Academic dishonesty and its relations to peer cheating and culture: A meta-analysis of the perceived peer cheating effect
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Academic cheating is a worldwide problem, which is exacerbated by perceived peer cheating. The present review of the literature quantitatively examined this perceived peer cheating effect. This meta-analysis included studies reporting correlations between students' own cheating and their perception of cheating in peers. The sample consisted of 43 effect sizes (38 studies) based on a total sample size of 24,181 demographically diverse participants from multiple countries (65% female) from papers published from 1941 to 2021. Results showed a perceived peer cheating effect of intermediate effect size (r = 0.37, 95% CI = 0.35 to 0.39), and that perceived peer cheating is among one of the strongest factors known to be associated with students' academic cheating. Moderator analyses using country level measures revealed this effect to be stronger in cultures that are high in power distance, collectivism, long-term orientation, restraint, and low in uncertainty avoidance and religiosity. The present findings indicate that the behavior of peers plays an important role in students’ academic cheating, suggesting that effective strategies to promote academic integrity will need to consider peer influences as well as the culture in which students are socialized.
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Individuals from collectivist cultures tend to be more tolerant of cheating and may even view it as a way to help each other.
“In contrast, research has found that individuals from collectivist cultures tend to be more tolerant of cheating (Brodowsky et al., 2020;Zhang & Yin, 2020;Zhao et al., 2022) and may even view it as a way to help each other (Harvey et al., 2020;Kadayam Guruswami et al., 2023).”
From Ethical use of artificial intelligence based tools in higher education: Are future business leaders ready? (2025) · cites this paper as Zhao et al., 2022
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Social-motivational factors affecting business students' cheating behavior in Hong Kong and China
Tsui, A., & Ngo, H. (2016) · Journal of Education for Business · doi:10.1080/08832323.2016.1231108 · Cited by 13 · see citing works
Cultural Influences on Accounting and Its Practices
Young, M. (2013) · Digital Commons (Liberty University) · Cited by 11 · see citing works
Social learning theory and academic dishonesty
Lersch, K. M. (1999) · International Journal of Comparative and Applied Criminal Justice · doi:10.1080/01924036.1999.9678635 · Cited by 8 · see citing works
Academic dishonesty :behaviors and attitudes of students at church-related colleges and universities
Bourassa, M. J. (2011) · OhioLink ETD Center (Ohio Library and Information Network) · Cited by 5 · see citing works
An investigation of academic dishonesty among undergraduates at Kansas State University
Walton, C. L. T. (2010) · K-State Research Exchange (Kansas State University) · Cited by 4 · see citing works
Gunawan, D. A. K., & Pramadi, A. (2018) · ANIMA Indonesian Psychological Journal · doi:10.24123/aipj.v33i2.1582 · Cited by 4 · see citing works
Academic Dishonesty among Associate Degree Nursing Students.
Krueger, L. (2013) · ProQuest LLC eBooks · Cited by 3 · see citing works
Kobayashi, E., & Farrington, D. (2019) · International Journal of Comparative and Applied Criminal Justice · doi:10.1080/01924036.2019.1578674 · Cited by 2 · see citing works
Peer influence on attitudes and behavior alien to institutional objectives
Wells, W. E. (1965) · UA Campus Repository (The University of Arizona) · Cited by 1 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Dwivedi, Y. K., Kshetri, N., Hughes, L., Slade, E. L., Jeyaraj, A., Kar, A. K., Baabdullah, A. M., Koohang, A., Raghavan, V., Ahuja, M., et al. (2023) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2023.102642 · Cited by 4,584 · see citing works
Lee, V. R., Pope, D., Miles, S., & Zárate, R. C. (2024) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2024.100253 · Cited by 130 · see citing works
Mumtaz, S., Carmichael, J., Weiss, M., & Nimon-Peters, A. (2025) · Education and Information Technologies · doi:10.1007/s10639-024-13099-8 · Cited by 55 · see citing works
It's Bad but It's Good: An Exploration of AI Cheating on Higher Educational Students Perspectives
Sholikhah, Z., Pramuka, B. A., Adawiyah, W. R., Yanuar, I., Respati, Y. A., & Pramuka, M. F. (2024) · 2024 IEEE Technology & Engineering Management Conference - Asia Pacific (TEMSCON-ASPAC) · doi:10.1109/temscon-aspac62480.2024.11024673 · Cited by 4 · see citing works
Bhattacharyya, S., Chatterjee, N., & Ramanathan, A. (2025) · International Journal for Educational Integrity · doi:10.1007/s40979-025-00193-8 · Cited by 4 · see citing works
- Authors
- Zhao, L., Mao, H., Compton, B. J., Peng, J., Fu, G., Fang, F., Heyman, G. D., & Lee, K.
- Affiliations
- Hangzhou Normal University (CN)
- University of California San Diego (US)
- Peking University (CN)
- University of Toronto (CA)
- Corresponding author
- Zhao, Li
- Fang, Fang
- Lee, Kang
- Venue
- Educational Research Review
- Publisher
- Elsevier BV
- Published
- 2022-06
- Volume
- 36
- Pages
- 100455
- ISSN
- 1747-938X (ISSN-L)
- OpenAlex
- W4229068789
- Cited by
- 120
- References
- 125
- Citations by year
- 2026: 30 · 2025: 26 · 2024: 37 · 2023: 22 · 2022: 5
- FWCI
- 4.26
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Educational Research Review (publishedVersion, cc-by-nc-nd)
- Funders
- National Natural Science Foundation of China (32171060)
- Jilin Office of Philosophy and Social Science
- Social Sciences and Humanities Research Council of Canada
- Topics
- Social Sciences › Safety Research › Academic integrity and plagiarism
- Social Sciences › Decision Sciences › Information Systems and Management › Ethics in Business and Education
- Physical Sciences › Mathematics › Statistics and Probability › Benford’s Law and Fraud Detection
- Keywords
- academic dishonesty, peer influence, power distance, collectivism, long-term orientation, uncertainty avoidance, religiosity, academic integrity, cross-cultural comparison
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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