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Ethical Practices in Modern Academia: Does Length of Educational Experience and Quality of Governance Contribute to a Deeper Understanding of Academic Integrity?

Journal: Business Ethics and Leadership (BEL) (Vol.9, No. 1)

Publication Date:

Authors : ; ;

Page : 95-108

Keywords : academic integrity; education indicators; ethical decisions; Generalized Additive Models; non-linear modeling; plagiarism; polynomial regression; similarity percentage;

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Abstract

Academic integrity is a fundamental component of higher education, yet its enforcement varies across countries due to differences in educational development and governance structures. This study investigates the relationship between educational indicators – Expected Years of Schooling and Mean Years of Schooling – and plagiarism levels, as measured by similarity percentages in checked documents. Additionally, the study examines the role of governance quality, represented by the Rule of Law index, in influencing plagiarism levels. The research utilises Plagiarism Checker X data as of February 1, 2024, providing the most recent plagiarism similarity percentages, while the educational indicators (Expected Years of Schooling and Mean Years of Schooling) and the Rule of Law index are based on the latest available data from 2022. A statistical analysis was conducted using Generalized Additive Models (GAM) and quadratic polynomial regression to identify potential non-linear relationships among these variables. The results indicate that Mean Years of Schooling have a statistically significant non-linear effect on similarity percentages, with a peak influence around 6–8 years before levelling off. Expected Years of Schooling also exhibits a weak but statistically significant negative relationship with similarity percentages, suggesting that higher schooling expectations contribute to reducing plagiarism. In contrast, the Rule of Law does not directly impact similarity percentages, implying that governance quality alone is not a strong predictor of academic integrity. A comparison between GAM and polynomial regression models shows similar explanatory power, with GAM offering greater flexibility in capturing non-linear patterns while polynomial regression provides a more explicit interpretative framework.

Last modified: 2025-04-14 16:54:52