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Article

Psychological Capital and Entrepreneurial Intention—The Mediation Role of Internet Entrepreneurial Self-Efficacy

by
Beatrice Adriana Balgiu
1,*,
Petruța Mihai
2 and
Teodora Daniela Chicioreanu
1
1
Department of Career and Educational Training, National University of Science and Technology Politehnica Bucharest, 060042 Bucharest, Romania
2
Department of Entrepreneurship and Management, National University of Science and Technology Politehnica Bucharest, 060042 Bucharest, Romania
*
Author to whom correspondence should be addressed.
Adm. Sci. 2025, 15(12), 464; https://doi.org/10.3390/admsci15120464
Submission received: 27 September 2025 / Revised: 17 November 2025 / Accepted: 19 November 2025 / Published: 26 November 2025
(This article belongs to the Special Issue Moving from Entrepreneurial Intention to Behavior)
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Abstract

Based on Social Cognitive Theory and Positive Psychology, this study addresses a research gap by examining internet entrepreneurial self-efficacy as a mediating mechanism between psychological capital and internet entrepreneurial intention in the digital context—a relationship rarely explored among engineering students in Eastern European economies. Using a quantitative, cross-sectional design, data were collected from 900 undergraduates enrolled in three Romanian technical universities who completed a set of specific instruments. Entrepreneurial intention was measured with the Individual Entrepreneurial Intent Scale adapted for online entrepreneurship; entrepreneurial self-efficacy in the online context was assessed using the Internet Entrepreneurial Self-Efficacy Scale; and psychological capital was measured with the Psychological Capital Questionnaire adapted for the student environment. The mediation analysis conducted through PROCESS-macro showed that psychological capital exerts a significant indirect effect on internet entrepreneurial intention via internet entrepreneurial self-efficacy. Psychological capital exerted a strong effect on internet entrepreneurial self-efficacy (β = 0.538), which in turn influenced the intention to start an online business (β = 0.213), while the direct effect on internet entrepreneurial intention remained relatively reduced (β = 0.037). The results indicate that positive psychological resources foster entrepreneurial intention by strengthening confidence specific to the digital environment. This study advances Social Cognitive Theory by demonstrating that internet entrepreneurial self-efficacy operates as the proximal cognitive pathway through which psychological capital is translated into entrepreneurial intention in online settings, clarifying how general psychological resources acquire domain-specific relevance in digital entrepreneurship.

1. Introduction

Technological advancements have redefined entrepreneurship by creating digital ecosystems, in which business opportunities are increasingly mediated by online platforms. This transformation has brought new forms of entrepreneurship to the forefront based on digital infrastructure, flexibility, and connectivity (Wang et al., 2020; Kollmann et al., 2021). This change requires not only technical adaptation, but also new psychological and cognitive capacities that enable individuals to identify and exploit digital opportunities. Within this context, internet entrepreneurship—focused on, but not limited to, e-commerce, online marketing, and platform-based business creation—has become a strategic driver of innovation and economic growth in both developed and emerging economies (Barroso & Laborda, 2022).
Despite its global relevance, research on internet entrepreneurship has been dominated by technological and educational perspectives, whereas the psychological mechanisms that enable individuals to translate digital competence into entrepreneurial action remain poorly understood (Primario et al., 2022; Vu et al., 2024). Higher education institutions are the first to be interested in understanding these mechanisms in order to prepare students who want to engage in entrepreneurial activities not only with technical skills but also on a psychological level (Compagnucci & Spigarelli, 2020). Among these mechanisms, Psychological Capital (PsyCap) has been recognized as a motivational resource that fosters perseverance and adaptability (Luthans et al., 2007a). Most studies have examined PsyCap in traditional entrepreneurship, where its self-efficacy dimension, refers to general business capabilities rather than internet-specific skills (Mahfud et al., 2020; Margaça et al., 2023; Maslakçı et al., 2024).
This aspect creates a theoretical tension: while PsyCap is expected to enhance entrepreneurial intention through self-efficacy (Bandura, 1997), it is unclear whether this mechanism operates similarly in digital contexts, where entrepreneurial success depends not only on psychological resilience but also on confidence in using online technologies. This issue becomes even more relevant for populations not primarily trained in business, such as engineering students. The digital environment may alter the cognitive pathways through which PsyCap functions, suggesting that internet entrepreneurial self-efficacy (IESE) could be the key cognitive mechanism that transforms psychological resources into intention.
Previous studies on European economies have only looked at this relationship in a limited manner (Ardelean, 2021). This leaves a research gap regarding how PsyCap interacts with IESE to shape entrepreneurial intention in transitional economies characterized by rapid digitalization, but uneven psychological and institutional support for entrepreneurship. To address this deficiency, we identified three research gaps. First, existing studies mostly examine the direct links between psychological capital (PsyCap) and entrepreneurial intention (IE), providing limited insight into the causal mechanisms connecting them (Newman et al., 2014; Zhang et al., 2020). The mediating role of Internet Entrepreneurial Self-Efficacy (IESE) has been less frequently tested as a cognitive process that channels PsyCap towards entrepreneurial motivation. Second, prior research has focused mainly on business students (Mahfud et al., 2020), neglecting engineering students whose technical competence, but moderate entrepreneurial training, offers a distinct perspective. Third, most of the evidence relates to traditional entrepreneurship, overlooking the online environment, where success depends on trust in digital technologies (Tian, 2022). To address this gap, the present study investigated the relationship between PsyCap, IESE, and internet entrepreneurial intention (IEI) among Romanian engineering students. This population represents a group that is theoretically relevant. Their technical background provides the digital skills necessary to form internet-based efficacy beliefs (Shekhar & Huang-Saad, 2021), while their modest exposure to business training allows us to isolate the motivational and cognitive effects of PsyCap.
This study contributes to the literature by advancing Social Cognitive Theory (SCT) (Bandura, 1997) through the integration of Psychological Capital (PsyCap)—a general psychological resource—and Internet Entrepreneurial Self-Efficacy (IESE)—a domain-specific cognitive belief—into a unified mediation model predicting Internet Entrepreneurial Intention (IEI). The study seeks to clarify the transformation of motivational resources into entrepreneurial intentions within the digital realm, thereby expanding the applicability of Social Cognitive Theory beyond conventional entrepreneurship. Practically, it considers directions for entrepreneurial education in technical universities, recommending the integration of the development of PsyCap components with the training of digital entrepreneurial skills to increase students’ readiness for online initiatives, such as integrating PsyCap modules in online business simulations.

2. Theoretical Framework

2.1. Conceptual Clarifications

Digital transformation has generated multiple forms of entrepreneurship, among which internet entrepreneurship denotes ventures that rely primarily on online infrastructure and web-based platforms for their core activities (Ojala, 2024; Al-Mamary, 2025). These ventures depend on connectivity, web-mediated interactions, and online transactional systems, such as e-commerce, digital marketplaces, and internet marketing. In this context, internet entrepreneurial self-efficacy (IESE) refers to individuals’ confidence in their ability to perform entrepreneurial tasks that require online technologies (Chang et al., 2020). As a context-specific form of self-efficacy, IESE encompasses beliefs regarding the use of digital tools, development and management of web-based content, and execution of e-commerce activities (Wang et al., 2020). IESE reflects perceived competence in handling the technical and managerial tasks characteristic of internet entrepreneurship and forms the cognitive basis for evaluating one’s potential to initiate and sustain online business activities (Wang et al., 2020; Yeh et al., 2021).

