2072-64142411-140610.17059/ekon.reg.2024-3-9Regional Differences of Women’s Online Shopping Behaviour in Kazakhstan: An In-Depth Analysis of the FactorsРегиональные различия в поведении женщин при совершении покупок в интернете в Казахстане: глубокий анализ факторовhttps://orcid.org/0000-0003-3412-3706KireyevaAnel A. КирееваАнель Ахметовна kireyeva.anel@ieconom.kzhttps://orcid.org/0000-0001-5390-5776Nurbatsin Akan S. Нурбацин Акан Сейткановичnakans_kz@mail.ruhttps://orcid.org/0000-0001-5442-7913Orynbet Perizat Zh. Орынбет Перизат Жангирқызы perizat.orynbet@mail.ruInstitute of Economics of the Science Committee of the Ministry of Science and Higher Education of the Republic of KazakhstanИнститут экономики Комитета науки Министерства науки и высшего образования Республики КазахстанKenzhegali Sagadiyev University of International BusinessУниверситет международного бизнеса имени Кенжегали СагадиеваTuran UniversityУниверситет Туран203732746Copyright © 2024 Anel A. Kireyeva, Akan S. Nurbatsin, Perizat Zh. OrynbetCopyright © 2024 Анель Ахметовна Киреева, Акан Сейтканович Нурбацин, Перизат Жангирқызы Орынбет2024Anel A. Kireyeva, Akan S. Nurbatsin, Perizat Zh. OrynbetАнель Ахметовна Киреева, Акан Сейтканович Нурбацин, Перизат Жангирқызы ОрынбетCC BY 4.0https://www.economyofregions.org/ojs/index.php/er/article/view/567https://www.economyofregions.org/ojs/index.php/er/article/download/567/332

Despite the growing popularity of online shopping, there is a lack of research on regional differences in consumer behaviour and preferences, particularly among women. The study aims to investigate the regional differences in women’s online shopping behaviour in Kazakhstan by conducting an in-depth analysis of the factors influencing female consumers. Using data from a survey of 400 women across different regions of Kazakhstan, logistic regression analysis was utilised to examine the relationship between online shopping frequency and several independent variables. The analysis found that the pandemic significantly affected online shopping behaviour in Kazakhstan, leading to decreased shopping frequency across all regions. Additionally, we found that women living in urban areas were significantly more likely to shop online frequently than those in rural areas, with an odds ratio of 0.504 (p = 0.014). The research also revealed notable differences in Internet penetration rates, with Karaganda, Pavlodar regions and Astana city having the highest rates among women (93.1 %, 93.0 %, and 94.5 %, respectively), while Atyrau and Kyzylorda regions had the lowest (80.7 % and 80.0 %). Therefore, it is recommended that policymakers should focus on expanding Internet infrastructure in remote regions by developing customised online marketplaces that meet the needs of urban areas like Almaty city. The findings of this study underscore the importance of considering regional differences in understanding the factors that drive online shopping behaviour in Kazakhstan. By investing in initiatives that promote e-commerce adoption and cater to consumers’ unique needs and preferences in different regions, policymakers can help foster a more inclusive and dynamic e-commerce ecosystem in Kazakhstan.

Несмотря на растущую популярность онлайн-покупок в настоящее время практически нет исследований, посвященных региональным различиям в поведении и предпочтениях потребителей, особенно среди женщин. Цель статьи — изучить региональные различия в поведении женщин при совершении покупок в интернете в Казахстане при помощи глубокого анализа факторов, влияющих на покупательниц. На основе данных опроса 400 женщин из разных регионов Казахстана была построена логистическая регрессионная модель для анализа взаимосвязи между частотой онлайн-покупок и несколькими независимыми переменными. Проведенный анализ показал, что пандемия значительно повлияла на поведение женщин при совершении покупок в интернете, приведя к снижению частоты покупок во всех регионах Казахстана. Кроме того, женщины, проживающие в городских районах, значительно чаще совершают покупки в интернете по сравнению с женщинами из сельской местности (отношение шансов 0,504 (p = 0,014)). Исследование также выявило заметные различия в уровне распространения интернета: самые высокие показатели среди женщин наблюдаются в Карагандинской, Павлодарской областях и Астане (93,1 %, 93,0 % и 94,5 % соответственно), а самые низкие — в Атырауской и Кызылординской областях (80,7 % и 80,0 %). Таким образом, необходима политика по расширению интернет-инфраструктуры в отдаленных регионах и созданию специализированных онлайн-рынков, которые отвечают потребностям городских районов, таких как Алматы. Результаты исследования подчеркивают важность учета региональных различий при изучении факторов, определяющих поведение при совершении покупок в интернете в Казахстане. Инвестиции в различные инициативы, способствующие внедрению электронной коммерции и учитывающие уникальные потребности и предпочтения потребителей в разных регионах, позволят сформировать более инклюзивную и динамичную экосистему электронной коммерции в Казахстане.

region, regional differences, rural areas, online shopping, online purchase, digital literacy, Kazakhstanрегион, региональные различия, сельская местность, онлайн-покупки, цифровая грамотность, Казахстан

The article has been prepared with the support of the Science Committee MSHE RK, AP14869297 "Priorities and mechanisms against rural women of Kazakhstan unequal access to the resources".

