Authors :
Dr. Richa Kalpesh Saxena; Janhavi Tripathi; Yash Joshi; Zaina Mangrolia; Kartavya Jain
Volume/Issue :
Volume 11 - 2026, Issue 10 - October
Google Scholar :
https://tinyurl.com/3a3dftb4
DOI :
https://doi.org/10.38124/ijisrt/26Oct1274
Note : A published paper may take 4-5
working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and
ResearchGate.
Abstract :
A huge amount of every search, click, cart addition and review can be recorded on ecommerce platforms, and businesses today are turning to analytics and operations research (OR) to take action based on these impressions. This study traces the evolution of research on online consumers' behavior from analytics and OR perspective. Using PRISMA framework, 1,382 records from Scopus were identified on 16 September 2026, and then screened to 595 English-language journal articles and reviews published from 2013 to 2026 from 377 sources, authored by 1,810 authors. The literature review was supplemented by a cluster map in VOSviewer, based on co-occurrence of keywords, and a thematic mapping and a three field plot in the software Biblioshiny were examined to gain insight into the performance.
Keywords :
Online Consumer Behaviour, E-Commerce, Operations Research, Analytics, Bibliometric Analysis, Biblioshiny.
References :
- Almashaleh, O., & Fatahi Valilai, O. (2026). Causal Drivers of Sustainable Social Media Engagement in the Textile Industry: A Double Machine Learning Approach. IEEE Transactions on Engineering Management, 73, 495–509. https://doi.org/10.1109/TEM.2025.3640875
- Al-Olfi, S. A. A. (2026). The Impact of Sustainable Supply Chain Strategies on E-Commerce Consumer Purchasing Behavior: Considering the Moderating Effect of Big Data Analytics. Journal of the Knowledge Economy, 17(1), 364–399. https://doi.org/10.1007/s13132-025-02671-3
- Al-Zagheer, H., Alsaaideh, M., Kilani, Y., Alkasasbeh, A. E. A. H., Alhanatleh, H., & Al-Sukkar, A. S. (2026). Exploring the influence of AI–driven personalization capabilities on consumer purchase intention: The mediating role of customer engagement. International Journal of Data and Network Science, 10(3), 1073–1082. https://doi.org/10.5267/j.ijdns.2026.4.019
- Anju, L., & Veni, S. (2026). A Machine Learning Framework for Predicting Online Purchasing Intensity Using Composite Behavioral Indices and Balanced Classification Techniques. Journal of Internet Services and Information Security, 16(1), 365–384. https://doi.org/10.58346/JISIS.2026.I1.021
- Aria, M., & Cuccurullo, C. (2017). bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959–975. https://doi.org/10.1016/j.joi.2017.08.007
- Awajan, I., Mohamad, M., & Al-Quran, A. (2021). Sentiment Analysis Technique and Neutrosophic Set Theory for Mining and Ranking Big Data from Online Reviews. IEEE Access, 9, 47338–47353. https://doi.org/10.1109/ACCESS.2021.3067844
- Barbado, R., Araque, O., & Iglesias, C. A. (2019). A framework for fake review detection in online consumer electronics retailers. Information Processing and Management, 56(4), 1234–1244. https://doi.org/10.1016/j.ipm.2019.03.002
- Deng, L., Hu, X., & Xiao, Y. (2026a). Blockchain-enabled strategic optimization: A decision analytics framework for multinational enterprises’ e-commerce entry model. PLOS ONE, 21(5 May). https://doi.org/10.1371/journal.pone.0335655
- Deng, L., Hu, X., & Xiao, Y. (2026b). Blockchain-enabled strategic optimization: A decision analytics framework for multinational enterprises’ e-commerce entry model. PLOS ONE, 21(5 May). https://doi.org/10.1371/journal.pone.0335655
- Donthu, N., Kumar, S., Mukherjee, D., Pandey, N., & Lim, W. M. (2021). How to conduct a bibliometric analysis: An overview and guidelines. Journal of Business Research, 133(March), 285–296. https://doi.org/10.1016/j.jbusres.2021.04.070
- Guha, S., & Kumar, S. (2018). Emergence of Big Data Research in Operations Management, Information Systems, and Healthcare: Past Contributions and Future Roadmap. Production and Operations Management, 27(9), 1724–1735. https://doi.org/10.1111/poms.12833
- Han, F., & Guan, J. (2024). Investment in Data Analytics with Manufacturer Encroachment. Mathematics, 12(9). https://doi.org/10.3390/math12091371
