Investigating the Effect of AI-Generated Customer Reviews on Purchase Intent and Perceived Authenticity in E-Commerce Environments
Bibliographic record
Abstract
The fast emergence of generative artificial intelligence (AI) in online marketplaces has prompted critical inquiries about consumer perceptions of AI-authored content. Online reviews function as a leading factor that influences buying decisions, although there is limited understanding of how review origin (whether composed by humans or AI) affects authenticity perceptions and willingness to purchase, especially in developing markets where e-commerce trust remains low. This research investigated the effect of AI-generated customer reviews on purchase intent and perceived authenticity among Nigerian e-commerce users. The research employed a survey experiment using a simulated product page from AliExpress, as well as 300 participants between 25 and 40 years old, to examine direct, mediating, and moderating effects through a PLS-SEM model. The findings indicate that AI-generated reviews are regarded as less genuine than human-written reviews (β = −1.418, p < .001). In addition, the study found perceived authenticity to be a significant predictor of purchase intent (β = +0.766, p < .001) while completely mediating the connection between review source and intent (indirect β = −1.086, p < .01). Unexpectedly, platform trust did not moderate this relationship. The research results enhance marketing theory by using the Theory of Reasoned Action (TRA) and the Elaboration Likelihood Model (ELM) in AI environments to demonstrate that authenticity serves as a crucial cognitive factor in digital persuasion. From a practical perspective, the research indicates the need for both local and global e-commerce platforms to maintain clear review disclosure to customers, while Nigerian regulatory authorities need to create disclosure standards for consumer protection. Nevertheless, this study confirms that authenticity continues to be a fundamental element of trust and purchasing behaviour, even within a marketplace driven by AI.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".