An Exploratory Study Using Electroencephalography (EEG) to Measure the Smartphone User Experience in the Short Term

References

• Ariely, D., & Berns, G. S. (2010). Neuromarketing: The hope and hype of neuroimaging in business. Nature Reviews Neuroscience, 11(4), 284–292. doi:10.1038/nrn2795
   PubMed Web of Science ®Google Scholar
• Bailenson, J. N., Pontikakis, E. D., Mauss, I. B., Gross, J. J., Jabon, M. E., Hutcherson, C. A. C., … John, O. (2008). Real-time classification of evoked emotions using facial feature tracking and physiological responses. International Journal of Human-computer Studies, 66(5), 303–317. doi:10.1016/j.ijhcs.2007.10.011
   Web of Science ®Google Scholar
• Balconi, M., & Lucchiari, C. (2008). Consciousness and arousal effects on emotional face processing as revealed by brain oscillations. A Gamma Band Analysis. International Journal of Psychophysiology, 67(1), 41–46. doi:10.1016/j.ijpsycho.2007.10.002
   Google Scholar
• Bazanova, O. M., & Vernon, D. (2014). Interpreting EEG alpha activity. Neuroscience & Biobehavioral Reviews, 44, 94–110. doi:10.1016/j.neubiorev.2013.05.007
   PubMed Web of Science ®Google Scholar
• Berka, C., Levendowski, D. J., Cvetinovic, M. M., Petrovic, M. M., Davis, G., Lumicao, M. N., … Olmstead, R. (2004). Real-time analysis of EEG indexes of alertness, cognition, and memory acquired with a wireless EEG headset. International Journal of Human-computer Interaction, 17(2), 151–170. doi:10.1207/s15327590ijhc1702_3
   Web of Science ®Google Scholar
• Bigdely-Shamlo, N., Mullen, T., Kothe, C., Su, K. M., & Robbins, K. A. (2015). The PREP pipeline: Standardized preprocessing for large-scale EEG analysis. Frontiers in Neuroinformatics, 9, 16. doi:10.3389/fninf.2015.00016
   PubMed Web of Science ®Google Scholar
• Bødker, M., Gimpel, G., & Hedman, J. (2009). The user experience of smartphones: A consumption values approach. In Proceedings of the Global Mobility Roundtable Conference, Cairo, Egypt.
   Google Scholar
• Cao, Y., Qu, Q., Duffy, V. G., & Ding, Y. (2019). Attention for web directory advertisements: A top-down or bottom-up process? International Journal of Human–Computer Interaction, 35(1), 89–98. doi:10.1080/10447318.2018.1432162
   Web of Science ®Google Scholar
• Chaudhuri, A., & Holbrook, M. B. (2001). The chain of effects from brand trust and brand affect to brand performance: The role of brand loyalty. Journal of Marketing, 65(2), 81–93. doi:10.1509/jmkg.65.2.81.18255
   Web of Science ®Google Scholar
• Chotpitayasunondh, V., & Douglas, K. M. (2016). How “phubbing” becomes the norm: The antecedents and consequences of snubbing via smartphone. Computers in Human Behavior, 63, 9–18. doi:10.1016/j.chb.2016.05.018
   Web of Science ®Google Scholar
• Comon, P. (1994). Independent component analysis, a new concept? Signal Processing, 36(3), 287–314. doi:10.1016/0165-1684(94)90029-9
   Web of Science ®Google Scholar
• Csikszentmihalyi, M. (1975). Beyond boredom and anxiety: The experience of play in work and games. San Francisco, CA: Jossey-Bass, Inc.
   Google Scholar
• Daliri, M. R. (2013). Kernel earth mover’s distance for EEG classification. Clinical EEG and Neuroscience, 44(3), 182–187. doi:10.1177/1550059412471521
   PubMed Web of Science ®Google Scholar
• 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(1), 9–21. doi:10.1016/j.jneumeth.2003.10.009
   PubMed Web of Science ®Google Scholar
• Dimoka, A., Davis, F. D., Gupta, A., Pavlou, P. A., Banker, R. D., Dennis, A. R., … & Kenning, P. H. (2012). On the use of neurophysiological tools in IS research: Developing a research agenda for NeuroIS. MIS Quarterly, 36(3), 679–702.
