrical potential is referred to as an Event Related Potential (ERP).
In a typical EEG study, the placement of the electrodes follows an internationally recognized
standard referred to as 10:20, meaning that the distance between electrodes is either 10% or
20% from the back, or front, of the head (Khazi et al., 2012). High-density recordings are
possible, using up to 256 electrodes, which are usually attached to a cap (or net) for the
convenience of placing them on the scalp. An electrolytic gel is used to improve the
conductivity from the skin to the electrode; this gel, however, can leave a residue.
Alternatively, so-called dry solutions are now possible; they offer a relatively quick setup time
and have been found to be more tolerated by participants (Hinrichs et al., 2020).

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Like fMRI, EEG recordings are susceptible to disturbances from other sources (i.e., “noise”),
which include interference from electrical machinery or equipment, eye blinking, and mouth
and jaw movements (Huster et al., 2014). Intriguingly, recent technological developments
have allowed portable and low-cost EEG solutions, that, by ensuring ecological validity
(Massaro, 2017), are greatly extending the use of EEG to a variety of contexts of relevance for
organizational research and practice. For instance, the possibility of collecting data from large
numbers of participants simultaneously (Krigolson et al., 2017, p. 8) is opening intriguing
avenues to extend organizational neuroscience research from the individual to dyadic and
team units of analyses. For one example, Stevens and Galloway (2017) used EEG data streams
and advanced methods of entropy to assess across-brain patterns when teams performed
coordinated tasks.
Technological advancement in the analysis of EEG data has also provided the opportunity to
identify EEG correlates of organizational behaviors (Waldman et al., 2011). A notable method
in this respect is called quantitative EEG (qEEG) (Nuwer, 1997). Here, methods of signal
analysis using Fourier transforms and time-frequency analysis are applied to EEG, focusing on
patterns of activity such as phase synchrony and magnitude synchrony. The qEEG method has
also found increasing use in neurofeedback applications (Massaro, 2015), wherein brain
activity is monitored and applied to modulate certain sensorial stimuli apt to be controlled by
the research participant or user.
Magnetoencephalography
Magnetoencephalography (MEG) is similar to EEG in that it also relies on the electromagnetic
signals that arise from neuronal activity. The difference between the two techniques is that
MEG measures brain activity using magnets rather than electrodes. Despite being close in
nature to EEG, MEG’s discovery was made almost half a century later, in 1968 by the nuclear
physicist David Cohen (Cohen, 1968). The initial recording devices were small and wearying
to use; the more modern, helmet-like magnetometers were developed in the 1980s. When
using MEG, participants sit, or lie, in a shielded room with their head placed in a scanner;
wearable and slightly more portable versions of MEG are available in a “helmet” form.
Like EEG, MEG offers a direct measure of neuronal activity. MEG boasts a higher temporal
resolution, of mere milliseconds, and spatial resolution (about 3 mm) than EEG because the
magnetic fields are less distorted than electrical fields as they pass through the skull. The
magnetic signals are weak and require superconducting sensors referred to as
superconducting quantum interference devices (SQUIDs) (Hari & Salmelin, 2011). Because of
these properties, MEG is ideally suited to reveal patterns of activation within and among
networks of cortical areas in the human brain; moreover, MEG can assess sub-cortical activity
(Singh, 2014), which can thus be directly associated to psychological states linked with
“inner” regions of the brain, such as correlates of emotions in the so-called limbic system.
Another benefit of MEG is that the setup time is less cumbersome than with EEG, because
there are no electrodes to place on the skull and no gel to use. To ensure accuracy in
localizing the source of brain activity, MEG is often paired with anatomical imaging (e.g., MRI)

