Skip to main content
SpringerLink
Log in

Mood Boards as a Tool for Studying Emotions as Building Blocks of the Collective Unconscious

  • Conference paper
  • First Online:
Culture and Computing (HCII 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12215))

Included in the following conference series:

  • 1892 Accesses

Abstract

We conducted an empirical study to answer the research question whether designers could generate richer affective content through mood boards when they are primed by archetypal media content, comparing to non-archetypal media content. Mood board making may stimulate more feedback from target users and help designers discover deeper insights about user needs and aspiration towards products. Today, mood board making has become an essential skill for designers. However, this technique did not gain adequate credits in terms of scientific evidence. It is necessary to assess the validity of mood boards to be an effective tool for studying unconscious emotions in design research. Four professional designers were asked to make mood boards for four different TV commercials (2× without archetypal content; 2× with archetypal content). All 16 mood boards are made online available to a group of 141 raters. In a random order all raters had to click on each mood board to view the full-size and give a rating of ‘attractiveness’ [0–100 score]. The GLM results of all ratings indicate that the attractiveness of the mood boards for archetypal media content and non-archetypal media content are significantly different (F = 15.674, df = 1, p < 0.001). The mood boards primed by archetypal media content (Mean = 54.42, SE = 1.55) are significantly more attractive than the mood boards primed by non-archetypal media content (Mean = 51.37, SE = 1.47). We conclude that mood boards are a enough good tool to investigate and use unconscious emotions what is relevant for addressing design challenges in different contexts.

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

Access this chapter

Log in via an institution
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

Similar content being viewed by others

Notes

  1. 1.

    We analyzed our data with IBM SPSS Statistics, version 25.

References

  1. Picard, R.W.: Affective Computing. MIT Press, Cambridge (2000)

    Google Scholar 

  2. Picard, R.W.: Affective computing: challenges. Int. J. Hum.-Comput. Stud. 59(1–2), 55–64 (2003)

    Google Scholar 

  3. Ivonin, L., et al.: Traces of unconscious mental processes in introspective reports and physiological responses. PLoS ONE 10(4), e0124519 (2015)

    Google Scholar 

  4. Mareis, C.: The epistemology of the unspoken: On the concept of tacit knowledge in contemporary design research. Des. Issues 28(2), 61–71 (2012)

    Google Scholar 

  5. Dijksterhuis, A.: Think different: the merits of unconscious thought in preference development and decision making. J. Personal. Soc. Psychol. 87(5), 586–598 (2004)

    Google Scholar 

  6. Koskinen, I., et al.: Design Research Through Practice: From the Lab, Field, and Showroom. Elsevier, Amsterdam (2011)

    Google Scholar 

  7. Kahneman, D.: Maps of bounded rationality: psychology for behavioral economics. Am. Econ. Rev. 93(5), 1449–1475 (2003)

    Google Scholar 

  8. Chang, H.-M., Emotions in archetypal media content. In: Industrial Design, p. 245. Eindhoven University of Technology, Eindhoven (2014)

    Google Scholar 

  9. Cassidy, T.D.: Mood boards: current practice in learning and teaching strategies and students’ understanding of the process. Int. J. Fash. Des. 1(1), 43–54 (2008)

    Google Scholar 

  10. McDonagh, D., Storer, I.: Mood boards as a design catalyst and resource: researching an under-researched area. Des. J. 7(3), 16–31 (2004)

    Google Scholar 

  11. Nagamachi, M.: Kansei engineering: a new ergonomic consumer-oriented technology for product development. Int. J. Ind. Ergon. 15(1), 3–11 (1995)

    Google Scholar 

  12. Barnes, C., Lillford, S.P.: Decision support for the design of affective products. J. Eng. Des. 20(5), 477–492 (2009)

