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Article

How Do Emotions and Social Ties Shape Digital Entrepreneurship? Evidence from Brazilian Higher Education

by
André Luiz Tavares Damasceno
1,*,
Cristiano Morini
2,
Edmundo Inácio Jr.
2 and
Dirceu da Silva
3
1
Departamento de Ensino, Instituto Federal do Sudeste de Minas Gerais (IF Sudeste MG), Manhuaçu 36909-300, MG, Brazil
2
Faculdade de Ciências Aplicadas (FCA), Universidade Estadual de Campinas (UNICAMP), Campinas 13083-970, SP, Brazil
3
Faculdade de Educação, Universidade Estadual de Campinas (UNICAMP), Campinas 13083-970, SP, Brazil
*
Author to whom correspondence should be addressed.
Adm. Sci. 2025, 15(12), 460; https://doi.org/10.3390/admsci15120460
Submission received: 18 September 2025 / Revised: 3 November 2025 / Accepted: 14 November 2025 / Published: 21 November 2025
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Abstract

Context: Digital entrepreneurship has attracted the attention of governments, investors, and researchers, who are directing their efforts and resources toward investigating its causes. Several studies have focused on the positive factors contributing to entrepreneurial intentions, with Theory of Planned Behaviour (TPB) being the most cited. This paper examines the relationship among TPB, emotions and social capital in the digital context. Objective: To evaluate the impact of social capital and anticipated emotions (positive and negative) on the digital entrepreneurial intentions of students from Higher Education Institutions (HEIs). Methodology: The research proposed seven hypotheses, including two new ones, all of which are embedded in the digital context. Data were collected using a questionnaire administered to undergraduate students in Business Administration, Engineering, and Information Technology. A total of 1110 valid responses were obtained. The data were analysed using Partial Least Squares Structural Equation Modelling (PLS-SEM). Results: Considering the TPB factors, Attitude (AT) and Perceived Behavioural Control (PBC) significantly impact Digital Entrepreneurial Intentions (DEI), while Subjective Norms (SN) show a statistically significant but weak effect (f2 < 0.02). Social Capital (SC) indirectly influences DEI by shaping attitudes. Anticipated Positive Emotions (APE) and Anticipated Negative Emotions (ANE) are statistically significant; however, their practical moderating effects are weak. Conclusions: Perceived Behavioural Control (PBC) emerges as the strongest predictor of Digital Entrepreneurial Intention (DEI), while Subjective Norms (SN) and emotional factors (APE and ANE), though statistically significant, exhibit limited practical influence. Practical implications: Understanding how anticipated emotions interact with SC in shaping DEI can help educators and policymakers develop more effective strategies to support aspiring entrepreneurs. Originality: This study highlights the relationships among TPB factors, SC, APE, and ANE, underscoring the complex role of emotions in the digital entrepreneurial process. This research enriches the literature by incorporating emotional and social dimensions into the Theory of Planned Behaviour (TPB), demonstrating that digitalisation reshapes, rather than displaces, the cognitive foundation of entrepreneurial action.

1. Introduction

Contemporary entrepreneurship is simultaneously tempting and challenging. Tempting because professionals of all ages today receive both objective and subjective stimuli to create new businesses, and challenging because today’s competitiveness requires confidence in one’s value proposition (Lagoudas et al., 2020), management capabilities, and the ability to adapt, identify and exploit opportunities that are often hidden (Santoso et al., 2020). Entrepreneurial activity is one of the most relevant forces sustaining the economies (Verma et al., 2025; Subramanian et al., 2017). Entrepreneurship has been a key instrument in promoting a country’s competitive capacity and constraining competition between nations (Passaro et al., 2017).
With this in mind, public and private agents have promoted entrepreneurship, especially in the innovation and high-tech sectors (Passaro et al., 2017; Trivedi, 2016). Among the challenges involved in promoting entrepreneurship are the creation and fostering of ecosystems (Cavallo et al., 2021; Hudek et al., 2021; Nambisan & Baron, 2021; Prashantham, 2021), companies (Fan et al., 2021) and conditions conducive to the emergence of new entrepreneurs (Haddoud et al., 2022; Huang, 2021; Kaminsky et al., 2021; Kowang et al., 2021; Sansone et al., 2021).
Entrepreneurship often assumes the condition of a lifestyle or personal project, stimulating autonomy and reducing unemployment (Iglesias-Sánchez et al., 2016; Mawson & Kasem, 2019). Staniewski and Awruk (2016) consider the ability to identify and solve problems vital to entrepreneurial action and emphasise the belief in one’s capacity to do so creatively and effectively. Personality traits, skills, and self-management (Slogar et al., 2021), and social context (Shahid & Ahsen, 2021) contribute significantly to entrepreneurial intentions.
Ajzen (1991) investigated the prediction of human behaviour to understand how specific psychological resources—attitudes, subjective norms, and perceived behavioural control—can influence, or even determine, an individual’s prior (or planned) intention to act in a certain way to accomplish a goal. The author named this model the Theory of Planned Behaviour (TPB).
TPB has been widely used in research on Entrepreneurial Intent (EI) in emerging countries (Akinwale et al., 2019; Arafat et al., 2020; Bogatyreva & Shirokova, 2017; Doanh, 2021; Joshi et al., 2020; Tran et al., 2018; da Cruz & Alvaro, 2013; Mahmood et al., 2019; Sher et al., 2020; Subramanian et al., 2017; Trivedi, 2016), including Brazil (da Cruz & Alvaro, 2013; Lasso et al., 2018; Wegner et al., 2020).
Researchers investigating the subject often employ Partial Least Squares Structural Equation Modelling (PLS-SEM). Despite this profusion of studies, we identified an opportunity to expand Ajzen’s (1991) TPB by adding sub-factors that have not yet been jointly explored in the literature: (i) Social Capital (SC), (ii) Anticipated Positive Emotions (APE) and (iii) Anticipated Negative Emotions (ANE). Accordingly, this article investigates how social capital and anticipated emotions (negative and positive) interact to influence Digital Entrepreneurial Intentions (DEI). Its academic relevance becomes more explicit when considering that the Business Administration and IT undergraduate students in the study sample are potential entrepreneurs. This is particularly relevant because these students are often exposed to interdisciplinary knowledge, combining managerial skills with technological expertise, which is critical for identifying and seizing opportunities in the digital economy. By understanding their entrepreneurial intentions, institutional leaders and course coordinators can better adapt curricula to foster skills such as innovation, emotional resilience, and networking, thereby preparing students more effectively for the challenges of digital entrepreneurship (Bae et al., 2014; Davidsson & Honig, 2003).
Although prior research has extensively applied the TPB to entrepreneurial contexts, few studies have explored how emotional and social dimensions jointly influence digital entrepreneurial intentions. This gap is especially pronounced in emerging economies like Brazil, where digital entrepreneurship is growing rapidly but remains under-researched. This study, therefore, addresses the following research question: How do social capital and anticipated emotions (positive and negative) interact to influence university students’ digital entrepreneurial intentions? By addressing this gap, the study extends the TPB model by incorporating emotional and social constructs in the context of digital entrepreneurship. Contributions state that integrating psychological and relational dimensions advances theories and offers practical insights for education, policy, and support programs for young digital entrepreneurs.
In addition to this introduction, the paper is organised into four further sections. Section 2 presents a literature review (based on articles identified by the RSL conducted) on (i) the Theory of Planned Behaviour (TPB) and Entrepreneurial Intention (EI) and (ii) PLS-SEM (analysing the studies that use this method). Section 3 explains the methodology. Section 4 presents the results, with discussion and data analysis. Finally, Section 5 outlines some conclusions.

