Examining the impact of big five personality traits and digital competencies on digital entrepreneurial intention: the mediating role of digital self-efficacy
- Open Access
- 01.12.2025
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Abstract
Introduction
Digital entrepreneurship, a rapidly expanding field, centers on using digital technologies to pursue business opportunities and drive digital economic development (Elnadi & Gheith, 2023; Duong et al., 2024; Huynh et al., 2025). It includes creating digital products and services and using internet technologies to identify and pursue entrepreneurial opportunities, and the digitization of traditional business processes (Caputo et al., 2021). This reconciliation of traditional entrepreneurship with the new ways of doing business in the digital era requires a deeper understanding of the unique factors that drive digital entrepreneurship.
Previous studies have focused on experience, education, and industry-specific knowledge as key drivers of digital entrepreneurial intention and success (Kraus et al., 2019; Liao et al., 2022). However, little is known about the individual-level antecedents that encourage engagement in digital entrepreneurship. One of the most widely studied antecedents in entrepreneurship is personality traits, particularly those described by the Big Five framework, which represent enduring patterns of thought, emotion, and behavior that shape outcomes such as opportunity recognition, innovation, and venture creation (McCrae et al., 1992; Antoncic & Prodan, 2008). Alongside these traits, digital competence has become increasingly vital in the digital entrepreneurial landscape. Entrepreneurs, like other individuals, are expected to be digitally competent and adaptable to meet rising demands and seize emerging opportunities (Rahman et al., 2024). Digital competence refers to the confident, critical, and responsible use of digital technologies for learning, work, and participation in society (Directorate-General for Education, 2019) and enables individuals to operate effectively in digital business environments (Huynh et al., 2025). Despite the relevance of both personality traits and digital competence, existing research has not yet examined how these two individual-level factors jointly influence digital entrepreneurial intentions.
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To address this gap and respond to the call by Elnadi and Gheith (2023), this study investigates the factors influencing digital entrepreneurial intention by examining the Big Five personality traits and digital competencies as key antecedents. Furthermore, it explores the mediating role of digital self-efficacy in shaping entrepreneurial intention within a digital context.
This research makes three key contributions. First, it advances the digital entrepreneurship literature by jointly examining personality traits and digital competence, two antecedents that, as mentioned, have been studied in isolation but never in combination. Second, it introduces digital self-efficacy as a mediating mechanism between these two antecedents and digital entrepreneurial intention, offering a more nuanced understanding than previous research (Hatlevik et al., 2018; Joo et al., 2018; Taneja et al., 2025), which typically treats digital self-efficacy as a byproduct of digital competence. Furthermore, although Bachmann et al. (2024) examined digital competencies and entrepreneurial self-efficacy in relation to entrepreneurial intention, their framework did not incorporate digital self-efficacy as a domain-specific construct. Our study builds on and expands this work by introducing digital self-efficacy as conceptually distinct from general self-efficacy, where success relies not only on motivation but also on the ability to navigate digital tools and systems effectively (Eastin & LaRose, 2000; Venkatesh & Bala, 2008). Third, grounded in Social Cognitive Theory (SCT) and based on a survey of 380 students in Taiwan, this study builds on and extends the model proposed by Bachmann et al. (2024) by incorporating personality traits to offer a more comprehensive understanding of digital entrepreneurial intentions.
The work is structured as follows. In the next section, we introduce the theoretical background under the lens of SCT and present our hypotheses. Next, we describe methods and results. Finally, we discuss our findings and their implications for entrepreneurship practice and future research, working to further explore the interplay between entrepreneurial intentions and the digital environment.
Theoretical background and hypotheses development
Digital entrepreneurship is the process of identifying and pursuing entrepreneurial opportunities by using technical operating systems and information communication technologies (Al-Mamary & Alraja, 2022). The key distinction between traditional and digital entrepreneurship lies in the integration of digital technology in various value chain activities within digital ventures (Dy, 2022).
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We examine the antecedents of digital entrepreneurial intentions using the lens of Social Cognitive Theory (SCT). According to Bandura (1986), human behavior is influenced by a dynamic interaction between cognitive, behavioral, and environmental factors. SCT highlights that an individual’s expectations about the future, belief in their own abilities (self-efficacy), personal goals, and intentions shape their actions. Thoughts, emotions, and beliefs play a crucial role in determining behavior. SCT offers a comprehensive framework for analyzing individual actions and their outcomes by integrating cognitive, behavioral, and environmental perspectives (Hmieleski & Baron, 2009).
Within this framework, the construct of self-efficacy plays a central role. Self-efficacy is considered a major element of SCT and is defined as people’s belief in their capacity to perform a given activity (Bandura, 1977). In the context of digital entrepreneurship, we refer specifically to digital or technological self-efficacy, which is the owner-entrepreneur’s confidence in effectively adopting and utilizing digital technologies (Malodia et al., 2023). Digital self-efficacy extends Albert Bandura’s concept of general self-efficacy to the realm of computer technology use (Bandura, 2010; Lucas et al., 2009), and a higher level of technological self-efficacy indicates greater confidence in the ability to use technology effectively (Aboobaker et al., 2023).
