Optimizing Classification Methods for Online Buyers' Purchase Intentions in Bangladesh

Main Article Content

Ikbal Ahmed
Md Mahmudul Hoque
Nayan banik
Atiqur Rahman
Mohammad Nur-E-Alam
Mohammad Aminul Islam

Abstract

The classification of online buyers' purchasing intentions is of paramount importance, especially in the context of the period of the COVID-19/post-COVID-19 pandemic, as it carries significant implications for the business industry. However, effectively managing the diverse ever-changing intentions of individual Internet customers remains a challenging task. This study aims to improve the classification techniques used to classify different sorts of online buyers' purchasing intents in Bangladesh. A comprehensive analysis of different classification algorithms reveals that the Random Forest algorithm outperformed other methods, achieving exceptional accuracy rates of 99.9% in training and 89.7% in testing. Conversely, the Gaussian Naive Bayes algorithm demonstrated comparatively lower accuracy, with training testing accuracies of 80% and 79%, respectively. This study contributes not only to a better understanding of online buyers' purchase intentions in Bangladesh but also provides valuable insights into the business industry. Moreover, our work highlights the potential for future investigations in recognizing Bangla numerals throug gestures to enhance the accuracy of categorizing online buyers' intended purchases. This research serves as a stepping stone for further advancements in classifying and understanding online buyers' purchase intentions, ultimately fostering more accurate decision-making in the realm of E-commerce in Bangladesh.

Downloads

Download data is not yet available.

Article Details

How to Cite
[1]
Ikbal Ahmed, Md Mahmudul Hoque, Nayan banik, Atiqur Rahman, Mohammad Nur-E-Alam, and Mohammad Aminul Islam , Trans., “Optimizing Classification Methods for Online Buyers’ Purchase Intentions in Bangladesh”, IJRTE, vol. 12, no. 6, pp. 17–24, Mar. 2024, doi: 10.35940/ijrte.E7987.12060324.
Section
Articles

How to Cite

[1]
Ikbal Ahmed, Md Mahmudul Hoque, Nayan banik, Atiqur Rahman, Mohammad Nur-E-Alam, and Mohammad Aminul Islam , Trans., “Optimizing Classification Methods for Online Buyers’ Purchase Intentions in Bangladesh”, IJRTE, vol. 12, no. 6, pp. 17–24, Mar. 2024, doi: 10.35940/ijrte.E7987.12060324.
Share |

References

Rahman, M. A., Islam, M. A., Esha, B. H., Sultana, N., and Chakravorty, S. (2018). Consumer buying behavior towards online shopping: An empirical study on Dhaka city, Bangladesh. Cogent Business and Management, 5(1), 1514940. https://doi.org/10.1080/23311975.2018.1514940

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

Rapert, M. I., Thyroff, A., and Grace, S. C. (2021). The generous consumer: Interpersonal generosity pro-social dispositions as antecedents to cause-related purchase intentions. Journal of Business Research, 132, 838-847. https://doi.org/10.1016/j.jbusres.2020.10.070

Rahman, M.M, Hasan, M.Z., Morshed M.G., Karim, S., and Alex, M.R. (2023). Forecasting student clothes purchases intention inbangladesh: a machine learning approach. International Journal of Recent Technology and Engineering (IJRTE), Vol. 11(6). https://doi.org/10.35940/ijrte.F7495.0311623

Shawon, S.S., Hasan, M.A., Nayeem, A.R., and Uddin, M.B. Online purchasing behaviour among Bangladeshi young generation: Influencing factors and impact. Asian Business Review., Vol. 8(3), pp:125-130, doi.org/10.18034/abr.v8i3.163. https://doi.org/10.18034/abr.v8i3.163

Tanvir, A.A., Khandokar, I.A., Islam, A.K.M.M., Islam, S., Shatabda, S. A gradient boosting classifier for purchase intention prediction of online shoppers. Heliyon 9 (2023) e15163. https://doi.org/10.1016/j.heliyon.2023.e15163

Mohamad Shariff, N. S., & Nur Hayani Izzati Abd Hamid. (2021). Consumers’ Buying Behavior Towards Online Shopping During The Covid-19 Pandemic: An Empirical Study In Malaysia. Malaysian Journal of Science Health & Technology, 7(2), 1–7. https://doi.org/10.33102/mjosht.v7i2.164.

