Introduction

With the rapid advancement of digital technology, the global economic system is undergoing accelerated transformation toward a digital economy characterized by information-driven dynamics, platform-based collaboration, and data value creation. This transformation is not only reconstructing industrial structures but also expanding the boundaries and methods of entrepreneurship, endowing entrepreneurial activities with greater flexibility, lower entry barriers, and higher technological dependence (Bernardino et al., 2023; Zou et al., 2024). As digital natives, college students generally possess strong digital tool proficiency and a high willingness to adopt emerging technologies. Coupled with intensifying employment competition and enhanced policy support, student entrepreneurship has become a convergence point for educational reform, economic transformation, and youth development (Lestari et al., 2024; Iskandar et al., 2024). Although surveys show high entrepreneurial interest among university students, their actual participation rates remain persistently low, revealing a structural contradiction between strong intentions and weak implementation (Harima et al., 2021). This phenomenon indicates that the transition from entrepreneurial intention to action involves complex psychological mechanisms and conversion barriers, which cannot be effectively addressed by policy incentives or educational interventions alone without targeting the core cognitive and motivational issues.

Previous studies have demonstrated that individual traits are crucial factors influencing the formation of entrepreneurial intentions, with entrepreneurial passion, entrepreneurial curiosity, and digital competence identified as three representative entrepreneurial predisposition variables (Al-Ghazali et al., 2022; Tabak et al., 2024). Entrepreneurial passion reflects an individual’s emotional identification with and enthusiasm for entrepreneurial activities, entrepreneurial curiosity represents their proactive inclination to explore novel things, new models, and uncertain situations, while digital competence constitutes a key capability for identifying opportunities, integrating resources, and executing plans in digital environments. The relationships between these traits and entrepreneurial intentions have been preliminarily verified in relevant studies (Triyono et al., 2023; Mir et al., 2023); however, existing research predominantly adopts direct path modeling and lacks systematic analysis of the mechanisms among these variables (Porkodi and Saranya, 2023). More importantly, these trait variables do not exist in isolation in real-world contexts—their effects typically require individuals’ cognitive processing and psychological mobilization before ultimately transforming into explicit intentions and behavioral preparedness (Dheer and Castrogiovanni, 2023). Consequently, merely analyzing the direct effects of individual traits on entrepreneurial intentions cannot adequately explain why some individuals with strong traits still lack entrepreneurial intent in reality, even exhibiting avoidance tendencies.

In the in-depth exploration of the relationship between individual traits and entrepreneurial intention, cognitive processing mechanisms have increasingly been regarded as potential mediating variables, with entrepreneurial alertness emerging as a representative cognitive factor (Duong et al., 2021). Entrepreneurial alertness reflects an individual’s acute ability to identify latent opportunities and cognitive sensitivity to changing trends, serving as a core psychological bridge linking personal traits and entrepreneurial intent (Otache et al., 2024).Moreover, whether individuals can effectively combine their trait advantages with cognitive judgment is significantly influenced by their motivational state. Entrepreneurial motivation determines whether individuals are willing to commit resources, overcome obstacles, and bear risks, making it a crucial moderating factor that drives traits into action (Kryeziu et al., 2024). However, although existing studies have sporadically highlighted the importance of cognitive and motivational variables, research systematically integrating traits, cognition, and motivation into a unified theoretical model remains scarce. Current literature is often characterized by weak logical path construction, ambiguous interaction mechanisms among variables, and insufficient theoretical grounding, failing to establish a comprehensive psychological mechanism model with strong explanatory power. Additionally, most prior studies have focused primarily on entrepreneurship education or behavioral intervention perspectives, lacking an in-depth investigation based on psychological structures. They also inadequately address the emerging challenges faced by college students in forming entrepreneurial intentions within the digital economy context.

To address these gaps, this study adopts an interaction perspective between cognition and motivation, establishing an influence pathway centered around individual traits, cognitive judgment, and motivational resources. The research aims to tackle prevalent issues such as excessive variable enumeration, ambiguous path relationships, and weak theoretical integration in entrepreneurial intention studies. By clarifying variable positioning and operational mechanisms, this model facilitates a deeper understanding of the psychological structure of college students’ entrepreneurial intentions, providing a more explanatory analytical framework for deciphering their cognitive antecedents and behavioral tendencies.

Literature review

Research progress

With the rapid development of the digital economy, university students have emerged as one of the most dynamic groups among new entrepreneurial actors. In an era characterized by platform-driven ecosystems, algorithm-based recommendations, and widespread remote collaboration, entrepreneurial activities have become increasingly diversified, exhibiting novel features such as asset-light models, high flexibility, and cognitive-driven dynamics (Herman, 2022; Zhou et al., 2022). University students actively engage in digital entrepreneurship through social platforms, e-commerce systems, and self-media operations, with their entrepreneurial behaviors relying more on personal cognitive structures and intrinsic motivations rather than solely on traditional resource endowments or organizational pathways (Yang et al., 2023). Against this backdrop, entrepreneurial intention has been widely regarded as a critical antecedent for evaluating their entrepreneurial potential and predicting future entrepreneurial behaviors. Understanding its psychological formation mechanism has thus become a central issue in digital entrepreneurship research.

