Impact of Cognitive Workload and Emotional Arousal on Performance in Cooperative and Competitive Interactions

  • Anuja Hariharan
  • Verena Dorner
  • Marc T. P. Adam
Conference paper
Part of the Lecture Notes in Information Systems and Organisation book series (LNISO, volume 16)

Abstract

We examine whether changes in the social environment (competitive or cooperative), affect the relationship between the internal state (cognitive workload and emotional arousal), and the performance in a given task. In a controlled experimental game setting, participants played cooperatively and competitively with different partners. EEG activity and heart rate changes measured cognitive workload and emotional arousal respectively. Cognitive workload was associated negatively with performance in the competitive but not the cooperative mode. By contrast, arousal was associated negatively with performance in the cooperative mode but not the competitive mode.

Keywords

Competition Cooperation EEG Heart rate NeuroIS 

References

  1. 1.
    Preece, J., Shneiderman, B.: The reader-to-leader framework: motivating technology-mediated social participation. AIS Trans Hum Comp Inter 1(1), 13–32 (2009)Google Scholar
  2. 2.
    Galegher, J., Kraut, R.E., Egido, C.: Intellectual Teamwork: Social and Technological Foundations of Cooperative Work. Psychology Press (2014)Google Scholar
  3. 3.
    Malhotra, D.: The desire to win: the effects of competitive arousal on motivation and behavior. Organ Behav Hum Decis Process 111(2), 139–146 (2010)CrossRefGoogle Scholar
  4. 4.
    Adam, M.T.P., Kroll, E.B., Teubner, T.: A note on coupled lotteries. Econ Lett 124(1), 96–99 (2014)CrossRefGoogle Scholar
  5. 5.
    Park, C.H., Son, K., Lee, J.H., Bae, S.H. Crowd vs. crowd: large-scale cooperative design through open team competition. Proceedings of the 2013 Conference on Computer Supported Cooperative Work, 1275–1284 (2013)Google Scholar
  6. 6.
    Feldmann, N., Adam, M.T.P., Bauer, M. Using serious games for idea assessment in service innovation: team formation matters. ECIS 2014 Proceedings, Tel Aviv, Israel, pp. 1–17 (2014)Google Scholar
  7. 7.
    Cornforth, D., Adam, M.T.P. Clustering evaluation, description, and interpretation for serious games. In: Loh, C.S., Sheng, Y., Ifenthaler, D. (eds.) Serious Games Analytics, pp. 135–155. Springer. ISBN 978-3-319-05833-7 (2015)Google Scholar
  8. 8.
    Bioplux PLUX – Wireless Biosignals. [online] http://www.plux.info/systems (2007). Accessed 14 July 2011
  9. 9.
    Greiner, B. The online recruitment system ORSEE 2.0: a guide for the organization of experiments in economics. In: Kremer, K., Macho, V. (eds.) Forschung und wissenschaftliches Rechnen, 2003, pp. 79–93. GWDG Bericht 63 [Research and Scientific Computing] (2004)Google Scholar
  10. 10.
    Hariharan, A., Adam, M.T.P., Teubner, T., Weinhardt, C.: Think, feel, bid: the impact of environmental conditions on the role of bidders' cognitive and affective processes in auction bidding. Electron. Markets, 1–17 (2016)Google Scholar
  11. 11.
    Ortiz de Guinea, A., Titah, R., Léger, P.-M.: Measure for measure: a two study multi-trait multi-method investigation of construct validity in IS research. Comp Hum Behav 29(3), 833–844 (2013)CrossRefGoogle Scholar
  12. 12.
    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)CrossRefGoogle Scholar
  13. 13.
    Pope, A.T., Bogart, E.H., Bartolome, D.S.: Biocybernetic system evaluates indices of operator engagement in automated task. Biol Psychol 40(1), 187–195 (1995)CrossRefGoogle Scholar
  14. 14.
    Charland, P., Léger, P.-M., Sénécal, S., Courtemanche, F., Mercier, J., Skelling, Y., Labonté-Lemoyne, E.: Assessing the multiple dimensions of engagement to characterize learning: a neurophysiological perspective. J Vis Exp 101, e52627 (2015)Google Scholar
  15. 15.
    Teubner, T., Adam, M.T.P., Riordan, R.: The impact of computerized agents on immediate emotions, overall arousal and bidding behavior in electronic auctions. J Assoc Inf Syst 16(10), 838–879 (2015)Google Scholar
  16. 16.
    Charland, P., Allaire-Duquette, G., Léger, P.-M.: Collecting neurophysiological data to investigate users’ cognitive states during game play. J Comput 2(3), 20–24 (2014)Google Scholar
  17. 17.
    Adam, M.T.P., Krämer, J., Müller, M.B.: Auction fever! How time pressure and social competition affect bidders’ arousal and bids in retail auctions. J Retail 91(3), 468–485 (2015)CrossRefGoogle Scholar
  18. 18.
    Léger, P.M., Davis, F.D., Cronan, T.P., Perret, J.: Neurophysiological correlates of cognitive absorption in an enactive training context. Comput Hum Behav 34, 273–283 (2014)CrossRefGoogle Scholar
  19. 19.
    Csikszentmihalyi, M.: Flow: the psychology of optimal experience, vol. 41. Harper Perennial, New York, NY (1991)Google Scholar
  20. 20.
    Bastarache-Roberge, M.-C., Léger, P.-M., Courtemanche, F., Sénécal, S., Fredette, M. Measuring flow using psychophysiological data in a multiplayer gaming context. In: Information Systems and Neuroscience, pp. 187–191. Springer (2015)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2017

Authors and Affiliations

  • Anuja Hariharan
    • 1
  • Verena Dorner
    • 1
  • Marc T. P. Adam
    • 2
  1. 1.Karlsruhe Institute of TechnologyKarlsruheGermany
  2. 2.The University of NewcastleNewcastleAustralia

Personalised recommendations