Content uploaded by Sebastiano Massaro
Author content
All content in this area was uploaded by Sebastiano Massaro on Dec 17, 2022
Content may be subject to copyright.
Methodological Challenges and Advances in Managerial
and Organizational Cognition
Neuroscience Methods: A Framework for Managerial and Organizational Cognition
Sebastiano Massaro,
Article information:
To cite this document: Sebastiano Massaro, "Neuroscience Methods: A Framework
for Managerial and Organizational Cognition" In Methodological Challenges and
Advances in Managerial and Organizational Cognition. Published online: 11 Dec 2017;
241-278.
Permanent link to this document:
https://doi.org/10.1108/S2397-52102017010
Downloaded on: 21 December 2017, At: 12:03 (PT)
References: this document contains references to 0 other documents.
To copy this document: permissions@emeraldinsight.com
The fulltext of this document has been downloaded 1 times since 2017*
Access to this document was granted through an Emerald subscription provided by
Token:BookSeriesAuthor:9BC3FA79-16C8-4656-AAC6-9AC855398206:
For Authors
If you would like to write for this, or any other Emerald publication, then please
use our Emerald for Authors service information about how to choose which
publication to write for and submission guidelines are available for all. Please visit
www.emeraldinsight.com/authors for more information.
About Emerald www.emeraldinsight.com
Emerald is a global publisher linking research and practice to the benefit of society.
The company manages a portfolio of more than 290 journals and over 2,350 books
and book series volumes, as well as providing an extensive range of online products
and additional customer resources and services.
Emerald is both COUNTER 4 and TRANSFER compliant. The organization is a partner
of the Committee on Publication Ethics (COPE) and also works with Portico and the
LOCKSS initiative for digital archive preservation.
*Related content and download information correct at time of download.
Downloaded by Doctor Sebastiano Massaro At 12:03 21 December 2017 (PT)
241
Neuroscience
Methods: A
Framework for
Managerial and
Organizational
Cognition
Sebastiano Massaro
ABSTRACT
In light of the growing interest in neuroscience within the
managerial and organizational cognition (MOC) scholarly
domain at large, this chapter advances current knowledge
on core neuroscience methods. It does so by building on the
theoretical analysis put forward by Healey and Hodgkinson
(2014, 2015), and by offering a thorough – yet accessible
– methodological framework for a better understanding of
key cognitive and social neuroscience methods. Classifying
neuroscience methods based on their degree of resolution,
functionality, and anatomical focus, the chapter outlines
their features, practicalities, advantages and disadvantages.
Specically, it focuses on functional magnetic resonance
CHAPTER
10
Methodological Challenges and Advances in Managerial and Organizational Cognition
New Horizons in Managerial and Organizational Cognition, 2018, 241–278
Edited by Robert J. Galavan, Kristian J. Sund and Gerard P. Hodgkinson
doi:10.1108/S2397-52102017010
Copyright © 2018 by Emerald Publishing Limited
All rights of reproduction in any form reserved
Downloaded by Doctor Sebastiano Massaro At 12:03 21 December 2017 (PT)
242 SEBASTIANO MASSARO
imaging, electroencephalography, magnetoencephalography,
heart rate variability, and skin conductance response.
Equipped with knowledge of these methods, researchers
will be able to further their understanding of the potential
synergies between management and neuroscience, to better
appreciate and evaluate the value of neuroscience methods,
and to look at new ways to frame old and new research
questions in MOC. The chapter also builds bridges between
researchers and practitioners by rebalancing the hype and
hopes surrounding the use of neuroscience in management
theory and practice.
Keywords: Affect and cognition; behavioral sciences; mana-
gerial and organizational cognition; neuroscience methods;
organizational neuroscience
The notion that cognitive capacities affect managerial
understanding, perceptions of, and actions toward organi-
zational environments is undeniably rooted in Herbert
Simon’s research agenda (Simon, 1955; March & Simon, 1958;
for a summary, see Porac, 2014; for a perspective on the Carn-
egie School, see Gavetti, Levinthal, & Ocasio 2007). From those
early seeds, research in managerial and organizational cognition
(MOC) has ourished, incorporating a wealth of insights from
the cognitive and behavioral sciences, giving rise to a scholarly
domain that investigates the cognitive systems and architectures
sustaining organizational life (see the Academy of Management
MOC Division’s Statement, https://moc.aom.org).
In the past few decades, MOC has grown vibrantly and
produced a number of seminal contributions (e.g., Gavetti &
Levinthal, 2000; Gavetti & Rivkin, 2007; Hodgkinson & Healey,
2008, 2011; Narayanan, Zane, & Kemmerer, 2011; Porac &
Thomas, 2002). The development of rened theoretical appa-
ratuses – from behavioral strategy (Hodgkinson, 2015; Powell,
Lovallo, & Fox, 2011) to the microfoundations movement
(Felin, Foss, & Ployhart, 2015; Gavetti, 2005), among others –
have advanced understanding of the ways in which individuals’
cognitive processes and their interactions, shape organizations.
Together with these theoretical advances, a reection on the
underlying methods has also become an integral part of the MOC
research agenda. Notably, this demand has led to the inuential
volume edited by Huff (1990), who put forward a fundamental
Downloaded by Doctor Sebastiano Massaro At 12:03 21 December 2017 (PT)
243
Neuroscience Methods
methodological framework and encouraged novel inquiries
aimed at capturing the mental processes of decision making. One
remarkable example, Hodgkinson and Johnson (1994) showed
that it is possible to study the competitive environment by focus-
ing on individuals’ mental processes. By using a cognitive taxo-
nomic interview approach, Hodgkinson and Johnson (1994) were
able to map the mental models of managers in retailing chains
and link such models to both intra- and inter-organizational
competitive structures. More recently, Hodgkinson and Healey
(2011) have further built on this achievement and inspired the
eld to focus further on the investigation of the psychological
foundations of management. By doing so, they have also brought
forward the potential of social cognitive neuroscience to advance
current scholarly knowledge in management.
This initiative has joined other insightful exchanges focused
on neuroscience across several elds interfacing with MOC,
spanning from strategic management (e.g., Healey & Hodg-
kinson, 2014; Laureiro-Martínez, Venkatraman, Cappa, Zollo,
& Brusoni, 2015; Powell, 2011) and neuroentrepreneurship
(e.g., de Holan, 2014), to organizational behavior (e.g., Becker,
Cropanzano, & Sanfey, 2011; Senior, Lee, & Butler, 2011). Corre-
spondingly, some initial empirical outputs based on neuroscience
methods have also begun to appear in the broader managerial lit-
erature (e.g., Bagozzi, Verbeke, Dietvorst, Belschak, van den Berg,
& Rietdijk, 2013; Laureiro-Martinez, Brusoni, Canessa, & Zollo,
2015; Waldman, Wang, Hannah, & Balthazard, 2017).
Yet, I also recognize that neuroscience is currently not
widely spread as a core research area within many business
schools and management departments. Thus, as is often the
case for emerging and novel research sectors (for an analy-
sis, see Hodgkinson & Healey, 2008), the growing momentum
behind the use of neuroscience has frequently been encountered
with skepticism, communication gaps, fears of complexity, and
so forth (e.g., Waldman, 2013). Moreover, trained neuroscien-
tists working in business schools’ are relatively rare, making
the required interdisciplinary knowledge transfer to and from
the management community more challenging than in other
research areas, such as economics and psychology.
As a result, there has been scant attention thus far on why
and how the neuroscience methodology can help to advance
the existing MOC literature, and the adequacy, challenges, and
advancements of neuroscience methods to MOC are yet to be
fully explored.
Downloaded by Doctor Sebastiano Massaro At 12:03 21 December 2017 (PT)
244 SEBASTIANO MASSARO
In the chapter I aim to bridge this substantial gap by focusing
specically on the way in which distinctive features of key social
and cognitive neuroscience (hereafter, neuroscience) methods can
expand knowledge on how “organization members model reality
and how such models interact with behaviors” – which are the
dening features of the MOC domain (see the Academy of Man-
agement MOC Division’s Statement, https://moc.aom.org).
My aim therefore is twofold: First, guided by the theoreti-
cal works of Healey and Hodgkinson (2014, 2015), I present a
rationale to support the advancement of MOC scholarship using
neuroscience methods. Second, I put forward a systematic expla-
nation of such methods, offering a much needed procedural,
yet still accessible, blueprint for each of the methods reviewed,
highlighting their key features, advantages, and disadvantages. I
realize that this coverage might be perceived as somehow more
technically oriented than other more established methodological
accounts in MOC. Yet, I also believe that such technical coverage
is needed to promote a fuller understanding of what neurosci-
ence methods may ultimately offer MOC to enable this interdis-
ciplinary partnership. Using supporting examples throughout, I
highlight various research avenues presented by the diversity and
complexity of the neuroscience approaches currently available to
MOC researchers.
All in all, the chapter brings forward a timely apparatus for
fostering the development of MOC investigations using (and/
or looking at) neuroscience and the anticipation of possible
misunderstandings and/or overexcitement on what the related
methods are and can effectively achieve (cf., Ashkanasy, Becker,
& Waldman, 2014; Lindebaum & Jordan, 2014). In conclusion,
I present my overall reections on how best to advance the eld
of MOC by enabling researchers interested in using neuroscience
methods to fully embrace calls for multidisciplinary and multilevel
scholarship advocated both in mainstream neuroscience (e.g.,
Gazzaniga, 2004) and the most recent MOC scholarship (e.g.,
Huff, Milliken, Hodgkinson, Galavan, & Sund, 2016).
MOC and Neuroscience: Between theory
and Methods
The guiding theoretical narrative of the present chapter is the over-
arching framework put forward by Hodgkinson and Healey (2011)
Downloaded by Doctor Sebastiano Massaro At 12:03 21 December 2017 (PT)
245
Neuroscience Methods
and Healey and Hodgkinson (2014, 2015; see also the introduc-
tory chapter of this book) on the use of neuroscience in MOC.
In their seminal article Hodgkinson and Healey (2011) reveal
how, notwithstanding several initiatives toward incorporating a
fuller behavioral and cognitive perspective, management theory
and research have minimized the exploration of key mental pro-
cesses (i.e., affective and non-conscious processes). Signicantly,
the authors go further and show that integrating management
theory with social cognitive neuroscience evidence can meaning-
fully address this need and advance our understanding of how
the capacities of both individuals and teams (see also Healey,
Vuori, & Hodgkinson, 2015) can shape rms’ dynamic capabili-
ties (Hodgkinson & Healey, 2011).
In parallel with this call, management research at large has
begun to uncover the potential of neuroscience in its domains,
giving rise to insightful exchanges and debates toward a more
inclusive disciplinary understanding of neuroscience in manage-
ment, a new research eld known as organizational (cognitive)
neuroscience (e.g., for differences between organizational and
organizational cognitive neuroscience, see Becker et al., 2011
and Senior et al., 2011, respectively; for an inclusive denition
of the eld, see Massaro & Pecchia, in press). Correspondingly,
the encounter of the managerial scholarly community with
neuroscience has brought forward concerns related to reduc-
tionist approaches (e.g., Ashkanasy et al., 2014; Lindebaum &
Jordan, 2014).