Joshi, R., Gupte, R. and Saravanan, P. (2018) A Random Forest Approach for Predicting Online Buying Behavior of Indian Customers. Theoretical Economics Letters, 8, 448-475. doi: 10.4236/tel.2018.83032. https://doi.org/10.4236/tel.2018.83032

Gu, S.; Ślusarczyk, B.; Hajizada, S.; Kovalyova, I.; Sakhbieva, A. Impact of the COVID-19 Pandemic on Online Consumer Purchasing Behavior. J. Theor. Appl. Electron. Commer. Res. 2021, 16, 2263-2281. https://doi.org/10.3390/jtaer16060125. https://doi.org/10.3390/jtaer16060125

Alotaibi, F.M. A Machine-Learning-Inspired Opinion Extraction Mechanism for Classifying Customer Reviews on Social Media. Appl. Sci. 2023, 13, 7266. https://doi.org/10.3390/app13127266. https://doi.org/10.3390/app13127266

Alarifi, G., Rahman, M. F., & Hossain, M. S. (2023). Prediction and Analysis of Customer Complaints Using Machine Learning Techniques. International Journal of E-Business Research (IJEBR), 19(1), 1-25. http://doi.org/10.4018/IJEBR.319716

Neger, M., and Uddin, B. (2020). Factors affecting consumers’ internet shopping behavior during the COVID-19 pemic: Evidence from Bangladesh. Chinese Business Review, 19(3), 91-104. https://doi.org/10.17265/1537-1506/2020.03.003

Moe, W. W. (2003). Buying, searching, or browsing: Differentiating between online shoppers using in-store navigational clickstream. Journal of consumer psychology, 13(1-2), 29-39. https://doi.org/10.1207/153276603768344762

Mobasher, B., Dai, H., Luo, T., and Nakagawa, M. (2002). Discovery evaluation of aggregate usage profiles for web personalization. Data mining knowledge discovery, 6, 61-82. https://doi.org/10.1023/A:1013232803866

Suchacka, G., and Chodak, G. (2017). Using association rules to assess purchase probability in online stores. Information Systems e-Business Management, 15, 751-780. https://doi.org/10.1007/s10257-016-0329-4

Suchacka, G., Skolimowska-Kulig, M., and Potempa, A. (2015). Classification Of E-Customer Sessions Based On Support Vector Machine. ECMS, 15, 594-600. https://doi.org/10.7148/2015-0594

Salcedo‐Sanz, S., Rojo‐Álvarez, J. L., Martínez‐Ramón, M., and Camps‐Valls, G. (2014). Support vector machines in engineering: an overview. Wiley Interdisciplinary Reviews: Data Mining Knowledge Discovery, 4(3), 234-267. https://doi.org/10.1002/widm.1125

Suchacka, G., Skolimowska-Kulig, M., and Potempa, A. (2015). A k-Nearest Neighbors method for classifying user sessions in e-commerce scenario. Journal of Telecommunications Information Technology.

Budnikas, G. (2015). Computerised recommendations on e-transaction finalisation by means of machine learning. Statistics in Transition. New Series, 16(2), 309-322. https://doi.org/10.59170/stattrans-2015-017

Clifton, B. (2012). Advanced web metrics with Google Analytics. John Wiley and Sons.

Yeung, W. L. (2016). A review of data mining techniques for research in online shopping behaviour through frequent navigation paths.

Awad, M. A., and Khalil, I. (2012). Prediction of user's web-browsing behavior: Application of markov model. IEEE Transactions on Systems, Man, Cybernetics, Part B (Cybernetics), 42(4), 1131-1142. https://doi.org/10.1109/TSMCB.2012.2187441

Shi, Y., Wen, Y., Fan, Z., and Miao, Y. (2013, November). Predicting the next scenic spot a user will browse on a tourism website based on markov prediction model. In 2013 IEEE 25th International Conference on Tools with Artificial Intelligence (pp. 195-200). IEEE. https://doi.org/10.1109/ICTAI.2013.38

Narvekar, M., and Banu, S. S. (2015). Predicting user's web navigation behavior using hybrid approach. Procedia Computer Science, 45, 3-12. https://doi.org/10.1016/j.procs.2015.03.073

Poggi, N., Moreno, T., Berral, J. L., Gavalda, R., and Torres, J. (2009). Self-adaptive utility-based web session management. Computer Networks, 53(10), 1712-1721. https://doi.org/10.1016/j.comnet.2008.08.022

Ding, A. W., Li, S., and Chatterjee, P. (2015). Learning user real-time intent for optimal dynamic web page transformation. Information Systems Research, 26(2), 339-359. https://doi.org/10.1287/isre.2015.0568

