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117-127 

117 
 

 

 

Article 

Digital-enhanced talent cultivation mechanisms in 
entrepreneurial universities: an AI-integrated 
multi-level analysis of student entrepreneurial 
intentions 
Zhiyuan Lyu1,2, Yusri Kamin1* 
1Faculty of Educational Sciences and Technology, Universiti Teknologi Malaysia, Malaysia 
2College of Continuing Education, WenZhou Polytechnic, Wenzhou Zhejiang, China 

A R T I C L E   I N F O 
 

Article history: 
Received 25 May 2025  
Received in revised form 
20 July 2025 
Accepted 07 August 2025 
 
Keywords:  
Entrepreneurial university,  
Digital talent cultivation, AI-enhanced learning, 
Entrepreneurial intention, Institutional theory, 
Personalized development pathways 
 
*Corresponding author 
Email address: 
p-yusri@utm.my 
 
DOI: 10.55670/fpll.futech.4.4.10 

A B S T R A C T 
 

This study examines talent cultivation in entrepreneurial universities and 
investigates how formal and informal factors affect students' entrepreneurial 
intentions. Analysis of 782 students from eight Chinese universities, enhanced 
by machine learning predictive models, reveals that informal culture, 
particularly entrepreneurial culture (𝛽𝛽 =  0.36), combined with AI-powered 
personalized learning pathways ( 𝛽𝛽 =  0.28), correlates significantly with 
entrepreneurial intentions. The interaction between curriculum and culture 
( 𝛽𝛽 = 0.23 ) suggests that educational efforts achieve greater effectiveness 
within supportive cultural environments. This research contributes to 
entrepreneurial talent development through institutional theory lenses and 
offers a contextual framework for universities to strategically shape 
entrepreneurial attitudes amid rapid changes in Chinese higher education. 

1. Introduction 
Over the past few decades, the complex structure of 

higher education has transformed tremendously, with 
entrepreneurial activity becoming increasingly important in 
university missions alongside conventional teaching and 
research functionalities [1]. This shift has positioned 
universities as crucial incubating institutions for 
entrepreneurial skills, particularly in China, where innovation 
policies emphasize entrepreneurship education [2]. The gap 
between substantial funding for entrepreneurial initiatives 
and their limited effectiveness in nurturing actual 
entrepreneurial intentions among students reveals 
uncertainties about talent cultivation systems' functioning. 
This disconnect manifests particularly in understanding how 
different institutional components collaboratively influence 
students' entrepreneurial attitudes and actions. From a 
human resource development (HRD) perspective, 
entrepreneurial universities represent strategic human 
capital cultivation ecosystems that systematically develop 
entrepreneurial competencies through evidence-based talent 
management approaches [3, 4]. The rapid advancement of 

artificial intelligence and digital technologies has 
fundamentally transformed entrepreneurial education 
landscapes. AI-powered tools enable universities to provide 
personalized learning experiences, predictive analytics for 
talent identification, and intelligent mentoring systems that 
significantly enhance traditional talent cultivation 
mechanisms [5, 6]. This digital transformation presents both 
opportunities and challenges for entrepreneurial universities 
seeking to optimize talent development ecosystems through 
evidence-based, technology-enhanced approaches. 
Entrepreneurial intention, defined as an individual's 
deliberate commitment to launch a business, represents a key 
precursor to actual entrepreneurial activity [7]. While 
numerous studies examine entrepreneurship education's role 
in achieving these objectives, many concentrate exclusively 
on teaching aspects rather than holistic talent development 
ecosystems within entrepreneurial universities [8]. 
Moreover, existing literature relies predominantly on single-
level analyses, overlooking operational nexuses of 
institutional components at various levels within college 

 

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November 2025| Volume 04 | Issue 04 | Pages 117- 127 

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Z. Lyu & Y. Kamin. /Future Technology                                                                          November 2025| Volume 04 | Issue 04 | Pages 117-127 

118 
 

settings [9]. This gap hinders a comprehensive understanding 
of optimal entrepreneurial talent nurturing approaches. 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
Institutional theory enables analysis of this complex 

phenomenon by distinguishing between formal institutions 
(programs, policies, regulations) and informal institutions 
(norms, cultures, networks, mentorships) [10]. This 
framework facilitates understanding how various talent 
cultivation mechanisms affect students' entrepreneurial 
intentions at granular scales. However, insufficient literature 
employs multi-level institutional analysis of talent cultivation 
mechanisms in entrepreneurial universities, particularly in 
Chinese settings where institutional framework 
configurations differ substantially from Western contexts 
[11]. Addressing this important gap in existing literature, this 
study investigates how universities stimulate entrepreneurial 
talent through formal and informal institutional constituents. 
The research examines talent cultivation mechanisms in 
entrepreneurial universities and their influence on students' 
entrepreneurial motivation through multi-level institutional 
theory lenses. This investigation assists university managers 
and policymakers in enhancing entrepreneurial education 
outcomes aligned with China's innovation-driven 
development strategy [12]. 

2. Literature review 
2.1 Problem context and research gaps 

The entrepreneurial university paradigm faces a critical 
challenge: despite substantial investments in 
entrepreneurship education infrastructure, student 
entrepreneurial intention conversion rates remain 
suboptimal, particularly in emerging economies. Chinese 
universities exemplify this paradox, where government-led 
initiatives have created extensive entrepreneurial education 
programs, yet actual student venture creation lags 
significantly behind policy expectations [2].  

This implementation gap suggests fundamental 
misalignment between talent cultivation mechanisms and 
student entrepreneurial development needs. Three 
interconnected problems emerge: (1) overemphasis on 
formal curriculum delivery without corresponding cultural 
transformation, (2) limited understanding of how digital 
technologies reshape traditional talent development 
pathways, and (3) absence of integrated frameworks 
connecting institutional support systems with individual 
entrepreneurial outcomes. These gaps necessitate a 
comprehensive investigation of multi-level institutional 
influences, particularly examining how formal and informal 
mechanisms interact within digitally-enhanced educational 
environments. 

 

 

2.2 Evolution of entrepreneurial universities 
Over recent decades, the entrepreneurial university 

concept has evolved, transforming institutions from passive 
knowledge providers into active, sophisticated ecosystems 
fostering entrepreneurial spirit and skills. Wurth [1] 
characterizes such universities as self-organizing systems 
wherein disparate teaching, research, and business 
enterprise methods operate without academic disciplinary 
restrictions. This perspective offers a clearer understanding 
of how various university environment elements contribute 
to talent development and nurturing goals. Chinese 
universities particularly exemplify this evolution, designing 
comprehensive entrepreneurial courses combining 
theoretical and practical components [2]. However, these 
programs often lack adequate integration across institutional 
levels, considerably decreasing the chances of fostering 
entrepreneurial intentions among students. Talent nurturing 
processes in entrepreneurial universities encompass varied 
formal and informal institutional components aimed at 
fostering entrepreneurial skills. Formal mechanisms typically 
comprise systematized entrepreneurship education 
programs, available incubation space, and subsidized policies 
[13]. Studies on entrepreneurial intentions indicate several 
important elements, particularly regarding educational 
activities. Vivekananth et al. [14] demonstrate that 
entrepreneurship education increases self-efficacy and self-
imposed intentions at university levels, with self-efficacy 
playing important mediating roles. This confirms the 
importance of educational intervention, but it does not 
account for the varied execution methods across institutional 
settings. 

Recent advances in educational technology have 
introduced AI-driven assessment tools and adaptive learning 
platforms personalizing entrepreneurial education based on 
individual student profiles, learning styles, and career 
aspirations [15, 16]. Bell and Bell [17] demonstrate that 
generative AI technologies significantly enhance 
entrepreneurial self-efficacy through personalized learning 
experiences, while Mac Aodha and Ramalingam [18] found AI-
powered tools improve students' entrepreneurial 
competencies, suggesting the need to integrate digital 
innovation into talent cultivation frameworks. Similarly, 
Jiatong et al. [19] emphasize entrepreneurial attitudes and 
creativity as bearing on intentions, indicating successful 
talent development integrates beyond traditional pedagogical 
methodologies to include psychological and artistic aspects. 
These deliberations extend talent cultivation discussions by 
suggesting systems should concentrate on entrepreneurial 
skills beyond technical education aspects. 

2.3 Institutional theory applications 
Institutional theory proves helpful in understanding 

different university components' contributions toward 
entrepreneurial activity. Rocha et al. [10] employ this theory 
to explain university entrepreneurial ecosystem effectiveness 
and regional diversity effects, proposing that contextual 
elements significantly adjust talent nurturing system potency. 
These varying degrees of context responsiveness emphasize 
the need to refine entrepreneurship educational approaches 
considering particular institutional frameworks. Bergmann et 
al. [20] develop this by analyzing the combined effects of 
entrepreneurial climate, gender, and formal education on 
startup activity, revealing sophisticated institutional impact 
forms beyond simple cause-and-effect relations. 
Relationships between formal and informal institutional 
components remain understudied in the literature, especially 

Abbreviations 
AI Artificial Intelligence 
HEI Higher Education Institution 
HLM Hierarchical Linear Modeling 
HRD Human Resource Development 
ICC Intraclass Correlation Coefficient 
ML Machine Learning 
SHAP SHapley Additive exPlanations 
STEM Science, Technology, Engineering, and Mathematics 
VR Virtual Reality 



Z. Lyu & Y. Kamin. /Future Technology                                                                          November 2025| Volume 04 | Issue 04 | Pages 117-127 

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in China, where institutional frameworks may vary greatly 
from Western contexts. 

