Skip to main content
Log in

What prompts consumers to purchase online? A machine learning approach

  • Published:
Electronic Commerce Research Aims and scope Submit manuscript

Abstract

With e-commerce emerging as a prominent mode of purchasing, there is a pressing need for businesses across the globe to understand online consumer purchase behavior and, in particular, their purchase intention. Information on purchase behavior provides valuable insights for designing marketing activities to reach wider target audiences, promote greater customer involvement, and achieve higher investment returns. This research builds a novel algorithm for predicting the purchase intention of e-commerce website users. The dataset for the study was publically available online. Under-sampling was used to remove the imbalance in the dataset, and two-stage feature selection was applied to identify the most important consumer characteristics. Then, the greedy search and the wrapper methods were used to generate a dataset comprising the five most relevant features. Subsequently, an improved machine learning model was proposed based on stacking well-known classifiers and compared against state-of-the-art Machine Learning classifiers using various measures to evaluate its performance. Our results showed that the proposed algorithm returned the best overall accuracies for 50–50, 66–34, and 80–20 splits of the dataset. It also outperformed other classifiers in extant literature. Our findings help e-commerce sites offer their users predictive and personalized recommendations.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  1. Abdullah, S., Lim, Y. J., Osman, A., Romle, A. R., & Salahuddin, S. N. (2016). Factors influencing online shopping behavior: The mediating role of purchase intention. Procedia Economics and Finance, 35, 401–410. https://doi.org/10.1016/S2212-5671(16)00050-2

    Article  Google Scholar 

  2. Abdou, D., & Jasimuddin, S. M. (2020). The use of the UTAUT model in the adoption of E-Learning technologies: An empirical study in france based banks. Journal of Global Information Management (JGIM), 28(4), 38–51.

    Article  Google Scholar 

  3. Aggawal, R., Pramesh, C.S., & Ranganathan, P. (2017). Common pitfalls in statistical analysis: Logistic regression. https://doi.org/10.4103/picr.PICR_87_17.

  4. Ahluwalia, P., & Merhi, M. I. (2020). Understanding country level adoption of E-Commerce: A theoretical model including technological, institutional, and cultural factors. Journal of Global Information Management (JGIM), 28(1), 1–22.

    Article  Google Scholar 

  5. Akram, U., Junaid, M., Zafar, A. U., Li, Z., & Fan, M. (2021). Online purchase intention in Chinese social commerce platforms: Being emotional or rational? Journal of Retailing and Consumer Services, 63, 102669.

    Article  Google Scholar 

  6. Akroush, M. N., & Al-Devei, M. M. (2015). An integrated model of factors affecting consumer attitudes towards online shopping. Business Process Management Journal, 21(6), 1353–1376. https://doi.org/10.1108/BPMJ-02-2015-0022

    Article  Google Scholar 

  7. Akter, S., & Wamba, S. F. (2016). Big data analytics in e-commerce: A systematic review and agenda for future research. https://doi.org/10.1007/s12525-016-0219-0.

  8. Alfonso, V., Boar, C., Frost, J., Gambacorta, L., & Liu, J. (2021). E-commerce in the pandemic and beyond: Online appendix, BIS bulletin no. 36.

  9. Alizadeh, S. H., & Harzevili, N. S. (2018). Mixture of latent multinomial naïve Bayes classifier. Applied Soft Computing. https://doi.org/10.1016/j.asoc.2018.04.020

    Article  Google Scholar 

  10. Alismaili, S. Z., Li, M., Shen, J., Huang, P., He, Q., & Zhan, W. (2020). Organisational-level assessment of cloud computing adoption: Evidence from the Australian SMEs. Journal of Global Information Management (JGIM), 28(2), 73–89.

    Article  Google Scholar 

  11. Ali, M., Tarhini, A., Brooks, L., & Kamal, M. M. (2021). Investigating the situated culture of multi-channel customer management: A case study in Egypt. Journal of Global Information Management (JGIM), 29(3), 46–74.

    Article  Google Scholar 

  12. Al-Hasan, A., Khuntia, J., & Yim, D. (2021). Cross-culture online knowledge validation and the exclusive practice of stem cell therapy. Journal of Global Information Management (JGIM), 29(2), 194–221.

