On the Use of Neurophysiological Tools in IS Research: … · 2016. 10. 6. · use neurophysiological tools in IS research along with a set of practical suggestions for developing

ISSUES AND OPINIONS

ON THE USE OF NEUROPHYSIOLOGICAL TOOLS IN ISRESEARCH: DEVELOPING A RESEARCH AGENDA

FOR NEUROIS1

Angelika DimokaTemple University

[email protected]

Rajiv D. BankerTemple University

[email protected]

Izak BenbasatUniversity of British Columbia

[email protected]

Fred D. DavisUniversity of Arkansas & Sogang University

[email protected]

Alan R. DennisIndiana University

[email protected]

David GefenDrexel University

[email protected]

Alok GuptaUniversity of Minnesota

[email protected]

Anja IschebeckUniversity of Graz

[email protected]

Peter H. KenningZeppelin University

[email protected]

Paul A. PavlouTemple University

[email protected]

Gernot Müller-PutzGraz University of Technology

[email protected]

René RiedlUniversity of Linz

[email protected]

Jan vom BrockeUniversity of Liechtenstein

[email protected]

Bernd WeberUniversity of Bonn

[email protected]

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. Thereis heated interest now in the social sciences in capturing presumably objective data directly from the humanbody, and this interest in neurophysiological tools has also been gaining momentum in IS research (termedNeuroIS). This article first reviews commonly used neurophysiological tools with regard to their majorstrengths and weaknesses. It then discusses several promising application areas and research questions whereIS researchers can benefit from the use of neurophysiological data. The proposed research topics arepresented within three thematic areas: (1) development and use of systems, (2) IS strategy and businessoutcomes, and (3) group work and decision support. The article concludes with recommendations on how touse neurophysiological tools in IS research along with a set of practical suggestions for developing a researchagenda 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 researcherscan use neurophysiological tools and how these tools canadvance IS research. Psychophysiological tools (e.g., eyetracking, skin conductance) and brain imaging tools (e.g.,fMRI, EEG) have recently received heated attention in thesocial sciences due to their ability to complement existingsources of data with data captured directly from the humanbody (Lieberman 2007). Neurophysiological tools enable themeasurement of human responses when people engage invarious activities, such as decision making, or react to variousstimuli, 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 extendedto IS (e.g., Cyr et al. 2009; Dimoka 2010, 2012; Dimoka et al.2011; Dimoka and Davis 2008; Dimoka et al. 2007; Gallettaet al. 2007; Moore et al. 2005; Randolph et al. 2006; Riedl2009; Riedl et al. 2010a). The term NeuroIS has been coinedto 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 paperexplores 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 strengthsand weaknesses. We then propose a research agenda whereIS research can benefit from the use of neurophysiologicaltools as categorized under three overarching domains of ISresearch (Taylor et al. 2010): (1) development and use ofsystems, (2) IS strategy and business outcomes, and (3) groupwork and decision support. For each of these three areas, wediscuss how various IS research topics can be informed by theuse of neurophysiological tools and how neurophysiologicaldata can complement and supplement existing sources of data.We then offer a set of recommendations on how to bestemploy neurophysiological tools in IS research, how to seam-lessly integrate neurophysiological data in the portfolio ofexisting approaches, tools, and data available to ISresearchers, and how to pursue NeuroIS. We finally concludeby discussing how NeuroIS can add to existing IS research,what important research findings we expect to obtain fromNeuroIS, what challenges NeuroIS might face in makingsubstantive contributions to the IS literature, and how toestablish NeuroIS as a viable subfield in IS research.

The paper proceeds as follows: the following section reviewsthe most commonly used neurophysiological tools and dis-cusses their strengths and limitations. The subsequent sectiondiscusses several research questions that may benefit from the

use of neurophysiological tools in three key areas of ISresearch (Taylor et al. 2010). We then offer recommendationsfor intelligently pursuing NeuroIS and promoting NeuroIS asa viable subfield.

Strengths and Weaknesses ofNeurophysiological Tools

This section discusses the strengths and weaknesses of popu-lar psychophysiological and neuroimaging tools that couldinform IS research. A brief description of these tools isavailable in Table 1 and Appendix A.

Major Strengths of Neurophysiological Tools

The promise of NeuroIS is to complement existing researchtools with neurophysiological tools that can provide reliabledata 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 thatsubjects cannot consciously manipulate their responses sincethese are not readily subject to manipulation. For example, itmight be possible to use fMRI as a lie detector as there isoften a higher (unconscious) activation in the prefrontalcortex of subjects who lie versus those who do not (Harris2010). As neurophysiological data are generally not suscep-tible to subjectivity bias, social desirability bias, and demandeffects,2 they could complement existing sources of data,triangulate across measurement data, and reduce commonmethod bias by not relying on any single measurementmethod (e.g., Dimoka et al. 2011). Neurophysiological toolsare particularly valuable for measuring IS constructs thatpeople 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 orfears), deep or hidden emotions (e.g., guilt, fears, and anger),automated processes (e.g., habit and automaticity), complexcognitive 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 captureunconscious 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 responseto act upon what she believes the experimenter is expecting her to do.

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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 processeswith 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 subjectiveand are not restricted to conscious awareness and revealedpreferences.

Neurophysiological data have the advantage of continuousreal-time measurement that allows collecting data contin-uously on a real-time basis while a subject is executing a taskor responding to a specific stimulus (usually not easilyafforded by self reports or observation). This enables a levelof temporal precision that allows a researcher to temporallymatch the task or stimulus to the neurophysiological responsevirtually in real-time. By permitting continuous real-timedata collection and powerful time-series analysis,neurophysiological tools are able to capture the flow of eithera single construct or many constructs simultaneously, thusallowing us to infer the temporal order of either thedimensions of a single IS construct or two or more ISconstructs that are spawned by a certain task or stimulus. Because temporal precedence is a key prerequisite ofcausality (Cook and Campbell 1979; Zheng and Pavlou2010), neurophysiological studies can potentially help infercausal relationships among IS constructs.

Major Weaknesses of NeurophysiologicalTools

Neurophysiological tools also have weaknesses (Riedl et al.2010a). Most weaknesses apply across all tools, albeit atdifferent 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, TableA2) 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 $10,000and $20,000 U.S. to equip a lab with physiological tools), thecost of neuroimaging tools is substantial (between $100 and$600 U.S. per scanning hour), given the need for technicianswith specialized knowledge. While the use of repeatedmeasures from each subject coupled with the precision ofobjective data require fewer subjects per study (for example,most fMRI studies only need 10 to 20 subjects), cost is still anissue that is best answered by the person paying for the studiesrelative to the expected insights (Camerer et al. 2004). Accessibility is another issue, since neurophysiological toolsreside in medical facilities dedicated to clinical use (Husing etal. 2006). Nonetheless, major universities worldwide havefacilities with neurophysiological tools, plus hospitals andclinics often rent their neuroimaging tools for researchpurposes.

Artificial Setting

The experimental context in which neurophysiological toolsare used creates an artificial environment that limits theexternal 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-bodytubes and require subjects to remain still during the study.

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Most neurophysiological tools use various sensors attachedto the human body that may themselves induce stress and biasthe results. Eye tracking tools often need subjects to wearspecial equipment, such as a headgear, creating an artificialsetting. While neurophysiological tools continuouslyenhance their interfaces, including improvements such as lessconstrained and less noisy fMRI scanners and eye trackerswithout a headgear, it is recommended that researchersreplicate the experiment in a more traditional setting andcompare the corresponding behavioral responses to test forexternal validity.

Labor-Intensive Data Extraction and Analysis

The ability of neurophysiological tools to collect real-timedata directly from the human body is accompanied by a dif-ficulty in extracting such intense amounts of data, includingcorrecting for movement (such as fMRI or eye tracking),preparation for proper recordings (such as electrode place-ment for EKG), sometimes manual data extraction (such asobservation in eye tracking studies), and enormous amountsof imaging data (such as fMRI/EEG). The analysis of vastamounts of neurophysiological data can thus be a dauntingtask, especially if there is a need for data preprocessing (suchas fMRI data). While advanced software tools can supportdata analysis for each tool, the statistical analysis of largeamounts of data from the human body is still a non-trivialtask. For example, fMRI data must go through slice timingcorrection, realignment, coregistration, segmentation, normal-ization, and smoothing in preparing for data analysis (for areview, see Dimoka 2011; Frackowiak et al. 2004; Friston2004).

Measurement Issues

First, there are content validity concerns about whetherneurophysiological data capture the constructs they areintended to measure. At the individual subject level,neurophysiological responses often vary from a commonbaseline and may be triggered differently by externalconfounds that cannot be perfectly isolated. For example,heart rate measured by EKG can be influenced by manystimuli that need to be experimentally controlled for. Thereare also differences between right- and left-handed people,women and men, and younger and older adults. Habituationto 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 suchissues, it is necessary to realize that neurophysiological datacould 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 infMRI and PET, brain images must be normalized to a commontemplate. Experimental designs can overcome cortical dif-ferences with a proper baseline for meaningful comparisons,such as within-subjects designs that help reduce error varianceby using each subject as her/his own control.

Mono-Operationalization Bias

In the manner in which neurophysiological tools are oftendeployed, 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 ofmeasures, and thus measure reliability (Cook and Campbell1979). Measurement error, thus, is unknown in such casessince 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 samestimuli. In many settings, this will simply not be feasible, andscholars should argue for the likelihood that their data is stillreasonable (Straub et al. 2004). Even with single measures,discriminant validity can still be assessed; however, forconvergent validity (factorial validity), multiple measures ofeach construct are required (Gefen et al. 2000).

Difficulty in Interpreting Neurophysiological Results

Neurophysiological results may also not be as straightforwardto interpret as traditional sources of data. This is largely dueto the mapping of neurophysiological measures to theoreticalconstructs. For example, the meaning of various eye trackingmeasures is still a debated issue, and eye fixations and gazehave 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-onemapping between a brain area and a theoretical construct hasmade it difficult to interpret the meaning of brain activations(Logothetis 2008). Simply put, a certain neurophysiologicalmeasure can be linked to several theoretical constructs.Furthermore, a lack of a standard terminology and definitionsmay create difficulties in interpreting neurophysiologicalresults.

Manipulation and Ethics

It is sometimes feared that neurophysiological tools could beused to manipulate behavior. However, they can only observe,



not manipulate behavior.3 Neurophysiological tools may alsocapture private issues that people may not be willing toconsciously share. Thus, NeuroIS must be governed by strictethical rules.

In conclusion, neurophysiological tools have several impor-tant strengths that can advance IS research. Nonetheless,they also have certain challenges that must be acknowledgedto fully harness their potential.

Developing a Research Agendafor NeuroIS

A research agenda for NeuroIS needs to draw upon the vastneuroscience literature in the social sciences that has beenrapidly expanding over the last two decades. IS researchersare thus advised to first become familiar with prominentneuroscience theories and more specifically with findingsabout the localization of various processes and constructs inthe brain (e.g., Dimoka et al. 2011). There is also a rapidlygrowing field termed social neuroscience that focuses onintegrating neuroscience theories with applications in psycho-logy, marketing, and economics (e.g., Lieberman 2009). SeeAppendix B for a detailed review.

