Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 11, 1244-1254 2025 Publisher: Learning Gate DOI: 10.55214/2576-8484.v9i11.11104 © 2025 by the author; licensee Learning Gate © 2025 by the author; licensee Learning Gate History: Received: 26 September 2025; Revised: 6 November 2025; Accepted: 11 November 2025; Published: 20 November 2025 * Correspondence: phamthu@thanhdong.edu.vn Factors influencing students’ decisions to start online businesses in the digital ERA at Thanh Dong University Pham Thi Thu1* 1Thanh Dong University, Hai Phong City, Vietnam; phamthu@thanhdong.edu.vn (P.T.T.). Abstract: This study examines the factors influencing Thanh Dong University students’ decisions to start online businesses in the digital era. A mixed-method design was adopted, in which the main phase used a cross-sectional survey with convenience sampling, yielding 300 valid questionnaires from second- to fourth-year students. This study employs the Structural Equation Modeling (SEM) technique using the SMARTPLS3 software to analyze the influence of factors: Educational Support, Relationship Support, Structural Support, and Entrepreneurial Experience on the factor of Entrepreneurial Intention. The factor of Entrepreneurial Intention affects the Decision to start an online business. The empirical results indicate that educational and relational support and prior entrepreneurial experience positively and significantly enhance entrepreneurial intention, while structural support shows no significant effect. Entrepreneurial intention, in turn, strongly promotes students’ decisions to start online businesses. These findings suggest that universities should strengthen practice-oriented entrepreneurship education, mentoring networks, and experiential start-up activities to foster students’ entrepreneurial intention and translate it into actual online business ventures in the digital age. The study contributes to enriching the literature on student digital entrepreneurship in emerging economies. Keywords: Digital transformation, Online business decision, Start-up, Thanh Dong University. 1. Introduction In the digital era, entrepreneurship not only drives economic growth and job creation but also represents an attractive career path for university students in general and those at Thanh Dong University in particular. The rapid development of AI, big data, e-commerce, the Internet of Things (IoT), cloud computing, and digital platforms has created opportunities to start businesses more easily than ever. Nevertheless, students face challenges related to practical experience, financing, managerial capability, and the ability to adapt to a volatile environment. Although numerous studies have examined entrepreneurial decision-making, there remains a paucity of in-depth research on the impacts of digital technologies and the specific factors shaping students’ decisions. Accordingly, this study aims to assess the factors influencing Thanh Dong University students’ decisions to start an online business in the digital context, thereby providing scientific and practical foundations for fostering entrepreneurial spirit within universities and proposing solutions to support students in launching successful ventures. 2. Literature Review Choo and Wong [1] are pioneering scholars in clarifying the link between entrepreneurial intention and behavior. Their study in Singapore shows that entrepreneurial intention is the most proximate antecedent of start-up behavior, shaped by two core cognitive evaluations that individuals derive from their environment: perceived desirability and perceived feasibility. Similarly, in the UK context, Henley [2] argues that intention functions as a bridge between aspiration and action, reflecting an individual’s 1245 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 11: 1244-1254, 2025 DOI: 10.55214/2576-8484.v9i11.11104 © 2025 by the author; licensee Learning Gate psychological readiness prior to entering self-employment. Taken together, these studies converge on the conclusion that intention is a critical intermediate stage that translates the influences of education, social factors, and experience into concrete entrepreneurial behavior. Mazzarol et al. [3] conducted a comparative study in Australia and Singapore, highlighting the role of entrepreneurial education and the social environment in stimulating students’ entrepreneurial motivation. According to the authors, training programs in small business, marketing, and management enhance students’ opportunity recognition and their confidence to start a venture. These findings underscore