    Google Scholar 

  13. Scherer, K.R.: What are emotions? And how can they be measured? Soc. Sci. Inf. 44(4), 695–729 (2005)

    Google Scholar 

  14. Wirth, W., Schramm, H.: Media and emotions. Commun. Res. Trends 24(3), 3–39 (2005)

    Google Scholar 

  15. Khalid, H.M., Helander, M.G.: Customer emotional needs in product design. Concurr. Eng. 14(3), 197–206 (2006)

    Google Scholar 

  16. Norman, D.A.: Emotional Design: Why We Love (or Hate) Everyday Things. Basic Books, New York (2004)

    Google Scholar 

  17. Desmet, P.M., Hekkert, P.: The basis of product emotions. In: Green, W.S., Jordan, P.W. (eds.) Pleasure with Products: Beyond Usability, pp. 58–66. Taylor & Francis, London (2002)

    Google Scholar 

  18. Desmet, P.: A multilayered model of product emotions. Des. J. 6(2), 4–13 (2003)

    Google Scholar 

  19. Hassenzahl, M.: Aesthetics in interactive products: correlates and consequences of beauty. In: Product Experience, pp. 287–302. Elsevier (2008)

    Google Scholar 

  20. Diefenbach, S., Hassenzahl, M.: The dilemma of the hedonic: appreciated, but hard to justify. Interact. Comput. 23(5), 461–472 (2011)

    Google Scholar 

  21. Hassenzahl, M., Diefenbach, S., Göritz, A.: Needs, affect, and interactive products: facets of user experience. Interact. Comput. 22(5), 353–362 (2010)

    Google Scholar 

  22. Jordan, P.: Designing Pleasurable Products: An Introduction to the New Human Factors. Taylor & Francis, London (2000)

    Google Scholar 

  23. Laugwitz, B., Held, T., Schrepp, M.: Construction and evaluation of a user experience questionnaire. In: Holzinger, A. (ed.) USAB 2008. LNCS, vol. 5298, pp. 63–76. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-89350-9_6

    Chapter  Google Scholar 

  24. Karapanos, E., Martens, J.-B., Hassenzahl, M.: Reconstructing experiences with iScale. Int. J. Hum.-Comput. Stud. 70(11), 849–865 (2012)

    Google Scholar 

  25. Osgood, C.E., et al.: Cross-cultural universals of affective meaning. In: May, W.H., Miron, M.S. (eds.) vol. 1, p. 486. University of Illinois Press, Illinois (1975)

    Google Scholar 

  26. Ekman, P.: Strong evidence for universals in facial expressions: a reply to Russell’s mistaken critique. Psychol. Bull. 115(2), 268–287 (1994)

    Google Scholar 

  27. Desmet, P.: Measuring emotion: development and application of an instrument to measure emotional responses to products. In: Blythe, M.A., et al. (eds.) Funology: From Usability to Enjoyment, pp. 111–123. Kluwer Academic Publishers, Dordrecht (2003)

    Google Scholar 

  28. Huisman, G., et al.: LEMtool: measuring emotions in visual interfaces. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. ACM (2013)

    Google Scholar 

  29. Hole, L., Williams, O.M.: The emotion sampling device (ESD). In: Proceedings of the 21st British HCI Group Annual Conference on People and Computers: HCI but not as we know it. BCS Learning & Development Ltd. (2007)

    Google Scholar 

  30. Desmet, P.M., Porcelijn, R., Van Dijk, M.: Emotional design: application of a research-based design approach. Knowl. Technol. Policy 20(3), 141–155 (2007)

    Google Scholar 

  31. Tanderup Gade, U.: Design boards as an alignment tool for cross-disciplinarity in engineering. In: Proceedings of the 18th International Conference on Engineering and Product Design Education (E&PDE 2016), Design Education: Collaboration and Cross-Disciplinarity. Institution of Engineering Designers, The Design Society, Aalborg (2016)

    Google Scholar 

  32. Zabotto, C.N., et al.: Automatic digital mood boards to connect users and designers with Kansei engineering. Int. J. Ind. Ergon. 74, 1–11 (2019)

    Google Scholar 

  33. McDonagh, D., Denton, H.: Exploring the degree to which individual students share a common perception of specific mood boards: observations relating to teaching, learning and team-based design. Des. Stud. 26(1), 35–53 (2005)