Dimoka et al. (2011) proposed seven specific opportunitiesthat IS researchers can pursue with the aid of neurophysio-logical tools to inform IS research: (1) localizing the neuralcorrelates of IS constructs,4 (2) capturing hidden or uncon-scious processes, (3) complementing existing data withneurophysiological 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 IStheories.

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 cancomplement extant sources of IS data. We focus on threeareas of IS research that have been suggested as adequatelyspanning 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 eacharea, we propose a set of research topics (Table 2) that seek tooffer a representative depiction of how IS research can benefitfrom the use of neurophysiological tools.

The examples in Table 1 are intended to be merely repre-sentative of how neurophysiological tools can be used toenrich some areas of IS research, and we neither seek to offeran exhaustive coverage of all IS research areas that could useneurophysiological tools nor imply that any other IS areamight not benefit from the use of neurophysiological tools. We simply encourage IS researchers to assess whether theirown topics of interest could be enhanced by the use of neuro-physiological tools. Additional opportunities for NeuroIS byincluding the moderating role of culture, gender, and age arediscussed in Appendix C.

Opportunities in Development andUse of Systems

The opportunities from using neurophysiological tools in thedevelopment and use of systems focus on (1) enhancing theindividual 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 andUse of Systems

There are several opportunities for IS research on encouragingindividual adoption and use of systems using neurophysio-logical tools; these are summarized in Table 3 and elaboratedin more detail below.

First, neurophysiological tools can shed light on our under-standing of the nature and dimensionality of constructs relatedto the adoption and use of systems by identifying their neuralcorrelates. While the neural correlates of the original TAMconstructs (perceived usefulness and ease of use) have alreadybeen identified (Dimoka and Davis 2008), the nature of otherconstructs 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 helpunderstand the nature of these constructs better and tease outdifferences among them.

3Similar to other lab studies, external stimuli can manipulate behavior,especially if they are administered in real-time based on neurophysiologicalresponses. Also, transcranial magnetic stimulation (TMS) can temporarilydesensitize certain brain areas, thereby affecting behavior. Nonetheless, suchmanipulations can only be induced in a lab setting.

4In terms of identifying the neural correlates of IS constructs, there areseveral ways to induce brain activation in response to IS constructs, such asimages of people who exhibit certain attributes (e.g., trustworthiness toinduce trust), experimental games (e.g., trust game), or scenarios that inducecertain perceptions, such as trust. Dimoka (2010) also proposed a methodto induce brain activation that underlies IS constructs by using measurementitems as triggers. This capability of neurophysiological tools has interestingimplications for assessing both reliability and construct validity.



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 IndividualAdoption and Use ofSystems

1. Understanding the nature and dimensionality of adoption and use of systems based ontheir 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 insightsabout system adoption and use that may not be inferred withexisting tools by identifying hidden or unconscious processesthat cannot be self-reported. For example, challenging theliterature that has viewed the two TAM constructs to bepurely cognitive, evidence showed that perceived usefulnessmay originate in the “emotional” areas of the brain, such asthe insular cortex (which is associated with emotional losses),and the anterior cingulate cortex that interfaces emotional andcognitive brain areas (Dimoka et al. 2011). This is consistentwith Cenfetelli (2004) and Venkatesh (2000) who showed therole of emotions in technology adoption, the marketingliterature that noted the role of emotions in the adoption ofinnovations (Wood and Moreau 2006), and Bagozzi’s (2007)call for including emotions in system use. Extending thisstream, neurophysiological tools can identify other hidden orunconscious 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 notcontrolled by self-reported conscious thoughts (e.g., Frankand Claus 2006; Lieberman 2007).

Third, identifying the neural correlates of the determinants ofsystem adoption and use opens avenues for designing systems

that trigger activation in certain brain areas, such as thecaudate nucleus for utility, or the dorsolateral prefrontalcortex (DLPFC) for reduced cognitive overload. Activationsin these brain areas can become a guide for how systems canbe designed to encourage adoption, and neurophysiologicaldata can inform the design of specific features to enhancesystem adoption and use. Neurophysiological tools can alsoassist system designers to directly test their designs withneurophysiological data, thus having less need to rely onsubjective self-reported data that may not track well withactual system use (Straub et al. 1995). These tools may beable to assess what Burton-Jones and Straub (2006) refer to asdeep structure usage. As another example, eye tracking toolsmay allow system designers to effectively place material onthe screen based on where the user is looking and where theuser’s eyes move when using a system. NeuroIS studies canalso help establish direct usability criteria by linking usabilitymetrics (e.g., efficiency, quality) with neurophysiologicalmetrics. Such neurophysiological studies can use existingsystems with different levels of usability on particular dimen-sions to examine how technical differences are represented asphysiological or neural differences. These studies may alsouncover additional constructs that drive the adoption and useof systems which users may have been unable to articulate viaself-reported measures, as well as emotional or hedonic pro-cesses which users may have been unwilling to report (e.g.,



anxiety, flow). Having identified the physiological or neuralcorrelates of usability, neurophysiological tools could enabledesigners to use these measures to evaluate future IT systemsand design modifications based on how well these systemsperform in practice. For example, if the evaluation of asystem with neurophysiological tools uncovers negative emo-tions, system designers could try to spot a flaw that causessuch emotional distress, thus helping design more effectivesystems. This could help us in designing systems to reducetechnostress (Ayyagari et al. 2011). System designers couldalso capture the user’s cognitive style (e.g., verbal, visual) andexamine whether cognitive style affects how people adopt anduse systems. As neurophysiological tools may identify theneural correlates of cognitive style (Benbasat and Taylor1978), they may help to objectively measure spatial attentionand processing style, thus exploring how individual dif-ferences in terms of cognitive style may play a role in thedesign of systems that encourage adoption.

Finally, neurophysiological tools may identify causal linksamong 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 shedlight on their temporal ordering (Dimoka and Davis 2008).Because emotional processes often precede cognitive ones(Pessoa 2008), and since perceived usefulness is shown to belinked with affective brain areas in the limbic system (Dimokaet al. 2011), neurophysiological tools could be used toexamine whether ease of use does precede usefulness, asTAM asserts. For example, knowledge of which TAM con-struct precedes the other in the brain could shed light onmediation hypotheses in the TAM model (Pavlou 2003) withpractical implications (e.g., Meuter et al. 2005). While fMRImight be able to infer the temporal dynamics between thesetwo constructs, given its limited temporal resolution, fMRImay need to be complemented by MEG or EEG, which havesuperior temporal resolution. Because causal inferencecannot be inferred solely by temporal precedence (Zheng andPavlou 2010), the use of TMS to temporarily inhibit brainactivity in isolated areas may be useful in order to studywhether the areas associated with perceived ease of use andusefulness are needed to determine the causal links leading toIT system adoption and use.

Assessing Information and Cognitive Overload

The proposed opportunities related to assessing informationand cognitive overload are summarized in Table 4 and arediscussed in more detail below.

Assessing information and cognitive overload has long beenan area of interest in the IS literature (e.g., Eppler and Mengis

2004; O’Reilly 1980). Such overload arises from having toomuch 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 reducinginformation and cognitive overload, IS researchers oftendesign systems that aim at reducing the information com-plexity of the task (enabling users to simplify their decisionmaking), or enhancing the user’s information processingcapabilities by better showing information (Galbraith 1974).However, the measurement of information and cognitiveoverload has been elusive in the literature due to self-reportsand expert evaluations (e.g., Payne et al. 1992). There is aneed for a direct measurement of information and cognitiveoverload, and neurophysiological tools have the potential tooffer such a direct measurement. For example, EKG coulddirectly measure whether a certain interface increases theuser’s heart rate, thus inferring anxiety or stress. Eye trackingtools can capture whether a user finds it difficult to identifyinformation by observing how her or his eyes wanderaimlessly on a computer screen. In terms of measuring cognitive overload, the cognitiveneuroscience literature has shown that activation in the pre-frontal cortex is associated with tasks of higher informationload (e.g., Linden et al. 2003) and working memory (e.g.,Braver et al. 1997). The DLPFC has been involved in suchhigher-order functions, namely cognition and problem olving(Rypma and D’Esposito 1999); specifically there is aninverted-U function that describes the level of brain activationin the DLPFC relative to the degree of cognitive overload asDLPFC 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 heartrate, SCR through sweat excretion, or fEMG through facialgestures.

Based on the neural correlates of cognitive overload, NeuroISstudies could test whether IT artifacts can reduce cognitiveoverload by measuring brain activity when users undertakecognitive tasks. Apart from directly measuring if cognitiveoverload is reduced with the aid of IT, neurophysiologicaltools could also shed light on whether the reduction of cogni-tive overload is due the simplification of the informationcomplexity of the task (viewed as reduction in DLPFCactivation) 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-Ufunction). These findings could also be used in the design ofIT systems that reduce cognitive overload, either by simpli-fying the task or by enhancing the user’s capabilities (orboth).



Table 4. Sample Research Opportunities in Assessing Information and Cognitive Overload


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


Encouraging Transactionsby Online Consumers

1. Understanding the neural correlates of the antecedents of e-commerce adoption byshedding light on their nature, dimensionality, distinction, and convergence.

2. Uncovering additional “hidden” predictors and inhibitors of e-commerce adoption, suchas deception, and identifying patterns for detecting website deception.

3. Designing collaborative tools to engage consumers in social learning by identifyingpatterns 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 ofcognitive overload (e.g., frustration) can identify problemsusers face when engaging in cognitive tasks. For example,neuroimaging tools could be used to test if IT systemsdesigned to enhance combinatorial auctions by offeringstructured information on the auction have the expected effect(Adomavicius et al. 2010). Neuroimaging data could also beused to improve the design of these IT interfaces byidentifying their ability to reduce cognitive overload andprevent mental collapse and emotional breakdown. Finally,neurophysiological tools could enable the evaluation of ITinterfaces, link neuroimaging data to economic (auction)outcomes, and measure constructs (cognitive overload andemotional processes) that are generally difficult to measure(Ba and Pavlou 2002).

Encouraging Transactions by Online Consumers

There are also opportunities for encouraging transactions byonline consumers in electronic markets to enhance theadoption of e-commerce, which are summarized in Table 5and discussed in more detail below.

Similar to encouraging system adoption by users (Venkateshand Davis 2000), there is a rich literature on constructs thatpromote 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 ispossible 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 ontheir distinctiveness or convergence. For example, Dimokaand Davis (2008) showed that website usefulness and ease ofuse 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 encouragewebsite adoption could be examined as well. These findingscan help specify the nature and dimensionality of these ante-cedent factors and result in more valid e-commerce adoptionmodels that better correspond to the functionality of thehuman body.

