Towards a neuroIS research methodology: Intensifying the discussion on Methods, Tools, And Measurement

Journal of the Association for Information Systems

Volumn 15, Issue , 2014, Pages 1-35

  Riedl, René ^a   Davis, Fred D ^b   Hevner, Alan R ^c  

^a UNIVERSITY OF APPLIED SCIENCES UPPER AUSTRIA   (Austria)

^b Springfield Neurological and Spine Institute   (United States)

^c UNIVERSITY OF SOUTH FLORIDA   (United States)

Author keywords

Brain; Diagnosticity; Intrusiveness; Methodology; Methods; Nervous System; NeuroIS; Neuroscience; Objectivity; Psychophysiology; Reliability; Sensitivity; Validity

Indexed keywords

EID: 84908455502     PISSN: 15583457     EISSN: 15369323     Source Type: Journal    
DOI: 10.17705/1jais.00377     Document Type: Editorial

References (88)

1. Allanson, J., & Fairclough, S. H. (2004). A research agenda for physiological computing. Interacting With Computers, 16(5), 857-878.
2. Astor, P. J., Adam, M. T. P., Jerčić, P., Schaaff, K., & Weinhardt, C. (2014). Integrating biosignals into information systems: A NeuroIS tool for improving emotion regulation. Journal of Management Information Systems, 30(3), 247-278.
3. Bacharach, S. S. (1989). Organizational theories: Some criteria for evaluation. The Academy of Management Review, 14(4), 496-515.
4. Bagozzi, R. P. (2011). Measurement and meaning in information systems and organizational research: Methodological and philosophical foundations, MIS Quarterly, 35(2), 261-292.
5. Barnett, V., & Lewis, T. (1994). Outliers in statistical data. Chichester: John Wiley & Sons.
6. Boucard, A., Marchand, A., & Nogues X. (2007). Reliability and validity of structural equation modeling applied to neuroimaging data: A simulation study. Journal of Neuroscience Methods, 166(2), 278-292.
7. Boucsein, W., & Backs, R. W. (2000). Engineering psychophysiology as a discipline: Historical and theoretical aspects. In R. W. Backs & W. Boucsein (Eds.), Engineering psychophysiology: Issues and Applications (pp. 3-30). New Jersey: Lawrence Erlbaum.
8. Brod, C. (1984). Technostress: The human cost of the computer revolution. Reading: Addison-Wesley.
9. Brown, G. G., Mathalon, D.H., Stern, H., Ford, J., Mueller, B., Greve, D. N., McCarthy, G., Voyvodic, J., Glover, G., Diaz, M., Yetter, E., Ozyurt, I. B., Jorgensen, K. W., Wible, C. G., Turner, J. A., Thompson, W. K., & Potkin, S. G. (2011). Function biomedical informatics research network. Multisite reliability of cognitive BOLD data. Neuroimage, 54(3), 2163-75.
10. Cacioppo, J. T., & Tassinary, L. G. (1990). Inferring psychological significance from physiological signals. American Psychologist, 45(1), 16-28.
11. Cacioppo, J. T., Petty, R. E., & Marshall-Goodell, B. (1985). Physical, social, and inferential elements of psychophysiological measurement. In P. Karoly (Ed.), Measurement strategies in health psychology (pp. 263-300). New York: John Wiley & Sons.
12. Campbell, D. T., & Fiske, D. W. (1959). Convergent and discriminant validation by the multitrait-multimethod matrix. Psychological Bulletin, 56(2), 81-105.
13. Carter, C. S., Pournajafi-Nazarloo, H., Kramer, K. M., Ziegler, T. E., White-Traut, R., & Bello, D. (2007). Oxytocin: Behavioral associations and potential as a salivary biomarker. Annals of the New York Academy of Sciences, 1098, 312-322.
14. Clark, L. A., & Watson, D. (1995). Constructing validity: Basic issues in objective scale development. Psychological Assessment, 7(3), 309-319.