An important antecedent of both self-efficacy and entrepreneurial intention is personality. Human behavior is also shaped by personality, and the decision to engage in entrepreneurial activities is influenced by individuals’ personality traits—even in the digital era. Personality traits are stable patterns of behavior across different situations (Specht, 2017), and they continue to play a crucial role in shaping individuals’ engagement with digital opportunities (González-Padilla et al., 2024). The Big Five model groups personality traits into five main categories: extraversion, neuroticism, agreeableness, conscientiousness, and openness (McCrae et al., 1992). Several studies have highlighted the relationship between personality traits and self-efficacy in shaping behavioral outcomes (Furnham & Cheng, 2024).
In this light, we formulate hypotheses linking each of the Big Five traits to digital self-efficacy. Extraversion refers to an individual’s tendency to engage in social situations and exhibit an outgoing disposition, forming the basis of a socially involved lifestyle (Wilson et al., 2005). Research suggests that extraverted individuals excel in socially driven careers (Barrick et al., 2001), are more likely to develop digital skills, and to strengthen their digital self-efficacy (Stajkovic et al., 2018). Their broad social networks can also enhance digital self-efficacy by facilitating support, skill development, and adaptability to digital tools (Furnham & Cheng, 2024). Therefore we postulate that:
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H1a: Extraversion is positively related to digital self-efficacy.
Agreeableness is a personality attribute that reflects a person’s tendency to prioritize others’ needs over their own (Graziano et al., 2007). While some studies have suggested that agreeableness is negatively associated with entrepreneurial behavior due to lower competitiveness, others argue that it can be beneficial in collaborative settings. Awwad and Al-Aseer (2021) found that entrepreneurs high in agreeableness may successfully scale small businesses. In line with this, we suggest that agreeable individuals may enhance their digital self-efficacy through supportive and collaborative interactions that increase confidence in navigating digital technologies, leading to the following hypothesis:
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H1b: Agreeableness is positively related to digital self-efficacy.
Conscientiousness describes the inclination to be organized, dependable, and responsible. Individuals high in conscientiousness are typically diligent, trustworthy, and disciplined (Thompson, 2008). In the entrepreneurial field, conscientiousness is associated with the long-term viability of business ventures (Ciavarella et al., 2004; Howard, 1995). Moreover, it facilitates task engagement and effort, supporting higher self-efficacy beliefs (Stajkovic et al., 2018). Structured and goal-oriented, conscientious individuals may be more confident in using digital tools to enhance their productivity and efficiency; therefore, we assume.
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H1c: Conscientiousness is positively related to digital self-efficacy.
Neuroticism reflects a person’s emotional instability and tendency to experience negative emotions, such as anxiety, fear, and self-doubt. It is also associated with poor self-regulation and difficulty managing stress (Widiger & Oltmanns, 2017). Highly neurotic individuals often lack the confidence to overcome challenges and may perceive digital tools as threatening or difficult to master (Pickering et al., 2016). Liu et al. (2024) also indicate that individuals with higher levels of neuroticism report lower self-efficacy and greater tension. Based on the previous arguments, we propose the following:
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H1d: Neuroticism is negatively related to digital self-efficacy.
Openness is characterized by curiosity, open-mindedness, and a preference for novelty and variety. It is associated with the ability to learn and adapt, and with a strong interest in new experiences (Costa & McCrae, 1999). Open individuals are typically more willing to embrace technological change and digital innovation. They tend to approach new challenges with a positive attitude (Maran et al., 2022), which may enhance their ability to develop digital skills and increase their confidence in using technology (Saleem et al., 2011). Based on what said above, we formulate:
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H1e: Openness is positively related to digital self-efficacy.
Beyond personality traits, digital competencies also play a critical role in shaping digital self-efficacy. Digital competencies refer to the ability to use and engage with digital technologies confidently, thoughtfully, and responsibly in contexts such as learning, professional work, and everyday life in society (Bachmann et al., 2024). They encompass an ensemble of attitudes, knowledge, skills, awareness, and values that are extremely important when using disruptive digital technologies and applications in an organization (Bachmann et al., 2024). More specifically, digital competence includes skills related to information management, communication, collaboration, digital content creation, security, and problem-solving. Possessing strong digital competence enhances an individual’s ability to manage technological and functional resources effectively, which in turn strengthens their digital self-efficacy, reflecting their confidence in handling specific tasks (Bandura, 1982). Therefore we postulate that:
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H2: Digital competencies are positively related to digital self-efficacy.