In response to such concerns, Healey and Hodgkinson (2014,
2015) have proposed a comprehensive socially situated perspective.
This perspective indicates how neuroscience ndings can advance
knowledge of organizational phenomena through interaction
with other social and contextual organizational features. This
argument is in line with the understanding of MOC as a domain
devoted to investigating individual, relational, and collective
cognition in organizational contexts. That is, cognition is an
“umbrella” construct (for a nuanced analysis, see Hodgkinson &
Healey, 2008) and the nervous system of organizational actors is
a part of the overall cognitive organizational architecture. Healey
and Hodgkinson (2015) compare this socially situated perspective
with an intra-individual view, the latter focusing on the role of
the brain in MOC (see Figure 1 in Healey & Hodgkinson, 2015,
p. 63). They argue that MOC cannot be entirely located in the
brain because the brain is just one among several elements that
modulates the complexity of cognition in organizations.
Downloaded by Doctor Sebastiano Massaro At 12:03 21 December 2017 (PT)
246 SEBASTIANO MASSARO
Following this call, I situate the use of neuroscience meth-
ods in MOC at the intersection between the intra-personal
and the inter-personal socially situated viewpoints proposed
by Healey and Hodgkinson (2015, as depicted in Figure 1.)
Indeed, these authors further suggest that meaningful insights
from neuroscience for MOC can be achieved by using multiple
approaches (see for a related discussion Sharp, Monterosso, &
Montague, 2012). Yet, because very little has been explored uti-
lizing neuroscience methodology per se thus far, the realization
of this call still remains a steep learning process for the MOC
community.
To address this shortfall, I take inspiration from Huff’s
(1990) comprehensive overview of research methods in MOC.
As Ginsberg (1992) explains, Huff’s contribution suggests that
interdisciplinary thinking can help to move beyond and above
traditional knowledge: “(…) mapping is most attractive as a
method for studying topics that are intrinsically cognitive for
explaining variance that is unexplained by other methods” (ital-
ics added; Huff & Fletcher, 1990, p. 142). In the following sec-
tions, I mirror this insight and review the distinctive approach to
capturing MOC provided by neuroscience methods. By extend-
ing earlier insights (Massaro, 2016), I present an enhanced
and detailed interdisciplinary overview of several neuroscience
Figure 1: Integrating MOC and Neuroscience Theory and Methods. This
Figure Follows the Framework by Healey and Hodgkinson (2014, 2015), Which
Contrasts the Intrapersonal and Socially Situated Perspectives on Neuroscience
in Management. Neuroscience Methods Work at the Intersection of these
Perspectives.
Downloaded by Doctor Sebastiano Massaro At 12:03 21 December 2017 (PT)
247
Neuroscience Methods
methods. Indeed, I embrace recent prompts to appreciate neuro-
science in its entirety (Massaro & Pecchia, in press), and under-
stand neuroscience as a research avenue grounded on a broader
theoretical perspective (Healey & Hodgkinson, 2014).
Thus, I discuss neuroscience methods as a set of tools able to
inform knowledge on the mental processes of decision making
beyond what can be explained currently by other approaches.
Indeed, MOC researchers have often limited their efforts to
the investigation of psychometric, self-report, and other obser-
vational data – notwithstanding construct validity issues (for
a review, see Hodgkinson & Healey, 2008) – while lacking
tools for probing more deeply into the unobservable mecha-
nisms underpinning dynamic processes in play (Godfrey & Hill,
1995).
As we shall see, each of the neuroscience methods reviewed
here allows researchers to “look under the hood” of cognition,
effectively providing measurable, objective, and possibly gener-
alizable neuro-physiological information on mental processes.
As such, these methods can inform the ndings of conventional
studies, possibly rening or strengthening existing knowledge.
Likewise, a neuroscience approach to MOC research promises
to provide information that differs from that which can be cap-
tured by behavioral methods, allowing an entry point to mental
constructs seized from both implicit, unobservable, and explicit,
observable, awareness (Becker & Menges, 2013).
A Methodological Framework: Three Ways
of Looking at Neuroscience Methods
In encountering neuroscience methods, several taxonomies are
available across different literatures. Here, I classify neurosci-
ence methods in three ways: by resolution, by functionality,
and by sites of inference. Readers should note that, while such
classications are presented here in the context of MOC, these
classications and methods are applicable to any other manage-
ment domain using neuroscience, from neurostrategy to organi-
zational neuroscience, and so forth.
Resolution. Traditionally in neuroscience, methods are clas-
sied according to a matrix based on each technique’s distinct
resolutions. Distinct resolutions indicate the ability of a given
technique to discriminate between points in space (i.e., spatial
Downloaded by Doctor Sebastiano Massaro At 12:03 21 December 2017 (PT)
248 SEBASTIANO MASSARO
resolution) and time (i.e., temporal resolution) (e.g., Menon, Gati,
Goodyear, Luknowsky, & Thomas, 1998).
Such an approach is highly valuable since it allows chart-
ing the techniques according to their ability to provide accurate
information either on the spatial location of a neural event, or
on the time in which it unfolds. This conceptualization is indeed
useful to couple the needs of an experimental design or of a
research question with the intrinsic technological capabilities of
each method, as I shall explain below. Thus, for instance, if a
researcher seeks to acquire information on “deep” brain regions
engaged while a participant is undertaking a cognitive task, high
spatial-resolution techniques, such as fMRI, are generally more
preferable than less specic ones, such as EEG. Conversely, if a
researcher is interested in understanding the precise timing of a
neural event, then EEG typically offers more precise temporal
information than fMRI.
Functionality. Recently, Massaro (2016) suggested that in
management and organizational studies, the classication of neu-
roscience methods by resolution should be coupled with insights
on their functionality provided by Kable (2011). That is, it is pos-
sible to conceive a classication of neuroscience methods on the
basis of their underlying testing rationale. Thus, methods can be
classied as association, necessity, or sufciency tests.
Association tests are those methods that involve the manip-
ulation of a mental state, the aligned recording of the neural
activity, and a correlation analysis between the two. In contrast,
necessity tests are those that imply a disruption of the neural
activity to show the role of a specic mental function. Suf-
ciency tests are those augmenting (or reducing) neural activity
and investigating if intervention results in a specic behavior
or mental state. Common necessity and sufciency methods
include lesion studies or transcranial magnetic stimulation,
which can assess the causality between experimental interven-
tions and neural states.
Sites of inference. While management scholars have already
beneted from the classications above, the overall approach
toward neuroscience has tended to study the brain alone (for a
critique see Massaro & Pecchia, in press). In turn, this parceled
and focalized understanding has led to a prolonged debate on
what neuroscience investigations in management should encom-
pass (e.g., Butler, Lee, & Senior, 2017), as well as provocative
promptings on whether neurons and brains can “manage”
Downloaded by Doctor Sebastiano Massaro At 12:03 21 December 2017 (PT)
249
Neuroscience Methods
organizations (for an analysis see Healey, Hodgkinson, &
Massaro, 2016).
Moving this conversation toward a more inclusive and per-
haps accessible understanding of neuroscience, calls for a com-
plementary and more overarching classication of the methods
suitable for use in MOC research. This third classication, is
grounded on a given method’s functional-anatomic sites of infer-
ence, namely the central (CNS) and peripheral nervous system
(PNS). While it is not the aim of this chapter to provide a full
anatomical coverage of the nervous system, I believe it is useful to
summarize some of its key features to ease readers’ command of
the neuroscience terminology before venturing into the descrip-
tions of its principal methods.
The human nervous system is a complex collection of nerves
and specialized cells (i.e., neurons and glial cells) (for a compre-
hensive account, see Mai & Paxinos, 2011). Nerves are bundles
of bers that depart from the brain and spinal cord and reach
out to other parts of our body. Neurons are specialized cells that
transmit signals between separate parts of the body, and glial
cells are specialized cells that support, defend, and partly nur-
ture neurons. As shown in Figure 2, the nervous system has two
main interacting anatomical components: the CNS and the PNS.
The CNS is primarily composed of the brain and the
spinal cord. The PNS consists of sensory neurons, ganglia (i.e.,
groups of neurons) and nerves that interconnect and join the
CNS. The PNS delivers information from the brain to the rest
of the body, and vice versa. Functionally, the nervous system
is classied under its two main branches: the somatic (i.e.,
voluntary component) and the autonomic (i.e., involuntary
components). Here, I will also focus on that branch of the
PNS called the automatic nervous system (ANS; Jänig, 1989).
The ANS regulates several body functions such as heart rate
(HR), pupil dilatation, respiration rate, and complex automatic
behavioral responses like the “rest-and-digest” and “ght-or-
ight” responses (McCorry, 2007). This regulation takes place
through two branches of the ANS: the sympathetic and the
parasympathetic branches. Importantly, these branches are
always working and acting in opposition, being involved either
in the preparation for action or in the relaxation of the body. The
sympathetic branch responds to arousing stimuli, while more
relaxing situations prompt responses from the parasympathetic
branch, as summarized in Table 1.
Downloaded by Doctor Sebastiano Massaro At 12:03 21 December 2017 (PT)
250 SEBASTIANO MASSARO
Before moving to a detailed description of the methods assess-
ing these components of the nervous system, readers should note
that the nervous system is a well-integrated and interconnected
structure. Moreover, I will present specic research illustrations
often taken from an intra-individual perspective as the major-
ity of studies in neuroscience have focused on this perspective
thus far. However, all the methods reviewed in the chapter can be
used to inform both an intra-personal and a socially situated per-
spective on cognition. Even experiments that must be performed
inside an experimental suite, like fMRI studies, can be employed
in social interactions through approaches such as hyper-scanning
(i.e., the simultaneous investigations of participants assessed with
neuroimaging tools; Montague et al., 2002). Thus, the ultimate
consideration on which neuroscience method a researcher should
Figure 2: Anatomical Divisions of the Human Nervous System.
Table 1: Parasympathetic and Sympathetic Activities.
Structure/Organ Sympathetic Parasympathetic
Heart Increased heart rate Decreased heart rate
Lung Bronchial muscle relaxed Bronchial muscle con-
tracted
Pupil Dilatation Constriction
Stomach/Intestine Reduced activity Increased activity
Salivation Reduced activity Increased activity
Downloaded by Doctor Sebastiano Massaro At 12:03 21 December 2017 (PT)
251
Neuroscience Methods
use in MOC, requires familiarity with the details of the technique
in question, coupled with a meticulous experimental design well
suited to target the specic research question to be addressed in
the investigator’s study. As there is a wealth of research in main-
stream neuroscience on the principles of empirical research design,
in the followings sections, I will refer readers to these whenever
appropriate.
Neuroscience Techniques for MOC
In providing a description of neuroscience methods for MOC, I
necessarily had to limit the focus of the chapter to some selected
techniques. Specically, I focus on the following association meth-
ods: functional magnetic resonance imaging (fMRI), electroen-
cephalography (EEG), magnetoencephalography (MEG), heart rate
variability (HRV), and skin conductance response (SCR). Table 2
provides an overall summary of the key features of these methods.
