Economists vs. Engineers—Assessing Students’ Entrepreneurial Intentions from the Perspective of Mindset and Resilience

Abstract

Given that student entrepreneurship contributes to the intensification of economic activities and the improvement of the social well-being of the parties involved, evaluating and fostering students’ entrepreneurial intentions can be a step in moving from intention to action in the entrepreneurial process. From this perspective, the present study assesses students’ entrepreneurial intentions and measures the impact of the most important determinants based on online questionnaires addressed to students from two different fields of study: economics and engineering. Using the collected data (N = 392 students) and analysis methods based on correlation and stratified multiple regression as well as non-parametric tests (Mann–Whitney U), the study reveals that students’ entrepreneurial intentions are influenced by mindset and resilience. The study indicates that the influences can vary significantly when the analyses include control variables, such as gender, field of study, year of study, professional experience, age, and country of origin. It is also important to note that the statistical significance of the results regarding the impact of resilience varies depending on the specifics of the control variables. This study considered both analyses of resilience (as a synthetic indicator) and its subcomponents. The results of this study have both theoretical and practical utility.

1. Introduction

In the context of the increasingly complex and dynamic global economy, entrepreneurship is a principal engine for innovation, economic growth, and social development. As traditional career paths evolve and new technologies transform industries, higher education institutions play a major role in preparing students with the skills, mindsets, and competencies needed to cope with uncertainty and create value in diverse contexts. Integrating entrepreneurship into higher education not only supports students’ ability to identify and exploit opportunities but also cultivates critical thinking, creativity, resilience, and a proactive attitude in finding solutions to various problems.

Moreover, universities represent important ecosystems for entrepreneurial activity, acting as incubators for new businesses and as bridges between research, economic activity, and society. Today’s students can become tomorrow’s entrepreneurs, which is why understanding their motivations and intentions allows not only to anticipate future trends in the entrepreneurial ecosystem, but also to support tailored educational and institutional interventions. Thus, empirical investigation of entrepreneurial intention among students paves the way to broader mechanisms of entrepreneurial behavior formation.

Entrepreneurial intention is a widespread research theme, with studies focusing on personal, academic, and social aspects. Entrepreneurial intention refers to the strong determination to invest skills, competencies, and resources into a business, being defined as the process of recognizing opportunities in a market, finding and exploiting the resources necessary to capitalize on these opportunities for long-term personal gain (Uddin & Bose, 2012).

Entrepreneurial intention implies attitudes, social norms, and powerful self-efficacy (Pérez-López, 2016). As previous studies stated, entrepreneurship is determined by certain personality traits that imply innovation, the desire to become successful, and coping with stress or challenges (Ahmed et al., 2021). Moreover, self-awareness and self-motivation may predict a determined and perseverant future entrepreneur (Bigos & Michalik, 2020), while self-esteem, self-efficacy, and the orientation toward positive results are essential indicators of a successful future entrepreneur (Farhat & Guevara, 2020).

Recent studies have demonstrated the significant influence of sociodemographic factors, such as age, year of study, level of education, and knowledge about entrepreneurship, on entrepreneurial intentions of students in economic and administrative fields (Millán et al., 2017). Education in the field of entrepreneurship that develops specific skills and attitudes is considered to improve the entrepreneurial intentions significantly (Han et al., 2015), but the support of the government in implementing favorable policies to assist the business environment is holding up the intentions (Tetteh et al., 2024). The students’ entrepreneurial intentions are strongly influenced by factors such as perceived educational support. Moreover, self-confidence is an essential mediator between contextual factors and entrepreneurial intentions. The entrepreneurial intentions may be encouraged by cultural values, economic development conditions in specific regions, or the political environment. The economic stability and the direct contribution of innovation play a role in shaping entrepreneurial intentions.

A positive attitude toward challenges and continuous personal improvement of skills are necessary to succeed (Szczygiel et al., 2024), so an entrepreneurial mindset is likely to influence the intention to start a business. The concept of entrepreneurial resilience indicates the ability to act efficiently under pressure, or in stressful or highly uncertain conditions, including adaptability (recovery resilience) and identifying opportunities (growing the level of resilience) (Hao et al., 2024). Entrepreneurial mindset and resilience are essential for entrepreneurial success, but they have different functions. While the entrepreneurial mindset refers to personality traits that can lead to the recognition of opportunities, namely creativity and innovation, resilience is the sum of the abilities to adapt, recover, and relaunch after a failure. Both constructs are connected, as a strong entrepreneurial mindset can increase resilience, and resilience supports the adaptability necessary to succeed in entrepreneurship (Tiwari & Homechaudhuri, 2022).

Several studies examined the key factors influencing entrepreneurial mindset correlated to resilience and found adaptability and stress management (maintaining an optimal performance level despite adversities) (Halberstadt & Alcorta de Bronstein, 2021), positive psychological thinking (responses to crisis and identifying the best solutions), and self-efficacy (adopting the necessary behavior based on needs and motivation to continue the entrepreneurial efforts) (Emrizal et al., 2020). The mediation and moderation roles of resilience on entrepreneurial intention are focused on self-efficacy and internal locus of control demonstrated by individuals in difficult contexts, such as pandemics or environmental disasters (Alshebami, 2022). Psychological resilience reduces the major impact of fear of failure on entrepreneurial intentions, with individuals becoming more inspired to continue entrepreneurial aspects regardless of possible causes of delay in business activities (Ukil & Jenkings, 2023).

The research questions that were the basis of this study were as follows: To what extent are the entrepreneurial intentions of students from the selected population influenced by entrepreneurial mindset and resilience? Are the influences of the two determinants similar or different depending on specific variables, such as gender, year of study, field of study, age, work experience, and residency?

The literature revealed a research gap represented by the lack of an empirical understanding of the relationship between mindset, resilience, and entrepreneurial intention. The present paper started from the need to identify the factors influencing the entrepreneurial intentions of the students, motivated by official statistical information that draws attention to the decrease in the rate of implementation of new businesses at the student level. Therefore, according to the data provided by the National Trade Register in Romania, the authority that manages the process of business registrations and closures, students who register new businesses benefit from facilities regarding registration fees, in the context of which statistics of establishments and deregistrations are collected. According to these statistics, on 1 January 2025 (compared to 1 January 2023), the annual growth rate of new businesses registered by students decreased from 0.18% to 0.12%. In addition, the same source also reported that annual deregistrations almost doubled (from 5276 in 2023 to 9251 in 2024). Under these conditions, it is the duty of authorities, educational institutions (which are tasked with developing entrepreneurial skills), and researchers to assess the causes of unfavorable trends.

Although numerous studies address students’ entrepreneurial intention in a broad context, there are still gaps regarding how entrepreneurial mindset and resilience influence this intention among students from less economically developed regions. Because Romania is organized into eight regions that present major differences in terms of the degree of economic development (and implicitly the intensity of entrepreneurial activities), a regional analysis was taken into account. The North-East region of Romania, although it is classified as one of the least developed regions at the level of the European Union (based on gross domestic product per capita), stands out for the presence of two large universities that offer educational services for two related fields: engineering and management, and economics and business. This region faces specific socio-economic challenges, and universities play a crucial role in stimulating entrepreneurship as a vector of local and regional development. Therefore, the present research complements the existing literature by highlighting relevant psychological and educational factors in the context of a student population from different fields. Furthermore, the results may provide valuable insights for similar institutions in other regions with comparable socio-economic profiles, thus contributing to the development of adapted educational policies and entrepreneurial training programs.

The literature review also indicated a gap in the comparative analysis of entrepreneurial intentions depending on the year of study or the specialization chosen by students (Xanthopoulou et al., 2024). Only a few studies found differences between the behavior of business students and that of students from other fields (Maresch et al., 2016; Nabi et al., 2017). These aspects were addressed in our research by considering two subsamples of respondents, engineering and management students and economics and business students, at different stages of their university studies. The impact of students’ age and gender on entrepreneurial intention is insufficiently investigated in the literature, and these are aspects that our research supplemented.

The main objective of the research was to assess entrepreneurial intentions, entrepreneurial mindset, and resilience and to quantify the relationships between them. Considering previous studies, the authors started from the hypothesis of direct and positive determinations between variables. Unlike previous studies, which approached resilience as a synthetic construct, the current study considered the structural elements of resilience (hardiness, coping, adaptability/flexibility, meaningfulness/purpose, optimism, regulation of emotion and cognition, and self-efficacy). A subsidiary objective of the research was to assess whether the interdependencies indicate differences when different criteria for grouping students are considered, such as gender, field of study in which they are enrolled, year of study, work experience, age, and country of residence.

The results of the study revealed that an entrepreneurial mindset and resilience had a favorable impact on entrepreneurial intentions. Beyond this valuable result, which aligns with the results of previous studies carried out on different samples and periods (Pfeifer et al., 2016; Mukhtar et al., 2021), the present research is distinguished by the particularities of the analyzed sample and by the decomposition of resilience into seven components.

To meet the assumed objectives, the work was structured as follows. The following section presents the theoretical framework of the research and points out the main hypotheses. The third section details the methodology, and the fourth and fifth sections present the results and initiate discussions based on the results obtained. The last section summarizes the conclusions, points out the limits of the research, and traces the future directions of research.

