Journal of Entrepreneurship, Management and Innovation (2026)
Volume 22 Issue 4: 115-138
DOI: https://doi.org/10.7341/20262246
JEL Codes: L26, I23, A22
Sara Marcelino Morgado, MS.c., University of Beira Interior, C-MAST—Centre for Mechanical and Aerospace Science and Technologies, Department of Electromechanical Engineering, Rua Marquês d’Ávila e Bolama, 6201-001, Portugal, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it. 
Nathalia Suchek, Ph.D., University of Beira Interior, NECE - Research Centre for Business Sciences, Department of Management and Economics, Rua Marquês d’Ávila e Bolama, 6201-001, Portugal, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.
Arminda do Paço, Ph.D., University of Beira Interior, NECE - Research Centre for Business Sciences, Department of Management and Economics, Rua Marquês d’Ávila e Bolama, 6201-001, Portugal, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.
Ricardo Gouveia Rodrigues, Ph.D., University of Beira Interior, NECE - Research Centre for Business Sciences, Department of Management and Economics, Rua Marquês d’Ávila e Bolama, 6201-001, Portugal, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.
Abstract
PURPOSE: Fostering an entrepreneurial mindset in health organizations can equip professionals to identify innovative opportunities to improve service quality. With the complexity and fast-evolving environment of the healthcare industry, along with the common pressure to increase efficiency, more attention has been paid to entrepreneurial endeavors. This paper arises from the need to identify useful entrepreneurship curricular content and modes of delivery that align with students’ and graduates’ training needs and experts’ viewpoints. The study also combines expert insights on entrepreneurship and healthcare sector trends to provide a tailored, empirical pedagogical framework that moves beyond previous generic and comparative approaches. METHODOLOGY: A survey of 227 students and graduates from five countries was conducted. Exploratory descriptive and cluster analyses were performed. Moreover, to identify current trends and challenges in the healthcare sector, as well as relevant topics for a training program to foster entrepreneurship, focus group sessions were conducted in these countries. FINDINGS: The study results provide a pedagogical framework for the modules of the training program proposal, taking into account the main challenges and trends of the healthcare sector, while also identifying the skills, pedagogical methods, preferred delivery mode, assessment, and certification. IMPLICATIONS: This study contributes to entrepreneurship education theory by providing a sector-specific perspective on how skills and pedagogical approaches shape entrepreneurial development in healthcare. From a practical standpoint, the study offers evidence-based guidance for designing health entrepreneurship curricula that integrate business, digital, regulatory, and interpersonal skills through real-world projects, mentorship, and ecosystem collaboration. ORIGINALITY & VALUE: By combining quantitative and qualitative data from students, graduates, and experts, this research provides a comprehensive and interdisciplinary understanding of training needs in healthcare innovation and an evidence-informed proposed framework.
Keywords: entrepreneurship, pedagogical framework, healthcare, skills development, training program, higher education.
INTRODUCTION
The healthcare sector assumes a central role in a country’s well-being and development (Haase & Franco, 2020). Healthcare organizations should provide services with efficiency and that comply with high-quality standards, as such services can irreversibly influence the patient’s health condition. Beyond the pressure of meeting increasing community expectations, healthcare organizations have to deal with challenges such as financial cutbacks, resource shortages, increased competition (Haase & Franco, 2020), regulatory changes, rising costs of health systems, and the increase in severe and chronic medical conditions (Amini et al., 2018; Antoniadou & Kanellopoulou, 2024). The ongoing expansion of new and expensive health technologies and Artificial Intelligence (AI) capabilities offers new challenges and opportunities for the healthcare industry development, which becomes relevant to keep up with the market expectations in such a landscape and equip health professionals to provide quality services in real-time with fewer medical errors (Antoniadou & Kanellopoulou, 2024; Patru et al., 2023). From the need to improve efficiency and take advantage of new technologies emerges the need to innovate healthcare delivery models, create new solutions for resource optimization, and, consequently, strengthen its market position by encouraging an entrepreneurial mindset. Additionally, creating new businesses to provide solutions for societal needs through entrepreneurship practices is also crucial for financial success, being significant in both developing and developed economies, as it has a positive impact on job creation and economic growth and development (Akkaya et al., 2024; Carpenter & Wilson, 2022; Shaikh et al., 2020). Given the potential of new technology integration in healthcare services to reduce costs and waiting times, promote equality of patient treatment, and increase healthcare access, healthcare students and recent graduates have shown a growing interest in developing entrepreneurial and innovation skills (Afeli & Adunlin, 2022; Akkaya et al., 2024; Patru et al., 2023). For this reason, innovation and entrepreneurship disciplines are now included not only in business-related courses, but also in several medical schools (Niccum et al., 2017). In the current fast-evolving healthcare system, the educational needs and goals of potential entrepreneurs are often unmet by the current education system. To provide patient-centered solutions in line with the emergence of disruptive technologies, personalized medicine, and changes in regulations, health professionals need to complement their education in clinical sciences with knowledge of entrepreneurship (Niccum et al., 2017).
The objective of this study is original as it integrates both learner-driven and expert-informed perspectives to co-identify relevant topics for a healthcare entrepreneurship training program. This study aims to propose a pedagogical framework, i.e., a structured set of principles, learning objectives, and instructional strategies that guide the design and delivery of educational experiences (Devlin, 1996), composed of relevant topics for a training program on entrepreneurship for the healthcare sector. One of the specific objectives of the investigation is to map the training needs of students and graduates in Medicine, Life Sciences, business, and non-business disciplines, given their crucial future role in the healthcare sector, as noted in the literature (Martin & Iucu, 2014; Secundo et al., 2016). To do so, a survey was carried out to collect and analyze students’ and graduates’ needs in terms of skills and knowledge they would like to enhance, the type of learning activities they would like to participate in, their preferred mode of delivery, as well as the preferred method of assessing, validating, and certifying their acquired skills and knowledge. A second specific objective of the investigation is to identify current trends and challenges in entrepreneurship in the healthcare sector, and the relevant topics for a training program, based on experts’ viewpoints through focus group sessions. The results can be useful in designing curricula for training programs to empower students to start their own businesses or pursue careers that fit their aspirations and the skill sets required by the health industry of the future, including entrepreneurial, digital, and green skills.
LITERATURE BACKGROUND
Entrepreneurship corresponds to the process of identifying and exploiting business opportunities to introduce new products or services into the market (Carpenter & Wilson, 2022). Currently, entrepreneurship is viewed not only as a skillset but also as a set of fundamental attitudes for creating new businesses (Carpenter & Wilson, 2022). Healthcare entrepreneurship can be defined as the pursuit of opportunities to provide value according to stakeholders’ perspectives through innovative solutions, under uncertain and complex conditions (Patru et al., 2023). An entrepreneurial mindset encompasses the capacity to recognize opportunities, be flexible and adaptable, think critically, and solve problems through creativity, communication, and collaboration (Zhang & Austin, 2023). Entrepreneurship education involves any pedagogical program or process that trains entrepreneurial attitudes and skills, contributing to the development of the competencies and qualifications required for entrepreneurship intention (Bae et al., 2014; Carpenter & Wilson, 2022; Rippa et al., 2023; Rocha et al., 2024). There is evidence that entrepreneurial skills, including knowledge of how to start and run a business, as well as relevant soft skills to attract resources, be creative, and pursue a vision, can be developed through education (Haase & Lautenschläger, 2011). Hereby, entrepreneurship education has the potential to foster business literacy, innovation, and strengthen economies (Carpenter & Wilson, 2022), and initiatives on funding entrepreneurship training are increasing (Fairlie, 2023).
Despite entrepreneurship education playing a relevant role at different levels of education, interest in higher education stands out due to the significant role of entrepreneurship among students and graduates in transferring knowledge from academia into profitable businesses (Rocha et al., 2024). Additionally, students are generally more ambitious, willing to experiment, and have access to up-to-date knowledge and advantageous conditions for starting a business (Rippa et al., 2023). Both business and non-business students can add value to the healthcare market through entrepreneurship initiatives. However, non-business students, including Science, Technology, Engineering, and Mathematics students, commonly manifest difficulties when commercializing their ideas due to a lack of skills in financial management, sales, human resources management, and marketing, among others (Martin & Iucu, 2014; Rocha et al., 2024). On the other hand, non-business students are well-prepared to conceive innovative ideas that can be converted into valuable products (Rocha et al., 2024; Secundo et al., 2016). In general, the literature widely acknowledges the relevance of intention as a predictor of behavior; however, no direct link between intention and action has been firmly established (Adam & Fayolle, 2015). Ajzen (1991) had already noted that intentions could explain only about 30% of the variance in behavior. Given this intention-behavior gap, there is a need to help these students realize their full potential through entrepreneurial education, thereby creating favorable conditions for them to meet healthcare market needs (Secundo et al., 2016).
Previous studies have addressed the development of entrepreneurial skills by students or recent graduates in the medical field. Patru et al. (2023) investigated the impact of digitalization and new technologies on young people with a medical background’s desire to become entrepreneurs. Akkaya et al. (2024) conducted a study to measure the psychological barriers to entrepreneurial intention among Turkish healthcare students, and Shaikh et al. (2020) studied the association between personality traits among student pharmacists and entrepreneurial and intrapreneurial intentions. Although some efforts have been made to understand what influences entrepreneurial intentions, less attention has been paid to the content of training to empower healthcare students to become entrepreneurs. While previous studies and frameworks have contributed significantly to the field, they present important limitations that this study seeks to address. For instance, Afeli & Adunlin (2022) focus on comparing curricular content between health and non-health institutions, without proposing a pedagogical framework grounded in the articulated needs of learners and stakeholders. Likewise, widely adopted models such as the European Commission’s EntreComp (Bacigalupo et al., 2016) and DigComp (European Commission, 2025) provide comprehensive, standardized competency structures but follow predominantly top-down approaches that are not tailored to specific sectors, such as healthcare. However, a gap in understanding the specific training needs of the healthcare sector persists. Despite the role of entrepreneurship education being recognized as relevant for fostering innovation and economic outcomes (Carpenter & Wilson, 2022), previous work on entrepreneurship in the healthcare sector has mainly focused not on the content of training but on the influencing factors of entrepreneurship intention. This study proposes to address this gap by combining student and graduate training needs with expert insights on healthcare sector trends to provide a tailored, empirical pedagogical framework that moves beyond generic and comparative approaches.
