Universities must provide high-quality training aligned with the needs of graduates who want to become entrepreneurs. This research aims to analyze students’ career preferences and their relationship with the preference for entrepreneurial careers over traditional jobs.
This study distributed 486 structured, self-administered questionnaires to undergraduate students majoring in business-related fields at higher education institutions (HEIs) in two countries with differing entrepreneurial traditions: Spain and the USA. A total of 181 valid responses were obtained, yielding a response rate of 37.2%. A multigroup structural equation modeling approach was used to test the research hypotheses.
The findings reveal that students who place a higher value on mathematics are more inclined to become entrepreneurs and less likely to seek traditional employment. This trend is particularly pronounced among American students, who, even when proficient in mathematics, are driven by their valuation of entrepreneurial endeavors. A positive relationship between college’s ability to provide adequate entrepreneurial skills and the intention to start a business was also revealed.
The results suggest that educational programs should prioritize enhancing students’ self-efficacy perceptions in entrepreneurship. To foster this entrepreneurial mindset, educational institutions are advised to incorporate comprehensive curricula encompassing both hard and soft skills.
In contrast to previous research, this study implements a bidirectional focus considering both high school students’ entrepreneurial intentions and HEIs’ perceived quality to boost student’s intention to start their own business.
1. Introduction
Universities face a significant challenge in adequately preparing future professionals for a rapidly evolving digital and globalized world. This challenge is a complex and multifaceted process involving numerous stakeholders and impacting all educational institutions, particularly universities’ competitiveness (Bileviciute et al., 2019). Labas et al. (2016) suggested that more competitive higher education institutions (HEIs) are better positioned to retain students.
HEI competitiveness entails not only addressing external factors like globalization but also managing internal changes efficiently, which influences their long-term financial sustainability (Rogers, 2019). To meet these demands, they must provide high-quality training aligned with the needs of graduate starting their own businesses. Universities must provide high-quality training aligned with the needs of graduates pursuing entrepreneurship. According to Finch et al. (2013), universities recognize their crucial role in preparing future entrepreneurs and are actively working to enhance their graduates’ entrepreneurial intentions (EI) (Weerathunga and Mallawarachchi, 2020). The success of students in entrepreneurial endeavors is closely related to their satisfaction with the HEI. Students who perceive the entrepreneurial education they received as high quality have usually developed the necessary skills for entrepreneurship. This, in turn, enhances perceived level of control in managing a company and boosts their self-confidence (Barba-Sánchez et al., 2022; Moriano et al., 2011).
In this vein, an inverted U-shaped relationship between age and entrepreneurship has been confirmed, meaning that an individual’s motivation to start new ventures decreases with age, as older individuals may prefer activities that offer immediate rewards, such as paid work or leisure time (Paray and Kumar, 2020). Therefore, it is crucial to study the EI of university students, as these intentions are powerful drivers for socioeconomic development at both the regional and national levels. Most literature focuses on students’ EI without considering the role of HEIs in enhancing their future EI or the influence of institutional factors in different countries (Syed et al., 2024). For example, adolescents in Mediterranean countries tend to prioritize intrinsic life goals, such as meaningful relationships and self-acceptance, over extrinsic aspirations, while those in the USA may focus more on image and power, highlighting the impact of cultural factors on motivational aspects (Tejerina-Arreal et al., 2014).
In contrast to previous studies, our research will analyze students’ career preferences and their inclination to pursue entrepreneurial careers compared to traditional employment. This study will focus on two HEI located in countries with differing entrepreneurial traditions, namely, Spain and the USA. We will use expectancy-value scores in mathematics as students who perceive themselves as capable of excelling in a particular career and value the associated outcomes are more likely to pursue that career (Wigfield and Eccles, 2000).
A bidirectional focus considering both high school students’ EI and HEI quality to boost student’s intentions for starting their own business will be implemented, the latter measured as the pupil’s satisfaction with the perceived service quality of the HEI (Toscano-Hernández et al., 2024). Also, the relationship between EI and their expectations will be analyzed, the potential differences with the expectancy-value-related motivational factors predicting students’ career preferences between Spain and the USA and the differences in the likelihood of engaging in business startups depending on the students’ self-efficacy linked with the HEI received education.
The paper is structured to first present the theoretical background and objectives of the study. The subsequent sections detail the methodology and sample, present the findings and conclude with a synthesis of outcomes, limitations and suggestions for future research.
2. Theoretical framework
Understanding the factors that contribute to EI is crucial. Upon reviewing the literature concerning EI, different research streams emerge, some of them focusing on social psychology, aiming to dissect human behaviors and elucidate the cognitive processes leading from attitudes and beliefs to actionable behavior.
Noteworthy contributions from this discipline include the seminal works of Ajzen (1991). Ajzen’s (1991) Theory of Planned Behavior (TPB) in particular has gained prominence, becoming one of the most used theories in social psychology (Ajzen, 2020). The second stream is specific to entrepreneurship, as delineated by Shapero and Sokol (2002).
Following these seminal works, research in EI has burgeoned, delving into various nuanced aspects of the phenomenon. Many studies have highlighted that only certain individuals opt for entrepreneurship while others decide not to (Syed et al., 2024). Entrepreneurship theory emphasizes the psychological dimension as a crucial aspect in entrepreneurial decision-making. Chatterjee and Das (2015) posed that entrepreneurship is viewed as a cognitive process shaped by psychological factors, underscoring the importance of understanding this phenomenon to rationalize EI in the decision-making process.
Understanding entrepreneurship requires distinguishing between two key phases: the emergence of an entrepreneurial mindset and the outcomes of entrepreneurial endeavors (Rauch and Frese, 2000). The processes by which individuals decide to become entrepreneurs differ from those that lead to entrepreneurial success (Utsch and Rauch, 2000). In this context, personality traits are likely to be more influential in the decision to pursue entrepreneurship than in determining later success (Pidduck et al., 2023).