the university’s role as a resource base that provides the knowledge foundation for entrepreneurial intention. Approaching the topic from a critical perspective, Alberti [4] evaluates the effectiveness of entrepreneurship education programs in Europe. The author observes that the impact of education on entrepreneurial intention materializes only when programs are practically oriented, featuring real projects, entrepreneur mentors, and strong market linkages. Programs that are predominantly theoretical or lack experiential components tend merely to raise awareness, falling short of fostering a clear entrepreneurial intention. This view is reinforced by Holmgren and From [5], who show that participation in entrepreneurship courses yields genuine value only when learners are immersed in contexts that require solving real-world problems. Turker and Sonmez Selçuk [6] in Turkey, the analytical framework is extended by examining the effects of social, educational, and individual factors on students’ entrepreneurial intentions. Their results show that entrepreneurship education and support from social networks have positive impacts, whereas gender and economic circumstances exert only limited influence. The study thus offers clear evidence of a strong association between the educational environment and university students’ entrepreneurial intentions. Arum and Müller [7] approach the topic through a social-capital lens, using evidence from the United States and Europe. They show that students from families with a business tradition have a higher propensity to engage in entrepreneurship than their peers. Families provide not only financial resources and information but also social norms that reduce perceived risk and enhance confidence to initiate a venture. Broader networks encompassing friends, faculty, and alumni further reinforce entrepreneurial intention by sharing experiential knowledge and opening avenues for collaboration. From an institutional-context perspective, Katz [8] analyzes the impact of digital transformation and entrepreneurship-support policies on business development in Latin America. He argues that countries with transparent institutions, robust online public services, and advanced digital infrastructure tend to exhibit significantly higher entrepreneurship rates. This indicates that structural factors, institutions, administrative procedures, infrastructure, and policy are critical antecedents that shape individuals’ perceptions of the feasibility of self-employment, thereby strengthening entrepreneurial intention. Krueger [9] was among the first to identify the link between entrepreneurial experience and intention. He demonstrates that individuals who have been exposed to or participated in small business projects tend to exhibit stronger entrepreneurial intentions than those without such experience. In the same vein, Basu and Virick [10] stress that hands-on, proactive activities such as running a project, piloting product sales, or entering idea competitions bolster self-efficacy and perceived behavioral control, thereby strengthening entrepreneurial intention. However, Davidsson [11] cautions that not all “experience” is beneficial: experiences that yield no tangible outcomes or lack reflective learning may fail to generate positive cognitive change. From a behavioral modeling perspective, Hair et al. [12] recommend using partial least squares structural equation modeling (PLS-SEM) to test relationships between intention and behavior when sample sizes are moderate and data are non-normally distributed. They advise applying reliability criteria (Cronbach’s alpha, rho_A, composite reliability) and measures of convergent and discriminant validity (AVE, HTMT) to ensure model validity. In the same vein, Kock and Hadaya [13] propose two procedures for determining the minimum required sample size: the inverse square root and gamma- 1246 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 11: 1244-1254, 2025 DOI: 10.55214/2576-8484.v9i11.11104 © 2025 by the author; licensee Learning Gate exponential methods, which enhance the robustness of complex structural models. Additionally, Tabachnick and Fidell [14] provide guidance on multivariate data handling, assumption testing, and bias mitigation to improve the accuracy of research on entrepreneurial behavior. Studies on student entrepreneurship in Vietnam, while increasing in number, still lack consensus on models and measurement scales. Most works remain descriptive, identifying motives or barriers without delving into online entrepreneurship, an emerging trend in the