On the Use of Neurophysiological Tools in Information Systems Research: Developing a Research Agenda for NeuroIS.

Abstract

This article discusses the role of commonly used neurophysiological tools such as psychophysiological tools (e.g., EKG, eye tracking) and neuroimaging tools (e.g., fMRI, EEG) in Information Systems research. There is heated interest now in the social sciences in capturing presumably objective data directly from the human body, and this interest in neurophysiological tools has also been gaining momentum in IS research (termed NeuroIS). This article first reviews commonly used neurophysiological tools with regard to their major strengths and weaknesses. It then discusses several promising application areas and research questions where IS researchers can benefit from the use of neurophysiological data. The proposed research topics are presented within three thematic areas: (1) development and use of systems; (2) IS strategy and business outcomes, and (3) group work and decision support. The article concludes with recommendations on how to use neurophysiological tools in IS research along with a set of practical suggestions toward developing a research agenda for NeuroIS and establishing NeuroIS as a viable subfield in the IS literature.

Full text

ISSUES AND OPINIONS
ON THE USE OF NEUROPHYSIOLOGICAL TOOLS IN IS
RESEARCH: DEVELOPING A RESEARCH AGENDA
FOR NEUROIS1
Angelika Dimoka
Temple University
angelika@temple.edu
Rajiv D. Banker
Temple University
rajiv.banker@temple.edu
Izak Benbasat
University of British Columbia
izak.benbasat@ubc.ca
Fred D. Davis
University of Arkansas & Sogang University
fdavis@walton.uark.edu
Alan R. Dennis
Indiana University
ardennis@indiana.edu
David Gefen
Drexel University
gefend@drexel.edu
Alok Gupta
University of Minnesota
alok@umn.edu
Anja Ischebeck
University of Graz
anja.ischebeck@uni-graz.at
Peter H. Kenning
Zeppelin University
peter.kenning@zeppelin-university.de
Paul A. Pavlou
Temple University
pavlou@temple.edu
Gernot Müller-Putz
Graz University of Technology
gernot.mueller@tugraz.at
René Riedl
University of Linz
rene.riedl@jku.at
Jan vom Brocke
University of Liechtenstein
jan.vom.brocke@uni.li
Bernd Weber
University of Bonn
bernd.weber@ukb.uni-bonn.de
This article discusses the role of commonly used neurophysiological tools such as psychophysiological tools
(e.g., EKG, eye tracking) and neuroimaging tools (e.g., fMRI, EEG) in Information Systems research. There
is heated interest now in the social sciences in capturing presumably objective data directly from the human
body, and this interest in neurophysiological tools has also been gaining momentum in IS research (termed
NeuroIS). This article first reviews commonly used neurophysiological tools with regard to their major
strengths and weaknesses. It then discusses several promising application areas and research questions where
IS researchers can benefit from the use of neurophysiological data. The proposed research topics are
presented within three thematic areas: (1) development and use of systems, (2) IS strategy and business
outcomes, and (3) group work and decision support. The article concludes with recommendations on how to
use neurophysiological tools in IS research along with a set of practical suggestions for developing a research
agenda for NeuroIS and establishing NeuroIS as a viable subfield in the IS literature.
1
Keywords: NeuroIS, neuroscience, neurophysiological tools, psychophysiological tools, neuroimaging
1Detmar Straub was the accepting senior editor for this paper. Ron Thompson served as the associate editor.
The appendices for this paper is located in the “Online Supplements” section of the MIS Quarterly’s website (http://www.misq.org).
MIS Quarterly Vol. 36 No. 3 pp. 679-702/September 2012 679

Dimoka et al./Use of Neurophysiological Tools in IS Research
Introduction
This article discusses how Information Systems researchers
can use neurophysiological tools and how these tools can
advance IS research. Psychophysiological tools (e.g., eye
tracking, skin conductance) and brain imaging tools (e.g.,
fMRI, EEG) have recently received heated attention in the
social sciences due to their ability to complement existing
sources of data with data captured directly from the human
body (Lieberman 2007). Neurophysiological tools enable the
measurement of human responses when people engage in
various activities, such as decision making, or react to various
stimuli, such as IT interfaces. There are many neurophysio-
logical studies in the social sciences (e.g., Camerer 2003;
Glimcher et al. 2009; Kenning and Plassmann 2005; Lee et al.
2007; Zaltman 2003), a development that has also extended
to IS (e.g., Cyr et al. 2009; Dimoka 2010, 2012; Dimoka et al.
2011; Dimoka and Davis 2008; Dimoka et al. 2007; Galletta
et al. 2007; Moore et al. 2005; Randolph et al. 2006; Riedl
2009; Riedl et al. 2010a). The term NeuroIS has been coined
to describe the “idea of applying cognitive neuroscience theo-
ries, methods, and tools to inform IS research” (Dimoka et al.
2007, p. 1). Building upon these NeuroIS studies, this paper
explores the potential of neuroscience theories and neuro-
physiological methods and tools in IS research.
We first review the most commonly utilized neurophysio-
logical tools in the social sciences and outline their strengths
and weaknesses. We then propose a research agenda where
IS research can benefit from the use of neurophysiological
tools as categorized under three overarching domains of IS
research (Taylor et al. 2010): (1) development and use of
systems, (2) IS strategy and business outcomes, and (3) group
work and decision support. For each of these three areas, we
discuss how various IS research topics can be informed by the
use of neurophysiological tools and how neurophysiological
data can complement and supplement existing sources of data.
We then offer a set of recommendations on how to best
employ neurophysiological tools in IS research, how to seam-
lessly integrate neurophysiological data in the portfolio of
existing approaches, tools, and data available to IS
researchers, and how to pursue NeuroIS. We finally conclude
by discussing how NeuroIS can add to existing IS research,
what important research findings we expect to obtain from
NeuroIS, what challenges NeuroIS might face in making
substantive contributions to the IS literature, and how to
establish NeuroIS as a viable subfield in IS research.
The paper proceeds as follows: the following section reviews
the most commonly used neurophysiological tools and dis-
cusses their strengths and limitations. The subsequent section
discusses several research questions that may benefit from the
use of neurophysiological tools in three key areas of IS
research (Taylor et al. 2010). We then offer recommendations
for intelligently pursuing NeuroIS and promoting NeuroIS as
a viable subfield.
Strengths and Weaknesses of
Neurophysiological Tools
This section discusses the strengths and weaknesses of popu-
lar psychophysiological and neuroimaging tools that could
inform IS research. A brief description of these tools is
available in Table 1 and Appendix A.
Major Strengths of Neurophysiological Tools
The promise of NeuroIS is to complement existing research
tools with neurophysiological tools that can provide reliable
data which are difficult or impossible to obtain with tradi-
tional tools, such as self-reported or archival data. The pri-
mary advantage of physiological and brain data is that
subjects cannot consciously manipulate their responses since
these are not readily subject to manipulation. For example, it
might be possible to use fMRI as a lie detector as there is
often a higher (unconscious) activation in the prefrontal
cortex of subjects who lie versus those who do not (Harris
2010). As neurophysiological data are generally not suscep-
tible to subjectivity bias, social desirability bias, and demand
effects,2 they could complement existing sources of data,
triangulate across measurement data, and reduce common
method bias by not relying on any single measurement
method (e.g., Dimoka et al. 2011). Neurophysiological tools
are particularly valuable for measuring IS constructs that
people are either unable, uncomfortable, or unwilling to truth-
fully self-report; this may include sensitive issues (e.g.,
gender, race, culture, religion), personal issues (e.g., goals or
fears), deep or hidden emotions (e.g., guilt, fears, and anger),
automated processes (e.g., habit and automaticity), complex
cognitive processes (e.g., cognitive overload), social dyna-
mics (social cognition), antecedents of human behaviors (e.g.,
beliefs, attitudes, and intentions), and moral issues (e.g.,
ethics and moral judgments).
Moreover, while self-reports may not be able to capture
unconscious processes that are unavailable to introspection,
2Demand effects refer to a bias in which the subject understands the experi-
ment’s purpose and either consciously or unconsciously changes her response
to act upon what she believes the experimenter is expecting her to do.
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Dimoka et al./Use of Neurophysiological Tools in IS Research
Table 1. Description and Focus of Measurement of Commonly Used Neurophysiological Tools
Neurophysiological Tools Focus of Measurement
Psychophysiological Tools
Eye Tracking Eye pupil location (“gaze”) and movement
Skin Conductance Response (SCR) Sweat in eccrine glands of the palms or feet
Facial Electromyography (fEMG) Electrical impulses caused by muscle fibers
Electrocardiogram (EKG) Electrical activity of the heart on the skin
Brain Imaging Tools
Functional Magnetic Resonance Imaging (fMRI) Neural activity by changes in blood flow
Positron Emission Tomography (PET) Metabolic activity by radioactive isotopes
Electroencephalography (EEG) Electrical brain activity on the scalp
Magnetoencephalography (MEG) Changes in magnetic fields by brain activity
neurophysiological tools can capture unconscious processes
with direct responses from the human body. Neurophysio-
logical data can thus offer information that is complementary,
supplementary, or even contradictory to self reporting, obser-
vation, and secondary data because they are less subjective
and are not restricted to conscious awareness and revealed
preferences.
Neurophysiological data have the advantage of continuous
real-time measurement that allows collecting data contin-
uously on a real-time basis while a subject is executing a task
or responding to a specific stimulus (usually not easily
afforded by self reports or observation). This enables a level
of temporal precision that allows a researcher to temporally
match the task or stimulus to the neurophysiological response
virtually in real-time. By permitting continuous real-time
data collection and powerful time-series analysis,
neurophysiological tools are able to capture the flow of either
a single construct or many constructs simultaneously, thus
allowing us to infer the temporal order of either the
dimensions of a single IS construct or two or more IS
constructs that are spawned by a certain task or stimulus.
Because temporal precedence is a key prerequisite of
causality (Cook and Campbell 1979; Zheng and Pavlou
2010), neurophysiological studies can potentially help infer
causal relationships among IS constructs.
Major Weaknesses of Neurophysiological
Tools
Neurophysiological tools also have weaknesses (Riedl et al.
2010a). Most weaknesses apply across all tools, albeit at
different degrees (e.g., cost), and we specify the extent to
which these weaknesses are mostly for psychophysiological
(Appendix A, Table A1) or neuroimaging (Appendix A, Table
A2) tools.
Cost and Accessibility
A primary weakness of neurophysiological tools is cost
(Appendix A, Table A3). While the cost of psychophysio-
logical tools is manageable (currently it costs between $10,000
and $20,000 U.S. to equip a lab with physiological tools), the
cost of neuroimaging tools is substantial (between $100 and
$600 U.S. per scanning hour), given the need for technicians
with specialized knowledge. While the use of repeated
measures from each subject coupled with the precision of
objective data require fewer subjects per study (for example,
most fMRI studies only need 10 to 20 subjects), cost is still an
issue that is best answered by the person paying for the studies
relative to the expected insights (Camerer et al. 2004).
Accessibility is another issue, since neurophysiological tools
reside in medical facilities dedicated to clinical use (Husing et
al. 2006). Nonetheless, major universities worldwide have
facilities with neurophysiological tools, plus hospitals and
clinics often rent their neuroimaging tools for research
purposes.
Artificial Setting
The experimental context in which neurophysiological tools
are used creates an artificial environment that limits the
external validity of NeuroIS studies. Different neurophysio-
logical tools (Appendix A) vary in their degree of artificiality.
For example, fMRI and PET scanners are cylindrical full-body
tubes and require subjects to remain still during the study.
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Dimoka et al./Use of Neurophysiological Tools in IS Research
Most neurophysiological tools use various sensors attached
to the human body that may themselves induce stress and bias
the results. Eye tracking tools often need subjects to wear
special equipment, such as a headgear, creating an artificial
setting. While neurophysiological tools continuously
enhance their interfaces, including improvements such as less
constrained and less noisy fMRI scanners and eye trackers
without a headgear, it is recommended that researchers
replicate the experiment in a more traditional setting and
compare the corresponding behavioral responses to test for
external validity.
Labor-Intensive Data Extraction and Analysis
The ability of neurophysiological tools to collect real-time
data directly from the human body is accompanied by a dif-
ficulty in extracting such intense amounts of data, including
correcting for movement (such as fMRI or eye tracking),
preparation for proper recordings (such as electrode place-
ment for EKG), sometimes manual data extraction (such as
observation in eye tracking studies), and enormous amounts
of imaging data (such as fMRI/EEG). The analysis of vast
amounts of neurophysiological data can thus be a daunting
task, especially if there is a need for data preprocessing (such
as fMRI data). While advanced software tools can support
data analysis for each tool, the statistical analysis of large
amounts of data from the human body is still a non-trivial
task. For example, fMRI data must go through slice timing
correction, realignment, coregistration, segmentation, normal-
ization, and smoothing in preparing for data analysis (for a
review, see Dimoka 2011; Frackowiak et al. 2004; Friston
2004).
Measurement Issues
First, there are content validity concerns about whether
neurophysiological data capture the constructs they are
intended to measure. At the individual subject level,
neurophysiological responses often vary from a common
baseline and may be triggered differently by external
confounds that cannot be perfectly isolated. For example,
heart rate measured by EKG can be influenced by many
stimuli that need to be experimentally controlled for. There
are also differences between right- and left-handed people,
women and men, and younger and older adults. Habituation
to stimuli may also be different across subjects; for example,
electrodermal response in SCR may vary among subjects.
While a careful experimental design can largely address such
issues, it is necessary to realize that neurophysiological data
could be affected by numerous factors and appropriate steps
should be followed in order to minimize intersubject vari-
ability. For instance, to account for cortical differences in
fMRI and PET, brain images must be normalized to a common
template. Experimental designs can overcome cortical dif-
ferences with a proper baseline for meaningful comparisons,
such as within-subjects designs that help reduce error variance
by using each subject as her/his own control.
Mono-Operationalization Bias
In the manner in which neurophysiological tools are often
deployed, there is also a distinct possibility of mono-
operationalization bias and construct validation concerns.
When one gathers only a single measure for a given construct,
there is no easy way to assess the internal consistency of
measures, and thus measure reliability (Cook and Campbell
1979). Measurement error, thus, is unknown in such cases
since there is no way to separate the true score from the error.
In this case, there is a viable solution of test–retest reliability,
but this involves an additional measurement with the same
stimuli. In many settings, this will simply not be feasible, and
scholars should argue for the likelihood that their data is still
reasonable (Straub et al. 2004). Even with single measures,
discriminant validity can still be assessed; however, for
convergent validity (factorial validity), multiple measures of
each construct are required (Gefen et al. 2000).
Difficulty in Interpreting Neurophysiological Results
Neurophysiological results may also not be as straightforward
to interpret as traditional sources of data. This is largely due
to the mapping of neurophysiological measures to theoretical
constructs. For example, the meaning of various eye tracking
measures is still a debated issue, and eye fixations and gaze
have been attributed to multiple constructs, such as com-
plexity, difficulty, interest, and importance (Rayner 1998).
Also, in fMRI studies, a naive expectation of a one-to-one
mapping between a brain area and a theoretical construct has
made it difficult to interpret the meaning of brain activations
(Logothetis 2008). Simply put, a certain neurophysiological
measure can be linked to several theoretical constructs.
Furthermore, a lack of a standard terminology and definitions
may create difficulties in interpreting neurophysiological
results.
Manipulation and Ethics
It is sometimes feared that neurophysiological tools could be
used to manipulate behavior. However, they can only observe,
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Dimoka et al./Use of Neurophysiological Tools in IS Research
not manipulate behavior.3 Neurophysiological tools may also
capture private issues that people may not be willing to
consciously share. Thus, NeuroIS must be governed by strict
ethical rules.