Existing research has repeatedly verified the influence of individual psychological traits on entrepreneurial intention. Entrepreneurial passion, as an intense emotional trait, manifests as an individual’s sense of identification and emotional engagement in entrepreneurial contexts, and has been identified as one of the key driving forces behind entrepreneurial actions (Wang et al., 2008). Studies have demonstrated that entrepreneurial passion can strengthen students’ beliefs, identification, and sense of purpose regarding entrepreneurial activities, thereby positively influencing entrepreneurial intention (Sariwulan et al., 2020). However, these studies often equate entrepreneurial passion with entrepreneurial motivation, overlooking the fundamental distinction between its role as an affective driving factor and the cognitive goal orientation inherent in motivation. This conflation may lead to biased identification of causal pathways in the research.

Another significant trait variable is entrepreneurial curiosity, which refers to an individual’s exploratory drive and problem awareness when encountering new technologies, emerging models, and uncharted domains. Related studies suggest that curiosity not only motivates university students to continuously monitor external changes but also enhances their openness to innovative opportunities and willingness to act, thereby increasing entrepreneurial propensity (Hamdan, 2019; Fawaid et al., 2022). However, existing literature predominantly adheres to a generalized “curiosity-sparks-exploration” rationale while lacking in-depth analysis of how curiosity specifically functions within entrepreneurial cognition and opportunity recognition mechanisms. This oversight often marginalizes its role in empirical pathway construction. Furthermore, digital competency has become a crucial foundation for university students’ participation in the digital economy, encompassing technical operation skills, platform adaptability, and data-driven decision-making capabilities—key determinants in transforming conceptual ideas into feasible digital practices (Magni et al., 2021a, 2021b). Individuals with strong digital competencies demonstrate greater agility in rapid response and opportunity identification within complex information environments, leading to more robust entrepreneurial expectations. Collectively, these studies indicate that entrepreneurial passion, entrepreneurial curiosity, and digital competency constitute pivotal individual factors shaping digital entrepreneurial intentions. Nevertheless, research remains scant when it comes to systematically examining digital competency as a core independent variable and thoroughly investigating its mechanistic role in cognitive construction and behavioral orientation.

In the pathway between trait variables and entrepreneurial intention, entrepreneurial alertness has been proposed as a crucial cognitive mediator, explaining how individuals transform intrinsic traits into concrete entrepreneurial intentions. Entrepreneurial alertness is manifested through an individual’s ability to perceive environmental changes, identify and evaluate potential opportunities, and structurally integrate diverse information (Tang et al., 2008). This competency becomes particularly pivotal in digital contexts characterized by information overload and high uncertainty.Empirical studies indicate that entrepreneurial passion indirectly enhances entrepreneurial alertness by stimulating individuals’ tendency to focus on opportunities, thereby elevating their intention levels (Hu et al., 2018). Entrepreneurial curiosity drives individuals to proactively acquire information and establish knowledge connections, improving their capacity to detect market gaps in dynamic scenarios (Levasseur et al., 2020). Meanwhile, individuals with strong digital competencies can leverage technological tools to enhance their filtering and integration of complex information, sharpening their entrepreneurial judgment (Chahal, 2022). Thus, entrepreneurial alertness—as a “cognitive processing hub”—likely mediates the relationship between these trait variables and entrepreneurial intention, serving as a critical nexus in the psychological mechanism of entrepreneurship.Despite these studies providing theoretical support for the mediating role of entrepreneurial alertness, most have yet to develop systematic structural models. Moreover, precise clarification remains lacking on how alertness adapts its cognitive translation process to different trait variables—an oversight that leads to fragmented pathway construction in current research.

Additionally, entrepreneurial motivation as an endogenous source of behavioral energy is increasingly emphasized in entrepreneurial intention research. According to self-determination theory, motivation not only determines whether individuals develop entrepreneurial intentions but also influences the intensity and direction of their intention formation process. Existing research indicates that even when individuals possess entrepreneurial traits such as passion, curiosity, or competencies, without adequate intrinsic motivation, their entrepreneurial intentions may remain ambiguous or unstable (Carsrud and Brännback, 2011). Conversely, when motivation levels are high: entrepreneurial passion is more likely to translate into volition; entrepreneurial curiosity can be effectively channeled toward goal-directed behavior; and digital competencies are more readily applied to constructing viable entrepreneurial pathways. Rivero and Ubierna (2021) and Chahal et al. (2024a, 2024b) further suggested that entrepreneurial motivation functions not merely as an independent predictor but also as a moderating factor within trait-behavior pathways, altering the strength of trait variables’ influence on entrepreneurial intention. These insights underscore the indispensability of entrepreneurial motivation as a moderating variable. However, whether motivation exerts consistent moderating effects across multidimensional trait variables, or demonstrates selective enhancement for specific traits, remains systematically unexamined. Empirical validation is urgently needed to clarify its generalizability and boundary conditions.

