Hierarchical Representation of Website Evaluation Model Using Survey and Perceptual Based Criteria

  • Jarosław WątróbskiEmail author
  • Artur Karczmarczyk
  • Jarosław Jankowski
  • Paweł Ziemba
  • Waldemar Wolski
Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 311)


The high availability of e-commerce websites which deliver similar services and products, as well as the harsh rivalry between competitors, increased the importance of systematic evaluation of the e-commerce websites’ quality, usability and user experience. Multiple methodologies for performing the evaluation are available, however, they are based mainly on survey data. In our previous research, we introduced perceptual measurements from eye tracker (ET) to the set of evaluation criteria. In this paper, we present an approach based on AHP (Analytic Hierarchy Process) to allow a thorough analysis of the complex structure of criteria and its impact on the final evaluation. Additionally, we combine the AHP outputs with the COMET (Characteristic Objects METhod) technique to build a fuzzy rule base that provides a stable model of the entire domain of evaluation criteria. The results of the conducted empirical verification of the proposed approach are presented and discussed. The main research findings show that the rankings obtained with the presented approach are very stable and the probability of a rank reversal phenomenon is low.


Website quality evaluation AHP COMET Eye tracking eQual 


  1. 1.
    Kemp, S.: Digital in 2017 global overview, January 2017Google Scholar
  2. 2.
    Kemp, S.: Digital in 2016, January 2016Google Scholar
  3. 3.
    Ecommerce News Europe: ecommerce in Europe, September 2016Google Scholar
  4. 4.
    Lindner, M.: Online sales will reach $523 billion by 2020 in the US, January 2016Google Scholar
  5. 5.
    Paul, R.: Just how big is the ecommerce market? you’ll never guess!, June 2015Google Scholar
  6. 6.
    Strzelecki, A., Furmankiewicz, M., Ziuziański, P.: The use of management dashboard in monitoring the efficiency of the internet advertising campaigns illustrated on the example of Google analytics. Studia Ekonomiczne 296, 136–150 (2016)Google Scholar
  7. 7.
    Grigera, J., Garrido, A., Panach, J.I., Distante, D., Rossi, G.: Assessing refactorings for usability in e-commerce applications. Empir. Softw. Eng. 21(3), 1224–1271 (2016)CrossRefGoogle Scholar
  8. 8.
    Sohaib, O., Kang, K.: Assessing web content accessibility of e-commerce websites for people with disabilities (2016)Google Scholar
  9. 9.
    Olsson, M.: Build a Profitable Online Business: The No-Nonsense Guide, 1st edn. Apress, Berkely (2013)Google Scholar
  10. 10.
    Kruger, R., Gelderblom, H., Beukes, W.: The value of comparative usability and UX evaluation for e-commerce organisations. In: CONF-IRM 2016 Proceedings, January 2016Google Scholar
  11. 11.
    Barnes, S.J., Vidgen, R.: The equal approach to the assessment of e-commerce quality: a longitudinal study of Internet Bookstores. In: Web Engineering, p. 161 (2005)Google Scholar
  12. 12.
    Webb, H.W., Webb, L.A.: SiteQual: an integrated measure of web site quality. J. Enterp. Inf. Manag. 17(6), 430–440 (2004)CrossRefGoogle Scholar
  13. 13.
    Parasuraman, A., Zeithaml, V.A., Malhotra, A.: ES-QUAL: a multiple-item scale for assessing electronic service quality. J. Ser. Res. 7(3), 213–233 (2005)CrossRefGoogle Scholar
  14. 14.
    Ahn, T., Ryu, S., Han, I.: The impact of the online and offline features on the user acceptance of internet shopping malls. Electron. Commer. Res. Appl. 3(4), 405–420 (2005)CrossRefGoogle Scholar
  15. 15.
    Barnes, S.J., Vidgen, R.: Measuring web site quality improvements: a case study of the forum on strategic management knowledge exchange. Ind. Manag. Data Syst. 103(5), 297–309 (2003)CrossRefGoogle Scholar
  16. 16.
    Yang, Z., Cai, S., Zhou, Z., Zhou, N.: Development and validation of an instrument to measure user perceived service quality of information presenting web portals. Inf. Manag. 42(4), 575–589 (2005)CrossRefGoogle Scholar
  17. 17.