Neurophysiological tools can help identify additional“hidden” factors that have not been captured by earlierstudies. For example, if a brain area that enables or inhibitswebsite adoption is linked to a hidden or unconscious processrecognized in the neuroscience literature, it may uncover newantecedents that have been neglected to date. Notably, decep-tion, such as phishing, has been an impediment to onlinetransactions. This valid point notwithstanding, detectingdeception may be difficult to study with behavioral studies



that ask subjects to detect fraud (Pavlou and Gefen 2005).This is where neurophysiological tools could be valuable byexposing subjects to fraudulent websites and identifyingdifferences in physiological or neural responses when subjectsinteract with various websites. Neurophysiological tools,such as fMRI, might be able to capture where deceptionoccurs in the brain while psychophysiological tools such aseye tracking may help identify how expert online consumersdetect fraud. These findings could be used to identify howlearning can be achieved in fearful situations, such asphishing websites, and how effective patterns for detectingdeceptive websites can be enhanced with the aid of IT tools.

IS researchers have long noted differences when consumersinteract with similar website interfaces. For example, Bapnaet al. (2004) identified distinct bidding strategies used byconsumers in online auctions. Further, they showed that newIT interfaces and minor variations in auction rules result ininteresting and logically sound bidding practices. Similarly,Adomavicius et al. (2007) showed that information revelationstrategies can affect bidder behavior in complex combinatorialauctions. Still, the theoretical explanation of bidder behavioris difficult to uncover with self-reported measures in experi-ments, surveys, or interviews, such as those dealing with trustand perceived risk (Pavlou and Gefen 2004). Neurophysio-logical tools could assist in the design of metrics for complexconstructs such as trust, perceived risk, cognitive effort, andcompetitive fervor toward reconciling observed actions withbidder intent and offer a better explanation of observed bidderbehavior. These perceptual measures are generally difficultto measure with self reports, thus neurophysiological data cancomplement and expand existing e-commerce and auctionmetrics.

Online collaborative shopping and social shopping networksare becoming popular by allowing consumers to incorporatetheir social circle in the otherwise impersonal online trans-action environment (e.g., Zhu et al. 2010). Understandinghow consumers interact with others is an important areawhere neurophysiological tools could help in the design ofcollaborative tools that enable consumers to engage in sociallearning and transact together within a social network.Neurophysiological data can be used to identify patterns ofcooperative social behavior by drawing upon the neuralcorrelates of social cognition (Adolphs 1999) to designcollaborative tools that support cooperative social behaviorand social learning.

Neurophysiological tools can also help assess how consumersreact to a website’s information design, including searchresults, banner adds, or news stories. There is an increasedinterest 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 informationdesign 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 whatconsumers see on a website and what thoughts and emotionsthe information triggers in their mind could be useful inimproving website design and facilitating transactions. Thereis an emerging literature on how eye tracking helps evaluatewebsite designs (e.g., Pan et al. 2004; Tzanidou 2003) byenabling direct real-time tracking of where a consumer islooking 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 facilitatebrowsing and transacting.

As website use becomes even more frequent and perhapshabitual, 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 learnedpatterns in website use that may not readily be uncovered withself reports or observation because such patterns may not beavailable for introspection. For example, eye tracking toolscan compare novice and expert website users to identifydifferences that may help novice users and improve their web-site interaction. Neurophysiological tools can also comparephysiological or neural differences across consumers whovisit a website for the first time versus repeat visitors, andthey can identify how learning to use a website occurs overtime across consumers. Eye tracking seems to be a very use-ful tool to study habitual tasks and how people evolve fromnovices to experts (Karn et al. 1997). These studies coulduncover useful insights about learning habits for first-timeversus repeat website users that could help website designerscustomize their websites to cater to different consumers (e.g.,first-time versus repeat).

Understanding and mitigating uncertainty has been touted animportant impediment for online markets given the physicaland 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 pricetransparency must occur in order to reduce uncertainty. How-ever, our current understanding of the construct of uncertaintyis relatively limited, and the IS literature has generally treatedseller and product uncertainty as a unitary construct. Theliterature on understanding and mitigating uncertainty couldbe advanced by neurophysiological tools. First, similar tohow Dimoka (2010) has shown that trust and distrust aredistinct constructs that reside in distinct brain areas, drawingon the neuroscience literature that has extensively studied



uncertainty (Appendix B), brain imaging studies couldlocalize the neural correlates of seller and product uncertaintyto identify if they are viewed by consumers as distinctconstructs.

Although the literature has focused on mitigating seller uncer-tainty through trust and other means, there is still very littlework on mitigating product uncertainty (Ghose 2009).Evidence from the literature shows that distinct brain areas areresponsible when evaluating products (product uncertainty)and people (seller uncertainty) (Grinband et al. 2006; Yoon etal. 2006). The neural correlates of product uncertainty mayshed light on its nature, such as whether it is linked toprimarily cognitive or emotional brain areas. This knowledgemay help design product quality signals that can effectivelyreduce product uncertainty. The neural correlates of productuncertainty may help test the effectiveness of product qualitysignals that have been proposed in the IS literature, such asproduct diagnosticity (e.g., Jiang and Benbasat 2004), productdescriptions (Jiang and Benbasat 2007), third-party certi-ficates (Li et al. 2009), and product condition disclosures(Ghose 2009). Neurophysiological tools can also look intothe timing of the neural correlates of product and selleruncertainty to infer whether consumers first assess product orseller uncertainty, thus guiding the design and timing of selleror product quality signals on websites.

A challenge for consumers is the difficulty of feeling andtrying physical products before transacting. Virtual reality,video, and static information displays, and other productquality signals that can enhance product understanding arealternative means available to web designers (Jiang andBenbasat 2007). Physiological tools can assess whether andto 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 dealingwith uncertain purchases. Also, the aesthetics of the overallpresentation of product quality signals can be enhanced withphysiological tools by measuring whether users have somenegative reactions to these signals, such as unwanted emo-tions. Finally, neurophysiological tools can be used to spot“fake” product quality signals that misrepresent productquality, helping consumers focus on legitimate signals.

Opportunities in Information Systems Strategyand Business Outcomes

The proposed research opportunities on IS strategy andbusiness 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 andachieving favorable business outcomes by enhancing strategicdecision making. This is summarized in Table 6 and dis-cussed in more detail below.

First, drawing upon the extensive neuroscience literature onindividual decision making, we argue that we can betterunderstand the basis of managerial decision making with theaid of neurophysiological tools. As reviewed in Appendix B,there is a rich literature on how people make decisions byusing calculative and emotional aspects in their decisionmaking (Ernst and Paulus 2005) and how simple emotionalcues can simplify complex and uncertain decisions under thesomatic marker hypothesis (Damasio 1994). Moreover, thereis evidence that more successful decision makers are thosewho combine both cognitive and emotional aspects (Hsu et al.2005). This actually corresponds to findings in the strategyliterature that intuition is useful for complex actions underuncertainty (Gigerenzer and Selten 2001). Building upon theneuroscience literature, neurophysiological tools couldexamine how to facilitate strategic decision making in uncer-tain environments, perhaps focusing on CIOs (chief informa-tion officers) as subjects (Banker et al. 2011). Althoughneurophysiological studies with CIOs and senior executivesmay be difficult to conduct, it is not uncommon to havestudies with real professionals. For instance, Lo and Repin(2002) examined professional foreign exchange traders whentrading 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 onIT strategy in turbulent environments that examines caseswhere strategic decisions must be made quickly and underuncertain and changing conditions (Pavlou and El Sawy2006). While there is a need to generalize findings fromartificial individual decision making by subjects to realdecision making by true executives, neurophysiological toolscan offer useful insights into how to facilitate decision makingvia cognitive and emotional markers and more effectivelyhelp IT strategy executives make better decisions.

Moreover, achieving alignment between IT and businessfunctions has been one of the most important goals of ISstrategy 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 inintegrating IT and business lies in coordinating actionsbetween these two functions at virtually all organizationallevels (Reich and Benbasat 1996). The neuroscience litera-



Table 6. Sample Opportunities in Developing Information Systems Strategy


Deveoping IS Strategy

1. Enhancing strategic decision making in uncertain environments by using both cognitiveand emotional markers.

2. Coordinating actions and goals across IT and business functions to promotecooperation between IT and business people.

3. Designing organizational incentives that are based on the functionality of the humanbody (e.g., aligned goals, theory of mind, and social coordination).

ture has identified the brain basis of joint action and has shedlight on how the brain helps coordinate actions, goals, andintentions (Newman-Norlund et al. 2007). Moreover, there isliterature on the neural correlates of social cooperation(Rilling et al. 2002) and theory of mind (McCabe et al. 2001).Extending these findings, neurophysiological tools couldexamine how to coordinate actions and goals across IT andbusiness functions and how to promote cooperation betweenIT and business people by designing appropriate incentivesthat are based on the underlying brain functionality of alignedgoals, theory of mind, and social coordination.

Finally, the IS strategy literature has focused on developingcapabilities 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 threecapabilities, it is not clear whether they correspond to howorganizations actually function. The distinction betweendynamic and improvisational capabilities is still an unsettledissue in the literature (Eisenhardt and Martin 2000). In thatneurophysiological tools can localize the neural correlates ofhuman activities, it would be very possible to examine wherespontaneous improvisation occurs in the brain and if it differsfrom planned change. In doing so, neurophysiological toolscould help resolve debates in the IS strategy literature thatcould not be easily examined otherwise.

Enhancing the Design of Organizational Systems

Neurophysiological tools can help with the design and use oforganizational systems (Table 7).

First, the successful implementation of organizational ERPsystems relies on reducing the functionality misfit that arisesdue to gaps between the functionalities offered by an ERPpackage and those required by the organization (Rolland andPrakash 2000). This misfit is difficult to assess because of thecomplexity of ERP systems and the lack of appropriate mea-

sures of functional needs from a usage perspective, similar toindividual adoption and use. Neurophysiological tools canidentify neurological indicators that help create metrics of themisfit in such ex ante analysis. In general, neurophysiologicaltools may offer complementary metrics from theories thatseek to reduce misfit, theories such as cognitive fit theory,which looks at the interaction between problem representationand problem-solving (Vessey 1991). As cognitive fit theoryviews the process as taking place within a mental model(Zhang 1997), neurophysiological tools could inform themetrics suggested by this theory by capturing the neuralcorrelates of cognitive fit and misfit and offering metrics thatcould be used in the design of organizational systems. Also,the fundamental tenets of how organizations manipulate thecharacteristics of the problem and task representation can bestudied with NeuroIS to define contextual metrics of mentalrepresentation 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 beentouted as one of the most important barriers to the success ofinterorganizational systems (IOS) (Barrett and Konsynski1982). Although the design of IOS with the user in mind isnecessary, much of the literature has focused on technologi-cally “optimal” systems that may not be necessarily consistentwith how people use systems in organizations (Minnery andFine 2009). Thus, there is potential value in gathering neuro-physiological responses when evaluating IOS prototypes, andneurophysiological tools may help complement existingsources of data and provide novel insights into the design ofIOS. The connection between IOS and organizational designis related to the construct of bounded rationality, which refersto “neurophysiological limitations to the information pro-cessing capacities (memory, computation and communica-tion)” (Bakos and Treacy 1986, p. 109). Neurophysiologicaltools can help assess the role of IOS in enhancing the infor-mation processing capacity of organizational users to informthe design of IOS. Besides, activation in certain brain areascan serve as a proxy for how organizational incentives shouldbe designed to effectively use IOS, and neurophysiologicaltools could conceivably help inform IS research.