In summary, individual characteristics such as entrepreneurial passion, entrepreneurial curiosity, and digital competency serve as direct antecedent variables in shaping entrepreneurial intention. Entrepreneurial alertness acts as a cognitive mediator in this process, facilitating the transformation from “perception” to “judgment.” Meanwhile, entrepreneurial motivation moderates the level of intention generation across various trait-intention pathways. Although existing literature provides theoretical support for the independent pathways of these variables, systematic research on their integrated interactions and multi-mechanism convergence within a unified framework remains limited, hindering a comprehensive understanding of university students’ entrepreneurial psychological processes.Furthermore, current studies lack a systematic, structurally coherent, and logically closed theoretical model. Empirical validation of multi-path, multi-level linkage mechanisms in the complex context of the digital economy remains unexplored. Therefore, this study proposes a “dual-mechanism model” based on theoretical integration, where entrepreneurial alertness serves as the mediator and entrepreneurial motivation as the moderator. This framework systematically examines the formation logic of university students’ entrepreneurial intention through two parallel pathways: “trait-cognition-behavior” and “trait-motivation-behavior.”

Research hypotheses

Based on the systematic review of prior research, university students’ entrepreneurial intention is driven by multiple individual factors. Among these, entrepreneurial passion, entrepreneurial curiosity, and digital competency represent three characteristic trait variables that may not only directly influence entrepreneurial intention but also transform into more stable behavioral tendencies through individuals’ cognitive processing mechanisms. Moreover, entrepreneurial motivation, as an intrinsic driving force, may alter the effect intensity of trait variables on intention, demonstrating a moderating effect. Therefore, this study integrates perspectives from cognition, behavior, and motivation by synthesizing Social Cognitive Theory (SCT), Theory of Planned Behavior (TPB), and Self-Determination Theory (SDT). We construct a dual-path entrepreneurial intention formation model combining “trait–cognition–behavior” and “trait–motivation–behavior” pathways, based on which research hypotheses are proposed.

Social Cognitive Theory (SCT) emphasizes that individual behavior stems not only from internal drivers but also relies on the observation, perception, and cognitive processing of external environmental cues. In the entrepreneurial context, this theory explains how individuals process external information through cognitive systems and translate it into behavioral tendencies. Specifically, entrepreneurial alertness—manifested as opportunity recognition, information integration, and judgment—serves as a critical mediating mechanism connecting individual traits with entrepreneurial intention. Entrepreneurial passion may enhance individuals’ kinetic focus on opportunities, entrepreneurial curiosity drives active information acquisition and processing, while digital competency strengthens their ability to discern and capture meaningful signals in complex information environments. Grounded in SCT, we derive a cognitive pathway whereby individual traits indirectly influence entrepreneurial intention through entrepreneurial alertness.

The Theory of Planned Behavior (TPB) focuses on the formation structure of behavioral intention, proposing that individual behavioral decisions are shaped by three key factors: attitudes toward the behavior, subjective norms, and perceived behavioral control (Ajzen, 1991; Chahal et al., 2024a, 2024b). In this study, entrepreneurial passion reflects university students’ positive affective endorsement of entrepreneurial behavior, constituting the attitudinal dimension. Entrepreneurial curiosity represents exploratory interest in the entrepreneurial process, belonging to the cognitive tendency in behavioral evaluation. Meanwhile, digital competency embodies individuals’ self-perceived capability to execute entrepreneurial actions, forming the cognitive basis of perceived behavioral control.TPB not only implies that these trait variables may directly enhance entrepreneurial intention but also provides a theoretical rationale for their indirect effects through cognitive mechanisms. Therefore, from the TPB perspective, this study considers both the direct effects of trait variables and the mediating role of entrepreneurial alertness.

Self-Determination Theory (SDT) further complements the moderating role of motivation in behavioral formation. The theory posits that the autonomy and persistence of behavior fundamentally rely on the internalization level of motivation (Ryan and Deci, 2000; Chahal et al., 2025). The more intrinsically driven the motivation, the more likely individuals are to voluntarily and consistently engage in specific behaviors.In the entrepreneurial context, entrepreneurial motivation influences whether individuals translate their existing passion, interest, and competencies into concrete behavioral intentions. When motivation levels are high, entrepreneurial traits are more likely to stimulate entrepreneurial intention. Conversely, when motivation is weak, even individuals with relevant traits may struggle to formulate clear entrepreneurial objectives. Thus, within the “trait–intention” pathway, entrepreneurial motivation may function as a critical moderating variable, shaping the significance and direction of these relationships.

Based on the above theoretical analysis and logical derivation, this study constructs a theoretical model with entrepreneurial passion, entrepreneurial curiosity, and digital competency as antecedent variables, entrepreneurial alertness as a mediating variable, and entrepreneurial motivation as a moderating variable, and proposes the research hypotheses shown in Table 1.

Table 1 Summary of Research Hypotheses.
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Research design

Data sources

This study focuses on university students in the context of China’s digital economy development. Samples were collected from five higher education institutions and vocational colleges in eastern, southern, western, northern, and central China, covering both regular undergraduate universities and polytechnic colleges. The sample includes a wide range of majors, such as management, economics, information engineering, and social sciences, ensuring representativeness and diversity. A purposive sampling method was employed, specifically targeting college students to examine their entrepreneurial intentions and influencing mechanisms in a digital economy environment.