Entrepreneurial intention reflects an individual or a group’s intent to create a new business or idea. It is a deliberate mental state that precedes action and focuses attention on the objective of starting a new company (Saúde et al., 2020). Digital entrepreneurial intention refers more specifically to an individual’s predisposition to engage in a new technology-based venture (Chang et al., 2020; Huang et al., 2022). In this study, digital entrepreneurial intention is conceptualized as the intention to engage in entrepreneurial activities by embracing digital technologies to generate new business avenues. Several studies have explored the relationship between personality traits and entrepreneurial intention. Li et al. (2022) found that conscientiousness, openness, and extraversion had a positive impact, while neuroticism had a significant negative effect. Similarly, Salameh et al. (2022) demonstrated that openness and extraversion are positively associated with entrepreneurial intention, as more sociable and outgoing individuals are more likely to pursue entrepreneurial careers. Based on the previous arguments, we propose the following hypotheses:
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H3a: Extraversion is positively related to digital entrepreneurial intention.
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H3b: Agreeableness is positively related to digital entrepreneurial intention.
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H3c: Conscientiousness is positively related to digital entrepreneurial intention.
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H3d: Neuroticism is negatively related to digital entrepreneurial intention.
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H3e: Openness is positively related to digital entrepreneurial intention.
According to Penrose’s (1959) resource-based perspective, an individual’s knowledge base shapes their ability to recognize and exploit entrepreneurial opportunities. Building on this theory, we argue that digital competencies can serve as a foundation for the development of digital entrepreneurial intentions. In support of this, Elnadi and Gheith (2023) found that digital competence indirectly influences the development of digital entrepreneurial intention among university students. Therefore, we affirm that:
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H4: Digital competencies are positively related to digital entrepreneurial intention.
Amani et al. (2024) demonstrated that self-efficacy plays a key role in strengthening entrepreneurs’ confidence in their ability to create favorable environments for business growth. Similarly, digital self-efficacy has been shown to positively influence digital entrepreneurial intention and to mediate the relationship between digital entrepreneurship education, perceived ease of use, and entrepreneurial outcomes (Wibowo et al., 2023). In line with this we propose that:
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H5: Digital self-efficacy is positively related to digital entrepreneurial intention.
Beyond direct effects, digital self-efficacy is expected to mediate several key relationships. According to SCT, personality traits and cognitive resources help us reach behavioral outcomes such as entrepreneurial intention (Bandura, 1986). Entrepreneurial self-efficacy has been described as an individual’s ability to mobilize motivation, cognitive resources, and action plans necessary to succeed in a given professional domain (Caputo et al., 2025). In light of digitalization, this study focuses on digital self-efficacy as a key variable influencing digital entrepreneurial intention (Chen, 2014).
Previous studies demonstrated that both the Big Five personality traits and digital self-efficacy act as key determinants of entrepreneurial behavior (Caputo et al., 2025). Drawing on this, we propose that beyond their direct effects, personality traits influence entrepreneurial intentions through digital self-efficacy. Extraversion, agreeableness, and conscientiousness are associated with confidence, adaptability, and goal-directed behavior, and are therefore expected to foster digital self-efficacy, which in turn supports digital entrepreneurial intention. Conversely, neuroticism, associated with emotional instability and self-doubt, is expected to weaken digital self-efficacy, thereby diminishing entrepreneurial intention. Drawing on the discussion above, we put forward the following hypotheses:
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H6a: Digital self-efficacy mediates the positive relationship between extraversion and digital entrepreneurial intention.
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H6b: Digital self-efficacy mediates the positive relationship between agreeableness and digital entrepreneurial intention.
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H6c: Digital self-efficacy mediates the positive relationship between conscientiousness and digital entrepreneurial intention.
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H6d: Digital self-efficacy mediates the negative relationship between neuroticism and digital entrepreneurial intention.
Bachmann et al. (2024) proposed that digital competencies influence entrepreneurial intention solely through the mediation of entrepreneurial self-efficacy. However, our study extends this perspective by demonstrating that digital self-efficacy not only has a direct effect on digital entrepreneurial intention but also serves as a mediating mechanism. Our study identifies digital self-efficacy as a key mediating factor and highlights its dual role, both as a direct predictor of digital entrepreneurial intention and as a mediator in the relationship between digital competencies and digital entrepreneurial intention. Therefore we postulate that:
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H6e: Digital self-efficacy mediates the positive relationship between digital competencies and digital entrepreneurial intention.
Figure 1 below summarizes the conceptual framework guiding this study, outlining the hypothesized relationships between the Big Five personality traits, digital competencies, digital self-efficacy, and digital entrepreneurial intentions.
Fig. 1
Research Framework
Methodology
Data collection and sample
Data was collected from a random sample of university participants in Taiwan between January and April 2024. In recent years, Taiwan has made significant progress in digital transformation. It ranked 9th in the 2023 Global Digital Competitiveness Index, demonstrating its strong ability to embrace and integrate digital technologies within enterprises and government institutions (Statista, 2023). This achievement has attracted foreign investment and fostered innovation across the island, making it a valuable setting for our study.