METHODS
This research draws on an empirical examination of a subset of data generated within a funded project that brings together five organizations from different European countries, each contributing complementary expertise: two universities from Poland (#PL) and Portugal (#PT), one research institute from Germany (#GE), one innovation hub from Greece (#GR) and one consultancy firm from Cyprus (#CY).
The project’s primary goal is to craft a transformative training program that equips university students and graduates with entrepreneurial, digital, and green skills. It aims to reduce skills mismatches by addressing labor-market needs, particularly in the health industry, based on input from employers and managers of health-related companies. In this context, data were gathered through a survey and focus groups, allowing for both quantitative measurement of key constructs and qualitative exploration of participants’ perspectives. This combination of methods provides a more comprehensive understanding of skills needs and preferences.
Survey
The survey was directed to students in disciplines that, according to the literature, are relevant to healthcare entrepreneurship. The targeted group comprised students and graduates in Medicine and Life Sciences due to their competencies in healthcare services. Business students were also included, as they have a relevant educational background for creating and managing businesses and are more frequently aware of the importance of strong negotiation skills, leadership, and a focus on market needs (Rocha et al., 2024). Engineering students are recognized in the entrepreneurship literature for typically having higher levels of entrepreneurial intention than non-engineering students and for creating new, high-quality firms (Rippa et al., 2023). Secundo et al. (2016) even referred to them as “the most suitable students to develop the capacity, competence and attitude to transform new ideas, technologies and inventions into commercially viable products and services to create economic and social value”. Information and Communication Technology (ICT) students are familiar with the potential to solve problems and create more efficient processes and product delivery. Entrepreneurship has been and continues to be shaped by digital transformation (Garcez et al., 2022), and the emergence of digital technologies, particularly in the medical field, opens both new challenges and opportunities (Patru et al., 2023). Therefore, students from ICT-related disciplines were also included in the survey. Participants were recruited primarily through university student populations via an online survey and through the research team’s professional and personal networks for both the survey and focus groups, resulting in a convenience sample. Data collection took place between May and September 2025. As participation was based on open dissemination channels, the response rate could not be estimated. Given that English was used as a common working language within the consortium and target population, no translation or back-translation procedures were applied. The instrument was reviewed internally by the research team to ensure clarity and consistency across contexts.
According to institutional guidelines, formal ethical approval was not required for this study. However, all participants provided informed consent, participation was voluntary, and responses were collected anonymously to ensure confidentiality. The questionnaire included sections dedicated to entrepreneurial attributes, necessary skills for entrepreneurship in healthcare, preferred pedagogical approaches, availability of resources and support, delivery modes, assessment methods, and certification preferences. The constructs and indicators used to gather the necessary information were inspired by several projects and studies. For instance, Health2Innovation (https://www.health2innovation.eu/) served as inspiration for collecting data on digital skills; in turn, the ENTRANCE Project (https://entranceproject.eu/) informed the development of questions related to management functions and skills. Finally, entrepreneurial skills (e.g., need for achievement, risk-taking and tolerance for ambiguity, innovativeness, dynamism, autonomy, and self-confidence) were operationalized based on prior research by Davidsson (1989), Kuip and Verheul (2004), Lumpkin and Dess (1996), and Robinson et al. (1991). Items were rated on a 5-point Likert scale (1 = Totally Disagree, 5 = Totally Agree). Respondents were asked to indicate their level of agreement regarding the importance of receiving training in specific skills for starting a healthcare-related business.
Exploratory analyses were conducted using IBM SPSS Statistics (version 29.0.1.0). Missing values were examined before conducting the main analyses. The proportion of missing data was low across the study variables, with a maximum of 2.2% for any variable. This level of missingness is well below the 10% threshold suggested by Hair et al. (2019), indicating that missing data were unlikely to substantially affect the analyses. Given this low level of missingness, missing values were replaced using mean imputation, in which each missing value was replaced by the variable’s sample mean. Multi-item constructs were scored as the mean of their items. Prior to computing scale scores, internal consistency was evaluated with Cronbach’s alpha for each construct. Demographic information was also collected on country, academic area, academic status (student/graduate), level of education (undergraduate, master’s, doctoral), gender, and age (continuous, later categorized for descriptive purposes). Descriptive statistics (mean, standard deviation, minimum, maximum, frequencies, and percentages) were calculated. Normality of the continuous variables was tested using the Kolmogorov-Smirnov test. Since most variables deviated significantly from a normal distribution, non-parametric statistical tests were applied. Bivariate correlations between entrepreneurial attributes and necessary skills were examined using Spearman’s rho. An exploratory cluster analysis was performed to identify participant profiles based on entrepreneurial attributes and perceived training needs. A K-means clustering algorithm was applied, with the optimal number of clusters determined by the Average Silhouette Width criterion. Cluster stability was assessed using the Jaccard stability index. To further validate the cluster solution, an alternative cluster check was conducted using a cross-algorithm consistency check, and a Random Forest classifier was used to test the non-random structure of the clusters.
Focus group
The five project partners conducted field research in each country through focus groups, each lasting approximately two hours and involving around five participants. The composition included one representative from the partner organization, two entrepreneurs from the healthcare industry, one university lecturer specializing in Business Administration or Entrepreneurship, and one career advisor. This mix of participants was intentionally designed to capture diverse perspectives and expertise, facilitating a comprehensive mapping of current trends, emerging opportunities, and key challenges within the healthcare market. The guidelines used to conduct the focus group were mainly inspired by the Health2Innovation and ENTRANCE European projects, but were adapted to accommodate the diversity of the participants. The discussions were recorded, transcribed, and analyzed in each team’s local language using thematic analysis, allowing key patterns to be systematically identified and categorized. Specifically, the thematic analysis followed an inductive approach, in which codes were defined from focus group data and a coding scheme was iteratively refined throughout the analysis. For the cross-country analysis, each national research team first conducted a thematic synthesis and produced a structured report written in English. These country-level reports were then compared and integrated by the core research team, allowing for the identification of overarching themes and cross-country patterns. To ensure consistency, the codes used in each team’s country reports were independently revised by two researchers and compiled in the final synthesis presented in this study (section 4.2). While formal saturation was not predefined, recurring patterns across participants suggested thematic sufficiency.
RESULTS
The results are organized according to the study’s objective of developing an empirically grounded pedagogical framework for healthcare entrepreneurship education by integrating learner-driven and expert-informed perspectives. First, the survey analysis characterizes the profile of students and graduates and identifies their perceived training needs, preferred pedagogical approaches, delivery formats, assessment methods, and certification options, thereby addressing the learner-driven component of the framework. Descriptive statistics indicate which skills, resources, and learning formats are most valued by potential participants, providing direct evidence for prioritizing training content and instructional strategies. Correlation analysis further examines how entrepreneurial intention and attitudes are associated with perceived training needs, helping clarify whether different entrepreneurial orientations are linked to distinct training priorities. Cluster analysis complements these findings by identifying exploratory student profiles based on entrepreneurial attributes and perceived training needs, offering insights for a possible differentiation of pedagogical strategies according to students’ levels of engagement and readiness. Finally, the focus group analysis incorporates expert-informed perspectives by identifying current healthcare market trends, sector-specific challenges, demanded skills, and relevant training topics. Together, these analyses support the construction of a pedagogical framework that is responsive both to learners’ expressed needs and to the practical demands of healthcare entrepreneurship.
Survey
Sample characteristics
The sample consisted of N=227 participants. Table 1 presents the respondents’ profiles by country, academic area, academic status, education level, gender, and age.
The respondents were relatively evenly distributed across the five participating countries and represented a variety of academic fields, with Health Sciences (Medicine and Other Health Sciences combined), accounting for 35.2% of the sample, followed by Engineering (25.1%) as the most common area of study. Most respondents were students (64.8%) and reported an undergraduate (51.1%) or master’s (38.3%) educational level.
Table 1. Respondents profile
|
Variables |
|
Frequency |
Percent |
|---|---|---|---|
|
Country |
Cyprus |
46 |
20.3 |
|
Germany |
31 |
13.7 |
|
|
Greece |
41 |
18.1 |
|
|
Poland |
54 |
23.8 |
|
|
Portugal |
53 |
23.3 |
|
|
Did not answer |
2 |
0.9 |
|
|
Academic area |
Business |
35 |
15.4 |
|
Engineering |
57 |
25.1 |
|
|
ICT related studies |
21 |
9.3 |
|
|
Life or Natural Sciences |
33 |
14.5 |
|
|
Medicine |
43 |
18.9 |
|
|
Other Health Sciences |
37 |
16.3 |
|
|
Did not answer |
1 |
0.4 |
|
|
Academic status |
Student |
147 |
64.8 |
|
Graduate |
79 |
34.8 |
|
|
Did not answer |
1 |
0.4 |
|
|
Education level |
Undergraduate |
116 |
51.1 |
|
Master |
87 |
38.3 |
|
|
Doctoral |
24 |
10.6 |
|
|
Gender |
Man |
92 |
40.5 |
|
Woman |
126 |
55.5 |
|
|
Other |
1 |
0.4 |
|
|
Prefer not to say |
7 |
3.1 |
|
|
Did not answer |
1 |
0.4 |
|
|
Age |
18-22 |
80 |
35.2 |
|
23-27 |
59 |
26.0 |
|
|
28-32 |
32 |
14.1 |
|
|
33-37 |
18 |
7.9 |
|
|
38-42 |
7 |
3.1 |
|
|
43-47 |
5 |
2.2 |
|
|
48-52 |
3 |
1.3 |
|
|
53-58 |
3 |
1.3 |
|
|
Did not answer |
20 |
8.8 |
Participants ranged in age from 18 to 58 years old (M = 26.65, SD = 7.77), with the majority (61.2%) aged 18 to 27 years.
Entrepreneurial attributes and training needs
Table 2 presents the means and standard deviations for all variables and constructs measured on a 5-point Likert scale (1 = Totally Disagree, 5 = Totally Agree). The means of the variables showed that, among entrepreneurial attributes, the positive attitudes towards entrepreneurship had the highest average (M = 3.73; SD = 0.72), while entrepreneurial intention in the healthcare sector showed moderate values (M = 2.91; SD = 1.13). The negative attitudes towards entrepreneurship recorded an average of 2.89 (SD = 0.87).