digital era. Moreover, no study has focused on the mediating role of entrepreneurial intention between education, networks, infrastructure, and experience. This reveals a notable theoretical gap in the Vietnamese context, where universities are vigorously implementing “Innovation and Student Entrepreneurship” programs. Recent surveys in Vietnam indicate that students exhibit a high interest in entrepreneurship, yet the rate of translating intention into action remains low due to constraints in capital, skills, and market access. This pattern is consistent with Alberti [4], who argues that intention is converted into behavior only when individuals perceive a supportive environment. Accordingly, clarifying the mechanisms through which factors such as entrepreneurship education, social networks, digital infrastructure, and personal experience shape intention and online start-up decisions is necessary to furnish evidence tailored to Vietnam. Drawing on the theoretical synthesis, this study adopts and extends the models of Choo and Wong [1], Henley [2] and Mazzarol et al. [3] positing entrepreneurial intention as a mediating variable between four groups of antecedents education (Edu), relational networks (Rel), infrastructure and institutions (Str), and experience (Know) and the outcome variable, the start-up decision (Dec). The proposed model both inherits foundational theoretical propositions and incorporates the distinctive features of online entrepreneurship in Vietnamese higher education, where intention and decision may unfold more rapidly owing to lower experimentation costs and the scalability afforded by digital platforms. 3. Theoretical Background The digital era marks a pivotal stage in human history, with digital technologies occupying a central role across most spheres of life. The surge of advanced technologies such as artificial intelligence (AI), big data, the Internet of Things (IoT), cloud computing, blockchain, and digital platforms has profoundly transformed how people work, communicate, and conduct business. In this context, traditional business models are increasingly supplanted by platform-based models in which information, data, and connectivity constitute the core value drivers. Entrepreneurship is regarded as a deliberate, intentional act [2]. Typically, entrepreneurial intention precedes the emergence of new ventures, often within roughly one year. Choo and Wong [1] argue that intention is the clearest indicator of entrepreneurial behavior and emphasize that venture creation is not a single event but a process that can take years to develop and implement. Consistently, Mazzarol et al. [3] consider entrepreneurial intention the initial step in the venture-formation process. Educational support. Popescu and Pohoata contend that education directly influences individual action [15]. Entrepreneurship education refers to lectures or courses that equip learners with the skills and knowledge necessary to start a business [5]. In a study of Turkish university students, Turker and Sonmez Selçuk [6] concluded that entrepreneurship education positively affects the development of entrepreneurial intention. However, other work has not found a clear association between entrepreneurship education and youths’ entrepreneurial intention in developing contexts [4]. Accordingly, the following hypothesis is proposed: H1: There is a relationship between entrepreneurship education and entrepreneurial intention. Relational support. Relational support is understood as assistance for entrepreneurial activity provided by family and friends. Family culture, along with prevailing values and available resources, can encourage individuals to engage in business activities. Numerous studies have shown that the probability of becoming an entrepreneur increases significantly when there is a prior entrepreneur in the 1247 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 11: 1244-1254, 2025 DOI: 10.55214/2576-8484.v9i11.11104 © 2025 by the author; licensee Learning Gate family [7]. In addition, family members and friends can strongly influence young people's career choices and entrepreneurial orientation. Accordingly, the following hypothesis is proposed: H2: There is a relationship between relational support and entrepreneurial intention. Structural/environmental support. The environment in which entrepreneurial activity occurs exerts an important influence on the intention to become an entrepreneur. Culture, incentives, and institutional support can operate either as barriers or as catalysts to the decision to undertake entrepreneurship. These