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Modelling and predicting an individual’s perception of advertising appeal


Existing research has found that people evaluate an ad as being more appealing when its design matches their psychological traits. Therefore, to personalise ad design or predict the advertising appeal that an individual perceives, it is especially important to understand what psychological traits moderate an ad’s design effect to a large degree. The present research addressed this question. We conducted a questionnaire survey in which we measured participants’ personality and sense of value according to the Big Five personality traits (Big Five) and Schwartz’s Basic Value (SBV), and asked them advertising appeal that they perceived on ads with various designs. By comparing models that predict perceived advertising appeal using the Big Five and the SBV, we found that the SBV moderates ad design’s effect to a greater extent than does the Big Five. This finding will have an impact on the research of ad personalisation, where researchers have focused on the Big Five and paid little attention to sense of value when examining people’s psychological traits. We also found that the personality sphere as measured by the different Big Five questionnaire inventories, of which the number and representation of items differed, moderates an ad design’s effect to a significantly different extent. We elicited potential requirements for the inventories to be used in such research, which will help researchers to select an inventory. We also confirmed that models that incorporate our findings outperformed the existing modelling approach in terms of prediction accuracy.

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  1. 1.

    E.g., Chen et al. (2015), Clark and Çallı (2014), Ding and Pan (2016), Hirsh et al. (2012), Kobayashi et al. (2019), Matic et al. (2017), Matz et al. (2017), Myers et al. (2010), Roffo and Vinciarelli (2016) and Sofia et al. (2016).

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    Sample size (N), ratio of male to female (M/F) and mean age (MA) were \(N = 129\), \(M/F = 0.7\) and \(MA = 16.9\) in Furnham and Rao (2002) and \(N = 158\), \(M/F = 0.9\) and \(MA = 19.2\) in Swami and Furnham (2012), respectively.

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    We utilised the Japanese version of the PVQ, which was studied in

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    \(\mathrm{Relative\,likelihood} = \exp (\frac{\mathrm{AIC}(m_\mathrm{D2})-\mathrm{AIC}(m_\mathrm{D4})}{2}).\)

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    More information can be found in the following links:,, and


  1. Azucar, D., Marengo, D., Settanni, M.: Predicting the Big 5 personality traits from digital footprints on social media: a meta-analysis. Pers. Individ. Differ. 124, 150–159 (2018)

    Article  Google Scholar 

  2. Brito-Costa, S., Moisão, A., De Almeida, H., Castro, F.V.: Psychometric properties of ten item personality inventory (TIPI). Int. J. Dev. Educ. Psychol. 1(2), 115–121 (2015)

    Google Scholar 

  3. Chamorro-Premuzic, T., Reimers, S., Hsu, A., Ahmetoglu, G.: Who art thou? Personality predictors of artistic preferences in a large UK sample: the importance of openness. Br. J. Psychol. 100(3), 501–516 (2009)

    Article  Google Scholar 

  4. Chamorro-Premuzic, T., Burke, C., Hsu, A., Swami, V.: Personality predictors of artistic preferences as a function of the emotional valence and perceived complexity of paintings. Psychol. Aesth. Creat. Arts 4(4), 196 (2010)

    Article  Google Scholar 

  5. Chen, J., Hsieh, G., Mahmud, J.U., Nichols, J.: Understanding individuals’ personal values from social media word use. In: Proceedings of the 17th ACM Conference on Computer Supported Cooperative Work & Social Computing, ACM, New York, NY, USA, CSCW ’14, pp. 405–414, (2014)

  6. Chen, J., Haber, E., Kang, R., Hsieh, G., Mahmud, J.: Making use of derived personality: the case of social media ad targeting. In: 9th International AAAI Conference on Web and Social Media, pp. 51–60 (2015)

  7. Chittaranjan, G., Blom, J., Gatica-Perez, D.: Mining large-scale smartphone data for personality studies. Pers. Ubiquitous Comput. 17(3), 433–450 (2013).

    Article  Google Scholar 

  8. Clark, L., Çallı, L.: Personality types and Facebook advertising: an exploratory study. J. Direct Data Dig. Mark. Pract. 15(4), 327–336 (2014).

