Current Trends and Future Directions of Big Data in Commerce: A Bibliometric Analysis Based on Scopus
Main Article Content
Abstract
Big data provides significant benefits across various sectors, including commerce. However, there remained a gap in bibliometric studies examining big data within the context of commerce, leaving research development in this field unclear. This study aimed to address this gap by conducting a bibliometric investigation into researchers' contributions to big data in commerce, including their affiliations and countries of origin. Additionally, the study sought to identify the most productive journals and highlight relevant and under-researched topics within this field. A bibliometric analysis approach was employed, analyzing 396 Scopus-indexed documents and using VOSviewer visualization to identify major recurring issues in the literature. The findings revealed that in 2021, the number of publications on big data in commerce peaked at 97 documents. Maalla, A., from Guangzhou College of Technology and Business, China, emerged as the most prolific author, while China led in publication output with 308 documents. The Journal of Physics Conference Series was identified as the most productive source. Computer Science was the most explored discipline, indicating a strong integration of technology with commerce. Keyword analysis divided research focus into four main clusters: analytical technology, platform optimization, supply chain management, and marketing strategy optimization. These findings provide a foundation for future research to explore areas such as Customer Experience Management, Blockchain Technology, Cloud Computing, Predictive Analytics, and Customer Segmentation, thereby enriching the academic literature and offering practical contributions to data-driven commerce.
Article Details

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Authors who publish their manuscripts through the Journal of Information Systems and Computer Science agree to the following:
- Copyright to the manuscripts of scientific papers in this Journal is held by the author.
- The author surrenders the rights when first publishing the manuscript of his scientific work and simultaneously the author grants permission / license by referring to the Creative Commons Attribution-ShareAlike 4.0 International License to other parties to distribute his scientific work while still giving credit to the author and the Journal of Information Systems and Computer Science as the first publication medium for the work.
- Matters relating to the non-exclusivity of the distribution of the Journal that publishes the author's scientific work can be agreed separately (for example: requests to place the work in the library of an institution or publish it as a book) with the author as one of the parties to the agreement and with credit to sJournal of Information Systems and Computer Science as the first publication medium for the work in question.
- Authors can and are expected to publish their work online (e.g. in a Repository or on their Organization's/Institution's website) before and during the manuscript submission process, as such efforts can increase citation exchange earlier and with a wider scope.
References
Akter, S., & Wamba, S. F. (2016). Big data analytics in E-commerce: A systematic review and agenda for future research. Electronic Markets, 26(2), 173–194. Scopus. https://doi.org/10.1007/s12525-016-0219-0
Alrumiah, S. S., & Hadwan, M. (2021). Implementing big data analytics in e-commerce: Vendor and customer view. IEEE Access, 9, 37281–37286. Scopus. https://doi.org/10.1109/ACCESS.2021.3063615
Alsmadi, A. A., Shuhaiber, A., Al-Okaily, M., Al-Gasaymeh, A., & Alrawashdeh, N. (2023). Big data analytics and innovation in e-commerce: Current insights and future directions. Journal of Financial Services Marketing. Scopus. https://doi.org/10.1057/s41264-023-00235-7
Behl, A., Dutta, P., Lessmann, S., Dwivedi, Y. K., & Kar, S. (2019). A conceptual framework for the adoption of big data analytics by e-commerce startups: A case-based approach. Information Systems and E-Business Management, 17(2–4), 285–318. Scopus. https://doi.org/10.1007/s10257-019-00452-5
Byrapu Reddy, S. R., Kanagala, P., Ravichandran, P., Pulimamidi, D. R., Sivarambabu, P. V., & Polireddi, N. S. A. (2024). Effective fraud detection in e-commerce: Leveraging machine learning and big data analytics. Measurement: Sensors, 33. Scopus. https://doi.org/10.1016/j.measen.2024.101138
Chen, S., & Sapna Kumari, C. (2024). Evaluation of E-commerce Supply Chain Cost Management Based on Big Data Intelligent Platform Processing. Lecture. Notes. Data Eng. Commun. Tech., 198, 253–264. Scopus. https://doi.org/10.1007/978-981-97-1983-9_23
Dong, S. (2015). Research on the big data model of E-Commerce in cloud networking based on consumer behavior. Metallurgical and Mining Industry, 7(9), 516–521. Scopus.