elements are shaped by government interventions and policies. The business environment, therefore, affects entrepreneurial intention. Although these aspects have been examined in prior studies, the author argues that shifts in the business environment and the economy’s digital transformation must also be taken into account [8]. Accordingly, the following hypothesis is proposed: H3: There is a relationship between structural support and entrepreneurial intention. Prior entrepreneurial experience. Prior entrepreneurial experience influences individuals' entrepreneurial ideas [9]. Such experience not only helps develop entrepreneurial intention but also accumulates benefits for subsequent business activities. Basu and Virick [10] also indicate that prior experience is positively associated with self-esteem and attitudes toward entrepreneurship. However, Davidsson [11] argues that prior entrepreneurial experience has an insignificant effect on individuals’ business knowledge and no substantial impact on their attitudes and entrepreneurial intentions. Accordingly, the following hypothesis is proposed: H4: There is a relationship between prior entrepreneurial experience and entrepreneurial intention. 4. Research Methodology 4.1. Sampling and Data Collection In this study, the official sample size comprised 320 observations. The collected data were analyzed using SmartPLS 3. In total, responses were obtained from 300 students between February 2025 and May 2025. 4.2. Data Processing After removing observations that did not meet the inclusion criteria, 300 valid cases remained and were processed using Excel and SmartPLS 3. The measurement model and the structural equation model (SEM) were then evaluated. Finally, bootstrapping was conducted to assess the reliability and significance of the SEM estimates. 4.3. Measurement Scales The questionnaire consisted of four sections corresponding to four factors: Educational Support (Edu), Relational Support (Rel), Structural Support (Str), and Entrepreneurial Knowledge/Attitude (Att). A 5-point Likert scale (1 = lowest to 5 = highest) was employed. In total, 19 observed indicators were included. Additional variables such as gender, years of study, and academic faculty were also recorded. 1248 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 11: 1244-1254, 2025 DOI: 10.55214/2576-8484.v9i11.11104 © 2025 by the author; licensee Learning Gate 5. Results and Discussion 5.1. Measurement Model Analysis Figure 1. Estimated Model Results. 5.2. Evaluation of the Formative Measurement Model The assessment of convergent validity for formative constructs relies on the redundant analysis technique proposed by Chin [16]. This method should be implemented following the recommendations of Hair et al. [17]. Accordingly, the author employs a single-item construct as the dependent variable, while the independent variable represents the latent construct measured in the formative model. The analytical model is illustrated in Figure 2. Figure 2. Results of the First-Stage PLS Path Model Estimation. The regression value of the path coefficient is R² = 82.1%, which is greater than 50%, indicating that the measurement model for Dec achieves satisfactory convergent validity. 1249 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 11: 1244-1254, 2025 DOI: 10.55214/2576-8484.v9i11.11104 © 2025 by the author; licensee Learning Gate Furthermore, the VIF coefficients of all indicators in the formative measurement model are below the threshold value of 5, confirming the absence of multicollinearity. Therefore, the next step in evaluating the statistical significance of the outer weights can be appropriately conducted. Table 1. Results of the Measurement Model Assessment. Construct Indicator Outer Loading Cronbach’s Alpha Composite Reliability (rho_A) Average Variance Extracted (AVE) Educational Support Edu1 0.912 0.87 0.887 0.793 Edu2 0.903 Edu3 0.855 Relational Support Rel1 0.876 0.868 0.88 0.791 Rel2 0.909 Rel3 0.882 Structural Support Str1 0.878 0.88 0.906 0.805 Str2 0.899 Str3 0.914 Prior Entrepreneurial Experience Know1 0.919 0.877 0.934 0.796 Know2 0.909 Know3 0.847 Entrepreneurial Intention Att1 0.897 0.908 0.913 0.784 Att2 0.865 Att3 0.886 Att4 0.894 Online Entrepreneurial Activity Dec1 0.872 0.892 0.895 0.755 Dec2 0.884 Dec3 0.855 Dec4 0.865 The assessment of internal consistency reliability (Table 1) shows that all values exceed the recommended threshold of 0.7, as suggested by Hair et al. [18]. Among the constructs, the Rel scale (Cronbach’s Alpha = 0.868; rho_a = 0.88) demonstrates the lowest level of internal