    Article  Google Scholar 

  9. Cohen, R.J., Swerdlik, M.E., Phillips, S.M.: Psychological Testing and Assessment: An Introduction to Tests and Measurement. Mayfield Publishing Co, Houston (1996)

    Google Scholar 

  10. Costa, P., McCrae, R.R.: The revised NEO personality inventory (NEO-PI-R). In: The SAGE Handbook of Personality Theory and Assessment: Volume 2-Personality Measurement and Testing, SAGE Publications Inc., pp. 179–198 (2008)

  11. Ding, T., Pan, S.: Personalized emphasis framing for persuasive message generation. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, Association for Computational Linguistics, Austin, Texas, pp. 1432–1441, (2016)

  12. de Montjoye, Y.A., Quoidbach, J., Robic, F., Pentland, A.S.: Predicting Personality Using Novel Mobile Phone-Based Metrics. In: Greenberg, A.M., Kennedy, W.G., Bos, N.D. (eds.) Social Computing, Behavioral-Cultural Modeling and Prediction, pp. 48–55. Springer Berlin Heidelberg, Berlin, Heidelberg (2013)

    Chapter  Google Scholar 

  13. Farnadi, G., Sitaraman, G., Sushmita, S., Celli, F., Kosinski, M., Stillwell, D., Davalos, S., Moens, M.F., De Cock, M.: Computational personality recognition in social media. User Model. User Adapted Interact. 26(2), 109–142 (2016).

    Article  Google Scholar 

  14. Ferwerda, B., Schedl, M., Tkalčič, M.: Using instagram picture features to predict users’ personality. In: International Conference on Multimedia Modeling, Springer, pp. 850–861 (2016)

  15. Ferwerda, B., Tkalčič, M.: Predicting users’ personality from instagram pictures: using visual and/or content features? In: Proceedings of the 26th Conference on User Modeling, Adaptation and Personalization, ACM, pp. 157–161 (2018)

  16. Fujishima, Y., Yamada, N., Tsuji, H.: Construction of short form of five factor personality questionnaire. Jpn. J. Pers. 13(2), 231–241 (2005).

    Article  Google Scholar 

  17. Furnham, A., Avison, M.: Personality and preference for surreal paintings. Pers. Individ. Differ. 23(6), 923–935 (1997)

    Article  Google Scholar 

  18. Furnham, A., Rao, S.: Personality and the aesthetics of composition: a study of Mondrian and Hirst. North Am. J. Psychol. 4(2), 233–242 (2002)

    Google Scholar 

  19. Goldberg, L.R.: An alternative Description of Personality: the Big-Five Factor structure. J. Pers. Soc. Psychol. 59(6), 1216 (1990)

    Article  Google Scholar 

  20. Goldberg, L.R., Johnson, J.A., Eber, H.W., Hogan, R., Ashton, M.C., Cloninger, C.R., Gough, H.G.: The international personality item pool and the future of public-domain personality measures. J. Res. Pers. 40(1), 84–96 (2006)

    Article  Google Scholar 

  21. Goldberg, Y., Levy, O.: word2vec Explained: deriving Mikolov et al.’s negative-sampling word-embedding method. CoRR abs/1402.3722, (2014) arXiv:1402.3722

  22. Gosling, S.D., Rentfrow, P.J., Swann Jr., W.B.: A very brief measure of the Big-Five personality domains. J. Res. Pers. 37(6), 504–528 (2003)

    Article  Google Scholar 

  23. Hirsh, J.B., Kang, S.K., Bodenhausen, G.V.: Personalized persuasion: tailoring persuasive appeals to recipients’ personality traits. Psychol. Sci. 23(6), 578–581 (2012). pMID: 22547658

    Article  Google Scholar 

  24. Hsieh, G., Chen, J., Mahmud, J.U., Nichols, J.: You read what you value: Understanding personal values and reading interests. In: Proceedings of the 32nd Annual ACM Conference on Human Factors in Computing Systems, ACM, New York, NY, USA, CHI ’14, pp. 983–986, (2014)

  25. Ishikawa, Y., Kobayashi, A., Minamikawa, A.: Predicting advertising appeal from receiver’s psychological traits and ad design features. In: Adjunct Publication of the 27th Conference on User Modeling, Adaptation and Personalization, ACM, New York, NY, USA, UMAP’19 Adjunct, pp.45–49, (2019)

  26. Ishikawa, Y., Kobayashi, A., Minamikawa, A., Ono, C.: Predicting a driver’s personality from daily driving behavior. Proc. Int. Driv. Symp. Human Fact. Driver Assess. Train. Veh. Des. 2019, 203–209 (2019)

    Google Scholar 

  27. Jiang, X., Hadid, A., Pang, Y., Granger, E., Feng, X.: Deep Learning in Object Detection and Recognition. Springer, Singapore (2019).