2.4 Global perspectives on digital entrepreneurship 
education 
Recent international studies provide comparative 

insights into the evolution of digital entrepreneurship 
education. European universities demonstrate advanced 
integration of AI-powered learning analytics, with 
institutions in Germany and Finland achieving 40% 
improvement in entrepreneurial competency development 
through personalized learning pathways [21]. American 
entrepreneurial universities emphasize ecosystem 
approaches, where digital platforms facilitate cross-
institutional collaboration and resource sharing [22]. 
Comparative analysis reveals distinct regional approaches: 
Western institutions prioritize individual-centered digital 
tools focusing on personal entrepreneurial journey mapping, 
while Asian contexts emphasize collective learning platforms 
and group-based digital collaboration [23]. These differences 
highlight the importance of contextual adaptation in digital 
entrepreneurship education design, supporting this study's 
focus on Chinese institutional environments where collective 
cultural values intersect with individual entrepreneurial 
aspirations. 

2.5 Research gaps summary 
Although notable research exists regarding 

entrepreneurship education and entrepreneurial intentions, 
glaring omissions persist concerning talent nurturing 
mechanisms in entrepreneurial universities. Several 
investigations take limited views, concentrating on particular 
educational interventions while neglecting entire support 
systems [12]. The interplay of various institutional 
components forming entrepreneurial outcomes remains 
uncaptured by these approaches. Additionally, studies 
employing multi-level analyses capable of explaining 
institutional factor impacts on entrepreneurial intentions in 
nested contexts remain scarce [9]. This highlights significant 
methodological issues given universities' multi-level 
institutional depth. Contextual specificity remains lacking, 
particularly regarding talent cultivation mechanism 
variations across institutional environments, especially in 
non-Western countries like China [11]. Resolving these issues 
requires integrated theoretical frameworks that acknowledge 
the complexity and multilevel nature of entrepreneurial 
talent cultivation phenomena within specific institutional 
settings. 

3. Theoretical framework and research hypotheses 
This study develops a multi-level framework 

synthesizing institutional theory with human resource 
development (HRD) principles to examine talent cultivation 
mechanisms' impact on student entrepreneurial intentions in 
entrepreneurial universities. Institutional theory provides 
the structural lens for understanding how formal regulations 
and informal cultural norms shape behavior [24], while HRD 
theory offers process-oriented insights into systematic 
competency development and talent management [3, 4]. This 
theoretical synthesis creates a unique analytical framework 
where institutional components are reconceptualized as 
strategic HRD interventions. Formal institutions (curriculum, 
platforms, policies) represent structured talent development 
programs, while informal institutions (culture, mentorship, 
networks) constitute organizational climate factors 
facilitating or constraining human capital development [25, 
26]. This integrated perspective advances beyond traditional 

institutional analysis by incorporating evidence-based talent 
management principles, thereby treating entrepreneurial 
universities as complex human capital development 
ecosystems rather than merely educational institutions. 
Institutional theory differentiates between informal and 
formal institutions, influencing individual behavior through 
regulatory, normative, and cognitive processes [24]. In 
university contexts, formal institutional components consist 
of structured, documented talent cultivation elements 
designed for implementation. These comprise 
entrepreneurship programs offering required knowledge 
fundamentals, practical platforms allowing experiential 
learning, and policies providing enabling conditions for 
entrepreneurial activity [8]. Zhang & Yang [2] assert these 
components profoundly shape entrepreneurial motivations 
through defined structures and diminished entrepreneurial 
challenges. However, their impact remains contingent upon 
the implementation degree, student motivation, and 
participation levels. 

Informal sociocultural interactions also serve as 
institutional factors shaping certain behaviors. 
Entrepreneurial culture within universities fosters normative 
and cognitive legitimation of entrepreneurial activity [20]. 
Mentorships assist in boosting students' self-entrepreneurial 
efficacy, while peers provide helpful networks for knowledge 
and emotional support [19]. Qi [27] notes these social 
informal components frequently impact entrepreneurial 
intentions more than formal educational processes like 
training programs. This suggests social aspects of 
entrepreneurial learning deserve serious consideration in 
higher education institutions' talent development strategies. 
This aligns with Liu's [28] observation that effective 
entrepreneurship education management must address not 
only operational skills but also entrepreneurship's mental 
aspects. 

These formal and informal cognitive components do not 
act separately; their interactions often prove multifaceted, 
potentially magnifying or mitigating impacts. Dabbous and 
Boustani [7] show that formal digital educational resources 
prove more useful when accompanied by informal supportive 
entrepreneurial cultures, while Smolka et al. [8] observe that 
compulsory entrepreneurship education yields limited 
results without informal support. Based on these arguments, 
this study proposes that strategically aligned and mutually 
reinforcing formal and informal institutional components 
strengthen the effects of underlying talent cultivation 
mechanisms on entrepreneurial intentions. This holistic 
understanding of entrepreneurial university phenomena 
contributes to explaining how such universities 
systematically foster entrepreneurial talent through multi-
layered formal and cultural systems pertaining to particular 
entrepreneurial learning environment structures and 
cultures. Based on the theoretical framework outlined above, 
the following hypotheses investigate talent cultivation 
mechanisms' influence on student entrepreneurial intentions: 

3.1 Formal institutional factors 
H1: Formal institutional factors positively influence student 
entrepreneurial intentions in entrepreneurial universities. 
• H1a: Entrepreneurship curriculum quality positively 

influences student entrepreneurial intentions. 
• H1b: Practice platform accessibility positively influences 

student entrepreneurial intentions. 
• H1c: Policy support adequacy positively influences student 

entrepreneurial intentions. 
 



Z. Lyu & Y. Kamin. /Future Technology                                                                          November 2025| Volume 04 | Issue 04 | Pages 117-127 

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3.2 Informal institutional factors 
H2: Informal institutional factors positively influence student 
entrepreneurial intentions in entrepreneurial universities. 
• H2a: Entrepreneurial culture positively influences student 

entrepreneurial intentions. 
• H2b: Mentorship quality positively influences student 

entrepreneurial intentions. 
• H2c: Peer network engagement positively influences 

student entrepreneurial intentions. 

3.3 Interaction effects 
H3: Formal and informal institutional factors interact 
synergistically to enhance their collective impact on student 
entrepreneurial intentions. 
• H3a: Entrepreneurship curriculum and entrepreneurial 

culture have a positive interaction effect on 
entrepreneurial intentions. 

• H3b: Practice platforms and mentorship quality have a 
positive interaction effect on entrepreneurial intentions. 

• H3c: Policy support and peer networks have a positive 
interaction effect on entrepreneurial intentions. 

3.4 Contextual factors 
H4: Student background characteristics moderate the 
influence of institutional factors on entrepreneurial 
intentions. 
• H4a: Formal institutional factors have a stronger influence 

on students without family entrepreneurial backgrounds. 
• H4b: The influence of informal institutional factors 

remains consistent across different demographic groups. 

3.5 Human resource development factors 
Drawing from strategic talent management literature, 

HRD factors focus on organizational-level talent development 
systems and processes [3, 4]. 
H5:Human resource development systems moderate the 
relationship between institutional factors and 
entrepreneurial intentions. 
• H5a: Strategic talent assessment mechanisms strengthen 

the formal institutional factors' influence on 
entrepreneurial intentions [26, 29]. 

• H5b: Comprehensive career development support 
enhances informal institutional factors' effectiveness [25, 
30]. 

3.6 Digital technology enhancement factors 
Building on digital transformation theory, digital 

enhancement represents technology-mediated learning 
innovations that transform traditional educational delivery [5, 
31]. 
H6:Digital technology integration amplifies talent cultivation 
effectiveness through personalized and adaptive learning 
mechanisms. 
• H6a: AI-powered personalization systems enhance formal 

curriculum delivery effectiveness [15, 32]. 
• H6b: Digital collaboration platforms strengthen peer 

network influences [33]. 
• H6c: Intelligent mentoring systems augment traditional 

mentorship quality [18, 34]. 

4. Research methodology 
This research utilizes mixed methods approaches, 

analyzing talent cultivation mechanisms' impact on student 
entrepreneurial intentions in Chinese entrepreneurial 
universities. This multi-level research question requires 
integrated approaches to institutional-level processes and 
individual-level results. Building upon established 

methodological constructs within entrepreneurship 
education research [8, 14], an overarching protocol 
combining quantitative survey research and qualitative 
analysis was developed. The methodological framework 
systematically examines multi-level institutional influences 
on student entrepreneurial intentions, incorporating 
machine learning algorithms identifying complex patterns in 
talent cultivation effectiveness. Random forest models 
analyze non-linear relationships between institutional factors 
and entrepreneurial outcomes, complementing traditional 
hierarchical linear modeling with predictive analytics 
capabilities [35, 36]. This enhanced methodological approach 
enables identification of previously undetected interaction 
effects and provides nuanced insights into the complex 
dynamics of entrepreneurial talent development. The 
framework integrates institutional theory as a theoretical 
foundation, guiding investigation of both formal and informal 
institutional factors within Chinese entrepreneurial 
university contexts. The research design employs mixed-
methods approaches, enabling comprehensive analysis of 
how institutional factors interact in shaping entrepreneurial 
intentions among university students. 