   Web of Science ®Google Scholar
• Dimoka, A., Pavlou, P. A., & Davis, F. D. (2011). Research commentary—NeuroIS: The potential of cognitive neuroscience for information systems research. Information Systems Research, 22(4), 687–702. doi:10.1287/isre.1100.0284
   Web of Science ®Google Scholar
• Ding, Y., Guo, F., Hu, M., & Cao, Y. (2017). Using event related potentials to investigate visual aesthetic perception of product appearance. Human Factors and Ergonomics in Manufacturing & Service Industries, 27(5), 223–232. doi:10.1002/hfm.v27.5
   Web of Science ®Google Scholar
• Ding, Y., Guo, F., Zhang, X., Qu, Q., & Liu, W. (2016). Using event related potentials to identify a user’s behavioural intention aroused by product form design. Applied Ergonomics, 55, 117–123. doi:10.1016/j.apergo.2016.01.018
   PubMed Web of Science ®Google Scholar
• Do Amaral, V., Ferreira, L. A., Aquino, P. T., & de Castro, M. C. F. (2013, February). EEG signal classification in usability experiments. In 2013 ISSNIP Biosignals and Biorobotics Conference: Biosignals and Robotics for Better and Safer Living (BRC) (pp. 1–5). Rio de Janeiro, Brazil: IEEE.
   Google Scholar
• Eldenfria, A., & Al-Samarraie, H. (2019). Towards an online continuous adaptation mechanism (OCAM) for enhanced engagement: An EEG study. International Journal of Human–Computer Interaction, 35(20), 1960–1974. DOI: 10.1080/10447318.2019.1595303.
   Web of Science ®Google Scholar
• Engel, A. K., Fries, P., & Singer, W. (2001). Dynamic predictions: Oscillations and synchrony in top–Down processing. Nature Reviews Neuroscience, 2(10), 704. doi:10.1038/35094565
   PubMed Web of Science ®Google Scholar
• Ewing, K. C., Fairclough, S. H., & Gilleade, K. (2016). Evaluation of an adaptive game that uses EEG measures validated during the design process as inputs to a biocybernetic loop. Frontiers in Human Neuroscience, 10, 223. doi:10.3389/fnhum.2016.00223
   PubMed Web of Science ®Google Scholar
• Fullwood, C., Quinn, S., Kaye, L. K., & Redding, C. (2017). My virtual friend: A qualitative analysis of the attitudes and experiences of smartphone users: Implications for smartphone attachment. Computers in Human Behavior, 75, 347–355. doi:10.1016/j.chb.2017.05.029
   Web of Science ®Google Scholar
• Garmer, K., Ylven, J., & Karlsson, I. M. (2004). User participation in requirements elicitation comparing focus group interviews and usability tests for eliciting usability requirements for medical equipment: A case study. International Journal of Industrial Ergonomics, 33(2), 85–98. doi:10.1016/j.ergon.2003.07.005
   Web of Science ®Google Scholar
• Guo, F., Li, M., Qu, Q., & Duffy, V. G. (2019). The effect of a humanoid robot’s emotional behaviors on users’ emotional responses: Evidence from pupillometry and electroencephalography measures. International Journal of Human–Computer Interaction, 35(20), 1947–1959, DOI: 10.1080/10447318.2019.1587938.
   Web of Science ®Google Scholar
• Halder, S., Käthner, I., & Kübler, A. (2016). Training leads to increased auditory brain–Computer interface performance of end-users with motor impairments. Clinical Neurophysiology, 127(2), 1288–1296. doi:10.1016/j.clinph.2015.08.007
   PubMed Web of Science ®Google Scholar
• Hassenzahl, M. (2007). Being and doing–A perspective on user experience and its measurement. Interfaces, 72, 10–12.
   Google Scholar
• Hibbeln, M. T., Jenkins, J. L., Schneider, C., Valacich, J., & Weinmann, M. (2017). How is your user feeling? Inferring emotion through human-computer interaction devices. Mis Quarterly, 41(1), 1–21. doi:10.25300/MISQ
   Web of Science ®Google Scholar
• Hollingsworth, C. L., & Randolph, A. B. (2015). Using NeuroIS to better understand activities performed on mobile devices. In Information Systems and Neuroscience (pp. 213–219), Cham: Springer.
   Google Scholar
• Hsu, C. L., & Lin, J. C. C. (2015). An empirical study of smartphone user behavior: The effect of innovation characteristics, brand equity and social influence. International Journal of Mobile Human Computer Interaction, 7(1), 1–24. doi:10.4018/ijmhci.2015010101
   Web of Science ®Google Scholar
• Inal, T. C., Serteser, M., Coşkun, A., Özpinar, A., & Ünsal, I. (2010). Indirect reference intervals estimated from hospitalized population for thyrotropin and free thyroxine. Croatian Medical Journal, 51(2), 124–130. doi:10.3325/cmj.2010.51.124
   PubMed Web of Science ®Google Scholar
• International Organization for Standardization. (2010). Ergonomics of human-system interaction: Part 210. Human-centred design for interactive systems (formerly known as 13407) (ISO 9241-210). Geneva, Switzerland: Author.