15. Cook, T. D., & Campbell, D. T. (1979). Quasi experimentation: Design and analytical issues for field settings. Chicago: Rand McNally.
16. Cronbach, L. J. (1971). Test validation. In Thorndike, R. L. (Ed.), Educational measurement (2nd edn.) (pp. 443-507). Washington DC: American Council on Education.
17. D’Esposito, M., Zarahn, E., Aguirre, G. K., & Rypma, B. (1999). The effect of normal aging on the coupling of neural activity to the BOLD hemodynamic response. NeuroImage, 10(1), 6-14.
18. Dawson, M. E., Schell, A. M., & Filion, D. L. (2007). The electrodermal system. In J. T. Cacioppo, L. G. Tassinary, & G. G. Berntson (Eds.), Handbook of psychophysiology (3rd edn.) (pp. 159-181). Cambridge: Cambridge University Press.
19. 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.
20. Dimoka, A. (2012). How to conduct a functional magnetic resonance (fMRI) study in social science research. MIS Quarterly, 36(3), 811-840.
21. Dimoka, A., Banker, R. D., Benbasat, I., Davis, F. D., Dennis, A. R., Gefen, D., Gupta, A., Ischebeck, A., Kenning, P., Müller-Putz, G., Pavlou, P. A., Riedl, R., vom Brocke, J., & Weber, B. (2012). On the use of neurophysiological tools in IS research: Developing a research agenda for NeuroIS. MIS Quarterly, 36(3), 679-702.
22. Dimoka, A., Pavlou, P. A., & Davis, F. D. (2007). NEURO-IS: The potential of cognitive neuroscience for information systems research. Proceedings of the 28th International Conference on Information Systems, 1-20.
23. Dimoka, A., Pavlou, P. A., & Davis, F. D. (2011). NeuroIS: The potential of cognitive neuroscience for information systems research. Information Systems Research, 22(4), 687-702.
24. Edwards, J. R., & Bagozzi, R. P. (2000). On the nature and direction of relationships between constructs and measures. Psychological Methods, 5(2), 155-174.
25. Fabiani, M., Gratton, G., Karis, D., & Donchin, E. (1987). The definition, identification, and reliability of measurement of the P300 component of the event-related brain potential. In P. K. Ackles, J. R. Jennings, & M. G. H. Coles (Eds.), Advances in psychophysiology (pp.1-78). Greenwich, CT: JAI Press.
26. Fliessbach, K., Rohe, T., Linder, N. S., Trautner, P., Elger, C. E., & Weber, B. (2010). Retest reliability of reward-related BOLD signals. Neuroimage, 50(3), 1168-76.
27. Foley, P., & Kirschbaum, C. (2010). Human hypothalamus-pituitary-adrenal axis responses to acute psychosocial stress in laboratory settings. Neuroscience and Biobehavioral Reviews, 35(1), 91-96.
28. Fridlund, A. J., & Cacioppo, J. T. (1986). Guidelines for human electromyographic research. Psychophysiology, 23(5), 567-89.
29. Gale, A., & Baker, S. (1981). In vivo or in vitro? Some effects of laboratory experiments, with particular reference to the psychophysiology experiment. In M. J. Christie, & P. G. Mellett (Eds.), Foundations of psychosomatics. Chichester: Wiley.
30. Gazzaniga, M. S., Russell, T., & Senior, C. (2009). Methods in mind. Cambridge: MIT Press.
31. Gratton, G. (2007). Biosignal processing. In J. T. Cacioppo, L. G. Tassinary, & G. G. Berntson (Eds.), Handbook of psychophysiology (3rd edn.) (pp. 834-858). Cambridge: Cambridge University Press.
32. Guijt, A. M., Sluiter, J. K., & Frings-Dresen, M. H. W. (2007). Test-retest reliability of heart rate variability and respiration rate at rest and during light physical activity in normal subjects. Archives of Medical Research, 38(1), 113-120.
