Skip to main content
SpringerLink
Log in

Leveraging Conscientiousness-Based Preferences in Information Visualization Design

  • Chapter
  • First Online:
Visualization Psychology

  • 284 Accesses

Abstract

Recent research on information visualization has shown how individual differences such as personality mediate how users interact with visualization systems. Although there is a robust body of research on this relationship, these studies focus on a particular subset of personality constructs. Therefore, there are still personality traits with untapped potential that can provide new findings and inform the design of user-centered visualization systems. This chapter focuses on the conscientiousness personality trait, which measures a person’s preference for an organized approach to life over a spontaneous one. In particular, we believe that conscientiousness may regulate how one prefers graphical encodings and organization. We leverage design guidelines based on user preferences and conscientiousness levels to prototype different information visualization systems. We conducted a user testing phase to understand how these prototypes affect user task efficiency, task efficacy, perceived ease of use, perceived usefulness, and preference. Our findings show that conscientiousness levels lead to distinct user preferences, suggesting an interaction effect between conscientiousness and design guidelines in task efficiency. Additionally, individuals with low conscientiousness scores appear to be faster at completing tasks independently of the design guidelines. Moreover, individuals with high and low conscientiousness scores prefer a visualization specifically designed based on their preferences. Finally, the design guidelines lead to different perceived ease-of-use scores. Our study sheds new light on the relevance of personality as an adaptation technique in the design pipeline of visualization systems.

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 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover 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.

    Allport [2] first defined personality traits as generalized and personalized determining tendencies, consistent and stable modes of an individual’s adjustment to his environment. Furthermore, the author built a vast lexical collection of adjectives that could describe these traits.

  2. 2.

    https://web.tecnico.ulisboa.pt/~tomas.alves/publications/Alves2022_Conscientiousness-InfoVis-Preferences.pdf.

References

  1. H. Al-Samarraie, A. Eldenfria, and H. Dawoud. The impact of personality traits on users’ information-seeking behavior. Information Processing & Management, 18:17–18, 10 2016.

    Google Scholar 

  2. G. W. Allport. Concepts of trait and personality. Psychological Bulletin, 24(5):284, 1927.

    Google Scholar 

  3. G. W. Allport. Pattern and growth in personality. The 1937 Allport and Stagner Texts in Personality Psychology, 1961.

    Google Scholar 

  4. T. Alves, B. Ramalho, J. Henriques-Calado, D. Gonçalves, and S. Gama. Exploring how personality models information visualization preferences. arXiv preprint arXiv:2008.13133, 2020.

    Google Scholar 

  5. D. Azucar, D. Marengo, and M. Settanni. Predicting the big 5 personality traits from digital footprints on social media: A meta-analysis. Personality and Individual Differences, 124:150–159, 2018.

    Article  Google Scholar 

  6. S. Berkovsky, R. Taib, I. Koprinska, E. Wang, Y. Zeng, J. Li, and S. Kleitman. Detecting personality traits using eye-tracking data. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, page 221. ACM, 2019.

    Google Scholar 

  7. N. Bolger and E. A. Schilling. Personality and the problems of everyday life: The role of neuroticism in exposure and reactivity to daily stressors. Journal of Personality, 59(3):355–386, 1991.

    Article  Google Scholar 

  8. E. T. Brown, A. Ottley, H. Zhao, Q. Lin, R. Souvenir, A. Endert, and R. Chang. Finding Waldo: Learning about users from their interactions. IEEE Transactions on Visualization and Computer Graphics, 20(12):1663–1672, 2014.

    Article  Google Scholar 

  9. S. Butt and J. G. Phillips. Personality and self-reported mobile phone use. Computers in Human Behavior, 24(2):346–360, 2008.

    Article  Google Scholar 

  10. B. J. Carducci. The psychology of personality: Viewpoints, research, and applications. John Wiley & Sons, 2009.

    Google Scholar 

  11. C. S. Carver and M. F. Scheier. On the self-regulation of behavior. Cambridge University Press, 2001.

    Google Scholar 

  12. D. Cashman, Y. Wu, R. Chang, and A. Ottley. Inferential tasks as a data-rich evaluation method for visualization. In EVIVA-ML: IEEE VIS Workshop on EValuation of Interactive VisuAl Machine Learning systems, volume 7, 2019.

    Google Scholar 

  13. I. L. Child. Personality in culture. Handbook of Personality Theory and Research, pages 82–145, 1968.

    Google Scholar 

  14. J. Cohen. Statistical power analysis for the behavioral sciences. Academic Press, 2013.

    Book  MATH  Google Scholar 

  15. C. Conati, G. Carenini, D. Toker, and S. Lallé. Towards user-adaptive information visualization. In Twenty-Ninth AAAI Conference on Artificial Intelligence, 2015.

