ChapterPDF Available

Neuroscientific methods for strategic management

October 2015

DOI:10.4324/9780203676615

In book: Research methods for strategic management (pp.253-282)
Chapter: NEUROSCIENTIFIC METHODS FOR STRATEGIC MANAGEMENT
Publisher: Routledge
Editors: Giovanni Battista Dagnino, Maria Cristina Cinici

Authors:

Sebastiano Massaro

The Organizational Neuroscience Laboratory | University of Surrey | Warwick University

Main components of an MRI scanner Source: http://www.themesotheliomalibrary.com

…

Spatial and temporal resolutions of neuroscience techniques Notes: The vertical axes show the spatial extent of the techniques; the horizontal axes represent the time intervals over which information can be collected with each technique. Recordings from the central nervous system are often limited in resolution by the properties of nervous tissue and of the specific method. Sources: (a) Churchland and Sejnowski, 1988; (b) Senior, Lee, and Butler, 2011.

…

Spectral analysis of right front coherence in leadership research Notes: The gradient shows the levels of coherence on 3 right frontal electrode locations, including areas at 0% (indicated by the minus signs), areas at 100% (indicated by plus signs), and in between. Dark regions with a + represent areas with high degrees of coherence (75% or higher); dark regions with minus signs characterize areas with low coherence (25% or lower). The numeric values indicate the summed averaged coherence scores for such brain regions in different leaders.

…

Figures - uploaded by Sebastiano Massaro

Content may be subject to copyright.

Content uploaded by Sebastiano Massaro

Content may be subject to copyright.

NEUROSCIENTIFIC METHODS FOR

STRATEGIC MANAGEMENT

Sebastiano Massaro1

In the past decade, social disciplines have looked with increasing interest at

neuroscience. From anthropology (Adenzato and Garbarini, 2006) to law (Greene

and Cohen, 2004; Jones and Shen, 2012), from politics (Connolly, 2002) to

sociology (Franks, 2010), the integration of neuroscientific aspects into social

studies has become a phenomenon of considerable interest. Business scholarship

has not been immune from this trend with contributions crossing leadership

(Ghadiri, Habermacher, and Peters, 2012), marketing (Ar iely and Ber ns, 2010; Lee,

Broderick, and Chamberlain, 2007) and strategy (Powell, 2011), among others.

Accordingly, universities have created dedicated centers of research; journals and

conferences have started to offer substantial space to the role of neuroscience in

management; and several researchers have developed international partnerships

aiming to extend these cutting-edge approaches.2

Although some scholars have questioned the appropriateness and viability of

neuroscience to effectively advance social analyses and business research (e.g.,

Bennett, Hacker, and Bennett, 2003; Gul and Pesendorfer, 2008; Lindebaum and

Zundel, 2013), the intensification and effects of this type of studies cannot be

denied. Indeed, knowing more on how our brain works can help to advance our

understanding of human cognition, emotions, behavior, and decision making, both

inside and outside organizations.

In this work, Iargue that to better appreciate how neuroscience can inform

research in management, understanding the rationale of relevant neuroscience

methods, is afundamental step currently missing in the scholarship.3Only through

this knowledge,will management scholars fully appreciatethe potential of the related

research and possibly incorporatefurther these instrumentsinto their exploratory

equipment.

By mainly concentrating on brain-imaging methods, this chapter provides an

opening review, for both those researchers who are new to neuroscience,and those

multidisciplinary-oriented scholars who are seeking to refresh their knowledge on

the topic. Following an introductory excursus on the early applications of these

methods to management studies, I will explain how they can be classified, and

supply a core description of relevant techniques. Moreover, I will offer some

evidence related to management scholarship, and in particular to strategy, one of

the fields most attentive to neuroscience (e.g., Powell, 2011; Powell, Lovallo, and

Fox, 2011). Finally, the chapter will pinpointcritical considerations for

management research related to employing neuroscience approaches.

The partnership between neuroscience and management

The idea of coupling descriptions of human behaviors and the brain has actively

engaged researchers for centuries (for ahistory of neuroscience see, e.g., Finger

[2001]).Ye t, the experimental partnership betweencognitive neuroscience and social

disciplines –nowadays often called social cognitive and affective neuroscience –has

acquired wider resonance only in the past couple of decades, thanks to the

emergence of functional neuroimagingof behavior (Raichle, 2003; 2009a), the

combination of neuroimaging and behavioral research approaches.This researcharea

generally refersto the use of technologies measuring hemodynamic,electromagnetic,

or biophysical properties and changes in the brain and in the nervous system,

following an experimental behavioral manipulation, task or stimulus, to provide

visual metrics(e.g.graph, image, scan) of the underlying brain regions and neural

functions.The resulting outcomes enable inferences about the relationshipsbetween

neural substratesand behavioral or mental processes associated.4

One of the first techniques used to measure brain activity, electroencephalography

(EEG), emerged in the 1930s when Berger (1929) demonstrated that electrical

activity from the brain could be measured by placing conducting material on the

scalp and amplifying the consequential signal. After Dawson (1951) developed a

method of signal averaging, management research suggested EEG’s use to

investigate the neural basis of performance decrements in the workplace (Scott,

1966).The successive, essentially misleading, idea that the left hemisphere of the

human brain would control only logic, analytical ability, sequential perception, and

language, while the right hemispherespatial and simultaneous perception,

imagination, and intuition (for a review see, e.g., Gazzaniga and LeDoux [1978])

further inspired management inquiries. Mintzberg (1976) addressed the challenge

of coupling neuroscience information with management research when he

imprecisely claimed that “right-brain is holistic and the left-brain logical,”

suggesting differences in the brain hemispheres were compelling for business

studies, training, and practice. Afterwards, such claims offered room to a body of

management neuromythology supported by a rather lay dissemination (Hines,

1987).Those outcomes allowed, for instance, to argue that executives tend to use

more right brain processing than analysts, and vice versa (Doktor, 1978), and

contributed to setting the basis for the development of frameworks seeking to

enhance managers’ analytical and intuitive skills (Robey andTaggart, 1982).

254 Massaro

Although EEG offered preliminary management-related findings obtained by

examining brain activity, it is with the advent of tomographic techniques that the

actual imaging takeover began.5In 1973, Godfrey Hounsfield (1973) introduced a

breakthrough technique: X-ray Computed Tomography (CT). It had immediate

impact; not only did it revolutionize medical clinical practice, facilitating screening

and diagnosis, but it also provoked behavioral scientists to consider new ways of

imaging the brain (Garvey and Hanlon, 2002; Raichle, 2009b; Rogers, 2003).

Subsequently, another type of tomography,Positron Emission Tomography (PET),

enabled creating autoradiographs of brain functions (Tilyou, 1991).This ushered in

the beginning of the “hemodynamic era”for functional brain-imaging:by injecting

a radioactive pharmaceutical in a subject, it was possible to quickly measure blood

flow changes and associate them with measurements of brain function (Phelps and

Mazziotta, 1985). PET also made possible“experimentalizing” a strategy of cognitive

subtraction with functional neuroimaging (Donders, 1969; Petersen et al., 1988),

which, although often questioned (Friston et al., 1996; Sartori and Umiltà, 2000),

has represented a pillar for numerous studies.Cognitive subtraction mostly relies on

assumptions of linearity and pure insertion: an elicited mental component evokes an

“extra” physiological activation that is the same regardless of preexisting mental and

physiological contexts (Price and Friston, 1997).This suggests that functional

imaging of behavioral processes can then be derived by subtraction of a control task

from an experimental assignment, so that differences in brain activity can be

attributed to selected mental components (Friston et al., 1996). Due to its logistics

(i.e. requirement for local presence of a particle accelerator) and concerns for

participants’ health (i.e., use of radioactive material) PET has not arisen as the

technique of choice for most of management inquiries. However, it has been

employed to identify neural substrates of phenomena such as planning (Dagher et

al., 1999), and risk-avoiding and ambiguity-avoiding behaviors (Smith et al., 2002).

More recently,another neuroimagingtechnique has offered theability to apprise

where activity is occurring in thebrain while we areperforming experimental

behavioral tasksor we areatrest. This is functional Magnetic Resonance Imaging

(fMRI), which grounds on nuclear magnetic resonance physics (Bloch, 1946;

Lauterbur, 1973; Purcell, To rrey,and Pound, 1946).The revolution in neuroscience

arrived in 1992, when researchers associatedMagnetic Resonance Imagingwith

brain activity-relatedchanges in blood oxygenation.The signal arising fromthe

unique combination of brainphysiology andnuclear physics became known as the

Blood Oxygen Level Dependent(BOLD)signal (Ogawa,Lee, and Tank, 1990).6There

rapidly followed severalevidences of BOLD signal changesin humans during

“brain activation,” giving officialbirth to fMRI (Bandettini et al., 1992; Kwong et

al., 1992; Ogawa et al., 1992). fMRI has dominated functional brain imagingof

behavior research ever since, and has been the neuroimagingtechniquebringing

thegreatest promises to management research.Forinstance, some encouraging

contributions have been those applied to strategic games (e.g., Sanfey et al., 2003)

and those investigating the neural underpinnings associated with strategic insight

and intuition (e.g., Vo l zand vonCramon, 2006).

Neuroscientific methods 255

Classifications: Between resolution and functionality

To fully appreciate how neuroscience techniques can attempt to “open the black

box of the brain,” it is important to classify them under a systemic outlook,

according to their distinctive characteristics. Experimental methodsin

neuroscience have traditionally been organized according to a matrixed perspective

that considers and emphasizes the distinct spatial and temporal resolutions of each

technique (Churchland and Sejnowski, 1988), as shown in Figure 10.1a.7

The concept of resolution is an essential prerequisite for understanding the

essence of each neuroimaging procedure. Simply speaking, it allows providing

answers for questions such as “how good is a brain scan image?”The response is

commonly disentangled into concepts of spatial or temporal resolution, which are

respectively the abilities to discriminate between two points in space and time

(Menon et al., 1998). A high spatial resolution determines a sharp image, while a

low one gives a“pixely” appearance to the image; for example,when two spatially

close (i.e., a few millimeters) anatomical structures are distinguishable in an image,

this has a higher resolution than one when they are not discernable. Spatial

resolution depends on the properties of the system that creates the images, such as

gradient strength and digitalizing rate (Bandettini, 2002), being therefore limited

by hardware and acquisition protocols. Severaltechniques provide spatial

information of the human brain with high resolution, including fMRI and PET.

However, understandingthe neuroscience of mental processesrequires

information not only on the spatial localization of brain activities, but also on their

temporal evolution. Analyses with a temporal resolution of milliseconds can be

conducted by electroencephalographic (EEG) and magnetoencephalographic

(MEG) methods, which are based on the electric or magnetic activity caused by

movements of ions inside and outside cellular membranes (e.g., Kristeva et al.,

1979).These methods provide almost real-time information on brain activity; yet,

EEG has lower spatial localization and resolution.

The importance of understandingthe properties of eachmethod is

fundamental. Limited resolutions bound practical applications of the techniques.

Moreover, each technique allows adifferent examination of the neural functions

specifically on the basis of its intrinsic characteristics. Somemight believe the

results obtained exploiting different resolutions of several techniques couldjust

converge in an overall explanation of the neural processes.This claim however is

inaccurate. Severalother influencesdetermine an experimental outcome, such as

whether the process is recording physiological brain activity, or instead

interfering withit,or stimulating the brain to change abehavioral response.

Therefore, theappropriate methods and levels at which to examine brain

function largely depend on the researchquestion being addressed (Stewart and

Walsh, 2006).

Organizational scholars have drawn from this resolution-based classification,

arguing its ease in depicting the relative advantages and disadvantages of each

method: as seen in Figure 10.1b, this categorization has helped delineating the

256 Massaro

FIGURE 10.1 Spatial and temporal resolutions of neuroscience techniques

Notes: The vertical axes show the spatial extent of the techniques; the horizontal axes represent the

time intervals over which information can be collected with each technique.Recordings from

the central nervous system are often limited in resolution by the properties of nervous tissue

and of the specific method.

Sources: (a) Churchland and Sejnowski, 1988; (b) Senior, Lee, and Butler, 2011.

(a)

(b)

Brain

Map

Col

umn

,!!.

layer

- 1

iii

Neuron

-2

Dendrite

-3

(ii

Synapse

Systems

Brain

Region

-4

Column

[ Layer

Neuron

Dendrite

Synapse

-3

-2

-1 0

Millisecond

Multiunit

recordings

Second Minute Hour

Time (log seconds)

fMRI

Single ce

recordings

Millisecond Second Minute Hour

Time (log scale)

Day

PET

Day

• Neuro

: psychology

Cognitive

n uropsychiatry

Year Lifetime

Millisecond

preliminaryexperimental boundaries of organizational neuroscience (Becker,

Cropanzano, and Sanfey, 2011; Senior, Lee, and Butler, 2011).

