koz"Вестник Северо-Казахстанского университета имени Манаша Козыбаева"Bulletin of Manash Kozybayev North Kazakhstan University2958-003X2958-0048М. Қозыбаев атындағы СҚУ10.54596/2958-0048-2026-2-328-346koz-2606Research ArticleИНФОРМАЦИОННО-КОММУНИКАЦИОННЫЕ ТЕХНОЛОГИИINFORMATION AND COMMUNICATION TECHNOLOGIESМЕТОДИКА МНОГОКРИТЕРИАЛЬНОЙ ОЦЕНКИ ЦИФРОВЫХ РИТЕЙЛ-ПЛАТФОРМ: ИНТЕГРАЦИЯ MCDA, WEB-АНАЛИТИКИ И UX-ДАННЫХMETHODOLOGY FOR MULTI-CRITERIA EVALUATION OF DIGITAL RETAIL PLATFORMS: INTEGRATION OF MCDA, WEB ANALYTICS, AND UX DATAhttps://orcid.org/0000-0001-8198-2672КуликоваВ. П.KulikovaV. P.

Профессор кафедры "информационно-коммуникационные технологии", кандидат технических наук,  Scopus ID: 57209829779, h-индекс 2

Петропавловск

Professor, Department of Information and Communication Technologies, Candidate of Technical Sciences

Petropavlovsk

v4lentina@mail.ruКухаренкоЕ. В.KukharenkoE. V.

ассоциативный профессор, кафедра ИКТ

Петропавловск

Assistant Professor, Department of Information and Communication Technologies, Candidate of Technical Sciences

Petropavlovsk

geny@ku.edu.kzhttps://orcid.org/0009-0001-9363-7259НикишинаО. А.NikishinaO. A.

ст. преподаватель. кафедра ИКТ

Петропавловск

Senior lecturer of the Department of Information and Communication Technologies, Master of Information Systems

Petropavlovsk

opronina@ku.edu.kzНАО "Северо-Казахстанский университет имени Манаша Козыбаева"КазахстанManash Kozybayev North Kazakhstan University NPLCKazakhstan20260907202602 (70)328346Copyright © Куликова В.П., Кухаренко Е.В., Никишина О.А., 20262026Куликова В.П., Кухаренко Е.В., Никишина О.А.Kulikova V.P., Kukharenko E.V., Nikishina O.A.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://vestnik.ku.edu.kz/jour/article/view/2606

В статье представлена методика многокритериальной оценки цифровых ритейл-платформ, основанная на интеграции методов MCDA, web-аналитики и UX-данных. Предлагаемая модель объединяет процедуры AHP и TOPSIS, что позволяет одновременно формализовать экспертные предпочтения и строить интегральный рейтинг альтернатив. Методика учитывает разнородные источники информации: количественные показатели веб-метрик, качественные UX-оценки, результаты наблюдений по методике mystery shopping. Описаны процедуры нормализации, вычисления весов критериев и алгоритм агрегирования оценок. Апробация выполнена на выборке онлайн-магазинов и оффлайн-ритейлеров, что подтвердило устойчивость и интерпретируемость модели, а также ее применимость для анализа цифровой зрелости и качества пользовательского опыта. Результаты исследования могут быть использованы в задачах UX-аудита, оценки интерфейсов, управлении цифровыми продуктами и стратегическом анализе e-commerce.

The article presents a methodology for multi-criteria evaluation of digital retail platforms based on the integration of Multi-Criteria Decision Analysis (MCDA), web analytics, and UX data. The proposed hybrid model combines AHP and TOPSIS methods, enabling the formalization of expert preferences and the construction of an integral ranking of alternatives. The methodology incorporates heterogeneous data sources, including quantitative web metrics, qualitative UX assessments, and mystery shopping results. Principles of data normalization, criteria weighting, and the algorithm for calculating the integral score are described. The methodology was tested on datasets from online stores and offline retailers, confirming its stability, interpretability, and applicability for assessing digital maturity and user experience quality. The results can be used for UX auditing, interface evaluation, digital product management, and strategic e-commerce analysis.

