Recommendation Explainability, Choice Visibility, and Personalization Fit as Drivers of Value-in-Use among E-Commerce Users in Indonesia
DOI:
https://doi.org/10.71238/jcvoe.v1i01.223Keywords:
E-Commerce; Recommendation Clarity; Option Visibility; Personalization Appropriateness; Perceived User Value.Abstract
This study examines the relationships between clarity of recommendations, visibility of options, appropriateness of personalization, and perceived user value among e-commerce users in Indonesia. A quantitative explanatory approach with a cross-sectional survey design was employed, involving 150 respondents selected through purposive sampling. Data were collected using a structured questionnaire with a five-point Likert scale and analyzed through multiple linear regression using SPSS version 25. The findings reveal that clarity of recommendations (β = 0.301; p < 0.001), visibility of options (β = 0.310; p < 0.001), and appropriateness of personalization (β = 0.427; p < 0.001) have positive and statistically significant relationships with perceived user value. Personalization appropriateness emerged as the strongest predictor, highlighting the importance of delivering relevant and contextually suitable product recommendations. The overall regression model was statistically significant (F = 66.174; p < 0.001), explaining 57.6% of the observed variation in perceived user value (R² = 0.576). These findings demonstrate that recommendation clarity, accessible product alternatives, and relevant personalization are important characteristics of value-oriented e-commerce experiences. The study contributes to digital commerce literature by integrating three consumer-centered recommendation dimensions within a unified empirical framework. Practically, the findings emphasize the importance of improving recommendation transparency, product comparison accessibility, and personalization relevance to enhance consumers' perceived shopping value.
References
Amarullah, D. (2023). How trust and perceived risk create consumer purchase intention in the context of e-commerce: moderation role of eWOM. International Journal of Electronic Marketing and Retailing, 14(1), 107–122. https://doi.org/10.1504/IJEMR.2023.127288
Aripin, M. Z., Azzahra, A., & Afridi, F. K. (2023). Strategies for the Utilization of Information Technology in Micro, Small, and Medium Business Marketing. American Journal of Economic and Management Business (AJEMB), 3(8), 1–13. https://doi.org/10.58631/ajemb.v3i8.108
Astria, D., & Santi, M. (2021). Pemanfaatan Aplikasi Whatsapp Bisnis Dalam Strategi Pemasaran Online Untuk Meningkatkan Jumlah Penjualan. … Ekonomi Syari’ah &Bisnis Islam ….
Auliani, L. N. (2023). Implementasi Enterprise Resource Planning Odoo dalam Optimalisasi Proses Bisnis PT XYZ. Qualilatitive Research of Business and Social Sciences, 1(1), 50–61.
BPS. (2025). Statistik E-Commerce 2024. BPS-Statistics Indonesia. https://www.bps.go.id/id/publication/2025/11/28/647323224ecc656c2933571b/e-commerce-statistics-2024.html
Canhoto, A. I., Keegan, B. J., & Ryzhikh, M. (2023). Snakes and ladders: Unpacking the personalisation-privacy paradox in the context of AI-enabled personalisation in the physical retail environment. Information Systems Frontiers, 26(3), 1005–1024. https://doi.org/10.1007/s10796-023-10369-7
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008
Gouthier, M. H. J., Nennstiel, C., Kern, N., & Wendel, L. (2022). The more the better? Data disclosure between the conflicting priorities of privacy concerns, information sensitivity and personalization in e-commerce. Journal of Business Research, 148, 174–189. https://doi.org/10.1016/j.jbusres.2022.04.034
Green, S. B. (1991). How many subjects does it take to do a regression analysis? Multivariate Behavioral Research, 26(3), 499–510. https://doi.org/10.1207/s15327906mbr2603_7
Harly, G. S., & Octavia, D. (2014). The Impact of Endorsement Fashion Blogger Towards Purchase Intention on Local Brand (2013 - 2014). JUrnal Manajemen Indonesia, 14(2), 140–152.
International Trade Administration. (2025). Indonesia - eCommerce. U.S. Department of Commerce. https://www.trade.gov/country-commercial-guides/indonesia-ecommerce
Iyengar, S. S., & Lepper, M. R. (2000). When choice is demotivating: Can one desire too much of a good thing? Journal of Personality and Social Psychology, 79(6), 995–1006. https://doi.org/10.1037/0022-3514.79.6.995
Krisdiyanto, A., & Dewi, K. (2023). Challenges and Solutions in Developing Eco-Friendly Electric Vehicles with Extended Range. West Science Interdisciplinary Studies, 1(09), 799–807. https://doi.org/10.58812/wsis.v1i09.232
Ling, S., Zheng, C., & Cho, D. (2023). How Brand Knowledge Affects Purchase Intentions in Fresh Food E-Commerce Platforms: The Serial Mediation Effect of Perceived Value and Brand Trust. Behavioral Sciences, 13(8). https://doi.org/10.3390/bs13080672
Song, Y. W., Lim, H. S., & Oh, J. (2021). “We think you may like this”: An investigation of electronic commerce personalization for privacy-conscious consumers. Psychology & Marketing, 38(10), 1723–1740. https://doi.org/10.1002/mar.21501
Suali, S. (2024). Analisis iklan media digital dan kualitas informasi terhadap keputusan pembelian pada situs e-commerce Blibli. JURSIMA, 12(2), 280–289. https://doi.org/10.47024/js.v12i2.887
Tam, K. Y., & Ho, S. Y. (2006). Understanding the impact of web personalization on user information processing and decision outcomes. MIS Quarterly, 30(4), 865–890. https://doi.org/10.2307/25148757
Tasci, A. D. A. (2021). A critical review and reconstruction of perceptual brand equity. International Journal of Contemporary Hospitality Management, 33(1), 166–198. https://doi.org/10.1108/IJCHM-03-2020-0186
Tintarev, N., & Masthoff, J. (2012). Evaluating the effectiveness of explanations for recommender systems: Methodological issues and empirical studies on the impact of personalization. User Modeling and User-Adapted Interaction, 22, 399–439. https://doi.org/10.1007/s11257-011-9117-5
Wandoko, W., & Panggati, I. E. (2022). The Influence of Digital Influencer, e-WOM and Information Quality on Customer Repurchase Intention toward Online Shop in e-Marketplace during Pandemic COVID-19: The Mediation Effect of Customer Trust. Journal of Relationship Marketing, 21(2), 148–167. https://doi.org/10.1080/15332667.2022.2035198
Wang, Y., Zhu, J., Liu, R., & Jiang, Y. (2024). Enhancing recommendation acceptance: Resolving the personalization–privacy paradox in recommender systems: A privacy calculus perspective. International Journal of Information Management, 76, 102755. https://doi.org/10.1016/j.ijinfomgt.2024.102755
Xiao, B., & Benbasat, I. (2007). E-commerce product recommendation agents: Use, characteristics, and impact. MIS Quarterly, 31(1), 137–209. https://doi.org/10.2307/25148784
Zayed, M. F., Gaber, H. R., & El Essawi, N. (2022). Examining the Factors That Affect Consumers’ Purchase Intention of Organic Food Products in a Developing Country. In Sustainability (Vol. 14, Number 10). https://doi.org/10.3390/su14105868
Zeithaml, V. A. (1988). Consumer perceptions of price, quality, and value: A means-end model and synthesis of evidence. Journal of Marketing, 52(3), 2–22. https://doi.org/10.1177/002224298805200302
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Ade Suhara, Tina Isnaeni (Author)
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Paper Template 