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to get a clear representation of the brain areas involved in a certain process or activity. As a
result, this pairing of techniques may incur additional costs, on top of an already expensive
procedure.
Functional Near-Infrared Spectroscopy
Functional near-infrared spectroscopy (fNIRS) is one of the most novel techniques proposed
for organizational neuroscience investigations. It is a noninvasive optical imaging technique
that measures changes in hemoglobin concentrations within the brain by means of
hemoglobin’s characteristic absorption spectra in the near-infrared range (Sangani et al.,
2015). Its origins trace to 1977, when Jöbsis (1977) demonstrated the possibility of detecting
changes in adult cortical oxygenation during hyperventilation by near-infrared spectroscopy.
What followed, a few decades later, was the proposal that BOLD signals could be measured
without fMRI. This finding led to functional applications of NIRS, with the first fNIRS human
studies, adopting single-site measurements, being performed in the early 1990s (Ferrari &
Quaresima, 2012).
Human tissues are relatively transparent to light in the NIR spectral window. NIR light is
either absorbed by pigmented compounds or scattered in tissues and can penetrate human
tissues (Delpy & Cope, 1997). Importantly, fNIRS is weakly sensitive to blood vessels larger
than 1 mm because they completely absorb the light. Given that the arterial blood volume
fraction is approximately 30% in a human brain (Ito et al., 2005), fNIRS offers the potential to
obtain information mainly concerning oxygenation changes occurring within the venous
compartment in the brain (Pinti et al., 2019). Like fMRI, fNIRS relies on the hemodynamic
response. As such, its temporal resolution is similar to fMRI’s, about 5 seconds; its spatial
resolution, however, is lower than that of fMRI, being around 10–20 mm (Glover, 2012).
One of the major strengths of fNIRS, and one of the reasons why it is a promising technology
for organizational neuroscience, is represented by the availability of relatively low-cost,
portable, wireless instruments. As with EEG, these devices can be used in simultaneous brain
activation studies on multiple subjects (e.g., Cui et al., 2012, Holper et al., 2012; Jiang et al.,
2012). Indeed, over the years, fNIRS has made important contributions toward the
understanding of how the human brain functions and its applications are continuing to grow
as more researchers from diverse fields utilize the technology.
Transcranial Magnetic Stimulation
Scientists have used electricity to stimulate the brain as early as the late 1800s (Sarmiento et
al., 2016). In 1939 Cerletti was the first to use electric stimulation, or electroconvulsive
therapy (aka shock therapy), to treat certain mental conditions (Cerletti, 1940). Some 40 years
later, Merton and Morton (1980) used electrical shocks to stimulate the motor cortex, a
technique referred to as transcranial electrical stimulation (TES). However, this approach was
a deeply painful and unpleasant process, so researchers began looking for more suitable and

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acceptable alternatives. In 1985 Barker et al. (1985) discovered that magnetic fields could
stimulate the brain, which prompted the development of transcranial magnetic stimulation
(TMS).
In lay terms, TMS is akin to the inverse of MEG. A transient electrical current is passed
through a coil, producing a magnetic field; this coil is then placed above a particular area of
the scalp so the magnetic field “travels” through the skull into a target brain area, where it
manipulates the activity of the target neurons. This manipulation can either activate neurons
or, if the current is pulsed repeatedly (rTMS), create a temporary “virtual lesion” (Bolognini &
Ro, 2010; Walsh & Cowey, 2000). Because of its ability to activate or temporarily deactivate
brain’s regions, TMS is one of the few functional techniques that can causally identify which
brain regions are recruited for a certain task or mental process. In other words, unlike
correlational methods, TMS allows researchers to test cause-effect hypotheses, for example,
that a certain brain area is responsible for a certain behavior. Unlike other methods that can
make such causal attributions (e.g., lesion studies, direct neuronal stimulation), TMS is safe
and noninvasive. However, it is worth noting that it can also affect cortical areas just beneath
the skull (Hallet, 2007).
TMS is not a recording technique as such, but it does have good accuracy in terms of the
specificity of the brain areas it stimulates, usually accurate to within a few millimeters. The
duration of the effect is contingent on the stimulation; given that it relies on electromagnetic
properties, the effects can be noticed almost immediately, within milliseconds (Stewart &
Walsh, 2006) and can last up to a few minutes (Eche et al., 2012).
Transcranial Direct Current Stimulation
Another technique using electromagnetic stimulation is transcranial direct current stimulation
(tDCS). The roots of tDCS date back well over 200 years, with Galvani and Volta’s work in the
late 1700s (Sarmiento et al., 2016). Yet it was not until 1998 with Priori’s work (Priori et al.,
1998) that tDCS was shown to affect cortical excitability (Brunoni et al., 2012).
The technique involves applying two electrodes to the scalp and running a constant, weak
current between them and through the target brain area, usually for a few minutes at a time.
Depending on the electrode generating the current pulse (the anode or the cathode), this
stimulation can either excite or inhibit the target area (Thair et al., 2017). Like TMS, tDCS is
not a recording technique; rather, it is a manipulation technique. Because it involves applying
a sustained current to a brain area, compared to TMS, the spatial resolution is poorer.
Moreover, because the current must be sustained, it can take minutes before effects are
observed (Thair et al., 2017; Wagner et al., 2007).

