The individual characteristics,organizational characteristics and research productivity of early career LIS researchers in China’s mainland: A crisp set qualitative comparative analysis (csQCA)

Abstract

There existed discrimination, including gender discrimination, first degree discrimination, etc., when assessing the future research productivity of PhD graduates in recruitment in China’s mainland. Were PhD students who did not possess certain conditions (e.g. first degree receiving from a non-key university) unable to achieve high research productivity after graduation? Previous studies focused on the “net effects” of individual and organizational characteristics on research productivity by using quantitative methods (e.g. regression analysis). However, researchers’ research productivity might be due to the interactions of multiple factors rather than a single factor. This study aimed to analyze the effects of the combined conditions (interactions) of individual and organizational characteristics on the research productivity of early career library and information science (LIS) researchers under the context of employment discrimination in the academic job market of China’s mainland. Early career LIS researchers who graduated from China’s mainland universities/institutions between 2011 and 2015 were selected as the sample (n = 62). csQCA was employed to analyze the data. The results revealed that the effects of a single condition did not directly contribute to the occurrence of high research productivity. There were two combinations of conditions that could contribute to the high research productivity of early career LIS researchers. The first combination that contributed to the high research productivity of an early career LIS researcher was receiving his or her bachelor’s degree from a key university, publishing higher than the median number of articles indexed by Web of Science core collections (WOS) during their PhD and working in a key university after PhD graduation. The second combination was being male, publishing more than the median number of articles indexed by the WOS and the local core journals index during their PhD, and working at a key university after PhD graduation.

Keywords

Doctoral supervisor employment discrimination first degree gender prestige of affiliation research productivity study abroad

Introduction

In 2008, China surpassed the United States to become the country that produces the most PhDs in the world (He, 2010). According to statistical reports released by the Ministry of Education of the People’s Republic of China (MEOC), the number of doctoral students enrolled in 2018 reached 95,500, approximately five times the number of students enrolled in 1999 (MEOC, 2000, 2019). There were 55,000, 58,000, and 60,700 doctoral graduates in 2016, 2017, and 2018, respectively (MEOC, 2017, 2018, 2019). In addition, the number of full-time teachers (they were required to do both teaching and research) in all institutions of higher education increased by 29,400, 31,300, and 39,500 in 2016, 2017, and 2018, respectively (MEOC, 2017, 2018, 2019). Previous investigations (Huang and Shen, 2019; Woolston and O’Meara, 2019) revealed that most doctoral graduates of China’s mainland desired to work in academia. Using the LIS discipline in China’s mainland as an example, at Nanjing University, a top Chinese university, all of the full-time PhD students who received their LIS PhDs in 2017 (NJU, 2018), 2019 (NJU, 2020), and 2020 (NJU, 2021) worked at universities (11/11 in 2017, 5/5 in 2019, and 11/11 in 2020). At Renmin University of China, another top Chinese university, 61.9% (13/21) of LIS PhDs graduating in 2021 worked at universities (RUC, 2021). As a result, the figures mentioned above indicate that approximately 40% of doctoral graduates from China’s mainland cannot find work in academia. The academic job market in China is even more competitive as the above projections do not include PhDs returning to China from overseas. In recent years, to encourage young overseas scholars to return to work in China, the Chinese government has developed many talent programs (Zhu, 2019), such as Young Scholar 1000 Talents Plan, NSFC Excellent Young Scholars Fund (Overseas), and so on. Recipients of these talent programs will receive significant funding from these programs in addition to high income (NATURECAREERS, 2022). For example, The Young Scholar 1000 Talents Plan (SEU, 2018) and the NSFC Excellent Young Scholars Fund (Overseas) (SJTU, 2022) will provide at least RMB 1 million ($ 0.157 million) in research funds to these recipients. Driven by these incentives, a creasing number of PhDs from overseas have returned to China (Cao et al., 2020). This has crowded out the academic job market for local PhDs of China’s mainland.

In the context of the oversupply of PhDs, universities are raising various thresholds to recruit junior researchers. In particular, the Chinese government launched the “Double First Class” (The World First Class University and First Class Academic Discipline Construction; in Chinese: 双一流) policy in 2015. The selected universities in the “Double First Class” receive preferential treatment in education policies, for example, Tsinghua University and Peking University (the best universities in China) are subsidized by RMB 5 billion ($ 78.5 million) a year, and the other top universities are supported with a minimum of RMB 1.8 billion ($ 28.2 million) (Li, 2020). Universities have placed special emphasis on the research productivity of their researchers to enter the “Double First Class” list. This is because research productivity is one of the most important requirements for universities to enter the “Double First Class” list and is also one of the most important indicators for university rankings. As a result, universities recruit junior researcher candidates with the hope that they will have good research productivity in the future. In the universities of China’s mainland, the recruitment of junior researchers is sometimes assisted by setting indicators such as gender, study abroad experience, mentorship, and the first degree (whether the candidates receive their bachelor’s degrees from a key university) of the candidate to help them select candidates. Accordingly, discrimination, including gender discrimination, first degree discrimination, study abroad experience discrimination, etc., is often found in the recruitment of universities in China’s mainland (Tian and Zhang, 2021). In recent years, the MEOC has repeatedly issued documents to strictly prohibit discrimination against gender, first degree, etc. in recruitment (MEOC, 2013). However, unfortunately, employment discrimination, such as gender discrimination, first degree discrimination, study abroad experience discrimination, etc., has become an unspoken rule in universities’ recruitment of junior researchers.