Panzner, M., and Cimiano, P. (2016). Comparing hidden markov models long short term memory neural networks for learning action representations. In Machine Learning, Optimization, Big Data: Second International Workshop, MOD 2016, Volterra, Italy, August 26-29, 2016, Revised Selected Papers 2 (pp. 94-105). Springer International Publishing. https://doi.org/10.1007/978-3-319-51469-7_8

Hidasi, B., Karatzoglou, A., Baltrunas, L., and Tikk, D. (2015). Session-based recommendations with recurrent neural networks. arXiv preprint arXiv:1511.06939. https://doi.org/10.3390/electronics11193079

Dharmasiri, M.A. Preprocessing data for Predicting Online Shoppers Purchasing Intention, Available online: https://medium.com/analytics-vidhya/preprocessing-data-for-predicting-online-shoppers-purchasing-intention-ml-ba78186b7e85, (Accessed on 19 June 2023).

Patil, S., Varadarajan, V., Mazhar, S. M., Sahibzada, A., Ahmed, N., Sinha, O., ... and Kotecha, K. (2022). Explainable Artificial Intelligence for Intrusion Detection System. Electronics, 11(19), 3079.

Gaurav, A., Agrawal, N., Al-Nema, M., and Gautam, V. (2022). Computational Approaches in the Discovery Development of Therapeutic Prophylactic Agents for Viral Diseases. Current Topics in Medicinal Chemistry, 22(26), 2190-2206. https://doi.org/10.2174/1568026623666221019110334

Muneer, A., and Fati, S. M. (2020). A comparative analysis of machine learning techniques for cyberbullying detection on Twitter. Future Internet, 12(11), 187. https://doi.org/10.3390/fi12110187

Hornik, K., Stinchcombe, M., and White, H. (1989). Multilayer feedforward networks are universal approximators. Neural networks, 2(5), 359-366. https://doi.org/10.1016/0893-6080(89)90020-8

Warner, B., and Misra, M. (1996). Understing neural networks as statistical tools. The American Statistician, 50(4), 284-293. https://doi.org/10.2307/2684922

Riedmiller, M., and Braun, H. (1993, March). A direct adaptive method for faster backpropagation learning: The RPROP algorithm. In IEEE international conference on neural networks (pp. 586-591). IEEE.

Günther, F., and Fritsch, S. (2010). Neuralnet: training of neural networks. R J., 2(1), 30. https://doi.org/10.32614/RJ-2010-006

Schiffmann, W., Joost, M., and Werner, R. (1994). Optimization of the backpropagation algorithm for training multilayer perceptrons. University of Koblenz: Institute of Physics.

Azar, A. T. (2013). Fast neural network learning algorithms for medical applications. Neural Computing Applications, 23(3-4), 1019-1034. https://doi.org/10.1007/s00521-012-1026-y

Alpaydin, E. (2020). Introduction to machine learning. MIT Press.

Chauhan, H., and Chauhan, A. (2013). Implementation of decision tree algorithm c4. 5. International Journal of Scientific Research Publications, 3(10), 1-3.

Garg, R., Kumar, A., Bansal, N., Prateek, M., and Kumar, S. (2021). Semantic segmentation of PolSAR image data using advanced deep learning model. Scientific Reports, 11(1), 1-18. https://doi.org/10.1038/s41598-021-94422-y

Subba, Dr. R. (2020). Consumer’s Predilection towards Online Shopping in selected areas of Bongaigaon Town of Assam. In International Journal of Recent Technology and Engineering (IJRTE) (Vol. 8, Issue 6, pp. 5111–5115). https://doi.org/10.35940/ijrte.f1115.038620

Consumer Online Purchase Decision and its Influencers in Uttrakhand: A Factor Analysis Method. (2020). In International Journal of Innovative Technology and Exploring Engineering (Vol. 8, Issue 10S2, pp. 90–99). https://doi.org/10.35940/ijitee.j1016.08810s219

Sonare, S., & Kamble, Dr. M. (2021). Ternary Classification of Product Based Reviews: Survey, Open Issues and New Approach for Sentiment Analysis. In Indian Journal of Artificial Intelligence and Neural Networking (Vol. 1, Issue 2, pp. 1–8). https://doi.org/10.54105/ijainn.b1008.041221

Most read articles by the same author(s)

<< < 1 2 3 4 5 6 7 > >>