    Google Scholar 

  16. P. J. Corr and G. Matthews. The Cambridge handbook of personality psychology. Cambridge University Press Cambridge, 2009.

    Book  Google Scholar 

  17. P. Costa and R. R. McCrae. The revised NEO personality inventory (NEO-PI-R). The SAGE Handbook of Personality Theory and Assessment, 2:179–198, 01 2008.

    Google Scholar 

  18. P. T. Costa Jr and R. R. McCrae. The Revised NEO Personality Inventory (NEO-PI-R). Sage Publications, Inc, 2008.

    Book  Google Scholar 

  19. P. T. Dinesen, A. S. Nørgaard, and R. Klemmensen. The civic personality: Personality and democratic citizenship. Political Studies, 62:134–152, 2014.

    Article  Google Scholar 

  20. X. Du, F. Shao, S. Wu, H. Zhang, and S. Xu. Water quality assessment with hierarchical cluster analysis based on Mahalanobis distance. Environmental Monitoring and Assessment, 189(7):335, 2017.

    Google Scholar 

  21. H. J. Eysenck. The biological basis of personality. Nature, 199:1031–4, 10 1963.

    Google Scholar 

  22. G. Farnadi, G. Sitaraman, S. Sushmita, F. Celli, M. Kosinski, D. Stillwell, S. Davalos, M.-F. Moens, and M. De Cock. Computational personality recognition in social media. User Modeling and User-Adapted Interaction, 26(2-3):109–142, 2016.

    Article  Google Scholar 

  23. D. B. d. S. Ferreira. A influência do modelo de aceitação da tecnologia na utilização da rede social Facebook na perspetiva do perfil pessoal. PhD thesis, Instituto Politécnico do Porto. Instituto Superior de Contabilidade e Administração do Porto, 2014.

    Google Scholar 

  24. A. Friedman, B. P. Knijnenburg, K. Vanhecke, L. Martens, and S. Berkovsky. Privacy aspects of recommender systems. In Recommender Systems Handbook, pages 649–688. Springer, 2015.

    Google Scholar 

  25. L. Goldberg. The structure of phenotypic personality traits. American Psychologist, 48:26–34, 02 1993.

    Google Scholar 

  26. T. M. Green and B. Fisher. Towards the personal equation of interaction: The impact of personality factors on visual analytics interface interaction. In 2010 IEEE Symposium on Visual Analytics Science and Technology, pages 203–210. IEEE, 2010.

    Google Scholar 

  27. J. J. Gross and O. P. John. Mapping the domain of expressivity: Multimethod evidence for a hierarchical model. Journal of Personality and Social Psychology, 74:170–91, 02 1998.

    Google Scholar 

  28. S. Halko and J. A. Kientz. Personality and persuasive technology: an exploratory study on health-promoting mobile applications. In International Conference on Persuasive Technology, pages 150–161. Springer, 06 2010.

    Google Scholar 

  29. J. Han, J. Pei, and M. Kamber. Data mining: concepts and techniques. Elsevier, 2011.

    MATH  Google Scholar 

  30. M. Ilayaraja and T. Meyyappan. Mining medical data to identify frequent diseases using apriori algorithm. In 2013 International Conference on Pattern Recognition, Informatics and Mobile Engineering, pages 194–199. IEEE, 2013.

    Google Scholar 

  31. C. G. Jung. Psychological types. Routledge, 2014.

    Book  Google Scholar 

  32. D. Kolb and A. Kolb. The Kolb Learning Style Inventory 4.0: Guide to Theory, Psychometrics, Research & Applications. Experience Based Learning Systems, 01 2013.

    Google Scholar 

  33. D. A. Kolb. Experiential learning: Experience as the source of learning and development. FT Press, 2014.

    Google Scholar 

  34. M. Kosinski, Y. Bachrach, P. Kohli, D. Stillwell, and T. Graepel. Manifestations of user personality in website choice and behaviour on online social networks. Machine Learning, 95(3):357–380, 2014.

    Article  MathSciNet  Google Scholar 

  35. C. C. Kuhlthau. A principle of uncertainty for information seeking. Journal of Documentation, 49(4):339–355, 1993.

    Article  Google Scholar 

  36. S. Lallé and C. Conati. The role of user differences in customization: a case study in personalization for infovis-based content. In Proceedings of the 24th International Conference on Intelligent User Interfaces, pages 329–339, 2019.

    Google Scholar 

  37. K. Lee and M. C. Ashton. Psychometric properties of the HEXACO personality inventory. Multivariate Behavioral Research, 39(2):329–358, 2004.