One of the significant factors influencing EI is the family environment. Herdjiono et al. (2018) demonstrated that family surroundings have a substantial and positive impact on entrepreneurial aspirations, shaping children’s behaviors and interests from an early age. The family serves as the primary setting for behavioral development, often functioning as the most influential educational environment throughout the child’s formative years. This influence becomes particularly pronounced when there is an entrepreneur in the family, increasing the likelihood that a child will choose an entrepreneurial path (Staniewski, 2016). Through continuous interaction, encouragement and collaborative efforts, the family fosters entrepreneurial inclinations not only within the familial context but also in broader social settings.
Another critical factor is an individual’s self-concept, which has been shown to significantly influence EI. Simanjuntak et al. (2016) highlighted that understanding one’s self-concept – comprising self-perception, abilities, limitations and attitudinal aspects like self-esteem – is essential for entrepreneurial success. Self-awareness enables individuals to better assess their environment and identify business opportunities. Developing a positive self-concept is particularly vital in stimulating entrepreneurial interest, as it helps individuals understand their abilities, emotions and limitations, fostering a strong belief in their capacity to recognize and seize opportunities (Newman et al., 2019).
Some studies have also emphasized the role of financial, social and psychological capitals in shaping entrepreneurial outcomes, as these are other drivers that contribute to EI (Elsafty et al., 2020). Envick (2005) showed that for an entrepreneur to start a new venture, some essential factors are his or her financial capital, the entrepreneur’s human capital (the social capital that allows the venture to leverage on the entrepreneur’s network to gain more strength) and the psychological capital that is in every sense as important as the other forms of entrepreneur capital as it effectively mobilizes the financial, human and social capital for success. Others have delved into the attitudes and behaviors an entrepreneur should have to be successful (Rehman et al., 2023). Notably, entrepreneurial success is shaped by a myriad of factors, ranging from age, regional developmental disparities to education (Bai et al., 2022).
In relation to age, Paray and Kumar (2020) confirmed an inverted U-shaped relationship between age and entrepreneurship, meaning that an individual’s motivation for starting new ventures decreases over their lifespan. As individuals grow older, they may prefer activities that offer immediate rewards, such as paid work or leisure time, in retirement (Syed et al., 2024). Entrepreneurship could be considered as an option more often among recent graduates than middle-aged professionals because immediate motivation and perceived rewards play an important role in determining their entrepreneurship intention (Lin and Wang, 2019).
2.1 Entrepreneurship intentions and expectations
EI among university students is related to beliefs, perceived social influence in their immediate environment, as well as to the individual’s skills and attitudes related to and about developing an entrepreneurial project (Rueda Sampedro et al., 2012). These results are relevant to focus actions to promote entrepreneurship in the educational stages before students joining the labor market.
Moreover, students’ attitudes toward mathematics have been correlated with their academic performance in the subject (Karjanto, 2017). This intricate relationship between entrepreneurial success and mathematical proficiency involves psychological, educational and socioeconomic dimensions. In addition, a broader set of general skills will allow the individual to spot opportunities faster and hence increasing the chances to become an entrepreneur (Molina-López et al., 2021). Finally, emphasis has been placed on the importance of specific training in entrepreneurship and business administration, arguing that the more someone knows about the topic, the more likely he or she will be to see entrepreneurship, versus a company job, as an attractive career option (Rubio-Andrés et al., 2023).
In this regard, specific entrepreneurship education has also been proven to be a key element for increasing EI. Dehghanpour Farashah (2013) stated that Entrepreneurship Education and Training (EET), such as awareness programs like career options and sources of funding, informal inspirational programs like interviews with entrepreneurs and field trips, active experimentation like consulting with entrepreneurs and computer simulation and skill-building and theoretical courses offered formally by universities are key factors that contribute to this initiative (Solomon et al., 2002).
Our first hypothesis (H1) suggests that motivational factors connected to Eccles’ expectancy-value theory significantly influence students’ career preferences. This theory suggests that students are more likely to pursue careers in fields where they believe they can succeed (expectancies) and find the work meaningful or important (values). It posits that individuals’ choices and behaviors are influenced by their expectations of success and the subjective task values they place on those activities. Students who perceive themselves as capable of excelling in a particular career path and value the outcomes associated with that path are more likely to choose that career.
Numerous studies have found a correlation between students’ attitudes toward mathematics and their academic performance and entrepreneurial success (Abd Rahim et al., 2023). According to Wang and Degol (2013), Expectancy-Value Theory posits that students’ achievement-related behaviors, such as their persistence and performance in mathematics, are influenced by two primary factors: expectations for success and the value they place on the task. Students who believe they are capable of excelling in mathematics (high expectancy) and who see the subject as important, useful or personally fulfilling (high value) are more likely to engage deeply in learning activities, apply effortful strategies and persist through academic challenges. The theory further delineates different types of value – intrinsic (enjoyment of the subject), utility (perceived relevance to future goals) and attainment (alignment with personal identity and self-worth) – all of which contribute to students’ willingness to dedicate time and effort to mathematics, leading to improved academic performance (González-Pérez et al., 2020; Wang and Degol, 2013).
Moreover, Expectancy-Value Theory provides a lens through which we can understand variations in students’ entrepreneurial inclinations. As highlighted by Maheshwari et al. (2023), cognitive and personality factors such as self-efficacy, behavioral control and personal attitudes are critical determinants of EI. When applied to mathematics, this suggests that students who perceive themselves as competent in mathematical reasoning and problem-solving may be more likely to explore careers that leverage these skills, including entrepreneurship. However, the connection is not automatic – if students perceive high utility value in mathematics as a tool for business decision-making, financial analysis or innovation, they are more likely to translate their mathematical competence into entrepreneurial motivation. This underscores the importance of fostering not just mathematical skills but also an appreciation for their applicability beyond academia (Aliedan et al., 2022; Maheshwari et al., 2023).