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Heart Rate Variability
Heart rate variability (HRV) analysis is a methodology that differs from those reviewed so far
because it does not directly focus on brain activity; instead, it looks at activity of the
autonomic nervous system (ANS) and its effects on behavior and cognition (Massaro &
Pecchia, 2019). Among other things, the ANS assists humans in the regulation of behavior,
bodily reflexes, and physiological states (Robertson et al., 2012). There are two branches of
the ANS, which work complementarily to one another: the sympathetic nervous system (SNS)
and the parasympathetic nervous system (PNS). The SNS is typically the system that prepares
the body for action in response to a stimulus or event—the so-called fight-or-flight response.
The PNS works to calm the body down—the rest-and-digest response.
While work by Rosenblueth and Simeone (1934) in the early 1930s paved the way for HRV, it
was only in the 1970s that research exposed the intimate relationship between heart rate and
the brain. This is best characterized by Lacey and Lacey (1978, p. 99), who suggested a “two-
way communication between the heart and the brain”: they found that as heart rate greatly
decelerated, the reaction time of an individual was faster, suggesting better attention and
preparation for action. As Morris and Thompson (1969) suggested, a lower heart rate is
associated with tasks that require careful attention to external stimuli, whereas a higher heart
rate is linked to cognitively demanding tasks. Scientists have rapidly advanced the
understanding and use of HRV in behavioral research, leading to the establishment of
international standards and parameters for its measurement (Task Force of the European
Society of Cardiology and the North American Society of Pacing Electrophysiology, 1996).
For the purpose of this article, HRV analysis can be best seen as an unobtrusive way of
assessing the effects of the ANS on the heart. HRV offers a measure of the fluctuation
between consecutive heartbeats resulting from ANS influence (Montano et al., 1994) and can
be affected, even on a short timescale, by factors such as breathing, stress, and physical
activity (Castaldo et al., 2017). Indeed, with equipment that has a sampling rate between 500
and 1,000 Hertz, even small changes in heart rate can be detected easily.
While HRV is becoming a popular feature in consumer-grade fitness products, it requires
several complex steps to ensure accuracy when used for research purposes. These steps
include, among others, looking at acquisition of the signal, correction of abnormal beats and
artifacts, computation of inter-beat intervals, and, finally, extraction of HRV features or
indices. Stable detector algorithms (e.g., Pan & Tompkins, 1985) are often deployed to
perform these steps and provide output visualizations for analysis, which generally include
extraction of the characteristics of the heart signal and waveform classification and
recognition.
The variations in heart rate can be evaluated by a number of analytical methods, each
returning specific features. The simplest are the time domain indices of HRV, which quantify
the amount of variability in measurements of the time period between successive heartbeats.

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Frequency domain analysis is also widely used in behavioral research, and it shows how
spectral power (mostly variance) distributes as a function of frequency. Some components are
distinguished in a spectrum calculated from short-term recordings of 2 to 5 minutes and
include High Frequency (HF; frequency activity in the 0.15–0.40 Hz range) and Low
Frequency (LF; frequency activity in the 0.04–0.15 Hz range) power. Several researchers
consider the LF/HF ratio as indicative of sympathetic to parasympathetic autonomic balance
(Eckberg, 1997; Montano et al., 1994).
Electrodermal Activity
Electrodermal activity (EDA) is another method that relies on physiological markers to infer
ANS activity. EDA refers to the electrical conductance of the skin and its changes in response
to sweat secretion resulting from sympathetic neuronal activity (Critchley, 2002). Its phasic
changes are often referred to as the galvanic skin responses (GSR). Like HRV, the
measurement is linked to the temporal qualities being the conductance per second (siemens).
The study of electrodermal activity of the skin began in 1888 by Romain Vigouroux
(Vigouroux, 1888), who examined skin conductance as a marker for clinical diagnosis, as
changes in EDA in response to various stimuli were measured (Dawson, Schell, & Filion,
2000). Throughout the 20th century, however, EDA became a widely used and sensitive index
of emotion-related sympathetic activity (Boucsein, 1992; Fowles et al., 1981; Venables &
Christie, 1980).
This coupling enables EDA to be used as an objective neuroscience index of human behavior,
such as, for example, emotional responses and conditioning. Indeed, lesion studies of patients
and use of imaging techniques have “clarified the contribution of certain brain regions—
implicated in emotion, attention, and cognition—to peripheral EDA responses” (Critchley,
2002, p. 132).
Eye Tracking
Eye tracking is generally a method of assessing either the point of gaze or the motion of an
eye relative to the head. This information is then used to infer people’s mental states based on
the rationale that that several brain regions are involved in visual processing and the
coordination of eye movements (Meißner & Oll, 2019).
Two of the most established eye tracking techniques are electro-oculography (EOG) and video-
oculography (VOG). EOG detects electrical activity, more specifically the voltage difference of
eye movements between the cornea and retina, quantified by recordings taken from
electrodes applied to the skin surface at fixed points around the eye(s) (Cowley et al., 2016).
Because it measures electrical signals associated with movement, it provides high temporal
resolution (milliseconds) and can even be used when the eyes are closed (e.g., during sleep or
at sleep onset). However, it offers limited spatial resolution, has a drifting baseline (i.e., there

