Designing Persuasive Mobile Live-Commerce Systems: An ELM–SOR Analysis of Interactive Interface Cues and Consumer Engagement

Wang Lin; Ng Siew Imm; Norazlyn Kamal Basha

doi:10.3991/ijim.v20i17.62832

Authors

Wang Lin Universiti Putra Malaysia, Serdang, Malaysia
Ng Siew Imm Universiti Putra Malaysia, Serdang, Malaysia https://orcid.org/0000-0002-6518-925X
Norazlyn Kamal Basha Universiti Putra Malaysia, Serdang, Malaysia

DOI:

https://doi.org/10.3991/ijim.v20i17.62832

Keywords:

Mobile live-commerce, interactive mobile interfaces, customer engagement, purchase intention, Elaboration Likelihood Model, Stimulus–Organism–Response

Abstract

Mobile live-streaming commerce has quickly changed digital shopping. It lets people interact in real time, see products in different ways, and connect with others through mobile apps. Even though many use these platforms, keeping users engaged remains a major challenge for designers and retailers. This study uses the elaboration likelihood model (ELM) and the stimulus–organism–response (SOR) framework to examine how interactive mobile interface cues affect customer engagement and purchase behaviour in mobile live-commerce settings. This study examined six interface stimuli: product information quality, streamer interaction quality, streamer credibility, bullet-screen consistency, monetary incentives, and popularity cues. Susceptibility to informational influence (SII) was included as a moderating factor. Data came from 500 Chinese Millennial mobile live-streaming shoppers and were analysed using partial least squares structural equation modelling (PLS-SEM). The findings show that all six interface cues help increase customer engagement. Streamer interaction quality and product information quality have the biggest impact. Customer engagement strongly predicts purchase intention and completely mediates the link between each interface cue and purchase intention. Furthermore, SII significantly amplifies the effects of streamer credibility, bullet-screen consistency, and popularity cues on user engagement but does not moderate the effects of information quality, interaction quality, or monetary incentives. These findings advance mobile human-computer interaction (HCI) research by elucidating how interactive interface features influence user engagement and by offering practical guidance for designing more engaging and personalised mobile live-commerce applications.

References

[1] Y. Peng, J. Ding, and Y. Zhang, "Combination of streamers' product description and viewers' comments: moderating effect of streamer–viewer relationship strength," Marketing Intelligence & Planning, vol. 42, no. 1, pp. 190–210, 2024, doi: 10.1108/MIP-03-2023-0112.

[2] K. Khotimah and Lusiana, "Live-Streaming Influencers and Customer Loyalty: A Systematic Review of Customer Satisfaction and Service Quality in TikTok Commerce," International Journal of Management Thinking, vol. 4, no. 1, pp. 1–17, Mar. 2026, https://doi.org/10.56868/ijmt.v4i1.109.

[3] Y. Sun, X. Shao, X. Li, Y. Guo, and K. Nie, "How live streaming influences purchase intentions in social commerce: an IT affordance perspective," Electronic Commerce Research and Applications, vol. 37, Art. no. 100886, Sep.–Oct. 2019, doi: 10.1016/j.elerap.2019.100886.

[4] Statista, "Livestream e-commerce sales in the United States from 2020 to 2026," 2024. [Online]. Available: https://www.statista.com/statistics/1276120/livestream-e-commerce-sales-united-states/

[5] A. Arora, J. L. Garcia, M. Harrison, and E. S. Paton, "It's showtime! How live commerce is transforming the shopping experience," McKinsey & Company, Jul. 2021. [Online]. Available: https://www.mckinsey.com/capabilities/tech-and-ai/our-insights/its-showtime-how-live-commerce-is-transforming-the-shopping-experience

[6] X. Luo, J.-H. Cheah, L. D. Hollebeek, and X.-J. Lim, "Boosting customers’ impulsive buying tendency in live-streaming commerce: The role of customer engagement and deal proneness," Journal of Retailing and Consumer Services, vol. 77, Art. no. 103644, Mar. 2024, doi: 10.1016/j.jretconser.2023.103644.

[7] F. J. Tapia Moreno, “Elaboration of Statistics Learning Objects for Mobile Devices”, Int. J. Interact. Mob. Technol., vol. 6, no. 2, pp. pp. 51–54, Apr. 2012. https://doi.org/10.3991/ijim.v6i2.1971.

[8] R. Ding, X. Chen, S. Wei, and J. Wang, "What drives trust building in live streaming E-commerce? From an elaboration likelihood model perspective," Industrial Management & Data Systems, vol. 125, no. 3, pp. 969–999, Jan. 2025, doi: 10.1108/IMDS-03-2024-0273.

[9] X. Gao, X. Xu, and Q. Li, "How the live streaming commerce viewers process the persuasive message: An ELM perspective and the moderating effect of mindfulness," Electronic Commerce Research and Applications, vol. 49, Art. no. 101087, Sep. 2021, doi: 10.1016/j.elerap.2021.101087.

