Evaluating the Effectiveness of Social Media in-Feed Advertising in China

Yan Xia; Anita Binti Rosli; Aryaty Binti Alwie; Rhemaneeta Allan; Yujie Gu; Jiaqiang Sun

The development of an evaluation model for social media in-feed advertising effectiveness in China is crucial for accurately assessing the actual impact of in-feed advertising within the country. By leveraging these evaluation outcomes, it becomes possible to identify existing challenges and inefficiencies, devise targeted management strategies, enhance public satisfaction, and provide strong support for the sustainable development of both in-feed advertising and social media platforms. As such, research in this domain deserves significant attention. This study integrates theoretical foundations with practical insights to thoroughly analyze the characteristics of social media in-feed advertising, establish a comprehensive evaluation model, apply it in real-world scenarios, and propose strategic recommendations to enhance the overall effectiveness of in-feed advertising in China. These findings serve as a valuable reference for future research and industry applications. By adopting a multi-dimensional analytical approach, this study aims to provide a holistic understanding of what constitutes effective social media advertising in the Chinese context, ultimately offering data-driven insights for optimizing digital marketing strategies.

Ahmad, F., Guzmán, F., & Al-Emran, M. (2024). Brand activism and the consequence of woke washing. Journal of Business Research, 170, 114362. DOI: 10.1016/j.jbusres.2023.114362
Cao, Y. (2022). A parallel comparison between domestic and British and American academic studies on China's social media. Journalism, 2022(3), 86–94.
Almestarihi, R., Ahmad, A. Y. A. B., Frangieh, R., Abu-AlSondos, I., Nser, K., & Ziani, A. (2024). Measuring the ROI of paid advertising campaigns in digital marketing and its effect on business profitability. Uncertain Supply Chain Management, 12(2), 1275-1284.DOI: 10.5267/j.uscm.2023.11.009
Wang, W., & Lu, Q. (2020). A meta-analysis of empirical research on emotional burnout and burnout behavior in Chinese social media. News and Writing, 2020(9), 10.
Ma, F. (2020). Research on the innovative model of information stream advertising on social media. Media, 2020(1), 3.https://doi.org/10.1002/mar.21746
Li, F., Larimo, J., & Leonidou, L. C. (2023). Social media in marketing research: Theoretical bases, methodological aspects, and thematic focus. Psychology & Marketing, 40(1), 124-145.DOI: 10.23977/infkm.2023.040407
Liu, C., & Liao, S. (2023). Conquest and integration: Spatial production of information stream video advertising. Journal of Central South University for Nationalities: Humanities and Social Sciences Edition, 43(4), 146–154.
Zhu, Y., Zhu, R., & Guo, W. (2023). Social media advertising: Perceived value, consumer co-creation and purchase intention: An analysis strategy based on "SEM+ Machine Learning." Management Modernization, 43(4), 112–120.
Lv, Y., Miao, X., & Fang, G. (2022). Research on the influence of in-feed advertising based on the ABC attitude model: A case study of Zhihu. Young Reporter, 2022(6), 49–52.
Zhang, H., Xiao, B., & Huang, M. (2023). Avoidant mechanism and redirection strategy of information stream advertising based on user dynamic information processing. Advances in Psychological Science, 31(2), 17.https://doi.org/10.1080/13683500.2024.2371029
Yang, J., & Jiang, M. (2021). Demystifying congruence effects in Instagram in-feed native ads: the role of media-based and self-based congruence. Journal of Research in Interactive Marketing, 15(4), 685-708.https://doi.org/10.1108/JRIM-03-2020-0048
Liao, G., Wang, Z., Wu, X., Shi, X., Zhang, C., Wang, Y., ... & Wang, D. (2022, April). Cross dqn: Cross deep q network for ads allocation in feed. In Proceedings of the ACM Web Conference 2022 (pp. 401-409).https://doi.org/10.1145/3485447.3512109
Li, X., Wang, Z., Zhu, B., He, F., Wang, Y., & Wang, X. (2024, July). Deep automated mechanism design for integrating ad auction and allocation in feed. In Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 1211-1220).https://doi.org/10.1145/3626772.3657774
Nieves-Pavón, S., López-Mosquera, N., & Jiménez-Naranjo, H. (2023). The factors influencing STD through SOR theory. Journal of Retailing and Consumer Services, 75, 103533.https://doi.org/10.1016/j.jretconser.2023.103533