MCDAAHPTOPSISweb-аналитикаUX-оценкаmystery shoppingцифровой ритейлмногокритериальная модельинтегральная оценкаe-commerceMCDAAHPTOPSISweb analyticsUX evaluationmystery shoppingdigital retailmulti-criteria modelintegral assessmente-commerceReferencesShankar V., Kalyanam K., Setia P., et al. How technology is changing retail // Journal of Retailing. 2021. Vol. 97, No. 1. P. 13–27. DOI: https://doi.org/10.1016/j.jretai.2020.10.001.Shankar, V., Kalyanam, K., Setia, P., et al. (2021). How technology is changing retail. Journal of Retailing, 97(1), 13–27. https://doi.org/10.1016/j.jretai.2020.10.001Verhoef P.C., Kannan P.K., Inman J.J. From multi-channel retailing to omni-channel retailing: introduction to the special issue on multi-channel retailing // Journal of Retailing. 2015. Vol. 91, No. 2. P. 174–181. DOI: https://doi.org/10.1016/j.jretai.2015.02.005.Verhoef, P.C., Kannan, P.K., & Inman, J. J. (2015). From multi-channel retailing to omni-channel retailing: Introduction to the special issue on multi-channel retailing. Journal of Retailing, 91(2), 174–181. https://doi.org/10.1016/j.jretai.2015.02.005Palmié M., Miehe L., Oghazi P., et al. Digital service ecosystems and retail meta-ecosystems // Technological Forecasting and Social Change. 2022. Vol. 176. Article 121452. DOI: https://doi.org/10.1016/j.techfore.2021.121452.Palmié, M., Miehe, L., Oghazi, P., et al. (2022). Digital service ecosystems and retail meta ecosystems. Technological Forecasting and Social Change, 176, Article 121452. https://doi.org/10.1016/j.techfore.2021.121452Hagberg J., Sundström M., Egels Zandén N. The digitalization of retailing: an exploratory framework // International Journal of Retail & Distribution Management. 2016. Vol. 44, No. 7. P. 694–712. DOI: https://doi.org/10.1108/IJRDM-09-2015-0140.Hagberg, J., Sundström, M., & Egels Zandén, N. (2016). The digitalization of retailing: An exploratory framework. International Journal of Retail & Distribution Management, 44(7), 694–712. https://doi.org/10.1108/IJRDM-09-2015-0140Gkikas D.C., Theodoridis P.K. Predicting online shopping behavior: using machine learning and Google Analytics to classify user engagement // Applied Sciences. 2024. Vol. 14, No. 23. Article 11403. DOI: https://doi.org/10.3390/app142311403.Gkikas, D.C., & Theodoridis, P. K. (2024). Predicting online shopping behavior: Using machine learning and Google Analytics to classify user engagement. Applied Sciences, 14(23), Article 11403. https://doi.org/10.3390/app142311403Mamakou X.J., Zaharias P., Milesi M. Measuring customer satisfaction in electronic commerce: the impact of e-service quality and user experience // International Journal of Quality & Reliability Management. 2024. Vol. 41, No. 3. P. 915–943. DOI: https://doi.org/10.1108/IJQRM-07-2021-0215.Mamakou, X.J., Zaharias, P., & Milesi, M. (2024). Measuring customer satisfaction in electronic commerce: The impact of e service quality and user experience. International Journal of Quality & Reliability Management, 41(3), 915–943. https://doi.org/10.1108/IJQRM-07-2021-0215Albert W., Tullis T. Measuring the User Experience: Collecting, Analyzing, and Presenting Usability Metrics. Burlington: Morgan Kaufmann, 2013. 313 p.Albert, W., & Tullis, T. (2013). Measuring the user experience: Collecting, analyzing, and presenting usability metrics. Morgan Kaufmann.Nielsen J. Usability Engineering. Boston: Academic Press, 1993. 362 p.Nielsen, J. (1993). Usability engineering. Academic Press.Wilson A. The use of mystery shopping in the measurement of service delivery // Service Industries Journal. 1998. Vol. 18, No. 3. P. 148–163.Wilson, A. (1998). The use of mystery shopping in the measurement of service delivery. Service Industries Journal, 18(3), 148–163.Saaty T. Decision making with the analytic hierarchy process // International Journal of Services Sciences. 2008. Vol. 1, No. 1. P. 83–98.Saaty, T. (2008). Decision making with the analytic hierarchy process. International Journal of Services Sciences, 1(1), 83–98.Hwang C. L., Yoon K. Multiple Attribute Decision Making: Methods and Applications. Berlin: Springer Verlag, 1981. 259 p.Hwang, C.L., & Yoon, K. (1981). Multiple attribute decision making: Methods and applications. Springer Verlag.Triantaphyllou E. Multi-Criteria Decision Making Methods: A Comparative Study. Dordrecht: Springer, 2000. 290 p.Triantaphyllou, E. (2000). Multi criteria decision making methods: A comparative study. Springer.Opricovic S., Tzeng G.-H. Compromise solution by MCDM methods: a comparative analysis of VIKOR and TOPSIS // European Journal of Operational Research. 