Kable(2011) has suggested acomplementary framework to organize

neuroscience techniques when applied to social sciences, on the ground of their

underlying testing rationale. Association tests are those experimental methods that

implicate a manipulation of a psychological state or behavior, the simultaneous

measurements of the neural activity, and the following analysis of the correlation

between the two.These include classic fMRI, PET, EEG, and MEG approaches.

Necessity tests are instead those that involve a disruption of the neural activity and

aim to show how this manipulation impairs a specific mental function. Sufficiency

tests are those enhancing a neural activity and seeking to establish that this process

results in a specific behavior or mental state.Necessity and sufficiency tests, such as

lesion studies, neuropharmacological or Transcranial Magnetic Stimulation (TMS)

experiments, are able to directly probe the causality between neural and mental

states.

Overall, it is quite straightforward to argue that knowledge of these classifi-

cations represents an important apparatus for scholars who both seek to understand

the technicalities, and also inquire what different kinds of evidence they should

gather to allow the most appropriate inferences about brain functions.

The techniques

The main neuroimaging techniques examined in this chapter include functional

Magnetic Resonance Imaging (fMRI), Positron Emission Tomography (PET), and

electroencephalography (EEG). These methods generally allow identifying brain

areas displaying increased activity, in comparison to controls,while the subjects are

performing specific tasks.

Functional Magnetic Resonance Imaging (fMRI)

Functional Magnetic Resonance Imaging (fMRI)is probably the most known and

widely appliedneuroscience methodologyin business research (Dimoka et al., 2012).

To understand the foundation of fMRI it is first necessary to appreciate the

underlying principles of Magnetic Resonance Imaging (MRI). MRI exploits the fact

that protons (atomic hydrogen nuclei) of our body in the presence of an external

magneticfield behave likecompass needles, aligning in parallel to that field (Le

Bihan, 1996). Simply put, after electromagnetic pulses are applied to these protons

(and then switchedoff), theyemit detectable and characteristic radio signals, allowing

acomputerto reconstruct images of the inner organs (for areview, see Brown and

Semelka [2010]).Imposing the magnetic field and pulses and acquiring the resulting

signals requires specific equipment,consisting of an MRI magnet, a system of coils

and signal amplifiersystems (Figure 10.2). An MRI scanner is acylindrical tube

whose core is constituted by avery powerful electro-magnet (Chapman, 2006); a

typical magnet, well-suited for fMRI research, has field strength of 3Te s l as (T).8

258 Massaro

Functional MRI uses these principles to detect the magnetic signal from

hydrogen nuclei in water (H2O). It relies on differences in magnetic properties

between venous (oxygen-poor) and arterial (oxygen-rich) blood, which allow

revealing the changes in blood oxygenation and flow that occur in response to

neural activity, the so-named neurovascular coupling (Logothetis et al., 2001;

Logothetis and Wandell, 2004). When a brain area is more active it requires more

oxygen, and as a consequence blood flow increases to the active area (Fox and

Raichle, 1986; Uludag et al., 2004).By using the BOLD signal (Ogawa et al., 1990)

fMRI allows researchers to examine activation maps showing which parts of the

brain are involved in a particular mental process (Bandettini et al., 1992; Ogawa et

al., 1992; Kwong et al., 1992).

Nonetheless, the extent, dynamics, and underlying mechanisms of neurovascular

coupling are not yet fully understood (Attwell and Iadecola, 2002; Magistretti and

Pellerin, 1999) and the BOLD signal depends on several parameters, so its

biophysical link with neuronal activation is not yet entirely straightforward

(Malonek et al., 1997). Moreover, the fact that fMRI experiments elicit a BOLD

signal does not indicate that subjects necessarily had psychological events associated

with that part of the brain (Poldrack, 2006).

Neuroscientific methods 259

FIGURE 10.2 Main components of an MRI scanner

Source: http://www.themesotheliomalibrary.com

Caring

These concerns have lead to some research issues for fMRI research, which

however can rely on high spatial (typically 3 millimeters) and high temporal (about

2 seconds) resolution (Song, Huettel, and McCharty, 2006).9For instance, the

spatial localization of the BOLD signal can be distant from the actual site of neural

activity, because the signal source includes various vascular networks sized from

capillaries to large draining veins, and the physiological delay necessary for the

mechanisms triggering the vascular response to work limits the temporal resolution

of the technique (Le Bihan et al., 2006). Research is constantly improving

resolution parameters, yet these may be ultimately limited by our physiology. For

example, the brain vascular supply is not regulated on the scale of individual

neurons and might then be restricted to 0.5-1.5 mm (Menon and Kim, 1999).

Nonetheless, recent work has suggested that water diffusion MRI (for a review,

see Beaulieu [2002]), could among other methods, overcome some of these limits:

changes in the magnitude of diffusion of water molecules within cerebral tissue

during neuronal activation would likely reflect transient changes in the

microstructure of the neurons, which can then be imagined (Le Bihan, 2003).

Although this suggestion has been challenged (Yacoub et al., 2008), captur ing such

effects would have a remarkable consequence on neuroimaging applications in

behavioral reserach, since they would be directly linked to neuronal events in

contrast to blood flow effects, which are secondary.

Despite these and other concerns, fMRI has been extensively recruited in social

sciences (e.g. Camerer, 2003; Crockett et al., 2008; Damasio, 1994; Glimcher and

Rustichini, 2004). Not surprisingly, it has also been the technique of choice to

investigate several strategic management paradigms.

For example, fMRI studies have significantly contributed to investigating the

neural basis for cooperation. Cooperation, the willful contribution of personal

effort to the completion of interdependent tasks, including jobs, has been a

mainstay in the management literature (e.g., Barnard, 1938; March and Simon,

1958; the whole special issue of the Academy of Management Journal 38(1) [1995]).

McCabe and colleagues (2001) employed fMRI in two-person reciprocity games

in which participants were facing both human and computer counterparts.They

found that cooperation with humans was highly correlated with increased

activation of brain regions responsible for joint attention and mutual gains, and

decreased activation of regions associated with immediate reward gratification.This

study prompted further exploration of other aspects key for management research,

such as the role of fairness and trust in the workplace. For instance, equity theory

(Adams, 1963; 1965) suggests that perceptions of fairness are job-related motiva-

tional grounds that can influence responses of job performers. Research has argued

that fair treatment has positive effects on individual employee attitudes (e.g.,

satisfaction and commitment) and individual behaviors (e.g., absenteeism and

citizenship behavior) (Colquitt et al., 2001; Moorman, 1991), while unfair

treatment conveys opposite behaviors and attitudes (Cohen-Charash and Mueller,

2007). Research has measured the neural responses and identified key correlates

underlying social exchanges and sense of fairness in volunteers playing different

260 Massaro

strategic games, such as the prisoner dilemma, the ultimatum game, or the

reciprocal trust game. For instance, King-Casas and colleagues (2005) found that

reciprocity expressed by one social actor strongly predicts trust expressed by his or

her partner, a behavioral finding mirrored by an increased activation in the dorsal

striatum as compared to control conditions.

In recent years, functional magnetic resonance imaging studies have begun to

covered other strategic management paradigms, embracing topics spanning from

exploration and exploitation (Daw et al., 2006) to escalation of commitment. For

example, research has widely established how the inability to plan ahead often

results in escalation of commitment, myopia of learning, or unnecessary risk taking

(Levinthal and March,1993). Escalation of commitment is that situation whenever

a manager, or any decision maker, keeps committing considerable resources to a

course of action in the hope of achieving a positive outcome, but instead

experiences disappointing results (Staw, 1981; Brockner, 1992).Campbell-

Meiklejohn and colleagues (2008) highlighted neural correlates of this complex

behavior: in comparison to control conditions, decisions not to escalate were

associated with increased activity in the anterior cingulate, left anterior insula,

posterior cingulate, and parietal cortices, but decreased activity in the ventro-

medial prefrontal cortex. Decisions to escalate were associated with a decrease of

activity in the anterior cingulate, right anterior insula, and inferior frontal gyrus,

but there was no increase in activity in comparison with the control condition,

which instead suggested increased activity in the ventro-medial prefrontal cortex.

The burst of wide applicability in presenting such imaging research outcomes

has perhaps raised the bitterest criticisms around fMRI results, despite scholars

having highlighted actual limitations of the methodology (Logothetis, 2008;

Poldrack, 2012). As seen above, spatial resolution barriers would not allow to map

the intimate nature of individual neurons (i.e., in a voxel there are about 5.5

millions neurons) and directly distinguish between functional activities relevant to

the task, irrelevant to the task, and noise.The averaging of imaging, which often

leads to ignoring differences between individuals, random effect analysis, and

statistical issues are other arguments often brought up to underlie problems of

research reproducibility (Vul et al., 2009; the whole Perspectives on Psychological

Science issue 4(3), [2009] is a must read, dedicated to the issue of correlations in

psychological research using fMRI). Recent investigations have started to address

these concerns and suggest that increased reproducibility can be achieved through

the combined results from multicenter fMRI studies (Stöcker et al., 2005), the

development of neuroimaging databases, the use of consistent protocols (Liu et al.,

2004), similar machineries, homogeneous sampling, and multiple comparisons

correction methods (Poldrack et al., 2011).

In any case,these considerations per se should not prevent management scholars

from exploring the use of this methodology, since the debate is a current challenge

accompanying the daily routine of every neuroimaging scientist. For one, an

analysis of the rise of brain imaging methods from a socio-historical point of view

(Beaulieu, 2000), has revealed that neuroscientists have a love-hate relationship

Neuroscientific methods 261

with their images: these are useful for blending data and convenient for communi-

cating results to a large audience; however, they hold incredible exposure to the

most disparate criticisms.

Positron Emission Tomography (PET)

Positron Emission Tomography (PET) was one of the first techniques used to

exploit the links between neural activity and metabolism to study brain functions

(Phelps and Mazziotta, 1985; Raichle and Snyder, 2007). It is an analytical nuclear

imaging technique, able to provide high spatial resolution images of functional

processes occurring in the brain, and it has traditionally been used to make in vivo

measurements of the anatomical distribution and rates of specific biochemical

reactions (Gulyas et al., 2002).The term nuclear signifies that the technique relies

on radioactively labeled molecules (tracers). Similar to MRI, PET requires

dedicated instrumentation, which includes a ring of detectors located around the

patient’s head (Turkington, 2001) (Figure 10.3).

In a typical PET experiment, a short-lived radioisotope of a biologically

relevant element (carbon, nitrogen, oxygen, fluorine) is produced locally using a

low-energy particle accelerator (i.e., a cyclotron). It is then synthetically bound to

a biomolecule, usually glucose or oxygen, or to a drug, to form a physiological

radiotracer able to emit positrons (positively charged particles of the mass of an

electron).This radiotracer is then injected intravenously into the subject, so that it

can bind to a specific receptor or enter into specific metabolic pathways. During

the natural process of radioactive decay the positron is emitted and travels for a

short distance within the brain, then collides with an electron.This impact

produces two coincidental rays (gamma rays), which can be measured by the

262 Massaro

FIGURE 10.3 Examples of PET detectors

Source: Humm, Rosenfeld, and Del Guerra, 2003

Caring

Caring Caring Caring

detectors around the subject’s head (Ter-Pogossian and Herscovitch, 1985). When

two opposite detectors on the ring simultaneously recognize a gamma ray, a

computerized system records this as a coincidence event.The computer records all

of the coincidence events that occur during the imaging period and then

reconstructs cross-sectional images. Bi- and three-dimensional images are often

accomplished with the aid of an X-ray CT scan performed on the subject during

the same session, in the same machine (Pelizzari et al., 1989). Since the tracer

accumulates in the brain in direct proportion to the blood flow, the greater the

flow, the greater the radioactive count rate.Thus, the distribution and intensity of

the uptake of the positron-emitting radiotracer indicates the underlying neural

activity, and the regional cerebral blood flow (rCBF) works as the dependent variable

(Raichle, 1979; Raichle, Martin, and Herscovitch, 1983).

PET presents several disadvantages in comparison to fMRI.Above all,it involves

the use of ionizing radiations, which have potential harmful effects on the research

subjects. Moreover, it affords relatively poorer spatial (4 millimeters) and temporal

(30–40 seconds) resolutions and generally involves one to two measurements per

subject, with each measurement reflecting neural activity averaged over one minute

(Kato, Taniwaki, and Kuwabara, 2000).