33. Habermas, J. (1990). Moral consciousness and communicative action. Cambridge: Polity Press.
34. Hevner, A., Davis, C., Collins, R. W. & Gill, T. G. (2014). A NeuroDesign model for IS research. Informing Science: The International Journal of an Emerging Transdiscipline, 17, 103-132.
35. Horvat-Gordon, M., Granger, D. A., Schwartz, E. B., Nelson, V. J., & Kivlighan, K. T. (2005). Oxytocin is not a valid biomarker when measured in saliva by immunoassay. Physiology and Behavior, 84(3), 445-448.
36. Huettel, S. A., Song, A. W. & McCarthy, G. (2009). Functional magnetic resonance imaging (2nd ed.). Massachusetts: Sinauer.
37. Jennings, J. R., & Gianaros, P. J. (2007). Methodology. In J. T. Cacioppo, L. G. Tassinary, & G. G. Berntson (Eds.), Handbook of psychophysiology (3rd edn.) (pp. 812-833). Cambridge: Cambridge University Press.
38. Jin, P. (1992). Toward a reconceptualization of the law of initial value. Psychological Bulletin, 111(1), 176-84.
39. Kantz, H., Kurths, J., & Mayer-Kress, G. (1998). Nonlinear analysis of physiological data. Berlin: Springer.
40. Kerlinger, F. N., & Lee, H. B. (2000). Foundations of behavioral research (4th edn.). Wadsworth: Thomson.
41. Koch, G. G. (1982). Intraclass correlation coefficient. In S. Kotz & N. L. Johnson (Eds.), Encyclopedia of statistical sciences 4 (pp. 213-217). New York: John Wiley & Sons.
42. Lawshe, C. H. (1975). A quantitative approach to content validity. Personnel Psychology, 28, 563–575.
43. Léger, P.-M., Riedl, R., & vom Brocke, J. (2014). Emotions and ERP information sourcing: The moderating role of expertise. Industrial Management & Data Systems, 114(3), 456-471.
44. Léger, P.-M., Sénecal, S., Courtemanche, F., Ortiz de Guinea, A., Titah, R., Fredette, M., Labonte-Lemoine, È. (2014). Precision is in the eye of the beholder: Application of eye fixation-related potentials to information systems research. Journal of the Association for Information Systems, 15(10), 651-678.
45. Loos, P., Riedl, R., Müller-Putz, G. R., 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, 2(6), 395-401.
46. Luczak, H., & Göbel, M. (2000). Signal processing and analysis in application. In R. W. Backs (Eds.), Engineering psychophysiology: Issues and applications (pp. 79-110). New Jersey: Lawrence Erlabaum Associates.
47. MacKenzie, S. B., Podsakoff, P. M., & Podsakoff, N. P. (2011). Construct measurement and validation procedures in MIS and behavioral research: Integrating new and existing techniques. MIS Quarterly, 35(2), 293-334.
48. McCullough, M. E., Churchland, P. S., & Mendez, A. J. (2013). Problems with measuring peripheral oxytocin: Can the data on oxytocin and human behavior be trusted. Neuroscience & Biobehavioral Reviews, 37(8), 1485-1492.
49. McEvoy, L. K., Smith, M. E., & Gevins, A. (2000). Test-retest reliability of cognitive EEG. Clinical Neurophysiology, 111(3), 457-463.
50. Mingers, J. (2001). Combining IS research methods: Towards a pluralist methodology. Information Systems Research, 12(3), 240-259.
51. Mukherjee, S., Yadav, R., Yung, I., Zajdel, D. P., & Oken, B. S. (2011). Sensitivity to mental effort and test-retest reliability of heart rate variability measures in healthy seniors. Clinical Neurophysiology, 122(10), 2059-2066.
52. Müller, R., & Büttner, P. (1994). A critical discussion of intraclass correlation coefficients. Statistic in Medicine, 13(23-24), 2465-2476.
53. Näpflin, M., Wildi, M., & Sarnthein, J. (2007). Test-retest reliability of resting EEG spectra validates a statistical signature of persons. Clinical Neurophysiology, 118(11), 2519-2524.
