Information systems (IS) community is increasingly interested in employing neuroscience tools and methods in order to develop new theories concerning Human–computer interaction (HCI) and further understand IS acceptance models. The new field of NeuroIS has been introduced to address these issues. NeuroIS researchers have proposed encephalography (EEG), among other neuroscience instruments, as a valuable usability metric, when used effectively in appropriately designed experiments. Moreover, numerous researchers have suggested that EEG frontal asymmetry may serve as an important metric of user experience. Based on the aforementioned evidence, this study aims to integrate frontal asymmetry with Technology acceptance model (TAM). Particularly, we assumed that frontal asymmetry might predict users’ perceptions regarding Usefulness and Ease of Use. Furthermore, we hypothesized that frontal asymmetry might also affect (influence) users’ Perceived Playfulness. Specifically, 82 (43 females and 39 males) undergraduate students were chosen to use a Computer-Based Assessment (while being connected to the EEG) in the context of an introductory informatics course. Results confirmed our hypothesis as well as points of theory about Information technology (IT) acceptance variables. This is one of the first studies to suggest that frontal asymmetry could serve as a valuable tool for examining IT acceptance constructs and better understanding HCI.
This is a preview of subscription content, log in to check access.
Buy single article
Instant access to the full article PDF.
Price includes VAT for USA
Subscribe to journal
Immediate online access to all issues from 2019. Subscription will auto renew annually.
This is the net price. Taxes to be calculated in checkout.
Agarwal, R., & Karahanna, E. (2000). Time flies when you’re having fun: Cognitive absorption and beliefs about information technology usage. MIS Quarterly, 24, 665–694.
Agarwal, R., & Prasad, J. (1999). Are individual differences germane to the acceptance of new information technologies? Decision Sciences, 30(2), 361–391.
Allen, J. J. B., Coan, J. A., & Nazarian, M. (2004). Issues and assumptions on the road from raw signals to metrics of frontal asymmetry in emotion. Biological Psychology, 67, 183–218.
Barclay, D., Higgins, C., & Thompson, R. (1995). The Partial Least Squares approach to causal modelling: Personal computer adoption and use as an illustration. Technology Studies, 2(1), 285–309.
Bhandari, U., & Chang, K. (2014). Role of emotions and aesthetics in ICT usage for underserved communities: a NeuroIS investigation. In Proceedings of ICIS 2014. Auckland: AIS
Chin, W. W. (1998). The partial least squares approach to structural equation Modeling. In G. A. Marcoulides (Ed.), Modern business research methods (pp. 295–336). Mahwah, NJ: Lawrence Erlbaum Associates.
Coan, J. A., & Allen, J.J.B. (2003). Frontal EEG asymmetry and the behavioral activation and inhibition systems. Psychophysiology, 40, 106–114.
Coan, J. A., & Allen, J. J. B. (2004). Frontal EEG asymmetry as a moderator and mediator of emotion. Biological Psychology, 67, 7–49.
Compeau, D., Higgins, C. A., & Huff, S. (1999). Social cognitive theory and individual reactions to computing technology: A longitudinal study. MIS Quarterly, 23, 145–158.
Davidson, R. J. (1988). EEG measures of cerebral asymmetry: Conceptual and methodological issues. International Journal of Neuroscience, 39, 71–89.
Davidson, R. J., Ekman, P., Saron, C., Senulis, J., & Friesen, W. V. (1990). Approach/withdrawal and cerebral asymmetry: Emotional expression and brain physiology. I. Journal of Personality and Social Psychology, 58, 330–341.
Davidson, R. J., & Fox, N. A. (1989). Frontal brain asymmetry predicts infants’ response to maternal separation. Journal of Abnormal Psychology, 98, 127–131.
Davidson, R. J., Taylor, N., & Saron, C. (1979). Hemisphericity and styles of information processing: Individual differences in EEG asymmetry and their relationship to cognitive performance. In Abstracts of the papers presented at the eighteenth annual meeting of the society for psychophysiological research. Psychophysiology. 16, 197.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13, 319–340.
Davis, F. D., Bagozzi, R. P., Warshaw, P. R. (1992). Extrinsic and intrinsic motivation to use computers in the workplace. Journal of Applied Social Psychology, 22(14), 1111–1132.
Dawson, G., Panagiotides, H., Klinger, L. G., & Hill, D. (1992). The role of frontal lobe functioning in the development of infant self-regulatory behavior. Brain and Cognition, 20, 162–176.
