Good and bad: how university innovation support influences college students' innovative behavior.

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Learn more: PMC Disclaimer | PMC Copyright Notice BMC Psychol . 2026 Apr 21;14:810. doi: 10.1186/s40359-026-04588-1 Good and bad: how university innovation support influences college students’ innovative behavior Liren Zhou 1 , Qiu Yan 2 , Chang Ni 1 , Ya You 1 , Jun Peng 3, ✉ Author information Article notes Copyright and License information 1 Yangzhou Polytechnic Institute, School of E-commerce & School of Innovation and Entrepreneurship, Yangzhou, China 2 Basic Courses Department, Zhenjiang College, Zhenjiang, China 3 College of Teacher Education, Ningbo University, No. 818, Fenghua Road, Jiangbei District, Ningbo, 315211 China ✉ Corresponding author. Received 2026 Mar 18; Accepted 2026 Apr 15; Collection date 2026. © The Author(s) 2026 Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/ . PMC Copyright notice PMCID: PMC13227898  PMID: 42015215 Abstract In modern higher education, university innovation support is widely regarded as a key strategy for promoting students’ innovative behavior. However, existing studies mostly adhere to a “more is better” perspective, ignoring its potential negative effects and individual boundary conditions, and failing to fully explore the dual psychological mechanisms and individual differences underlying its role. Based on data from 534 questionnaires, this study uses Mplus 8.0 to explore the underlying mechanism between university innovation support and college students’ innovative behavior. Research findings: (1) University innovation support is positively related to college students’ innovative behavior; (2) Creative self-efficacy mediates the relationship between university innovation support and college students’ innovative behavior; (3) Psychological entitlement mediates the relationship between university innovation support and college students’ innovative behavior; (4) Creative self-efficacy and psychological entitlement serve as chain mediators in the relationship between university innovation support and college students’ innovative behavior; (5) Learning goal orientation moderates the relationship between university innovation support and college students’ innovative behavior, such that the positive relationship is stronger when learning goal orientation is high than when it is low. The conclusions of this study can provide important practical guidance for universities to optimize innovation support systems by balancing the intensity of support to avoid the cultivation of students’ psychological entitlement, balancing the rationality and pertinence of support, and, at the same time, paying attention to individual differences in students’ motivation to accurately stimulate college students’ innovative behavior. Keywords: Innovation Support, Creative Self-Efficacy, Psychological Entitlement, Learning Goal Orientation, Innovative Behavior Introduction In modern higher education, university innovation support is widely regarded as a key strategy for promoting students’ innovative behavior [ 1 , 2 ]. Existing literature mainly follows a “more is better” approach, arguing that university support (both structural and psychological) creates necessary conditions for students’ innovative engagement. It also highlights a psychological factor—confidence — as the main mediating mechanism: abundant institutional resources boost students’ confidence in their creative abilities, thereby promoting their innovative behavior [ 1 – 3 ]. However, this optimistic view misses important research gaps, leaving critical questions unanswered. First, current studies overlook the potential negative effect of university innovation support. Drawing on the “too-much-of-a-good-thing” (TMGT) perspective and framework [ 4 – 6 ], excessive external support might be associated with negative psychological states rather than merely fostering positive outcomes, yet few studies have explored this non-linear, non-unidirectional relationship in higher-education innovation contexts [ 7 , 8 ]. A key gap exists in the absence of a dual-pathway perspective: while appropriate university support can boost students’ confidence in their ability to innovate (positive path), excessive support may also foster an unwarranted sense of entitlement, which undermines their intrinsic motivation to engage in innovative activities (negative path) [ 5 , 9 ]. Current research does not compare these two opposing psychological mechanisms, resulting in an incomplete understanding of how university innovation support influences students’ innovative efforts and performance. Second, current literature treats students as a uniform group, neglecting individual boundary conditions that moderate the impact of university innovation support [ 10 , 11 ]. It remains unclear why some students experience the “resource curse”—where excessive support decreases their autonomy and initiative—while others effectively leverage the provided support to enhance their innovative behavior. Most existing models assume a “one-size-fits-all” effect of resources, ignoring individual differences in students’ motivational traits [ 1 , 12 ]. Specifically, regarding students’ intrinsic drive towards deep mastery of knowledge and continuous skill development rather than merely seeking external validation or easy resources [ 13 , 14 ], there is a lack of thorough exploration of how university innovation support interacts with students’ innovative behavior. Against this backdrop, this study aims to address two key questions: (1) Do two opposing psychological mechanisms—one enhancing confidence in innovation ability (positive) and the other promoting an unwarranted sense of entitlement (negative)—serve as competitive mediators in this relationship? (2) How does students’ intrinsic motivation aimed at mastery and skill influence the internal process between university innovation support and students’ innovative behavior? Theoretical framework and hypothesis development Theoretical framework This study draws on Social Cognitive Theory (SCT) and develops an “environmental support-individual cognition-behavioral output” model to explain the relationships among the five core variables. At the heart of SCT is the principle of triadic reciprocal determinism, the dynamic interaction among personal factors, environment, and behavior, which suggests two-way interactions among these factors [ 15 – 17 ]. This principle offers a strong theoretical basis for understanding how individuals interpret and respond to external support differently, leading to varying innovative behaviors. Within this model, university innovation support