In this line, self-efficacy and outcome expectations play a significant role in mediating the effect of entrepreneurial education on the intention to start a business. Moreover, financial and numeracy competencies could influence EI (Ligouri et al., 2020; Kuswanto et al., 2023). Individuals with elevated levels of financial literacy and numeracy skills tend to manifest EI (Denanyoh et al., 2015; Ogundimu et al., 2016). Furthermore, a strong mathematical groundwork can increase the probability of engaging in entrepreneurial pursuits by facilitating financial decision-making, identification of opportunities and risk evaluation. A positive association between high expectancy-value scores in mathematics could indicate a preference for entrepreneurial careers over traditional employment (Hulleman et al., 2016; Hsu et al., 2014). This collective evidence underscores the pivotal role of expectancy-value-related motivational factors in predicting students’ career preferences, with higher scores linked to a greater inclination toward entrepreneurial careers, highlighting the importance of mathematical skills and entrepreneurial education in fostering entrepreneurial aspirations:
Expectancy-value motivational factors predict students’ career preferences, with higher expectancy-value scores linked to a stronger inclination toward entrepreneurship than pursuing employment in other fields.
2.2 Entrepreneurship intentions and cultural differences
Research has shown that education in entrepreneurship has a positive effect on new business activity in developed countries, particularly in ventures with high-growth potential (Levie and Autio, 2008). The impact of entrepreneurship education is primarily observed through enhanced opportunity perception rather than through perceived start-up skills, as indicated by Levie and Autio (2008).
Cultural differences are a well-known factor influencing entrepreneurial activity, with consistent national variations in entrepreneurial behavior (Bosma et al., 2008). Lent et al. (2000) proposed that both the immediate personal environment, including significant others, and the broader sociocultural context influence an individual’s career choices. They suggest that the broader sociocultural factors shape EI indirectly by influencing the individual’s immediate environment. Therefore, in the context of entrepreneurial career decisions, culture impacts EI through social norms that are interconnected with the immediate individual environment (Lent et al., 2000). Krueger et al. (2000) further contended that cultural impact on EI operates through subjective norms, one of the key components of the TPB (Ajzen, 1991). Subjective norms refer to societal pressures or expectations about entrepreneurial behavior.
Moriano et al. (2011) supported this view by demonstrating that cultural differences influence the effect of subjective norms on EI across several countries, including Spain and the USA. While the TPB model emphasizes three components – attitude toward behavior, subjective norm and perceived behavioral control (PBC) – self-efficacy, a concept closely related to PBC, has been shown to have a stronger correlation with s. The authors highlighted that attitudes and self-efficacy universally influence EI, while the impact of subjective norms varies across cultures (Moriano et al., 2011). This variation suggests that the social environment’s influence on entrepreneurial aspirations is not uniform and depends on the cultural context. They also recommended further research to identify the specific referent groups relevant to entrepreneurship in different cultures.
In Spain, entrepreneurship tends to have a lower technological component compared to countries like the USA. This is likely influenced by societal references to high-tech ventures, which are more prevalent in the USA. According to Trejo Berumen et al. (2018), the technological environment – measured by the percentage of business R&D expenditure – is 14 points higher in the USA than in Spain (46% vs 32%). This technological gap shapes the entrepreneurial landscape and the types of role models available in each country.
In addition, cultural differences between Spain and the USA extend to motivational factors. Spanish adolescents prioritize intrinsic goals such as meaningful relationships and self-acceptance over extrinsic aspirations like power and image (Tejerina-Arreal et al., 2014). This difference in life values affects students’ career choices and EI, particularly in how expectancy-value-related motivational factors shape their preferences.
Given these insights, it is reasonable to hypothesize that cultural variations between Spain and the USA influence the motivational factors driving students’ career preferences. Therefore, we propose the following hypothesis:
Significant differences exist in the expectancy-value-related motivational factors predicting students’ career preferences in Spain and the USA.
2.3 Entrepreneurship intentions and student self-efficacy perception
Innovation and entrepreneurship education is an important measure used to cultivate the innovative spirit of college students and improve the perceived quality with the educational service received. For applied sciences colleges and universities, innovation and entrepreneurship education are both necessary and urgent in terms of the types and quality of talent cultivation, the promotion of entrepreneurship and the later employment of college students (Chen et al., 2019a; Yang, 2020 in Gao et al., 2021).
The pursuit of employability has consequences for HEI student satisfaction and for the latter intention to start their own business, as well as for the student’s self-efficacy perception (Tian et al., 2025). Kotler and Clarke (1987) defined satisfaction as a state felt by a person who has experienced a performance or outcome that meets his or her expectations (Rubio-Andrés et al., 2023). Satisfaction is a function of the relative level of expectations and perceived performance, which is the intentional performance resulting in one’s contentment (Malik et al., 2010). Many studies have attempted to establish why some students are more satisfied than others, and there have been several attempts to identify its determinants (Tan et al., 2016). Knowing and understanding all the elements that may influence student satisfaction is a valuable resource for research (De Cuyper et al., 2012; Bordean and Sonea, 2018). Students satisfied with their learning environment and experience may have better learning outcomes which, consequently, may generate more qualified professionals and entrepreneurs (Duque and Weeks, 2010; Yusoff et al., 2015). Following Denson et al. (2010), an important predictor of student satisfaction is the ability to enroll in courses and activities at a HEI as it is the student who decides to join this type of activities that, a priori, brings them closer to the company (Hew et al., 2020).
At the same time, one of the most relevant elements for students is the use of innovative didactic activities aligned with business reality, which prepares students to be more competitive. In their study, Barba-Sánchez et al. (2022) showed that university activities and courses related to entrepreneurship have a direct influence on students’ EI and on their satisfaction with the entrepreneurship education received, contributing to students’ level of perceived ability to manage a company as well as their self-confidence. Similar conclusions were obtained by Tian et al. (2025) for the case of China, showing that HEI that focus on developing an entrepreneurial mindset can significantly influence students’ intentions to pursue entrepreneurship.
