Beyond Rational Choice: Habit Drives Generation Z's Social Commerce Adoption in the Digital Transformation Era
DOI:
https://doi.org/10.35870/ijmsit.v6i2.8485Keywords:
Social Commerce, UTAUT, Digital Transformation, Gen Z, Behavioral IntentionAbstract
Social commerce has emerged as a growing element of Indonesia’s digital economy, although the psychological drivers behind its adoption among Generation Z remain relatively underexplored. Much of the existing research applies the UTAUT framework within traditional e-commerce contexts, providing limited insight into how socially embedded digital environments influence this generation’s adoption behavior. This study investigates the roles of Performance Expectancy, Effort Expectancy, Social Influence, and Habits in shaping Generation Z’s Behavioral Intentions toward social commerce. Data were obtained through a quantitative survey involving 151 Generation Z respondents selected using purposive sampling and subsequently analyzed through multiple linear regression with IBM SPSS Statistics 27. The results reveal that only Habits exhibit a significant positive relationship with Behavioral Intentions, whereas Performance Expectancy, Effort Expectancy, and Social Influence show no statistically significant influence. These findings indicate that Generation Z’s tendency to engage with social commerce in this context appears to stem primarily from established digital routines rather than deliberate judgments of usefulness, ease, or interpersonal influence.
Downloads
References
Agrebi, S., & Jallais, J. (2015). Explain the intention to use smartphones for mobile shopping. Journal of Retailing and Consumer Services, 22, 16–23. https://doi.org/10.1016/j.jretconser.2014.09.003
Ampornklinkaew, C. (2025). The role of social media influencers in influencing consumers’ imitation intentions. Digital Business, 5(2), 100143. https://doi.org/10.1016/j.digbus.2025.100143
Bandura, A. (2023). Social Cognitive Theory: An Agentic Perspective on Human Nature. John Willey & Sons, Inc.
BPS. (2024). Statistik Telekomunikasi Indonesia.
Creswell, J. W. (2014). Research Design: Qualitative, Quantitative, and Mixed Methods Approaches (4th ed.). SAGE Publications.
Davis, F. D., & Granić, A. (2024). The Technology Acceptance Model. Springer International Publishing. https://doi.org/10.1007/978-3-030-45274-2
Dwivedi, Y. K., Ismagilova, E., Sarker, P., Jeyaraj, A., Jadil, Y., & Hughes, L. (2023). A Meta-Analytic Structural Equation Model for Understanding Social Commerce Adoption. Information Systems Frontiers, 25(4), 1421–1437. https://doi.org/10.1007/s10796-021-10172-2
Francis, T., & Hoefel, F. (2018). ‘True Gen’: Generation Z and its implications for companies.
Goodstats. (2024). Tren Social Commerce Makin Populer, Masyarakat Kini Hobi Belanja di Media Sosial.
Gupta, A., Yousaf, A., & Mishra, A. (2020). How pre-adoption expectancies shape post-adoption continuance intentions: An extended expectation-confirmation model. International Journal of Information Management, 52, 102094. https://doi.org/10.1016/j.ijinfomgt.2020.102094
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2014). Multivariate data analysis (7th ed.). Pearson Education.
Hajli, M. N. (2014). A study of the impact of social media on consumers. International Journal of Market Research, 56(3), 387–404. https://doi.org/10.2501/IJMR-2014-025
Hendrajaya, C. T., Brahmasari, I. A., & Ratih, I. A. B. (2024). The influence of effort expectancy, performance expectancy, and social influence on perceived risk, behavioral intention, and actual use moderated by user trust in social commerce in Indonesia. Edelweiss Applied Science and Technology, 8(6), 4683–4699. https://doi.org/10.55214/25768484.v8i6.3017
Hossain, M. A., & Dwivedi, Y. K. (2014). What improves citizens’ privacy perceptions toward RFID technology? A cross-country investigation using mixed method approach. International Journal of Information Management, 34(6), 711–719. https://doi.org/10.1016/j.ijinfomgt.2014.07.002
Huwaida, L. A., Yusuf, A., Satria, A. N., Darmawan, M. A., Ammar, M. F., Yanuar, M. W., Hidayanto, A. N., & 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(2), 100256. https://doi.org/10.1016/j.joitmc.2024.100256
Ibrahim, I. (2025). Determinasi User Satisfaction dan Behavioral Intention Pengguna TikTok: Perspektif UTAUT. Indonesian Journal of Digital. Indonesian Journal of Digital Business. https://ejournal.upi.edu/index.php/IJDB/article/view/95077
Katadata. (2023). Perilaku Konsumen E-commerce Indonesia 2023.
Kiran, K. P., & Sailaja, V. N. (2025). Exploring behavioral intentions of consumers towards different digital payment services through the interplay of perceived risks and adoption factors. Humanities and Social Sciences Communications, 12(1), 1141. https://doi.org/10.1057/s41599-025-05468-6
Kotler, P., Kartajaya, H., & Setiawan, I. (2021). Marketing 5.0: Technology for Humanity. John Willey & Sons.