Under the context of employment discrimination in the academic job market in China’s mainland, are PhD students who do not possess certain conditions (e.g. no study abroad experience, first degree receiving from a non-key university) unable to achieve high research productivity after graduation? In this paper, Qualitative Comparative Analysis (QCA) was conducted to identify the effects of the combined conditions (interaction) of gender, first degree, study abroad experience, supervisor’s academic status, publications during PhD, and prestige of affiliation on the research productivity of early career researchers (as measured as whether an early career researcher had published at least one WOS article as the first or corresponding author after receiving their PhDs) in LIS in China’s mainland. This study makes three contributions. First, this paper introduced QCA into the analysis of the factors influencing the research productivity of early career LIS researchers, bringing inspiration to the research design of studies in this research area. Second, the findings of this paper could provide insights for PhD students (especially those who did not possess certain conditions, for example, no study abroad experience, first degree receiving from a non-key university) who were interested in pursuing an academic career in the future. Third, our study could provide insights for universities to recruit early career researchers who would achieve high research productivity based on candidates’ research productivity during their PhDs rather than by setting discriminatory conditions.

Literature review

The study of the factors influencing the research productivity of researchers is not a new topic. The factors influencing the research productivity of researchers can be divided into intrinsic and extrinsic factors (Meneses and Moreno, 2019), that is, individual characteristics and organizational characteristics. Bland et al. (2005) developed a systematic model to predict faculty research productivity. The model included eight individual characteristics, 15 organizational characteristics, and four leadership characteristics. Based on the data from the University of Minnesota Medical School, their regression results revealed that the model was suitable to predict faculty research productivity. Similar to the model of Bland et al. (2005), our study also focused on the effects of individual characteristics and organizational characteristics on research productivity. However, our study was conducted in the context of China’s mainland, especially the context of academic employment discrimination in China’s mainland. China’s employment discrimination has distinct domestic characteristics (Tian and Zhang, 2021). This might be related to traditional Chinese cultural-social values. For example, females were usually bound by their familial responsibilities, as well as the social pressures of being a good wife, a loving mother, and an obedient daughter in China (Dai et al., 2021). In terms of study abroad, Chinese universities tended to recruit candidates who had international experience, particularly those who earned their PhDs in Western countries, as they thought study abroad experience was a safeguard to ensure more international collaboration and international influence (Hu, 2021; Rhoads and Hu, 2012). Based on 5297 LIS job advertisements, Tian and Zhang (2021) found that there exists serious discrimination, including gender discrimination, first degree discrimination, etc., in the LIS job market in China’s mainland. Therefore, the following related variables our study focused on were reviewed.

Gender

The differences in roles in the social division of labor made women often encounter more difficulties (e.g. fewer opportunities, resources, and protected time) in academic research than men (Ha et al., 2021). The difficulty of balancing work and family and receiving less mentoring could limit females’ research productivity and career development (Desselle et al., 2018). Numerous studies indicated that females’ research productivity was generally lower than that of males (Behmer Hansen et al., 2020; Ngaage et al., 2019; Siddique et al., 2021). In universities, there were significantly more male faculty members than female faculty members (Opesade et al., 2017), and males tended to have higher academic status and scholarly achievement (Khoshpouri et al., 2021). Males tended to outperform females in terms of the number of publications (Desselle et al., 2018), the number of citations (O’Neill et al., 2021), and the H-index (Albahari and Bashir, 2020; Chauvin et al., 2019; Ngaage et al., 2019).

First degree

The first degree not only affected an individual’s later career choice but also influenced an individual’s future career development (Khalafallah et al., 2020). Key universities tended to provide students with better cultural, social, and academic capital (Useem and Karabel, 1986), which could have a considerable impact on students’ later academic development. With superior faculties and infrastructure, key universities not only engaged students in academic research but also provided them with professional guidance to help them achieve success in their future academic work (Grimm et al., 2014; Long et al., 2009). Previous studies revealed that researchers who graduated from key universities were more likely to achieve high levels of academic success in the future (Grimm et al., 2014; Gu et al., 2011; Jean and Felbaum, 2019).