- Haripriya, R., Khare, N., Pandey, M., Bhatia, G. K., Nandurkar, R., Hada, M., Biswas, S., Choudhary, J., Singh, D. P., & Gupta, S. (2026). Privacy-preserving federated analytics for digital marketing using adaptive weighted aggregation. Cluster Computing, 29(6). https://doi.org/10.1007/s10586-026-06206-6
- Jia, X., Xu, M., Xu, C., & Zhu, P. (2026a). An online review-driven decision-making method for electric vehicle selection based on satisfaction analysis. Expert Systems with Applications, 300. https://doi.org/10.1016/j.eswa.2025.130518
- Jia, X., Xu, M., Xu, C., & Zhu, P. (2026b). An online review-driven decision-making method for electric vehicle selection based on satisfaction analysis. Expert Systems with Applications, 300. https://doi.org/10.1016/j.eswa.2025.130518
- Jin, K., Zhang, Y., Fang, T., & Blanco-Encomienda, F. J. (2026). A scalable framework for building and validating knowledge-driven synthetic consumer agents with large language models. Journal of Industrial Information Integration, 53. https://doi.org/10.1016/j.jii.2026.101183
- Khan, Mohd. Y., Kumari, A., Pandey, Y., & Hasan, M. (2026). Retrospection of online reviews research landscape: foundation, current trends and future research directions. International Journal of Process Management and Benchmarking, 22(2), 149–173. https://doi.org/10.1504/IJPMB.2026.151148
- Khubchandani, S., & Raman, R. (2026). Insights into Gen Z online food ordering behavior: leveraging eye-tracking and AI for cognitive analysis. Benchmarking, 33(5), 1558–1576. https://doi.org/10.1108/BIJ-12-2024-1133
- Koçak, B. B. (2026). Forecasting box office revenue using AI algorithms and consumer search behavior. Journal of Revenue and Pricing Management, 25(4), 352–367. https://doi.org/10.1057/s41272-025-00568-z
- Kumar, G., & Rishiwal, V. (2020). Malicious User Nodes Detection by Web Mining Based Artificial Intelligence Technique. International Journal of Uncertainty, Fuzziness and Knowldege-Based Systems, 28(1), 1–24. https://doi.org/10.1142/S0218488520500014
- Kumar, R., Talaei, N. M., Kumar, A., Coussement, K., & Oztekin, A. (2026). Leveraging online reviews to decode quality-induced customer dissatisfaction: From perception to product discouragement. Decision Sciences, 57(3), 235–255. https://doi.org/10.1111/deci.70019
- Kyriakidis, A., & Tsafarakis, S. (2025a). Extracting knowledge from customer reviews: an integrated framework for digital platform analytics. International Transactions in Operational Research, 32(4), 2061–2086. https://doi.org/10.1111/itor.13537
- Kyriakidis, A., & Tsafarakis, S. (2025b). Extracting knowledge from customer reviews: an integrated framework for digital platform analytics. International Transactions in Operational Research, 32(4), 2061–2086. https://doi.org/10.1111/itor.13537
- Lee, E., Wang, Y., & Zhao, H. (2026). Who will generate buzz for you? Predicting review writers for local businesses and online sellers. Information and Management, 63(7). https://doi.org/10.1016/j.im.2026.104409
- Li, Z., & Hu, J. (2026). Research on analysis and improvement strategies of consumer loyalty on e-commerce platforms based on big data. International Journal of Information and Communication Technology, 27(16). https://doi.org/10.1504/IJICT.2026.152149
- Liu, Q., & Pareti, M. (2026). AI-driven sentiment analysis of grape wine consumers: A comparative study between China and Italy. Journal of Retailing and Consumer Services, 93. https://doi.org/10.1016/j.jretconser.2026.104888
- Mariani, M. M., & Fosso Wamba, S. (2020). Exploring how consumer goods companies innovate in the digital age: The role of big data analytics companies. Journal of Business Research, 121, 338–352. https://doi.org/10.1016/j.jbusres.2020.09.012
- Mei, L., Yang, Y., & Deng, Y. (2026a). Revisiting the S-O-R Framework in Live-Streaming Commerce: An AI-Driven Emotion Recognition Approach. Tehnicki Vjesnik, 33(4), 1430–1435. https://doi.org/10.17559/TV-20250906002961
- Mei, L., Yang, Y., & Deng, Y. (2026b). Revisiting the S-O-R Framework in Live-Streaming Commerce: An AI-Driven Emotion Recognition Approach. Tehnicki Vjesnik, 33(4), 1430–1435. https://doi.org/10.17559/TV-20250906002961