2. Theoretical Framework

2.1. Determinants of Entrepreneurial Intentions

The factors explaining the entrepreneurial intention of individuals have been sought in theories of human behavior, such as the theory of planned behavior, the Big Five and the HEXACO model of personality traits, and social capital theory. Studies on entrepreneurial intentions among students are of great interest because this educated population constitutes a fertile pool for the transition from intention to behavior in entrepreneurial activity.

The theory of planned behavior (TPB) stipulates that attitude, subjective norms, and perceived control are factors that determine human behavior (Ajzen, 1991), including entrepreneurial intention (Kautonen et al., 2015). In searching for the determinants of entrepreneurial intention, some studies started from the factors identified in TPB as influencing human behavior but added other determinants, depending on the context of the analysis, the objectives pursued, and the sample considered, to create a clearer picture of the intention to initiate and manage businesses. Thus, attitude, perceived social norms, perceived entrepreneurial capacity, and entrepreneurship education directly influence entrepreneurship intention (Sampene et al., 2023). Furthermore, the findings indicate that entrepreneurship education serves as a mediating variable in the nexus between the factors from the TPB and entrepreneurial intention. Additionally, perceived university support moderated the relationship between entrepreneurship education and entrepreneurial intention. Previous authors, in a study on entrepreneurial intention among engineering students, analyzed the impact of TPB factors, including influences such as curiosity and well-being. The results of this study show that entrepreneurial intention is positively influenced by attitudes oriented toward entrepreneurship, perceived behavioral control, and curiosity, and negatively determined by subjective norms, while psychological well-being does not impact entrepreneurial intention (Balgiu & Simionescu-Panait, 2024).

The HEXACO model is a six-dimensional framework of personality traits that expands upon the well-known Big Five model: honesty–humility, emotional stability, extraversion, agreeableness, conscientiousness, and openness to experience (Zhao et al., 2009). Numerous studies have focused on some or all personality traits in entrepreneurial intention, in many cases correlated with other influencing factors. For instance, personal traits, opportunity recognition skills, and risk-taking are directly associated with entrepreneurial intention (Bergner et al., 2021), while openness, conscientiousness, extraversion, and humility have an indirect and positive relationship with entrepreneurial intention through career adaptability (Tsaknis et al., 2022). As for training, the studies have shown that undergraduate management students’ entrepreneurial intentions are positively determined by personality traits and entrepreneurship education (Ratnamiasih et al., 2024). Investigating the entrepreneurial traits of university students in the health field, other studies concluded that entrepreneurial intention is positively influenced by the quality of their proactiveness, innovativeness, internal locus of control, problem-solving capacity, and autonomy (Mohamed et al., 2023).

Social capital theory has been developed and extended in the fields of sociology, economics, and entrepreneurship (Aldrich & Zimmer, 1986; Davidsson & Honig, 2003). Social capital, an aspect with a major impact on resilience, helps individuals with an efficient entrepreneurial mindset because the support of a network may encourage and sustain an entrepreneur in difficult times (Pongtanalert & Assarut, 2022). Creating a strong relationship within the working team increases the level of flexibility and resilience of the entrepreneur, so promoting soft skills and positive psychology may help face future challenges (Abonil et al., 2024). The entrepreneurial mindset is based on consciousness and constant focus on the decision process, potential risks, and finding proper strategies to succeed, therefore enforcing the resilience (Indrianti et al., 2024). Some researchers include social capital among the determinants of students’ entrepreneurial intentions, finding that entrepreneurial attitude orientation, social capital, and psychological capital influence the formation of entrepreneurial intentions among polytechnic students (Mahfud et al., 2020).

Recent literature links entrepreneurship to sustainable development goals (SDGs), highlighting how sustainable values and social responsibility have become additional motivations for young entrepreneurs (Apostolopoulos et al., 2018; Ivasciuc & Ispas, 2023).

In the context of higher education, research shows that the educational environment and entrepreneurial culture within universities contribute significantly to shaping entrepreneurial intentions (Fayolle & Gailly, 2015). Entrepreneurship education programs, mentoring, and networking opportunities can strengthen the entrepreneurial mindset and the ability of students to transform ideas into concrete initiatives (Liñán et al., 2011).

2.2. Entrepreneurial Mindset and Entrepreneurial Intention

The previously presented theories led us to the idea that entrepreneurial intention is determined by a series of traits and modes of behavior that lead an individual to act toward starting a business. These traits create the entrepreneurial mindset as the combination of ‘motives, skills, and thought processes that distinguish entrepreneurs from non-entrepreneurs and that contribute to entrepreneurial success’ (Davis et al., 2015), or as a set of practices for harnessing uncertainty and rapid change, meaning action-oriented thinking and continuous innovation (McGrath & MacMillan, 2000). Although approaches in the literature on entrepreneurial mindset (EM) are rather confusing, three aspects are distinguished in the definition of the concept: emotional, cognitive, and behavioral. EM involves passion and optimism, a specific way of thinking, and the ability and willingness of the future entrepreneur to act to capitalize on a profit opportunity (Kuratko et al., 2021). However, the impact of EM on entrepreneurial intention (EI) and entrepreneurial behavior remains insufficiently addressed in the literature (Cui & Bell, 2022). The present paper aims to fill this gap.

The relationship between entrepreneurial mindset and entrepreneurial intention among students has been the subject of several studies. Some research revealed that there is a significant relationship between entrepreneurship education, entrepreneurial creativity, entrepreneurial mindset, and entrepreneurial intention in business education students (Ediagbonya, 2022). Researchers found that the entrepreneurial mindset has a mediating role in the relationship between entrepreneurship education and entrepreneurial intention among students (Sun et al., 2023; Mukhtar et al., 2021). Self-efficacy, positive outcome expectations, and entrepreneurial identity are predictors of the entrepreneurial intention of business students (Pfeifer et al., 2016). According to Oulhou and Ibourk (2023), the perceived quality of entrepreneurial training, together with the level of satisfaction associated with it, has a significant influence on the development of entrepreneurship and entrepreneurial intention among Moroccan university students.

Based on the literature review, the first hypothesis assumed in this study was as follows:

H1. 

Entrepreneurial mindset (EM) has a positive impact on entrepreneurial intention (EI) among students.

2.3. Resilience Capacity and Entrepreneurial Intention

The impact of resilience on entrepreneurial intention indicated a positive influence, especially on the ability to confront business challenges and maintain proper motivation to continue being successful. Previous research suggested that entrepreneurial resilience has a strong positive impact on entrepreneurial intention (K. Wu et al., 2023). At the university student level, a study demonstrated that a higher score in resilience leads to an improvement in the perceived control of entrepreneurial intention, with both attitude and subjective norms having a positive impact (Cruz et al., 2022). In addition, it has been proven that resilient people tend to apply their educational knowledge to entrepreneurial ideas strongly. Education positively influences entrepreneurial intentions, resilience, and opportunity awareness, and plays the role of a mediator (Bui et al., 2025). Entrepreneurship education and entrepreneurial intentions are developing a positive correlation mediated by entrepreneurial perceptiveness and resilience, indicating the importance of the connectivity between solid business knowledge, determination, and future targets in professional life (Hoang et al., 2023).

Analyses focused on the relationship between resilience and entrepreneurial intentions of students revealed that resilience has a positive impact on EI because a high level of resilience correlates with perceived control over EI, a positive and open attitude to failure (Cruz et al., 2022), and also enhances entrepreneurial abilities that are essential for success. Resilience helps students overcome fear about their future career and lack of professional experience, transforming these obstacles into opportunities and positive perspectives that support entrepreneurial intentions. Resilient students are more focused on developing new ideas (Ukil & Almashayekhi, 2024). Resilience encompasses traits such as optimism, resourcefulness, and the ability to cope with stress, which are crucial for entrepreneurial success (Yuan et al., 2024).

The results of previous studies focused on the interdependence relationship between entrepreneurial intentions and resilience capacity, which was the basis for the second hypothesis:

H2. 

A higher resilience capacity (R) has a positive impact on EI.

Differing from previous studies, and taking into account the complexity of the concept of resilience, the present study proposed to test the impact of the seven primary components of resilience on EI. The 25 items that assessed student resilience were grouped to quantify the seven subdimensions: hardiness (i.e., commitment/challenge/control), coping, adaptability/flexibility, meaningfulness/purpose, optimism, regulation of emotion and cognition, and self-efficacy. Thus, seven adjacent hypotheses (H2.1–H2.7) were formulated starting from the relationship of direct and positive determination between the subdimensions of resilience and the entrepreneurial intentions of the students:

H2.1. 

Hardiness has a positive impact on students’ EI.

H2.2. 

Coping has a positive impact on students’ EI.

H2.3. 

Adaptability/flexibility has a positive impact on students’ EI.

H2.4. 

Meaningfulness/purpose has a positive impact on students’ EI.

H2.5. 

Optimism has a positive impact on students’ EI.

H2.6. 

Regulation of emotion and cognition has a positive impact on students’ EI.

H2.7. 

Self-efficacy has a positive impact on students’ EI.