Table 7. Sample Research Opportunities in Optimizing the Design of Organizational Systems


Designing OrganizationalSystems

1. Creating metrics for the ex ante gap between ERP functionalities and the organization’sfunctional 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


Technological Fairness andUnfairness in OrganizationalSettings

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 ISresearch is the use of IT to reproduce genuine artifacts inmuseum organizations and other archaeological sites (Palludand Straub 2007). Although a variety of IT artifacts havebeen designed to augment reality (Vlahakis et al. 2002), thereis a debate whether users appreciate such artificial IT artifacts. Measuring visitors’ perceptions, such as emotions andaesthetics, may be used to enhance self-reported measures.Neurophysiological tools can also offer measures of whatpeople perceive in IT versus genuine artifacts. For example,an fMRI study that presents subjects with different artifactscan identify differences in brain activations between IT-generated and genuine artifacts. These differences can becompared to the neuroscience literature on emotions and canbe linked to constructs that relate to aesthetics, such aspleasure/displeasure. In the neuroscience literature, pleasantpictures activate the nucleus accumbens and medial prefrontalcortex (Sabatinelli et al. 2007), while displeasure activates thesuperior temporal gyrus, amygdala, and hippocampus (Brittonet al. 2006). Also, other cognitive and emotional processesmay be activated differentially in the brain between IT andgenuine artifacts due to other factors that may enter a visitor’smindset (Pallud and Straub 2007). Those may be related tocontent (activation in the anterior cingulate cortex due toutility) (McClure et al. 2004), ease of use (activation inDLPFC due to calculation) (Dimoka et al. 2011), promotions(activation in caudate nucleus due to higher rewards)(Delgado et al. 2005), anger (activation in orbitofrontalcortex) (Murphy et al. 2003), or authenticity (whose neuralcorrelates have still not been identified).

Promoting Technological Fairness inOrganizational 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 moredetail below.

First, decisions in organizations need collaborative agreementbetween several individuals or subunits to share technologycosts and to enhance the benefit generated by the technology.Incentives of these multiple decision makers must be wellaligned to enable collaborative agreements. These issues areaccentuated in interorganizational contexts such as in tele-communications network and IT standards setting. The litera-ture has shown that besides economic returns from thedecision to share technology costs, perceptions about thefairness of the sharing arrangement affect whether individualdecision makers accept or reject the arrangement (Tabibniaand Lieberman 2007). Fairness and unfairness have been twoimportant issues in studies of human decision making thatoften bring into play both emotional and cognitive brainprocesses. In the cognitive neuroscience literature, Sanfey etal. (2003) used fMRI to identify the neural correlates of theprocesses involved in decision making when subjects playedthe Ultimatum game. Unfair offers activated the insularcortex (a highly emotional area) and the DLPFC (a highlycognitive area). Activation in the insular cortex predictedwhether a person might reject an offer (while the DLPFC didnot), testifying to the role of emotional processes in economic



decision making. In contrast, Knoch et al. (2006) proposedthat the DLPFC is associated with rejecting unfair offers orpunishing unfair behavior by showing that subjects withtemporarily disrupted activity in the right DLPFC (usingTMS) were more likely to accept unfair offers in theUltimatum game. These findings testify to the role of bothcognitive and emotional processes when judging fair andunfair offers. Moral and social values temper the role ofmaterial effects and self-interest in influencing human deci-sions. Neurophysiological tools analyzing activation inemotional and cognitive regions of the brain at the individuallevel can shed light on what organizational incentives willlead to acceptance or rejection of technology investmentproposals 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 withthe same monetary value. It is possible that these findings canbe used to design incentives for technology decision makersby attempting to maximize the activation in the reward areasof the brain under different compensation schemes. Also,Dulebohn et al. (2009) showed that unfair outcomes activatedthe anterior insular cortex and DLPFC (similar to Sanfey et al.2003) while unfair procedures triggered both the ventrolateralprefrontal cortex and the superior temporal sulcus. Theauthors distinguished between procedural and distributivejustice as distinct constructs whose neural correlates reside indifferent brain areas. Decision makers may find the conductof the process or the outcome of that process, or both, to beunfair. These findings testify to the importance of emotionalresponses to unfair offers relative to activating the brain’sreward areas for material outcomes. However, hormones,such as serotonin, modulate behavioral reactions to unfairness(Crockett et al. 2008). Clearly, the neuroscience literature canprovide guidance in designing incentives that go beyondmaterial rewards to ensure that technology-related decisionsare 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) understandingand building online trust.

Enhancing Online Group Collaborationand 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 failto incorporate the comments of others into their own thinking,which often results in poor decisions (Dennis 1996). It isunclear whether this is due to a lack of attention or due to adeliberate choice to disregard others, although some researchsuggests that it may be due to a lack of attention (Heninger etal. 2006). Attention has been examined extensively in theneuroscience literature, and several findings about the neuraland physiological correlates of attention have been identified. The dorsolateral prefrontal and parietal cortices have beenassociated with attention in fMRI studies (Knudsen 2007).Psychophysiological tools, such as EEG, EKG, and SCR, canbe used to measure emotion, attention, and arousal whengroup members engage in collaborative activities, thus testingwhether group members fail to attend to information or deli-berately discount information from others. Once the cause ofthe 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 isoften to promote cooperation and to prevent competitionamong group members. Neuroimaging tools can be used todesign interventions that enable or prevent the neural cor-relates of collaboration and competition, respectively (Decetyet al. 2004). The design of collaborative technologies couldinclude interventions that facilitate in-group collaboration andprevent in-group competition. Such IT designs could betested with fMRI to verify their ability in enhancing activityin the neural correlates associated with collaboration andreduce activity in the brain areas associated with competition(inferior parietal cortex and medial prefrontal cortex). Inaddition, physiological tools such as fEMG could captureusers’ reactions when collaborative technologies are used toprevent negative reactions in group interaction. Such directand objective tests using neurophysiological tools could thenbe used to help refine the design of collaborative technologiesand implement them in actual group settings.

Third, it is well known that lean digital media such as e-mailhave a smaller number of cues than audio or video. It hasrecently been argued that communication through such leanmedia is inherently biased, leading people to sense a morenegative tone in e-mail (Byron 2008) and reducing theirfeeling of cooperativeness. Neurophysiological tools couldassess emotional responses to e-mail (and other media) mes-sages, thus enabling a better understanding of whether leandigital media is indeed more negatively biased than other,richer media. If so, IS researchers could strive to understandaspects that lead to this bias and find solutions to addressthem.



Table 9. Sample Research Opportunities in Enhancing Online Group Collaboration and DecisionSupport


Enhancing GroupCollaboration and DecisionSupport

1. Testing if group members unintentionally fail to pay attention to information from othersor 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 withbrain imaging tools for assessing alternative measures ofattention. For instance, deficits in attention can be due tosecondary tasks that disrupt a person’s attention to primarytasks. Indeed, the neuroscience literature has shown that if asecondary task becomes automated, dual-task interference isreduced and distinct brain areas are associated with consciousversus automated task processing (Kunde et al. 2007). Thesefindings could be used to design collaborative technologiesthat help secondary tasks become fully automated, thusenabling group members to dedicate their full attention toprimary tasks. Automaticity and habit have been primarilylinked to two brain areas (medial temporal lobe and basalganglia) (e.g., Graybiel 2008; Kubler et al. 2006), making itpossible to test whether dual-task interference can bemitigated with the aid of collaborative technologies.

Designing Online Decision Aids

We also discuss three research opportunities for designingdecision aids in online markets (Table 10).

Decision aids are integral components of online markets, andthe IS literature has focused on designing decision aids in theform of recommendation agents to help to enhance decisionmaking in online markets (e.g., Adomavicius and Tuzhilin2005; Xiao and Benbasat 2007). Similar to system andwebsite adoption, the design of decision aids could also beinformed by the use of neurophysiological tools.

One set of studies can focus on how decision aids can bedesigned to give consumers advice related to sensitive issues,such as sexual habits, diseases, and drugs. Since consumersare likely to be embarrassed when dealing with such sensitiveissues, decision aids could be designed to create rapport with

consumers and enable them to answer sensitive questions byreducing embarrassment (e.g., Al-Natour et al. 2009). In thatsubjects may not admit anxiety via self reports, neurophysio-logical tools may uncover such emotions. The neuroscienceliterature has made much progress in identifying the neuralcorrelates of many emotional processes (Murphy et al. 2003;Phan et al. 2002), that can be used to identify which emotionsthe sensitive questions trigger. Neurophysiological studiescan 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 understandhow people respond to sensitive issues, and this knowledgecan be used to design decision aids that would enableconsumers to truthfully respond to sensitive questions andreceive valuable personal advice.

Although IT artifacts, such as systems or websites, are notassociated with an anthropomorphic element, decision aids inthe form of recommendation agents often include a humanoidface (avatar), which plays a role in their adoption (Qiu andBenbasat 2009). The neuroscience literature has studied howpeople respond to various faces, and these findings can beused to inform the design of decision aids with a humaninterface. Because these interfaces are associated with acertain ethnicity and gender, their adoption may depend onthe ethnicity/gender the avatar was designed to have and theuser’s ethnicity and gender, as predicted by theories ofsimilarity. Qiu and Benbasat (2010) found differences acrossconsumers in terms of how they use recommendation agentsthat differ on their ethnicity and gender, but the behavioraldata could not fully explain these differences, perhapsbecause social desirability bias prevented users fromadmitting that they favor avatar interfaces of the same eth-nicity and gender. Because one of the basic advantages ofneurophysiological tools is to get direct responses that are notbiased by subjectivity and social desirability, brain activity



Table 10. Sample Opportunities in Designing Online Decision Aids


Designing Online DecisionAids

1. Designing decision aids to create rapport with consumers to enable them to respondtruthfully to sensitive questions.

2. Building decision aids whose humanoid faces (avatars) spawn activations in brainareas 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


Understanding and BuildingOnline 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 totransact with an agent of a certain ethnicity and gender, andwhether brain activation differs depending on the subject’sown demographics.

Understanding and Building Online Trust

Neurophysiological tools can help better understand thenature and dynamics of trust and distrust and how systems canbe designed to help build trust online, as summarized in Table11 and discussed in detail below.

