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Studies in Higher Education

ISSN: 0307-5079 (Print) 1470-174X (Online) Journal homepage: www.tandfonline.com/journals/cshe20

Investigating the impact of doctoral student competency on research performance: a comprehensive analysis

Jin Wang

To cite this article: Jin Wang (11 Apr 2025): Investigating the impact of doctoral student competency on research performance: a comprehensive analysis, Studies in Higher Education, DOI: 10.1080/03075079.2025.2490138

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Investigating the impact of doctoral student competency on research performance: a comprehensive analysis Jin Wang

School of Public Policy&Management, China University of Minning and Technology, Xuzhou, People’s Republic of China

ABSTRACT This study classifies the dimensions of doctoral student competency and research performance, focusing on the relationship between these two constructs. Building upon existing literature, we developed a competency and performance measurement scale. The findings provide strong evidence that doctoral student competency comprises four dimensions, while research performance consists of three. A survey was conducted using a random sample of 771 doctoral students from three universities in Eastern China with established doctoral programs. The developed scale demonstrated good convergent and discriminant validity. The results reveal that the doctoral competency framework significantly predicts research performance. Specifically, among the four dimensions of competency, knowledge and skills and self-concept primarily affect research attitude; cognition-traits has a strong impact on research behavior, while both cognition-traits and self-concept influence research output. These findings contribute to further discussions on improving doctoral research performance through a competency-driven approach. Based on these findings, insights are provided on how to cultivate and select doctoral students using a competency-driven framework, as well as how to enhance their efficiency and productivity.

ARTICLE HISTORY Received 4 December 2024 Accepted 2 April 2025

KEYWORDS Doctoral student; doctoral student competency; research performance; predictive model; higher education

1. Introduction

There are cross-correlations between education and competences, as public development heavily relies on the education system’s ability to provide adequate skills. The European Union’s Strategic Framework for Education and Training Cooperation (2021–2030) highlights sustained development through knowledge in education, research, innovation, and creativity. Doctoral studies may be regarded as the highest level of educational attainment. High achievers have reached their status through a stringent selection process (Firman et al. 2020). In higher education’s final stage, doctoral studies are interconnected within a unified research environment, making the research performance and quality of doctoral students strategically important. Research performance, as a set of abilities, involves a correlation of various objective and subjective factors (Edgar and Geare 2013). This study examines research performance comprehensively, focusing on its two core elements: research and performance. Research, an essential academic activity, is a fundamental expectation for doctoral stu- dents (Hedjazi and Behravan 2011). Research performance is multidimensional, encompassing both objective indicators (e.g. publication output, citation rates) and subjective factors (e.g. motivation, self-efficacy). In many countries, including China, doctoral education serves as a critical driver of

© 2025 Society for Research into Higher Education

CONTACT Jin Wang [email protected] School of Public Policy&Management, China University of Minning and Technology, No. 1 University Road, Xuzhou, Jiangsu Province, People’s Republic of China

Supplemental data for this article can be accessed online at https://doi.org/10.1080/03075079.2025.2490138.

STUDIES IN HIGHER EDUCATION https://doi.org/10.1080/03075079.2025.2490138

innovation and knowledge production, prompting policymakers and institutions to refine doctoral programs through various reforms (e.g. Project 211, Project 985, and the ‘Double First-Class’ initiat- ive). These reforms increasingly incorporate competency frameworks to enhance doctoral students’ research capabilities (Deng and Zhengmei 2020). Amid these reforms, a growing body of research has highlighted the importance of doctoral student competency – encompassing a wide range of skills, knowledge, and personal attributes – in shaping research outcomes (Firman et al. 2020). Despite the increasing attention on this topic, there is not much research that really delves into the relationship between ability and research outcomes, especially in the particular context of doc- toral education in China. In fact, competency-based frameworks are in their infancy here. The impor- tance of doctoral students’ abilities in research is self-evident, so it is critical to understand how these abilities affect research performance. Developing these competencies is not only the core of doctoral education, but also directly related to whether students can make meaningful contributions to the academic field. Despite the widespread recognition of the importance of these competencies, many voices remain concerned about the adequacy of current educational models to prepare doctoral stu- dents for the challenges of advanced research. To address this, the first task is to identify which com- petencies are most closely associated with high-quality research outcomes. It cannot be overlooked that these competencies have a profound impact on a student’s academic development and overall success, and understanding the mechanisms behind them is critical. This study proactively explores the intricate links between the key competencies demonstrated by doctoral students in their research practice and their research performance, focusing not only on the contribution of these competencies to the quantity of research output, but also on their positive role in improving the quality of research. In other words, this study seeks to reveal how each competence plays a role in the actual research process, and to provide a strong theoretical basis and empirical support for curriculum design, tutor guidance, and higher education policy making.

2. Literature review

2.1. Competency theory

In 1973, American psychologist David C. McClelland first proposed the concept of ‘competency’ and gave a very inspiring definition. Competency is not only a set of quantifiable indicators, but a multi- dimensional combination of abilities, covering many factors such as ability, skill, personal trait and motivation (McClelland 1973). As his research progressed, McClelland actively developed a range of effective and reliable measurement tools to measure motivation, attitude, knowledge, and obser- vable behavioral skills. In an effort to provide a clear distinction between high performers and average performers, he continually revises and refines his theory. In fact, his initiative provided a fresh perspective for subsequent scholars and laid the foundation for today’s comprehensive under- standing of competency. McClelland’s ideas go far beyond superficial numerical comparisons and provide a broad framework for understanding how individuals behave in different professional set- tings. This theory has inspired scholars around the world and enriched the connotation of compe- tency from the initial skill assessment tool to a complex system covering multiple dimensions such as cognition, emotion and motivation. Among the many capability models, Spencer and Spencer’s (1993) iceberg model is the most striking. It graphically compares visible knowledge and skills to the ‘tip’ of an iceberg, while self-concept, personality traits, and motivation hidden beneath the surface are seen as the core forces driving performance. Similarly, the onion model pro- posed by Cheetham and Chivers (1996) stratifies abilities, with the outer layer being easily observed behaviors and the inner layer being personal values, traits and deep motivations.

In addition, the capability structure model provides a more nuanced approach to assessing role needs. It not only focuses on explicit characteristics, but also digs deeper into the hidden but critical factors for success to help organizations accurately identify and deploy high performers (Manzoor, Othman, and Pomares 2021). Although the iceberg model and onion model initially served business

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management, their adaptability made this theoretical framework gradually penetrate into the field of higher education. Research by Spöttl and Musekamp (2017) and others reveals that these models play an irreplaceable role in competency-based teaching strategies. In fact, they not only promote the accumulation of knowledge, but also stimulate the students’ inner passion, prompting them to develop the personal qualities and internal motivation necessary for lifelong learning. At the same time, through the introduction and application of these theories, the difference between high performers and ordinary performers has been more clearly distinguished, which provides theor- etical basis and practical guidance for higher education reform.

In recent years, China’s doctoral education is undergoing a transformation. In the past, aca- demic knowledge transfer has been dominant; Nowadays, universities are making every effort to cultivate students’ comprehensive abilities to cope with the challenges of globalization (Hu et al. 2017). At the same time, universities are actively drawing on international research stan- dards and aiming to enhance the competitiveness of graduates in a fierce job market, which is not only reshaping the education model, but also instilling momentum for future develop- ment (Fu 2017). Today, major educational reforms are no longer limited to academic depth, but focus on building a complete set of ability systems to provide a solid guarantee for students’ future development in multiple fields. The core of doctoral training has always been research; However, the competency-based teaching framework introduced in recent years is going all out to upgrade students’ intellectual level and vocational skills (Li and Huang 2024). Studies have shown that this model has achieved remarkable results in balancing academic knowledge with key competencies such as communication, leadership, and innovation (Huang, Wang, and Ma 2017; Wei and Liu 2019). Doctoral students should have cross-border competitiveness, can not only study cutting-edge research, but also be able to take on responsibilities in academia, industry and public service. Meanwhile, competency-based training models in fields such as engineering and business have been shown to help students better enter the workforce (Liu, Luo, and Ye 2022). This integrated approach provides graduates with strong support for the rapid changes in the global economy. Research has also shown that this competency-based approach inspires students’ passion for interdisciplinary research, enhances their project management skills, and enhances their contribution to socio-economic progress (Wu and Wu 2024).

In general, competency models emphasize the existence of defining individual traits between high performers and average performers in a particular role, organization, or cultural context. These traits include motivation, personality, perceived self, attitudes, values, professional skills, cog- nitive abilities, and behavioral performance. When scientifically measured, these indicators are effective in differentiating high-level performance from average (Brown 1993; McClelland 1973; Spencer and Spencer 1993). Competency models provide a conceptual framework for this, showing that occupational effectiveness arises not only from unchanging skills, but also from the dynamic interaction between cognitive processing, emotional regulation, and goal-driven motiv- ation. This perspective suggests that high-level workplace performance is actually the result of the synergies of all three, enabling professionals to better adapt to changing environments and increasingly complex organizational challenges.

2.2. Research performance

Research performance can be defined as the quality of a paper that contributes to the dissemination and visibility of knowledge gained through research (Bazeley 2010). However, scholars have not yet reached a consensus on a single standardized term to describe academic research. Various terms have been used in the literature, including ‘scientific research’ (Xu et al. 2021), ‘scientific productivity’ (Andrews 1979; Bazeley 2010; Pettit 1970), ‘research performance’ (Javed, Ahmad, and Khahro 2020; Wood 1990), ‘research output’ (Pinto and Teixeira 2020), and ‘research activities’ (Manzoor, Othman, and Pomares 2021). Each term highlights different aspects of the research process, reflecting dimen- sions such as volume (productivity), quality (performance), and dissemination (output). Research

STUDIES IN HIGHER EDUCATION 3

performance encompasses a variety of scholarly endeavors, including submitting articles to aca- demic or professional journals, getting published in these journals, authoring or co-authoring books and monographs, writing reviews for books, and presenting papers at professional confer- ences (Li, Yang, and Cai 2022). Although the terms ‘research productivity’ and ‘research performance’ are frequently used interchangeably in existing literature (Fan et al. 2020), their specific meanings and emphases vary depending on disciplinary context and research objectives. The existing litera- ture shows that the level of scientific research output mainly depends on several key factors. Recent research shows that many factors have a profound impact on the scientific research perform- ance of university teachers. Dreijerink, Handgraaf, and Antonides (2022) argued that sustained per- sonal effort and a focus on career development are important prerequisites for scientific success. At the same time, the study points out that researchers have full autonomy in selecting topics, which has played a significant role in improving work efficiency and scientific research output. Previous studies have shown that the level of research performance often depends on the personality charac- teristics of researchers, among which intrinsic motivation, the enthusiasm from the heart, promotes researchers to continuously produce high-quality research results. Moreover, this effect is not limited to a single cultural or regional context.

2.3. Doctoral student competency and research performance

Competency theory reveals the phenomenon that people with deep intrinsic qualities – such as strong motivation, strong values, positive attitudes, and unique personal characteristics – tend to perform better at work. They are able to flexibly use their strengths to cope with various challenges (Spöttl and Musekamp 2017). Competency not only covers multiple dimensions, it is an organic com- bination of innate traits and acquired skills, which can be continuously developed and optimized to significantly improve overall performance. More importantly, these core elements are dynamic and can be adjusted and shaped according to different situational needs (Mueller-Frommeyer et al. 2017). Drawing on prior scholarship, Asif et al. (2013) introduced a total quality management frame- work for higher education, highlighting various critical success factors. Meanwhile, Gómez, Aranda, and Santos (2016) employed a sample of 351 student reports compiled by professionals to conduct a two-year longitudinal study among social science majors. Van Loo and Semeijn (2004) further posited that competence can be evaluated either through self-assessment or via expert ratings pro- vided by key informants or observers. However, competence evaluation through self-assessment is often biased by personal perceptions, which can limit the accuracy of the evaluation. Despite these diverse approaches, most competence evaluations have heavily relied on student perceptions. More- over, the majority of existing competence scales cater to undergraduates, with only a few designed for postgraduates. Given the distinct learning environments and objectives at these two academic levels, applying undergraduate-focused scales directly to postgraduates can introduce bias and diminish accuracy. Therefore, the present study develops a doctoral competency scale from the per- spective of professionals. For doctoral students, competency represents their ability and internal resources, enabling them to tackle challenging assignments, ultimately contributing to their success (Hobfoll 2002). Those with high competency levels are better equipped to meet various demands in academic research, demonstrating increased commitment and a high degree of engage- ment. Furthermore, competencies such as professional knowledge and research skills allow doctoral students to meet academic requirements effectively, resulting in positive outcomes in both personal and academic domains. Ferla, Valcke, and Schuyten (2010) reported that learners’ self-perceived aca- demic competence exerts a measurable influence on both their study engagement and overall per- formance. Ren et al. (2020) found that a critical self-examination of one’s academic abilities strongly impacts academic success. These findings underscore the relationship between self-perceived com- petency and actual research performance. Building on this, we hypothesize that doctoral students’ perceived competence is positively associated with their research performance.

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In China, doctoral education has traditionally emphasized the thorough acquisition of knowledge and mastery of academic disciplines. Previous studies have primarily explored broader aspects of doctoral education in the country, such as institutional structures, academic expectations, and student outcomes (Niu and Zhang 2021). Research on doctoral student competencies remains limited. Most existing studies focus on either exploring general competencies (Miao and Ma 2011 Xin, Sun, and Miao 2014;) or examining specific competency areas (Gong et al. 2014; Li and Cai 2015). There’s a noticeable gap in the literature when it comes to exploring the competency charac- teristics needed at the doctoral level. Although relevant competency models have been proposed in several countries, the predictive validity of these models, especially the relationship between these models and doctoral research performance, is still poorly understood. Therefore, further research is necessary to gain a deeper understanding of doctoral competency and to identify effective practical experiences that can be applied in China and internationally.

3. Methodology

In eastern China, specifically in Beijing and Jiangsu Province, we conducted a questionnaire survey targeting 800 doctoral students from three research-oriented universities with doctoral enrollment qualifications. The survey was conducted using random sampling, with both paper-based and elec- tronic questionnaires distributed to participants. After excluding 29 invalid questionnaires, we obtained 771 valid responses, yielding an effective response rate of 96.38%. The statistical character- istics of the valid samples in this study are presented in Table 1. A analysis indicates that the effective research sample aligns well with the demographic characteristics of doctoral students, ensuring its scientific validity and representativeness.

Drawing on Spencer’s competency iceberg model and the Graduate Competency Scale devel- oped by Chen Zhixia (Chen and Guo 2018), this study developed a Doctoral Competency Scale. The revised scale includes four dimensions – knowledge and skills, self-concept, cognition-traits, and motivation – with 49 items. The research performance questionnaire was adapted from the questionnaires used for evaluating research performance in higher education institutions (Li, Ge, and Yin 2009) and the impact of doctoral students’ psychological capital on research performance (Wei 2015). The questionnaire addressed three dimensions: research attitude performance, research behavior performance, and research output performance, consisting of nine measurement items in total. Responses were recorded using a five-point Likert scale, ranging from 1 (‘strongly disagree’) to 5 (‘strongly agree’). The internal consistency of the doctoral competency scale, measured by Cron- bach’s α, was 0.951. Subscales for knowledge and skills, self-concept, thinking traits, and motivation had Cronbach’s α values above 0.920, indicating strong reliability. Similarly, the research perform- ance questionnaire showed good internal consistency, with subscale values above 0.800 and an overall Cronbach’s α of 0.841. Given that personal attributes – such as gender, age, academic

Table 1. Descriptive statistics of sample characteristics.

Characteristics Category Frequency (n) Percentage (%)

Gender Male 510 66.1 Female 261 33.9

Age group 21 years and below 51 6.7 22–24 years 108 14.0 25–27 years 291 37.7 28 years and above 321 41.6

Academic year Doctoral year 1 72 9.3 Doctoral year 2 201 26.1 Doctoral year 3 228 29.6 Doctoral year 4 135 17.5 Doctoral year 5 and above 135 17.5

Field of study Humanities and social sciences 288 37.4 Science and engineering 483 62.6

STUDIES IN HIGHER EDUCATION 5

year, and field of study – may significantly influence research variables other than the independent variable and ultimately shape the relationships among these variables, this study incorporates these attributes as control variables in the analysis. Data analysis was performed using SPSS 27 and AMOS 21. We selected SPSS 27 for its robust capabilities in handling descriptive statistics, correlation, and regression analyses, and chose AMOS 21 for its specialized features in structural equation modeling and confirmatory factor analysis. First, data quality was assessed with the variance inflation factor. Next, confirmatory factor analysis was conducted to validate research variables. Lastly, descriptive statistics, Pearson correlation, and regression analyses were used to examine the effect of doctoral competency on research performance.

4. Results

4.1. Testing For common method bias

The variables in this study were collected through self-reported data from respondents, which may introduce common method bias. Therefore, before conducting statistical analyses, a bias test was performed. All independent variables were included in regression models with attitude performance, behavioral performance, and outcome performance as dependent variables. Variance inflation factor (VIF) values ranged from 1.16–1.28, below the accepted threshold of 5, indicating minimal common method bias.

4.2. Confirmatory factor analysis and validity testing

Given that both the doctoral competence and research performance scales are adapted, this study uses Confirmatory Factor Analysis (CFA) with Structural Equation Modeling (SEM) to assess construct validity and evaluate the appropriateness of their dimensional structures. Scholars recommend using both convergent and discriminant validity methods in CFA to assess the structural validity of measurement scales (Stöber 2001). Convergent validity measures the correlation between a scale and other measures of the same construct, while discriminant validity assesses the lack of correlation with measures of different constructs. This study uses standardized factor loadings, composite reliability, and Average Variance Extracted (AVE) to test convergent validity. Discriminant validity is examined by comparing the correlation coefficients between latent variables with the square root of their AVE. Statistical analysis was conducted on variables like doctoral competence and research performance, with results shown in Table 2.

Table 2 shows that the competency model for doctoral students meets the recommended thresholds for absolute fit indices (RMSEA, AGFI), relative fit indices (CFI, NFI), and parsimony fit index (PNFI), indicating a good model fit. The standardized factor loadings, composite reliability, and average variance extracted also meet the required benchmarks, supporting convergent validity.

4.3. Statistics and correlation matrix analysis of research variables.

Before conducting hypothesis testing, descriptive statistics and Pearson correlation analyses were performed on doctoral competency and research performance, including their dimensions, to pre- liminarily assess the homogeneity, correlation strength, and direction of the study variables. In our initial analysis, the control variables exhibited negligible correlations with the dependent vari- able, research performance, as indicated by absolute correlation coefficients near zero and non-sig- nificant p-values (e.g. the correlation between gender and research performance was r = −0.05, p = .45). These analyses provide a foundation for subsequent statistical analyses. The results for the mean, standard deviation, and correlation coefficients are presented in Table 3.

Table 3 shows that the Sqrt(AVE) for each doctoral competence dimension exceeds its corre- lations with related variables, indicating good discriminant validity. Additionally, research

6 J. WANG

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STUDIES IN HIGHER EDUCATION 7

performance demonstrates strong convergent and discriminant validity. Similarly, the mean scores for doctoral competence, work performance, and their respective dimensions are above 3 (on a 5- point scale), with relatively low standard deviations, implying that the research variables generally meet the homogeneity assumption. Therefore, further regression analysis is justified. The Pearson correlation coefficients between doctoral competence and research performance, as well as their respective dimensions, are significantly positive, establishing a basis for examining the specific impact of doctoral competence on research performance. There is a significant correlation between doctoral competence and research attitude performance, research behavior perform- ance, and research outcome performance (P ≤ .01). The correlation strengths, in descending order, are research outcome performance (r = 0.547), research attitude performance (r = 0.529), and research behavior performance (r = 0.496). This indicates that enhancing doctoral competence can improve research attitude performance, research behavior performance, and research outcome performance.

To determine the specific impact of doctoral students’ competencies on research performance, a regression analysis was conducted, treating the three dimensions of research performance as depen- dent variables and the four dimensions of competency as independent variables. We used a stepwise regression approach, adding each competency dimension to the model one by one. The standar- dized regression coefficients were significant (P ≤ .05). To highlight the key findings, we present the standardized regression coefficients (β) and the F-statistic in Table 4.

The results reveal four key dimensions that appear in regression equations used to assess research attitude, behavior, and outcome performance, and play a crucial role in predicting research attitude performance (F = 22.086). Looking in more detail, through standardized regression coefficient (β) analysis, knowledge and skills were found to be the strongest predictors of study attitude perform- ance, followed by self-concept, motivation, and cognitive characteristics. In addition, this model also has significant predictive power for research behavior performance (F = 26.884) and research outcome performance (F = 39.051). Digging into the regression coefficients, we found that cognitive characteristics had the most significant impact on study behavior, followed by knowledge and skills, self-concept, and motivation. In terms of the performance of research outcomes, cognitive charac- teristics still dominate, followed by self-concept, knowledge and skills, and motivation. Overall, our study found that knowledge and skills significantly drive attitude performance, while cognitive characteristics play a dominant role in behavior and outcomes.

Table 3. Descriptive statistics and correlation analysis results of research variables.

Research Variables 1 2 3 4 5 6 7

1.Knowledge and skills (0.767) 2.Self-concept 0.318** (0.783) 3.Cognition-traits 0.356** 0.371** (0.769) 4.Motivation 0.258** 0.319** 0.265** (0.746) 5.Research attitude performance 0.384** 0.372** 0.324** 0.529** (0.772) 6.Research behavior performance 0.404** 0.351** 0.442** 0.496** 0.461** (0.785) 7.Research output performance 0.397** 0.412** 0.526** 0.547** 0.397** 0.372** (0.744) M 3.709 3.661 3.605 3.835 3.641 3.791 3.668 SD 0.974 0.976 1.020 0.901 0.899 0.841 1.069

Note: The correlation coefficients are presented in the lower left part of the matrix, while the square roots of the Average Variance Extracted (Sqrt(AVE)) are placed along the diagonal.**⍰＜0.01。

Table 4. Competency dimensions and β coefficients in the regression of research performance.

Knowledge and Skills Self-concept Cognition-traits Motivation F

Research Attitude Performance 0.263 0.222 0.132 0.212 22.086 Research Behavior Performance 0.247 0.146 0.284 0.138 26.884 Research Output Performance 0.191 0.192 0.376 0.179 39.051

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5. Discussion

Our study delves into the key competencies behind doctoral research performance and how these competencies predict research outcomes. Through the analysis of the data, we summarized the fol- lowing key findings:

The competence framework of doctoral students is undoubtedly an important predictor of research performance. Both correlation analysis and regression analysis show that this framework can effectively evaluate scientific research results. Secondly, we find that knowledge, skills and self-concept have different emphasis in shaping research attitudes, behaviors and outcomes: knowl- edge, skills and self-concept are the main factors contributing to positive research attitudes, while cognitive characteristics more significantly affect research behaviors and interact with self- concept to directly determine research outcomes. In China, the traditional emphasis on academic knowledge and technical skills is gradually shifting to comprehensive ability cultivation, which helps students improve their scientific research ability and overall output (Shen, Wang, and Bian 2021).

First, the positive link between knowledge, skills, self-concept, and research attitude emphasizes the need to foster.

both technical expertise and self-confidence in doctoral students. A student’s research attitude, shaped by their motivation and mindset, is critical to their research success. In China, where the focus has historically been on academic rigor and technical skills, encouraging the development of self-concept alongside academic knowledge could help students build stronger academic identi- ties and navigate the pressures of their studies more effectively (He and Zhu 2023). Therefore, train- ing programs should focus not only on technical skills but also on building students’ self-confidence. Based on the promotion of Chinese studies to an international perspective, institutions can adopt a mentorship model, conduct workshops to enhance self-esteem, and implement a student achieve- ment recognition system to promote balanced personal and professional development (Bueno 2023).

Second, the impact of cognitive traits on research behavior underscores the importance of qual- ities like critical thinking, persistence, and adaptability in shaping how doctoral students approach their research. Research behavior reflects not only consistency but also the strategies students use to tackle their research tasks. Given the significant role of cognitive traits, doctoral programs should prioritize the cultivation of these traits in order to enhance students’ sense of research engagement and output. In China, in the face of strict academic standards, it is particularly important to develop students’ resilience and cognitive flexibility to help them overcome various challenges in their studies. Studies have shown that by participating in problem solving workshops, actively par- ticipating in research simulations, and completing tasks in real situations, students are able to proac- tively improve their cognitive flexibility and perseverance to better cope with these challenges (Allen, Smith, and Tenenbaum 2019; Levecque, Anseel, De Beuckelaer, Van der Heyden, and Gisle 2017). Notably, doctoral students internationally face similar pressures from rigorous academic requirements, competitive environments, and dynamic research conditions . The identified teaching interventions demonstrate cross-cultural applicability. As such, this provides useful insights into improving student readiness through a structured experiential learning framework applicable to different academic cultures (Gross and Rutland 2017).

Research shows that cognitive characteristics and self-concept are interwoven and affect the research results together. For example, the intelligence level and self-confidence of doctoral stu- dents often play a decisive role in events such as paper presentations and academic conferences. Good cognitive traits enable them to meet a variety of complex intellectual challenges, while firm self-cognition injects more confidence into their academic expression. In China, in the face of increasingly stringent research quality requirements, this organic fusion of internal awareness and confidence is often the key to doctoral success (Li and Zhang 2021). International studies have also found that the balance between rigorous cognitive ability and sufficient self-confidence can

STUDIES IN HIGHER EDUCATION 9

help enhance students’ academic resilience and better cope with complex research problems (Cassidy 2015). Therefore, we suggest a comprehensive cultivation strategy: both to enhance the intelligence level of doctoral students and to enhance their self-confidence in all aspects by increas- ing the opportunities to present research results, obtain feedback and peer communication, so as to achieve synergistic development of both (Xing and Liu 2023).

This study provides a new perspective on doctoral education and validates the effectiveness of competency frameworks in linking individual skills with research outcomes. This framework can be used to gain a deeper understanding of the intrinsic drivers of doctoral academic success (Gillham and Schilling 2023). Unlike past studies that focused on individual abilities or over-empha- sized certain skills, we find that abilities are intertwined and collectively shape scientific performance. The framework provides useful inspiration for integrating traditionally separate knowledge, skills and self-concepts into a unified training program. By identifying key competencies that predict research performance, it not only broadens the discussion of doctoral education, but also lays the ground- work for revealing the core factors behind academic success, and for evidence-based training strat- egies that are applicable to different academic settings.

These findings have practical and far-reaching implications for teachers, supervisors and policy makers in the field of doctoral education. Globally, doctoral education faces a number of common challenges, such as the increasing complexity of interdisciplinary research and the increas- ing diversity of professional environments (Gallemí-Pérez and Chávez-Medina 2021). In China, doc- toral training has been focusing more on knowledge transfer and academic rigor for a long time. The introduction of competency framework just provides a new way of thinking, which makes the train- ing mode gradually shift from simple academic orientation to a more comprehensive competency orientation (Yang and Xia 2020). From the perspective of this study, this shift is significant, not only reflecting a more modern and humane education concept, but also providing more possibilities for students’ future development. In practice, the competency framework can help teachers better design and evaluate training programs, especially through competency assessment at the entrance stage, so that educational institutions can know in advance whether new students have the potential to cope with future challenges (Wan 2014).

In addition, more noteworthy is the personalized development program of doctoral students. Identifying the ability level of students as early as possible and making targeted training plans can effectively meet the growth needs of different students. Especially in the context of China’s current scientific research environment, which is relatively strict and has high academic expectations, personalized training methods can not only help reduce the risk of doctoral students dropping out, but also create a more dynamic and inclusive scientific research atmosphere. And as institutions around the world face the challenge of diverse student needs, academic rigor, and building inclusive and resilient research communities (Kaatz et al. 2019), personalized cultivation is showing broad applicability in a variety of academic environment. For example, training programs that promote cognitive flexibility can be achieved by encouraging interdisciplinary thinking and diverse approaches to research; For students with low self-confidence, timely feedback through structured tutor guidance can enhance their academic identity. Students who need to improve their cognitive skills can benefit substantially by participating in critical thinking and frustration building activities, such as peer discussions, frustration coping workshops and collaborative research projects (Shen, Wang, and Bian 2021).

At the policy level, the competency framework provides an effective tool for improving doctoral education, both for institutional management and for country-level reform. As China’s academic and research reforms deepen, ensuring that doctoral programs are aligned with competency-based stan- dards will help produce high-quality talent that meets domestic and international standards. Policy- makers can drive this shift through incentives and the development of competency-based accreditation standards (Verderame, Freedman, Kozlowski, and Mccormack 2018), thereby aligning doctoral education more closely with global and national research priorities and significantly increas- ing its societal impact. At the same time, other countries face similar challenges in balancing

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domestic talent needs with international competitiveness, and they can adapt this framework through their own policy mechanisms, whether through decentralized institutional initiatives (such as the systems in many European countries) or through industry-academic collaboration (common in North America) (Perkmann and Walsh 2007).