In this line, Marulanda-Valencia and Valencia-Arias (2019) conducted an extensive bibliometric analysis on entrepreneurial self-efficacy, emphasizing its critical role as a motivational factor that enhances EI. Their study highlights that self-efficacy is shaped not only by educational experiences but also by individual traits and external influences, reinforcing the need to treat it as a distinct construct from general satisfaction with educational services. Sandoval Álvarez and Bado Zuñiga (2022) explored the impact of entrepreneurial education on EI, revealing that the relationship between education and intention is mediated by both attitudes toward entrepreneurship and self-efficacy. This research underscores that while student satisfaction with educational quality can influence self-efficacy, the two constructs should not be used interchangeably.
This self-confidence is related to a person’s knowledge about themself, which might turn into being aware of what they want to do in their future career and having the capabilities required to do so (Moriano et al., 2011; Molina-López et al., 2024). Furthermore, it has been argued that Entrepreneurial Self-Efficacy is important for a person to act as an entrepreneur (Lukito, 2014; Otache et al, 2024). In our study, we take a look at how this self-efficacy linked with HEI received education might has an influence on the likelihood to have EI:
Student self-efficacy linked with HEI received education might has an influence on the likelihood of engaging in business startup activities.
3. Sample and methodology
To better isolate and examine the impact of cultural context, American and Spanish institutions have been selected due to their similarities. These institutions exhibit comparable academic offerings, student demographics, support services and international opportunities, making them suitable for a comparative analysis.
Students were selected from business administration programs in the USA and Spain that offered similar curricula, with a focus on equivalent core courses. To ensure the programs’ comparability, we consulted with deans and program directors to confirm the alignment of course content. The survey was then adapted to reflect the distinct educational contexts of each country, following pilot tests conducted with small groups of students in both locations. Information sessions were held at each university to explain the study’s goals and invite participation, and the survey link was distributed to all final-year business students.
The total sample consisted of 181 final-year business administration students: 100 from Spain and 81 from the USA. Both groups have a balanced gender distribution, with the American sample consisting of 55% female and 45% male students, and the Spanish sample comprising 52% female and 48% male students. The economic backgrounds of students from both universities are diverse, predominantly middle class. The average age of the Spanish students was 20.73 years, while that of the American students was 21.16 years. Overall, the samples are highly comparable, reflecting similar gender distribution, economic diversity and average age across both universities.
Thus, two comparable private universities were selected, each with similar average student income levels (adjusted for cost-of-living differences) and with business faculties actively promoting entrepreneurship-related initiatives, such as student entrepreneurship clubs. These institutions offer a diverse range of programs designed to foster students’ entrepreneurial self-efficacy and interest, including study abroad opportunities and business plan competitions. In addition, both universities feature leadership programs through which students develop essential life lessons and soft skills – not through formal instruction, but through meaningful, experience-based learning.
The questionnaire was divided into sections based on expectancy-value theory, measuring key variables such as EI, career preferences, as well as academic and soft skills. Statements were rated on a seven-point Likert scale (1 = “strongly disagree” to 7 = “strongly agree”). The survey was adapted and validated for both cultural contexts through back-translation from English to Spanish, following Eccles and Wigfield’s approach (1995) for measuring career aspirations and educational choices. The questionnaire was adapted in collaboration with professors from both Spanish and American universities to enhance students’ comprehension of the items analyzed. An online meeting was held to agree on the most appropriate student profiles to complete the questionnaire in each country, ensuring comparability across contexts, as outlined in Table 1. The final version of the questionnaire was then administered online during class time, with students participating voluntarily and anonymously. Before starting the survey, participants were informed of the study’s aims/objectives and the right to refuse participation or withdraw from the study at any time.
Table of cross-cultural sample analysis
| Variable | USA | Spain |
|---|---|---|
| Sex | Female: 55% | Female: 52% |
| Male: 45% | Male: 48% | |
| Average age (years) | 21.16 | 20.73 |
| Business administration degree students | 95% | 85% |
| Last year students | 80% | 75% |
The following variables were included:
Entrepreneurial intentions: This section evaluated students’ aspirations to start their own businesses, using statements like “In the future, I would like to start my own business” and “I want to be my own boss.”
Work for others intentions: This section captured students’ preferences for other types of employment. Participants responded to items such as “In the future, I would like to work for others.”
Expectancy-value motivational factors (Table 2): Following Eccles and Wigfield (1995), the questionnaire measured students’ beliefs about their abilities and the importance they placed on mathematics. These included expectancy-related factors (students’ beliefs in their ability to succeed, like “I find it easy to learn new things in math”) and value-related factors (how much they valued mathematics for their future professional intentions like “Math will allow me to further my professional prospects”:
Expectancy-related factors (Expectancy) were measured using five items (Exp1, Exp2, Exp3, Exp4 and Exp5).
Value-related factors (Value) were measured in three items related to utility (Ut1, Ut2 and Ut3) and two items related to Attainment (Att1 and Att2).
Academic skills (hard skills): To evaluate the link between student self-efficacy and the received education, we measured students’ perceived technical or academic competencies with items like “I am confident in my academic skills to become an entrepreneur.”
Soft skills: This section assessed non-technical competencies necessary for entrepreneurial success with items such as “I feel confident in leading projects.”
Items for expectancy-value motivational factors
| Expectations | |
| Exp1 | The math courses are easy for me |
| Exp2 | I expect to do well in a STEM degree |
| Exp3 | I find it easy to learn new things in math |
| Exp4 | I am one of the best students in math |
| Exp5 | Compared to other courses, I am good at math |
| Value | |
| Ut1 | Math is useful for the career I want to have in the future |
| Ut2 | Learning math will be useful in my daily life |
| Ut3 | Compared to other courses, math will be very useful for what I plan to do in the future |
| Att1 | Math will help me achieve what I want to do in the future |
| Att2 | Math will allow me to further my professional prospects |
Data analysis was performed using a Structural Equation Modeling with the statistical software STATA 15.1 (Sysoyeva et al., 2021; Ashrafi et al., 2018; Ricci‐Cabello et al., 2018).