According to the Connor–Davidson Resilience Scale (CD-RISC-25), hardiness is a personality trait that has a protective role in case a person goes through adverse situations. In correlation with resilience, it refers to commitment, control, and challenge. Coping refers to how a person uses their strengths to manage adverse situations. Adaptability represents an essential element of resilience, helping people to navigate through hard and significant changes in their lives. Meaningfulness/purpose refers to the ability of the person to feel valued, despite life difficulties. Optimism is the power to maintain a positive direction in life despite difficult contexts and is seen as a significant predictor of resilience because it helps to cope with stress. Regulation of emotion and cognition involves finding the inner resources to activate positive emotions, cognitive revaluation, and reconstruction, refocusing the person toward a purpose in life. Self-efficacy is defined as the capacity of someone to believe in actions that produce a behavior oriented to personal performance.

2.4. Entrepreneurial Intention and Socio-Demographic Factors

Previous studies have provided contradictory evidence. Therefore, the present study aims to develop the analysis by assessing the influence of socio-demographic factors on students’ entrepreneurial intention. In this sense, the third hypothesis was formulated:

H3. 

EI depends on control variables, such as gender, year of study and field/specialization, age, work experience, and country of origin.

Since this hypothesis takes into account several variables, in order to provide more clarity in the research, a separate analysis of each determining factor was chosen. Thus, seven sub-adjacent hypotheses were formulated, corresponding to the seven socio-demographic variables targeted.

H3.1. 

The entrepreneurial intentions of male students are higher than the entrepreneurial intentions of female students.

Previous results of various studies highlight gender as having a differentiating role in the entrepreneurial intentions of students. Male students showed high entrepreneurial intentions and self-trust in entrepreneurial skills (Da Costa et al., 2023), a significant increase in positive psychology regarding entrepreneurial self-efficacy and control after assimilating knowledge in the entrepreneurial area (Shinnar et al., 2014), and also improved entrepreneurial intentions and creativity levels compared to women (Phipps & Prieto, 2015). On the other hand, women tend to record a lower level of entrepreneurial intentions, being more influenced by social and subjective norms (Maes et al., 2014), with a weak effect on career adaptability and entrepreneurial intentions (Zhang et al., 2024).

The differences depending on gender indicated that female students have a more powerful attitude and develop social abilities and a desire to succeed in entrepreneurship activities compared to male students (Zaharah et al., 2012).

H3.2. 

The entrepreneurial intentions of students in the final years of studies are higher than the intentions of students in the first years of studies.

Although limited, previous research reveals that the entrepreneurial intentions of first-year students are higher than those in their final years. Some of the factors are changes in expected support from the university and family, or the uncertainty of economic policies in their own country (Xanthopoulou et al., 2024). Even so, if the university offers students the opportunity to take entrepreneurship courses, they can strengthen their entrepreneurial intentions (Letsoalo & Rankhumise, 2020).

H3.3. 

The entrepreneurial intentions of students pursuing a study program in the field of economics and business are stronger than the entrepreneurial intentions of students pursuing a study program in the field of engineering and management.

Students in technical majors generally have a lower level of entrepreneurial intentions compared to their peers studying business (Arias & Flad, 2025). Business administration students have higher self-esteem and confidence in entrepreneurship (through achieving goals, optimizing results, and creating a network of relationships) (Jin et al., 2014), developing interactions with mentors or entrepreneurship specialists during the years of study. In addition, entrepreneurial intentions are strengthened by the participation of a larger number of business students in extra-curricular activities (Cekule et al., 2023). However, the effectiveness of these activities may vary, and previous studies have not always found direct correlations with entrepreneurial intentions (Wegner et al., 2020). Although the results recorded in the past indicate that engineering students are reluctant to formulate entrepreneurial intentions, they have a high level of entrepreneurial intentions through participating in management and entrepreneurship courses and junior enterprises (JEs) (Almeida et al., 2021).

H3.4. 

Students’ entrepreneurial intentions intensify as they age.

As they age, students’ entrepreneurial intentions register increases in level compared with younger students (Atitsogbe et al., 2021; Sahinidis et al., 2021). The results of several studies show that the increase is due to motivation, level of knowledge, and attitude toward entrepreneurship (Ashokan et al., 2019). However, there is also research indicating that age is not a significant predictor of students’ entrepreneurial intentions (Ozyilmaz, 2011).

The correlation between age and entrepreneurial intentions may change over time. Managing self-efficacy, although it has a positive influence on entrepreneurial intentions immediately after graduation, decreases over time (Valencia-Arias et al., 2023).

H3.5. 

Students who have work experience show greater entrepreneurial intentions.

Some results indicate a weighted influence of work experience on entrepreneurial intentions. Students’ work experience has no significant moderating effect on the relationship between self-efficacy and entrepreneurial intentions (Militaru et al., 2017), a result also supported by another study that signals an insignificant relationship with controlling behavior, entrepreneurial attitudes, and intentions (Malebana & Mahlaole, 2023). However, in certain contexts, work experience positively influences entrepreneurial intentions. Young students who have worked in the past have firmly expressed their desire to open a business (Bignotti & Le Roux, 2020). In addition, students with work experience have a higher level of entrepreneurial intentions than students without experience, even if the difference is statistically insignificant (Fatoki, 2014). Work experience may come with skills, advantages, and high confidence that can increase the level of entrepreneurial intentions (Gielnik et al., 2018).

H3.6. 

The entrepreneurial intentions of university students who study in their homeland are lower than those studying in other countries.

People who study abroad experience entrepreneurial effectiveness, expectations, and entrepreneurial interest differently from others who study in their homeland (Uskuri & Sesen, 2023). Thus, the university’s location may influence the choice of an entrepreneurial career. Studying outside home countries can enrich entrepreneurial self-efficacy—adaptability in the face of risks, identifying innovative solutions in times of crisis, and finding opportunities have a significant impact on students’ entrepreneurial intentions (J. Wu et al., 2022). Factors such as the entrepreneurial perception in other countries, the multiculturalism of entrepreneurship concepts (J. Wu & Rudnák, 2021), and the university environment’s approach to entrepreneurship (access to entrepreneurial education) correlate positively with entrepreneurial intentions (Lopez & Alvarez, 2019).

Therefore, the theoretical framework for this study is primarily rooted in the theory of planned behavior, built on attitude, subjective norms, and perceived controlled behavior, in the HEXACO model of personality traits, and also in social capital theory, which has developed the most recent approaches to the determinants of entrepreneurial intention among students. The overall theoretical model for this research considers that students’ entrepreneurial intentions are determined by entrepreneurial mindset and resilience, which are the main hypotheses that we will test in this study.

3. Materials and Methods

To assess the students’ perception of the three variables (entrepreneurial mindset, entrepreneurial intentions, and entrepreneurial resilience), a questionnaire was applied, built on the entrepreneurial mindset—the Entrepreneurial Mindset Profile (EMP) Personality Scales—authors’ contribution (developed after EMP dimensions) (Davis et al., 2015), adaptation of the scale of Linan, F. & Chen, Y.W.—the Entrepreneurial Intention Questionnaire (Linán & Chen, 2009; Singh & Prasad, 2016), and the CD-RISC scale (Connor & Davidson, 2003). The Entrepreneurial Mindset questionnaire was developed by the authors based on two dimensions, personality and skill, and has 18 items, the Entrepreneurial Intentions questionnaire has 10 items, and the CD-RISC scale has 25 items.

The final questionnaire consisted of 53 items (which assessed entrepreneurial mindset, entrepreneurial intentions, and resilience) to which were added 6 items describing demographic attributes regarding respondents: gender, age, year of study, work experience (if any), country of origin, and field of study in which they are enrolled. To ensure suitability and accessibility for the targeted sample, the questionnaire was pre-tested. At the end of the pre-testing operation, it was found that no revisions were required at the level of the items, as the participants in the pre-test confirmed that they had no difficulties in understanding and answering the questions in the questionnaire.

The questionnaire was distributed in electronic format to all study groups in the fields of engineering and management and economics and business from two targeted universities: Gheorghe Asachi Technical University of Iasi (GATU) and Alexandru Ioan Cuza University of Iasi (AICU). The arguments underlying the limitation of the analysis to only two universities were: (a) universities that have study programs that include in the curriculum basic disciplines for the formation of entrepreneurial skills (such as entrepreneurial economics, marketing, business financing, market analysis, etc.) were considered; (b) to ensure homogeneity at the sample level, the selection of respondents from the same economic environment (who respond to the same types of challenges, who have the same business opportunities, and who are exposed to similar threats) was considered. The simultaneous application of the two selection criteria oriented the analysis toward the two public universities previously presented, which are representative of the academic and economic environment of the North-East region of Romania.

The questionnaire distribution period was December 2024 to April 2025. The total target population was 7982 students (573 in the field of engineering and management, 7409 in the field of economics and business). The response rate (based on the entire target population) was 5%.

The questionnaire was sent in anonymized form (without collecting email addresses or other data indicating the identity of the respondents). Respondents agreed to the collection of personal data, such as gender, year of study, field of study, work experience, age, and country of origin.

As shown in

Table 1. Information on the sample structure.

Fields of Study	Total Population	Respondents	Response Rate	Years of Study and No. of Respondents
				1	2	3	4
1. Engineering and management	573	218	38.0%	74	57	52	36
2. Economics and business	7409	174	2.3%	72	61	24	15
Total sample	7982	392	5.0%	146	118	77	51

, there is a significant difference in the response rate across the two study fields. This difference can be explained by the fact that no constraints on survey participation were imposed. However, the advantage of data authenticity should be viewed with caution because it does not compensate for the disadvantages of a low response rate, which limits the possibilities of generalizing results.