   Google Scholar
• Karapanos, E., Zimmerman, J., Forlizzi, J., & Martens, J. B. (2010). Measuring the dynamics of remembered experience over time. Interacting with Computers, 22(5), 328–335. doi:10.1016/j.intcom.2010.04.003
   Web of Science ®Google Scholar
• Khushaba, R. N., Wise, C., Kodagoda, S., Louviere, J., Kahn, B. E., & Townsend, C. (2013). Consumer neuroscience: Assessing the brain response to marketing stimuli using electroencephalogram (EEG) and eye tracking. Expert Systems with Applications, 40(9), 3803–3812. doi:10.1016/j.eswa.2012.12.095
   Web of Science ®Google Scholar
• Kim, J. Y., & Yoon, M. Y. (2014, June). The use of EEG to measure emotional response to tactile sensation in evaluation of DSLR camera usability. In International Conference on Human-Computer Interaction (pp. 351–356), Cham: Springer. doi:10.1177/1753193414537758.
   Google Scholar
• Klimesch, W., Sauseng, P., & Hanslmayr, S. (2007). EEG alpha oscillations: The inhibition–Timing hypothesis. Brain Research Reviews, 53(1), 63–88. doi:10.1016/j.brainresrev.2006.06.003
   PubMed Web of Science ®Google Scholar
• Kramer, D. (2007). Predictions of performance by EEG and skin conductance. Indiana Undergraduate Journal of Cognitive Science, 2, 3–13.
   Google Scholar
• Lallemand, C., Gronier, G., & Koenig, V. (2015). User experience: A concept without consensus? Exploring practitioners’ perspectives through an international survey. Computers in Human Behavior, 43, 35–48. doi:10.1016/j.chb.2014.10.048
   Web of Science ®Google Scholar
• Law, E. L. C., Van Schaik, P., & Roto, V. (2014). Attitudes towards user experience (UX) measurement. International Journal of Human-computer Studies, 72(6), 526–541. doi:10.1016/j.ijhcs.2013.09.006
   Web of Science ®Google Scholar
• Lee, H., Lee, J., & Seo, S. (2009, July). Brain response to good and bad design. In International Conference on Human-Computer Interaction (pp. 111–120), Berlin, Heidelberg: Springer.
   Google Scholar
• Léger, P. M., Davis, F. D., Cronan, T. P., & Perret, J. (2014). Neurophysiological correlates of cognitive absorption in an enactive training context. Computers in Human Behavior, 34, 273–283. doi:10.1016/j.chb.2014.02.011
   Web of Science ®Google Scholar
• Lim, S. L., Bentley, P. J., Kanakam, N., Ishikawa, F., & Honiden, S. (2014). Investigating country differences in mobile app user behavior and challenges for software engineering. IEEE Transactions on Software Engineering, 41(1), 40–64. doi:10.1109/TSE.2014.2360674
   Web of Science ®Google Scholar
• Masaki, H., Ohira, M., Uwano, H., & Matsumoto, K. I. (2011, July). A quantitative evaluation on the software use experience with electroencephalogram. In International Conference of Design, User Experience, and Usability (pp. 469–477), Berlin, Heidelberg: Springer.
   Google Scholar
• Mashapa, J., Chelule, E., Van Greunen, D., & Veldsman, A. (2013, September). Managing user experience–Managing change. In IFIP Conference on Human-Computer Interaction (pp. 660–677), Berlin, Heidelberg: Springer.
   Google Scholar
• McMahan, T., Parberry, I., & Parsons, T. D. (2015). Modality specific assessment of video game player’s experience using the Emotiv. Entertainment Computing, 7, 1–6. doi:10.1016/j.entcom.2015.03.001
   Web of Science ®Google Scholar
• Nacke, L. E. (2010, May). Wiimote vs. controller: Electroencephalographic measurement of affective gameplay interaction. In Proceedings of the international academic conference on the future of game design and technology (pp. 159–166). British Columbia: ACM.
   Google Scholar
• Nah, F. F. H., Yelamanchili, T., & Siau, K. (2017, July). A review on neuropsychophysiological correlates of flow. In International Conference on HCI in Business, Government, and Organizations (pp. 364–372), Cham: Springer. doi:10.3389/fcimb.2017.00364
   Google Scholar
• Norman, D. A. (2004). Emotional design: Why we love (or hate) everyday things. New York, NY: Basic Civitas Books.