Nonetheless, PET has provided intriguing insights on topics central to strategic

management. One of the most productive lines of research developed around the

concept of planning. Planning has been a growing topic in strategy inquiries since

the 1950s (Payne,1957), and has been identified as a variable able to both impact

firm performance and have a role in strategic decision making (Ansoff, 1991;

Armstrong, 1982; Mintzberg, 1994). Strategic planning decisions emerge from

complex interactions among individuals with subjective interests and perception;

understanding the respective neural correlates can inform further on both planning

processes and theories. Several neuroimaging studies have independently addressed

the issue. Associating PET studies with the Tower of London (TOL) task – an

adaptation of the To w er of Hanoi (Anzai and Simon, 1979), which consists of

moving colored balls within a limited number of moves in order to achieve a given

goal configuration – researchers shed a light on the anatomic and physiological

correlates of planning processes. Longer planning times and fewer moves to

complete a problem are associated with significantly higher regional cerebral blood

flow in the left prefrontal cortex, whereas execution time is negatively correlated

with both left and right prefrontal rCBF (Baker et al., 1996; Dagher et al., 1999).

With the preponderant emergence of fMRI, more widely accessible and

cheaper, the use of PET in social sciences has seemingly plateaued. Nevertheless,

PET may still hold a relevant role for management scholarships.This technique

measures blood flow in absolute terms (while fMRI measures changes in blood

oxygenation), permitting therefore a more precise comparison between subjects,

sessions, and brain regions (Minoshima et al., 1994).Therefore, there is considerable

reliability for research investigating associations within subjects across different

tasks.

Moreover, PET has the unique ability to measure cerebral metabolism, hence

Neuroscientific methods 263

associate differences in molecular synthesis with difference in behavior (Phelps and

Mazziotta, 1985). For instance, in the striatum, differences in the synthesis of

dopamine, a molecule frequently implicated in impulsive behaviors, has been

associated withdifferences in reversal learning (Cools et al., 2009).This

phenomenon is connected to decisions made under emotional situations and

conflicts (Fellows and Farah, 2003; Kovalchik and Allman, 2005), which are

circumstances often experienced across several management levels (Huy, 2002).

Electroencephalography (EEG)

While measuring regional cerebral flow can provide a detailed anatomical mapping

of active brain areas, the time resolution of the related methods is generally too

slow to reveal the rapid flux of neuronal communication. Conversely, surface

recordings of the electric fields emanating from active populations of neurons offer

a higher degree of temporal resolution (on the order of milliseconds), but yield a

less complete picture of anatomical sources.This method, called electroen-

cephalography (EEG), is the oldest non-invasive method to measure brain activity

(Nunez, 1995).

The existence of electrical currents in the brain was discovered by Richard

Caton (1875), and the first electroencephalographic experiment was performed in

1929 by Hans Berger (1929), in which he discovered the alpha rhythm, waves with

a uniform rhythm typical of a subject awake in a quiet resting state (Adrian and

Matthews, 1934). Since this pioneering discovery, researchers have conducted

thousands of experiments, leading to advances in both the recording systems and

the understanding of brain functions (Freeman and Quiroga, 2013). Nowadays, it

is acknowledged that our brain produces several types of brainwaves with different

frequencies, and each of them is associated with particular mental states. As an

example, beta waves have a frequency of 15–38 Hz and are characteristic of

individuals who are fully awake and aler t (Nunez, 1995).

EEG employs advanced signal processing methods to infer data about the brain

through the scalp and skull (Niedermeyer and Lopes da Silva, 1995). It develops

around the concept that neurons are excitable cells, which transmit information

through electrical or chemical signals via dedicated structures called synapses.

Populations of neurons are connected into networks and communicate with each

other repeatedly by sending electrical impulses.The technique specifically

measures the resulting electrical currents that flow underneath the scalp while

short extensions of certain cortical neurons (the dendrites of pyramidal neurons)

are excited (Atwood and Mackay, 1989). When the brain processes an event,

thousands of these cells are activated at the same time, causing a fluctuation in

voltage.In order to measure these signals a cap with several electrodes is placed on

the subject’s head.By quantifying the differences between the electrodes,the flow

and strength of the electric field can be inferred (Tyner et al., 1989). Since the

signals that reach the scalp are very small (usually in the range of 1 to 100 µV) they

are then amplified and converted into a digital form (Luck, 2005). However,

264 Massaro

because EEG detects electrical signals at the scalp, it can only measure activity

coming from the cortex, making almost impossible evaluating direct activation in

deeper lying structures (Bronzino, 1995). Moreover, EEG has a very high temporal

resolution, hence allowing for very fast measurement within milliseconds.

However, the low spatial resolution makes it challenging to precisely localize the

source of the signal.

To be able to extract the correct electrical signal associated with a behavioral

experimental task and distinguish it from the background noise, a functional EEG

study requires multiple averaged measurements. By averaging signals, researchers

can indicate that a certain task causes a specific activation of the measured brain

region at a specific time stamp. Simply put, the resulting response is called event-

related potential (ERP) (Squires et al., 1976).

The preparation of a standard EEG setup takes a relatively long time (Lebedev

and Nicolelis, 2006); however, an EEG assessment can be accomplished while

people are seated and engaged in everyday activities, including conversations

associated with the type of task the experiment involves. Being relatively

inexpensive, non-invasive,and non-harmful for the participants of a study,EEG has

been one of the most applied techniques in management studies.

Already in the 1980s, Robey and Ta ggart (1981; 1982) sought to establish a

linkage between measures of managerial styles and brain activity recorded with

EEG. Drawing on the insights on hemispheric dominance and on the initial claims

of Doktor (1978) they argued relationships between cerebral dominance and scales

able to assess distinct strategic leadership types. More recently, Waldman and

colleagues (2011) focused on inspirational management and its association with

electrical brain activity, by recording subjects’ beta waves in terms of coherence. In

this way they were able to measure the coordinated activity between multiple parts

of the brain when the subjects were presented a visual task on activities related to

inspirational leadership (Figure 10.4).They showed that coherence in the right

frontal areas of the brain could offer the basis for social visionary communication,

which helps to build followers’ perceptions of charismatic leaders. Although this

research pipeline has ambitious potential, with some authors foreseeing the

eventuality to train managers to “replicate such brain patterns,” its results shall be

understood with caution (as reported in Dvorak and Badal [2007]).

EEG has been employed to understand several other management constructs,

such as punishment behavior.This topic has received increasing attention from

management researchers (Simons, 1991), as it is associated with important variables

such as power, reward, cooperation, and fairness. For instance, when managers are

considered to have punished others unfairly, they not only impair their own

reputations, but also risk eliciting negative attitudes and counterproductive

behaviors,weakening the perceived legitimacy of their authority (Ball, Treviño,and

Sims, 1994). Knoch and colleagues (2010) disclosed that the right lateral prefrontal

cortex may play a central role in punishment behavior: subjects with an active PFC

region seem most likely to punish an unfair proposal, even though the action has

disadvantages for themselves,and vice versa.

Neuroscientific methods 265

Lesion studies, VLSM, TMS, and MEG

It is central to highlight that while the techniques examined so far can identify

brain activation, they cannot independently determine which of these areas are

indispensable for performing the experimental task.This information can instead

be provided by neuropsychological studies.

Among them, lesion studies are the oldest approaches to the study of mental

functions. Already in 1861, Paul Broca (1861) suggested a relation between

language and the brain’s left hemisphere, setting the basis for localizing human

brain function by studying the correlation between a behavioral disorder and the

site of a brain injury.This approach has been the milestone for a long tradition of

neuropsychological research, grounding therationale of cognitivedissociation

266 Massaro

FIGURE 10.4 Spectral analysis of right front coherence in leadership research

Notes: The gradient shows the levels of coherence on 3 right frontal electrode locations,including

areas at 0% (indicated by the minus signs), areas at 100% (indicated by plus signs), and in

between. Dark regions with a + represent areas with high degrees of coherence (75% or

higher); dark reg ions with minus signs characterize areas with low coherence (25% or lower).

The numeric values indicate the summed averaged coherence scores for such brain regions in

different leaders.

Source:Waldman, Balthazard, and Peterson, 2011

Caring

Caring Caring

Caring

Caring Caring

Caring

(Caramazza, 1986; Shallice, 1988). Single dissociation occurs when damage in a

brain region causes a disruption in one specific mental function but not in another,

allowing inference that those functions are independent of each other (Kolb and

Whishaw, 2009). Alternatively, double dissociation is conceivable when a subject

with brain damage shows poor performance on one task and good performance

on another task, while other patients show the opposite performance.This allows

inference that the two related mental processes function independently of each

other. Several researchers have criticized the logic or some of its applications,

arguing, for example, that double dissociations do not necessarily imply a difference

in processing mechanisms between tasks (Bullinaria and Chater, 1995; Chater and

Ganis, 1991).

Despitethese criticisms and the advancements provided by modern

neuroimaging techniques, managementresearch can still gather important

investigative elements from lesion studies (Rorden and Karnath, 2004). One

approach, for example, is that of grouping subjects according to the lesions’

locations and comparing the performance of each group.This can be exemplified

in attention studies. Attention is a topic of great interest in strategic management,

since it fosters questions not only in problem solving (Bower, 1986; Newell and

Simon, 1972), but also in aspects such as strategic issue diagnosis (Dutton, Fahey,

and Narayanan,1983), and organizational mindfulness (Levinthal and Rerup, 2006;

Weick and Sutcliffe, 2006). In parallel, neuroscience studies on attention have

proposed the existence of three systems of attention: orienting, alerting, and

executive control (Posner et al., 2007). Research has compared their efficiencies

between differently brain-damaged subjects (frontal, temporal, and parietal lesions)

and healthy controls using the Attention Network Te st (ANT) (Raz and Buhle,

2006). A reduced efficiency of the executive network was found in patients with

frontal lobe and parietal lobe injuries, patients with parietal lobe injuries showed a

deficit in the orienting network, and analysis of lateralization indicated the right

hemisphere superiority to the alerting system.

Subjects with brain damages, as those recruited for that research, can easily be

enrolled from different sources (i.e., ischemic, tumor removal, degenerative diseases

patients). However, aside from the fairly obvious ethical concerns, there are some

practical implications in conducting large-scale lesions studies in management,

since they require dedicated infrastructure and personnel. Moreover, a major

drawback of this approach is that brain damage is not under easy experimental

controls (Brett, Johnsrude, and Owen, 2002).This ongoing uncertainty means that

it is difficult to control for phenomena such as brain reorganization, different

severity of lesion, and more generally individual differences.

A combination of imaging and lesion studies could prove useful to overcome

these difficulties and advance management investigations (Shallice, 2003). Voxel-

based Lesion-Symptom Mapping (VLSM) is a relatively recent method for analyzing

the relationships between behavioral deficits in a neurological population and

lesion sites associated with those deficits.The major advantage of VLSM over

classic lesion studies is that it allows researchers to examine such data without

Neuroscientific methods 267

articulating behavioral and lesion sites’ boundaries (e.g., parietal patients vs. frontal

patients) (Bates et al., 2003). For instance, Driscoll and colleagues (2012) have

elucidated the neural bases of self-reported emotional empathy comparing a group

of Vietnam combat veterans who had traumatic brain injuries with a group of

non-brain-injured veterans, by using VLSM on computed tomographic scans.

Empathy is essential for managing relations in organizations, and research has

suggested that the ability to understand others’ emotions enables a manager to

foster strategic management (Nonaka and Toyama, 2007).

Another method alternative to classical lesion studies is offered by Transcranial

Magnetic Stimulation (TMS). However, differently from classic lesion studies, this

approach does not involve permanent brain damage.Transcranial magnetic

stimulation is a non-invasive technique that electromagnetically induces very brief

electrical current pulses through a coil placed above the brain area to be stimulated;

this produces weak electrical currents in the underlying neurons (Walsh and

Cowey, 2000).TMS thus holds a unique role in understanding how the brain

works,because it can be used to disengage a brain area for a minimal time,allowing

scientists to understand its functional role (Pascual-Leone, Bartes-Faz, and Keenan,

1999).TMS has a temporal resolution of milliseconds, while the spatial resolution

depends on the coil and the target area, which can be located thanks to a navigator

device (Stewart andWa lsh, 2006).