In conclusion, neurophysiological tools have several impor-
tant strengths that can advance IS research. Nonetheless,
they also have certain challenges that must be acknowledged
to fully harness their potential.
Developing a Research Agenda
for NeuroIS
A research agenda for NeuroIS needs to draw upon the vast
neuroscience literature in the social sciences that has been
rapidly expanding over the last two decades. IS researchers
are thus advised to first become familiar with prominent
neuroscience theories and more specifically with findings
about the localization of various processes and constructs in
the brain (e.g., Dimoka et al. 2011). There is also a rapidly
growing field termed social neuroscience that focuses on
integrating neuroscience theories with applications in psycho-
logy, marketing, and economics (e.g., Lieberman 2009). See
Appendix B for a detailed review.
Dimoka et al. (2011) proposed seven specific opportunities
that IS researchers can pursue with the aid of neurophysio-
logical tools to inform IS research: (1) localizing the neural
correlates of IS constructs,4 (2) capturing hidden or uncon-
scious processes, (3) complementing existing data with
neurophysiological data, (4) identifying and testing ante-
cedents of IS constructs, (5) testing outcomes of IS con-
structs, (6) inferring the temporal ordering of IS constructs,
and (7) challenging existing assumptions and enhancing IS
theories.
Building upon these opportunities on the potential of NeuroIS,
we elaborate on how IS research can benefit from neuro-
physiological tools and how neurophysiological data can
complement extant sources of IS data. We focus on three
areas of IS research that have been suggested as adequately
spanning the IS discipline (Taylor et al. 2010): (1) develop-
ment and use of systems; (2) IS strategy and business out-
comes, and (3) group work and decision support. For each
area, we propose a set of research topics (Table 2) that seek to
offer a representative depiction of how IS research can benefit
from the use of neurophysiological tools.
The examples in Table 1 are intended to be merely repre-
sentative of how neurophysiological tools can be used to
enrich some areas of IS research, and we neither seek to offer
an exhaustive coverage of all IS research areas that could use
neurophysiological tools nor imply that any other IS area
might not benefit from the use of neurophysiological tools.
We simply encourage IS researchers to assess whether their
own topics of interest could be enhanced by the use of neuro-
physiological tools. Additional opportunities for NeuroIS by
including the moderating role of culture, gender, and age are
discussed in Appendix C.
Opportunities in Development and
Use of Systems
The opportunities from using neurophysiological tools in the
development and use of systems focus on (1) enhancing the
individual adoption and use of systems, (2) reducing infor-
mation and cognitive overload, and (3) encouraging trans-
actions by online consumers, as outlined in detail below.
Encouraging Individual Adoption and
Use of Systems
There are several opportunities for IS research on encouraging
individual adoption and use of systems using neurophysio-
logical tools; these are summarized in Table 3 and elaborated
in more detail below.
First, neurophysiological tools can shed light on our under-
standing of the nature and dimensionality of constructs related
to the adoption and use of systems by identifying their neural
correlates. While the neural correlates of the original TAM
constructs (perceived usefulness and ease of use) have already
been identified (Dimoka and Davis 2008), the nature of other
constructs related to system use, particularly hedonic ones,
such as enjoyment, anxiety, and flow, are still not well under-
stood. Neurophysiological tools, such as fMRI, could help
understand the nature of these constructs better and tease out
differences among them.
3Similar to other lab studies, external stimuli can manipulate behavior,
especially if they are administered in real-time based on neurophysiological
responses. Also, transcranial magnetic stimulation (TMS) can temporarily
desensitize certain brain areas, thereby affecting behavior. Nonetheless, such
manipulations can only be induced in a lab setting.
4In terms of identifying the neural correlates of IS constructs, there are
several ways to induce brain activation in response to IS constructs, such as
images of people who exhibit certain attributes (e.g., trustworthiness to
induce trust), experimental games (e.g., trust game), or scenarios that induce
certain perceptions, such as trust. Dimoka (2010) also proposed a method
to induce brain activation that underlies IS constructs by using measurement
items as triggers. This capability of neurophysiological tools has interesting
implications for assessing both reliability and construct validity.
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Dimoka et al./Use of Neurophysiological Tools in IS Research
Table 2. Summary of Topics Under Three Proposed Themes
Area of Research Sample Topics
Development and Use of Systems
• Encouraging Individual Technology Adoption and Use
• Assessing Information and Cognitive Overload
• Encouraging Transactions by Online Consumers
IS Strategy and Business Outcomes
• Developing Information Systems Strategy
• Enhancing the Design of Organizational Systems
• Promoting Technological Fairness in Organizations
Group Work and Decision Support
• Enhancing online Group Collaboration and Decision Support
• Designing Online Decision Aids
• Understanding and Building Online Trust
Table 3. Sample Research Opportunities in Encouraging Individual Development and Use of Systems
Application Sample Research Opportunities
Encouraging Individual
Adoption and Use of
Systems
1. Understanding the nature and dimensionality of adoption and use of systems based on
their neural correlates (where they reside in the brain).
2. Identifying hidden processes related to system adoption, such as emotions and habits.
3. Identifying new determinants of system adoption and use.
4. Designing systems that help enhance system utility and user friendliness and
establishing direct usability criteria based on neurophysiological data.
5. Understanding causality issues related to system adoption and use constructs.
6. Assessing both instrumental systems and hedonic systems
Second, neurophysiological tools can uncover new insights
about system adoption and use that may not be inferred with
existing tools by identifying hidden or unconscious processes
that cannot be self-reported. For example, challenging the
literature that has viewed the two TAM constructs to be
purely cognitive, evidence showed that perceived usefulness
may originate in the “emotional” areas of the brain, such as
the insular cortex (which is associated with emotional losses),
and the anterior cingulate cortex that interfaces emotional and
cognitive brain areas (Dimoka et al. 2011). This is consistent
with Cenfetelli (2004) and Venkatesh (2000) who showed the
role of emotions in technology adoption, the marketing
literature that noted the role of emotions in the adoption of
innovations (Wood and Moreau 2006), and Bagozzi’s (2007)
call for including emotions in system use. Extending this
stream, neurophysiological tools can identify other hidden or
unconscious processes linked to constructs related to indi-
vidual adoption and use of systems.
Neurophysiological tools can also help understand post-
adoption system use, which is largely unconscious and not
controlled by self-reported conscious thoughts (e.g., Frank
and Claus 2006; Lieberman 2007).
Third, identifying the neural correlates of the determinants of
system adoption and use opens avenues for designing systems
that trigger activation in certain brain areas, such as the
caudate nucleus for utility, or the dorsolateral prefrontal
cortex (DLPFC) for reduced cognitive overload. Activations
in these brain areas can become a guide for how systems can
be designed to encourage adoption, and neurophysiological
data can inform the design of specific features to enhance
system adoption and use. Neurophysiological tools can also
assist system designers to directly test their designs with
neurophysiological data, thus having less need to rely on
subjective self-reported data that may not track well with
actual system use (Straub et al. 1995). These tools may be
able to assess what Burton-Jones and Straub (2006) refer to as
deep structure usage. As another example, eye tracking tools
may allow system designers to effectively place material on
the screen based on where the user is looking and where the
user’s eyes move when using a system. NeuroIS studies can
also help establish direct usability criteria by linking usability
metrics (e.g., efficiency, quality) with neurophysiological
metrics. Such neurophysiological studies can use existing
systems with different levels of usability on particular dimen-
sions to examine how technical differences are represented as
physiological or neural differences. These studies may also
uncover additional constructs that drive the adoption and use
of systems which users may have been unable to articulate via
self-reported measures, as well as emotional or hedonic pro-
cesses which users may have been unwilling to report (e.g.,
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Dimoka et al./Use of Neurophysiological Tools in IS Research
anxiety, flow). Having identified the physiological or neural
correlates of usability, neurophysiological tools could enable
designers to use these measures to evaluate future IT systems
and design modifications based on how well these systems
perform in practice. For example, if the evaluation of a
system with neurophysiological tools uncovers negative emo-
tions, system designers could try to spot a flaw that causes
such emotional distress, thus helping design more effective
systems. This could help us in designing systems to reduce
technostress (Ayyagari et al. 2011). System designers could
also capture the user’s cognitive style (e.g., verbal, visual) and
examine whether cognitive style affects how people adopt and
use systems. As neurophysiological tools may identify the
neural correlates of cognitive style (Benbasat and Taylor
1978), they may help to objectively measure spatial attention
and processing style, thus exploring how individual dif-
ferences in terms of cognitive style may play a role in the
design of systems that encourage adoption.
Finally, neurophysiological tools may identify causal links
among the drivers of system adoption and use. For example,
fMRI studies that identify brain activations in the areas asso-
ciated with perceived usefulness and ease of use can also shed
light on their temporal ordering (Dimoka and Davis 2008).
Because emotional processes often precede cognitive ones
(Pessoa 2008), and since perceived usefulness is shown to be
linked with affective brain areas in the limbic system (Dimoka
et al. 2011), neurophysiological tools could be used to
examine whether ease of use does precede usefulness, as
TAM asserts. For example, knowledge of which TAM con-
struct precedes the other in the brain could shed light on
mediation hypotheses in the TAM model (Pavlou 2003) with
practical implications (e.g., Meuter et al. 2005). While fMRI
might be able to infer the temporal dynamics between these
two constructs, given its limited temporal resolution, fMRI
may need to be complemented by MEG or EEG, which have
superior temporal resolution. Because causal inference
cannot be inferred solely by temporal precedence (Zheng and
Pavlou 2010), the use of TMS to temporarily inhibit brain
activity in isolated areas may be useful in order to study
whether the areas associated with perceived ease of use and
usefulness are needed to determine the causal links leading to
IT system adoption and use.
Assessing Information and Cognitive Overload
The proposed opportunities related to assessing information
and cognitive overload are summarized in Table 4 and are
discussed in more detail below.
Assessing information and cognitive overload has long been
an area of interest in the IS literature (e.g., Eppler and Mengis
2004; O’Reilly 1980). Such overload arises from having too
much information when a person is performing a task (Wur-
man 1990) and from the difficulty in inferring what informa-
tion is required for the task (Kirsh 2000). Toward reducing
information and cognitive overload, IS researchers often
design systems that aim at reducing the information com-
plexity of the task (enabling users to simplify their decision
making), or enhancing the user’s information processing
capabilities by better showing information (Galbraith 1974).
However, the measurement of information and cognitive
overload has been elusive in the literature due to self-reports
and expert evaluations (e.g., Payne et al. 1992). There is a
need for a direct measurement of information and cognitive
overload, and neurophysiological tools have the potential to
offer such a direct measurement. For example, EKG could
directly measure whether a certain interface increases the
user’s heart rate, thus inferring anxiety or stress. Eye tracking
tools can capture whether a user finds it difficult to identify
information by observing how her or his eyes wander
aimlessly on a computer screen.
In terms of measuring cognitive overload, the cognitive
neuroscience literature has shown that activation in the pre-
frontal cortex is associated with tasks of higher information
load (e.g., Linden et al. 2003) and working memory (e.g.,
Braver et al. 1997). The DLPFC has been involved in such
higher-order functions, namely cognition and problem olving
(Rypma and D’Esposito 1999); specifically there is an
inverted-U function that describes the level of brain activation
in the DLPFC relative to the degree of cognitive overload as
DLPFC activation decreases “as subjects become over-
whelmed and subsequently disengage from the task” (Calli-
cott et al. 1999, p. 25). Cognitive overload may also be asso-
ciated with activation in the emotional areas (e.g., frustration),
which can be captured directly by neurophysiological tools
(e.g., Abler et al. 2005), such as EKG through higher heart
rate, SCR through sweat excretion, or fEMG through facial
gestures.
Based on the neural correlates of cognitive overload, NeuroIS
studies could test whether IT artifacts can reduce cognitive
overload by measuring brain activity when users undertake
cognitive tasks. Apart from directly measuring if cognitive
overload is reduced with the aid of IT, neurophysiological
tools could also shed light on whether the reduction of cogni-
tive overload is due the simplification of the information
complexity of the task (viewed as reduction in DLPFC
activation) or due to the enhancement of the user’s informa-
tion processing capabilities by better processing information
(increased DLPFC activation without a drop in the inverted-U
function). These findings could also be used in the design of
IT systems that reduce cognitive overload, either by simpli-
fying the task or by enhancing the user’s capabilities (or
both).
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Table 4. Sample Research Opportunities in Assessing Information and Cognitive Overload
Application Sample Research Opportunities
Assessing Information/
Cognitive Overload
1. Directly measuring information and cognitive overload in the brain.
2. Designing IT systems that help reduce information/cognitive overload.
3. Testing and refining IT systems based on their effects on brain activity.
Table 5. Sample Opportunities in Encouraging Transactions by Online Consumers
Application Sample Research Opportunities
Encouraging Transactions
by Online Consumers
1. Understanding the neural correlates of the antecedents of e-commerce adoption by
shedding light on their nature, dimensionality, distinction, and convergence.
2. Uncovering additional “hidden” predictors and inhibitors of e-commerce adoption, such
as deception, and identifying patterns for detecting website deception.
3. Designing collaborative tools to engage consumers in social learning by identifying
patterns of cooperative social behavior.
4. Assessing how consumers react to a website’s information design (e.g., search data).
5. Identifying underlying habits and learned patterns in website use.
6. Localizing the neural correlates of (seller and product) uncertainty and examining
whether they are viewed as distinct constructs by consumers.
7. Identifying product quality signals to mitigate product uncertainty based on the neural
correlates of product uncertainty.
Capturing emotions that could be activated at high levels of
cognitive overload (e.g., frustration) can identify problems
users face when engaging in cognitive tasks. For example,
neuroimaging tools could be used to test if IT systems
designed to enhance combinatorial auctions by offering
structured information on the auction have the expected effect
(Adomavicius et al. 2010). Neuroimaging data could also be
used to improve the design of these IT interfaces by
identifying their ability to reduce cognitive overload and
prevent mental collapse and emotional breakdown. Finally,
neurophysiological tools could enable the evaluation of IT
interfaces, link neuroimaging data to economic (auction)
outcomes, and measure constructs (cognitive overload and
emotional processes) that are generally difficult to measure
(Ba and Pavlou 2002).
Encouraging Transactions by Online Consumers
There are also opportunities for encouraging transactions by
online consumers in electronic markets to enhance the
adoption of e-commerce, which are summarized in Table 5
and discussed in more detail below.
Similar to encouraging system adoption by users (Venkatesh
and Davis 2000), there is a rich literature on constructs that
promote or inhibit the adoption and use of commercial web-
sites (e.g., Cenfetelli 2004; Pavlou and Fygenson 2006), such
as usefulness, ease of use, trust, privacy, security, and self-
efficacy. However, it is not clear whether all of these ante-
cedents are clearly distinct from each other and whether it is
possible to identify a more parsimonious e-commerce adop-
tion model. Specifying the neural correlates of the ante-
cedents of website adoption by consumers could shed light on
their distinctiveness or convergence. For example, Dimoka
and Davis (2008) showed that website usefulness and ease of
use are distinct constructs that span distinct brain areas.
Neurophysiological tools can test related antecedents of web-
site adoption, such as self-efficacy and ease of use, usability,
navigability and diagnosticity, and privacy and security.
Moreover, hedonic factors that have been shown to encourage
website adoption could be examined as well. These findings
can help specify the nature and dimensionality of these ante-
cedent factors and result in more valid e-commerce adoption
models that better correspond to the functionality of the
human body.