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is background interference to the signal not related to eye movement), and exhibits high-
frequency noise, as a result of factors such as power lines or clenching of the jaw (Eggert,
2007).
Devices for VOG measurements are camera-based and track the movements of the eye via
changes in features, such as the pupil, iris, sclera, eyelid, and light source reflections on the
surface of the cornea. Because VOG does not require touching the participants, VOG offers a
more ecologically valid data collection procedure relative to EOG and is more widely used.
Compared to EOG, VOG devices provide better spatial resolution (typically 0.1–1 degrees of
visual angle); with a sampling typically between 30 Hz and 1 kHz, the temporal resolution of
many visual eye tracking devices is quite impressive (1 to 30 ms; Cognolato et al., 2018).
Computational Methods
Computational methods, generally referred to as computational neuroscience, are yet to be
included as part of the organizational neuroscience toolkit. The roots of computational
neuroscience can be traced back to 1952, when Hodgkin and Huxley were able to quantify
with a formula the action potential (think of an explosion of electrical activity that is created
by a depolarizing current) of a neuron (Hodgkin & Huxley, 1952). However, it was not until the
seminal book Theoretical Neuroscience: Computational and Mathematical Modeling of Neural
Systems by Dayan and Abbott (2003) that a unifying understanding of computational
neuroscience emerged in the literature.
In sum, computational neuroscience is best characterized by two approaches. On the one
hand, theoretically, it uses experimental and analytical means to understand the structure and
function of single neurons, neural circuits, or brain systems. On the other hand,
computationally, it involves the simulation of numerical/analytical models and/or their
experimental verification (Schutter, 2008).
While there is much potential for computational neuroscience to advance management
research, the barrier to entry is substantially high. The mathematical knowledge required is
extensive and without the necessary formal background, many may find it
“intimidating” (Anderson, 2014). However, tools designed for computational modeling in
neuroscience can assist researchers in their studies (Blundell et al., 2018). Finally, while
computational neuroscience is yet to blossom in organizational research, there are several
instances in which the use of machine learning and Artificial Intelligence (AI) is finding
positive reception within the management scholarly community (Lévesque et al., 2020).
The Current Literature in Organizational Neuroscience
To analyze the state-of-the-art research thus far in organizational neuroscience, a literature
review among some the most established management scholarly journals was performed. The
journals listed in the Financial Times 50 list <https://www.ft.com/content/
3405a512-5cbb-11e1-8f1f-00144feabdc0#axzz4J1D8IOXa> pertaining to management and

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organizational disciplines were selected; this list was later integrated with other journals
generally recognized as top of the field, or outlets that specifically cover organizational
neuroscience (Table 3). While practitioner-oriented journals were initially excluded, this body
of knowledge was later combined with a broader search on public repositories (i.e., Google
Scholar, PubMed).
Table 3. Journals Selected for the Initial Literature Search
Journals (*FT List)
Academy of Management Journal*
Academy of Management Review*
Administrative Science Quarterly*
Entrepreneurship Theory and Practice*
Human Relations*
Human Resource Management*
Journal of Applied Psychology*
Journal of Business Ethics*
Journal of Business Venturing*
Journal of Management*
Journal of Management Studies*
Management Science*
Organization Science*
Organization Studies*
Organizational Behavior and Human Decision Processes*
Research Policy*
Strategic Management Journal*
Journal of Organizational Behaviour
Journal of Management Inquiry

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Journals (*FT List)
Leadership Quarterly
Note. FT = Financial Times
The search included keywords such as “organizational neuroscience,” “neuroscience,” “fMRI,”
“EEG,” “eye tracking,” “heart rate variability,” and so forth, including possible terms’
variations. While the term “organizational neuroscience” appeared in a peer-reviewed article
only in 2011 (Becker & Cropanzano, 2010) an extended search was performed covering the
period between 2005 and 2020.
A total of 631 papers were retrieved. The key inclusion criteria were for work that: (a)
experimentally used neuroscience methods and/or (b) primarily used neuroscience theory to
advance management and organizational studies. Those works that returned the search words
just as pertaining to bibliographies or showed contributions with a minimal theoretical role of
neuroscience were excluded. The short list amounted to 197 papers, of which, following
analysis of the abstracts, 89 were retained (concordance rate among authors k = 0.87). Next,
these works were clustered into more “traditional” disciplinary areas as elaborated in the
following sections of this article. The areas primarily covered were Organizational Behavior,
Applied Psychology, Business Ethics, Entrepreneurship, Strategic Management, and Human
Resources and Occupational Research.
Organizational Behavior and Applied Psychology
Organizational behavior topics dominate the current body of works in organizational
neuroscience (Becker & Cropanzano, 2010; Becker et al., 2011; Butler & Senior, 2007).
Methodologically, fMRI and EEG are the most prominent techniques used in this domain (and
more generally across the retrieved literature).Niven and Boorman (2016) claimed that fMRI
has the potential to cover a “diverse range of organizational phenomena, including leadership,
coaching, justice, social influence, Machiavellianism, decision-making, and self-control.”
Waldman and colleagues (Waldman et al., 2011) pioneered a substantial body of works
leveraging the properties of EEG to investigate leadership behavior in the workplace. These,
however, are not the sole techniques that can help to advance investigations of organizational
phenomena and actors: HRV (Matta et al., 2017), computational neuroscience (Baldassi et al.,
2020; Webb et al., 2020), eye-tracking (Lohse & Johnson, 1996; Stock-Homburg et al., 2020,),
and electrodermal activity (Christopoulos et al., 2016; Lajante et al., 2012) have all shown the
potential to advance both organizational behavior theory and practice.
One such reason is that neuroscience investigations allow the identification of the “inner
working” mechanisms of organizational actors, which in turn are useful to understand more
about their behaviors and to craft possible interventions to improve them (Waytz & Mason,
2013). For example, a study using HRV by Matta and colleagues (2017) found that
inconsistency of fairness, or fair treatment, had a greater impact on stress levels, assessed via