- Miura, K., Feng, X., Yamagata, H., Mikami, Y., & Zhang, K. (2026). Consumer pre-purchase safety behavior in online marketplaces: examining the roles of knowledge, two-layer perceived risk, and self-efficacy across product categories. Journal of Marketing Analytics. https://doi.org/10.1057/s41270-026-00502-1
- Modi, N., & Kumar, Y. (2026). Eye gaze analytics for enhancing online travel booking experiences: a cognitive and behavioral study. Quality and Quantity, 60(4), 12345–12370. https://doi.org/10.1007/s11135-025-02446-7
- Moher, D., Liberati, A., Tetzlaff, J., & Altman, D. G. (2010). Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement. International Journal of Surgery, 8(5), 336–341. https://doi.org/10.1016/j.ijsu.2010.02.007
- Ng, C. Y., Tang, Y. M., & Ho, G. T. S. (2026). A blended sentiment analysis, large language model, and multi-criteria decision-making (BSLMM) approach to sustainable product innovation. Soft Computing, 30(7), 4685–4701. https://doi.org/10.1007/s00500-026-11402-y
- Nguyen, T., Ta, N. P., Nguyen Hoang Phuong, T., Rodgers, W., & Lopez, A. (2026). Validating segmented algorithmic pathways for ethical online retail personalisation. International Journal of Retail and Distribution Management, 54(9), 983–1000. https://doi.org/10.1108/IJRDM-02-2026-0129
- Oh, C., Roumani, Y., Nwankpa, J. K., & Hu, H.-F. (2017). Beyond likes and tweets: Consumer engagement behavior and movie box office in social media. Information and Management, 54(1), 25–37. https://doi.org/10.1016/j.im.2016.03.004
- Okyere Sefa, A. A., Rezaei, M., & Fatahi Valilai, O. (2026). An analytics-driven method for building ethical customer digital twins using neuromarketing and social media data. Decision Analytics Journal, 18. https://doi.org/10.1016/j.dajour.2026.100689
- Pitka, T., Bucko, J., Krajči, S., Krídlo, O., Guniš, J., Šnajder, Ľ., Antoni, Ľ., & Eliaš, P. (2025). Time analysis of online consumer behavior by decision trees, GUHA association rules, and formal concept analysis. Journal of Marketing Analytics, 13(1), 29–52. https://doi.org/10.1057/s41270-023-00274-y
- Popescu, C., Gabor, M. R., & Stancu, A. (2025). E-Commerce Revolution: How the Pandemic Reshaped the US Consumer Shopping Habits: A PACF and ARIMA Approach. Systems, 13(9). https://doi.org/10.3390/systems13090802
- Pournarakis, D. E., Sotiropoulos, D. N., & Giaglis, G. M. (2017). A computational model for mining consumer perceptions in social media. Decision Support Systems, 93, 98–110. https://doi.org/10.1016/j.dss.2016.09.018
- Ray, R. K., & Singh, A. (2025). From online reviews to smartwatch recommendation: An integrated aspect-based sentiment analysis framework. Journal of Retailing and Consumer Services, 82. https://doi.org/10.1016/j.jretconser.2024.104059
- Salehan, M., & Kim, D. J. (2016). Predicting the performance of online consumer reviews: A sentiment mining approach to big data analytics. Decision Support Systems, 81, 30–40. https://doi.org/10.1016/j.dss.2015.10.006
- Song, P., Chang, J., & Wu, L. (2026). Strategic data sales on online platforms with consumer privacy concerns and protection. Industrial Management and Data Systems. https://doi.org/10.1108/IMDS-12-2025-1803
- Ströhle, P., Flath, C. M., & Gärttner, J. (2019). Leveraging customer flexibility for car-sharing fleet optimization. Transportation Science, 53(1), 42–61. https://doi.org/10.1287/trsc.2017.0813
- Suman, A., Bala, P. K., & Chakraborty, S. (2026). Technology Roadmapping for GenAI Chatbots in Online Shopping Context. International Journal of Human-Computer Interaction. https://doi.org/10.1080/10447318.2026.2684316
- Sumanth, Y., & Somasekhar, G. (2026). A bibliometric and thematic evolution analysis of artificial intelligence in online customer experience. Discover Artificial Intelligence, 6(1). https://doi.org/10.1007/s44163-026-01867-z
- Susarla, P., Purnell, D., & Scott, K. (2026). Zillow’s artificial intelligence failure and its impact on perceived trust in information systems. Journal of Information Technology Teaching Cases, 16(1), 13–19. https://doi.org/10.1177/20438869241279865