functions as the main environmental input and opportunity structure [ 18 , 19 ]. As a complex environmental force shaping student behavior [ 20 ], it includes tangible resources (funding, tools), informational support (training, mentorship), and social cues (recognition, normative expectations), collectively creating the conditions and perceived opportunities for innovation [ 1 , 19 , 21 ]. Two contrasting cognitive and affective mechanisms serve as key interpreters of this environmental input: creative self-efficacy and psychological entitlement. Creative self-efficacy, defined as the belief in one’s ability to generate creative ideas, mediates the relationship between supportive environments and increased innovative action, converting empowerment, organizational culture, and leadership into creative performance [ 22 , 23 ]. It interprets external support as validation and a resource to overcome challenges, fueling effort and persistence [ 22 – 24 ]. Conversely, psychological entitlement—a sense of special deservingness—acts as a distorted filter, perceiving support as something owed rather than a valuable resource [ 25 ]. This perception diminishes motivation, fosters complacency, and is associated with anger, perceived injustice, and decreases in proactive behavior when expectations are unmet [ 26 , 27 ]. It can also influence how individuals use creativity for recognition independently of self-efficacy [ 28 ], creating two opposing pathways that turn support into either constructive or inhibitive forces [ 26 , 29 ]. Learning goal orientation, a stable personal trait, functions as a higher-level cognitive schema and self-regulatory guide [ 30 , 31 ]. It influences how information is processed and preps individuals for self-regulated learning (such as planning and effort regulation), and is adaptive for cognition, motivation, and performance [ 31 ]. People with a strong learning orientation adopt a “growth mode,” seeing innovation support as an opportunity to develop skills and boost their creative beliefs [ 32 ]. Ultimately, innovative behavior is the observable result of this three-part process [ 33 ]. It consists of concrete actions aimed at generating new, useful ideas, representing the final product of environmental support filtered through a learning-goal orientation and the dominant cognitive-motivational state [ 33 , 34 ]. SCT therefore combines environment, personal factors, and behavior into a unified explanatory system [ 35 ]. Innovation support and innovative behavior An individual’s innovative behavior, defined as the proactive generation, promotion, and execution of novel ideas, is inherently resource-intensive and fraught with uncertainty [ 36 , 37 ]. College students often face significant obstacles when turning abstract ideas into tangible prototypes. These barriers mainly include a lack of financial resources, limited professional networks, and insufficient specialized knowledge [ 38 ]. A strong university support system directly addresses and eases these real-world challenges [ 18 ]. By providing accessible workspaces, dedicated academic guidance, and structured extracurricular innovation programs, universities greatly reduce the barriers to experimentation [ 18 , 39 ]. In such an environment with ample resources, the perceived risks and potential costs of project failure are greatly diminished, and fear of failure can be more effectively managed [ 18 , 40 ]. This allows students greater freedom and independence to engage in exploratory, trial-and-error activities without the overwhelming fear of serious academic or financial consequences [ 18 , 41 ]. Institutional support effectively reduces the inherent risks of exploring new intellectual territory and helps students transform resource constraints into opportunities for innovative action [ 39 , 41 ]. Through continuous structural support, universities turn passive learners into proactive problem-solvers who consistently seek creative solutions [ 18 , 42 ]. Therefore, a solid resource base provided by the university serves as a direct catalyst for development, resulting in increased proactive experimentation and tangible innovation among students [ 36 , 38 ]. Based on the above analysis, this study posits the following hypothesis: H1: University innovation support is positively related to college students' innovative behavior. The mediating role of creative self-efficacy Creative self-efficacy, defined as an individual’s confidence in their ability to organize and execute actions for creative tasks, is greatly influenced by external environmental cues, verbal encouragement, and mastery experiences [ 43 ]. Innovation processes in higher education are inherently marked by high uncertainty and complexity, along with significant risks of failure, which are often associated with intense psychological anxiety and self-doubt among students engaged in these uncharted academic pursuits [ 44 , 45 ]. In the university setting, Innovation Support acts as a vital environmental facilitator, directly nurturing these key sources of creative self-efficacy [ 43 , 46 ]. When universities proactively provide tangible resources—such as advanced incubation labs, initial seed funding, and unrestricted access to professional raw materials—they eliminate physical and financial obstacles that hinder student-led innovative efforts [ 46 ]. This support allows students the freedom to experiment, enabling them to gain early, low-risk mastery experiences without fearing severe academic or financial penalties [ 47 ]. Intangible Innovation Support, including expert mentorship, entrepreneurial workshops, and a campus culture tolerant of failure, serves as an impactful form of verbal and social persuasion [ 48 ]. Faculty mentors’ endorsement of new ideas signals institutional trust and indicates students’ potential cognitive skills for innovation [ 49 ]. Seeing peers succeed in developing prototypes using university resources offers vicarious learning, further strengthening creative self-efficacy [ 45 , 50 ]. This ecosystem lessens cognitive load, emotional exhaustion, and stress associated with high-stakes creative tasks, diminishing the fear of failure and boosting students’ belief in their creative abilities [ 51 ]. Ultimately, students internalize external support, thereby strengthening their conviction in their creative competence, highlighting the critical role of university Innovation Support in enhancing creative self-efficacy [ 33 ]. Based on the above analysis, the following hypothesis is proposed. H2a: University innovation support is positively related to college students' creative self-efficacy. With a solid foundation in creative self-efficacy as a psychological