    Google Scholar 

  34. Garner, S., McDonagh-Philp, D.: Problem interpretation and resolution via visual stimuli: the use of ‘mood boards’ in design education. J. Art Des. Educ. 20(1), 57–64 (2001)

    Google Scholar 

  35. Lucero, A., Aliakseyeu, D., Martens, J.-B.: Funky wall: presenting mood boards using gesture, speech and visuals. In: Proceedings of the Working Conference on Advanced Visual Interfaces. ACM (2008)

    Google Scholar 

  36. McDonagh, D., Bruseberg, A., Haslam, C.: Visual product evaluation: exploring users’ emotional relationships with products. Appl. Ergon. 33(3), 231–240 (2002)

    Google Scholar 

  37. Visser, F.S., et al.: Contextmapping: experiences from practice. CoDesign 1(2), 119–149 (2005)

    Google Scholar 

  38. Zhu, L.: Application of service design tools in product development process. In: 3rd International Conference on Management Science and Innovative Education - MSIE 2017. DEStech Transactions on Social Science, Education and Human Science, Jinan (2017)

    Google Scholar 

  39. Nenkov, G.Y., Scott, M.L.: “So cute I could eat it up”: priming effects of cute products on indulgent consumption. J. Consum. Res. 41(2), 326–341 (2014)

    Google Scholar 

  40. Philippot, P.: Inducing and assessing differentiated emotion-feeling states in the laboratory. Cogn. Emot. 7(2), 171–193 (1993)

    Google Scholar 

  41. Gross, J.J., Levenson, R.W.: Emotion elicitation using films. Cogn. Emot. 9(1), 87–108 (1995)

    Google Scholar 

  42. Rottenberg, J., Ray, R.D., Gross, J.J.: Emotion elicitation using films. In: Coan, J.A., Allen, J.J.B. (eds.) Handbook of Emotion Elicitation and Assessment, pp. 9–28. Oxford University Press, Oxford (2007)

    Google Scholar 

  43. Lench, H.C., Flores, S.A., Bench, S.W.: Discrete emotions predict changes in cognition, judgment, experience, behavior, and physiology: a meta-analysis of experimental emotion elicitations. Psychol. Bull. 137(5), 834–855 (2011)

    Google Scholar 

  44. Edell, J.A., Burke, M.C.: The power of feelings in understanding advertising effects. J. Consum. Res. 14(3), 421–433 (1987)

    Google Scholar 

  45. Baumgartner, H., Sujan, M., Padgett, D.: Patterns of affective reactions to advertisements: the integration of moment-to-moment responses into overall judgments. J. Mark. Res. 34(2), 219–232 (1997)

    Google Scholar 

  46. Van Rompay, T.J., Pruyn, A.T., Tieke, P.: Symbolic meaning integration in design and its influence on product and brand evaluation. Int. J. Des. 3(2), 19–26 (2009)

    Google Scholar 

  47. Mark, B.M., Pearson, C.: The Hero and the Outlaw: Building Extraordinar Brands Through the Power of Archetypes. McGraw-Hill Books, New York (2001)

    Google Scholar 

  48. Rapaille, G.C.: 7 Secrets of Marketing in a Multi-Cultural World. Executive Excellence Publication, Provo (2001)

    Google Scholar 

  49. Tsai, S.-P.: Investigating archetype-icon transformation in brand marketing. Mark. Intell. Plan. 24(6), 648–663 (2006)

    Google Scholar 

  50. Caldwell, M., Henry, P., Alman, A.: Constructing audio-visual representations of consumer archetypes. Qual. Mark. Res.: Int. J. 13(1), 84–96 (2010)

    Google Scholar 

  51. Chang, H.-M., Díaz, M., Català, A., Chen, W., Rauterberg, M.: Mood boards as a universal tool for investigating emotional experience. In: Marcus, A. (ed.) DUXU 2014. LNCS, vol. 8520, pp. 220–231. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-07638-6_22

    Chapter  Google Scholar 

  52. Midi-Minuit, P., Bardou-Jacquet, A.: Honda - The Cog (2012). http://www.youtube.com/watch?v=bl2U1p3fVRk&hd=1. Accessed 6 Nov 2019