The formal data collection began in December 2023. With the assistance of student affairs departments and class advisors at each institution, the research team distributed electronic questionnaire links or paper questionnaires through campus administrative systems. Both online and offline distribution methods were used to accommodate different institutional teaching and technological conditions. The survey was conducted anonymously, with all participants explicitly reading and confirming their informed consent at the beginning of the questionnaire, acknowledging their voluntary participation and ensuring that collected data would be used solely for academic research, kept strictly confidential, and analyzed in an anonymized statistical format.Approximately 420 questionnaires were distributed, with 381 valid responses obtained, yielding an effective response rate of approximately 90.7%. The overall collection results were satisfactory, with balanced regional sample distribution supporting subsequent structural modeling and statistical analysis.

Variable measurement

This study focuses on examining the influence of individual trait variables—entrepreneurial passion (EP), entrepreneurial curiosity (EC), and digital competency (DC)—on university students’ entrepreneurial intention (EI), while introducing entrepreneurial alertness (EA) as a mediating variable and entrepreneurial motivation (EM) as a moderating variable to construct a structural model. All variables were measured based on established authoritative literature, with appropriate cultural adaptations made for the Chinese university student context. Each item was rated using a five-point Likert scale (1 = “strongly disagree,” 5 = “strongly agree”) to reflect respondents’ level of agreement with the given statements.

The design of internal factors closely aligns with university students’ behavioral and psychological responses in the digital economy context. Entrepreneurial passion measures individuals’ emotional engagement and goal commitment in digital entrepreneurial tasks. Entrepreneurial curiosity reflects their willingness to explore emerging technological trends, informational scenarios, and business models. Digital competency assesses students’ self-evaluated proficiency in using digital tools, adapting to online interaction environments, and understanding platform-based entrepreneurial mechanisms, embodying their foundational capability to undertake entrepreneurial tasks in a digital economy. These three dimensions were measured using five items each, adapted from the scales of Magni et al. (2021a, 2021b) and Kuckertz and Wagner (2010). Entrepreneurial intention was measured via two items based on the studies of Ajzen (1991) and Liñán and Chen (2009), evaluating students’ subjective inclination toward future entrepreneurial engagement. Entrepreneurial alertness adopted Tang et al.‘s (2012) classic three-dimensional model, employing three items to assess students’ scanning ability for environmental changes, opportunity evaluation skills, and acumen in resource connection. Finally, the entrepreneurial motivation scale referenced Ryan and Deci’s (2000) self-determination theory and was divided into intrinsic and extrinsic motivation dimensions, totaling six items.

The questionnaire also included demographic variables such as gender (GE), academic year (GR), major type (MA), and institution category (SC), all collected through closed-ended categorical options and used as control variables for sample characterization and subsequent heterogeneity analysis. Gender was coded as a binary variable (1 = male, 2 = female), academic year included four options from freshman to senior year (1 = freshman, 2 = sophomore, 3 = junior, 4 = senior), major types were classified into management, economics, engineering, and social sciences categories (coded 1-4 respectively), and institution category was divided into regular undergraduate universities and vocational colleges (1 = regular undergraduate, 2 = vocational college). The final questionnaire contained 24 measurement items and 4 demographic items, which were finalized after small-sample pretesting and expert evaluation, demonstrating sound structural logic and measurement reliability that met the requirements for subsequent structural equation modeling analysis.

Data analysis methods

Before the formal implementation of the questionnaire, the study conducted a preliminary pilot test with 30 university students. The reliability of the data was analyzed using SPSS 26.0, with Cronbach’s Alpha values calculated for each variable dimension to assess internal consistency and clarity of item descriptions. This stage was solely used for preliminary questionnaire refinement and was not included in the main analysis.

For formal data analysis, the study employed the Structural Equation Modeling (SEM) approach to examine the path relationships among variables in the research model, as well as to assess mediating and moderating effects. Given that the study involved multiple latent variables and interaction paths, with its focus on theoretical exploration and prediction rather than model fit optimization—and considering that the data did not strictly meet the normality assumption—Partial Least Squares Structural Equation Modeling (PLS-SEM) was adopted. This method is particularly suitable for small-to-medium sample sizes and complex path relationships in social science research, offering advantages such as robustness to non-normal data distribution and high analytical efficiency.

The data analysis process consisted of two stages: measurement model and structural model. First, the measurement model was evaluated to assess the reliability and validity of the latent variables, including outer loadings, average variance extracted (AVE), composite reliability (CR), and Cronbach’s Alpha values. All latent variables had to meet the following criteria to proceed to the structural model analysis phase: outer loadings > 0.6, AVE > 0.5, and both CR and Alpha values > 0.7. Subsequently, the significance of path relationships was examined, including analyses of mediating and moderating effects. The significance of these effects was evaluated using the Bootstrapping resampling method (5000 repetitions).