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HRV, than being consistently treated in an unfair manner. Another study expanded on this
topic by testing the effect of perceptions of unfair pay on healthy individuals. Falk et al. (2018)
found that perceptions of unfair pay affect the productivity and health of employees: higher
levels of unfairness go along with lower heart rate variability. Thus, this finding provides a
physiologically informed understanding of the association between outcomes that are
perceived as unfair and physiological correlates. Moreover, it has been found that in unfair
outcomes, greater activation is present in areas of the brain associated with emotions, such as
the anterior cingulate cortex, anterior insula, and the dorsolateral prefrontal cortex (Dulebohn
et al., 2009).
One of the first and most studied areas of research in organizational behavior using
neuroscience is leadership. Studies in leadership have covered both the examination of the
leaders themselves and the effects on teams and followers (Boyatzis, 2014; Waldman et al.,
2011). Charismatic leadership in particular continues to be proposed as a key area for
organizational neuroscience research (Van Vugt & von Rueden, 2020). Existing works examine
how followers of charismatic leaders show deactivation of prefrontal cortex activity (Schjoedt
et al., 2011), the emergence of leadership following eye-tracking in teams (Gerpott et al.,
2018), and how inspirational statements intertwined with a leader’s group relationship show
increased activation in brain areas that are typically implicated in controlling semantic
information processing (Molenberghs et al., 2017), among other topics. Moreover, there are
reports of strong correlations between brain activations and leadership performance
(Tuncdogan et al., 2017). For instance, interconnectedness between regions of the brain
focused on creativity and those associated with expressing and regulating emotions, such as
those associated to right hemispheric coherence, show significant correlation with visionary
communication and charismatic leadership (Waldman et al., 2011).
Other leadership research aspects have been investigated with neuroscience as well. For
instance, by using EEG, researchers have been able to identify transformational leaders with
92.5% accuracy (Balthazard et al., 2012). In the same study, the authors looked at the
literature differentiating transformational and nontransformational leaders: the prefrontal and
frontal lobes were salient across most of the literature, but there was also inclusion of
findings in the temporal, central, parietal areas, and occipital lobes. This evidence indicates
that a wide neural network may be at play for a transformational leader. In a follow-up study,
it was found through observing connectivity in different regions of the brain that less EEG
coherence in the frontal lobes is associated with greater adaptive decision-making for military
leaders (Hannah et al., 2013). Another work found that there is a marked difference in the left
prefrontal cortex of leaders who displayed confidence and optimism in comparison to those
leaders who did not (Peterson et al., 2008). Finally, in recall of experiences with resonant
versus dissonant leaders, memories of resonant leaders are shown to activate areas
associated to the mirror neuron system (MNS) and the default mode network (DMN) (Boyatzis
et al., 2012). The default mode network is generally related to social cognition and activated
when people are interacting with others (Raichle & Snyder, 2007). The mirror neuron system
is also related to social cognition; however, it is reported to be salient in empathy and
imitation as well (Decety & Michalska, 2010).