2. Literature Review

2.1. Theory of Planned Behaviour and Entrepreneurial Intention

The Theory of Planned Behaviour (Ajzen, 1991) posits that individuals make decisions in a reasoned manner, systematically using available information and considering the implications of their actions, and behave in a certain way. According to TPB, the factors responsible for behavioural variation relate to Attitude (AT), Subjective Norms (SN), and Perceived Behavioural Control (PBC) (Ajzen, 1991). Peng et al. (2021) conclude that these factors can be mediated by experience, leading future entrepreneurs to weigh risks and understand their skills more realistically (Iglesias-Sánchez et al., 2016). The intention-action gap occurs when the desire to start a business fails to translate into actual entrepreneurial behaviour (Bogatyreva & Shirokova, 2017).
The EI may remain dormant but is stable over time (Joensuu-Salo et al., 2015). Family (Hadjimanolis, 2016; Ting et al., 2020; Vracheva et al., 2019) and the university environment affects EI through subjective norms (Vracheva et al., 2019). Entrepreneurs’ confidence is positively affected by the quality of financial and legal institutions (Shirokova et al., 2020).
Akinwale et al. (2019) and Tchokoté et al. (2025) highlight AT as the key determinant for EI. In the context of TBP, attitude refers to an individual’s positive or negative evaluation of entrepreneurship based on expected outcomes (Ajzen, 1991). Individuals who perceive entrepreneurship as a path to personal fulfilment, financial success, or autonomy are more likely to develop a favourable attitude toward EI (Arafat et al., 2020; Mahmood et al., 2019; Passaro et al., 2017; Vracheva et al., 2019). Entrepreneurial education can influence these perceptions by shaping beliefs about the feasibility and desirability of starting a business (Kakouris et al., 2020). Therefore, fostering a positive attitude toward entrepreneurship requires exposure to entrepreneurial knowledge and reinforcement of its potential benefits and rewards.
Subjective norms (SN) refer to the perceived social pressure to engage (or not) in entrepreneurial activities, shaped by the expectations and opinions of family, friends, mentors, and broader societal influences (Ajzen, 1991). These norms often serve as a source of motivation, particularly in collectivist cultures, where social acceptance plays a critical role in shaping behaviour. However, empirical findings on the impact of subjective norms remain divergent. While some studies emphasise the importance of social support and endorsement in strengthening EI, Krueger et al. (2000) found that subjective norms do not significantly influence EI. Similarly, Arafat et al. (2020) in a study with Indian university students reported limited evidence of their effect. These divergent findings suggest that the influence of subjective norms on EI may vary depending on cultural, demographic, and contextual factors.
Perceived behavioural control (PBC), the third factor in the TPB, refers to an individual’s perception of the ease or difficulty of performing a specific behaviour, influenced by both internal factors (such as skills, knowledge, and confidence) and external conditions (such as available resources and potential obstacles) (Ajzen, 1991). A high level of perceived behavioural control increases the likelihood that an intention will translate into an action, as individuals feel more capable of overcoming challenges. Ahmed et al. (2021) associate PBC with risk propensity and stress tolerance, suggesting that individuals who perceive themselves as resilient and capable of managing uncertainty are more likely to develop EI. This concept is closely related to self-efficacy, defined by Bandura (1977) as an individual’s belief in their ability to organise and execute actions required to achieve specific goals. Studies indicate that entrepreneurial self-efficacy is crucial in shaping entrepreneurial behaviour by reinforcing individuals’ confidence in starting and sustaining a business (Newman et al., 2019; Zhao et al., 2005). Therefore, fostering PBC through education, mentorship, and exposure to entrepreneurial experiences can enhance an individual’s likelihood of engaging in entrepreneurship.