4. Results
Various indices assessing model fit were used, such as χ2/df, root mean square error of approximation (RMSEA), the Tucker–Lewis index (TLI) and the comparative fit index (CFI). Existing recommendations propose that a satisfactory fit (see Table 3) is indicated by RMSEA < 0.06, CFI > 0.90, TLI > 0.90 and χ2/df < 2 (e.g. Byrne, 1998; Hu and Bentler, 1999; Raykov and Marcoulides, 2000). The fit statistics for the structural equation model offer important insights into the model’s adequacy and its goodness of fit. The likelihood ratio (chi2_ms) shows a chi-square value of 96.4 with 52 degrees of freedom, and the associated p-value is 0.000. All models described in this work meet these criteria and were estimated using full information maximum likelihood to handle missing data (Enders, 2010).
Model fit
| Model | Chi-square | df | Chi-square/df | RMSEA | CFI | TLI |
|---|---|---|---|---|---|---|
| Same form model | 96.4 | 52 | 1,853 | 0.055 | 0.953 | 0.947 |
The variance components indicate how much of the variation in each dependent variable is explained by the model (fitted variance) and how much remains unexplained (residual variance). For instance, the model explains a significant portion of the variance in Exp1, with a fitted variance of 3.044352 and a residual variance of only 0.5320561. The R-squared values across different dependent variables are generally high, suggesting a strong model fit. Exp2 has an outstanding R-squared value of 0.9237137, indicating that over 92% of the variance in Exp2 is explained by the model. Similarly, Exp1, Exp3 and several value measures (Ut2 and Ut3) also show high R-squared values (above 0.80), reinforcing the model’s robustness in these areas.
The Cronbach’s alpha (Table 4) values provided for the scales show the reliability of the sets of items designed to measure the underlying constructs of expectancy and value. A higher Cronbach’s alpha value suggests that the items have relatively high internal consistency. Cronbach’s alpha for the expectancy scale is 0.9564. For the value scale (Ut1 to Ut3, Att1 and Att2), the Cronbach’s alpha is 0.9308. This is also a very high value, showing excellent internal consistency. The high Cronbach’s alpha values for both scales prove that the items within each scale are consistently measuring their respective constructs (expectancy and value). This excellent internal consistency ensures that the observed relationships and predictive power of these constructs are based on reliable and coherent measurements. Therefore, the findings derived from these scales can be considered robust and trustworthy. The average variance extracted (AVE) values for expectancy and value variables suggest that on average the items measuring expectancy and value reliably reflect the underlying constructs they are intended to measure. Higher AVE values generally indicate better reliability and validity of the measurement model. Thus, these findings support the robustness of using expectancy and utility variables in understanding motivational factors influencing career preferences and choices.
CFA factor loadings, AVE and Cronbach’s alpha reliabilities
| Variable | Loadings | AVE | Cronbach’s alpha |
|---|---|---|---|
| Expectancies | 0.818 | 0.956 | |
| Exp1 | 0.915 | ||
| Exp2 | 0.964 | ||
| Exp3 | 0.935 | ||
| Exp4 | 0.805 | ||
| Exp5 | 0.895 | ||
| Value | 0,721 | 0,931 | |
| Ut1 | 0,700 | ||
| Ut2 | 0,940 | ||
| Ut3 | 0,910 | ||
| Att1 | 0,881 | ||
| Att2 | 0,792 |
The results of the study support the hypothesis (H1) that expectancy-value-related motivational factors significantly predict students’ career preferences (Figure 1). Specifically, the path coefficients (Table 5) show that there is a positive, but not statistically significant, relationship between students’ expectations in mathematics and their preference for entrepreneurship (b = 0.054, p = 0.105). In contrast, the relationship between expectations and the preference for working for others is positive and statistically significant (b = 0.134, p < 0.01).
The structural diagram maps relationships between two main constructs, Expectancy and Value, and their associated variables. Expectancy links to five experiences labeled E x p 1 to E x p 5, each displaying numerical values for relationship strength. Value connects to six factors, three utility factors labeled U t 1 to U t 3 and two attitudes labeled A t t 1 and A t t 2, each also associated with numerical values. Circular nodes representing error terms are placed beside each experience and factor, containing relevant symbols and values. Arrows indicate the direction and flow of influence between nodes, including a direct link between Expectancy and Value with its own numerical value.Expectancy-value model with the whole data set
Source: Authors’ own work
The structural diagram maps relationships between two main constructs, Expectancy and Value, and their associated variables. Expectancy links to five experiences labeled E x p 1 to E x p 5, each displaying numerical values for relationship strength. Value connects to six factors, three utility factors labeled U t 1 to U t 3 and two attitudes labeled A t t 1 and A t t 2, each also associated with numerical values. Circular nodes representing error terms are placed beside each experience and factor, containing relevant symbols and values. Arrows indicate the direction and flow of influence between nodes, including a direct link between Expectancy and Value with its own numerical value.Expectancy-value model with the whole data set
Source: Authors’ own work
Path coefficients from the expectancy-value model with the whole data set
| Path coefficient | b | SD |
|---|---|---|
| Expectancy → Entrepreneurship | 0.054 | (0.071) |
| Expectancy → Work for others | 0.134 | (0.073)* |
| Value → Entrepreneurship | 0.382 | (0.066)*** |
| Value → Work for others | −0.303 | (0.067)*** |
b = standardized path coefficient; *p < 0,05 (5%); **p < 0,01 (1%); ***p < 0,001 (0,1%)
On the contrary, the value students place on mathematics is positively associated with their preference for entrepreneurial careers (b = 0.382, p < 0.01). This shows that higher value scores are linked to a greater preference for becoming entrepreneurs. Conversely, the value placed on mathematics is negatively and significantly related to the preference for working for others (b = −0.303, p < 0.001). This suggests that students who highly value mathematics could be less inclined to prefer careers working for others.