Table 2. Descriptive statistics
|
Category |
Item |
Description |
Mean |
SD |
|---|---|---|---|---|
|
Entrepreneurial attributes |
EntInt |
Entrepreneurial Intention in Healthcare Industry (Cronbach’s alpha = 0.883) |
2.91 |
1.13 |
|
EntInt1 |
I am considering becoming an entrepreneur in the healthcare industry. |
2.99 |
1.22 |
|
|
EntInt2 |
I am pretty sure that, sooner or later, I will be an entrepreneur in the healthcare industry. |
2.84 |
1.18 |
|
|
PosAttEnt |
Positive attitude towards entrepreneurship (Cronbach’s alpha = 0.633) |
3.73 |
0.72 |
|
|
PosAttEnt1 |
I associate entrepreneurship with the opportunity to realise myself and my dreams. |
3.93 |
0.87 |
|
|
PosAttEnt2 |
I associate entrepreneurship with seizing opportunities that are not an option when being an employee. |
3.83 |
0.96 |
|
|
PosAttEnt3 |
I associate entrepreneurship with being completely autonomous in my professional decisions. |
3.44 |
0.99 |
|
|
NegAttEnt |
Negative attitudes towards entrepreneurship (Cronbach’s alpha = 0.637) |
2.89 |
0.87 |
|
|
NegAttEnt1 |
I associate entrepreneurship with lack of financial security. |
2.93 |
1.16 |
|
|
NegAttEnt2 |
I associate entrepreneurship with lack of work-life balance. |
2.87 |
1.12 |
|
|
NegAttEnt3 |
I associate entrepreneurship with a continuous exposure to failure. |
2.87 |
1.14 |
|
|
Training needs |
DAS |
Data analysis skills (Cronbach’s alpha = 0.866) |
4.32 |
0.63 |
|
DAS1 |
Recognizing new opportunities. |
4.27 |
0.72 |
|
|
DAS2 |
Combining business information from different sources. |
4.36 |
0.72 |
|
|
DAS3 |
Evaluating usefulness of resources. |
4.32 |
0.69 |
|
|
MPS |
Management and planning skills (Cronbach’s alpha = 0.885) |
4.30 |
0.64 |
|
|
MPS1 |
Identifying appropriate business strategies. |
4.38 |
0.68 |
|
|
MPS2 |
Determining logistics for manufacture/delivery of products. |
4.18 |
0.81 |
|
|
MPS3 |
Evaluating the outcomes of work. |
4.28 |
0.75 |
|
|
MPS4 |
Developing an action plan which includes the basic steps to achieve the goals of my activity/idea (e.g., setting milestones). |
4.37 |
0.79 |
|
|
MPS5 |
Developing a business plan describing how to achieve the goals of my business. |
4.31 |
0.80 |
|
|
SFCS |
Sales, financing and contracting skills (Cronbach’s alpha = 0.866) |
4.21 |
0.64 |
|
|
SFCS1 |
Selling techniques. |
4.05 |
0.84 |
|
|
SFCS2 |
Negotiating contracts. |
4.31 |
0.75 |
|
|
SFCS3 |
Raising funds. |
4.24 |
0.78 |
|
|
SFCS4 |
Drawing up and managing budgets. |
4.30 |
0.76 |
|
|
SFCS5 |
Setting prices. |
4.14 |
0.84 |
|
|
GWS |
Group work skills (Cronbach’s alpha = 0.893) |
4.34 |
0.65 |
|
|
GWS1 |
Developing motivated teams of people. |
4.36 |
0.76 |
|
|
GWS2 |
Empowering others and delegating work as appropriate. |
4.29 |
0.78 |
|
|
GWS3 |
Managing conflict. |
4.32 |
0.77 |
|
|
GWS4 |
Managing stress and obtaining balance. |
4.35 |
0.81 |
|
|
GWS5 |
Identifying my strengths and weaknesses and those of my team. |
4.39 |
0.75 |
|
|
RUS |
Dealing with risk and uncertainty skills (Cronbach’s alpha = 0.836) |
3.67 |
0.88 |
|
|
RUS1 |
Having a strong preference for high-risk projects (with the possibility of very high but uncertain returns). |
3.54 |
1.08 |
|
|
RUS2 |
Taking bold and high-impact actions to achieve the company’s objectives. |
3.88 |
0.93 |
|
|
RUS3 |
Adopting a bold and aggressive stance to make the most of potential opportunities. |
3.60 |
1.03 |
|
|
ACS |
Achievement skills (Cronbach’s alpha = 0.794) |
4.18 |
0.74 |
|
|
ACS1 |
Deriving a sense of achievement and satisfaction from my work. |
4.04 |
0.91 |
|
|
ACS2 |
Learning as much as possible from my business. |
4.34 |
0.80 |
|
|
ACS3 |
When doing something, doing it with excellence. |
4.15 |
0.91 |
|
|
INS |
Innovativeness skills (Cronbach’s alpha = 0.725) |
3.96 |
0.72 |
|
|
INS1 |
Changing the way things are done. |
3.87 |
0.86 |
|
|
INS2 |
Always searching new and better ways of doing things. |
4.28 |
0.81 |
|
|
INS3 |
Coming up with new, wild, or even crazy ideas. |
3.72 |
1.03 |
|
|
DYS |
Dynamism skills (Cronbach’s alpha = 0.685) |
3.72 |
0.75 |
|
|
DYS1 |
Initiating actions to which competitors respond instead of responding to actions initiated by competitors. |
3.91 |
0.81 |
|
|
DYS2 |
Being the first to introduce new products/services, management techniques, operating technologies, etc. |
3.91 |
0.90 |
|
|
DYS3 |
Actively seeking to drive our competitors out of the market. |
3.35 |
1.14 |
|
|
AUS |
Autonomy skills (Cronbach’s alpha = 0.771) |
4.11 |
0.72 |
|
|
AUS1 |
Accepting both positive and negative consequences of my decisions and actions. |
4.21 |
0.81 |
|
|
AUS2 |
Depending on my efforts to influence the outcome of events in my life. |
3.89 |
0.90 |
|
|
AUS3 |
Making things happen instead of waiting and watching things happen. |
4.23 |
0.89 |
|
|
SCS |
Self-confidence skills (Cronbach’s alpha = 0.741) |
3.88 |
0.79 |
|
|
SCS1 |
Accomplishing under no direct supervision of anyone. |
3.77 |
1.03 |
|
|
SCS2 |
Having the ability to cope under new, untested conditions. |
4.16 |
0.81 |
|
|
SCS3 |
Asserting myself against the opinion of the majority. |
3.73 |
1.05 |
|
|
DGS |
Digital skills (Cronbach’s alpha = 0.920) |
4.18 |
0.65 |
|
|
DGS1 |
Electronic Health Record (EHR) Management - efficiently and securely managing patient data and enhancing care quality and coordination. |
4.23 |
0.78 |
|
|
Training needs |
DGS2 |
Health Information Exchange (HIE) - demand sharing of health information across platforms, improving patient outcomes and care continuity. |
4.14 |
0.83 |
|
DGS3 |
Telehealth Technology Competence - providing accessible care remotely, particularly important in expanding healthcare reach. |
4.11 |
0.80 |
|
|
DGS4 |
Cybersecurity Awareness in Health IT - sensitive health data against breaches, ensuring patient trust and regulatory compliance. |
4.27 |
0.83 |
|
|
DGS5 |
Mobile Health (mHealth) App Development - creating accessible health applications, supporting self-management, and patient engagement. |
4.19 |
0.81 |
|
|
DGS6 |
Artificial Intelligence for Healthcare Solutions - enhancing diagnostics, treatment personalization, and operational efficiencies. |
4.21 |
0.87 |
|
|
DGS7 |
Interoperability of Health Systems - integrating diverse health IT systems, facilitating comprehensive care delivery. |
4.11 |
0.80 |
|
|
DGS8 |
Digital Imaging and Diagnostic Technologies - advancing diagnostic precision and supporting remote analysis capabilities. |
4.22 |
0.82 |
|
|
DGS9 |
Health Data Privacy and Compliance - ensuring data protection, privacy, and adherence to health regulations. |
4.16 |
0.91 |
|
|
Pedagogical Method |
PM1 |
Asking students to decide their own problem-solving procedures. |
3.87 |
0.87 |
|
PM2 |
Encourage academics and students’ collaboration for common assignments. |
4.08 |
0.84 |
|
|
PM3 |
To work with and examine case studies. |
4.14 |
0.81 |
|
|
PM4 |
Engaging in real-world projects (e.g., problem-based learning). |
4.51 |
0.70 |
|
|
PM5 |
Using an experiential learning approach (e.g., organize visits to local firms). |
4.31 |
0.80 |
|
|
Resources & Support |
SP1 |
Mentorship programs. |
4.20 |
0.77 |
|
SP2 |
Funding opportunities. |
4.30 |
0.78 |
|
|
SP3 |
Networking events. |
4.18 |
0.8 |
|
|
SP4 |
Real entrepreneurs’ stories. |
4.09 |
0.87 |
|
|
Delivery Mode |
DM1 |
In-person (face-to-face). |
4.03 |
0.93 |
|
DM2 |
Remote (online). |
3.63 |
1.00 |
|
|
DM3 |
Online – Synchronous (happening, existing, or arising at precisely the same time). |
3.57 |
0.99 |
|
|
DM4 |
Online – Asynchronous (not simultaneous or concurrent in time). |
3.37 |
1.12 |
|
|
DM5 |
Hybrid (mixture of online and face-to-face). |
3.94 |
0.99 |
|
|
DM6 |
Hyflex (student-centred model of class delivery that can integrate in-class instruction, online synchronous video sessions and asynchronous content delivery) |
3.82 |
1.03 |
|
|
Assessment & certification |
AS1 |
Tests with open ended questions |
3.48 |
1.08 |
|
AS2 |
Tests with multiple choice questions |
3.60 |
1.10 |
|
|
AS3 |
Laboratory work and follow-up lab reports |
4.09 |
0.90 |
|
|
AS4 |
Presentations |
3.80 |
1.07 |
|
|
AS5 |
Group projects |
4.04 |
0.96 |
|
|
AS6 |
Essays and reports |
3.57 |
1.16 |
|
|
AS7 |
Written exams |
3.04 |
1.19 |
|
|
CE1 |
Course Certificate |
4.36 |
0.77 |
|
|
CE2 |
ECTS credits |
4.14 |
0.93 |
Regarding the perceived importance of training needs, training in group work skills (M = 4.34), management and planning skills (M = 4.30), and data analysis skills (M = 4.32) obtained the highest mean values (≥ 4.30). The perceived importance of training in digital skills (M = 4.18) and achievement skills (M = 4.18) was moderately high. In contrast, training needs related to dynamism (M = 3.72) and dealing with risk and uncertainty (M = 3.67) presented lower mean values.