2.2. Social Capital

Social capital (SC) is a multidimensional construct that positively impacts entrepreneurial capacity by facilitating access to resources, information, and social support (Zheng et al., 2014). It encompasses elements such as generalised trust, formal organisational associations, and civic engagement (Amini Sedeh et al., 2021), which contribute to the development of EI. Family influence plays a critical role in shaping EI through positive role models, emotional support, and resource sharing (Ayalew & Zeleke, 2018; Edelman et al., 2016; Gubik & Farkas, 2016; Hoang & Le, 2021). Educational environments, particularly universities, also foster social capital by expanding networks and promoting skills like risk-taking, stress management, and resilience (Ahmed et al., 2021; Muscio et al., 2021).
Social networks enhance social capital by providing emotional reinforcement, increasing confidence, and nurturing an entrepreneurial mindset (Pérez-Fernández et al., 2020). These networks offer opportunities for knowledge exchange, mentorship, and partnerships that are crucial to entrepreneurial success, particularly in the early stages of venture creation. By connecting individuals to broader communities, social networks amplify access to diverse resources, which can be instrumental in shaping entrepreneurial attitudes and intentions.
For underprivileged groups, social capital can bridge institutional gaps, enabling entrepreneurial initiatives even with limited financial resources (Manolova et al., 2019; Raijman, 2001). Additionally, technological competencies and access to information networks can strengthen an entrepreneur’s confidence and self-efficacy (van der Westhuizen & Goyayi, 2020; Ayalew & Zeleke, 2018), reinforcing the perception that success depends more on internal abilities than external conditions (Hadjimanolis, 2016). Despite the decisive role of social capital, a supportive institutional environment remains essential in fostering EI (Smith & Beasley, 2011).
While traditional studies (Zheng et al., 2014) confirm a direct relationship between social capital and entrepreneurial intention, digital entrepreneurship introduces distinct contextual dynamics. Online ventures often rely more on technological self-efficacy, virtual collaboration, and platform-based interactions than on conventional face-to-face networks. These structural differences may weaken the direct impact of social capital, rendering it an indirect driver, mediated by AT and PBC.

2.3. Emotions

Emotions—APE and ANE—play a critical role in shaping EI. Liu and Wang (2021) argue that fear of failure is one of the most potent psychological barriers to the realisation of entrepreneurial intentions, as it triggers feelings of frustration, self-doubt, and perceived incompetence. These negative emotions can undermine an individual’s self-efficacy—the belief in their ability to succeed—which is essential for entrepreneurial behaviour. When self-efficacy diminishes, individuals may perceive obstacles as insurmountable, leading to lower motivation to pursue entrepreneurial activities. Furthermore, fear of failure can distort how entrepreneurs assess risk, leading to overly cautious decision-making and a reduced willingness to act.
In contrast, positive emotions, such as enthusiasm and optimism, have been found to strengthen self-efficacy and enhance the likelihood of entrepreneurial engagement (Zabel et al., 2024; Londono et al., 2021; Luong & Lee, 2021). Balancing emotional responses with cognitive processes is crucial for fostering sustainable EI. While positive emotions can fuel creativity and motivation, they must be tempered by rational thinking to ensure sound decision-making (Chen et al., 2021; Zollo et al., 2021).
Educational programmes aimed at developing resilience and emotional regulation play a pivotal role in helping potential entrepreneurs manage emotional fluctuations (Elshaer & Saad, 2021; Soomro et al., 2024). Resilience, in particular, is essential for overcoming setbacks and navigating the uncertainties inherent in entrepreneurial ventures (Renko et al., 2021; Duchek, 2018). Moreover, students with high levels of proactivity and emotional intelligence tend to exhibit stronger entrepreneurial intentions, as they are better equipped to handle challenges and capitalise on opportunities (Zhang & Wang, 2024).
These emotional and psychological factors significantly influence an individual’s capacity and motivation to pursue entrepreneurial ventures.

3. Methodology

3.1. Bibliographic Portfolio

Academic papers were extracted from the Scopus and Web of Science databases in three stages: the first in December 2021, the second in February 2022, and the third in January 2025. The process is detailed in the following subsections.

3.1.1. Stage 1

Stage 1 focused on identifying papers that addressed “digital business,” “digital mindset,” “social capital”, and “theory of planned behaviour.” Table 1 presents the search terms and strategies used during the first stage of document extraction.

3.1.2. Stage 2

Stage 2 focused on identifying papers addressing “emotions” and “entrepreneurial intention.” Table 2 presents the search terms and strategies used in the second stage of document extraction.

3.1.3. Stage 3

Stage 3 focused on updating articles related to the topic published after February 2022. At this stage, a query was applied that combined all previously used search terms. Table 3 presents the search terms and strategies employed in the third stage of document extraction. This stage was conducted separately from Stage 1 to ensure search accuracy and to double-check the completeness of the results.

3.1.4. Final Portfolio

The documents extracted from Scopus and Web of Science were imported into MS Excel 365 for portfolio refinement. After the initial extraction, the refinement process involved removing (i) duplicates, (ii) papers with missing information (year, title or author), and (iii) studies not aligned with the research scope. Table 4 summarises the results of each stage.
In the end, the final portfolio comprised 1607 papers, which served as the basis for the literature review and the formulation of hypotheses presented in the next section.

3.2. Hypotheses

3.2.1. Existing Hypotheses

Table 5 summarises the hypotheses of the selected papers and their respective authors. All of these were utilised in the digital context.

3.2.2. New Hypotheses

This section presents the two new hypotheses formulated and explored in the present work. These hypotheses, summarised in Table 6, explore the moderating effects of Anticipated Negative Emotions (ANE) and Anticipated Positive Emotions (APE) on Social Capital (SC) and Digital Entrepreneurial Intentions (DEI).
Emotions and desires are closely related and exert a substantial influence on entrepreneurial intention (Londono et al., 2021; Luong & Lee, 2021). Social networks can positively impact affectivity and entrepreneurial mindset (Pérez-Fernández et al., 2020). Although positive emotions stimulate entrepreneurial intention, they must be balanced with cognition to ensure sound judgment (Chen et al., 2021). Elshaer and Saad (2021) cite resilience as an important characteristic. Business education and social capital promote entrepreneurial behaviour (Lee et al., 2021), requiring development through psychological mechanisms, including emotion and cognition (Cai et al., 2021).
The following subsection outlines the model developed to achieve the research objectives.