Liang, T.-P., & Turban, E. (2011). Introduction to the Special Issue Social Commerce: A Research Framework for Social Commerce. International Journal of Electronic Commerce, 16(2), 5–14. https://doi.org/10.2753/JEC1086-4415160201
Limayem, Hirt, & Cheung. (2007). How Habit Limits the Predictive Power of Intention: The Case of Information Systems Continuance. MIS Quarterly, 31(4), 705. https://doi.org/10.2307/25148817
Makarim, A., & Wahyudi, E. (2025). Pengaruh Performance Expectancy dan Effort Expectancy terhadap E-commerce Adoption Usage melalui Behavioral Intention. Indonesian Journal of Digital Business. https://ejournal.upi.edu/index.php/IJDB/article/view/95694
Nasution, R. P., F. Sembiring, B. K., & Situmorang, S. H. (2024). The Effect of Social Influence and Habit on Use Behavior with Behavioural Intention as an Intervening Variable in the Gojek Application User at the Medan Helvetia District. International Journal of Research and Review, 11(6), 147–157. https://doi.org/10.52403/ijrr.20240617
Octaviani, D. R., Sucherly, Prabowo, H., & Sari, D. (2023). Determinants of Indonesian Gen Z’s purchase behavior on online travel platforms: Extending UTAUT model. Innovative Marketing, 19(4), 54–65. https://doi.org/10.21511/im.19(4).2023.05
Pradita, F. N. S., Adityaputra, M. R., & Gunadi, W. (2025). Analysis of Gen Z’s Continuous Intention to Use Tiktok Shop in DKI Jakarta Through the UTAUT2 Perspective. Journal Research of Social Science, Economics, and Management, 5(3), 3561–3578. https://doi.org/10.59141/jrssem.v5i3.1107
Putra, A. O., Lita, R. P., & Besra, E. (2025). Habit, Trust, dan Continuous Intention sebagai Determinasi Use Behavior pada Pengguna PLN Mobile di Sumatera Barat. Jurnal Manajemen Stratejik Dan Simulasi Bisnis, 6(2), 1–14. https://doi.org/10.25077/mssb.6.2.1-14.2025
Refsi, F., & Soma, M. A. (2025). The Influence of Performance Expectancy, Effort Expectancy, Social Influence, Facilitating Conditions, And Financial Literacy on Use Behavior of Buy Now Pay Later Services with Behavior Intention as A Mediating Variable Among Generation Z In the Bandung Raya Area. International Journal of Educational Research & Social Sciences, 6(2), 281–286. https://doi.org/10.51601/ijersc.v6i2.981
Roscoe, J. T.. (1975). Fundamental research statistics for the behavioral sciences [by] John T. Roscoe. Holt, Rinehart and Winston.
Sadiq, S., Kaiwei, J., Aman, I., & Mansab, M. (2025). Examine the factors influencing the behavioral intention to use social commerce adoption and the role of AI in SC adoption. European Research on Management and Business Economics, 31(1), 100268. https://doi.org/10.1016/j.iedeen.2024.100268
Sekaran, & Bougie. (2016). Research Methods for Business: A Skill-Building Approach (S. Wiley, Ed.; 7th ed.). West Sussex.
Sugiyono. (2013). Metode penelitian kuantitatif, kualitatif dan R&D. Alfabeta.
Twenge, J. M. (2017). iGen: Why today’s super-connected kids are growing up less rebellious, more tolerant, less happy — and completely unprepared for adulthood. Atria Books.
Venkatesh, Thong, & Xu. (2012). Consumer Acceptance and Use of Information Technology: Extending the Unified Theory of Acceptance and Use of Technology. MIS Quarterly, 36(1), 157. https://doi.org/10.2307/41410412
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User Acceptance of Information Technology: Toward A Unified View1. MIS Quarterly, 27(3), 425–478. https://doi.org/10.2307/30036540
Wardani, L. P. A. K., & Masdiantini, P. R. (2022). Pengaruh Ekspektasi Kinerja, Ekspektasi Usaha, Faktor Sosial Budaya, Motivasi Hedonis dan Nilai Harga terhadap Minat Penggunaan Quick Response Code Indonesian Standard. Jurnal Ilmiah Akuntansi Dan Humanika, 12(1). https://ejournal.undiksha.ac.id/index.php/JJA/article/view/38188
Wibowo, A. H., Mursityo, Y. T., & Herlambang. (2019). Pengaruh Performance Expectancy, Effort Expectancy dan Social Influence terhadap Behavioral Intention dalam Implementasi Aplikasi SIMPG PT Perkebunan Nusantara XI Surabaya. Jurnal Pengembangan Teknologi Informasi Dan Ilmu Komputer, 3(9).
Zheng, H., Han, F., Huang, Y., Wu, Y., & Wu, X. (2025). Factors influencing behavioral intention to use e-learning in higher education during the COVID-19 pandemic: A meta-analytic review based on the UTAUT2 model. Education and Information Technologies, 30(9), 12015–12053. https://doi.org/10.1007/s10639-024-13299-2
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Hubi Rahmat Andika, Ishak, Roiyan One Febriani, Dian Eka Aprilia Fitria Ningrum
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
1. Copyright Retention and Open Access License
Authors retain copyright of their work and grant the journal non-exclusive right of first publication under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
This license allows unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
2. Rights Granted Under CC BY 4.0
Under this license, readers are free to:
- Share — copy and redistribute the material in any medium or format
- Adapt — remix, transform, and build upon the material for any purpose, including commercial use
- No additional restrictions — the licensor cannot revoke these freedoms as long as license terms are followed
3. Attribution Requirements
All uses must include:
- Proper citation of the original work
- Link to the Creative Commons license
- Indication if changes were made to the original work
- No suggestion that the licensor endorses the user or their use
4. Additional Distribution Rights
Authors may:
- Deposit the published version in institutional repositories
- Share through academic social networks
- Include in books, monographs, or other publications
- Post on personal or institutional websites
Requirement: All additional distributions must maintain the CC BY 4.0 license and proper attribution.
5. Self-Archiving and Pre-Print Sharing
Authors are encouraged to:
- Share pre-prints and post-prints online
- Deposit in subject-specific repositories (e.g., arXiv, bioRxiv)
- Engage in scholarly communication throughout the publication process
6. Open Access Commitment
This journal provides immediate open access to all content, supporting the global exchange of knowledge without financial, legal, or technical barriers.
