arbiter
JournalsAccount

EXPRESS: Theory of Machine: Lay Beliefs About Algorithmic Data Processing Drive Recommendation Acceptance

Edmond Alcheikh Kozah & Ana Valenzuela

Journal of Interactive Marketing2026https://doi.org/10.1177/10949968261423544article
AJG 3ABDC A
Weight
0.50

Abstract

Data is an indispensable asset in the AI ecosystem. This paper investigates consumers’ lay understanding of the different types of data that AI systems use to generate recommendations, and how this understanding influences their likelihood of accepting those recommendations. Across one pilot and four studies, we establish consumers’ mental construction of three different datatypes and experimentally validate two mechanisms that shape recommendation acceptance: perceived “individuality threat” associated with these datatypes and their “processing acceptability”.

Open via your library →

Cite this paper

https://doi.org/https://doi.org/10.1177/10949968261423544

Or copy a formatted citation

@article{edmond2026,
  title        = {{EXPRESS: Theory of Machine: Lay Beliefs About Algorithmic Data Processing Drive Recommendation Acceptance}},
  author       = {Edmond Alcheikh Kozah & Ana Valenzuela},
  journal      = {Journal of Interactive Marketing},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1177/10949968261423544},
}

Paste directly into BibTeX, Zotero, or your reference manager.

Flag this paper

EXPRESS: Theory of Machine: Lay Beliefs About Algorithmic Data Processing Drive Recommendation Acceptance

Flags are reviewed by the Arbiter methodology team within 5 business days.

Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.