Doctoral supervisor

The doctoral supervisor was an important factor (Yuan et al., 2016) and even played a key role in influencing the research performance of researchers during their PhD studies and their future research work (Gu et al., 2011). The academic status of doctoral supervisors affected the academic ability of their doctoral students (Zaed et al., 2020). A supervisor with more academic experience and upper-level academic status was conducive to improving his or her doctoral students’ academic performance (Gu et al., 2011). Supervisors could not only provide guidance for students in their research but also in their career choices and jobs (Schultz et al., 2020). Students’ academic potential could be stimulated under the guidance of an experienced supervisor, thus helping them succeed in their future research work (Choo et al., 2020; Nafukho et al., 2019; Teodorescu, 2000). For students who were interested in pursuing an academic career, choosing a supervisor with high academic abilities could be of great help to the success of their future research work (Smith et al., 2008).

Study abroad

Studying at foreign research institutions was an important way for researchers to improve their academic productivity (Carayol and Matt, 2006). In foreign countries or regions, researchers could meet different academic partners, broaden their academic collaboration networks, and improve their research productivity through collaboration with international colleagues (Akbaritabar et al., 2018). Through study and academic exchange abroad, researchers could improve their internationalization and publish more high-impact research papers (Baloch et al., 2021). A researcher’s level of internationalization could greatly affect one’s future research productivity (Baloch et al., 2021; Horta et al., 2018). Several studies indicated that researchers who earned their PhD abroad were more likely to have their publications cited than those who graduated locally (Katranidis et al., 2017; Sahoo et al., 2017; Tavares et al., 2019).

Research productivity during their PhDs

Numerous studies revealed that there was a correlation between the research productivity of researchers during their PhD studies and their future research productivity (Fu et al., 2021; Williamson and Cable, 2003). Research productivity during their PhD could be used to predict the future achievements of an individual’s academic career (Luby et al., 2020; Nurhudatiana and Anggraeni, 2015). First, compared with researchers with average academic levels, researchers with higher academic levels had more opportunities to collaborate with their supervisors to publish research papers during their PhD (Megel et al., 1988). The research productivity during their PhD could build up their experience and strengths for their future research work. These experiences and strengths could help them achieve higher academic achievements (Fu et al., 2021; Swihart et al., 2016). Second, higher research productivity during their PhD could help graduates enter prestigious research institutions, which in turn could provide them with more comprehensive research support (Anderson et al., 2020). In this way, the research productivity of researchers could also be well enhanced.

The prestige of affiliations

In addition to individual characteristics, the prestige of affiliations was also a factor that influenced the future research productivity of researchers (Hedjazi and Behravan, 2011; Rhaiem and Amara, 2020; Smith et al., 2008). Researchers working in prestigious research institutions tended to have higher research skills and research productivity (Long et al., 2009). First, prestigious research institutions often had high recruitment thresholds and performance assessment requirements. In other words, junior researchers who were able to work at prestigious universities generally had strong research capabilities and were required to produce consistently rich academic results (Amara et al., 2015). Prestigious research institutions also tended to hire high-level researchers (Long and McGinnis, 1981). Second, prestigious research institutions could provide researchers with better research infrastructure and research funding support. Furthermore, colleagues could make valuable suggestions for researchers. This helped researchers improve their research productivity (Anderson et al., 2020).

Generally, previous studies examined the “net effects” of individual characteristics (e.g. gender) and organizational characteristics (the prestige of affiliations) on research productivity by using quantitative methods (e.g. regression analysis). The net effects correspond to the effects of each independent variable on the dependent variable after controlling for the influences of the other independent variables on the dependent variable (Luís, 2019; Ragin, 2006a; Skarmeas et al., 2014; Woodside, 2013). However, researchers’ research productivity might be due to the interactions of multiple factors rather than a single factor. In contrast to previous quantitative studies that focused on “net effects,” this study used QCA to analyze the effects of combined conditions (interactions) of individual and organizational characteristics on the research productivity of early career LIS researchers from a configuration perspective.

Methods

Overview of QCA

QCA is a configuration research method proposed by Ragin to study complex causality through Boolean algebra and set theory (Ragin, 1987). Ragin (2008) notes that QCA is a set theory research method for small and medium-sized cases (usually approximately 60 cases). It combines the strengths of qualitative and quantitative analysis to identify multiple combinations of conditions that lead to outcomes from complex cases (Ragin, 2000). QCA differs from traditional quantitative research in that traditional quantitative research tends to focus on the net effects of a single variable on outcomes whereas QCA focuses on analyzing the effect of multiple combinations of conditions on outcomes. That is, QCA does not focus on the extent to which a single condition can contribute to the outcome but rather on what combinations of conditions can be used to form a “recipe” that leads to the outcome (Woodside, 2013).