Results

Descriptive statistics

The descriptive statistics of study variables are presented in Table 2. The overall mean values of key variables were at mid-high levels, indicating that university students generally demonstrate strong entrepreneurial psychological traits in the digital economy context. Specifically, entrepreneurial motivation showed the highest mean value (3.985), suggesting most students exhibit relatively positive intrinsic and extrinsic driving willingness when facing entrepreneurial opportunities brought by the digital economy. Entrepreneurial curiosity and digital capability also reached mean values of 3.914 and 3.847, respectively, demonstrating students’ pronounced exploratory tendencies toward new technologies and information environments, along with a certain foundation of digital skills to support entrepreneurial activities.

Table 2 Descriptive Statistics Results.
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Entrepreneurial alertness showed a slightly lower mean value (3.623) compared with other individual trait variables, indicating that although students generally possess high entrepreneurial motivation and curiosity, there remains room for improvement in actively identifying opportunities and integrating resources. This observation was further confirmed by its relatively high standard deviation, suggesting significant individual differences in entrepreneurial alertness among students. Entrepreneurial passion and entrepreneurial intention showed comparable mean scores (3.762 and 3.792, respectively), indicating students’ emotional investment in entrepreneurial goals roughly matches their actual entrepreneurial willingness.Regarding control variables, gender distribution was relatively balanced, with sophomores and juniors constituting the majority of grade distribution. The sample covered various major types and institution categories, exhibiting good diversity that facilitates subsequent examination of heterogeneous characteristics. Overall, the sample data demonstrated favorable distribution characteristics and discriminability, providing a solid foundation for subsequent structural modeling and mediation/moderation effect tests.

Measurement model evaluation

The reliability and validity test results are presented in Table 3. All latent variables exhibited Cronbach’s Alpha values exceeding 0.7, indicating good internal consistency of the scales. The composite reliability (CR) values all surpassed 0.85, further confirming the strong overall reliability of each latent variable. Additionally, the average variance extracted (AVE) for all variables exceeded 0.5, demonstrating satisfactory convergent validity of the latent variables. All item outer loadings were greater than 0.6, with no significantly low measurement items observed, indicating that the items effectively reflected their corresponding latent constructs. In summary, all latent variables in the model demonstrated good reliability and convergent validity, meeting the prerequisite conditions for subsequent structural model analysis.

Table 3 Reliability and Validity Test Results of Latent Variables.
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Structural model results

Table 4 presents the path coefficients among the latent variables in the structural model. The results indicate varying levels of significance across different paths, with partial support for the proposed hypotheses, thereby validating the influence mechanism of university students’ entrepreneurial intention in the digital economy context.

Table 4 Path Analysis Results of Structural Model.
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The path coefficient from entrepreneurial passion (EP) to entrepreneurial intention (EI) was 0.072 (p = 0.226), failing to reach statistical significance, thus rejecting H1. Although entrepreneurial passion is often considered a critical intrinsic driver of entrepreneurial behavior, its effect did not appear to directly translate into stronger entrepreneurial intention under the digital economy setting examined in this study. A plausible explanation is that, in an environment dominated by digital platforms with evolving entry barriers, emotional engagement and subjective enthusiasm alone may be insufficient to support concrete entrepreneurial decisions—students may still require clear path planning and resource integration capabilities. Furthermore, digital entrepreneurship practices emphasize rational judgment and operational skills, meaning pure passion may not effectively mitigate risk perceptions and could instead be suppressed under high uncertainty.

The path coefficient from entrepreneurial curiosity (EC) to entrepreneurial intention (EI) was -0.116 (p = 0.016). While statistically significant, this negative relationship led to the rejection of H2. This finding contrasts with previous research emphasizing cognitive interest as a driver of entrepreneurial motivation. It possibly suggests that in the digital economy context, students’ curiosity manifests more as broad exposure to information, technology, and trends rather than business-oriented implementation. Information overload and technological fragmentation may trap individuals in perpetual exploration without forming focused entrepreneurial objectives, thereby increasing cognitive load and paradoxically diminishing intention.Furthermore, in a rapidly iterating and highly uncertain digital ecosystem, excessive curiosity or “exploratory dispersion” may exacerbate decision paralysis and opportunity costs, ultimately suppressing entrepreneurial intention.

The path coefficient from digital capability (DC) to entrepreneurial intention (EI) was 0.238 (p = 0.000), showing a significant positive relationship, supporting H3. This finding highlights the critical role of digital literacy in digital entrepreneurship scenarios. In platform-centric, tool-mediated digital entrepreneurial ecosystems, individuals’ entrepreneurial execution no longer relies solely on traditional management skills or social capital but heavily depends on their competencies in information processing, platform interaction, content creation, and technological integration.University students with stronger digital capabilities are more likely to develop entrepreneurial models through social media, content platforms, or e-commerce systems. By leveraging algorithmic logic and digital traffic to obtain user feedback and business opportunities, they enhance perceived feasibility and practical confidence in entrepreneurship.

The path coefficient from entrepreneurial motivation (EM) to entrepreneurial intention (EI) was −0.129 (p = 0.004), revealing a significant negative relationship. A plausible explanation is that sampled university students exhibited characteristics of “passive motivation,” such as undertaking non-autonomous entrepreneurial plans to fulfill academic requirements, comply with institutional policies, or respond to employment pressures. Consequently, such motivation fails to genuinely translate into positive intention and may even produce counterproductive effects.