First, trust and its related distrust are complex social processesthat affect many group processes. Still, the study of trust, andeven more so distrust, has only scratched the surface of theserich 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 whythey mostly combine into an overall factor when they dealwith distinct aspects of trust is still unknown. Neurophysio-logical tools could possibly shed light on this issue byexamining how these interrelated trust dimensions, such ascredibility and benevolence (Pavlou and Dimoka 2006),reside in the brain. This might be done experimentally bymanipulating 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 shedlight on trust formation. If neurophysiological tools can

clearly show that these dimensions of trust indeed havedistinct neural correlates, IS researchers could design systemsthat separately affect each dimension of trust. For example,advice-giving systems may provide different informationabout each dimension of trust (Wang and Benbasat 2007). Prior IS research argued that integrity and ability precedebenevolence 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 beentaken by Dimoka (2010), who identified the neural correlatesof trust as the caudate nucleus (confident expectations aboutanticipated rewards) (King-Casas et al. 2005), anterior para-cingulate cortex (predicting how the trustee will act in thefuture) (McCabe et al. 2001), and orbitofrontal cortex (uncer-tainty from the trustor’s willingness to be vulnerable) (Krainet al. 2006). Riedl et al. (2010b) identified another arearelated to reward processing, namely the thalamus, replicatingBaumgartner et al. (2008). These findings shed light on theformation of trust and could guide the design of IT tools tobuild different trust dimensions.

Third, neurophysiological tools can also help examine howother constructs related to trust such as familiarity, satisfac-tion, and trust propensity help build trust. The IS literaturehas shown that familiarity and trust propensity build trustgradually (Gefen 2000). This is consistent with the neuralcorrelates of trust where brain areas such as the anteriorparacingulate cortex (associated with the trustor’s predictionsof the trustee’s actions) have a more enduring nature thanbrain areas associated with calculating uncertainty (orbito-frontal cortex) and anticipating rewards (caudate nucleus)(Dimoka 2010). Future research could study how familiarity



with the trustee and with trust propensity shape the neuralcorrelates of trust over time, thus extending the IS literatureon trust by better integrating familiarity with trust formation.

Fourth, besides examining the nature of trust, neurophysio-logical tools could identify antecedents of trust by studyinghow systems (e.g., websites) and IT-enabled signals andincentives (e.g., third-party assurances) activate the identifiedneural correlates of trust. It is already known that website andadvice-giving systems can build trust (Lim et al. 2006; Wangand Benbasat 2007); however, little is known about whatactually happens as the brain analyzes different trust-buildingstimuli. Particular antecedents of trust (e.g., feedback sys-tems, reputation signals, design cues) could be tested toidentify areas of brain activation. Using the elaborationlikelihood model, Kim and Benbasat (2009, 2010) showedthat trust-assuring arguments are more effective when peoplehave high product involvement while third-party assurancesare more effective in building trust when product involvementis low. However, product involvement and central versusperipheral information processing are difficult to measurewith traditional tools, creating another opportunity for neuro-physiological tools to complement existing tools. Suchstudies may identify different trust-building processes byshowing activity in the caudate nucleus (increase in potentialrewards from trust), or anterior paracingulate cortex (pre-dicting that the trustee will act cooperatively), or orbitofrontalcortex (reduction in uncertainty). Such studies might, in fact,complement existing findings about the reasoning processesthat people use to build trust (Komiak and Benbasat 2008;Wang and Benbasat 2008).

Fifth, neurophysiological tools could also examine thetemporal dynamics among the dimensions of trust, helping ISresearch delve more deeply into trust formation. fMRI tools,with their superior spatial resolution, coupled with MEG orEEG, with their superior temporal resolution, could be usedtogether to examine the temporal ordering of the activationsin the brain areas associated with trust (Dimoka 2010). Suchstudies could inform us as to whether and when people focuson the vulnerability associated with trust, rewards, andinferring the trustee’s actions. These findings could informthe design of IT-enabled trust-building stimuli.

Finally, neurophysiological tools might challenge existingassumptions in the trust literature. For example, Dimoka(2010) showed that distrust is not the opposite of trust, butrather is a distinct construct that is linked to distinct brainareas associated with fear of loss (insular cortex) and intenseemotions (amygdala). This may explain Wang and Ben-basat’s (2008) findings where they showed that knowledge-based processes (via explanations) affect trust but not distrustin advice-giving systems while “awareness of the unknown”

about the reasoning of such IT systems leads users to formdistrust beliefs (Komiak and Benbasat 2008). This also helpsexplain Pavlou and Dimoka’s (2006) findings that benev-olence (which is associated with emotional areas) is moreinfluential on price premiums than credibility (which isassociated with cognitive brain areas).

Discussion

The discussion first offers recommendations for pursuingNeuroIS by using neurophysiological tools to complement theexisting portfolio of empirical IS approaches, tools, and data. Second, we discuss how to establish NeuroIS as a viablesubfield in IS research that can contribute and add value to theIS literature.

Recommendations for PursuingNeuroIS Research

First and foremost, it is important to clarify that NeuroIS isnot a panacea for all IS research issues, and our objective issimply to propose some promising avenues for IS researchwith neurophysiological tools. Although we do not seek toexclude any IS areas from using neurophysiological tools, wehasten to add that there may be several areas of IS researchthat neurophysiological tools could offer little or even nohelp.

Second, IS researchers must assess when to use neuro-physiological tools and when existing tools are sufficient. Arule-of-thumb is that when existing tools can adequatelymeasure a research question, neurophysiological tools maynot be necessary. As Izak Benbasat noted during a 2009INFORMS panel “NeuroIS if necessary, but not necessarilyNeuroIS.” Thus, there must be a good rationale for usingneurophysiological tools (Kosslyn 1999), such as usingneurophysiological data to supplement existing sources ofdata or to address an issue that could not be adequatelyexamined with existing tools.

Third, despite the proposed advantages of neurophysiologicalmeasures, no single neurophysiological measure is usuallysufficient on its own, and it is advisable to use many datasources to triangulate across measures, which is alwaysadvisable in IS research (e.g., Straub et al. 2004). On the onehand, when there is a good correspondence between existingdata and neurophysiological data, we may infer that existingdata and resulting models closely correspond to the human orbrain functionality, thus validating and rendering higherconfidence to existing theories. However, there is seldom a



one-to-one correspondence between a neurophysiologicalmeasure and a theoretical construct (Huettel and Payne 2009),and it is important to treat neurophysiological measuresmerely as proxies for complex theoretical concepts (similar toall measures). Therefore, caution should be raised when thecorrelation between psychometric and neurophysiologicaldata are extremely high (Vul et al. 2009), and such correla-tions are not an artifact of the measurement method (Saxe etal. 2006). On the other hand, if there is a poor correspon-dence across neurophysiological and existing data, such a lowcorrespondence does not necessarily imply that one measureis 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 richnessprovided by multiple sources of measures can be used toenhance the ecological validity of IS studies.

Fourth, similar to all studies with human subjects, neuro-physiological studies require approval by the researcher’sinstitutional review board. Depending on the institution anduse of particular neurophysiological tools by prior investi-gators, the time and effort needed may differ. Still, moststudies with neurophysiological tools qualify for an expeditedreview, similar to behavioral studies such as surveys.

Fifth, as also noted by Dimoka et al. (2007), the field ofNeuroIS 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 ISresearchers can draw upon to inform their theories. Once thefield of NeuroIS is established, neuropsychological toolsbecome more accessible, and clear guidelines for NeuroISstudies agreed upon, it will become increasingly easier toconduct empirical studies with neurophysiological tools.

Finally, IS researchers should conduct a cost–benefit analysiswhen using neurophysiological tools. Each tool has differentstrengths and weaknesses that must be assessed relative totheir costs (Appendix A).

Toward a Viable NeuroIS Subfieldof IS Research

As noted in many of the examples offered in this paper, thevalue of NeuroIS largely lies in combining neurophysiologicaldata with other sources of data. The benefit of any new toollies in how it works together with, draws upon, and com-plements existing tools, and neurophysiological tools are noexception. It needs to be emphatically stated that neuro-physiological tools should not be seen as an attempt toreplace, but rather to complement and supplement existing IS

tools. Integrating neurophysiological data with other sourcesof data should be an important goal of NeuroIS. The litera-ture has generally shown high correlations between brain andpsychometric measures (Vul et al. 2009), and there is a debateabout whether correlations are artificially inflated due tocharacteristics of the focal statistical methods (e.g., Lieber-man et al. 2009). Future research is needed to assess the trueextent of these correlations for different IS constructs, varyingfrom purely cognitive to highly emotional ones. Potentialdifferences in the extent of these correlations may shed lighton the value of neurophysiological data versus other sourcesof data in measuring IS constructs. Nonetheless, differencesbetween neurophysiological and existing measures should notnecessarily imply that either approach is better, but it mayimply that there is a need for cross-validation to measurecomplex IS constructs that are hard to capture accurately witha single data source. Differences between neurophysiologicaland self-reported data may imply that either respondents areunwilling or unable to self-report certain constructs, or simplythat the human body simply cannot represent the richness ofpsychometric measures (Logothetis 2008).

Another promising role of neurophysiological tools is toinform debates that cannot be fully resolved with existingtools. Many of the examples offered in this paper revolvearound identifying the distinction, convergence, and dimen-sionality of IS constructs that are still unresolved in the ISliterature. Furthermore, competing theories can be resolvedwith neurophysiological tools that may help explain whichtheory is more likely to correspond to the body’s func-tionality. For example, Dimoka (2010) has tackled the stillunanswered question of whether trust and distrust are distinctconstructs or whether they are part of the same continuum. Inher study, the fMRI results showed that trust and distrust areassociated with the activation of different brain areas, thusoffering evidence that they are two distinct constructsassociated with different neurological processes. Neuro-physiological tools may uncover new constructs that havebeen ignored in the IS literature (perhaps because they couldnot 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 todevelop better IS theories.

Because neurophysiological tools have the inherent attractionof being able to open the black box of the human brain andmany studies have numerous interesting findings, there maybe a rush among IS researchers to conduct NeuroIS studieswithout developing adequate knowledge of the neuroscienceliterature and sufficient expertise with neurophysiologicaltools. It is thus necessary to devise guidelines for conductingNeuroIS studies. As with other statistical and methodological



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 toolsneed 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 promoteNeuroIS. Moreover, it would be useful to include NeuroIS asa topic in IS doctoral seminars and have dedicated doctoralcourses and seminars that review NeuroIS studies, offerempirical guidelines on neurophysiological tools (Dimoka2012), and discuss promising NeuroIS topics. Moreover,given the cost requirements of neurophysiological tools,sponsoring NeuroIS studies is a challenging task, and creatinga support structure to facilitate IS researchers to obtainfunding would also help promote NeuroIS. Finally, includingeditors and reviewers with expertise in cognitive neurosciencetheories and neurophysiological tools would enable NeuroISstudies in IS journals to be rigorously reviewed, thus ensuringthat only high-quality studies are published in major ISjournals.

Conclusion

Establishing NeuroIS as a viable subfield in IS research mustfocus on promising opportunities to enhance IS research,promote specialized events, establish and disseminate bestpractices, secure funding, and build a community of NeuroISscholars. While IS researchers are initially encouraged toborrow neuroscience theories and benefit from existingknowledge on using neurophysiological tools, they musteventually contribute to this emerging literature by offering aunique NeuroIS perspective. For example, localizing theneural correlates of IT-related constructs could be a goodstarting 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 couldprovide value. Above and beyond identifying potential appli-cations for research that has yet to be conducted, we hope thispaper entices NeuroIS researchers to be both intelligentconsumers and also diligent contributors to the rapidlyexpanding neuroscience literature.