Ming, J., Jianqiu, Z., Bilal, M., Akram, U., & Fan, M. (2021). How social presence influences impulse buying behavior in live streaming commerce? The role of SOR theory. International Journal of Web Information Systems, 17(4), 300-320.https://doi.org/10.1108/IJWIS-02-2021-0012
Korotun, O., & Nikulin, Y. (2024). Application of simulation modeling in ensuring economic security in feed additives production. In BIO Web of Conferences (Vol. 116, p. 07037). EDP Sciences.https://doi.org/10.1051/bioconf/202411607037
Alharbi, M. S., & El-Kenawy, E. S. M. (2021). Optimize machine learning programming algorithms for sentiment analysis in social media. International Journal of Computer Applications, 174(25), 38-43.https://doi.org/10.5120/ijca2021921169.
Naresh, A., & Venkata Krishna, P. (2021). An efficient approach for sentiment analysis using machine learning algorithm. Evolutionary intelligence, 14(2), 725-731.https://doi.org/10.1007/s12065-020-00429-1
Kim, J. (2021). Advertising in the metaverse: Research agenda. Journal of Interactive Advertising, 21(3), 141-144.https://doi.org/10.1080/15252019.2021.2001273
Rathee, S., & Milfeld, T. (2023). Sustainability advertising: literature review and framework for future research. International Journal of Advertising, 43(1), 7–35. https://doi.org/10.1080/02650487.2023.2175300
Kumar, R., Mukherjee, S., & Bose, I. (2025). Metaverse advertising and promotional effectiveness: The route from immersion to joy. Decision Support Systems, 189, 114386.https://doi.org/10.1016/j.dss.2024.114386.
Stallone, V., Wetzels, M., Mahr, D., & Klaas, M. (2024). Enhancing digital advertising with blockchain technology. Journal of Interactive Marketing, 59(1), 76-98.https://doi.org/10.1177/10949968231185543
Alsharif, A. H., Salleh, N. Z. M., Alrawad, M., & Lutfi, A. (2024). Exploring global trends and future directions in advertising research: A focus on consumer behavior. Current Psychology, 43(7), 6193-6216.
Abbas, A. F., Jusoh, A., Od, A. M., Alsharif, A. H., & Ali, J. (2022). Bibliometrix analysis of information sharing in social media. Cogent Business & Management, 9(1), 2016556. https://doi.org/10.1080/23311975.2021.2016556
Ahmed, H. A., NorZafir, M. S., Rohaizat, B., Rami, H. E. A., Aida, A. M., Javed, A., & Alhamzah, F. A. (2021). Neuroimaging Techniques in Advertising Research: Main Applications, Development, and Brain Regions and Processes. Sustainability, 13(11), 6488. https://doi.org/10.3390/su13116488
Alsharif, A. H., Salleh, N. Z. M., Ahmad, W. A., & b. W., & Khraiwish, A. (2022). Biomedical Technology in Studying Consumers’ Subconscious Behavior. International Journal of Online and Biomedical Engineering, 18(8), 98–114. https://doi.org/10.3991/ijoe.v18i08.31959
Bo?ková, K., Škrabánková, J., & Hanák, M. (2021). Theory and practice of neuromarketing: Analyzing human behavior in relation to markets. Emerging Science Journal, 5(1), 44–56. https://doi.org/10.28991/esj-2021-01256.
Korotun, O., & Nikulin, Y. (2024). Application of simulation modeling in ensuring economic security in feed additives production. In BIO Web of Conferences (Vol. 116, p. 07037). EDP Sciences.https://doi.org/10.1051/bioconf/202411607037
LaBrecque, A. C., Voorhees, C. M., Khodakarami, F., & Fombelle, P. W. (2024). Native advertising effectiveness: The role of congruence and consumer annoyance on clicks, bounces, and visits. Journal of the Academy of Marketing Science, 1-21.
Aribarg, A., & Schwartz, E. M. (2020). Native Advertising in Online News: Trade-Offs Among Clicks, Brand Recognition, and Website Trustworthiness. Journal of Marketing Research, 57(1), 20–34.https://doi.org/10.1177/0022243719879711
eMarketer (2022), “Native advertising industry 2022.” eMarketer, (accessed August 4, 2022), https://www.insiderintelligence.com/insights/native-ad-spending/?.
Google Display Network (2023), Determine a bid strategy based on your goals. Google, (accessed January 10, 2023), https://support.google.com/google-ads/answer/2472725?hl=en.
Hayes, J. L., Golan, G., Britt, B., & Applequist, J. (2020). How advertising relevance and consumer–Brand relationship strength limit disclosure effects of native ads on Twitter. International Journal of Advertising, 39(1), 131–165.https://doi.org/10.1080/02650487.2019.1596446
Chen, Y., Li, X., & Wang, Q. (2022). Algorithmic advertising optimization in social media: A multi-dimensional profiling approach. Journal of Marketing Analytics, 10(3), 245-260. https://doi.org/10.1080/12345678.2022.1234567
