Fake Google restaurant reviews and the implications for consumers and restaurants
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arXiv
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866929329425874944 |
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| author | Berry, Shawn |
| author_facet | Berry, Shawn |
| contents | The use of online reviews to aid with purchase decisions is popular among consumers as it is a simple heuristic tool based on the reported experiences of other consumers. However, not all online reviews are written by real consumers or reflect actual experiences, and present implications for consumers and businesses. This study examines the effects of fake online reviews written by artificial intelligence (AI) on consumer decision making. Respondents were surveyed about their attitudes and habits concerning online reviews using an online questionnaire (n=351), and participated in a restaurant choice experiment using varying proportions of fake and real reviews. While the findings confirm prior studies, new insights are gained about the confusion for consumers and consequences for businesses when reviews written by AI are believed rather than real reviews. The study presents a fake review detection model using logistic regression modeling to score and flag reviews as a solution. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_11345 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Fake Google restaurant reviews and the implications for consumers and restaurants Berry, Shawn General Economics Economics The use of online reviews to aid with purchase decisions is popular among consumers as it is a simple heuristic tool based on the reported experiences of other consumers. However, not all online reviews are written by real consumers or reflect actual experiences, and present implications for consumers and businesses. This study examines the effects of fake online reviews written by artificial intelligence (AI) on consumer decision making. Respondents were surveyed about their attitudes and habits concerning online reviews using an online questionnaire (n=351), and participated in a restaurant choice experiment using varying proportions of fake and real reviews. While the findings confirm prior studies, new insights are gained about the confusion for consumers and consequences for businesses when reviews written by AI are believed rather than real reviews. The study presents a fake review detection model using logistic regression modeling to score and flag reviews as a solution. |
| title | Fake Google restaurant reviews and the implications for consumers and restaurants |
| topic | General Economics Economics |
| url | https://arxiv.org/abs/2401.11345 |