base, it shifts from a latent cognitive state to an active intrinsic motivation that propels tangible Innovative Behavior [ 45 ]. In an academic setting, Innovative Behavior rarely occurs in isolation; it is a demanding, iterative process involving problem identification, unconventional idea generation, rigorous prototype development, and real-world application, often accompanied by technical setbacks and interpersonal resistance [ 52 ]. High creative self-efficacy is essential, influencing an individual’s selection of challenging creative tasks, the cognitive effort invested, and persistence in the face of inevitable obstacles [ 45 , 50 ]. College students with strong creative self-efficacy actively seek opportunities for innovation and bravely challenge established academic paradigms [ 45 , 47 , 51 ]. When encountering experimental failures, critical peer feedback, or temporary resource shortages, they do not view setbacks as inherent intellectual limitations; instead, they maintain mental flexibility and see hurdles as surmountable with alternative strategies [ 45 ]. This resilience helps sustain intrinsic motivation and allows them to allocate cognitive resources to innovative projects [ 47 ]. Strong self-regulation mechanisms associated with high creative self-efficacy encourage students to mobilize social networks, confidently present ideas to stakeholders, and obtain approvals—key steps in turning abstract ideas into concrete innovative behavior [ 33 , 44 , 46 ]. This intrinsic motivation is not spontaneous; it is a direct psychological outcome of students’ absorption of external university innovation support, with institutional resources fostering creative self-efficacy to boost innovation efforts [ 43 , 44 ]. Based on the above analysis, the following hypothesis is proposed. H2b: Creative self-efficacy is positively related to students' innovative behavior. Based on H2a and H2b, this study posits the following hypothesis: H2: Creative self-efficacy mediates the relationship between university innovation support and college students' innovative behavior. The mediating role of psychological entitlement Within university ecosystems, excessive support for innovation includes extensive laboratory access, generous seed funding, lenient grading for creative projects, and unconditional expert mentorship. Such support, without strict performance requirements, unintentionally fosters deep cognitive distortion among students. Recent studies show that while university innovation support, campus innovation climate, and rich resource provision can significantly enhance students’ innovative behavior and motivation, over-reducing perceived cost and pressure may distort students’ value–cost assessment of innovation tasks [ 1 , 18 ]. This phenomenon is best explained by psychological entitlement, a persistent mindset in which individuals believe they inherently deserve special treatment, higher status, and ongoing resources, regardless of their actual effort or achievement [ 53 ]. When universities consistently provide excessive support for young innovators by removing obstacles and bailing out failed projects, they create an artificial resource-abundant academic environment. Students in this overly supportive bubble view these resources as fundamental rights rather than earned privileges or conditional tools. Unconditional structural backing alters their self-perception, nurturing a false sense of exceptionalism and inflated expectations of continuous external support [ 54 ]. Instead of recognizing the cost and scarcity of the innovation infrastructure, these students develop an egocentric worldview, assuming the university is obligated to help their journey and ensure success. Seeing peers receive similar support without corresponding effort or results further normalizes this distortion, fostering a widespread entitlement culture [ 55 , 56 ]. Therefore, well-meaning but excessive university support for innovation ironically hampers healthy cognitive growth, planting the roots of unwarranted psychological entitlement among students [ 1 , 18 , 54 ]. Based on the above analysis, the following hypothesis is proposed. H3a: University innovation support is positively related to college students' psychological entitlement. Once this deeply entrenched psychological entitlement takes root, it becomes a severe motivational paradox that paralyzes students’ engagement in genuine, effort-intensive academic endeavors. True innovative behavior is grueling: a highly iterative, demanding process requiring profound persistence, the ability to endure repeated experimental failures, and a willingness to invest massive, unrecognized cognitive effort before tangible breakthroughs. However, students with high psychological entitlement develop maladaptive effort-reward expectations. They subconsciously hold an irrational transactional mindset, expecting immediate, outsized external rewards—public recognition, academic honors, or additional funding—for minimal or superficial intellectual labor [ 18 , 54 ]. When these overly supported students encounter inevitable friction, technical setbacks, and critical feedback in the execution of innovation, their entitled behavioral patterns collapse. Believing they deserve success without struggle, they view normal academic hurdles and rigorous peer reviews as unfair offenses or institutional betrayals rather than constructive challenges, thereby intensifying cognitive dissonance rather than promoting adaptive conceptual change [ 57 ]. This cognitive dissonance triggers defensive reactions: instead of increasing effort or adjusting strategies, they withdraw from tasks, abandoning the arduous work needed to realize novel ideas [ 53 , 58 ]. Their fixation on unearned status prevents cooperative knowledge-sharing, critical for complex innovation, even though mentor support, knowledge sharing, and challenging—but not hindering—pressures are precisely the factors shown to foster innovative behavior [ 54 , 58 ]. By superficially participating in innovation without committing to the grueling work, they see their actual innovative behavior plummet. Clearly, while universities offer resources to encourage proactive efforts, excessive support for innovation can unintentionally foster maladaptive psychological entitlement, undermining the perseverance and effort needed for true innovation [ 1 , 54 , 58 ]. Based on the above analysis, the following hypothesis is proposed. H3b: Psychological entitlement is negatively related to students' innovative behavior. Based on H3a and H3b, this study posits the following hypothesis: H3: Psychological entitlement mediates the relationship between university innovation