Although promising, the neurophysiological effects of TMS are not fully

understood, often leading to difficulties in interpretation of the results; it may have

excitatory, as well as inhibitory effects on brain regions, and differences in TMS

stimulation parameters can influence experimental results (Rorden and Karnath,

2004). Moreover, current TMS systems are able to directly disrupt regions only

near the scalp, usually evoke slight changes in behavior, and may induce epileptic

seizures if applied at high intensities (Sack and Linden, 2003).These limitations

make the technique not yet ideal for investigating long-term and social effects, such

as those characterizing organizations. Thus, one of the most promising research

directions is to explore multimodal imaging modalities, the combined use of two or

more experimental techniques able to complement each other (e.g., TMS and

fMRI) (Siebner and Rothwell, 2003; Babiloni et al., 2004).

Another neuroimaging method that might receive increasing resonance in

management studies is magnetoencephalograpy (MEG). It uses signals emerging from

the scalp and measures fluctuations in the magnetic field as a result of changes in

neural activity (Hämäläinen et al., 1993). Since the fields have strength of only

50–500fT (about 100 million times weaker than the Earth’s magnetic field), MEG

instrumentation requires the use of special devices placed on the subject’s head

(superconducting SQUID-based magnetometers), and a magnetically shielded

room (Vrba and Robinson, 2002). Magnetoencephalography is specular to TMS:

while MEG detects magnetic fields generated by neural currents, TMS induces

currents in the brain via magnetic fields. Moreover, MEG provides elevated

temporal resolution and, due to poor signal degradation, results in high spatial

discrimination of neural contributions (Pascual-Marqui, Michel, and Lehmann,

268 Massaro

1994). Finally, it allows for absolute measures, which are not dependent on the

choice of a reference, introducing new opportunities to further investigation of

strategic management topics.

Other neuroscience methods in management research

While this workhaslargely concentrated on neuroimaging technologies, it is neces-

sary to mention that several other neuroscientific approaches (e.g., those measuring

autonomic parameters, neurogenetics, and neuropharmacologytechniques) can

provide important information for management and strategyinquiries.

For instance, a method with the potential to offer novel insights in strategic

management research is that of eye-tracking. Eye-movement data usually consist of

eye fixations, when the gaze position is relatively still so that the foveae remain

directed at a particular point in space and information is extracted from the

stimulus (Pieters, 2008).The rationale of this method is then aimed at determining

the spatial point at which a viewer’s foveae are directed and the extent of time they

remain focused there. For instance, eye-tracking has been employed in the

evaluation of facial perception, which is an important antecedent to successful

social and business communication, since human social inferences are derived

largely from viewing facial expression (Schulte-Mecklenbeck, Kühberger, and

Ranyard,2011). Similarly, it can be employed to provide insights in the processing

of risky decisions (Glöckner and Herbold, 2011).

Research in strategy could also be further supported by neurogenetics experi-

mental procedures,investigating the basis of cognition,sociality,and behavior.Such

method has already been applied to business disciplines, for instance to entrepre-

neurship (Nicolau and Shane, 2010).This type of studies generally relies on

comparisons between twins or examines allelic differences, suggesting that genetic

variances translate into functional differences. Neurogenetics may be particularly

useful for strategy research by linking polymorphisms of selected genes affecting

neurotransmitter systems, or by employing genome-wide approaches to investigate

mental functions and behavioral phenotypes. For instance, research on exploration

and exploitation has shown that basal ganglia support learning to exploit decisions

that have yielded positive outcomes in the past, while the prefrontal cortex is

associated to strategic exploratory decisions when the magnitude of potential

outcomes is unknown. Distinct genetic processes sustain these differences: genes

controlling striatal dopamine function (DARPP-32 and DRD2) are associated with

exploitation, while a gene controlling prefrontal dopamine function (COMT) is

associated with “directed exploration” (Frank et al., 2009).

Although the sampling collecting procedures for these studies are quite simple

(a saliva or blood sample is usually sufficient), these analyses require advanced

expertise and facilities. Moreover, due to the intimate nature of the approach,

research findings are at high risk of producing serious ethical concerns or stigma-

tization (i.e.,associating specific polymorphisms to supposed deviant attitudes or to

targeted populations) (Illes and Racine, 2005).

Neuroscientific methods 269

Finally, neuropharmacological studies rely on the rationale that specific compounds

excite or inhibit particular neurotransmitter actions (neurotransmitter loading or

depletion), thereby influencing a subject’s behavior. Also in this case, these

approaches hold important concerns, in particular in relation to cognitive

enhancement (Bostrom and Sandberg, 2009). Examples of this methodological

approach are studies,which employ neuropeptides such as oxytocin and vasopressin

(Heinrichs, von Dawans, and Domes, 2009). For example, research has proposed a

role for oxytocin in modulating trust, thus influencing cooperative relations.

Administration of intranasal oxytocin increased the amount of money that a social

actor was ready to offer to a “trustee,” who could return either a smaller or larger

sum back to the person (Kosfeld et al., 2005). However, oxytocin did not increase

monetary distributions when the feedback was determined by a random draw,

indicating that these results are specific to the social interaction between the two

actors. In support to this research, imaging studies revealed that oxytocin decreased

amygdala activity, independently from the experimental scenario, providing further

insights into the neural mechanisms by which this neuropeptide regulates

cooperation (Petrovic et al., 2008).

Ethics, hype, and hope

Despite its complexity and technicalities, neuroscience research has engaged the

interest and curiosity of several audiences,including non-expert scholars (Frazzetto

and Anker, 2009). Since the 1990s we have seen the rise of a neuroculture (Rolls,

2012), with “neuro” concepts increasingly assimilated in the social sciences,

including management research.

In response to this phenomenon, some scholars have argued that managerial,

organizational, and strategy frameworks involve dynamic systems, multilevel

analyses, depend on environment,interaction with people, tasks, and structures, and

these paradigms cannot be fully appreciated with neuroscientific methods currently

available (Powell, 2011). Others have associated neuroimaging research with

phreonological cults (Dobbs, 2005; Simpson, 2005; Uttal, 2001), pointing out

methods, such as fMRI, inform about the location of neural activities, yet offer a

very plastic snapshot of the complex mental and behavioral processes occurring in

the brain (Coltheart, 2006; Page, 2006). On the other hand, researchers have

responded that functional neuroimaging allows for broader and more complex

explanations, and have proposed connectivist and network frameworks (Cowell,

Huber, and Cottrell, 2009; Rogers et al., 2007; Rubinov and Sporns, 2010).

Moreover, neuroimaging methods were primarily conceived for clinical

applications, and only later have been applied to behavioral and management

inquiries. What could happen if some incidental pathological abnormality emerges

during a management study? What if a non-clinical researcher thinks there is an

abnormality, which is instead just an ordinary physiological variant, and worries the

subject unreasonably?

These ethical issues are not insignificant (Grossman and Bernat, 2004). An

270 Massaro

unexpected finding may turn the naive desire of a volunteer to have a picture of

his or her brain into a major incident with severe consequences impacting both

health and everyday life (Kirschen, Jaworska, and Illes,2006). And if it is unethical

not to provide result interpretations, detecting pathological abnormalities is a

relatively frequent event, especially with functional neuroimaging systems

(Katzman, Dagher, and Patronas, 1999).Therefore,in order to minimize the impact

of incidental findings, research protocols should include informed consent and

adhere to detailed guidelines (Illes et al., 2004) and research outputs should be

examined and reported by qualified personnel able to flag minor normal variants

as well as pathologies (Illes et al., 2004).

Despite these vibrant considerations and debates, it is possible to claim that

learning about the brain can help to understand further people’s behaviors in firms

and organizations; thus neuroscience methods can add to the understanding of

management and strategy frameworks’ elements on the basic neural process

involved.To this end, knowledge on the techniques presented in this chapter

represents a key instrument to acquire new awareness about those paradigms, and

to ascertain the basis for a durable and doable association between neuroscience

and management research. Nevertheless, researchers should not only understand

and recognize both these tools’ potentials and limitations, but also be careful about

getting into the hype of including a“brain-talk” or neuroimage with any and every

research output. For instance, there is growing evidence that an untrained audience

too often trusts catchy neuroscience claims blindly (Racine, Bar-Ilan, and Illes,

2005;Weisberg et al., 2008). Once research results are publicized, especially when

linked to personality or social constructs, non-experts often relate with lay

interpretations of these outcomes. Although this phenomenon should not be

confused with the merits of sound research (Beck, 2010), it is also true that the way

in which some findings are presented tends to be vigorously loaded (Racine et al.,

2010). Extensively incorporating brain region labels and scans, perhaps supported

by amateurish statistics or imprecise anatomical knowledge, may become just

rhetoric, if not supported by clear experimental and scientific agendas and precise

methodological disclosure (Illes, 2006; McCabe and Castel, 2008;Weisberg et al.,

2008). Similarly, management scholars shall rethink the epistemological urge of

outlining new “neuro” disciplines (Bennett, Hacker, and Bennett, 2003; Legrenzi

and Umiltà, 2011). For one, here I have provided an introductory review on how

experimental neuroscience for management and strategy must necessarily be

considered as a set of instruments, suggesting that also uprising “neuroman-

agement” discussions must not disengage from the fuller understanding of the

underlying neuroscience.

The hope in and the competitive advantage of neuroscience and management

is thus an integrated framework, established through systematic understanding of

neuroscientific methods, multidirectional communication, and planned collabo-

rations between scholars as the most appropriate means to achieve a fuller

knowledge on human strategic behavior.

Neuroscientific methods 271

Notes

1I would like to thank Sigal Barsade,James Ber ry, Giambattista Dagnino,Mar tin Kilduff,

and Simcha Jong for their useful suggestions.

2Examples of these evidences include e.g. the Zhejiang University’s Neuromanagement

Laboratory; dedicated sessions at the Academy of Management Meetings; the Open

Research Area NESSHI (www.n e sshi.eu/) and the Human Brain

(https://www.humanbrainproject.eu/) projects.

3An all-inclusive analysis of the neuroscience methods would have to be book-length to

cover eachof the techniques presented in thischapter.Afew examplesof

neuroscience-specific texts able to exhaustively address these topics, which however do

not touch management paradigms, are: Cabeza and Kingstone, 2001; Senior, Russell,

and Gazzaniga, 2006; To g a and Mazziotta, 2002.

4The notion of functional neuroimaging of behavior employed in this work seeks to

highlight the differences with the use of these techniques in clinical practice (i.e.,

clinical functional neuroimaging). I will interchangeably use the terms mental and

behavioral to broadly encompass cognitive, emotional,and affective processes. Readers

must note that there is direct inference when the investigator infers something about

the role of particular brain regions in cognitive function. Reverse inference, which is

instead not recommended, occurs when the investigator infers the engagement of

particular cognitive functions based on activation in particular brain regions (Poldrack

2006).

5Tomographic techniques are those methods that allow imaging a body by sections

through a penetrating wave. They allow imaging of a slice through, rather than a

projection of a three-dimensional structure (Natterer and Ritman,2002).

6Technically a T2* relaxation time.

7This work does not review classic electrophysiological techniques (e.g., single- and

multi-unit recordings, patch clamp; for more information on these methods, see

Bretschneider and de Weille, 2006), and methods that currently have received marginal

applications in the strategic management scholarship (e.g., Magnetic Resonance

Spectroscopy [MRS]). Moreover, the work will solely cover neuroscience methods in

humans, hence excluding those applications carried out in primates (for more

information on this, see Murray and Baxter, 2006).

83 Teslas are roughly 60 thousand times greater than the Earth’s magnetic field.

9MRI and fMRI are also characterized by high contrast resolution, which is the ability to

distinguish the differences between two arbitrarily similar but not identical tissues,such

as white and grey matter (Bushberg et al., 2002).

References

AA.VV. (2009) Perspectives on Psychological Science, 4(3).

AA.VV. (1995) Academy of Management Journal, 38(1).

Adams, J. S. (1963) “Toward an understanding of inequity”, Journal of Abnormal Social

Psychology, 67(5): 422–436.

Adams, J. S. (1965) “Inequity in social exchange,” in L. Berkowitz (ed.) Advances in

Experimental Social Psychology, 267–269, New York: Academic Press.

Adenzato, M. and Garbarini, F. (2006) “The As if in cognitive science, neuroscience and

anthropology: A journey among robots, blacksmiths and neurons”, Theory & Psychology,

16(6): 747-759.

Adrian, E. D. and Matthews, B. H. (1934) “The Berger rhythm: Potential changes from the

occipital lobes in man,” Brain, 57(4): 355-385.