    Article  Google Scholar 

  38. H. M. Lefcourt. Locus of control: Current trends in theory and research. Psychology Press, 2014.

    Book  Google Scholar 

  39. H. Levenson. Reliability and Validity of the I, P, and C Scales-A Multidimensional View of Locus of Control. ERIC, 1973.

    Google Scholar 

  40. M. Lima and A. Simões. NEO-PI-R manual professional. Lisboa: CEGOC, 2000.

    Google Scholar 

  41. Z. Liu, R. J. Crouser, and A. Ottley. Survey on individual differences in visualization. arXiv preprint arXiv:2002.07950, 2020.

    Google Scholar 

  42. N. Majumder, S. Poria, A. Gelbukh, and E. Cambria. Deep learning-based document modeling for personality detection from text. IEEE Intelligent Systems, 32(2):74–79, 2017.

    Article  Google Scholar 

  43. D. P. McAdams. Three lines of personality development. European Psychologist, 2015.

    Google Scholar 

  44. R. R. McCrae and O. P. John. An introduction to the five-factor model and its applications. Journal of Personality, 60(2):175–215, 1992.

    Article  Google Scholar 

  45. L. McInnes, J. Healy, and S. Astels. HDBSCAN: Hierarchical density based clustering. Journal of Open Source Software, 2(11):205, 2017.

    Google Scholar 

  46. H. A. Murray. Explorations in personality: A clinical and experimental study of fifty men of college age. Oxford Univ. Press, 1938.

    Google Scholar 

  47. I. B. Myers, M. H. McCaulley, N. L. Quenk, and A. L. Hammer. MBTI manual: A guide to the development and use of the Myers-Briggs Type Indicator, volume 3. Consulting Psychologists Press Palo Alto, CA, 1998.

    Google Scholar 

  48. T. P. O’brien, L. Bernold, and D. Akroyd. Myers-Briggs type indicator and academic achievement in engineering education. International Journal of Engineering Education, 14, 01 1998.

    Google Scholar 

  49. N. Oscar, S. Mejía, R. Metoyer, and K. Hooker. Towards personalized visualization: Information granularity, situation, and personality. In Proceedings of the 2017 Conference on Designing Interactive Systems, pages 811–819, 2017.

    Google Scholar 

  50. A. Ottley, H. Yang, and R. Chang. Personality as a predictor of user strategy: How locus of control affects search strategies on tree visualizations. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems, pages 3251–3254, 2015.

    Google Scholar 

  51. D. Ozer and V. Benet. Personality and the prediction of consequential outcomes. Annual Review of Psychology, 57:401–21, 02 2006.

    Google Scholar 

  52. S. Petrovic. A comparison between the silhouette index and the Davies-Bouldin index in labelling IDS clusters. In Proceedings of the 11th Nordic Workshop of Secure IT Systems, pages 53–64, 2006.

    Google Scholar 

  53. L. Qiu, H. Lin, J. Ramsay, and F. Yang. You are what you tweet: Personality expression and perception on Twitter. Journal of Research in Personality, 46(6):710–718, 2012.

    Article  Google Scholar 

  54. J. B. Rotter. Social learning and clinical psychology. Prentice-Hall, Inc, 1954.

    Book  Google Scholar 

  55. J. B. Rotter. General expectancies for internal versus external control of reinforcement. Psychological Monographs, 80:1–28, 02 1966.

    Google Scholar 

  56. J. B. Rotter. Some problems and misconceptions related to the construct of internal versus external control of reinforcement. Journal of Consulting and Clinical Psychology, 43(1):56, 1975.

    Google Scholar 

  57. M. T. Russell, R. B. Cattell, A. Cattell, H. E. Cattell, and D. L. Karol. 16PF fifth edition administrator’s manual. Institute for Personality and Ability Testing, Incorporated, 1994.

    Google Scholar 

  58. S. M. Sarsam and H. Al-Samarraie. A first look at the effectiveness of personality dimensions in promoting users’ satisfaction with the system. SAGE Open, 8(2):2158244018769125, 2018.

    Google Scholar 

  59. J. Sheidin, J. Lanir, C. Conati, D. Toker, and T. Kuflik. The effect of user characteristics in time series visualizations. In Proceedings of the 25th International Conference on Intelligent User Interfaces, pages 380–389, 2020.

    Google Scholar 

  60. M. N. Shiota, D. Keltner, and O. P. John. Positive emotion dispositions differentially associated with big five personality and attachment style. The Journal of Positive Psychology, 1(2):61–71, 2006.