We relied on a convenience sample of 440 participants enrolled in entrepreneurship courses across different universities in Taiwan. We believe this sample offers a unique context for two main reasons: (a) Taiwan is one of the countries that most actively engaged in digital transformation recently, providing a dynamic environment for studying entrepreneurial behavior (Leung & Cossu, 2019); and (b) participants who have taken entrepreneurship courses are particularly well-suited for examining entrepreneurial intention, as they are more likely to have been exposed to relevant concepts, skills, and motivations (Bui et al., 2025). To ensure participant anonymity, we explicitly stated that no personal data would be publicly disclosed and that only aggregated results would be reported. To address missing data, we employed listwise deletion, removing 60 cases with incomplete responses from the dataset. This approach ensures that only complete responses are included in the final analysis, helping to maintain the integrity of the original responses and avoid potential bias introduced by data imputation (Enders, 2010). After removing 60 cases due to missing data, the final sample consisted of 380 responses, with 216 male (56.84%) and 164 female (43.16%), resulting in a response rate of 86.36%. Among the respondents, 69.73% were under the age of 22, 76.05% had short-term work experience, and 72.36% were undergraduate.
Measures
All the items of variables were adopted from the previous studies, and a seven-point Likert scale point was used, ranging from “strongly disagree” to “strongly agree”.
To measure the Big Five personality traits, we used a shortened version of the scale developed by Soane and Chmiel (2005). Extraversion (α = 0.910) was assessed with items such as “I really enjoy talking to people” and “I am a very active person.” AgreeablenesS (α = 0.743) included items like “I generally try to be thoughtful and considerate” and “I never get into arguments with my family and co-workers.” Conscientiousness (α = 0.828) was measured using items such as “I am pretty good about pacing myself to get things done on time” and “I am always dependable and organized.” Neuroticism (α = 0.802) was evaluated through items like “Under immense stress and burden, I feel like I am falling apart” and “I often feel tense and anxious.” Finally, Openness (α = 0.865) was assessed with items such as “I am full of ideas” and “I carry conversations to a higher level.”
To assess Digital Competencies, we employed the five-item scale developed by Singh et al. (2024) (α = 0.845), with sample statements including “I am competent in adapting to new technology during digital entrepreneurial training” and “I have prepared myself for future digital business challenges.” Digital Self-Efficacy (α = 0.808) was measured using items from Ulfert-Blank and Schmidt (2022) and Xin and Ma (2023), including “I can operate effectively despite persistent stress, pressure, and disagreement” and “I can run capital well in the digital economy.” Lastly, Digital Entrepreneurial Intention (α = 0.901) was assessed using six items from Singh et al. (2024), such as “I remain informed on the news of successful tech entrepreneurs” and “After completing my course, I plan to become a digital entrepreneur.” Table 1 below presents means, standard deviations, and Pearson correlation coefficients to illustrate variable distributions and interrelationships.
Table 1
Descriptive statistics and pearson correlation
Mean | SD | ET | AG | CO | NE | OP | DCs | DSE | DEI | |
|---|---|---|---|---|---|---|---|---|---|---|
ET | 5.01 | 1.16 | ||||||||
AG | 4.45 | 0.89 | 0.524** | |||||||
CO | 4.97 | 1.02 | 0.034 | 0.002 | ||||||
NE | 4.86 | 0.97 | − 0.004 | − 0.011 | 0.537** | |||||
OP | 5.21 | 1.28 | − 0.017 | − 0.003 | 0.339** | 0.422** | ||||
DCs | 5.11 | 1.14 | 0.760** | 0.703** | 0.012 | − 0.029 | − 0.041 | |||
DSE | 5.29 | 1.25 | 0.718** | 0.051 | 0.007 | − 0.029 | − 0.020 | 0.776** | ||
DEI | 4.99 | 1.18 | 0.519** | 0.043 | − 0.028 | − 0.020 | − 0.048 | 0.647** | 0.711** |
Data analysis
Evaluation of the measurement model
Partial least squares structural equation modeling (PLS-SEM) was employed in this study, as it meets the necessary criteria for assessing measurement reliability and validity (Hair et al., 2021). First, the composite reliability ratings are greater than 0.80 and less than 0.95 (see Table 2 for more details), exceeding the 0.70 minimum threshold for confirming internal consistency reliability. Second, to ensure indicator reliability, the outer loadings of each item should exceed 0.708. This threshold indicates that this construct explains more than half of the variance in the indicator, thereby providing sufficient indicator reliability. As can be assessed from Table 3, all items were kept because their loadings were greater than the cut-off value (Table 3). Third, if the extracted average variance (AVE) is 0.50 or higher, the construct explains 50% or more of the variance in the construct’s indicators (Hair et al., 2021). The AVE values in this study ranged from 0.565 for digital self-efficacy to 0.720 for extraversion, indicating that convergent validity is supported.