статья была подготовлена при поддержке Комитета науки МНВО РК, AP14869297 "Приоритеты и механизмы по преодолению неравного доступа сельских женщин Казахстана к ресурсам".

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Management Information Systems Quarterly, 36(1), 157-178. https://doi.org/10.2307/41410412Abdunurova, A. (2019). Digital behavior of consumers in Kazakhstan: approaches to online purchase and the relation to advertising in social networks. Central Asian Economic Review, (1), 55-64.Al-Qeisi, K., Dennis, C., Alamanos, E., & Jayawardhena, C. (2014). Website design quality and usage behavior: Unified Theory of Acceptance and Use of Technology. Journal of Business Research, 67(11), 2282–2290. https://doi.org/10.1016/j.jbusres.2014.06.016Aslam, W., Arif, I., Farhat, K., & Khursheed, M. (2018). The Role of Customer Trust, Service Quality and Value Dimensions in Determining Satisfaction and Loyalty: An Empirical Study of Mobile Telecommunication Industry in Pakistan. Market-Tržište, 30(2), 177-194. https://doi.org/10.22598/mt/2018.30.2.177Banerjee, S., & Seetharaman, P. (2022). How attractive is a locale to e-tailers? Introducing a regional e-tailing adoption model for non-metropolitan India. IIMB Management Review, 34(2), 116-129. https://doi.org/10.1016/j.iimb.2022.07.004 Bureau of National Statistics. (2021). Annual statistical collections of the Republic of Kazakhstan. https://stat.gov.kz/official (In Kazakh)Chen, L. D., Gillenson, M. L., & Sherrell, D. L. (2002). Enticing online consumers: An extended technology acceptance perspective. Information & Management, 39(8), 705-719. https://doi.org/10.1016/S0378-7206(01)00127-6 Chiang, K. P., & Dholakia, R. R. (2003). Factors driving consumer intention to shop online: an empirical investigation. Journal of Consumer psychology, 13(1-2), 177-183. https://doi.org/10.1207/S15327663JCP13-1&2_16Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. Management Information Systems Quarterly, 13(3), 319-340. https://doi.org/10.2307/249008Dittmar, H., Long, K., & Meek, R. (2004). Buying on the internet: Gender differences in on-line and conventional buying motivations. Sex Roles, 50, 423-444. https://doi.org/10.1023/B:SERS.0000018896.35251.c7Garbarino, E., & Strahilevitz, M. (2004). Gender differences in the perceived risk of buying online and the effects of receiving a site recommendation. Journal of Business Research, 57(7), 768-775. https://doi.org/10.1016/S0148-2963(02)00363-6Gefen, D., Karahanna, E., & Straub, D. W. (2003). Trust and TAM in online shopping: An integrated model. Management Information Systems Quarterly, 27(1), 51-90. https://doi.org/10.2307/30036519 Gu, Z., Wei, J., & Xu, F. (2016). An empirical study on factors influencing consumers' initial trust in wearable commerce. Journal of Computer Information Systems, 56(1), 79-85. https://doi.org/10.1080/08874417.2015.11645804 Hasan, B. (2010). Exploring gender differences in online shopping attitude. Computers in Human Behavior, 26(4), 597-601. https://doi.org/10.1016/j.chb.2009.12.012Hosmer, D. W., Lemeshow, S., & Sturdivant, R. X. (2013). Applied logistic regression. Hoboken, New Jersey: John Wiley & Sons. https://doi.org/10.1002/9781118548387 Hsiao, K. L., & Chen, C. C. (2016). What drives in-app purchase intention for mobile games? An examination of perceived values and loyalty. Electronic commerce research and applications, 16, 18-29. https://doi.org/10.1016/j.elerap.2016.01.001 Kim, J., & Lee, K. H. (2019). Influence of integration on interactivity in social media luxury brand communities. Journal of Business Research, 99, 422-429. https://doi.org/10.1016/j.jbusres.2017.10.001 Khamzina, Z., Buribayev, Y., Yermukanov, Y., & Alshurazova, A. (2020). Is it possible to achieve gender equality in Kazakhstan: Focus on employment and social protection. International Journal of Discrimination and the Law, 20(1), 5-20. https://doi.org/10.1177/1358229120927904 