These methods constitute the most often used and debated neuro-
science tools within management research more generally.
While these techniques do not cover the entire spectrum
of the neuroscience methods currently available to researchers,
I have purposely concentrated on these particular techniques
because they are some of the most useful approaches to inform
MOC. Indeed, as noted earlier, these techniques are all well suited
to map onto both the intra-personal and socially situated per-
spectives of neuroscience in MOC (Healey & Hodgkinson, 2014,
2015). Moreover, given the application of neuroscience to MOC
is at an early stage, it is sensible for readers to begin the encounter
with neuroscience by rst understanding cognition as it naturally
unfolds, and then exploring the opportunities for its manipula-
tion – an approach which also opens a set of important ethical
questions.
Mapping the Central Nervous System
The most popular and popularized technique capable of providing
maps of the “thinking brain” is undoubtedly fMRI. This method
enables the creation of functional maps of the brain’s activity by
capturing changes associated with the cerebral blood ow. Yet,
several other neuroscience tools possess the capacity to measure
the brain’s activity by assessing neurons’ electric potentials (EEG)
Downloaded by Doctor Sebastiano Massaro At 12:03 21 December 2017 (PT)
252 SEBASTIANO MASSARO
or the deriving magnetic elds (MEG). As we shall see, these tech-
niques are all non-invasive and allow, among other features, the
ability to capture how the “functioning” brain responds (e.g., is
“activated”) to experimental tasks (i.e., stimuli), as well as how
it operates at rest.
FUNCTIONAL MAGNETIC RESONANCE IMAGING
fMRI experiments are performed in a dedicated, shielded, neuro-
imaging suite when a research participant is laying still and at on
his/her back, and with his/her head located inside a magnetic reso-
nance (MR) magnet (i.e., the “scanner”). This magnet generates
a powerful magnetic eld – typically of 1.5 or 3 Tesla (T). As the
Table 2: Overall Comparison of the Rationale, Key
Technical Features, and Costs of Neuroscience Methods
Covered in the Chapter.
Method fMRI EEG MEG HRV SCR
Basic prin-
ciple
Cerebral
blood ow
(e.g., BOLD
signal)
Electrical
impulses of
pyramidal
neurons
Neurons-
generated
magnetic
elds
Modulation
of ANS on
heartbeats
variations
over time
Degree of
conductance
of a small
amount of
current pass-
ing through
the skin
(i.e., eccrine
glands)
Temporal
resolution
Low High High High High
Spatial reso-
lution (brain
localization)
High Very low Low — —
Availability Medium/
High
High Little High High
Ecological
validity and
portability
Little Some (port-
able tools)
Little High High
Indicative
costs
> millions
$ (imaging
suite) around
500/800
$ per subject/
session (usu-
ally 1 hour)
From 200 $
to 100.000 $
> millions
$ (imaging
suite) and
around 1,000
$ per subject/
session (usu-
ally 1 hour)
2,000
$ (reliable
full set-up)
10,000$
(reliable full
set-up)
Downloaded by Doctor Sebastiano Massaro At 12:03 21 December 2017 (PT)
253
Neuroscience Methods
magnetic eld reaches areas deep below the skull, it is possible to
acquire high-denition images of brain regions which are situated
below the cortex, the outer layer of the brain. This is a powerful
feature that is not generally available when using other methods
reviewed in the chapter. Yet, it is also important to note that fMRI
per se does not provide images of the anatomy of the brain. Such
images can instead be obtained through structural imaging and
coupled to functional analyses (Faro & Mohamed, 2010).
fMRI measures the brain’s functional activity by assessing
dynamic changes in the blood ow – the so named blood-oxygen-
ated level dependent signal or BOLD (Logothetis, Pauls, Augath,
Trinath, & Oeltermann, 2001; Ogawa, Lee, Kay, & Tank, 1990).
This means that fMRI does not directly assess neuronal activity.
Rather, the main rationale behind the use of fMRI in “mapping
cognition” is based on the evidence that when a brain region or
network of regions is engaged in an activity (e.g., an experimen-
tal task), the relative blood ow increases in that area (see, e.g.,
Zago, Lorusso, Ferrucci, & Priori, 2011). Due to the magnetic
properties of the oxygenated blood it is possible to interpret the
resulting BOLD signal as a specic “hemodynamic” function.
This reects brain functional activation during a given experi-
mental condition compared to a control or baseline condition
(Aguirre, Zarahn, & D’Esposito, 1998). fMRI images are then
reconstructed through a series of complex statistical and analyti-
cal processes (for a typical fMRI output see Figure 3)1.
These images, at their core, are composed of three-dimen-
sional components (i.e., voxels) carrying information on the
“scanned” brain. The voxel’s size determines the spatial resolu-
tion of fMRI (about 1 mm3). The temporal resolution, usually in
the order of seconds, is poorer compared to other methods that
are able to directly capture neuronal activity (i.e., EEG), essen-
tially because the BOLD signal yields a delay compared to the
physical site of activation (Kim, Richter, & Uǧurbil, 1997).
In addition, when performing or evaluating fMRI research, it
is important to pay attention to three core experimental features:
the type of stimulus used, the design of the task and key steps of
signal analysis. Two types of stimuli are generally used in fMRI
research: block and event-related (see Amaro & Barker, 2006).
In the block design, the repetitions of a stimulus are clustered
together into a few short “blocks” per each stimulus. This design
generally holds reasonable statistical power and is recommended
for between-subjects research. In the event-related design, differ-
ent stimuli are spread throughout the experimental session. While
Downloaded by Doctor Sebastiano Massaro At 12:03 21 December 2017 (PT)
254 SEBASTIANO MASSARO
more complex, this design is more suitable for both between- and
within analysis, thus better mirroring traditional MOC research
strategies. A mixed approach, combining both block and event-
related designs, is also a common research strategy (Petersen &
Dubis, 2012).
With regard to task design, several options are available (for
a review see Richards, Plate, & Ernst, 2013). The most diffused
design is cognitive subtraction, which confronts the activity of
distinct brain regions when engaged in a cognitive task (Friston
et al., 1996). Cognitive conjunction is another common design,
which allows for the identication of activated brain regions
as a cognitive process unfolds in its different phases (Price &
Friston, 1997). Finally, parametric designs and functional inte-
gration are more recent and sophisticated forms of design in
which correlations between brain activity and changes in a cho-
sen variable are measured, together with the mutual association
between different brain regions’ activities (see Penny, Friston,
Ashburner, Kiebel, & Nichols, 2011). Recently such approaches
have been used in studies when participants are not dealing with
an experimental task, also known as the resting state (Di Mar-
tino et al., 2008).
Usually, the choice of research design in fMRI is coupled
with a priori hypotheses on the neural sites involved in the
Figure 3: fMRI Output of a Participant Performing a Working Memory Task,
Showing Functional Activation of Bilateral and Superior Frontal and Parietal Cortex
(Source: Adapted from Graner, Oakes, French, & Riedy, 2013; https://commons.
wikimedia.org/wiki/File:FMRI_scan_during_working_memory_tasks.jpg).
Downloaded by Doctor Sebastiano Massaro At 12:03 21 December 2017 (PT)
255
Neuroscience Methods
task, stimulus, or behavior under investigation. Thus, research-
ers should identify a given region of interest (ROI) which then
enables a clear-cut approach in the subsequent analysis (Pol-
drack, 2007). It is also important to keep in mind that while
ROI are useful “guides,” it is good practice for every research
output using fMRI to include information on whole-brain scans
performed, and that any neuroimaging study should always be
supported by behavioral evidence and necessary computational
models.
Finally, the analysis of fMRI information necessitates a
combination of processing steps. These steps should be clearly
documented in the relevant research outputs and performed
with rigorous procedures in order to maintain the good qual-
ity of the signals and avoid the occurrence of false-positives (see
Logothetis, 2008). The processing steps include: temporal cor-
rection of the images acquired (i.e., slice timing); correction for
any head – and thus brain – movement; stereotactic normaliza-
tion, which normalizes the subject’s brain into standard refer-
ences; and smoothing, which aims to better the signal-to-noise
ratio and facilitate comparison between groups. Strother (2006)
and Amaro and Barker (2006) provide detailed technical expla-
nations of these steps. Interestingly, Laureiro-Martínez (2017), in
this book, provides a visual outline of how these steps mirror
the processes involved when analyzing think-aloud protocols in
MOC research.
Pros and Cons. fMRI, as seen, has high spatial resolution,
allowing for the identication of specic brain regions associated
with a particular function. Moreover, fMRI is applicable to dif-
ferent types of tasks within MOC research. For this reason, as
in all neuroscience-based research, it is vital to ensure that each
experimental design is t for investigating the research ques-
tion and hypotheses under investigation. It is also important to
mention that brain “activation” may be due to several spurious
causes (e.g., individual differences, unspecic mechanisms, unre-
lated physiological processes) or analytical missteps (see Eklund,
Nichols, & Knutsson, 2016) beyond the experimental task. To
respond to this problem, research seeking to develop controls
and analytical lters is a rapidly growing avenue (see, e.g., White,
O’Leary, Magnotta, Arndt, Flaum, & Andreasen, 2001).
Criticism on the reverse inference problem which, while com-
mon in all neuroscience methods, has seen its peak with fMRI
research (Poldrack, 2006). The reverse inference problem con-
cerns backward reasoning, regarding specically the issue of what
Downloaded by Doctor Sebastiano Massaro At 12:03 21 December 2017 (PT)
256 SEBASTIANO MASSARO
mental process can be inferred from a measured brain activity.
This type of inference can be highly problematic. For example,
if an “emotional” brain region, like the amygdala, which is well
known to be associated with fearful behavior (Davis, 1992), was
to be active during an attention task, a researcher could be misled
into thinking that attention may cause fear, potentially resulting
in spurious research ndings.
fMRI suffers from an intrinsic low temporal resolution and
may well be prohibitive for researchers, with costs reaching up
to 800 US dollars per participant/session. Importantly for MOC,
the physical demands of the technique mean that fMRI research
cannot be performed outside the imaging suite, raising issues of
ecological validity in transferring laboratory ndings to organi-
zational contexts. Ecological validity is the degree to which the
behavior identied in an experimental study reects the behavior
that occurs in natural (in our case organizational) settings. Given
the technique’s limitations, I suggest that in a context of inte-
grated MOC, the ecological boundaries sought in any MOC or
management study are important considerations to be addressed
in the research design.
Finally, due to the analytical complexity involved with fMRI,
it is necessary to pay close attention to the statistical approach
used in the analyses and to ensure that correlations between brain
activations and mental processes are both accurate and reproduc-
ible (Zarahn, Aguirre, & D’Esposito, 1997).
Advancing MOC research using fMRI. fMRI is a versatile
and informative technique for MOC research. For instance, in
another chapter of this book, Laureiro-Martínez (2017) illus-
trates how fMRI offers a useful template to map similarities with
‘distant’ methods, such as the think-aloud protocol, further sup-
porting the call for interdisciplinary methods in MOC evident
throughout this volume.