Among the pedagogical approaches, engagement in real-world projects was most valued (M = 4.51), followed by experiential learning (M = 4.31). Regarding resources and support, participants placed high importance on funding opportunities (M = 4.30) and mentorship programs (M = 4.20). Networking events (M = 4.18) and real entrepreneurs’ stories (M = 4.09) also received high scores.
The in-person delivery mode was the highest rated (M = 4.03), followed by the hybrid format (M = 3.94). Among the assessment methods, laboratory work (M = 4.09) and group projects (M = 4.04) were the most valued. Traditional assessments, such as written exams, tests with open-ended or multiple-choice questions, essays, and reports, obtained the lowest averages. Both certification options were well evaluated, with the course certificate being slightly more valued (M = 4.36) than ECTS credits (M = 4.14).
Internal consistency was assessed using Cronbach’s alpha. Most constructs demonstrated acceptable reliability, with Cronbach’s alphas above the commonly accepted threshold of 0.70. However, three constructs presented values between 0.60 and 0.69, i.e., NegAttEnt (α = 0.637), PosAttEnt (α = 0.633), and DYS (α = 0.685). These values fall within the lower acceptable range often considered in exploratory research (Hair et al., 2019). Nevertheless, they indicate modest internal consistency and should therefore be interpreted with caution.
Correlation
Spearman’s correlation was used to examine the relationships between entrepreneurial attributes and the perceived importance of various entrepreneurial skills, i.e., the average score obtained for training needs items. Table 3 displays the correlation coefficients (ρ) and significance levels. Positive coefficients indicate that higher perceived importance of a given skill is associated with higher entrepreneurial intention. Effect sizes were interpreted according to Cohen’s (1988) guidelines: ρ = 0.10–0.29 (weak), ρ = 0.30–0.49 (moderate), ρ = ≥ 0.50 (strong).
Table 3. Spearman correlations between entrepreneurial attributes and necessary skills
|
|
EntInt |
PosAttEnt |
NegAttEnt |
DAS |
MPS |
SFCS |
GWS |
RUS |
ACS |
INS |
DYS |
AUS |
SCS |
|
PosAttEnt |
0.238** |
||||||||||||
|
NegAttEnt |
0.026 |
0.151* |
|||||||||||
|
DAS |
0.084 |
0.256** |
0.011 |
||||||||||
|
MPS |
0.138* |
0.286** |
-0.054 |
0.674** |
|||||||||
|
SFCS |
0.191** |
0.218** |
-0.028 |
0.560** |
0.686** |
||||||||
|
GWS |
0.1 |
0.257** |
-0.062 |
0.664** |
0.656** |
0.644** |
|||||||
|
RUS |
0.133* |
0.146* |
0.093 |
0.247** |
0.241** |
0.325** |
0.336** |
||||||
|
ACS |
0.184** |
0.274** |
-0.097 |
0.526** |
0.577** |
0.561** |
0.609** |
0.265** |
|||||
|
INS |
0.155* |
0.196** |
-0.013 |
0.353** |
0.430** |
0.426** |
0.443** |
0.348** |
0.611** |
||||
|
DYS |
0.122 |
0.158* |
0.103 |
0.288** |
0.301** |
0.376** |
0.326** |
0.448** |
0.406** |
0.510** |
|||
|
AUS |
0.157* |
0.229** |
-0.009 |
0.511** |
0.547** |
0.508** |
0.646** |
0.346** |
0.657** |
0.544** |
0.485** |
||
|
SCS |
0.084 |
0.316** |
0.093 |
0.340** |
0.322** |
0.388** |
0.411** |
0.441** |
0.361** |
0.460** |
0.455** |
0.514** |
|
|
DGS |
0.192** |
0.158* |
-0.073 |
0.388** |
0.521** |
0.464** |
0.521** |
0.212** |
0.497** |
0.380** |
0.298** |
0.476** |
0.381** |
Note: * Correlation is significant at the 0.05 level (2-tailed); ** Correlation is significant at the 0.01 level (2-tailed).
Spearman’s correlation coefficients revealed several significant associations between entrepreneurial attributes and the perceived importance of various skills. However, the strength of these associations varied, and most coefficients should be interpreted as weak to moderate at most. Overall, positive attitudes towards entrepreneurship (PosAttEnt) demonstrated the strongest and most consistent relationships across skills, with significant correlations with all competency categories, ranging from weak to moderate (ρ = 0.146-0.316, p < 0.05). The highest was with self-confidence skills (SCS) (ρ = 0.316, p < 0.01), suggesting that individuals with a more favorable perception of entrepreneurship tend to assign greater importance to training in self-confidence-related skills. Nevertheless, the size of this association remains modest. A high level of self-confidence skills has been suggested by many studies as an entrepreneur’s standard characteristic (Robinson et al., 1991). Moderate correlations were also observed with management and planning skills (MPS) (ρ = 0.286, p < 0.01) and achievement skills (ACS) (ρ = 0.274, p < 0.01), indicating a link between positive attitudes toward entrepreneurship and training needs associated with strategic organization and goal achievement.
Entrepreneurial intention (EntInt) was positively associated with perceived training needs in different skills, albeit generally at weak levels, contrary to Lumpkin and Dess’ (1996) evidence. The stronger correlations were observed with sales, financing, and contracting skills (SFCS) (ρ = 0.191, p < 0.01), achievement skills (ACS) (ρ = 0.184, p < 0.01), and digital skills (DGS) (ρ = 0.192, p < 0.01), although their magnitude remained small. Thus, while statistically significant, these results indicate only limited practical associations between entrepreneurial intention and the perceived importance of these competence domains. Respondents with stronger entrepreneurial intentions may therefore place slightly greater importance on receiving training in digital, commercial, financial, and achievement-related skills, but the weak effect sizes suggest that these relationships should be interpreted cautiously. This result was also found in the report of some countries analyzed in the scope of the HealthInnovation project (e.g., Portugal, Spain).
The negative attitudes towards entrepreneurship (NegAttEnt) displayed fewer significant associations. Its only notable link was a weak positive correlation with the positive entrepreneurial view itself (ρ = 0.151, p < 0.05), which might reflect a nuanced perception in which some respondents acknowledge both the advantages and challenges of entrepreneurship simultaneously. The coefficient’s small magnitude indicates that this finding should not be overinterpreted.
Overall, the correlation analysis indicates that entrepreneurial intention is only weakly associated with the perceived importance of most competence domains. The strongest and most consistent pattern concerns positive attitudes towards entrepreneurship, although even these associations are generally modest. Therefore, the findings should be interpreted as evidence of limited but meaningful associations rather than strong predictive relationships.
Cluster analysis
An exploratory cluster analysis was conducted in R (R Core Team, 2025) using the cluster (Maechler et al., 2025) and factoextra (Kassambara and Mundt, 2020) R packages to identify distinct groups of participants based on their entrepreneurial attributes and the perceived need for entrepreneurial training. The data were partitioned using the K-means clustering algorithm, a non-hierarchical partitioning method that aims to minimize the within-cluster sum of squares (WSS). Given the algorithm’s sensitivity to initial centroid placement, 25 random starts (nstart = 25) were employed to ensure solution stability and global optima convergence. All variables were standardized (z-scores) prior to analysis to prevent discrepancies in variable scales from biasing distance calculations.
While the Average Silhouette Width indicated a relatively weak inherent structure (0.20 for k=2; 0.17 for k=3; 0.15 for k=4), subsequent stability analysis via bootstrap resampling (B=100) revealed that both solutions are acceptable (see Table 4 and Table 5 for the 3-cluster and 2-cluster solutions, respectively). Bootstrap resampling suggested that the three-cluster solution was reproducible within this sample. However, this result should be interpreted alongside the low silhouette values, which indicate weak separation between clusters. The 3-cluster solution achieved a mean Jaccard stability index of 0.916, well above the 0.85 threshold for highly stable clusters (Hennig, 2007). Given that k=3 offers greater theoretical granularity while maintaining stability and a low dissolution rate (3.33%), it was selected as the solution for this exploratory study.