support and college students' innovative behavior. Creative self-efficacy and psychological entitlement Although creative self-efficacy is traditionally universally celebrated as a highly critical cognitive asset driving positive academic engagement and innovative behavior [ 59 , 60 ], emerging psychological discourse warns that the unbridled accumulation of self-belief can inadvertently trigger a series of dark, maladaptive cognitive transformations, particularly within resource-saturated university environments that are supported by abundant innovation support [ 51 , 61 ]. Research shows that when an individual’s internalized belief in their ability to generate novel ideas reaches an exceptionally high level, it often turns into intellectual arrogance and a greatly inflated self-image, creating a strong foundation for psychological entitlement [ 62 , 63 ]. In higher education innovation ecosystems, students with robust creative self-efficacy are highly adept at rapid ideation and opportunity recognition [ 61 , 64 ], making them the earliest recipients of faculty praise, peer admiration, and preliminary seed funding, which further reinforces their self-belief and deepens their connection with innovation support [ 46 ]. As these students fluidly navigate brainstorming sessions and receive external validation for their cognitive talents, they subconsciously view themselves as intellectual elites or indispensable “star” assets to the university’s innovation portfolio, echoing findings that high perceived competence and empowerment fuel stronger innovation roles in academic settings [ 44 , 46 ]. This elevated self-view alters their psychological contract with the institution. Instead of maintaining a humble, growth-oriented mindset, they fall victim to a toxic “potential-equals-merit” distortion, erroneously equating creative concept capability with the grueling labor to execute it into innovative behavior [ 44 , 63 , 64 ]. Perceiving their creative potential as scarce and valuable, they deduce the university owes them continuous premium compensation—a core trait of psychological entitlement [ 62 ]. Thus, strong creative self-efficacy, especially when amplified by generous innovation support, becomes a direct precursor to psychological entitlement, shifting their narrative from productive to demanding, such that in well-funded incubators, they aggressively demand VIP access, unconditional mentorship, and immunity from critiques, seeing resource denial as an offense [ 46 , 60 , 61 ]. Based on the above analysis, the following hypothesis is proposed. H4b: College students' creative self-efficacy is positively related to their psychological entitlement. Based on H2a, H4b, and H3b, this study posits the following hypothesis: H4: Creative self-efficacy and psychological entitlement serve as chain mediators in the relationship between university innovation support and college students' innovative behavior. The moderating role of learning goal orientation Although university innovation support provides the critical scaffolding for student creativity, inconsistent empirical outcomes regarding actual performance indicate individual-level boundary conditions that shape how students process and utilize these external resources [ 1 , 44 ]. Research shows that environmental cues such as abundant funding or expert mentorship do not always produce proactive behaviors; their effectiveness depends on a person’s intrinsic motivational makeup [ 65 , 66 ]. Here, learning goal orientation—an intrinsic desire to develop competence, master new skills, and comprehend problem-solving processes rather than seeking external validation—emerges as a decisive psychological moderator [ 67 , 68 ]. In practice, students with high learning goal orientation in the autonomous, failure-prone university innovation ecosystem view institutional support as a tool for deep learning [ 69 , 70 ]. Focused on task mastery, they avoid the “resource curse” from excessive pampering [ 71 ]. Unconditional laboratory access or seed funding does not cause complacency; instead, it synergizes with their cognitive persistence to enable rigorous, iterative experiments [ 72 ]. When facing technical setbacks in prototype development, they use the provided mentorship and infrastructure to decode failures and refine strategies to promote innovative behavior [ 1 , 44 , 73 ]. Conversely, students with low learning goal orientation—driven by a fear of appearing incompetent or seeking superficial validation—maladaptively interact with support [ 68 ]. Excessive sponsorship removes developmental friction, becoming an intellectual crutch that fosters dependency [ 71 ]. Without task mastery as an anchor, external support diminishes self-regulation, stagnating or reversing the positive trajectory between innovation support and innovative behavior [ 74 ]. Based on the above analysis, the following hypothesis is proposed. H5: Learning goal orientation moderates the relationship between university innovation support and college students' innovative behavior, such that the positive relationship is stronger when learning goal orientation is high than when it is low. The theoretical framework of this study is shown in Fig. 1 below. Fig. 1. Open in a new tab Theoretical framework Method Data collection This study received ethical approval from the Human Research Ethics Committee of Yangzhou Polytechnic (approval number: EA-2025021; approval date: May 15, 2025). All subsequent pre-test and formal data collection procedures were strictly carried out in accordance with the ethical standards approved by the committee; all procedures in this study adhered to the ethical standards of the institutional review board and the Declaration of Helsinki. Data were collected through a structured questionnaire. The questionnaire included three parts: (1) a cover letter explaining the study’s purpose, importance, and assuring anonymity and voluntary participation; (2) an informed consent form; and (3) the main section with demographic questions and measurement items for the core constructs. All constructs were assessed using established scales, totaling 28 items. A pre-test was conducted with 50 university students from June 10 to June 27, 2025. Results showed satisfactory preliminary psychometric properties: Cronbach’s alpha coefficients for all scales were above 0.80, factor loadings exceeded 0.65, and the average variance extracted (AVE) values were over 0.50. The average completion time was 5 min (SD = 1.4). Based on feedback from 8 participants, minor wording adjustments were made for clarity, and the revised version was confirmed to be clear. The required sample size was determined using the rule of thumb