54. Näpflin, M., Wildi, M., & Sarnthein, J. (2008).Test-retest reliability of EEG spectra during a working memory task. Neuroimage, 43(4), 687-693.
55. O’Donnel, R. D., & Eggemeier, F. T. (1986). Workload assessment methodology. In Boff, K. R., Kaufman, L., & Thomas, J. P. (Eds.), Handbook of perception and human performance—cognitive processes and performance (pp. 42.1-42.49). New York: Wiley.
56. Ortiz de Guinea, A., & Webster, J. (2013). An investigation of information systems use patterns: Technological events as triggers, the effects of time, and consequences for performance. MIS Quarterly, 37(4), 1165-1188.
57. Ortiz de Guinea, A., Titah, R., & Léger, P.-M. (2013). Measure for measure: A two-study multi-trait multi-method investigation of construct validity in information systems research. Computers in Human Behavior, 29(3), 833-844.
58. Ortiz de Guinea, A., Titah, R., & Léger, P.-M. (2014). Explicit and implicit antecedents of users' behavioral beliefs in information systems: A neuropsychological investigation. Journal of Management Information Systems, 30(4), 179-210.
59. Parasuraman, R., & Rizzo, M. (2008). Introduction to neuroergonomics. In R. Parasuraman & M. Rizzo (Eds.), Neuroergonomics: The brain at work (pp. 3-12). Oxford: Oxford University Press.
60. Pizzagalli, D. A. (2007). Electroencephalography and high-density electrophysiological source localization. In J. T. Cacioppo, L. G. Tassinary, & G. G. Berntson (Eds.), Handbook of psychophysiology (3rd edn.) (pp. 56-84). Cambridge: Cambridge University Press.
61. Plichta, M. M., Schwarz, A. J., Grimm, O., Morgen, K., Mier, D., Haddad, L., Gerdes, A. B., Sauer, C., Tost, H., Esslinger, C., Colman, P., Wilson, F., Kirsch, P., & Meyer-Lindenberg, A. (2012). Test-retest reliability of evoked BOLD signals from a cognitive-emotive fMRI test battery. Neuroimage, 60(3), 1746-1758.
62. Riedl, R. (2009). Zum Erkenntnispotenzial der kognitiven Neurowissenschaften für die Wirtschaftsinformatik: Überlegungen anhand exemplarischer Anwendungen. NeuroPsychoEconomics, 4(1), 32-44.
63. Riedl, R. (2013). On the biology of technostress: Literature review and research agenda. DATA BASE for Advances in Information Systems, 44(1), 18-55.
64. Riedl, R., & Javor, A. (2012). The biology of trust: Integrating evidence from genetics, endocrinology and functional brain imaging. Journal of Neuroscience, Psychology, and Economics, 5(2), 63-91.
65. Riedl, R., Banker, R. D., Benbasat, I., Davis, F. D., Dennis, A. R., Dimoka, A., Gefen, D., Gupta, A., Ischebeck, A., Kenning, P., Müller-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 Association for Information Systems, 27, 243-264.
66. 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.
67. Riedl, R., Kindermann, H., Auinger, A., & Javor, A. (2012). Technostress from a neurobiological perspective: System breakdown increases the stress hormone cortisol in computer users. Business & Information Systems Engineering, 4(2), 61-69.
68. Riedl, R., Kindermann, H., Auinger, A., & Javor, A. (2013). Computer breakdown as a stress factor during task completion under time pressure: Identifying gender differences based on skin conductance. Advances in Human-Computer Interaction, 2013(7).
69. Riedl, R., Mohr, P., Kenning, P., Davis, F., & Heekeren, H. (2014). Trusting humans and avatars: A brain imaging study based on evolution theory. Journal of Management Information Systems, 30(4), 83-113.
