Research complexity increases with scientists’ academic age: Evidence from library and information science

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Highlights

  • Complexity is adopted as a novel perspective to characterize scientists’ research portfolios.

  • An econometric approach is employed to measure the research portfolio complexity (RPC) by modeling a heterogeneous author-topic bipartite network.

  • Scientists of similar productivity and impact may differ in RPC.

  • Senior scientists have higher RPC than their junior colleagues, even after removing the accumulated advantages.

  • Scientists with the highest RPC have distinct dynamic patterns of RPC.

Abstract

With the continued aging of the scientific workforce, the impact of this trend on scientists’ research performance has attracted increasing attention. The literature has predominantly focused on the productivity, impact, and collaboration pattern of scientists of different ages. A research gap is found in investigating the differences in the research topics studied by junior and senior scientists. This study focuses on the complexity of a scientist's research portfolio (RPC). Based on the concept of economic complexity, RPC was measured to characterize the capability of scientists to study complex research topics. An economic algorithm was adopted to estimate RPC on heterogeneous author-topic bipartite networks using bibliographic data from the field of Library and Information Science between 1971 and 2020. Through comparisons among scientist groups, RPC shows promise in distinguishing outstanding scientists from peers who have similar values of other indicators (e.g., citations and H-index). The change in RPC was further probed across scientists’ careers and an increasing trend with academic age was found, even after removing the accumulated advantages of senior scientists. Moreover, top-ranked scientists distinguish themselves from their peers by a higher RPC in the first year and a greater growth rate during their careers. While many researchers have their highest RPC in the first year, most top-ranked scientists reach their peak RPC later in their careers. The results provide helpful references for studies on the aging effect in academia.

Introduction

As the drivers of scientific advances, scientists and their research patterns have long been a central interest in science of science (Fortunato et al., 2018). Understanding how the characteristics of scientists affect their careers and how individual practices shape modern science provides valuable references for policy-makers and researchers (Gök et al., 2016; Jia et al., 2017; Larivière et al., 2015). With the aging of the scientific workforce, the impact of scientists’ age on their research performance and behaviors has become an important research topic (Blau & Weinberg, 2017; Merton, 1973). To study this problem, the literature primarily relies on metrics derived from scientists’ research portfolios, i.e., the collection of research output of an individual scientist. Research portfolios are often constructed based on publications and aggregated to different levels, including collaborators and topics (Srivastava et al., 2007; Tripodi et al., 2020). While existing studies primarily focus on the aging effect on productivity (Győrffy et al., 2020; Sugimoto et al., 2016), impact (Sugimoto et al., 2016; Thelwall & Fairclough, 2020), and collaboration (Bu et al., 2018; Wang et al., 2017) of scientists, understanding how the content of scientists’ research portfolios evolve with their academic age is yet another important research line that deserves more attention (Packalen & Bhattacharya, 2019; Zeng et al., 2019).

Indeed, scientists do not build up their research portfolios at random. With the large amount of knowledge needed to enter a new research domain, the evolution of scientists’ research portfolios tends to be path dependent, which is closely related to knowledge they master or gain through collaboration (Tripodi et al., 2020; Zeng et al., 2019). With the increase in academic age and seniority, research of a higher complexity becomes feasible for scientists. In addition, the “essential tension” between two types of research, namely, exploitation and exploration, drives scientists to switch research topics during their careers (Kuhn, 1977). Exploitation research, which involves refinement and consolidation in established fields, shows promise for increasing productivity and reducing risks. In contrast, research that explores uncharted waters is risky but may give rise to novel discoveries and unprecedented impact (Foster et al., 2015; March, 1991). Since scientists differ in their specialties and research strategies, research portfolios differ widely.

This is especially true for scientists of different academic ages or career stages. Previous studies have shown that junior and senior researchers have different preferences in choosing research topics (Bateman & Hess, 2015; Packalen & Bhattacharya, 2019). For example, senior scientists tend to be more conservative, e.g., prefer older ideas and criticize new ones (Cui et al., 2022). However, novelty does not tell the whole story of their research portfolio. It is recognized that senior scientists have advantages in accumulating resources, such as experience, funding, and collaboration (Abramo et al., 2016; Wang et al., 2017). Senior scientists may be able to conduct complex studies that their junior colleagues are unable to perform due to insufficient intellectual or financial resources. However, there are few studies on the relationship between scientists’ age and the complexity of their research. To this end, this study quantifies and compares the research portfolio complexity (RPC) of scientists of different academic ages, revealing a positive relationship between age and complexity.