Ansoff, H. I. (1991) “Critique of Henry Mintzberg”sThe design school: reconsidering the

basic premises of strategic management,” Strategic Management Journal, 12(6): 449–461.

272 Massaro

Anzai,Y. and Simon, H. A. (1979) “The theory of learning by doing,” Psycholgical Review,

86(2): 124–140.

Ariely,D. and Ber ns, G. S. (2010)“Neuromarketing: The hope and hype of neuroimaging in

business,” Nature Reviews Neuroscience, 11(4): 284–292.

Armstrong, J. S. (1982) “The value of for mal planning for strategic decisions: Review of

empirical research,” Strategic Management Journal, 3(3): 197–211.

Attwell, D. and Iadecola, C. (2002) “The neural basis of functional brain imaging signals,”

TRENDS in Neurosciences, 25(12): 621–625.

Atwood, H. L. and MacKay,W. A. (1989) Essentials of Neurophysiology, New York: Decker.

Babiloni, F., Mattia, D., Babiloni, C., Astolfi, L., Salinari, S., Basilisco, A., and Cincotti, F.

(2004) “Multimodal integration of EEG, MEG and fMRI data for the solution of the

neuroimage puzzle,” Magnetic Resonance Imaging, 22(10): 1471–1476.

Baker, S. C., Rogers, R. D., Owen, A. M., Frith, C. D.,Dolan, R. J., Frackowiak, R. S. J.,and

Robbins, T. W. (1996) “Neural systems engaged by planning: A PET study of the Tower

of London task,” Neuropsychologia, 34(6): 515–526.

Ball, G. A., Trevino, L. K. and Sims, H. P. (1994) “Just and unjust punishment: Influences on

subordinate performance and citizenship,” Academy of Management Journal, 37(2):

299–322.

Bandettini, P. A. (2002) “fMRI: The spatial, temporal, and interpretive limits of functional

MRI,” in Davis, K., Charney, D., Coyle, J. and Nemeroff (eds), Neuropsychopharmacology:

The Fifth Generation of Progress, 343–356, Philadelphia: Lippincott Williams and Wilkins.

Bandettini, P. A. (2009) “What’s new in neuroimaging methods?,” Annals of the New York

Academy of Sciences, 1156(1): 260–293.

Bandettini, P. A.,Wong, E. C., Hinks, R. S., Tikofsky, R. S. and Hyde, J. S. (1992) “Time

course EPI of human brain function during task activation,” Magnetic Resonance in

Medicine, 25(2): 390–397.

Barnard, C. I. (1938) The Functions of the Executive, Cambridge, MA: Harvard University

Press.

Bates, E.,Wilson, S. M., Saygin,A. P., Dick, F.,Sereno, M. I., Knight, R.T., and Dronkers, N.

F. (2003) “Vox e l-based lesion–symptom mapping,” Nature Neuroscience, 6(5): 448–450.

Beaulieu, A. (2000) “The Space inside the skull: Dig ital representations, brain. mapping and

cognitive neuroscience in the decade of the brain,” Ph.D. Dissertation, Science and

Technology Dynamics, University of Amsterdam, the Netherlands.

Beaulieu, C. (2002) “The basis of anisotropic water diffusion in the nervous system: A

technical review,” NMR in Biomedicine, 15(7–8): 435–455.

Beck, D. M. (2010)“The appeal of the brain in the popular press,”Perspectives on Psychological

Science, 5(6): 762–766.

Becker,W. J., Cropanzano R., and Sanfey A. G. (2011) “Organizational neuroscience: Taking

organizational theory inside the neural black box,” Journal of Management, 37(4): 933–961.

Bennett, M. R., Hacker, P. M. S., and Bennett, M. R. (2003) Philosophical Foundations of

Neuroscience, Oxford: Blackwell.

Berger, H. (1929) “Uber das Elektrenkephalogramm des Menschen,” Arch. Psychiatrie Nerv.,

87: 527–570.

Bloch, F. (1946) “Nuclear induction,” Physical Review, 70(7–8): 460.

Bostrom, N. and Sandberg, A. (2009) “Cognitive enhancement: Methods, ethics, regulatory

challenges,” Science and Engineering Ethics, 15(3): 311–341.

Bower, J. L. (1986) Managing the Resource Allocation Process,Vol. 3, Cambridge, MA: Harvard

Business Press.

Bretschneider, F. and De Weille, J. R. (2006) Introduction to Electrophysiological Methods and

Neuroscientific methods 273

Instrumentation, London: Elsevier.

Brett, M., Johnsrude, I. S., and Owen,A. M. (2002) “The problem of functional localization

in the human brain,” Nature Reviews Neuroscience, 3(3): 243–249.

Broca,P. (1861)“Perte de la parole,ramollissement chronique et destruction partielle du lobe

antérieur gauche du cerveau,” Bulletin Society Anthropology, 2: 235–238.

Brockner, J. (1992) “The escalation of commitment to a failing course of action: Toward

theoretical progress,” Academy of Management Review, 17(1): 39–61.

Bronzino, J. D. (ed.) (1995) The Biomedical Engineering Handbook,Vol. 1, Boca Raton, FL:

CRC Press.

Brown,M. A. and Semelka, R. C. (2011) MRI: Basic Principles andApplications, Hoboken, NJ:

Wiley.

Bullinaria, J. A. and Chater, N. (1995) “Connectionist modeling: Implications for cognitive

neuropsychology,” Language and Cognitive Processes, 10(3–4): 227–264.

Bushberg, J.T., Seibert, J. A., Leidholdt, Jr, E. M., and Boone, J. M. (2002) The Essential

Physics of Medical Imaging, Philadelphia, PA: Lippincott Williams & Wilkins.

Cabeza, R. and Kingstone, A. (eds) (2001) Handbook of Functional Neuroimaging of Cognition,

Cambrdige, MA: MIT Press.

Camerer, C. F. (2003) Behavioral Game Theory: Experiments in Strategic Interaction, Princeton,

NJ: Princeton University Press.

Campbell-Meiklejohn, D. K.,Woolrich, M. W., Passingham, R. E., and Rogers, R. D. (2008)

“Knowing when to stop: The brain mechanisms of chasing losses,” Biological Psychiatry,

63(3): 293–300.

Caramazza, A. (1986) “On drawing inferences about the structure of normal cognitive

systems from the analysis of patterns of impaired performance: The case for single-

patient studies,” Brain and Cognition, 5(1): 41–66.

Caton, R. (1875) “Electrical currents of the brain,” Journal of Nervous and Mental Disease, 2(4):

610.

Chapman, B. L. (2006) “Gradients: The heart of the MRI machine,” Current Medical Imaging

Reviews, 2(1): 131–138.

Chater, N. and Ganis, G. (1991) “Double dissociation and isolable cognitive processes,”

Behaviour, 5(9): 668–672.

Churchland, P. S. and Sejnowski, T. J. (1988) “Perspectives on cognitive neuroscience,”

Science, 242(4879): 741–745.

Cohen-Charash, Y. and Mueller, J. S. (2007) “Does perceived unfairness exacerbate or

mitigate interpersonal counterproductive work behaviors related to envy?,” Journal of

Applied Psychology, 92(3): 666–680.

Colquitt, J. A., Conlon, D. E.,Wesson, M. J., Porter, O. L. H., and Ng, K.Y. (2001) “Justice at

the millennium: A meta-analytic review of 25 years of organizational justice research,”

Journal of Applied Psychology, 86(3): 425–445.

Coltheart, M. (2006) “What has functional neuroimaging told us about the mind (so far)?,”

Cortex, 42: 323–331.

Connolly, W. E. (2002) Neuropolitics: Thinking, Culture, Speed, Minneaoplis, MN: University

of Minnesota Press.

Cools, R., Frank, M. J., Gibbs, S. E., Miyakawa, A., Jagust, W., and D’Esposito, M. (2009)

“Striatal dopamine predicts outcome-specific reversal learning and its sensitivity to

dopaminergic drug administration,” The Journal of Neuroscience, 29(5): 1538–1543.

Cowell, R. A., Huber, D. E., and Cottrell, G. W. (2009) “Virtual brain reading: A connec-

tionist approach to understanding fMRI,” Paper presented at the 31st Annual Meeting of

the Cognitive Science Society.

274 Massaro

Crockett, M. J., Clark, L., Tabibnia, G., Lieberman, M. D., and Robbins, T. W. (2008)

“Serotonin modulates behavioral reactions to unfairness,” Science, 320(5884): 1739–1739.

Cushman, F. and Greene, J. D. (2012) “Finding faults: How moral dilemmas illuminate

cognitive structure,” Social Neuroscience, 7(3): 269–279.

Dagher,A., Owen,A. M., Boecker, H., and Brooks, D. J. (1999) “Mapping the network for

planning: A correlational PET activation study with the Tower of London task,” Brain,

122(10): 1973–1987.

Damasio, A. R. (1994) Descartes’ Error: Emotion, Reason, and the Human Brain, London:

Penguin Books.

Daw, N. D., O’Doherty, J. P, Seymour, B., Dayan, P., and Dolan, R. J. (2006) “Cortical

substrates for exploratory decisions in humans,”Nature, 441, 876–879.

Dawson, D. G. (1951) “A summation technique for detecting small signals in a large irregular

background,” Journal of Physiology, 494: 251–326.

Dimoka, A., Banker, R. D., Benbasat, I., Davis, F. D. , Dennis, A. R., Gefen, D., and Weber, B.

(2012) “On the use of neuropyhsiological tools in IS research: Developing a research

agenda for NeuroIS,” MIS Quarterly, 36(3): 679–702.

Dobbs, D. (2005) “Fact or phrenology?,” Scientific Amer ican Mind, April 1–8: 24–31.

Doktor, R. (1978) “Problem solving styles of executives and management scientists,” TIMS

Studies in the Management Sciences, 8(2): 123–134.

Donders, F. C. (1969) “On the speed of mental processes,” Acta Psychologica, 30: 412–431.

Driscoll, D. M., Dal Monte, O., Solomon, J., Krueger, F. and Grafman, J. (2012) “Empathic

deficits in combat veterans with traumatic brain injury: A voxel-based lesion-symptom

mapping study,” Cognitive and Behavioral Neurology, 25(4): 160–166.

Dutton, J. E., Fahey, L. and Narayanan,V. K. (1983) “Toward understanding strategic issue

diagnosis,” Strategic Management Journal, 4(4): 307–323.

Dvorak, P. and Badal, J. (2007) “This is your brain on the job,” The Wall Street Journal,

September 20. Retrieved at: http://online.wsj.com/news/articles/

SB119024585835733168

Fellows,L. K. and Farah, M. J. (2003) “Ventromedial frontal cortex mediates affective shifting

in humans: Evidence from a reversal learning paradigm,” Brain, 126(8): 1830–1837.

Finger, S. (2001) Origins of Neuroscience: A History of Explorations into Brain Function, New

York: Oxford University Press.

Fox, P.T. and Raichle, M. E. (1986) “Focal physiological uncoupling of cerebral blood flow

and oxidative metabolism during somatosensory stimulation in human subjects,”

Proceedings of the National Academy of Sciences, 83(4): 1140–1144.

Frank, M. J., Doll, B. B., Oas-Terpstra, J. and Moreno, F. (2009) “Prefrontal and striatal

dopaminergic genes predict individual differences in exploration and exploitation,”

Nature Neuroscience, 12(8): 1062–1068.

Franks, D. D. (2010) Neurosociology: The Nexus Between Neuroscience and Social Psychology. New

York: Springer.

Frazzetto,G. andAnker, S. (2009) “Neuroculture,” Nature Reviews Neuroscience,10(11): 815–821.

Freeman,W. J. and Quiroga, R. Q.(2013) Imaging Brain Function with EEG: AdvancedTemporal

and Spatial Analysis of Electroencephalographic Signals, New York: Springer.

Friston, K. J. (2005) “Models of brain function in neuroimaging,” Annual Review of

Psychology, 56: 57–87.

Friston, K. J., Price, C. J., Fletcher, P., Moore, C., Frackowiak,R. S. J., and Dolan, R. J. (1996)

“The trouble with cognitive subtraction,” Neuroimage, 4(2): 97–104.

Garvey, C. J. and Hanlon, R. (2002) “Computed tomography in clinical practice,” BMJ:

British Medical Journal, 324(7345): 1077.

Gazzaniga, M. and LeDoux, J. (1978) The Integrated Mind, New York: Plenum.

Neuroscientific methods 275

Ghadiri, A., Habermacher, A., and Peters, T. (2012) “Neuroleadership: The backdrop,” in

Neuroleadership: A Journey Through the Brain for Business Leaders, 1–15, Berlin: Springer.

