Shopping Attributes and Low Price Appeal: The Role of Trust in Driving Impulse Buying on Shopee Live Streaming

The rapid expansion of live-streaming commerce has transformed online shopping into an interactive and immersive experience that increasingly stimulates consumers' impulse buying behavior. Despite the growing popularity of Shopee Live in Indonesia, limited research has examined the mechanisms through which shopping attributes and promotional pricing influence impulse buying through trust formation. This study investigates the effects of shopping attributes and low price appeal on impulse buying and examines the mediating role of trust among Shopee Live users. A quantitative explanatory design was employed using a cross-sectional survey of 170 consumers who had previously purchased products through Shopee Live. Data were analyzed using Structural Equation Modeling (SEM) with AMOS, while additional construct assessments were conducted using SmartPLS 4. The findings indicate that shopping attributes significantly influence impulse buying (β = 0.444, p < 0.001), and low price appeal also has a positive effect on impulse buying (β = 0.273, p = 0.050). Low price appeal significantly influences trust (β = 0.671, p = 0.002), whereas the effect of shopping attributes on trust is not significant (β = 0.063, p = 0.761). Furthermore, trust positively affects impulse buying (β = 0.293, p < 0.001) and significantly mediates the relationship between low price appeal and impulse buying (β = 0.267, p = 0.003). However, trust does not mediate the relationship between shopping attributes and impulse buying (β = 0.134, p = 0.060). The proposed model explains 83.1% of the variance in impulse buying and 49.9% of the variance in trust. This study extends the Stimulus–Organism–Response framework by demonstrating that trust in live-streaming commerce is driven primarily by economic cues rather than experiential shopping attributes. The findings further reveal two distinct pathways underlying impulse buying behavior in Shopee Live: a direct experiential pathway and an economic-trust pathway, offering important theoretical and managerial implications for digital commerce platforms and online sellers.

live-streaming commerce; shopping attributes; low price appeal; trust; impulse buying.

1. Akram, U., Hui, P., Khan, M. K., Yan, C., & Akram, Z. (2018). Factors affecting online impulse buying: Evidence from Chinese social commerce environment. Sustainability, 10(2), 352. https://doi.org/10.3390/su10020352 [Google Scholar] [Crossref]

2. APJII. (2025). Laporan survei penetrasi internet Indonesia 2025. Asosiasi Penyelenggara Jasa Internet Indonesia. [Google Scholar] [Crossref]

3. Apasrawirote, D., & Yawised, K. (2022). Factors influencing consumers' behavioral intention to use live streaming commerce. Frontiers in Psychology, 13, Article 933524. [Google Scholar] [Crossref]

4. Byrne, B. M. (2022). Structural equation modeling with AMOS: Basic concepts, applications, and programming (4th ed.). Routledge. [Google Scholar] [Crossref]

5. Cambra-Fierro, J., Melero-Polo, I., & Sese, F. J. (2021). Managing customer experiences in service ecosystems. Journal of Service Theory and Practice, 31(2), 173–196. [Google Scholar] [Crossref]

6. Chandrruangphen, P., Assarut, N., & Sinthupinyo, S. (2022). The influence of live streaming attributes on consumer trust and purchase intention in social commerce. Asia Pacific Journal of Marketing and Logistics, 34(9), 1937–1956. [Google Scholar] [Crossref]

7. Chen, C. C., & Lin, Y. C. (2018). What drives live-streaming usage intention? The perspectives of flow, entertainment, social interaction, and endorsement. Telematics and Informatics, 35(1), 293–303. [Google Scholar] [Crossref]

8. DataReportal. (2025). Digital 2025: Indonesia. https://datareportal.com/reports/digital-2025-indonesia [Google Scholar] [Crossref]

9. Dodds, W. B., Monroe, K. B., & Grewal, D. (1991). Effects of price, brand, and store information on buyers' product evaluations. Journal of Marketing Research, 28(3), 307–319. [Google Scholar] [Crossref]

10. Erdem, T., & Swait, J. (2004). Brand credibility, brand consideration, and choice. Journal of Consumer Research, 31(1), 191–198. [Google Scholar] [Crossref]

11. Gefen, D., Karahanna, E., & Straub, D. W. (2003). Trust and TAM in online shopping: An integrated model. MIS Quarterly, 27(1), 51–90. [Google Scholar] [Crossref]

12. Guo, L., Hu, X., Lu, J., & Ma, L. (2023). Understanding consumers' impulsive buying behavior in live-streaming commerce: The role of interactivity and trust. Electronic Commerce Research and Applications, 57, Article 101220. [Google Scholar] [Crossref]

13. Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2022). Multivariate data analysis (9th ed.). Cengage Learning. [Google Scholar] [Crossref]

14. Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2022). A primer on partial least squares structural equation modeling (PLS-SEM) (3rd ed.). Sage. [Google Scholar] [Crossref]