Table 4. 3-clusters solution
|
Variable |
Cluster 1 |
Cluster 2 |
Cluster 3 |
F_value |
p_value |
|
(n=73) |
(n=121) |
(n=33) |
|||
|
EntInt |
0.239 |
-0.059 |
-0.312 |
4.001 |
0.020 |
|
PosAttEnt |
0.447 |
-0.145 |
-0.458 |
13.366 |
< 0.001 |
|
NegAttEnt |
0.003 |
-0.059 |
0.209 |
0.931 |
0.396 |
|
DAS |
0.816 |
-0.155 |
-1.238 |
92.391 |
< 0.001 |
|
MPS |
0.728 |
0.016 |
-1.668 |
153.17 |
< 0.001 |
|
SFCS |
0.752 |
-0.069 |
-1.409 |
101.32 |
< 0.001 |
|
GWS |
0.849 |
-0.120 |
-1.437 |
132.45 |
< 0.001 |
|
RUS |
0.655 |
-0.255 |
-0.513 |
30.168 |
< 0.001 |
|
ACS |
0.869 |
-0.147 |
-1.382 |
128.51 |
< 0.001 |
|
INS |
0.784 |
-0.200 |
-1.001 |
64.676 |
< 0.001 |
|
DYS |
0.788 |
-0.235 |
-0.880 |
58.613 |
< 0.001 |
|
AUS |
0.868 |
-0.161 |
-1.330 |
119.2 |
< 0.001 |
|
SCS |
0.737 |
-0.204 |
-0.884 |
50.701 |
< 0.001 |
|
DGS |
0.706 |
-0.096 |
-1.209 |
68.479 |
< 0.001 |
Table 5. 2-clusters solution
|
Variable |
Cluster 1 |
Cluster 2 |
F_value |
p_value |
|---|---|---|---|---|
|
(n=120) |
(n=107) |
|||
|
EntInt |
0.237 |
-0.266 |
15.258 |
< 0.001 |
|
PosAttEnt |
0.309 |
-0.347 |
27.123 |
< 0.001 |
|
NegAttEnt |
-0.037 |
0.042 |
0.356 |
0.551 |
|
DAS |
0.538 |
-0.604 |
108.947 |
< 0.001 |
|
MPS |
0.545 |
-0.611 |
112.907 |
< 0.001 |
|
SFCS |
0.569 |
-0.638 |
129.238 |
< 0.001 |
|
GWS |
0.621 |
-0.696 |
172.576 |
< 0.001 |
|
RUS |
0.351 |
-0.394 |
36.341 |
< 0.001 |
|
ACS |
0.553 |
-0.620 |
118.397 |
< 0.001 |
|
INS |
0.492 |
-0.552 |
84.404 |
< 0.001 |
|
DYS |
0.402 |
-0.451 |
50.199 |
< 0.001 |
|
AUS |
0.558 |
-0.626 |
121.840 |
< 0.001 |
|
SCS |
0.495 |
-0.556 |
85.947 |
< 0.001 |
|
DGS |
0.522 |
-0.585 |
99.545 |
< 0.001 |
Theoretically, the three-cluster solution was considered useful because it differentiated respondents by their degree of entrepreneurial engagement and their perceived need for entrepreneurial training. Rather than producing only a broad high-versus-low distinction, the three-cluster solution identified an intermediate group whose responses were close to the sample mean and who did not show a clear prioritization of training needs. This intermediate profile was considered substantively relevant to the study because it may represent students who are neither clearly entrepreneurially engaged nor disengaged and may therefore require different educational strategies. Nevertheless, this interpretation is exploratory and should not be treated as evidence of stable or externally validated student profiles.
The robustness of the 3-cluster solution was further evaluated using the R packages fpc (Hennig, 2026) and mclust (Scrucca et al., 2023) via a cross-algorithm consistency check. Comparison with Partitioning Around Medoids (PAM) yielded an Adjusted Rand Index (ARI) of 0.618, indicating substantial agreement. The comparison with Hierarchical Clustering (Ward’s method) showed a moderate ARI of 0.460. A detailed inspection of the confusion matrices reveals that Cluster 2 remains highly consistent across all methods (agreement > 90%), whereas Clusters 1 and 3 exhibit higher sensitivity to the choice of algorithm. This suggests that while the central profiles of the segments are stable – as evidenced by the high Jaccard bootstrap coefficients (> 0.90) – the boundaries between specific segments are less distinct, a common characteristic in complex social sciences datasets.
To evaluate the distinctiveness of the 3-cluster partition, a Random Forest classification model was employed for predictive validation, using R packages caret (Kuhn, 2008) and randomForest (Liaw & Wiener, 2002). The dataset was split into a training set (80%) and an independent test set (20%). The model demonstrated excellent performance on the test set, achieving an overall Accuracy of 93.18% and a Cohen’s Kappa of .883. According to the benchmarks established by Landis & Koch (1977), a Kappa value exceeding .80 indicates ‘almost perfect’ agreement. This statistical evidence shows that despite some overlap in the cluster boundaries, the three groups represent distinct, identifiable segments with highly consistent internal profiles. While the initial silhouette analysis suggested fuzzy boundaries, the predictive performance suggests that the variables provide sufficient signal to distinguish between the groups and do not follow a random structure.
The K-means cluster analysis yielded a three-cluster solution (Table 4), with clusters differing across both the entrepreneurial profile variables and the perceived training needs. The ANOVA statistics are therefore reported to characterize the variables that most contributed to the differentiation of the exploratory profiles. One-way ANOVA confirmed statistically significant differences between clusters across most variables (all p < 0.001), with the exception of NegAttEnt (F = 0.931, p = 0.396), which did not significantly discriminate between groups. The three-cluster solution provided an exploratory segmentation of respondents in both entrepreneurial orientation and perceived training needs (Figure 1). Given the low Average Silhouette Width values, these results should be interpreted as indicative of tentative response patterns rather than as evidence of clearly separated or externally validated student types.
Cluster 1 emerged as the most entrepreneurially engaged group, combining above-average entrepreneurial intention (EntInt: z = +0.239) and positive attitudes towards entrepreneurship (PosAttEnt: z = +0.447) with consistently elevated perceived training needs across all competency areas, particularly in training related to achievement (ACS: z = +0.869), autonomy (AUS: z = +0.868), and group work (GWS: z = +0.849). This suggests that higher entrepreneurial motivation may be associated with a broader and stronger perceived need for entrepreneurial training.
Cluster 2 presented an undifferentiated profile, with entrepreneurial orientation variables close to the sample mean (EntInt: z = −0.059; PosAttEnt: z = −0.145) and modest negative deviations in perceived training needs, most notably in risk and uncertainty (RUS: z = −0.255), dynamism (DYS: z = −0.235), self-confidence skills (SCS: z = −0.204), and innovativeness skills (INS: z = −0.20) suggesting neither pronounced engagement nor clear prioritisation of training areas.
Note: EntInt - Entrepreneurial Intention in Healthcare Industry; PosAttEnt - Positive attitudes towards entrepreneurship; NegAttEnt - Negative attitudes towards entrepreneurship; DAS - Data analysis skills; MPS - Management and planning skills; SFCS - Sales, financing and contracting skills; GWS - Group work skills; RUS - Dealing with risk and uncertainty skills; ACS - Achievement skills; INS - Innovativeness skills; DYS - Dynamism skills; AUS - Autonomy skills; SCS - Self-confidence skills; DGS - Digital skills.
Figure 1. Cluster profiles
Cluster 3 displayed the least favourable entrepreneurial orientation, with below-average entrepreneurial intention (EntInt: z = −0.312), reduced positive attitudes (PosAttEnt: z = −0.458), and a slight tendency towards negative attitudes (NegAttEnt: z = +0.209), accompanied by substantially negative perceived training needs across all competency areas, most markedly in management and planning (MPS: z = −1.668), group work (GWS: z = −1.437), sales and contracting (SFCS: z = −1.409), and achievement skills (ACS: z = −1.382), reflecting a broadly generalised undervaluation of entrepreneurial training across this group.
By analyzing graduates’ and students’ preferences, it was possible to achieve the first specific research goal: mapping the training needs of students and graduates in Medicine, Life Sciences, business, and non-business disciplines.
Focus group results
The focus group participants were invited to discuss (1) the current trends in the healthcare market in their country, (2) the challenges this sector is facing and how to overcome them, (3) the specific skills currently in demand in the health market, (4) the importance of training to business creation in the scope of the health market and how to introduce it in the courses related to health, (5) relevant topics/modules to include in the course/training programme to be developed in the scope of the funded project. This made it possible to achieve the second specific objective of the investigation: to include experts’ viewpoints on current trends and challenges in entrepreneurship in the healthcare sector and on relevant topics for a training program.
Current trends
According to Table 6, participants highlighted that current trends in the healthcare sector are strongly shaped by digital transformation, including the adoption of cloud computing, hospital management software, digital applications, and technologies such as augmented reality for medical training and hospital administration. Although some digital solutions, such as AI, are still in the early stages of implementation, promising applications are emerging in personalized medicine, diagnostics, and hardware monitoring. The sector is also moving towards a patient-centered approach, with an emphasis on personalized care, remote patient monitoring, and improved residential care services. Other trends include the potential use of blockchain, advancements in interoperability, a stronger focus on preventive medicine, cross-sectoral collaboration, and the development of technologies such as advanced ultrasound solutions and robotic applications for both surgical procedures and specific functions, such as punctures, and mechatronics.
Table 6. Current trend according to focus group insights
Challenges
Participants identified multiple challenges that hinder innovation and entrepreneurship in the healthcare sector (Table 7). Regulatory and certification processes remain complex and slow, with particular challenges in combining software-as-a-medical-device with AI, navigating intricate frameworks, obtaining required certifications, and adapting to shifting certification timelines. There is a clear gap in entrepreneurship skills and literacy, with demand for specialized knowledge in regulatory compliance, clinical trial management, health economics, and immersive technology applications, alongside the need to foster transdisciplinary collaboration, decision-making under uncertainty, and substance over trends in entrepreneurial education. Resistance to innovation persists within established healthcare practices, making it difficult to convince stakeholders of the value of new solutions. Personal circumstances, including lack of awareness about entrepreneurship support opportunities, mental health factors, and external conditions such as the economic environment, further limit entrepreneurial potential. Additional challenges include securing initial capital, restrictions on patient access to advanced technologies, rising costs and time requirements for market entry, deficiencies in hospital management, the impact of population ageing, and insufficient collaboration between students from different academic disciplines, which limits the generation of innovative business ideas.
Table 7. Challenges according to focus group insights
Demanded skills
Participants identified a set of skills considered essential for success in healthcare entrepreneurship (Table 8), in line with some of the results from the European project Health2Innovation. Prior work experience before beginning a specialization was viewed as an advantage. Soft skills and mindset were emphasized as critical, including adaptability, willingness to learn, self-efficacy, proactivity, resilience, and openness to accepting mistakes and rejection. High employability profiles were associated with adaptability to the job market, a strong career orientation, and spontaneity, which can be fostered through artistic activities. Effective communication, teamwork, critical thinking, leadership, and cultural awareness were highlighted as vital for guiding multidisciplinary, multicultural teams and conveying complex medical information to diverse audiences. The ability to ask unexpected questions, navigate uncertainty, and maintain a strong focus on understanding user needs were also seen as important traits. Technical skills in high demand include regulatory acumen to navigate local and international healthcare regulations, proficiency in digital health technologies, data analytics, clinical research methodologies, and immersive technologies such as virtual and augmented reality. Expertise in medical certification processes, quality management systems, and compliance with international standards, such as ISO 13485 for medical devices, was identified as a key competency, along with project management skills that encompass methodologies, tools, and resources for conducting innovation projects.