for Structural Equation Modeling (SEM), which states that the sample size should be at least 10 times the number of measurement items [ 75 ]. With 28 items, this indicated a baseline of 280 participants. Considering the model’s complexity, which includes mediation analysis, and the potential for invalid responses, we increased the target sample size. Our goal was to collect approximately 560 valid responses. To achieve this and enhance sample diversity, questionnaires were distributed to 22 universities across six countries: China (6 universities), South Korea (3), Kazakhstan (3), the United Kingdom (3), the United States (4), and New Zealand (3). Each university received 30 questionnaires, for a total of 660. Formal data collection was conducted from July 11 to September 2, 2025. With permission from university administrations or department heads, questionnaires were distributed in class or during student group meetings. A total of 660 questionnaires were handed out, and 583 were returned, resulting in a response rate of 88.3%. All participants were adult university students who provided informed consent. Following established data screening procedures [ 76 – 78 ], 49 invalid questionnaires were excluded for specific reasons: incomplete responses (22), logical inconsistencies (e.g., contradictory answers; 12), patterned responding (e.g., straight-lining; 9), and abnormal completion times (less than 2 min or more than 8 min; 6). As a result, 534 valid questionnaires remained for final analysis. Although slightly below the initial target, the final sample of 534 significantly exceeds the baseline requirement of 280 for SEM analysis and is considered adequate for testing the proposed model. 534 valid samples have an average age of 21.6 years (standard deviation 2.3). The other demographic details of the valid sample are shown in Table 1 . Table 1. Demographic Characteristics of the Sample ( N = 534) Variables Category Frequency Percentage (%) Gender Male 238 44.6 Female 296 55.4 Nationality Chinese 154 28.8 South Korean 75 14.0 Kazakhstani 74 13.9 United Kingdom 79 14.8 United States 103 19.3 New Zealander 49 9.2 Major Engineering & Technology 145 27.2 Business & Economics 117 21.9 Humanities & Social Sciences 122 22.8 Natural Sciences 95 17.8 Arts & Design 55 10.3 Open in a new tab Measurement of the variables University innovation support was assessed using a 4-item scale developed by Xu et al. [ 79 ]. This original scale was written in Chinese and designed for the Chinese context. In this study, the scale was translated forward and backward following standard cross-cultural research procedures, and its reliability and validity were re-verified with a multi-country sample to ensure its suitability for cross-cultural use. A representative item was: “My university has clear educational philosophies and policies that encourage college students’ innovative practice and exploration.” Creative self-efficacy was assessed using a 4-item scale developed by Tierney & Farmer [ 80 ] that measures individuals’ confidence in their creative abilities in innovative activities. A representative item is: “I have confidence in my ability to solve academic and innovative problems creatively.” Psychological entitlement was assessed using a 9-item scale developed by Campbell et al. [ 25 ] that assesses individuals’ sense of entitlement to university innovation resources and opportunities. A representative item is: “I honestly feel I am more deserving of university innovation resources and opportunities than others.” Learning goal orientation was assessed using a 5-item scale developed by VandeWalle [ 81 ] that measures individuals’ tendency to pursue challenging tasks and to focus on developing innovative and academic abilities. A representative item is: “I am willing to select a challenging academic or innovative task from which I can learn a lot.” Individual innovation behavior was assessed using a 6-item scale developed by Scott & Bruce [ 82 ], which focuses on individuals’ proactive behaviors in generating, implementing and promoting innovative ideas in academic and campus settings. A representative item is: “I often proactively seek opportunities to apply new technologies, new processes, or new methods in my academic learning and innovative practice.” All items employed a 5-point Likert scale, with response options ranging from 1 (Strongly Disagree) to 5 (Strongly Agree). Tools In this study, Mplus 8.0 was utilized for the data analyses that followed. Sample common method bias test This study used procedural and statistical controls to reduce potential common-method bias. During questionnaire design, reversing item order and enforcing strict anonymity protocols helped decrease priming and social desirability bias. Additionally, Latent common method factor analysis confirmed bias control: the model fit improved significantly (RMSEA decreased by 0.01, SRMR decreased by 0.02, and CFI and NFI increased by 0.03 and 0.02, respectively). Therefore, common method bias may exist but is within an acceptable range. Results Reliability, validity, and collinearity diagnostics As shown in Table 2 , all item factor loadings exceeded 0.60, meeting the minimum standard for factor loadings. Composite reliability (CR) values ranged from 0.87 to 0.94, and average variance extracted (AVE) values all surpassed 0.50. These results indicate that the measurement scale demonstrates high internal consistency reliability and sufficient convergent validity. Additionally, Table 3 shows that the square root of the AVE for each latent variable was higher than the correlations among the other relevant latent variables. This supports the discrimination validity standard. Table 2. Results of CFA and reliability and validity testing Variable Item Estimate SE p SMC CR AVE UIS UIS1 0.78 0.02 < 0.001 0.61 0.87 0.63 UIS2 0.79 0.02 < 0.001 0.62 UIS3 0.82 0.02 < 0.001 0.67 UIS4 0.79 0.02 < 0.001 0.63 CSE CSE1 0.79 0.02 < 0.001 0.62 0.88 0.65 CSE2 0.83 0.02 < 0.001 0.68 CSE3 0.79 0.02 < 0.001 0.62 CSE4 0.82 0.02 < 0.001 0.67 PE PE1 0.88 0.02 < 0.001 0.77 0.94 0.78 PE2 0.89 0.02 < 0.001 0.79 PE3 0.88 0.02 < 0.001 0.78 PE4 0.88 0.02 < 0.001 0.77 PE5 0.87 0.02 < 0.001 0.76 PE6 0.88 0.02 < 0.001 0.77 PE7 0.88 0.02 < 0.001 0.77 PE8 0.88 0.02 < 0.001 0.78 PE9 0.89 0.02 < 0.001 0.78 LGO LGO1 0.85 0.01 < 0.001 0.73 0.94 0.75 LGO2 0.88 0.01 < 0.001 0.77 LGO3 0.86 0.01 < 0.001 0.73 LGO4 0.88 0.01 < 0.001 0.77 LGO5 0.87 0.01 < 0.001 0.75 IB IB1 0.85 0.01 < 0.001 0.73 0.93 0.68 IB2 0.81 0.02 < 0.001 0.66 IB3 0.81 0.02 < 0.001 0.66 IB4 0.84 0.02 < 0.001 0.70 IB5 0.79 0.02 < 0.001 0.62 IB6 0.83 0.02 < 0.001 0.68 Open in a new tab UIS University Innovation Support, CSE Creative