    Barnes, S., Vidgen, R.: WebQual: an exploration of website quality. In: ECIS 2000 Proceedings, p. 74 (2000)Google Scholar
  18. 18.
    Kabir, G., Hasin, M.: Comparative analysis of topsis and fuzzy topsis for the evaluation of travel website service quality. Int. J. Qual. Res. 6(3), 169–185 (2012)Google Scholar
  19. 19.
    Brans, J.P., Mareschal, B.: Promethee methods. In: Figueira, J., Greco, S., Ehrogott, M. (eds.) Multiple Criteria Decision Analysis: State of the Art Surveys. ISOR, vol. 78, pp. 163–186. Springer, New York (2005). CrossRefGoogle Scholar
  20. 20.
    Sun, C.C., Lin, G.T.: Using fuzzy TOPSIS method for evaluating the competitive advantages of shopping websites. Expert Syst. Appl. 36(9), 11764–11771 (2009)CrossRefGoogle Scholar
  21. 21.
    Bilsel, R.U., Büyüközkan, G., Ruan, D.: A fuzzy preference-ranking model for a quality evaluation of hospital web sites. Int. J. Intell. Syst. 21(11), 1181–1197 (2006)CrossRefzbMATHGoogle Scholar
  22. 22.
    Wątróbski, J., Ziemba, P., Jankowski, J., Wolski, W.: PEQUAL-e-commerce websites quality evaluation methodology. In: 2016 Federated Conference on Computer Science and Information Systems (FedCSIS), pp. 1317–1327. IEEE (2016)Google Scholar
  23. 23.
    Chen, L., Pu, P.: Eye-tracking study of user behavior in recommender interfaces. In: De Bra, P., Kobsa, A., Chin, D. (eds.) UMAP 2010. LNCS, vol. 6075, pp. 375–380. Springer, Heidelberg (2010). CrossRefGoogle Scholar
  24. 24.
    Chang Lee, K., Wook Chae, S.: Exploring the effect of the human brand on consumers’ decision quality in online shopping: an eye-tracking approach. Online Inf. Rev. 37(1), 83–100 (2013)CrossRefGoogle Scholar
  25. 25.
    Cowen, L., Ball, L.J., Delin, J.: An eye movement analysis of web page usability. In: Faulkner, X., Finlay, J., Détienne, F. (eds.) People and Computers XVI-Memorable Yet Invisible, pp. 317–335. Springer, London (2002). CrossRefGoogle Scholar
  26. 26.
    Bojko, A.: Eye Tracking the User Experience. Rosenfeld Media, Brooklyn (2013)Google Scholar
  27. 27.
    Ziemba, P., Wątróbski, J., Karczmarczyk, A., Jankowski, J., Wolski, W.: Integrated approach to e-commerce websites evaluation with the use of surveys and eye tracking based experiments, pp. 1019–1030, September 2017Google Scholar
  28. 28.
    Wątróbski, J., Jankowski, J., Karczmarczyk, A., Ziemba, P.: Integration of eye-tracking based studies into e-commerce websites evaluation process with eQual and TOPSIS methods. In: Wrycza, S., Maślankowski, J. (eds.) SIGSAND/PLAIS 2017. LNBIP, vol. 300, pp. 56–80. Springer, Cham (2017). CrossRefGoogle Scholar
  29. 29.
    Saaty, T.L.: What is the analytic hierarchy process? In: Mitra, G., Greenberg, H.J., Lootsma, F.A., Rijkaert, M.J., Zimmermann, H.J. (eds.) Mathematical Models for Decision Support. NATO ASI Series, vol. 48, pp. 109–121. Springer, Heidelberg (1988). CrossRefGoogle Scholar
  30. 30.
    McSpadden, K.: You now have a shorter attention span than a goldfish. Time Online Mag. Accessed 7 May 2015 (2016)Google Scholar
  31. 31.
    Weatherhead, R.: Say it quick, say it well-the attention span of a modern internet consumer. The Guardian Online 19 (2012).
  32. 32.