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Similarly, research has found that coworkers can unwittingly mimic behavior by engaging the
so-called mirror neuron system (Becker & Cropanzano, 2010). Several studies have discussed
in what ways coworker dynamics can influence individuals in organizations. For instance,
research has shown that preference selection is influenced by coworkers who are deemed to
be socially relevant (Mason et al., 2009); that social interactions can act as a stabilizer for
stress levels dependent on the support provided to colleagues (Baethge et al., 2020); and that
a coworker can act as an enabler to adopt innovation based on implicit and explicit emotions
(Håkonsson et al., 2016). Along with this body of research, one study has found that
“Machiavellians” show enhanced activation of regions in the brain including the MNS and that
these are crucial to perception of emotions (Bagozzi et al., 2013).
Another timely area of research that has recently found room in organizational neuroscience
concerns gender issues. Research has shown that the “female brain” may be more attuned to
social risks (Irwin, et al., 2015), as the naturally high levels of the neurotransmitter oxytocin
in females may enhance trusting behavior (Ryan, 2017). This concept may have implications
for several workplace scenarios in which women may be seen as more likely to treat others
“as if they are trustworthy” (Irwin et al., 2015), as a result of their high levels of trust.
Similarly, research focusing on the relationship between justice information processing and
neural activity has shown that this link is significantly influenced by gender differences, with
greater neural activation for females than males during consideration of justice information.
Specifically, researchers have found higher levels of brain activation in the ventromedial
prefrontal cortex (vmPFC) and ventral striatum brain regions during procedural justice in
females relative to males (Dulebohn et al., 2009). These findings suggest that females may
react more to (un)fairness, even if these reactions do not seem perceptible to others through
expressions and behavior changes (Dulebohn et al., 2016). While there may be certain neural
differences occurring between genders, using gender as a basis of differentiation in research
may create some unfavorable research biases. For instance, Halko and colleagues included
only male entrepreneurs, as prior research indicated that “why women entrepreneurs are less
inclined to grow their businesses” (Halko et al., 2017, p. 386).
In addition to the topics reviewed here, there is a growing body of research using
neuroscience to examine a variety of behavioural constructs relevant for the workplace. Some
examples include the impact of facial stigmas on interview performance (Madera & Hebl,
2011) and the use of one-time financial compensation to regain trust (Haesevoets et al., 2018).
Finally, an area of investigation that is likely to emerge in the next few years is research on
emotions. Despite limited empirical research to date, researchers (Healey et al., 2017, 2018)
have proposed a framework that connects neuroscience to social and environmental forces as
significant components of complex emotional organizational life. More recently, Massaro
(2020) advanced a theoretical model based on the combination of organizational neuroscience
and Affective Events Theory (Weiss & Cropanzano, 1996) and reviewed neuroscience research
on emotions with a specific look at workplace phenomena.

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Business Ethics
An area of research that has seen exponential interest in using neuroscience theory and
insights is the field of business ethics, generally under the broader framework of neuroethics.
The term “neuroethics” is “concerned with ethical, legal and social policy implications of
neuroscience, and with aspects of neuroscience research itself” (Illes & Bird, 2006, p. 511).
Thus, the focus is not only on the research and findings associated with ethical research
topics, such as morality and justice (Cropanzano et al., 2017; Massaro & Becker, 2015), but
also on the ethical use of organizational neuroscience research, findings, and implementations
(Robertson et al., 2017).
As regards the first aspect, research has indicated activation of specific brain regions in moral
dilemmas or situations—of note, the vmPFC and amygdalae—likely acting as sites for socially
inappropriate behaviors and to assess reward or punishment (e.g., Greene & Haidt, 2002). In
a meta-analysis of 123 data sets, researchers found that the medial prefrontal cortex, lateral
orbitofrontal cortex, amygdala, temporoparietal junction, precuneus, and anterior insula were
all areas activated in and/or associated with moral tasks (Eres et al., 2018). Building on these
insights, Orlitzky (2017) suggested that a dual system is involved and responsible in shaping
moral judgement. Concurrently, Cropanzano et al. (2017) proposed a framework that
untangles key processes of deontic justice: the use of justice rules to assess events, cognitive
empathy, and affective empathy.
Waldman and colleagues have also found that the interaction between moral relativism (i.e.,
having ethical principles that adapt to situations) and idealism (i.e., an absolute attempt to do
no harm to others, regardless of situation) in a leader may mediate “the effects of the brain’s
default mode network—in the prediction of ethical leadership” (Waldman et al., 2017, p.
1285). This finding was later supported by studies looking at the balance between the Task
Positive Network (TPN) and DMN for ethical leadership (Rochford et al., 2017), which
proposed the opportunity to encourage ethical awareness training involving engagement of
the DMN.
Finally, in examining learning strategies appropriate for moral decision-making, Christopoulos
et al. (2017) explored via a computational neuroscience approach how differently valuation of
choice is updated in dynamic environments. They suggested that it is important for
organizations to understand such strategies in order to design systems and processes to
“support or encourage desirable moral behaviours” (p. 710).
Entrepreneurship
Entrepreneurship is another recent area of interest in organizational neuroscience, although
as recently as 2014 it had no empirical neuroscience studies published (Nicolaou & Shane,
2014; Nofal et al., 2018). It is not that the literature was quiet on recommendations: the early
2000s showed calls for using fMRI and EEG to infer correlates relevant for entrepreneurial
cognition (Baron & Ward, 2004). Yet a decade later, even behavioral experimental approaches
were still considered rare among the literature in entrepreneurship (Williams et al., 2019).