4.1 Data collection and sampling 
Data collection occurred across eight entrepreneurial 

universities located in different Chinese regions, preselected 
based on well-established entrepreneurship education 
programs and diverse institutional profiles. Following 
sampling methods utilized by Zhang and Yang [2], stratified 
random sampling ensured adequate representation across 
study fields, study levels, and sociocultural demographic 
variables. The sample included 782 undergraduate and 
graduate students participating in various entrepreneurial 
education courses. Demographic features showed even 
distribution by gender (53% female), study fields (42% STEM, 
38% business, 20% other), and institutional strata (68% 
undergraduate, 32% graduate). This sampling approach 
permits robust multi-level analysis and corresponds with 
contextual variance characterizing Chinese higher education 
systems. 

4.2 Machine learning analysis approach 
The Random Forest algorithm was selected for pattern 

recognition analysis due to its superior performance in 
handling non-linear relationships, interaction effects, and 
mixed data types, which are characteristic of educational 
research [35, 36]. Unlike traditional regression models, 
Random Forest captures complex interaction patterns 
without prior specification, making it particularly suitable for 
exploring emergent relationships in multi-level institutional 
data. Model interpretability was ensured through SHAP 
(SHapley Additive exPlanations) value analysis, decomposing 
each prediction into feature contributions. Feature 
importance rankings revealed that informal institutional 
factors contributed 42% to model predictions, while formal 
factors contributed 31%, with interaction effects accounting 
for 27%. This algorithmic validation corroborates 
hierarchical modeling results while revealing additional non-
linear patterns, particularly in technology-enhanced learning 
pathways where traditional statistical methods showed 
limited explanatory power. 

4.3 Measurement instruments 
Measurement instruments were developed through 

iterative processes informed by established scales in 
entrepreneurship literature. Entrepreneurial intention, the 
primary dependent variable, was measured using modified 



Z. Lyu & Y. Kamin. /Future Technology                                                                          November 2025| Volume 04 | Issue 04 | Pages 117-127 

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versions of six-item scales validated by Vivekananth et al. [14], 
assessing students' commitment to pursue entrepreneurial 
activities. For independent variables, formal institutional 
factors were measured using multi-item scales addressing 
curriculum quality, practice platform accessibility, and policy 
support adequacy. Informal institutional factors were 
assessed through scales measuring entrepreneurial culture 
perception, mentorship quality, and peer network 
engagement. As indicated in Table 1, all measurement scales 
demonstrated satisfactory reliability (Cronbach's α > 0.80) 
and validity indicators, consistent with methodological 
standards established in previous studies [9, 19]. 

4.4 Analytical approach 
The analytical approach employs hierarchical linear 

modeling (HLM), accounting for nested data structures, with 
individual students clustered within university environments. 
This multi-level analytical technique, similar to that employed 
by Zamfir et al. [9], allows simultaneous examination of 
individual-level variations in entrepreneurial intentions and 
institutional-level differences in talent cultivation 
mechanisms. Following Bergmann et al. [20], increasingly 
complex models were specified, testing direct effects, cross-
level interactions, and potential mediating mechanisms. 
Control variables include demographic factors (age, gender, 
family entrepreneurial background) and university 
characteristics (size, location, entrepreneurial orientation), 
which previous research identified as potentially 
confounding factors [12]. This methodological approach 
offers several advantages over single-level analyses prevalent 
in existing research. It explicitly accounts for educational 
influences' nested nature, recognizing students' embedding 
within specific institutional contexts, shaping entrepreneurial 
development. The approach enables examination of cross-
level interaction effects between institutional characteristics 
and individual attributes, providing insights into how talent 
cultivation mechanisms function differently across diverse 
student populations.  

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Mixed-methods dimensions enhance finding 
interpretability by contextualizing quantitative patterns 
within students' lived experiences navigating entrepreneurial 
pathways. These methodological strengths directly address 
limitations identified in previous research [10, 13] and align 
with calls for contextually sensitive approaches studying 
entrepreneurship education outcomes in diverse institutional 
settings. 

5. Research Results 
5.1 Descriptive statistics and correlation analysis 

This study reveals compelling findings regarding talent 
cultivation mechanisms' influence on student 
entrepreneurial intentions in Chinese entrepreneurial 
universities. Preliminary descriptive statistics indicated 
moderate to high entrepreneurial intentions among surveyed 
students (M = 3.76, SD = 0.92), suggesting generally positive 
orientations toward entrepreneurship. Among formal 
institutional factors, the entrepreneurship curriculum 
received the highest ratings (M = 3.58, SD = 0.97), followed by 
practice platforms (M = 3.49, SD = 1.05) and policy support 
(M = 3.16, SD = 1.12), indicating potential disparities in formal 
support mechanism implementation. Informal institutional 
factors generally received higher evaluations, with 
entrepreneurial culture (M = 3.92, SD = 0.85) and peer 
networks (M = 3.73, SD = 0.88) rated particularly favorably, 
while mentorship quality (M = 3.45, SD = 1.09) showed 
greater variability, reflecting Qi's [27] observation that 
informal cultural elements often constitute entrepreneurial 
university environments' most salient aspects. As illustrated 
in Figure 1, correlation analysis revealed significant 
associations between all talent cultivation mechanisms and 
entrepreneurial intentions, with correlation coefficients 
ranging from r = 0.32 to r = 0.59 (all p < 0.001). Notably, 
informal institutional factors demonstrated stronger 
correlations with entrepreneurial intentions (average r = 0.54) 
compared to formal factors (average r = 0.41), aligning with 
Liu's [28] assertion that psychological and social dimensions 
often exert greater influence on entrepreneurial development 
than structured educational interventions.  

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

             Table 1. Key variables and measurement approach  

Variable Type Variables Measurement Data Level 
Dependent Entrepreneurial Intention 6-item scale  

(α = 0.89) 
Individual 

Formal Institutional · Entrepreneurship Curriculum 
· Practice Platforms 
· Policy Support 

Multi-item scales 
(α = 0.82-0.85) 

Institutional 

Informal Institutional · Entrepreneurial Culture 
· Mentorship Quality 
· Peer Networks 

Multi-item scales 
(α = 0.83-0.88) 

Institutional/ 
Individual 

HRD Factors · Career Development Support 
· Digital Learning Platform Usage 
· Talent Assessment Systems 

Multi-item scales 
(α = 0.84-0.87) 

Individual/ 
Institutional 

Digital Enhancement 
Factors 

· AI-Powered Learning Analytics 
· Personalized Development Algorithms 
· Digital Mentoring Platforms 
· Virtual Reality Training Modules 

Multi-item scales 
(α = 0.86-0.89) 

Individual/ 
Institutional 

Talent Management · Performance Feedback Mechanisms 
· Professional Development Planning 
· Competency-Based Evaluation 

Multi-item scales 
(α = 0.81-0.85) 

Institutional 

Control Variables · Student Demographics 
· University Characteristics 
· HRD Program Participation 
· Digital Technology Adoption 

Standard measures Mixed 

Analysis Method Hierarchical Linear Modeling (HLM) with cross-level interactions 
Machine Learning (Random Forest) for pattern recognition 

- - 

Note: All scales use 5-point Likert format (1 = strongly disagree to 5 = strongly agree) 



Z. Lyu & Y. Kamin. /Future Technology                                                                          November 2025| Volume 04 | Issue 04 | Pages 117-127 

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Intercorrelation patterns further suggested potential 
interaction effects between formal and informal factors, with 
the strongest correlations observed between entrepreneurial 
culture and peer networks (r = 0.56, p < 0.001), indicating the 
interconnected nature of informal institutional elements in 
entrepreneurial universities. 
5.2 Hierarchical linear modeling analysis 

Hierarchical linear modeling results confirmed the 
appropriateness of multi-level analysis, with an intraclass 
correlation coefficient (ICC = 0.29) indicating 29% of the 
variance in entrepreneurial intentions attributable to 
university-level differences. Model testing proceeded 
sequentially: Model 1 included only control variables, Model 
2 added formal institutional factors, Model 3 incorporated 
informal institutional factors, and Model 4 tested interaction 
effects. The analytical approach mirrors that employed by 
Zamfir et al. [9], though it extends their framework by 
explicitly modeling cross-level interactions between 
institutional factors. Results revealed that while all formal 
institutional factors demonstrated significant positive effects 
in Model 2, their coefficients substantially reduced when 
informal factors were introduced in Model 3, suggesting 
potential mediation effects. Entrepreneurship curriculum 
maintained the strongest influence among formal factors (β 
= 0.28, p < 0.001), followed by practice platforms (β = 0.23, 
p < 0.001) and policy support (β = 0.17, p < 0.01).  

 

 

 

 

These findings extend Smolka et al.'s [8] results 
regarding entrepreneurship education effectiveness by 
demonstrating differential impacts across formal 
mechanisms and highlighting the complementary role of 
informal factors. 

5.3 Effects of formal and informal institutional factors 
Among informal institutional factors, entrepreneurial 

culture emerged as the most influential predictor (β = 0.36, 
p < 0.001), followed by mentorship quality (β = 0.31, p < 
0.001) and peer networks (β  = 0.26, p < 0.001). Cultural 
factors' prominence aligns with Bergmann et al.'s [20] 
findings regarding entrepreneurial climate importance, while 
mentorship quality's substantial influence supports Jiatong et 
al.'s [19] emphasis on self-efficacy as a critical mediating 
mechanism. These results suggest universities may need 
greater emphasis on cultivating supportive entrepreneurial 
cultures and mentorship programs rather than focusing 
exclusively on formal curricular interventions. Most notably, 
Model 4 revealed significant interaction effects between 
formal and informal institutional factors. Positive interaction 
between entrepreneurship curriculum and entrepreneurial 
culture (β  = 0.23, p < 0.001) indicates formal education 
produces substantially stronger effects when embedded 
within supportive cultural environments (as depicted in 
Figure 2), providing empirical validation for theoretical 
frameworks proposed by Dabbous and Boustani [7].  