    Book  Google Scholar 

  28. Kobayashi, A., Ishikawa, Y., Minamikawa, A.: A study on effect of big five personality traits on ad targeting and creative design. In: Oinas-Kukkonen, H., Win, K.T., Karapanos, E., Karppinen, P., Kyza, E. (eds.) Persuasive Technology: Development of Persuasive and Behavior Change Support Systems, pp. 257–269. Springer International Publishing, Cham (2019)

    Chapter  Google Scholar 

  29. Le, Q., Mikolov, T.: Distributed representations of sentences and documents. In: Proceedings of the 31st International Conference on International Conference on Machine Learning - Volume 32,, ICML’14, p II–1188–II–1196 (2014)

  30. Matic, A., Pielot, M., Oliver, N.: “OMG! how did it know that?”: Reactions to highly-personalized ads. In: Adjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization, ACM, New York, NY, USA, UMAP ’17, pp. 41–46, (2017)

  31. Matz, S.C., Kosinski, M., Nave, G., Stillwell, D.J.: Psychological targeting as an effective approach to digital mass persuasion. Proc. Natl. Acad. Sci. USA. 114(48), 12714–12719 (2017).

    Article  Google Scholar 

  32. Matz, S.C., Segalin, C., Stillwell, D., Müller, S.R., Bos, M.W.: Predicting the personal appeal of marketing images using computational methods. J. Consum. Psychol. 29(3), 370–390 (2019).

    Article  Google Scholar 

  33. Mekala, D., Gupta, V., Paranjape, B., Karnick, H.: SCDV : Sparse Composite Document Vectors using soft clustering over distributional representations. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, Association for Computational Linguistics, Copenhagen, Denmark, pp. 659–669, (2017)

  34. Mukta, M.S.H., Ali, M.E., Mahmud, J.: User generated vs. supported contents: Which one can better predict basic human values? In: Spiro, E., Ahn, Y.Y. (eds.) Social Informatics, pp. 454–470. Springer International Publishing, Cham (2016)

    Chapter  Google Scholar 

  35. Murakami, Y., Murakami, C.: Scale construction of a “Big Five” personality inventory. Jpn. J. Personal. 6(1), 29–39 (1997)

    Article  Google Scholar 

  36. Myers, S.D., Sen, S., Aliosha, A.: The moderating effect of personality traits on attitudes toward advertisements. Manag. Mark. Chall. Knowl. Soc. 5(3), 3–20 (2010)

    Google Scholar 

  37. Mønsted, B., Mollgaard, A., Mathiesen, J.: Phone-based metric as a predictor for basic personality traits. J. Res. Personal. 74, 16–22 (2018).

    Article  Google Scholar 

  38. Namikawa, T., Tani, I., Wakita, T., Kumagai, R., Nakane, A., Noguchi, H.: Development of a short form of the Japanese Big-Five Scale, and a test of its reliability and validity. Jpn. J. Psychol. 83(2), 91–99 (2012)

    Article  Google Scholar 

  39. Nascimento, S.M., Linhares, J.M., Montagner, C., João, C.A., Amano, K., Alfaro, C., Bailão, A.: The colors of paintings and viewers’ preferences. Vision Res. 130, 76–84 (2017)

    Article  Google Scholar 

  40. Oshio, A., Shingo, A., Cutrone, P.: Development, reliability, and validity of the Japanese version of ten item personality inventory (TIPI-J). Jpn. J. Pers. 21(1), 40–52 (2012)

    Google Scholar 

  41. Roberts, B.W., Kuncel, N.R., Shiner, R., Caspi, A., Goldberg, L.R.: The power of personality: The comparative validity of personality traits, socioeconomic status, and cognitive ability for predicting important life outcomes. Perspect. Psychol. Sci. 2(4), 313–345 (2007)