2.2. Psychological Capital and Entrepreneurial Intention

The fundamental constructs of positive psychology—self-efficacy, optimism, hope, and resilience—have been integrated into a higher-order concept called Psychological Capital defined as “a positive psychological state of development of the individual, observable in the confrontation with problems and adversities” (Luthans et al., 2007b, p. 3). The relationship between psychological capital and an individual’s tendency to initiate a business has garnered increasing attention in the recent literature (Tian, 2022; Maslakçı et al., 2024; Kun, 2025). In entrepreneurship research, PsyCap has emerged as a key psychological resource that enables individuals to persevere in the face of uncertainty, recover from setbacks, and pursue opportunities (Niu, 2023).
A growing body of evidence suggests that PsyCap is positively related to entrepreneurial intention (EI), which is considered a valid predictor of entrepreneurial behavior (Maheshwari & Kha, 2022; Wardana et al., 2024). Systematic reviews and meta-analyses substantiate that PsyCap plays a significant role in shaping EI (Newman et al., 2014; Zhang et al., 2020; Tian, 2022; Margaça et al., 2023; Haddoud et al., 2024; Mahfud et al., 2024). From the perspective of Social Cognitive Theory (Bandura, 1997), PsyCap enhances goal persistence and cognitive evaluation of challenges, both of which affect entrepreneurial cognition. Optimism strengthens expectations of success; hope supports goal-directed action; resilience facilitates recovery from failures; and general self-efficacy enhances confidence in one’s ability to act (Luthans et al., 2007a). Together, these mechanisms increase the likelihood of forming entrepreneurial intentions (EI), as individuals perceive entrepreneurship as both achievable and rewarding (Tian, 2022). Given that PsyCap reflects an individual’s positive psychological resources, its influence on entrepreneurial intention is likely to operate through specific cognitive mechanisms—most notably self-efficacy beliefs. According to Social Cognitive Theory, motivational resources, such as optimism, hope, resilience, and general efficacy, shape goal-directed cognition by influencing how individuals appraise challenges and anticipate successful performance. This perspective provides the basis for examining how specific entrepreneurial beliefs further shape intentions. Based on these findings, we formulate the following hypothesis:
H1. 
High levels of psychological capital (PsyCap) are significantly positively correlated with internet entrepreneurial intention (IEI).

2.3. Entrepreneurial Self-Efficacy and Intention

Entrepreneurial self-efficacy (ESE) is conceptualized as an individual’s belief in their own ability to successfully initiate, manage, and complete entrepreneurial activities, including both operational tasks and strategic decisions related to entrepreneurial endeavors (Primario et al., 2022). According to the Social Cognitive Theory (Bandura, 1997; Newman et al., 2019), self-efficacy represents a motivational core mechanism that shapes the extent to which individuals invest effort, persist in the face of challenges, and ultimately translate their intention into entrepreneurial action. Higher perceived self-efficacy enhances confidence in one’s capacity to organize and execute entrepreneurial behaviors, thereby increasing the likelihood of success.
Empirical studies converge in recognizing ESE as a fundamental determinant of entrepreneurial intention (EI), especially among students (Auer Antončič et al., 2021; Pham et al., 2023). Research consistently shows that students with high levels of ESE are more likely to transition from intention to concrete entrepreneurial behaviors, demonstrating greater perseverance when facing uncertainty and risk (Duong & Bernat, 2019; Auer Antončič et al., 2021; Pham et al., 2023). Dharmanegara et al. (2022) emphasize its transformative role in bridging theoretical training and tangible entrepreneurial action. Similarly, research across cultural contexts, as exemplified by China, confirms that ESE interacts with entrepreneurship education and social networks to exert a direct and substantial influence on EI (Zhongnong, 2024).
The growing digitalization of entrepreneurship has led to more context-specific formulations of self-efficacy. Cyber-entrepreneurial self-efficacy reflects confidence in internet-based entrepreneurial skills, including website development, e-commerce, and social media marketing, and has been shown to predict cyber-entrepreneurial intentions (Chang et al., 2020).
Referring to Social Cognitive Theory, IESE captures a contextualized form of personal agency that links general psychological resources to domain-specific entrepreneurial behaviors. Consequently, IESE operates both as a direct antecedent of Internet Entrepreneurial Intention (IEI) and as a mediating cognitive mechanism through which broader psychological resources, such as PsyCap, are converted into entrepreneurial motivation in the digital domain. Within Social Cognitive Theory, domain-specific self-efficacy functions as a proximal cognitive driver of intention by shaping perceived capability, expected outcomes, and effort investment.
H2. 
Internet entrepreneurial self-efficacy (IESE) is significantly and positively correlated with internet entrepreneurial intention (IEI).
SCT posits that self-efficacy develops through mastery experience, cognitive appraisal, and affective regulation. PsyCap supports these processes by sustaining persistence, positive expectations, and resilience when individuals engage with digital tasks, thereby facilitating the development of IESE.
H3. 
Psychological capital (PsyCap) is significantly and positively correlated with internet entrepreneurial self-efficacy (IESE).

2.4. Entrepreneurial Self-Efficacy as Key Mediator

Some studies have highlighted the mediating role of entrepreneurial self-efficacy in the relationship between PsyCap and entrepreneurial intention. For example, Maslakçı et al. (2024) demonstrated that PsyCap exerts a significant positive effect on EI with ESE acting as a central mechanism that transmits this influence. Similar findings were reported by Kholifah et al. (2024), who highlighted the significant mediating effect of ESE in transmitting the influence of psychological capital on entrepreneurial intention.
These findings highlight that positive psychological resources, including resilience, optimism, hope, and general self-efficacy, create a motivational foundation that fosters the development of specific entrepreneurial self-beliefs. When faced with external demands or pressures, people who have a positive outlook tend to view difficulties as controllable and solvable, and react proactively to pressure. An optimistic outlook increases the entrepreneur’s flexibility in an entrepreneurial endeavor, allowing him to face obstacles and overcome mistakes (Chang et al., 2020). For example, Mahfud et al. (2020) showed that an individual’s positive psychological state contributes to the emergence of ESE, providing optimism and confidence in their entrepreneurial abilities. Extending these insights to the digital context, recent studies have highlighted that self-efficacy adapted to online entrepreneurial activities—termed digital entrepreneurial self-efficacy or internet entrepreneurial self-efficacy (IESE)—performs a similar mediating role. For example, Tennakoon et al. (2024) and Ridwan et al. (2024) found that digital entrepreneurial self-efficacy transmits the effects of online education and motivational factors on entrepreneurial intention. Similarly, Yeh et al. (2021) argued that IESE has a significant impact on entrepreneurial performance on the internet.
Building on Social Cognitive Theory, the present study conceptualizes IESE as the central psychological mechanism through which PsyCap stimulates Internet Entrepreneurial Intention (IEI). PsyCap supplies the motivational energy—hope, optimism, and resilience—essential for the commencement of goal-directed action, whereas IESE signifies the domain-specific confidence that such action can achieve success in the online context. Thus, the mediation model proposed here integrates the motivational and cognitive components of human agency to explain how psychological strength is transformed into entrepreneurial intention in digital contexts. Following SCT (Bandura, 1997), broader motivational states, such as PsyCap, influence behavior indirectly through their impact on self-efficacy, which serves as the immediate cognitive mechanism guiding intention. Therefore, IESE represents the pathway through which psychological resources are converted into perceived capability and, subsequently, entrepreneurial intention in digital settings. These considerations motivated the inclusion of IESE as a mediating mechanism in the proposed model.
H4. 
Internet entrepreneurial self-efficacy (IESE) mediates the relationship between psychological capital (PsyCap) and internet entrepreneurial intention (IEI).
Figure 1 illustrates the hypothetical relationships between variables in the proposed mediation model.