Acknowledgments

We would like to thank the senior editor, Detmar W. Straub, for histremendous support and hands-on guidance in the development ofthis manuscript. We also thank the associate editor and three

anonymous reviewers for their constructive and developmentalcomments and suggestions that have helped us improve our work. The paper is based on discussions that took place at a retreat inGmunden, Austria, in September 2009, and we thank the Town ofGmunden for providing financial support to organize the retreat.

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About the Authors

Angelika Dimoka is an associate professor of Marketing andManagement of Information Systems at the Fox School of Business,Temple University. She is also director of the Center for NeuralDecision Making. She holds a Ph.D. from the Viterbi School ofEngineering (specialization in Neuroscience and Brain Imaging)with a minor in MIS from the Marshall School of Business at theUniversity of Southern California. Angelika’s research interests arein the areas of cognitive neuroscience and functional neuroimagingin marketing and MIS (Neuromarketing and NeuroIS), quantitativeanalysis of uncertainty in online marketplaces, and modeling ofinformation pathways in the brain. Her research has appeared (or isscheduled to appear) in Information Systems Research, MISQuarterly, NeuroImage, IEEE Transactions in Biomedical Engi-neering, Annals of Biomedical Engineering, IEEE in Biology andMedicine, and Neuroscience Methods.

Rajiv D. Banker is professor and Merves Chair in Accounting andInformation Technology at the Fox School of Business, TempleUniversity. He received a Doctorate in Business Administrationfrom Harvard University. He has received numerous awards for hisresearch and teaching, and has published more than 150 articles injournals including Management Science, Information SystemsResearch, MIS Quarterly, Operations Research, Academy of

Management Journal, Strategic Management Journal, andEconometrica.

Izak Benbasat (Ph.D., University of Minnesota, 1974; DoctoratHonoris Causa, Université de Montréal, 2009) is a Fellow of theRoyal Society of Canada, CANADA Research Chair in InformationTechnology Management at the Sauder School of Business,University of British Columbia, Canada, and a Special TermVisiting Professor at the Guanghua School of Management, PekingUniversity, for 2011–2013. He currently serves on the editorialboards of Journal Management Information Systems and Informa-tion Systems Journal. He was editor-in-chief of Information SystemsResearch, editor of the Information Systems and Decision SupportSystems Department of Management Science, and a senior editor ofMIS Quarterly. He became a Fellow of the Association for Infor-mation Systems (AIS) in 2002, received the LEO Award forLifetime Exceptional Achievements in Information Systems fromAIS in 2007, and was conferred the title of Distinguished Fellow bythe Institute for Operations Research and Management Sciences(INFORMS) Information Systems Society in 2009.

Fred Davis is Distinguished Professor and David Glass Chair inInformation Systems at the Walton College of Business, Universityof Arkansas. He is also a Visiting Professor of Service SystemsManagement and Engineering at Sogang Business School, Seoul,Korea. He received his Ph.D. from MIT, and his research interestsinclude user acceptance of technology, computer assisted decisionmaking, NeuroIS, and service systems innovation. His work on thisarticle was partially supported by Sogang Business School’s WorldClass University Project (R32-20002) funded by the KoreanResearch Foundation.

Alan R. Dennis is a professor of Information Systems and holds theJohn T. Chambers Chair of Internet Systems in the Kelley School ofBusiness at Indiana University. He is a senior editor at MISQuarterly, and the founding publisher of MIS Quarterly Executive.He has written more than 100 research papers and four books (twoon data communications and networking, and two on systems analy-sis and design). His research focuses on four main themes: the useof computer technologies to support team creativity and decisionmaking; knowledge management; the use of the Internet to improvebusiness and education; and professional issues facing IS academics(e.g., business school rankings and the difficulty of publishing andgetting tenure in IS).

David Gefen is a professor of MIS at Drexel University, Phila-delphia, where he teaches strategic management of IT, databaseanalysis and design, and VB.NET. He received his Ph.D. in CISfrom Georgia State University and a Master of Sciences in MIS fromTel-Aviv University. His research focuses on trust and culture asthey apply to the psychological and rational processes involved inERP, CMC, and e-commerce implementation management, and tooutsourcing. David’s wide interests in IT adoption stem from his 12years of experience in developing and managing large informationsystems. His research findings have been published in MIS Quar-terly, Information Systems Research, IEEE Transactions on Engi-



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, Journalof the AIS, Communications of the AIS, and elsewhere. David is anauthor 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 DecisionSciences Department at the Carlson School of Management. Hereceived his Ph.D. in MSIS from The University of Texas at Austinin 1996. His research has been published in various top rankedinformation systems, economics, and computer science journals suchas MIS Quarterly, Management Science, Information SystemsResearch, Communications of the ACM, Journal of MIS, DecisionSciences, 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 Technologyand Management, and Journal of Organizational Computing andElectronic Commerce.

Anja Ischebeck is a professor of Cognitive Psychology andNeurosciences at the Institute of Psychology of the University Graz,Austria. She recieved her doctoral degree from the NijmegenInstitute for Cognition and Information (NICI) of the University ofNijmegen, Netherlands. She has worked at the Max-Planck Institutefor Human Cognitive and Brain Sciences in Leipzig, Germany, aswell as at the Medical University Innsbruck, Austria. Her mainresearch interests are number and language processing, attention,and the basis of human learning and memory. She has published intop-level neuroscience journals such as Neuroimage, Journal ofCogntive Neuroscience, Human Brain Mapping, and CerebralCortex.

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 ofEconomic Psycyhology, and Advances in Consumer Research, aswell as Journal of Neuroimaging, Neuroreport, and Brain ResearchBulletin. He has received several best paper awards and grants fromthe German government.

Paul A. Pavlou is an associate professor of Management Infor-mation Systems, Marketing, and Strategic Management and aStauffer Senior Research Fellow at the Fox School of Business atTemple University. He received his Ph.D. from the University ofSouthern 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 appearedin MIS Quarterly, Information Systems Research, Journal of Man-agement Information Systems, Journal of the AIS, Communicationsof the AIS, and Decision Sciences, among others.

Gernot Müller-Putz is associate professor and Head of the Institutefor Knowledge Discovery, Graz University of Technology, Austria. In 2004, he received his Ph.D. in electrical engineering from GrazUniversity of Technology, where, beginning in 2000, he worked onnon-invasive electroencephalogram-based (EEG) brain–computerinterfaces (BCI) for the control of neuroprosthetic devices. In 2008,he recieved his “venia docendi” for medical informatics (“TowardsEEG-Based Control of Neuroprosthetic Devices”) at the faculty ofcomputer science at Graz University of Technology. His researchinterest include EEG-based neuroprosthesis control, hybrid BCIsystems, the human somatosensory system and assistive technology.

René Riedl is an associate professor of Business Informatics at theUniversity of Linz, Austria. He serves on the executive board of theInstitute of Human Resources and Organizational Development inManagement at the University of Linz. His main research interestsare 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 theProceedings of the International Conference on InformationSystems, among others.

Jan vom Brocke holds the Hilti Chair in Business Process Manage-ment at the University of Liechtenstein. He is director of theInstitute of Information Systems and president of the LiechtensteinChapter of the Association for Information Systems. Jan has over10 years of experience in BPM projects and has published more than170 refereed papers in the proceedings of internationally perceivedconferences and established IS journals, including Business ProcessManagement Journal and Communications of the Association forInformation Systems. He has authored or edited 15 books, includingSpringer’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 advisorto a wide range of institutions. Jan has beeb a visiting professor atthe University of Muenster in Germany, the LUISS University inRome, the University of Turku in Finland, and the University of St.Gallen in Switzerland.

Bernd Weber is a neuroscientist at the Department of Epileptologyand the Center for Economics and Neuroscience at the University ofBonn. After graduating from medical school in 2003, he worked asa research associate and in 2005 started as head of the neuroimagingplatform at the Life & Brain Science Center at the University ofBonn, which hosts two MR scanners. His research interests includeplasticity of the human brain in pathology and health with a focus onsocial and economic decision making. In 2008 he became a researchassociate at the German Institute of Economic Research in Berlin(DIW). He is cofounder and on the board of directors of the Centerfor Economics and Neuroscience in Bonn. In 2010 he received aHeisenberg-Professorship of the German Research Foundation(DFG) at the University of Bonn.


ISSUES AND OPINIONS

ON THE USE OF NEUROPHYSIOLOGICAL TOOLS IN ISRESEARCH: DEVELOPING A RESEARCH AGENDA

FOR NEUROIS

Angelika DimokaTemple University

[email protected]

Rajiv D. BankerTemple University

[email protected]

Izak BenbasatUniversity of British Columbia

[email protected]

Fred D. DavisUniversity of Arkansas & Sogang University

[email protected]

Alan R. DennisIndiana University

[email protected]

David GefenDrexel University

[email protected]

Alok GuptaUniversity of Minnesota

[email protected]

Anja IschebeckUniversity of Graz

[email protected]

Peter KenningZeppelin University

[email protected]

Paul A. PavlouTemple University

[email protected]

Gernot Müller-PutzGraz University of Technology

[email protected]

René RiedlUniversity of Linz

[email protected]

Jan vom BrockeUniversity of Liechtenstein

[email protected]

Bernd WeberUniversity of Bonn

[email protected]

Appendix A

Description of Neurophysiological Tools

The description of neurophysiological tools is broken down into psychophysiological 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).

MIS Quarterly Vol. 36 No. 3–Appendices/September 2012 A1


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 thattracked the eye’s pupil while accounting for head movement. More recent devices either use a remote camera mounted on the screen that tracksthe 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 (Rayner1998). Eye fixation is a spatially motionless gaze (about 2 seconds) on a particular area in a visual display lasting between 100 and 300milliseconds with a velocity below 100 degrees per second. Pupil dilation gauges a person’s interest in the image they are viewing. Area ofinterest is the region of the display that is specified by the researcher. Gaze duration is the total duration and average spatial location ofconsecutive eye fixations on a particular area (which ends when the eye fixation moves outside the area of interest). Studies have shown thatpeople tend to have an intense gaze when looking at faces (e.g., Djamasbi et al. 2008). There are additional metrics that can be obtained byeye tracking tools, such as the number or percentage of voluntary/involuntary fixations (Jacob and Karn, 2003). The metrics obtained by eyetracking tools have been linked to cognitive and emotional processes, such as eye fixation to cognitive processing (Pan et al. 2004) andsurprising 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 computerusability studies (Jacob and Karn 2003). This is because eye tracking is useful for comparing different versions of a computer interface orthe effectiveness of different systems. Eye tracking has recently been extended in IS research. For example, Cyr et al. (2009) compared websitedesigns across cultures by examining the area of the website that users focused on. Djamasbi et al. (2010b) showed that users found a pagewith images of people’s faces to be more appealing than a page without images of faces. In their study, users performed their tasks morequickly 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 activitiesthat 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 researchersto analyze the location and timing of visualization data. However, eye tracking has some disadvantages. First, eye tracking does not captureperipheral vision, which constitutes the majority of human vision, and people look at things without fixating on them. Second, if subjects areaware that they are being observed with an eye tracker, it may bias the natural setting of the experiment. Finally, what subjects saw does notnecessarily 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 skintemporarily becomes a better electricity conductor when certain external or internal stimuli occur that cause an increase in the activity of humansweat glands (Randolph et al. 2005). SCR tools measures activation in the sympathetic nervous system that changes the sweat levels in theeccrine glands of the palms (or feet or arms). SCR uses electrodes that are commonly placed on the palmar side of the second phalanx of thefirst and second fingers that send an imperceptibly small electric current through the two electrodes (Jacob and Karn 2003) (Figure A2). Theelectrodes typically capture a continuous signal that is determined by the subject’s sympathetic nervous system (Moore and Dua 2004).