2004. Vol. 156, No. 2. P. 445–455. DOI: https://doi.org/10.1016/S0377 2217(03)000201.Opricovic, S., & Tzeng, G.-H. (2004). Compromise solution by MCDM methods: A comparative analysis of VIKOR and TOPSIS. European Journal of Operational Research, 156(2), 445–455. https://doi.org/10.1016/S0377-2217(03)00020 1Zavadskas E.K., Turskis Z. A new additive ratio assessment (ARAS) method in multicriteria decision-making // Technological and Economic Development of Economy. 2010. Vol. 16, No. 2. P. 159–172. DOI: https://doi.org/10.3846/tede.2010.10.Zavadskas, E.K., & Turskis, Z. (2010). A new additive ratio assessment (ARAS) method in multicriteria decision-making. Technological and Economic Development of Economy, 16(2), 159–172. https://doi.org/10.3846/tede.2010.10Taha H.A. Operations Research: An Introduction. 10th ed. Harlow: Pearson, 2017. 848 p.Taha, H.A. (2017). Operations research: An introduction (10th ed.). Pearson.Keeney R.L., Raiffa H. Decisions with Multiple Objectives: Preferences and Value Trade-offs. Cambridge: Cambridge University Press, 1993. 569 p.Keeney, R.L., & Raiffa, H. (1993). Decisions with multiple objectives: Preferences and value trade offs. Cambridge University Press.Wei Y.-M. A hybrid multi-criteria decision-making framework for the strategic evaluation of business development models // Information. 2025. Vol. 16, No. 6. Article 454. DOI: https://doi.org/10.3390/info16060454.Wei, Y.-M. (2025). A hybrid multi-criteria decision-making framework for the strategic evaluation of business development models. Information, 16(6), Article 454. https://doi.org/10.3390/info16060454Rodríguez Carrillo M.L., Pérez Domínguez L., Romero López R., et al. A systematic literature review on the use of multicriteria decision-making methods for SME innovation assessment // Frontiers in Artificial Intelligence. 2025. Vol. 8. Article 1605756. DOI: https://doi.org/10.3389/frai.2025.1605756.Rodríguez Carrillo, M.L., Pérez Domínguez, L., Romero López, R., et al. (2025). A systematic literature review on the use of multicriteria decision-making methods for SME innovation assessment. Frontiers in Artificial Intelligence, 8, Article 1605756. https://doi.org/10.3389/frai.2025.1605756Alkan N., Kahraman C. Prioritization of supply chain digital transformation strategies using multi-expert Fermatean fuzzy analytic hierarchy process // Informatica. 2023. Vol. 34, No. 1. P. 1–33. DOI: https://doi.org/10.15388/22 INFOR493.Alkan, N., & Kahraman, C. (2023). Prioritization of supply chain digital transformation strategies using multi-expert Fermatean fuzzy analytic hierarchy process. Informatica, 34(1), 1–33. https://doi.org/10.15388/22-INFOR493Torbacki W. An integrated MCDA framework for prioritising emerging technologies in the transition from Industry 4.0 to Industry 5.0 // Applied Sciences. 2025. Vol. 15, No. 15. Article 8168. DOI: https://doi.org/10.3390/app15158168.Torbacki, W. (2025). An integrated MCDA framework for prioritising emerging technologies in the transition from Industry 4.0 to Industry 5.0. Applied Sciences, 15(15), Article 8168. https://doi.org/10.3390/app15158168Kulikova V., Iklassova K., Kazanbayeva A. Development of a decision making method to form the indicators for a university development plan // Eastern European Journal of Enterprise Technologies. 2019. Vol. 3/3(99). P. 12–21. DOI: https://doi.org/10.15587/17294061.2019.169193.Kulikova, V., Iklassova, K., & Kazanbayeva, A. (2019). Development of a decision making method to form the indicators for a university development plan. Eastern European Journal of Enterprise Technologies, 3/3(99), 12–21. https://doi.org/10.15587/1729-4061.2019.169193Kurmasheva L., Kurmashev I., Kulikov V., Kulikova V., Tajigitov A. The use of data mining in the management of the career guidance work of the university // Annals of Data Science. 2024. Vol. 12. P. 1923–1940. DOI: https://doi.org/10.1007/s40745024 00585 6.Kurmasheva, L., Kurmashev, I., Kulikov, V., Kulikova, V., & Tajigitov, A. (2024). The use of data mining in the management of the career guidance work of the university. Annals of Data Science, 12, 1923–1940. https://doi.org/10.1007/s40745-024-00585-6Куликов В.П., Куликова В.П. Практика информационной поддержки принятия решений: учеб. пособие. - Петропавловск: СКУ им. М. Козыбаева, 2023. - 203 с. ISBN 978 601 223 649 1. УДК 001.891. ББК 72. К90Kulikov, V.P., & Kulikova, V.P. (2023). Praktika informacionnoj podderzhki prinyatiya reshenij [Uchebnoe posobie]. - Petropavlovsk: SKU im. M. Kozybaeva. ISBN 978 601 223 649 1.

The authors declare that there are no conflicts of interest present.