DETERMINANTS AND CLASSIFICATIONS OF ONLINE SHOPPING CONSUMERS’ PURCHASE INTENTION IN INDONESIA

Reninta Dewi Nugraheni

Abstract


Online shopping is still growing significantly, which is an upward trend for e-commerce platforms. It has grown in popularity among consumers for a number of reasons, driving Indonesian consumer behavior. This study aims to identify determinants and classifications of Indonesian online shopping consumers’ purchase intention using the decision tree methodology. The notion underpinning this study is the phenomena of online shopping as a consumer behavior transition in Indonesia which has increased gradually over the last decades. The data was collected using a structured questionnaire and 820 respondents participated in this study. Then, the data was analyzed using C5.0 and CART 5.0 algorithms. The results show that the most influential determinants of the frequency of online shopping include the consumers’ occupation, choice of e-commerce platforms, product origin, and product quality. The influence of several factors on customers' intentions to engage in shopping online can be determined, including job status, e-commerce platform, product sources, and product quality.

JEL: L81, L86, Q5.


Keywords


e-commerce; consumer behavior; online shopping

Full Text:

PDF

References


Ali, F., & Sohail, M. (2018). Effects of Corporate Social Responsibility on Consumer Purchase Intention. Pakistan Journal of Humanities and Social Sciences, 6(4), 477–491. www.pjhss.com

Aliedan, M. M., Elshaer, I. A., Alyahya, M. A., & Sobaih, A. E. E. (2022). Influences of University Education Support on Entrepreneurship Orientation and Entrepreneurship Intention: Application of Theory of Planned Behavior. Sustainability (Switzerland), 14(20). https://doi.org/10.3390/su142013097

Alsoud, M. A. S., & Othman, I. bin L. (2018). The Determinant of Online Shopping Intention in Jordan: A Review and Suggestions for Future Research. International Journal of Academic Research in Business and Social Sciences, 8(8). https://doi.org/10.6007/ijarbss/v8-i8/4507

Assiroj, P., Warnars, H. L. H. S., & Fauzi, A. (2018). Comparing CART and C5.0 Algorithm Performance of Human Development Index. 2018 Third International Conference on Informatics and Computing (ICIC), 1–5. https://doi.org/10.1109/IAC.2018.8780439

Baati, K., & Mohsil, M. (2020). Real-Time Prediction of Online Shoppers’ Purchasing Intention Using Random Forest. In Artificial Intelligence Applications and Innovations (pp. 43–51). https://doi.org/10.1007/978-3-030-49161-1_4

Beck, S., & Kenning, P. (2015). The Influence of Retailers’ Family Firm Image on New Product Acceptance: An Empirical Investigation in the German FMCG Market. International Journal of Retail & Distribution Management, 43(12), 1126–1143. https://doi.org/10.1108/IJRDM-06-2014-0079

Benediktus, N., & Oetama, R. S. (2020). Algoritma Klasifikasi Decision Tree C5.0 untuk Memprediksi Performa Akademik Siswa. 14 ULTIMATICS, XII(1). https://www.kaggle.com/aljarah/xAPI-Edu-Data

Chen, D., Sain, S. L., & Guo, K. (2012). Data mining for the online retail industry: A case study of RFM model-based customer segmentation using data mining. Journal of Database Marketing and Customer Strategy Management, 19(3), 197–208. https://doi.org/10.1057/dbm.2012.17

Chua, T. H. H., & Chang, L. (2016). Follow me and like my beautiful selfies: Singapore teenage girls’ engagement in self-presentation and peer comparison on social media. Computers in Human Behavior, 55, 190–197. https://doi.org/10.1016/j.chb.2015.09.011

Ente, D. R., Thamrin, S. A., Kuswanto, H., Arifin, S., & Andreza. (2020). Klasifikasi Faktor-Faktor Penyebab Penyakit Diabetes Melitus di Rumah Sakit UNHAS Menggunakan Algoritma C4.5 *. Indonesian Journal of Statistics and Its Applications, 4(1), 80–88.

Giningroem, D. S. W. P., Setyawati, N. W., & Wijayanti, M. (2022). Consumer Experiences, Time Saving Orientation, and Price Saving Orientation on Actual Behavior to Use Application Online Food Delivery through Convenience Motivation. East Asian Journal of Multidisciplinary Research, 1(11), 2549–2560. https://doi.org/10.55927/eajmr.v1i11.1989

Giustin, F. N., Sari, B. N., & Padilah, T. N. (2022). Application of C5.0 Algorithm in Prediction of Learning Outcomes in Calculus Subject. In Journal of Applied Engineering and Technological Science (Vol. 3, Issue 2).