    Jankowski, J., Kazienko, P., Wątróbski, J., Lewandowska, A., Ziemba, P., Zioło, M.: Fuzzy multi-objective modeling of effectiveness and user experience in online advertising. Expert Syst. Appl. 65, 315–331 (2016)CrossRefGoogle Scholar
  33. 33.
    Nielsen, J.: Usability Engineering. Elsevier, Amsterdam (1994)zbMATHGoogle Scholar
  34. 34.
    International Organization for Standardization: ISO 9241–11: Ergonomic Requirements for Office Work with Visual Display Terminals (VDTs): Part 11: Guidance on Usability (1998)Google Scholar
  35. 35.
    ISO/IEC 25010: 2011. Systems and software engineering-Systems and software Quality Requirements and Evaluation (SQuaRE)-System and software quality models (2011)Google Scholar
  36. 36.
    Hassenzahl, M.: User experience (UX): towards an experiential perspective on product quality. In: Proceedings of the 20th Conference on l’Interaction Homme-Machine, pp. 11–15. ACM (2008)Google Scholar
  37. 37.
    Jankowski, J., Wątróbski, J., Kolomvatsos, K., Kazienko, P.: Fuzzy modeling of user behaviors and virtual goods purchases in social networking platforms. JUCS - J. Univers. Comput. Sci. 22(3), 416–437 (2016)MathSciNetGoogle Scholar
  38. 38.
    Fernandez, A., Insfran, E., Abrahão, S.: Usability evaluation methods for the web: a systematic mapping study. Inf. Softw. Technol. 53(8), 789–817 (2011)CrossRefGoogle Scholar
  39. 39.
    Boulding, W., Kalra, A., Staelin, R., Zeithaml, V.A.: A dynamic process model of service quality: from expectations to behavioral intentions. J. Mark. Res. 30(1), 7 (1993)CrossRefGoogle Scholar
  40. 40.
    Barnes, S.J., Vidgen, R.: An evaluation of cyber-bookshops: the webqual method. Int. J. Electron. Commer. 6(1), 11–30 (2001)CrossRefGoogle Scholar
  41. 41.
    Webb, H., Webb, L.: Business to consumer electronic commerce website quality: integrating information and service dimensions. In: AMCIS 2001 Proceedings, p. 111 (2001)Google Scholar
  42. 42.
    La Porte, T.M., Demchak, C.C., Friis, C.: Webbing governance: global trends across national-level public agencies. Commun. ACM 44(1), 63–67 (2001)CrossRefGoogle Scholar
  43. 43.
    Elling, S., Lentz, L., de Jong, M.: Website evaluation questionnaire: development of a research-based tool for evaluating informational websites. In: Wimmer, M.A., Scholl, J., Grönlund, Å. (eds.) EGOV 2007. LNCS, vol. 4656, pp. 293–304. Springer, Heidelberg (2007). CrossRefGoogle Scholar
  44. 44.
    Ping Zhang, G.M.: User expectations and rankings of quality factors in different web site domains. Int. J. Electron. Commer. 6(2), 9–33 (2001)CrossRefGoogle Scholar
  45. 45.
    Roth, S.P., Tuch, A.N., Mekler, E.D., Bargas-Avila, J.A., Opwis, K.: Location matters, especially for non-salient features-an eye-tracking study on the effects of web object placement on different types of websites. Int. J. Hum.-Comput. Stud. 71(3), 228–235 (2013)CrossRefGoogle Scholar
  46. 46.
    Hu, L., Zhang, W., Xu, Q.: The determinants of online payment method choice: insight from an eye-tracking study. In: WHICEB, p. 80 (2013)Google Scholar
  47. 47.
    Menon, R.V., Sigurdsson, V., Larsen, N.M., Fagerstrøm, A., Foxall, G.R.: Consumer attention to price in social commerce: eye tracking patterns in retail clothing. J. Bus. Res. 69(11), 5008–5013 (2016)CrossRefGoogle Scholar
  48. 48.
    Hernández-Méndez, J., Muñoz-Leiva, F.: What type of online advertising is most effective for etourism 2.0? an eye tracking study based on the characteristics of tourists. Comput. Hum. Behav. 50, 618–625 (2015)CrossRefGoogle Scholar
  49. 49.