These findings affirm that both expectancy and value components of motivational factors play a crucial role in shaping students’ career preferences. Higher expectancy-value scores are indeed associated with a greater inclination toward entrepreneurial careers compared to careers working for others, which supports H1. Thus, fostering positive expectations and values related to mathematics might encourage students to pursue entrepreneurial paths.
The analysis of the subsample for Spanish students (Table 6) offers interesting insights into how expectancy-value-related motivational factors influence their career preferences. The path coefficients reveal that there is a significant and negative relationship between students’ expectations in mathematics and their preference for entrepreneurship (b = −0.417, SD = 0.105, p < 0.001). This suggests that higher expectations are associated with a lower preference for entrepreneurial careers and with a greater preference for careers working for others (b = 0.377, SD = 0.112, p < 0.001).
Path coefficients from the expectancy-value model for the Spanish students
| Path coefficient | b | SD | p-value |
|---|---|---|---|
| Expectancy → Entrepreneurship | −0.417 | (0.105) | 0.000*** |
| Expectancy → Work for others | 0.377 | (0.112) | 0.001*** |
| Value → Entrepreneurship | 0.060 | (0.109) | 0.584 |
| Value → Work for others | −0.181 | (0.285) | 0.122 |
b = standardized path coefficient; *p < 0,05 (5%); **p < 0,01 (1%); ***p < 0,001 (0,1%)
The value students place on mathematics shows a slightly positive but not statistically significant relationship with their preference for entrepreneurial careers (b = 0.060, SD = 0.109). This implies that within this subsample the value does not predict a preference for entrepreneurial careers. In summary, while for Spanish students (Figure 2), expectations in mathematics play a critical role in shaping their career preferences, the value placed on mathematics does not significantly affect their career choices.
The structural equation model shows interconnected latent constructs labelled Expectancy, Value, Entrepreneurship, and Work for Others. Expectancy connects to five observed variables labelled E x p 1 to E x p 5, each with numerical values indicating relationship strength. Value is linked to three utility factors labelled U t 1 to U t 3 and two attitudes labelled A t t 1 and A t t 2, each with corresponding numerical values. Expectancy flows towards both Entrepreneurship and Value, while Value connects to Entrepreneurship and Work for Others. Entrepreneurship also links to Work for Others. Positive and negative numerical values along the arrows indicate different strengths and directions of relationships. Circular nodes next to observed variables represent error terms, each labelled with values.Expectancy-value model for Spanish students
Source: Authors’ own work
The structural equation model shows interconnected latent constructs labelled Expectancy, Value, Entrepreneurship, and Work for Others. Expectancy connects to five observed variables labelled E x p 1 to E x p 5, each with numerical values indicating relationship strength. Value is linked to three utility factors labelled U t 1 to U t 3 and two attitudes labelled A t t 1 and A t t 2, each with corresponding numerical values. Expectancy flows towards both Entrepreneurship and Value, while Value connects to Entrepreneurship and Work for Others. Entrepreneurship also links to Work for Others. Positive and negative numerical values along the arrows indicate different strengths and directions of relationships. Circular nodes next to observed variables represent error terms, each labelled with values.Expectancy-value model for Spanish students
Source: Authors’ own work
The analysis of the US students (Figure 3) subsample provides a contrasting perspective on how expectancy-value-related motivational factors influence career preferences. For US students, there is a significant and positive relationship between students’ expectations and their preference for entrepreneurship (b = 0.409, SD = 0.103, p < 0.001). This suggests that higher expectations are associated with a stronger inclination toward entrepreneurial careers and a lower preference for working for established organizations. Similarly, the value placed on mathematics (Table 7) shows a significant and positive relationship with entrepreneurial preference (b = 0.288, SD = 0.111, p < 0.001), indicating that students who highly value mathematics are more likely to pursue entrepreneurship.
The structural equation model shows relationships between latent constructs labelled Expectancy, Value, Entrepreneurship, and Work for Others. Expectancy connects to five observed variables labelled E x p 1 to E x p 5, with numerical values ranging from 2.0 to 2.8 and relationship strengths between 0.84 and 0.97. Value links to three utility factors labelled U t 1 to U t 3 and two attitudes labelled A t t 1 and A t t 2, with values between 2.0 and 3.3 and relationship strengths from 0.82 to 0.96. Expectancy connects to Entrepreneurship with a strength of 0.41 and to Value with a strength of 0.25, while Value connects to Entrepreneurship with 0.29 and to Work for Others with 0.0051. Entrepreneurship connects to Work for Others with negative 0.36, and Expectancy connects directly to Work for Others with negative 0.33. Circular nodes beside each observed variable represent error terms, each with a numerical value.Expectancy-value model for American students
Source: Authors’ own work
The structural equation model shows relationships between latent constructs labelled Expectancy, Value, Entrepreneurship, and Work for Others. Expectancy connects to five observed variables labelled E x p 1 to E x p 5, with numerical values ranging from 2.0 to 2.8 and relationship strengths between 0.84 and 0.97. Value links to three utility factors labelled U t 1 to U t 3 and two attitudes labelled A t t 1 and A t t 2, with values between 2.0 and 3.3 and relationship strengths from 0.82 to 0.96. Expectancy connects to Entrepreneurship with a strength of 0.41 and to Value with a strength of 0.25, while Value connects to Entrepreneurship with 0.29 and to Work for Others with 0.0051. Entrepreneurship connects to Work for Others with negative 0.36, and Expectancy connects directly to Work for Others with negative 0.33. Circular nodes beside each observed variable represent error terms, each with a numerical value.Expectancy-value model for American students
Source: Authors’ own work
Path coefficients from the expectancy-value model for the American students
| Path coefficient | b | SD | p-value |
|---|---|---|---|
| Expectancy → Entrepreneurship | 0.409 | (0.103) | 0.000*** |
| Expectancy → Work for others | −0.330 | (0.111) | 0.009** |
| Value → Entrepreneurship | 0.288 | (0.110) | 0.003** |
| Value → Work for others | 0.005 | (0.118) | 0.965 |
b = standardized path coefficient; *p < 0,05 (5%); **p < 0,01 (1%); ***p < 0,001 (0,1%)
Therefore, in analyzing the relationship between mathematical proficiency and entrepreneurial inclinations across different students (H2), we observe a significant divergence between Spanish and American students. Our findings suggest that Spanish students who excel in mathematics often perceive their optimal career path as working for established organizations. Conversely, American students with high mathematical expectancies tend to view their skills as a foundation for entrepreneurial ventures. Quantitatively, the impact of high expectations on entrepreneurial preferences exhibits a negative coefficient for Spanish students (b = −0.417), indicating that as their expectations increase, their inclination toward entrepreneurship diminishes. In contrast, for US students, the coefficient is positive (b = 0.409), meaning that higher expectations strengthen their entrepreneurial aspirations.