3. Materials and Methods

3.1. Participants and Procedure

This study adopted a cross-sectional methodology and used self-report data. Participants were recruited from three comprehensive technical universities located in different Romanian regions, ensuring a heterogeneous distribution of respondents across geographical and institutional contexts. Data were gathered using an online survey distributed via Google Forms, between October and December 2023. The survey link was distributed by course instructors to entire student groups rather than to individual volunteers, thereby increasing the representativeness of the sample and reducing the likelihood of self-selection. Participation was voluntary and uncompensated (lacking monetary or academic incentives), which further mitigated motivational distortion. To reduce potential common method bias, procedural safeguards were applied during data collection, including full anonymity and randomization of item order across the questionnaires. On average, respondents completed the survey in approximately 12 to 14 min. The measurement tools were part of a larger investigation exploring entrepreneurial tendencies in academic environments. To maintain data integrity, survey access was limited in order to prevent multiple submissions from the same participant. The eligibility criteria included being at least 18 years old, native Romanian speaker, and currently enrolled at a technical university. Individuals who did not meet these criteria, submitted incomplete or inconsistent responses, or attempted to respond multiple times were excluded from the final dataset. A conventional sampling strategy was used because of the exploratory nature of the study. This approach is consistent with previous research in the field of entrepreneurship that has used student populations (e.g., Mahfud et al., 2020; Kholifah et al., 2024), where homogeneity of educational background and age increases internal validity and reduces external variability. Although conventional sampling limits generalizability, the inclusion of three major technical universities and diverse engineering subfields increases the representativeness and ensures adequate variability within the sample.

3.2. Ethical Considerations

The study was conducted in accordance with ethical principles, including the World Medical Association Declaration of Helsinki from 1975, as revised in 2013. This study was approved by the relevant departmental ethics committee of the National University of Science and Technology Politehnica Bucharest (Reg. No. 3048/16.10.2023). Before participation, all respondents received an online information sheet describing the study’s aims, confidentiality, and voluntary nature. Informed consent was obtained electronically before accessing the questionnaire, and the participants could withdraw at any time without penalty.

3.3. Measures

Psychological Capital Questionnaire—PCQ (Luthans et al., 2007a) measures a set of resources represented by self-efficacy, optimism, hope, and resilience combined into the PsyCap super factor (Luthans et al., 2007a, 2007b). The validated scale in Romanian for employees in organizations has demonstrated good psychometric properties (Lupșa & Vîrgă, 2020). Applying the tool in a student environment requires only minimal adaptation of the items. The concept of psychological capital is universal and can be applied to any field in which individuals pursue their objectives. Thus, we replaced terms specific to the organizational context with others relevant to the student environment (e.g., “work” with “academic studies”). A similar approach has been used successfully in other studies that applied the PCQ to student samples (Hazan Liran & Miller, 2019). The 24 items scored on a 6-point Likert-type scale (1—strongly disagree to 6—strongly agree) constitute a subscale for: self-efficacy (6 items)—the individual’s belief in their own ability to mobilize motivation and cognitive resources (e.g., I trust myself when analyzing a long-term study-related problem to find a solution); hope (6 items)—perseverance in achieving goals (e.g., There are plenty of ways to solve any problem with my college assignments); resilience (6 items, one of which is reverse)—the ability to recover from problems, conflicts, and adversities (e.g., Usually, when I study, I easily get past the stressful aspects); optimism (e.g., 6 items, of which two items are reversed)—anticipating positive outcomes (e.g., I am optimistic about what will happen to me regarding my student life). Scores ranged from 24 to 120. Thus, high scores indicate high levels of PsyCap. Under conditions of minimal item adaptation, the scale was first pretested on 19 engineering students for item comprehensibility and clarity, and the psychometric properties were analyzed. The results of the confirmatory factor analysis (CFA) for this study supported the validity of the instrument for the purpose of the research: χ2/df = 3.16, CFI = 0.956, TLI = 0.949, NFI = 0.938, RMSEA = 0.049 [90%CI: 0.045–0.053], SRMR = 0.039. Composite reliability (CR) and average variance extracted (AVE) were calculated based on factor loadings. Thus, factor loadings for items vary as follows: for the self-efficacy factor, between 0.744–0.830 (CR = 0.897, AVE = 0.594), for the hope factor, between 0.714–0.757 (CR = 0.871, AVE = 0.530), for the resilience factor, between 0.697–0.789 (CR = 0.857, AVE = 0.502), and for the optimism factor, between 0.644–0.769 (CR = 0.843, AVE = 0.518).
Internet Entrepreneurial Self-Efficacy Scale—IESES (Wang et al., 2020) measures internet entrepreneurial self-efficacy through 16 items included in three subscales: leadership—the ability to lead partners and make decisions in internet-based businesses (e.g., I possess the ability to be a leader); technology utilization—the ability to use multimedia tools and web applications (e.g., I can install and manipulate basic hardware types to help my business); internet marketing and e-commerce—the ability to provide high-quality services to online customers (e.g., I can create a unique e-commerce website). The items were evaluated on a 7-point Likert scale, ranging from 1—strongly disagree, to 7—strongly agree. Scores for each subscale are obtained by summing the responses to their respective items. The scale was validated in Romanian students and demonstrated good psychometric properties (Balgiu et al., 2024). In the current sample, CFA highlighted an overall acceptable model fit: χ2/df = 4.50, CFI = 0.951, TLI = 0.952, NFI = 0.946, RMSEA = 0.063 [90%CI: 0.057–0.069], SRMR = 0.051. Factor loadings for items vary as follows: leadership factor, from 0.644 to 0.760 (CR = 0.824, AVE = 0.485), technology utilization factor, from 0.581 to 0778 (CR = 0.807, AVE = 0.515) and internet marketing and e-commerce factor, from 0.686 to 0.854 (CR = 0.916, AVE = 0.610).
Individual Entrepreneurial Intent Scale—IEIS (Thompson, 2009), is designed to evaluate a person’s inclination toward entrepreneurship within conventional business contexts. In this study, the scale was modified to capture intentions related to online entrepreneurial ventures. The example of statements from the adapted version are as follows: I intend to launch an online business in the future and, I am saving money to start an online business. The instrument consisted of 10 main items, including three reverse-coded items. In addition, four filler items, referred to as “red herrings” by Thompson (2009), are incorporated to detect and reduce potential response bias. Participants responded using a 6-point Likert scale ranging from 1—definitely false to 6—definitely true. A composite score was calculated by summing the values of all relevant items. The scale was translated forward and backward and a pilot pre-test was conducted with engineering students to ensure clarity and comprehension, leading to minor linguistic adjustments. The CFA indicated that the model demonstrated an excellent fit to the data: χ2/df = 3.14, CFI = 0.991, TLI = 0.985, NFI = 0.990, RMSEA = 0.067 [90%CI: 0.044–0.081], SRMR = 0.075. Factor loadings for the items varied from 0.570 to 0.834 (CR = 0.860, AVE = 0.510).
A demographic questionnaire was used to collect information including (i) sex, (ii) age, (iii) year of study, (iv), and technical subfield studied.
Below are summarized the goodness-of-fit indices values for each instrument (Table 1).