    Wang, Q., Yang, S., Liu, M., Cao, Z., Ma, Q.: An eye-tracking study of website complexity from cognitive load perspective. Decis. Support Syst. 62, 1–10 (2014)CrossRefGoogle Scholar
  50. 50.
    Khodambashi, S., Gilstad, H., Nytrø, Ø.: Usability evaluation of clinical guidelines on the web using eye-tracker. Stud. Health Technol. Inform. 228, 95 (2016)Google Scholar
  51. 51.
    Štrach, P., Slivkin, N.: Adaptation needed: eye-tracking study of cross-cultural differences in perception of B2B websites (2017)Google Scholar
  52. 52.
    Yuan, X., Guo, M., Ren, F., Peng, F.: Usability analysis of online bank login interface based on eye tracking experiment. Sens. Transducers 165(2), 203 (2014)Google Scholar
  53. 53.
    Albayrak, D., Cagiltay, K.: Analyzing Turkish e-government websites by eye tracking. In: 2013 Joint Conference of the 23rd International Workshop on Software Measurement and the 2013 Eighth International Conference on Software Process and Product Measurement (IWSM-MENSURA), pp. 225–230. IEEE (2013)Google Scholar
  54. 54.
    Pan, B., Zhang, L.: An eyetracking study on online hotel decision making: the effects of images and umber of options (2016)Google Scholar
  55. 55.
    Kim, S., Stoel, L.: Dimensional hierarchy of retail website quality. Inf. Manag. 41(5), 619–633 (2004)CrossRefGoogle Scholar
  56. 56.
    Chmielarz, W., Zborowski, M.: Comparative analysis of electronic banking websites in selected banks in Poland in 2014. In: 2015 Federated Conference on Computer Science and Information Systems (FedCSIS), pp. 1499–1504. IEEE (2015)Google Scholar
  57. 57.
    Chmielarz, W.: Evaluation of selected mobile applications stores from the user’s perspective. Online J. Appl. Knowl. Manag. 3(1), 21–36 (2015)Google Scholar
  58. 58.
    Chmielarz, W.: Methods of comparative analysis of electronic bankings’ websites. case of Poland. In: Chroust, G., Kotsis, G., Risak, V., Rozsenich, N., Zinterhof, P. (eds.) 1-st CEE Symposium on Business Informatics, Osterreichische Computer Gesellschaft, Vienna, pp. 73–84. Citeseer (2009)Google Scholar
  59. 59.
    Zenebe, A., Zhou, L., Norcio, A.F.: User preferences discovery using fuzzy models. Fuzzy Sets Syst. 161(23), 3044–3063 (2010)MathSciNetCrossRefGoogle Scholar
  60. 60.
    Del Vasto-Terrientes, L., Valls, A., Slowinski, R., Zielniewicz, P.: ELECTRE-III-H: an outranking-based decision aiding method for hierarchically structured criteria. Expert Syst. Appl. 42(11), 4910–4926 (2015)CrossRefGoogle Scholar
  61. 61.
    Kaya, T.: Multi-attribute evaluation of website quality in e-business using an integrated fuzzy ahptopsis methodology. Int. J. Comput. Intell. Syst. 3(3), 301–314 (2010)MathSciNetCrossRefGoogle Scholar
  62. 62.
    Huang, J., Jiang, X., Tang, Q.: An e-commerce performance assessment model: its development and an initial test on e-commerce applications in the retail sector of china. Inf. Manag. 46(2), 100–108 (2009)CrossRefGoogle Scholar
  63. 63.
    Guitouni, A., Martel, J.M., Vincke, P., North, P.: A framework to choose a discrete multicriterion aggregation procedure. Defence Research Establishment Valcatier (DREV) (1998)Google Scholar
  64. 64.
    Konys, A., Wątróbski, J., Różewski, P.: Approach to practical ontology design for supporting COTS component selection processes. In: Selamat, A., Nguyen, N.T., Haron, H. (eds.) ACIIDS 2013. LNCS (LNAI), vol. 7803, pp. 245–255. Springer, Heidelberg (2013). CrossRefGoogle Scholar
  65. 65.
    Chmielarz, W., Zborowski, M.: Aspects of mobility in e-marketing from the perspective of a customer. In: 2016 Federated Conference on Computer Science and Information Systems (FedCSIS), pp. 1329–1333. IEEE (2016)Google Scholar
  66. 66.
