Exploring the influence of coauthorship with top scientists on researchers’ affiliation, research topic, productivity, and impact

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Highlights

  • The study is designed to explore the influence of different proximities and types of coauthorship with top scientists on their performance.

  • We identified the winners of four awards as top authors.

  • The closeness and type of coauthorships between the top and ordinary scientists are identified based on the author sequence and role.

  • The change of researcher performance considers four aspects quantitatively: affiliation, research topic, productivity, and impact.

Abstract

Research studies have found that coauthorship with top scientists positively correlates with researchers’ career advancement. However, the influence of different proximities and types of coauthorship with top scientists on their performance has rarely been discussed. We identified the winners of four awards as top authors. We also evaluated the effect on the researchers’ affiliation change, research topic, productivity, and impact before and after three top-ordinary scientist coauthorship types (strong, moderate, and weak), examining the effect after top-top and ordinary-ordinary scientist coauthorships. Additionally, a coauthorship closeness indicator was proposed, considering the team size and author role to measure the collaboration relationship between coauthors. The results reveal that the top scientist in strong coauthorship obtained the highest affiliation change rate. For the top-ordinary coauthorship, the affiliation change rate for top scientists is higher than for ordinary scientists. For other aspects (the coauthor number, research topic, productivity, and impact), the rate after strong and moderate coauthorships increases compared to weak top-ordinary coauthorship type for top and ordinary scientists. Therefore, top scientists obtain a partner with skills, and ordinary scientists obtain more guidance. Strong and moderate coauthorships are win-win relationships for top-ordinary coauthorship types.

Introduction

Collaboration is a critical aspect of scientific research, and collaborative papers are increasingly common in the journals of many disciplines (Hara et al., 2003; Larivière et al., 2015). Previous research has demonstrated that authors are inclined to collaborate with top scientists in their research field because top scientists have advantages in knowledge skills, academic impact, and scientific research resources. (Beaver and Rosen, 1978) explained that collaboration offers a method to overcome intellectual isolation. There is little debate about the benefits of collaboration for top academics.

Coauthorship is an invariant and verifiable indicator for investigating scientific collaboration behavior (Glänzel and Schubert, 2004). Several studies have assessed the effect of coauthorship with senior researchers on their junior collaborators. Coauthorship with international or experienced scientists positively correlates with researchers’ career advancement (AlShebli et al., 2020; Amjad & Munir, 2021; Iglič et al., 2017). Yin and Zhi (2017) demonstrated that the academic elite play a central role in an organization's workflow structure, as they control the flow of critical resources, and academic elites are commonly believed to positively affect their colleagues’ performance. Li et al. (2019) found that junior researchers who coauthor work with top scientists enjoy a persistent competitive advantage throughout the rest of their careers.

Nevertheless, collaboration does not always happen between senior and junior collaborators. Academic collaboration comprises more than one coauthorship type. Collaboration publications are usually coauthored by two or more well-known and senior scholars. In this context, top-top, top-ordinary, and ordinary-ordinary scientist coauthorships were considered according to the author's academic status to distinguish the coauthorship type. Within the informetrics and scientometrics fields, top scientists were proxied as Prize winners. Further, three types of top-ordinary coauthorships (strong, moderate, and weak) were considered according to the role of the coauthors. Strong coauthorship indicates that one scientist is the first author and the other is the corresponding author. Moderate coauthorship indicates that one scientist is the first or corresponding author and the other is an ordinary author. In weak coauthorship, the authors are both ordinary authors (i.e., not the first or corresponding author).

This research aims to classify the coauthorship type and distinguish the influence of different types on the coauthors. The most common way to measure coauthorship quantitatively is a bibliometric analysis using publication data. Bibliometric analyses (such as the number of publications, citations, coauthors, and research topics) can provide an overview of the coauthorship influence (Franceschet & Costantini, 2010; Gazni et al., 2012; Lariviere et al., 2006). Most coauthorship analyses focus on what factors would lead to academic collaboration. Less research has explained how these factors change before and after academic collaboration. This work focuses on four factors (researchers’ affiliation change, research topic, productivity, and impact) to quantify the influence of coauthorship on top scientists. The measurements of researchers’ affiliation change, research topic, and productivity are convincing. The raw citation count could arguably proxy the researcher's impact, especially for multiauthor papers. Thus, we propose that the coauthorship closeness calculation should consider the author rank in the author list to quantify the scholar impact changes for coauthored papers.