    Article  Google Scholar 

  13. Aljarah, I., Al-Zoubi, A. M., Faris, H., Hassonah, M. A., Mirjalili, S., & Saadeh, H. (2018). Simultaneous feature selection and support vector machine optimization using the grasshopper optimization algorithm. Cogn Comput, 10, 478–495. https://doi.org/10.1007/s12559-017-9542-9

    Article  Google Scholar 

  14. Alt, R., & Zimmermann, H.-D. (2019). Electronic markets on platform competition. Electron Markets, 29, 143–149. https://doi.org/10.1007/s12525-019-00353-y

    Article  Google Scholar 

  15. Amoroso, D. L., Roman, F. L., & Morco, R. (2016). E-Commerce online purchase intention: Importance of corporate social responsibility issues. In I. Lee (Ed.), Encyclopedia of E-Commerce Development, Implementation, and Management (pp. 1610–1626). IGI Global. https://doi.org/10.4018/978-1-4666-9787-4.ch114

    Chapter  Google Scholar 

  16. Amrita Nair-Ghaswalla. (2021). https://www.thehindubusinessline.com/news/why-building-trust-in-e-comm-is-a-challenge/article23930512.ece.

  17. Akter, S., Dwivedi, Y. K., Biswas, K., Michael, K., Bandara, R. J., & Sajib, S. (2021). Addressing Algorithmic Bias in AI-Driven Customer Management. Journal of Global Information Management (JGIM), 29(6), 1–27.

    Article  Google Scholar 

  18. Ariffin, S. K., Mohan, T., & Goh, Y. N. (2018). Influence of consumers’ perceived risk on consumers’ online purchase intention. Journal of Research in Interactive Marketing.

  19. Baabdullah, A., Davies, G., Dwivedi, Y. K., Kumar, V., Rana, N., & Shareef, M. A. (2019). Purchase intention in an electronic commerce environment: A trade-off between controlling measures and operational performance. Informational Technology & People, 32(6), 1345–1375. https://doi.org/10.1108/ITP-05-2018-0241

    Article  Google Scholar 

  20. Baati, K., & Mohsil, M. (2020). Real-time prediction of online shoppers’ purchasing intention using random forest. In IFIP International Conference on Artificial Intelligence Applications and Innovations (pp. 43–51). Springer, Cham.

  21. Balgomera, K., Cruz, A. E. D., Santiago, J. E. G., & Fernandez, R. R. (2022). Consumer trust in mobile phone industry: Comparative study on traditional commerce & e-commerce. Journal of Business and Management Studies, 4(2), 100–116.

    Article  Google Scholar 

  22. Ballestar, M. T., Grau-Carles, Pilar, & Sainz, Jorge. (2018). Predicting customer quality in e-commerce social networks: A machine learning approach. Review of Managerial Science, 13(3), 589–603. https://doi.org/10.1007/s11846-018-0316-x

    Article  Google Scholar 

  23. Bawack, R. E., Wamba, S. F., Carillo, K. D. A., & Akter, S. (2022). Artificial intelligence in E-Commerce: a bibliometric study and literature review. Electronic Markets, 32, 1–42.

    Article  Google Scholar 

  24. Beaver, J., Jia, Y., Liu, Y.-W., Nanduri, J., & Oka, A. (2020). Microsoft uses machine learning and optimization to reduce e-commerce fraud. INFORMS Journal on Applied Analytics. https://doi.org/10.1287/inte.2019.1017

    Article  Google Scholar 

  25. Bag, S., Tiwari, M. K., & Chan, F. T. (2019). Predicting the consumer’s purchase intention of durable goods: An attribute-level analysis. Journal of Business Research, 94, 408–419.

    Article  Google Scholar 

  26. Belgiu, M., & Dragut, L. (2016). Random forest in remote sensing: A review of applications and future directions. https://doi.org/10.1016/j.isprsjprs.2016.01.011.

  27. Brown, M., Pope, N., & Voges, K. (2003). Buying or browsing?: An exploration of shopping orientations and online purchase intention. European Journal of Marketing, 37(11/12), 1666–1684. https://doi.org/10.1108/03090560310495401

    Article  Google Scholar 

  28. Boroon, L., Abedin, B., & Erfani, E. (2021). The dark side of using online social networks: A review of individuals’ negative experiences. Journal of Global Information Management (JGIM), 29(6), 1–21.

    Article  Google Scholar 

  29. Cai, J., Luo, J., Wang, S., & Yang, S. (2017). Feature selection in machine learning: A new perspective. Neurocomputing. https://doi.org/10.1016/j.neucom.2017.11.077

    Article  Google Scholar 

  30. Chaffey. (2022). https://www.smartinsights.com/ecommerce/ecommerce-analytics/ecommerce-conversion-rates/.

  31. Chau, N. T., Deng, H., & Tay, R. (2021). A perception-based model for mobile commerce adoption in vietnamese small and medium-sized enterprises. Journal of Global Information Management (JGIM), 29(1), 44–67.

