Do articles with multiple corresponding authorships have a citation advantage? A double machine learning analysis approach

Ruonan Cai, Wencan Tian, Rundong Luo, Zhichao Fang and Zhigang Hu ()
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Ruonan Cai: Shandong University
Wencan Tian: Beijing Normal University
Rundong Luo: Shandong University
Zhichao Fang: Renmin University of China
Zhigang Hu: South China Normal University

Scientometrics, 2025, vol. 130, issue 5, No 2, 2523-2550

Abstract: Abstract Multiple corresponding authorships imply a higher level of commitment, which also implies a higher level of quality. In this study, we explore the relationship between the phenomenon of multiple corresponding authorships and citation counts. To do this, an empirical research is conducted, and Double Machine Learning models—a novel method—are used. Our results of the case study based on the field of “Chemistry & Medicine” demonstrate that, when controlling for other variables, articles with multiple corresponding authors indeed tend to receive more citations than those with a single corresponding author. In addition, the citation advantage is more pronounced when multiple corresponding authors are from different institutions. However, an excessive number of corresponding authors may weaken the citation advantage. These findings remain robust as they still hold when incorporating interdisciplinarity as a control variable and using other methods (e.g., Propensity Score Matching).

Keywords: Multiple corresponding authorships; Citation advantage; Double machine learning; Scientific collaboration; Team science (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s11192-025-05242-0

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