Table 2
Reliability and convergent validity assessment
Construct items | Factor loading | Cronbach’s alpha (α) | AVE | CR | |
|---|---|---|---|---|---|
Extraversion (ET) | ET1 ET2 ET3 ET4 ET5 | 0.807 0.864 0.881 0.842 0.828 | 0.901 | 0.714 | 0.916 |
Agreeableness (AG) | AG1 AG2 AG3 AG4 | Deleted 0.797 0.834 0.803 | 0.743 | 0.626 | 0.853 |
Conscientiousness (CO) | CO1 CO2 CO3 CO4 | 0.869 0.897 0.809 Deleted | 0.828 | 0.738 | 0.871 |
Neuroticism (NE) | NE1 NE2 NE3 NE4 | 0.776 0.790 0.809 0.790 | 0.802 | 0.659 | 0.865 |
Openness (OP) | OP1 OP2 OP3 OP4 | 0.853 0.884 0.835 0.804 | 0.865 | 0.711 | 0.908 |
Digital Competencies (DCs) | DCs1 DCs2 DCs3 DCs4 DCs5 | 0.855 0.839 0.887 0.872 0.834 | 0.891 | 0.736 | 0.923 |
Digital Self-Efficacy (DSE) | DSE1 DSE2 DSE3 DSE4 DSE5 DSE6 | 0.857 0.856 0.889 0.853 0.855 0.882 | 0.910 | 0.747 | 0.927 |
Digital Entrepreneurial Intention (DEI) | DEI1 DEI2 DEI3 DEI4 DEI5 DEI6 | 0.809 0.848 0.861 0.841 0.803 0.738 | 0.820 | 0.669 | 0.870 |
Table 3
Assessment of discriminant validity using Fornell-Larcker
AG | CO | DCs | DEI | DSE | ET | NE | OP | |
|---|---|---|---|---|---|---|---|---|
AG | 0.792 | |||||||
CO | 0.449 | 0.811 | ||||||
DCs | 0.415 | 0.326 | 0.836 | |||||
DEI | 0.574 | 0.578 | 0.454 | 0.808 | ||||
DSE | 0.537 | 0.549 | 0.314 | 0.461 | 0.825 | |||
ET | 0.513 | 0.293 | 0.555 | 0.401 | 0.477 | 0.845 | ||
NE | 0.527 | 0.411 | 0.481 | 0.553 | 0.504 | 0.558 | 0.796 | |
OP | 0.531 | 0.457 | 0.401 | 0.513 | 0.492 | 0.501 | 0.421 | 0.844 |
All square roots of AVE values on the diagonals should be greater than the correlation between each corresponding row and column value in Table 3, indicating that the core construct measures discriminant between construct variances using the discriminant validity criteria (Fornell & Larcker, 1981). Furthermore, to examine discriminant validity, we employed the Heterotrait-Monotrait ratio (HTMT) of correlations that are depicted in Table 4. In our analysis, all HTMT values are lower than the more cautious threshold value of 0.85. As a result, these findings validate the discriminant validity of the measurement model. To address common method bias, procedural safeguards such as respondent anonymity, varied scale formats, and randomized item ordering were implemented. Statistically, Harman’s single-factor test (Podsakoff et al., 2003) was conducted to assess potential common method bias arising from self-reported data. The unrotated exploratory factor analysis revealed that the first factor accounted for 39.95% of the total variance, which is below the recommended 50% threshold (Fuller et al., 2016), suggesting that CMB is unlikely to threaten the validity of the findings. Additionally, all variance inflation factors (VIFs) were below 3.3, and all tolerance values exceeded 0.10, indicating no significant collinearity issues, as can be seen in Table 5.
Table 4
Assessment of discriminant validity using Heterotrait–Monotrait ratio (HTMT)
AG | CO | DCs | DEI | DSE | ET | NE | OP | |
|---|---|---|---|---|---|---|---|---|
AG | ||||||||
CO | 0.346 | |||||||
DCs | 0.401 | 0.369 | ||||||
DEI | 0.446 | 0.404 | 0.419 | |||||
DSE | 0.429 | 0.381 | 0.526 | 0.421 | ||||
ET | 0.430 | 0.319 | 0.413 | 0.342 | 0.458 | |||
NE | 0.415 | 0.476 | 0.360 | 0.242 | 0.342 | 0.360 | ||
OP | 0.377 | 0.418 | 0.347 | 0.389 | 0.225 | 0.365 | 0.343 |
Table 5
Assessment of common method bias and collinearity statistics
Collinearity | ||
|---|---|---|
Tolerance | VIF | |
Extraversion | 0.442 | 2.258 |
Agreeableness | 0.676 | 1.479 |
Conscientiousness | 0.721 | 1.386 |
Neuroticism | 0.518 | 1.927 |
Openness | 0.459 | 2.174 |
Digital Competencies | 0.408 | 2.445 |
Digital Self-Efficacy | 0.422 | 2.367 |
Digital Entrepreneurial Intention | 0.512 | 1.950 |
Harman’s Single-Factor Test | ||
Variance Explained by First Factor (%) | 39.95% | |
Hypotheses testing
The structural model was validated by reporting the coefficient of determination (R2), path coefficient (β), p-values, effect size (f2), and t-values using a bootstrapping approach with 5,000 sub-samples, as recommended by Hair et al. (2019). Results from this analysis are reported in Table 6. Apart from H2 and H3b, the main effects are positive and significant at the 1% level or above. According to Cohen (1988) and Hair and colleagues (2021), f2 values are small, medium, or large at thresholds of 0.02, 0.15, and 0.35, respectively.