    Article  Google Scholar 

  32. Chaudhary, A., & Kamal, R. (2016). An improved random forest classifier for multi-class classification. Information Processing in Agriculture. https://doi.org/10.1016/j.inpa.2016.08.002

    Article  Google Scholar 

  33. Chaudhuri, N., Gupta, G., Vamsi, V., & Bose, I. (2021). On the platform but will they buy? Predicting customers’ purchase behavior using deep learning. Decision Support Systems, 149, 113622.

    Article  Google Scholar 

  34. Chawla, N. V., Chen, Y., Mursalin, M., & Zhang, Y. (2017). Automated epileptic seizure detection using improved correlation-based feature selection with random forest classifier. Neurocomputing. https://doi.org/10.1016/j.neucom.2017.02.053

    Article  Google Scholar 

  35. Chang, C. L., & Wu, S. (2021). Using online social networks to globalize and popularize product brands in different cultural areas: A relational network model. Journal of Global Information Management (JGIM), 29(6), 1–30.

    Google Scholar 

  36. Changchit, C., Klaus, T., & Treerotchananon, A. (2021). Using customer review systems to support purchase decisions: A comparative study between the US and Thailand. Journal of Global Information Management (JGIM), 29(6), 1–24.

    Article  Google Scholar 

  37. Chen, C.-W., Chou, S., & Lin, J.-Y. (2015). Female online shoppers: Examining the mediating roles of e-satisfaction and e-trust on e-loyalty development. Internet Research, 25(4), 542–561. https://doi.org/10.1108/IntR-01-2014-0006

    Article  Google Scholar 

  38. Chen, Y. F., & Chang, S. H. (2016). The online framing effect: The moderating role of warning, brand familiarity, and product type. Electronic Commerce Research, 16(3), 355–374.

    Article  Google Scholar 

  39. Chen, M. Y., & Teng, C. I. (2013). A comprehensive model of the effects of online store image on purchase intention in an e-commerce environment. Electronic Commerce Research, 13(1), 1–23.

    Article  Google Scholar 

  40. Chen, N., & Yang, Y. (2021). The impact of customer experience on consumer purchase intention in cross-border E-commerce—Taking network structural embeddedness as mediator variable. Journal of Retailing and Consumer Services, 59, 102344.

    Article  Google Scholar 

  41. Cheng, G., Cherian, J., Sial, M. S., Mentel, G., Wan, P., Álvarez-Otero, S., & Saleem, U. (2021). The relationship between csr communication on social media, purchase intention, and e-wom in the banking sector of an emerging economy. Journal of Theoretical and Applied Electronic Commerce Research, 16(4), 1025–1041.

    Article  Google Scholar 

  42. Cheng, Y., & Jiang, H. (2021). Customer–brand relationship in the era of artificial intelligence: Understanding the role of chatbot marketing efforts. Journal of Product & Brand Management, 31(2), 252–264.

    Article  Google Scholar 

  43. Cheng, L., Hu, H., & Wu, C. (2021). Spammer group detection using machine learning technology for observation of new spammer behavioral features. Journal of Global Information Management (JGIM), 29(2), 61–76.

    Article  Google Scholar 

  44. Chevalier. (2022). https://www.statista.com/statistics/1286420/consumer-trust-merchants-e-commerce-fraud-prevention-country/.

  45. Chu, S.-C., Sauer, P. L., & Yim, M.Y.-C. (2017). Is augmented reality technology an effective tool for e-commerce? An interactivity and vividness perspective. https://doi.org/10.1016/j.intmar.2017.04.001

    Article  Google Scholar 

  46. Cision. (2022). https://www.prnewswire.com/news-releases/global-e-commerce-market-reached-us-13-trillion-in-2021-301488606.html.

  47. Dastane, O. (2020). Impact of digital marketing on online purchase intention: Mediation effect of customer relationship management. Journal of Asian Business Strategy, 10, 142–158.