Glimcher, P. W. and Rustichini, A. (2004) “Neuroeconomics: The consilience of brain and

decision,” Science, 306(5695): 447–452.

Glöckner,A. and Herbold, A. K. (2011) “An eye tracking study on information processing

in risky decisions: Evidence for compensatory strategies based on automatic processes,”

Journal of Behavioral Decision Making, 24(1): 71–98.

Greene, J.and Cohen, J. (2004) “For the law, neuroscience changes nothing and everything,”

Philosophical Tra nsactions of the Royal Society, 359(1451): 1775–1785.

Grossman, R. I. and Bernat, J. L. (2004) “Incidental research imaging findings Pandora’s

costly box,” Neurology, 62(6): 849–850.

Gul, F. and Pesendorfer, W. (2008) “The case for mindless economics,” in Caplin, A. and

Schotter, A. (eds), The Foundations of Positive and Normative Economics, 3–39, Oxford:

Oxford University Press.

Gulyas, B., Halldin, C., Sandell, J., Karlsson, P., Sóvágó, J., Kárpáti, E., and Farde, L. (2002)

“PET studies on the brain uptake and regional distribution of [11C] vinpocetine in

human subjects,” Acta Neurologica Scandinavica, 106(6): 325–332.

Hämäläinen, M., Hari, R., Ilmoniemi, R. J., Knuutila, J., and Lounasmaa, O. V. (1993)

“Magnetoencephalography: Theory, instrumentation, and applications to noninvasive

studies of the working human brain,” Reviews of Modern Physics, 65(2): 413.

Heinrichs, M., von Dawans, B., and Domes, G. (2009) “Oxytocin, vasopressin, and human

social behavior,” Frontiers in Neuroendocrinology, 30(4): 548–557.

Hines, T. (1987) “Left brain/right brain mythology and implications for management and

training,” Academy of Management Review, 12: 600–606.

Hounsfield, G. N. (1973) “Computerized transverse axial scanning (tomography): Part 1.

Description of system,” British Journal of Radiology, 46(552): 1016–1022.

Humm, J. L., Rosenfeld, A. and Del Guerra, A. (2003) “From PET detectors to PET

scanners,” European Journal of Nuclear Medicine and Molecular Imaging, 30(11): 1574–1597.

Huy, Q. N. (2002) “Emotional balancing of organizational continuity and radical change:

The contribution of middle managers,”Administrative Science Quarterly, 47(1): 31–69.

Illes, J. (ed.) (2006) Neuroethics: Defining the Issues in Theory, Practice and Policy, New York:

Oxford University Press.

Illes, J. and Racine, E. (2005) “Imaging or imagining? A neuroethics challenge informed by

genetics,” The Amer ican Journal of Bioethics, 5(2): 5–18.

Illes, J., Moser, M. A., McCormick, J. B., Racine, E., Blakeslee, S., Caplan, A., and Weiss, S.

(2009) “Neurotalk: improving the communication of neuroscience research,” Nature

Reviews Neuroscience, 11(1): 61–69.

Illes, J., Rosen,A. C., Huang, L., Goldstein, R. A., Raffin, T. A., Swan, G., and Atlas, S. W.

(2004) “Ethical consideration of incidental findings on adult brain MRI in research,”

Neurology, 62(6): 888–890.

Jones, O. D. and Shen,F. X.(2012) “Law and neuroscience in the United States,” in Spranger,

T. M. (ed.) International Neurolaw: A Comparative Analysis, 349–380, Berlin: Springer.

Kable, J. W. (2011) “The cognitive neuroscience toolkit for the neuroeconomist,” Journal of

Neuroscience, Psychology, and Economics, 4(2): 63–84.

Kato, M., Taniwaki, T. and Kuwabara,Y. (2000) “The advantages and limitations of brain

function analyses by PET,” Clinical Neurology, 40(12): 1274.

Katzman, G. L., Dagher, A. P., and Patronas, N. J. (1999) “Incidental findings on brain

magnetic resonance imaging from 1000 asymptomatic volunteers,” JAMA: the Journal of

the American Medical Association, 282(1): 36–39.

King-Casas, B., To mlin, D., Anen, C., Camerer, C. F., Quartz, S. R., and Montague, P. R.

276 Massaro

(2005) “Getting to know you: Reputation and trust in a two-person economic

exchange,” Science, 308(5718): 78–83.

Kirschen, M. P. ,Jaworska, A., and Illes, J. (2006) “Subjects’ expectations in neuroimaging

research,” Journal of Magnetic Resonance Imaging, 23(2): 205–209.

Knoch, D., Gianotti, L. R., Baumgartner, T. and Fehr, E. (2010) “A neural marker of costly

punishment behavior,” Psychological Science, 21(3): 337–342.

Kolb, B. and Whishaw, I. Q. (2009) Fundamentals of Human Neuropsychology, New York:

Macmillan.

Kosfeld, M., Heinrichs, M., Zak, P. J., Fischbacher, U., and Fehr, E. (2005) “Oxytocin

increases trust in humans,” Nature, 435(7042): 673–676.

Kovalchik, S. and Allman, J. (2006) “Measur ing reversal learning: Introducing the Variable

Iowa Gambling Task in a study of young and old normals,” Cognition & Emotion, 20(5):

714–728.

Kristeva, R., Keller, E., Deecke, L., and Kornhuber, H. H. (1979) “Cerebral potentials

preceding unilateral and simultaneous bilateral finger movements,” Electroencephalography

and Clinical Neurophysiology, 47(2): 229–238.

Kwong, K. K., Belliveau,J. W.,Chesler, D. A., Goldberg, I. E.,Weisskoff, R. M., Poncelet,B.

P. , and Tu rner,R. (1992)“Dynamic magnetic resonance imaging of human brain activity

during primary sensory stimulation,” Proceedings of the National Academy of Sciences, 89(12):

5675–5679.

Lauterbur, P. C. (1973) “Image formation by induced local interactions: examples employing

nuclear magnetic resonance,” Nature, 242(5394): 190–191.

Le Bihan, D. (1996) “Functional MRI of the brain principles, applications and limitations,”

Journal of Neuroradiology, 23(1): 1–5.

Le Bihan, D. (2003) “Looking into the functional architecture of the brain with diffusion

MRI,” Nature Reviews Neuroscience, 4(6): 469–480.

Le Bihan, D., Urayama, S. I.,Aso, T., Hanakawa, T., and Fukuyama, H. (2006) “Direct and

fast detection of neuronal activation in the human brain with diffusion MRI,” Proceedings

of the National Academy of Sciences, 103(21): 8263–8268.

Lebedev, M. A. and Nicolelis, M. A. (2006) “Brain–machine interfaces: Past, present and

future,” TRENDS in Neurosciences, 29(9): 536–546.

Lee, N., Broderick, A. J., and Chamberlain, L. (2007) “What is ‘Neuromarketing?’ A

discussion and agenda for future research,” International Journal Of Psychophysiology, 63(2):

199–204.

Legrenzi, P. and Umiltà, C. (2011) Neuromania: On the Limits of Brain Science, New York:

Oxford University Press.

Levinthal, D. A. and March, J. G. (1993) “The myopia of learning,” Strategic Management

Journal, 14(S2): 95–112.

Levinthal, D. and Rerup, C. (2006) “Crossing an apparent chasm: Bridging mindful and less-

mindful perspectives on organizational learning,” Organization Science, 17(4): 502–513.

Lindebaum, D. and Zundel, M. (2013) “Not quite a revolution: Scrutinizing organizational

neuroscience in leadership studies,” Human Relations, 66(6): 857–877.

Liu, J. Z., Zhang, L., Brown, R. W., and Yue, G. H. (2004) “Reproducibility of fMRI at 1.5

T in a strictly controlled motor task,” Magnetic Resonance in Medicine, 52(4): 751–760.

Logothetis, N. K. (2008) “What we can do and what we cannot do with MRI,” Nature,

453(7197): 869–878.

Logothetis, N. K. and Wandell, B. A. (2004) “Interpreting the BOLD signal,” Annual Review

of Physiology, 66: 735–769.

Logothetis, N. K., Pauls, J., Augath,M., Tr i nath, T., and Oeltermann, A. (2001)

“Neurophysiological investigation of the basis of the fMRI signal,” Nature, 412(6843):

Neuroscientific methods 277

150–157.

Luck, S. J. (2005) “Ten simple rules for designing ERP exper iments,” in T.C. Handy (ed.),

Event-Related Potentials: A Methods Handbook, 16–32, Cambridge, MA: MIT Press.

Magistretti, P. J., Pellerin, L., Rothman, D. L., and Shulman, R. G. (1999) “Energy on

demand,” Science, 283(5401): 496–497.

Malonek, D., Dirnagl, U., Lindauer, U., Yamada, K., Kanno, I., and Grinvald, A. (1997)

“Vascular imprints of neuronal activity: Relationships between the dynamics of cortical

blood flow, oxygenation, and volume changes following sensory stimulation,” Proceedings

of the National Academy of Sciences, 94(26): 14826–14831.

March, J. G. and Simon, H. A. (1958) Organizations, Oxford: Wiley.

McCabe, D. P. and Castel, A. D. (2008) “Seeing is believing: The effect of brain images on

judgments of scientific reasoning,” Cognition, 107(1): 343–352.

McCabe, K., Houser, D., Ryan, L., Smith,V., and Trouard, T. (2001) “A functional imaging

study of cooperation in two-person reciprocal exchange,” Proceedings of the National

Academy of Sciences, 98(20): 11832–11835.

Menon, R. S. and Kim, S. G. (1999) “Spatial and temporal limits in cognitive neuroimaging

with fMRI,” TRENDS in Cognitive Sciences, 3(6): 207–216.

Menon, R. S., Gati, J. S., Goodyear, B. G., Luknowsky, D. C. and Thomas, C. G. (1998)

“Spatial and temporal resolution of functional magnetic resonance imaging,” Biochemistry

and Cell Biology, 76(2-3): 560–571.

Minoshima, S., Koeppe,R. A., Frey, K. A. and Kuhl, D. E. (1994) “Anatomic standardization:

linear scaling and nonlinear warping of functional brain images,” Journal of Nuclear

Medicine, 35(9): 1528–1537.

Mintzberg, H, (1976) “Planning on the left and managing on the right,” Harvard Business

Review, 54(4): 49–58.

Mintzberg, H. (1994) “The f all and rise of strategic planning,”Harvard Business Review, 72(1):

107–114.

Moorman, R. H. (1991) “Relationship between organizational justice and organizational

citizenship behaviors. Do fairness perception influence employee citizenship?,” Journal of

Applied Psychology, 76(6): 845–855.

Murray, E. A. and Baxter, M. G. (2006) “Cognitive neuroscience and nonhuman primates:

Lesion studies,” in Senior, C. E., Russell, T. E., and Gazzaniga, M. S. (eds), Methods in

Mind, 43–69. Cambrdige, MA: MIT Press.

Natterer, F. andRitman, E. L. (2002) “Past and future directions in x ray computed

tomography (CT),”International Journal of Imaging Systems and Technology,12(4):

175–187.

Newell,A. and Simon, H. A. (1972) Human Problem Solving,Vo l. 14, Englewood Cliffs, NJ:

Prentice-Hall.

Nicolaou, N. and Shane, S. (2009) “Born entrepreneurs? The genetic foundations of

entrepreneurship,” Journal of BusinessVe ntur ing, 23: 1–22.

Niedermeyer, E. and Lopes da Silva, F.H. (eds) (1999) Electroencephalography: Basic Principles,

Clinical Applications, and Related Fields, 4th edn, Baltimore: Williams &Wilkins.

Nonaka, I. and Toyama, R. (2007) “Strategic management as distributed practical wisdom

(phronesis),” Industrial and Corporate Change, 16(3): 371–394.

Nunez, P. L. (1995) Neocortical Dynamics and Human EEG Rhythms, New York: Oxford

University Press.

Ogawa, S., Lee, T. M., Kay,A. R., and Tank, D. W. (1990) “Brain magnetic resonance imaging

with contrast dependent on blood oxygenation,” Proceedings of the National Academy of

Sciences, 87(24): 9868–9872.

Ogawa, S., Tank, D. W., Menon, R., Eller mann, J. M., Kim, S. G., Merkle, H., and Ugurbil,

278 Massaro

K. (1992) “Intrinsic signal changes accompanying sensory stimulation: functional brain

mapping with magnetic resonance imaging,” Proceedings of the National Academy of Sciences,

89(13): 5951–5955.