Hodgkinson and Healey (2008) provide a comprehensive
review of topics on which fMRI and other neuroscience methods
can be applied, spanning from memory to attention, and beyond.
It is indeed clear that any mental process, particularly from an
intra-individual perspective focused on a microfoundational
behavioral understanding of MOC, necessarily has some
underlying components based on neural systems. One area in
which fMRI can help to substantially advance MOC research is
the interplay between cognition and affect in decision making.
In one illustrative study, Vuilleumier, Armony, Driver, and
Dolan (2001) used event-related fMRI to investigate whether
Downloaded by Doctor Sebastiano Massaro At 12:03 21 December 2017 (PT)
257
Neuroscience Methods
brain responses to fearful vs. neutral faces were controlled by
attention. Participants were asked to assess stimuli at given
locations, and faces or unrelated stimuli (e.g., houses) were
presented at relevant (or not) places. Moreover, the faces
presented to the participants showed either a fearful or a
neutral expression. The researchers found that the activation
of fusiform gyri by faces was strongly affected by attention, but
the left amygdala response to fearful faces was not (Vuilleumier
et al., 2001). Additionally, the fusiform gyri activity was
greater for fearful faces, regardless of the attention. These
ndings highlight the differential effects on how information
from attention and emotion is processed, thus seeking to help
clarify the long-standing debate on the roles of emotion and
attention in management (e.g., Simon, 1987).
Similarly, the ability to regulate emotional responses is
another important domain for managerial research (Hodgkin-
son & Healey, 2011; Healey, Hodgkinson, & Massaro, 2017).
Ochsner, Bunge, Gross, and Gabrieli (2002) used fMRI to study
the brain systems engaged in reappraising negatively salient situ-
ations. The researchers showed that increased activation of the
brain’s prefrontal regions (the brain’s “executive centers”) and
decreased activation of the amygdala and orbitofrontal cortex
were associated with emotional reappraisal (Ochsner et al., 2002).
This suggests a distinct cognitive role in emotional reappraisal
strategies, particularly in negative situations. This evidence may
help to advance both research and practice in those situations in
which managers are exposed to decision making in negative sce-
narios, such as when facing an organizational crisis (e.g., D’Aveni
& MacMillan, 1990).
Finally, fMRI has proven to be an important tool in uncov-
ering the neural mechanisms of moral decision making (see
Cropanzano, Massaro, & Becker, 2017). In an inuential study,
Sanfey, Rilling, Aronson, Nystrom, and Cohen (2003) used fMRI
in an Ultimatum Game to investigate the neural correlates of cog-
nitive and emotional decision making. They showed that unfair
offers triggered activity in brain areas related to both cognition
(i.e., dorsolateral prefrontal cortex) and emotion (i.e., anterior
insula). Due to the increase in activity of the anterior insula when
unfair offers were rejected by research participants, the authors
were able to propose a direct role for emotions in moral decision-
making (Sanfey et al., 2003).
This body of evidence supports the call for investigating
further the involvement of emotions in managerial situations
Downloaded by Doctor Sebastiano Massaro At 12:03 21 December 2017 (PT)
258 SEBASTIANO MASSARO
involving attention, cognitive regulation, and decision making.
This is an important opportunity to advance current scholarship
focusing on affect (e.g., Cote & Miners, 2006) because fMRI
enables the mapping of the neural systems associated with these
constructs that are often beyond what is observable in traditional
MOC research.
ELECTROENCEPHALOGRAPHY
Since the early efforts made to record brain electrical activity
by Berger (see, e.g., Millett, 2001), EEG has rapidly become
one of the most used techniques in cognitive neuroscience. EEG
provides temporally precise information about the state of the
brain in a given period, and information about activity changes
induced by tasks, stimuli, or other events relative to a control
condition, within a specic time span (i.e., event-related poten-
tials or ERPs).
EEG records signals principally resulting from the electrical
activity of a population of cortical neurons named pyramidal neu-
rons (Nunez & Cutillo, 1995). Neurons are electrically charged
cells and when a group of adjacent neurons are charged, they
produce local currents that can be captured by the EEG appara-
tus. The ability to record these electrical signals gives EEG a very
high resolution (i.e., milliseconds), enabling researchers to make
inferences on the temporal unfolding of a cognitive process (for
possible applications in MOC, see e.g., Dietrich & Kanso, 2010).
Conversely, the spatial resolution of EEG is less detailed. The
electrical events are generated below the scalp and thus need to
pass through different layers of tissue, which often results in an
inaccurate representation of the brain activity (Nunez & Srin-
ivasan, 2006). These layers, and the skull in particular, induce
a distorting effect so that the recorded activity becomes a sum
of several underlying sources (Makeig, Bell, Jung, & Sejnowski,
1996) and are the main reason for the poor spatial resolution of
EEG (i.e., about 5–10 centimeters).
This issue leads to another important point for MOC research-
ers to note. Even with newly surfacing topographic methods such
as quantied EEG (qEEG), it is not possible to directly infer the
activity of brain regions which are located deep below the scalp.
To provide a practical example, it is not feasible to directly “map”
activities of areas such the hippocampus (i.e., a region involved in
memory) or the amygdala (e.g., involved in emotional responses
to fearful stimuli).
Downloaded by Doctor Sebastiano Massaro At 12:03 21 December 2017 (PT)
259
Neuroscience Methods
A standard EEG research design requires participants to per-
form a task while having conducting electrodes placed on precise
locations of the scalp (see Towle et al., 1993). The electrodes are
then connected to a digital amplier that captures the electrical
signals and transfers them to a computer for processing and anal-
ysis. Electrodes are either applied with a conducting gel/solution
or are dry and are often positioned within a head-cap or helmet
to facilitate wearability and preparation of the experiment (see
also Grozea, Voinescu, & Fazli, 2011; Lopez-Gordo, Sanchez-
Morillo, & Valle, 2014). The number of electrodes ranges from 8
up to 256: the higher the number of electrodes, the more reliable
the signal output will be. However, the use of high-density set-
ups also requires lengthy preparation time and such equipment is
rarely portable. Figure 4 shows an example of what a typical EEG
recording output looks like.
Pros and Cons. EEG enables researchers to capture instanta-
neous brain dynamics and monitor changes in the brain’s activity
and associated mental processes. Additionally, an EEG setup is rel-
atively cheap, has fairly low maintenance and running costs, and
does not require constant in-house R&D expertise (e.g., a machine
technician, a radiologist) with associated costs, unlike fMRI.
Recently, the use of portable devices and dry electrodes has
opened up new opportunities for EEG-based investigations. Such
Figure 4: EEG Traces during Resting State. Time is Expressed in Seconds on
the Horizontal Axis, While Amplitude on a Scale of 100 μV on the Vertical Axis
(Source: Adapted from Cherninskyi, 2017; https://commons.wikimedia.org/wiki/
File:Human_EEG_without_alpha-rhythm.png).
Downloaded by Doctor Sebastiano Massaro At 12:03 21 December 2017 (PT)
260 SEBASTIANO MASSARO
devices are usually quite affordable and promise high ecological
validity thanks to their portability. However, researchers should
note that such affordable and portable devices commonly use a
low number of electrodes (e.g., about 10), and often rely on pro-
prietary algorithms for data analysis, thus not always ensuring
fuller experimental control and research transparency.
EEG research also has important limitations: EEG signals
have a high noise-to-signal ratio because they can detect signals
from different sources other than the brain site of interest, such
as eye blinking (Hoffmann & Falkenstein, 2008). Thus, as in any
of the neuroscience techniques reviewed here, it is important to
employ accurate artifact reduction and ltering algorithms before
processing and analyzing data (Joyce, Gorodnitsky, & Kutas,
2004). Moreover, EEG has a fairly poor spatial resolution. This
yields some challenges in inferring where the signal is truly gener-
ated. A mixed methods approach integrating EEG and fMRI and
incorporating increasingly rened analyses is advancing possible
solutions to tackle this problem (Ritter & Villringer, 2006).
Advancing MOC research using EEG. Recently, EEG appli-
cations have enjoyed growing interest in management research.
The resulting insights have mostly concentrated on leadership and
qEEG approaches thus far (e.g., Balthazard, Waldman, Thatcher,
& Hannah, 2012). However, the potential of EEG to advance
MOC paradigms extends farther than that.
For instance, Klimesch (1999) explains that EEG oscillations
in the alpha and theta bands (i.e., common waveforms classi-
ed according to their frequency, amplitude, shape, and sites
on the scalp) distinctively reveal cognitive and memory task
performance. Performance is generally related to an increase in
the spectral power of alpha and a decrease in theta. In addition,
alpha frequency yields substantial individual differences and its
asynchrony is positively correlated with long-term memory, while
theta synchronization is positively correlated with the ability to
encode novel information (Klimesch, 1999). Such ndings suggest
that by appreciating distinct waveforms, EEG represents a useful
technique for “mapping” research participants’ performance to
specic cognitive tasks, such as memory. This in turn offers meas-
urable and objective variables to better understand the responses
of individuals to complex workplace tasks requiring the retrieval
of information stored in their long- memory repository. EEG may
also promote novel insights into the micro-foundations of trans-
active memory systems in organizations (Argote & Ren, 2012).
Downloaded by Doctor Sebastiano Massaro At 12:03 21 December 2017 (PT)
261
Neuroscience Methods
Attention is another important area of research for MOC
(e.g., Ocasio, 1997). In another illustrative study, Jung, Makeig,
Stensmo, and Sejnowski (1997) demonstrate that in attention
tasks, human alertness varies on a precise temporal scale and
variation in the EEG spectrum relates to the level of alertness
of the participants. These insights reveal important implications
for research on managerial attention. Indeed, Jung et al. (1997)
showed that accurate and almost real-time estimation of a par-
ticipant’s level of alertness is feasible using EEG measures, thus
supporting the technique’s ability to monitor mental states in
attention-critical organizational settings.
While these are just a few examples, they clearly demonstrate
that EEG can address a number of questions related to how
MOC processes unfold. Along these lines, MOC research may
use EEG as a technique to advance knowledge on how managers
and employees respond to different workplace or organizational
dynamics. In this way, researchers can explore intriguing ques-
tions such as what type of information can, and to what extent,
affect an organizational actor’s overall cognitive ability and reac-
tions to different organizational cues.
MAGNETOENCEPHALOGRAPHY
MEG is a technique for recording the magnetic eld produced
from the electrical activity of neurons, where such electrical
activity is coupled to the generation of magnetic elds (Hansen,
Kringelbach, & Salmelin, 2010). In MEG, the uctuation of these
magnetic elds has similar temporal features to those seen in
EEG. Moreover, this activity can be captured both continuously
(i.e., as a sequence of oscillations) or as a change in response to
experimental events, tasks, or stimuli. Given that the magnetic
activity generated by the neurons is overall weak (10−15 Tesla),
MEG relies on a system of superconducting sensors, called super-
conducting quantum interference devices (SQUIDs) to detect the
arising eld (Hari & Salmelin, 2012).