A2 MIS Quarterly Vol. 36 No. 3–Appendices/September 2012


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 topsychological processes, such as attention and orienting reflexes (Raskin 1973). SCR has been used to study the role of emotions in decisionmaking. For example, Bechara et al. (1999) used the Iowa gambling task to measure decision making as an index of somatic states. In a similarstudy, Crone et al. (2004) examined the pattern of heart rate (with EKG) and skin conductance (with SCR) that preceded risky choices followingthe 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 minimalintervention on subjects because it is usually placed on the subject’s fingers, palms, feet, or arms. However, the main disadvantage of SCRis its lack of predictable measurement, which makes SCR measures potentially unreliable. Moreover, SCR measures are highly subject tohabituation effects, which often make repeated SCR measures unreliable. Finally, it is not conclusive what SCR measures represent in termsof 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 aheartbeat, an electrical signal spreads from the top to the bottom of the heart and sets the rhythm of the heartbeat. These signals are capturedby 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 alsobeen used to study the role of emotions in decision making. Miu et al. (2008) used EKG while subjects played the Iowa gambling task2 tocapture the heart rate of their emotional responses in order to examine the role of anxiety on decision making. EKG is also associated withanger, which is accompanied by a tonic increase in heart rate. EKG may also be used to capture sadness, which increases blood pressure anddecreases 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 fromthe interpretation of its findings because heart rate may be affected by a very large set of factors, plus it is virtually impossible to pinpoint whichparticular 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 acceptor 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 receivesany 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 theobjective 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 earnmoney, 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.



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 fromtwo main facial muscles, the corrugator supercilli and zygomaticus. fEMG is measured with small (2 to 4 mm) electrodes placed on the leftside of the face (Figure A4), and the raw fEMG signal must be amplified and filtered. Because emotional expression is linked to the contractionof the face, facial muscle activity is linked to emotional reactions (Schwartz et al. 1976), and fEMG offers a direct measure of electrical activityfrom 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 (whichcontrols smiling) is correlated with positive stimuli and mood states (such as pleasure and enjoyment). fEMG can also measure activity at theorbicularis oculi, which captures the magnitude of a blink reflex. Larger blinks are associated with unpleasant stimuli while smaller blinks areassociated 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 withhigher recall of commercial ads (Hazlett and Hazlett 1999). Moreover, fEMG was closely linked to real-time emotion-specific events duringthe 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-timefashion without any cognitive effort from the subject. Second, fEMG is minimally intrusive. Third, fEMG is relatively inexpensive and widelyaccessible in behavioral labs. However, fEMG has several limitations. First, fEMG can only get data from a small number of facial musclesbecause of the limited number of electrodes that can be attached to the subject’s face. Second, the quality of the fEMG measures isquestionable 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 arebeing measured.

Figure A4. Sample Pictures of Facial Electromyography Tools



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 etal. 1990). Neural activity in a brain area leads to an increase in blood oxygenation, which is referred to as hemodynamic response, typicallypeaking about 4 or 5 seconds after the onset of neural activity. The hemodynamic response, which was shown to be directly linked to neuralactivity (Logothetis et al. 2001) can be captured with an fMRI scanner (Figure A5) by exploiting the blood’s magnetic properties (oxygen-richversus oxygen-depleted), termed blood oxygen level dependent (BOLD) (Ogawa et al. 1990). fMRI results are often shown with statisticalparametric activation maps (SPMs) that contain statistical results of fMRI data computed in a 3D space (voxel or volumetric pixel). As fMRIdoes not capture absolute levels of blood oxygenation, fMRI results compare the relative intensity of BOLD signals across conditions (Fristonet al. 1994). Hence, to locate areas of brain activation, a single high-resolution structural image is also acquired. With modern fMRI scannersand 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 thebrain 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 locateddeep within the brain (see Murphy et al. 2003; Phan et al. 2002). Second, fMRI is non-invasive. Third, because fMRI is widely used, thereare standard data-analysis approaches to compare across studies. Nonetheless, fMRI also has some disadvantages (Savoy 2005): First, fMRIhas modest temporal resolution (a few seconds). Thus, inferring causal relationships between two brain activities may require complementingfMRI data with the higher temporal resolution EEG or MEG data. Second, fMRI data must be interpreted carefully because correlation doesnot necessarily infer causation,4 and brain activity is complex and often nonlocalized (Kenning and Plassmann 2005). Third, there is noconsensus 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 ofphotons (high-energy gamma quants) is produced that travel in opposite directions. PET can detect this pair of simultaneously generatedphotons and calculate its point of origin from the arrival times. From the distribution of the detected photons, a 3D image is created thatrepresents 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. TMStemporarily suppresses specific brain areas, thus creating a “virtual” lesion. TMS helps infer causality by showing that a certain function cannot be performedwhen a brain area is temporarily disrupted (Miller 2008). TMS and fMRI are sometimes used jointly, because fMRI allows more precise assessment of the impactof the TMS on brain areas connected to the targeted area (se Knoch et al. 2006).



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 comparableto fMRI. The greatest disadvantage of PET is its invasive and potentially harming nature since subjects have to be intravenously injected witha 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 electricalpotentials 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) (FigureA7). 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 (asit is not constrained by the bulky and enclosed fMRI or PET scanners that may cause claustrophobia), it is tolerant to subjects’ movements thatare not allowed in fMRI, and it is silent (which is important for auditory stimuli). Moreover, EEG has excellent temporal resolution in the orderof milliseconds, and is preferred when timing resolution is needed (Kenning and Plassmann 2005). Finally, EEG captures brain activity directlyin 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 theouter layers of the cortex, it is largely insensitive to electrical activity in deeper brain areas (Mathalon et al. 2003). Thus, compared to designsthat are feasible with fMRI or PET, EEG studies require relatively simpler paradigms.

Figure A7. Sample Pictures of EEG Tools



However, EEG and fMRI are not mutually exclusive, and it is possible to simultaneously use both tools to take advantage of the high temporalresolution of EEG with the high spatial resolution of fMRI. However, the two data sources may not reflect the exact same brain activity dueto different timing; there are also technical difficulties associated with integrating fMRI and EEG data. Nonetheless, there is much researchon 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 fieldsthat are induced by synchronized neuronal electrical potentials. Similar to EEG, MEG signals are also derived from the summation of thepotentials of ionic currents caused by dendritic neurons. Since the brain’s magnetic field is relatively very small (~10 micro Tesla), MEG usesextremely 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 onstatistical assumptions. MEG is more effective in registering activity in deeper brain structures than EEG is, but does so at a lower spatialresolution 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



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 inTable A1. Interested researchers are encouraged to consult with either their own institutions for exact costs for renting the tools or with thecommercial 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 ISresearchers may find it useful to review and integrate the neuroscience literature on the particular topic they are examining. This appendixpresents 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 hypothesisposits that somatic markers (associations about emotional processes), are summed into a single state that facilitates decision making in thepresence of various uncertain options. The somatic markers have been associated with the ventromedial prefrontal cortex, and damage to thisbrain 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 cognitiveneuroscience literature linked the anterior paracingulate and medial prefrontal cortex as the brain areas linked to predicting of others’ behaviorin an exemplar study by McCabe et al. (2001). Related work on mirror neurons (neurons that mirror another person’s behavior), was linkedto 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; Krainet al. 2006), and assessing various utility trade-offs (e.g., Camerer 2003). The prefrontal cortex (primarily the orbitofrontal and dorsolateralprefrontal cortex), and the limbic system (mostly the anterior cingulate cortex and amygdala), are the two brain areas mostly associated withdecision making (e.g., Ernst and Paulus 2005). Moreover, the prefrontal cortex was shown to be responsible primarily for the calculativeaspects 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). Theventrolateral prefrontal cortex (BA47), is the primary area associated with future intentions by interacting with other brain areas that provideinput 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 Sirigu2009).

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 lateralprefrontal cortex while emotional information is processed in the dorsal frontomedial cortex. Cognitive effort and working memory forshort-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).



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 hascreated virtual “maps” of the human brain and body by indicating where activity occurs when people engage in various activities. Dimokaet al. (2011), offer an extensive summary of many human processes that are likely to be of interest to IS research, categorized under (1) decisionmaking, (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., Ernstand Paulus 2005; McClure et al. 2004a). Uncertain decision making has been linked to the orbitofrontal and parietal cortex (e.g., Huettel etal. 2005; Krain et al. 2006). Decision-making under different conditions is associated with different brain areas, with risk focusing on thenucleus 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 frontaland 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 prefrontaland medial parietal cortices (e.g., Iacobini et al. 2004; Katayose 2006). Finally, spatial cognition is linked to the medial temporal lobe and thehippocampus (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 termsof the general processing of emotions, the medial prefrontal cortex and anterior cingulate cortex are the two primary brain areas (see Damasio1996; Phan et al. 2002). Moreover, the literature has focused on specific emotions. Anxiety has been linked to the amygdala and theventromedial prefrontal cortex (e.g., Bishop 2007; Wager 2006). Disgust is linked to the insular cortex (e.g., Lane et al. 1997), fear to theamydgala (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, suchas social cognition (which is associated with the temporal lobe) (Adolphs 1999; 2001), and moral judgment (which is associated with thefrontopolar 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 linkedto the amygdala and insular cortex) ( Dimoka 2010; Winston et al. 2002), cooperation (which is linked to the orbitofrontal cortex) (Rilling etal. 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 correlateshave been examined in the vast neuroscience literature. Nonetheless, the neural correlates of these processes could be a good starting pointfor 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 studiesare 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 werethe early pioneers of social neuroscience followed by marketers, and many interesting findings have been found by identifying the neuralcorrelates 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 havebeen 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 otherboth behaviorally and also neurally. Bhatt and Camerer (2005), challenged another well-established economic theory that effective decision-



making should be governed by rational cognitive processes without relying on emotions. The authors showed that subjects who had goodcooperation 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. Forexample, 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 economicgain while a different brain area, associated with losses (insular cortex), is activated in the prospect of economic loss (Kuhnen and Knutson2005). 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 specificgains), 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 ambiguousgambles. 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 howconsumers respond to marketing and advertising (Ariely and Berns 2010; Kenning et al. 2007; Lee et al. 2007; Zaltman 2003). In terms of howconsumers make purchasing decisions, Braeutigam et al. (2004), explained the neurological basis of predictable versus unpredictable purchasesand linked them to immediate and delayed rewards that are associated with different brain areas. Predictable and impulse purchases aregoverned by different neurological processes, thus explaining why consumers radically differ in how they make predictable and impulsepurchasing 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 withreasoning and calculation and increased activation in areas associated with emotions and self-reflections, helping explain why marketers investin brand building to reduce consumers’ rational calculation when deciding across competing brands. In a well-publicized Coke versus PepsifMRI study, McClure et al. (2004b), explained that people prefer Coke because of brand recognition (by differentially activating the dorsolateralprefrontal cortex that governs cognitive information processing), over Pepsi in a non-blind tasting. However, similar brain areas, mostlyassociated with pleasant emotions, were activated for both refreshments in blind tasting. These findings explained that consumers prefer Cokeover 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 areconverging under the umbrella of the neuroscience literature to provide a unified theory of human behavior. Therefore, we expectneurophysiological 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 inferringneural 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 andare 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 politicalcorrectness. Neurophysiological data that may be more immune from subjectivity bias may be what is needed to push IS research on cultureforward.

