Published: 2023-12-10
Analysis of E-Commerce Purchase Patterns Using Big Data: An Integrative Approach to Understanding Consumer Behavior
DOI: 10.35870/ijsecs.v3i3.1840
Caroline, Yuswardi, Yulianto Umar Rofi'i
-
Caroline:
Universitas Sultan Fatah
-
Yuswardi:
Universitas Jabal Ghafur
- Yulianto Umar Rofi'i: Institut Teknologi dan Bisnis Muhammadiyah Bali
Abstract
This research undertakes a meticulous examination of the Indonesian e-commerce industry, aiming to unravel the intricate patterns governing consumer behavior within this rapidly evolving digital landscape. Employing an extensive dataset and cutting-edge data analysis methodologies, this study discerns pivotal trends that have engendered transformative shifts in Indonesia's e-commerce sector. A conspicuous trend uncovered is the escalating reliance on instant messaging platforms and social media conduits for e-commerce transactions. This pronounced transition underscores the remarkable adaptability of businesses to the digital milieu, thereby accentuating the significance of a digitally oriented business paradigm. Furthermore, this research brings to light the prevailing predilection among non-formal e-commerce enterprises, whose revenues predominantly dwell below the IDR 300 million threshold. Notably, the Cash on Delivery (COD) method remains the preeminent payment mechanism. These observations illuminate the structural underpinnings of the market and consumer payment proclivities, thereby exerting a discernible influence on pricing strategies and payment processing mechanisms adopted by enterprises. Moreover, the study delves into the transformative effects of the COVID-19 pandemic, which have expedited the digital metamorphosis of both consumers and e-commerce enterprises. This acceleration has ushered in a new epoch characterized by novel opportunities and concomitant challenges within the e-commerce domain. In summation, this research furnishes a multidimensional and academically rigorous perspective on the Indonesian e-commerce landscape, furnishing actionable insights indispensable for businesses and policymakers alike. The comprehension of these evolving trends is indispensable for strategic formulation and policy calibration, enabling adept navigation of the dynamic e-commerce milieu.
Keywords
E-Commerce; Consumer Behavior; Big Data Analysis; Digital Transformation; COVID-19 Impact
Article Information
This article has been peer-reviewed and published in the International Journal Software Engineering and Computer Science (IJSECS). The content is available under the terms of the Creative Commons Attribution 4.0 International License.
-
Issue: Vol. 3 No. 3 (2023)
-
Section: Articles
-
Published: 2023-12-10
-
License: CC BY 4.0
-
Copyright: © 2023 Authors
-
DOI: 10.35870/ijsecs.v3i3.1840
AI Research Hub
This article is indexed and available through various AI-powered research tools and citation platforms. Our AI Research Hub ensures that scholarly work is discoverable, accessible, and easily integrated into the global research ecosystem.
Caroline, Universitas Sultan Fatah
Development Economics Study Program, Faculty of Economics and Social Sciences, Universitas Sultan Fatah, Demak Regency, Central Java Province, Indonesia
Yuswardi, Universitas Jabal Ghafur
Informatics Engineering Study Program, Universitas Jabal Ghafur, Pidie District, Aceh Province, Indonesia
Yulianto Umar Rofi'i, Institut Teknologi dan Bisnis Muhammadiyah Bali
Digital Business Study Program, Institut Teknologi dan Bisnis Muhammadiyah Bali, Jembrana Regency, Bali Province, Indonesia
-
Zubaidah, I. and Latief, M.J., 2022. Analisis Proses Pengambilan Keputusan Konsumen E-commerce Shopee di Lingkungan RT08/RW10 Sriamur Bekasi. Jurnal EMT KITA, 6(2), pp.324-333. DOI: https://doi.org/10.35870/emt.v6i2.674.
-
Delvisa, E., 2023. Pengaruh E-Commerce, Pengetahuan Kewirausahaan dan Sistem Informasi Akuntansi pada Minat Berwirausaha. Jurnal EMT KITA, 7(3), pp.630-636. DOI: https://doi.org/10.35870/emt.v7i3.1170.
-
Wattimena, F.Y. and Rofi'i, Y.U., 2023. E-Commerce Product Recommendation System Using Case-Based Reasoning (CBR) and K-Means Clustering. International Journal Software Engineering and Computer Science (IJSECS), 3(2), pp.162-173. DOI: https://doi.org/10.35870/ijsecs.v3i2.1527.