In a MEG experiment, the participant sits inside a shielded
imaging suite and wears a helmet which contains hundreds of
super sensitive magnetometers (SQUIDs). These SQUIDs ena-
ble the researcher to record the magnetic eld generated by the
“activated” neurons during the experimental session and in turn,
derive inferences on the temporal and spatial properties of the
correlates.
Downloaded by Doctor Sebastiano Massaro At 12:03 21 December 2017 (PT)
262 SEBASTIANO MASSARO
Pros and Cons. MEG yields several benets, namely precise
temporal resolution and a better spatial resolution relatively to
EEG. This is because the magnetic elds are not greatly distorted
by the tissues underlying the scalp and the large amount of sen-
sors allows for the production of a more detailed “map” of the
signal. Unlike EEG, MEG can precisely locate where the brain
signal is generated on the cortex. Consequently, researchers are
able to make detailed inferences about both the location and the
duration of the cortical activity (Hansen et al., 2010). However,
it is also true that the spatial resolution is less accurate compared
to an MRI. Figure 5 illustrates a comparison between fMRI and
MEG outputs. In addition, a MEG experiment is relatively easy
to set up and requires a shorter preparation period than that
required for EEG.
Notwithstanding these benets, the cost of an MEG suite is
quite prohibitive, at present reaching the order of millions of US
dollars. Indeed, because the environment is affected by numer-
ous electromagnetic sources, to avoid any signal interference, the
MEG scanner is required to be placed in a protected shielded
chamber. This set-up also requires availability of liquid helium,
increasing maintenance and running costs. Moreover, there are
comparatively fewer MEG centers available in the world than
fMRI ones.
Advancing MOC research using MEG. As far as I know,
MEG has not yet been used in management research. This is
most probably due to both the high cost of the equipment and
Figure 5: Visual Comparison of fMRI and MEG Imaging Outputs (Source:
Adapted from Human Connectome Project, http://www.human
connectome.org/about/project/resting-MEG.html).
Downloaded by Doctor Sebastiano Massaro At 12:03 21 December 2017 (PT)
263
Neuroscience Methods
the sub-optimal trade-off with the resolution parameters. Yet,
MEG has recently seen an increasing use in cognate areas such as
neuroeconomics.
In one revealing study, researchers found that well connected
resting-state brain networks are correlated with better cognitive
performance (Dolan, 2008). Building on this evidence, Douw et
al. (2011) explained the relationship between resting-state MEG
functional brain and global and domain-specic cognitive perfor-
mance. The authors found that higher performance was related
to increased local connectivity in the theta band and to higher
network clustering, among other features. Moreover, the authors
identied some gender differences within their sample of par-
ticipants: women showed a smaller clustering and shorter band
length, while higher cognitive scores in men were associated with
increased theta band clustering. These results highlight the value
of MEG in examining the complex underpinning of cognitive
processing and may also promote further research on, for exam-
ple, how gender differences are conceptualized and investigated
in MOC (e.g., Powell, Buttereld, & Parent, 2002).
A classic study by Tallon-Baudry, Meyniel and Bourgeois-
Gironde (2011) investigated how the human brain responds
to economic monetary stimuli. It is well known that monetary
incentives “trigger” the reward system in the brain (e.g., Thut et
al., 1997). Tallon-Baudry et al. (2011) went further and explored
how the specic features of monetary stimuli are identied by the
brain. They found that the ventral visual pathway of the brain
can distinguish between coins and neutral stimuli in one-tenth
of a second, regardless of participants’ familiarity with the cur-
rency. These ndings support the idea that the representation of
money is non-specic and independent from past experience,
opening interesting avenues for research on mental representa-
tion in MOC.
Mapping the Peripheral Nervous System
While the techniques reviewed above focus on the brain, I now
move on to discuss two other neuroscience methods that assess
activity of the PNS – cardiovascular measures and electroder-
mal activity. While these measures have been thus far largely
excluded from the ongoing conversation in MOC and organi-
zational neuroscience at large, as I shall now explain, they rep-
resent reliable, cost-effective, and ecologically valid methods for
Downloaded by Doctor Sebastiano Massaro At 12:03 21 December 2017 (PT)
264 SEBASTIANO MASSARO
MOC investigations. Due to these features, these methods also
hold promise for empirically facilitating a view of neuroscience in
MOC that leverages the socially situated perspective provided by
Healey and Hodgkinson (2014, 2015).
CARDIOVASCULAR MEASURES
The heart is an involuntary muscle which provides a constant
blood ow all over the body. The cardiac cycle consists of a
sequence of events between heartbeats and is composed by two
main moments: diastole, in which the heart is at rest and the
blood ows into the heart, and systole, when the electrical activ-
ity generated by pacemaker cells leading to contraction and the
blood is pumped out of the heart (Saladin & Miller, 1998). Along
with this activity, the heart is under control of the ANS, which
inuences the overall electrical activity of the organ (Burgess,
Trinder, Kim, & Luke, 1997).
This electrical activity can be detected via an electrocardio-
gram (ECG) acquired by placing several electrodes on the par-
ticipant’s chest. These electrodes record the electrical potential
produced by the heart’s muscles contraction over one heartbeat,
generating a waveform, where the peak of ventricular respond-
ing is named as R peak. From ECG data, two main indexes of
the ANS can be inferred: HR and HRV. Indeed, the sympathetic
branch of the ANS induces the heart to beat faster by releasing
noradrenalin, while the parasympathetic (vagal) branch causes
the heart to slow down by means of acetylcholine release (Levy
& Martin, 1984).
HR is an index dened as the amount of R peaks within 1
minute (expressed in beats/minute or bpm). An adult HR ranges
from 60 to 100 bmp. HR is affected by individual characteristics
such as age, tness, and lifestyle. In addition, stress and emotional
states can affect HR. Such inuencing characteristics and factors
not only call for attention in controlling these during an experi-
ment but are also suggestive of opportunities for designing tar-
geted research (i.e., looking at the role of stress in MOC).
More recently, a set of indexes that focuses on the varia-
tion over time of the interval between consecutive heartbeats
– HRV – has emerged in the management and MOC literature
(see Massaro & Pecchia, in press). HRV is dened as the uc-
tuation over time of the interval between consecutive heartbeats
taken at the R peak (Sztajzel, 2004). HRV offers several meas-
ures that can be grouped into three main categories. The rst
Downloaded by Doctor Sebastiano Massaro At 12:03 21 December 2017 (PT)
265
Neuroscience Methods
category includes time domain measures (Kleiger, Stein, Bosner,
& Rottman, 1992), which are the simplest indexes to com-
pute, obtained by traditional descriptive statistics of heartbeats,
such as mean and variation of consecutive RR intervals. These
indexes strictly correlate and are assumed to reect ANS para-
sympathetic activity.
The second category covers frequency-domain measures
which are based on the relative portion of different frequency
areas (Montano et al., 2009). HRV can be evaluated in terms of
very-low frequency (VLF; 0.003–0.04 Hz), low frequency (LF;
0.04–0.15 Hz), and high frequency (HF; 0.15–0.4 Hz), by look-
ing both at the peak of the frequency and at the spectral power
(i.e., the area below the curve in Figure 6).
HF is generally interpreted as a marker of vagal modulation,
while LF is interpreted as being a marker of both sympathetic
and parasympathetic activity. Despite some controversy, the HF
to LF ratio (HF/LF) has been recurrently used as an index to
describe the global instantaneous balance between sympathetic
and vagal nerve activities (i.e., the sympatho-vagal balance;
Malliani, 1999).
The third category of HRV measures covers nonlinear indexes
(Mansier et al., 1996). Different to the linear indexes referred
to above, these are non-stationary and are suitable to appreciate
Figure 6: HRV Power Spectrum Showing High, Low, and Very Low Frequencies
(From the Right to the Left).
Downloaded by Doctor Sebastiano Massaro At 12:03 21 December 2017 (PT)
266 SEBASTIANO MASSARO
how HRV reects a chaotic system – which is dynamic, nonlinear,
and rapidly evolves over time. The most commonly used HRV
nonlinear features are entropy, scaling exponents, and fractal
dimensions.
Pros and Cons. These cardiovascular measures are relatively
easy to measure, non-invasive, versatile, and are generally low
cost. The use of wireless and portable instruments to record ECG
data, such as cardiac bio-patches, has allowed researchers to cap-
ture cardiovascular indexes in different experimental settings,
thereby helping to preserve ecological validity.
The main drawback of these measures is associated with
inter- and intra-individual differences, which necessitate a within-
subject design and the need for procient analytical expertise.
Moreover, it is important for researchers to note that these indexes
have low specicity and are useful to provide information on the
overall cognitive states of research participants. Recently, Fooken
(2017) has shown that HRV holds large external validity, offering
support for the use of this method in MOC research.
Advancing MOC research using cardiovascular measures.
HRV measures have been widely used to assess central constructs
in organizational theory and research (for a detailed review see
Massaro & Pecchia, in press). For example, research has associ-
ated HRV with the cognitive dimensions of the ow state asso-
ciated with a task and such measures have been used to infer a
participant’s mental load (Keller, Bless, Blomann, & Kleinböhl,
2011). Recently, Castaldo, Montesinos, Melillo, Massaro, and
Pecchia (2018a) have shown that HRV features are highly cor-
related with performance over a repeated mental task and that
HRV features and dynamics diminish with repetitions, while per-
formance increases. Moreover, Tripathi, Mukundan, and Mathew
(2003) have shown that manipulating cognitive demands in men-
tal task variants reveal the susceptibility of certain spectral com-
ponents of HRV to cognition, in particular, when the cognitive
load is centered on working memory.
HRV also holds implications for the practical implementa-
tions of neuroscience methods in organizations. Leveraging on
the increasing portability and wearability of equipment capable
of recording ECG data (e.g., bio-patches), controlled HRV pro-
tocols may soon make organizational interventions aimed at per-
formance enhancement more efcient (for a preliminary study on
short time recordings, see, e.g., Castaldo et al., 2018b). There is
rising evidence of the effectiveness of HRV neurofeedback – a
protocol that aims to communicate to people on how to change
Downloaded by Doctor Sebastiano Massaro At 12:03 21 December 2017 (PT)
267
Neuroscience Methods
their level of physiological arousal by modulating their own
responses (McCraty, 2005).
ELECTRODERMAL ACTIVITY
Our skin, which is highly innervated by our PNS, protects the
body from external agents and preserves our physiological bal-
ance. These functions are partly controlled by the activity of two
kinds of glands: apocrine and eccrine. The palms of our hands
possess an elevated concentration of eccrine glands, and their
activity can be inferred by the recording of electrodermal activity
(Boucsein, 2012).
Skin conductance, measured in microsimens (mS), is the
most accessible and most widely used form of measuring elec-
trodermal activity (Prokasy, 2012). As the glands’ activity
increases the amount of electrolytes on the skin, skin conduct-
ance involves the conductance of a small amount of current
passing through the skin, which in turn reects the activity of
the sympathetic nervous system.