 

   Figure 1. Descriptive statistics and correlation analysis of talent cultivation mechanisms 

 



Z. Lyu & Y. Kamin. /Future Technology                                                                          November 2025| Volume 04 | Issue 04 | Pages 117-127 

123 
 

 

 

 

Similarly, interaction between practice platforms and 
mentorship quality (β = 0.20, p < 0.01) suggests experiential 
learning opportunities yield greater benefits when 
complemented by quality guidance, consistent with Zhang 
and Yang's [2] qualitative observations regarding 
entrepreneurship education contextual enablers in Chinese 
universities. 
5.4 Interaction Effects and Robustness Analysis 

Supplementary analyses confirmed the robustness of the 
findings across different model specifications and subgroup 
analyses. Notably, formal institutional factors' influence 
proved more pronounced for students without family 
entrepreneurial backgrounds, suggesting university support 
mechanisms' particular vitality for first-generation 
entrepreneurs. Conversely, informal factors' effects remained 
relatively consistent across demographic groups, indicating 
their universal importance in entrepreneurial talent 
cultivation. These patterns extend Rocha et al.'s [10] findings 
regarding institutional effects' contextual sensitivity by 
identifying specific student characteristics moderating 
institutional influences on entrepreneurial intentions. As 
shown in Table 2, hierarchical linear modeling results 
demonstrate both formal and informal institutional factors' 
significant effects on entrepreneurial intentions, with 
informal factors showing stronger direct effects and 
important interaction effects with formal factors. These 
findings highlight the importance of adopting integrated 
approaches to entrepreneurial talent cultivation, strategically 
aligning formal educational structures with supportive 
cultural and social environments. 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

6. Discussion and implications 
This research offers an in-depth analysis of talent 

nurturing mechanisms in entrepreneurial universities, 
presenting subtle details on the impact of multi-level 
interactions on student entrepreneurial intentions. Using 
institutional theory, the study constructs comprehensive 
analytical frameworks categorizing and analyzing complex 
interactions between formal and informal institutional 
components, thereby enhancing understanding of 
entrepreneurial talent superstructure, particularly within 
Chinese higher education contexts. The most striking results 
problematize contemporary curriculum-based viewpoints by 
showing certain informal institutional components, 
particularly entrepreneurial culture, demonstrate much 
stronger impacts on entrepreneurial intentions than formal 
mechanisms (β = 0.36). This highlights the significant impact 
of culture and society on entrepreneurial ecosystem 
development. Additionally, the study explains formal and 
informal institutional components' mutual influence, where 
curriculum-culture interaction effects (β = 0.23) illustrate 
that educational interventions' effectiveness wholly depends 
on the institutional context. From human resource 
development perspectives, these findings provide crucial 
insights into how entrepreneurial universities function as 
strategic talent development organizations [4, 37]. Informal 
institutional factors' dominance ( β  = 0.36 for 
entrepreneurial culture) suggests effective entrepreneurial 
talent cultivation requires sophisticated HRD approaches 
beyond traditional training models, incorporating 
comprehensive organizational culture transformation, 
systematic mentorship programs, and integrated support 
systems [3, 26]. This aligns with contemporary talent 

   Figure 2. Interaction between curriculum and culture: effect on entrepreneurial intentions 

 



Z. Lyu & Y. Kamin. /Future Technology                                                                          November 2025| Volume 04 | Issue 04 | Pages 117-127 

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management theories emphasizing the importance of 
creating holistic learning ecosystems where individual 
development outcomes are significantly influenced by 
organizational climate and cultural factors [25, 30]. 
Significant interaction effects between formal and informal 
factors (β = 0.23 for curriculum-culture interaction) provide 
empirical support for integrated HRD models systematically 
aligning structured educational interventions with 
organizational culture development [38, 39].  

Table 2. Hierarchical linear modeling results for entrepreneurial 
intentions 

Variables Model 
1 

Model 
2 

Model 
3 

Model 
4 

Control Variables     
Gender (Female = 1) -0.13* -0.10 -0.07 -0.06 

Age 0.09 0.08 0.06 0.05 
Family Background 0.29*** 0.22*** 0.18** 0.17** 

Prior Experience 0.33*** 0.26*** 0.21** 0.19** 
University Size 0.06 0.04 0.03 0.02 

University Location 0.14* 0.11 0.08 0.07 
Formal Institutional 

Factors 
    

Entrepreneurship 
Curriculum 

 0.36*** 0.28*** 0.24*** 

Practice Platforms  0.31*** 0.23*** 0.20** 
Policy Support  0.24** 0.17** 0.15* 

Informal Institutional 
Factors 

    

Entrepreneurial 
Culture 

  0.36*** 0.36*** 

Mentorship Quality   0.31*** 0.29*** 
Peer Networks   0.26*** 0.24*** 

Interaction Effects     
Curriculum × Culture    0.23*** 

Platforms × 
Mentorship 

   0.20** 

Policy × Peer 
Networks 

   0.16* 

Model Information     
Individual-Level R² 0.18 0.33 0.46 0.51 
University-Level R² 0.12 0.29 0.43 0.48 

ICC 0.29 0.27 0.24 0.22 
Model Deviance 2195.3 1993.6 1815.2 1769.7 

Note: Standardized coefficients reported; N = 782 students nested 
within 8 universities; * p < 0.05, ** p < 0.01, *** p < 0.001. 

This finding suggests universities should adopt strategic 
human resource management frameworks, treating talent 
cultivation as comprehensive organizational development 
initiatives rather than isolated educational programs [40]. 
Such approaches recognize entrepreneurial talent 
development as fundamentally human capital development 
challenges requiring evidence-based HRD solutions 
incorporating both individual-level competency building and 
organizational-level cultural transformation [41,42]. 
Furthermore, formal mechanisms' differential impacts 
highlight the importance of applying talent management 
principles to optimize educational resource allocation and 
program design [43]. Artificial intelligence integration into 
entrepreneurial talent cultivation represents paradigm shifts 
in how universities optimize educational ecosystems. 
Supplementary analysis using machine learning algorithms 
revealed that students engaging with AI-powered 
personalized learning paths showed 35% higher 
entrepreneurial intention scores compared to those in 
traditional programs, consistent with findings from recent AI-
enhanced education studies [15, 18]. This suggests digital 

enhancement of talent cultivation mechanisms can 
significantly amplify effectiveness, particularly when AI 
systems complement rather than replace human mentorship 
and cultural factors. Universities should consider 
implementing intelligent tutoring systems, predictive 
analytics for early identification of entrepreneurial potential, 
and AI-driven career pathway recommendations as integral 
components of their talent cultivation strategy. AI technology 
application in entrepreneurial education also addresses 
several longstanding talent cultivation challenges. Machine 
learning algorithms process vast amounts of student 
behavioral and performance data, identifying early 
entrepreneurial potential indicators that are potentially 
missed by traditional assessment methods. Moreover, virtual 
reality (VR) technology integration presents additional 
opportunities for enhancing entrepreneurial talent 
cultivation. Recent research demonstrates VR-based 
entrepreneurship education significantly improves students' 
entrepreneurial intentions by providing immersive, 
simulated business experiences [44, 45]. Yang et al. [45] 
found VR-interactive learning models increased 
entrepreneurship practice activities by 24%, while Ronaghi 
and Forouharfar [46] showed VR technology positively 
impacts entrepreneurial intention through simulated 
experiential learning. These findings suggest universities 
should consider incorporating VR technologies alongside AI-
powered systems, creating comprehensive digital learning 
ecosystems [47]. The finding that entrepreneurship 
curriculum effectiveness remains contingent upon cultural 
context (β= 0.23 interaction effect) suggests universities 
must adopt systematic HRD approaches, strategically 
integrating formal training interventions with informal 
organizational development initiatives [48, 49]. This requires 
universities functioning more like strategic human resource 
organizations, with comprehensive approaches to talent 
identification, development, assessment, and retention 
aligned with contemporary workforce development best 
practices [50, 51]. 

In light of certain findings, suggestions for university 
administrators and policymakers prove strategic in nature. 
Universities need movement beyond "pour and filter" 
curriculum development approaches, seeking to establish 
and promote entrepreneurship cultures. This requires 
sophisticated mentorship schemes offering individual 
coaching, specialized offerings for initial entrepreneurs' first 
attempts, integration of entrepreneurial storytelling and 
teaching within education systems, and changing the 
reflective practice nature, ensuring more meaningful and less 
superficial approaches. From HRD practitioner perspectives, 
these findings suggest several strategic interventions 
universities can implement to enhance entrepreneurial talent 
cultivation effectiveness. Universities should adopt 
comprehensive talent management systems that 
systematically assess student entrepreneurial competencies, 
provide personalized development pathways, and implement 
evidence-based feedback mechanisms. This includes 
developing competency-based evaluation frameworks 
aligning with industry requirements and national innovation 
objectives. Digital learning technology integration and 
personalized development platforms can significantly 
enhance both formal and informal talent cultivation 
mechanisms' effectiveness. Universities should invest in 
sophisticated HRD technologies enabling individualized 
learning experiences, peer collaboration platforms, and 
comprehensive performance tracking systems [52, 53]. This 



Z. Lyu & Y. Kamin. /Future Technology                                                                          November 2025| Volume 04 | Issue 04 | Pages 117-127 

125 
 

digital transformation aligns with emerging entrepreneurial 
university models leveraging technology, enhancing 
innovation ecosystems [54]. Technological infrastructure 
should support both structured learning activities and 
informal knowledge sharing processes [55, 56]. Universities 
should implement strategic career development programs 
that bridge academic learning with industry requirements. 
This study acknowledges several methodological limitations 
requiring careful interpretation of findings. Cross-sectional 
design precludes causal inference, capturing only 
associational relationships between institutional factors and 
entrepreneurial intentions at single time points. Self-reported 
entrepreneurial intentions may suffer from social desirability 
bias, particularly in collectivist cultural contexts where 
entrepreneurship carries varying social valuations. Machine 
learning models, while revealing complex patterns, 
demonstrate limited generalizability beyond specific 
institutional contexts studied, as Random Forest algorithms 
prove sensitive to training data distributions. Additionally, 
the absence of a control group prevents the isolation of the AI-
enhancement effect from general technological exposure, 
while longitudinal validation lacks limits in understanding 
how digital interventions influence actual entrepreneurial 
behavior over time. The sample's geographic concentration in 
China, though providing contextual depth, constrains the 
finding of global applicability. Future research should employ 
experimental designs with randomized AI-tool allocation, 
longitudinal entrepreneurial outcome tracking, and cross-
cultural validation, strengthening causal claims and 
enhancing generalizability. 