The path between entrepreneurial passion (EP) and entrepreneurial alertness (EA) did not reach significance, with a coefficient of 0.094 (p = 0.135). This suggests that while emotional engagement may enhance subjective motivation, it does not effectively improve individuals’ ability to recognize external opportunities in digital contexts. This result indirectly supports that passion may operate more on sustaining entrepreneurship than on the initial opportunity identification stage.

Entrepreneurial curiosity (EC) demonstrated a significant positive impact on entrepreneurial alertness (EA), with a path coefficient of 0.208 (p = 0.001). In the digital economy context, while entrepreneurial curiosity may not directly boost entrepreneurial intention, it could indirectly function by enhancing individuals’ sensitivity to market opportunities and digital trends. Specifically, curiosity-driven information sensitivity helps students improve their ability to identify potential demands and emerging models in digital scenarios, thereby providing cognitive support for entrepreneurial intentions.

Meanwhile, the path coefficient from digital capability (DC) to entrepreneurial alertness (EA) was 0.301 (p = 0.000), demonstrating a significant positive relationship. This result further underscores that digital capability serves not only as a foundational resource at the operational level but also as core cognitive capital. In data-driven digital entrepreneurial platforms with real-time feedback, individuals with technological application skills demonstrate enhanced entrepreneurial alertness through better comprehension of user behavior and identification of market gaps. This cognitive advantage provides critical support for the formation and reinforcement of entrepreneurial intention.

The path coefficient from entrepreneurial alertness (EA) to entrepreneurial intention (EI) was 0.231 (p = 0.001), indicating a statistically significant positive relationship, thus validating H4. As the cognitive embodiment of information acquisition and opportunity recognition in the digital era, alertness significantly facilitates university students’ entrepreneurial decision-making. This confirms that in complex technological environments, those who can identify market niches earlier exhibit stronger entrepreneurial predisposition.

In summary, the structural model analysis demonstrates that in the context of the digital economy’s innovation wave, H3 and H4 received significant support, whereas H1 and H2 were either unsupported or showed contradictory directions. This further substantiates the decisive role of cognitive capability in digital entrepreneurial behavior. Additionally, all variance inflation factor (VIF) values were below 2, eliminating multicollinearity concerns and confirming the model’s reliability.

Mediation effect analysis

This study further employed the Bootstrapping method to examine three mediation pathways, assessing the mediating role of entrepreneurial alertness (EA) in the relationship between entrepreneurial passion (EP), entrepreneurial curiosity (EC), and digital capability (DC) on entrepreneurial intention (EI) (Table 5). The results demonstrated that both EC and DC significantly and indirectly influenced university students’ EI through EA, whereas the mediated effect of EP was statistically insignificant.

Table 5 Mediation Effect Test Results.
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Specifically, entrepreneurial curiosity (EC) exhibited a significant indirect effect on EI through EA (β = 0.032, p = 0.042), supporting H6. These findings indicate that university students’ novelty-seeking tendencies in the digital economy context significantly enhance their sensitivity to external entrepreneurial opportunities, thereby fostering EI. As an intrinsically driven cognitive trait, curiosity may indirectly contribute to intention formation by activating individuals’ information processing and opportunity-recognition abilities.Similarly, digital capability (DC) demonstrated a highly significant indirect effect on EI through EA (β = 0.117, p < 0.001), validating H7. This suggests that students’ mastery of digital skills—including information retrieval, technology application, and digital resource integration—strengthens their ability to identify and evaluate potential entrepreneurial opportunities, thereby increasing their entrepreneurial propensity. DC thus functions not only as a crucial adaptive resource in the digital economy but also as a key cognitive mediator in the formation of entrepreneurial intention.

In contrast, the indirect effect of entrepreneurial passion (EP) on entrepreneurial intention (EI) through entrepreneurial alertness (EA) was statistically insignificant (β = −0.004, p = 0.467), failing to support H5. This result suggests that while passion, as a positive emotional state, may directly drive behavioral motivation, it does not meaningfully enhance EI by improving opportunity recognition capabilities. One plausible explanation is that passion primarily functions at the volitional level by energizing motivation rather than systematically processing and evaluating opportunity signals, leading to its weak mediating role in the cognitive pathway.

In summary, entrepreneurial alertness (EA) serves as a significant mediator in transforming cognitive entrepreneurial traits (such as curiosity and digital capability) into entrepreneurial intention, but fails to establish an effective mediating mechanism for affective traits (such as passion). This finding highlights how distinct types of personal traits differentially influence mediation pathways. It underscores that, in the digital economy context, entrepreneurship education should prioritize cultivating students’ cognitive sensitivity and discernment to effectively foster entrepreneurial intention formation.

Moderating effect analysis

This study introduced interaction terms to examine the moderating role of entrepreneurial motivation in the pathways through which entrepreneurial individual traits influence entrepreneurial intention. As shown in Table 6, entrepreneurial motivation demonstrated significant moderating effects in some pathways, exhibiting conditional enhancement effects.