15. Kline, R. B. (2023). Principles and practice of structural equation modeling (5th ed.). Guilford Press. [Google Scholar] [Crossref]

16. Kukar-Kinney, M., & Close, A. G. (2010). The determinants of consumers' online shopping cart abandonment. Journal of the Academy of Marketing Science, 38(2), 240–250. [Google Scholar] [Crossref]

17. Li, Y., Li, X., & Cai, S. (2023). Persuasion knowledge and trust formation in live-streaming commerce. Journal of Retailing and Consumer Services, 73, Article 103311. [Google Scholar] [Crossref]

18. Liu, Y., & Wang, C. (2022). Price promotions and consumer perceived quality in online shopping environments. Journal of Retailing and Consumer Services, 66, Article 102904. [Google Scholar] [Crossref]

19. Luo, X., Tong, S., Fang, Z., & Qu, Z. (2023). Frontiers: Machines versus humans: The impact of artificial intelligence chatbot disclosure on customer purchases. Marketing Science, 42(1), 1–18. [Google Scholar] [Crossref]

20. Masitoh, D., Suryadi, N., & Putri, R. A. (2024). Live shopping attributes, trust, and impulse buying behavior in social commerce. Journal of Retailing and Consumer Services, 76, Article 103566. [Google Scholar] [Crossref]

21. Mehrabian, A., & Russell, J. A. (1974). An approach to environmental psychology. MIT Press. [Google Scholar] [Crossref]

22. Momentum Works. (2025). E-commerce in Southeast Asia 2025 report. Momentum Works. [Google Scholar] [Crossref]

23. Rasool Lakhan, G., Ullah, M., Arshad, M., & Ahmad, M. S. (2021). Internet usage and digital transformation of consumer behavior. Journal of Open Innovation: Technology, Market, and Complexity, 7(2), 99. [Google Scholar] [Crossref]

24. Sasongko, B., & Artanti, Y. (2024). The influence of price promotion and flash sale on impulse buying behavior in e-commerce platforms. Cogent Business & Management, 11(1), Article 2334512. [Google Scholar] [Crossref]

25. Sun, Y., Shao, X., Li, X., Guo, Y., & Nie, K. (2019). How live streaming influences purchase intentions in social commerce: An IT affordance perspective. Electronic Commerce Research and Applications, 37, Article 100886. [Google Scholar] [Crossref]

26. We Are Social. (2025). Digital 2025 global overview report. https://wearesocial.com [Google Scholar] [Crossref]

27. Wongkitrungrueng, A., & Assarut, N. (2020). The role of live streaming in building consumer trust and engagement with social commerce sellers. Journal of Business Research, 117, 543–556. [Google Scholar] [Crossref]

28. Xu, X., Wu, J. H., & Li, Q. (2023). Understanding consumers' impulse buying in live-streaming commerce through the stimulus-organism-response framework. Internet Research, 33(4), 1345–1368. [Google Scholar] [Crossref]

29. Zakiah, N. (2025). Consumer engagement and impulsive purchasing behavior in Indonesian live-streaming commerce. Jurnal Manajemen dan Pemasaran Digital, 9(1), 1–15. [Google Scholar] [Crossref]

30. Zhang, M., Qin, F., Wang, G. A., & Luo, C. (2023). The impact of live-streaming interactions on consumers' impulse buying behavior. Electronic Commerce Research, 23(2), 819–843. [Google Scholar] [Crossref]

31. Zhao, Y., Chen, X., & Wang, J. (2024). Promotional strategies and trust formation in live-streaming commerce. Journal of Retailing and Consumer Services, 78, Article 103731. [Google Scholar] [Crossref]

32. Zheng, X., Men, J., Yang, F., & Gong, X. (2022). Understanding impulse buying in mobile commerce: An investigation into hedonic browsing and price promotions. Electronic Commerce Research and Applications, 52, Article 101113. [Google Scholar] [Crossref]

33. Browne, M. W., & Cudeck, R. (1993). Alternative ways of assessing model fit. In K. A. Bollen & J. S. Long (Eds.), Testing structural equation models (pp. 136–162). Sage. [Google Scholar] [Crossref]

34. Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50. [Google Scholar] [Crossref]

35. Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115–135. [Google Scholar] [Crossref]

36. Hu, L. T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1–55. [Google Scholar] [Crossref]

37. Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric theory (3rd ed.). McGraw-Hill. [Google Scholar] [Crossref]

• A Study on Utilisation on Vidiyal Payanam with Special Reference to Udumalpet
• Influence of Sales Promotional Strategies on Water Purifier Purchase: A Study
• Voluptatem exercitat
• Beatae voluptatum eanfjnjfnjnj
• Analysis of Marketing Strategies in Newbridge College During Covid-19 Pandemic