consistency reliability, whereas the Att scale (Cronbach’s Alpha = 0.908; rho_a = 0.913) exhibits the highest. Overall, all constructs show strong internal consistency reliability. The evaluation of convergent validity (Table 1) indicates that the Average Variance Extracted (AVE) values for all constructs, Edu (0.793), Rel (0.791), Str (0.805), Know (0.796), Att (0.784), and Dec (0.755), exceed the 0.50 threshold, confirming good convergence across all measurement scales. Furthermore, the HTMT ratio was used to detect potential issues related to discriminant validity [19]. The HTMT ratios for all construct pairs are below the threshold of 0.85 (Table 2), indicating satisfactory discriminant validity. Table 2. Heterotrait–Monotrait (HTMT) Ratios. Att Dec Edu Know Rel Str Att Dec 0.534 Edu 0.291 0.063 Know 0.268 0.23 0.136 Rel 0.288 0.201 0.067 0.056 Str 0.27 0.127 0.113 0.072 0.032 The assessment of cross-loadings, HTMT ratios, and the bootstrapping test for HTMT coefficients was conducted to evaluate discriminant validity. The HTMT index values for pairs of latent constructs are presented in a matrix table, with a comparison threshold of 0.85. When the HTMT value is below 1250 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 11: 1244-1254, 2025 DOI: 10.55214/2576-8484.v9i11.11104 © 2025 by the author; licensee Learning Gate 0.85, the two sets of indicators measuring the respective latent variables are considered to possess satisfactory discriminant validity, thereby meeting the condition for subsequent analysis. Observation of the HTMT matrix shows that all HTMT values are below the 0.85 threshold, confirming that the constructs meet the required discriminant validity criteria. Consequently, the bootstrapping test was performed to further validate the precision and robustness of the discriminant validity assessment. Table 3. Outer Loadings and Cross-Loadings. Item Att Edu Know Rel Str Att1 0.897 0.205 0.245 0.279 0.209 Att2 0.865 0.232 0.193 0.181 0.224 Att3 0.886 0.265 0.225 0.238 0.221 Att4 0.894 0.225 0.237 0.216 0.219 Edu1 0.258 0.912 0.125 -0.006 0.124 Edu2 0.239 0.903 0.081 -0.066 0.024 Edu3 0.193 0.855 0.119 -0.069 0.116 Know1 0.263 0.081 0.919 0.009 0.073 Know2 0.247 0.152 0.909 0.063 0.076 Know3 0.136 0.084 0.847 -0.052 -0.003 Rel1 0.215 -0.023 0.009 0.876 0.024 Rel2 0.260 -0.034 0.030 0.909 0.006 Rel3 0.214 -0.078 0.006 0.882 -0.009 Str1 0.172 0.081 0.047 0.036 0.878 Str2 0.223 0.091 0.045 -0.013 0.899 Str3 0.253 0.090 0.078 0.005 0.914 The assessment of cross-loadings, HTMT ratios, and the bootstrapping test for HTMT coefficients was conducted to evaluate discriminant validity. The HTMT index values for pairs of latent constructs are presented in a matrix table, with a comparison threshold of 0.85. When the HTMT value is below 0.85, the two sets of indicators measuring the respective latent variables are considered to possess satisfactory discriminant validity, thereby meeting the condition for subsequent analysis. Observation of the HTMT matrix shows that all HTMT values are below the 0.85 threshold, confirming that the constructs meet the required discriminant validity criteria. Consequently, the bootstrapping test was performed to further validate the precision and robustness of the discriminant validity assessment. The assessment of cross-loadings, HTMT ratios, and HTMT bootstrapping was conducted to evaluate discriminant validity. The HTMT coefficients of latent variable pairs are presented in a matrix and compared against the threshold of 0.85. If the HTMT value is below 0.85, the two sets of indicators for those latent constructs are considered to possess adequate discriminant validity, meeting the condition for subsequent analysis. Observation of the matrix reveals that all HTMT ratios are below 0.85, confirming that discriminant validity is achieved. Therefore, the HTMT bootstrapping test was performed to confirm the statistical precision and reliability of the discriminant validity results. 1251 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 11: 1244-1254, 2025 DOI: 10.55214/2576-8484.v9i11.11104 © 2025 by the author; licensee Learning Gate Table 4. Total Effects. Original Sample Sample Mean Standard Deviation T Statistics P Values Att -> Dec_ 0.487 0.489 0.041 11.796 0.000 Edu -> Att 0.229 0.230 0.051 4.471 0.000 Edu -> Dec_ 0.111 0.112 0.026 4.238 0.000 Know -> Att 0.210 0.211 0.046 4.545 0.000 Know -> Dec_ 0.102 0.103 0.025 4.163 0.000 Rel -> Att 0.266 0.268 0.049 5.466 0.000 Rel -> Dec_ 0.130 0.131 0.027 4.801 0.000 Str -> Att 0.208 0.211 0.051 4.112 0.000 Str -> Dec_ 0.101 0.103 0.027 3.794 0.000 We observe that entrepreneurial intention (Att) has a significant impact on the decision to start an online business (Dec), whereas educational support (Edu), relational support (Rel), structural support (Str), and prior entrepreneurial experience (Know) exert smaller effects on entrepreneurial intention. 