Table 6 Moderating Effect Results.
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When the interaction term of entrepreneurial motivation was introduced between entrepreneurial passion and entrepreneurial intention, its moderating effect did not reach statistical significance (β = 0.041, p = 0.174), indicating that hypothesis H8 was not supported. This result suggests that although entrepreneurial passion, as a positive emotional experience, may directly enhance individuals’ entrepreneurial behavioral intentions, its influence on entrepreneurial intention does not significantly change with different levels of motivation. In other words, the strength of motivation does not significantly alter the driving effect of passion on entrepreneurial intention, indicating that while affective factors have broad effects in the activation stage, they show low sensitivity in regulating the intention formation process. In contrast, entrepreneurial motivation showed a significant moderating effect on the pathway between entrepreneurial curiosity and entrepreneurial intention (β = 0.068, p = 0.042), supporting hypothesis H9. This suggests that when individuals have higher levels of entrepreneurial motivation, the positive effect of entrepreneurial curiosity on entrepreneurial intention becomes more pronounced. This result can be interpreted as meaning that curiosity-driven information exploration behavior is more easily transformed into specific goal-oriented entrepreneurial behaviors under strong motivational support, with the interaction strengthening the cognitive connection between opportunity attention and action intention.Further analysis also revealed that entrepreneurial motivation had a significant positive moderating effect on the pathway between digital capability and entrepreneurial intention (β = 0.110, p = 0.001), supporting hypothesis H10. This result emphasizes that in high entrepreneurial motivation states, individuals’ digital capabilities not only provide fundamental tool support for identifying opportunities but are more likely to facilitate the conversion of cognitive abilities into action tendencies. This mechanism indicates that higher motivation levels make individuals more inclined to internalize their technological resource cognition into actual entrepreneurial motivations, thereby significantly increasing intention levels.

Discussion

Against the backdrop of rapid digital economic development, this study validated the formation mechanism of entrepreneurial intention among college students. The results indicate that digital capability and entrepreneurial alertness have significant positive effects on entrepreneurial intention, while entrepreneurial passion and curiosity do not directly translate into higher levels of intention—in some cases, they even demonstrate negative effects. Meanwhile, entrepreneurial alertness plays a crucial mediating role in the pathways through which curiosity and digital capability influence intention, while entrepreneurial motivation exerts significant moderating effects on the impact of curiosity and digital capability on intention. These findings reveal that cognitive traits, compared to affective factors, are more effective in driving students’ entrepreneurial decision-making within the digital economy context, and also demonstrate that the interaction between motivation and capability can further strengthen intention formation.

Compared with existing research, this study exhibits certain differences and innovations in both theory and methodology. First, Wang et al. (2021) found that digital skills significantly enhance entrepreneurial intention by strengthening entrepreneurial self-efficacy, but their study did not account for the cognitive chain of entrepreneurial alertness. While consistently confirming the importance of digital capability, this research further highlights that digital capability indirectly promotes intention formation by enhancing opportunity identification sensitivity, thereby expanding the understanding of the capability-cognition-intention pathway. Second, Othman et al. (2023) emphasized that students’ curiosity in digital entrepreneurship education helps stimulate novelty and learning interest, yet their findings primarily focused on positive effects. In contrast, this study reveals that curiosity in information-overloaded and uncertain contexts may lead to a decline in intention. This divergence illustrates the negative consequences of “exploration overload” in the digital economy, enriching the interpretation of curiosity’s dual effects. Finally, Nuringsih et al. (2024) demonstrated that digital entrepreneurship is closely linked to sustainable development, emphasizing the importance of external environmental support. In contrast, this study focuses on individual-level intrinsic psychological mechanisms and, by incorporating the moderating effect of entrepreneurial motivation, elucidates the interaction between cognitive resources and psychological drives, further deepening the understanding of the dynamic cognitive pathways through which individual traits influence entrepreneurial intention.

The theoretical contribution of this study lies in constructing an integrated model of “digital capability—entrepreneurial alertness - entrepreneurial motivation - entrepreneurial intention,” revealing the underlying mechanism through which individual cognitive traits and psychological motivations collectively influence entrepreneurial decision-making via interaction in the digital economy context. This expands the boundaries of entrepreneurial psychological variables in studying entrepreneurial intentions. Additionally, by introducing digital capability and opportunity identification—contextually adaptive and practice-oriented external and cognitive factors—into the framework of entrepreneurial motivation’s moderating effects, this study moves beyond previous research that mainly focused on single traits or direct effects. It thereby enriches the paradigm for analyzing psychological driving forces within the contexts of entrepreneurship education and the digital economy.