Kleisiari, C., Duquenne, M. N., & Vlontzos, G. (2021). E-Commerce in the Retail Chain Store Market: An Alternative or a Main Trend? Sustainability, 13(8), 4392. https://doi.org/10.3390/su13084392 Leong, M. K., & Chaichi, K. (2021). The Adoption of Technology Acceptance Model (TAM) and trust in influencing online purchase intention during the Covid-19 pandemic: Empirical evidence from Malaysia. International Journal of Academic Research in Business and Social Science, 11(8), 468-478. http://dx.doi.org/10.6007/IJARBSS/v11-i8/10422 Li, C., Zeng, J., Jin, X., & Chu, W. (2021). Quality index evaluation model of MPP sold under the E-commerce platform. Mathematical Problems in Engineering, 1-9. https://doi.org/10.1155/2021/5589839 Long, J. S., & Freese, J. (2014). Regression models for categorical dependent variables using Stata. College Station, Texas: Stata Press. Lu, J., Hung, K., Wang, L., Schuett, M. A., & Hu, L. (2016). Do perceptions of time affect outbound-travel motivations and intention? An investigation among Chinese seniors. Tourism Management, 53, 1-12. https://doi.org/10.1016/j.tourman.2015.09.003 Mahmood, M., Batool, S. H., Rafiq, M., & Safdar, M. (2022). Examining digital information literacy as a determinant of women's online shopping behavior. Information Technology & People, 35(7), 2098-2114. https://doi.org/10.1108/ITP-05-2021-0397Molla, A., & Licker, P. S. (2005). eCommerce adoption in developing countries: A model and instrument. Information & Management, 42(6), 877-899. https://doi.org/10.1016/j.im.2004.09.002Nitzl, C., Roldan, J. L., & Cepeda, G. (2016). Mediation analysis in partial least squares path modeling: Helping researchers discuss more sophisticated models. Industrial Management & Data Systems, 116(9), 1849-1864. https://doi.org/10.1108/IMDS-07-2015-0302Pavlou, P. A. (2003). Consumer acceptance of electronic commerce: Integrating trust and risk with the technology acceptance model. International Journal of Electronic Commerce, 7(3), 101-134. https://doi.org/10.1080/10864415.2003.11044275 Peng, C. Y. J., Lee, K. L., & Ingersoll, G. M. (2002). An introduction to logistic regression analysis and reporting. The Journal of Educational Research, 96(1), 3-14. https://doi.org/10.1080/00220670209598786 Riffai, M. M. M. A., Grant, K., & Edgar, D. (2012). Big TAM in Oman: Exploring the promise of on-line engagement via e-commerce adoption by SMEs. International Journal of Information Management, 32(3), 239-250. https://doi.org/10.1016/j.ijinfomgt.2011.11.007 Rodgers, S., & Harris, M. A. (2003). Gender and e-commerce: An exploratory study. Journal of Advertising Research, 43(3), 322-329. https://doi.org/10.1017/S0021849903030307 Sharma, A., & Jhamb, D. (2020). Changing consumer behaviours towards online shopping-an impact of Covid 19. Academy of Marketing Studies Journal, 24(3), 1-10. Sayyida, S., Hartini, S., Gunawan, S., & Husin, S. N. (2021). The impact of the COVID-19 pandemic on retail consumer behavior. Aptisi Transactions on Management, 5(1), 79-88. https://doi.org/10.33050/atm.v5i1.1497 Sigala, M. (2020). Tourism and COVID-19: Impacts and implications for advancing and resetting industry and research. Journal of Business Research, 117, 312-321. https://doi.org/10.1016/j.jbusres.2020.06.015 Stafford, T. F., Turan, A., & Raisinghani, M. S. (2004). International and cross-cultural influences on online shopping behavior. Journal of Global Information Technology Management, 7(2), 70-87. https://doi.org/10.1080/1097198X.2004.10856373 Toleuuly, A., Yessengeldin, B., Khussainova, Z., Yessengeldina, A., Zhanseitov, A., & Jumabaeva, S. (2020). Features of e-commerce risk management in modern conditions. Academy of Strategic Management Journal, 19(1), 1-6. Venkatesh, V., Thong, J. Y., & Xu, X. (2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. Management Information Systems Quarterly, 36(1), 157-178. https://doi.org/10.2307/41410412