    Article  Google Scholar 

  61. P. M. d. Silva, G. A. Dias, and J. R. d. Almeida. Modelo de aceitação de tecnologia (TAM) aplicado ao Sistema de Informação da Biblioteca Virtual em Saúde (BVS) nas Escolas de Medicina da Região Metropolitana do Recife. PhD thesis, Universidade Federal da Paraíba, Brasil, 2013.

    Google Scholar 

  62. R. Subramanian, J. Wache, M. K. Abadi, R. L. Vieriu, S. Winkler, and N. Sebe. Ascertain: Emotion and personality recognition using commercial sensors. IEEE Transactions on Affective Computing, 9(2):147–160, 2016.

    Article  Google Scholar 

  63. V. Venkatesh and H. Bala. Technology acceptance model 3 and a research agenda on interventions. Decision Sciences, 39(2):273–315, 2008.

    Article  Google Scholar 

  64. M. A. d. S. Vilar. Modelo de Aceitação da Tecnologia adaptado às compras online. PhD thesis, [sn], 2013.

    Google Scholar 

  65. J. Wache, R. Subramanian, M. K. Abadi, R.-L. Vieriu, N. Sebe, and S. Winkler. Implicit user-centric personality recognition based on physiological responses to emotional videos. In Proceedings of the 2015 ACM on International Conference on Multimodal Interaction, pages 239–246. ACM, 2015.

    Google Scholar 

  66. J. Wang, J. Wang, Q. Ke, G. Zeng, and S. Li. Fast approximate k-means via cluster closures. In Multimedia Data Mining and Analytics, pages 373–395. Springer, 2015.

    Google Scholar 

  67. C. Xiong, L. Padilla, K. Grayson, and S. Franconeri. Examining the components of trust in map-based visualizations. In R. Kosara, K. Lawonn, L. Linsen, and N. Smit, editors, EuroVis Workshop on Trustworthy Visualization (TrustVis). The Eurographics Association, 2019.

    Google Scholar 

  68. C. Ziemkiewicz, R. J. Crouser, A. R. Yauilla, S. L. Su, W. Ribarsky, and R. Chang. How locus of control influences compatibility with visualization style. In 2011 IEEE Conference on Visual Analytics Science and Technology (VAST), pages 81–90. IEEE, 2011.

    Google Scholar 

  69. C. Ziemkiewicz and R. Kosara. Preconceptions and individual differences in understanding visual metaphors. Computer Graphics Forum, 28(3):911–918, 2009.

    Article  Google Scholar 

  70. C. Ziemkiewicz, A. Ottley, R. J. Crouser, A. R. Yauilla, S. L. Su, W. Ribarsky, and R. Chang. How visualization layout relates to locus of control and other personality factors. IEEE Transactions on Visualization and Computer Graphics, 19(7):1109–1121, 2012.

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by national funds through Fundação para a Ciência e a Tecnologia (FCT) with references UIDB/50021/2020 and SFRH/BD/144798/2019, which is a doctoral grant awarded to the first author.

Author information

Authors and Affiliations

  1. HUMAN-LAB, Graphics and Interaction, INESC-ID and Instituto Superior Técnico—University of Lisbon, Lisboa, Portugal

    Tomás Alves, Bárbara Ramalho, Daniel Gonçalves & Sandra Gama

  2. CICPSI, Faculdade de Psicologia, Universidade de Lisboa, Lisboa, Portugal

    Joana Henriques-Calado

Authors
  1. Tomás Alves
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Bárbara Ramalho
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Daniel Gonçalves
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Joana Henriques-Calado
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Sandra Gama
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Tomás Alves .

Editor information

Editors and Affiliations

  1. University of Colorado Boulder, Boulder, CO, USA

    Danielle Albers Szafir

  2. Department of Computer Science, King's College London, London, UK

    Rita Borgo

  3. OeRC, Department of Engineering Science, University of Oxford, Oxford, UK

    Min Chen

  4. Department of Public Health, Swansea University, Swansea, UK

    Darren J. Edwards

  5. School of Interactive Arts & Technology, Simon Fraser University, Surrey, BC, Canada

    Brian Fisher

  6. Northeastern University, Boston, USA

    Lace Padilla

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Alves, T., Ramalho, B., Gonçalves, D., Henriques-Calado, J., Gama, S. (2023). Leveraging Conscientiousness-Based Preferences in Information Visualization Design. In: Albers Szafir, D., Borgo, R., Chen, M., Edwards, D.J., Fisher, B., Padilla, L. (eds) Visualization Psychology. Springer, Cham. https://doi.org/10.1007/978-3-031-34738-2_13

Download citation

Publish with us

Policies and ethics

Search

Navigation