Table 8. Demanded skills according to focus group insights
Importance of training to business creation
Participants agreed that targeted training plays a decisive role in enabling healthcare entrepreneurship by equipping individuals with both interpersonal and entrepreneurial skills (Table 9). Interpersonal skills training can enhance soft skills essential for business creation, while dedicated courses can provide knowledge in business planning, financial management, market analysis, and regulatory compliance, as well as strengthen leadership capabilities and team management from a leader’s perspective. Training was also seen as a means to foster innovation through pedagogical approaches such as product-based learning, case-based learning, and flipped classrooms, creating supportive ecosystems that encourage experimentation and the development of novel solutions. Contact with real-world perspectives, including testimonials from former students and industry professionals, together with networking opportunities between healthcare professionals, researchers, and entrepreneurs, was considered vital for building collaborations. Practical experiences, including curricular internships, classroom-based activities, and project-based work, were regarded as the most effective way to prepare students for business creation, while avoiding approaches that could foster imposter syndrome. The promotion of multidisciplinary collaboration, through joint subjects or co-supervised theses across different faculties, was highlighted as a way to combine perspectives and expertise. Other priorities included co-creation between academia and market players, lifelong learning aligned with professional needs, flexible curricula that adapt to student profiles, project management training for certification and implementation processes, fundraising and investment pitch preparation, and the integration of virtual and augmented reality tools.
Table 9. Importance of training according to focus group insights
Important training topics
Participants outlined a comprehensive set of training topics essential for preparing future healthcare entrepreneurs (Table 10). Funding and financial management were seen as foundational, including basic financial literacy, the ability to monetize ideas, practical learning through case studies, and sessions on securing investment, financial forecasting, budgeting, and revenue model development. Business planning and strategy development should address market analysis, competitive positioning, and sustainable growth strategies tailored to the healthcare sector. A strong understanding of the regulatory landscape in healthcare and clinical trial management is necessary to navigate compliance requirements and ensure product efficacy. Digital health technologies, such as telemedicine, health apps, and wearables, should be explored, along with their integration into healthcare delivery systems. Ethical considerations, patient safety, and privacy standards were regarded as critical components of responsible innovation. Other important topics include leadership and team management, design thinking, co-creation, investment pitch preparation, and project management. Participants also recommended analyzing both successful and failed cases to enhance collective learning, as well as promoting practical and mobility experiences to expose learners to diverse work methodologies and foster a global mindset. Courses should adopt a discussion-based approach that develops communication, listening, and interpretive skills, while modules on resilience, effective communication, and leadership would complement technical training.
Table 10. Important training topics
Pedagogical framework
In the focus group sessions, some training topics were suggested: funding and financial management, business planning, market analysis, regulatory landscape in healthcare, digital health technologies, ethics, leadership and team management, design thinking, project management, and soft skills (resilience, effective communication and leadership). One question raised was that success should not only be presented, but also prompt students to reflect on their problems and failures. The mobility experiences and contact with companies already operating in the market were also suggested. It was also highlighted that innovative pedagogical methods, such as product-based learning, case-based learning, and flipped classrooms, should be implemented. According to focus group results, it would be enriching to facilitate networking opportunities between students and entrepreneurs, include internships in the training offer, promote work in multidisciplinary teams/projects, and allow curricula flexibility. The questionnaire results were very clear, pointing to the need to improve all general skills and competences presented (information analysis, managing and planning, sales and financing, working with people, achievement, innovativeness, autonomy, self-confidence) as well as the digital skills. Regarding pedagogical methods, the respondents would prefer engaging in real-world projects (e.g., problem-based learning) and would like to have more funding opportunities and mentorship. They would prefer face-to-face courses and be evaluated by laboratory work and reports. Having a certificate or ECTS at the end would also be appreciated.
Regarding cluster analysis results, three exploratory student profiles were identified, showing descriptive differences in both entrepreneurial orientation and awareness of competences needed. Students in Cluster 1 showed a comparatively higher entrepreneurial orientation, as evidenced by consistently above-average scores across all competency areas, indicating a higher perceived importance of the skills needed to pursue entrepreneurship. For this profile, the pedagogical framework could consider depth over breadth, providing sufficiently challenging and applied learning experiences, such as project-based learning involving real healthcare entrepreneurship challenges, mentoring, and participation in incubation programs. Students in Cluster 2 displayed scores consistently close to the sample mean across both training needs and attitudinal variables, representing the less clearly differentiated exploratory profile. The absence of clearly differentiated scores may reflect a lack of informed awareness rather than genuine indifference, and the pedagogical intervention could therefore begin with experiential and exploratory learning strategies designed to activate awareness of skills needed. Cluster 3 may require particular attention, with students attributing below-average importance across all skills needed, accompanied by below-average entrepreneurial intention and a slight tendency towards more negative attitudes. Below-average scores in this context reflect a lower perceived importance of these training needs, and the pedagogical intervention could initially emphasize awareness-building and attitudinal change before competency training. Finally, negative attitudes towards entrepreneurship did not significantly differentiate the clusters, indicating that resistance or skepticism towards entrepreneurship is relatively uniformly distributed across the student population and must therefore be addressed as a transversal component across the curriculum.
Based on the collected data on focus group insights and student and graduate questionnaires, a framework proposal is presented in Figure 2.
Figure 2. Pedagogical framework proposal
To make the mixed-methods integration underlying the pedagogical framework explicit, Table 11 presents a joint display linking each framework component to the corresponding survey and focus group evidence and the resulting pedagogical implication.
Table 11. Systematization of research evidence and framework components
|
Framework component |
Evidence |
Resulting pedagogical implication | |
|---|---|---|---|
|
Modules |
Soft Skills |
Survey evidence: Group work skills received the highest score among the competence domains. Autonomy skills were also highly rated, as were self-confidence skills and achievement skills. |
The framework should include soft skills as a transversal component, developed through teamwork, real-world projects, experiential activities, leadership exercises, communication tasks, and multidisciplinary collaboration. |
|
Business Opportunities and Funding |
Survey evidence: Data analysis skills were considered important, including recognizing new opportunities, combining business information from different sources and evaluating usefulness of resources. Resources and support on funding opportunities were also highly valued by respondents. Focus group evidence: Participants identified securing initial capital as a major challenge for healthcare entrepreneurs. They also recommended training on how to raise money and secure investment. |
The framework should include content on funding sources, investment readiness, fundraising strategies, and networking with investors, mentors, entrepreneurs, and relevant healthcare market actors. |
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Regulatory Landscape |
Survey evidence: The survey did not include a direct item on healthcare regulation or certification. However, related digital skills in health data privacy and compliance, ensuring adherence to health regulations, were highly valued. |
The framework should include a dedicated module on healthcare regulation, certification pathways, compliance, data protection, medical device requirements, and the regulatory implications of digital health and AI-based solutions. |
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Pitching |
Focus group evidence: Participants recommended including investment pitches and training on how to convince investors. They also highlighted the importance of convincing the market and customers of the value of innovative healthcare solutions. |
The framework should include pitch preparation, investor communication, value proposition development, storytelling, presentation skills, and exercises where learners present healthcare business ideas to peers, mentors, or external stakeholders. |
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Business Model |
Survey evidence: Management and planning skills were among the most highly rated competence domains. Respondents valued identifying appropriate business strategies, determining logistics for manufacture/delivery of products, and developing business plans. |
The framework should include business model development, business planning, market analysis, strategic positioning, and action planning. |
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Clinical Trial Management |
Focus group evidence: Participants explicitly identified clinical trial management as a specialized skill needed in healthcare entrepreneurship. They also recommended understanding the process of designing, conducting, and managing clinical trials to ensure compliance and efficacy. |
The framework should include an introductory component on clinical trial design, clinical validation, evidence generation, compliance requirements, and the role of clinical trials in bringing healthcare innovations to market. |
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Digital Technologies |
Survey evidence: Digital skills were highly rated by respondents. Relevant items included EHR management, health information exchange, telehealth technology competence, cybersecurity awareness, mHealth app development, AI for healthcare solutions, interoperability of health systems, digital imaging and diagnostic technologies, and health data privacy and compliance. |
The framework should include digital health technologies as a core component, covering practical applications, opportunities, risks, implementation challenges, interoperability, privacy, cybersecurity, AI, telehealth, mHealth, and immersive technologies. |
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Marketing and Commercialization |
Survey evidence: Respondents valued sales, financing and contracting skills, including selling techniques, negotiating contracts, and setting prices. Focus group evidence: Participants highlighted market analysis, competitive positioning, sustainable growth strategies, convincing customers and the market of the value of innovative solutions, and understanding users and their needs. |
The framework should include market analysis, customer discovery, user needs assessment, pricing, commercialization strategies, competitive positioning, sales techniques, and communication of value in healthcare markets. |
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Financial Management |
Survey evidence: Respondents considered sales, financing and contracting skills important, including drawing up and managing budgets and setting prices. |
The framework should include financial literacy, budgeting, revenue models, pricing, investment readiness, financial forecasting, and cost structures. |
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Project Management |
Survey evidence: Management and planning skills were highly rated, including evaluating outcomes, and developing action plans. |
The framework should use project-based learning as a central pedagogical strategy and include explicit training in project planning, milestones, implementation, budgeting, monitoring, evaluation, and healthcare innovation project management. |
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Design Thinking and Co-creation |
Focus group evidence: Participants explicitly mentioned design thinking and co-creation. They also recommended partnerships between academia and market players, multidisciplinary work, and collaborative approaches to developing new healthcare solutions. |
The framework should include design thinking and co-creation activities, involving students, academics, healthcare professionals, entrepreneurs, users, and market actors in the development and validation of healthcare innovation ideas. |
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Ethics |
Focus group evidence: Participants identified ethics of business, ethical considerations in healthcare innovation, patient privacy, patient safety, and compliance with standards as important training topics. |
The framework should include ethics as a dedicated and transversal topic, covering responsible innovation, patient safety, privacy, data protection, compliance, and ethical decision-making in healthcare entrepreneurship. |
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Implementation |
Pedagogical Methods |
Survey evidence: Respondents most valued engagement in real-world projects, followed by experiential learning approaches and case studies. Collaboration between academics and students for common assignments was also positively rated. |
The framework should privilege active and experiential pedagogies, including problem-based learning, project-based learning, case studies, internships, flipped classroom activities, contact with entrepreneurs, and multidisciplinary teamwork. |
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Delivery Mode |
Survey evidence: In-person delivery was the highest-rated mode, followed by hybrid delivery. Focus group evidence: Participants recommended practical and mobility experiences, contact with companies already operating in the market, physical or virtual collaborations with professionals and students from other countries and cultures, lifelong learning, and flexible curricula adapted to students’ profiles. |
The framework should prioritize face-to-face and hybrid learning formats, while allowing flexibility through virtual collaboration, mobility experiences, company contact, and adaptable learning paths. |
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Assessment and Certification |
Survey evidence: Laboratory work and follow-up lab reports were the most valued assessment method, followed by group projects. Both course certificates and ECTS credits are valued by respondents. |
The framework should assess students through applied outputs such as projects, lab reports, group work, presentations, pitches, case analyses, and practical assignments. Certification through course certificates and/or ECTS credits should be considered. |
The originality of this research lies in synthesizing, for the first time, modules, skills, trends, challenges, pedagogical methods, delivery modes, and assessment and certification tailored to healthcare entrepreneurship. This synthesis was possible due to an original empirical work with participants from different European Countries to identify useful content in accordance with both students of relevant disciplines to healthcare innovation and experts in entrepreneurship. This dual empirical grounding and sector-specific focus address a gap in the literature, which has rarely combined educational design with the evolving technological, regulatory, and organizational dynamics of healthcare entrepreneurship. Therefore, the here-presented study advances the literature on healthcare entrepreneurship education by moving beyond general competency frameworks to propose an empirically grounded pedagogical framework tailored to the specificities of the healthcare sector. While prior studies have emphasized the importance of entrepreneurship education and interdisciplinary training (Martin & Iucu, 2014; Secundo et al., 2016), they tend to remain conceptual or focused on single disciplines. By contrast, this study integrates evidence from students, graduates, and experts’ perspectives, enabling a multi-actor view of training needs. Furthermore, it operationalizes these insights into structured pedagogical components, namely learning objectives, delivery modes, and assessment methods, consistent with the definition of pedagogical frameworks by Devlin (2006).