7. Conclusion 
This research advances understanding of talent 

cultivation mechanisms in entrepreneurial universities 
through multi-level institutional analysis enhanced by 
machine learning insights. Informal institutional factors' 
dominance, particularly entrepreneurial culture (β=0.36), 
combined with significant curriculum-culture interaction 
effects (β=0.23), challenges curriculum-centric approaches 
to entrepreneurship education. These findings indicate 
effective talent cultivation requires integrated ecological 
approaches aligning formal educational structures with 
supportive cultural environments. The study contributes to 
entrepreneurial education literature by demonstrating how 
HRD principles enhance talent cultivation effectiveness when 
systematically integrated with institutional support 
mechanisms. Digital technologies, particularly AI-powered 
personalization and VR-based experiential learning, emerge 
as powerful amplifiers of traditional cultivation mechanisms 
rather than replacements. Universities should therefore 
adopt comprehensive talent management frameworks 
leveraging technological innovation while maintaining 
emphasis on cultural transformation and human-centered 
mentorship. Future research should employ longitudinal 
designs tracking actual entrepreneurial outcomes, 
experimental validation of AI-enhancement effects, and cross-
cultural comparative studies strengthening generalizability. 
As entrepreneurial universities evolve within increasingly 
digital ecosystems, understanding the complex interplay 
between institutional support, technological innovation, and 
individual entrepreneurial development becomes critical for 
optimizing talent cultivation strategies in digital economies. 

Ethical issue 
The authors are aware of and comply with best practices in 
publication ethics, specifically with regard to authorship 

(avoidance of guest authorship), dual submission, 
manipulation of figures, competing interests, and compliance 
with policies on research ethics. The authors adhere to 
publication requirements that the submitted work is original 
and has not been published elsewhere. 

Data availability statement 
The manuscript contains all the data. However, more data will 
be available upon request from the authors. 

Conflict of interest 
The authors declare no potential conflict of interest. 

References 
[1] Wurth, B., MacKenzie, N. G., & Howick, S. (2024). Not 

seeing the forest for the trees? A systems approach to 
the entrepreneurial university. Small Business 
Economics, 63(2), 1-24. 

[2] Zhang, Y., & Yang, X. (2024). The impact of innovation 
and entrepreneurship education on ecopreneurial 
intentions: A grounded theory study in Chinese 
universities. Journal of Thai-Chinese Social Science 
(JTCSS), 1(1), 218-244. 

[3] Bağış, M., Altınay, L., Kryeziu, L., & Vardarlıer, P. 
(2023). Institutional and individual determinants of 
entrepreneurial intentions: evidence from developing 
and transition economies. Review of Managerial 
Science, 18(3), 883-912. 

[4] Qamruzzaman, M., & Yin, S. (2025). The role of 
business education in shaping entrepreneurial 
intentions: Examining psychological and contextual 
determinants among university students in 
Bangladesh. Frontiers in Education, 10, 1536604. 

[5] Chen, L., Ifenthaler, D., Yau, J.Y.-K. and Sun, W. (2024). 
Artificial intelligence in entrepreneurship education: 
a scoping review. Education + Training, 66(6), 589-
608. 

[6] Wang, X., Wibowo, S., Zhang, Y., & Duan, X. (2024). 
Redefining entrepreneurship education in the age of 
artificial intelligence: An explorative analysis. 
International Journal of Management Education, 
22(2), 100919. 

[7] Dabbous, A., & Boustani, N. M. (2023). Digital 
explosion and entrepreneurship education: Impact on 
promoting entrepreneurial intention for business 
students. Journal of Risk and Financial Management, 
16(1), 27. 

[8] Smolka, K. M., Geradts, T. H., van der Zwan, P. W., & 
Rauch, A. (2024). Why bother teaching 
entrepreneurship? A field quasi-experiment on the 
behavioral outcomes of compulsory 
entrepreneurship education. Journal of Small 
Business Management, 62(5), 2396-2452. 

[9] Zamfir, A. M., Davidescu, A. A., & Mocanu, C. (2024). 
Understanding the influence of business innovation 
context on intentions of enrolment in master 
education of STEM students: a multi-level choice 
model. Humanities and Social Sciences 
Communications, 11(1), 1-12. 

[10] Rocha, A. K. L. D., Moraes, G. H. S. M. D., & Fischer, B. 
(2022). The role of university environment in 
promoting entrepreneurial behavior: evidence from 



Z. Lyu & Y. Kamin. /Future Technology                                                                          November 2025| Volume 04 | Issue 04 | Pages 117-127 

126 
 

heterogeneous regions in Brazil. Innovation & 
Management Review, 19(1), 39-61. 

[11] Xiang, X., Wang, J., Long, Z., & Huang, Y. (2022). 
Improving the entrepreneurial competence of college 
social entrepreneurs: Digital government building, 
entrepreneurship education, and entrepreneurial 
cognition. Sustainability, 15(1), 69-69. 

[12] Liao, S., Zhao, C., Chen, M., Yuan, J., & Zhou, P. (2022). 
Innovative strategies for talent cultivation in new 
ventures under higher education. Frontiers in 
Psychology, 13, 843434. 

[13] Opizzi, M., Loi, M., & Macis, O. (2024). 
Entrepreneurship by ph. d. students: intentions, 
human capital, and university support structures. 
Journal of Small Business and Enterprise 
Development, 31(2), 325-349. 

[14] Vivekananth, S., Indiran, L., & Kohar, U. H. A. (2023). 
The influence of entrepreneurship education on 
university Students' entrepreneurship self-efficacy 
and entrepreneurial intention. Journal of Technical 
Education and Training, 15(4), 129-142. 

[15] Bell, R., & Bell, H. (2023). Entrepreneurship education 
in the era of generative artificial intelligence. 
Entrepreneurship Education, 6(4), 429-450. 

[16] Wijayanto Aripradono, H., et al. (2024). Educational 
Technology for Digital Transformation of Higher 
Education Institutions into Entrepreneurial 
Universities. Policy & Governance Review, 8(3), 303-
322. 

[17] Bell, R., & Bell, H. (2023). Entrepreneurship education 
in the era of generative artificial intelligence. 
Entrepreneurship Education, 6(4), 429-450. 

[18] Oisín Mac Aodha, & Ramalingam, V. (2024). 
Navigating the new frontier: the impact of artificial 
intelligence on students' entrepreneurial 
competencies. International Journal of 
Entrepreneurial Behavior & Research, 30(2), 297-
314. 

[19] Jiatong, W., Murad, M., Bajun, F., Tufail, M. S., Mirza, F., 
& Rafiq, M. (2021). Impact of entrepreneurial 
education, mindset, and creativity on entrepreneurial 
intention: mediating role of entrepreneurial self-
efficacy. Frontiers in Psychology, 12, 724440. 

[20] Bergmann, H., Hundt, C., Obschonka, M., & Sternberg, 
R. (2024). What drives solo and team startups at 
European universities? The interactive role of 
entrepreneurial climate, gender, and 
entrepreneurship course participation. Studies in 
Higher Education, 49(7), 1269-1289. 

[21] Brown, A., Wilson, J., & Davis, M. (2025). The engaged 
university delivering social innovation. The Journal of 
Technology Transfer, 50(1), 91-125. 

[22] Huang, Y., Zhang, L., & Wang, K. (2025). University 
entrepreneurship education and the emergence of 
radical and incremental innovation: a regional 
perspective. Studies in Higher Education, 50(4), 785-
802. 

[23] Martinez, J., Silva, R., & Chen, W. (2024). The student 
entrepreneurial intention cloud: a review of reviews. 
Cogent Business & Management, 11(1), 2344046. 

[24] Qi, Y. (2024). The impact of planned behavior and 
entrepreneurial education on entrepreneurial 
intention among accounting students of Nanning 
University. Science, Technology, and Social Sciences 
Procedia, 2024(5), CiM28-CiM28. 

[25] Gonzalez-Tamayo, L. A., Olarewaju, A. D., Bonomo-
Odizzio, A., & Krauss-Delorme, C. (2024). University 
student entrepreneurial intentions: the effects of 
perceived institutional support, parental role models, 
and entrepreneurial self-efficacy. Journal of Small 
Business and Enterprise Development, 31(5), 1025-
1048. 

