Designing Live Biofeedback for Groups to Support Emotion Management in Digital Collaboration

  • Michael T. Knierim
  • Dominik Jung
  • Verena Dorner
  • Christof Weinhardt
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10243)

Abstract

Digital collaboration of individuals has increased in diverse areas such as gaming, learning and product innovation. Across scenarios, adequate intra- and interpersonal emotion management is increasingly acknowledged to be beneficial to cognitive and affective interaction outcomes. Unfortunately, individuals differ notably in their emotion management abilities. Additionally, many types of computer mediated collaboration lack the richness of affective cues traditionally found in face-to-face interaction. We envision psychophysiology-based emotion feedback as an automated tool to improve emotion management, and therefore group performance and satisfaction. The presented prototype presents a first iteration of this idea, centered around information on emotional arousal derived from peripheral nervous system measures.

Keywords

Digital collaboration Emotion management Live biofeedback Group feedback 

References

  1. 1.
    Cole, H., Griffiths, M.D.: Social interactions in massively multiplayer online role-playing gamers. Cyberpsychol. Behav. 10, 575–583 (2007)CrossRefGoogle Scholar
  2. 2.
    Zwass, V.: Co-creation: toward a taxonomy and an integrated research perspective. Int. J. Electron. Commer. 15, 11–48 (2010)CrossRefGoogle Scholar
  3. 3.
    Engel, D., Woolley, A.W., Jing, L.X., Chabris, C.F., Malone, T.W.: Reading the mind in the eyes or reading between the lines? Theory of mind predicts collective intelligence equally well online and face-to-face. PLoS ONE 9, 1–16 (2014)Google Scholar
  4. 4.
    Woolley, A.W., Chabris, C.F., Pentland, A., Hashmi, N., Malone, T.W.: Evidence for a collective intelligence factor in the performance of human groups. Science 330(6004), 686–688 (2010). http://science.sciencemag.org/content/330/6004/686 CrossRefGoogle Scholar
  5. 5.
    Mayer, J.D., Roberts, R.D., Barsade, S.G.: Human abilities: emotional intelligence. Annu. Rev. Psychol. 59, 507–536 (2008)CrossRefGoogle Scholar
  6. 6.
    Menges, J.I., Kilduff, M.: Group emotions: cutting the gordian knots concerning terms, levels of analysis, and processes. Acad. Manag. Ann. 9, 845–928 (2015)CrossRefGoogle Scholar
  7. 7.
    Derks, D., Fischer, A.H., Bos, A.E.R.: The role of emotion in computer-mediated communication: a review. Comput. Hum. Behav. 24, 766–785 (2008)CrossRefGoogle Scholar
  8. 8.
    Mikolajczak, M.: Going beyond the ability-trait debate: the three-level model of emotional intelligence. Electron. J. Appl. Psychol. 5, 25–31 (2009)Google Scholar
  9. 9.
    Peira, N., Fredrikson, M., Pourtois, G.: Controlling the emotional heart: heart rate biofeedback improves cardiac control during emotional reactions. Int. J. Psychophysiol. 91, 225–231 (2014)CrossRefGoogle Scholar
  10. 10.
    Snyder, J., Matthews, M., Chien, J., Chang, P.F., Sun, E., Abdullah, S., Gay, G.: MoodLight : exploring personal and social implications of ambient display of biosensor data. In: CSCW 2015, pp. 143–153 (2015)Google Scholar
  11. 11.
    Chanel, G., Mühl, C.: Connecting brains and bodies: applying physiological computing to support social interaction. Interact. Comput. 27, 534–550 (2015)CrossRefGoogle Scholar
  12. 12.
    Hariharan, A., Adam, M.T.P., Dorner, V., Lux, E., Müller, M.B., Pfeiffer, J., Weinhardt, C.: Brownie: a platform for conducting NeuroIS experiments, SSRN 2639047. (2016)Google Scholar
  13. 13.
    Berntson, G.G., Quigley, K.S., Lozano, D.: Cardiovascular psychophysiology. In: Cacioppo, J.T., Tassinary, L.G., Berntson, G.G. (eds.) Handbook of Psychophysiology, pp. 182–210. Cambridge University Press, Cambridge (2007)CrossRefGoogle Scholar
  14. 14.
    Lafferty, J.C., Pond, A.W.: Desert Survival Situation. Human Synergistics International, Plymouth (1987)Google Scholar
  15. 15.
    Guzman, E., Bruegge, B.: Towards emotional awareness in software development teams. In: Proceedings of the 9th Joint Meeting on Foundations of Software Engineering, pp. 671–674 (2013)Google Scholar
  16. 16.
    Calacci, D., Lederman, O., Shrier, D., Pentland, A.: Breakout: an open measurement and intervention tool for distributed peer learning groups, pp. 1–6. arXiv Preprint. arXiv:1607.01443 (2016)
  17. 17.
    Fernández, J.M., Augusto, J.C., Trombino, G., Seepold, R., Madrid, N.M.: Self-aware trader: a new approach to safer trading. J. Univ. Comput. Sci. 19, 2292–2319 (2013)Google Scholar
  18. 18.
    Pousman, Z., Stasko, J.: A taxonomy of ambient information systems: four patterns of design. In: Proceedings of the Working Conference on Advanced Visual Interfaces, pp. 67–74 (2006)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Michael T. Knierim
    • 1
  • Dominik Jung
    • 1
  • Verena Dorner
    • 1
  • Christof Weinhardt
    • 1
  1. 1.Institute for Information Systems and Marketing (IISM)Karlsruhe Institute of Technology (KIT)KarlsruheGermany

Personalised recommendations