Table 6
PLS effects results
Hypotheses | β | SE | t-value | p value | LLCI | ULCI | Result |
|---|---|---|---|---|---|---|---|
H1a: ET → DSE | 0.242 | 0.065 | 5.982 | p ≤.001 | 0.156 | 0.330 | Supported |
H1b: AG → DSE | 0.051 | 0.060 | 0.974 | ns | −0.042 | 0.218 | Unsupported |
H1c: CO → DSE | 0.275 | 0.062 | 3.463 | p ≤.001 | 0.197 | 0.354 | Supported |
H1d: NE → DSE | −0.154 | 0.061 | 2.657 | 0.003** | −0.268 | −0.041 | Supported |
H1e: OP → DSE | 0.218 | 0.064 | 2.941 | 0.001** | 0.054 | 0.378 | Supported |
H2: DCs → DSE | 0.193 | 0.066 | 2.205 | 0.002** | 0.093 | 0.254 | Supported |
H3a: ET → DEI | 0.227 | 0.065 | 4.561 | p ≤.001 | 0.104 | 0.390 | Supported |
H3b: AG → DEI | 0.043 | 0.058 | 0.991 | ns | −0.035 | 0.171 | Unsupported |
H3c: CO → DEI | 0.214 | 0.060 | 3.410 | 0.001** | 0.091 | 0.339 | Supported |
H3d: NE → DEI | −0.139 | 0.065 | 2.412 | 0.005** | −0.257 | −0.032 | Supported |
H3e: OP → DEI | 0.189 | 0.061 | 2.984 | 0.007** | 0.067 | 0.288 | Supported |
H4: DCs → DEI | 0. 254 | 0.060 | 5.956 | p ≤.001 | 0.095 | 0.362 | Supported |
H5: DSE→ DEI | 0. 315 | 0.064 | 6.658 | p ≤.001 | 0.130 | 0.445 | Supported |
With respect to the effect of the big five personality traits on digital self-efficacy, the H1 hypothesis states that extraversion was positively related to digital self-efficacy (β = 0.107, f2 = 0.008, t = 2.666, p <.01). Thus, H1a is confirmed. In contrast, we found no significant influence of agreeableness on digital self-efficacy (β = 0.062, f2 = 0.130, t = 1.113). Hence, H1b is not confirmed. This study found that conscientiousness had a strongly positive effect on digital self-efficacy (β = 0.273, f2 = 0.002, t = 6.673, p <.001). Therefore, H1c is confirmed. Besides, the findings denote that neuroticism was negatively related to digital self-efficacy (β = −0.163, f2 = 0.004, t = 2.974, p <.01). H1d is confirmed. Additionally, this study also reveals a strongly significant effect of openness to experience on digital self-efficacy (β = 0.384, f2 = 0.001, t = 6.805, p <.001). Thus, H1e is confirmed.
The results indicate a positive relationship between digital competencies and digital self-efficacy, with digital competencies significantly influencing digital self-efficacy (β = 0.262, f² = 0.003, t = 3.869, p <.001), confirming H2. Regarding the relationship between the Big Five personality traits and digital entrepreneurial intention, the findings show that extraversion had no significant effect on digital entrepreneurial intention (β = −0.075, f² = 0.231, t = 1.769), leading to the rejection of H3a. Similarly, agreeableness was not significantly related to digital entrepreneurial intention (β = 0.087, f² = 0.181, t = 1.329), thus H3b is not supported. However, conscientiousness was positively associated with digital entrepreneurial intention (β = 0.212, f² = 0.004, t = 3.366, p <.01), confirming H3c. The results also show that neuroticism had a negative effect on digital entrepreneurial intention (β = −0.145, f² = 0.008, t = 2.565, p <.1), supporting H3d. Additionally, openness to experience positively influenced digital entrepreneurial intention (β = 0.193, f² = 0.004, t = 3.065, p <.01), confirming H3e. Regarding the direct effect of digital competencies on digital entrepreneurial intention, the findings reveal a significant positive relationship (β = 0.260, f² = 0.002, t = 3.914, p <.001), confirming H4. Furthermore, the results indicate that digital self-efficacy strongly influences digital entrepreneurial intention (β = 0.301, f² = 0.001, t = 4.246, p <.001), supporting H5.
The results of the mediation analysis are summarized in Table 7. Regarding the indirect effects of the Big Five personality traits on digital entrepreneurial intention through digital self-efficacy, the findings indicate that extraversion (β = 0.032, t = 2.339, p <.05), conscientiousness (β = 0.082, t = 3.274, p <.01), neuroticism (β = −0.049, t = 2.309, p <.05), and openness to experience (β = 0.116, t = 3.709, p <.001) had significant indirect effects. However, agreeableness (β = 0.019, t = 1.050) did not exhibit a significant indirect effect.