Delorme, A., & Makeig, S. (2004). EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. Journal of Neuroscience Methods, 134, 9–21.
Dien, J. (1998). Issues in the application of the average reference: Review, critiques, and recommendations. Behavior Research Methods, Instruments, & Computers, 30, 34–43.
Dimoka, A. (2010). What does the brain tell us about trust and distrust? evidence from a functional neuroimaging study. MIS Quarterly, 34(2), 373–396.
Dimoka, A., & Davis, F. D. (2008). Where does tam reside in the brain? the neural mechanisms underlying technology adoption. In ICIS 2008 Proceedings. Paper 169
Dimoka, A., Pavlou, P. A., & Davis, F. D. (2007). NEUROIS: The potential of cognitive neuroscience for information systems research. In Proceedings of the 28th international conference on information systems (ICIS) (pp 1–20).
Dimoka, A., Pavlou, P. A., & Davis, F. D. (2010). NEURO-IS: The potential of cognitive neuroscience for information systems research. In Information systems research. Articles in advance (pp. 1–18).
Falk, R. F., & Miller, N. B. (1992). A primer for soft modeling. Akron, OH: University of Akron Press.
Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention and behayior: An introduction to theory and research. Reading, MA: Addison-Wesley.
Fornell, C., & Bookstein, F. L. (1982). Two structural equation models: LISREL and PLS applied to consumer exit-voice theory. Journal of Marketing Research, 19, 440–452.
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equations models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50.
Fox, N. A. (1994). Dynamic cerebral processes underlying emotion regulation. In, The development of emotion regulation: Biological and behavioral considerations. Monographs of the Society for Research in Child Development, 59(2–3), 240.
Fox, N. A., Henderson, H. A., Rubin, K. H., Calkins, S. D., & Schmidt, L. A. (2001). Continuity and discontinuity of behavioral inhibition and exuberance: Psychophysiological and behavioral influences across the first four years of life. Child Development, 72, 1–21.
Galletta, D., et al. (2007). Does our web site stress you out? In Proceedings of the international conference on information systems. Paper 50 (research in progress).
Glimcher, P. W., et al. (Eds.). (2009). Neuroeconomics: Decision making and the brain. Amsterdam: Academic Press.
Harmon-Jones, E. (2003). Clarifying the emotive functions of asymmetrical frontal cortical activity. Psychophysiology, 40, 838–848.
Harmon-Jones, E., Gable, P. A., & Peterson, C. K. (2010). The role of asymmetric frontal cortical activity in emotion-related phenomena: A review and update. Biological Psychology, 84, 451–462.
Harmon-Jones, E., Harmon-Jones, C., Serra, R., & Gable, P. A. (2011). The effect of commitment on relative left frontal cortical activity: Tests of the action-based model of dissonance. Personality & Social Psychology Bulletin, 37(3), 395–408.
Hollingsworth, C. L., Randolph, A. B. (2015) Using NeuroIS to better understand activities performed on mobile devices. In: F. Davis, R. Riedl, vom Brocke J., P. M. Léger & A. Randolph (Eds.), Information systems and neuroscience. Lecture notes in information systems and organisation (Vol. 10). Cham: Springer.
Kapoor, A., & Picard, R. W. (2005). Multimodal affect recognition in learning environments. In Proceedings of the 13th annual ACM international conference on multimedia (pp. 677–682). Singapore: Hilton
Lee, J. C., & Tan, D. S. (2006). Using a low-cost electroencephalogramph for task classification in HCI research. In Proceedings of the 19th annual ACM symposium on user interface software and technology in Montreux, Switzerland, October 15–18 (pp. 81–90). New York: ACM Press.
Lee, Y. C. (2008). The role of perceived resources in online learning adoption. Computers & Education, 50(4), 1423–1438.
Liapis, C., & Chatterjee, S. (2011). On a NeuroIS design science model. Lecture Notes in Computer Science. 6629, 440–451.
Loos, P., Riedl, R., Müller-Putz, G., vom Brocke, J., Davis, F. D., Banker, R. D., & Léger, P. -M. (2010). NeuroIS: Neuroscientific approaches in the investigation and development of information systems. Business & Information Systems Engineering, 6, 395–401.
Milic, N. (2017). Consumer grade brain-computer interfaces: An entry path into NeuroIS Domains. In: F. Davis, R. Riedl, vom Brocke J., P. M. Léger & A. Randolph (Eds.), Information systems and neuroscience. Lecture notes in information systems and organisation (Vol. 16). Cham: Springer.