Details on the sample structure are presented in Table 1. The necessary clarification is that, for the two fields of study, the duration of schooling is different. In the field of engineering and management, the duration of studies is 4 years, these being delimited in Table 1 by the distinct numbering 1, 2, 3, and 4. At the GATU level, there were no responses from master’s students. On the other hand, at AICU, the duration of studies is 3 years. Table 1 delimits the three years of studies (by the distinct coding 1, 2, and 3), to which was added the year “4”, in which the students from master’s programs were included (15 in number).

In our analysis, the data entered into the analysis were grouped into three categories: dependent variable (EI); independent variables (ME and R); control variables (socio-demographic data). The first two categories of variables (dependent and independent) were evaluated based on a five-step Likert scale (1—total disagreement, 2—partial disagreement, 3—neutral, 4—partial agreement, 5—total agreement).

The control variables were quantified and coded as follows: (a) gender (G): 1—male; 2—female; (b) year of study (Y): 1, 2, 3, 4—bachelor degree, master’s degree; (c) field of study (F): 1—engineering and management, 2—economics and business; (d) work experience (We): 1—no experience; 2—less than 1 year of experience; 3—1–3 years of experience; 4—more than 3 years of experience; (e) age (A): 1—students aged 18 to 19; 2—students aged 20; 3—students aged 21; 4—students aged 22; 5—students aged 23; 6—students over 23 years old; (f) country (C): 1—Romania; 2—Republic of Moldova. The structure of the data, analyzed from the perspective of control variables, is presented in

Table 2. Distribution of data related to the surveyed sample.

	No.	Percent
Gender
Male	118	30%
Female	274	70%
Total	392	100%
Year of study
1	146	37%
2	118	30%
3	77	20%
4	51	13%
Total	392	100%
Field of study (University)
Engineering and management (GATU)	218	56%
Economics and business (AICU)	174	44%
Total	392	100%
Work experience
No experience	245	63%
Up to one year of experience	96	24%
1–3 years of experience	32	8%
More than 3 years of experience	19	5%
Total	392	100%
Age
Students aged 18 to 19	112	29%
Students aged 20	117	30%
Students aged 21	78	20%
Students aged 22	51	13%
Students aged 23	16	4%
Students over 23 years old	18	4%
Total	392	100%
Country
Romania	314	80%
Republic of Moldova	78	20%
Total	392	100%

.

According to the data in Table 2, the sample is mainly represented by female respondent students (70%) registered in the first 3 years of studies (87%), with a relatively balanced distribution between the two study programs (56% vs. 44%), who have no work experience (63%), are aged between 18 and 21 years (79%), and have Romanian citizenship (80%). Since the questionnaire was freely distributed, some variables such as respondents’ gender and response rate could not be controlled. Therefore, taking into account the particularities of the sample of respondents, the results should be interpreted in accordance with these limitations of the research.

This study assesses the interdependencies between entrepreneurial mindset, entrepreneurial intentions, and resilience for a specific sample. Specifically, the analyses were carried out on a sample of students who follow study programs in two related fields: engineering and management and economics and business. Based on the literature review, the conceptual framework of the research was defined as in Figure 1. The methods, techniques and tools used are detailed in

Table 3. Methods, techniques, and tools used to achieve the objectives.

Objectives	Methods, Techniques, and Tools Used
Construction of reliability testing	Alpha Cronbach
Description of the data	Descriptive statistics, correlation analyses
Evaluation of determinations between variables	ANOVA test, multiple regression analyses, Mann–Whitney U test

.

Regression analysis is a method often used in assessing the determinants of students’ entrepreneurial intentions. For example, Nguyen et al. (2025) used multivariate regression and showed that personality traits and resilience significantly affect entrepreneurial intentions among students. Hernández-Sánchez et al. (2020) applied regression analysis models and provided evidence on the determining relationship between the perceptions of economic and social context (especially the pandemic), personality variables, and entrepreneurial intention. They showed that proactivity and optimism mediate these relationships. Alsaidan and Zhang (2018) used correlation and linear regression and showed that entrepreneurial self-efficacy moderates the relationship between students’ perception of career guidance and entrepreneurial intentions. In two of the three studies previously presented (Nguyen et al., 2025; Hernández-Sánchez et al., 2020), data were collected using 5-point Likert scales, with responses ranging from strong disagreement to strong agreement.

When the conditions for applying parametric analyses were not fully met, researchers also turned to nonparametric methods. For example, Tausif et al. (2021) compared the entrepreneurial intentions of students from different countries using the Mann–Whitney U test. They showed that entrepreneurial intentions are strongly influenced by the culture of the countries from which the students come. Instead, attitude and perceived behavioral control explain students’ entrepreneurial intent, regardless of the country they come from. Aljarodi et al. (2023) used the Mann–Whitney U test and binomial logistic regression analysis and highlighted gender differences in entrepreneurial activity.

In this study, the following equations were used to estimate the score of the components of each variable:

EM = a₁·EM₁ + a₂·EM₂ + ... + a₁₈·EM₁₈

(1)

R = b₁·R₁ + b₂·R₂ + ... + b₂₅·R₂₅

(2)

EI = c₁·EI₁ + c₂·EI₂ + ... + c₁₀·EI₁₀

(3)

In these equations, a_i, b_j, and c_l are the estimation parameters for EM, R, and EI. The variables (EM₁-EM₁₈, R₁-R₂₅, and EI₁-EI₁₀) recorded values between 1 and 5, corresponding to the Likert scale used. Each variable is expressed as the arithmetic mean of each component. To provide more adequate knowledge, following the model established by the theory (CD-RISC), the resilience variable has following subcomponents: hardiness (i.e., commitment/challenge/control) (items 5, 10, 11, 12, 22, 23, 24); coping (items 2, 7, 13, 15, 18); adaptability/flexibility (items 1, 4, 8); meaningfulness/purpose (items 3, 9, 20, 21); optimism (items 6, 16); regulation of emotion and cognition (items 14, 19), and self-efficacy (items 17, 25).

To evaluate the determination relationships between the selected variables, regression analyses were performed. Specifically, the evaluation of the links between entrepreneurial intentions and independent variables was considered both at the level of the entire sample and at the level of the subsamples delimited according to the control variables. The equations used to estimate the regression models applied in the present study are as follows:

Model 1:

EI = β₀ + β₁·EM + β₂·R + α

(4)

Model 2:

EI = β₀ + β₁·EM + β₂·Rh+ β₃·Rc+ β₄·Ra + β₅·Rm + β₆·Ro + β₇·Rr + β₈·Rs + α

(5)

In Equations (4) and (5), βi is the coefficient of the linear regression equations, and α is the error term. Regression analyses were performed both on the entire sample and on sample sections, a context in which specific selection variables were used (such as gender, year of study, work experience, field of study, age, and residence). Nonparametric methods (Mann–Whitney U tests) were also applied to test the identified differences. SPSS software (version 22) was used to run the analyses.

4. Results

The first analyses aimed at testing the reliability and nature of the data. In this regard, Cronbach’s alpha test (to evaluate the reliability of the construct) was applied. The results presented in

Table 4. Cronbach’s alpha coefficients.

Scope	Valid Cases	Excluded Cases	Total Cases	Cronbach’s Alpha	Cronbach’s Alpha Based on Standardized Items	No. of Items
All variables (EI, EM, R)	392	0	392	0.921	0.929	53
Entrepreneurial Intention (EI)	392	0	392	0.815	0.822	10
Entrepreneurial Mindset (EM)	392	0	392	0.807	0.826	18
Resilience (R)	392	0	392	0.895	0.902	25

Source: Own processing.

reveal a high level of internal consistency for the construct used. Although the analysis provided several results (such as the Cronbach alpha coefficient if an item is eliminated), the study synthesized only the main results, which prove that the reliability conditions are met.

For a construct to prove reliability, the Cronbach alpha coefficient must register values greater than 0.7 (Hair et al., 2010; Šerbetar & Sedlar, 2016). For all tested cases, the analyzed coefficient took values higher than the minimum allowed limit.

Descriptive statistics provided useful information on the sample structure. According to the data in

Table 5. Descriptive statistics.

Variables	N	Minimum	Maximum	Mean	Std. Deviation	Variance
EI	392	1.10	5	3.40	0.70	0.49
EM	392	2.44	5	4.02	0.44	0.20
R	392	2.04	5	4.03	0.51	0.26
Rh	392	2.29	5	4.18	0.59	0.34
Rc	392	2.40	5	3.94	0.60	0.36
Ra	392	1.67	5	4.09	0.65	0.42
Rm	392	1.25	5	3.91	0.70	0.48
Ro	392	1.00	5	3.79	0.85	0.72
Rr	392	1.00	5	3.83	0.88	0.77
Rs	392	1.00	5	4.26	0.77	0.59
G	392	1	2	1.70	0.459	0.211
Y	392	1	4	2.14	1.171	1.372
F	392	1	2	1.44	0.497	0.247
We	392	1	4	1.55	0.838	0.703
A	392	1	6	2.48	1.372	1.882
C	392	1	2	1.20	0.400	0.160

, the average value of dependent variables (EI) is 3.40, a situation that can be explained by the fact that the respondents are only in their student years, prioritizing the formation of professional skills. The independent variables (EM and R) have slightly higher mean values (4.02 and 4.03) and also have standard deviations and smaller variances. A strong argument in this regard is represented by the structure of the subjects related to the curricula in the two fields. Disciplines such as economics (micro and macro), entrepreneurship, organizational management, financial management, performance management, and management of small and medium-sized enterprises contribute to the formation of professional skills but also to the development of entrepreneurial thinking and resilience.