First, regarding skills and relevant topics, the survey and focus groups together show a pattern: students and recent graduates value training in practical, technical, and teamwork skills for entrepreneurship in healthtech and medtech, while entrepreneurial intention remains an attribute with only moderate value. Participants rated the perceived importance of training in data analysis, group work, management/planning, sales/financing, and digital skills consistently high, and they expressed strong preferences for experiential, real-world learning (the highest pedagogical mean for project- and problem-based activities). This is in line with Haase and Lautenschläger (2011), who state that entrepreneurs need genuine knowledge and information about the business creation process and management. It is also crucial to include training in negotiation, leadership, creativity, risk tolerance, opportunity-seeking, and communication skills. The insights indicate that entrepreneurship education should focus on experiencing entrepreneurship, instead of the traditional teaching (Haase & Lautenschläger, 2011). The pattern of high perceived need for concrete entrepreneurial skills but only moderate intention to found or join ventures aligns with prior works (Adam & Fayolle, 2015), showing that skills availability and positive attitudes do not automatically translate into entrepreneurial action. Our findings suggest that concerns about financial security, work-life balance, and exposure to failure emerged as salient barriers in the quantitative items and were echoed in focus group discussions. Importantly, the exploratory cluster analysis suggests that these patterns are not uniform across the student population. Three distinct profiles were identified, differing in both entrepreneurial orientation and perception of training needs. The heterogeneous nature of student entrepreneurial profiles has potential implications for entrepreneurship education (Matsushita & Takahashi, 2026). These profiles suggest that a differentiated pedagogical intervention may be useful, ranging from advanced, applied learning experiences for Cluster 1, through exploratory and awareness-activating strategies for Cluster 2, to prior attitudinal and motivational intervention before competence training for Cluster 3. By examining the characteristics and formation processes of each exploratory student profile, it becomes possible to discuss which educational interventions may enable shifts between profiles, an approach that the present framework explicitly operationalizes across the three identified clusters.
Secondly, it was also possible to identify current trends (digital transformation, AI, patient-centered approach, etc.) and challenges (regulatory frameworks, funding constraints, cultural resistance to innovation, etc.) to allow the training program to address emerging needs, enhance practical applicability, and better prepare learners for future professional demands. The identified challenges and trends are largely consistent with existing literature on healthcare innovation. Regulatory complexity, particularly in areas such as AI-enabled medical devices, is widely recognized as a major barrier to innovation (Vayena et al., 2018). Similarly, trends such as digital transformation, personalized medicine, and the growing role of artificial intelligence are well documented (Ghavami & Giulietti, 2026; Komala et al., 2023). However, this study provides novel insights by highlighting emerging dynamics that remain underexplored. These include the increasing mismatch between the pace of regulatory processes and market needs, the rising entry barriers due to certification costs and timelines, and the development of deeper technological innovation, such as immersive applications. Additionally, the findings point to upstream ecosystem challenges, such as limited interdisciplinary collaboration at the educational level, suggesting that barriers to innovation may originate earlier than typically addressed in the literature.
In terms of practical implications for curriculum design, and based on the combined quantitative and qualitative evidence, it was concluded that an effective pedagogical framework for health entrepreneurship should include the following elements:
- Core curriculum components: business fundamentals (market analysis, business planning, financing), regulatory and ethical aspects of healthcare, digital health technologies (EHR, telehealth, AI basics), and commercialization pathways (IP, reimbursement, procurement).
- Skill development focus: hands-on training in data analysis and digital tools; negotiation, budgeting, and fundraising simulations; team leadership and conflict management exercises.
- Preferred teaching methods: project-based learning, real-world collaborations with local firms and hospitals, case studies, and mentorship from entrepreneurs and clinicians.
- Delivery and assessment: preferably in-person delivery and assessment through practical and collaborative experiences, namely laboratory work and group projects. Both certification alternatives contemplated in the survey received positive evaluations, with the course certificate being considered slightly more valuable than ECTS credits.
These recommendations respond directly to participants’ stated preferences (high means for experiential learning, mentorship, funding support, and real entrepreneur stories) and address the gap between competence and intention by emphasizing applied, scaffolded experiences that reduce perceived risk and increase confidence. Critically, the exploratory cluster profiles identified in this study indicate that these recommendations may benefit from some degree of adaptation: whilst students resembling Cluster 1 may be more receptive to engage with the full breadth of the proposed curriculum, Cluster 2 students may require prior experiential exposure to develop awareness of their competency gaps, and students resembling Cluster 3 may require dedicated motivational and attitudinal intervention before meaningful competence development can occur. Furthermore, the finding that negative attitudes towards entrepreneurship did not significantly differentiate the clusters suggests that addressing misconceptions and resistance towards entrepreneurship should be treated as a transversal component of the curriculum, regardless of student profile. This way, the study emphasizes that entrepreneurship in healthcare requires a hybrid training needs profile combining technical expertise, regulatory knowledge, digital literacy, and interpersonal capabilities. The findings also reinforce the importance of experiential and ecosystem-embedded education models in healthcare entrepreneurship.
For educators, the results highlight the need to design curricula that reflect the complexity of healthcare innovation systems and to apply innovative pedagogical approaches that encourage practical activities, creativity, and the development of soft skills, all adapted to market demands. For policymakers, they underscore the importance of strengthening institutional partnerships and supporting infrastructure so that the knowledge generated in academia can be value-adding to the healthcare industry. Regarding the policy and ecosystem recommendations, higher education institutions should strengthen partnerships with incubators, hospitals, and industry to provide seed funding, mentorship, and networking opportunities—resources that participants rated as important. Career services and entrepreneurship centers can play a coordinating role by offering tailored bootcamps, pitch events, and access to legal and regulatory expertise.
Thus, this study’s results suggest that fostering entrepreneurship in healthcare requires not only development of skills through training but also structural alignment between education, industry, and policy ecosystems, enabling individuals to navigate the unique challenges and opportunities of this highly regulated and innovation-intensive sector.
CONCLUSION
This study maps the training needs and pedagogical preferences of students and recent graduates across health, engineering, ICT, and business disciplines and synthesizes expert perspectives from focus groups to propose a sector-specific pedagogical framework for healthtech and medtech entrepreneurship. Concerning entrepreneurial attributes and needed competences to healthcare entrepreneurship, it was possible to find that: (i) learners demand strong technical (data analysis, management, and digital), as well as interpersonal (teamwork, autonomy, and achievement) skills; (ii) experiential, project based learning and mentorship are the most valued delivery methods; and (iii) despite positive attitudes toward entrepreneurship, intention to pursue entrepreneurial careers is moderate, constrained by perceived financial and lifestyle risks.
Results suggest that higher education institutions should adopt a blended, practice-oriented curriculum that couples digital and regulatory literacy with business fundamentals, provides structured mentorship and funding pathways, and validates learning through applied assessments and microcredentials. The exploratory needs assessment for healthcare entrepreneurship education presented in this paper can guide evidence-based curriculum design and future pilot testing of training programs.
Regarding the research limitations, it is possible to point out: the response rate could not be estimated, the convenience sample (N = 227) across five countries provides useful cross national insight but is not nationally representative, possible creation of self-selection bias, due to the non-random sampling method followed and a possible higher availability of students already interested in entrepreneurship or innovation to participate, leading to limited generalisation of the results to all students and graduates in healthcare-related disciplines; cultural and institutional differences may moderate preferences; the measurement constraints, this is, some multi item scales showed modest internal consistency, which suggests caution when interpreting fine grained differences across constructs; the cross sectional design in which the data capture intentions and preferences at one point in time, but cannot establish causal links between training exposure and entrepreneurial outcomes; the self report bias (responses reflect perceived needs and attitudes rather than observed behaviour or demonstrated competence); the English-language survey may have affected cross-country comparability, and the framework has not yet been implemented or evaluated.
Another limitation is that the validation of the identified clusters is restricted to internal and robustness-based approaches. As the same variables were used both to construct and assess the clusters, the analysis does not provide external validation in the strict sense. Future research should address this limitation by examining the extent to which these profiles are associated with independent outcomes, such as subsequent entrepreneurial behavior, engagement in entrepreneurship-related activities, or external assessments. Such efforts would provide stronger evidence regarding the external validity and practical relevance of the identified profiles.
Future work should evaluate pilot curricula that implement the proposed framework and measure the effects of pedagogical elements on entrepreneurial intention and skill acquisition. Complementary studies could strengthen the content of education programs by including employer and patient perspectives.