    Article  Google Scholar 

  48. Dong, M., Du, S., Hou, X., Liu, S., Ma, W., Wei, Q., Wen, H., Zhang, Y., & Zhu, B. (2021). IoT equipment monitoring system based on C5.0 decision tree and time series analysis. IEEE Access. https://doi.org/10.1109/ACCESS.2021.3054044

    Article  Google Scholar 

  49. Dospinescu, O., Necula, S.-C., Pavaloaia, V.-D., & Strimbei, C. (2018). Enhancement of e-commerce websites with semantic web technologies. Sustainability. https://doi.org/10.3390/su10061955

    Article  Google Scholar 

  50. Dwivedi, Y. K., Ismagilova, E., Hughes, D. L., Carlson, J., Filieri, R., Jacobson, J., & Wang, Y. (2021). Setting the future of digital and social media marketing research: Perspectives and research propositions. International Journal of Information Management, 59, 102168.

    Article  Google Scholar 

  51. Erlangga, H. (2021). Effect of digital marketing and social media on purchase intention of Smes food products. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 12(3), 3672–3678.

    Article  Google Scholar 

  52. Esmeli, R., Bader-El-Den, M., & Abdullahi, H. (2021). Towards early purchase intention prediction in online session based retailing systems. Electronic Markets, 31(3), 697–715.

    Article  Google Scholar 

  53. Fan, W., Lu, B., & Zhou, M. (2015). Social presence, trust, and social commerce purchase intention: An empirical research. Computers in Human Behavior. https://doi.org/10.1016/j.chb.2015.11.057

    Article  Google Scholar 

  54. Faraoni, M., Pellicelli, A. C., Rialti, R., & Zollo, L. (2019). Exploring e-loyalty antecedents in B2C e-commerce: Empirical results from an Italian grocery retailer. British Food Journal, 121(2), 574–589. https://doi.org/10.1108/BFJ-04-2018-0216

    Article  Google Scholar 

  55. Feng, Q., Gong, J., & Liu, J. (2015). Urban flood mapping based on unmanned aerial vehicle remote sensing and random forest classifier-A case of Yuyao. China. https://doi.org/10.3390/w7041437

    Article  Google Scholar 

  56. Forrester Analytics. (2019). https://www.forrester.com/report.

  57. Hamami, F., & Muzakki, A. (2021). Machine learning pipeline for online shopper intention classification. In AIP Conference Proceedings (Vol. 2329, No. 1, p. 050014). AIP Publishing LLC.

  58. Gallego-Gomez, C., De-Pablos-Heredero, C., & Montes-Botella, J. L. (2021). The Impact of customer relationship management systems on dynamic capabilities at firms: An application to the banking industry. Journal of Global Information Management (JGIM), 29(1), 103–122.

    Article  Google Scholar 

  59. Guo, Y., & Wang, C. (2020). The impact mechanisms of psychological learning climate on employees’ innovative use of information systems. Journal of Global Information Management (JGIM), 28(2), 52–72.

    Article  Google Scholar 

  60. Gholami, R., Emrouznejad, A., Alnsour, Y., Kartal, H. B., & Veselova, J. (2020). The impact of smart meter installation on attitude change towards energy consumption behavior among northern ireland households. Journal of Global Information Management (JGIM), 28(4), 21–37.

    Article  Google Scholar 

  61. Gholami, R., Nishant, R., & Emrouznejad, A. (2021). Modeling residential energy consumption: An application of IT-based solutions and big data analytics for sustainability. Journal of Global Information Management (JGIM), 29(2), 166–193.

    Article  Google Scholar 

  62. Guo, L., Hua, L., Jia, R., Zhao, B., Wang, X., & Cui, B. (2019, July). Buying or browsing?: Predicting real-time purchasing intent using attention-based deep network with multiple behavior. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (pp. 1984–1992).

  63. Haponik. (2021). https://addepto.com/best-machine-learning-use-cases-ecommerce/.

  64. Hung, W., Tseng, C., Chang, F., & Ho, C. (2021). Effects of utilitarian and hedonic emotion on the use of online banking services. Journal of Global Information Management (JGIM), 29(6), 1–20.

    Article  Google Scholar 

  65. Hong, L., Yu, H., Yu, Y., Liang, P., & Xu, J. (2021). The role of customer-task fit between service interaction and value co-creation: Evidence from China. Journal of Global Information Management (JGIM), 29(6), 1–15.

    Article  Google Scholar 

  66. Ibrahim, O., Mardani, A., Nilashi, M., Roudposhti, V.M., Samad, S., & Streimikiene, D. (2018). A new model for customer purchase intention in e-commerce recommendation agents.

  67. Islam, M., Kang, M., & Haile, T. T. (2021). Do hedonic or utilitarian types of online product reviews make reviews more helpful?: A new approach to understanding customer review helpfulness on amazon. Journal of Global Information Management (JGIM), 29(6), 1–18.