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Thus, de Holan stated that more research was needed in this respect to grow
“neuroentrepreneurship,” arguing that “we cannot afford to keep ignoring the foundational
micro-antecedent of any human decision” (de Holan, 2014, p. 3). More recently, Massaro et al.
(2020) expanded on this view by putting forward a methodological framework to conduct
neuroimaging research in entrepreneurship.
Yet, in the past few years, despite framed under a “biology of entrepreneurship” perspective,
several studies have been performed within the organizational neuroscience remit by
identifying neurobiological markers for entrepreneurs (e.g., Eckhardt & Shane, 2010;
Nicolaou et al., 2021; Shane, 2003; Shane & Venkataraman, 2000; White et al., 2007;). For
instance, Bönte and colleagues (2015) found a significant association between prenatal
testosterone exposure (PTE) and entrepreneurial intent. PTE results in “masculine patterns,”
one of which is evident in the behavior of risk taking. This is an important finding for
entrepreneurs that resonates with general human behaviors: People have biases in loss
aversion—namely, when making choices for themselves, they will take a less risky option in
comparison to when deciding for others (Andersson et al., 2016).
More recently, empirical work using fMRI has begun to appear in the entrepreneurship
literature. By using fMRI, Shane and colleagues (2020) found that high passion of
entrepreneurs (specifically founders of a start-up) could increase the engagement of investors
watching the pitch by 39%. This finding corroborates the body of emerging research on the
effect that passion can have in a pitch setting to influence funding decisions (e.g., Murnieks et
al., 2016).
Passion indeed has a strong association with entrepreneurs’ perception of their ventures or
start-ups. In another fMRI study, entrepreneurs and parents were monitored to understand in
what ways reward, self-regulation, and confidence were associated with their bonding to the
venture or the children (Lahti et al., 2019) Parents and entrepreneurs showed similar neural
correlates of bonding, suggesting that founding entrepreneurs relate to their ventures as
parents relate to their children.
Strategic Management
Another rather recent field contributing to organizational neuroscience is strategic
management research. Powell suggested that “in strategic management, neuroscience offers
new opportunities for strategy researchers to validate constructs, test theories, measure
variables, and generate ideas, and it may offer ways to improve strategy practice” (Powell,
2011, p. 1495).
Laureiro-Martínez et al. (2015) used fMRI to look at attention and decision-making
performance and found that consistently high performance depends on the ability to shift
between exploitation and exploration, which in turn depends on stronger activation of brain
regions responsible for attentional and cognitive control. In another case, research using

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heart rate measures has suggested that focusing on and projecting positive emotions can
influence and encourage teams in exploration of alternate ideas and solutions (Håkonsson et
al., 2016).
Human Resources and Occupational Research
Human resources (HR) and occupational research has traditionally had a variety of works
using biologically informed methods or paradigms. Even though little organizational
neuroscience research was found in a search of the selected HR journals, the field has
accommodated neuroscience by investigating topics such as stress at work and employees’
emotional responses. Unlike other areas of research, the focus has been primarily on
biomarkers rather than brain imaging methods (Schulte & Hauser, 2012; Soo-Quee Koh &
Choon-Huat Koh, 2007). This is likely due to the familiarity and facility of HR and occupational
researchers with occupational health assessments that often provide opportunities to gather
data from blood work or laboratory assays.
For example, in looking at cortisol assessments, Lundberg and Hellström (2002), analyzed the
relationship between workload and morning salivary cortisol in women. They found positive
correlations between the amount of overtime at work and morning salivary cortisol, with
workers having excessive overtime reporting cortisol levels almost twice as high as those of
women with moderate overtime or normal working hours. Building on this evidence, Elfering
et al. (2017) also found an impact of job demands and control on weekend cortisol levels, with
this effect being fully mediated by after-work fatigue. Similarly, Klumb et al. (2017) assessed
variations in negative social interactions at work and at home along with workers’ affect and
cortisol levels, finding that only women showed a tendency for slowed decline of cortisol
levels on more socially stressful days.
Research in the occupational domain has also seen increasing use of ANS measurements. For
instance, McLaren (1997) compared male police officers with clerical workers on heart rate,
blood pressure, and self-reported levels of stress and arousal, and found that clerical workers
reported higher levels of stress and the police officers reported higher levels of arousal. in a
comparable setting, Krick and Felfe (2020) revealed a positive effect of mindful intervention
among police officers on heart rate variability coupled with a stronger reduction of
psychological strain, health complaints, and negative affect.
Organizational Neuroscience Research Practice
It would be wrong here to not cover some concerns of the second aspect of neuroethics
previously mentioned. For a review of common pitfalls in organizational neuroscience
research thus far, readers can consult the work of Jack et al. (2019).
There has been a substantial critique of the use of neuroscience in organizational research—
cautioning against it being a “panacea” and exposing concerns about a reductionist approach
unsuitable to investigate organizational life (Lindebaum, 2015; Tracey & Schluppeck, 2014).