It is not new to gauge the complexity of individual research. Previous studies have proposed the definition of research complexity based on the notion of diversity. For example, Ma & Li (2021) measured the complexity of a publication through the diverse types of non-text elements it used, e.g., tables, graphs, and equations. Based on cited/citing articles (Wang et al., 2017; Wu et al., 2019) and disciplines (Abramo et al., 2018), researchers quantified the complexity of scientific work through the authors’ effort to synthesize diverse knowledge and produce their own work. Despite measuring the research complexity using related attributes of a publication, few studies have considered the interaction between scientists and their research outputs. While many aspects of research complexity are hard to explicitly measure, the joint distribution of research outputs and related scientists provides a viable way to estimate implicit complexity based on observable results. This research question is explored with the help of a widely adopted economic measurement of countries’ economic complexity (Hidalgo, 2021; Hidalgo & Hausmann, 2009).

Similar to measuring the complexity of a scientist's research portfolio, economists also face the problem of measuring the complexity of a country's economic system. This is valuable, as countries with similar GDP per capita may differ greatly in their industry composition (e.g., microchips vs. oil), which reflects differences in the capabilities of countries. Without making assumptions about what constitutes the capabilities of countries, Hidalgo & Hausmann (2009) proposed a simple yet effective indicator to measure the economic complexity of countries based on the products they produced. The algorithm is driven by two straightforward ideas: (1) capable countries (i.e., countries with a high economic complexity) should be able to produce a diverse set of complex products, and (2) complex products should be produced only by a limited number of capable countries. This metric has been proven informative through its explanatory power on various economic indicators, including GDP and income inequality (Hartmann et al., 2017; Hidalgo, 2021; Hidalgo & Hausmann, 2009; Mealy et al., 2019). In the Methodology section, how the two ideas are operationalized is elaborated in an iterative manner, as well as the rationale for using this algorithm to measure scientists’ RPC.

Based on the notion of economic complexity, RPC is defined as the volume of resources (e.g., knowledge, funding, and labor) required to conduct related studies in a specific research portfolio. A scientist's research portfolio is represented by a collection of research topics the researcher has worked on. By analogy with the relationship of a country and their products (Hidalgo & Hausmann, 2009; Tacchella et al., 2012), their economic algorithm was used to estimate the RPC on heterogeneous author-topic bipartite networks. RPC is constituted by the total complexity of related research topics, while topic complexity, which reflects the skill set or knowledge required to conduct the research, depends on the number of related scientists and their RPC. The validity of the complexity measurement is empirically examined in the library and information science field. Specifically, this study attempts to answer the following research questions:

  • 1. How can the complexity of research portfolios be quantified based on the heterogeneous relationships between scientists and their research topics?

  • 2. How does the complexity of scientists’ research portfolios change with their academic ages? Do top-ranked scientists have distinct dynamic patterns?

The present work contributes to the understanding of scientists’ research behaviors by introducing a quantitative measurement of RPC and investigating the dynamic patterns of RPC across scientists’ careers. (1) It quantifies RPC by considering heterogeneous author-topic relationships. The usefulness of RPC in distinguishing excellent scientists from their peers with similar values of other indicators was also shown. Therefore, complexity can serve as a valuable perspective to characterize scientists’ research portfolios. (2) Through quantitative comparisons among scientists from various age groups, this study found an increasing pattern in the relationship between scientists’ RPC and academic age. Moreover, distinctions between top-ranked scientists and other scholars in their first-year RPC and growth rate during their careers were revealed. The results provide helpful references for studies on the aging effects on scientists.

Section snippets

Aging and scientists’ research patterns

Aging is an important factor that affects scientists’ research patterns, including productivity, creativity, and collaboration (Abramo et al., 2016; Lu et al., 2021; Merton, 1973; Sugimoto et al., 2016). With an aging population and the growing “burden of knowledge”, the mean age of the scientific workforce and age at first publication have significantly increased in recent decades (Blau & Weinberg, 2017; Jones, 2009). This manifests in the growing need for research on the aging effects on

Dataset

Library and Information Science (LIS) was selected as an exemplary discipline to test the complexity measurement approach. It is an active research area familiar to the authors and has scientists with various specialties and research interests jointly study information-related topics, which allows for in-depth analyses of the results. The LIS journal list was first obtained from the 2019 version of Journal Citation Reports (JCR), which resulted in 87 periodicals. Scopus was chosen as our major

Results and discussion

In this section, the validity of the algorithm and the correlation between RPC and other indicators was first investigated. Next, special attention is paid to the relationship between scientists’ academic age and their RPC.

Discussion

This study investigates the relationship between LIS scientists’ academic age and the complexity of their research portfolios through the lens of an economic approach. In this section, the implications of the results and the limitations are discussed, with future directions that may deepen our understanding of the research complexity.