   Google Scholar
• Park, J., Han, S. H., Kim, H. K., Cho, Y., & Park, W. (2013). Developing elements of user experience for mobile phones and services: Survey, interview, and observation approaches. Human Factors and Ergonomics in Manufacturing & Service Industries, 23(4), 279–293. doi:10.1002/hfm.20316
   Web of Science ®Google Scholar
• Pavlou, P., Davis, F., & Dimoka, A. (2007). Neuro IS: the potential of cognitive neuroscience for information systems research. Twenty Eighth International Conference on Information Systems 2007 Proceedings (pp. 122). Quebec, Montreal: AIS Electronic Library.
   Google Scholar
• Perlow, L. A. (2012). Sleeping with your smartphone: How to break the 24/7 habit and change the way you work. Boston, MA: Harvard Business Press.
   Google Scholar
• Roux, F., & Uhlhaas, P. J. (2014). Working memory and neural oscillations: Alpha–Gamma versus theta–Gamma codes for distinct WM information? Trends in Cognitive Sciences, 18(1), 16–25. doi:10.1016/j.tics.2013.10.010
   PubMed Web of Science ®Google Scholar
• Sammler, D., Grigutsch, M., Fritz, T., & Koelsch, S. (2007). Music and emotion: Electrophysiological correlates of the processing of pleasant and unpleasant music. Psychophysiology, 44(2), 293–304. doi:10.1111/psyp.2007.44.issue-2
   PubMed Web of Science ®Google Scholar
• Sanchez-Franco, M. J. (2006). Exploring the influence of gender on the web usage via partial least squares. Behaviour & Information Technology, 25(1), 19–36. doi:10.1080/01449290500124536
   Web of Science ®Google Scholar
• Shiv, B. (2007). Emotions, decisions, and the brain. Journal of Consumer Psychology, 17(3), 174–178. doi:10.1016/S1057-7408(07)70025-6
   Web of Science ®Google Scholar
• Skadberg, Y. X., & Kimmel, J. R. (2004). Visitors’ flow experience while browsing a web site: Its measurement, contributing factors and consequences. Computers in Human Behavior, 20(3), 403–422. doi:10.1016/S0747-5632(03)00050-5
   Web of Science ®Google Scholar
• Smithson, J. (2000). Using and analysing focus groups: Limitations and possibilities. International Journal of Social Research Methodology, 3(2), 103–119. doi:10.1080/136455700405172
   Google Scholar
• Taghizadeh-Sarabi, M., Daliri, M. R., & Niksirat, K. S. (2015). Decoding objects of basic categories from electroencephalographic signals using wavelet transform and support vector machines. Brain Topography, 28(1), 33–46. doi:10.1007/s10548-014-0371-9
   PubMed Web of Science ®Google Scholar
• Tatum, W. O., Husain, A. M., Benbadis, S. R., & Kaplan, P. W. (2014). Handbook of EEG interpretation. New York, NY: Demos Medical Publishing.
   Google Scholar
• van Boxtel, G. J., Denissen, A. J., Jäger, M., Vernon, D., Dekker, M. K., Mihajlović, V., & Sitskoorn, M. M. (2012). A novel self-guided approach to alpha activity training. International Journal of Psychophysiology, 83(3), 282–294. doi:10.1016/j.ijpsycho.2011.11.004
   PubMed Web of Science ®Google Scholar
• Venkatesh, V., Morris, M. G., & Ackerman, P. L. (2000). A longitudinal field investigation of gender differences in individual technology adoption decision-making processes. Organizational Behavior and Human Decision Processes, 83(1), 33–60. doi:10.1006/obhd.2000.2896
   PubMed Web of Science ®Google Scholar
• Wang, & Minor, M. S. (2008). Validity, reliability, and applicability of psychophysiological techniques in marketing research. Psychology & Marketing, 25(2), 197–232. doi:10.1002/mar.20206
   Web of Science ®Google Scholar
• Wang, C. C., & Hsu, M. C. (2014). An exploratory study using inexpensive electroencephalography (EEG) to understand flow experience in computer-based instruction. Information & Management, 51(7), 912–923. doi:10.1016/j.im.2014.05.010
   Web of Science ®Google Scholar
• Wolpaw, J. R., Birbaumer, N., McFarland, D. J., Pfurtscheller, G., & Vaughan, T. M. (2002). Brain–Computer interfaces for communication and control. Clinical Neurophysiology, 113(6), 767–791. doi:10.1016/S1388-2457(02)00057-3
   PubMed Web of Science ®Google Scholar