Skin conductance is usually measured in terms of oscilla-
tions between tonic and phasic activity (Lim et al., 1997). While
the former describes variations in skin conductance level irrel-
evant to a task, the phasic tone denotes conductance changes
induced by stimulus presentation and it is elicited within 5 sec-
onds from the stimulus. The phasic increase of skin conductance
arising after a stimulus presentation is known as skin conduct-
ance response (SCR) (see Figure 7). When arousing stimuli acti-
vate cognitive processing, the body responds by stimulating the
eccrine glands.
Pros and cons. Electrodermal activity has been widely used
in neuroscience and cognitive research (e.g., Schmidt & Walach,
2000). This method not only allows for the gathering of con-
tinuous data, but also results in producing data that is easily
Figure 7: A Typical Pattern of Skin Conductance Response over 1 Minute
(Source: Adapted from: https://commons.wikimedia.org/wiki/File:Gsr.svg).
Downloaded by Doctor Sebastiano Massaro At 12:03 21 December 2017 (PT)
268 SEBASTIANO MASSARO
detectable and reliable. The experimental set-up is unobtrusive,
compact, often wearable and wireless, and relatively cheap (i.e.,
usually less than 500 US dollars).
Notwithstanding these advantages, the related analysis
aligned to this method has some key limitations. Notably, it is
not possible to assess the valence of a response using this method.
That is, even in presence of an increase in electrodermal activ-
ity, it is not possible to infer the nature of the emotive state the
participant is experiencing (e.g., positive vs. negative emotions).
Moreover, several confounding factors can affect the quality of
the data, such as external temperature, repetition of experimental
design, and physiological conditions of the participant.
Advancing MOC research using electrodermal activity.
Measuring electrodermal activity is a particularly useful way
to collect data on cognitive and affective processes as they are
manifesting within the body. For instance, SCR is considered to
be a good marker of individual state and trait characteristics of
emotional responsiveness. Indeed, SCR has been widely used in
decision making research.
Notably, SCR has offered substantial support to the somatic
marker hypothesis (Bechara & Damasio, 2005). The somatic
marker hypothesis suggests that several somatic markers are
linked to the ventromedial prefrontal cortex (vmPFC), an area
within the brain implicated in executive and strategic decisions.
Bechara, Damasio, Tranel and Damasio (2005) found that indi-
viduals with impaired vmPFC underperformed in decision mak-
ing tasks and did not manifest any SCR modulation in response
to fair conditions or losses. Bechara et al. (2005) concluded that
SCR is an ideal marker to infer information about emotional
arousal in decision making. This research highlights the potential
of electrodermal recordings to extend research in MOC. SCR in
particular provides the ability to “map” the fast-paced states of
individuals and thus can be used to explore the reactions of man-
agers and employees to organizational cues (Hodgkinson and
Healey, 2011).
Discussion and Conclusions
In this chapter, I have built on the theoretical insights offered
by Healey and Hodgkinson (2014, 2015) on the use of neu-
roscience in management in order to present and discuss key
neuroscience methods that offer the non-incremental potential
Downloaded by Doctor Sebastiano Massaro At 12:03 21 December 2017 (PT)
269
Neuroscience Methods
to advance MOC research and more generally, management
research. For each of the methods reviewed in the chapter, I
have presented an overview, their benets and limitations, and
offered some examples of how such methods might advance
MOC research.
The review of neuroscience methods provided in the chapter
should enable management researchers to acquire a more techni-
cal introduction to neuroscience techniques and provide research-
ers with a more cohesive vision of MOC and neuroscience. I am
also hopeful that leveraging this knowledge, MOC researchers
will be empowered to better understand and identify the most
suitable methods to tackle their research questions (i.e., what they
should measure), appreciate the boundaries and opportunities of
each technique, as well as some of the key associated practicali-
ties, costs, and benets of each.
Specically, I have divided these techniques according to
their main anatomo-functional sites of inference. This approach
was utilized for several reasons. First, it has allowed me to align
my review with the theoretical insights for studying neurosci-
ence in MOC advanced by Hodgkinson and Healey (2014,
2015). Second, my framework sustains the idea that neurosci-
ence in MOC can extend investigations beyond the brain per
se (Massaro & Pecchia, in press). Lastly, I hope that my sys-
tematization will alert researchers to the fact that the most
appropriate research method to be used is always the one most
closely aligned with the research questions being investigated.
For instance, if ecological validity is a priority, then the port-
able and wearable tools usable in EEG and HRV would offer a
non-trivial advantage. On the contrary, if researchers were more
interested in understanding the neural correlates of managers’
mental processes, then the techniques featuring higher spatial
resolution would likely be their primary choice.
Within this chapter, I have also argued that the theoretical
advancements achievable by using neuroscience in MOC will nec-
essarily result from a careful integration of the aforementioned
techniques with more traditional ones. For one, I mentioned the
compelling need to support any neuroscience study with behav-
ioral data. Thus, as Huff (1990) demonstrated, novel methods in
MOC, like neuroscience methods, should ultimately be used to
gain additional sources of insight into organizational life. Keep-
ing this core concept as reference, and extending earlier insights
on neuroscience methods in management (Massaro, 2016), here
I have argued that neuroscience techniques can complement
Downloaded by Doctor Sebastiano Massaro At 12:03 21 December 2017 (PT)
270 SEBASTIANO MASSARO
current techniques within MOC, pending a fuller understanding
of their methodological underpinnings.
To this end, I believe that this methodological knowledge will
also enable researchers to address some important questions at
the frontiers of MOC, such as ‘Can neuroscience reliably address
issues of construct validity in this eld? What is the most suit-
able theoretical position able to merge MOC and neuroscience?
How can emerging topics in MOC research, such as emotional
self-regulation, morality, and cooperation, be advanced by neuro-
science methods?’ As discussed throughout, readers should con-
sult Hodgkinson and Healey (2008) for a comprehensive review
of topics and arguments in MOC on which neuroscience methods
can be benecially applied.
The insights of the chapter can also be extended to the needs
of practitioners. Indeed, interest in using neuroscience methods
to inform business applications in decision making is an area of
application that is rapidly expanding (Waytz & Mason, 2013).
For one, despite known limitations and caveats (see Massaro,
2015), neurofeedback represents one of the most auspicious
opportunities to convert neuroscience research into business prac-
tice. Added to this, increasing news of brain–computer interfaces
and “neuroscience-informed” approaches in the workplace are
becoming regular headlines in the media, showing an increasing
demand from the “real-world” of academics enabled with exper-
tise ready to address and inform novel business opportunities.
All in all, I am condent that the knowledge presented in
this chapter offers useful insights to deepen understanding of the
cognitive architecture of organizational life. At the same time,
I caution readers not to fall for the “seductive allure” of neu-
roscience (Weisberg, Keil, Goodstein, Rawson, & Gray, 2008).
Neuroscience and its methods require specialized knowledge,
and expertise, often accompanied by complex analytical skills.
Over-simplifying the underlying methodology of a study risks
invalidating its ndings, resulting in the production of amateurish
research and questionable insights, or possibly worse, replicating
knowledge which may be already well established in mainstream
neuroscience. Unfortunately, we are already witnessing a growing
body of “improvisational” neuroscience experts, and accompany-
ing research missteps, are rapidly surfacing in the management
literature at large, even in top scholarly outlets. Concluding the
chapter on a more optimistic note, I recommend that the manage-
ment community at large work in closer partnership with trained
neuroscientists and, albeit not straightforwardly, seek to establish
Downloaded by Doctor Sebastiano Massaro At 12:03 21 December 2017 (PT)
271
Neuroscience Methods
a common working language. Hopefully, this chapter offers a
supportive step toward this end and will thus enable researchers
to fully “cross the traditions” (Hodgkinson & Healey, 2008) of
these exciting disciplines.
Note
1. For ease of dissemination some of the images included in this chapter
are purposively taken from available online open sources.
Acknowledgments
I am indebted to the editors of this book, Robert J. Galavan, Kristian J. Sund,
and Gerard P. Hodgkinson, for the invitation to write this chapter, their kind
availability, and useful feedback. I would also like to thank Daniella Laureiro-
Martinez for her suggestions on an earlier version of this manuscript and to
Mark P. Healey and Karen Nokes for their help at the nal stages of revision.
I am grateful to Giovanni Gavetti for the long conversations (and longer hikes)
that have massively helped me to renew my attention to the neurocognitive sci-
ences. Special thanks, beyond his editorial duties, to Gerard P. Hodgkinson for
the numerous dialogues over the past few years and his enthusiasm in promoting
intersections and distinctions between management and neuroscience.
References
Aguirre, G. K., Zarahn, E., & D’Esposito, M. (1998). The variability of human,
BOLD hemodynamic responses. Neuroimage, 8(4), 360–369.
Amaro, E., & Barker, G. J. (2006). Study design in fMRI: Basic principles. Brain
and Cognition, 60(3), 220–232.
Argote, L., & Ren, Y. (2012). Transactive memory systems: A microfoundation of
dynamic capabilities. Journal of Management Studies, 49(8), 1375–1382.
Ashkanasy, N. M., Becker, W. J., & Waldman, D. A. (2014). Neuroscience and
organizational behavior: Avoiding both neuro-euphoria and neuro-phobia. Jour-
nal of Organizational Behavior, 35(7), 909–919.
Bagozzi, R. P., Verbeke, W. J., Dietvorst, R. C., Belschak, F. D., van den Berg,
W. E., & Rietdijk, W. J. (2013). Theory of mind and empathic explanations of
Machiavellianism: A neuroscience perspective. Journal of Management, 39(7),
1760–1798.
Balthazard, P. A., Waldman, D. A., Thatcher, R. W., & Hannah, S. T. (2012). Dif-
ferentiating transformational and non-transformational leaders on the basis of
neurological imaging. The Leadership Quarterly, 23(2), 244–258.
Bechara, A., & Damasio, A. R. (2005). The somatic marker hypothesis: A neural
theory of economic decision. Games and Economic Behavior, 52(2), 336–372.
Downloaded by Doctor Sebastiano Massaro At 12:03 21 December 2017 (PT)
272 SEBASTIANO MASSARO
Bechara, A., Damasio, H., Tranel, D., & Damasio, A. R. (2005). The Iowa gam-
bling task and the somatic marker hypothesis: Some questions and answers.
Trends in Cognitive Sciences, 9(4), 159–162.
Becker, W. J., Cropanzano, R., & Sanfey, A. G. (2011). Organizational neurosci-
ence: Taking organizational theory inside the neural black box. Journal of Man-
agement, 37(4), 933–961.
Becker, W. J., & Menges, J. I. (2013). Biological implicit measures in HRM and
OB: A question of how not if. Human Resource Management Review, 23(3),
219–228.
Boucsein, W. (2012). Electrodermal activity. New York: Springer Science & Busi-
ness Media.
Burgess, H. J., Trinder, J., Kim, Y., & Luke, D. (1997). Sleep and circadian inu-
ences on cardiac autonomic nervous system activity. American Journal of Physi-
ology-Heart and Circulatory Physiology, 273(4), H1761–H1768.
Butler, M., Lee, N., & Senior, C. (2017). Organisational cognitive neuroscience
drives theoretical progress, or: The curious case of the straw man murder. Human
realtions, 70(10), 1171–1190.
