Authors :
Shraddha Priyadarshi
Volume/Issue :
Volume 11 - 2026, Issue 9 - September
Google Scholar :
https://tinyurl.com/dwaexuez
DOI :
https://doi.org/10.38124/ijisrt/26sep1155
Note : A published paper may take 4-5
working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and
ResearchGate.
Abstract :
Background:
Short-form video platforms increasingly personalize users’ feeds through recommendation systems that repeatedly
expose them to products, aesthetics, and consumption narratives. This study examines whether repeated algorithmic
exposure is associated with changes in perceived product interest and uncertainty about the origins of consumer desire.
Methods:
The manuscript analyzes aggregate descriptive survey information supplied for 200 participants in India who reported
short-form video use. Measures cover platform use, algorithmic awareness, perceived personalization, repeated exposure,
hypothetical pre- and post-exposure interest, and consumer behaviors such as saving, searching, purchasing, and later
questioning a purchase. Results are reported descriptively because the supplied information is aggregate rather than
respondent-level raw data.
Keywords :
Algorithmic Recommendation; Short-Form Video; Consumer Behavior; Personalization; Preference Formation; Consumer Desire; Social Media; Instagram Reels; Youtube Shorts.
References :
- Aoki, K., & Matsui, T. (2025). Does algorithmic recommendation complement or substitute advertising and influencers? Computers in Human Behavior, 172, 108735.
- Dekker, I., et al. (2025). For you vs. for everyone: The effectiveness of algorithmic personalization in driving social media engagement. Telematics and Informatics, 101, 102300.
- Kaplan, A., et al. (2026). When “For You” isn't for you: Measuring user agency in TikTok's algorithmic feed. Proceedings of the International AAAI Conference on Web and Social Media, 20, 1175–1189.
- Lu, Y. (2026). How TikTok influences Gen Z's consumer decisions: A systematic review. International Journal of Consumer Studies, 50(3), e70206.
- Siles, I., et al. (2024). Analyzing user engagement with TikTok's short format video recommendations using data donations. Proceedings of the CHI Conference on Human Factors in Computing Systems.
- Vombatkere, K., et al. (2024). TikTok and the art of personalization: Investigating exploration and exploitation on social media feeds. arXiv:2403.12410.
- Zou, J., et al. (2022). An empirical investigation of personalization factors on TikTok. Proceedings of the Web Conference 2022.
Background:
Short-form video platforms increasingly personalize users’ feeds through recommendation systems that repeatedly
expose them to products, aesthetics, and consumption narratives. This study examines whether repeated algorithmic
exposure is associated with changes in perceived product interest and uncertainty about the origins of consumer desire.
Methods:
The manuscript analyzes aggregate descriptive survey information supplied for 200 participants in India who reported
short-form video use. Measures cover platform use, algorithmic awareness, perceived personalization, repeated exposure,
hypothetical pre- and post-exposure interest, and consumer behaviors such as saving, searching, purchasing, and later
questioning a purchase. Results are reported descriptively because the supplied information is aggregate rather than
respondent-level raw data.
Keywords :
Algorithmic Recommendation; Short-Form Video; Consumer Behavior; Personalization; Preference Formation; Consumer Desire; Social Media; Instagram Reels; Youtube Shorts.
