Advertisement

Identification of a Multi-criteria Assessment Model of Relation Between Editorial and Commercial Content in Web Systems

  • Jarosław JankowskiEmail author
  • Wojciech Sałabun
  • Jarosław Wątróbski
Conference paper
  • 412 Downloads
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 506)

Abstract

Together with the increasing role of Internet in commercial activity growing intensity of marketing content is observed. Advertising clutter is interfering with web usability and is affecting processing of the editorial content by web users. Therefore, effective way to manage marketing content is needed. This problem can be solved by using a proper combination of multi-criteria decision-analysis methods. The presented research shows a unique approach to identify assessment model of tradeoffs between the editorial content and the intensity of marketing components. The fuzzy model is identified on the basis of the experiment with the use of eye tracker and a combination of PROMETHEE and COMET methods. As a result, we obtained the assessment model, which is a relation between a set of defined inputs and a set of permissible outputs with the property that each input is related to exactly one output (assessment). Therefore, this model can be used online to manage web systems with balance between editorial and commercial content.

Keywords

Web systems MCDA COMET Fuzzy logic 

References

  1. 1.
    Benway, J.P., Lane, D.M.: Banner Blindness: Web Searchers Often Miss ObviousGoogle Scholar
  2. 2.
    Burke, R.R., Srull, T.K.: Competitive interference and consumer memory for advertising. J. Consum. Res. 15, 55–68 (1988)CrossRefGoogle Scholar
  3. 3.
    Du, H., Xu, Y.: Research on multi-objective optimization decision model of web advertising- takes recruitment advertisement as an example. Int. J. Adv. Comput. Tech. 4(10), 329–336 (2012)Google Scholar
  4. 4.
    Flavian, C., Gurrea, R., Orus, C.: A heuristic evaluation of websites design for achieving the web success. Int. J. Serv. Stand. 5(1), 17–41 (2008)Google Scholar
  5. 5.
    Gibbs, W.: Examining users on news provider web sites: a review of methodogy. J. Usab. Stud. 3, 129–148 (2008)Google Scholar
  6. 6.
    Brajnik, G., Gabrielli, S.: A review of online advertising effects on the user experience. Int. J. Hum. Comput. Interact. 26(10), 971–997 (2010)CrossRefGoogle Scholar
  7. 7.
    Goldstein, D.G., McAfee, R.P., Suri, S.: The cost of annoying ads. In Proceedings of the 22nd International Conference on World Wide Web, pp. 459–470 (2013)Google Scholar
  8. 8.
    Ha, L.: Advertising clutter in consumer magazines: dimensions and effects. J. Adv. Res. 36(4), 76–85 (1996)Google Scholar
  9. 9.
    Kalyanaraman, S., Ivory, J., Maschmeyer, L.: Interruptions and online information processing: the role of interruption type, interruption content, and interruption frequency. In: Proceedings of 2005 Annual Meeting of International Communication Association, pp. 1–32 (2005)Google Scholar
  10. 10.
    Ha, L., McCann, K.: An integrated model of advertising clutter in offline and online media. Int. J. Advers. 27(4), 569–592 (2008)CrossRefGoogle Scholar
  11. 11.
    Jankowski, J., Ziemba, P., Wątróbski, J., Kazienko, P.: Towards the tradeoff between online marketing resources exploitation and the user experience with the use of eye tracking. In Intelligent Information and Database Systems, pp. 330–343 (2016)Google Scholar
  12. 12.
    Jankowski, J., Wątróbski, J., Ziemba, P.: Modeling the impact of visual components on verbal communication in online advertising. Comput. Collect. Intel. pp. 44–53, (2015)Google Scholar
  13. 13.
    Krammer, V.: An effective defense against intrusive web advertising. In: Proceedings of the 2008 6th Annual Conference on Privacy, Security and Trust (PST’08), pp. 3–14 (2008)Google Scholar
  14. 14.
    Lang, A.: The limited capacity model of mediated message processing. J. Commun. 50(1), 4670 (2000)CrossRefGoogle Scholar