3.4. Data Analysis Strategy

Descriptive analysis was used to assess data normality and the internal consistency of the verified scales through skewness and kurtosis statistics, as well as Cronbach’s alpha and McDonald’s omega coefficients. Correlational analysis (Pearson’s coefficients) was used to determine the existence of congruence between the variables. The mediation effects were examined using the PROCESS-macro (Hayes, 2022), which operates within a structural equation modelling (SEM) framework. Bootstrapping with 5000 resamples was used to estimate indirect effects, and 95% bias-corrected confidence intervals were used to assess statistical significance. Indirect effects were considered significant when the confidence interval did not include zero (Hayes, 2022). The maximum likelihood estimation (MLE) was chosen. Statistical significance was set at p < 0.05. PsyCap was the independent variable and internet entrepreneurial intention was the dependent variable. The internal consistency of the scales was assessed using α (Cronbach’s alpha) and ω (McDonald’s omega). Before performing the analyses, all variables were standardized (z scores) to eliminate scale differences between variables. All data were analyzed using SPSSv24 (IBM, New York, NY, USA) and JASP 0.19.1.0 (Amsterdam University, Amsterdam, The Netherlands).

4. Results

4.1. Sociodemographic Characteristics of the Sample

The study sample consisted of 900 university students, with an average age of 21.06 years (SD = 2.91). Of these, 520 were identified as males (Mean age = 21.03, SD = 2.41) and 380 as females (Mean age = 21.12, SD = 3.48). Regarding academic progression, 418 participants (46.45%) were enrolled in the first or second year of their programs, while the remaining 482 students (53.55%) were in their third or fourth years. The participants represented a variety of academic disciplines, including Information Technology & Communications and Automation (26.44%), Medical Engineering (25%), Transportation Studies (13.01%), Business Engineering and Management (13.33%), Civil Engineering (12%), and Electrical Engineering (10.22%) (Table 2). The coverage of several engineering subfields contributed to a diverse sample, supporting the representativeness of the participating student population.

4.2. Common Method Bias (CMB) Assessment

The possibility of respondent social desirability was calculated in two ways: using Harman’s single-factor test (Podsakoff et al., 2003). Thus, an exploratory factor analysis (EFA) was conducted, which illustrated a factorial solution of six distinct factors greater than 1 (KMO = 0.946; Bartlett’s test of sphericity = 16,280.551; df = 861; p < 0.001). These account for 59.80% of the total variance. The first factor captured 32.14% of the data variance and scores below the 50% recommended threshold (Fuller et al., 2016). Second, a model with one latent factor was tested through confirmatory factor analysis (CFA), in which poor fit indices were observed: χ2 = 10,619.286, df = 860, χ2/df = 12.348, CFI = 0.546, TLI = 0.523, RMSEA = 0.112 [90%CI: 0.110–0.116], SRMR = 0.112. These results provide no significant evidence of CMB in the present study.

4.3. Descriptive and Correlational Analysis

Table 3 presents the descriptive and correlational analyses. All correlations between variables were positive: PsyCap correlated moderately with IESE (r = 0.497) and weakly with IEI (r = 0.333). IESE correlated moderately with IEI (r = 0.544) (all at p < 0.001). These results support H1–H3, confirming that the analyzed constructs are significantly related, as predicted by the theoretical framework. The pattern of moderate intercorrelations is consistent with a partial mediation mechanism, suggesting that psychological resources influence entrepreneurial intention primarily through domain-specific self-efficacy rather than through strong direct effects. In line with the guidelines proposed by Kim (2013) for assessing normality in large samples (n > 300), data can be considered approximately normally distributed when the absolute value of skewness does not exceed 2, and kurtosis remains below an absolute value of 7. The skewness and kurtosis indices were close to zero, suggesting a relatively normal distribution of data for each variable. The α and ω coefficient values were high (above 0.83), indicating good internal consistency of the instruments used to measure the constructs.

4.4. Analysis of Mediation Effects

In the mediation model, psychological capital had a direct, weak, yet significant effect on entrepreneurial intention (β = 0.037; p = 0.012) and revealed a strong connection between psychological capital and online entrepreneurial self-efficacy (β = 0.538; p < 0.001). Although both the indirect (β = 0.114) and direct (β = 0.037) effects were relatively reduced, they remained theoretically meaningful, illustrating the subtle motivational role of psychological resources commonly observed in behavioral intention research. This result suggests that a higher level of psychological resources (resilience, optimism, self-efficacy, and hope) is associated with a stronger perception of one’s ability to act entrepreneurially in an online environment. The perception of online entrepreneurial self-efficacy positively influenced the intention to engage in entrepreneurial activities in that environment (β = 0.213, p < 0.001). The mediating effect (β = 0.114, p < 0.001) supported the hypothesis of significant mediation (Table 4).
The confidence interval (95%CI: 0.095–0.134) did not contain a value of zero, confirming the robustness of the effect. The total effect of PsyCap on IEI (both direct and indirect) was β = 0.151 (p < 0.001), indicating that a large part of the influence of PsyCap on entrepreneurial intention was transmitted through perceived self-efficacy.
The model supports a partial mediation effect: entrepreneurial self-efficacy explains a significant part of the influence of PsyCap on internet entrepreneurial intention, but the direct relationship between PsyCap and IEI remains significant even after the mediator is included in the model. Figure 2 provides a visual representation of the results of the analysis. This diagram presents PsyCap as an independent variable, IEI as a dependent variable, and IESE as a mediator. The total variance of internet entrepreneurial intention was R2 = 0.301, which indicates that the model has good explanatory validity according to the standards in applied psychological research (where R2 of 0.25–0.40 is considered “moderate to good”) (Hair et al., 2022). However, about 70% of variance in entrepreneurial intention remains unexplained, suggesting the presence of additional predictors. Thus, H4 was supported, indicating that IESE mediates the relationship between PsyCap and the intention to open an online business.

