Advertisement

Multicriteria Selection of Online Advertising Content for the Habituation Effect Reduction

  • Anna Lewandowska
  • Jarosław JankowskiEmail author
  • Wojciech Sałabun
  • Jarosław Wątróbski
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11432)

Abstract

While extant research has examined the various areas related to interactive marketing, techniques to overcome habituation effects without negative impact on users are still less explored. Usually effects such as vividness are used and they have an influence on consumer skepticism toward websites and brands, which negatively influences their attitudes. This research contributes to the field through exploration and identification of consumer responses to multimedia content focused on reduction of habituation effects through visual elements with high intensity used. The results were obtained from the natural responses of web users and subjective experiments in which a group of observers rated differently arranged banners. Proposed decision support model based on the COMET method helps to validate proposed scenarios towards compromise solutions.

Notes

Acknowledgments

The work was supported by the National Science Centre of Poland, the decisions no. 2017/27/B/HS4/01216 (JJ), 2016/23/N/HS4/01931 (WS) and by the Faculty of Computer Science and Information Technology, West Pomeranian University of Technology, Szczecin statutory funds.

References

  1. 1.
    Hoffman, D.L., Novak, T.P.: Marketing in hipermedia computer-mediated enviroments: conceptual foundations. J. Mark. 60(3), 50–68 (1996)Google Scholar
  2. 2.
    Benway, J.P.: Banner blindness: the irony of attention grabbing on the World Wide Web. Proc. Hum. Factors Ergon. Soc. Annu. Meet. 42(5), 463–467 (1998)Google Scholar
  3. 3.
    Burke, M., Hornof, A., Nilsen, E., Gorman, N.: High-cost banner blindness: ads increase perceived workload, hinder visual search, and are forgotten. ACM Trans. Comput. Hum. Interact. (TOCHI) 12(4), 423–445 (2005)Google Scholar
  4. 4.
    ITU-R.REC.BT.500-11. Methodology for the subjective assessment of the quality for television pictures (2002)Google Scholar
  5. 5.
    Jankowski, J.: Integration of collective knowledge in fuzzy models supporting web design process. In: Jędrzejowicz, P., Nguyen, N.T., Hoang, K. (eds.) ICCCI 2011. LNCS (LNAI), vol. 6923, pp. 395–404. Springer, Heidelberg (2011).  https://doi.org/10.1007/978-3-642-23938-0_40Google Scholar
  6. 6.
    Jankowski, J., Wątróbski, J., Ziemba, P.: Modeling the impact of visual components on verbal communication in online advertising. In: Núñez, M., Nguyen, N.T., Camacho, D., Trawiński, B. (eds.) ICCCI 2015. LNCS (LNAI), vol. 9330, pp. 44–53. Springer, Cham (2015).  https://doi.org/10.1007/978-3-319-24306-1_5Google Scholar
  7. 7.
    Wątróbski, J., Jankowski, J., Piotrowski, Z.: The selection of multicriteria method based on unstructured decision problem description. In: Hwang, D., Jung, J.J., Nguyen, N.-T. (eds.) ICCCI 2014. LNCS (LNAI), vol. 8733, pp. 454–465. Springer, Cham (2014).  https://doi.org/10.1007/978-3-319-11289-3_46Google Scholar
  8. 8.
    Krammer, V.: An effective defense against intrusive web advertising. In: Proceedings of the 2008 Sixth Annual Conference on Privacy, Security and Trust (PST 2008), pp. 3–14. IEEE Computer Society, Washington, DC (2008)Google Scholar
  9. 9.
    Lewandowska (Tomaszewska), A., Jankowski, J.: The negative impact of visual web advertising content on cognitive process: towards quantitative evaluation. Int. J. Hum. Comput. Stud. 108, 41–49 (2017)Google Scholar
  10. 10.
    Mantiuk, R.K., Tomaszewska, A., Mantiuk, R.: Comparison of four subjective methods for image quality assessment. Comput. Graph. Forum 31(8), 2478–2491 (2012)Google Scholar
  11. 11.
    McCoy, S., Everard, A., Polak, P., Galletta, D.F.: The effects of online advertising. Commun. ACM 50(3), 84–88 (2007)Google Scholar
  12. 12.
    Rust, T.: Advertising Media Models. Lexington Books, Lexington (1989)Google Scholar
  13. 13.
    Yoo, C.Y., Kim, K., Stout, P.A.: Assessing the effects of animation in online banner advertising: hierarchy of effects model. J. Interact. Advertising 4(2), 49–60 (2004)Google Scholar
  14. 14.
    Zha, W., Wu, H.D.: The impact of online disruptive ads on users? Comprehension, evaluation of site credibility, and sentiment of intrusiveness. Am. Commun. J. 16(2), 15–28 (2014)Google Scholar
  15. 15.
    Zorn, S., Olaru, D., Veheim, T., Zhao, S., Murphy, J.: Impact of animation and language on banner click-through rates. J. Electron. Commer. Res. 13(2), 173–183 (2012)Google Scholar
  16. 16.
    Wątróbski, J., Jankowski, J., Ziemba, P., Karczmarczyk, A., Zioło, M.: Generalised framework for multi-criteria method selection. Omega (2018)Google Scholar
  17. 17.
    Sałabun, W.: The characteristic objects method: a new distance-based approach to multicriteria decision-making problems. J. Multi Criteria Decis. Anal. 22(1–2), 37–50 (2015)Google Scholar
  18. 18.
    Wang, X., Triantaphyllou, E.: Ranking irregularities when evaluating alternatives by using some ELECTRE methods. Omega 36(1), 45–63 (2008)Google Scholar
  19. 19.
    Piegat, A., Sałabun, W.: Identification of a multicriteria decision-making model using the characteristic objects method. Appl. Comput. Intell. Soft Comput. 2014, 14 (2014)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Anna Lewandowska
    • 1
  • Jarosław Jankowski
    • 1
    Email author
  • Wojciech Sałabun
    • 1
  • Jarosław Wątróbski
    • 2
  1. 1.Faculty of Computer Science and Information TechnologyWest Pomeranian University of Technology in SzczecinSzczecinPoland
  2. 2.Faculty of Economics and ManagementUniversity of SzczecinSzczecinPoland

Personalised recommendations