Castaldo, R., Montesinos, L., Melillo, P., Massaro, S., Pecchia, L. (2018a). To
what extent can we shorten HRV analysis in wearable sensing? A case study
on mental stress detection. In H. Eskola, O. Väisänen, J. Viik & J. Hyttinen
(Eds). EMBEC & NBC 2017. EMBEC 2017, NBC 2017. IFMBE Proceedings
(Vol. 65). Singapore: Springer.
Castaldo, R., Montesinos, L., Wan, T.S., Serban, A., Massaro, S., & Pecchia,
L. (2018b). Heart rate variability analysis and performance during a repeated
mental workload task. In H. Eskola, O. Väisänen, J. Viik, J. Hyttinen (Eds),
EMBEC & NBC 2017. EMBEC 2017, NBC 2017. IFMBE Proceedings
(Vol. 65). Singapore: Springer.
Cherninskyi, A. (2017). The sample of human EEG with in resting state. Avail-
able at: https://commons.wikimedia.org/wiki/
Cote, S., & Miners, C. T. (2006). Emotional intelligence, cognitive intelligence,
and job performance. Administrative Science Quarterly, 51(1), 1–28.
Cropanzano, R. S., Massaro, S., & Becker, W. J. (2017). Deontic justice and
organizational neuroscience. Journal of Business Ethics, 144(4), 733–754.
D’Aveni, R. A., & MacMillan, I. C. (1990). Crisis and the content of managerial
communications: A study of the focus of attention of top managers in surviving
and failing rms. Administrative Science Quarterly, 634–657.
Davis, M. (1992). The role of the amygdala in fear and anxiety. Annual Review
of Neuroscience, 15(1), 353–375.
de Holan, P. M. (2014). It’s all in your head: Why we need neuroentrepreneur-
ship. Journal of Management Inquiry, 23(1), 93–97.
Dietrich, A., & Kanso, R. (2010). A review of EEG, ERP, and neuroimaging stud-
ies of creativity and insight. Psychological Bulletin, 136(5), 822.
Di Martino, A., Scheres, A., Margulies, D. S., Kelly, A. M. C., Uddin, L. Q.,
Shehzad, Z., … Milham, M. P. (2008). Functional connectivity of human stria-
tum: A resting state FMRI study. Cerebral Cortex, 18(12), 2735–2747.
Dolan, R. J. (2008). Neuroimaging of cognition: Past, present, and future.
Neuron, 60(3), 496–502.
Downloaded by Doctor Sebastiano Massaro At 12:03 21 December 2017 (PT)
273
Neuroscience Methods
Douw, L., Schoonheim, M. M., Landi, D., Van der Meer, M. L., Geurts, J. J.
G., Reijneveld, J. C., … Stam, C. J. (2011). Cognition is related to resting-state
small-world network topology: An magnetoencephalographic study. Neurosci-
ence, 175, 169–177.
Eklund, A., Nichols, T. E., & Knutsson, H. (2016). Cluster failure: Why fMRI
inferences for spatial extent have inated false-positive rates. Proceedings of the
National Academy of Sciences, 201602413.
Faro, S. H., & Mohamed, F. B. (Eds.). (2010). BOLD fMRI: A guide to functional
imaging for neuroscientists. New York: Springer Science & Business Media.
Felin, T., Foss, N. J., & Ployhart, R. E. (2015). The microfoundations movement
in strategy and organization theory. The Academy of Management Annals, 9(1),
575–632.
Fooken, J. (2017). Heart rate variability indicates emotional value during pro-
social economic laboratory decisions with large external validity. Scientic
Reports, 7.
Friston, K. J., Price, C. J., Fletcher, P., Moore, C., Frackowiak, R. S. J., & Dolan,
R. J. (1996). The trouble with cognitive subtraction. Neuroimage, 4(2), 97–104.
Gavetti, G. (2005). Cognition and hierarchy: Rethinking the microfoundations
of capabilities’ development. Organization Science, 16(6), 599–617.
Gavetti, G., & Levinthal, D. (2000). Looking forward and looking backward:
Cognitive and experiential search. Administrative Science Quarterly, 45(1),
113–137.
Gavetti, G., Levinthal, D., & Ocasio, W. (2007). Perspective—Neo-Carnegie: The
Carnegie school’s past, present, and reconstructing for the future. Organization
Science, 18(3), 523–536.
Gavetti, G., & Rivkin, J. W. (2007). On the origin of strategy: Action and cogni-
tion over time. Organization Science, 18(3), 420–439.
Gazzaniga, M. S. (2004). The cognitive neurosciences. Cambridge, MA: MIT
Press.
Ginsberg, A. (1992). Mapping strategic thought (Book Review). Academy of
Management Review, 17(1), 145–148.
Godfrey, P. C., & Hill, C. W. (1995). The problem of unobservables in strategic
management research. Strategic Management Journal, 16(7), 519–533.
Graner, J. L., Oakes, T. R., French, L. M., & Riedy, G. (2013). Functional MRI in
the investigation of blast-related traumatic brain injury. Frontiers in Neurology,
4, 16.
Grozea, C., Voinescu, C. D., & Fazli, S. (2011). Bristle-sensors—low-cost exible
passive dry EEG electrodes for neurofeedback and BCI applications. Journal of
Neural Engineering, 8(2), 025008.
Hansen, P., Kringelbach, M., & Salmelin, R. (Eds.). (2010). MEG: An introduc-
tion to methods. New York: Oxford University Press.
Hari, R., & Salmelin, R. (2012). Magnetoencephalography: From SQUIDs to
neuroscience: Neuroimage 20th anniversary special edition. Neuroimage, 61(2),
386–396.
Healey, M. P., & Hodgkinson, G. P. (2014). Rethinking the philosophical and
theoretical foundations of organizational neuroscience: A critical realist alterna-
tive. Human Relations, 67(7), 765–792.
Downloaded by Doctor Sebastiano Massaro At 12:03 21 December 2017 (PT)
274 SEBASTIANO MASSARO
Healey, M. P., & Hodgkinson, G. P. (2015). Toward a theoretical framework for
organizational neuroscience. In Organizational Neuroscience (pp. 51–81). Emer-
ald Group Publishing Limited.
Healey, M. P., Hodgkinson, G. P., & Massaro, S. (2016, January). Can brains
manage? Academy of Management Proceedings, 2016(1), p. 14005.
Healey, M. P., Hodgkinson, G. P., & Massaro, S. (2017, January). Emotion reg-
ulation in organizations: Integrating neural and social processes. Academy of
Management Proceedings, 2017(1).
Healey, M. P., Vuori, T., & Hodgkinson, G. P. (2015). When teams agree while
disagreeing: Reexion and reection in shared cognition. Academy of Manage-
ment Review, 40(3), 399–422.
Hodgkinson, G. P. (2015) The behavioural strategy perspective. In M. Jenkins,
V. Ambrosini & N. Collier (Eds.), Advanced strategic management: A multi-
perspective approach (3rd ed., pp. 201–228). Basingstoke: Palgrave Macmillan
Hodgkinson, G. P., & Healey, M. P. (2008). Cognition in organizations. Annual
Review Psychology, 59, 387–417.
Hodgkinson, G. P., & Healey, M. P. (2011). Psychological foundations of dynamic
capabilities: Reexion and reection in strategic management. Strategic Manage-
ment Journal, 32(13), 1500–1516.
Hodgkinson, G. P., & Johnson, G. (1994). Exploring the mental models of com-
petitive strategists: The case for a processual approach. Journal of Management
Studies, 31(4), 525–552.
Hoffmann, S., & Falkenstein, M. (2008). The correction of eye blink artefacts
in the EEG: A comparison of two prominent methods. PLoS One, 3(8), e3004.
Huff, A. S. (1990). Mapping strategic thought. New York: John Wiley & Sons.
Huff, A. S., & Fletcher, K. E. (1990). Conclusion: Key mapping decisions. Map-
ping Strategic Thought, 403–412.
Huff, A. S., Milliken, F. J., Hodgkinson, G. P., Galavan, R. J., & Sund, K. J.
(2016). A conversation on uncertainty in managerial and organizational cogni-
tion. In Uncertainty and Strategic Decision Making. Bingley, UK: Emerald Group
Publishing Limited.
Jänig, W. (1989). Autonomic nervous system. In R. F. Schmidt & G. Thews
(Eds.),Human physiology (pp. 333-370). Springer Berlin.
Joyce, C. A., Gorodnitsky, I. F., & Kutas, M. (2004). Automatic removal of eye
movement and blink artifacts from EEG data using blind component separation.
Psychophysiology, 41(2), 313–325.
Jung, T. P., Makeig, S., Stensmo, M., & Sejnowski, T. J. (1997). Estimating alert-
ness from the EEG power spectrum. IEEE Transactions on Biomedical Engineer-
ing, 44(1), 60–69.
Kable, J. W. (2011). The cognitive neuroscience toolkit for the neuroeconomist: A
functional overview. Journal of Neuroscience, Psychology, and Economics, 4(2), 63.
Keller, J., Bless, H., Blomann, F., & Kleinböhl, D. (2011). Physiological aspects
of ow experiences: Skills-demand-compatibility effects on heart rate vari-
ability and salivary cortisol. Journal of Experimental Social Psychology, 47(4),
849–852.
Downloaded by Doctor Sebastiano Massaro At 12:03 21 December 2017 (PT)
275
Neuroscience Methods
Kim, S. G., Richter, W., & Uǧurbil, K. (1997). Limitations of temporal resolution
in functional MRI. Magnetic Resonance in Medicine, 37(4), 631–636.
Kleiger, R. E., Stein, P. K., Bosner, M. S., & Rottman, J. N. (1992). Time domain
measurements of heart rate variability. Cardiology Clinics, 10(3), 487–498.
Klimesch, W. (1999). EEG alpha and theta oscillations reect cognitive and
memory performance: A review and analysis. Brain Research Reviews, 29(2),
169–195.
Laureiro-Martínez, D., Brusoni, S., Canessa, N., & Zollo, M. (2015). Understand-
ing the exploration–exploitation dilemma: An fMRI study of attention control and
decision-making performance. Strategic Management Journal, 36(3), 319–338.
Laureiro-Martínez, D., Venkatraman, V., Cappa, S., Zollo, M., & Brusoni, S.
(2015). Cognitive neurosciences and strategic management: Challenges and
opportunities in tying the knot. In Gavetti G. & Ocasio W. (Eds.), Advances in
Strategic Management (Vol. 32), Cognition and Strategy (pp. 351–370). Bingley,
UK: Emerald Group Publishing Limited.
Levy, M. N., & Martin, P. J. (1984). Neural control of the heart. In N. Sperelakis
(Ed.), Physiology and Pathophysiology of the Heart (pp. 337–354). Boston, MA:
Springer US.
Lim, C. L., Rennie, C., Barry, R. J., Bahramali, H., Lazzaro, I., Manor, B., &
Gordon, E. (1997). Decomposing skin conductance into tonic and phasic compo-
nents. International Journal of Psychophysiology, 25(2), 97–109.