70. Salinsky, M. C., Oken, B. S., & Morehead, L. (1991). Test-retest reliability In EEG frequency-analysis electroencephalography. Clinical Neurophyisiology, 79(5), 382-392.
71. Schell, A. M., Dawson, M. E., Nuechterlein, K. H., Subotnik, K. L., & Ventura, J. (2002). The temporal stability of electrodermal variables over a one-year period in patients with recent-onset schizophrenia and normal subjects. Psychophysiology, 39(2), 124-132.
72. Schultheiss, O. C., & Stanton, S. J. (2009). Assessment of salivary hormones. In E. Harmon-Jones & J. S. Beer (Eds.), Methods in social neuroscience (pp. 17-44). Guilford: New York.
73. Simon, H. A. (1980). The behavioral and social sciences. Science, 209(4452), 72-78.
74. Straub, D. W. (1989). Validating instruments in MIS research. MIS Quarterly, 13(2), 147-169.
75. Straub, D. W., Ang, S., & Evaristo, R. (1994). Normative standards for MIS research. DATA BASE for Advances in Information Systems, 25(1), 21-34.
76. Straub, D. W., Gefen, D., & Boudreau, M.-C. (2004). Validation guidelines for IS positivist research. Communications of the Association for Information Systems, 13(24), 380-427.
77. Stroobant, N., & Vingerhoets, G. (2001). Test-retest reliability of functional transcranial Doppler ultrasonography. Ultrasound in Medicine & Biolology, 27(4), 509-14.
78. Strube, M. J., & Newman L. C. (2007). Psychometrics. In J. T. Cacioppo, L. G. Tassinary, & G. G. Berntson (Eds.), Handbook of psychophysiology (3rd edn.) (pp. 789-811). Cambridge: Cambridge University Press.
79. Tams, S., Hill, K., Ortiz de Guinea, A., Thatcher, J., & Grover, V. (2014). NeuroIS—alternative or complement to existing methods? Illustrating the holistic effects of neuroscience and self-reported data in the context of technostress research. Journal of the Association for Information Systems, 15(10), 723-752.
80. Tervaniemi, M., Lehtokoski, A., Sinkkonen, J., Virtanen, J., Ilmoniemi, R. J., & Näätänen R. (1999). Test-retest reliability of mismatch negativity for duration, frequency and intensity changes. Clinical Neurophysiology, 110(8), 1388-1393.
81. Thesen, T., & Murphy, C. (2002). Reliability analysis of event-related brain potentials to olfactory stimuli. Psychophysiology, 39(6), 733-738.
82. Vance, A., Anderson, B., Kirwan, C. B., & Eargle, D. W. (2014). Using measures of risk perception to predict information security behavior: Insights from electroencephalography (EEG). Journal of the Association for Information Systems, 15(10), 679-722.
83. Venkatesh, V., Brown, S. A., & Bala, H. (2013). Bridging the qualitative-quantitative divide: Guidelines for conducting mixed methods research in information systems. MIS Quarterly, 37(1), 21-54.
84. Vogel, D. R., & Wetherbe, J. C. (1984). MIS research: A profile of leading journals and universities. DATA BASE for Advances in Information Systems, 16(1), 3-14.
85. vom Brocke, J., & Liang, T.-P. (2014). Guidelines for neuroscience studies in information systems research. Journal of Management Information Systems, 30(4), 211-234.
86. vom Brocke, J., Riedl, R., & Léger, P.-M. (2013). Application strategies for neuroscience in information systems design science research. Journal of Computer Information Systems, 53(3), 1-13.
87. Weil, M. M., & Rosen, L. D. (1997). Technostress: Coping with technology work home play. New York: Wiley.
88. Wilder, J. (1967). Stimulus and response: The law of initial value. Bristol, UK: John Wright & Sons.