Eastin, M. S., Brinson, N. H., Doorey, A., & Wilcox, G. (2016). Living in a big data world: Predicting mobile commerce activity through privacy concerns. Computers in Human Behavior, 58, 214–220. Scopus. https://doi.org/10.1016/j.chb.2015.12.050
Ellili, N., Nobanee, H., Alsaiari, L., Shanti, H., Hillebrand, B., Hassanain, N., & Elfout, L. (2023). The applications of big data in the insurance industry: A bibliometric and systematic review of relevant literature. Journal of Finance and Data Science, 9. Scopus. https://doi.org/10.1016/j.jfds.2023.100102
Fang, Q., Hu, Y., Lv, S., Guo, L., Xiao, L., & Hu, Y. (2015). IIRS: A novel framework of identifying commodity entities on e-commerce big data. In Sun Y. & Li J. (Eds.), Lect. Notes Comput. Sci. (Vol. 9098, pp. 473–480). Springer Verlag; Scopus. https://doi.org/10.1007/978-3-319-21042-1_44
Fauzi, M. A., Kamaruzzaman, Z. A., & Abdul Rahman, H. (2023). Bibliometric review on human resources management and big data analytics. International Journal of Manpower, 44(7), 1307–1327. Scopus. https://doi.org/10.1108/IJM-05-2022-0247
Ge, F., Li, Q., & Nazir, S. (2023). The Impact of E-Commerce Live Broadcast on Happiness With Big Data Analysis. Journal of Organizational and End User Computing, 35(1). Scopus. https://doi.org/10.4018/JOEUC.333619
Le, T. M., & Liaw, S.-Y. (2017). Effects of pros and cons of applying big data analytics to consumers’ responses in an e-commerce context. Sustainability (Switzerland), 9(5). Scopus. https://doi.org/10.3390/su9050798
Li, L., Chi, T., Hao, T., & Yu, T. (2018). Customer demand analysis of the electronic commerce supply chain using Big Data. Annals of Operations Research, 268(1–2), 113–128. Scopus. https://doi.org/10.1007/s10479-016-2342-x
Mahajan, K., Bordoloi, D., Barboza, C., Bansal, D., Madhava Rao, B., & Sri Varshini, S. (2024). Big Data with Cloud Computing Model for Customer Need Identification in E-Commerce Industry. In Mahato G.C., S. S., & Dash S. (Eds.), Int. Conf. Recent Trends Comput. Sci. Technol., ICRTCST - Proc. (pp. 155–159). Institute of Electrical and Electronics Engineers Inc.; Scopus. https://doi.org/10.1109/ICRTCST61793.2024.10578354
Munshi, A., Alhindi, A., Qadah, T. M., & Alqurashi, A. (2023). An Electronic Commerce Big Data Analytics Architecture and Platform. Applied Sciences (Switzerland), 13(19). Scopus. https://doi.org/10.3390/app131910962
Pande, L., & Sengupta, S. (2024). Digital Commerce and Big Data revolutionizing the tourism industry: A review article. IEEE Int. Conf. Converg. Technol., I2CT. 2024 IEEE 9th International Conference for Convergence in Technology, I2CT 2024. Scopus. https://doi.org/10.1109/I2CT61223.2024.10544348
Pandey, D. K., Hunjra, A. I., Bhaskar, R., & Al-Faryan, M. A. S. (2023). Artificial intelligence, machine learning and big data in natural resources management: A comprehensive bibliometric review of literature spanning 1975–2022. Resources Policy, 86. Scopus. https://doi.org/10.1016/j.resourpol.2023.104250
Peng, Z. L., & Huang, Y. L. (2014). Research on E-commerce intelligence based on IOT and big data. Appl. Mech. Mater., 496–500, 1889–1894. Scopus. https://doi.org/10.4028/www.scientific.net/AMM.496-500.1889
Philippov, S. A., Zakharov, V. N., Stupnikov, S. A., & Kovalev, D. Yu. (2015). Organization of big data in the global e-Commerce platforms. In Kalinichenko L. & Starkov S. (Eds.), CEUR Workshop Proc. (Vol. 1536, pp. 119–124). CEUR-WS; Scopus. https://www.scopus.com/inward/record.uri?eid=2-s2.0-84962287417&partnerID=40&md5=89f7cb511c3626283b116fb56a59a16e
Ramkumar, A., Kulkarni, P., Obaid, A. J., Abdulbaqi, A. S., & Al Yakin, A. (2023). Big Data Analytics and Its Application in E-Commerce. In Kristiawan M., La’biran R., Arrang J.R.T., Obaid A.J., Muthmainnah null, Apriani E., & Elngar A.A. (Eds.), AIP Conf. Proc. (Vol. 2736, Issue 1). American Institute of Physics Inc.; Scopus. https://doi.org/10.1063/5.0170687
Ran, J., Ma, H., & Ran, R. (2024). The role of big data and IoT in logistics supply chain management of e-commerce. Journal of Computational Methods in Sciences and Engineering, 24(2), 813–822. Scopus. https://doi.org/10.3233/JCM-237067