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This body of work generally contends that organizational neuroscience may have little regard
for the sensitivities of collecting data using neuroimaging techniques and risk of “reverse
engineering” management constructs; moreover, it often confronts the quality and
reproducibility of fMRI studies (e.g., Lindebaum, 2015). This position has, in turn, resulted in
backlash from many supporters of organizational neuroscience (Ashkanasy, 2013; Butler et al.,
2017; Cropanzano & Becker, 2013; Healey & Hodgkinson, 2014), and heeded calls for taking a
more balanced approach. Overall, as it is apparent that organizational neuroscience is gaining
acceptance and interest, it is important to reiterate that reductionists’ attempts are beyond
the scope of the field and that, if dealt with appropriately, most of the methodological issues
exposed so far in organizational neuroscience can be (and should have been) addressed with
existing knowledge from mainstream neuroscience.
Phua and Christopoulos (2014) raised three issues that historically have led to
misinterpretation of neuroscience in understanding behavior and that can offer a good
platform to emulate for greater acceptance of organizational neuroscience. First, they
recommend the need to look at how psychological models can work, not just which brain
regions are activated; second, they recommend caution in using reverse inference—that is,
inferring that a certain cognitive or behavioral process during the observation of brain
activation has a direct and definitive involvement; and, third, they emphasize ensuring
scholarly responsibility in appropriately dealing with complexity of neuroscience data sets and
experimental designs.
Discussion and Opportunities for Future Research
The research evidence presented in this article puts forward organizational neuroscience as a
thriving, novel scholarly field that, despite still being parceled across domains and topics,
covers a variety of themes of interest for management and organizational research at large.
Moving forward, researchers are tasked to build on such evidence and further advance
research within an integrative, interdisciplinary approach. Indeed, as virtually any behavior
and mental state can be associated with some neuro-physiological correlate, research
opportunities are countless. While researchers must be attentive not to merely concept-
monger established neuroscience evidence in management narrative, there are several
opportunities to provide innovative contributions to both theory and practice.
For instance, management research at large can focus on identifying discrete biomarkers for
emotions in the workplace. This avenue will be particularly appropriate as it would fully
leverage the ability of neuroscience to capture data beyond participants’ awareness. Similarly,
by using neuroimaging it will be possible to tackle important issues affecting contemporary
businesses, such as assessing neural responses on entrepreneurial evaluations to expose
potential judgement biases. Cross-fertilizing with neuroeconomics, strategic management will
benefit by further extending the boundaries of behavioral strategy. For instance, valuable
research questions involve investigating the neural micro foundations of dynamic capabilities.
Similarly, research in organizational behavior and applied psychology will benefit by further
researching team dynamics, possibly using multimodalities with wearables on HRV and EDA

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to complement recent EEG evidence. Finally, as anticipated throughout this article, many of
the methods reviewed here—computational neuroscience above others–have been used in just
a few publications in mainstream management journals, thus offering space for novel works to
appear.
Within these thriving opportunities, managerial and organizational scholars can also take a
more active role in neuroscience. For instance, many tasks and protocols used in mainstream
neuroscience offer stylized facts, largely due to noise-reduction constraints. However, still
little is known about the ability of these stimuli to fully reproduce real-life organizational
settings. The task for organizational neuroscientists is to work jointly with organizational and
neuroscience scholars and practitioners to validate protocols that can either confirm existing
procedures or create new stimuli specifically apt to investigating the workplace.
Correspondingly, by reflecting the importance of ecological validity, conducting research on
settings that are more natural to the corporate environment offers intriguing opportunities for
neuroscientists to move their research outside of the lab. Given the increasing number of
commercially available wearable tools, spanning from EEG caps to HRV devices, this seems to
be a highly promising and accessible avenue.
More broadly, organizational neuroscience can contribute to increasing movements of open
science. Sharing data sets and preregistering protocols will offer further validity to research,
minimize doubts of researchers, and ensure that the field can develop rigorously. In such a
way, organizational neuroscience will also offer a natural entry point for management
scholarship at large to take part in more extensive dialogues with other social, behavioural,
and natural disciplines.
In conclusion, just as it was nearly impossible to cover all the benefits, issues, and
technicalities of neuroscience methods, it is challenging to present the countless research
opportunities that will populate organizational neuroscience in the years to come. To facilitate
this research avenue, this article comprehensively reviewed the emerging field of
organizational neuroscience. Specifically, the related methods were discussed and existing
knowledge in the field was systematized. While organizational neuroscience has received
increased attention, enthusiasm, and scrutiny, it is still in an infant stage. It is therefore
fundamental for both novice and more seasoned researchers to become familiar with the
foundation of this field and appreciate the more complete theoretical and methodological
potential that neuroscience puts forward to advance management and organizational studies.
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