-
Favaretto, M., De Clercq, E., Schneble, C.O. and Elger, B.S., 2020. What is your definition of Big Data? Researchers’ understanding of the phenomenon of the decade. PloS one, 15(2), p.e0228987. DOI: https://doi.org/10.1371/journal.pone.0228987.
-
Sivarajah, U., Kamal, M.M., Irani, Z. and Weerakkody, V., 2017. Critical analysis of Big Data challenges and analytical methods. Journal of business research, 70, pp.263-286. DOI: https://doi.org/10.1016/j.jbusres.2016.08.001.
-
Akter, S. and Wamba, S.F., 2016. Big data analytics in E-commerce: a systematic review and agenda for future research. Electronic Markets, 26, pp.173-194. DOI: https://doi.org/10.1007/s12525-016-0219-0.
-
Le, T.M. and Liaw, S.Y., 2017. Effects of pros and cons of applying big data analytics to consumers’ responses in an e-commerce context. Sustainability, 9(5), p.798. DOI: https://doi.org/10.3390/su9050798.
-
Zineb, E.F., Najat, R.A.F.A.L.I.A. and Jaafar, A.B.O.U.C.H.A.B.A.K.A., 2021. An intelligent approach for data analysis and decision making in big data: a case study on e-commerce industry. International Journal of Advanced Computer Science and Applications, 12(7).
-
Song, Z., Sun, Y., Wan, J., Huang, L. and Zhu, J., 2019. Smart e-commerce systems: current status and research challenges. Electronic Markets, 29, pp.221-238. DOI: https://doi.org/10.1007/s12525-017-0272-.
-
Alrumiah, S.S. and Hadwan, M., 2021. Implementing big data analytics in e-commerce: Vendor and customer view. Ieee Access, 9, pp.37281-37286.DOI: https://doi.org/10.1109/ACCESS.2021.3063615.
-
Anshari, M., Almunawar, M.N., Lim, S.A. and Al-Mudimigh, A., 2019. Customer relationship management and big data enabled: Personalization & customization of services. Applied Computing and Informatics, 15(2), pp.94-101.DOI: https://doi.org/10.1016/j.aci.2018.05.004.
-
Rane, N., 2023. Enhancing Customer Loyalty through Artificial Intelligence (AI), Internet of Things (IoT), and Big Data Technologies: Improving Customer Satisfaction, Engagement, Relationship, and Experience. Internet of Things (IoT), and Big Data Technologies: Improving Customer Satisfaction, Engagement, Relationship, and Experience (October 13, 2023). DOI: http://dx.doi.org/10.2139/ssrn.4616051.
-
Praveen, K.B., Kumar, P., Prateek, J., Pragathi, G. and Madhuri, J., 2020. Inventory management using machine learning. International Journal of Engineering Research & Technology (IJERT), 9(06), pp.866-869.
-
Kietzmann, J., Paschen, J. and Treen, E., 2018. Artificial intelligence in advertising: How marketers can leverage artificial intelligence along the consumer journey. Journal of Advertising Research, 58(3), pp.263-267. DOI: https://doi.org/10.2501/JAR-2018-035
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Authors who publish with this journal agree to the following terms:
1. Copyright Retention and Open Access License
Authors retain copyright of their work and grant the journal non-exclusive right of first publication under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
This license allows unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
2. Rights Granted Under CC BY 4.0
Under this license, readers are free to:
- Share — copy and redistribute the material in any medium or format
- Adapt — remix, transform, and build upon the material for any purpose, including commercial use
- No additional restrictions — the licensor cannot revoke these freedoms as long as license terms are followed
3. Attribution Requirements
All uses must include:
- Proper citation of the original work
- Link to the Creative Commons license
- Indication if changes were made to the original work
- No suggestion that the licensor endorses the user or their use
4. Additional Distribution Rights
Authors may:
- Deposit the published version in institutional repositories
- Share through academic social networks
- Include in books, monographs, or other publications
- Post on personal or institutional websites
Requirement: All additional distributions must maintain the CC BY 4.0 license and proper attribution.
5. Self-Archiving and Pre-Print Sharing
Authors are encouraged to:
- Share pre-prints and post-prints online
- Deposit in subject-specific repositories (e.g., arXiv, bioRxiv)
- Engage in scholarly communication throughout the publication process
6. Open Access Commitment
This journal provides immediate open access to all content, supporting the global exchange of knowledge without financial, legal, or technical barriers.