Roy, S., Salve, A. R., Shah, J. A., Kadam, S., Muda, I., & Dash, M. (2022). Artificial Intelligence Based Rural E-Commerce Boosting Using Big Data. Proc. Int. Conf. Contemp. Comput. Informatics, IC3I, 2087–2093. Scopus. https://doi.org/10.1109/IC3I56241.2022.10073248
Sahoo, S. (2022). Big data analytics in manufacturing: A bibliometric analysis of research in the field of business management. International Journal of Production Research, 60(22), 6793–6821. Scopus. https://doi.org/10.1080/00207543.2021.1919333
Samsul, S. A., Yahaya, N., & Abuhassna, H. (2023). Education big data and learning analytics: A bibliometric analysis. Humanities and Social Sciences Communications, 10(1). Scopus. https://doi.org/10.1057/s41599-023-02176-x
Sharma, D., Maurya, S., Punhan, R., Ojha, M. K., & Ojha, P. (2023). E-Commerce: Reach Customers and Drive Sales with Data Science and Big Data Analytics. Int. Conf. Innov. Technol., INOCON. 2023 2nd International Conference for Innovation in Technology, INOCON 2023. Scopus. https://doi.org/10.1109/INOCON57975.2023.10101132
Sun, C., Gao, R., & Xi, H. (2014). Big data based retail recommender system of non E-commerce. Int. Conf. Comput. Commun. Netw. Technol., ICCCNT. 5th International Conference on Computing Communication and Networking Technologies, ICCCNT 2014. Scopus. https://doi.org/10.1109/ICCCNT.2014.6963129
Tamasiga, P., Ouassou, E. H., Onyeaka, H., Bakwena, M., Happonen, A., & Molala, M. (2023). Forecasting disruptions in global food value chains to tackle food insecurity: The role of AI and big data analytics – A bibliometric and scientometric analysis. Journal of Agriculture and Food Research, 14. Scopus. https://doi.org/10.1016/j.jafr.2023.100819
Thayyib, P. V., Mamilla, R., Khan, M., Fatima, H., Asim, M., Anwar, I., Shamsudheen, M. K., & Khan, M. A. (2023). State-of-the-Art of Artificial Intelligence and Big Data Analytics Reviews in Five Different Domains: A Bibliometric Summary. Sustainability (Switzerland), 15(5). Scopus. https://doi.org/10.3390/su15054026
Wu, P.-J., & Lin, K.-C. (2018). Unstructured big data analytics for retrieving e-commerce logistics knowledge. Telematics and Informatics, 35(1), 237–244. Scopus. https://doi.org/10.1016/j.tele.2017.11.004
Xiong, Z. K., Wan, P. Z., & Cai, J. P. (2014). Study on e-commerce platform operation mechanism in big data environmen. In Lin Z., Hu H., Zhang Y., Qiao J., & Xu J. (Eds.), Appl. Mech. Mater. (Vols. 687–691, pp. 2776–2779). Trans Tech Publications Ltd; Scopus. https://doi.org/10.4028/www.scientific.net/AMM.687-691.2776
Yesudas, M., Menon, G., & Ramamurthy, V. (2014). Intelligent operational dashboards for smarter commerce using big data. IBM Journal of Research and Development, 58(5–6). Scopus. https://doi.org/10.1147/JRD.2014.2346131
Yim, S. T., Son, J. C., & Lee, J. (2022). Spread of E-commerce, prices and inflation dynamics: Evidence from online price big data in Korea. Journal of Asian Economics, 80. Scopus. https://doi.org/10.1016/j.asieco.2022.101475
Zhang, B., Du, Z., Wang, B., & Wang, Z. (2019). Motivation and challenges for e-commerce in e-waste recycling under “Big data” context: A perspective from household willingness in China. Technological Forecasting and Social Change, 144, 436–444. Scopus. https://doi.org/10.1016/j.techfore.2018.03.001
Zhang, D., & Huang, M. (2022). A Precision Marketing Strategy of e-Commerce Platform Based on Consumer Behavior Analysis in the Era of Big Data. Mathematical Problems in Engineering, 2022. Scopus. https://doi.org/10.1155/2022/8580561
Zhang, X., & Chen, M. (2014). Application of big data technology in unstructured data management for the railway freight E-commerce. In Zhang J., Zhang X., Yi P., & Wang K. (Eds.), ICLEM: Syst. Plan., Supply Chain Manag., Saf. - Proc. Int. Conf. Logist. Eng. Manag. (pp. 1155–1161). American Society of Civil Engineers (ASCE); Scopus. https://doi.org/10.1061/9780784413753.175
Zhao, Y., Li, D., & Pan, L. (2015). Cooperation or competition: An evolutionary game study between commercial banks and big data-based e-commerce financial institutions in China. Discrete Dynamics in Nature and Society, 2015. Scopus. https://doi.org/10.1155/2015/890972
Zheng, K., Zhang, Z., & Song, B. (2020). E-commerce logistics distribution mode in big-data context: A case analysis of JD.COM. Industrial Marketing Management, 86, 154–162. Scopus. https://doi.org/10.1016/j.indmarman.2019.10.009