The decomposition of the resilience variable (R) on the subcomponents reveals that self-efficacy (4.26), resistance (hardiness) (4.18), and adaptability/flexibility (4.09) are the pillars that support the resilience of the students in the analyzed sample. Students’ perceptions of the other four variables were lower (Rc: 3.94; Rm: 3.91; Rr: 3.83; Ro: 3.79).

Regarding the control variables (Table 5), the respondents are particularized by the following observations: the preponderance of female respondents is noted (mean G: 1.70, where 1—male, 2—female); the respondents in the first three years of studies predominate (mean Y: 2.14); more students are studying in the field of engineering and management (218 of 392, mean F: 1.44); the vast majority are without work experience (mean We: 1.55, where 1—no experience and 4—more than 3 years of experience); most of the students are aged between 18 and 21 years (mean A: 2.48, where 1—students aged 18 to 19 and 6—students over 23 years old), and are studying in the country of residence (mean C: 1.20, where 1—Romania and 2—Republic of Moldova).

The correlation analysis indicated that the vast majority of variables show a low correlation (

Table 6. Correlation analysis.

	EI	EM	R	Rh	Rc	Ra	Rm	Ro	Rr	Rs	G	Y	F	We	A	C
EI	1	0.39 **	0.37 **	0.35 **	0.27 **	0.27 **	0.26 **	0.27 **	0.20 **	0.33 **	−0.01	−0.08	−0.24 **	0.06	−0.09	0.06
EM	0.40 **	1	0.71 **	0.70 **	0.54 **	0.60 **	0.38 **	0.41 **	0.47 **	0.57 **	0.18 **	−0.05	0.05	0.12 *	−0.04	−0.04
R	0.37 **	0.71 **	1	0.89 **	0.78 **	0.76 **	0.67 **	0.66 **	0.70 **	0.74 **	0.13 **	−0.07	0.04	0.09	−0.08	−0.08
Rh	0.35 **	0.70 **	0.89 **	1	0.60 **	0.60 **	0.52 **	0.49 **	0.57 **	0.68 **	0.13*	−0.03	0.03	0.06	−0.06	−0.09
Rc	0.27 **	0.54 **	0.78 **	0.60 **	1	0.54 **	0.43 **	0.43 **	0.47 **	0.54 **	0.14 **	−0.03	0.07	0.06	−0.07	−0.04
Ra	0.27 **	0.61 **	0.76 *	0.61 **	0.55 **	1	0.35 **	0.53 **	0.60 **	0.48 **	0.07	−0.04	0.03	0.17 **	0.00	−0.02
Rm	0.26 **	0.38 **	0.67 **	0.52 **	0.43 **	0.35 **	1	0.31 **	0.29 **	0.44 **	0.16 **	−0.08	0.05	0.02	−0.11 *	−0.07
Ro	0.27 **	0.42 **	0.69 **	0.49 **	0.43 **	0.53 **	0.30 **	1	0.54 **	0.43 **	−0.02	−0.08	0.00	0.10	−0.05	−0.05
Rr	0.20 **	0.47 **	0,7 **	0.57 **	0.47 **	0.60 **	0.29 **	0.54 **	1	0.39 **	−0.06	−0.05	0.04	0.05	−0.05	−0.10
Rs	0.33 **	0.59 **	0.74 **	0.68 **	0.54 **	0.48 **	0.44 **	0.43 **	0.39 **	1	0.19 **	−0.04	−0.01	0.04	−0.04	−0.03
G	−0.01	0.18 **	0.13 **	0.13 *	0.14 **	0.07	0.16 **	−0.02	−0.06	0.19 **	1	−0.03	0.09	−0.10	−0.10	0.05
Y	−0.08	−0.05	−0.07	−0.03	−0.04	−0.04	−0.08	−0.08	−0.05	−0.04	−0.03	1	−0.08	0.28 **	0.75 **	−0.01
F	−0.24 **	0.05	0.04	0.03	0.07	0.03	0.05	0.00	0.04	−0.01	0.09	−0.08	1	−0.09	0.00	−0.19 **
We	0.06	0.18 *	0.09	0.06	0.06	0.17 **	0.02	0.10	0.05	0.04	−0.10	0.28 **	−0.09	1	0.39 **	−0.07
A	−0.09	−0.04	−0.08	−0.06	−0.07	0.00	−0.11 *	−0.05	−0.05	−0.04	−0.10	0.75 **	0.00	0.39 **	1	−0.03
C	0.06	−0.04	−0.08	−0.09	−0.04	−0.02	−0.07	−0.05	−0.10	−0.03	0.05	−0.01	−0.19 **	−0.07	−0.03	1

** Correlation is significant at the 0.01 level. * Correlation is significant at the 0.05 level.

). Moderate correlation levels were found between the variables EM and R (0.71) and A and Y (0.747). To evaluate the impact of these correlations between variables, when running the regression analysis, tests for multicollinearity were also taken into account. The association between the last two variables is natural because there is a direct correlation between student age and student years (as the student goes through their studies, they advance in age). Positive associations were identified between EI and EM (and, respectively, between EI and R). Based on this initial information, it can be assumed (presumably) that EM and R could have a positive impact on EI.

According to the data in

Table A1. Descriptive statistics by gender group, field of study, and country of origin.

	N		Mean		Std. Dev.
	Male	Female	Male	Female	Male	Female
EI	118	274	3.42	3.39	0.66	0.71
EM	118	274	3.9	4.07	0.48	0.42
R	118	274	3.92	4.07	0.55	0.49
	E&M	E&B	E&M	E&B	E&M	E&B
EI	218	174	3.55	3.22	0.63	0.70
EM	218	174	4.00	4.05	0.43	0.44
R	218	174	4.01	4.05	0.47	0.51
	RO	RM	RO	RM	RO	RM
EI	314	78	3.38	3,4	0.65	0,7
EM	314	78	4.03	4.02	0.45	0.39
R	314	78	4.08	4.03	0.53	0.51

Note: Engineering and management—(E&M); Economics and business (E&B); RO—Romania; RM—Republic of Moldova.

, the entrepreneurial initiatives of male students (3.42) are slightly higher than the entrepreneurial initiatives of female students (3.39), which confirms hypothesis H3.1. These findings confirm the results of previous studies (Da Costa et al., 2023; Phipps & Prieto, 2015). In contrast, hypothesis H3.3 is refuted, because the data in Table A1 reveal that the entrepreneurial initiatives of students in the field of economics and business (3.22) are inferior to the entrepreneurial initiatives of students in the field of engineering and management (3.55), which contradicts the conclusions of other studies (Arias & Flad, 2025). The data in the same table support hypothesis H3.6, as students studying in another country were found to have a more favorable perception of entrepreneurial intentions compared to students pursuing higher education in the country where they were born. These findings are consistent with Wu et al. (2022) and Uskuri and Sesen (2023).

According to the data in

Table A2. Descriptive statistics by year of study and work experience.

Y		EI	EM	R	We		EI	EM	R
1	Mean	3.54	4.08	4.09	1	Mean	3.36	3.99	3.99
	N	146	146	146		N	245	245	245
	Std. Deviation	0.64	0.43	0.50		Std. Deviation	0.69	0.44	0.50
2	Mean	3.28	3.99	3.99	2	Mean	3.48	4.04	4.08
	N	118	118	118		N	96	96	96
	Std. Deviation	0.72	0.47	0.51		Std. Deviation	0.71	0.46	0.55
3	Mean	3.28	3.96	3.96	3	Mean	3.46	4.11	4.16
	N	77	77	77		N	32	32	32
	Std. Deviation	0.77	0.44	0.52		Std. Deviation	0.64	0.44	0.47
4	Mean	3.46	4.03	4.02	4	Mean	3.46	4.18	4.04
	N	51	51	51		N	19	19	19
	Std. Deviation	0.63	0.43	0.54		Std. Deviation	0.79	0.41	0.55

, students’ entrepreneurial intentions are more intense in the first year of college (3.54), then decrease (to 3.28 in years 2 and 3), and then intensify again in the fourth year (3.40). The same trend is shown by EM and R. These results partially refute the H3.2 hypothesis that the entrepreneurial intentions of students in their final years are higher than the entrepreneurial intentions of students in their first year, even though the university offers students the opportunity to take entrepreneurship courses, which can stimulate students’ entrepreneurial intentions (Letsoalo & Rankhumise, 2020). Table A2 also presents information on the intensity of entrepreneurial intentions, taking into account the length of service (work experience) of the responding students. According to the data, entrepreneurial intentions intensify slightly in the first years of activity but decrease as students acquire more seniority. These results disprove the H3.5 hypothesis, according to which students with more work experience have higher entrepreneurial intentions. Our findings contradict the results of other studies, which claim that students’ work experience stimulates the intention to open a business (Bignotti & Le Roux, 2020; Fatoki, 2014).

According to the data in

Table A3. Descriptive statistics organized according to the age of the student respondents.