    Chmielarz, W., Zborowski, M.: Comparative analysis of electronic banking websites in Poland in 2014 and 2015. In: Ziemba, E. (ed.) Information Technology for Management. LNBIP, vol. 243, pp. 147–161. Springer, Cham (2016). CrossRefGoogle Scholar
  67. 67.
    Chmielarz, W., Zborowski, M.: Comparative analysis of e-banking services in Poland in 2016. In: Wrycza, S., Maślankowski, J. (eds.) SIGSAND/PLAIS 2017. LNBIP, vol. 300, pp. 43–55. Springer, Cham (2017). CrossRefGoogle Scholar
  68. 68.
    Wątróbski, J., Jankowski, J., Ziemba, P.: Multistage performance modelling in digital marketing management. Econ. Sociol. 9(2), 101 (2016)CrossRefGoogle Scholar
  69. 69.
    Wątróbski, J., Ziemba, P., Jankowski, J., Zioło, M.: Green energy for a green city—a multi-perspective model approach. Sustainability 8(8), 702 (2016)CrossRefGoogle Scholar
  70. 70.
    Ziemba, P., Wątróbski, J., Zioło, M., Karczmarczyk, A.: Using the PROSA method in offshore wind farm location problems. Energies 10(11), 1755 (2017)CrossRefGoogle Scholar
  71. 71.
    Verly, C., Smet, Y.D.: Some results about rank reversal instances in the PROMETHEE methods. Int. J. Multicriteria Decis. Mak. 3(4), 325 (2013)CrossRefGoogle Scholar
  72. 72.
    Sałabun, W.: The characteristic objects method: a new distance-based approach to multicriteria decision-making problems: the comet: a new distance-based approach to MCDM problems. J. Multi-Criteria Decis. Anal. 22(1–2), 37–50 (2015)CrossRefGoogle Scholar
  73. 73.
    Saaty, T.L.: How to make a decision: the analytic hierarchy process. Eur. J. Oper. Res. 48(1), 9–26 (1990)CrossRefzbMATHGoogle Scholar
  74. 74.
    Saaty, T.L.: Decision making with the analytic hierarchy process. Int. J. Ser. Sci. 1(1), 83–98 (2008)Google Scholar
  75. 75.
    Macharis, C., Springael, J., De Brucker, K., Verbeke, A.: PROMETHEE and AHP: the design of operational synergies in multicriteria analysis.: strengthening PROMETHEE with ideas of AHP. Eur. J. Oper. Res. 153(2), 307–317 (2004)CrossRefzbMATHGoogle Scholar
  76. 76.
    Sałabun, W., Piegat, A.: Comparative analysis of MCDM methods for the assessment of mortality in patients with acute coronary syndrome. Artif. Intell. Rev. 48(4), 1–15 (2017)Google Scholar
  77. 77.
    Faizi, S., Sałabun, W., Rashid, T., Wątróbski, J., Zafar, S.: Group decision-making for hesitant fuzzy sets based on characteristic objects method. Symmetry 9(8), 136 (2017)MathSciNetCrossRefGoogle Scholar
  78. 78.
    Bashir, Z., Wątróbski, J., Rashid, T., Sałabun, W., Ali, J.: Intuitionistic-fuzzy goals in zero-sum multi criteria matrix games. Symmetry 9(8), 158 (2017)MathSciNetCrossRefGoogle Scholar
  79. 79.
    Faizi, S., Rashid, T., Sałabun, W., Zafar, S., Wątróbski, J.: Decision making with uncertainty using hesitant fuzzy sets. Int. J. Fuzzy Syst. 3(16), 1–11 (2017)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Jarosław Wątróbski
    • 1
    Email author
  • Artur Karczmarczyk
    • 2
  • Jarosław Jankowski
    • 2
  • Paweł Ziemba
    • 3
  • Waldemar Wolski
    • 1
  1. 1.University of SzczecinSzczecinPoland
  2. 2.Department of Computer ScienceWest Pomeranian University of Technology in SzczecinSzczecinPoland
  3. 3.Department of TechnologyThe Jacob of Paradies UniversityGorzów WielkopolskiPoland

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