5. Discussion

The results of this study confirmed the proposed hypotheses, supporting the essential role of Psychological Capital (PsyCap) in shaping Internet Entrepreneurial Intention (IEI), both directly and indirectly, through Internet Entrepreneurial Self-Efficacy (IESE). While PsyCap stimulates entrepreneurial intention through optimism, hope, and resilience—psychological resources that support proactive behavior—its direct effect on IEI was observed to be relatively reduced (β = 0.037). This type of small effect is common in behavioral-intentional studies, where psychological mechanisms tend to exert subtle but consistent influences on decision-making. This weaker direct association, compared to studies in traditional entrepreneurship (Zhao et al., 2020), is most likely a consequence of the specificity of the digital context. Although engineering students possess developed technical skills, translating them into entrepreneurial confidence requires domain-specific efficacy. PsyCap influences entrepreneurial intention mainly through IESE—the critical cognitive mechanism that transforms general psychological resources into the confidence needed to create and manage online initiatives. IESE emerged as a significant mediator (indirect effect β = 0.114), confirming that entrepreneurial motivation is predominantly shaped by self-efficacy beliefs specific to the digital domain.
The mediation model explained a moderate proportion of variance in IEI (R2 = 0.301), consistent with the multifactorial nature of entrepreneurial intention. The remaining unexplained variance suggests that additional variables—such as perceived opportunities, subjective norms, or contextual digital experience—may also contribute to the formation of internet entrepreneurial intention.
These results are consistent with earlier research highlighting the mediating function of entrepreneurial self-efficacy in digital entrepreneurship. For example, Bachmann et al. (2024) showed that digital competencies influence entrepreneurial intention primarily through individual entrepreneurial orientation and self-efficacy, implying that digital skills alone are insufficient without a strong entrepreneurial mindset. Similarly, Mahfud et al. (2020) and Al-Qadasi et al. (2023) demonstrated that self-efficacy acts as a partial mediator linking psychological capital to entrepreneurial intention, even when applied to technology-based contexts. These findings also are consistent with prior research showing that cognitive and psychological resources play a central role in digital entrepreneurship. Al-Omoush (2022) discovered that intellectual capital enhances e-business entrepreneurial orientation through knowledge-based mechanisms, thereby supporting the notion that resource variables like psychological capital influence online entrepreneurial intention mainly through cognitive mediators such as self-efficacy.
In this study, IESE represents this proximal mechanism within the digital domain: students high in PsyCap—characterized by hope, optimism, resilience, and efficacy—are more confident in managing online business tasks, which strengthens their entrepreneurial intention. The persistence of a significant direct path indicates that PsyCap also exerts an independent motivational influence, consistent with SCT’s principle of reciprocal interaction between personal agency, cognition, and environment.
The present results are further supported by recent research conducted in digital and non-business contexts. Zhao et al. (2020) confirmed that internet-specific self-efficacy mediates the link between psychological factors and online entrepreneurial intention. Yuan et al. (2024) found that PsyCap predicts entrepreneurial intentions among university students, while Haddoud et al. (2024) demonstrated that self-efficacy mediates the impact of entrepreneurship education on entrepreneurial behavior. Likewise, Sooknannan et al. (2024) documented how entrepreneurial self-efficacy develops among technical students, supporting the relevance of the PsyCap–IESE–IEI mechanism beyond business disciplines. These findings suggest that in technology-focused learning environments, domain-specific self-efficacy functions as the cognitive link between psychological resources and entrepreneurial motivation. Compared with prior research primarily focused on business students and traditional entrepreneurial settings (Newman et al., 2019; Mahfud et al., 2024), the current study expands the understanding of PsyCap and IESE to engineering education, emphasizing that these psychological mechanisms are transferable across academic domains.
The results indicate that, from a theoretical standpoint, PsyCap is a requisite yet insufficient condition for entrepreneurial intention. Psychological resources establish motivational readiness but must be complemented by domain-specific efficacy and environmental support to produce intentional behavior. Following the logic of Necessary Condition Analysis (Dul, 2020), PsyCap can be seen as a threshold variable: its absence prevents the emergence of entrepreneurial intention, while its presence alone does not guarantee it without adequate self-efficacy and contextual enablers.
From a contextual perspective, the Romanian educational and technological environment defines the boundary conditions of this study. Engineering students possess strong digital literacy but limited entrepreneurial experience, which amplifies the mediating role of IESE as a cognitive mechanism linking motivation and action. At the same time, Romania’s moderate level of digital infrastructure and its still-emerging entrepreneurial ecosystem create both opportunities and constraints for translating psychological resources into entrepreneurial behavior.
Although IESE depends on specific digital competences such as web use and online marketing, its development is inherently psychological. It requires perseverance and adaptability (resilience and hope), risk-taking and confidence in success (optimism), and proactive engagement (self-efficacy). This interpretation is consistent with Liu et al. (2019) and Tsai et al. (2020), who showed that PsyCap enhances confidence in digital abilities and motivates sustained entrepreneurial engagement.
Finally, a notable contribution of this study is its focus on engineering students—a population characterized by technical expertise but limited entrepreneurial education. Even within this group, the development of PsyCap increased IESE and, consequently, internet entrepreneurial intention. These findings support educational recommendations (Compagnucci & Spigarelli, 2020; Shekhar & Huang-Saad, 2021) advocating for integrated training programs that combine psychological resource development, digital skills, and entrepreneurship education. Such approaches can build both the motivational and cognitive foundations required for participation in the digital entrepreneurial ecosystem.
Study limitations and future research directions. This study has several limitations that should be acknowledged. The study focused intentionally on Romanian engineering students, a theoretically relevant group characterized by digital literacy and limited entrepreneurial training. This focus allows for the isolation of psychological and cognitive mechanisms underlying digital entrepreneurial intention. However, future research should test the generalizability of the PsyCap–IESE–IEI model among other student populations and across different cultural and institutional contexts. This would allow for a broader understanding of how contextual and cultural factors influence internet entrepreneurial intention.
Second, self-report measures were used in this study, which may introduce social desirability bias (CMB). Despite employing procedural remedies (e.g., participants anonymity) and conducting statistical tests for CMB, future studies could benefit from incorporating objective indicators of entrepreneurial behavior, such as actual business initiation or company registration, to reduce bias and strengthen the ecological validity of the results.
The study utilized a cross-sectional design, which constrains the capacity to determine causal relationships. Although the model demonstrated significant mediation effects, future research should explore multi-group analyses and longitudinal designs to track how PsyCap and IESE evolve over time and influence actual entrepreneurial behavior and to examine whether structural relationships are invariant across gender, levels of entrepreneurial experience, or other relevant subgroups.
Furthermore, while this study conceptualizes IESE as a multidimensional construct encompassing leadership, digital skills, and internet marketing competencies, the construct was analysed as a single mediator in the model. Future research could investigate additional factors, such as digital literacy, artificial intelligence skills, and the role of entrepreneurial education, to examine how they interact with PsyCap to influence entrepreneurial intention.
Another limitation concerns the omission of control variables such as gender, academic specialization, or prior entrepreneurial exposure. These factors are known to influence entrepreneurial intention and could partly account for the observed variance in IEI. Future research should include such demographic and academic variables to verify whether the PsyCap–IESE–IEI relationships remain robust when these influences are controlled.

6. Conclusions

This research enhances the existing literature on digital entrepreneurship by presenting and empirically validating a mediation model that connects Psychological Capital (PsyCap) to Internet Entrepreneurial Intention (IEI) via Internet Entrepreneurial Self-Efficacy (IESE). The results confirm that psychological resources enhance entrepreneurial motivation primarily by fostering domain-specific confidence in managing online entrepreneurial tasks. This finding expands Social Cognitive Theory, demonstrating that general motivational resources (hope, optimism, resilience, and self-efficacy) are transformed into entrepreneurial intentions through specific cognitive mechanisms adapted to the digital context.
From a theoretical perspective, the study shows why PsyCap functions as a psychological resource that indirectly shapes entrepreneurial intention by strengthening online self-efficacy. The practical implications derive directly from the study’s theoretical contribution, which shows that psychological resources influence internet entrepreneurial intention primarily through domain-specific self-efficacy in digital learning environments. The results lead to the idea that in technical universities, entrepreneurship education needs to be intensified and deserves to focus on developing students’ PsyCap level and digital entrepreneurial self-efficacy, along with digital skills. Technical universities could incorporate PsyCap-building modules—such as resilience training, goal-setting workshops, and digital self-efficacy simulations—into digital entrepreneurship curricula. These findings offer helpful perspectives for educators and policymakers aiming to foster digital entrepreneurial readiness among technical students.