Depending on the condition type, QCA can be classified as crisp-set QCA (csQCA), multivalue QCA (mvQCA), and fuzzy-set QCA (fsQCA). csQCA is suitable for dichotomous conditions. mvQCA is suitable for the conditions of multivalued classification. fsQCA is suitable for continuous data. In this study, the vast majority of the data were dichotomous. Therefore, the csQCA method was selected for our study.

There are four major steps in QCA analysis. The first step is to build a truth table. A truth table is built to convert raw data to a binary form, that is, converting the raw data to 0s or 1s. Each row of a truth table represents a combination of conditions that could lead to an outcome. The second step is to analyze the necessary conditions for the outcome. In QCA, the consistency score is the main criterion to measure whether a condition is a necessary condition for the outcome (Ragin, 2006b)—if the consistency score exceeds 0.9, the condition can be considered a necessary condition for the outcome (Ragin, 1987). The third step is to conduct sufficiency analysis of configurations. Before obtaining the configurations, two key thresholds—the case frequency threshold and the original consistency threshold—need to be set. The case frequency threshold includes condition combinations in the analysis only when they occur at least x times in the sample; those configurations with a case frequency threshold of 0 are configured as logical remainders. The raw consistency threshold is the consistency of the condition combinations of at least x before they are included in the configuration analysis by the software. In small-scale sample studies, the threshold for case frequency can be set to 1 (Schneider and Wagemann, 2012). For the raw consistency threshold, Fiss (2011) gives the minimum acceptable threshold for the original consistency threshold: 0.75. Referring to previous studies (Schneider and Wagemann, 2012), we set the case frequency threshold to 1 and the raw consistency threshold to 0.8 in the current study. Then, the combinations of different conditions leading to the outcome need to be calculated. Finally, the combinations of conditions that are suitable for explaining the outcome are then interpreted concerning empirical facts and theoretical grounds.

Conditions, outcome, and data collection

Currently, a doctoral degree in LIS in China’s mainland typically takes 3 years of full-time study. Institutions with doctoral degrees in LIS authorized in China’s mainland were used as the data source. Early career researchers are usually defined as researchers within 10 years of obtaining their PhDs (Christian et al., 2021; Pain, 2014). According to the definition, the early career LIS researchers in this study were defined as the full-time PhD students who enrolled between 2008 and 2012 (usually graduated from 2011 to 2015) in the field of LIS in China’s mainland. The sample was sufficiently “heterogeneous” in that the PhD students were in the same discipline but had different personal characteristics. The conditions of the current study were gender, first degree, study abroad status (hereinafter referred to as study abroad), supervisor’s academic status (hereinafter referred to as supervisor), number of CSSCI (Chinese Social Sciences Citation Index, a well-known social science index database in China) articles as the first or corresponding author during PhD studies (hereinafter referred to as CSSCI (PhD)), publishing an article indexed by Web of Science core collections or not as the first author or corresponding author during PhD studies (hereinafter referred to as WOS (PhD)), and whether the affiliation (the university that PhDs work for after graduation) is a key university (hereinafter referred to as affiliation).

At present, one of the conditions for PhD students to obtain a doctoral degree in the field of LIS in China’s mainland is that they are required to publish a certain number (usually 3) of research articles indexed by the CSSCI (hereinafter referred to as CSSCI articles). Under the current research evaluation in China’s mainland, publishing articles in Web of Science core collections (WOS articles; i.e. SCI or SSCI articles) is considered a very important indicator of a researcher’s high academic performance. Initially, we intended to use the mean numbers of WOS articles as the first or corresponding author (and his or her affiliation was the first/corresponding affiliation) between the year of the researcher’s PhD graduation and December 31, 2020 as the threshold of the outcome. However, we found that the median number of WOS articles was 0 (see Table 3). Therefore, the research productivity in this study (i.e. the outcome variable of this study) was defined as whether the early career LIS researcher had published at least one WOS article as the first or corresponding author (and his or her affiliation was the first/corresponding affiliation) during the year after PhD graduation and December 31, 2020 (hereinafter referred to as WOS (work)).

There were three major steps in the data collection. First, we manually collected PhD graduates’ names through the alumni on the websites of the universities/institutions with LIS doctoral degree-granting authority and the CNKI doctoral dissertation database (a well-known doctoral dissertation database in China’s mainland). Second, we determined each graduate’s gender, undergraduate school, study abroad status, doctoral supervisor’s academic status, and affiliation through the acknowledgments and personal introduction in his or her doctoral dissertation, search engines, personal homepage, and personal relationships of the authors of this paper. Third, we obtained the number of publications, including CSSCI (PhD), WOS (PhD), and WOS (work), through the appendices of their doctoral dissertations, the CNKI database (a well-known academic database in China’s mainland), and the Web of Science core collections.