    Article  Google Scholar 

  68. Kamalul Ariffin, S., Mohan, T., & Goh, Y.-N. (2018). Influence of consumers’ perceived risk on consumers’ online purchase intention. Journal of Research in Interactive Marketing, 12(3), 309–327. https://doi.org/10.1108/JRIM-11-2017-0100

    Article  Google Scholar 

  69. Kabir, M. R., Ashraf, F. B., & Ajwad, R. (2019). Analysis of different predicting model for online shoppers’ purchase intention from empirical data. In 2019 22nd International Conference on Computer and Information Technology (ICCIT) (pp. 1–6). IEEE.

  70. Kashyap, A. K., & Kumar, A. (2018). Leveraging utilitarian perspective of online shopping to motivate online shoppers. International Journal of Retail & Distribution Management, 46(3), 247–263. https://doi.org/10.1108/IJRDM-08-2017-0161

    Article  Google Scholar 

  71. Kaur, S., Lal, A. K., & Bedi, S. S. (2017). Do vendor cues influence purchase intention of online shoppers? An empirical study using SOR framework. Journal of Internet Commerce, 16(4), 343–363.

    Google Scholar 

  72. Kondrateva, G., Ammi, C., & Baudier, P. (2020). Understanding restaurant clients’ intention to use mobile applications: A comparative study of France and Russia. Journal of Global Information Management (JGIM), 28(3), 1–16.

    Article  Google Scholar 

  73. Kim, J. B. (2012). An empirical study on consumer first purchase intention in online shopping: Integrating initial trust and TAM. Electronic Commerce Research, 12(2), 125–150.

    Article  Google Scholar 

  74. Kumar. (2021). https://vitalflux.com/e-commerce-machine-learning-use-cases-examples/.

  75. Kumar, A., Kabra, G., Mussada, E. K., Dash, M. K., & Rana, P. S. (2019). Combined artificial bee colony algorithm and machine learning techniques for prediction of online consumer repurchase intention. Neural Computing and Applications, 31(2), 877–890.

    Article  Google Scholar 

  76. Kumar, U. D. (2017). Business analytics: The science of data-driven decision making. Wiley.

    Google Scholar 

  77. Laxman, L. K. P. (2021). Legal and regulatory challenges in facilitating a sustainable ASEAN E-commerce sector. In Handbook of Research on Innovation and Development of E-Commerce and E-Business in ASEAN (pp. 1–25). IGI Global.

  78. Lam, H. Y., Tsang, Y. P., Wu, C. H., & Chan, C. Y. (2021). Intelligent E-vendor relationship management for enhancing global B2C E-commerce ecosystems. Journal of Global Information Management (JGIM), 29(3), 1–25.

    Article  Google Scholar 

  79. Li, Q., Liang, N., & Li, E. Y. (2018). Does friendship quality matter in social commerce? An experimental study of its effect on purchase intention. Electronic Commerce Research, 18(4), 693–717.

    Article  Google Scholar 

  80. Li, Q., Gu, M., Zhou, K., & Sun, X. (2015). Multi-classes feature engineering with sliding window for purchase prediction in mobile commerce. In 2015 IEEE International Conference on Data Mining Workshop (ICDMW) (pp. 1048–1054). IEEE.

  81. Li, C., Liu, Y., & Du, R. (2021). The effects of review presentation formats on consumers’ purchase intention. Journal of Global Information Management (JGIM), 29(6), 1–20.

    Google Scholar 

  82. Li, X., Lu, K., & Shaouf, A. (2016). The effect of web advertising visual design on online purchase intention: An examination across gender. Computers in Human Behavior, 60, 622–634. https://doi.org/10.1016/j.chb.2016.02.090

    Article  Google Scholar 

  83. Liao, S. H., Hu, D. C., Chung, Y. C., & Huang, A. P. (2021). Risk and opportunity for online purchase intention–A moderated mediation model investigation. Telematics and Informatics, 62, 101621.

    Article  Google Scholar 

  84. Lin, J., Li, T., & Guo, J. (2021). Factors influencing consumers’ continuous purchase intention on fresh food e-commerce platforms: An organic foods-centric empirical investigation. Electronic Commerce Research and Applications, 50, 101103.

    Article  Google Scholar 

  85. Ling, C., Zhang, T., & Chen, Y. (2019). Customer purchase intent prediction under online multi-channel promotion: A feature-combined deep learning framework. IEEE Access, 7, 112963–112976.