Lindebaum, D., & Jordan, P. J. (2014). A critique on neuroscientic methodolo-
gies in organizational behavior and management studies. Journal of Organiza-
tional Behavior, 35(7), 898–908.
Logothetis, N. K. (2008). What we can do and what we cannot do with fMRI.
Nature, 453(7197), 869–878.
Logothetis, N. K., Pauls, J., Augath, M., Trinath, T., & Oeltermann, A. (2001).
Neurophysiological investigation of the basis of the fMRI signal. Nature,
412(6843), 150–157.
Lopez-Gordo, M. A., Sanchez-Morillo, D., & Valle, F. P. (2014). Dry EEG elec-
trodes. Sensors, 14(7), 12847–12870.
Mai, J. K., & Paxinos, G. (Eds.). (2011). The human nervous system. London,
UK: Academic Press.
Makeig, S., Bell, A. J., Jung, T. P., & Sejnowski, T. J. (1996). Independent compo-
nent analysis of electroencephalographic data. Advances in Neural Information
Processing Systems, 145–151.
Malliani, A. (1999). The pattern of sympathovagal balance explored in the fre-
quency domain. Physiology, 14(3), 111–117.
Mansier, P., Clairambault, J., Charlotte, N., Médigue, C., Vermeiren, C., LePape,
G., … Swynghedauw, B. (1996). Linear and non-linear analyses of heart rate
variability: A minireview. Cardiovascular Research, 31(3), 371–379.
March, J. G., & Simon, H. A. (1958). Organizations. Hoboken, NJ: Wiley-Blackwell.
Massaro, S. (2015). Neurofeedback in the workplace: From neurorehabilitation
hope to neuroleadership hype?. International Journal of Rehabilitation Research,
38(3), 276–278.
Downloaded by Doctor Sebastiano Massaro At 12:03 21 December 2017 (PT)
276 SEBASTIANO MASSARO
Massaro, S. (2016) Neuroscientic methods applications in strategic man-
agement. In G. Dagnino & C. Cinci (Eds.), Strategic management: A research
method handbook (pp. 254–282). New York, NY: Routledge.
Massaro, S., & Pecchia, L. (in press). Heart rate variability (HRV) analysis: A
methodology for organizational neuroscience. Organizational Research Methods.
10.1177/1094428116681072.
McCorry, L. K. (2007). Physiology of the autonomic nervous system. American
Journal of Pharmaceutical Education, 71(4), 78.
McCraty, R. (2005). Enhancing emotional, social, and academic learning with
heart rhythm coherence feedback. Biofeedback, 33(4), 130–134.
Menon, R. S., Gati, J. S., Goodyear, B. G., Luknowsky, D. C., & Thomas, C. G.
(1998). Spatial and temporal resolution of functional magnetic resonance imag-
ing. Biochemistry and Cell Biology, 76(2–3), 560–571.
Millett, D. (2001). Hans Berger: From psychic energy to the EEG. Perspectives in
Biology and Medicine, 44(4), 522–542.
Montague, P. R., Berns, G. S., Cohen, J. D., McClure, S. M., Pagnoni, G., Dha-
mala, M., … Fisher, R. E. (2002). Hyperscanning: Simultaneous fMRI during
linked social interactions. Neuroimage, 16(4), 1159–1164.
Montano, N., Porta, A., Cogliati, C., Costantino, G., Tobaldini, E., Casali, K. R.,
& Iellamo, F. (2009). Heart rate variability explored in the frequency domain: a
tool to investigate the link between heart and behavior. Neuroscience & Biobe-
havioral Reviews, 33(2), 71–80.
Narayanan, V. K., Zane, L. J., & Kemmerer, B. (2011). The cognitive perspective
in strategy: An integrative review. Journal of Management, 37(1), 305–351.
Nunez, P. L., & Cutillo, B. A. (1995). Neocortical dynamics and human EEG
rhythms. New York, NY: Oxford University Press.
Nunez, P. L., & Srinivasan, R. (2006). Electric elds of the brain: the neurophys-
ics of EEG. New York, NY: Oxford University Press.
Ocasio, W. (1997). Towards an attention-based view of the rm. Strategic Man-
agement Journal, 187–206.
Ochsner, K. N., Bunge, S. A., Gross, J. J., & Gabrieli, J. D. (2002). Rethinking
feelings: An FMRI study of the cognitive regulation of emotion. Journal of Cog-
nitive Neuroscience, 14(8), 1215–1229.
Ogawa, S., Lee, T. M., Kay, A. R., & Tank, D. W. (1990). Brain magnetic reso-
nance imaging with contrast dependent on blood oxygenation. Proceedings of
the National Academy of Sciences, 87(24), 9868–9872.
Penny, W. D., Friston, K. J., Ashburner, J. T., Kiebel, S. J., & Nichols, T. E. (Eds.).
(2011). Statistical parametric mapping: The analysis of functional brain images.
Academic Press.
Petersen, S. E., & Dubis, J. W. (2012). The mixed block/event-related design.
Neuroimage, 62(2), 1177–1184.
Poldrack, R. A. (2006). Can cognitive processes be inferred from neuroimaging
data? Trends in Cognitive Sciences, 10(2), 59–63.
Poldrack, R. A. (2007). Region of interest analysis for fMRI. Social Cognitive
and Affective Neuroscience, 2(1/4), 67.
Downloaded by Doctor Sebastiano Massaro At 12:03 21 December 2017 (PT)
277
Neuroscience Methods
Porac, J. (2014). Managerial and organizational cognition. Wiley Ency-
clopedia of Management. New York, NY: John Wiley & Sons, Ltd. DOI:
10.1002/9781118785317
Porac, J. F., & Thomas, H. (2002). Managing cognition and strategy: Issues,
trends and future directions. Handbook of Strategy and Management, 165–181.
Powell, G. N., Buttereld, D. A., & Parent, J. D. (2002). Gender and managerial
stereotypes: Have the times changed?. Journal of Management, 28(2), 177–193.
Powell, T. C. (2011). Neurostrategy. Strategic Management Journal, 32(13),
1484–1499.
Powell, T. C., Lovallo, D., & Fox, C. R. (2011). Behavioral strategy. Strategic
Management Journal, 32(13), 1369–1386.
Price, C. J., & Friston, K. J. (1997). Cognitive conjunction: A new approach to
brain activation experiments. Neuroimage, 5(4), 261–270.
Prokasy, W. (Ed.). (2012). Electrodermal activity in psychological research. Lon-
don, UK: Academic Press.
Richards, J. M., Plate, R. C., & Ernst, M. (2013). A systematic review of fMRI
reward paradigms used in studies of adolescents vs. adults: the impact of task
design and implications for understanding neurodevelopment. Neuroscience &
Biobehavioral Reviews, 37(5), 976–991.
Ritter, P., & Villringer, A. (2006). Simultaneous Eeg–fmri. Neuroscience & Biobe-
havioral Reviews, 30(6), 823–838.
Saladin, K. S., & Miller, L. (1998). Anatomy & physiology. New York, NY:
WCB/McGraw-Hill.
Sanfey, A. G., Rilling, J. K., Aronson, J. A., Nystrom, L. E., & Cohen, J. D. (2003).
The neural basis of economic decision-making in the ultimatum game. Science,
300(5626), 1755–1758.
Schmidt, S., & Walach, H. (2000). Electrodermal activity (EDA)-state-of-the-art
measurement and techniques for parapsychological purposes. The Journal of
Parapsychology, 64(2), 139.
Senior, C., Lee, N., & Butler, M. (2011). PERSPECTIVE—organizational cogni-
tive neuroscience. Organization Science, 22(3), 804–815.
Sharp, C., Monterosso, J., & Montague, P. R. (2012). Neuroeconomics: A bridge
for translational research. Biological Psychiatry, 72(2), 87–92.
Simon, H. A. (1955). A behavioral model of rational choice. The Quarterly Jour-
nal of Economics, 69(1), 99–118.
Simon, H. A. (1987). Making management decisions: The role of intuition and
emotion. The Academy of Management Executive (1987–1989), 57–64.
Strother, S. C. (2006). Evaluating fMRI preprocessing pipelines. IEEE Engineer-
ing in Medicine and Biology Magazine, 25(2), 27–41.
Sztajzel, J. (2004). Heart rate variability: A noninvasive electrocardiographic
method to measure the autonomic nervous system. Swiss Medical Weekly, 134,
514–522.
Tallon-Baudry, C., Meyniel, F., & Bourgeois-Gironde, S. (2011). Fast and auto-
matic activation of an abstract representation of money in the human ventral
visual pathway. PloS One, 6(11), e28229.
Downloaded by Doctor Sebastiano Massaro At 12:03 21 December 2017 (PT)
278 SEBASTIANO MASSARO
Thut, G., Schultz, W., Roelcke, U., Nienhusmeier, M., Missimer, J., Maguire, R.
P., & Leenders, K. L. (1997). Activation of the human brain by monetary reward.
Neuroreport, 8(5), 1225–1228.
Towle, V. L., Bolaños, J., Suarez, D., Tan, K., Grzeszczuk, R., Levin, D. N., …
Spire, J. P. (1993). The spatial location of EEG electrodes: Locating the best-
tting sphere relative to cortical anatomy. Electroencephalography and Clinical
Neurophysiology, 86(1), 1–6.
Tripathi, K. K., Mukundan, C. R., & Mathew, T. L. (2003). Attentional modula-
tion of heart rate variability (HRV) during execution of PC based cognitive tasks.
Ind J Aerospace Med, 47(1), 1–10.
Vuilleumier, P., Armony, J. L., Driver, J., & Dolan, R. J. (2001). Effects of atten-
tion and emotion on face processing in the human brain: An event-related fMRI
study. Neuron, 30(3), 829–841.
Waldman, D. A. (2013). Interdisciplinary research is the key. Frontiers in Human
Neuroscience, 7, 562.
Waldman, D. A., Wang, D., Hannah, S., & Balthazard, P. (2017). A neurologi-
cal and ideological perspective of ethical leadership. Academy of Management
Journal, 60(4), 1285–1306.
Waytz, A., & Mason, M. (2013). Your brain at work (digest summary). Harvard
Business Review, 91(7/8), 102–111.
Weisberg, D. S., Keil, F. C., Goodstein, J., Rawson, E., & Gray, J. R. (2008). The
seductive allure of neuroscience explanations. Journal of Cognitive Neurosci-
ence, 20(3), 470–477.
White, T., O’Leary, D., Magnotta, V., Arndt, S., Flaum, M., & Andreasen, N. C.
(2001). Anatomic and functional variability: the effects of lter size in group
fMRI data analysis. Neuroimage, 13(4), 577–588.
Zago, S., Lorusso, L., Ferrucci, R., & Priori, A. (2011). Functional neuroimaging:
A historical perspective. In P. Bright & A. Ruskin (eds.), Neuroimaging-Methods.
(Vol. 1). InTech Open Access Publisher.
Zarahn, E., Aguirre, G. K., & D’Esposito, M. (1997). Empirical analyses of
BOLD fMRI statistics. NeuroImage, 5(3), 179–197.
Downloaded by Doctor Sebastiano Massaro At 12:03 21 December 2017 (PT)