Page, M. (2006) “What can’t functional neuroimaging tell the cognitive psychologist?,”

Cortex, 42(3): 428–443.

Pascual-Leone, A.,Walsh,V., and Rothwell, J. (2000) “Transcranial magnetic stimulation in

cognitive neuroscience–virtual lesion, chronometry,and functional connectivity,” Current

Opinion in Neurobiology, 10(2): 232–237.

Pascual-Marqui, R. D., Michel, C. M., and Lehmann, D. (1994) “Low resolution electro-

magnetic tomography: A new method for localizing electrical activity in the brain,”

International Journal of Psychophysiology, 18(1): 49–65.

Payne, B. (1957) “Steps in long-range planning,” Harvard Business Review, 35(2): 95–101.

Pelizzari, C. A., Chen, G.T., Spelbring, D. R.,Weichselbaum, R. R., and Chen, C.T. (1989)

“Accurate three-dimensional registration of CT, PET, and/or MR images of the brain,”

Journal of Computer AssistedTo mography, 13(1): 20–26.

Petersen, S. E., Fox, P.T., Posner, M. I., Mintun, M., and Raichle, M. E. (1988) “Positron

emission tomographic studies of the cortical anatomy of single- word processing,”

Nature, 331(6157): 585–589.

Petrovic, P., Kalisch, R., Singer, T., and Dolan, R. J. (2008) “Oxytocin attenuates affective

evaluations of conditioned faces and amygdala activity,” The Journal of Neuroscience,

28(26): 6607–6615.

Phelps, M. E. and Mazziotta, J. C. (1985) “Positron emission tomography: human brain

function and biochemistry,” Science, 228(4701): 799–809.

Pieters, R. (2008) “A review of eye-tracking research in marketing,”in Naresh, K. M. (ed.),

Review of Marketing Research,Vol. 4, 123–214, Bingley, UK: Emerald Group Publishing

Limited.

Poldrack, R. A. (2006) “Can cognitive processes be inferred from neuroimaging data?,”

TRENDS in Cognitive Sciences, 10(2): 59–63.

Poldrack, R. A. (2012) “The future of fMRI in cognitive neuroscience,” Neuroimage, 62(2):

1216–1220.

Poldrack, R. A., Kittur, A., Kalar, D., Miller, E., Seppa, C., Gil,Y., and Bilder, R. M. (2011)

“The cognitive atlas: To w ard a knowledge foundation for cognitive neuroscience,”

Frontiers in Neuroinformatics, 5: 5–17.

Powell, T. C. (2011) “Neurostrategy,” Strategic Management Journal, 32(13): 1484–1499.

Powell, T. C., Lovallo, D., and Fox, C. R. (2011) “Behavioral strategy,” Strategic Management

Journal, 32(13): 1369–1386.

Price, C. J. and Friston, K. J. (1997) “Cognitive conjunction: A new approach to brain

activation experiments,” Neuroimage, 5(4): 261–270.

Purcell, E. M., To rrey, H. C., and Pound, R. V. (1946) “Resonance absorption by nuclear

magnetic moments in a solid,” Physical Review, 69(1–2): 37.

Racine, E., Bar-Ilan, O., and Illes, J. (2005) “fMRI in the public eye,” Nature Reviews

Neuroscience, 6(2): 159–164.

Racine, E.,Waldman, S., Rosenberg, J., and Illes, J. (2010) “Contemporary neuroscience in

the media,” Social Science & Medicine, 71(4): 725–733.

Raichle, M. E. (1979) “Quantitative in vivo autoradiography with positron emission

tomography,” Brain Research Reviews, 1(1): 47–68.

Raichle, M. E.(2003) “Functional brain imaging and human brain function,” The Journal of

Neuroscience, 23(10): 3959–3962.

Raichle, M. E. (2009a) “A paradigm shift in functional brain imaging,” The Journal of

Neuroscience, 29(41): 12729–12734.

Neuroscientific methods 279

Raichle, M. E. (2009b) “A brief history of human brain mapping,” TRENDS in

Neurosciences, 32(2): 118–126.

Raichle, M. E.and Snyder,A. Z. (2007) “A default mode of brain function: A brief history

of an evolving idea,” Neuroimage, 37(4): 1083–1090.

Raichle, M. E., Martin,W. R., Herscovitch, P., Mintun, M. A., and Markham, J. (1983) “Brain

blood flow measured with intravenous H2(15)O. II. Implementation and validation,”

Journal of Nuclear Medicine, 24(9): 790–798.

Raz, A. and Buhle, J. (2006) “Typologies of attentional networks,” Nature Reviews

Neuroscience, 7(5): 367–379.

Robey, D. and Ta ggart, W. (1981) “Measur ing managers’ minds: The assessment of style in

human information processing,” Academy of Management Review, 6(3): 375–383.

Robey, D. and Taggart, W. (1982) “Human information processing in information and

decision support systems,” MIS Quarterly, 6(2): 61–73.

Rogers,B.P.,Morgan,V. L.,Newton,A.T., and Gore,J. C. (2007) “Assessing functional connec-

tivity in the human brain by fMRI,” Magnetic Resonance Imaging,25(10): 1347–1357.

Rogers, L. F. (2003) “Helical CT: The revolution in imaging,” American Journal of

Roentgenology, 180(4): 883–883.

Rolls, E.T. (2012) Neuroculture: On the Implications of Brain Science, New York: Oxford

University Press.

Rorden, C. and Karnath, H. O. (2004)“Using human brain lesions to infer function: A relic

from a past era in the fMRI age?,” Nature Reviews Neuroscience, 5(10): 812–819.

Rubinov, M. and Sporns, O. (2010) “Complex network measures of brain connectivity:Uses

and inter pretations,” Neuroimage, 52(3): 1059–1069.

Sack, A.T. and Linden, D. E. (2003) “Combining transcranial magnetic stimulation and

functional imaging in cognitive brain research: Possibilities and limitations,” Brain

Research Reviews, 43(1): 41–56.

Sanfey, A. G., Rilling, J. K.,Aronson, J. A., Nystrom, L. E., and Cohen, J. D. (2003) “The

neural basis of economic decision-making in the ultimatum game,” Science, 300(5626):

1755–1758.

Sartori, G. and Umiltà, C. (2000) “How to avoid the fallacies of cognitive subtraction in

brain imaging,” Brain and Language, 74(2): 191–212.

Schulte-Mecklenbeck, M., Kühberger,A., and Ranyard, R. (2011) “The role of process data

in the development and testing of process models of judgment and decision making,”

Judgment and Decision Making, 6(8): 733–739.

Scott Jr.,W. E. (1966) “Activation theory and task design,” Organizational Behavior and Human

Performance, 1(1): 3-30.

Senior, C. E., Russell, T. E., and Gazzaniga, M. S. (2006) Methods in Mind, Cambr idge, MA:

MIT Press.

Senior, C., Lee, N., and Butler, M. (2011) “PERSPECTIVE – Organizational Cognitive

Neuroscience,” Organization Science, 22(3): 804–815.

Shallice, T. (1988) From Neuropsychology to Mental Structure, New York: Cambridge University

Press.

Shallice, T. (2003) “Functional imaging and neuropsychology findings: How can they be

linked?,” Neuroimage, 20(S1): 46–54.

Siebner, H. and Rothwell, J. (2003) “Transcranial magnetic stimulation: New insights into

representational cortical plasticity,” Experimental Brain Research, 148(1): 1–16.

Simons, R.(1991) “Strategic orientation and top management attention to control systems,”

Strategic Management Journal, 12(1): 49–62.

Simpson, D. (2005) “Phrenology and the neurosciences: Contributions of FJ Gall and JG

Spurzheim,” ANZ Journal of Surgery, 75(6): 475–482.

280 Massaro

Smith, K., Dickhaut, J., McCabe, K., and Pardo, J. V. (2002) “Neuronal substrates for choice

under ambiguity, risk, gains, and losses,” Management Science, 48(6): 711–718.

Song, A. W., Huettel, S. A., and McCarthy, G. (2006) “Functional neuroimaging: Basic

principles of functional MRI,” in Cabeza, R. and Kingstone, A. (eds) Handbook of

Functional Neuroimaging of Cognition, 21–52, Cambridge, MA: MIT Press.

Squires, K. C.,Wickens, C., Squires, N. K., and Donchin, E. (1976) “The effect of stimulus

sequence on the waveform of the cortical event-related potential,” Science, 193(4258):

1142–1146.

Staw, B. M. (1981) “The escalation of commitment to a course of action,” Academy of

Management Review, 6(4): 577–587.

Stewart, L. and Walsh,V. (2006) “Transcranial magnetic stimulation in human cognition,” in

Senior, C. E., Russell, T. E., and Gazzaniga, M. S. (eds), Methods in Mind, 1–27,

Cambridge, MA: MIT Press.

Stöcker, T., Schneider, F., Klein, M., Habel, U., Kellermann, T., Zilles, K., and Shah, N. J.

(2005) “Automated quality assurance routines for fMRI data applied to a multicenter

study,” Human Brain Mapping, 25(2): 237–246.

Tang,Y. Y. and Posner, M. I. (2009).“Attention training and attention state training,” Trends

in Cognitive Sciences, 13(5): 222–227.

Taggart, W., Robey, D., and Kroeck, K. (1985) “Managerial decision styles and cerebral

dominance: An empirical study,” Journal of Management Studies, 22(2): 175–192.

Ter-Pogossian, M. M. and Herscovitch, P. (1985) “Radioactive oxygen-15 in the study of

cerebral blood flow, blood volume, and oxygen metabolism,” Seminars in Nuclear Medicine,

15(4): 377–394.

Tilyou, S. M. (1991) “The evolution of positron emission tomography,” Journal of Nuclear

Medicine, 32(4): 15N–26N.

To g a, A. W. and Mazziotta, J. C. (eds) (2002) Brain Mapping: The Methods,Vol. 1, San Diego:

Elsevier.

Turkington, T.G. (2001) “Introduction to PET instrumentation,” Journal of Nuc lear Medicine

Technology, 29(1): 4–11.

Ty ner, F. S., Knott, J. R.,and Mayer Jr,W. B. (1989) Fundamentals of EEG Technology: Clinical

Correlates, Philadelphia: Lippincott Williams and Wilkins.

Uludag, K., Dubowitz, D. J.,Yoder, E. J., Restom, K., Liu, T. T., and Buxton, R. B. (2004)

“Coupling of cerebral blood flow and oxygen consumption dur ing physiological

activation and deactivation measured with fMRI,” Neuroimage, 23(1): 148–155.

Uttal,W. R.(2001) The New Phrenology: The Limits of Localizing Cognitive Processes in the Brain.

Cambridge, MA: MIT Press.

Vol z, K. G. and von Cramon, D. Y. (2006)“What neuroscience can tellabout intuitive processes

in the context of perceptual discove ry,” Journal of Cognitive Neuroscience, 18(12):2077–2087.

Vrba, J. and Robinson, S. E. (2002) “SQUID sensor array configurations for magnetoen-

cephalography applications,” Superconductor Science and Technology, 15(9): R51.

Vul, E., Harris, C., Winkielman, P., and Pashler, H. (2009) “Puzzlingly high correlations in

fMRI studies of emotion, personality, and social cognition,” Perspectives on Psychological

Science, 4(3): 274–290.

Waldman, D. A., Balthazard, P. A., and Peterson, S. J. (2011) “Leadership and neuroscience:

Can we revolutionize the way that inspirational leaders are identified and developed?,”

Academy of Management Perspectives, 25(1): 60–74.

Wal sh, V. and Cowey, A. (2000) “Transcranial magnetic stimulation and cognitive

neuroscience,” Nature Reviews Neuroscience, 1(1): 73–80.

Weick, K. E. and Sutcliffe, K. M. (2006) “Mindfulness and the quality of organizational

attention,” Organization Science, 17(4): 514–524.

Neuroscientific methods 281

Weisberg, D. S., Keil, F. C., Goodstein, J., Rawson, E., and Gray, J. R. (2008) “The seductive

allure of neuroscience explanations,” Journal of Cognitive Neuroscience, 20(3): 470–477.

Willingham, D.T. and Dunn, E. W. (2003) “What neuroimaging and brain localization can

do, cannot do and should not do for social psychology,” Journal of Personality and Social

Psychology, 85(4): 662.

Yacoub, E., Uludag, K., Ugurbil, K., and Harel, N. (2008) “Decreases in ADC observed in

tissue areas during activation in the cat visual cortex at 9.4 T using high diffusion sensiti-

zation,” Magnetic Resonance Imaging, 26(7): 889–896.