Author Contributions

Conceptualization, B.A.B.; methodology, B.A.B.; software, B.A.B.; validation, B.A.B., and T.D.C.; formal analysis, B.A.B.; investigation, B.A.B. and P.M.; resources, B.A.B. and P.M.; data curation, B.A.B. and P.M.; writing—original draft preparation, B.A.B.; writing—review and editing, B.A.B.; visualization, B.A.B., T.D.C., and P.M.; supervision, B.A.B.; project administration, B.A.B.; funding acquisition, B.A.B. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by The National Program for Research of the National Association of Technical Universities (GNaCARUT 2023) grant number 9/06.10.2023, ID: 520235419 And the APC was funded by grant number 9/06.10.2023, ID: 520235419.

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki, and approved by the Institutional Review Board of the Ethics Commission for Scientific Research of the National University of Science and Technology Politehnica Bucharest (protocol code Reg. No. 3048 and date of approval 16 October 2023).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors on request.

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

The following abbreviations are used in this manuscript:
IESEInternet entrepreneurial self-efficacy
IEIInternet entrepreneurial intention
ESEEntrepreneurial Self-Efficacy
EIEntrepreneurial Intention
PsyCapPsychological capital
SCTSocial Cognitive Theory
EFAExploratory Factor Analysis
KMOKaiser-Meyer-Olkin
CFAConfirmatory Factor Analysis
CFIComparative fit index
TLITucker–Lewis index
NFINormed fit index
RMSEARoot mean squared error of approximation
SRMRStandardized root mean square residual
AVEAverage variance extracted
CRComposite reliability

