“Algorithmic Transparency” or “Algorithm Panic”? A Literature Review and Frontier Issues on AIGC Advertising Transparency and Consumer Responses

The quick dissemination of generative artificial intelligence (AIGC) is changing the production methods of the advertising industry, and has also pushed “whether to disclose and how to disclose” to the center of regulation and the market. On a global scale, AI labeling systems have been implemented one after another, while academia still has obvious disagreements about what AIGC advertising transparency will bring-Is transparency a good medicine for restoring trust, or a hidden danger that damages advertising effectiveness? This research, based on literature from the Web of Science Core Collection and the China National Knowledge Infrastructure (CNKI), adopts a systematic review method to sort out the conceptual connotation, effect evidence, theoretical explanations, boundary conditions, and research gaps of AIGC advertising transparency. The paper ascertains that the impact of AIGC advertising transparency on consumers has a “double-edged sword” characteristic, that is to say, its net effect depends on the interaction between disclosed information and consumers’ existing expectations. The uncanny valley theory, CASA theory, the persuasion knowledge model, and attribution theory are not competing with one another; rather, they separately explain different links in the temporal chain of “expectation formation—expectation violation—defense activation—attribution judgment.” The direction and strength of the effect are also moderated by multiple factors, including consumer characteristics, brand and product factors, advertising formats, and platform contexts. In this context, this paper constructs an integrated theoretical framework and proposes future issues, such as differentiation of disclosure framing, differential effects of advertising formats, dynamic transparency, and human-AI collaborative transparency, providing a foundation for subsequent empirical research and regulatory practice.

Authors

Institutions

Publication Details

Journal
Media studies.
Published
2026-10-29
DOI
https://doi.org/10.70267/ms.v3n3.6983
Primary Topic
AI in Service Interactions
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

“Algorithmic Transparency” or “Algorithm Panic”? A Literature Review and Frontier Issues on AIGC Advertising Transparency and Consumer Responses

Xiyuan Wu
5 links
Media studies.
AI in Service Interactions
article

“Algorithmic Transparency” or “Algorithm Panic”? A Literature Review and Frontier Issues on AIGC Advertising Transparency and Consumer Responses

Xiyuan Wu
article en

Abstract

The quick dissemination of generative artificial intelligence (AIGC) is changing the production methods of the advertising industry, and has also pushed “whether to disclose and how to disclose” to the center of regulation and the market. On a global scale, AI labeling systems have been implemented one after another, while academia still has obvious disagreements about what AIGC advertising transparency will bring-Is transparency a good medicine for restoring trust, or a hidden danger that damages advertising effectiveness? This research, based on literature from the Web of Science Core Collection and the China National Knowledge Infrastructure (CNKI), adopts a systematic review method to sort out the conceptual connotation, effect evidence, theoretical explanations, boundary conditions, and research gaps of AIGC advertising transparency. The paper ascertains that the impact of AIGC advertising transparency on consumers has a “double-edged sword” characteristic, that is to say, its net effect depends on the interaction between disclosed information and consumers’ existing expectations. The uncanny valley theory, CASA theory, the persuasion knowledge model, and attribution theory are not competing with one another; rather, they separately explain different links in the temporal chain of “expectation formation—expectation violation—defense activation—attribution judgment.” The direction and strength of the effect are also moderated by multiple factors, including consumer characteristics, brand and product factors, advertising formats, and platform contexts. In this context, this paper constructs an integrated theoretical framework and proposes future issues, such as differentiation of disclosure framing, differential effects of advertising formats, dynamic transparency, and human-AI collaborative transparency, providing a foundation for subsequent empirical research and regulatory practice.

Media studies.Vol. 3(3)
Southwestern University of Finance and Economics (CN)
Openalex Percentile: Top 14%
AI in Service Interactions
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Laika access coming soon