Conclusion

When we talk about the aging of scientists, it is often considered to have a negative impact on scientists’ performance, e.g., decreased productivity (Abramo et al., 2016) and reluctance to try new ideas (Cui et al., 2022; Packalen & Bhattacharya, 2019). Despite the mixed evidence, these indicators may not tell the full story of a scientist's research portfolio. In this article, the complexity of scientists’ research portfolios was studied based on the bipartite network of scientists and their

CRediT authorship contribution statement

Zhentao Liang: Conceptualization, Methodology, Formal analysis, Writing – original draft. Zhichao Ba: Conceptualization, Methodology, Formal analysis, Writing – review & editing. Jin Mao: Writing – review & editing. Gang Li: Funding acquisition, Supervision, Writing – review & editing.

Acknowledgments

This work was jointly supported by the National Natural Science Foundation of China (Nos. 72004094, 71921002, and 72174154). In addition, we would like to thank the four anonymous reviewers for their valuable comments and suggestions, which helped us improve this paper. Zhentao Liang would also like to give special thanks to his wife Lishan Mai and family for their support throughout the hard times.

References (75)

  • R. Rousseau et al.

    Reflections on the activity index and related indicators

    Journal of Informetrics

    (2012)
  • D. Vanderelst et al.

    Scientometrics reveals funding priorities in medical research policy

    Journal of Informetrics

    (2013)
  • H. Woźniakowski

    A survey of information-based complexity

    Journal of Complexity

    (1985)
  • G. Abramo et al.

    The combined effects of age and seniority on research performance of full professors

    Science and Public Policy

    (2016)
  • V. Aman

    Does the Scopus author ID suffice to track scientific international mobility? A case study based on Leibniz laureates

    Scientometrics

    (2018)
  • C. Antonelli et al.

    Knowledge complexity and the mechanisms of knowledge generation and exploitation: The European evidence

    Research Policy

    (2020)
  • P. Azoulay et al.

    Does science advance one funeral at a time?

    American Economic Review

    (2019)
  • B. Balassa

    Trade liberalisation and “revealed” comparative advantage1

    The Manchester School

    (1965)
  • P.-A. Balland et al.

    The geography of complex knowledge

    Economic Geography

    (2017)
  • T.S. Bateman et al.

    Different personal propensities among scientists relate to deeper vs. broader knowledge contributions

    Proceedings of the National Academy of Sciences

    (2015)
  • F. Battiston et al.

    Taking census of physics

    Nature Reviews Physics

    (2019)
  • D. Bawden et al.

    Waiting for Carnot”: Information and complexity

    Journal of the Association for Information Science and Technology

    (2015)
  • D.M. Blau et al.

    Why the US science and engineering workforce is aging rapidly

    Proceedings of the National Academy of Sciences

    (2017)
  • C. Bloch

    Heterogeneous impacts of research grant funding

    Research Evaluation

    (2020)
  • Y. Bu et al.

    Analyzing scientific collaboration with “giants” based on the milestones of career

    Proceedings of the Association for Information Science and Technology

    (2018)
  • H. Cui et al.

    Aging scientists and slowed advance

    (2022)
  • R.J. Daniels

    A generation at risk: Young investigators and the future of the biomedical workforce

    Proceedings of the National Academy of Sciences

    (2015)
  • A. Ebadi et al.

    How to boost scientific production? A statistical analysis of research funding and other influencing factors

    Scientometrics

    (2016)
  • S. Fortunato et al.

    Science of science

    Science

    (2018)
  • J.G. Foster et al.

    Tradition and innovation in scientists’ research strategies

    American Sociological Review

    (2015)
  • A. Gök et al.

    The impact of research funding on scientific outputs: Evidence from six smaller European countries

    Journal of the Association for Information Science and Technology

    (2016)
  • B. Győrffy et al.

    Is there a golden age in publication activity?—An analysis of age-related scholarly performance across all scientific disciplines

    Scientometrics

    (2020)
  • R. Hausmann et al.

    The atlas of economic complexity

    (2014)
  • C.A. Hidalgo

    Economic complexity theory and applications

    Nature Reviews Physics

    (2021)
  • C.A. Hidalgo et al.

    The building blocks of economic complexity

    Proceedings of the National Academy of Sciences of the United States of America

    (2009)
  • R. Hill et al.

    Adaptability and the pivot penalty in science

    (2021)
  • J.P.A. Ioannidis et al.

    Updated science-wide author databases of standardized citation indicators

    PLoS Biology

    (2020)
  • Cited by (1)

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