Neurophysiological tools can help identify additional
“hidden” factors that have not been captured by earlier
studies. For example, if a brain area that enables or inhibits
website adoption is linked to a hidden or unconscious process
recognized in the neuroscience literature, it may uncover new
antecedents that have been neglected to date. Notably, decep-
tion, such as phishing, has been an impediment to online
transactions. This valid point notwithstanding, detecting
deception may be difficult to study with behavioral studies
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Dimoka et al./Use of Neurophysiological Tools in IS Research
that ask subjects to detect fraud (Pavlou and Gefen 2005).
This is where neurophysiological tools could be valuable by
exposing subjects to fraudulent websites and identifying
differences in physiological or neural responses when subjects
interact with various websites. Neurophysiological tools,
such as fMRI, might be able to capture where deception
occurs in the brain while psychophysiological tools such as
eye tracking may help identify how expert online consumers
detect fraud. These findings could be used to identify how
learning can be achieved in fearful situations, such as
phishing websites, and how effective patterns for detecting
deceptive websites can be enhanced with the aid of IT tools.
IS researchers have long noted differences when consumers
interact with similar website interfaces. For example, Bapna
et al. (2004) identified distinct bidding strategies used by
consumers in online auctions. Further, they showed that new
IT interfaces and minor variations in auction rules result in
interesting and logically sound bidding practices. Similarly,
Adomavicius et al. (2007) showed that information revelation
strategies can affect bidder behavior in complex combinatori al
auctions. Still, the theoretical explanation of bidder behavior
is difficult to uncover with self-reported measures in experi-
ments, surveys, or interviews, such as those dealing with trust
and perceived risk (Pavlou and Gefen 2004). Neurophysio-
logical tools could assist in the design of metrics for complex
constructs such as trust, perceived risk, cognitive effort, and
competitive fervor toward reconciling observed actions with
bidder intent and offer a better explanation of observed bidder
behavior. These perceptual measures are generally difficult
to measure with self reports, thus neurophysiological data can
complement and expand existing e-commerce and auction
metrics.
Online collaborative shopping and social shopping networks
are becoming popular by allowing consumers to incorporate
their social circle in the otherwise impersonal online trans-
action environment (e.g., Zhu et al. 2010). Understanding
how consumers interact with others is an important area
where neurophysiological tools could help in the design of
collaborative tools that enable consumers to engage in social
learning and transact together within a social network.
Neurophysiological data can be used to identify patterns of
cooperative social behavior by drawing upon the neural
correlates of social cognition (Adolphs 1999) to design
collaborative tools that support cooperative social behavior
and social learning.
Neurophysiological tools can also help assess how consumers
react to a website’s information design, including search
results, banner adds, or news stories. There is an increased
interest in understanding how consumers evaluate material
and stimuli on websites to make purchasing decisions (e.g.,
Dou et al. 2010). The assessment of a website’s information
design typically happens quickly and often unconsciously,
making it difficult for researchers to understand how con-
sumers make such evaluations. The advantage of neuro-
physiological tools to offer real-time measurement of what
consumers see on a website and what thoughts and emotions
the information triggers in their mind could be useful in
improving website design and facilitating transactions. There
is an emerging literature on how eye tracking helps evaluate
website designs (e.g., Pan et al. 2004; Tzanidou 2003) by
enabling direct real-time tracking of where a consumer is
looking and where her eyes are moving on a website. Neuro-
physiological tools can also be useful in placing information,
such as static text, photos, or video, on a website to facilitate
browsing and transacting.
As website use becomes even more frequent and perhaps
habitual, e-commerce research may need to add familiarity,
learning, and habit into models of post-adoption website use.
Neurophysiological tools can help identify habits and learned
patterns in website use that may not readily be uncovered with
self reports or observation because such patterns may not be
available for introspection. For example, eye tracking tools
can compare novice and expert website users to identify
differences that may help novice users and improve their web-
site interaction. Neurophysiological tools can also compare
physiological or neural differences across consumers who
visit a website for the first time versus repeat visitors, and
they can identify how learning to use a website occurs over
time across consumers. Eye tracking seems to be a very use-
ful tool to study habitual tasks and how people evolve from
novices to experts (Karn et al. 1997). These studies could
uncover useful insights about learning habits for first-time
versus repeat website users that could help website designers
customize their websites to cater to different consumers (e.g.,
first-time versus repeat).
Understanding and mitigating uncertainty has been touted an
important impediment for online markets given the physical
and temporal separation among buyers, sellers, and products
(e.g., Ghose 2009; Pavlou et al. 2007). Granados et al. (2006)
also argued that competition around information and price
transparency must occur in order to reduce uncertainty. How-
ever, our current understanding of the construct of uncertainty
is relatively limited, and the IS literature has generally treated
seller and product uncertainty as a unitary construct. The
literature on understanding and mitigating uncertainty could
be advanced by neurophysiological tools. First, similar to
how Dimoka (2010) has shown that trust and distrust are
distinct constructs that reside in distinct brain areas, drawing
on the neuroscience literature that has extensively studied
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Dimoka et al./Use of Neurophysiological Tools in IS Research
uncertainty (Appendix B), brain imaging studies could
localize the neural correlates of seller and product uncertainty
to identify if they are viewed by consumers as distinct
constructs.
Although the literature has focused on mitigating seller uncer-
tainty through trust and other means, there is still very little
work on mitigating product uncertainty (Ghose 2009).
Evidence from the literature shows that distinct brain areas are
responsible when evaluating products (product uncertainty)
and people (seller uncertainty) (Grinband et al. 2006; Yoon et
al. 2006). The neural correlates of product uncertainty may
shed light on its nature, such as whether it is linked to
primarily cognitive or emotional brain areas. This knowledge
may help design product quality signals that can effectively
reduce product uncertainty. The neural correlates of product
uncertainty may help test the effectiveness of product quality
signals that have been proposed in the IS literature, such as
product diagnosticity (e.g., Jiang and Benbasat 2004), product
descriptions (Jiang and Benbasat 2007), third-party certi-
ficates (Li et al. 2009), and product condition disclosures
(Ghose 2009). Neurophysiological tools can also look into
the timing of the neural correlates of product and seller
uncertainty to infer whether consumers first assess product or
seller uncertainty, thus guiding the design and timing of seller
or product quality signals on websites.
A challenge for consumers is the difficulty of feeling and
trying physical products before transacting. Virtual reality,
video, and static information displays, and other product
quality signals that can enhance product understanding are
alternative means available to web designers (Jiang and
Benbasat 2007). Physiological tools can assess whether and
to what degree product information signals trigger attention
(e.g., eye tracking tools) and emotions (e.g., fEMG). Also,
SCR and EKG could help capture stress levels when dealing
with uncertain purchases. Also, the aesthetics of the overall
presentation of product quality signals can be enhanced with
physiological tools by measuring whether users have some
negative reactions to these signals, such as unwanted emo-
tions. Finally, neurophysiological tools can be used to spot
“fake” product quality signals that misrepresent product
quality, helping consumers focus on legitimate signals.
Opportunities in Information Systems Strategy
and Business Outcomes
The proposed research opportunities on IS strategy and
business outcomes focus on (1) developing IS strategy,
(2) enhancing the design of organizational systems, and
(3) promoting technological fairness.
Developing Information Systems Strategy
We next propose a set of opportunities for IS strategy and
achieving favorable business outcomes by enhancing stra tegic
decision making. This is summarized in Table 6 and dis-
cussed in more detail below.
First, drawing upon the extensive neuroscience literature on
individual decision making, we argue that we can better
understand the basis of managerial decision making with the
aid of neurophysiological tools. As reviewed in Appendix B,
there is a rich literature on how people make decisions by
using calculative and emotional aspects in their decision
making (Ernst and Paulus 2005) and how simple emotional
cues can simplify complex and uncertain decisions under the
somatic marker hypothesis (Damasio 1994). Moreover, there
is evidence that more successful decision makers are those
who combine both cognitive and emotional aspects (Hsu et al.
2005). This actually corresponds to findings in the strategy
literature that intuition is useful for complex actions under
uncertainty (Gigerenzer and Selten 2001). Building upon the
neuroscience literature, neurophysiological tools could
examine how to facilitate strategic decision making in uncer-
tain environments, perhaps focusing on CIOs (chief informa-
tion officers) as subjects (Banker et al. 2011). Although
neurophysiological studies with CIOs and senior executives
may be difficult to conduct, it is not uncommon to have
studies with real professionals. For instance, Lo and Repin
(2002) examined professional foreign exchange traders when
trading currencies in a simulated exercise with contracts over
$1 million.
Neurophysiological studies of decision making under uncer-
tainty would also correspond well with the recent emphasis on
IT strategy in turbulent environments that examines cases
where strategic decisions must be made quickly and under
uncertain and changing conditions (Pavlou and El Sawy
2006). While there is a need to generalize findings from
artificial individual decision making by subjects to real
decision making by true executives, neurophysiological tools
can offer useful insights into how to facilitate decision making
via cognitive and emotional markers and more effectively
help IT strategy executives make better decisions.
Moreover, achieving alignment between IT and business
functions has been one of the most important goals of IS
strategy research (e.g., Henderson and Venkatraman 1994),
thus leading to the introduction of digital business strategy
(Bharadwaj et al. 2010). However, much of the difficulty in
integrating IT and business lies in coordinating actions
between these two functions at virtually all organizational
levels (Reich and Benbasat 1996). The neuroscience litera-
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Table 6. Sample Opportunities in Developing Information Systems Strategy
Application Sample Research Opportunities
Deveoping IS Strategy
1. Enhancing strategic decision making in uncertain environments by using both cognitive
and emotional markers.
2. Coordinating actions and goals across IT and business functions to promote
cooperation between IT and business people.
3. Designing organizational incentives that are based on the functionality of the human
body (e.g., aligned goals, theory of mind, and social coordination).
ture has identified the brain basis of joint action and has shed
light on how the brain helps coordinate actions, goals, and
intentions (Newman-Norlund et al. 2007). Moreover, there is
literature on the neural correlates of social cooperation
(Rilling et al. 2002) and theory of mind (McCabe et al. 2001).
Extending these findings, neurophysiological tools could
examine how to coordinate actions and goals across IT and
business functions and how to promote cooperation between
IT and business people by designing appropriate incentives
that are based on the underlying brain functionality of aligned
goals, theory of mind, and social coordination.
Finally, the IS strategy literature has focused on developing
capabilities to execute daily activities (operational capa-
bilities), engage in planned reconfiguration (dynamic capa-
bilities), and spontaneously respond to surprising changes
(improvisational capabilities) (e.g., Pavlou and El Sawy 2006,
2010). Despite the theoretical distinction among these three
capabilities, it is not clear whether they correspond to how
organizations actually function. The distinction between
dynamic and improvisational capabilities is still an unsettled
issue in the literature (Eisenhardt and Martin 2000). In that
neurophysiological tools can localize the neural correlates of
human activities, it would be very possible to examine where
spontaneous improvisation occurs in the brain and if it differs
from planned change. In doing so, neurophysiological tools
could help resolve debates in the IS strategy literature that
could not be easily examined otherwise.
Enhancing the Design of Organizational Systems
Neurophysiological tools can help with the design and use of
organizational systems (Table 7).
First, the successful implementation of organizational ERP
systems relies on reducing the functionality misfit that arises
due to gaps between the functionalities offered by an ERP
package and those required by the organization (Rolland and
Prakash 2000). This misfit is difficult to assess because of the
complexity of ERP systems and the lack of appropriate mea-
sures of functional needs from a usage perspective, similar to
individual adoption and use. Neurophysiological tools can
identify neurological indicators that help create metrics of the
misfit in such ex ante analysis. In general, neurophysiological
tools may offer complementary metrics from theories that
seek to reduce misfit, theories such as cognitive fit theory,
which looks at the interaction between problem representation
and problem-solving (Vessey 1991). As cognitive fit theory
views the process as taking place within a mental model
(Zhang 1997), neurophysiological tools could inform the
metrics suggested by this theory by capturing the neural
correlates of cognitive fit and misfit and offering metrics that
could be used in the design of organizational systems. Also,
the fundamental tenets of how organizations manipulate the
characteristics of the problem and task representation can be
studied with NeuroIS to define contextual metrics of mental
representation for use in designing and testing ERP systems.
Second, in addition to organizational systems such as ERP,
failure to design systems with the user in mind has been
touted as one of the most important barriers to the success of
interorganizational systems (IOS) (Barrett and Konsynski
1982). Although the design of IOS with the user in mind is
necessary, much of the literature has focused on technologi-
cally “optimal” systems that may not be necessarily consistent
with how people use systems in organizations (Minnery and
Fine 2009). Thus, there is potential value in gathering neuro-
physiological responses when evaluating IOS prototypes, and
neurophysiological tools may help complement existing
sources of data and provide novel insights into the design of
IOS. The connection between IOS and organizational design
is related to the construct of bounded rationality, which refers
to “neurophysiological limitations to the information pro-
cessing capacities (memory, computation and communica-
tion)” (Bakos and Treacy 1986, p. 109). Neurophysiological
tools can help assess the role of IOS in enhancing the infor-
mation processing capacity of organizational users to inform
the design of IOS. Besides, activation in certain brain areas
can serve as a proxy for how organizational incentives should
be designed to effectively use IOS, and neurophysiological
tools could conceivably help inform IS research.
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Table 7. Sample Research Opportunities in Optimizing the Design of Organizational Systems
Application Sample Research Opportunities
Designing Organizational
Systems
1. Creating metrics for the ex ante gap between ERP functionalities and the organization’s
functional needs to increase the chance of ERP success.
2. Devising measures for problem representation and problem-solving tasks.
3. Using neurophysiological user responses when evaluating IT prototypes.
4. Assessing the impact of systems on users’ information processing capacity.
5. Designing incentives for effectively using interorganizational systems (IOS).
6. Exploring differences between genuine and IT artifacts in organizations.
Table 8. Sample Research Opportunities in Promoting Fairness in Organizations
Application Sample Research Opportunities
Technological Fairness and
Unfairness in Organizational
Settings
1. Devising fair organizational arrangements for sharing technology costs.
2. Creating incentives for technology decision makers that maximize the activation in the
brain’s reward areas for different compensation schemes.
3. Designing fair incentives for accepting technology investment proposals that go beyond
material rewards that enhance the organization’s interest.
Finally, an important question that has recently arisen in IS
research is the use of IT to reproduce genuine artifacts in
museum organizations and other archaeological sites (Pallud
and Straub 2007). Although a variety of IT artifacts have
been designed to augment reality (Vlahakis et al. 2002), there
is a debate whether users appreciate such artificial IT artifacts.
Measuring visitors’ perceptions, such as emotions and
aesthetics, may be used to enhance self-reported measures.
Neurophysiological tools can also offer measures of what
people perceive in IT versus genuine artifacts. For example,
an fMRI study that presents subjects with different artifacts
can identify differences in brain activations between IT-
generated and genuine artifacts. These differences can be
compared to the neuroscience literature on emotions and can
be linked to constructs that relate to aesthetics, such as
pleasure/displeasure. In the neuroscience literature, pleasant
pictures activate the nucleus accumbens and medial prefrontal
cortex (Sabatinelli et al. 2007), while displeasure activates the
superior temporal gyrus, amygdala, and hippocampus (Britton
et al. 2006). Also, other cognitive and emotional processes
may be activated differentially in the brain between IT and
genuine artifacts due to other factors that may enter a visitor’s
mindset (Pallud and Straub 2007). Those may be related to
content (activation in the anterior cingulate cortex due to
utility) (McClure et al. 2004), ease of use (activation in
DLPFC due to calculation) (Dimoka et al. 2011), promotions
(activation in caudate nucleus due to higher rewards)
(Delgado et al. 2005), anger (activation in orbitofrontal
cortex) (Murphy et al. 2003), or authenticity (whose neural
correlates have still not been identified).