  86. Liu, Y., & Du, R. (2020). Examining the effect of reviewer socioeconomic status disclosure on customers’ purchase intention. Journal of Global Information Management (JGIM), 28(3), 17–35.

    Article  Google Scholar 

  87. Liu, Q., Zhang, B., Wang, L., Zhang, X., & Li, Y. (2021). Information cascades and online shopping: A cross-cultural comparative study in China and the United States. Journal of Global Information Management (JGIM), 29(3), 26–45.

    Article  Google Scholar 

  88. Lissitsa, S., & Kol, O. (2021). Four generational cohorts and hedonic m-shopping: Association between personality traits and purchase intention. Electronic Commerce Research, 21(2), 545–570.

    Article  Google Scholar 

  89. Ma, S., Lin, Y., & Pan, G. (2021). Does cross-border e-commerce contribute to the growth convergence?: An analysis based on Chinese provincial panel data. Journal of Global Information Management (JGIM), 29(5), 86–111.

    Article  Google Scholar 

  90. Meghani. (2018). https://www.forbesindia.com/article/leaderboard/study-reveals-big-trust-deficit-between-businesses-and-consumers-over-digital-data/50995/1.

  91. Meng, L. M., Duan, S., Zhao, Y., Lü, K., & Chen, S. (2021). The impact of online celebrity in livestreaming E-commerce on purchase intention from the perspective of emotional contagion. Journal of Retailing and Consumer Services, 63, 102733.

    Article  Google Scholar 

  92. Mokryn, O., Bogina, V., & Kuflik, T. (2019). Will this session end with a purchase? Inferring current purchase intent of anonymous visitors. Electronic Commerce Research and Applications, 34, 100836.

    Article  Google Scholar 

  93. Mou, J., Cui, Y., & Kurcz, K. (2020). Trust, risk and alternative website quality in B-buyer acceptance of cross-border E-commerce. Journal of Global Information Management (JGIM), 28(1), 167–188.

    Article  Google Scholar 

  94. Nagy, S., & Hajdú, N. (2021). Consumer acceptance of the use of artificial intelligence in online shopping: Evidence from Hungary. Amfiteatru Economic, 23(56), 155–173.

    Google Scholar 

  95. Namogoo. (2021). https://www.namogoo.com/blog/consumer-behavior-psychology/customer-purchase-intention/.

  96. Narang, N. (2020). A study on future and challenges of electronic e-commerce in India. EPRA International Journal of Multidisciplinary Research (IJMR), 6, 58–63.

    Google Scholar 

  97. Netti, K., & Radhika, Y. (2015). A novel method for minimising loss of accuracy in naïve Bayes classifier. https://doi.org/10.1109/ICCIC.2015.7435801.

  98. Noviantoro, T., & Huang, J. P. (2021). Applying data mining techniques to investigate online shopper purchase intention based on clickstream data. Review of Business, Accounting, & Finance, 1(2), 130–159.

    Google Scholar 

  99. Omigie, N. O., Zo, H., Ciganek, A. P., & Jarupathirun, S. (2020). Understanding the continuance of mobile financial services in Kenya: The roles of utilitarian, hedonic, and personal values. Journal of Global Information Management (JGIM), 28(3), 36–57.

    Article  Google Scholar 

  100. Panda. (2020). https://www.business-standard.com/article/companies/insurance-firms-looks-to-bridge-online-trust-deficit-amid-covid-19-crisis-120041601833_1.html.

  101. Pandya, J., & Pandya, R. (2015). C5.0 algorithm to improved decision tree with feature selection and reduced error pruning.

  102. Patel. (2021). https://medium.com/m/global-identity?redirectUrl=https%3A%2F%2Ftowardsdatascience.com%2Fwhat-is-feature-engineering-importance-tools-and-techniques-for-machine-learning2080b0269f10#:~:text=Feature%20engineering%20is%20the%20process,design%20and%20train%20better%20features.

  103. Pillai, R., Sivathanu, B., & Dwivedi, Y. K. (2020). Shopping intention at AI-powered automated retail stores (AIPARS). Journal of Retailing and Consumer Services, 57, 102207.

    Article  Google Scholar 

  104. Potempa, A., Skolimowska-Kulig, M., & Suchacka, G. (2015). Classification of e-customer sessions based on support vector machine. https://doi.org/10.7148/2015-0594.

  105. Rahman, M. S., Hossain, M. A., Zaman, M. H., & Mannan, M. (2020). E-service quality and trust on customer’s patronage intention: Moderation effect of adoption of advanced technologies. Journal of Global Information Management (JGIM), 28(1), 39–55.