This research explores how different proximities and types of coauthorship with top scientists affect the researcher's future affiliation change, research topic, productivity, and impact. The present study attempts to examine the following questions:

  • RQ1: How can collaboration types be categorized with top scientists according to the coauthor role?

  • RQ2: What effects do different coauthorship types have on top and ordinary scientists?

  • RQ3: Which coauthorship type could have win-win advantages for top and ordinary scientists?

  • RQ4: Does the coauthorship closeness correlate with other scholar performance indicators?

To this end, three kinds of top-ordinary scientist collaboration types were identified. Top-top and ordinary-ordinary scientist coauthorships were considered the baseline effect. In addition, the coauthorship closeness calculation considers the author rank in the author list to measure the collaboration relationship between coauthors.

Further, academic performance is a complex and multifaceted concept. This work considers four aspects that involve bibliometric data for which scholars’ affiliations in publications over time are a valuable source for studies of scientist mobility (Laudel, 2003). The Scopus author ID exists for every author publishing in sources covered by Scopus and is a good solution to identify changes in a scientist's affiliation (Aman, 2018). The research topic is measured using the number of normalized keywords. Productivity is the average annual number of papers, and authors who publish more papers are likely to have more collaborators; therefore, the coauthor number is also considered. Further, the impact is the average citation count.

The remainder of the paper proceeds as follows. The method section explains the data processing and methods for this analysis. Next, the results section presents the researcher affiliation change, research topic, productivity, and impact after collaboration with top scientists. The results reveal the correlation between coauthorship closeness and traditional performance indicators (coauthor numbers, author citation count, h-index, cited-by count, and document count). The discussion section summarizes the influence of different closeness and types of coauthorships with top scientists and answers the research questions. Finally, the conclusion section notes the implications and limitations of this study.

The terms in this study are defined as follows:

  • Strong coauthorship: One scientist is the first author, and the other is the corresponding author.

  • Moderate coauthorship: One scientist is the first or corresponding author, and the other is an ordinary author.

  • Weak coauthorship: The authors are both ordinary authors (i.e., not the first or corresponding author).

  • Coauthorship closeness: This indicator between two authors considers the team size and author role in the coauthored papers.

  • Affiliation change: In this study, we used the new institution name as the proxy for the affiliation change. Several possible situations may result in a new institution. The author may have a new position after working with a top scientist. Moreover, the scientist may have multiple jobs or may have changed affiliations.

Section snippets

Scientific collaboration

Scientific collaboration has evolved gradually to be one of the most significant forms of knowledge production (Xia et al., 2017). Previous bibliometric studies have focused on the influencing factors, advantages, and collaboration patterns through the coauthorship network (Hou et al., 2021; Lu et al., 2021; Hennemann et al., 2012). The coauthorship network embedded in the bibliographic information provides a stable connection trace for scientific collaboration (Tsai & Brusilovsky, 2016).

Research overview

The study explores how the closeness and type of coauthorship with top scientists affect researcher performance, as displayed in Fig. 1. Scientific papers were obtained from the Web of Science (WoS) category, “information and library science” (Table 1) and the winners of the Derek de Solla Price Memorial Medal, The Award of Merit, Geard Salton Awards and Karen Spärck Jones Award were collected for the top author. The names and the corresponding year of those winners were listed in Appendix A.

Coauthorship intensity

In this section, we analyzed the frequency of ordinary author coauthorship with a top author and the citation counts of the ordinary author. The correlation between citation count and frequency is 0.00567953. Further, we ranked the citation count and frequency. The correlation between citation rank and frequency rank is 0.14362834.

In addition, we analyzed the time span of the ordinary author's coauthorship with the top author and the citation count of the ordinary author. The correlation

Discussion and implications

Many previous studies have proposed that ordinary scientists’ coauthorship with top scientists positively affects their career advancement (Amjad et al., 2017; Qi et al., 2017). This section directly addresses the research questions proposed in the present study.

Conclusion

Previous studies have focused on ordinary scientist coauthorship with top scientists, benefiting their careers. However, how the coauthorship type with top scientists affects ordinary scientists has rarely been explored. We primarily researched the following stages: identifying the winners of four awards as the top scientists and evaluating performance indicators before and after various coauthorship types. In addition, we proposed the coauthorship closeness indicator.

First, we classified the

Acknowledgments

This work was partially funded by the Ministry of Education of the Republic of Korea, the National Research Foundation of Korea (NRF-2020S1A5B1104865) and the National Natural Science Foundation of China (NSFC) Grant No. 72104220.

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