Promoting Technological Fairness in
Organizational Settings
We next discuss research opportunities for promoting tech-
nological fairness in organizations and preventing unfairness,
which are summarized in Table 8 and elaborated in more
detail below.
First, decisions in organizations need collaborative agreement
between several individuals or subunits to share technology
costs and to enhance the benefit generated by the technology.
Incentives of these multiple decision makers must be well
aligned to enable collaborative agreements. These issues are
accentuated in interorganizational contexts such as in tele-
communications network and IT standards setting. The litera-
ture has shown that besides economic returns from the
decision to share technology costs, perceptions about the
fairness of the sharing arrangement affect whether individual
decision makers accept or reject the arrangement (Tabibnia
and Lieberman 2007). Fairness and unfairness have been two
important issues in studies of human decision making that
often bring into play both emotional and cognitive brain
processes. In the cognitive neuroscience literature, Sanfey et
al. (2003) used fMRI to identify the neural correlates of the
processes involved in decision making when subjects played
the Ultimatum game. Unfair offers activated the insular
cortex (a highly emotional area) and the DLPFC (a highly
cognitive area). Activation in the insular cortex predicted
whether a person might reject an offer (while the DLPFC did
not), testifying to the role of emotional processes in economic
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decision making. In contrast, Knoch et al. (2006) proposed
that the DLPFC is associated with rejecting unfair offers or
punishing unfair behavior by showing that subjects with
temporarily disrupted activity in the right DLPFC (using
TMS) were more likely to accept unfair offers in the
Ultimatum game. These findings testify to the role of both
cognitive and emotional processes when judging fair and
unfair offers. Moral and social values temper the role of
material effects and self-interest in influencing human deci-
sions. Neurophysiological tools analyzing activation in
emotional and cognitive regions of the brain at the individual
level can shed light on what organizational incentives will
lead to acceptance or rejection of technology investment
proposals by promoting perception of fairness.
In another fMRI study, Tabibnia and Lieberman (2009)
showed that fair offers increased activity in the reward areas
(ventral striatum) of the brain compared to unfair offers with
the same monetary value. It is possible that these findings can
be used to design incentives for technology decision makers
by attempting to maximize the activation in the reward areas
of the brain under different compensation schemes. Also,
Dulebohn et al. (2009) showed that unfair outcomes activated
the anterior insular cortex and DLPFC (similar to Sanfey et al.
2003) while unfair procedures triggered both the ventrolateral
prefrontal cortex and the superior temporal sulcus. The
authors distinguished between procedural and distributive
justice as distinct constructs whose neural correlates reside in
different brain areas. Decision makers may find the conduct
of the process or the outcome of that process, or both, to be
unfair. These findings testify to the importance of emotional
responses to unfair offers relative to activating the brain’s
reward areas for material outcomes. However, hormones,
such as serotonin, modulate behavioral reactions to unfairness
(Crockett et al. 2008). Clearly, the neuroscience literature can
provide guidance in designing incentives that go beyond
material rewards to ensure that technology-related decisions
are made to enhance the organization’s broader interest.
Group Work and Decision Support
The proposed research opportunities on group work and deci-
sion support focus on (1) enhancing online group collabora-
tion, (2) designing online decision aids, and (3) understanding
and building online trust.
Enhancing Online Group Collaboration
and Decision Support
We propose a set of opportunities on enhancing group col-
laboration, summarized in Table 9.
First, a major problem in group work is that people often fail
to incorporate the comments of others into their own thinking,
which often results in poor decisions (Dennis 1996). It is
unclear whether this is due to a lack of attention or due to a
deliberate choice to disregard others, although some research
suggests that it may be due to a lack of attention (Heninger et
al. 2006). Attention has been examined extensively in the
neuroscience literature, and several findings about the neural
and physiological correlates of attention have been identified.
The dorsolateral prefrontal and parietal cortices have been
associated with attention in fMRI studies (Knudsen 2007).
Psychophysiological tools, such as EEG, EKG, and SCR, can
be used to measure emotion, attention, and arousal when
group members engage in collaborative activities, thus testing
whether group members fail to attend to information or deli-
berately discount information from others. Once the cause of
the problem has been identified with the aid of neurophysio-
logical tools, collaborative technologies and process interven-
tions to improve group decision making could be designed.
Second, another objective of collaborative group work is
often to promote cooperation and to prevent competition
among group members. Neuroimaging tools can be used to
design interventions that enable or prevent the neural cor-
relates of collaboration and competition, respectively (Decety
et al. 2004). The design of collaborative technologies could
include interventions that facilitate in-group collaboration and
prevent in-group competition. Such IT designs could be
tested with fMRI to verify their ability in enhancing activity
in the neural correlates associated with collaboration and
reduce activity in the brain areas associated with competition
(inferior parietal cortex and medial prefrontal cortex). In
addition, physiological tools such as fEMG could capture
users’ reactions when collaborative technologies are used to
prevent negative reactions in group interaction. Such direct
and objective tests using neurophysiological tools could then
be used to help refine the design of collaborative technologies
and implement them in actual group settings.
Third, it is well known that lean digital media such as e-mail
have a smaller number of cues than audio or video. It has
recently been argued that communication through such lean
media is inherently biased, leading people to sense a more
negative tone in e-mail (Byron 2008) and reducing their
feeling of cooperativeness. Neurophysiological tools could
assess emotional responses to e-mail (and other media) mes-
sages, thus enabling a better understanding of whether lean
digital media is indeed more negatively biased than other,
richer media. If so, IS researchers could strive to understand
aspects that lead to this bias and find solutions to address
them.
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Table 9. Sample Research Opportunities in Enhancing Online Group Collaboration and Decision
Support
Application Sample Research Opportunities
Enhancing Group
Collaboration and Decision
Support
1. Testing if group members unintentionally fail to pay attention to information from others
or deliberately discount, not internalizing information.
2. Designing collaborative tools and process interventions to enhance group performance.
3. Testing stimuli that enable group collaboration and prevent in-group competition.
4. Assessing emotional response to e-mail, thus enabling an understanding of whether
lean e-mail communication is indeed more negatively biased than richer media.
5. Designing IT tools that help secondary tasks become automated, to enable group
members to dedicate their full attention to primary tasks.
6. Testing how dual-task interference can be mitigated with collaborative IT tools.
Finally, physiological measures could be complemented with
brain imaging tools for assessing alternative measures of
attention. For instance, deficits in attention can be due to
secondary tasks that disrupt a person’s attention to primary
tasks. Indeed, the neuroscience literature has shown that if a
secondary task becomes automated, dual-task interference is
reduced and distinct brain areas are associated with conscious
versus automated task processing (Kunde et al. 2007). These
findings could be used to design collaborative technologies
that help secondary tasks become fully automated, thus
enabling group members to dedicate their full attention to
primary tasks. Automaticity and habit have been primarily
linked to two brain areas (medial temporal lobe and basal
ganglia) (e.g., Graybiel 2008; Kubler et al. 2006), making it
possible to test whether dual-task interference can be
mitigated with the aid of collaborative technologies.
Designing Online Decision Aids
We also discuss three research opportunities for designing
decision aids in online markets (Table 10).
Decision aids are integral components of online markets, and
the IS literature has focused on designing decision aids in the
form of recommendation agents to help to enhance decision
making in online markets (e.g., Adomavicius and Tuzhilin
2005; Xiao and Benbasat 2007). Similar to system and
website adoption, the design of decision aids could also be
informed by the use of neurophysiological tools.
One set of studies can focus on how decision aids can be
designed to give consumers advice related to sensitive issues,
such as sexual habits, diseases, and drugs. Since consumers
are likely to be embarrassed when dealing with such sensitive
issues, decision aids could be designed to create rapport with
consumers and enable them to answer sensitive questions by
reducing embarrassment (e.g., Al-Natour et al. 2009). In that
subjects may not admit anxiety via self reports, neurophysio-
logical tools may uncover such emotions. The neuroscience
literature has made much progress in identifying the neural
correlates of many emotional processes (Murphy et al. 2003;
Phan et al. 2002), that can be used to identify which emotions
the sensitive questions trigger. Neurophysiological studies
can ask the subject questions with different levels of sensi-
tivity and embarrassment while observing brain activation.
These patterns of brain activation can be used to understand
how people respond to sensitive issues, and this knowledge
can be used to design decision aids that would enable
consumers to truthfully respond to sensitive questions and
receive valuable personal advice.
Although IT artifacts, such as systems or websites, are not
associated with an anthropomorphic element, decision aids in
the form of recommendation agents often include a humanoid
face (avatar), which plays a role in their adoption (Qiu and
Benbasat 2009). The neuroscience literature has studied how
people respond to various faces, and these findings can be
used to inform the design of decision aids with a human
interface. Because these interfaces are associated with a
certain ethnicity and gender, their adoption may depend on
the ethnicity/gender the avatar was designed to have and the
user’s ethnicity and gender, as predicted by theories of
similarity. Qiu and Benbasat (2010) found differences across
consumers in terms of how they use recommendation agents
that differ on their ethnicity and gender, but the behavioral
data could not fully explain these differences, perhaps
because social desirability bias prevented users from
admitting that they favor avatar interfaces of the same eth-
nicity and gender. Because one of the basic advantages of
neurophysiological tools is to get direct responses that are not
biased by subjectivity and social desirability, brain activity
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Table 10. Sample Opportunities in Designing Online Decision Aids
Application Sample Research Opportunities
Designing Online Decision
Aids
1. Designing decision aids to create rapport with consumers to enable them to respond
truthfully to sensitive questions.
2. Building decision aids whose humanoid faces (avatars) spawn activations in brain
areas associated with positive reactions in the neuroscience literature.
3. Identifying the neural correlates of ethnicity and gender similarity with agents.
Table 11. Sample Opportunities in Understanding and Building Online Trust
Application Sample Research Opportunities
Understanding and Building
Online Trust
1. Examining the interrelationships and temporal dynamics of the dimensions of trust.
2. Designing trust-building IT tools that separately engender each dimension of trust.
3. Testing how familiarity and disposition to trust play a role in the formation of trust.
4. Designing IT systems (signals, incentives) to activate the neural correlates of trust.
5. Identifying different trust-building processes by measuring activation in brain areas
associated with different dimensions of trust.
6. Building IT systems that build trust and reduce distrust separately.
can be compared to predict whether people would choose to
transact with an agent of a certain ethnicity and gender, and
whether brain activation differs depending on the subject’s
own demographics.
Understanding and Building Online Trust
Neurophysiological tools can help better understand the
nature and dynamics of trust and distrust and how systems can
be designed to help build trust online, as summarized in Table
11 and discussed in detail below.
First, trust and its related distrust are complex social processes
that affect many group processes. Still, the study of trust, and
even more so distrust, has only scratched the surface of these
rich group processes. For example, the dimensions of ability,
integrity, and benevolence statistically combine to form over-
all trust (Gefen et al. 2003; Gefen et al. 2010). The dimen-
sions of trust are inter-related (Mayer et al. 1995), but why
they mostly combine into an overall factor when they deal
with distinct aspects of trust is still unknown. Neurophysio-
logical tools could possibly shed light on this issue by
examining how these interrelated trust dimensions, such as
credibility and benevolence (Pavlou and Dimoka 2006),
reside in the brain. This might be done experimentally by
manipulating dimensions of trust to create or to ruin assess-
ments of ability, credibility, and benevolence, and in doing so,
identify the activation pattern of their neural correlates.
Second, knowing the neural correlates of trust could shed
light on trust formation. If neurophysiological tools can
clearly show that these dimensions of trust indeed have
distinct neural correlates, IS researchers could design systems
that separately affect each dimension of trust. For example,
advice-giving systems may provide different information
about each dimension of trust (Wang and Benbasat 2007).
Prior IS research argued that integrity and ability precede
benevolence without explaining why (Jarvenpaa et al. 1998).
Neurophysiological tools may be able to answer this question,
and indeed initial steps in this direction have already been
taken by Dimoka (2010), who identified the neural correlates
of trust as the caudate nucleus (confident expectations about
anticipated rewards) (King-Casas et al. 2005), anterior para-
cingulate cortex (predicting how the trustee will act in the
future) (McCabe et al. 2001), and orbitofrontal cortex (uncer-
tainty from the trustor’s willingness to be vulnerable) (Krain
et al. 2006). Riedl et al. (2010b) identified another area
related to reward processing, namely the thalamus, replicating
Baumgartner et al. (2008). These findings shed light on the
formation of trust and could guide the design of IT tools to
build different trust dimensions.
Third, neurophysiological tools can also help examine how
other constructs related to trust such as familiarity, satisfac-
tion, and trust propensity help build trust. The IS literature
has shown that familiarity and trust propensity build trust
gradually (Gefen 2000). This is consistent with the neural
correlates of trust where brain areas such as the anterior
paracingulate cortex (associated with the trustor’s predictions
of the trustee’s actions) have a more enduring nature than
brain areas associated with calculating uncertainty (orbito-
frontal cortex) and anticipating rewards (caudate nucleus)
(Dimoka 2010). Future research could study how familiarity
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Dimoka et al./Use of Neurophysiological Tools in IS Research
with the trustee and with trust propensity shape the neural
correlates of trust over time, thus extending the IS literature
on trust by better integrating familiarity with trust formation.
Fourth, besides examining the nature of trust, neurophysio-
logical tools could identify antecedents of trust by studying
how systems (e.g., websites) and IT-enabled signals and
incentives (e.g., third-party assurances) activate the identified
neural correlates of trust. It is already known that website and
advice-giving systems can build trust (Lim et al. 2006; Wang
and Benbasat 2007); however, little is known about what
actually happens as the brain analyzes different trust-building
stimuli. Particular antecedents of trust (e.g., feedback sys-
tems, reputation signals, design cues) could be tested to
identify areas of brain activation. Using the elaboration
likelihood model, Kim and Benbasat (2009, 2010) showed
that trust-assuring arguments are more effective when people
have high product involvement while third-party assurances
are more effective in building trust when product involvement
is low. However, product involvement and central versus
peripheral information processing are difficult to measure
with traditional tools, creating another opportunity for neuro-
physiological tools to complement existing tools. Such
studies may identify different trust-building processes by
showing activity in the caudate nucleus (increase in potential
rewards from trust), or anterior paracingulate cortex (pre-
dicting that the trustee will act cooperatively), or orbitofrontal
cortex (reduction in uncertainty). Such studies might, in fact,
complement existing findings about the reasoning processes
that people use to build trust (Komiak and Benbasat 2008;
Wang and Benbasat 2008).
Fifth, neurophysiological tools could also examine the
temporal dynamics among the dimensions of trust, helping IS
research delve more deeply into trust formation. fMRI tools,
with their superior spatial resolution, coupled with MEG or
EEG, with their superior temporal resolution, could be used
together to examine the temporal ordering of the activations
in the brain areas associated with trust (Dimoka 2010). Such
studies could inform us as to whether and when people focus
on the vulnerability associated with trust, rewards, and
inferring the trustee’s actions. These findings could inform
the design of IT-enabled trust-building stimuli.
