Accesses as a Moderator of Article Length and Citations
DOI:
https://doi.org/10.70747/cr.v4i4.698Palabras clave:
correlation analysis, article citations, artificial neural networks, statistical methods, Inteligencia ArtificialResumen
The literature has identified that the length of the article is related to the number of citations. This paper presents a correlation analysis of the moderator effect of the number of accesses to an article on the relationship between the article length and its citations using three regression techniques. This analysis concerns the manuscripts published in the Journal of International Business Studies between 1970 and 2021. For this analysis, we use a classic linear regression model and two machine learning regression techniques: model trees and artificial neural networks. The main results show moderation of the article’s accessibility in the relationship between article length and citations. Also, it affects the citation rate, but article length does not have a significant relationship with this rate. The conclusions show that analyzed hypotheses have a reduced correlation, but the regression method influences how the models are built in the statistical procedures. Specifically, artificial neural network models show better hypothesis-correlation than linear regression-based models.
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Derechos de autor 2025 Jose Satsumi Lopez Morales, Rafael Rivera- Lopez, Antonio Huerta-Estevez, Marco Antonio Cruz Chávez
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