Table 7
Results of mediation analysis
Factors | Indirect Effect | Total Effect | ||||||
|---|---|---|---|---|---|---|---|---|
Path | β | SE | t-value | p-value | LLCI | ULCI | ||
ET | ET → DSE → DEI | 0.140 | 0.055 | 2.118 | p ≤.001 | 0.075 | 0.212 | 0.140 |
AG | AG → DSE → DEI | −0.005 | 0.018 | 0.524 | ns | −0.035 | 0.038 | −0.005 |
CO | CO → DSE → DEI | 0.151 | 0.057 | 3.340 | p ≤.001 | 0.086 | 0.229 | 0.151 |
NE | NE → DSE → DEI | −0.136 | 0.056 | 2.764 | 0.005** | −0.215 | −0.044 | −0.136 |
OP | OP → DSE → DEI | 0.113 | 0.067 | 2.010 | 0.007** | 0.064 | 0.171 | 0.113 |
DCs | DCs → DSE → DEI | 0.162 | 0.063 | 3.619 | p ≤.001 | 0.078 | 0.297 | 0.162 |
Additionally, the results confirm that digital competencies significantly influence digital entrepreneurial intention through digital self-efficacy (β = 0.060, t = 2.401, p <.05). These findings provide partial support for the indirect effects of the Big Five personality traits via digital self-efficacy and confirm the mediation pathways proposed in H4 and H5.
Discussion and implications
This study examined the factors influencing digital entrepreneurial intention, focusing on the role of Big Five personality traits and digital competencies as key antecedents. It also explored the mediating role of digital self-efficacy in shaping entrepreneurial intention within a digital context.
By jointly examining personality traits and digital competence, two antecedents that have typically been studied in isolation, our study contributes to the digital entrepreneurship literature by offering a more integrated framework. The findings of this study highlight the direct relationship between Big Five personality traits and digital self-efficacy, showing that extraversion, conscientiousness, neuroticism and openness to experience significantly influence digital self-efficacy, while agreeableness does not. This is consistent with Awwad & Al-Aseer’s (2021) findings, which suggested that extroverted, risk-taking, and enthusiastic entrepreneurs are more likely to seek and share information, helping them acquire knowledge that supports their decision to launch a business. Although we hypothesized a positive relationship between agreeableness and digital self-efficacy (Wang et al., 2016), our analysis did not support this relationship. This may be because agreeableness is a stable trait shaped by long-term person-situation interactions, influencing social behavior but not directly affecting domain-specific beliefs like digital self-efficacy (Caprara et al., 2010).
While digital competencies are positively associated with digital self-efficacy, the relationship is relatively weak. This nuanced result adds to existing literature by suggesting that digital competence alone is not sufficient and what matters is the individual’s confidence in applying these skills. Our findings support Bachmann et al. (2024), reinforcing the idea that digital competencies are a foundational but indirect source of entrepreneurial confidence. Extending Bachmann et al.’s (2024) perspective, our study introduces digital self-efficacy as a domain-specific construct, conceptually distinct from general self-efficacy, where success relies not only on motivation but also on the ability to navigate digital tools and systems effectively (Eastin & LaRose, 2000; Venkatesh & Bala, 2008).
Our findings regarding the direct effect of the Big Five traits on digital entrepreneurial intention are partially consistent with previous studies (Li et al., 2022; Salameh et al., 2022). Specifically, we found that extraversion and agreeableness do not significantly influence digital entrepreneurial intention, which is inconsistent with some prior research (e.g., Wanget al., 2016), where these traits were found to have a positive impact on entrepreneurial intention. However, our results align with Salameh et al. (2022), who found that conscientiousness and openness to experience are positively associated with digital entrepreneurial intention. These traits are particularly relevant in the digital domain, as individuals with higher levels of conscientiousness and openness are more likely to exhibit stronger entrepreneurial intentions.
Our study highlights the strong positive influence of digital competencies on digital entrepreneurial intention. This finding aligns with Elnadi and Gheith (2023), who emphasize that digital competencies contribute to digital entrepreneurial intention by leveraging the mediating effects of entrepreneurial alertness and digital inventiveness. Additionally, our results support the work of Awwad and Al-Aseer (2021), suggesting that highly innovative and adaptive individuals who enjoy tackling new challenges are more likely to develop strong entrepreneurial intentions and pursue business ventures. Furthermore, we confirm the negative impact of neuroticism on digital entrepreneurial intention, consistent with Li et al. (2022). Entrepreneurs with lower levels of neuroticism tend to experience fewer negative emotions, such as anxiety and despair, making them better equipped to navigate the uncertainties of entrepreneurship. Our study provides new evidence that digital self-efficacy mediates the relationship between digital competencies and entrepreneurial intention, underscoring the importance of belief systems in translating skills into action. This mediating mechanism has not been emphasized in previous models (e.g., Bachmann et al., 2024), making it a central contribution to our study.