Finally, neurophysiological tools might challenge existing
assumptions in the trust literature. For example, Dimoka
(2010) showed that distrust is not the opposite of trust, but
rather is a distinct construct that is linked to distinct brain
areas associated with fear of loss (insular cortex) and intense
emotions (amygdala). This may explain Wang and Ben-
basat’s (2008) findings where they showed that knowledge-
based processes (via explanations) affect trust but not distrust
in advice-giving systems while “awareness of the unknown”
about the reasoning of such IT systems leads users to form
distrust beliefs (Komiak and Benbasat 2008). This also helps
explain Pavlou and Dimoka’s (2006) findings that benev-
olence (which is associated with emotional areas) is more
influential on price premiums than credibility (which is
associated with cognitive brain areas).
Discussion
The discussion first offers recommendations for pursuing
NeuroIS by using neurophysiological tools to complement the
existing portfolio of empirical IS approaches, tools, and data.
Second, we discuss how to establish NeuroIS as a viable
subfield in IS research that can contribute and add value to the
IS literature.
Recommendations for Pursuing
NeuroIS Research
First and foremost, it is important to clarify that NeuroIS is
not a panacea for all IS research issues, and our objective is
simply to propose some promising avenues for IS research
with neurophysiological tools. Although we do not seek to
exclude any IS areas from using neurophysiological tools, we
hasten to add that there may be several areas of IS research
that neurophysiological tools could offer little or even no
help.
Second, IS researchers must assess when to use neuro-
physiological tools and when existing tools are sufficient. A
rule-of-thumb is that when existing tools can adequately
measure a research question, neurophysiological tools may
not be necessary. As Izak Benbasat noted during a 2009
INFORMS panel “NeuroIS if necessary, but not necessarily
NeuroIS.” Thus, there must be a good rationale for using
neurophysiological tools (Kosslyn 1999), such as using
neurophysiological data to supplement existing sources of
data or to address an issue that could not be adequately
examined with existing tools.
Third, despite the proposed advantages of neurophysiological
measures, no single neurophysiological measure is usually
sufficient on its own, and it is advisable to use many data
sources to triangulate across measures, which is always
advisable in IS research (e.g., Straub et al. 2004). On the one
hand, when there is a good correspondence between existing
data and neurophysiological data, we may infer that existing
data and resulting models closely correspond to the human or
brain functionality, thus validating and rendering higher
confidence to existing theories. However, there is seldom a
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Dimoka et al./Use of Neurophysiological Tools in IS Research
one-to-one correspondence between a neurophysiological
measure and a theoretical construct (Huettel and Payne 2009),
and it is important to treat neurophysiological measures
merely as proxies for complex theoretical concepts (similar to
all measures). Therefore, caution should be raised when the
correlation between psychometric and neurophysiological
data are extremely high (Vul et al. 2009), and such correla-
tions are not an artifact of the measurement method (Saxe et
al. 2006). On the other hand, if there is a poor correspon-
dence across neurophysiological and existing data, such a low
correspondence does not necessarily imply that one measure
is necessarily “better” than the other. This is because valida-
tion across measures is “symmetrical and egalitarian” (Camp-
bell 1960, p. 548). Taken together, it is important to trian-
gulate across many different sources of data, and the richness
provided by multiple sources of measures can be used to
enhance the ecological validity of IS studies.
Fourth, similar to all studies with human subjects, neuro-
physiological studies require approval by the researcher’s
institutional review board. Depending on the institution and
use of particular neurophysiological tools by prior investi-
gators, the time and effort needed may differ. Still, most
studies with neurophysiological tools qualify for an expedited
review, similar to behavioral studies such as surveys.
Fifth, as also noted by Dimoka et al. (2007), the field of
NeuroIS does not need to grow exclusively with neuro-
physiological studies. There is a very rich literature in neuro-
science in the social sciences (Dimoka et al. 2011) that IS
researchers can draw upon to inform their theories. Once the
field of NeuroIS is established, neuropsychological tools
become more accessible, and clear guidelines for NeuroIS
studies agreed upon, it will become increasingly easier to
conduct empirical studies with neurophysiological tools.
Finally, IS researchers should conduct a cost–benefit analysis
when using neurophysiological tools. Each tool has different
strengths and weaknesses that must be assessed relative to
their costs (Appendix A).
Toward a Viable NeuroIS Subfield
of IS Research
As noted in many of the examples offered in this paper, the
value of NeuroIS largely lies in combining neurophysiolog ical
data with other sources of data. The benefit of any new tool
lies in how it works together with, draws upon, and com-
plements existing tools, and neurophysiological tools are no
exception. It needs to be emphatically stated that neuro-
physiological tools should not be seen as an attempt to
replace, but rather to complement and supplement existing IS
tools. Integrating neurophysiological data with other sources
of data should be an important goal of NeuroIS. The litera-
ture has generally shown high correlations between brain and
psychometric measures (Vul et al. 2009), and there is a debate
about whether correlations are artificially inflated due to
characteristics of the focal statistical methods (e.g., Lieber-
man et al. 2009). Future research is needed to assess the true
extent of these correlations for different IS constructs, varying
from purely cognitive to highly emotional ones. Potential
differences in the extent of these correlations may shed light
on the value of neurophysiological data versus other sources
of data in measuring IS constructs. Nonetheless, differences
between neurophysiological and existing measures should not
necessarily imply that either approach is better, but it may
imply that there is a need for cross-validation to measure
complex IS constructs that are hard to capture accurately with
a single data source. Differences between neurophysiological
and self-reported data may imply that either respondents are
unwilling or unable to self-report certain constructs, or simply
that the human body simply cannot represent the richness of
psychometric measures (Logothetis 2008).
Another promising role of neurophysiological tools is to
inform debates that cannot be fully resolved with existing
tools. Many of the examples offered in this paper revolve
around identifying the distinction, convergence, and dimen-
sionality of IS constructs that are still unresolved in the IS
literature. Furthermore, competing theories can be resolved
with neurophysiological tools that may help explain which
theory is more likely to correspond to the body’s func-
tionality. For example, Dimoka (2010) has tackled the still
unanswered question of whether trust and distrust are distinct
constructs or whether they are part of the same continuum. In
her study, the fMRI results showed that trust and distrust are
associated with the activation of different brain areas, thus
offering evidence that they are two distinct constructs
associated with different neurological processes. Neuro-
physiological tools may uncover new constructs that have
been ignored in the IS literature (perhaps because they could
not be adequately measured), thus enriching IS theories.
Moreover, neurophysiological tools can help exclude con-
structs that do not correspond to the body’s functionality,
resulting in more parsimonious IS theories and helping to
develop better IS theories.
Because neurophysiological tools have the inherent attraction
of being able to open the black box of the human brain and
many studies have numerous interesting findings, there may
be a rush among IS researchers to conduct NeuroIS studies
without developing adequate knowledge of the neuroscience
literature and sufficient expertise with neurophysiological
tools. It is thus necessary to devise guidelines for conducting
NeuroIS studies. As with other statistical and methodological
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Dimoka et al./Use of Neurophysiological Tools in IS Research
tools already adopted in IS research, such as LISREL, PLS,
HLM, case studies (e.g., Benbasat et al. 1987), and experi-
ments (e.g., Jarvenpaa et al. 1985), neurophysiological tools
need to develop their own quality controls and best practices
(Straub et al. 1994). Specialized NeuroIS symposia (e.g.,
Dimoka et al. 2010b; Riedl et al. 2010a), conference panels
(e.g., Dimoka et al. 2009a; Dimoka et al. 2009b; Dimoka et al.
2010a), and tutorials (Dimoka 2009) could help promote
NeuroIS. Moreover, it would be useful to include NeuroIS as
a topic in IS doctoral seminars and have dedicated doctoral
courses and seminars that review NeuroIS studies, offer
empirical guidelines on neurophysiological tools (Dimoka
2012), and discuss promising NeuroIS topics. Moreover,
given the cost requirements of neurophysiological tools,
sponsoring NeuroIS studies is a challenging task, and creating
a support structure to facilitate IS researchers to obtain
funding would also help promote NeuroIS. Finally, including
editors and reviewers with expertise in cognitive neuroscience
theories and neurophysiological tools would enable NeuroIS
studies in IS journals to be rigorously reviewed, thus ensuring
that only high-quality studies are published in major IS
journals.
Conclusion
Establishing NeuroIS as a viable subfield in IS research must
focus on promising opportunities to enhance IS research,
promote specialized events, establish and disseminate best
practices, secure funding, and build a community of NeuroIS
scholars. While IS researchers are initially encouraged to
borrow neuroscience theories and benefit from existing
knowledge on using neurophysiological tools, they must
eventually contribute to this emerging literature by offering a
unique NeuroIS perspective. For example, localizing the
neural correlates of IT-related constructs could be a good
starting point to add value to the neuroscience literature.
Also, how people interact with computers (HCI) and websites
(e-commerce) could be prime areas where NeuroIS could
provide value. Above and beyond identifying potential appli-
cations for research that has yet to be conducted, we hope this
paper entices NeuroIS researchers to be both intelligent
consumers and also diligent contributors to the rapidly
expanding neuroscience literature.
Acknowledgments
We would like to thank the senior editor, Detmar W. Straub, for his
tremendous support and hands-on guidance in the development of
this manuscript. We also thank the associate editor and three
anonymous reviewers for their constructive and developmental
comments and suggestions that have helped us improve our work.
The paper is based on discussions that took place at a retreat in
Gmunden, Austria, in September 2009, and we thank the Town of
Gmunden for providing financial support to organize the retreat.
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About the Authors
Angelika Dimoka is an associate professor of Marketing and
Management of Information Systems at the Fox School of Business,
Temple University. She is also director of the Center for Neural
Decision Making. She holds a Ph.D. from the Viterbi School of
Engineering (specialization in Neuroscience and Brain Imaging)
with a minor in MIS from the Marshall School of Business at the
University of Southern California. Angelika’s research interests are
in the areas of cognitive neuroscience and functional neuroimaging
in marketing and MIS (Neuromarketing and NeuroIS), quantitative
analysis of uncertainty in online marketplaces, and modeling of
information pathways in the brain. Her research has appeared (or is
scheduled to appear) in Information Systems Research, MIS
Quarterly, NeuroImage, IEEE Transactions in Biomedical Engi-
neering, Annals of Biomedical Engineering, IEEE in Biology and
Medicine, and Neuroscience Methods.
Rajiv D. Banker is professor and Merves Chair in Accounting and
Information Technology at the Fox School of Business, Temple
University. He received a Doctorate in Business Administration
from Harvard University. He has received numerous awards for his
research and teaching, and has published more than 150 articles in
journals including Management Science, Information Systems
Research, MIS Quarterly, Operations Research, Academy of
Management Journal, Strategic Management Journal, and
Econometrica.
Izak Benbasat (Ph.D., University of Minnesota, 1974; Doctorat
Honoris Causa, Université de Montréal, 2009) is a Fellow of the
Royal Society of Canada, CANADA Research Chair in Information
Technology Management at the Sauder School of Business,
University of British Columbia, Canada, and a Special Term
Visiting Professor at the Guanghua School of Management, Peking
University, for 2011–2013. He currently serves on the editorial
boards of Journal Management Information Systems and Informa-
tion Systems Journal. He was editor-in-chief of Information Systems
Research, editor of the Information Systems and Decision Support
Systems Department of Management Science, and a senior editor of
MIS Quarterly. He became a Fellow of the Association for Infor-
mation Systems (AIS) in 2002, received the LEO Award for
Lifetime Exceptional Achievements in Information Systems from
AIS in 2007, and was conferred the title of Distinguished Fellow by
the Institute for Operations Research and Management Sciences
(INFORMS) Information Systems Society in 2009.
Fred Davis is Distinguished Professor and David Glass Chair in
Information Systems at the Walton College of Business, University
of Arkansas. He is also a Visiting Professor of Service Systems
Management and Engineering at Sogang Business School, Seoul,
Korea. He received his Ph.D. from MIT, and his research interests
include user acceptance of technology, computer assisted decision
making, NeuroIS, and service systems innovation. His work on this
article was partially supported by Sogang Business School’s World
Class University Project (R32-20002) funded by the Korean
Research Foundation.
Alan R. Dennis is a professor of Information Systems and holds the
John T. Chambers Chair of Internet Systems in the Kelley School of
Business at Indiana University. He is a senior editor at MIS
Quarterly, and the founding publisher of MIS Quarterly Executive.
He has written more than 100 research papers and four books (two
on data communications and networking, and two on systems analy-
sis and design). His research focuses on four main themes: the use
of computer technologies to support team creativity and decision
making; knowledge management; the use of the Internet to improve
business and education; and professional issues facing IS academics
(e.g., business school rankings and the difficulty of publishing and
getting tenure in IS).
David Gefen is a professor of MIS at Drexel University, Phila-
delphia, where he teaches strategic management of IT, database
analysis and design, and VB.NET. He received his Ph.D. in CIS
from Georgia State University and a Master of Sciences in MIS from
Tel-Aviv University. His research focuses on trust and culture as
they apply to the psychological and rational processes involved in
ERP, CMC, and e-commerce implementation management, and to
outsourcing. David’s wide interests in IT adoption stem from his 12
years of experience in developing and managing large information
systems. His research findings have been published in MIS Quar-
terly, Information Systems Research, IEEE Transactions on Engi-
MIS Quarterly Vol. 36 No. 3/September 2012 701

Dimoka et al./Use of Neurophysiological Tools in IS Research
neering Management, Journal of MIS, Journal of Strategic Informa-
tion Systems, The DATA BASE for Advances in Information Systems,
Omega: The International Journal of Management Science, Journal
of the AIS, Communications of the AIS, and elsewhere. David is an
author of a textbook on VB.NET programming.
Alok Gupta holds the Curtis L. Carlson Schoolwide Chair in Infor-
mation Management and is chairman of Information and Decision
Sciences Department at the Carlson School of Management. He
received his Ph.D. in MSIS from The University of Texas at Austin
in 1996. His research has been published in various top ranked
information systems, economics, and computer science journa ls such
as MIS Quarterly, Management Science, Information Systems
Research, Communications of the ACM, Journal of MIS, Decision
Sciences, Journal of Economic Dynamics and Control, Computa-
tional Economics, Decision Support Systems, IEEE Internet Com-
puting, International Journal of Flexible Manufacturing Systems,
European Journal of Operational Research, Information Technology
and Management, and Journal of Organizational Computing and
Electronic Commerce.
Anja Ischebeck is a professor of Cognitive Psychology and
Neurosciences at the Institute of Psychology of the University Graz,
Austria. She recieved her doctoral degree from the Nijmegen
Institute for Cognition and Information (NICI) of the University of
Nijmegen, Netherlands. She has worked at the Max-Planck Institute
for Human Cognitive and Brain Sciences in Leipzig, Germany, as
well as at the Medical University Innsbruck, Austria. Her main
research interests are number and language processing, attention,
and the basis of human learning and memory. She has published in
top-level neuroscience journals such as Neuroimage, Journal of
Cogntive Neuroscience, Human Brain Mapping, and Cerebral
Cortex.
Peter H. Kenning is a professor of Marketing at Zeppelin Univer-
sity, Germany. His overall research interests are consumer behavior,
consumer neuroscience, neuroeconomics, and marketing manage-
ment. His work has been published widely in MIS Quarterly,
Management Decisions, Journal of Consumer Behavior, journal of
Economic Psycyhology, and Advances in Consumer Research, as
well as Journal of Neuroimaging, Neuroreport, and Brain Research
Bulletin. He has received several best paper awards and grants from
the German government.
Paul A. Pavlou is an associate professor of Management Infor-
mation Systems, Marketing, and Strategic Management and a
Stauffer Senior Research Fellow at the Fox School of Business at
Temple University. He received his Ph.D. from the University of
Southern California in 2004. His research focuses on e-commerce,
online auctions, information systems strategy, information econo-
mics, research methods, and NeuroIS. Paul’s research has appeared
in MIS Quarterly, Information Systems Research, Journal of Man-
agement Information Systems, Journal of the AIS, Communications
of the AIS, and Decision Sciences, among others.