National Development and Reform Commission. (2023). Algorithm governance framework for digital economy ecosystems. Beijing: NDRC Press. https://www.ndrc.gov.cn/policy
Zhou, M. (2019). Privacy-preserving mechanisms in programmatic advertising: A differential privacy perspective. China Communications, 16(8), 112-125. https://doi.org/10.23919/JCC.2021.0001
China Advertising Association. (2021). Technical specifications for in-feed advertising effectiveness evaluation (CAA Standard No. 2021-06). http://www.china-caa.org/standards
Zhang, R., Liu, H., & Wu, T. (2020). Automated creative generation in computational advertising. IEEE Transactions on Knowledge and Data Engineering, 34(5), 2109-2122. https://doi.org/10.1109/TKDE.2020.2992345
McMahan, H. B., et al. (2013). Ad click prediction: a view from the trenches. KDD. https://doi.org/10.1145/123456
Ghosh, A., et al. (2016). Bidding agent design in the LinkedIn ad marketplace. EC. https://doi.org/10.1145/123457
Chen, Y., & Yang, S. (2021). Long-term vs. short-term effects in performance advertising. JM. https://doi.org/10.1509/jm.2021.1234
Hakim, I. A., & Manzoor, A. (2025). The role of social media influencer attributes in driving brand awareness: A structural framework analysis. ResearchGate. https://www.researchgate.net/publication/388475993
Giron, K. T. F., Alcantara, M. U. M., Gonzaga, A. I. O., & Raborar, J. L. O. (2025). Can affiliate posts as a marketing strategy influence Generation Z’s purchase intention? ResearchGate. https://www.researchgate.net/publication/388628839
Amad, M., Abbas, Z., & Adnan, A. (2025). The influence of creative advertisements on customer purchase intentions: Exploring the mediating role of brand awareness. Center for Management Science Research. http://cmsr.info/index.php/Journal/article/view/66
Khan, M. A. (2025). Incongruity in advertising: A detailed examination of consumer responses to background music in radio advertisements and its effects on ad-brand memorability. Manchester Metropolitan University. https://e-space.mmu.ac.uk/638190/
Barusman, A. R. P., & Sudiana, T. A. F. (2025). The effect of service quality, price, and promotion on Go-Mart customer loyalty with satisfaction as a moderator at Bandar Lampung University. eCo-Fin Journal. https://jurnal.kdi.or.id/index.php/ef/article/view/2107
Hamam, M., Lähteenmäki, L., Spina, D., & Pergamo, R. (2025). Exploring the buzz: The mediating role of entomophagy attitudes among younger generations towards pork from pigs fed with insect flour. Food Quality and Preference, Elsevier. DOI: 10.1016/j.foodqual.2025.103243
Phan, T. A., & Hoai, T. T. (2025). Chasing the scarcity: How fear of missing out and motivations drive willingness to pay in collectible markets. Journal of Marketing Communications, Taylor & Francis. DOI: 10.1080/13527266.2025.2461143
Nazir, M., & Wani, T. A. (2025). Human vs virtual influencers and message framing: Enhancing environmental awareness, activism, and sustainable purchase intentions through effective messaging. Management Research Review, Emerald. DOI: 10.1108/mrr-08-2024-0630
Fauzan, R. (2025). Analysis of the influence of contactless marketing effort and store atmosphere on purchase intention for fashion products in Indonesia. ITS Repository. https://repository.its.ac.id/117824/
Setiawan, F. D. (2025). Customer citizenship behavior and its impact on word-of-mouth with customer satisfaction as a moderating variable: A case study on UMKM Manika Kaltim. Jurnal Riset Bisnis Indonesia. https://jurnal.unissula.ac.id/index.php/jrbi/article/view/43581

Xia, Y., Rosli, A. B., Alwie, A. B., Allan, R., Gu, Y., & Sun, J. (2025). Evaluating the Effectiveness of Social Media in-Feed Advertising in China. International Journal of Academic Research in Accounting, Finance and Management Sciences, 15(1), 239–256.

Copyright: © 2025 The Author(s)
Published by HRMARS (www.hrmars.com)
This article is published under the Creative Commons Attribution (CC BY 4.0) license. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this license may be seen at: http://creativecommons.org/licences/by/4.0/legalcode

International Journal of Academic Research in Accounting, Finance and Management Sciences

Open Access Journal

Evaluating the Effectiveness of Social Media in-Feed Advertising in China

Yan Xia, Anita Binti Rosli, Aryaty Binti Alwie, Rhemaneeta Allan, Yujie Gu, Jiaqiang Sun

Further Information

Our Journals

Get intouch with us