Self-Efficacy, PE Psychological Entitlement, LGO Learning Goal Orientation, IB Innovative Behavior, CR composite reliability, AVE average variance extracted Table 3. Variables’ correlation coefficient and the square root of AVE AVE UIS CSE PE LGO IB UIS 0.63 0.79 CSE 0.65 0.30 0.81 PE 0.78 0.33 0.30 0.88 LGO 0.75 0.42 0.26 0.14 0.87 IB 0.68 0.26 0.31 -0.26 0.25 0.82 Open in a new tab The values in bold and italic font signify the square root of the Average Variance Extracted (AVE), whereas the figures in the lower triangular part of the matrix denote the Pearson correlation coefficients Model fit indices Table 4 displays the results of the confirmatory factor analysis comparing various measurement models. The basis for distinguishing among the alternative models is explained as follows: The 1-factor model merges all five constructs—university innovation support, creative self-efficacy, psychological entitlement, learning goal orientation, and innovative behavior—into a single latent variable. The 2-factor model treats university innovation support as a separate external support factor, while grouping creative self-efficacy, psychological entitlement, learning goal orientation, and innovative behavior into a single factor representing internal psychological states and behavioral performance. The 3-factor model further separates individual-related constructs by isolating innovative behavior, resulting in three distinct groups: university innovation support, the combined internal psychological factor (CSE + PE + LGO), and innovative behavior. The 4-factor model refines this structure by isolating learning goal orientation, while maintaining (CSE + PE) as a work-related psychological factor, alongside independent university innovation support and innovative behavior. Finally, the hypothesized 5-factor model, with all five constructs as separate latent variables, demonstrated the best fit to the data ( χ²/df = 1.18, CFI = 0.99, TLI = 0.99, RMSEA = 0.02, SRMR = 0.03). Table 4. Results of comparisons with alternative models Model Factor χ²/df CFI TLI RMSEA SRMR 1-Factor UIS + CSE + PE + LGO + IB 18.59 0.46 0.42 0.18 0.22 2-Factor UIS, (CSE + PE + LGO + IB) 16.15 0.54 0.50 0.17 0.21 3-Factor UIS, (CSE + PE + LGO), IB 10.37 0.72 0.69 0.13 0.17 4-Factor UIS, (CSE + PE), LGO, IB 4.19 0.90 0.89 0.08 0.10 5-Factor UIS, CSE, PE, LGO, IB 1.18 0.99 0.99 0.02 0.03 Open in a new tab Mediation effects Table 5 presents the results of the mediating effect analysis, including the total, direct, and specific indirect effects linking university innovation support to innovative behavior. The total effect of university innovation support on innovative behavior was significant and positive (Estimate = 0.31, SE = 0.06, z = 5.06, p < 0.001), while the direct effect of university innovation support on innovative behavior remained significant and positive after controlling for indirect pathways (Estimate = 0.37, SE = 0.06, z = 6.37, p < 0.001), which supports H1. Table 5. Test of mediating effect Path Effect SE p Lower 2.5% Upper 2.5% Hypothesis Total 0.31 0.06 < 0.001 0.19 0.43 - Direct 0.37 0.06 < 0.001 0.26 0.48 H1 (Y) Total Indirect -0.06 0.04 0.172 -0.13 0.03 - Ind1: UIS → CSE → IB 0.12 0.03 < 0.001 0.07 0.18 H2 (Y) Ind2: UIS → PE → IB -0.14 0.03 < 0.001 -0.21 -0.08 H3 (Y) Ind3: UIS → CSE → PE → IB -0.04 0.01 0.002 -0.06 -0.02 H4 (Y) Open in a new tab The total indirect effect was not significant (Estimate = -0.06, SE = 0.04, z = -1.37, p = 0.172), likely because the positive specific indirect effect through creative self-efficacy is canceled out by the negative specific indirect effects through psychological entitlement and the chain mediation of creative self-efficacy and psychological entitlement. This pattern aligns with the concept of competitive mediation. Regarding the specific indirect effects, the results revealed three significant pathways: the indirect effect of university innovation support on innovative behavior through creative self-efficacy was positive and significant (Estimate = 0.12, SE = 0.03, z = 4.44, p < 0.001), with a 95% confidence interval [0.07, 0.18] that did not contain zero, thereby supporting H2; the indirect effect of university innovation support on innovative behavior through psychological entitlement was negative and significant (Estimate = -0.14, SE = 0.03, z = -4.48, p < 0.001), with a 95% confidence interval [-0.21, -0.08] that did not contain zero, supporting H3; and the chain mediation effect through creative self-efficacy and psychological entitlement was negative and significant (Estimate = -0.04, SE = 0.01, z = -3.16, p = 0.002), with a 95% confidence interval [-0.06, -0.02] that did not contain zero, which supports H4. Moderating effect Table 6 presents the results of the structural model, including the effects of main variables, interaction terms, and demographic control variables on innovative behavior. The results of the analysis revealed significant main effects and a positive moderating effect of learning goal orientation on the relationship between university innovation support and innovative behavior. Specifically, university innovation support had a significantly positive impact on innovative behavior (Estimate = 0.36, SE = 0.07, t = 5.51, p < 0.001), and learning goal orientation also exerted a significantly positive main effect on innovative behavior (Estimate = 0.12, SE = 0.05, t = 2.50, p = 0.012). More importantly, the interaction term between university innovation support and learning goal orientation had a significantly positive coefficient (Estimate = 0.17, SE = 0.04, t = 3.86, p < 0.001), indicating that learning goal orientation plays a significant positive moderating role in the relationship between university innovation support and innovative behavior, supporting H5. Additionally, the control variables, including gender, nationality, major, and age, had no significant effects on innovative behavior (all p > 0.05), indicating that the model effectively controlled for potential confounding factors and that the results are more reliable. It can be clearly observed that among the control variables, gender (Estimate = -0.04, SE = 0.09, t = -0.43, p = 0.671), nationality (Estimate = -0.02, SE = 0.03, t = -0.56, p = 0.578), major (Estimate = -0.05, SE = 0.03, t = -1.67, p = 0.096), and age (Estimate = 0.02, SE = 0.02, t = 1.21, p = 0.227) all had no significant predictive effects on innovative behavior (IB), which further confirms the effectiveness of the model’s control for confounding variables. Table 6. Test of moderating effect DV IV Estimate SE t p Lower 2.5% Upper 2.5% Hypothesis IB Gender -0.04 0.09 -0.43 0.671 -0.22 0.14 Nationality -0.02 0.03 -0.56 0.578 -0.07 0.04 Major -0.05 