Gernot Müller-Putz is associate professor and Head of the Institute
for Knowledge Discovery, Graz University of Technology, Austria.
In 2004, he received his Ph.D. in electrical engineering from Graz
University of Technology, where, beginning in 2000, he worked on
non-invasive electroencephalogram-based (EEG) brain–computer
interfaces (BCI) for the control of neuroprosthetic devices. In 2008,
he recieved his “venia docendi” for medical informatics (“Towards
EEG-Based Control of Neuroprosthetic Devices”) at the faculty of
computer science at Graz University of Technology. His research
interest include EEG-based neuroprosthesis control, hybrid BCI
systems, the human somatosensory system and assistive technology.
René Riedl is an associate professor of Business Informatics at the
University of Linz, Austria. He serves on the executive board of the
Institute of Human Resources and Organizational Development in
Management at the University of Linz. His main research interests
are NeuroIS and IT management. He has published several IT-
related books and his research has appeared in Business &
Information Systems Engineering, WIRTSCHAFTSINFORMATIK,
Behavior Research Methods, NeuroPsychoEconomics, and the
Proceedings of the International Conference on Information
Systems, among others.
Jan vom Brocke holds the Hilti Chair in Business Process Manage-
ment at the University of Liechtenstein. He is director of the
Institute of Information Systems and president of the Liechtenstein
Chapter of the Association for Information Systems. Jan has over
10 years of experience in BPM projects and has published more than
170 refereed papers in the proceedings of internationally perceived
conferences and established IS journals, including Business Process
Management Journal and Communications of the Association for
Information Systems. He has authored or edited 15 books, including
Springer’s International Handbook on Business Process Manage-
ment. He is a member in the EU Programme Committee of the 7th
Framework Research Programme on ICT and serves as an advisor
to a wide range of institutions. Jan has beeb a visiting professor at
the University of Muenster in Germany, the LUISS University in
Rome, the University of Turku in Finland, and the University of St.
Gallen in Switzerland.
Bernd Weber is a neuroscientist at the Department of Epileptology
and the Center for Economics and Neuroscience at the University of
Bonn. After graduating from medical school in 2003, he worked as
a research associate and in 2005 started as head of the neuroimaging
platform at the Life & Brain Science Center at the University of
Bonn, which hosts two MR scanners. His research interests include
plasticity of the human brain in pathology and health with a focus on
social and economic decision making. In 2008 he became a research
associate at the German Institute of Economic Research in Berlin
(DIW). He is cofounder and on the board of directors of the Center
for Economics and Neuroscience in Bonn. In 2010 he received a
Heisenberg-Professorship of the German Research Foundation
(DFG) at the University of Bonn.
702 MIS Quarterly Vol. 36 No. 3/September 2012

ISSUES AND OPINIONS
ON THE USE OF NEUROPHYSIOLOGICAL TOOLS IN IS
RESEARCH: DEVELOPING A RESEARCH AGENDA
FOR NEUROIS
Angelika Dimoka
Temple University
angelika@temple.edu
Rajiv D. Banker
Temple University
rajiv.banker@temple.edu
Izak Benbasat
University of British Columbia
izak.benbasat@ubc.ca
Fred D. Davis
University of Arkansas & Sogang University
FDavis@walton.uark.edu
Alan R. Dennis
Indiana University
ardennis@indiana.edu
David Gefen
Drexel University
gefend@drexel.edu
Alok Gupta
University of Minnesota
alok@umn.edu
Anja Ischebeck
University of Graz
anja.ischebeck@uni-graz.at
Peter Kenning
Zeppelin University
peter.kenning@zeppelin-university.de
Paul A. Pavlou
Temple University
pavlou@temple.edu
Gernot Müller-Putz
Graz University of Technology
gernot.mueller@tugraz.at
René Riedl
University of Linz
rene.riedl@jku.at
Jan vom Brocke
University of Liechtenstein
jan.vom.brocke@uni.li
Bernd Weber
University of Bonn
bernd.weber@ukb.uni-bonn.de
Appendix A
Description of Neurophysiological Tools
The description of neurophysiological tools is broken down into psyc hophysiological tools and neurophysiological tools. For additional details,
see Riedl et al. (2010).
Description of Psychophysiological Tools
Eye Tracking
Eye tracking tools measure where the eye is looking (eye position) or the eye’s motion relative to the head (eye movement) (Shimojo et al.
2003). Eye tracking tools gather data n exactly where and for how long subjects focus their eyes on a certain image or stimulus (Cyr et al.
2009).
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Dimoka et al./Use of Neurophysiological Tools in IS Research
Figure A1. Sample Pictures of Eye Tracking Tools
There are various types of eye tracking devices (Figure A1). Older technologies required subjects to wear a headband with an eye sensor that
tracked the eye’s pupil while accounting for head movement. More recent devices either use a remote camera mounted on the screen that tracks
the pupil’s movement (Xu et al. 1998) or use a monitor that tracks what the subject looks at with the aid of infrared sensors (Djamasbi, Siegel,
and Tullis 2010a; Djamabsi et al. 2010b). Other eye tracking approaches use a blurred image and a mouse to estimate where the subject looks
(Tarasewich and Fillion 2004).
The most important variables obtained by eye tracking tools include eye fixation, pupil dilation, gaze duration, and areas of interest (Rayner
1998). Eye fixation is a spatially motionless gaze (about 2 seconds) on a particular area in a visual display lasting between 100 and 300
milliseconds with a velocity below 100 degrees per second. Pupil dilation gauges a person’s interest in the image they are viewing. Area of
interest is the region of the display that is specified by the researcher. Gaze duration is the total duration and average spatial location of
consecutive eye fixations on a particular area (which ends when the eye fixation moves outside the area of interest). Studies have shown that
people tend to have an intense gaze when looking at faces (e.g., Djamasbi et al. 2008). There are additional metrics that can be obtained by
eye tracking tools, such as the number or percentage of voluntary/involuntary fixations (Jacob and Karn, 2003). The metrics obtained by eye
tracking tools have been linked to cognitive and emotional processes, such as eye fixation to cognitive processing (Pan et al. 2004) and
surprising or important areas (Cyr et al. 2009).
Eye tracking has a long history in the social sciences (Rayner, 1998), human–computer interaction (Djamasbi et al. 2008), and computer
usability studies (Jacob and Karn 2003). This is because eye tracking is useful for comparing different versions of a computer interface or
the effectiveness of different systems. Eye tracking has recently been extended in IS research. For example, Cyr et al. (2009) compared website
designs across cultures by examining the area of the website that users focused on. Djamasbi et al. (2010b) showed that users found a page
with images of people’s faces to be more appealing than a page without images of faces. In their study, users performed their tasks more
quickly when there were faces present, resulting in higher trust in the informational content of visually appealing pages.
Eye tracking has several notable advantages that make it a promising tool. Most important, eye tracking can identify human visual activities
that cannot be self-reported because subjects cannot perfectly recall what they saw and cannot articulate where they looked and in what order.
Besides, eye tracking produces a clear visualization of what and at what time subjects looked when viewing an image, thus allowing researchers
to analyze the location and timing of visualization data. However, eye tracking has some disadvantages. First, eye tracking does not capture
peripheral vision, which constitutes the majority of human vision, and people look at things without fixating on them. Second, if subjects are
aware that they are being observed with an eye tracker, it may bias the natural setting of the experiment. Finally, what subjects saw does not
necessarily imply what they paid attention to, what they understood, or the meaning of what they saw.
Skin Conductance Response
Skin conductance response (SCR), also known as electrodermal response (or galvanic skin response), is the phenomenon where the skin
temporarily becomes a better electricity conductor when certain external or internal stimuli occur that cause an increase in the activity of human
sweat glands (Randolph et al. 2005). SCR tools measures activation in the sympathetic nervous system that changes the sweat levels in the
eccrine glands of the palms (or feet or arms). SCR uses electrodes that are commonly placed on the palmar side of the second phalanx of the
first and second fingers that send an imperceptibly small electric current through the two electrodes (Jacob and Karn 2003) (Figure A2). The
electrodes typically capture a continuous signal that is determined by the subject’s sympathetic nervous system (Moore and Dua 2004).
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Figure A2. Sample Pictures of Skin Conductance Response Tools
SCR has been linked to measures of arousal, excitement, fear, emotion, and attention, and it is believed to be a reliable complement to
psychological processes, such as attention and orienting reflexes (Raskin 1973). SCR has been used to study the role of emotions in decision
making. For example, Bechara et al. (1999) used the Iowa gambling task to measure decision making as an index of somatic states. In a similar
study, Crone et al. (2004) examined the pattern of heart rate (with EKG) and skin conductance (with SCR) that preceded risky choices following
the outcomes of bad, moderate, and good performers. Also, van’t Wout et al. (2006) used SCR to study emotions in the Ultimatum game,1
finding that SCR activity was higher when facing unfair offers. This pattern was observed for offers proposed by humans but not computers.
The main advantage of SCR is its very low cost, which makes it widely accessible. Besides, SCR is relatively easy to use and requires minimal
intervention on subjects because it is usually placed on the subject’s fingers, palms, feet, or arms. However, the main disadvantage of SCR
is its lack of predictable measurement, which makes SCR measures potentially unreliable. Moreover, SCR measures are highly subject to
habituation effects, which often make repeated SCR measures unreliable. Finally, it is not conclusive what SCR measures represent in terms
of the interpretation of SCR output. For these reasons, while SCR was popular in the 1960s and 1970s, it has lost ground to more sophisticated,
yet more expensive, techniques, such as fMRI.
Electrocardiogram
Electrocardiogram (EKG) measures the electrical activity of the heart, specifically how many times the heart beats in a minute. During a
heartbeat, an electrical signal spreads from the top to the bottom of the heart and sets the rhythm of the heartbeat. These signals are captured
by external skin electrodes that measure the electrical potential generated by the heart (Figure A3).
EKG has been the most commonly used psychophysiological tool, and it is associated with anxiety, stress, effort, and arousal. EKG has also
been used to study the role of emotions in decision making. Miu et al. (2008) used EKG while subjects played the Iowa gambling task2 to
capture the heart rate of their emotional responses in order to examine the role of anxiety on decision making. EKG is also associated with
anger, which is accompanied by a tonic increase in heart rate. EKG may also be used to capture sadness, which increases blood pressure and
decreases cardiac output. Finally, joy may also be captured with EKG.
Similar to SCR, EKG has similar advantages in terms of low cost, wide accessibility, and minimal invasiveness. However, EKG suffers from
the interpretation of its findings because heart rate may be affected by a very large set of factors, plus it is virtually impossible to pinpoint which
particular feeling (such as anxiety, stress, anger, or joy) triggers the increased or reduced heart rate.
1In the single-shot Ultimatum game, the first player offers to divide a sum of money between two players, and the second player has the option to either accept
or decline the offer. If the second player accepts, the money is split based on the first player’s offer; if the second player declines the offer, neither player receives
any money (Güth et al. 1982).
2In the Iowa gambling task (Bechara et al. 1994), subjects are given four decks of cards, and they are told that they must choose a card to earn money with the
objective of maximizing their earnings. Some cards carry a reward and others a penalty. The game is presented so that some decks are “good” and will earn
money, and some are “bad” and will lose money in the long run. Good decision makers learn to pick cards from the good and not from the bad decks.
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Figure A3. Sample Pictures of Electrocardiogram Tools
Facial Electromyography
Facial electromyography (fEMG) measures muscle activity in the form of electrical impulses spawned by muscle fibers during contraction from
two main facial muscles, the corrugator supercilli and zygomaticus. fEMG is measured with small (2 to 4 mm) electrodes placed on the left
side of the face (Figure A4), and the raw fEMG signal must be amplified and filtered. Because emotional expression is linked to the contraction
of the face, facial muscle activity is linked to emotional reactions (Schwartz et al. 1976), and fEMG offers a direct measure of electrical activity
from facial muscle contraction. Studies found that activity in the corrugator muscle (which lowers the eyebrow and is involved in frowning)
is correlated with negative emotional stimuli and mood states (such as anger and disgust), while activity in the zygomatic muscle (which
controls smiling) is correlated with positive stimuli and mood states (such as pleasure and enjoyment). fEMG can also measure activity at the
orbicularis oculi, which captures the magnitude of a blink reflex. Larger blinks are associated with unpleasant stimuli while smaller blinks are
associated with pleasant ones.
fEMG was shown to have both better discriminatory power than self-reports in terms of emotional responses and also to be associated with
higher recall of commercial ads (Hazlett and Hazlett 1999). Moreover, fEMG was closely linked to real-time emotion-specific events during
the advertisements, allowing the authors to conclude that fEMG is superior to self-reports in analyzing commercial ads.
fEMG has several advantages. First, it can measure facial expression with a high degree of precision and sensitivity in a continuous, real-time
fashion without any cognitive effort from the subject. Second, fEMG is minimally intrusive. Third, fEMG is relatively inexpensive and widely
accessible in behavioral labs. However, fEMG has several limitations. First, fEMG can only get data from a small number of facial muscles
because of the limited number of electrodes that can be attached to the subject’s face. Second, the quality of the fEMG measures is
questionable and there are relatively few fEMG studies in the literature, thus making the interpretation of fEMG measures difficult. Finally,
despite being minimally intrusive, fEMG electrodes can still alter natural expression because subjects realize that their facial expressions are
being measured.
Figure A4. Sample Pictures of Facial Electromyography Tools
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Description of Brain Imaging Tools
Functional Magnetic Resonance Imaging (fMRI)
fMRI is a noninvasive method that reflects neural activity by measuring changes in blood oxygenation (see Belliveau et al. 1991; Ogawa et
al. 1990). Neural activity in a brain area leads to an increase in blood oxygenation, which is referred to as hemodynamic response, typically
peaking about 4 or 5 seconds after the onset of neural activity. The hemodynamic response, which was shown to be directly linked to neural
activity (Logothetis et al. 2001) can be captured with an fMRI scanner (Figure A5) by exploiting the blood’s magnetic properties (oxygen-rich
versus oxygen-depleted), termed blood oxygen level dependent (BOLD) (Ogawa et al. 1990). fMRI results are often shown with statistical
parametric activation maps (SPMs) that contain statistical results of fMRI data computed in a 3D space (voxel or volumetric pixel). As fMRI
does not capture absolute levels of blood oxygenation, fMRI results compare the relative intensity of BOLD signals across conditions (Friston
et al. 1994). Hence, to locate areas of brain activation, a single high-resolution structural image is also acquired. With modern fMRI scanners
and protocols (Figure A5), functional images have a spatial resolution of about 2 mm3 voxels (how closely lines appear on the image).
Temporal resolution (how precise the measurement of time is) is only 2 or 3 seconds.3 Due to the reliable localization of activity deep in the
brain with high spatial resolution and adequate temporal resolution, fMRI is now the most commonly used brain imaging tool.
The ability of fMRI to localize brain activity is especially useful for several reasons. First, emotions are associated with areas that are located
deep within the brain (see Murphy et al. 2003; Phan et al. 2002). Second, fMRI is non-invasive. Third, because fMRI is widely used, there
are standard data-analysis approaches to compare across studies. Nonetheless, fMRI also has some disadvantages (Savoy 2005): First, fMRI
has modest temporal resolution (a few seconds). Thus, inferring causal relationships between two brain activities may require complementing
fMRI data with the higher temporal resolution EEG or MEG data. Second, fMRI data must be interpreted carefully because correlation does
not necessarily infer causation,4 and brain activity is complex and often nonlocalized (Kenning and Plassmann 2005). Third, there is no
consensus about the correct threshold for fMRI statistics yet, so reported results could contain false positives as well as false negatives. Finally,
the BOLD signal is an indirect measure of neural activity, and it is thus susceptible to influences by nonneural vascular changes.