Second, neurophysiological tools may help compare how people across cultures respond to various designs. The neuroscience has broadlyexamined cultural differences (e.g., Gutchess et al. 2006; Zhu et al. 2007). For example, Sabbagh et al. (2006), identified significant differencesin the brain’s executive functioning of small children in a cross-cultural study in the United States and China. In contrast, the authors did notfind differences in the brain areas related to the theory of mind, implying that American and Chinese children do not differ in the way theypredict how others will behave. Similarly, IS research has extensively studied cultural differences; for example, Cyr et al. (2009), used acombination 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), andCyr and Trevor-Smith (2004), found significant cultural differences in how people interact with websites. Building upon these neuraldifferences and similarities in culture, IS researchers can examine how various IT designs are viewed across cultures using neurophysiologicaltools. 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 termsof the overall visual design.

Third, neurophysiological tools may help explore cultural differences in terms of communication, language, and training. Despite the extensivestudy 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 educationand socialization across cultures (Hofstede 1980); however, existing tools could not tease out the exact reason, and neurophysiological toolscan delve deeper into the underling origins of such cultural differences. Neurophysiological tools, for example, could focus on potentialdifferences in patterns of brain activation when people from different cultures engage in oral or written communication. How oral or writtenlanguage makes a difference in terms of how people communicate could also be examined with neurophysiological tools that could capturepotential differences in brain or physiological responses during communication. Being able to stipulate the brain area and functionality opensnew opportunities to answer such questions. Physiological tools could also explore differences in how culturally diverse people communicateand 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 culturaldifferences by either trying to eliminate or exacerbate them, and neurophysiological tools could test whether and how IT training has its desiredgoals.

Finally, cultural factors may play a role in the design of trust-enhancing IT designs (e.g., Sia et al. 2009). Neurophysiological tools could helpexamine the neurological and physiological aspects of trust building across cultures to shed light on potential differences on how culture affectstrust formation. For example, fMRI can compare whether the neural correlates of various dimensions of trust (Section 3.3.3), are activateddifferently in these brain areas using the same trust-building stimuli across cultures, and how dissimilar trust-building stimuli have differentialeffects on the various dimensions of trust across different cultures.



Table C1. Sample Research Opportunities in Understanding Cultural Differences


Cultural Differences

1. Examining neural and physiological differences across cultures2. Comparing how people across cultures respond to different IT designs3. Exploring cultural differences in communication, language, and training4. Examining how culture plays a role in the design of trust-building systems

Table C2. Sample Research Opportunities in Understanding Gender Differences


Gender Differences

1. Testing whether gender differences are primarily due to nature or nurture2. Explaining observed behavioral differences across genders in terms of their neurological or

physiological origin3. Exploring communication, language, training differences across genders4. 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 weelaborate in detail below.

Neurophysiological tools could be useful to study gender differences because gender is also a sensitive topic that is often subject to socialdesirability bias and political correctness. While we propose to examine various gender differences, it is important to note that our discussionis 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 ofsocialization and experience (sociocultural gender) (Lueptow et al. 1995). Gender is a complex sociocultural construct that distinguishes socialrelationships among women and men (Santos et al. 2006), and it is the outcome of historic and cultural processes that have developed throughsociocultural 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 explaineddifferences in math ability. Moreover, Cyr et al. (2009), found that sociocultural gender values were a more salient moderator of an expandedtechnology adoption model than biological gender. Nonetheless, there are also biological gender differences in technology use (e.g., Gefenand Straub 1997; Gefen and Ridings 2005; Venkatesh and Morris 2000). Accordingly, Trauth (2002), argues for a social construction theoryto 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. Forexample, sociolinguists have shown that men and women communicate differently (e.g., Tannen 1994), probably due to both nature (biologicalgender), and nurture (sociocultural gender). Also, it is a well-established fact that the male brain is larger than the female one, even whencorrections are made for body size (Rushton and Ankney 1996). However, this male “advantage” in brain size does not imply a malepredominance 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 thetraditional argument for mathematical superiority in men is not biological but sociocultural, driven by differences in masculinity and femininitywith both boys and girls, with masculine traits performing better. Differences in cognitive ability are also likely to be a function of biologicaltraits (genetics, hormones, brain anatomy), and of socialization, cultural values, and social norms (Cahill 2006). Therefore, neurophysiologicaltools could help identify whether observed neurological or physiological differences can be attributed to biological/anatomical or socioculturalgender, 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 eBaysellers, implying that, at least partly, the observed behavioral differences across biological gender have their origins in neurophysiologicaldifferences. Alternative explanations range from the role of hormones and different brain structures across anatomical gender (Brizendine



2006). Neurophysiological tools may test the wide-held assumption in the sociolinguistics literature that men communicate with social powerin mind while women communicate with empathy (Kilbourne and Weeks 1997), thus helping resolve the question of whether it is a matter ofpreexisting 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 useof language (Gefen and Straub 1997), socialization in virtual communities (Gefen and Ridings 2005), and IT training (Venkatesh and Morris2000). For example, neurophysiological tools could examine differences in communication, language, and socialization across biological andsociocultural gender, thus helping to explain observed behavioral differences in how women and men participate in social communities, howthey communicate with others, and how they use written and oral language. Moreover, neurophysiological tools may explore the extent towhich IT training differentially affects men and women, thus designing IT systems that will have distinct training patterns for men and forwomen.

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; Mosset al. 2006), differences that attribute to women being more sympathetic to websites with hedonic artifacts and interested in commercialwebsites that enable socialization (Van Slyke et al. 2002). Rodgers and Harris (2003), noted that lack of hedonic artifacts and socializationmay be the reasons that women are less involved in commercial websites. Cyr et al. (2007), found enjoyment to have a significant impact one-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 contextof online recommendation agents. In contrast, however, Djamasbi et al. (2007), did not find differences in terms of how women and menrecognize specific IT artifacts on websites. In sum, there are many observed behavioral differences and similarities across gender that cannotbe fully explained by existing research tools, and neurophysiological tools can delve deeper into the neurological and psychophysiologicalunderpinnings 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 weelaborate 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 (AppendixA). 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 eyetracking responses), skin conductance (that may affect SCR responses), and muscle flexibility (that may affect fEMG responses). There arealso neurological differences across age groups. For example, there are differences in the fMRI signal associated with ageing, specifically thatthe time lag of fMRI activation is prolonged with increased ageing (Taoka et al. 1998). Moreover, the mechanisms that underlie the fMRI signalwere shown to be altered with ageing, making the interpretation of fMRI signals for older people more difficult (D’Esposito et al. 2003). Thereis also evidence that EEG signals also change with age (Gaudreau et al. 2001). In summary, several of the responses obtained by the proposedneurophysiological tools could be moderated by age (e.g., Buckner 2004), thus creating opportunities for contrasting the various physiologicaland neuroimaging responses across populations that vary in age.

Table C3. Sample Research Opportunities in Understanding Age Differences


Age Differences

1. Studying differences in technology adoption and use across age groups2. Examining the reduction of cognitive overload among older adults3. Exploring differences in strategic decision-making in organizational settings4. Responding to organizational incentives regarding monetary and status rewards5. Designing collaborative tools to enhance group decision-making across age groups



In terms of individual adoption and use, for example, NeuroIS studies could examine how age may affect the nature and determinants of theadoption and use of systems by examining the neural correlates and physiological responses of people across age groups. Such studies couldfocus on “hidden” processes that users are unlikely to easily or willingly self-report, such as emotions and habits. Also, usability may varyacross age groups, and neurophysiological data can identify direct usability criteria for different ages. Besides, instrumental and hedonicsystems may be adopted and used differently across age groups, and neurophysiological studies can complement existing IS studies with directneural and physiological data. Information and cognitive overload is also likely to differ across age groups, and it may be especially salientamong older adults. Thus, the proposed research opportunities on cognitive and information overload may be studied across populations ofdifferent ages with emphasis on older adults who may be of greater need for IT solutions to help them overcome information and cognitiveoverload.

In terms of information systems strategy, managerial decision making could also differ across age groups in terms of relying on cognitive andemotional markers to make decisions. Also, organizational incentives could work differently for different age groups, and differences in thefunctionality of the human body could be useful in designing appropriate incentives. Promoting cooperation between IT and business functionscould also be moderated by age, and neurophysiological tools could assist with the coordination of actions and goals among IT and businesspeople who may be similar or different in terms of their age. Age could also be included as a moderator in studies devising fair organizationalarrangements for sharing technology costs and designing fair incentives. This is because people of different ages may react differently tomaterial versus status rewards, and various neurophysiological tools could capture such differences in terms of how people of different agesprocess 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 anddecision support. For example, collaborative tools could be designed differently for groups that differ in their age composition to prevent groupmembers from deliberately discounting and not internalizing information from others. Dual-task interference could be different across agegroups, and neurophysiological tools could be designed to help group members of different ages process interventions, enable group membersto dedicate their full attention to primary tasks, prevent in-group competition, and avoid negative emotional responses that harm group decisionmaking. Decision aids can also be designed differently for people of different age groups and specifically study how decision aids can createrapport with consumers of different ages to enable interaction with them and truthful responses to sensitive questions, enhancing their decisionmaking. Finally, designing trust-building systems that seek to activate the neural or physiological correlates of trust could be designeddifferently for people of different ages who may have different bases for trust. In sum, age could be a key moderator in neurophysiologicalstudies related to group work and decision support.

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On the Use of Neurophysiological Tools in IS Research: … · 2016. 10. 6. · use neurophysiological tools in IS research along with a set of practical suggestions for developing