5.3. Discussion of Research Findings The PLS-SEM analysis indicates that the proposed research model satisfies the reliability and validity criteria for all measurement scales. First, all VIF values are below 3, confirming the absence of multicollinearity and ensuring the stability of the estimated coefficients. Regarding direct effects, the bootstrapping results reveal that all path coefficients of the independent variables are statistically significant at the 5% level (p ≤ 0.05). This implies that the exogenous constructs play an important explanatory role for the dependent variable. As for indirect effects, the results in the Specific Indirect Effects table show that educational, relational, and structural support, as well as prior entrepreneurial experience, influence the decision to start an online business (Dec) through entrepreneurial intention (Att). All indirect paths are statistically significant, confirming the mediating role of entrepreneurial intention in transmitting the effects of supportive factors to actual entrepreneurial decision-making. In terms of total effects, the findings indicate that the overall influence of the exogenous constructs (Edu, Rel, Str, Know) on Dec primarily occurs indirectly via Att, since no direct relationship exists between these variables and the decision to start a business. This highlights that entrepreneurial intention serves as the key linkage transforming external support factors into tangible entrepreneurial actions. Regarding R² coefficients, the R² values of Att and Dec fall within the moderate range (25%–50%), suggesting that while the included factors reasonably explain the dependent variables, additional variables outside the model may also influence entrepreneurial intention and decision. The f² analysis provides further evidence of the relative importance of each factor. Following Cohen [20] guidelines, the path Att → Dec shows a large effect size, underscoring the crucial role of entrepreneurial intention in shaping entrepreneurial decisions. Meanwhile, Edu, Rel, and Know exhibit small effects on Att, and notably, Str is statistically insignificant (p > 0.05), indicating that structural or environmental support is not yet a decisive factor in motivating students’ entrepreneurial intentions within this study’s context. Finally, the Q² results are all greater than zero, confirming that the model has predictive relevance. However, since Q² values remain below the moderate threshold (25%–50%), the model’s predictive power is still limited. This opens directions for future research, such as incorporating additional potential variables (e.g., personal traits, financial motivation, entrepreneurial self-efficacy) to enhance the model’s explanatory and predictive capacity. 1252 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 11: 1244-1254, 2025 DOI: 10.55214/2576-8484.v9i11.11104 © 2025 by the author; licensee Learning Gate 6. Managerial Implications The findings confirm that entrepreneurial intention serves as a pivotal mediator, transforming the effects of education (Edu), relationships (Rel), structural–institutional conditions (Str), and experience (Know) on the online entrepreneurship decision (Dec). Among these, three factors education, relationships, and experience exert positive effects, whereas the structural–institutional factor does not exhibit a statistically significant impact. Based on this, several implications are drawn: First, entrepreneurship education. Higher education institutions should enhance the practical orientation of their programs by integrating theory with practice and employing real projects or online business simulations. Incorporating content such as e-commerce, digital marketing, and innovation- driven entrepreneurship will help students develop opportunity recognition and implementation skills. Universities should also invite entrepreneurs and technology experts to teach or mentor to strengthen real-world relevance. Second, networks and social capital. The results indicate that ties with faculty, peers, alumni, and entrepreneurs play an important motivational role for entrepreneurial intention. Accordingly, institutions should develop multi-layered support networks that connect students with the local business ecosystem through forums, fairs, and