References

  1. Al-Mamary, Y. H. (2025). The transformative power of artificial intelligence in entrepreneurship: Exploring AI’s capabilities for the success of entrepreneurial ventures. Future Business Journal, 11(1), 104. [Google Scholar] [CrossRef]
  2. Al-Omoush, K. S. (2022). Understanding the impact of intellectual capital on e-business entrepreneurial orientation and competitive agility: An empirical study. Information Systems Frontiers, 24(2), 549–562. [Google Scholar] [CrossRef]
  3. Al-Qadasi, N., Zhang, G., Al-Awlaqi, M. A., Alshebami, A. S., & Aamer, A. M. (2023). Factors influencing entrepreneurial intention of university students in Yemen: The mediating role of entrepreneurial self-efficacy. Frontiers in Psychology, 14, 1111934. [Google Scholar] [CrossRef] [PubMed]
  4. Ardelean, B. O. (2021). Role of technological knowledge and entrepreneurial orientation on entrepreneurial success: A mediating role of psychological capital. Frontiers in Psychology, 12, 814733. [Google Scholar] [CrossRef]
  5. Auer Antončič, J., Veselinovič, D., Antončič, B., Grbec, D. L., & Li, Z. (2021). Financial self-efficacy in family business environments. Journal of Enterprising Culture, 29(2), 207–219. [Google Scholar] [CrossRef]
  6. Bachmann, N., Rose, R., Maul, V., & Hölzle, K. (2024). What makes for future entrepreneurs? The role of digital competencies for entrepreneurial intention. Journal of Business Research, 174, 114481. [Google Scholar] [CrossRef]
  7. Balgiu, B. A., Cotoară, D. M., & Simionescu-Panait, A. (2024). Validation of the Internet Entrepreneurial Self-Efficacy scale among Romanian technical students. PLoS ONE, 19(10), e0312929. [Google Scholar] [CrossRef]
  8. Bandura, A. (1997). Self-efficacy: The exercise of control. W. H. Freeman. [Google Scholar]
  9. Barroso, M., & Laborda, J. (2022). Digital transformation and the emergence of the Fintech sector: Systematic literature review. Digital Business, 2, 100028. [Google Scholar] [CrossRef]
  10. Chang, S.-H., Shu, Y., Wang, C.-L., Chen, M.-Y., & Ho, W.-S. (2020). Cyber-entrepreneurship as an innovative orientation: Does positive thinking moderate the relationship between cyber-entrepreneurial self-efficacy and cyber-entrepreneurial intentions in non-IT students? Computers in Human Behavior, 107, 105975. [Google Scholar] [CrossRef]
  11. Compagnucci, L., & Spigarelli, F. (2020). The third mission of the university: A systematic literature review on potentials and constraints. Technological Forecasting and Social Change, 161, 120284. [Google Scholar] [CrossRef]
  12. Dharmanegara, I. B. A., Rahmayanti, P. L. D., & Yasa, N. N. K. (2022). The role of entrepreneurial self-efficacy in mediating the effect of entrepreneurship education and financial support on entrepreneurial behavior. International Journal of Social Science and Business, 6(2), 165–173. [Google Scholar] [CrossRef]
  13. Dul, J. (2020). Necessary Condition Analysis (NCA): Logic and methodology of “necessary but not sufficient” causality. Organizational Research Methods, 23(4), 812–831. [Google Scholar] [CrossRef]
  14. Duong, C. D., & Bernat, T. (2019, September 4–6). Entrepreneurial self-efficacy and intention among Vietnamese students: A meta-analytic path analysis based on the theory of planned behavior [Conference proceeding]. 23rd International Conference on Knowledge-Based and Intelligent Information & Engineering Systems (KES), Procedia Computer Science (Volume 159, pp. 2447–2460), Budapest, Hungary. [Google Scholar] [CrossRef]
  15. Fuller, C. M., Simmering, M. J., Atinc, G., Atinc, Y., & Babin, B. J. (2016). Common methods variance detection in business research. Journal of Business Research, 69(8), 3192–3198. [Google Scholar] [CrossRef]
  16. Haddoud, M. Y., Nowiński, W., Laouiti, R., & Onjewu, A.-K. E. (2024). Entrepreneurial implementation intention: The role of psychological capital and entrepreneurship education. The International Journal of Management Education, 22(2), 100982. [Google Scholar] [CrossRef]
  17. Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2022). A primer on partial least squares structural equation modeling (PLS-SEM) (3rd ed.). Sage. [Google Scholar]
  18. Hayes, A. F. (2022). Introduction to mediation, moderation, and conditional process analysis (3rd ed.). The Guilford Press. [Google Scholar]
  19. Hazan Liran, B., & Miller, P. (2019). The role of psychological capital in academic adjustment among university students. Journal of Happiness Studies, 21(4), 1169. [Google Scholar] [CrossRef]
  20. Kholifah, N., Nurtanto, M., Mutohhari, F., Majid, N. W. A., & Fawaid, M. (2024). The role of digital technology competency and psychological capital in vocational education students: The mediating role of entrepreneurial self-efficacy. Journal of Technical Education and Training, 16(2), 238–250. Available online: https://publisher.uthm.edu.my/ojs/index.php/JTET/article/view/15640 (accessed on 18 November 2025). [CrossRef]
  21. Kim, H. Y. (2013). Statistical notes for clinical researchers: Assessing normal distribution (2) using skewness and kurtosis. Restorative Dentistry & Endodontics, 38(1), 52–54. Available online: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3591587/ (accessed on 18 November 2025). [CrossRef] [PubMed]
  22. Kollmann, T., Kleine-Stegemann, L., Cruppe, K. D., & Then-Bergh, C. (2021). Eras of digital entrepreneurship. Business & Information Systems Engineering, 64(1), 15–31. [Google Scholar] [CrossRef]
  23. Kun, D. (2025). The influence of entrepreneurial psychological capital on college students’ entrepreneurial intention and development strategies. Academic Journal of Business & Management, 6(12), 193–198. [Google Scholar] [CrossRef]
  24. Liu, X., Lin, C., Zhao, G., & Zhao, D. (2019). Research on the effects of entrepreneurial education and entrepreneurial self-efficacy on college students’ entrepreneurial intention. Frontiers in Psychology, 10, 869. [Google Scholar] [CrossRef]
  25. Lupșa, D., & Vîrgă, D. (2020). Psychological capital questionnaire (PCQ): Analysis of the Romanian adaptation and validation. Psihologia Resurselor Umane, 16(1), 27–39. Available online: https://www.hrp-journal.com/index.php/pru/article/view/450 (accessed on 18 November 2025).
  26. Luthans, F., Avolio, B. J., Avey, J. B., & Norman, S. M. (2007a). Positive psychological capital: Measurement and relationship with performance and satisfaction. Personnel Psychology, 60(3), 541–572. [Google Scholar] [CrossRef]
  27. Luthans, F., Youssef, C., & Avolio, B. J. (2007b). Psychological capital: Developing the human competitive edge. Oxford University Press. [Google Scholar]
  28. Maheshwari, G., & Kha, K. L. (2022). Investigating the relationship between educational support and entrepreneurial intention in Vietnam: The mediating role of entrepreneurial self-efficacy in the theory of planned behavior. International Journal of Management Education, 20(1), 100553. [Google Scholar] [CrossRef]
  29. Mahfud, T., Hidayat, R. A., Thai, D., Kholifah, N., & Sari, A. I. (2024). Let’s be an entrepreneur through education! The role of entrepreneurial attitude orientation and psychological capital among university students. Global Business & Finance Review, 29(8), 1–16. [Google Scholar] [CrossRef]
  30. Mahfud, T., Triyono, M. B., Sudira, P., & Mulyani, Y. (2020). The influence of social capital and entrepreneurial attitude orientation on entrepreneurial intentions: The mediating role of psychological capital. European Research on Management and Business Economics, 26(1), 33–39. [Google Scholar] [CrossRef]
  31. Margaça, C., Sánchez García, J. C., & Hernández Sánchez, B. (2023). Psychological capital and entrepreneurship: A systematic literature review of a growing research agenda. Intangible Capital, 19(2), 276–292. [Google Scholar] [CrossRef]
  32. Maslakçı, A., Sürücü, L., & Şeşen, H. (2024). Positive psychological capital and university students’ entrepreneurial intentions: Does gender make a difference? International Journal for Educational and Vocational Guidance, 24(1), 125–150. [Google Scholar] [CrossRef]
  33. Newman, A., Obschonka, M., Schwarz, S., Cohen, M., & Nielsen, I. (2019). Entrepreneurial self-efficacy: A systematic review of the literature on its theoretical foundations, measurement, antecedents, and outcomes, and an agenda for future research. Journal of Vocational Behavior, 110, 403–419. [Google Scholar] [CrossRef]
  34. Newman, A., Ucbasaran, D., Zhu, F., & Hirst, G. (2014). Psychological capital: A review and synthesis. Journal of Organizational Behavior, 35(S1), 120–138. [Google Scholar] [CrossRef]
  35. Niu, X. R. (2023). The relationship between psychological capital, entrepreneurial ability, and entrepreneurial performance of digital entrepreneurs. International Journal of Science and Business, 23(1), 106–119. [Google Scholar] [CrossRef]
  36. Ojala, A. (2024). Digital entrepreneurship. In Reference module in social sciences. Elsevier. [Google Scholar] [CrossRef]