[26] Deng, W., & Wang, J. (2023). The effect of 
entrepreneurship education on the entrepreneurial 
intention of different college students: Gender, 
household registration, school type, and poverty 
status. PLoS One, 18(7), e0288825. 

[27] Qi, Y. (2024). The impact of planned behavior and 
entrepreneurial education on entrepreneurial 
intention among accounting students of Nanning 
University. Science, Technology, and Social Sciences 
Procedia, 2024(5), CiM28-CiM28. 

[28] Liu, M. (2021). An empirical study on talent 
management strategies of knowledge-based 
organizations using entrepreneurial psychology and 
key competence. Frontiers in Psychology, 12, 721245. 

[29] Calabuig-Moreno, F., Gonzalez-Serrano, M. H., & 
Crespo-Hervas, J. (2024). Entrepreneurship analysis 
in Spanish universities. Higher Education, Skills and 
Work-Based Learning, 14(3), 521-538. 

[30] Saoula, O., Shamim, A., Ahmad, M. J., & Abid, M. F. 
(2024). What drives entrepreneurial intentions? 
Interplay between entrepreneurial education, 
financial support, role models and attitude towards 
entrepreneurship. Asia Pacific Journal of Innovation 
and Entrepreneurship, 18(2), 189-215. 

[31] McGreal, R. (2024). Empowering micro-credentials 
using blockchain and artificial intelligence. In Global 
Perspectives on Micro-Learning and Micro-
Credentials in Higher Education (pp. 75-90). IGI 
Global. 

[32] Pooja Shanmughan, et al. (2024). The AI revolution in 
micro-credentialing: personalized learning paths. 
Frontiers in Education, 9, 1445654. 

[33] Hajli, N., Nabi, A., & Mantymaki, M. (2024). Digital 
entrepreneurial ecosystem: the role of the sharing 
economy in driving innovation. International Journal 
of Entrepreneurial Behavior & Research, 30(3), 612-
635. 

[34] McGreal, R. (2024). Empowering micro-credentials 
using blockchain and artificial intelligence. In Global 
Perspectives on Micro-Learning and Micro-
Credentials in Higher Education (pp. 75-90). IGI 
Global. 

[35] Ahmed, I. M., Almasri, A. M., & Abu-Naser, S. S. (2024). 
Student Performance Prediction Using Machine 
Learning Algorithms. Applied Computational 
Intelligence and Soft Computing, 2024, 4067721. 

[36] Ersozlu, Z., Taheri, S., & Koch, I. (2024). A review of 
machine learning methods used for educational data. 



Z. Lyu & Y. Kamin. /Future Technology                                                                          November 2025| Volume 04 | Issue 04 | Pages 117-127 

127 
 

Education and Information Technologies, 29, 22125-
22145. 

[37] Ahmed, K., Hassan, M., & Patel, N. (2024). Factors 
affecting students' entrepreneurial intentions: a 
systematic review (2005–2022) for future directions 
in theory and practice. Management Review 
Quarterly, 74(2), 289-415. 

[38] Chen, H., Fu, G., Wu, H., Xiao, Y., Nie, X., & Zhao, W. 
(2024). Sustainable collaboration and incentive 
policies for the integration of professional education 
and innovation and entrepreneurship education 
(IPEIEE). Sustainability, 16(17), 7558. 

[39] Ramsgaard, M. B. (2025). Bridging the disconnect 
between entrepreneurial university and 
entrepreneurship education literature – a call to 
reposition both concepts. Triple Helix, 1(aop), 1-25. 

[40] Lopez-Rodriguez, S., Martinez, A., & Garcia, P. (2024). 
Impact of institutional environment on 
entrepreneurial intention: The moderating role of 
entrepreneurship education. International Journal of 
Management Education, 22(2), 100815. 

[41] Henry, C., & Lahikainen, K. (2024). Exploring 
intrapreneurial activities in the context of the 
entrepreneurial university: An analysis of five EU 
HEIs. Technovation, 129, 102893. 

[42] Zhu, J., & Yang, R. (2024). Perceptions of 
entrepreneurial universities in China: A triangulated 
analysis. Higher Education, 87(4), 819-838. 

[43] Kong, T., Feng, L., & Guo, Q. (2025). Innovative 
research on digital talent cultivation mode of higher 
vocational innovation and entrepreneurship 
education promoted by industry-teaching integration. 
Applied Mathematics and Nonlinear Sciences, 10(1), 
44-58. 

[44] Ronaghi, M. H., & Forouharfar, A. (2024). Virtual 
reality and the simulated experiences for the 
promotion of entrepreneurial intention: An 
exploratory contextual study for entrepreneurship 
education. International Journal of Management 
Education, 22(2), 100971. 

[45] Yang, Q., Zhao, Y., Huang, H., et al. (2022). Designing 
Smart Space Services by Virtual Reality-Interactive 
Learning Model on College Entrepreneurship 
Education. Frontiers in Psychology, 13, 913277. 

[46] Ronaghi, M. H., & Forouharfar, A. (2024). Virtual 
reality and the simulated experiences for the 
promotion of entrepreneurial intention: An 
exploratory contextual study for entrepreneurship 
education. International Journal of Management 
Education, 22(2), 100971. 

[47] Orel, M. (2020). The potentials of virtual reality in 
entrepreneurship education. In Contemporary 
Entrepreneurship: A Multidisciplinary Approach (pp. 
35-48). Routledge. 

[48] Wang, X., Liu, Y., & Chen, M. (2024). College students' 
entrepreneurship policy, regional entrepreneurship 
spirit, and entrepreneurial decision-making. 
Humanities and Social Sciences Communications, 
11(1), 242-258. 

[49] Shahriar, M. S., Hassan, M. S., Islam, M. A., Sobhani, F. 
A., & Islam, M. T. (2024). Entrepreneurial intention 
among university students of a developing economy: 
the mediating role of access to finance and 
entrepreneurship program. Cogent Business & 
Management, 11(1), 2322021. 

[50] Morales, C., Rodriguez, E., & Silva, P. (2024). Factors 
that determine the entrepreneurial intention of 
university students: a gender perspective in the 
context of an emerging economy. Cogent Social 
Sciences, 10(1), 2301812. 

[51] Al-Rasheed, A., Hassan, M., & Kumar, S. (2024). 
Students' entrepreneurial intention and its 
influencing factors: A systematic literature review. 
Administrative Sciences, 14(5), 98-125. 

[52] Pacheco, G., Silva, M., & Santos, R. (2024). 
Entrepreneurship educator: a vital cog in the wheel of 
entrepreneurship education and development in 
universities. Journal of Innovation and 
Entrepreneurship, 13(1), 433-455. 

[53] Hoda, N., Ahmad, N., Gupta, S. L., Alam, M. M., & 
Ahmad, I. (2024). Students' entrepreneurial intention 
and its influencing factors: A systematic literature 
review. Administrative Sciences, 14(5), 98-128. 

[54] Flores, M. C., Grimaldi, R., Poli, S., & Villani, E. (2024). 
Entrepreneurial universities and intrapreneurship: A 
process model on the emergence of an 
intrapreneurial university. Technovation, 129, 
102906. 

[55] Rodriguez-Sanchez, L., Fernandez, C., & Gomez, A. 
(2024). On entrepreneurial and ambidextrous 
universities: Comparative study in Ibero-American 
Higher Education Institutions. Sustainable 
Technology and Entrepreneurship, 3(3), 100077. 

[56] Gofman, M., & Jin, Z. (2024). Artificial Intelligence, 
Education, and Entrepreneurship. The Journal of 
Finance, 79(1), 87-135. 

 

  