3.3. Model of Digital Entrepreneurial Intentions, Social Capital and Emotions

Our proposed model comprises seven constructs. Table 7 summarises each construct’s code, description, and classification (mediator or moderator) to facilitate the understanding of the concepts discussed in the following sections.
Figure 1 illustrates the conceptual model developed from the hypotheses discussed and those proposed following the portfolio analysis. The codes consist of four letters identifying the construct, followed by a sequential indicator number.
Table 8 presents the relationship between the construct indicators and the authors who developed the questions. The TPB constructs (AT, PBC, SN, and DEI) are presented first, followed by the constructs added to the researchers’ proposed model (SC, APE, and ANE).
The following subsection presents the criteria for sample selection and data collection techniques.

3.4. Ethics Committee

The Research Ethics Committee in Human and Social Sciences of UNICAMP (CEP-CHS), CAAE no. 58711822.6.0000.8142, approved the research. A 5-point Likert-scale questionnaire comprising 29 questions was administered (the questionnaire can be shared upon request).

3.5. Sample Selection and Data Collection

Data were collected through a questionnaire distributed to undergraduate students in Business Administration, Engineering and Information Technology, all aged 18 years or older, from public institutions in São Paulo.
This choice is justified by São Paulo’s status as Brazil’s most economically developed state, with a higher propensity to adopt technology, and by the fact that most of the researchers involved in this study are based there. The selected programmes also have a strong entrepreneurial orientation. The minimum sample size was calculated using G*Power 3.1 software.
The sample composition presents a potential limitation, as only 10.63% of respondents were enrolled in Information Technology programmes, a central area of digital entrepreneurship. This imbalance may have influenced the magnitude of certain relationships, particularly those associated with technology-based entrepreneurial behaviour.
Although the sample is composed of a group (IT) that may present some bias, the use of the SmartPLS FIMIX module (Hair et al., 2024) with two partitions indicated the existence of “two” groups with an incidence of 97.9% in segment 1 and 2.1% in segment 2, indicating that segment 2 is negligible; therefore, it can be considered that the data comes from a single sample.

3.6. Data Analysis

Data analysis used the Partial Least Squares Structural Equation Modelling (PLS-SEM) approach. The quality of the model was assessed in terms of content, convergent, and discriminant validity. While convergent validity assesses the extent to which indicators measure the intended construct, discriminant validity assesses the extent to which indicators are distinct and representative (Hair, 2014).
PLS-SEM is widely used to explain correlations among multiple variables (Hair et al., 2019) and is suitable for testing theoretical frameworks (Hair et al., 2019). Microsoft Excel was used for data tabulation, and SmartPLS 4 was used to run the PLS-SEM algorithm—the measurement model evaluation aimed to assess the overall quality and reliability of the set of indicators.

4. Results

4.1. Sample Description

When using the G*Power software and the conditions suggested by Cohen (1988), with seven predictors, a 5% significance level, a statistical power of 80%, and a medium effect size (f2 = 0.15), the calculation suggested a sample of 103 observations. The questionnaire was applied in both online and in-person formats. In the end, we obtained 1110 responses.

4.2. Descriptive Statistics

Of the 1110 respondents, 58.1% are men and 41.89% are women. As for courses, 57.30% were majoring in Engineering, 32.87% in Business Administration, and 10.63% in Information Technology (Computer Science, Information Systems, Software Engineering, etc.) (Table 9).
The following sections present calculations regarding the structural model’s (i) validity and reliability and (ii) evaluation.

4.3. Measuring Model

As previously mentioned, model validity and reliability are verified through (i) convergent validity and (ii) discriminant validity. After analysing the SmartPLS 4 results, we adjusted the model by removing six of the 29 indicators—SC_01, SC_02, SC_03, APE_01, APE_04, and DEI_02—leaving 23 indicators.
To ensure methodological transparency, the six excluded indicators (SC_01, SC_02, SC_03, APE_01, APE_04, and DEI_02) and their respective factor loadings are detailed in Appendix A (Table A1). All present external loadings were below 0.70, and cross-discrimination was low. Although the AVE of the Subjective Norms (SN) construct remained at 0.398, it was decided to maintain it due to its theoretical relevance in the Theory of Planned Behaviour (Ajzen, 1991). The composite consistency above 0.70 indicates adequate reliability and supports its inclusion in the final model.
The six indicators were removed due to low outer loadings (<0.70) and low cross-loading discrimination, in accordance with Hair et al. (2019). This adjustment improved both convergent validity (AVE) and internal consistency reliability across the remaining constructs.
Table 10 presents the Fornell-Larcker criterion, Cronbach’s alpha, classical composite reliability (rho_c) and average variance extracted (AVE) values calculated using SmartPLS 4. The square roots of the AVE values (shown diagonally in Table 10) should be greater than the correlations among the corresponding constructs.
While Cronbach’s alphas for SN, PCB, APE, and DEI are below 0.7, all constructs’ classical composite reliabilities (rho_c) are equal to or exceed 0.70 (Hair, 2014). Furthermore, except for SN, all AVE values exceed 0.5. Therefore, the model can be considered adjusted.
Although the Average Variance Extracted (AVE) for Subjective Norms (SN) is slightly below the 0.50 threshold, the construct was retained due to its theoretical relevance within the TPB framework and its acceptable composite reliability (ρc = 0.797).
This decision aligns with Hair et al. (2019), who argue that marginally low AVE values can be tolerated when reliability and theoretical consistency are satisfactory. Having completed the validation stage, the following section presents the structural model analysis.