Data processing and calibration

Before using csQCA for analysis, each condition needed to be dichotomized to assign the condition a value of 1 or 0 according to certain criteria. Here, “1” indicated the presence of the condition, and “0” indicated the absence of the condition. In the final condition configuration, if the condition was prefixed with the symbol “~,” it meant that the condition was set as “0”; otherwise, it was set as “1.” The results of the conditional dichotomization are shown in Table 1.

Table 1.

The results of the conditional dichotomization.

Conditions/outcome	Items	Description	Assignment
Conditions	First degree	The researcher received his or her bachelor’s degree from a key university.	1
	First degree	The researcher received his or her bachelor’s degree from a non-key university.	0
	Gender	The researcher was male.	1
	Gender	The researcher was female.	0
	Supervisor	The doctoral supervisor of the researcher was a senior professor.	1
	Supervisor	The doctoral supervisor of the researcher was not a senior professor.	0
	Study abroad	The researcher had at least 1 year of study abroad experience.	1
	Study abroad	The researcher did not have at least 1 year of study abroad experience.	0
	Affiliation	The researcher worked in a key university after PhD graduation.	1
	Affiliation	The researcher worked in a non-key university after PhD graduation.	0
	WOS (PhD)	The number of WOS articles published by the researcher during his or her PhD was greater than 0.	1
	WOS (PhD)	The number of WOS articles published by the researcher during his or her PhD was 0.	0
	CSSCI (PhD)	The number of CSSCI articles published by the researcher during his or her PhD was greater than or equal to the median of the sample.	1
	CSSCI (PhD)	The number of CSSCI articles published by the researcher during his or her PhD was less than the median of the sample.	0
Outcome	WOS (work)	The researcher published at least one WOS article as the first or corresponding author (and his or her affiliation was the first/corresponding affiliation) during the year of graduation and December 31, 2020.	1
Outcome	WOS (work)	The researcher published 0 WOS article as the first or corresponding author (and his or her affiliation was the first/corresponding affiliation) during the year of graduation and December 31, 2020.	0

The key universities in this study were referred to “985 Project” universities or “211 Project” universities in China’s mainland. There are four levels of titles of full-time teachers in universities of China’s mainland, ranked from lowest to highest: teaching assistants, lecturers, associate professors, and professors. It should be noted that full-time teachers in universities of China’s mainland are required to do both teaching and research. Generally, lecturers, associate professors, and professors in universities of China’s mainland correspond to assistant professors, associate professors, and professors in American universities, respectively. However, in China’s mainland, the lecturers and associate professors are further divided into three levels respectively (i.e. level 10 lecturer, level 9 lecturer, level 8 lecturer, level 7 associate professor, level 6 associate professor, and level 5 associate professor), and professors are divided into four levels (i.e. level 4 professors, level 3 professors, level 2 professors, and level 1 professors). Level 1 professors and level 2 professors are referred to as professors who have made significant achievements in their professional fields. In addition, in China’s mainland, the professors of “Changjiang (Yangtze River) Scholars Award Program” (WSU, 2018; WUSTL, 2017; Xinhua, 2018) and the “senior professors in the field of humanities and social sciences (in Chinese: 人文社科资深教授)”(WHU, 2022; ZJU, 2012) are the highest academic titles in the field of humanities and social sciences. In this study, doctoral supervisors with the title of “Changjiang (Yangtze River) Scholars Award Program,” “senior professors in the field of humanities and social sciences,” level 1 professor, or level 2 professor were defined as senior professors.

Safeguarding the privacy of early career LIS researchers

It should be noted that the biographical details of the early career LIS researchers form the dataset of our study. The following measures were used to protect the privacy of the early career LIS researchers of our study. In this study, the corresponding author collected and processed the raw data. The first author performed the analysis (QCA) based on the dataset processed by the corresponding author. The other authors did not have access to any datasets. In the final dataset used for analysis, the corresponding author removed personally identifiable information about the researchers, including names, universities (institutions) where they received their bachelor’s degrees and Ph.Ds., year of PhD receipt, names of their PhD supervisors, affiliations, etc. To prevent disclosure of the personally identifiable information of early career LIS researchers, the corresponding author encrypted the raw data with encryption software (7-Zip). The authors were committed to strictly protecting the privacy of the early career LIS researchers included in our study.