Based on the research findings, the following practical recommendations are proposed: Regarding digital capability development, universities should incorporate practical modules such as data analysis, content creation, and platform operations into curricula. This enables students to independently build business models and apply emerging technologies to solve real-world problems within digital environments. Establishing entrepreneurship laboratories or virtual simulation platforms can help students develop technical integration and tool application skills in real-world data and market scenarios. Such targeted training is more conducive to the formation and strengthening of entrepreneurial intentions.For guiding entrepreneurial curiosity, institutional mechanisms should be designed to transform exploratory interests into directed practical actions. For example, integrating competition projects, entrepreneurship incubator boot camps, and similar formats can channel students’ curiosity about new technologies and trends into concrete business solution development and market validation processes. This approach not only prevents exploration dispersion caused by information overload but also maintains entrepreneurial goal focus and execution during ongoing exploration.To enhance entrepreneurial alertness, universities should establish interdisciplinary information-sharing and resource-linking mechanisms, providing students with multi-channel market insights and innovation opportunities. Through opportunity identification training, simulated investment negotiations, and university-enterprise collaborative innovation projects, students can develop quicker responsiveness to potential demands and gain the ability to align external resources with their own conditions. Such training helps internalize cognitive sensitivity into action-oriented entrepreneurial decisions, thereby facilitating the realization of entrepreneurial intentions.

This study has several noteworthy limitations. First, the sample was confined to university and vocational college students in China. Despite covering different regions and institutional types, the specific cultural and institutional contexts may limit the generalizability of the findings. Second, the data were primarily collected through self-reported questionnaires, which may introduce subjective biases and social desirability effects among respondents, potentially impacting the objectivity of variable measurement and the robustness of the results. Third, the cross-sectional research design only reveals correlations between variables and fails to fully capture the evolving mechanisms of entrepreneurial intention in dynamic processes, thereby limiting the validity of causal inferences to some extent.

Conclusion

Drawing on 381 valid responses from universities and vocational colleges across China’s five major geographical regions (east, south, west, north, and center), this research systematically examines factors influencing university students’ entrepreneurial intentions, with particular emphasis on the mediating and moderating roles played by motivation and entrepreneurial alertness. Using structural equation modeling (SEM), the analysis yielded the following key findings:

  1. (1)

    A strong positive direct relationship exists between digital capability (DC) and entrepreneurial intention (EI). After adjusting for demographic and academic factors including gender, year in program, discipline, and institutional type, the standardized effect of DC on EI was β = 0.238 (p = 0.000). Entrepreneurial alertness (EA) was similarly found to be a significant positive predictor of EI (β = 0.231, p = 0.000). These results suggest that functional abilities and opportunity recognition together form essential cognitive elements that encourage the formation of entrepreneurial intentions in the context of a digital economy. However, entrepreneurial passion (EP) did not significantly predict EI (β = 0.072, p = 0.226), and entrepreneurial curiosity (EC) was negatively associated with EI (β = −0.116, p = 0.016). This indicates that purely affective involvement and general exploratory traits are not enough to directly result in increased entrepreneurial intentions.

  2. (2)

    The relationship between cognitive traits and entrepreneurial intention (EI) is significantly mediated by entrepreneurial alertness (EA). Mediation testing confirmed notable indirect effects from both entrepreneurial curiosity (EC) and digital capability (DC) on EI through EA (EC → EA → EI: β = 0.032, p = 0.042; DC → EA → EI: β = 0.117, p = 0.000). However, entrepreneurial passion (EP) did not produce a meaningful indirect effect (β = -0.004, p = 0.467). This highlights that, in digital economic environments, the ability to recognize, evaluate, and leverage opportunities forms the essential pathway translating cognitive dispositions into intentional outcomes.

  3. (3)

    Entrepreneurial motivation (EM) enhances the pathway from cognitive traits to intention. Analysis of moderation effects indicated that EM significantly intensified the relationship between entrepreneurial curiosity (EC) and EI (interaction term γ = 0.068, p = 0.042), as well as between digital capability (DC) and EI (γ = 0.110, p = 0.001). In contrast, EM did not significantly moderate the effect of entrepreneurial passion (EP) on EI (γ = 0.041, p = 0.174). These results suggest that stronger motivation more effectively converts exploratory tendencies and competency-based advantages into intentional actions, whereas it has little influence on factors primarily driven by emotion.

This study uncovers the complex mechanisms by which cognitive traits and psychological motivation shape college students’ entrepreneurial intentions within the digital economy. Future research may advance along several paths. First, expanding the sample to include students from diverse countries and regions would allow systematic comparison across different levels of digital economic development, entrepreneurship education systems, and institutional settings. Such efforts could help verify the cross-cultural validity of the intention formation model and identify possible moderating roles of external institutional factors.Second, regarding data collection, going beyond self-reported measures is recommended. Incorporating outcomes from business competitions, evaluations from incubation programs, peer or mentor assessments, and behavioral traces from digital platforms would provide a multidimensional perspective on entrepreneurial motivation, digital literacy, and alertness. Data integration techniques should be applied to improve the objectivity and robustness of measurements.Finally, longitudinal tracking and experimental interventions are encouraged to monitor changes in entrepreneurial intention over time or under different educational conditions. Future work should also compare how various pedagogical methods—such as traditional courses, virtual simulations, and cross-disciplinary labs—affect the development of entrepreneurial alertness and motivation. These approaches would offer deeper causal insights and illuminate the dynamic processes through which entrepreneurial intentions evolve.