  37. Pham, V. H., Nguyen, T. K. C., Nguyen, T. B. L., Tran, T. T. T., & Nguyen, T. V. N. (2023). Subjective norms and entrepreneurial intention: A moderated-serial mediation model. Journal of Entrepreneurship, Management and Innovation, 19(1), 113–140. [Google Scholar] [CrossRef]
  38. Podsakoff, P. M., MacKenzie, S. B., Lee, J. Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879–903. [Google Scholar] [CrossRef]
  39. Primario, S., Rippa, P., & Secundo, G. (2022). Rethinking entrepreneurial education: The role of digital technologies to assess entrepreneurial self-efficacy and intention of STEM students. IEEE Transactions on Engineering Management, 71(6), 2829–2842. [Google Scholar] [CrossRef]
  40. Ridwan, M., Fiodian, V. Y., Religia, Y., & Hardiana, S. R. (2024). Investigating the effect of intrinsic and extrinsic motivation in shaping digital entrepreneurial intention: The mediating role of self-efficacy. Asia Pacific Journal of Innovation and Entrepreneurship, 19(2), 190–206. [Google Scholar] [CrossRef]
  41. Shekhar, P., & Huang-Saad, A. (2021). Examining engineering students’ participation in entrepreneurship education programs: Implications for practice. International Journal of STEM Education, 8(40), 1–15. [Google Scholar] [CrossRef]
  42. Sooknannan, R., Coetzee, M., & Potgieter, I. L. (2024). Business innovation intent among college students: Contributions of psychological capital and entrepreneurial self-efficacy. Journal of Psychology in Africa, 34(5), 471–476. [Google Scholar] [CrossRef]
  43. Tennakoon, N., Radulović, S., & Kyaruzi, I. S. (2024, October 25). The impact of digital education on the success of entrepreneurship: Exploring the interplay between online learning, digital self-efficacy, and entrepreneurial intention in the digital economy [Conference presentation]. 13th International Scientific Conference “Employment, Education and Entrepreneurship, Belgrade, Serbia. [Google Scholar]
  44. Thompson, E. (2009). Individual entrepreneurial intent: Construct clarification and development of an internationally reliable metric. Entrepreneurship Theory and Practice, 33(3), 669–694. [Google Scholar] [CrossRef]
  45. Tian, L. (2022). The influence of work values of college students on entrepreneurial intention: The moderating role of psychological capital. Frontiers in Education, 7, 1023537. [Google Scholar] [CrossRef]
  46. Tsai, F. S., Leonard, K. M., & Srivastava, S. (2020). The role of psychological capital in entrepreneurial contexts. Frontiers in Psychology, 11, 582133. [Google Scholar] [CrossRef] [PubMed]
  47. Vu, T. H., Do, A. D., Ha, D. L., Hoang, D. T., Le, T. A. V., & Le, T. T. H. (2024). Antecedents of digital entrepreneurial intention among engineering students. International Journal of Information Management Data Insights, 4(1), 100233. [Google Scholar] [CrossRef]
  48. Wang, Y. S., Tseng, T.-H., Wang, Y.-M., & Chu, C.-W. (2020). Development and validation of an internet entrepreneurial self-efficacy scale. Internet Research, 30(2), 653–675. [Google Scholar] [CrossRef]
  49. Wardana, L. W., Martha, J. A., Wati, A. P., Narmaditya, B. S., Setyawati, A., Maula, F. I., Mahendra, A. M., & Suparno, S. (2024). Does entrepreneurial self-efficacy really matter for entrepreneurial intention? Lesson from COVID-19. Cogent Education, 11(1), 2317231. [Google Scholar] [CrossRef]
  50. Yeh, C. H., Lin, H. H., Wang, Y. M., Wang, Y. S., & Lo, C. W. (2021). Investigating the relationships between entrepreneurial education and self-efficacy and performance in the context of internet entrepreneurship. The International Journal of Management Education, 19(3), 100565. [Google Scholar] [CrossRef]
  51. Yuan, R., Chen, Y., & Zheng, Y. (2024). The impact of deep learning based-psychological capital with ideological and political education on entrepreneurial intentions. Scientific Report, 14, 18132. [Google Scholar] [CrossRef]
  52. Zhang, S. N., Li, Y. Q., Liu, C. H., & Ruan, W. Q. (2020). Critical factors identification and prediction of tourism and hospitality students’ entrepreneurial intention. Journal of Hospitality, Leisure, Sport & Tourism Education, 26, 100234. [Google Scholar] [CrossRef]
  53. Zhao, J., Wei, G., Chen, K. H., & Yien, J. M. (2020). Psychological capital and university students’ entrepreneurial intention in China: Mediation effect of entrepreneurial capitals. Frontiers in Psychology, 10, 2984. [Google Scholar] [CrossRef]
  54. Zhongnong, C. (2024). Entrepreneurship education, social network and entrepreneurial self-efficacy among Chinese college students. Asia Pacific Journal of Management and Sustainable Development, 12(1), 55–68. [Google Scholar] [CrossRef]
Admsci 15 00464 g001
Figure 1. Conceptual model of the hypothesized relationship.
Figure 1. Conceptual model of the hypothesized relationship.
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Admsci 15 00464 g002
Figure 2. The mediation model of internet entrepreneurial self-efficacy in the relationship between psychological capital and internet entrepreneurial intention. Note: PsyCap—Psychological Capital; IESE—Internet Entrepreneurial Self-Efficacy; IEI—Internet Entrepreneurial Intention. The values on the arrows represent standardized regression coefficients (β, * p < 0.05, *** p < 0.001); a—effect of PsyCap on IESE; b—effect of IESE on IEI; c—total effect of PsyCap on IEI; c’—direct effect of PsyCap on IEI after including the mediator.
Figure 2. The mediation model of internet entrepreneurial self-efficacy in the relationship between psychological capital and internet entrepreneurial intention. Note: PsyCap—Psychological Capital; IESE—Internet Entrepreneurial Self-Efficacy; IEI—Internet Entrepreneurial Intention. The values on the arrows represent standardized regression coefficients (β, * p < 0.05, *** p < 0.001); a—effect of PsyCap on IESE; b—effect of IESE on IEI; c—total effect of PsyCap on IEI; c’—direct effect of PsyCap on IEI after including the mediator.
Admsci 15 00464 g002
Table 1. Goodness-of-fit indices resulting from CFA for each instrument.
Table 1. Goodness-of-fit indices resulting from CFA for each instrument.
Scalesχ2/dfCFITLINFIRMSEASRMR
PCQ3.160.9560.9490.9380.0490.039
IESES4.500.9510.9520.9460.0630.051
IEIS3.140.9910.9850.9900.0670.075
Note: PCQ-Psychological Capital Questionnaire; IESES-Internet Entrepreneurial Self-Efficacy Scale; IEIS-Individual Entrepreneurial Intent Scale; χ2/df-Chi-square to degrees of freedom ratio; CFI-Comparative fit index; TLI-Tucker–Lewis index; NFI-Normed fit index; RMSEA-Root mean square error of approximation; SRMR-Standardized root mean square residual.
Table 2. Socio-demographic information of the sample.
Table 2. Socio-demographic information of the sample.
StudentsN = 900
SexMales—57.77%
Females—42.23%
AgeM = 21.06 years
Years of studyEarly years—46.45%
Final years—53.55%
Field of studyInformation Technology & Communications—26.44%
Medical Engineering—25%
Transportation Studies—13.01%
Business Engineering and Management—13.33%
Civil Engineering—12%
Electrical Engineering—10.22%
Table 3. Descriptive and correlational analysis for key variables.
Table 3. Descriptive and correlational analysis for key variables.
Variables123
PsyCap–
IESE0.497 ***–
IEI0.333 ***0.544 ***–
Mean3.8124.7123.606
SD0.7431.0411.173
Skewness−0.112−0.108−0.093
Kurtosis−0.333−0.326−0.473
α0.9260.9100.833
ω0.9290.9120.834
Note: PsyCap-psychological capital; IESE-internet entrepreneurial self-efficacy; IEI-internet entrepreneurial intention; *** p < 0.001.
Table 4. Direct, indirect, and total effects.
Table 4. Direct, indirect, and total effects.
Direct EffectParameter Estimates (β)SEz-Values95%CIp
PsyCap → IESE0.5380.03117.4000.477–0.5980.001
IESE → IEI0.2130.01415.6480.186–0.2380.001
PsyCap → IEI0.0370.0152.5130.008–0.0650.012
Indirect effect
PsyCap → IESE → IEI0.1140.01011.6350.095–0.1340.001
Total effect
PsyCap → IEI0.1510.01410.6160.123–0.1790.001
Note: PsyCap—Psychological Capital; IESE—Internet Entrepreneurial Self-Efficacy; IEI—Internet Entrepreneurial Intention.
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MDPI and ACS Style

Balgiu, B.A.; Mihai, P.; Chicioreanu, T.D. Psychological Capital and Entrepreneurial Intention—The Mediation Role of Internet Entrepreneurial Self-Efficacy. Adm. Sci. 2025, 15, 464. https://doi.org/10.3390/admsci15120464

AMA Style

Balgiu BA, Mihai P, Chicioreanu TD. Psychological Capital and Entrepreneurial Intention—The Mediation Role of Internet Entrepreneurial Self-Efficacy. Administrative Sciences. 2025; 15(12):464. https://doi.org/10.3390/admsci15120464

Chicago/Turabian Style

Balgiu, Beatrice Adriana, Petruța Mihai, and Teodora Daniela Chicioreanu. 2025. "Psychological Capital and Entrepreneurial Intention—The Mediation Role of Internet Entrepreneurial Self-Efficacy" Administrative Sciences 15, no. 12: 464. https://doi.org/10.3390/admsci15120464

APA Style

Balgiu, B. A., Mihai, P., & Chicioreanu, T. D. (2025). Psychological Capital and Entrepreneurial Intention—The Mediation Role of Internet Entrepreneurial Self-Efficacy. Administrative Sciences, 15(12), 464. https://doi.org/10.3390/admsci15120464

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