Results

Overview of the dataset

A total of 62 LIS PhD graduates with complete personal information and publication records were obtained. Among the 62 LIS PhDs, 22 graduated from Wuhan University, 21 graduated from Nanjing University, five graduated from Central China Normal University, four graduated from Chinese Academy of Sciences, four graduated from Nankai University, three graduated from Peking University, and three graduated from Jilin University. The basic personal information of the sample is presented in Table 2.

Table 2.

Basic information of the sample (n = 62).

Conditions	Items	Frequency	Percentage (%)
First degree	Key university	44	70.97
First degree	Non-key university	18	29.03
Gender	Male	31	50
Gender	Female	31	50
Supervisor*	Senior professor	24	38.71
Supervisor*	Non-senior professor	38	61.29
Study abroad	Yes	15	24.19
Study abroad	No	47	75.81
Affiliation	Key university	33	53.23
	Non-key university	29	46.77

Among the 62 LIS PhDs, 24 were supervised by senior professors, and 38 were supervised by non-senior professors. In the dataset of this study, the number of unduplicated supervisors was 38. This was because one supervisor might supervise more than one PhD student. Among the 38 unduplicated supervisors, 12 were senior professors, and 26 were non-senior professors.

The results of descriptive statistics of WOS (PhD), CSSCI (PhD), and WOS (work) of the early career LIS researchers are presented in Table 3 (n = 62).

Table 3.

Descriptive statistics of WOS (PhD), CSSCI (PhD), and WOS (work) of the sample.

Variables	WOS (PhD)	CSSCI (PhD)	WOS (work)
Minimum	0	0	0
Maximum	2	13	13
Median	0.00	4.00	0.00
Mean	0.35	4.08	1.34
Std. error of mean	0.083	0.391	0.342
Std. deviation	0.655	3.080	2.691

As shown in Table 3, the WOS (work), which was the outcome variable of this study (research productivity), had a minimum of 0, a maximum of 13, and a median of 0. Additionally, we counted the number of researchers with WOS (work) of 0. The results showed that 39 (out of 62) of the early career LIS researchers had not yet published a WOS article as the first or corresponding author (and his or her affiliation was the first/corresponding affiliation) during the year after PhD graduation and December 31, 2020.

Analysis of necessary conditions for high research productivity

The results of the necessity test for each condition are presented in Table 4. If the consistency score of a single condition exceeds 0.9, the condition can be regarded as a necessary condition for the outcome. That is, the occurrence of this condition can lead to the occurrence of the outcome (Ragin, 1987). Table 4 showed that the consistency scores of gender, first degree, study abroad, supervisor, affiliation, WOS (PhD), and CSSCI (PhD) were lower than 0.9. That is, none of the conditions was a necessary condition for high research productivity. The results indicated that the reasons for high research productivity among early career LIS researchers were complex and diverse.

Table 4.

Analysis of the necessary conditions for high academic productivity.

Conditions	Consistency	Coverage
First degree	0.652174	0.340909
Gender	0.608696	0.451613
Supervisor	0.434783	0.416667
Study abroad	0.391304	0.6
Affiliation	0.695652	0.484848
WOS (PhD)	0.608696	0.875
CSSCI (PhD)	0.478261	0.34375

Analysis of configurations for high research productivity

The fs/QCA 3.0 software was employed to calculate solutions of multiple sets of configurations that produced high research productivity. The generated solutions can be divided into complex solutions, parsimonious solutions, and intermediate solutions, according to the inclusion degrees of logical remainders (Nenonen et al., 2020). Logical remainder refers to the combination of conditions that may exist logically but is not observed among the empirical cases (Rihoux and Ragin, 2009). In QCA, the logical remainder is used to simplify the results (Verweij and Trell, 2019). This can make the results of QCA more parsimonious and have stronger interpretation ability (Duller, 2022). There are no logical remainders in the complex solution. This will cause the complex solution tends to be too complex for a theoretically meaningful explanation. The parsimonious solution contains all the logical remainders but does not evaluate the rationality of the logical remainders. This makes the results of the parsimonious solution too parsimonious and lacks theoretical support. The intermediate solution only contains the logical remainder that conforms to the theoretical expectation and empirical evidence. The results of the intermediate solution will be more reliable and the complexity will be moderate (Schneider and Wagemann, 2013). As a result, in QCA, the intermediate solution is the best choice for result interpretation (Blackman et al., 2013; Rihoux and Ragin, 2009). Therefore, the intermediate solution was analyzed in this study. The results of the intermediate solution are presented in Table 5.

Table 5.

Intermediate solutions for high academic productivity.