  53. Wieden, Kennedy: Honda Civic - Everyday (2006). http://www.youtube.com/watch?v=rcyfVQ1eobM&hd=1. Accessed 6 Nov 2019

  54. Lang, P.J., Bradley, M.M., Cuthbert, B.N.: International affective picture system (IAPS): technical manual and affective ratings. In: Technical Report, pp. 1–5. NIMH Center for the Study of Emotion and Attention, Gainesville (1997)

    Google Scholar 

  55. Bradley, M., Lang, P.: The international affective digitized sounds (IADS-2): affective ratings of sounds and instruction manual Gainesville. In: Technical Report. The Center for Research in Psychophysiology, Florida (2007)

    Google Scholar 

  56. Zajonc, R.B.: Feeling and thinking: preferences need no inferences. Am. Psychol. 35(2), 151–175 (1980)

    Google Scholar 

  57. Maloney, A.: Preference ratings of images representing archetypal themes: an empirical study of the concept of archetypes. J. Anal. Psychol. 44(1), 101–116 (1999)

    Google Scholar 

  58. Friedman, M.: The use of ranks to avoid the assumption of normality implicit in the analysis of variance. J. Am. Stat. Assoc. 32(200), 675–701 (1937)

    MATH  Google Scholar 

  59. Hollander, M., Wolfe, D.A., Chicken, E.: Nonparametric Statistical Methods. Wiley Series in Probability and Statistics, vol. 751, 3rd edn, p. 848. Wiley, Hoboken (2013)

    MATH  Google Scholar 

  60. Wilcoxon, F.: Individual comparisons by ranking methods (1945). In: Kotz, S., Johnson, N.L. (eds.) Breakthroughs in Statistics, pp. 196–202. Springer, New York (1992). https://doi.org/10.1007/978-1-4612-4380-9_16

  61. Epstein, S.: Integration of the cognitive and the psychodynamic unconscious. Am. Psychol. 49(8), 709 (1994)

    Google Scholar 

Download references

Acknowledgements

The authors thank ‘The Archive for Research in Archetypal Symbolism’ (ARAS) for the help with identification and selection of archetypal stimuli.

Funding

This work was supported in part by the Erasmus Mundus Joint Doctorate (EMJD) in Interactive and Cognitive Environments (ICE), which is funded by Erasmus Mundus [FPA no. 2010–2012], by Industrial Design Department from Eindhoven University of Technology (Netherlands), and by Department of Management from Universitat Politècnica de Catalunya (Spain).

Author information

Authors and Affiliations

  1. Mendix Technology B.V., Rotterdam, The Netherlands

    Huang-Ming Chang

  2. Keytree Ltd., London, UK

    Leonid Ivonin

  3. Universitat Politècnica de Catalunya, Vilanova, Spain

    Marta Diaz & Andreu Catala

  4. Eindhoven University of Technology, Eindhoven, The Netherlands

    Matthias Rauterberg

Authors
  1. Huang-Ming Chang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Leonid Ivonin
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Marta Diaz
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Andreu Catala
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Matthias Rauterberg
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Matthias Rauterberg .

Editor information

Editors and Affiliations

  1. Eindhoven University of Technology, Eindhoven, The Netherlands

    Matthias Rauterberg

Ethics declarations

Written consent was acquired from each participant prior to the empirical sessions. This was a non-clinical study without any harming procedure and all data were collected anonymously. Therefore, according to the Netherlands Code of Conduct for Scientific Practice (principle 1.2 on page 5), ethical approval was not sought for execution of this study.

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Chang, HM., Ivonin, L., Diaz, M., Catala, A., Rauterberg, M. (2020). Mood Boards as a Tool for Studying Emotions as Building Blocks of the Collective Unconscious. In: Rauterberg, M. (eds) Culture and Computing. HCII 2020. Lecture Notes in Computer Science(), vol 12215. Springer, Cham. https://doi.org/10.1007/978-3-030-50267-6_1

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-50267-6_1

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-50266-9

  • Online ISBN: 978-3-030-50267-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation