Configurational paths for the enhancement of college students’ scientific research and innovation competencies
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Cite this article:
Yang, X., Zhang, J., Guo, P., Luo, J., Xu, H., Li, J., Song, X., & Liao, Q.
(2025). Configurational paths for the enhancement of college students’ scientific research and innovation competencies.
Social Behavior and Personality: An international journal,
53(11),
e15537.
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Enhancing the research and innovation competencies of university students has become a central issue in higher education reform. This study investigated how internal factors (learning autonomy, teamwork proficiency, communication skills, knowledge accretion, and psychological safety) and external factors (institutional guidance and support, research policy, and research environment and atmosphere) influenced the improvement of students’ research and innovation competencies. Using responses from a sample of 104 students, we conducted a fuzzy-set qualitative comparative analysis. The results showed that the research environment and atmosphere was the most significant influencing factor, followed by institutional guidance and support. We identified three distinct configurations of influencing factors: external factor dominated, internal factor dominated, and mixed. This study contributes to understanding of the potential combinations of conditions that can enhance students’ research and innovation competencies.
Article Highlights
- This study applied fuzzy-set qualitative comparative analysis to examine college students’ scientific research and innovation competencies.
- Three types of factor configurations affected research and innovation competencies: external factor dominated, internal factor dominated, and mixed.
- The research environment and atmosphere was the most important factor affecting research and innovation competencies.
In the era of accelerated globalization and the burgeoning knowledge economy, scientific research and innovation competencies have become critical benchmarks for evaluating university students’ academic proficiency and competitiveness (van Laar et al., 2017). Given the surging demand for innovative talents at the national level in China, augmenting the research and innovation competencies of university students has ascended to the forefront of higher education reform initiatives (Tao et al., 2020). Although numerous studies have delved into individual factors influencing research and innovation competencies, including personal traits, resource availability, and faculty mentorship, these factors often interact in complex ways (Y. W. Li & Liang, 2025; Tao & Xie, 2022). Analyzing them in isolation may not comprehensively uncover the underlying complexity. Therefore, adopting a multidimensional and systematic approach to explore avenues for enhancing university students’ research and innovation competencies has substantial theoretical and practical significance.
The assessment of university students’ scientific research and innovation competencies necessitates a multidimensional perspective. By taking into account aspects such as knowledge application, research outputs, participation levels, cognitive skills, and practical expertise, a more scientifically rigorous evaluation of students’ overall competencies can be achieved. Existing research has explored the impact of both internal and external factors on students’ research and innovation competencies (Barra et al., 2024; Huang et al., 2024; McClintock & Fainstad, 2022). However, the majority of these studies concentrated on single factors, rather than fully analyzing the interactions among multiple antecedent elements and their combined effects. Therefore, to build upon these previous findings, we integrated internal and external factors from a configurational vantage point, drawing on extant research and relevant theories to systematically identify the key determinants of university students’ research and innovation competencies.
Internal Factors
In the contemporary knowledge-based economy, university students’ scientific research and innovation competencies have been widely recognized as a crucial driver of social progress and technological advancement (Huang et al., 2024; McClintock & Fainstad, 2022; Sakellari et al., 2024). Many factors influence these competencies, with the internal factors of learning autonomy, teamwork proficiency, communication skills, knowledge accretion, psychological safety, institutional guidance and support, research policy support, and research environment and atmosphere playing a pivotal role (Berkat et al., 2025).
Learning Autonomy
Learning autonomy among university students is a fundamental internal factor underpinning their research and innovation competencies (Klemenčič, 2017). Little (2022) defined autonomy as the ability of students to engage in independent critical thinking, make informed decisions, and execute them effectively. In the current educational milieu, universities face challenges such as traditional teaching paradigms, insufficient student motivation, inefficient online–offline teaching configurations, and antiquated evaluation systems, all of which significantly impede the enhancement of students’ autonomous learning abilities (X. Li & Eng, 2024). Autonomous learning nurtures adaptability and problem-solving acumen, bolsters self-confidence, and serves as the bedrock for lifelong learning (Zhu, 2023).
Teamwork Proficiency
Teamwork proficiency refers to the ability to assume the role of an individual contributor, team member, or team leader within a collaborative group (Driskell et al., 2018). It ensures that each team member can channel their creativity toward collective goals (X. P. Zhao et al., 2008). University students with strong teamwork skills are more adept at sharing and integrating ideas and knowledge with their peers, thereby fostering a more innovative environment (Martín-Hernández et al., 2022). This collective intelligence enables teams to better address complex research challenges and drive innovation by capitalizing on the strengths of each member. Effective teamwork allows students to maximize their individual strengths when undertaking research tasks, leading to collective innovation (Pyo et al., 2021).
Communication Skills
Robust communication skills promote innovation and its practical application, exerting a significant influence on the implementation of innovation projects (Dias-Oliveira et al., 2024). In an increasingly competitive job market, university students must not only possess forward-thinking ideas but also the confidence and ability to articulate and showcase their thoughts (Zhang, 2021). Moreover, a substantial portion of scientific research culminates in reports or papers. Students’ ability to compose clear and well-structured research papers, present their ideas persuasively, and collaborate effectively with mentors and team members often stems from their excellent communication skills during the innovation process (Y. J. Wang et al., 2024).
Knowledge Accretion
Francis Bacon, the renowned British philosopher, famously posited that knowledge is power. In the current era of information overload, this concept has taken on new dimensions. Knowledge not only entails the accumulation of information but also the ability to apply it effectively to solve real-world problems. Innovation through knowledge hinges on a solid foundation of learning and accumulation (X. Li, 2023). When confronted with research challenges, a robust knowledge base enables students to apply their learning flexibly to identify innovative solutions.
Psychological Safety
Psychological safety refers to the belief that one can take interpersonal risks, expose vulnerabilities, and express opinions without the fear of embarrassment, reprimand, or neglect (Andersson et al., 2020). The presence of psychological safety is closely associated with enhanced team learning, innovation competencies, leaders’ inclusiveness, and a sense of belonging among team members (McClintock & Fainstad, 2022). Researchers encounter numerous uncertainties and interpersonal risks during the innovation process, which can significantly circumscribe their self-awareness and emotional expression (Domínguez-Escrig et al., 2022). Therefore, cultivating an organizational climate that encourages dissent, discussion, and criticism is essential for fostering individual development, learning, and innovation (Andersson et al., 2020). When psychological safety is ensured, innovators are more likely to engage in innovative behaviors (Vladić et al., 2021).
On the basis of the above analysis, it is necessary to clarify the role that task significance plays in achieving scientific research and innovation competencies. Thus, we proposed the following hypotheses:
Hypothesis 1: Learning autonomy will lead to university students’ scientific research and innovation competencies.
Hypothesis 2: Teamwork proficiency will lead to university students’ scientific research and innovation competencies.
Hypothesis 3: Communication skills will lead to university students’ scientific research and innovation competencies.
Hypothesis 4: Knowledge accretion will lead to university students’ scientific research and innovation competencies.
Hypothesis 5: Psychological safety will lead to university students’ scientific research and innovation competencies.
External Factors
A supportive environment is the foundation of success. Whether in teamwork or personal development, suitable resources and abundant external conditions serve as reliable means for achieving goals and creating value. An effective research environment nurtures students’ active exploration and collaboration, thereby enhancing their innovation competencies (Yin & Xin, 2024). Within a robust external scientific research innovation ecosystem, institutional guidance, policy support, and the research environment all play indispensable roles (Eyiusta & Esen, 2025).
Institutional Guidance and Support
A comprehensive mechanism for supporting and enhancing mentorship is pivotal in cultivating university students’ research and innovation competencies. As the primary institutions in this cultivation process, universities’ outstanding faculty members are not only instrumental in propelling students’ research innovations but also embody the ethos of higher education (Mlika et al., 2022). Tao and Xie (2022) underscored the significant impact of mentors’ guidance on students’ research and innovation competencies: Mentors can enhance students’ autonomous learning, kindle their research interests and potential, and thus contribute positively to their innovation competencies. Therefore, strengthening institutional team building and improving mentorship competencies may serve as external buttresses for fostering students’ research and innovation abilities.
Research Policy Support
Universities provide a solid material foundation for students’ research endeavors through dedicated research funds, laboratory equipment, and well-maintained facilities. In addition, research policies—such as incentive mechanisms and performance evaluations—have been shown to stimulate students’ enthusiasm for scientific inquiry, thereby enhancing their innovation competencies (Berkat et al., 2025). Prior research has highlighted that institutional frameworks facilitating undergraduate participation in research projects can significantly broaden research opportunities and improve student engagement in academic inquiry (Sun, 2023). These policy supports may act as crucial external conditions that foster students’ development in research and innovation.
Research Environment and Atmosphere
A vibrant research environment may further stoke individuals’ enthusiasm for scientific inquiry. In addition, research laboratories play an irreplaceable role in nurturing students’ competencies and innovation potential. By participating in research projects, utilizing advanced research tools, and engaging with rich research materials, students can continuously elevate their research literacy and innovation abilities (Wu et al., 2023). A well-designed university research training system provides an effective means of fostering students’ research and innovation competencies (H. Wang & Chen, 2024). By investing more in experimental equipment and optimizing resource allocation to provide students with well-equipped research centers, the process of reforming research practices can be expedited. This new nurturing environment significantly bolsters students’ research and innovation competencies. Thus, we proposed the following hypotheses:
Hypothesis 6: The presence of institutional guidance and support will lead to university students’ scientific research and innovation competencies.
Hypothesis 7: The presence of research policy support will lead to university students’ scientific research and innovation competencies.
Hypothesis 8: The presence of research environment and atmosphere will lead to university students’ scientific research and innovation competencies.
Model Construction
Building upon the theoretical edifices of the scholars mentioned above, this study delved into the interactive effects of internal and external factors on university students’ scientific research and innovation competencies. We constructed a model driven by eight factors: autonomy, teamwork, communication skills, knowledge accumulation, psychological safety, institutional guidance and support, policy support, and environmental atmosphere. Our objective was to further explore the configurational effects of these internal and external factors on the enhancement of students’ scientific research and innovation competencies. The theoretical model is illustrated in Figure 1.
Figure 1. Theoretical Model Driving Scientific Research and Innovation Competencies
Method
Participants and Procedure
Participants were 48 men (46.15%) and 56 women (53.85%). All were college students, so we did not collect age data. There were seven (6.7%) freshmen, 36 (34.62%) sophomores, 47 (45.19%) juniors, and 14 (13.46%) seniors. The regional distribution was as follows: 60 (57.69%) students from Hena, six (5.77%) students from Gansu, nine (8.65%) students from Harbin, 17 (16.35%) students from Beijing, and 12 (11.54%) students from Tianjin.
In a presurvey the Cronbach’s alpha coefficients for the eight variables exceeded .80, indicating strong correlations among the items, good reliability, and satisfactory internal consistency. We then scrutinized the validity of the questionnaire. The Kaiser–Meyer–Olkin measure of sampling adequacy was greater than .90, and Bartlett’s test of sphericity produced a significant chi-square value of 4115.01 (p < .001). Additionally, the factor loadings for all measurement items were above .60, with most items surpassing .70, corroborating the validity of the measurement scale.
We conducted data collection primarily through field research, randomly distributing 117 questionnaires on university campuses. All participants provided informed consent. After excluding 13 forms due to high consistency in answers, we retained 104 valid questionnaires for an effective response rate of 88.89%.
Measures
Following the recommendations of Lyu et al. (2024), one author translated all scales into Chinese and then back-translated them into English, and the other authors checked the accuracy of the translation. The survey included 34 items divided across nine dimensions. All items were graded on a 5-point Likert scale ranging from 1 (totally inconsistent) to 5 (totally consistent).
Learning Autonomy
To measure learning autonomy we revised five items from Little (2022) and Zhu (2023). A sample item is “I am capable of actively participating in scientific research and innovation activities.” Cronbach’s alpha was .95 in this study, showing the scale was reliable.
Teamwork Proficiency
To measure teamwork proficiency we used the four-item scale developed by Martín-Hernández et al. (2022). A sample item is “I am good at collaborating with others in scientific research projects.” Cronbach’s alpha was .95 in this study, indicating the scale was reliable.
Communication Skills
To measure communication skills, we adjusted and merged some items from Zhang (2021) to create a four-item scale. A sample item is “I am able to clearly express my own ideas to others when solving scientific research problems.” Cronbach’s alpha was .95 in this study, indicating the scale was reliable.
Knowledge Accretion
We used four items from the five-item scale developed by X. Li (2023), omitting one item with a factor loading under .50. A sample item is “I am able to flexibly apply the accumulated knowledge to solve scientific research problems.” Cronbach’s alpha was .95 in this study, indicating the scale was reliable.
Psychological Safety
After referring to Andersson et al. (2020) and Vladić et al. (2021), we made adjustments and merged some items to create three items to measure psychological safety. A sample item is “When I propose innovative ideas, I don’t worry about being questioned and criticized by others.” Cronbach’s alpha was .91 in this study, indicating the scale was reliable.
Institutional Guidance and Support
We referred to Tao and Xie (2022), adjusted and merged some of their items, and created four items to measure institutional guidance and support. A sample item is “The school has a sound institutional system that provides support for scientific research and innovation cultivation activities.” Cronbach’s alpha was .94 in this study, indicating the scale was reliable.
Research Policy Support
After referring to Sun (2023), we compiled our own four-item scale measuring research policy support. A sample item is “The school has relevant research policies that provide a material foundation for us to carry out scientific research and innovation activities.” Cronbach’s alpha was .95 in this study, indicating the scale was reliable.
Research Environment and Atmosphere
To measure research environment and atmosphere, we used five items developed by Wu et al. (2023). A sample item is “The school has relevant scientific research and innovation clubs and organizations.” Cronbach’s alpha was .94 in this study, indicating the scale was reliable.
Scientific Research and Innovation Competencies
Scientific research and innovation competencies was an overall evaluation variable, so we created one item for assessment purposes: “What level do you think your current scientific research and innovation ability is at overall?”
Data Analysis
Fuzzy-set qualitative comparative analysis diverges from traditional empirical statistical methods in its emphasis on analyzing complex causal relationships. The improvement of university students’ research and innovation abilities is a complex and systematic process, resulting from the concurrent influence of multiple antecedent factors. The depth and breadth of the impact of these factors on research and innovation abilities varies. When intricate interactions occur between factors, fuzzy-set qualitative comparative analysis emerges as an effective tool for identifying multiple pathway combinations and their corresponding outcomes.
Prior to conducting the formal analysis, it was imperative to calibrate the data to determine whether each case belonged to a particular set and its degree of membership within that set. Considering factors such as the relatively large mean of the data in this study, the calibration thresholds according to Ong and Johnson (2023) were set as follows: fully belonging (M + 1 SD), fully not belonging (M – 1 SD), and the mean itself (cross-over point). We used the calibrating (M, n1, n2, n3) function in Fuzzy-Set Qualitative Comparative Analysis 3.0 software to perform the calculations and calibration for all variables (see Table 1).
Table 1. Data Calibration Points
Results
Necessity Test
We performed a necessity test of each condition to ascertain whether the conditions were necessary for the outcome. This served as a prerequisite for conducting the configuration analysis. The results of the consistency and coverage tests of the single-condition variables are shown in Table 2.
Table 2. Necessity Analysis of Single-Condition Variables
Note. SRICs = scientific research and innovation competencies.
According to the necessity analysis results, the condition variable for which the consistency was highest (> .90) was research environment and atmosphere, indicating that a positive research environment was a necessary condition for achieving high scientific research and innovation competencies. Conversely, for nonhigh scientific research and innovation competencies, the condition variable for nonresearch environment and atmosphere had a consistency greater than .90, suggesting that the absence of a supportive research environment hindered innovation capability. The consistency of the other condition variables did not exceed .90, implying that they were not necessary conditions for the outcome variable.
Configuration Analysis
After conducting the necessity analysis for individual condition variables, we constructed a truth table. Following C. Zhao et al. (2024), this study integrated both intermediate and parsimonious solutions to summarize the condition combinations influencing students’ scientific research and innovation competencies (see Table 3). From the configuration results, there were seven paths driving high research innovation competencies (S1, S2, S3, S4, S5, S6, S7). The consistency values for these configuration paths were all greater than .80, indicating that they were sufficient conditions for high scientific research and innovation competencies. The overall consistency was .97, signifying that approximately 97% of cases were consistent with these configurations.
Table 3. Configuration Analysis for High Scientific Research and Innovation Capability
Note. ● = a core condition; ○ = a peripheral condition; ✖ = the absence of a core condition; × = the absence of a peripheral condition; — = the condition has no impact on the outcome.
We used the following notations in forming the configurations: “~” (tilde) indicates the absence or low level of a condition (e.g., “~A” means “not A” or “low A”). “·” () denotes a logical AND between conditions, requiring both to be present (e.g., “A·B” stands for “A and B”). “/” (slash) denotes a logical OR, indicating that either condition suffices (e.g., “A/B” means “A or B”). We used the following acronyms in the configurations: LA = learning autonomy; TP = teamwork proficiency; CS = communication skills; KA = knowledge accretion; PS = psychological safety; IGS = institutional guidance and support; RPS = research policy support; REA = research environment and atmosphere.
Configurations S1 and S2: ~KA·REA·PS·IGS·RPS·TP/CS. In these two configuration paths, high REA and low KA were core conditions, while the three external factors served as auxiliary conditions. These configurations emphasized the significant impact of external factors on the enhancement of university students’ scientific research and innovation competencies, with TP and CS acting as substitutes.
Configurations S3 and S4: REA·IGS·~KA·~PS·TP/RPS. In these paths, both REA and IGS were core conditions, while TP and RPS were substitutive auxiliary conditions. KA and PS did not serve as core conditions. This suggests that under the premise of a high REA and strong IGS, and with low KA and PS, the presence of either high teamwork ability or high research policy support promoted the enhancement of students’ scientific research and innovation competencies.
Configuration S5: REA·LA·TP·CS·KA·PS. In this path, REA was a core condition, with all five internal factors serving as auxiliary conditions. This indicated that under the condition of a high REA, university students needed to possess higher and more comprehensive internal factors to achieve improvement in scientific research and innovation competencies.
Configurations S6 and S7: REA·LA·TP·CS·RPS·PS/IGS. In these paths, REA was a core condition; LA, TP, and CS were auxiliary conditions; and PS and IGS were substitutive auxiliary conditions. This suggested that within a high REA, if students had high LA, TP, and CS, while also possessing either high PS or high IGS, their scientific research and innovation competencies were effectively improved.
On the basis of the above analysis, enhancement of university students’ scientific research and innovation competencies was achieved through the organic combination of internal and external factors. Specifically, the improvement in competencies could be categorized into three configuration paths: internal factor dominated (S5), mixed (S3 and S6), and external factor dominated (S1, S2, S4, and S7). Additionally, REA appeared only as a core condition; LA, TP, CS, and RPS appeared only as auxiliary conditions; and KA, PS, and IGS appeared both as core and auxiliary conditions. This suggests that REA was the primary factor for enhancing university students’ scientific research and innovation competencies.
Robustness Analysis
We conducted a robustness analysis to assess the stability and reliability of the research results. This can be achieved through methods such as adjusting calibration standards, altering the consistency threshold, or removing cases. In accordance with You and Gao (2023), we conducted our robustness test by raising the consistency threshold from .80 to .85. After comparison, the overall consistency before and after the robustness test remained at .97, and the number and content of the configuration paths remained unchanged, confirming the stability of the research results.
Discussion
Theoretical Implications
This study identified seven configuration paths (S1, S2, S3, S4, S5, S6, S7) that contribute to high scientific research and innovation competencies in college students. Among the examined factors, research environment and atmosphere was present in every configuration path and played a crucial role. Beymer et al. (2023) indicated that an inclusive and open research environment significantly enhances students’ sense of innovative efficacy. In different research contexts, students need to possess a variety of research innovation skills. External factors, such as institutional guidance, research policy support, and a conducive research environment, are essential for the effective implementation of research activities and the achievement of high-quality research outcomes. We found that high scientific research and innovation competencies among university students were the result of the synergistic effect of multiple factors. Students must improve their research abilities across various dimensions; universities need to integrate diverse resources; and close cooperation is required between universities, the government, and society to provide students with a favorable external research environment, thereby ensuring the comprehensive enhancement of their scientific research and innovation competencies.
Practical Implications
First, to stimulate students’ internal motivation we recommend cultivating their autonomy and independent research abilities. In the process of research exploration, university students need to possess strong autonomy and independent thinking abilities. Self-determination theory posits that autonomy is a core element in stimulating intrinsic motivation (Ryan & Deci, 2020). Thus, universities should cultivate students’ psychological resilience through systematic mental health education and professional psychological counseling, thereby enhancing students’ confidence and courage when facing research challenges. This would increase their autonomy in research, endowing them with more decision-making power and supporting them in making independent decisions based on their research needs (Adali & Li, 2025). Simultaneously, educators should advocate critical thinking and encourage students to independently explore problem-solving approaches, thus sowing the seeds of research autonomy. Moreover, the establishment of research scholarships and the organization of research competitions may effectively stimulate students’ enthusiasm and motivation for research. These measures could enable students to take a more proactive role in research activities, significantly enhancing their research innovation competencies.
Second, institutions should strengthen external support by encouraging policy-driven support for students’ research development. External support and policy-driven measures are of crucial importance for students’ research activities, ensuring the smooth progress of students’ research and the full realization of their potential. Tao et al. (2020) contended that universities should seize the opportunity presented by the Chinese Ministry of Education’s commitment to deepening reform of undergraduate education to comprehensively improve students’ research competencies during the talent-cultivation process. A comprehensive institutional guidance and support system should be established, with dedicated research management departments in place. Under the guidance of educational research institutions, the various organizational roles within universities should be utilized to enhance the scientific nature of curriculum design. Additionally, a robust research management system should be established, with outstanding mentors providing students with the necessary care and support. Optimizing research policies is also essential, such as creating special funds, providing advanced equipment, and establishing reward mechanisms to offer students practical opportunities and collaborative platforms. This would enable students to make better use of available resources and improve the quality and effectiveness of their research.
Third, institutions should create an innovative atmosphere by building a positive research environment and cultivating an academic culture. A high-quality research environment is a key medium for cultivating students’ innovation competencies. Currently, universities face challenges in fostering a research atmosphere, such as a weak error-tolerance mechanism and insufficient undergraduate participation in research, which restricts students’ perception and practical experience in research. Therefore, universities should construct a multidimensional support system. At the hardware level, universities should increase investment in laboratory and research platform construction, supply advanced instruments, and provide intelligent learning spaces to ensure material support for innovative practices. At the academic ecosystem level, universities should establish regular communication mechanisms through interdisciplinary forums, cutting-edge lectures, and joint research projects with enterprises, expanding students’ academic horizons and promoting intellectual exchanges. Professors should lead students in forming research teams, strengthening academic exchanges, and ensuring rights protection (Kim & Jang, 2021). In the research teams, a mentor–graduate student–undergraduate linkage model should be implemented to cultivate students’ problem-solving skills in real-world research contexts.
Limitations and Future Research
This study has the following shortcomings: First, the research participants were drawn from across China, but we did not study regional differences. Second, we did not classify and analyze the differences among college students from different disciplines. In future research, we plan to carry out more targeted discussions by incorporating dimensions such as regional differences and disciplinary backgrounds. At the same time, we hope to integrate methods such as structural equation modeling for analysis to gain a deeper understanding of the mechanisms for enhancing college students’ scientific research and innovation competencies.
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Figure 1. Theoretical Model Driving Scientific Research and Innovation Competencies
Table 1. Data Calibration Points
Table 2. Necessity Analysis of Single-Condition Variables
Note. SRICs = scientific research and innovation competencies.
Table 3. Configuration Analysis for High Scientific Research and Innovation Capability
Note. ● = a core condition; ○ = a peripheral condition; ✖ = the absence of a core condition; × = the absence of a peripheral condition; — = the condition has no impact on the outcome.
This project was supported by the National Social Science Fund of China (24CGL012), the Soft Science Research Program of Henan Province (252400410317; 242400410602), the Henan Universities Philosophy and Social Sciences Innovative Team Support Program (2024-CXTD-13), the Henan Province College Students’ Innovation and Entrepreneurship Training Program (202410464079), the Planning Project of Philosophy and Social Science of Henan Province (2024ZZX013), the Research and Practice Project on Higher Education Teaching Reform in Henan Province (2024SJGLX0316), the College Students’ Innovation and Entrepreneurship Training Program of Henan University of Science and Technology (2024314), and the Henan Province Science and Technology Research Project (242102210122; 232102210153).
The data that support the findings of this study are available on request from the corresponding author.
Xiaona Yang, Business School, Henan University of Science and Technology, 263 Kaiyuan Avenue, Luolong District, Luoyang, Henan Province, People’s Republic of China. Email: [email protected]
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