LONGITUDINAL MODELING OF E-COMMERCE CHOICE USING LATENT GROWTH CURVE TO ASSESS INFLUENCING FACTORS AMONG LATE ADOLESCENTS
DOI:
https://doi.org/10.47080/hv8z4172Keywords:
Latent Growth Curve Modeling, E-commerce, Consumer BehaviorAbstract
The rapid growth of e-commerce in Indonesia has significantly influenced consumer behavior, particularly among late adolescents aged 18–21 years. This study examines the dynamic factors affecting e-commerce preferences, including price, service quality, and customer loyalty, using Latent Growth Curve Modeling (LGCM). This method was chosen for its ability to analyze variable changes longitudinally, allowing the identification of growth patterns and factors influencing shifts in consumer behavior over time. Data were collected through an online survey involving 400 respondents over three time periods. The study’s findings reveal that price is the most stable variable (intercept 0.5302, slope 0.0811), whereas service quality (intercept 0.8127, slope -0.0285) and loyalty (intercept 0.8508, slope -0.0188) show slight declines. Innovation, functioning as a covariate, significantly affects the intercept of all variables, particularly loyalty, although its impact on growth rates varies. The model demonstrates a good fit, with RMSEA (0.0730), CFI (0.9844), and TLI (0.9402), confirming its validity. Visualizations indicate that loyalty evolves more dynamically than service quality, highlighting the crucial role of innovation in customer engagement. This study emphasizes the need for e-commerce platforms to prioritize innovation and service quality improvements to foster long-term loyalty. These findings provide valuable insights into consumer behavior dynamics and offer strategic recommendations for achieving competitive advantage in the digital marketplace.
References
Brandmaier, A. M., Lindenberger, U., & McCormick, E. M. (2024). Optimal two-time point longitudinal models for estimating individual-level change: Asymptotic insights and practical implications. Developmental Cognitive Neuroscience, 70(101450), 1-7.
Chen, J., & Zhang, X. (2022). L2 development of phraseological knowledge via a xu-argument based continuation task: A latent curve modeling approach. System, 106(102767).
Chen, K., Luo, S., & Tong, D. Y. (2024). Cross border e-commerce development and enterprise digital. Heliyon, 10(15).
Cheong, J., MacKinnon, D. P., & Khoo, S. T. (2023). Investigating mediation processes using parallel process latent growth curve modeling. Structural Equation Modeling, 289-309.
Curran, P. J., & Bauer, D. J. (2021). The Disaggregation of Within-Person and Between-Person Effects in Longitudinal Models Using Latent Growth Curve Modeling. Annual Review of Clinical Psychology, 259-284.
Daniswara, A. A., & Nuryana2, K. D. (2023). Data Preprocessing Pola Pada Penilaian Mahasiswa Program Profesi Guru. Journal of Informatics and Computer Science, 05(01), 97-100.
Efrida, A., Hamidi, W., & Desweni, S. P. (2023). Analisis Faktor-Faktor yang Mempengaruhi Kesejahteraan Petani Kelapa Sawit Dengan Pendapatan Petani Sebagai Variabel Intervening di Kecamatan Tambusai Utara. Journal Economy And Currency Study (JECS), 95-104.
Grimm, K. J., Ram, N., & Estabrook, R. (2022). Growth Modeling: Structural Equation and Multilevel Modeling Approaches. 2nd Edition. Guilford Press.
Hakim, M., & Mulyapradana, A. (2020). Pengaruh Penggunaan Media Daring dan Motivasi Belajar Terhadap Kepuasan Mahasiswa Pada Saat Pandemi Covid-19 . Widya Cipta: Jurnal Sekretari dan Manajemen , 4(2), 154-160.
Halim, M. A., Sari, L. F., & Nofranita, W. (2023). PERSEPSI KONSUMEN TERKAIT ETIKA, KEPUASANDAN LOYALITAS PELANGGAN DI SEKTOR RETAIL ONLINE. Menara Ekonomi, 79-92.
Harahap, N., & Sutarman. (2023). Penaksiran Parameter Generalized Linear Mixed ModelMenggunakan Maximum Likelihood. IJM: Indonesian Journal of Multidisciplinary, 1(6), 2289-2296.
Huwaida, L. A., Yusuf, A., Satria, A. N., Darmawan, M. A., Ammar, M. F., Yanuar, M. W., . . . Yaiprasert, C. (2024). Generation Z and Indonesian Social Commerce: Unraveling key drivers of their shopping decisions . Journal of Open Innovation: Technology, Market, and Complexity, 10.
İŞBİLEN, E. (2023, April 12). Apa itu Estimasi Kemungkinan Maksimum (MLE)? (builtin) Dipetik Oktober 3, 2024, dari https://builtin.com/data-science/maximum-likelihood-estimation
Martin, A. J., Collie, R. J., Stephan, M., Flesken, A., Halcrow, F., & McCourt, B. (2024). What is the role of teaching support in students’ motivation and engagement trajectories during adolescence? A four-year latent growth modeling study. Learning and Instruction, 92(101910), 1-11.
Nest, G. v., Passos, V. L., Candel , M. J., & Breukelen, G. J. (2020). An overview of mixture modelling for latent evolutions in longitudinal data: Modelling approaches, fit statistics and software. Advances in Life Course Research, 43(100323), 1-17.
Oktavia, C., Warsito, B., & Kadarrisman, V. G. (2023). Implementasi Metode SEMPLS, CSI, dan CLI untuk Pengukuran Loyalitas Pelanggan Shopee Menggunakan R Shiny. Jurnal Sains dan Teknologi, 603-612.
Putri, M. A., & Pratama, M. (2024). Analisis Faktor-Faktor Yang Mempengaruhi Kepuasan Konsumen E-Commerce Pada Mahasiswa Di Kota Padang . INNOVATIVE: Journal Of Social Science Research, 4(4), 11765-11775.
Rodriguez , D. (2023). Area under the Curve as an Alternative to Latent Growth Curve Modeling When Assessing the Effects of Predictor Variables on Repeated Measures of a Continuous Dependent Variable. Stats, 674-688.
Schoenherr, J., Strohmaier, A. R., & Schukajlow , S. (2024). Learning with visualizations helps: A meta-analysis of visualization interventions in mathematics education. Educational Research Review, 45(100639), 1-17.
Utami, Y., Rasmanna, P. M., & Khairunnisa. (2023). Uji Validitas dan Uji Reliabilitas Instrument Penilaian Kinerja Dosen. SAINTEK (Jurnal Sains dan Teknologi), 4(2), 21-24.
Wang, J., & Fang, S. (2024). The developmental trajectory of college students’ psychological flexibility: Based on latent growth model. Journal of Contextual Behavioral Science, 32(100765).
Wang, L., Zhang, Z., & McArdle, J. J. (2020). Estimating and Interpreting Latent Growth Curve Models with Time-Varying Covariates. Structural Equation Modeling, 1-16.
Wang, X., Jingjie, L., Lu, Q., Quanhao, Y., Fan, J., Gao, F., . . . Zhu, Z. (2022). Childhood experiences of threat and deprivation predict distinct depressive symptoms: A parallel latent growth curve model. Journal of Affective Disorders, 319, 244-251
Zulfikar, R. (2019). PENGARUH DEMOGRAFIS TERHADAP TINGKAT PREFERENSI, KEPUASAN DAN PERSEPSI RISIKO MASYARAKAT PADA PRODUK HIJAU DI SEKTOR OBAT TRADISIONAL. Ekuitas: Jurnal Ekonomi dan Keuangan, 168-187.