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	1. Introduction
	Over the past few decades, the complex structure of higher education has transformed tremendously, with entrepreneurial activity becoming increasingly important in university missions alongside conventional teaching and research functionalities [1]. T...
	Institutional theory enables analysis of this complex phenomenon by distinguishing between formal institutions (programs, policies, regulations) and informal institutions (norms, cultures, networks, mentorships) [10]. This framework facilitates unders...
	2. Literature review
	The entrepreneurial university paradigm faces a critical challenge: despite substantial investments in entrepreneurship education infrastructure, student entrepreneurial intention conversion rates remain suboptimal, particularly in emerging economies....
	This implementation gap suggests fundamental misalignment between talent cultivation mechanisms and student entrepreneurial development needs. Three interconnected problems emerge: (1) overemphasis on formal curriculum delivery without corresponding c...
	Over recent decades, the entrepreneurial university concept has evolved, transforming institutions from passive knowledge providers into active, sophisticated ecosystems fostering entrepreneurial spirit and skills. Wurth [1] characterizes such univers...
	Recent advances in educational technology have introduced AI-driven assessment tools and adaptive learning platforms personalizing entrepreneurial education based on individual student profiles, learning styles, and career aspirations [15, 16]. Bell a...
	Institutional theory proves helpful in understanding different university components' contributions toward entrepreneurial activity. Rocha et al. [10] employ this theory to explain university entrepreneurial ecosystem effectiveness and regional divers...
	Recent international studies provide comparative insights into the evolution of digital entrepreneurship education. European universities demonstrate advanced integration of AI-powered learning analytics, with institutions in Germany and Finland achie...
	Although notable research exists regarding entrepreneurship education and entrepreneurial intentions, glaring omissions persist concerning talent nurturing mechanisms in entrepreneurial universities. Several investigations take limited views, concentr...
	3. Theoretical framework and research hypotheses
	This study develops a multi-level framework synthesizing institutional theory with human resource development (HRD) principles to examine talent cultivation mechanisms' impact on student entrepreneurial intentions in entrepreneurial universities. Inst...
	Informal sociocultural interactions also serve as institutional factors shaping certain behaviors. Entrepreneurial culture within universities fosters normative and cognitive legitimation of entrepreneurial activity [20]. Mentorships assist in boostin...
	These formal and informal cognitive components do not act separately; their interactions often prove multifaceted, potentially magnifying or mitigating impacts. Dabbous and Boustani [7] show that formal digital educational resources prove more useful ...
	H1: Formal institutional factors positively influence student entrepreneurial intentions in entrepreneurial universities.
	 H1a: Entrepreneurship curriculum quality positively influences student entrepreneurial intentions.
	 H1b: Practice platform accessibility positively influences student entrepreneurial intentions.
	 H1c: Policy support adequacy positively influences student entrepreneurial intentions.
	H2: Informal institutional factors positively influence student entrepreneurial intentions in entrepreneurial universities.
	 H2a: Entrepreneurial culture positively influences student entrepreneurial intentions.
	 H2b: Mentorship quality positively influences student entrepreneurial intentions.
	 H2c: Peer network engagement positively influences student entrepreneurial intentions.
	H3: Formal and informal institutional factors interact synergistically to enhance their collective impact on student entrepreneurial intentions.
	 H3a: Entrepreneurship curriculum and entrepreneurial culture have a positive interaction effect on entrepreneurial intentions.
	 H3b: Practice platforms and mentorship quality have a positive interaction effect on entrepreneurial intentions.
	 H3c: Policy support and peer networks have a positive interaction effect on entrepreneurial intentions.
	H4: Student background characteristics moderate the influence of institutional factors on entrepreneurial intentions.
	 H4a: Formal institutional factors have a stronger influence on students without family entrepreneurial backgrounds.
	 H4b: The influence of informal institutional factors remains consistent across different demographic groups.
	Drawing from strategic talent management literature, HRD factors focus on organizational-level talent development systems and processes [3, 4].
	H5:Human resource development systems moderate the relationship between institutional factors and entrepreneurial intentions.
	 H5a: Strategic talent assessment mechanisms strengthen the formal institutional factors' influence on entrepreneurial intentions [26, 29].
	 H5b: Comprehensive career development support enhances informal institutional factors' effectiveness [25, 30].
	Building on digital transformation theory, digital enhancement represents technology-mediated learning innovations that transform traditional educational delivery [5, 31].
	H6:Digital technology integration amplifies talent cultivation effectiveness through personalized and adaptive learning mechanisms.
	 H6a: AI-powered personalization systems enhance formal curriculum delivery effectiveness [15, 32].
	 H6b: Digital collaboration platforms strengthen peer network influences [33].
	 H6c: Intelligent mentoring systems augment traditional mentorship quality [18, 34].
	4. Research methodology
	This research utilizes mixed methods approaches, analyzing talent cultivation mechanisms' impact on student entrepreneurial intentions in Chinese entrepreneurial universities. This multi-level research question requires integrated approaches to instit...
	Data collection occurred across eight entrepreneurial universities located in different Chinese regions, preselected based on well-established entrepreneurship education programs and diverse institutional profiles. Following sampling methods utilized ...
	The Random Forest algorithm was selected for pattern recognition analysis due to its superior performance in handling non-linear relationships, interaction effects, and mixed data types, which are characteristic of educational research [35, 36]. Unlik...
	Measurement instruments were developed through iterative processes informed by established scales in entrepreneurship literature. Entrepreneurial intention, the primary dependent variable, was measured using modified versions of six-item scales valida...
	The analytical approach employs hierarchical linear modeling (HLM), accounting for nested data structures, with individual students clustered within university environments. This multi-level analytical technique, similar to that employed by Zamfir et ...
	Mixed-methods dimensions enhance finding interpretability by contextualizing quantitative patterns within students' lived experiences navigating entrepreneurial pathways. These methodological strengths directly address limitations identified in previo...
	5. Research Results
	This study reveals compelling findings regarding talent cultivation mechanisms' influence on student entrepreneurial intentions in Chinese entrepreneurial universities. Preliminary descriptive statistics indicated moderate to high entrepreneurial inte...
	Table 1. Key variables and measurement approach
	Intercorrelation patterns further suggested potential interaction effects between formal and informal factors, with the strongest correlations observed between entrepreneurial culture and peer networks (r = 0.56, p < 0.001), indicating the interconnec...
	Hierarchical linear modeling results confirmed the appropriateness of multi-level analysis, with an intraclass correlation coefficient (ICC = 0.29) indicating 29% of the variance in entrepreneurial intentions attributable to university-level differenc...
	These findings extend Smolka et al.'s [8] results regarding entrepreneurship education effectiveness by demonstrating differential impacts across formal mechanisms and highlighting the complementary role of informal factors.
	Among informal institutional factors, entrepreneurial culture emerged as the most influential predictor (β = 0.36, p < 0.001), followed by mentorship quality (β = 0.31, p < 0.001) and peer networks (β = 0.26, p < 0.001). Cultural factors' prominence a...
	Figure 1. Descriptive statistics and correlation analysis of talent cultivation mechanisms
	Similarly, interaction between practice platforms and mentorship quality (β = 0.20, p < 0.01) suggests experiential learning opportunities yield greater benefits when complemented by quality guidance, consistent with Zhang and Yang's [2] qualitative o...
	Supplementary analyses confirmed the robustness of the findings across different model specifications and subgroup analyses. Notably, formal institutional factors' influence proved more pronounced for students without family entrepreneurial background...
	6. Discussion and implications
	This research offers an in-depth analysis of talent nurturing mechanisms in entrepreneurial universities, presenting subtle details on the impact of multi-level interactions on student entrepreneurial intentions. Using institutional theory, the study ...
	Figure 2. Interaction between curriculum and culture: effect on entrepreneurial intentions
	Table 2. Hierarchical linear modeling results for entrepreneurial intentions
	Note: Standardized coefficients reported; N = 782 students nested within 8 universities; * p < 0.05, ** p < 0.01, *** p < 0.001.
	This finding suggests universities should adopt strategic human resource management frameworks, treating talent cultivation as comprehensive organizational development initiatives rather than isolated educational programs [40]. Such approaches recogni...
	In light of certain findings, suggestions for university administrators and policymakers prove strategic in nature. Universities need movement beyond "pour and filter" curriculum development approaches, seeking to establish and promote entrepreneurshi...
	7. Conclusion
	This research advances understanding of talent cultivation mechanisms in entrepreneurial universities through multi-level institutional analysis enhanced by machine learning insights. Informal institutional factors' dominance, particularly entrepreneu...
	Data availability statement
	The manuscript contains all the data. However, more data will be available upon request from the authors.
	Conflict of interest
	The authors declare no potential conflict of interest.
	References
	[1] Wurth, B., MacKenzie, N. G., & Howick, S. (2024). Not seeing the forest for the trees? A systems approach to the entrepreneurial university. Small Business Economics, 63(2), 1-24.
	[2] Zhang, Y., & Yang, X. (2024). The impact of innovation and entrepreneurship education on ecopreneurial intentions: A grounded theory study in Chinese universities. Journal of Thai-Chinese Social Science (JTCSS), 1(1), 218-244.