  15. 15.
    McCoy, S., Everard, A., Polak, P., Galletta, D.F.: The effects of online advertising. Commun. ACM 50(3), 84–88 (2007)CrossRefGoogle Scholar
  16. 16.
    Nielsen, J.H., Huber, J.: The Effect of Brand Awareness on Intrusive AdvertisingGoogle Scholar
  17. 17.
    Piegat, A., Sałabun, W.: Comparative analysis of MCDM methods for assessing the severity of chronic liver disease. In: International Conference on Artificial Intelligence and Soft Computing, pp. 228–238 (2015)Google Scholar
  18. 18.
    Piegat, A., Sałabun, W.: Identification of a multicriteria decision-making model using the characteristic objects method. Appl. Comput. Intel. Soft Comput. (2014)Google Scholar
  19. 19.
    Piegat, A., Sałabun, W.: Nonlinearity of human multi-criteria in decision-making. J. Theor. Appl. Comput. Sci. 6(3), 36–49 (2012)Google Scholar
  20. 20.
    Ping, Z.: Pop-Up Animations: Impacts and Implications for Website Design and Online Advertising, HCI and MIS: Applications 5 (2006)Google Scholar
  21. 21.
    Pollifroni, M.: Multidimensional analysis applied to the quality of the websites: some empirical evidences from the italian public sector. Econ. Sociol 7(4), 128–138 (2014)CrossRefGoogle Scholar
  22. 22.
    Portnoy, F., Marchionini, G.: Modeling the effect of habituation on banner blindness as a function of repetition and search type: gap analysis for future work. In: Proceedings of the 28th of the International Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA’10), pp. 4297–4302 (2010)Google Scholar
  23. 23.
    Rosenkrans, G.: The creativeness and efectiveness of online interactive rich media advertising. J. Interact. Advers. 9(2), (2009)Google Scholar
  24. 24.
    Sałabun, W.: Application of the fuzzy multi-criteria decision-making method to identify nonlinear decision models. Int. J. Comput. Appl. 89(15), 1–6 (2014)Google Scholar
  25. 25.
    Sałabun, W.: Reduction in the number of comparisons required to create matrix of expert judgment in the comet method. Manag. Prod. Eng. Rev. 5(3), 62–69 (2014)Google Scholar
  26. 26.
    Sałabun, W.: The characteristic objects method: a new distancebased approach to multicriteria decisionmaking problems. J. MCDA 22(1–2), 37–50 (2015)Google Scholar
  27. 27.
    Sałabun, W.: The use of fuzzy logic to evaluate the nonlinearity of human multi-criteria used in decision making. P. Elektro. (Elec. Rev.) 88(10b), 235–238 (2012)Google Scholar
  28. 28.
    Wątróbski, J., Jankowski, J.: Knowledge Management in MCDA Domain. In: Proceedings of the FedCCSIS. Annual Computer Science and Information Systems 5, pp. 1445–1450 (2015)Google Scholar
  29. 29.
    Wątróbski, J., Jankowski, J.: Guideline for MCDA Method Selection in Production Management Area In: Rewski, P., Novikov, D., Bakhtadze, N., Zaikin, O. (eds.) New Frontiers in Information and Production Systems Modelling and Analysis. Intel. Syst. Refer. Lib. 98, pp. 119–138 (2016)Google Scholar
  30. 30.
    Yoo, CY., Kim, K.: Assessing the effects of animation in online banner advertising: hierarchy of effects model. J. Interact. Advers. 4(2), 49-60Google Scholar
  31. 31.
    Zha, W., Wu, H.D.: The impact of online disruptive ads on users’ com-prehension, evaluation of site credibility, and sentiment of intrusiveness. Am. Commun. J. 16(2), 1528 (2014)Google Scholar
  32. 32.
    Ziemba, P., Piwowarski, M., Jankowski, J., Wątróbski, J.: Method of criteria selection and weights calculation in the process of Web projects evaluation. In: Hwang, D., Jung, J.J., Nguyen, N.-T. (eds.) ICCCI 2014. LNCS 8733, pp. 684–693 (2014)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2017

Authors and Affiliations

  • Jarosław Jankowski
    • 1
    • 2
    Email author
  • Wojciech Sałabun
    • 1
  • Jarosław Wątróbski
    • 1
  1. 1.West Pomeranian University of TechnologySzczecinPoland
  2. 2.Wrocław University of TechnologyWrocławPoland

Personalised recommendations