A	1 (18–19 Years Old)			2 (20 Years Old)			3 (21 Years Old)			4 (22 Years Old)			5 (23 Years Old)			6 (over 20 Years Old)
	Mean	N	SD	Mean	N	SD	Mean	N	SD	Mean	N	SD	Mean	N	SD	Mean	N	SD
EI	3.47	112	0.64	3.43	117	0.71	3.37	78	0.73	3.37	51	0.73	3.13	16	0.60	3.29	18	0.81
EM	4.04	112	0.42	4.05	117	0.45	3.99	78	0.42	4.01	51	0.45	3.90	16	0.58	4.06	18	0.50
R	4.07	112	0.48	4.03	117	0.52	4.03	78	0.49	3.99	51	0.50	3.78	16	0.70	4.02	18	0.56

Note: Std. deviation—SD.

, the entrepreneurial intentions of 18–19-year-old students are more intense than the entrepreneurial intentions of 20–23-year-old students. A slight increase in entrepreneurial intentions is observed in students over 23 years old. This hypothesis partially confirms the H3.4 hypothesis, according to which students’ entrepreneurial intentions intensify as they age, convergent with the findings of Atitsogbe et al. (2021) and Sahinidis et al. (2021), but unconfirmed by other evidence (Ozyilmaz, 2011).

To test the interdependence relationships between the dependent variable (EI) and the independent variables (EM and R), multiple regression analyses were run. Each regression analysis was replayed for control variables to highlight differences specific to the sample structure. To ensure the validity of the results, tests were carried out on the models (R-squared; F-statistic). These tests highlight the extent to which the variance of the dependent variable is explained by the independent variables. At the same time, the degree of fulfillment of specific conditions was taken into account: numerical expression of the variables, the existence of a linearity relationship between variables (not necessarily absolute), the absence/presence of extreme values, the nature of the variance of the errors (Durbin–Watson Test), and the distribution of the residual variable. The results are summarized in

Table 7. Regression analysis—EI dependent variables.

Variables	Model 1
	M1	M1-G1	M1-G2	M1-F 1	M1-F 2	M1-C1	M1-C2	M1-Y1	M1-Y4	M1-We1	M1-We2	M1-A1	M1-A3
Constant	0.72 (0.02)	0.84 (0.07)	0.50 (0.21)	0.37 (0.31)	0.89 (0.06)	0.58 (0.08)	1.35 (0.08)	0.87 (0,7)	−0.25 (0.71)	0.98 (0.01)	0.53 (0.35)	1.09 (0.05)	0.68 (0.36)
EM	0.41 (0.00)	0.31 (0.06)	0.50 (0.00)	0.54 (0.00)	0.27 (0.11)	0.93 (0.00)	0.42 (0.06)	0.77 (0.00)	0.58 (0.03)	0.47 (0.00)	0.13 (0.50)	0.67 (0.00)	0.16 (0.55)
R	0.25 (0.00)	0.34 (0.02)	0.21 (0.06)	0.26 (0.02)	0.30 (0.04)	0.30 (0.00)	0.12 (0.53)	−0.12 (0.35)	0.34 (0.09)	0.13 (0.27)	0.59 (0.00)	−0.08 (0.62)	0.51 (0.03)
R2	0.17	0.23	0.17	0.26	0.14	0.20	0.10	0.21	0.42	0.14	0.27	0.16	0.17
R2 Adj.	0.17	0.22	0.16	0.26	0.13	0.19	0.07	0.20	0.39	0.13	0.25	0.15	0.15
D-B Test	1.95	1.88	1.81	2.27	1.97	1.83	1.94	1.84	2.13	1.81	1.83	2.19	1.74
F Test	40.96	17,3	26.78	28.26	14.38	28.37	3.97	19.07	17.14	19.03	17.13	10.39	7.81
F Sig.	0.00	0.00	0.00	0.00	0.00	0.00	0.02	0.00	0.00	0.00	0.00	0.00	0.00
Std.Dev.	0.99	0.99	0.99	0.99	0.99	0.99	0.98	0.99	0.98	0.99	0.99	0.99	0.99

Note: D-B Test—Durbin–Watson test.

and

Table 8. Regression analysis. M2: EI dependent variables; subcomponents of resilience.

Variables	Model 2
	M2	M2- G1	M2- G2	M2- F 1	M2- F 2	M2- C1	M2- C2	M2- Y1	M2- Y2–4	M2- We1	M2- We2	M2- A1	M2- A3
Constant	0.68 (0.03)	0.77 (0.11)	0.34 (0.41)	0.31 (0.41)	0.90 (0.07)	0.63 (0.06)	1.33 (0.09)	0.87 (0.07)	0.85 (0.03)	0.98 (0.01)	0.10 (0.85)	0.95 (0.10)	0.63 (0.08)
EM	0.43 (0.00)	0.35 (0.08)	0.49 (0.00)	0.54 (0.00)	0.32 (0.09)	0.42 (0.00)	0.37 (0.11)	0.86 (0.00)	0.12 (0.40)	0.46 (0.00)	0.33 (0.06)	0.75 (0.00)	0.31 (0.02)
Rh	0.06 (0.52)	−0.13 (0.42)	0.13 (0.30)	0.04 (0.72)	0.08 (0.64)	0.11 (0.33)	0.04 (0.84)	−0.09 (0.56)	0.14 (0.28)	0.10 (0.48)	0.03 (0.84)	0.05 (0.77)	0.06 (0.60)
Rc	−0.001 (0.90)	0.23 (0.04)	−0.11 (0.26)	0.03 (0.69)	0.00 (0.99)	−0.07 (0.41)	0.22 (0.18)	−0.16 (0.12)	0.11 (0.29)	−0.04 (0.70)	0.05 (0.68)	−0.21 (0.11)	0.07 (0.41)
Ra	−0.01 (0.87)	0.06 (0.60)	−0.05 (0.59)	0.02 (0.84)	−0.04 (0.76)	−0.06 (0.50)	0.10 (0.54)	0.01 (0.93)	−0.04 (0.69)	−0.06 (0.54)	0.08 (0.53)	0.06 (0.67)	−0.06 (0.47)
Rm	0.08 (0.13)	0.06 (0.55)	0.13 (0.06)	0.13 (0.04)	0.06 (0.48)	0.08 (0.20)	0.12 (0.34)	0.18 (0.04)	0.06 (0.42)	0.03 (0.71)	0.22 (0.02)	0.12 (0.23)	0.08 (0.26)
Ro	0.09 (0.06)	0.13 (0.87)	0.13 (0.04)	0.01 (0.82)	0.18 (0.03)	1.56 (0.00)	0.24 (0.04)	−0.08 (0.92)	0.13 (0.03)	0.07 (0.30)	0.13 (0.09)	−0.02 (0.80)	0.14 (0.02)
Rr	−0.61 (0.23)	0.05 (0.62)	0.13 (0.04)	−0.08 (0.19)	−0.04 (0.61)	0.04 (0.15)	0.14 (0.22)	0.00 (0.98)	−0.08 (0.21)	−0.06 (0.38)	0.10 (0.21)	−0.08 (0.39)	−0.54 (0.37)
Rs	0.83 (0.17)	0.07 (0.41)	0.15 (0.07)	0.11 (0.03)	0.01 (0.88)	0.07 (0.06)	0,5 (0.68)	−0.12 (0.17)	0.19 (0.02)	0.09 (0.29)	0.09 (0.25)	−0.05 (0.67)	0.14 (0.05)
R2	0.19	0.26	0.21	0.29	0.17	0.23	0.22	0.25	0.22	0.15	0.28	0.20	0.22
R2 Adj.	0.17	0.21	0.19	0.26	0.12	0.21	0.13	0.21	0.20	0.12	0.24	0.14	0.20
D-B Test	1.95	1.89	1.79	2.31	2.03	1.88	1.87	1.81	1.94	1.82	1.89	2.17	1.90
F Test	11.21	4.77	9.03	10.65	4.07	4.07	2.42	4.82	8.47	5.28	6.65	3.31	9.53
F Sig.	0.00	0.00	0.00	0.00	0.00	0.00	0.02	0.00	0.00	0.00	0.00	0.00	0.00
Std.Dev.	0.99	0.97	0.99	0.98	0.98	0.97	0.95	0.97	0.98	0.98	0.97	0.96	0.97

.

The regression model (1), which refers to independent variables (EM and R), proved to be valid because, in all tested cases, the F Test took sufficiently high values, and F.sig recorded values lower than 0.05. The Durbin–Watson test provided values very close to 2, which means that the errors are independent (there are no correlations between the error variable and the independent variables). Between 14% and 42% of the variation of the dependent variable is explained by the independent variables.

The regression analysis applied to the entire sample reveals that the model is valid, and the two independent variables (EM and R) have a positive, statistically significant influence on the dependent variable (EI) (the hypotheses H1 and H2 being confirmed). These results confirm the findings of other studies (e.g., Ediagbonya, 2022; Cruz et al., 2022; K. Wu et al., 2023). Analysis from the perspective of control variables reveals that for male students, only the resilience factor has a significant influence (Model M1-G1, β = 0.34, p-value = 0.02), while for female students, only the EM variable has a statistically significant influence (Model M1-G2, β = 0.50, p-value = 0.00). The findings of the present study are thus different from the results of other research, for example that carried out by Phipps and Prieto (2015), which emphasizes that the entrepreneurial intentions and creativity levels of male students are superior compared to those of women, or that of Zaharah et al. (2012), which concludes that female students have a more powerful attitude and develop social abilities and a desire to succeed in entrepreneurship activities compared to male students. From the perspective of the field of study, EM exerts significant influence on EI only in the case of students in the field of engineering and management (Model M1-F1, β = 0.54, p-value = 0.00), while resilience has a statistically significant influence on both groups of students (Model M1-F1, β = 0.26, p-value = 0.02 and Model M1-F2, β = 0.30, p-value = 0.04).