4.4. Structural Model

4.4.1. Model Analysis

Table 11 presents Cohen’s (f2), Pearson’s (r2) and Stone-Geisser’s (Q2) coefficients, calculated for the dependent variables (AT and DEI) based on the independent variable (SC). These three coefficients indicate the model’s explanatory power.
The effect size (f2), presented in Table 11, as proposed by Cohen (1988), classifies values of 0.02, 0.15 and 0.35 as (i) low, (ii) medium and (iii) moderate, respectively.
Pearson’s coefficient of determination (r2) measures the correlation between two variables, with values of 0.02, 0.13, and 0.26 listed as high, medium, and low in social and behavioural sciences (Cohen, 1988).
The predictive validity of the model is confirmed when Q2 values are greater than zero (Hair, 2014). This condition is met in the present analysis, as shown in the table above, with AT = 0.052 and DEI = 0.675.

4.4.2. Resulting Model

Figure 2 illustrates the final structural model with the corresponding path coefficients. The R2 analysis indicates that the DEI construct accounts for 69.3% of its variance, with Attitude (AT), Perceived Behavioural Control (PBC), Subjective Norms (SN), and Social Capital (SC) accounting for 69.3% of the variance.
Among these, PBC shows the most potent effect on DEI, accounting for 15.2% of the explained variance, thus representing the model’s most influential construct. Additionally, the constructs Anticipated Negative Emotions (ANE) and Anticipated Positive Emotions (APE) act as moderators in the relationship between Social Capital (SC) and Digital Entrepreneurial Intention (DEI).
The following section presents the results of the hypothesis test.

4.5. Hypothesis Testing

Table 12 presents the result of hypothesis testing. The analysis was performed using SmartPLS 4 with the bootstrapping procedure (10,000 subsamples; 5% significance level) to calculate the t-statistics and p-values.
As shown in Table 12, all hypotheses—except H2 (SC → DEI)—were supported at the 5% significance level. The paths SC → AT (H1), AT → DEI (H3), SN → DEI (H4), and PBC → DEI (H5) presented significant path coefficients (p < 0.05), reinforcing the robustness of the TPB constructs.
The moderating effects of anticipated emotions (H6 and H7) were also statistically significant (p = 0.032 and p = 0.045, respectively), although their practical relevance was weak (f2 < 0.02).
In the following section, we discuss these results.

4.6. Discussion of Results

4.6.1. Social Capital and Attitudes (H1)

The positive effect of social capital on attitudes confirms that academic environments help students internalise pro-entrepreneurial behaviours through networking and collaboration. However, consistent with Doanh (2021), this effect remains indirect with respect to entrepreneurial intention.

4.6.2. Subjective Norms (H4)

Subjective Norms (H4) demonstrated statistical significance (t = 2.354; p = 0.019), although the effect size was weak (f2 < 0.02). This suggests that while students recognise social expectations regarding entrepreneurship, these influences play a secondary role compared with personal attitudes and perceived behavioural control.

4.6.3. Perceived Behavioural Control (H5)

PBC exhibits the strongest path coefficient (β = 0.152), confirming it as the primary driver of digital entrepreneurial intentions. This finding reinforces the role of self-efficacy and resource perception as critical enablers of entrepreneurial behaviour.

4.6.4. Emotional (H6, H7)

Both anticipated positive and negative emotions (H6 and H7) were statistically significant (p = 0.032 and p = 0.045, respectively). However, their weak effect sizes (f2 < 0.02) indicate limited practical relevance. These findings suggest that emotional anticipation exerts a slight influence on digital entrepreneurial intentions, whereas rational-cognitive factors—such as PBC and attitude—remain dominant.

4.6.5. Synthesis

Together, these findings extend the Theory of Planned Behaviour (TPB) by demonstrating that digital entrepreneurship depends primarily on internal psychological control (PBC and AT), while social or emotional factors remain peripheral but non-negligible.
The results demonstrate that Perceived Behavioural Control (PBC) prevails over Subjective Norms (SN) in the context of digital entrepreneurship. This predominance can be interpreted from the perspective of technological self-efficacy (Bandura, 1977; Hair et al., 2024), since the digital environment favours autonomy and reduces dependence on traditional social approval. Thus, individuals with greater technological proficiency tend to act more independently of normative pressures. Similarly, the non-significant effect of direct Social Capital (SC) on Digital Entrepreneurial Intention (DII) may reflect the transformation of trust networks: in digital ecosystems, informational capital (access to knowledge, data, and skills) surpasses relational capital based on personal ties. This perspective reinforces that digital trust and interactions mediated by virtual platforms reconfigure the traditional dynamics of social capital in entrepreneurship.