Configurations		Raw coverage	Unique coverage	Consistency
Configuration 1	~first degreestudy abroad~CSSCI (PhD)	0.0869565	0	1
Configuration 2	~first degreestudy abroad~affiliation	0.0869565	0	1
Configuration 3	first degreeWOS (PhD)affiliation	0.391304	0.26087	1
Configuration 4	gendersupervisor~affiliation*CSSCI (PhD)	0.0869565	0.0869565	1
Configuration 5	genderWOS (PhD)CSSCI (PhD)*affiliation	0.217391	0.130435	1
Configuration 6	first degreegender~supervisoraffiliation~CSSCI (PhD)	0.0869565	0.0434783	1
Configuration 7	~first degree~gendersupervisoraffiliationCSSCI (PhD)	0.0434783	0.0434783	1
	Solution coverage: 0.782609
	Solution consistency: 1

In QCA, the symbol “+” indicates “logical or”; the symbol “*” indicates “logic and”; and the symbol “~” indicates “logical not,” which corresponds to the condition “absence” (the condition is set as “0”).

In the intermediate solution, a total of seven sets of configurations were obtained. The overall coverage of the seven configurations was 78.3%. That is, the seven configurations could explain 78.3% of the early career LIS researchers with high research productivity. The consistency of each configuration was 1 (exceeding the consistency threshold: 0.8), indicating that each configuration could contribute to the occurrence of high research productivity. The higher the coverage of a configuration, the stronger its empirical association with the outcome (Ragin and Strand, 2008). In Table 5, the raw coverage of Configuration 1, Configuration 2, Configuration 4, and Configuration 6 was the same, which indicated that they had the same explanatory power for the outcome (high research productivity). However, the raw coverages of those four configurations were not high (all of them were lower than 10%). Referring to the study of Grimaldi et al. (2019), two configurations with coverage over 10% were selected for interpretation. The two configurations were Configuration 3 (first degree * WOS (PhD) * affiliation) and Configuration 5 (gender * WOS (PhD) * CSSCI (PhD) * affiliation).

According to the guidelines of previous studies (Fiss, 2011; Misangyi and Acharya, 2014), the conditions that appeared in both intermediate and parsimonious solutions were specified as core conditions, and the conditions that appeared only in intermediate solutions were specified as peripheral conditions. Table 6 showed the results of configurations to achieve high research productivity. In Table 6, spaces indicated that the presence or absence of the condition in a configuration had no effects on the outcome. Black circles denoted the presence of a condition, which meant the condition was set as 1. Specifically, large black circles “●” denote core conditions, and small black circles “•” denoted peripheral conditions. The core conditions were those factors that play a very important role in the occurrence of the outcome, and the peripheral conditions were less important than core conditions for the results (Ragin and Fiss, 2008).

Table 6.

Configurational conditions for high research productivity.

Conditions	Configurations
Conditions	3	5
First degree	•
Gender		•
Study abroad
Supervisor
WOS (PhD)	●	●
CSSCI (PhD)		•
Affiliation	●	●
Raw coverage	0.391304	0.217391
Unique coverage	0.26087	0.130435
Consistency	1	1

Discussion

The results of the necessary condition analysis (see Table 4) showed that none of the conditions had a consistency score above 0.9. This result indicated that there was no single condition that had strong explanatory power for early career LIS researchers to achieve high research productivity. In other words, there was no single condition that can be a necessary condition for early career LIS researchers to achieve high research productivity. The results indicated that the causal mechanisms for achieving high research productivity for early career LIS researchers were complex and that the effects of a single condition did not directly contribute to the occurrence of high research productivity.

Our study revealed that the two configurations, configuration 3 and configuration 5, were suitable to explain the causal mechanism of high research productivity for early career LIS researchers.

Configuration 3 was “first degree * WOS (PhD) * affiliation.” The configuration revealed that early career LIS researchers with undergraduate degrees from key universities, having published at least one WOS article during their PhD, and working at a key university after their PhD were able to achieve high research productivity while their genders, supervisors, and study abroad experience did not affect research productivity. First, in terms of the first degree, educational background was an important factor influencing researchers’ research productivity (Gu et al., 2011; Littell et al., 2008). On the one hand, researchers who received their bachelor’s degrees from key universities tended to have strong learning abilities. On the other hand, undergraduates studying in key universities had more access to academic research than undergraduates studying in non-key universities. Key universities were also able to provide their undergraduates with systematic guidance and support, thus facilitating their academic initiation, helping them conduct research activities (Grimm et al., 2014), and accumulating experience for later academic research. Second, researchers’ early publications could be used to predict their later research productivity (Finkelstein, 1984; Horta and Santos, 2016). A PhD student who published a WOS article as the first author or corresponding author during his or her PhD could reflect strong research abilities. The academic achievements during their PhD helped a PhD student to conduct independent research in the future and provided a guarantee to enter a prestigious research institution (e.g. a key university) after their PhD graduation. Third, after joining a prestigious research institution, researchers could better conduct their research work thanks to the various high-quality resources provided by the prestigious research institution. Previous studies revealed that different levels of research institutions had impacts on researchers’ research performance (Cole and Cole, 1972). The research productivity of researchers at prestigious research institutions was higher than that of researchers at lower prestigious research institutions (Akbaritabar et al., 2018; Long et al., 2009). In summary, early career LIS researchers could achieve high research productivity under the interaction of the three conditions (receiving a bachelor’s degree from a key university, publishing at least one WOS article during their PhD, and working at a key university after PhD graduation). In Configuration 3, working at a key university and having published at least one WOS article during the PhD were core conditions (see Table 6). This indicated that for this type of early career LIS researchers, working at a key university and having published at least one WOS article during their PhD played very important roles in achieving high research productivity in the future. It should be noted that configuration 3 had the highest raw coverage (39.1%) among all the configurations. This result indicated that configuration 3 had better accuracy and wider applicability in predicting early career LIS researchers to achieve high research productivity.