In addition, mentor training and career development also benefit from this research. In China, mentorship roles have traditionally been more hierarchical, and the transition to mentorship requires some cultural adaptation (Shen, Wang, and Bian 2021). We hope that this study will promote the development of doctoral education in the direction of more emphasis on ability cultivation and prompt people to re-examine the way of cultivating doctoral research ability. For international edu- cators, strategic mentor training programs – when culturally appropriate – can be effective in improving doctoral students’ research resilience, interdisciplinary collaboration, and creative problem solving. These competencies are in line with the European University Association’s Research Career Development Principles and the American Council of Graduate Schools’ emphasis on trans- ferable skills, indicating that components of the core framework have cross-cultural applicability. Focusing on the key competencies that directly affect the success of research not only enhances the academic performance of doctoral students, but also enhances their ability to respond to chal- lenges, collaborate in teams, and think creatively in order to better adapt to today’s interdisciplinary and dynamic research environment (Wu and Wu 2024).

Technology is equally important in supporting capacity-building. Digital tools, online platforms, and virtual mentor programs open up entirely new ways for doctoral students to develop key com- petencies (Jung 2018). Future research could further explore how these technological interventions can be used to improve students’ critical thinking, collaboration, and self-awareness.

In China, the adoption of a competency-based doctoral education model is actually a process of combining traditional academic values with cutting-edge international educational practices. Project 211, Project 985 and the ‘Double First-class’ programs have been promoting excellence and inno- vation in higher education, providing a solid foundation for integrating competency-based curricu- lum and assessment mechanisms into doctoral education. This shift also provides comparative value to international peers who are exploring similar reform trajectories. Combining these national reforms with the multidimensional competence framework proposed in this paper, Chinese doctoral programs are expected to make significant breakthroughs in research training. At the same time, putting cognitive flexibility and self-concept at the core of the reform will not only enable graduates to better cope with complex, high-end scientific research challenges (Verderame et al. 2018), but also help them excel in China’s rapidly evolving academic environment – a dual adaptive capacity increasingly recognized as critical in global doctoral education frameworks (European University Association 2021).

6. Conclusion

In recent years, the importance of scientific research ability has been widely concerned, and many studies have emphasized its far-reaching impact on the development of doctoral students. Scientific research ability not only directly determines whether doctoral students can obtain high quality aca- demic results, but also profoundly affects their contribution to the subject field. However, in the past, China’s doctoral education often paid more attention to the imparts of knowledge and academic rigor, and did not pay enough attention to the cultivation of doctoral students’ comprehensive ability. Nowadays, the emergence of competence model provides a new way to solve this problem, and also builds an effective platform for predicting the future scientific research potential of doctoral students. This competency-based approach also aims to provide useful insights into the international doctoral education system that enhances students’ research capabilities and academic contributions.

In fact, the key skills required for doctoral research tend to focus on critical thinking, problem solving, and solid professional knowledge. These abilities are precisely the core conditions for

STUDIES IN HIGHER EDUCATION 11

doctoral students to carry out high-level research and achieve innovative breakthroughs. Therefore, the in-depth analysis of the influence of ability traits on scientific research results is helpful to find new ways to improve the quality of doctoral education, such as optimizing the curriculum design, improving the teaching model and carrying out targeted and personalized training programs.

In addition, this study further explores the association between doctoral student competence and research performance, and reveals how to effectively predict and enhance research output through a competency-based framework. The results of the study provide practical suggestions for the reform of the doctoral education system, such as improving the ability assessment system and providing more timely and personalized support measures. Obviously, the introduction of a capacity-oriented training model can not only help doctoral students better cope with the increasingly complex scien- tific research challenges, but also build a more innovative academic team (Gu et al. 2011).

Overall, understanding the deep relationship between doctoral ability and research performance is strategically important for redefining doctoral education. The analysis combined with reliable empirical data can make up for the shortcomings in the current research, which not only helps to improve the academic ability of doctoral students in an all-round way, but also further stimulates the vitality of academic innovation. Through this series of studies, doctoral programs can refine cur- riculum offerings, optimize mentor strategies, and introduce targeted interventions to produce doc- toral talents capable of making high-quality scientific contributions.

Disclosure statement No potential conflict of interest was reported by the author(s).

ORCID Jin Wang http://orcid.org/0009-0003-0966-6618

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