Gopinath, R. (2019). Online Shopping Consumer Behaviour of Perambalur District. International Journal of Research, VIII(V), 542–547. https://www.researchgate.net/publication/332961054

Gu, S., & Wu, Y. (2019). Using the Theory of Planned Behaviour to Explain Customers’ Online Purchase Intention. World Scientific Research Journal, 5. https://doi.org/10.6911/WSRJ.201909_5(9).0026

Gupta, B., Uttarakhand, P., & Rawat, I. A. (2017). Analysis of Various Decision Tree Algorithms for Classification in Data Mining. In International Journal of Computer Applications (Vol. 163, Issue 8).

Ha, N. T., Nguyen, T. L. H., Pham, T. Van, & Nguyen, T. H. T. (2021). Factors Influencing Online Shopping Intention: An Empirical Study in Vietnam. Journal of Asian Finance, Economics and Business, 8(3), 1257–1266. https://doi.org/10.13106/jafeb.2021.vol8.no3.1257

Huynh-Cam, T. T., Chen, L. S., & Le, H. (2021). Using decision trees and random forest algorithms to predict and determine factors contributing to first-year university students’ learning performance. Algorithms, 14(11). https://doi.org/10.3390/a14110318

Jothi, C. A., & Gaffoor, A. M. (2017). Impact of Social Media in Online Shopping. ICTACT Journal on Management Studies, 3(3), 576–586. https://doi.org/10.21917/ijms.2017.0079

Kardinasari, R., Iskandar, T. Z., Nugraha, Y., & Jatnika, R. (2019). Social Sensitivity Effect to Public Service Competence and Its Impact on the Head of Sub-district Performance in West Java Province. Journal of Psychology Research, 9(1). https://doi.org/10.17265/2159-5542/2019.01.004

Karimi, S., Holland, C. P., & Papamichail, K. N. (2018). The Impact of Consumer Archetypes on Online Purchase Decision-Making Processes and Outcomes: A Behavioural Process Perspective. Journal of Business Research, 91, 71–82. https://doi.org/10.1016/j.jbusres.2018.05.038

Lee, J. E., & Chen-Yu, J. H. (2018). Effects of price discount on consumers’ perceptions of savings, quality, and value for apparel products: mediating effect of price discount affect. Fashion and Textiles, 5(1). https://doi.org/10.1186/s40691-018-0128-2

Lee, J., Jung, O., Lee, Y., Kim, O., & Park, C. (2021). A comparison and interpretation of machine learning algorithm for the prediction of online purchase conversion. Journal of Theoretical and Applied Electronic Commerce Research, 16(5). https://doi.org/10.3390/jtaer16050083

Lewis, R. J. (2000). An Introduction to Classification and Regression Tree (CART) Analysis. https://www.researchgate.net/publication/240719582

Lim, Y. J., Osman, A., Salahuddin, S. N., Romle, A. R., & Abdullah, S. (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

Lin, C. L., & Fan, C. L. (2019). Evaluation of CART, CHAID, and QUEST algorithms: a case study of construction defects in Taiwan. Journal of Asian Architecture and Building Engineering, 18(6), 539–553. https://doi.org/10.1080/13467581.2019.1696203

Lu, L.-C., Chang, W.-P., & Chang, H.-H. (2014). Consumer Attitudes toward Blogger’s Sponsored Recommendations and Purchase Intention: The Effect of Sponsorship Type, Product Type, and Brand Awareness. Computers in Human Behavior, 34, 258–266. https://doi.org/10.1016/j.chb.2014.02.007

Meeprom, S., & Silanoi, T. (2020). Investigating the perceived quality of a special event and its influence on perceived value and behavioural intentions in a special event in Thailand. International Journal of Event and Festival Management, 11(3), 337–355. https://doi.org/10.1108/IJEFM-09-2019-0043

Pallabi, M. (2015). Motivator of Online Shopping: The Income Factor. Asian Journal of Research in Banking and Finance, 5(11), 34–46. https://doi.org/10.5958/2249-7323.2015.00132.7

Pasharibu, Y., Soerijanto, J. A., & Jie, F. (2020). Intention to Buy, Interactive Marketing, and Online Purchase Decisions. Jurnal Ekonomi Dan Bisnis, 23(2), 339–356.

Prasad, S., Garg, A., & Prasad, S. (2019). Purchase decision of generation Y in an online environment. Marketing Intelligence and Planning, 37(4), 372–385. https://doi.org/10.1108/MIP-02-2018-0070

Rehman, A. U., Bashir, S., Mahmood, A., Karim, H., & Nawaz, Z. (2022). Does e-shopping service quality enhance customers’ e-shopping adoption? An extended perspective of unified theory of acceptance and use of technology. PLoS ONE, 17(2 February). https://doi.org/10.1371/journal.pone.0263652

Rehman, S. F. U., & Ansari, M. F. (2023). Predicting Customer Satisfaction of Online Shoppers Using AI – A Theoretic Framework. IJARCCE, 12(1). https://doi.org/10.17148/ijarcce.2023.12112

Rudansky-Kloppers, S. (2014). Investigating Factors Influencing Customer Online Buying Satisfaction In Gauteng, South Africa. International Business & Economics Research Journal, 13(5), 1187–1198.