Further analysis reveals that the value placed on mathematical skills does not significantly affect the preference for entrepreneurial careers among Spanish students (b = −0.060). However, it significantly and positively influences the entrepreneurial preferences of US students (b = 0.288). One possible explanation for this divergence is the broader perception of mathematics within different educational and professional contexts. While US students may associate mathematical proficiency with entrepreneurial potential, Spanish students might link it more closely to structured career paths within established institutions.
These findings underscore the importance of contextual factors in shaping career preferences. While mathematical proficiency signals capability across both groups, the career paths students ultimately choose may be influenced by broader societal expectations, labor markets structures and educational frameworks. Cultural norms could play a role in shaping these patterns, as American culture often emphasizes individualism and self-reliance – qualities that align with entrepreneurial ambitions – whereas Spanish culture places greater value on stability and collective support, potentially encouraging careers within established organizations. This divergence aligns with Hofstede’s (2001) cultural dimensions theory, which highlights that the US ranks significantly higher in individualism and lower in uncertainty avoidance compared to Spain, fostering a greater acceptance of entrepreneurial risk-taking. Therefore, the results strongly support H2, suggesting contextual differences between the two groups and emphasizing the need for tailored approaches in career counseling and educational interventions. However, future research would be needed to further explore these dynamics by incorporating direct measures of cultural attitudes and institutional influences.
Finally, our third hypothesis (H3) explores how university students’ self-efficacy, gained through the soft and academic skills acquired through their education influences their EI. The hypothesis suggests that in the university students gain both soft skills and academic knowledge, which boost their confidence in their ability to start a business, making them more inclined to pursue entrepreneurial ventures.
Results revealed a significant positive influence between confidence in the skills and abilities acquired through entrepreneurship and business education and EI (Figure 4). In terms of entrepreneurial engagement (Table 8), the standardized coefficient stood at 0.185, with a standard error of 0.086, resulting in a z-value of 2.16 and a p-value of 0.031. This suggests that students who report higher confidence in their entrepreneurial skills and abilities developed through their education, are markedly more inclined to engage in entrepreneurial endeavors.
The satisfaction model for entrepreneurship depicts two primary skill sets: soft skills and academic skills, each with a mean value of two point six. Soft skills have a path coefficient of zero point seven and academic skills have a path coefficient of zero point eight, both leading to confidence, which is standardised at one. Confidence then has a path coefficient of zero point one nine leading to entrepreneurship, which has a mean value of three. Error terms for entrepreneurship, soft skills, and academic skills are represented by epsilon one, epsilon two, and epsilon three respectively, with epsilon one having a value of zero point nine seven.Satisfaction model for entrepreneurship
Source: Authors’ own work
The satisfaction model for entrepreneurship depicts two primary skill sets: soft skills and academic skills, each with a mean value of two point six. Soft skills have a path coefficient of zero point seven and academic skills have a path coefficient of zero point eight, both leading to confidence, which is standardised at one. Confidence then has a path coefficient of zero point one nine leading to entrepreneurship, which has a mean value of three. Error terms for entrepreneurship, soft skills, and academic skills are represented by epsilon one, epsilon two, and epsilon three respectively, with epsilon one having a value of zero point nine seven.Satisfaction model for entrepreneurship
Source: Authors’ own work
Path coefficients from the satisfaction model for entrepreneurship
| Path coefficient | b | SD | p-value |
|---|---|---|---|
| Confidence → entrepreneurship | 0.185 | (0.857) | 0.031 |
b = standardized path coefficient
Confidence in the soft skills and abilities developed through entrepreneurship and business education plays a crucial role in shaping students’ EI. When students feel capable, they are more likely to see themselves as ready to start a business and take meaningful steps toward it. This sense of preparedness drives greater engagement with entrepreneurial activities and reinforces their belief in their potential as future entrepreneurs.
Therefore, institutional support, particularly through education that strengthens student’s confidence in their own competencies, significantly impact on entrepreneurial outcome. Unlike individual dimensions such as personal expectations and values, institutional factors involve the broader educational environment and its ability to equip students with essential skills. High confidence with college-provided education, encompassing both soft skills and academic foundations, directly influences students’ readiness to undertake entrepreneurial ventures. By enhancing educational programs and support services, institutions can effectively foster an environment conducive to entrepreneurship, demonstrating the critical role of institutional in shaping entrepreneurial outcomes.
5. Conclusions
The results of this study underscore the robustness of using the expectancy and value model to understand how motivational factors shape career preferences and choices. A significant finding of our research is the clear cultural disparity between Spanish and American students. Despite their strong mathematical abilities, Spanish students tend to prioritize stable employment, showing a cultural preference for security and traditional career paths. In contrast, Americans use their mathematical proficiency to engage in entrepreneurial pursuits, demonstrating a cultural focus on innovation and self-sufficiency. These discoveries have significant implications, indicating that instilling an entrepreneurial mindset involves more than just teaching technical skills; it needs the development of a culture that appreciates and encourages entrepreneurial initiatives.
