Predictors of citations: an analysis of highly-cited-papers in hospitality and tourism research using a machine learning approach

Abstract

In the dynamic nature of hospitality and tourism (H&T) research, it is increasingly difficult to distinguish highly-cited-papers (HCPs) due to the rapid proliferation of publications. This study employs machine learning techniques to identify the predictors of citation counts in H&T research over both short-term (5-year) and long-term (20-year) periods using HCPs. The analysis integrates a theoretical framework comprising normative theory and social constructivist theory. The findings indicate that international citation, PlumXmetrics, and early citations are the most effective determinants in both periods. Furthermore, while the importance of international citations is evident in both periods, the order of importance of the other two predictors changes. PlumXmetrics are more important in the long-term, while early citations are more important in the short-term. In conclusion, this comprehensive and up-to-date study of citation dynamics provides valuable insights for scholars and other stakeholders interested in enhancing the visibility and influence of H&T literature.