Our results also confirm that digital self-efficacy is a critical driver of digital entrepreneurial intention, aligning with Xin and Ma (2023). Additionally, individuals with a strong understanding of the digital entrepreneurial ecosystem are better equipped to navigate challenges and seize opportunities, ultimately enhancing their confidence in pursuing digital entrepreneurial intention which is a perspective supported by Vu et al. (2024). Furthermore, our results reinforce Ulfert-Blank & Schmidt’s (2022) assertion that digital self-efficacy can predict workplace success, particularly in environments characterized by the rapid evolution of digital systems.
Finally, our study underscores the pivotal mediating role of digital self-efficacy in shaping digital entrepreneurial intentions. Building on social cognitive theory, we demonstrate that beyond the direct influence of the Big Five personality traits, digital self-efficacy functions as a key mechanism, reinforcing the impact of extraversion, agreeableness, and conscientiousness while mitigating the negative effects of neuroticism. Additionally, extending Bachmann’s (2024) perspective, we establish that digital self-efficacy mediates the relationship between digital competencies and entrepreneurial intentions.
Implications for research and practice
This study has several academic and managerial implications. Specifically, the findings support social cognitive theory by exploring the role of personal inputs (the Big Five personality traits) and digital competencies via digital self-efficacy in developing digital entrepreneurial intention. The study broadens social cognitive theory by illustrating how digital self-efficacy mediates the impact of personality traits and digital competencies, optimizing efficiency and confidence in shaping digital entrepreneurial intention in the digital age.
Furthermore, this study responds to Elnadi and Gheith’s (2023) call by emphasizing the critical role of the Big Five traits in driving digital entrepreneurial intention through digital self-efficacy. It also addresses a gap identified by Bachmann et al. (2024) by showing that digital competencies influence both self-efficacy and digital self-efficacy in the formation of digital entrepreneurial intention. In particular, digital competencies have a stronger relationship with digital self-efficacy than with self-efficacy, and their full potential is realized when linked to digital self-efficacy in shaping digital entrepreneurial intentions.
Additionally, this study contributes to the literature on digital competencies by highlighting that they are dynamic, continuously evolving with advancements in technology, market needs, and context. Finally, it differentiates between general self-efficacy and digital self-efficacy (Xin & Ma, 2023), emphasizing that while the former relates to confidence in handling general challenges, the latter is more relevant in digital entrepreneurship, focusing specifically on confidence in managing digital technologies and overcoming related challenges.
From a practical perspective, these findings offer valuable insights for entrepreneurs, managers, and educational institutions seeking to promote digital entrepreneurial activities. Understanding the interplay between personality traits, digital competencies, and digital self-efficacy can help organizations foster stronger entrepreneurial intentions. Managers can design targeted training programs to enhance digital competencies, thereby boosting digital self-efficacy and increasing confidence in pursuing digital entrepreneurial opportunities. Similarly, entrepreneurship courses can integrate these insights into their curricula, emphasizing both digital competencies and self-efficacy to equip students with the necessary skills and confidence for success in the digital economy.
Limitations and future research
This study has several limitations that require further investigation. First, its cross-sectional design limits causal interpretation and may not fully capture the evolving nature of digital entrepreneurship. As technological infrastructures rapidly advance and digital technologies continuously reshape entrepreneurial opportunities (Kraus et al., 2019), longitudinal research could better track how personality traits, digital competence, and digital self-efficacy influence digital entrepreneurial intention over time. Second, the sample size was rather small and included a significantly homogeneous collection of participants. This homogeneity limits the generalizability of the findings, as the results may not fully capture the perspectives and behaviors of experienced entrepreneurs in real-world digital ventures. Furthermore, this study did not incorporate all dimensions of digital self-efficacy outlined by Ulfert-Blank and Schmidt (2022), potentially limiting its ability to capture digital self-efficacy’s full impact on digital entrepreneurial intentions. Future research should integrate a more comprehensive set of digital self-efficacy scales for a deeper understanding of its role in digital entrepreneurship. Finally, future research could explore contextual boundary conditions, such as regional differences in digital infrastructure, that may shape the strength of these relationships and determine when digital self-efficacy becomes most critical.
Conclusions
The objective of this study was to examine the influence of Big Five personality traits and digital competencies on digital entrepreneurial intention, with a particular focus on the mediating role of digital self-efficacy. Results of our analysis reveal that, while agreeableness may positively influence self-efficacy in general or social contexts, its impact appears limited in digital domains. This suggests that fostering digital self-efficacy may require greater emphasis on traits such as openness to experience and emotional stability, which are more directly linked to confidence in digital tasks. Neuroticism, which reflects a tendency toward anxiety, insecurity, and emotional instability, negatively affects digital entrepreneurial intention, likely because entrepreneurship often involves uncertainty and complexity. Such understanding can inform the design of targeted interventions, such as entrepreneurship education programs that not only build digital competence but also strengthen digital self-efficacy and promote traits like openness and conscientiousness.
Declarations
Conflict of interest
The authors declare that they have no conflict of interest related to this study.
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