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TRUVACE RECORD VERSION
record: TRV-2026-2549
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-11-12T07:52:12.925492Z
status: published
lens: p_space
sector: lifestyle
headline: Greedy and biased algorithms in AI recommendation systems: effects on emotional arousal and impulsive buying
dek: Purpose This study aims to examine the influence of greedy and biased recommendation algorithms on consumers’ emotional arousal, impulsive buying behavior and post-purchase dissonance in artificial intelligence (AI)-driven short video platforms, such as TikTok, Instagram Reels and YouTube Shorts. It further investigates the moderating roles of promotion-proneness and inferences of manipulative intent. Design/methodology/approach A quantitative survey was conducted with 766 Vietnamese consumers who had recently m…
gain_title: (none)
problem_title: A survey of 766 Vietnamese consumers found that greedy and biased recommendation algorithms on short-video platforms heightened emotional arousal, which led to impulsive buying and then to post-purchase dissonance.
trace_subject: (none)
gain_reading: (none)
gain_evidence: (none)
problem_reading: A survey of 766 Vietnamese consumers found that greedy and biased recommendation algorithms on short-video platforms heightened emotional arousal, which led to impulsive buying and then to post-purchase dissonance.
problem_evidence: Greedy and biased algorithms significantly enhance emotional arousal, which mediates the pathway to impulsive buying. | Impulsive buying, in turn, heightens post-purchase dissonance.
quick_read: A survey of 766 Vietnamese consumers who had recently bought something influenced by AI recommendations examined how algorithm design affects shopping on short-video platforms such as TikTok, Instagram Reels and YouTube Shorts. It found that greedy and biased recommendation algorithms raised emotional arousal, which in turn drove impulsive buying.

Impulsive buying then increased post-purchase dissonance, the discomfort buyers feel after a purchase. The algorithms had no direct effect on dissonance; the effect ran only through arousal and impulsive buying, a result the authors describe as fully supported serial mediation.

The authors say the findings point to a tension between engagement-focused personalization and consumer well-being. Because the data are self-reported and cross-sectional from one country, causal conclusions and generalization to other markets remain uncertain.
limitation: The study relies on cross-sectional, self-reported data from Vietnamese consumers, which may limit causal interpretation and generalizability to other cultural and market contexts.
tag: Evidence-backed problem
key_points: The study surveyed 766 Vietnamese consumers who had recently made purchases influenced by AI recommendations. | It examined platforms including TikTok, Instagram Reels and YouTube Shorts. | Greedy and biased algorithms significantly raised emotional arousal, which mediated the path to impulsive buying. | Impulsive buying heightened post-purchase dissonance, but algorithms had no direct effect on dissonance. | Promotion-proneness strengthened the link from arousal to impulsive buying. | Inferences of manipulative intent amplified the link from impulsive buying to dissonance. | The authors suggest transparency about algorithmically or commercially prioritized content, user controls and less manipulative promotions.
rundown: Researchers used partial least squares structural equation modeling to test a framework built on cognitive dissonance theory and behavioral decision theory. The model included mediation and moderation tests of how algorithm features shape consumer emotions and purchase behavior.

Two traits changed the strength of the effects. Consumers prone to promotions showed a stronger step from emotional arousal to impulsive buying, while those who inferred manipulative intent showed a stronger step from impulsive buying to post-purchase dissonance.

For platforms and marketers, the authors recommend balancing engagement-oriented personalization with consumer autonomy. They suggest more transparency, meaningful user controls, and post-purchase reassurance, customer support and satisfaction guarantees to limit regret and keep trust.
sources:
- peer_reviewed | Journal of Systems and Information Technology | https://doi.org/10.1108/jsit-08-2025-0375 | 2026-09-23
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