Regarding the country of origin, the impact of independent variables (EM and R) is significant only for students from Romania (Model M1-C1, β = 0.93 and β = 0.30, p-value = 0.00). Although not statistically significant, for first-year students, the negative impact of R on EI should be noted (Model M1-Y1, β = −0.12, p-value = 0.35); and for fourth-year students, the impact remains statistically insignificant (but becomes positive) for this group of students (Model M1-Y4, β = 0.34, p-value = 0.09); EM retains the positive, statistically significant influence (Models M1-Y1 and M1-Y4, where p-value < 0.05). Although not shown in Table 7, analyses were also performed for students in years 2 and 3. For students in years 2–3, the specificity is that the influence of EM remains statistically insignificant. On the other hand, the influence of resilience becomes statistically significant (positive impact).

When the regression analysis is run taking into account the students’ work experience, the results reveal two diametrically opposite situations: for students who have no work experience, only the influence of the first variable (EM) is relevant (statistically significant) (Model M1-We1, β = 0.47, p-value = 0.00), while for students who have up to one year of seniority, the influence of resilience becomes statistically significant (Model M1-We2, β = 0.59, p-value = 0.00). The results of the analyses for students who have more than 1 year of seniority are not presented in Table 7 because no relevant data were obtained.

The analysis at the level of the age groups of the respondents revealed that the entrepreneurial intentions of the students are positively influenced only by the EM for students with younger ages (18–19 years old, Model M1-A1, β = 0.67, p-value = 0.00). With age (21–22 years), only the influence of resilience becomes statistically significant (Model M1-A3, β = 0.51, p-value = 0.03). For students aged 23 years or older, neither of the two predictors are relevant in the analysis performed. The results achieved based on this control variable (A3; 4) are relatively similar to the results obtained for the years of study (Y2; 3).

Table 8 presents the results of the regression analysis with consideration of the subcomponents of resilience. The regression model (2), which takes into account EM and the subcomponents of the R variable, proved to be valid because, in all cases, the F.sig values recorded were lower than 0.05. The Durbin–Watson test provided values very close to 2, which means that the errors are independent. Between 15% and 28% of the variation of the dependent variable is explained at the expense of the independent variables.

The analyses carried out at the level of the entire sample reveal that only EM has a significant influence on EI (Model M2, β = 0.34, p-value = 0.00). The analyses carried out at the level of subsamples, delimited by the gender of the respondents, reveal that the entrepreneurial intentions of the male respondents are influenced only by their ability to adapt (coping subcomponent) (Model M2-G1, β = 0.23, p-value = 0.04). In contrast, the entrepreneurial intentions of female respondents are influenced by optimism (Ro) and the ability to manage emotions and make decisions under pressure (Rr). For this group of students, EM retains a positive and statistically significant influence (Model M2-G2, β = 0.49, p-value = 0.00).

The analysis from the perspective of the fields of study revealed significant differences. The entrepreneurial intentions of students enrolled in engineering and management studies are influenced (positively and statistically significantly) by EM (Model M2-F1, β = 0.54, p-value = 0.00), meaningfulness/purpose (Rm), and self-efficacy (Rs). On the other hand, the entrepreneurial intentions of the students enrolled in economics and business studies are influenced (significantly) only by the optimism subcomponent (Ro). These results are important because they cover a gap in the literature, the studies that carry out comparative analysis of entrepreneurial intentions according to the students’ chosen field or year of study being insufficient (Xanthopoulou & Sahinidis, 2024). The results of our study differ from those of other studies, which found that students in technical majors generally have a lower level of entrepreneurial intentions compared to their peers studying business (Arias & Flad, 2025) but are convergent with other research that concluded that positive outcome expectations are predictors of entrepreneurial intention in business students (Pfeifer et al., 2016).

When the two subsamples are grouped according to the country of origin, the entrepreneurial intentions of the students are deferred by EM (only for Romanian students, Model M2-C1, β = 0.42, p-value = 0.00) and optimism (for both groups of students, p-value < 0.05).

The analysis of the subsamples organized at the level of years of study showed that the entrepreneurial intentions of first-year students are influenced by EM (Model M2-Y1, β = 0.86, p-value = 0.00) and meaningfulness/purpose EM (β = 0.6, p-value = 0.04). In contrast, for students in years 2–4, entrepreneurial intentions are influenced by two subcomponents of resilience: optimism (Ro) and self-efficacy (Rs). The entrepreneurial intentions of students who have no work experience are influenced only by EM (Model M2-We1, β = 0.46, p-value = 0.00). In contrast, the entrepreneurial intentions of students who have already experimented in the workplace are significantly influenced by meaningfulness/purpose (Rm). The analysis from an age perspective revealed that EM influences the entrepreneurial intentions of all the students surveyed (Models M2-A1 and A2, p-value < 0.05). In addition, the entrepreneurial intentions of students who are over 20 years old are still influenced by optimism (Model M2-A3, β = 0.14, p-value = 0.02).

Given the moderate correlations between some variables, multicollinearity was verified in the regression analyses. This was assessed using a tolerance and variance inflation factor (VIF). According to the rigors of statistics, a tolerance of less than 0.20 and/or a VIF value greater than or equal to 5 indicates the presence in the model of a problem related to the collinearity of factors. In the present study, tests conducted on each model showed VIF values between 1.44 and 3.24, and the tolerance was between 0.31 and 0.69 (

Table A4. Tolerance and variance inflation factor (VIF).

Model		Unstandardized Coefficients		Standardized Coefficients	t	Sig.	Collinearity Statistics
		B	Std. Error	Beta			Tolerance	VIF
Model 1	(Constant)	0.72	0.30		2.41	0.02
	EM	0.41	0.10	0.26	3.99	0.00	0.49	2.032
	R	0.25	0.09	0.19	2.86	0.00	0.49	2.032
Model 2	(Constant)	0.68	0.31		2.22	0.03
	EM	0.43	0.11	0.27	3.91	0.00	0.44	2.27
	Rh	0.06	0.10	0.05	0.64	0.52	0.31	3.24
	Rc	−0.00	0.07	0.00	−0.01	0.99	0.54	1.86
	Ra	−0.01	0.07	−0.01	−0.17	0.87	0.45	2.22
	Rm	0.08	0.06	0.08	1.51	0.13	0.69	1.44
	Ro	0.09	0.05	0.11	1.91	0.06	0.61	1.63
	Rr	−0.06	0.05	−0.08	−1.21	0.23	0.52	1.94
	Rs	0.08	0.06	0.09	1.39	0.17	0.49	2.05

Dependent variable: EI.

from Appendix A). These results prove that regression models are not influenced by correlations between EM and R (0.71) and A and Y (0.747).

In light of the above, it can be concluded that the most important predictors of the entrepreneurial intentions of the surveyed students are EM and R. These factors explain, on average, only 20% of the variation in entrepreneurial intentions. The analyses at the level of the subcomponents of resilience also revealed other determining factors, such as meaningfulness/purpose, optimism, self-efficacy, coping, and regulation of emotion and cognition.

To test these results, but also subject to a deviation from the normal distribution of data, non-parametric tests were also performed. The conditions for running this analysis were met: the dependent variable is expressed by a five-point scale; independent variables are organized into independent groups; there is no relationship between the observations in each group; and the analyzed variables are not distributed normally. Given that the distributions have a different shape, the Mann–Whitney U test was used to compare the mean ranges (

Table 9. Mann-Whitney U test.

	Gen (G)					Field (F)					Country (C)
	Mean Rank		Mann–Whitney U			Mean Rank		Mann–Whitney U			Mean Rank		Mann–Whitney U
	1	2	MWU	Z	Sig.	1	2	MWU	Z	Sig.	1	2	MWU	Z	Sig.
EI	197.36	196.13	16,064	−0.10	0.92	219.02	174.0	14,057	−4.41	0.00	193.67	207.90	60,811	−0.94	0.32
	Year of study (Y)					Age (A)					Work experience (We)
	Mean Rank		Mann–Whitney U			Mean Rank		Mann–Whitney U			Mean Rank		Mann–Whitney U
	1	2	MWU	Z	Sig.	1	2	MWU	Z	Sig.	1	2	MWU	Z	Sig.
EI	214.99	185.53	15,258	−2.49	0.01	187.97	210.72	15,917	−1.93	0.05	203.20	193.82	14,929	−0.74	0.46

). The independent variables were: gender (1—men, 2—women); field of study (1—engineering and management, 2—economics and business); country of residence (1—Romania, 2—Republic of Moldova); the year of study (1—first year of study, 2—last year of study); age (1—students 18–19 years old; 2—over 20 years old); work experience (1—no experience, 2—with experience).