Minas, R. K., Kazman, R., & Tempero, E. (2017). Neurophysiological Impact of Software design processes on software developers. In D. Schmorrow & C. Fidopiastis (Eds.), Augmented cognition. Enhancing cognition and behavior in complex human environments. AC 2017. Lecture notes in computer science (Vol. 10285). Cham: Springer.
Moon, J., & Kim, Y. (2001). Extending the TAM for a world-wide-web context. Information and Management, 38(4), 217–230.
Moore, M. M., Storey, V. C., & Randolph, A. B. (2005). User profiles for facilitating conversations with locked-in users. In Proceedings of the international conference on information systems (pp. 923–936).
Moridis, C. N., Terzis, V., Economides, A. A., Karlovasitou, A., & Karabatakis, V. E. (2012). Integrating TAM with EEG frontal asymmetry. In 7th mediterranean conference on information systems (MCIS 2012).
Ong, C., & Lai, J. (2006). Gender differences in perceptions and relationships among dominants of e-learning acceptance. Computers in Human Behaviour, 22(5), 816–829.
Randolph, A. B., Karmakar, S., & Jackson, M. M. (2006). Toward predicting control of a brain-computer interface. In Proceedings of the international conference on information systems (pp. 803–812).
Riedl, R., Banker, R. D., Benbasat, I., Davis, F. D., Dennis, A. R., Dimoka, A., Gefen, D., Gupta, A., Ischebeck, A., Kenning, P., Mόller-Putz, G., Pavlou, P. A., Straub, D. W., vom Brocke, J., & Weber, B. (2010a). On the foundations of NeuroIS: reflections on the Gmunden Retreat 2009. Communications of the AIS, 27, 243–264.
Riedl, R., Hubert, M., & Kenning, P. (2010b). Are there neural gender differences in online trust? An fMRI study on the perceived trustworthiness of eBay offers. MIS Quarterly, 34(2), 397–428.
Ringle, C. M., Wende, S., & Will, A. (2005). SmartPLS 2.0 (beta). University of Hamburg, Germany. http://www.smartpls.de.
Steiner, A.R.W., & Coan, J. A. (2011). Prefrontal asymmetry predicts affect, but not beliefs about affect. Biological Psychology, 88, 65–71.
Terzis, V., & Economides, A. A. (2011). The acceptance and use of computer based assessment. Computers & Education, 56(4), 1032–1044.
Terzis, V., Moridis, C. N., Economides, A. A. (2012). The effect of emotional feedback on behavioral intention to use computer based assessment. Computers & Education, 59(2), 710–721.
Urbach, N., & Ahlemann, F. (2010). Structural Equation Modeling in information system using Partial Least Square. Journal of Information Technology Theory and Application, 11(2), 5–40.
Venkatesh, V. (1999). Creation of favorable user perceptions: Exploring the role of intrinsic motivation. MIS Quarterly, 23, 239–260.
Venkatesh, V., & Davis, F. D. (1996). A model of the antecedents of perceived ease of use: Development and test. Decision Sciences, 27, 451–481.
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478.
Vom Brocke, J., Riedl, R., & Léger, P. M. (2011). Neuroscience in design-oriented research: Exploring new potentials. In H. Jain, A. P. Sinha & P. Vitharana (Eds.), Service-oriented perspectives in design science research, Lecture notes in computer science (Vol. 6629, pp. 427–439). Berlin: Springer.
Willems, R. M., Van der Haegen, L., Fisher, S. E., Francks, C. (2014). On the other hand: Including left-handers in cognitive neuroscience and neurogenetics. Nature Reviews Neuroscience, 15(3), 193–201.
Wilson, G. M., & Sasse, M. A. (2004). From doing to being: Getting closer to the user experience. Interacting with Computers, 16(4), 697–705.
Conflict of interest
The authors declare that they have no conflict of interest.
All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. This article does not contain any studies with animals performed by any of the authors.
Informed consent was obtained from all individual participants included in the study.
About this article
Cite this article
Moridis, C.N., Terzis, V., Economides, A.A. et al. Using EEG Frontal Asymmetry to Predict IT User’s Perceptions Regarding Usefulness, Ease of Use and Playfulness. Appl Psychophysiol Biofeedback 43, 1–11 (2018). https://doi.org/10.1007/s10484-017-9379-8
- EEG frontal asymmetry
- Perceived playfulness
- Perceived usefulness
- Perceived ease of use