TRV-2026-2173Version 1 · Certified
Reason for this version
Certified into the record
Canonical text (the exact bytes fingerprinted)
TRUVACE RECORD VERSION record: TRV-2026-2173 version: 1 kind: certified reason: Certified into the record timestamp: 2026-10-22T18:13:20.395955Z status: published lens: g_space sector: lifestyle headline: GenAI literacy and perceived decision quality in e-commerce: the mediating role of perceived information overload and the moderating role of consumer involvement dek: Generative artificial intelligence (GenAI) is increasingly used to support product search, comparison, and evaluation in e-commerce. However, consumers differ in their ability to use and critically evaluate GenAI-generated information, which may shape their perceived information overload and decision outcomes. Grounded in Information Processing Theory, this study examines the association between GenAI literacy and perceived decision quality, the mediating role of perceived information overload, and the moderatin… gain_title: Among 401 Chinese college students, greater GenAI literacy was associated with lower perceived information overload and higher perceived decision quality in e-commerce. problem_title: (none) trace_subject: (none) gain_reading: Among 401 Chinese college students, greater GenAI literacy was associated with lower perceived information overload and higher perceived decision quality in e-commerce. gain_evidence: GenAI literacy is positively associated with perceived decision quality and negatively associated with perceived information overload problem_reading: (none) problem_evidence: (none) quick_read: A survey of 401 Chinese college students examined how skill in using and critically evaluating generative AI relates to online shopping decisions. Analysis found that more literate users reported less information overload and rated their own purchase decisions more highly. The work matters because AI tools are increasingly used for product search and comparison, and the results suggest consumer capability shapes whether they help or burden shoppers. Because the data are self-reported associations from one student sample, causation and wider applicability remain unestablished. limitation: The evidence comes from a single online survey of Chinese college students and reports associations in perceived outcomes, so it does not show causal effects or actual decision quality. tag: Evidence-backed gain key_points: The sample was an online survey of 401 Chinese college students, analyzed with confirmatory factor analysis and bootstrapped mediation and moderation procedures. | Perceived information overload was negatively associated with perceived decision quality and explained a significant indirect link from GenAI literacy to decision quality. | Consumer involvement moderated the literacy-overload link, which was stronger at higher involvement levels. | The authors recommend strengthening critical GenAI literacy and adapting AI-supported information presentation to different involvement levels. rundown: The study is grounded in Information Processing Theory and treats GenAI literacy as the ability to use and critically evaluate AI-generated information during product search, comparison, and evaluation. Results show a mediation pattern: literacy relates to less perceived overload, and less overload relates to better perceived decision quality, producing a significant indirect association alongside the direct one. Involvement acted as a moderator, so motivated consumers with higher literacy reported a stronger reduction in perceived cognitive burden. The authors frame capability and motivation as jointly shaping perceptions. sources: - peer_reviewed | Frontiers in Psychology | https://doi.org/10.3389/fpsyg.2026.1919451 | 2026-10-21 prev: 0000000000000000000000000000000000000000000000000000000000000000
- sha256
- 171d6d341d2995d2e88865c8acc7605a29348df20b3e9eb6d52f8fd2cfd722b9
- previous
- 0000000000000000000000000000000000000000000000000000000000000000
Verify this record
How to verify without trusting this page
Fetch the canonical text of any version from /api/record/TRV-2026-2173 and hash it yourself — for example shasum -a 256 on the saved canonical field. The result must equal content_hash, and each version’s text ends with prev:followed by the prior version’s hash (version 1 chains to 64 zeros). If a single character of any version had been altered since certification, the chain would not reproduce.
ace