These contrasting patterns highlight the broader implications of how cultural perceptions and institutional contexts shape career preferences. For Spanish institutions, there may be an opportunity to cultivate a more entrepreneurial spirit by integrating mathematical applications with entrepreneurial education and providing support structures that encourage risk-taking and innovation. Meanwhile, American institutions could continue to build on their strengths by maintaining and enhancing support for students who leverage their mathematical skills in entrepreneurial contexts.
Finally, the study of Spanish and American university students reveals a positive relationship between the confidence provided in college through adequate entrepreneurial skills – encompassing both academic and soft skills – and the intention to start a business. Students who acquired higher level of confidence through their university education, feel assured in their knowledge are significantly more inclined to pursue entrepreneurship. This finding aligns with existing research which underscores the perceived competency in fostering EI (Bileviciute et al., 2019; Labas et al., 2016; Rogers, 2019; Finch et al., 2013; Weerathunga and Mallawarachchi, 2020).
6. Discussion and limitations
6.1 Theoretical implications
Spanish students who consider themselves proficient in quantitative disciplines tend to show a preference for conventional employment over engaging in entrepreneurial activities. Existing research suggests that students with a strong mathematical aptitude are often more inclined to take up entrepreneurial paths (Hulleman, 2016; Karjanto, 2017); nevertheless, this discrepancy may be linked to differing perceptions of entrepreneurship within the Spanish context.
Following Moriano et al. (2011), it is quite possible that Spanish students may not perceive entrepreneurship as an inherently appealing or innovative career choice due to it being less associated with technological or innovative aspects. In the USA, entrepreneurship, in contrast, is frequently associated with technological progress and innovation, thereby significantly increasing its attractiveness (Trejo Berumen et al., 2018). This underscores the importance of further exploring how cultural perceptions of entrepreneurship, particularly in relation to technological advancement, impact students’ career decisions.
However, as shown in this study, universities can play a more active role in promoting entrepreneurship among young university students because students confirm that there is a positive relationship between confidence gained from the quality of university services focused on providing adequate entrepreneurial skills – which include both hard and soft skills – and their intention to start a company. In the Spanish case, it seems clear that support structures need to be provided to encourage risk-taking, making innovation more closely linked to entrepreneurship. In the American case, these structures should continue offering support for students to take advantage of their mathematical skills in business contexts.
6.2 Managerial implications
Given the impact of quality college education and students’ confidence in their aspirations to start their own business, our results suggest that educational programs should prioritize improving students’ perceptions of entrepreneurship self-efficacy. To foster this entrepreneurial mindset, educational institutions are advised to incorporate comprehensive curricula encompassing both hard and soft skills essential for entrepreneurship. Technical skills, such as advanced mathematical and technical expertise, lay the groundwork for developing innovative solutions and managing business operations. Simultaneously, soft skills like leadership, communication and problem-solving cultivate the self-assurance and resilience necessary to navigate the complexities of entrepreneurship.
Moreover, hands-on experiences like internships, project-based learning and mentorship initiatives should be integrated to bridge the gap between theoretical knowledge and practical application. These experiences boost students’ confidence in starting and overseeing their own ventures. Furthermore, establishing a supportive entrepreneurial environment within universities via incubators, innovation hubs and networking platforms can offer the essential resources and encouragement for students to explore and pursue entrepreneurial endeavors. As noted by Zhou and Zhou (2022), innovation needs to be emphasized throughout their entire time at college or university.
6.3 Limitations and future research
While our findings offer valuable insights into the EI of university students, not all entrepreneurs, it must be acknowledged, are university-educated. Consequently, the conclusions and recommendations drawn from this study may not be universally applicable to all aspiring entrepreneurs. Furthermore, our research focuses on potential entrepreneurs rather than those who have already started entrepreneurial activities. Therefore, it would be helpful to compare these results with the experiences of established entrepreneurs to confirm our findings.
Also, another limitation is the clarification of the complex link between student’s motivation and mathematics self-efficacy and their further contribution to entrepreneurial inclination. This link is complex and requires further empirical investigation. Future research might explore this relationship more thoroughly, considering possible mediators or alternative explanations (using for that purpose, p. e., semistructured interviews).
Another limitation pertains to the random selection of participants within each university to ensure representativeness, as this study is a comparative analysis of two similar universities in different countries. Despite potentially restricting the broader relevance of the results, this focused approach facilitated a thorough and meticulous examination of two comparable cases. This methodology provided valuable insights into the cultural and contextual variations influencing EI. Nevertheless, broadening the study to include students from various countries would allow for the development of a more comprehensive and up-to-date understanding of global EI.
Also, and following Tan et al. (2016), we are aware that to generalize the results of the research the unit of analysis should also be expanded to include students from various HEIs in a specific country, incorporating public and private universities as well. All over the world both public and private organizations are hoping to increase entrepreneurialism, and much of this hope is being put at the feet of EI within universities (Carpenter and Wilson, 2022).
This study uses a quantitative approach to explore the relationship between students’ perceptions and motivational factors, but future research could benefit from a mixed-methods design. Incorporating qualitative insights would provide a deeper understanding of students’ preferences and intentions. In addition, including contextual variables to explain cross-country differences from both qualitative and quantitative perspectives would enrich the findings and offer a more holistic view of the cultural and environmental factors influencing students’ EI.
In conclusion, the case study methodology used in this paper, despite its limitations, allowed for a nuanced exploration of how similar educational settings can lead to diverse entrepreneurial outcomes influenced by cultural aspects. It would be interesting for future research to delve deeper into the various services offered by universities that could most help to foster an entrepreneurial spirit in addition to encompassing a wider array of educational backgrounds and institutions to improve the generalization of the findings and foster a more holistic understanding of entrepreneurial drivers among diverse populations.