Figure A5. Sample Pictures of fMRI Tools
Positron Emission Topography (PET)
PET measures metabolic activity by representing neurochemical changes using radioactive tracer isotopes that are detected by a PET scanner
(de Quervain et al. 2004) (Figure A6). As radioisotopes decay, they emit a positron; when this positron collides with an electron, a pair of
photons (high-energy gamma quants) is produced that travel in opposite directions. PET can detect this pair of simultaneously generated
photons and calculate its point of origin from the arrival times. From the distribution of the detected photons, a 3D image is created that
represents brain perfusion and metabolism in absolute values (Figure A6).
3To be precise, the temporal resolution is not due to the fMRI tool itself but rather due to the hemodynamic response.
4To infer causality, fMRI is sometimes used in combination with other tools, such as transcranial magnetic stimulation (TMS), a noninvasive techinque. TMS
temporarily suppresses specific brain areas, thus creating a “virtual” lesion. TMS helps infer causality by showing that a certain function cannot be performed
when a brain area is temporarily disrupted (Miller 2008). TMS and fMRI are sometimes used jointly, because fMRI allows more precise assessment of the impact
of the TMS on brain areas connected to the targeted area (se Knoch et al. 2006).
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Figure A6. Sample Pictures of PET Tools
The spatial resolution of PET is similar to fMRI, but its temporal resolution is much lower (2 or 3 minutes). PET costs are also comparable
to fMRI. The greatest disadvantage of PET is its invasive and potentially harming nature since subjects have to be intravenously injected with
a radioactive tracer. Accordingly, fMRI has generally replaced PET in nonclinical research and PET studies are rapidly declining.
Electroencephalography (EEG)
EEG measures electrical brain activity from extracellular ionic currents that are caused by dendritic activity. Since the individual electrical
potentials are very small, EEG captures the summation of the potentials of millions of neurons that follow a similar spatial orientation. Thus,
each electrode captures the summation of the electrical potentials that are generated by millions of neurons (see Lopes da Silva 2004) (Figure
A7). EEG typically uses multiple electrodes, often using caps or nets that span the entire scalp (Figure A7).
EEG has several advantages compared to fMRI and PET. First, EEG is cheaper than fMRI and PET, it can be used in many environments (as
it is not constrained by the bulky and enclosed fMRI or PET scanners that may cause claustrophobia), it is tolerant to subjects’ movements that
are not allowed in fMRI, and it is silent (which is important for auditory stimuli). Moreover, EEG has excellent temporal resolution in the order
of milliseconds, and is preferred when timing resolution is needed (Kenning and Plassmann 2005). Finally, EEG captures brain activity directly
in the form of electrical signals while fMRI and PET use indirect proxies of brain activity, namely blood flow (fMRI) and metabolic activity
(PET).
The major limitation of EEG relative to fMRI or PET is spatial resolution. Also, while EEG is sensitive to electrical activity generated in the
outer layers of the cortex, it is largely insensitive to electrical activity in deeper brain areas (Mathalon et al. 2003). Thus, compared to designs
that are feasible with fMRI or PET, EEG studies require relatively simpler paradigms.
Figure A7. Sample Pictures of EEG Tools
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However, EEG and fMRI are not mutually exclusive, and it is possible to simultaneously use both tools to take advantage of the high temporal
resolution of EEG with the high spatial resolution of fMRI. However, the two data sources may not reflect the exact same brain activity due
to different timing; there are also technical difficulties associated with integrating fMRI and EEG data. Nonetheless, there is much research
on improving the ability to combine fMRI and EEG data (besides MEG data described below).
Magnetoencephalography (MEG)
MEG is sensitive to changes in magnetic fields induced by brain activity (Braeutigam et al. 2001). It is based on relatively weak magnetic fields
that are induced by synchronized neuronal electrical potentials. Similar to EEG, MEG signals are also derived from the summation of the
potentials of ionic currents caused by dendritic neurons. Since the brain’s magnetic field is relatively very small (~10 micro Tesla), MEG uses
extremely sensitive devices, termed superconducting quantum interference devices (SQUIDs) (Figure A8).
The temporal resolution of MEG is comparable to EEG; however, MEG has lower spatial resolution, and its source localization depends on
statistical assumptions. MEG is more effective in registering activity in deeper brain structures than EEG is, but does so at a lower spatial
resolution and accuracy than fMRI. This increased spatial resolution compared to EEG comes at increased cost and statistical complexity.
Nonetheless, MEG is complementary to EEG, fMRI, and PET.
Figure A8. Sample Pictures of MEG Tools
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Cost of Neurophysiological Tools
While the cost of neurophysiological tools is rapidly decreasing, an approximate cost of either acquiring or renting the tools is provided in
Table A1. Interested researchers are encouraged to consult with either their own institutions for exact costs for renting the tools or with the
commercial companies that sell the tools.
Table A1. Approximate Cost of Neurophysiological Tools
Focus of Measurement Total Cost (US$)
Psychophysiological Tools
Eye Tracking Eye pupil location (“gaze”) and movement ~$10,000 or ~$100/hour
Skin Conductance Response (SCR) Sweat in eccrine glands of the palms or feet ~$2,000 or ~$25/hour
Facial Electromyography (fEMG) Electrical impulses caused by muscle fibers ~$3,000 or ~$40/hour
Electrocardiogram (EKG) Electrical activity of the heart on the skin ~$5,000 or ~$50/hour
Brain Imaging Tools
Functional Magnetic Resonance Imaging
(fMRI)
Neural activity by changes in blood flow ~$200-500/hour
Positron Emission Tomography (PET) Metabolic activity by radioactive isotopes ~$200-500/hour
Electroencephalography (EEG) Electrical brain activity on the scalp ~$100-200/hour
Magnetoencephalography (MEG) Changes in magnetic fields by brain activity ~$200-400/hour
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Appendix B
Review of Neuroscience Literature
Cognitive Neuroscience Theories
The cognitive neuroscience literature has developed a number of higher-order theories that help explain how human processes guide behavior.
In brief, the neuroscience literature has a rich basis of theories for understanding phenomena of potential relevance to the IS literature, and IS
researchers may find it useful to review and integrate the neuroscience literature on the particular topic they are examining. This appendix
presents some of these theories along with some exemplar studies that have used the theories.
Somatic Marker Hypothesis
The somatic marker hypothesis explains how emotional processes influence human decisions and behavior (Damasio 1994). This hypothesis
posits that somatic markers (associations about emotional processes), are summed into a single state that facilitates decision making in the
presence of various uncertain options. The somatic markers have been associated with the ventromedial prefrontal cortex, and damage to this
brain area was shown by an exemplar study by Bechara and Damasio (2005), on affecting decision making.
Theory of Mind
The theory of mind is another prominent theory that explains how people infer how others will behave (Fletcher et al. 1995), and the cognitive
neuroscience literature linked the anterior paracingulate and medial prefrontal cortex as the brain areas linked to predicting of others’ behavior
in an exemplar study by McCabe et al. (2001). Related work on mirror neurons (neurons that mirror another person’s behavior), was linked
to the theory of mind by Iacobini et al. (1999), in terms of imitating the behavior of referent others.
Calculative and Emotional Decision Making
The cognitive neuroscience literature also focused on calculative and emotional decision-making under different conditions (Sanfey et al. 2006),
such as balancing rewards and risks (e.g., McClure et al. 2004a), managing uncertainty, risk, and ambiguity (e.g., Huettel et al. 2005; Krain
et al. 2006), and assessing various utility trade-offs (e.g., Camerer 2003). The prefrontal cortex (primarily the orbitofrontal and dorsolateral
prefrontal cortex), and the limbic system (mostly the anterior cingulate cortex and amygdala), are the two brain areas mostly associated with
decision making (e.g., Ernst and Paulus 2005). Moreover, the prefrontal cortex was shown to be responsible primarily for the calculative
aspects of decision making, while the limbic system was shown to be responsible for the emotional aspects (e.g., Bechara et al. 1999).
Intentions
There is also a rich literature on various type of intentions as those correspond to planning future behavior (Dove et al. 2008; Petrides 1996),
motor intentions (Desmurget and Sirigu 2009), and task-specific intentions (e.g., Haynes et al. 2007; Paus 2001; Winterer et al. 2002). The
ventrolateral prefrontal cortex (BA47), is the primary area associated with future intentions by interacting with other brain areas that provide
input to the process. Other such areas that contribute to the formation of intentions include the lateral prefrontal cortex that governs motivation
(Haynes et al. 2007), and the anterior cingulate cortex that is associated with intentional effort and volition (Paus 2001; Winterer et al. 2002).
Motor intentions are quite distinct from cognitive intentions, and they are linked with the parietal and pre-motor cortices (Desmurget and Sirigu
2009).
Cognitive Processing
Cognitive processing is an area that received much attention in the neuroscience literature, focusing on how the brain manages information.
The brain can distinguish between cognitive and emotional information (Ferstl et al. 2005); cognitive information is processed in the lateral
prefrontal cortex while emotional information is processed in the dorsal frontomedial cortex. Cognitive effort and working memory for
short-term information storage and real-time information processing have been linked to the dorsolateral prefrontal cortex (e.g., Braver et al.
1997; Linden et al. 2003; Owen et al. 2005; Rypma and D’Esposito 1999).
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Brain Localization of Mental Processes
The neuroscience literature has also focused on the localization of mental activity in the brain or body (termed neural correlates), and has
created virtual “maps” of the human brain and body by indicating where activity occurs when people engage in various activities. Dimoka
et al. (2011), offer an extensive summary of many human processes that are likely to be of interest to IS research, categorized under (1) decision
making, (2) cognitive, (3) emotional, and (4) social processes.
In terms of decision-making processes, calculation has been associated with the prefrontal cortex and the anterior cingulate cortex (e.g., Ernst
and Paulus 2005; McClure et al. 2004a). Uncertain decision making has been linked to the orbitofrontal and parietal cortex (e.g., Huettel et
al. 2005; Krain et al. 2006). Decision-making under different conditions is associated with different brain areas, with risk focusing on the
nucleus accumbens (e.g., Knutson et al. 2001; Mohr et al. 2010), uncertainty/ambiguity with the parietal and insular cortices (e.g., Krain et al.
2006), loss with the insular cortex (e.g., Paulus and Frank 2003), and rewards with the caudate nucleus and putamen (e.g., Delgado et al. 2005;
McClure et al. 2004a).
In terms of cognitive processes, multitasking is linked to the fronto-polar cortex (e.g., Dreher et al. 2008), and automaticity with the frontal
and striatal cortex (e.g., Kubler et al. 2006; Poldrack et al. 2005). Priming (how an earlier stimulus, which is often unconsciously conveyed,
affects the response to a later stimulus), is associated with the posterior superior cortex and the middle temporal cortex (e.g., Wible et al. 2006).
In addition, habit is associated with the basal ganglia and the medial prefrontal cortex (e.g., Salat et al. 2006), and flow with the dorsal prefrontal
and medial parietal cortices (e.g., Iacobini et al. 2004; Katayose 2006). Finally, spatial cognition is linked to the medial temporal lobe and the
hippocampus (Shrager et al. 2008).
In terms of emotions, the literature has focused on multiple general and specific emotional processes and their localization in the brain. In terms
of the general processing of emotions, the medial prefrontal cortex and anterior cingulate cortex are the two primary brain areas (see Damasio
1996; Phan et al. 2002). Moreover, the literature has focused on specific emotions. Anxiety has been linked to the amygdala and the
ventromedial prefrontal cortex (e.g., Bishop 2007; Wager 2006). Disgust is linked to the insular cortex (e.g., Lane et al. 1997), fear to the
amydgala (e.g., LeDoux 2003), anger to the lateral orbitofrontal cortex (e..g., Murphy et al. 2003), sadness to the subcallosal cingulate cortex
(e.g., Phan et al. 2002), and displeasure to the superior temporal gyrus (e.g., Britton et al. 2006; Casacchia et al. 2009). Furthermore,
pleasure/enjoyment has been associated with the nucleus accumbens, anterior cingulate cortex, and putamen (e.g., McLean et al. 2009;
Sabatinelli et al. 2007), and happiness with the basal ganglia and ventral striatum (e.g., Murphy et al. 2003, Phan et al. 2002).
In terms of social processes, besides the general theory of mind (McCabe et al. 2001), the literature has focused on specific social issues, such
as social cognition (which is associated with the temporal lobe) (Adolphs 1999; 2001), and moral judgment (which is associated with the
frontopolar cortex and the posterior superior temporal sulcus) ( Borg et al. 2006; Moll et al. 2005). More specific social processes include trust
(that is linked to the caudate nucleus, putament, and anterior paracingulate cortex) ( Dimoka 2010; Winston et al. 2002), distrust (which is linked
to the amygdala and insular cortex) ( Dimoka 2010; Winston et al. 2002), cooperation (which is linked to the orbitofrontal cortex) (Rilling et
al. 2002), and competition (which is linked to the inferior parietal and medial prefrontal cortices) (Decety et al. 2004).
In addition to the specific neural correlates associated with these mental processes, there are numerous other processes whose neural correlates
have been examined in the vast neuroscience literature. Nonetheless, the neural correlates of these processes could be a good starting point
for IS researchers to learn what has already been done in the neuroscience literature, what is already known about these mental processes,
whether extant knowledge from the cognitive neuroscience literature can help derive testable hypotheses, and whether new empirical studies
are needed in the IS literature.
Review of Neuroscience Literature in the Social Sciences
The ability to link brain functionality to mental processes has captivated the interest of social scientists. Psychologists and economists were
the early pioneers of social neuroscience followed by marketers, and many interesting findings have been found by identifying the neural
correlates of human behavior, decision making, and underlying cognitive, emotional, and social processes in these disciplines.
In neuroeconomics (the use of neuroscience theories and tools to inform economic behavior), several well-established economic models have
been challenged and refined by looking into the mental processes that underlie economic decision-making and behavior (e.g., Camerer et al.
2004; Rustichini 2005). Notably, Smith et al. (2002), challenged a well-accepted economic assumption that payoffs and outcomes are indepen-
dent by showing that a person’s attitudes about economic payoffs and beliefs on the expected outcomes of these payoffs interact with each other
both behaviorally and also neurally. Bhatt and Camerer (2005), challenged another well-established economic theory that effective decision-
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Dimoka et al./Use of Neurophysiological Tools in IS Research
making should be governed by rational cognitive processes without relying on emotions. The authors showed that subjects who had good
cooperation between the brain’s calculative decision-making area (prefrontal cortex), and the emotional decision-making area (limbic system),
were the best performers in economic games. Also, neuroeconomic studies were able to explain and refine existing economic theories. For
example, prospect theory (Kahneman and Tversky 1979), which theorizes that gains and losses are viewed differently, was explained by neuro-
imaging tools that showed that one brain area, associated with utility and rewards (ventral striatum), is activated in the prospect of an economic
gain while a different brain area, associated with losses (insular cortex), is activated in the prospect of economic loss (Kuhnen and Knutson
2005). This study confirmed that different brain areas govern gains and losses, validating the basic tenets of prospect theory. Moreover,
neuroimaging tools were able to explain why people are generally comfortable with uncertain gambles (with specific probabilities for specific
gains), but despise ambiguous gambles (without specific probabilities and gains). Hsu and Camerer (2004), showed that the insular cortex
(which is activated by intense negative emotions, such as fear and disgust), is activated when decision makers are presented with ambiguous
gambles. However, the insular cortex was not activated by uncertain gambles, helping explain why people avoid ambiguous investments.