    Article  Google Scholar 

  42. Roffo, G., Vinciarelli, A.: Personality in computational advertising: A benchmark. In: Tkalcic, M., Carolis, B.D., de Gemmis, M., Kosir, A. (eds.) Proceedings of the 4th Workshop on Emotions and Personality in Personalized Systems co-located with ACM Conference on Recommender Systems (RecSys 2016), Boston, MA, USA, September 16, 2016,, CEUR Workshop Proceedings, vol. 1680, pp. 18–25, (2016) URL

  43. Schwartz, S.: An overview of the Schwartz theory of basic values. Online Read. Psychol. Cult. (2012).

    Article  Google Scholar 

  44. Schwartz, S.H.: A proposal for measuring value orientations across nations. Quest. Package Eur. Soc. Surv. 259(290), 261 (2003)

    Google Scholar 

  45. Segalin, C., Perina, A., Cristani, M., Vinciarelli, A.: The pictures we like are our image: continuous mapping of favorite pictures into self-assessed and attributed personality traits. IEEE Trans. Affect. Comput. 8(2), 268–285 (2016)

    Article  Google Scholar 

  46. Shimonaka, Y., Nakazato, K., Gondo, Y., Takayama, M.: Construction and factorial validity of the Japanese NEO-PI-R. Jpn. J. Personal 6(2), 138–147 (1998).

    Article  Google Scholar 

  47. Simms, L., Williams, T.F., Simms, E.N.: Assessment of the Five Factor Model, vol. 1. Oxford University Press, Oxford (2016).

    Book  Google Scholar 

  48. Sofia, G., Marianna, S., George, L., Panos, K.: Investigating the role of personality traits and influence strategies on the persuasive effect of personalized recommendations. In: 4th Workshop on Emotions and Personality in Personalized Systems (EMPIRE), p 9 (2016)

  49. Stachl, C., Au, Q., Schoedel, R., Buschek, D., Völkel, S., Schuwerk, T., Oldemeier, M., Ullmann, T., Hussmann, H., Bischl, B. et al.: Behavioral patterns in smartphone usage predict big five personality traits. (2019), URL

  50. Swami, V., Furnham, A.: The effects of symmetry and personality on aesthetic preferences. Imagin. Cogn. Personal. 32(1), 41–57 (2012)

    Article  Google Scholar 

  51. Tibshirani, R.: Regression shrinkage and selection via the lasso. J Royal Stat Soc Ser B (Methodological) 58(1), 267–288 (1996)

    MathSciNet  MATH  Google Scholar 

  52. Tkalčič, M., Tasic, J.F.: Colour spaces: perceptual, historical and applicational background. In: Zajc, B., Tkalčič, M. (eds.) The IEEE Region 8 EUROCON 2003, vol. 1, pp. 304–308. Computer as a Tool, (2003).

  53. Valdez, P., Mehrabian, A.: Effects of color on emotions. J. Exp Psychol General 123(4), 394 (1994)

    Article  Google Scholar 

  54. Vinciarelli, A., Mohammadi, G.: A survey of personality computing. IEEE Trans. Affect. Comput. 5(3), 273–291 (2014).

    Article  Google Scholar 

  55. Vrieze, S.: Model selection and psychological theory: a discussion of the differences between the Akaike information criterion (AIC) and the Bayesian information criterion (BIC). Psychol. Methods 17, 228–43 (2012).

    Article  Google Scholar 

  56. Wada, S.: Construction of the Big Five Scales of personality trait terms and concurrent validity with NPI. Jpn. J. Psychol. 67(1), 61–67 (1996).

    Article  Google Scholar 

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The authors wish to acknowledge Atsunori Minamikawa and Chihiro Ono, KDDI Research, Inc., for their help in reviewing the paper.

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Correspondence to Yuichi Ishikawa.

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Ishikawa, Y., Kobayashi, A. & Kamisaka, D. Modelling and predicting an individual’s perception of advertising appeal. User Model User-Adap Inter 31, 323–369 (2021).

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  • Advertising
  • Design
  • Psychological traits
  • Personality
  • Big Five
  • Sense of value