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NeuroIS for Decision Support: The Case of Filmmakers and Audience Test Screenings

  • Sandra Pelzer
  • Marc T. P. AdamEmail author
  • Simon Weaving
Conference paper
Part of the Lecture Notes in Information Systems and Organisation book series (LNISO, volume 29)

Abstract

The application of neuroscience theories, methods, and tools holds great potential for the development of novel decision support systems. In this paper, we develop a theoretical framework for how NeuroIS may support the test screening process of filmmakers where decisions are made about what narrative material is shown to the audience, what sequence it is to be ordered, and what emotional value it must carry. While current methods for audience test screenings commonly rely on standardized questionnaires and focus groups, decision support systems may employ neuroscience tools as built-in functions to provide the filmmaker with novel insights into how their movie is ultimately perceived by the audience. Thereby, a key focus lies on the coherence between the emotional experience intended by the filmmaker and the emotional experience exhibited by the audience. Further, NeuroIS allows an evaluation of how the emotional experience to specific cinematic moments affects overall movie satisfaction.

Keywords

Audience testing Decision support systems Filmmaker NeuroIS 

References

  1. 1.
    vom Brocke, J., Riedl, R., Léger, P.-M.: Application strategies for neuroscience in information systems design science research. J. Comput. Inf. Syst. 53, 1–13 (2013)CrossRefGoogle Scholar
  2. 2.
    Adam, M.T.P., Gimpel, H., Maedche, A., Riedl, R.: Design blueprint for stress-sensitive adaptive enterprise systems. Bus. Inf. Syst. Eng. 59, 277–291 (2017)CrossRefGoogle Scholar
  3. 3.
  4. 4.
    The numbers: movies released in 2017. https://www.the-numbers.com/movies/year/2017 (2017)
  5. 5.
    Chisholm, D., Fernandez-Blanco, V., Ravid, S., Walls, W.: Economics of motion pictures: the state of the art. J. Cult. Econ. 39, 1–13 (2015)CrossRefGoogle Scholar
  6. 6.
    Eliashberg, J., Elberse, A., Leenders, M.: The motion picture industry: critical issues in practice, current research & new research directions. Mark. Sci. 25, 638–661 (2005)CrossRefGoogle Scholar
  7. 7.
    Bauer, I.: Screenwriting Fundamentals: The Art and Craft of Visual Writing. Routledge, New York/London (2017)Google Scholar
  8. 8.
    Schreibman, M.: The Film Director Prepares: A Practical Guide to Directing for Film and TV. Lone Eagle Publishing, New York (2006)Google Scholar
  9. 9.
    Murch, W.: In the Blink of an Eye. A Perspective on Film Editing. Silman-James Press, Hollywood (1992)Google Scholar
  10. 10.
    Gregor, S., Lin, A.C.H., Gedeon, T., Riaz, A.: Neuroscience and a nomological network for the understanding and assessment of emotions in information systems research. J. Manage. Inf. Syst. 30, 13–48 (2014)CrossRefGoogle Scholar
  11. 11.
    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, 838–879 (2015)Google Scholar
  12. 12.
    Koller, M., Walla, P.: Measuring affective information processing in information systems and consumer research: introducing startle reflex modulation. In: ICIS 2012 Proceedings, pp. 1–16, Orlando, USA (2012)Google Scholar
  13. 13.
    Astor, P.J., Adam, M.T.P., Jerčić, P., Schaaff, K., Weinhardt, C.: Integrating biosignals into information systems: a NeuroIS tool for improving emotion regulation. J. Manage. Inf. Syst. 30, 247–278 (2013)CrossRefGoogle Scholar
  14. 14.
    Dmochowski, J.P., Bezdek, M.A., Abelson, B.P., Johnson, J.S., Schumacher, E.H., Parra, L.C.: Audience preferences are predicted by temporal reliability of neural processing. Nat. Commun. 5, 1–9 (2014)CrossRefGoogle Scholar
  15. 15.
    Christoforou, C., Christou-Champi, S., Constantinidou, F., Theodorou, M.: From the eyes and the heart: a novel eye-gaze metric that predicts video preferences of a large audience. Front. Psychol. 6, 1–11 (2015)CrossRefGoogle Scholar
  16. 16.
    Russel, J.A.: A circumplex model of affect. J. Pers. Soc. Psyc. 39(6), 1161–1178 (1980)CrossRefGoogle Scholar
  17. 17.
    Léger, P.-M., Sénecal, S., Courtemanche, F., Ortiz de Guinea, A., Titah, R., Fredette, M., Labonte-LeMoyne, É.: Precision is in the eye of the beholder: application of eye fixation-related potentials to information systems research. J. Assoc. Inf. Syst. 15, 651–678 (2014)Google Scholar
  18. 18.
    Hariharan, A., Adam, M.T.P., Dorner, V., Lux, E., Müller, M.B., Pfeiffer, J., Weinhardt, C.: Brownie: A platform for conducting NeuroIS experiments. J. Assoc. Inf. Syst. 18, 264–296 (2016)Google Scholar
  19. 19.
    Jung, D., Adam, M.T.P., Dorner, V., Hariharan, A.: A practical guide for human lab experiments in information systems research: a tutorial with Brownie. J. Syst. Inf. Technol. 19, 228–256 (2017)CrossRefGoogle Scholar
  20. 20.
    Aurier, P., Guintcheva, G.: Using affect-expectations theory to explain the direction of the impacts of experiential emotions on satisfaction. Psyc. Mark. 31, 900–913 (2014)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Sandra Pelzer
    • 1
  • Marc T. P. Adam
    • 2
    Email author
  • Simon Weaving
    • 2
  1. 1.Karlsruhe Institute of TechnologyKarlsruheGermany
  2. 2.The University of NewcastleNewcastleAustralia

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