mentoring programs. Dedicated online platforms should be created to facilitate experience sharing, collaboration, and opportunity discovery. Third, institutional environment and policy support. Although the empirical results do not show a clear effect, this likely reflects students’ limited exposure to current entrepreneurship policies. Thus, communication and dissemination of policies should be strengthened, while simultaneously improving digital infrastructure, administrative procedures, and mechanisms for financial support and co-working space provision to encourage students to experiment with online business ideas. Fourth, practical experience. Hands-on engagement in small business activities, course-based projects, or internships at start-ups fosters greater self-efficacy and proactiveness in students’ decision- making. Universities should expand “learning-by-doing” initiatives, encouraging students to participate in real projects, pilot experimental online ventures, or intern with entrepreneurial firms to accumulate managerial skills and experience. Finally, at the student level, the findings indicate that forming a strong entrepreneurial intention is a prerequisite for enacting entrepreneurial behavior. Students should proactively cultivate digital competencies, creative thinking, and soft skills through coursework, extracurricular activities, and online learning platforms. Self-directed learning and adaptability will help translate ideas into effective, real-world action. Overall. To catalyze digital entrepreneurship among students, an integrated support ecosystem is essential, comprising: (i) practice-oriented curricula paired with intensive mentoring; (ii) networked linkages among universities, businesses, and public agencies; and (iii) an enabling institutional environment and digital infrastructure. When these three components operate in concert, they provide a robust foundation for students at Thanh Dong University and Vietnamese students more broadly to convert entrepreneurial intention into concrete action, thereby diffusing an innovation-driven mindset in the era of digital transformation. 7. Conclusion This study identifies and tests the factors influencing Thanh Dong University students’ decisions to engage in online entrepreneurship through a model comprising four exogenous constructs: entrepreneurship education (Edu), social relationships (Rel), structural institutional support (Str), and entrepreneurial experience (Know), with entrepreneurial intention (Att) serving as a mediating variable leading to the start-up decision (Dec). The analysis indicates that education, relationships, and experience positively influence entrepreneurial intention and, through intention, indirectly affect students’ online start-up decisions. Conversely, structural institutional support does not demonstrate a statistically significant effect, 1253 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 11: 1244-1254, 2025 DOI: 10.55214/2576-8484.v9i11.11104 © 2025 by the author; licensee Learning Gate suggesting that students have yet to effectively access or utilize existing entrepreneurship policies and support infrastructures. Overall, the study confirms that entrepreneurial intention is the critical bridge between antecedent factors and entrepreneurial behavior, while elucidating the mechanism through which online start-up decisions are formed in Vietnam’s higher education context. The findings provide a scientific basis for policy design and curricular adjustment, thereby contributing to the development of a student entrepreneurship ecosystem aligned with digital transformation and innovation. Transparency: The author confirms that the manuscript is an honest, accurate, and transparent account of the study; that no vital features of the study have been omitted; and that any discrepancies from the study as planned have been explained. This study followed all ethical practices during writing. Copyright: © 2025 by the author. This article is an open-access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). References [1] S. Choo and M. Wong, "Entrepreneurial intention: Triggers and barriers to new venture creations in Singapore," Singapore Management Review, vol. 28, no. 2, pp. 47-64, 2006. [2] A. Henley, "Entrepreneurial aspiration and transition into self-employment: Evidence from British longitudinal data," Entrepreneurship & Regional Development, vol. 19, no. 3, pp. 253-280, 2007. https://doi.org/10.1080/08985620701223080 [3] T. Mazzarol, T. Volery, N. Doss, and V. 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