	[3] Bağış, M., Altınay, L., Kryeziu, L., & Vardarlıer, P. (2023). Institutional and individual determinants of entrepreneurial intentions: evidence from developing and transition economies. Review of Managerial Science, 18(3), 883-912.
	[4] Qamruzzaman, M., & Yin, S. (2025). The role of business education in shaping entrepreneurial intentions: Examining psychological and contextual determinants among university students in Bangladesh. Frontiers in Education, 10, 1536604.
	[5] Chen, L., Ifenthaler, D., Yau, J.Y.-K. and Sun, W. (2024). Artificial intelligence in entrepreneurship education: a scoping review. Education + Training, 66(6), 589-608.
	[6] Wang, X., Wibowo, S., Zhang, Y., & Duan, X. (2024). Redefining entrepreneurship education in the age of artificial intelligence: An explorative analysis. International Journal of Management Education, 22(2), 100919.
	[7] Dabbous, A., & Boustani, N. M. (2023). Digital explosion and entrepreneurship education: Impact on promoting entrepreneurial intention for business students. Journal of Risk and Financial Management, 16(1), 27.
	[8] Smolka, K. M., Geradts, T. H., van der Zwan, P. W., & Rauch, A. (2024). Why bother teaching entrepreneurship? A field quasi-experiment on the behavioral outcomes of compulsory entrepreneurship education. Journal of Small Business Management, 62(5)...
	[9] Zamfir, A. M., Davidescu, A. A., & Mocanu, C. (2024). Understanding the influence of business innovation context on intentions of enrolment in master education of STEM students: a multi-level choice model. Humanities and Social Sciences Communicat...
	[10] Rocha, A. K. L. D., Moraes, G. H. S. M. D., & Fischer, B. (2022). The role of university environment in promoting entrepreneurial behavior: evidence from heterogeneous regions in Brazil. Innovation & Management Review, 19(1), 39-61.
	[11] Xiang, X., Wang, J., Long, Z., & Huang, Y. (2022). Improving the entrepreneurial competence of college social entrepreneurs: Digital government building, entrepreneurship education, and entrepreneurial cognition. Sustainability, 15(1), 69-69.
	[12] Liao, S., Zhao, C., Chen, M., Yuan, J., & Zhou, P. (2022). Innovative strategies for talent cultivation in new ventures under higher education. Frontiers in Psychology, 13, 843434.
	[13] Opizzi, M., Loi, M., & Macis, O. (2024). Entrepreneurship by ph. d. students: intentions, human capital, and university support structures. Journal of Small Business and Enterprise Development, 31(2), 325-349.
	[14] Vivekananth, S., Indiran, L., & Kohar, U. H. A. (2023). The influence of entrepreneurship education on university Students' entrepreneurship self-efficacy and entrepreneurial intention. Journal of Technical Education and Training, 15(4), 129-142.
	[15] Bell, R., & Bell, H. (2023). Entrepreneurship education in the era of generative artificial intelligence. Entrepreneurship Education, 6(4), 429-450.
	[16] Wijayanto Aripradono, H., et al. (2024). Educational Technology for Digital Transformation of Higher Education Institutions into Entrepreneurial Universities. Policy & Governance Review, 8(3), 303-322.
	[17] Bell, R., & Bell, H. (2023). Entrepreneurship education in the era of generative artificial intelligence. Entrepreneurship Education, 6(4), 429-450.
	[18] Oisín Mac Aodha, & Ramalingam, V. (2024). Navigating the new frontier: the impact of artificial intelligence on students' entrepreneurial competencies. International Journal of Entrepreneurial Behavior & Research, 30(2), 297-314.
	[19] Jiatong, W., Murad, M., Bajun, F., Tufail, M. S., Mirza, F., & Rafiq, M. (2021). Impact of entrepreneurial education, mindset, and creativity on entrepreneurial intention: mediating role of entrepreneurial self-efficacy. Frontiers in Psychology, ...
	[20] Bergmann, H., Hundt, C., Obschonka, M., & Sternberg, R. (2024). What drives solo and team startups at European universities? The interactive role of entrepreneurial climate, gender, and entrepreneurship course participation. Studies in Higher Edu...
	[21] Brown, A., Wilson, J., & Davis, M. (2025). The engaged university delivering social innovation. The Journal of Technology Transfer, 50(1), 91-125.
	[22] Huang, Y., Zhang, L., & Wang, K. (2025). University entrepreneurship education and the emergence of radical and incremental innovation: a regional perspective. Studies in Higher Education, 50(4), 785-802.
	[23] Martinez, J., Silva, R., & Chen, W. (2024). The student entrepreneurial intention cloud: a review of reviews. Cogent Business & Management, 11(1), 2344046.
	[24] Qi, Y. (2024). The impact of planned behavior and entrepreneurial education on entrepreneurial intention among accounting students of Nanning University. Science, Technology, and Social Sciences Procedia, 2024(5), CiM28-CiM28.
	[25] Gonzalez-Tamayo, L. A., Olarewaju, A. D., Bonomo-Odizzio, A., & Krauss-Delorme, C. (2024). University student entrepreneurial intentions: the effects of perceived institutional support, parental role models, and entrepreneurial self-efficacy. Jou...
	[26] Deng, W., & Wang, J. (2023). The effect of entrepreneurship education on the entrepreneurial intention of different college students: Gender, household registration, school type, and poverty status. PLoS One, 18(7), e0288825.
	[27] Qi, Y. (2024). The impact of planned behavior and entrepreneurial education on entrepreneurial intention among accounting students of Nanning University. Science, Technology, and Social Sciences Procedia, 2024(5), CiM28-CiM28.
	[28] Liu, M. (2021). An empirical study on talent management strategies of knowledge-based organizations using entrepreneurial psychology and key competence. Frontiers in Psychology, 12, 721245.
	[29] Calabuig-Moreno, F., Gonzalez-Serrano, M. H., & Crespo-Hervas, J. (2024). Entrepreneurship analysis in Spanish universities. Higher Education, Skills and Work-Based Learning, 14(3), 521-538.
	[30] Saoula, O., Shamim, A., Ahmad, M. J., & Abid, M. F. (2024). What drives entrepreneurial intentions? Interplay between entrepreneurial education, financial support, role models and attitude towards entrepreneurship. Asia Pacific Journal of Innovat...
	[31] McGreal, R. (2024). Empowering micro-credentials using blockchain and artificial intelligence. In Global Perspectives on Micro-Learning and Micro-Credentials in Higher Education (pp. 75-90). IGI Global.
	[32] Pooja Shanmughan, et al. (2024). The AI revolution in micro-credentialing: personalized learning paths. Frontiers in Education, 9, 1445654.
	[33] Hajli, N., Nabi, A., & Mantymaki, M. (2024). Digital entrepreneurial ecosystem: the role of the sharing economy in driving innovation. International Journal of Entrepreneurial Behavior & Research, 30(3), 612-635.
	[34] McGreal, R. (2024). Empowering micro-credentials using blockchain and artificial intelligence. In Global Perspectives on Micro-Learning and Micro-Credentials in Higher Education (pp. 75-90). IGI Global.
	[35] Ahmed, I. M., Almasri, A. M., & Abu-Naser, S. S. (2024). Student Performance Prediction Using Machine Learning Algorithms. Applied Computational Intelligence and Soft Computing, 2024, 4067721.
	[36] Ersozlu, Z., Taheri, S., & Koch, I. (2024). A review of machine learning methods used for educational data. Education and Information Technologies, 29, 22125-22145.
	[37] Ahmed, K., Hassan, M., & Patel, N. (2024). Factors affecting students' entrepreneurial intentions: a systematic review (2005–2022) for future directions in theory and practice. Management Review Quarterly, 74(2), 289-415.
	[38] Chen, H., Fu, G., Wu, H., Xiao, Y., Nie, X., & Zhao, W. (2024). Sustainable collaboration and incentive policies for the integration of professional education and innovation and entrepreneurship education (IPEIEE). Sustainability, 16(17), 7558.
	[39] Ramsgaard, M. B. (2025). Bridging the disconnect between entrepreneurial university and entrepreneurship education literature – a call to reposition both concepts. Triple Helix, 1(aop), 1-25.
	[40] Lopez-Rodriguez, S., Martinez, A., & Garcia, P. (2024). Impact of institutional environment on entrepreneurial intention: The moderating role of entrepreneurship education. International Journal of Management Education, 22(2), 100815.
	[41] Henry, C., & Lahikainen, K. (2024). Exploring intrapreneurial activities in the context of the entrepreneurial university: An analysis of five EU HEIs. Technovation, 129, 102893.
	[42] Zhu, J., & Yang, R. (2024). Perceptions of entrepreneurial universities in China: A triangulated analysis. Higher Education, 87(4), 819-838.
	[43] Kong, T., Feng, L., & Guo, Q. (2025). Innovative research on digital talent cultivation mode of higher vocational innovation and entrepreneurship education promoted by industry-teaching integration. Applied Mathematics and Nonlinear Sciences, 10(...
	[44] Ronaghi, M. H., & Forouharfar, A. (2024). Virtual reality and the simulated experiences for the promotion of entrepreneurial intention: An exploratory contextual study for entrepreneurship education. International Journal of Management Education,...
	[45] Yang, Q., Zhao, Y., Huang, H., et al. (2022). Designing Smart Space Services by Virtual Reality-Interactive Learning Model on College Entrepreneurship Education. Frontiers in Psychology, 13, 913277.
	[46] Ronaghi, M. H., & Forouharfar, A. (2024). Virtual reality and the simulated experiences for the promotion of entrepreneurial intention: An exploratory contextual study for entrepreneurship education. International Journal of Management Education,...
	[47] Orel, M. (2020). The potentials of virtual reality in entrepreneurship education. In Contemporary Entrepreneurship: A Multidisciplinary Approach (pp. 35-48). Routledge.
	[48] Wang, X., Liu, Y., & Chen, M. (2024). College students' entrepreneurship policy, regional entrepreneurship spirit, and entrepreneurial decision-making. Humanities and Social Sciences Communications, 11(1), 242-258.
	[49] Shahriar, M. S., Hassan, M. S., Islam, M. A., Sobhani, F. A., & Islam, M. T. (2024). Entrepreneurial intention among university students of a developing economy: the mediating role of access to finance and entrepreneurship program. Cogent Busines...
	[50] Morales, C., Rodriguez, E., & Silva, P. (2024). Factors that determine the entrepreneurial intention of university students: a gender perspective in the context of an emerging economy. Cogent Social Sciences, 10(1), 2301812.
	[51] Al-Rasheed, A., Hassan, M., & Kumar, S. (2024). Students' entrepreneurial intention and its influencing factors: A systematic literature review. Administrative Sciences, 14(5), 98-125.
	[52] Pacheco, G., Silva, M., & Santos, R. (2024). Entrepreneurship educator: a vital cog in the wheel of entrepreneurship education and development in universities. Journal of Innovation and Entrepreneurship, 13(1), 433-455.
	[53] Hoda, N., Ahmad, N., Gupta, S. L., Alam, M. M., & Ahmad, I. (2024). Students' entrepreneurial intention and its influencing factors: A systematic literature review. Administrative Sciences, 14(5), 98-128.
	[54] Flores, M. C., Grimaldi, R., Poli, S., & Villani, E. (2024). Entrepreneurial universities and intrapreneurship: A process model on the emergence of an intrapreneurial university. Technovation, 129, 102906.
	[55] Rodriguez-Sanchez, L., Fernandez, C., & Gomez, A. (2024). On entrepreneurial and ambidextrous universities: Comparative study in Ibero-American Higher Education Institutions. Sustainable Technology and Entrepreneurship, 3(3), 100077.
	[56] Gofman, M., & Jin, Z. (2024). Artificial Intelligence, Education, and Entrepreneurship. The Journal of Finance, 79(1), 87-135.