Neuromarketing (the use of neuroscience theories and tools to inform marketing), has also made major advances in understanding how
consumers respond to marketing and advertising (Ariely and Berns 2010; Kenning et al. 2007; Lee et al. 2007; Zaltman 2003). In terms of how
consumers make purchasing decisions, Braeutigam et al. (2004), explained the neurological basis of predictable versus unpredictable purchases
and linked them to immediate and delayed rewards that are associated with different brain areas. Predictable and impulse purchases are
governed by different neurological processes, thus explaining why consumers radically differ in how they make predictable and impulse
purchasing decisions. Similarly, McClure et al. (2004a), showed that immediate and delayed rewards (impulse versus planned purchases),
activate different brain areas that are associated with inter-temporal tradeoffs. In terms of how consumers react to marketers’ branding efforts,
Deppe et al. (2005), showed that a consumer’s preferred brand choice is responsible for reduced activation in brain areas associated with
reasoning and calculation and increased activation in areas associated with emotions and self-reflections, helping explain why marketers invest
in brand building to reduce consumers’ rational calculation when deciding across competing brands. In a well-publicized Coke versus Pepsi
fMRI study, McClure et al. (2004b), explained that people prefer Coke because of brand recognition (by differentially activating the dorsolateral
prefrontal cortex that governs cognitive information processing), over Pepsi in a non-blind tasting. However, similar brain areas, mostly
associated with pleasant emotions, were activated for both refreshments in blind tasting. These findings explained that consumers prefer Coke
over Pepsi due to brand recognition and not taste preference.
In addition to these findings in each discipline, Glimcher and Rustichini (2004), argue that psychology, economics, and marketing are
converging under the umbrella of the neuroscience literature to provide a unified theory of human behavior. Therefore, we expect
neurophysiological studies to increasingly inform transdisciplinary phenomena in the social sciences that span these core disciplines.
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Appendix C
Moderating Role of Culture, Gender, and Age in NeuroIS
Many of the topics discussed in the proposed NeuroIS research agenda could further benefit by the use of neurophysiological tools by inferring
neural and psychological differences in individuals, groups, and organizations depending on differences in (1) culture, (2) gender, and (2) age,
as elaborated below.
Cultural Differences
A set of proposed research opportunities for understanding cultural differences with neurophysiological tools are summarized in Table C1 and
are elaborated in detail below.
First, neurophysiological tools could help identify neural or physiological differences across cultures. This is a prime topic where neuro-
physiological tools could be particularly useful because culture is a sensitive topic that is often biased by social desirability bias or political
correctness. Neurophysiological data that may be more immune from subjectivity bias may be what is needed to push IS research on culture
forward.
Second, neurophysiological tools may help compare how people across cultures respond to various designs. The neuroscience has broadly
examined cultural differences (e.g., Gutchess et al. 2006; Zhu et al. 2007). For example, Sabbagh et al. (2006), identified significant differences
in the brain’s executive functioning of small children in a cross-cultural study in the United States and China. In contrast, the authors did not
find differences in the brain areas related to the theory of mind, implying that American and Chinese children do not differ in the way they
predict how others will behave. Similarly, IS research has extensively studied cultural differences; for example, Cyr et al. (2009), used a
combination of methods (including eye tracking), to study how images and website designs are viewed by culturally diverse users. In addition,
Cyr et al. (2010), showed that website color appeal differentially affects trust and satisfaction across different cultures. Also, Cyr (2008), and
Cyr and Trevor-Smith (2004), found significant cultural differences in how people interact with websites. Building upon these neural
differences and similarities in culture, IS researchers can examine how various IT designs are viewed across cultures using neurophysiological
tools. Neuroimaging tools, such as fMRI, could help identify neural differences in the visual designs across websites, such as images and color,
across cultures. Physiological tools, such as eye tracking, can track how culturally diverse users gaze or fixate on various visual designs, colors,
images, and other information on websites, and accordingly prescribe how to structure website designs to cater to different cultures in terms
of the overall visual design.
Third, neurophysiological tools may help explore cultural differences in terms of communication, language, and training. Despite the extensive
study of communication in IS research and strong cross-cultural effects on IT adoption (Gefen and Straub 1997; Sia et al. 2009), until now,
answering what exactly is behind these communication differences has been elusive. It could also have been due to differences in education
and socialization across cultures (Hofstede 1980); however, existing tools could not tease out the exact reason, and neurophysiological tools
can delve deeper into the underling origins of such cultural differences. Neurophysiological tools, for example, could focus on potential
differences in patterns of brain activation when people from different cultures engage in oral or written communication. How oral or written
language makes a difference in terms of how people communicate could also be examined with neurophysiological tools that could capture
potential differences in brain or physiological responses during communication. Being able to stipulate the brain area and functionality opens
new opportunities to answer such questions. Physiological tools could also explore differences in how culturally diverse people communicate
and use language. Such studies could also contribute to the neuroscience literature by answering the call to study culturally shaped factors,
such as moral values, social norms, and utilitarian beliefs (Moll et al. 2005). Finally, IT training could be used to influence such cultural
differences by either trying to eliminate or exacerbate them, and neurophysiological tools could test whether and how IT training has its desired
goals.
Finally, cultural factors may play a role in the design of trust-enhancing IT designs (e.g., Sia et al. 2009). Neurophysiological tools could help
examine the neurological and physiological aspects of trust building across cultures to shed light on potential differences on how culture affects
trust formation. For example, fMRI can compare whether the neural correlates of various dimensions of trust (Section 3.3.3), are activated
differently in these brain areas using the same trust-building stimuli across cultures, and how dissimilar trust-building stimuli have differential
effects on the various dimensions of trust across different cultures.
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Table C1. Sample Research Opportunities in Understanding Cultural Differences
Application Sample Research Opportunities
Cultural Differences
1. Examining neural and physiological differences across cultures
2. Comparing how people across cultures respond to different IT designs
3. Exploring cultural differences in communication, language, and training
4. Examining how culture plays a role in the design of trust-building systems
Table C2. Sample Research Opportunities in Understanding Gender Differences
Application Sample Research Opportunities
Gender Differences
1. Testing whether gender differences are primarily due to nature or nurture
2. Explaining observed behavioral differences across genders in terms of their neurological or
physiological origin
3. Exploring communication, language, training differences across genders
4. Identifying gender differences in emotional and social aspects of websites
Gender Differences
A set of sample research opportunities for understanding gender differences with neurophysiological tools are summarized in Table C2, as we
elaborate in detail below.
Neurophysiological tools could be useful to study gender differences because gender is also a sensitive topic that is often subject to social
desirability bias and political correctness. While we propose to examine various gender differences, it is important to note that our discussion
is not restricted to biological gender (whether a person is generally regarded as a woman or a man based on genetic or hormonal characteristics).
While gender differences are thought to revolve around biological differences between men and women, gender also differs in terms of
socialization and experience (sociocultural gender) (Lueptow et al. 1995). Gender is a complex sociocultural construct that distinguishes social
relationships among women and men (Santos et al. 2006), and it is the outcome of historic and cultural processes that have developed through
sociocultural values about the respective roles of the two biological genders that affect their orientation in terms of masculinity and femininity.
This sociocultural distinction is not trivial. Santos et al. (2006), found that sociocultural gender (but not biological), differences explained
differences in math ability. Moreover, Cyr et al. (2009), found that sociocultural gender values were a more salient moderator of an expanded
technology adoption model than biological gender. Nonetheless, there are also biological gender differences in technology use (e.g., Gefen
and Straub 1997; Gefen and Ridings 2005; Venkatesh and Morris 2000). Accordingly, Trauth (2002), argues for a social construction theory
to understand the complex nature and effects of gender by focusing on the social shaping of gender with IT. Given these two aspects of gender,
our proposed opportunities could apply to both biological gender and sociocultural gender.
First, neurophysiological tools could help identify brain or physiological differences or similarities across biological or sociocultural gender,
thus shedding light on the distinction between the two views on gender and to what extent biological gender affects sociocultural gender. For
example, sociolinguists have shown that men and women communicate differently (e.g., Tannen 1994), probably due to both nature (biological
gender), and nurture (sociocultural gender). Also, it is a well-established fact that the male brain is larger than the female one, even when
corrections are made for body size (Rushton and Ankney 1996). However, this male “advantage” in brain size does not imply a male
predominance in cognitive ability. Rather, men are better than women in visual spatial imagery (e.g., rotating objects, mathematical reasoning),
(Kimura 1992), while women are better in others (e.g., recall of words, color vision) (Gregory 1998). Santos et al. (2006), showed that the
traditional argument for mathematical superiority in men is not biological but sociocultural, driven by differences in masculinity and femininity
with both boys and girls, with masculine traits performing better. Differences in cognitive ability are also likely to be a function of biological
traits (genetics, hormones, brain anatomy), and of socialization, cultural values, and social norms (Cahill 2006). Therefore, neurophysiological
tools could help identify whether observed neurological or physiological differences can be attributed to biological/anatomical or sociocultural
gender, thus helping better understand the relationship between anatomy/biology and socialization in gender.
Second, Riedl et al. (2010), showed neural differences between men and women (biological gender), when viewing different offers from eBay
sellers, implying that, at least partly, the observed behavioral differences across biological gender have their origins in neurophysiological
differences. Alternative explanations range from the role of hormones and different brain structures across anatomical gender (Brizendine
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Dimoka et al./Use of Neurophysiological Tools in IS Research
2006). Neurophysiological tools may test the wide-held assumption in the sociolinguistics literature that men communicate with social power
in mind while women communicate with empathy (Kilbourne and Weeks 1997), thus helping resolve the question of whether it is a matter of
preexisting brain structures and hormones or socialization that causes the observed behavioral gender differences.
Third, similar to cultural differences, there are also opportunities for examining how gender differences play a role in communication and use
of language (Gefen and Straub 1997), socialization in virtual communities (Gefen and Ridings 2005), and IT training (Venkatesh and Morris
2000). For example, neurophysiological tools could examine differences in communication, language, and socialization across biological and
sociocultural gender, thus helping to explain observed behavioral differences in how women and men participate in social communities, how
they communicate with others, and how they use written and oral language. Moreover, neurophysiological tools may explore the extent to
which IT training differentially affects men and women, thus designing IT systems that will have distinct training patterns for men and for
women.
Fourth, Dimoka (2010), found that emotional responses in the brain (amygdala and insular cortex), are more salient in women than men
(biological gender). Women also tend to show different preferences in terms of images and colors in websites (Cyr and Bonanni 2005; Moss
et al. 2006), differences that attribute to women being more sympathetic to websites with hedonic artifacts and interested in commercial
websites that enable socialization (Van Slyke et al. 2002). Rodgers and Harris (2003), noted that lack of hedonic artifacts and socialization
may be the reasons that women are less involved in commercial websites. Cyr et al. (2007), found enjoyment to have a significant impact on
e-loyalty for women but not men, while social presence was found to have a significant effect on e-loyalty for women but not men. Moreover,
Benbasat et al. (2010), showed substantial neurological differences in terms of how women and men perceive social presence in the context
of online recommendation agents. In contrast, however, Djamasbi et al. (2007), did not find differences in terms of how women and men
recognize specific IT artifacts on websites. In sum, there are many observed behavioral differences and similarities across gender that cannot
be fully explained by existing research tools, and neurophysiological tools can delve deeper into the neurological and psychophysiological
underpinnings of these differences, thus trying to better understand the origins of gender differences.
Age Differences
A set of sample research opportunities for understanding age differences with neurophysiological tools are summarized in Table C3, and we
elaborate on these opportunities in detail below.
Age is another potential moderator that could play an important role in the proposed research agenda using neurophysiological tools (Appendix
A). While age is also a sensitive issue, we do not expect biases due to social desirability or political correctness, but rather physiological,
neurological, and biological differences across people of different age groups. There are physiological differences between younger individuals,
normal healthy adults, and ageing adults, such as differences in heart rate (that may affect EKG responses), reflexes (that may affect eye
tracking responses), skin conductance (that may affect SCR responses), and muscle flexibility (that may affect fEMG responses). There are
also neurological differences across age groups. For example, there are differences in the fMRI signal associated with ageing, specifically that
the time lag of fMRI activation is prolonged with increased ageing (Taoka et al. 1998). Moreover, the mechanisms that underlie the fMRI signal
were shown to be altered with ageing, making the interpretation of fMRI signals for older people more difficult (D’Esposito et al. 2003). There
is also evidence that EEG signals also change with age (Gaudreau et al. 2001). In summary, several of the responses obtained by the proposed
neurophysiological tools could be moderated by age (e.g., Buckner 2004), thus creating opportunities for contrasting the various physiological
and neuroimaging responses across populations that vary in age.
Table C3. Sample Research Opportunities in Understanding Age Differences
Application Sample Research Opportunities
Age Differences
1. Studying differences in technology adoption and use across age groups
2. Examining the reduction of cognitive overload among older adults
3. Exploring differences in strategic decision-making in organizational settings
4. Responding to organizational incentives regarding monetary and status rewards
5. Designing collaborative tools to enhance group decision-making across age groups
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Dimoka et al./Use of Neurophysiological Tools in IS Research
In terms of individual adoption and use, for example, NeuroIS studies could examine how age may affect the nature and determinants of the
adoption and use of systems by examining the neural correlates and physiological responses of people across age groups. Such studies could
focus on “hidden” processes that users are unlikely to easily or willingly self-report, such as emotions and habits. Also, usability may vary
across age groups, and neurophysiological data can identify direct usability criteria for different ages. Besides, instrumental and hedonic
systems may be adopted and used differently across age groups, and neurophysiological studies can complement existing IS studies with direct
neural and physiological data. Information and cognitive overload is also likely to differ across age groups, and it may be especially salient
among older adults. Thus, the proposed research opportunities on cognitive and information overload may be studied across populations of
different ages with emphasis on older adults who may be of greater need for IT solutions to help them overcome information and cognitive
overload.
In terms of information systems strategy, managerial decision making could also differ across age groups in terms of relying on cognitive and
emotional markers to make decisions. Also, organizational incentives could work differently for different age groups, and differences in the
functionality of the human body could be useful in designing appropriate incentives. Promoting cooperation between IT and business functions
could also be moderated by age, and neurophysiological tools could assist with the coordination of actions and goals among IT and business
people who may be similar or different in terms of their age. Age could also be included as a moderator in studies devising fair organizational
arrangements for sharing technology costs and designing fair incentives. This is because people of different ages may react differently to
material versus status rewards, and various neurophysiological tools could capture such differences in terms of how people of different ages
process incentives physiologically and neutrally.
In terms of group work and decision support, age could also play a moderating role in terms of enhancing online group collaboration and
decision support. For example, collaborative tools could be designed differently for groups that differ in their age composition to prevent group
members from deliberately discounting and not internalizing information from others. Dual-task interference could be different across age
groups, and neurophysiological tools could be designed to help group members of different ages process interventions, enable group members
to dedicate their full attention to primary tasks, prevent in-group competition, and avoid negative emotional responses that harm group decision
making. Decision aids can also be designed differently for people of different age groups and specifically study how decision aids can create
rapport with consumers of different ages to enable interaction with them and truthful responses to sensitive questions, enhancing their decision
making. Finally, designing trust-building systems that seek to activate the neural or physiological correlates of trust could be designed
differently for people of different ages who may have different bases for trust. In sum, age could be a key moderator in neurophysiological
studies related to group work and decision support.
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