Judging by the average ranks, the gender of the respondents influences the entrepreneurial intentions of male and female students to approximately equal degrees. However, the result is not statistically significant. The differences in country of origin, age, and work experience also proved to be statistically insignificant. On the other hand, with a relevant statistical significance, it can be appreciated that students’ entrepreneurial intentions differ depending on the year of study. For first-year students, the influences are stronger. The summary of the results of the analyses carried out is presented in

Table 10. Synthesis of the analysis results—EI.

Source	H1	H2	H2.1	H2.2	H2.3	H2.4	H2.5	H2.6	H2.7	H3.1	H3.2	H3.3	H3.4	H3.5	H3.6
Descriptive statistic	-	-	-	-	-	-	-	-	-	c	pc	c	i	c	i
Regression analysis	c	c	i	pc	i	pc	pc	pc	pc	-	-	-	-	-	-
Mann–Whitney U test	-	-	-	-	-	-	-	-	-	c(si)	c	c	c(si)	i	i

Note: c—confirmed; pc—partially confirmed; i—infirmed; si—statistically insignificant.

.

5. Discussion

The present study confirms the direct association between resilience and entrepreneurial intentions through the importance of helping individuals adapt to adverse situations, thus making entrepreneurship a career option for students (Renko et al., 2016). In addition, in agreement with previous studies, the entrepreneurial mindset has a positive and significant influence on entrepreneurial intention, with a mediating role of entrepreneurial self-efficacy (Wang et al., 2021; Sahid et al., 2024). The self-efficacy as an element of resilience influences entrepreneurial intentions using entrepreneurial attitude and control (Tsai et al., 2016; Rosique-Blasco et al., 2018). As some studies suggested (Haase et al., 2012; Yordanova & Tarrazon, 2010; Gallegos et al., 2024), the hypothesis regarding gender differences from an entrepreneurial intention perspective is confirmed: male students may record a higher level than female students.

As demonstrated above, the entrepreneurial mindset has a direct impact on entrepreneurial intentions among students (Akbari et al., 2024) through the relationship between personality traits, resilience, and attitude (Al-Ghazali et al., 2022). According to previous research, the students of business specializations, through the specificity of courses at university, have stronger entrepreneurial intentions compared to those of engineering (Herman & Stefanescu, 2017), although the adaptation and intensive orientation of universities’ entrepreneurial education in recent years also support an interest in entrepreneurship for the second category of students. Positive differences are observed in the entrepreneurial intentions of students enrolled at university in their home country compared to those studying in another country, as those in the second category experience a higher level of resilience and expectations regarding their future entrepreneurial career (St-Jean et al., 2014).

Even though there are studies that indicate an increase in the level of students’ entrepreneurial intentions as they accumulate knowledge in the field (in the last year of college compared to the first), other studies, such as Dzomonda et al. (2015) and Slomski et al. (2024), disprove this hypothesis by capturing factors that interfere over time, such as the business environment or psychological factors (attitude and perceived behavioral control) that can lead to a decrease in interest in entrepreneurship, as in the current research. In this case, the university could interfere by introducing entrepreneurship courses, which could stimulate students’ interest in starting new businesses.

Although some studies on the influence of work experience on students’ entrepreneurial intentions show that there is a tendency toward an increase among experienced students, their authors point out that the difference is not statistically significant and may vary depending on the context and the economic status of the country (Chukhray et al., 2021). Our research sample consists of subjects from different economic backgrounds, with complex and diverse educational and cultural environments, and with different levels of personal and educational development (years 1–4 of study) and diverse perspectives on entrepreneurship, depending on the degree of knowledge and understanding of this concept.

Our findings led us to valuable practical implications. Universities can develop and implement extracurricular activities focused on entrepreneurship through workshops, business incubators, and business partnerships. Moreover, academic support can also be provided through the organization and functioning of entrepreneurial communities that create and strengthen the relationships between students and the business environment. Thus, students will understand and put into practice their entrepreneurial resilience and innovative capabilities. The integration of entrepreneurial education into the curriculum should also include aspects of creativity development, resilience, adaptability, failure management, using experiential learning, real-world applications, problem-solving, and critical thinking. The adoption of a supportive learning climate can lead to the development of a resilient entrepreneurial mindset. Universities can make efforts to support students by providing information on access to funding sources and regulations, and possibly government facilities.

The authors of the present study suggest that future research should be directed toward doctoral students as respondents to investigate the differences in entrepreneurial intention compared to bachelor’s and master’s students. Comparative research could also be conducted on the perceptions of the entrepreneurial intentions of students from other majors (arts, medicine, literature, etc.), as students from outside the business field also benefit from entrepreneurial leadership courses, which improve their entrepreneurial mindset, regardless of their academic field (Mars et al., 2024). Future research could consider the impact of the family environment on students’ entrepreneurial intentions, as well as longitudinal studies that track the influence of theoretical entrepreneurial knowledge put into practice after graduation from university by opening new businesses. Expanding the research through comparative analyses with students from universities in other countries is also the intention of the authors. For example, past studies indicate that entrepreneurial intentions may vary significantly across American, Asian, and European students, arguing that cultural sensitivity to motivators and barriers influences the decision to start a business (Giacomin et al., 2011). In Mediterranean and South American countries, the hierarchical structure of society as well as traditions lead to the effective identification of entrepreneurial opportunities, although power distance and predominantly long-term orientation determine differences in entrepreneurial intentions (Barrero & Delgado-García, 2025).

6. Conclusions

Given that entrepreneurship plays a key role in stimulating economic development by generating innovation, creating jobs, producing wealth, and even addressing broader socio-economic challenges, encouraging entrepreneurship and moving from intention to action in this regard are objectives on the public policy agenda. Since today’s students can become tomorrow’s entrepreneurs, analyzing the intimate springs of entrepreneurial intention among this category paves the way for broader mechanisms for shaping entrepreneurial behavior by supporting tailored educational and institutional interventions.

The main objective of the research was to assess entrepreneurial mindset and resilience as determinants of entrepreneurial intentions among students. Taking into account previous studies, the research started from the hypothesis of direct and positive determinations between variables (EI, EM, and R), but unlike previous studies, which approached resilience as a synthetic construct, the present study took into account its constituent elements (resistance, adaptation, adaptability/flexibility, meaning/purpose, optimism, emotion and cognition, regulation, and self-efficacy). At the same time, the research assessed the interdependencies between variables according to different criteria for grouping students, such as gender, field of study in which they are enrolled, year of study, professional experience, age, and country of residence. In addition, another objective proposed by this paper aimed to complete the specialized literature, which indicated gaps regarding comparative analysis of entrepreneurial intentions according to the field chosen by students, year of study, age, and gender. Thus, two subsamples of respondents were analyzed, engineering and management students and economics and business students, at different stages of their university studies.

The regression analysis applied to the entire sample revealed that the model is valid, and the two independent variables (EM and R) have a positive, statistically significant influence on the dependent variable (EI). The analysis from the perspective of the control variables revealed that, in the case of male students, only the resilience factor has a significant influence, while, in the case of female students, only the EM variable has a statistically significant influence. From the perspective of the field of study, we note that ME exerts a significant influence on EI only in the case of students in the field of engineering and management, while resilience has a statistically significant influence on both groups of students. Regarding the environment of origin, the impact of the independent variables (ME and R) is significant only for students from Romania. Although it is not statistically significant, for first-year students, it should be noted that there is a negative impact of R on EI, while for final-year students, ME maintains its positive, statistically significant influence. The professional experience of students makes the influence of R on EI significant, but in the case of a lack of work experience, the EM variable is relevant. In conclusion, we can state that, in general, the results of our study confirm the findings of other works, but nuances are revealed when the control variables are taken into account.

This research is useful from both a theoretical and a practical perspective. From a theoretical point of view, the study complements the specialized literature and provides additional evidence regarding the determinants of students’ entrepreneurial intentions. At the same time, the study analyzes a sample that has not been the subject of previous debates. The practical significance of the results comes from their usefulness for the formulation of local policies to stimulate entrepreneurial intentions, as well as for the adequacy of the university/college curriculum focused on the development of entrepreneurial skills. Universities can develop and implement extracurricular activities focused on understanding and stimulating entrepreneurship through workshops, business incubators, partnerships with companies, and the development of entrepreneurial communities that integrate students, the business environment, and institutional partners.

The results of the present study should be interpreted with caution due to the limitations of the research. One limitation concerns the representativeness of the sample. The results are representative only for the North-East region for students enrolled in two of the most sought-after universities (which attract over 90% of the young people attracted by two fields of study, economics and business and engineering and management). Regarding the two study programs, the possibilities for generalizing the results are limited due to the low response rate to the questionnaire, but also due to imbalances at the sample level. Another limitation is represented by the variables included in the analysis. In future analyses, both the expansion of the representativeness of the analyses and the addition of the number of variables analyzed will be considered. The limitations may also come from the small number of participating universities, the lack of PhD students, and also from the number of countries participating in the research.

As future directions of research, extension of the analyses will better answer the research question. In this regard, the identification of new factors that have led to the decrease in intensity of students’ entrepreneurial activities remains on the list of priorities. Future research could also focus on the impact of family environment on students’ entrepreneurial intentions, as well as longitudinal analyses on the influence of theoretical entrepreneurial knowledge put into practice after university graduation by opening new businesses. Research can also be extended by comparing the entrepreneurial intentions of students from universities in different countries.