Skip to main content

Using a Cognitive Analysis Grid to Inform Information Systems Design

  • Conference paper
  • First Online:
Information Systems and Neuroscience

Abstract

Following our first conceptualization of a cognitive analysis grid (CA grid) for IS research in 2014, the CA grid was improved and tested in a proof of concept manner. The theory and application of this method are briefly explained, along with lessons learned from a first experiment. The next steps in the validation of this method include applying it to a wider group of naïve participants. This will allow to draw statistical parallels between the cognitive demand of the interface and the performance of the users based on their cognitive profile. Ultimately, this technique should be useful both in NeuroIS research and user experience (UX) tests to guide hypotheses and explain user’s performance.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    Memory remains an important component of both the task and the cognitive profile of the individual, but since the content of every user’s memory is different; it is difficult to make valid attributions at the level of the task.

References

  1. Ortiz de Guinea, A., Titah, R., Léger, P.-M.: Explicit and implicit antecedents of users’ behavioral beliefs in information systems: a neuropsychological investigation. J. Manage. Inf. Syst. 30(4), 179–210 (2014)

    Google Scholar 

  2. Loos, P., Riedl, R., Müller-Putz, G.R., vom Brocke, J., Davis, F.D., Banker, R.D., Léger, P.M.: NeuroIS: neuroscientific approaches in the investigation and development of information systems. Bus. Inf. Syst. Eng. 2(6), 395–401 (2010)

    Google Scholar 

  3. Goodhue, D.L., Thompson, R.L.: Task-technology fit and individual performance. MIS Q. 19(2), 213–236 (1995)

    Article  Google Scholar 

  4. Gu, L., Wang, J.: A study of exploring the “Big Five” and task technology fit in web-based decision support systems. Issues Inf. Syst. 10(2), 210–217 (2009)

    Google Scholar 

  5. Strong, D.M., Dishaw, M.T., Bandy, D.B.: Extending task technology fit with computer self-efficacy. ACM SIGMIS Database 37(2–3), 96–107 (2006)

    Article  Google Scholar 

  6. Ortiz de Guinea, A., Webster, J.: An Investigation of information systems use patterns: technological events as triggers, the effect of time, and consequences for performance. MIS Q. 37(4), 1165–1188 (2013)

    Google Scholar 

  7. Dumont, L., Chamard, É., Léger, P.-M., Ortiz de Guinea, A. Sénécal, S.: Cognitive analysis grid for IS research. In: Gmunded Retreat on NeuroIS (2014)

    Google Scholar 

  8. Baddeley, A.D., Hitch, G.: Working memory. Psychol. Learn. Motiv. 8, 47–89 (1974)

    Article  Google Scholar 

  9. Miyake, A., Friedman, N.P., Emerson, M.J., Witzki, A.H., Howerter, A., Wager, T.D.: The unity and diversity of executive functions and their contributions to complex “Frontal Lobe” tasks: a latent variable analysis. Cogn. Psychol. 41(1), 49–100 (2000)

    Article  Google Scholar 

  10. Kramer, J.H., Mungas, D., Possin, K.L., Rankin, K.P., Boxer, A.L., Rosen, H.J., Bostrom, A., Sinha, L., Berhel, A., Widmeyer, M.: NIH EXAMINER: conceptualization and development of an executive function battery. J. Int. Neuropsychol. Soc. 20(01), 11–19 (2014)

    Article  Google Scholar 

  11. Zekveld, A.A., Heslenfeld, D.J., Johnsrude, I.S., Versfeld, N.J., Kramer, S.E.: The eye as a window to the listening brain: neural correlates of pupil size as a measure of cognitive listening load. NeuroImage 101, 76–86 (2014)

    Article  Google Scholar 

  12. Paas, F., Tuovinen, J.E., Tabbers, H., Van Gerven, P.W.: Cognitive load measurement as a means to advance cognitive load theory. Educ. Psychol. 38(1), 63–71 (2003)

    Article  Google Scholar 

  13. Dumont, L., Gagnon, R., ElMouderrib, S., Gagnon, B., Théoret, H.: Validation of the French translation of the NIH-EXAMINER (In Progress)

    Google Scholar 

  14. Berka, C., Levendowski, D.J., Lumicao, M.N., Yau, A., Davis, G., Zivkovic, V.T., Olmstead, R.E., Tremoulet, P.D., Craven, P.L.: EEG correlates of task engagement and mental workload in vigilance, learning, and memory tasks. Aviat. Space Environ. Med. 78(Suppl. 1), B231–B244 (2007)

    Google Scholar 

  15. Sammer, G., Blecker, C., Gebhardt, H., Bischoff, M., Stark, R., Morgen, K., Vaitl, D.: Relationship between regional hemodynamic activity and simultaneously recorded EEG-theta associated with mental arithmetic-induced workload. Hum. Brain Mapp. 28(8), 793–803 (2007)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Laurence Dumont .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Dumont, L., Chénier-Leduc, G., de Guise, É., de Guinea, A.O., Sénécal, S., Léger, PM. (2015). Using a Cognitive Analysis Grid to Inform Information Systems Design. In: Davis, F., Riedl, R., vom Brocke, J., Léger, PM., Randolph, A. (eds) Information Systems and Neuroscience. Lecture Notes in Information Systems and Organisation, vol 10. Springer, Cham. https://doi.org/10.1007/978-3-319-18702-0_26

Download citation

Publish with us

Policies and ethics