    Article  Google Scholar 

  106. Rehman, I. H., Ahmad, A., Akhter, F., & Aljarallah, A. (2021). A dual-stage SEM-ANN analysis to explore consumer adoption of smart wearable healthcare devices. Journal of Global Information Management (JGIM), 29(6), 1–30.

    Article  Google Scholar 

  107. Santo, P. E., & Marques, A. M. A. (2021). Determinants of the online purchase intention: hedonic motivations, prices, information and trust. Baltic Journal of Management.

  108. Sakar, C. O., Polat, S. O., Katircioglu, M., & Kastro, Y. (2019). Real-time prediction of online shoppers’ purchasing intention using multilayer perceptron and LSTM recurrent neural networks. Neural Computing and Applications, 31(10), 6893–6908.

    Article  Google Scholar 

  109. SCCG. (2022). https://www.sccgltd.com/featured-articles/trust-in-blockchain-for-ecommerce-confidence/.

  110. Sethi, R. S., Kaur, J., & Wadera, D. (2018). Purchase intention survey of millennials towards online fashion stores. Academy of Marketing Studies Journal, 22(1), 1–16.

    Google Scholar 

  111. Sengupta, S. (2020). How Does culture impact customer evaluation in online complaining?: Evidence from Germany and India. Journal of Global Information Management (JGIM), 28(2), 131–159.

    Article  Google Scholar 

  112. Shaw. (2022). https://www.bigcommerce.com/blog/ecommerce-machine-learning/#business-benefits-of-ecommerce-machine-learning.

  113. Shankar, A., Yadav, R., Gupta, M., & Jebarajakirthy, C. (2021). How does online engagement drive consumers’ webrooming intention?: A moderated-mediation approach. Journal of Global Information Management (JGIM), 29(6), 1–25.

    Article  Google Scholar 

  114. Shihab, M. R., & Putri, A. P. (2019). Negative online reviews of popular products: Understanding the effects of review proportion and quality on consumers’ attitude and intention to buy. Electronic Commerce Research, 19(1), 159–187.

    Article  Google Scholar 

  115. Shin, D. (2021). A cross-national study on the perception of algorithm news in the east and the west. Journal of Global Information Management (JGIM), 29(2), 77–101.

    Article  Google Scholar 

  116. Siknun, G.P., & Sitanggang, I.S. (2016). Web-based classification application for forest fire data using shiny framework and the C5.0 algorithm. https://doi.org/10.1016/j.proenv.2016.03.084.

  117. Singh. (2019). https://www.cnbctv18.com/views/the-webs-trust-deficit-why-indians-prefer-cash-for-online-purchases-4287981.htm.

  118. Soleimani, M. (2021). Buyers’ trust and mistrust in e-commerce platforms: A synthesizing literature review. Information Systems and e-Business Management 1–22.

  119. Song, P., & Liu, Y. (2020). An XGBoost algorithm for predicting purchasing behaviour on E-commerce platforms. Tehnički vjesnik, 27(5), 1467–1471.

  120. Srivastava, P. R., & Eachempati, P. (2021). Intelligent employee retention system for attrition rate analysis and churn prediction: An ensemble machine learning and multi-criteria decision-making approach. Journal of Global Information Management (JGIM), 29(6), 1–29.

    Article  Google Scholar 

  121. Statista. (2021). https://www.statista.com/topics/846/amazon/#dossierKeyfigures.

  122. Statista. (2022). https://www.statista.com/statistics/617136/digital-population-worldwide/.

  123. Statista. (2022). https://www.statista.com/forecasts/1262881/mobile-app-download-worldwide-by-segment.

  124. Statista. (2022). https://www.statista.com/statistics/251666/number-of-digital-buyers-worldwide/.

  125. Statista. (2022). https://www.statista.com/statistics/607716/worldwide-artificial-intelligence-market-revenues/.

  126. Sumarliah, E., Khan, S. Z., & Khan, R. U. (2021). Modest wear e-commerce: Examining online purchase intent in Indonesia. Research Journal of Textile and Apparel.

  127. Sun, Y., Yu, Z., Li, L., Chen, Y., Kataev, M. Y., Yu, H., & Wang, H. (2021). Technological innovation research: A structural equation modelling approach. Journal of Global Information Management (JGIM), 29(6), 1–22.