Saleem, A., Aslam, J., Kim, Y. B., Nauman, S., & Khan, N. T. (2022). Motives towards e-Shopping Adoption among Pakistani Consumers: An Application of the Technology Acceptance Model and Theory of Reasoned Action. Sustainability (Switzerland), 14(7). https://doi.org/10.3390/su14074180

Šebalj, D., Franjković, J., & Hodak, K. (2017). Shopping Intention Prediction using Decision Trees. Millenium - Journal of Education, Technologies, and Health, 2(4), 13–22. https://doi.org/10.29352/mill0204.01.00155

Sinha, J., & Kim, J. (2012). Factors Affecting Indian Consumers’ Online Buying Behavior". Innovative Marketing, 8(2), 46–57. www.tradechakra.com,

Sowmya, R., & Suneetha, K. R. (2017). Data Mining with Big Data.

Sussman, R., & Gifford, R. (2019). Causality in the Theory of Planned Behavior. Personality and Social Psychology Bulletin, 45(6), 920–933. https://doi.org/10.1177/0146167218801363

Truong, N. X. (2018). The Impact of Hallyu 4.0 and Social Media on Korean Products Purchase Decision of Generation C in Vietnam. The Journal of Asian Finance, Economics and Business, 5(3), 81–93. https://doi.org/10.13106/jafeb.2018.vol5.no3.81

Vasic, N., Kilibarda, M., & Kaurin, T. (2019). The Influence of Online Shopping Determinants on Customer Satisfaction in the Serbian Market. Journal of Theoretical and Applied Electronic Commerce Research, 14(2), 70–89. https://doi.org/10.4067/S0718-18762019000200107

Venkatesh, V., Speier-Pero, C., & Schuetz, S. (2022). Why do People Shop Online? A Comprehensive Framework of Consumers’ Online Shopping Intentions and Behaviors. Information Technology & People, 35(5), 1590–1620. https://doi.org/10.1108/ITP-12-2020-0867

Vieira, A. (2015). Predicting Online User Behaviour using Deep Learning Algorithms. Machine Learning, 1–21. http://arxiv.org/abs/1511.06247

Vijay, T. S., Prashar, S., & Sahay, V. (2019). The Influence of Online Shopping Values and Web Atmospheric Cues on E-Loyalty: Mediating Role of E-Satisfaction. Journal of Theoretical and Applied Electronic Commerce Research, 14(1), 1–15. https://doi.org/10.4067/S0718-18762019000100102

Vyas, A., & Bissa, G. (2017). A Study on Customer Preference towards Online Shopping with Special Reference to Bikaner City. International Journal of Engineering Technology Science and Research (IJETSR), 4(9), 675–681.

Wahyudin, Moh., Yuliando, H., & Savitri, A. (2020). Consumer Behavior Intentions to Purchase Daily Needs through Online Store Channel. AgriTECH, 40(4), 306–311. https://doi.org/10.22146/agritech.49232

Wang, L. W., & Le, Q. L. (2016). Customer Satisfaction towards Online Shopping at Electronics Shopping Malls in Vietnam- A Conceptual Model to Enhance Business Success through Efficient Websites and Logistics Services. Journal of Stock & Forex Trading, 05(01). https://doi.org/10.4172/2168-9458.1000164

Wu, C., Yang, F., Wu, Y., & Han, R. (2019). Prediction of Crime Tendency of High-Risk Personnel using C5.0 Decision Tree Empowered by Particle Swarm Optimization. Mathematical Biosciences and Engineering, 16(5), 4135–4150. https://doi.org/10.3934/mbe.2019206

Yamasari, Y., Nugroho, S. M. S., Yoshimoto, K., Takahashi, H., & Purnomo, M. H. (2019). Expanding Tree-Based Classifiers using Meta-Algorithm Approach: An Application for Identifying Students’ Cognitive Level. International Journal of Innovative Computing, Information and Control, 15(6), 2085–2107.

Zacharis, N. Z. (2018). Classification and regression trees (CART) for predictive modeling in blended learning. International Journal of Intelligent Systems and Applications, 10(3), 1–9. https://doi.org/10.5815/ijisa.2018.03.01

Zhang, Y., Trusov, M., Stephen, A. T., & Jamal, Z. (2017). Online Shopping and Social Media: Friends or Foes? Journal of Marketing, 81(6), 24–41. https://doi.org/10.1509/jm.14.0344

ADDITIONAL REFERENCES

APJII. (2022). Profil Internet Indonesia 2022 (Issue June).




DOI: https://doi.org/10.26418/jebik.v13i1.61399

Refbacks

  • There are currently no refbacks.


JOURNAL INDEXING

               More...

 

PUBLISHED BY

Faculty of Economics and Business
Universitas Tanjungpura





Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.

 

real time web analytics View My Stats