5. Conclusions

The study confirms that Attitude (AT) and Perceived Behavioural Control (PBC) are the main predictors of Digital Entrepreneurial Intention (DEI), with PBC showing the most substantial contribution (β = 0.152). Subjective Norms (SN) and Anticipated Emotions (positive and negative) were statistically significant but of weak practical importance (f2 < 0.02), indicating that social and emotional dimensions play a secondary, albeit measurable, role in shaping digital entrepreneurial behaviour.
These findings highlight that fostering self-efficacy, digital skills, and proactive attitudes may be more effective than focusing solely on emotional or social influences when designing entrepreneurship education programmes.
Emotional states are context-dependent and can be shaped by educational and environmental stimuli that enhance resilience and self-efficacy. The results indicate that positive and negative emotions (H6: ANE × SC → DEI; H7: APE × SC → DEI, supported) do not moderate the relationship between SC and DEI, but they do indirectly affect attitudes and self-efficacy.
The proposed model is limited to Brazil. Replicating this model in other cultural contexts may help us understand the moderating effect of emotions in DEI. Some findings suggest that interpretations vary in grey areas of knowledge, which may provide an opportunity to tackle the same problem in alternative contexts. As in this study, validating hypotheses with low path coefficients may provide others with new opportunities to understand this topic and test new hypotheses in new environments.
Although the sample imbalance is acknowledged—only 10.63% of IT students—future research could advance by conducting multi-group analyses or segmentations (e.g., IT vs. other areas) to assess whether academic background moderates the relationships predicted by the TCP. This approach could clarify whether the underrepresentation of IT students influences the strength of pathways associated with digital self-efficacy and technology-mediated entrepreneurial intentions.
While there is evidence supporting the hypothesis, the practical impact of the relationship may be limited, opening a range of new possibilities to challenge the existing TPB.
Furthermore, anticipated emotions tend to fluctuate significantly over time. Future research should consider using panel data to track these changes over extended periods. Data collection occurred post-pandemic, soon after in-person environments reopened. Focusing on fields with high digital entrepreneurial activity and São Paulo’s institutions enhanced feasibility but limited generalizability.
This study has some limitations that should be considered when interpreting the findings. The sample imbalance, particularly the underrepresentation of IT students, limits the generalizability of the results. Future studies should replicate the model across diverse academic contexts and examine how disciplinary background moderates digital entrepreneurial intentions. Moreover, further investigations could employ longitudinal data to assess the stability of these relationships over time.
This study expands the Theory of Planned Behaviour (TPB) by demonstrating that, in the context of digital entrepreneurship, emotional and social constructs—although statistically significant—play a peripheral role. This finding reinforces the dominance of cognitive components (Perceived Behavioural Control and Attitude) as the main drivers of entrepreneurial intention in technology-mediated environments. Thus, the research contributes to the literature by integrating emotional and social dimensions into the TPB, revealing that digitalisation redefines, rather than replaces, the cognitive core of entrepreneurial action.

Author Contributions

Conceptualization, A.L.T.D. and C.M.; Formal analysis, A.L.T.D. and D.d.S.; Investigation, A.L.T.D. and C.M.; Methodology, A.L.T.D., C.M., E.I.J. and D.d.S.; Software, A.L.T.D., E.I.J. and D.d.S.; Supervision, E.I.J.; Validation, D.d.S.; Writing—review & editing, C.M. and E.I.J. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki, and approved by The Research Ethics Committee in Human and Social Sciences of UNICAMP (CEP-CHS), CAAE (protocol code no. 58711822.6.0000.8142 and date 12 August 2022).

Informed Consent Statement

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

Data Availability Statement

The research data were collected through a specific form, created in Google Forms. The data are kept in Google Drive in an account managed by UNICAMP, with access restricted to Mr. André Luiz Tavares Damasceno (1st author) and Mr. Cristiano Morini (2nd author). In the informed consent form, participants were informed that the data were collected for specific use in this research. The users’ consent data were collected in written form, and their reuse in other similar research is not permitted. However, the authors are available to guarantee the veracity of the data used in the study in question.

Conflicts of Interest

The authors declare no conflicts of interest.

Appendix A

Table A1. Excluded items and their respective factor loadings.
Table A1. Excluded items and their respective factor loadings.
ConstructItem’s CodeBrief Description of the ItemOriginal Factor LoadingExclusion CriteriaReference
Social Capital (SC)SC_01“I know people who can help me start a digital business”0.58Loading < 0.70Adapted of Doanh (2021)
Social Capital (SC)SC_02“My online social network provides useful resources for digital entrepreneurship”0.61Loading < 0.70Doanh (2021)
Social Capital (SC)SC_03“I can easily obtain entrepreneurial information from my contacts”0.64Loading < 0.70Liao and Welsch (2005)
Anticipated Positive Emotions (APE)APE_01“I think I would feel proud if I started a digital business”0.67Loading < 0.70Boyd and Vozikis (1994)
Anticipated Positive Emotions (APE)APE_04“I will feel personal fulfilment by having my own digital business”0.66Loading < 0.70Liñán and Chen (2009)
Digital Entrepreneurial Intention (DEI)DEI_02“I am determined to create a digital business in the near future”0.62Loading < 0.70Liñán and Fayolle (2015)