    Google Scholar 

  128. Talukder, M. S., Chiong, R., Corbitt, B., & Bao, Y. (2020). Critical factors influencing the intention to adopt m-Government services by the elderly. Journal of Global Information Management (JGIM), 28(4), 74–94.

    Article  Google Scholar 

  129. Trivedi, S. K., & Dey, S. (2013). An enhanced genetic programming approach for detecting unsolicited emails. In 2013 IEEE 16th International Conference on Computational Science and Engineering (pp. 1153–1160). IEEE.

  130. Trivedi, S. K., & Dey, S. (2014). Interaction between feature subset selection techniques and machine learning classifiers for detecting unsolicited emails. ACM SIGAPP Applied Computing Review, 14(1), 53–61.

    Article  Google Scholar 

  131. Trivedi, S. K., & Dey, Sh. (2018). Analysing user sentiment of Indian movie reviews: A probabilistic committee selection model. The Electronic Library, 36(4), 590–606. https://doi.org/10.1108/EL-08-2017-0182

    Article  Google Scholar 

  132. Trivedi, S. K., & Dey, S. (2019). A modified content-based evolutionary approach to identify unsolicited emails. Knowledge and Information Systems, 60(3), 1427–1451.

    Article  Google Scholar 

  133. Varsha, P. S., Akter, S., Kumar, A., Gochhait, S., & Patagundi, B. (2021). The impact of artificial intelligence on branding: A bibliometric analysis (1982–2019). Journal of Global Information Management (JGIM), 29(4), 221–246.

    Article  Google Scholar 

  134. Vali, H., Jingjun (David) Xu., & Yildirim, M. B. (2021). Comparative Reviews vs. Regular Consumer Reviews: Effects of Presentation Format and Review Valence. Journal of Global Information Management (JGIM), 29(6), 1–29.

  135. Ventre, I., & Kolbe, D. (2020). The impact of perceived usefulness of online reviews, trust and perceived risk on online purchase intention in emerging markets: A Mexican perspective. Journal of International Consumer Marketing, 32(4), 287–299.

    Article  Google Scholar 

  136. Verhagen, T., & Bloemers, D. (2018). Exploring the cognitive and affective bases of online purchase intentions: A hierarchical test across product types. Electronic Commerce Research, 18(3), 537–561.

    Article  Google Scholar 

  137. Wu, M. Y. (2022). https://www.business.com/articles/build-trust-with-ecommerce-customers/.

  138. Xiao, L., Mou, J., & Huang, L. (2021). Factors influencing chinese online health service use: A valence framework perspective. Journal of Global Information Management (JGIM), 29(5), 138–160.

    Article  Google Scholar 

  139. Yaseen, A. (2021). Next-wave of E-commerce: Mobile customers churn prediction using machine learning. Lahore Garrison University Research Journal of Computer Science and Information Technology, 5(2), 62–72.

    Article  Google Scholar 

  140. Yang, G., Wang, Y., Lu, F., Yu, L., & Ma, S. (2021). What determines the pattern of china’s cross-border E-commence with the world? Journal of Global Information Management (JGIM), 29(5), 55–70.

    Article  Google Scholar 

  141. Zhao, S., Fang, Y., Zhang, W., & Jiang, H. (2020). Trust, perceived benefit, and purchase intention in C2C E-commerce: An empirical examination in China. Journal of Global Information Management (JGIM), 28(1), 121–141.

    Article  Google Scholar 

  142. Zhang, D., Pee, L. G., & Cui, L. (2021). Artificial intelligence in E-commerce fulfillment: A case study of resource orchestration at Alibaba’s Smart Warehouse. International Journal of Information Management, 57, 102304.

    Article  Google Scholar 

  143. Zhang, C., & Srite, M. (2021). The role of national culture values and trust in online sharing hospitality platform acceptance. Journal of Global Information Management (JGIM), 29(3), 103–130.

    Article  Google Scholar 

  144. Zheng, B., & Liu, B. (2018). A scalable purchase intention prediction system using extreme gradient boosting machines with browsing content entropy. In 2018 IEEE International Conference on Consumer Electronics (ICCE) (pp. 1–4). IEEE.

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Leven J. Zheng.

Ethics declarations

Conflict of interest

On behalf of all authors, the corresponding author states that there is no conflict of interest.

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Trivedi, S.K., Patra, P., Srivastava, P.R. et al. What prompts consumers to purchase online? A machine learning approach. Electron Commer Res (2022). https://doi.org/10.1007/s10660-022-09624-x

Download citation

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10660-022-09624-x

Keywords

Navigation