- Tao, D., Yang, P., & Feng, H. (2020). Utilization of text mining as a big data analysis tool for food science and nutrition. Comprehensive Reviews in Food Science and Food Safety, 19(2), 875–894. https://doi.org/10.1111/1541-4337.12540
- Tao, J., Fang, X., & Zhou, L. (2026). Toward a Knowledge Discovery Method to Fake Review Detection. Information Systems Frontiers, 28(3), 1109–1125. https://doi.org/10.1007/s10796-026-10715-5
- Toudas, K. S., Nika, P. P., Giannakopoulos, N. T., Sakas, D. P., & Karountzos, P. (2026). ESG-Driven Digital Performance Measurement and Decision Support in Vegan Food Firms. Administrative Sciences, 16(5). https://doi.org/10.3390/admsci16050206
- Tussyadiah, I. P., & Zach, F. (2017). Identifying salient attributes of peer-to-peer accommodation experience. Journal of Travel and Tourism Marketing, 34(5), 636–652. https://doi.org/10.1080/10548408.2016.1209153
- Verma, D., Dewani, P. P., Behl, A., & Dwivedi, Y. K. (2023). Understanding the impact of eWOM communication through the lens of information adoption model: A meta-analytic structural equation modeling perspective. Computers in Human Behavior, 143(August 2022), 107710. https://doi.org/10.1016/j.chb.2023.107710
- Wang, J., Li, C., & Shi, Y. (2026a). Live-streaming encroachment and blockchain resistance in online retail channels. International Journal of Production Economics, 295. https://doi.org/10.1016/j.ijpe.2026.109984
- Wang, J., Li, C., & Shi, Y. (2026b). Live-streaming encroachment and blockchain resistance in online retail channels. International Journal of Production Economics, 295. https://doi.org/10.1016/j.ijpe.2026.109984
- Wang, P., & Xu, Z. (2020). A Novel Consumer Purchase Behavior Recognition Method Using Ensemble Learning Algorithm. Mathematical Problems in Engineering, 2020. https://doi.org/10.1155/2020/6673535
- Xiang, Z., Du, Q., Ma, Y., & Fan, W. (2017). A comparative analysis of major online review platforms: Implications for social media analytics in hospitality and tourism. Tourism Management, 58, 51–65. https://doi.org/10.1016/j.tourman.2016.10.001
- Xu, X., Wang, X., Li, Y., & Haghighi, M. (2017). Business intelligence in online customer textual reviews: Understanding consumer perceptions and influential factors. International Journal of Information Management, 37(6), 673–683. https://doi.org/10.1016/j.ijinfomgt.2017.06.004
- Yang, Z., Wang, Q., Qiu, L., & Cheng, H. K. (2026). Identifying purchase-evoking social media posts: A theory-driven deep multimodal learning framework. Production and Operations Management. https://doi.org/10.1177/10591478261474393
- Yassine, A., Singh, S., Hossain, M. S., & Muhammad, G. (2019). IoT big data analytics for smart homes with fog and cloud computing. Future Generation Computer Systems, 91, 563–573. https://doi.org/10.1016/j.future.2018.08.040
- Zhu, Q., Wang, Y., Xu, X., & Sarkis, J. (2025a). How loud is consumer voice in product deletion decisions? Retail analytic insights. Journal of Retailing and Consumer Services, 82. https://doi.org/10.1016/j.jretconser.2024.104110
- Zhu, Q., Wang, Y., Xu, X., & Sarkis, J. (2025b). How loud is consumer voice in product deletion decisions? Retail analytic insights. Journal of Retailing and Consumer Services, 82. https://doi.org/10.1016/j.jretconser.2024.104110
A huge amount of every search, click, cart addition and review can be recorded on ecommerce platforms, and businesses today are turning to analytics and operations research (OR) to take action based on these impressions. This study traces the evolution of research on online consumers' behavior from analytics and OR perspective. Using PRISMA framework, 1,382 records from Scopus were identified on 16 September 2026, and then screened to 595 English-language journal articles and reviews published from 2013 to 2026 from 377 sources, authored by 1,810 authors. The literature review was supplemented by a cluster map in VOSviewer, based on co-occurrence of keywords, and a thematic mapping and a three field plot in the software Biblioshiny were examined to gain insight into the performance.
Keywords :
Online Consumer Behaviour, E-Commerce, Operations Research, Analytics, Bibliometric Analysis, Biblioshiny.