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Admsci 15 00460 g001
Figure 1. Model of entrepreneurial intention (Elaborated by the authors). PS: The constructs added to the TPB were SC, APE and ANE.
Figure 1. Model of entrepreneurial intention (Elaborated by the authors). PS: The constructs added to the TPB were SC, APE and ANE.
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Figure 2. Adjusted final model.
Figure 2. Adjusted final model.
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Table 1. Terms and search strategies used for paper extraction in the Scopus and Web of Science databases in Stage 1.
Table 1. Terms and search strategies used for paper extraction in the Scopus and Web of Science databases in Stage 1.
Added TermsSourceSearch Strategy
Digital business.
Digital entrepreneurship.
Technological business.
Tech entrepreneurship.
Entrepreneurship
Startup: start-up.
Entrepreneurial intention.
Entrepreneurial digital. Mindset.
Entrepreneurial attitude.
Theory of planned behaviour.
Social Capital		ScopusTITLE-ABS-KEY ((digital AND business) OR (digital AND entrepreneurship) OR (technological AND business) OR (tech AND entrepreneurship) OR (entrepreneurship) OR (startup) OR (start-up)) AND ((“entrepreneurial AND intention”) OR (“entrepreneurial AND digital mindset”) OR (“entrepreneurial AND attitude”) OR (“theory of planned behavior”) OR (“theory AND of AND planned AND behavior”)) AND ((“TPB”) OR (“theory AND of AND planned AND behavior (TPB)”)) OR ((social AND capital AND digital AND entrepreneur) OR (digital AND entrepreneurship))
Web of ScienceTS = (digital business) OR TS = (digital entrepreneurship) OR TS = (technological business) OR TS = (tech entrepreneurship) OR TS = (entrepreneurship) OR TS = (startup) OR TS = (start-up) AND TS = (entrepreneurial AND intention OR entrepreneurial AND digital mindset OR entrepreneurial AND attitude OR theory of planned behavior OR theory AND of AND planned AND behavior) AND TS = (TPB OR theory AND of AND planned AND behavior (TPB)) OR TS = (social AND capital AND digital AND entrepreneur OR digital AND entrepreneurship)
Table 2. Terms and search strategies used for paper extraction in the Scopus and Web of Science databases in Stage 2.
Table 2. Terms and search strategies used for paper extraction in the Scopus and Web of Science databases in Stage 2.
Added TermsSourceSearch Strategy
Emotions.
Entrepreneurial intention		ScopusTITLE-ABS-KEY (“emotions” AND “entrepreneurial intention”)
Web of ScienceTS = (emotions) AND TS = (entrepreneurial intention)
Table 3. Terms and search strategies used for article extraction in the Scopus and Web of Science databases in Stage 3.
Table 3. Terms and search strategies used for article extraction in the Scopus and Web of Science databases in Stage 3.
SourceSearch Strategy
ScopusTITLE-ABS-KEY ((digital AND business) OR (digital AND entrepreneurship) OR (technological AND business) OR (tech AND entrepreneurship) OR (entrepreneurship) OR (startup) OR (start-up)) AND ((“entrepreneurial AND intention”) OR (“entrepreneurial AND digital mindset”) OR (“entrepreneurial AND attitude”) OR (“theory of planned behavior”) OR (“theory AND of AND planned AND behavior”)) AND ((“TPB”) OR (“theory AND of AND planned AND behavior (TPB)”)) OR ((social AND capital AND digital AND entrepreneur) OR (digital AND entrepreneurship)) AND (LIMIT-TO (PUBYEAR, 2022) OR LIMIT-TO (PUBYEAR, 2023) OR LIMIT-TO (PUBYEAR, 2024) OR LIMIT-TO (PUBYEAR, 2025))
TITLE-ABS-KEY (“emotions” AND “entrepreneurial intention”) AND (LIMIT-TO (PUBYEAR, 2022) OR LIMIT-TO (PUBYEAR, 2023) OR LIMIT-TO (PUBYEAR, 2024) OR LIMIT-TO (PUBYEAR, 2025))
Web of ScienceTS = ((digital AND business) OR (digital AND entrepreneurship) OR (technological AND business) OR (tech AND entrepreneurship) OR (entrepreneurship) OR (startup) OR (start-up)) AND TS = ((entrepreneurial AND intention) OR (entrepreneurial AND digital mindset) OR (entrepreneurial AND attitude) OR (theory of planned behavior) OR (theory AND of AND planned AND behavior)) AND TS = (TPB OR (theory AND of AND planned AND behavior (TPB))) OR TS = ((social AND capital AND digital AND entrepreneur) OR (digital AND entrepreneurship)) AND PY = (2022 OR 2023 OR 2024 OR 2025)
TS = (emotions) AND TS = (entrepreneurial intention) AND PY = (2022 OR 2023 OR 2024 OR 2025)
Table 4. Results of document extraction in Stages 1, 2 and 3.
Table 4. Results of document extraction in Stages 1, 2 and 3.
StagesStep 1:
Initial
Extraction		Stage 2:
Duplicates		Step 3:
Missing
Information		Step 4:
Studies
not Aligned		Final
Results
Stage 11985(−) 25(−) 90(−) 1205(=) 665
Stage 270(−) 23(−) 1(−) 0(=) 46
Stage 33678(−) 302(−) 3(−) 2477(=) 896
Total57333509436821607
Source: Authors.
Table 5. Hypotheses developed by previous studies.
Table 5. Hypotheses developed by previous studies.
IDHypothesisAuthors
H1Social Capital influences Attitudes.(Doanh, 2021; Henley et al., 2017; Mao & Ye, 2021; Popescu et al., 2016).
H2Social Capital Influences Digital Entrepreneurial Intentions.(Hoong et al., 2019; Pérez-Macías et al., 2018; Sultana et al., 2019).
H3Attitudes influence Digital Entrepreneurial Intentions.Adapted from (Ajzen, 1991, 2002, 2011).
H4Subjective Norms Influence Digital Entrepreneurial Intentions.Adapted from (Ajzen, 1991, 2002, 2011).
H5Perceived Behavioural Control influences Digital Entrepreneurial Intentions.Adapted from (Ajzen, 1991, 2002, 2011).
Table 6. New hypotheses proposed by the study (anticipated positive and negative emotions).
Table 6. New hypotheses proposed by the study (anticipated positive and negative emotions).
IDHypothesis
H6Anticipated Negative Emotions negatively moderate the relationship between Social Capital and Digital Entrepreneurial Intentions.
H7Anticipated Positive Emotions positively moderate the relationship between Social Capital and Digital Entrepreneurial Intentions.
Table 7. Code and description of the model constructs.