0.03 -1.67 0.096 -0.12 0.01 Age 0.02 0.02 1.21 0.227 -0.02 0.06 UIS 0.36 0.07 5.51 < 0.001 0.23 0.49 CSE 0.37 0.06 6.56 < 0.001 0.26 0.48 PE -0.41 0.04 -9.77 < 0.001 -0.49 -0.33 LGO 0.12 0.05 2.50 0.012 0.03 0.21 UIS × LGO 0.17 0.04 3.86 < 0.001 0.08 0.26 H5 (Y) Open in a new tab To further examine the moderating mechanism, a simple slope analysis was conducted, classifying learning goal orientation (LGO) into high and low levels based on the mean ± 1 standard deviation. Specifically, the mean of LGO was 3.50 and the standard deviation was 0.80; thus, the high-LGO condition was 4.30 (M + 1 SD) and the low-LGO condition was 2.70 (M − 1 SD). As shown in Table 7 , university innovation support had a significant positive effect on innovative behavior at both levels of LGO. When LGO was high, the effect of university innovation support on innovative behavior was stronger (slope = 0.56, SE = 0.09, t = 5.98, p < 0.001, 95% CI [0.38, 0.75]). When LGO was low, the effect remained significant but weaker (slope = 0.16, SE = 0.07, t = 2.16, p = 0.031, 95% CI [0.02, 0.30]). These results further confirm the positive moderating effect of learning goal orientation, such that higher LGO strengthens the positive relationship between university innovation support and innovative behavior. Table 7. Results of Simple Slope Analysis LGO Level Intercept Slope (UIS → IB) SE t p Lower 2.5% Upper 2.5% High (M + 1SD) -0.16 0.56 0.09 5.98 < 0.001 0.38 0.75 Low (M − 1SD) -0.44 0.16 0.07 2.16 0.031 0.02 0.30 Open in a new tab Discussion Research findings This study, grounded in the core tenets of SCT, validates an integrated “environmental support–individual cognition–behavioral output” model, with all five hypotheses supported. The findings provide a nuanced SCT-based explanation of how environmental inputs are differentially processed through personal cognitive factors to shape behavioral outcomes. Notably, the demographic control variables, including gender, nationality, major, and age, showed no significant effects on college students’ innovative behavior (see Table 6 ). University innovation support had a significant positive direct effect on innovative behavior (Estimate = 0.37, p < 0.001), confirming its role as a critical environmental opportunity structure within the triadic reciprocal determinism framework. The mediation analysis offers profound insights into the agentic cognitive processes central to SCT. The total indirect effect was non-significant (-0.06, p = 0.172), yet the three specific indirect paths were all significant, revealing a clear case of competitive mediation driven by opposing cognitive appraisals. This aligns with SCT’s emphasis on individuals’ active interpretation of their environment. Creative self-efficacy, a core SCT construct representing belief in one’s creative capabilities, functioned as a positive mediator (indirect effect = 0.12, p < 0.001). It translates environmental support into heightened agentic belief and effort, consistent with SCT’s principle that efficacy beliefs are key determinants of behavioral initiation and persistence. Conversely, psychological entitlement served as a negative mediator (indirect effect = -0.14, p < 0.001), acting as a distorted cognitive filter that interprets support as a baseline entitlement rather than a resource for agentic development, thereby stifling proactive effort. The opposing signs and comparable magnitudes of these paths empirically demonstrate that different cognitive interpretations of the same environmental factor (university innovation support) can produce competing behavioral influences, which explains the non-significant net indirect effect. The significant negative chain mediation through creative self-efficacy to psychological entitlement and then to innovative behavior (-0.04, p = 0.002) further reveals that university innovation support, although helpful in strengthening individual creative self-efficacy, may also trigger psychological entitlement, which could inhibit innovative behavior. Secondly, the analysis of learning goal orientation powerfully illustrates the moderating role of personal cognitive schemas within SCT’s triadic model. The positive moderating effect was significant (UIS×LGO interaction = 0.17, p < 0.001). Simple slope analysis revealed that the link between university innovation support and innovative behavior was stronger when learning goal orientation was high (simple slope = 0.56, p < 0.001) than when it was low (0.16, p = 0.031). This 0.40 difference in slope strength substantiates that individuals with a high learning goal orientation, who embody a mastery-oriented cognitive schema, are more effective in their self-regulatory processes. They are more likely to cognitively engage with environmental support, set challenging learning goals, and persistently mobilize that support for skill development, thereby significantly amplifying its positive impact on innovative action. This result vividly encapsulates SCT’s core premise: behavior is the product of a dynamic, reciprocal interaction among personal factors (such as learning goal orientation), environmental influences (such as university innovation support), and their cognitive processing. Theoretical contribution This study offers several significant theoretical contributions to the literature on university innovation support and student innovative behavior: First, we challenge the prevailing “more is better” paradigm common in discussions about educational support. Drawing on the TMGT perspective and framework, this research uncovers the dual effects of innovation support, showing that an excess of resources can paradoxically hinder student innovation [ 4 – 6 ]. This greatly enhances our understanding of the complex dynamics involved in resource allocation in higher education. Second, our study introduces a new dual-pathway model that uncovers both positive and negative psychological mechanisms. We go beyond simply emphasizing creative self-efficacy as a positive mediator [ 1 – 3 ] by adding psychological entitlement as a competing negative pathway. Examining these two opposing forces and their chain mediation provides a more comprehensive and detailed explanation of how university innovation support shapes student behavior, filling an important gap in the existing literature [ 9 ]. Third, this research incorporates individual motivational traits by identifying learning goal orientation as a key boundary condition. Unlike previous studies that often treated students as a uniform group, we show that students’ intrinsic motivation for mastery significantly moderates the relationship between university support and innovative behavior [ 10 , 11 ]. This finding provides a more