asuВестник Атырауского университета имени Халела ДосмухамедоваBulletin of the Khalel Dosmukhamedov Atyrau University2077-01972790-332XАтырауский университет имени Халела Досмухамедова10.47649/vau.26.v81.i2.29asu-2878Research ArticleЭКОНОМИКАECONOMICSМОДЕЛИРОВАНИЕ ВОСПРИЯТИЯ УМНОГО ГОРОДА ЖИТЕЛЯМИ НА ОСНОВЕ ФАКТОРНОГО АНАЛИЗАMODELING RESIDENTS’ PERCEPTION OF A SMART CITY BASED ON FACTOR ANALYSIShttps://orcid.org/0000-0002-6612-0991КулбаеваА.KulbayevaA.

Айгерим Кулбаева – PhD, старший преподаватель,

Рабиға Сұлтан Бегім 14Б, г. Туркестан

Aigerim Kulbayeva – PhD, Senior Lecturer,

Rabiga Sultan Begim 14B, Turkestan

aigerim.kulbayeva@iuth.edu.kzhttps://orcid.org/0000-0003-3625-6203ТубековаД.TubekovaD.

Дана Тубекова – PhD, старший преподаватель,

Рабиға Сұлтан Бегім 14Б, г. Туркестан

Danа Tubekova – PhD, Senior Lecturer, 

Rabiga Sultan Begim 14B, Turkestan

dana.tubekova@iuth.edu.kzhttps://orcid.org/0000-0002-7461-0384НахипбековаС.NakhipbekovaS.

Сымбат Нахипбекова – PhD, ассоцированный профессор,

Рабиға Сұлтан Бегім 14Б, г. Туркестан

Symbat Nakhipbekova – PhD, Associate Professor,

Rabiga Sultan Begim 14B, Turkestan

nahypbekova.symbat@iuth.edu.kzhttps://orcid.org/0000-0001-7695-5303БаймбетоваА.BaimbetovaA.

Асель Баймбетова – кандидат экономических наук, доцент, и.о. профессора кафедры Туризма и гостеприимства,

ул. Каныша Сатпаева, 2, г. Астана

Assel Baimbetova – Candidate of economic science, Acting Professor of the Department of Tourism and hospitality,

Kanysh Satbayev 2, Astana

baibaiassel@gmail.comМеждународный университет туризма и гостеприимстваКазахстанInternational University of Tourism and HospitalityKazakhstanЕвразийский национальный университет имени Л.Н. ГумилеваКазахстанL.N. Gumilyov Eurasian National UniversityKazakhstan202604102026812380395Copyright © Кулбаева А., Тубекова Д., Нахипбекова С., Баймбетова А., 20262026Кулбаева А., Тубекова Д., Нахипбекова С., Баймбетова А.Kulbayeva A., Tubekova D., Nakhipbekova S., Baimbetova A.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://asu.ejournal.kz/jour/article/view/2878

В данной статье рассматривается проблема моделирования принятия концепции «умного города» жителями на основе результатов факторного анализа. Цель исследования заключается в выявлении отношения населения к элементам Smart City, уровня их оценки и структуры восприятия, а также в определении влияния социально-демографических факторов на восприятие концепции Smart City. Эмпирической базой исследования послужили данные социологического опроса, проведённого среди жителей городов Туркестан и Алматы. Исследование основано на результатах анкетирования 680 респондентов. В процессе анализа данных были использованы методы описательной статистики, тесты сравнения средних значений, а также коэффициенты ранговой корреляции Спирмена и Кендалла. Дополнительно оценивались однородность ответов и степень взаимосвязи внутри тематических факторных групп. Результаты исследования показали, что по направлениям Smart Governance и Smart Mobility оценки жителей являются статистически однородными, тогда как по направлениям Smart Environment, Smart Living и Smart City Resources выявлены статистически значимые различия средних значений. Корреляционный анализ подтвердил наличие тесных и статистически значимых взаимосвязей между ответами респондентов по всем факторным группам (p < 0,001), что свидетельствует о системном характере восприятия элементов Smart City населением. Анализ социально-демографических переменных показал, что только уровень образования имеет устойчивую и статистически значимую связь с принятием элементов Smart City, в то время как влияние возраста, пола и места проживания не носит универсального характера. Полученные результаты подчёркивают важность учёта особенностей восприятия населения, уровня образования и применения citizen-centric подхода при разработке стратегий развития умных городов.

This article examines the modeling of residents’ acceptance of the Smart City concept based on the results of factor analysis. The aim of the study is to identify residents’ attitudes toward Smart City elements, their evaluation levels, and the structure of perception, as well as to assess the impact of socio-demographic factors. The empirical basis of the research consists of survey data collected from residents of the cities of Turkestan and Almaty. The study is based on the results of a questionnaire survey involving 680 respondents. Data analysis employed descriptive statistics, mean comparison tests, and Spearman’s and Kendall’s rank correlation coefficients. In addition, the homogeneity of responses and the degree of interrelationships within thematic factor groups were assessed. The findings indicate that residents’ evaluations of Smart Governance and Smart Mobility are statistically homogeneous, whereas statistically significant differences in mean values were identified for Smart Environment, Smart Living, and Smart City Resources. Correlation analysis revealed strong and statistically significant relationships among respondents’ answers across all factor groups (p < 0.001), indicating a systematic perception of Smart City elements among residents. The analysis of sociodemographic variables demonstrated that education level is the only factor showing a stable and statistically significant association with Smart City acceptance, while the effects of age, gender, and place of residence are not universal. The results highlight the importance of considering residents’ perceptions, educational background, and the application of a citizen-centric approach in the development of Smart City strategies.

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