Configuration 5 was “gender * WOS (PhD) * CSSCI (PhD) * affiliation.” The configuration drew a portrait of early career LIS researchers who achieved high research productivity: male PhDs who published more CSSCI articles during their PhD, published at least one WOS article, and worked at key universities after graduation. Configuration 5 was consistent with previous studies in predicting researchers who achieved high research productivity. First, gender was associated with high academic performance (Ha et al., 2021). Previous studies found that men outperform women in academic research (Behmer Hansen et al., 2020). Second, publications during their PhD were an important predictor of an individual’s future research productivity. Researchers with high research productivity published more papers during their doctoral studies than those with average research productivity (Megel et al., 1988). Third, prestigious research institutions could provide researchers with richer academic resources, better research infrastructure, and financial support. Under such circumstances, researchers could fully exploit their academic potential and thus produce more research productivity (Long et al., 2009; Ogunsola et al., 2020; Rhaiem and Amara, 2020). In configuration 5, having published at least one WOS article during their PhD and working at a key university after graduation were core conditions (see Table 6) while gender and publishing more CSSCI articles during their PhD were peripheral conditions. This suggested that having published articles in high-level journals during their PhD and working at a key university had greater impacts on an individual’s future research productivity compared to gender and having published more articles in low-level journals during their PhD.

It should be noted that both configuration 3 and configuration 5 contained the conditions of having published at least one WOS article during their PhD and working at a key university. Moreover, the two conditions were the core conditions of both configurations. This indicated that having published at least one WOS article during their PhD and working at a key university after graduation had more general and important impacts on the high research productivity of early career LIS researchers in the future than gender, receiving a bachelor’s degree from a key university, and publishing more CSSCI articles during their PhD.

Conclusions

In this study, we used QCA to identify the combinations of conditions that affected the research productivity of early career LIS researchers in China’s mainland. Our study revealed that no single condition could be a necessary condition for early career LIS researchers to achieve high research productivity. There were two combinations of conditions that could contribute to the high research productivity of early career LIS researchers. One was “first degree * WOS (PhD) * affiliation,” and the other was “gender * WOS (PhD) * CSSCI (PhD) * affiliation.” In the two combinations, having published at least one WOS article during their PhD and working at a key university after graduation had more general and important impacts on achieving high research productivity in the future for early career LIS researchers.

This study had limitations. The sample size of this study was small. The sample for this study was 62 early career LIS researchers. Although QCA itself was a research method geared toward small samples (usually approximately 60 cases), the generalizability of the findings of this study would be limited by the sample size compared to a large sample. Therefore, a larger sample size is required in future studies.

Despite the limitations of our study, this study had implications for junior research staff recruitment at universities and for doctoral students in their studies. When recruiting junior researchers (e.g. assistant professors), universities should not evaluate only one of the candidates’ characteristics but rather a combination of several of their characteristics. Universities should consider prioritizing a candidate’s research productivity during their PhD, including the number of articles published in core journals in the home country and the number of WOS articles, especially whether any WOS articles had been published as the first or corresponding author, rather than whether the candidate had studied abroad and whether his or her supervisor was a senior professor. For PhD students who were interested in pursuing an academic career, they should strive to achieve high research productivity during their PhD, especially to publish WOS articles as the first or corresponding author; and to be able to work at a key university after graduation.

Footnotes

Acknowledgements

We would like to thank the anonymous reviewers for their insightful comments that helped us improve the article.

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Qianjin Zong

Author biographies

Zhihong Huang is an undergraduate student at the School of Economics and Management, South China Normal University, China.

Qianjin Zong is a Lecturer at the School of Economics and Management, South China Normal University, China. His research interests include research policy, scientometrics, peer review, and information management.

Yuqing Xie earns her bachelor’s degree in management science from the South China Normal University, China.

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