nuanced theoretical perspective that considers individual differences, explaining why some students flourish with support while others may face a “resource curse,” thereby guiding more personalized intervention strategies [ 13 , 14 ]. Management enlightenment This study provides valuable practical insights for university administrators, educators, and policymakers aiming to promote effective student innovation while avoiding potential challenges. First, given the stable and positive direct effect of university innovation support on innovative behavior, universities should prioritize the steady supply and structural optimization of core innovation resources, ensuring that support remains targeted and moderate rather than pursuing blind expansion or excessive input, so as to lay a reliable environmental foundation for students’ innovative practices. Second, in light of the positive mediating role of creative self-efficacy, universities should integrate confidence-building interventions into routine teaching and innovation guidance, helping students gain substantial experience in innovation, receive constructive process feedback, and learn from peer examples to continuously consolidate their creative self-belief. Third, considering the negative mediating effect of psychological entitlement that reflects the too-much-of-a-good-thing effect, universities should reform the allocation mode of innovation resources by replacing unconditional universal support with competitive and conditional provision, clarifying the developmental nature of institutional support, and retaining appropriate task challenges to prevent excessive care from inducing distorted cognitive tendencies. Fourth, to address the negative chain mediation in which strong creative self-efficacy may breed psychological entitlement under abundant support, universities should strengthen mindset education and balanced feedback for students with high creative confidence, guiding them to associate creative competence with sustained effort and practical responsibility rather than overvaluing self-potential. Finally, based on the significant moderating role of learning goal orientation, universities should abandon the one‑size‑fits‑all support model and implement differentiated governance, providing sufficient developmental space and high-level resources for students with strong learning goal orientation while offering step-by-step guidance and low-threshold tasks for those with weak learning goal orientation to enhance their innovative initiative progressively. Limitations and future directions Despite this study’s contributions, it is important to acknowledge its limitations, which also pave the way for future research: Firstly, this research employs a cross-sectional survey design, which limits our ability to establish causal relationships with certainty between university innovation support, psychological mechanisms, and student innovative behavior. While our theoretical model and statistical analyses suggest directional influences, a cross-sectional approach cannot fully capture the dynamic, evolving nature of these constructs over time. Future research should adopt longitudinal designs to track changes in students’ perceptions of support, their self-efficacy, entitlement, and innovative outputs over several academic periods. Additionally, experimental or quasi-experimental studies could be employed to manipulate levels of innovation support, thereby providing stronger evidence for causality [ 83 ]. Secondly, while this study effectively draws on the TMGT perspective and framework to explore competing mediation pathways [ 5 , 9 ], it does not explicitly test for a non-linear (e.g., U-shaped or inverted U-shaped) direct effect of university innovation support on student innovative behavior. Although the TMGT perspective and framework often implies such curvilinear relationships, our focus was on the dual psychological paths. Future research could extend this work by directly investigating whether university innovation support exhibits a curvilinear effect, with moderate levels optimal and extremely low or high levels yielding diminishing or even negative returns. This would provide a more complete understanding of the “optimal” level of support and further enrich the application of the TMGT framework in educational contexts. Conclusion This study thoroughly examines the positive and negative effects of university innovation support on college students’ innovative behavior, challenging the typical assumption that “more is better.” It confirms that support generally boosts innovation, mainly by increasing creative self-efficacy. However, a key finding shows that psychological entitlement acts as a negative mediator, indicating that excessive or undeserved support can harm genuine innovative motivation. Additionally, learning goal orientation was identified as a critical moderator, strengthening the positive effects when students are intrinsically motivated. These findings are important for universities to develop strategic support systems for innovation. Focusing on quality over quantity, building genuine self-efficacy, reducing entitlement, and nurturing students’ natural desire for mastery will more effectively unlock students’ innovative potential. Acknowledgements Not applicable. Authors’ contributions Liren Zhou was responsible for the study’s conceptualization, methodology design, formal data analysis, investigation, and the writing of the original manuscript. Qiu Yan, Chang Ni, and Ya You jointly handled methodology implementation, investigation, resource acquisition, and data curation. Jun Peng contributed to conceptualization, overall research design, supervision, and the critical review and editing of the manuscript. Funding This study received no funding support. Data availability Available from the corresponding author upon reasonable request. Declarations Ethics approval and consent to participate Before starting data collection, this study received ethical approval from the Human Research Ethics Committee at Yangzhou Polytechnic (approval number: EA-2025021; approval date: May 15 2025). The process strictly adhered to the Declaration of Helsinki and China’s “Ethical Review Measures for Scientific and Medical Research Involving Humans.” During data collection, participants were fully informed, and their consent was properly obtained. Additionally, all participants are adults, and no minors are involved. Consent for publication Not applicable. Competing interests The authors declare no competing interests. Footnotes Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. References 1. Feng J, Sun G. 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