[10] L. Fitria, R. Eliza, and S. Sari, “Impact of Social Media Advertising on Business Performance: Evidence from Micro, Small, and Medium Enterprises in Dumai City, Indonesia”, IJMT, vol. 3, no. 2, pp. 16–31, Jan. 2026. https://doi.org/10.56868/ijmt.v3i2.101.

[11] R. E. Petty and J. T. Cacioppo, "Message elaboration versus peripheral cues," in Communication and Persuasion: Central and Peripheral Routes to Attitude Change. New York, NY: Springer New York, 1986, ch. 5, pp. 141–172. doi: 10.1007/978-1-4612-4964-1_5.

[12] M. Srivastava and G. K. Saini, "A bibliometric analysis of the elaboration likelihood model (ELM)," Journal of Consumer Marketing, vol. 39, no. 7, pp. 726–743, Sep. 2022, doi: 10.1108/JCM-09-2021-4914.

[13] R. R. Mantilla Guiza, “Enhancing Algorithmic Design through Mobile-Supported Constructivist Learning: A Problem-Based Approach”, Int. J. Interact. Mob. Technol., vol. 20, no. 06, pp. pp. 125–141, Mar. 2026.

[14] M. Zhang, L. Sun, F. Qin, and G. A. Wang, "E-service quality on live streaming platforms: swift guanxi perspective," Journal of Services Marketing, vol. 35, no. 3, pp. 312–324, Jun. 2021, doi: 10.1108/JSM-01-2020-0009.

[15] G. A. Farah, M. Ahmad, H. Muqarrab, J. A. Turi, and S. Bashir, "Online shopping behavior among university students: Case study of Must University," Advances in Social Sciences Research Journal, vol. 5, no. 4, pp. 228–242, Apr. 2018, doi: 10.14738/assrj.54.4429.

[16] S.-C. Yang, "The Impact of Live Streamers' Characteristics and Content Information Quality on Audience Participation Intentions: A Para-social Interaction Perspective," Rev. Socionetwork Strategy, vol. 19, no. 2, pp. 401–427, Oct. 2025, doi: 10.1007/s12626-025-00183-9.

[17] M. Siddique and A. Lee, "From Interaction to Conversion: The Influence of Streamer Interaction Quality (SIQ) on Consumer Adoption of Live Streaming Commerce," in Int. Textile Apparel Assoc. Annu. Conf. Proc., vol. 82, no. 1, Dec. 2025, doi: 10.31274/itaa.21768.

[18] C. Song and Y.-C. Liu, "The effect of live-streaming shopping on the consumer's perceived risk and purchase intention in China," in Proc. 23rd Int. Conf. Electron. Commerce (ICEC), 2021, Art. no. 21, doi: 10.1145/3481084.3481102.

[19] X. Zhu and L. Li, "What Does Bullet Screen Bring to Video Platform? A Theoretical Analysis Comparing Different Bullet Screen Modes," J. Theor. Appl. Electron. Commer. Res., vol. 20, no. 4, Art. no. 338, 2025, doi: 10.3390/jtaer20040338.

[20] Q. Zeng, Q. Guo, W. Zhuang, Y. Zhang, and W. Fan, "Do real-time reviews matter? Examining how bullet screen influences consumers’ purchase intention in live streaming commerce," Information Systems Frontiers, pp. 1–17, 2022.

[21] J. Chen, L. Teng, Y. Yu, and X. Yu, "The effect of online information sources on purchase intentions between consumers with high and low susceptibility to informational influence," Journal of Business Research, vol. 69, no. 2, pp. 467–475, 2016.

[22] V. Mirabi, H. Akbariyeh, and H. Tahmasebifard, "A study of factors affecting on customers purchase intention," Journal of Multidisciplinary Engineering Science and Technology (JMEST), vol. 2, no. 1, 2015.

[23] Ohanian, R., 1990. Construction and validation of a scale to measurecelebrity endorsers’ perceived expertise, trustworthiness, and attractiveness. J. Advert. 19 (3), 39–52. https://doi.org/10.1080/00913367.

[24] Mou, J., Cui, Y., & Kurcz, K. (2019). Bibliometric and visualized analysis of research on major e-commerce journals using Citespace. Journal of electronic commerce research, 20(4), 219-237.

[25] Vivek, S. D., Beatty, S. E., Dalela, V., & Morgan, R. M. (2014). A generalized multidimensional scale for measuring customer engagement. Journal of marketing theory and practice, 22(4), 401-420.

[26] Chen, J., Teng, L., Yu, Y., & Yu, X. (2016). The effect of online information sources on purchase intentions between consumers with high and low susceptibility to informational influence. Journal of Business Research, 69(2), 467-475.