Table 7. Code and description of the model constructs.
CodeConstructIndependentMediator?Moderator?Dependent
SCSocial CapitalYes
ATAttitudes Yes
SNSubjective NormsYes
PBCPerceived Behavioural ControlYes
ANEAnticipated Negative Emotions Yes
APEAnticipated Positive Emotions Yes
DEIDigital Entrepreneurial Intentions Yes
Table 8. Information about model constructs and items.
Table 8. Information about model constructs and items.
ConstructIndicatorIndicator DescriptionQuestion IDAuthor
ATAT_01Conviction to find a viable solution when dealing with new problems.17Murugesan and Dominic (2014)
AT_02Conviction to find a viable solution when dealing with new opportunities.18Murugesan and Dominic (2014)
AT_03Conviction to find a viable solution when facing new challenges from existing resources.19Murugesan and Dominic (2014)
PBCPBC_01Perception of having knowledge and skills to start a digital business.21Liñán and Chen (2009)
PBC_02Fear of failure diminished in the face of the desire to create a digital business.26Liñán and Chen (2009)
SNSN_01Increased confidence in performing tasks based on advice from family members.04Moussa and Kerkeni (2021)
SN_02Increased confidence in achieving results based on advice from family members.05Moussa and Kerkeni (2021)
SN_03Increased confidence in performing tasks based on examples from family members.06Moussa and Kerkeni (2021)
SN_04Increased confidence in achieving results based on examples from family members.07Moussa and Kerkeni (2021)
SN_05Family approval of the decision to start a digital business.15Liñán and Chen (2009)
SN_06Friend’s approval of the decision to start a digital business.16Liñán and Chen (2009)
DEIDEI_01Propensity to create a digital business given the availability of opportunity and resources.20Liñán and Chen (2009)
DEI_02Capacity to develop and deal with the challenges of a digital business.22Liñán and Chen (2009)
DEI_03Confidence in the ability to create a digital business increases as this desire does.27Salamzadeh et al. (2022)
SCSC_01Increased confidence in performing tasks based on advice from the social circle.08Luo et al. (2022)
SC_02Increased confidence in achieving results based on advice from the social circle.09Luo et al. (2022)
SC_03Encouragement of positive behaviour through relationships outside the family.10Salamzadeh et al. (2022)
SC_04Increased confidence in creating a successful digital business based on advice from family members.11Moussa and Kerkeni (2021)
SC_05Increased confidence in creating a successful digital business based on examples from family members.12Moussa and Kerkeni (2021)
SC_06Increased confidence in creating a successful digital business based on advice from social circles.13Baughn et al. (2006)
SC_07Increased confidence in creating a successful digital business based on examples from social circles.14Baughn et al. (2006)
SC_08Increased social relationships from digital business studies.25Zhu et al. (2019)
ANEANE_01Fear of disappointing family members.01Izard and Izard (1977)
ANE_02Fear of failing professionally.02Izard and Izard (1977)
ANE_03Fear of disappointing the social circle.03Izard and Izard (1977)
APEAPE_01Positive emotional support from social relations.23Izard and Izard (1977)
APE_02Prior preparation for daily emotional conflicts.24Goleman (1995)
APE_03Avoid bad behaviour driven by the desire to create a digital business.28Kanonuhwa et al. (2018)
APE_04Encourage good behaviour to create a digital business.29Kanonuhwa et al. (2018)
Table 9. Sample description.
Table 9. Sample description.
CharacteristicsFrequency (%)
GenderMan58.11
Woman41.89
MajorEngineering57.30
Business Administration32.07
IT10.63
Table 10. Fornell-Larcker criterion, Cronbach’s alpha, Composite Reliability and Average Variance Extracted (AVE) values.
Table 10. Fornell-Larcker criterion, Cronbach’s alpha, Composite Reliability and Average Variance Extracted (AVE) values.
ConstructATSCPBCANEAPEDEISN
AT0.825
SC0.2250.713
PBC0.3550.5530.824
ANE−0.172−0.054−0.1660.772
APE0.2210.5500.666−0.0910.861
DEI0.2800.5150.718−0.1340.7890.805
SN0.2020.3820.250−0.0030.1890.2090.631
Cronbach’s alpha0.7640.7810.5340.7190.6640.4830.746
Composite reliability0.8640.8380.8080.8110.8510.7840.797
Average variance extracted0.6810.5090.6790.5970.7420.6480.398
Source: Authors.
Table 11. Structural model values.
Table 11. Structural model values.
Constructf2r2Q2
AT0.0030.0510.052
DEI0.6620.6930.675
Table 12. Results of the structural model hypothesis testing.
Table 12. Results of the structural model hypothesis testing.
HypothesisRelationt-Statisticsp-ValueSupported?
H1SC → AT3.864<0.001Yes
H2SC → DEI0.4710.638No
H3AT → DEI5.126<0.001Yes
H4SN → DEI2.3540.019Yes
H5PBC → DEI6.107<0.001Yes
H6ANE × SC → DEI2.1430.032Yes
H7APE × SC → DEI2.0010.045Yes
Source: Authors.
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MDPI and ACS Style

Damasceno, A.L.T.; Morini, C.; Inácio Jr., E.; da Silva, D. How Do Emotions and Social Ties Shape Digital Entrepreneurship? Evidence from Brazilian Higher Education. Adm. Sci. 2025, 15, 460. https://doi.org/10.3390/admsci15120460

AMA Style

Damasceno ALT, Morini C, Inácio Jr. E, da Silva D. How Do Emotions and Social Ties Shape Digital Entrepreneurship? Evidence from Brazilian Higher Education. Administrative Sciences. 2025; 15(12):460. https://doi.org/10.3390/admsci15120460

Chicago/Turabian Style

Damasceno, André Luiz Tavares, Cristiano Morini, Edmundo Inácio Jr., and Dirceu da Silva. 2025. "How Do Emotions and Social Ties Shape Digital Entrepreneurship? Evidence from Brazilian Higher Education" Administrative Sciences 15, no. 12: 460. https://doi.org/10.3390/admsci15120460

APA Style

Damasceno, A. L. T., Morini, C., Inácio Jr., E., & da Silva, D. (2025). How Do Emotions and Social Ties Shape Digital Entrepreneurship? Evidence from Brazilian Higher Education. Administrative Sciences, 15(12), 460. https://doi.org/10.3390/admsci15120460

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