282 Massaro

Using Functional Neuroimaging to Advance Entrepreneurial Cognition Research

Article

Full-text available

Sep 2020
J SMALL BUS MANAGE

This paper advances current understandings of why and how neuroimaging can enrich the study of entrepreneurship. We discuss the foundations of this cross-disciplinary research area and its evolving boundaries, focusing on explaining and providing actionable insights on how two of the most widely used brain-imaging methods can be leveraged for use in entrepreneurship research. We provide guidelines aimed to equip entrepreneurship scholars with the fundamentals needed to design and evaluate research involving these neuroscience instruments. In so doing, we delineate examples related to entrepreneurial cognition and propose several ways in which this domain of research can be enhanced with neuroimaging.

Neuromanagement and neuromarketing: neuroscience in organizations

Chapter

Jul 2023

This chapter assesses neuroscience, related technologies and applications, and its implications for organisational studies and management. It also reviews neuroscientific tools to understand the neural features of management decision-making and customer/consumer behaviour, its origins, and underlying principles. For this purpose, the chapter starts with a brief overview and history of neuroscience, its use in organisations, and a subsequent focus on the technology driving neuroscience and, simultaneously, the latest developments in this developing discipline. After revealing the different implications in organisations, particularly in management and strategy, leadership, marketing, and economics, it will next point out emerging ethical and philosophical challenges and debates in neuroscience in organisational studies that emerged throughout its development. The chapter will end by addressing potential future implications and research directions for neuroscience in organisations and drawing conclusions.

Neuromanagement and Neuromarketing: Neuroscience in Organisations

Chapter

Jan 2023

This chapter assesses neuroscience, related technologies and applications, and its implications for organisational studies, management and marketing. It also reviews neuroscientific tools to understand the neural features of management decision-making, and customer/consumer behavior, its origins, and underlying principles.

Micro-Foundations of Strategic Decision-Making in Family Business Organisations: A Cognitive Neuroscience Perspective

Article

Feb 2022
LONG RANGE PLANN

Emotion significantly affects strategic decision-making by entrepreneurs in family business organisations (FBOs). This paper proposes a cognitive framework of management in FBOs that emphasises the importance of emotion, based on a micro-foundational perspective: that is, an integrated hierarchy of cognitive processes underlying FBOs’ strategic decision-making and their interactions with external affective events. Previous studies that used traditional behavioural methodologies are reviewed with reference to the proposed cognitive framework to highlight importance of understanding effect of emotion-cognition interactions on entrepreneurs’ strategic decision-making process. New techniques using biological, physiological, and neuroscientific tools are then introduced as complementary methods for this line of research. Finally, future research directions are discussed with a focus on implicit cognitive processing, complex emotions, and cognitive interventions.

Chapter 10: Neuroscience Methods: A Framework for Managerial and Organizational Cognition

Chapter

Full-text available

Dec 2017

Sebastiano Massaro

The Organizational Neuroscience of Emotions

Preprint

Full-text available

Aug 2019

Sebastiano Massaro

In book: Cambridge Handbook of Workplace Affect Publisher: Cambridge University Press

The Organizational Neuroscience of Emotions

Preprint

Full-text available

Aug 2019

Sebastiano Massaro

This work contributes to research in workplace affect by presenting an Organizational Neuroscience perspective on emotions. Methodological motivations are explored and a theoretical parallel drawn between Affective Event Theory (Weiss & Cropanzano, 1996) and neural circuitries of information processing. Neuroscience research relevant to the organizational affective literature is then explained by covering the broad domains of intra-individual and inter-personal affect. Topics addressed include basic emotions, emotional contagion, and emotional intelligence, among others. Suggestions for future research emerge at the end. Massaro S. (2019). The Organizational Neuroscience of Emotions. In: The Cambridge Handbook of Workplace Affect; Eds.: Yang L., Cropanzano R.S., Daus C., & Tur V.A.M.; Chapter 3, Cambridge University Press

Why and how do founding entrepreneurs bond with their ventures? Neural correlates of entrepreneurial and parental bonding

Article

Full-text available

May 2018
J BUS VENTURING

This paper investigates why and how founding entrepreneurs bond with their ventures. We develop and test theory about the nature of bonding in a functional magnetic resonance imaging (fMRI) study of 42 subjects (21 entrepreneurs and 21 parents). We find that entrepreneurs and parents show similar signs of affective bonding, that self-confidence plays a role in bonding style, and that the degree to which entrepreneurs include their ventures in the self and to which parents include their child in the self influences their ability to make critical assessments. Our findings suggest that bonding is similar for entrepreneurs and parents and that venture stimuli influence reward systems, self-regulatory functions, and mental factors that are associated with judgment.

Neuromanagement Under the Light of Maqasid Sharia

Article

Jan 2020

On a Correlative and Evolutionary SWOT Analysis

Article

Full-text available

May 2019

Charis M. Vlados

Purpose The purpose of this paper is to counter-propose a new approach of SWOT analysis, which can be used in the strategic planning of the contemporary organizations. Design/methodology/approach This paper, after presenting the conceptual context of the existing (conventional) SWOT analysis, presents the existing criticism within the international literature. Then, it articulates gradually the new evolutionary and correlative SWOT analysis, by using the approaches and the literature of evolutionary economics, and the Stra.Tech.Man approach in business dynamics. In conclusion, it presents the new conceptual framework on which a new correlative SWOT analysis can be based. Findings Main finding of this research is that the interpretation of the conventional SWOT analysis tends to study the strengths and the weaknesses of the business with an analytical dichotomy. The conventional SWOT analysis conceptualizes, usually implicitly, the opportunities and threats of the external environment as having the same impact to all the socioeconomic agents, without exception. However, by using a correlative interpretation of SWOT analysis, we understand that the opportunities and threats are always “potential,” depending on the organization’s strategic capability to exercise its comparative strengths and weaknesses. Originality/value In the existing literature of SWOT analysis, despite the growing criticism, there is no critique that can give systemic and correlative answers to the articulation of business strategy in SWOT terms. The Stra.Tech.Man approach, also, is a conceptual framework to study the evolutionary adaptation of all the kinds of socioeconomic organizations.

Justice at the Millennium: A Meta-Analytic Review of 25 Years of Organizational Justice Research

Article

Full-text available

Jun 2001

The field of organizational justice continues to be marked by several important research questions, including the size of relationships among justice dimensions, the relative importance of different justice criteria, and the unique effects of justice dimensions on key outcomes. To address such questions, the authors conducted a meta-analytic review of 183 justice studies. The results suggest that although different justice dimensions are moderately to highly related, they contribute incremental variance explained in fairness perceptions. The results also illustrate the overall and unique relationships among distributive, procedural, interpersonal, and informational justice and several organizational outcomes (e.g., job satisfaction, organizational commitment, evaluation of authority, organizational citizenship behavior, withdrawal, performance). These findings are reviewed in terms of their implications for future research on organizational justice.

Transcranial Magnetic Stimulation in Human Cognition

Chapter

Jun 2006

Experts discuss the wide variety of investigative tools available to cognitive neuroscience, including transcranial magnetic stimulation, neuroscience computation, fMRI, imaging genetics, and neuropharmacology, with particular emphasis on convergence of techniques and innovative uses. The evolution of cognitive neuroscience has been spurred by the development of increasingly sophisticated investigative techniques to study human cognition. In Methods in Mind, experts examine the wide variety of tools available to cognitive neuroscientists, paying particular attention to the ways in which different methods can be integrated to strengthen empirical findings and how innovative uses for established techniques can be developed. The book will be a uniquely valuable resource for the researcher seeking to expand his or her repertoire of investigative techniques. Each chapter explores a different approach. These include transcranial magnetic stimulation, cognitive neuropsychiatry, lesion studies in nonhuman primates, computational modeling, psychophysiology, single neurons and primate behavior, grid computing, eye movements, fMRI, electroencephalography, imaging genetics, magnetoencephalography, neuropharmacology, and neuroendocrinology. As mandated, authors focus on convergence and innovation in their fields; chapters highlight such cross-method innovations as the use of the fMRI signal to constrain magnetoencephalography, the use of electroencephalography (EEG) to guide rapid transcranial magnetic stimulation at a specific frequency, and the successful integration of neuroimaging and genetic analysis. Computational approaches depend on increased computing power, and one chapter describes the use of distributed or grid computing to analyze massive datasets in cyberspace. Each chapter author is a leading authority in the technique discussed. Contributors: Peyman Adjamian, Peter A. Bandettini, Mark Baxter, Anthony S. David, James Dobson, Ian Foster, Michael Gazzaniga, Dietmar G. Heinke, Stephen Hall, John M. Henderson, Glyn W. Humphreys, Andreas Meyer-Lindenburg, Venkata Mattay, Elisabeth A. Murray, Gina Rippon, Tamara Russell, Carl Senior, Philip Shaw, Krish D. Singh, Marc A. Sommer, Lauren Stewart, John D. Van Horn, Jens Voeckler, Vincent Walsh, Daniel R. Weinberger, Michael Wilde, Jeffrey Woodward, Robert H. Wurtz, Eun Young Yoon, Yong Zhao Carl Senior, Tamara Russell and Michael S. Gazzaniga

Cognitive Neuroscience and Nonhuman Primates: Lesion Studies

Chapter

Jun 2006

Behavioral game theory: Experiments in strategic interaction

Book

Sep 2003

C.F. Camerer

Game theory, the formalized study of strategy, began in the 1940s by asking how emotionless geniuses should play games, but ignored until recently how average people with emotions and limited foresight actually play games. This book marks the first substantial and authoritative effort to close this gap. Colin Camerer, one of the field's leading figures, uses psychological principles and hundreds of experiments to develop mathematical theories of reciprocity, limited strategizing, and learning, which help predict what real people and companies do in strategic situations. Unifying a wealth of information from ongoing studies in strategic behavior, he takes the experimental science of behavioral economics a major step forward. He does so in lucid, friendly prose. Behavioral game theory has three ingredients that come clearly into focus in this book: mathematical theories of how moral obligation and vengeance affect the way people bargain and trust each other; a theory of how limits in the brain constrain the number of steps of "I think he thinks . . ." reasoning people naturally do; and a theory of how people learn from experience to make better strategic decisions. Strategic interactions that can be explained by behavioral game theory include bargaining, games of bluffing as in sports and poker, strikes, how conventions help coordinate a joint activity, price competition and patent races, and building up reputations for trustworthiness or ruthlessness in business or life.

Leadership and Neuroscience: Can We Revolutionize the Way That Inspirational Leaders Are Identified and Developed?

Article

Feb 2011

Functional Brain Imaging and Human Brain Function

Article

May 2003

Marcus E. Raichle

The Integrated Mind

Article

Jan 1978

In this book we are trying to illuminate the persistent and nag ging questions of how mind, life, and the essence of being relate to brain mechanisms. We do that not because we have a commit ment to bear witness to the boring issue of reductionism but be cause we want to know more about what it's all about. How, in deed, does the brain work? How does it allow us to love, hate, see, cry, suffer, and ultimately understand Kepler's laws? We try to uncover clues to these staggering questions by con sidering the results of our studies on the bisected brain. Several years back, one of us wrote a book with that title, and the ap proach was to describe how brain and behavior are affected when one takes the brain apart. In the present book, we are ready to put it back together, and go beyond, for we feel that split-brain studies are now at the point of contributing to an understanding of the workings of the integrated mind. We are grateful to Dr. Donald Wilson of the Dartmouth Medi cal School for allowing us to test his patients. We would also like to thank our past and present colleagues, including Richard Naka mura, Gail Risse, Pamela Greenwood, Andy Francis, Andrea El berger, Nick Brecha, Lynn Bengston, and Sally Springer, who have been involved in various facets of the experimental studies on the bisected brain described in this book.

The neural basis of functional brain imaging signals

Article

Dec 2002
TRENDS NEUROSCI

The haemodynamic responses to neural activity that underlie the blood-oxygen-level-dependent (BOLD) signal used in functional magnetic resonance imaging (fMRI) of the brain are often assumed to be driven by energy use, particularly in presynaptic terminals or glia. However, recent work has suggested that most brain energy is used to power postsynaptic currents and action potentials rather than presynaptic or glial activity and, furthermore, that haemodynamic responses are driven by neurotransmitter-related signalling and not directly by the local energy needs of the brain. A firm understanding of the BOLD response will require investigation to be focussed on the neural signalling mechanisms controlling blood flow rather than on the locus of energy use.

Just and Unjust Punishment: Influences on Subordinate Performance and Citizenship

Article