Funding Information
This research was funded by RESTLESS project funded by the Erasmus+ Program KA220-HED – Cooperation partnerships in higher education, under the number 2023-2-PL01-KA220-HED-000176832, and NECE - Research Centre for Business Sciences funded by the Multiannual Funding Program of R&D Centers of FCT—Fundação para a Ciência e Tecnologia, Portugal, under Grant number UID/04630/2025, DOI: 10.54499/UID/04630/2025.
References
Adam, A., & Fayolle, A. (2015). Bridging the entrepreneurial intention-behaviour gap: the role of commitment and implementation intention. International Journal of Entrepreneurship and Small Business, 25(1), 36–54. https://doi.org/10.1504/IJESB.2015.068775
Afeli, S. A., & Adunlin, G. (2022). Curriculum content for innovation and entrepreneurship education in US pharmacy programs. Industry and Higher Education, 36(1), 13–18. https://doi.org/10.1177/0950422220986314
Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50, 179–211. https://doi.org/10.1016/0749-5978(91)90020-T
Akkaya, B., Popescu, C., & Üstgörül, S. (2024). How can we remove psychological entrepreneurship barriers on entrepreneurship intention for health organizations in the future? Sustainability, 16(8). https://doi.org/10.3390/su16083503
Amini, Z., Arasti, Z., & Bagheri, A. (2018). Identifying social entrepreneurship competencies of managers in social entrepreneurship organizations in healthcare sector. Journal of Global Entrepreneurship Research, 8(1). https://doi.org/10.1186/s40497-018-0102-x
Antoniadou, M., & Kanellopoulou, A. (2024). Educational approach: Application of SWOT analysis for assessing entrepreneurial goals in senior dental students. European Journal of Investigation in Health, Psychology and Education, 14(3), 753–766. https://doi.org/10.3390/ejihpe14030049
Bacigalupo, M., Kampylis, P., Punie, Y., & Brande, G. Van Den. (2016). EntreComp: The Entrepreneurship Competence Framework. https://doi.org/10.2791/593884
Bae, T. J., Qian, S., Miao, C., & Fiet, J. O. (2014). The relationship between entrepreneurship education and entrepreneurial intentions: A meta-analytic review. Entrepreneurship Theory and Practice, 38(2), 217–254. https://doi.org/10.1111/etap.12095
Carpenter, A., & Wilson, R. (2022). A systematic review looking at the effect of entrepreneurship education on higher education student. International Journal of Management Education, 20(2), 100541. https://doi.org/10.1016/j.ijme.2021.100541
Davidsson, P. (1989). Continued entrepreneurship and small firm growth. Stockholm School of Economics.
Devlin, M. (2006). Challenging accepted wisdom about the place of conceptions of teaching in university teaching improvement. International Journal of Teaching and Learning in Higher Education, 18(2), 112–119. https://hdl.handle.net/10536/DRO/DU:30006685
Devlin, M. (1996). Older and wiser? A comparison of the learning and study strategies of mature age and younger teacher education students. Higher Education Research & Development, 15(1), 51-60. https://doi.org/10.1080/0729436960150104
European Commission. (2025). DigComp 3.0 European Digital Competence Framework. https://doi.org/10.2760/7379058
Fairlie, R. W. (2023). Evaluating entrepreneurship training: How important are field experiments for estimating impacts? Journal of Economics and Management Strategy, 32(3), 607–635. https://doi.org/10.1111/jems.12420
Garcez, A., Silva, R., & Franco, M. (2022). The hard skills bases in digital academic entrepreneurship in relation to digital transformation. Social Sciences, 11(5). https://doi.org/10.3390/socsci11050192
Ghavami, D., & Giulietti, N. (2026). A decade of innovation in healthcare: Automation, bio-printing and digital twin technologies for personalized therapies. International Journal of Pharmaceutics: X, 11, 100526. https://doi.org/10.1016/j.ijpx.2026.100526
Haase, H., & Franco, M. (2020). Leadership and collective entrepreneurship: Evidence from the health care sector. Innovation: The European Journal of Social Science Research, 33(3), 368–385. https://doi.org/10.1080/13511610.2020.1756231
Haase, H., & Lautenschläger, A. (2011). The “teachability dilemma” of entrepreneurship. International Entrepreneurship and Management Journal, 7(2), 145–162. https://doi.org/10.1007/s11365-010-0150-3
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). Multivariate data analysis (8th ed.). Andover: Cengage.
Hennig, C. (2007). Cluster-wise assessment of cluster stability. Computational Statistics & Data Analysis, 52, 258–271. https://doi.org/10.1016/j.csda.2006.11.025
Hennig, C. (2026). fpc: Flexible Procedures for Clustering (R package version 2.2-14). https://doi.org/10.32614/CRAN.package.fpc
Kassambara, A., & Mundt, F. (2020). Factoextra: Extract and visualize the results of multivariate data analyses (Version 1.0.7) [Computer software]. CRAN. https://CRAN.R-project.org/package=factoextra
Komala, M. G., Ong, S. G., Qadri, M. U., Elshafie, L. M., Pollock, C. A., & Saad, S. (2023). Investigating the regulatory process, safety, efficacy and product transparency for nutraceuticals in the USA, Europe and Australia. Foods, 12(2), 427. https://doi.org/10.3390/foods12020427
Kuhn, M. (2008). Building predictive models in R using the caret package. Journal of Statistical Software, 1–26. https://doi.org/10.18637/jss.v028.i05
Kuip, I., & Verheul, I. (2004). Early development of entrepreneurial qualities: The role of initial education. International Journal of Entrepreneurship Education, 2(2), 203-226.
Landis, J. R., & Koch, G. G. (1977). The measurement of observer agreement for categorical data. The International Biometric Society, 33(1), 159–174. https://doi.org/10.2307/2529310
Liaw, A. & Wiener, M. (2002). Classification and regression by RandomForest. R News, 2(3), 18-22. http://CRAN.R-project.org/doc/Rnews/
Lumpkin, G. T., & Dess, G. G. (1996). Clarifying the entrepreneurial orientation construct and linking it to performance. The Academy of Management Review, 21(1), 135–172. https://doi.org/10.2307/258632
Maechler, M., Rousseeuw, P., Struyf, A., Hubert, M., & Hornik, K. (2025). cluster: Cluster analysis basics and extensions (Version 2.1.8) [Computer software]. CRAN. https://CRAN.R-project.org/package=cluster
Martin, C., & Iucu, R. B. (2014). Teaching entrepreneurship to educational sciences students. Procedia - Social and Behavioral Sciences, 116, 4397–4400. https://doi.org/10.1016/j.sbspro.2014.01.954
Matsushita, S., & Takahashi, K. (2026). Unpacking entrepreneurial intention: Three student types revealed by cluster analysis of build vs. activity roles. The International Journal of Management Education, 24(2), 101390. https://doi.org/10.1016/j.ijme.2026.101390
Niccum, B. A., Sarker, A., Wolf, S. J., & Trowbridge, M.J. (2017). Innovation and entrepreneurship programs in US medical education: A landscape review and thematic analysis. Medical Education Online, 22(1). https://doi.org/10.1080/10872981.2017.1360722
Patru, L., Birchi, F. A., & Patru, C. L. (2023). The relationship between digital technology and the development of the entrepreneurial competencies of young people in the medical field. Electronics, 12(8). https://doi.org/10.3390/electronics12081796
R Core Team. (2025). R: A language and environment for statistical computing. R Foundation for Statistical Computing. http://www.r-project.org
Rippa, P., Ferruzzi, G., Holienka, M., Capaldo, G., & Coduras, A. (2023). What drives university engineering students to become entrepreneurs? Finding different recipes using a configuration approach. Journal of Small Business Management, 61(2), 353–383. https://doi.org/10.1080/00472778.2020.1790291
Robinson, P. B., Stimpson, D. V., Huefner, J. C., & Hunt, H. K. (1991). An attitude approach to the prediction of entrepreneurship. Entrepreneurship Theory and Practice, 15(4), 13–32. https://doi.org/10.1177/104225879101500405
Rocha, R. G., Paço, A. do, & Alves, H. (2024). Entrepreneurship education for non-business students: A social learning perspective. International Journal of Management Education, 22(2), 100974. https://doi.org/10.1016/j.ijme.2024.100974
Scrucca, L., Fraley, C., Murphy, T. B., & Raftery, A. E. (2023). Model-based clustering, classification, and density estimation using mclust in R. Chapman and Hall/CRC. https://doi.org/10.1201/9781003277965
Secundo, G., Ndou, V., & Del Vecchio, P. (2016). Challenges for instilling entrepreneurial mindset in scientists and engineers: What works in European universities? International Journal of Innovation and Technology Management, 13(5), 1–23. https://doi.org/10.1142/S0219877016400125
Shaikh, N. F., Nili, M., Dwibedi, N., & Suresh Madhavan, S. (2020). Initial validation of an instrument for measuring entrepreneurial and intrapreneurial intentions in student pharmacists. American Journal of Pharmaceutical Education, 84(7), 928–937. https://doi.org/10.5688/ajpe7624
Vayena, E., Blasimme, A., & Cohen, I. G. (2018). Machine learning in medicine: Addressing ethical challenges. PLoS Med, 15(11), 4–7. https://doi.org/10.1371/journal.pmed.1002689
Zhang, P. C., & Austin, Z. (2023). To succeed as health care innovators, pharmacists need to cultivate an entrepreneurial mindset. Canadian Pharmacists Journal, 156(3), 110–111. https://doi.org/10.1177/17151635231164622
Biographical notes
Sara Morgado Marcelino received her master’s degree in Industrial Engineering and Management at the University of Beira Interior in 2022. Currently, she is a PhD student at University of Beira Interior, with a main interest in the field of Lean thinking, sustainability, circular economy and decision support tools.
Nathalia Suchek (Ph.D. in Management) is currently Innovation Manager at the University of Beira Interior and a Researcher of NECE - Research Centre for Business Sciences. Her research interests are entrepreneurship, innovation, sustainability, and circular economy.
Arminda do Paço (Ph.D. in Management) is a Full Professor in the Department of Management and Economics, University of Beira Interior, Portugal. She is currently the Dean of the Faculty of Human and Social Sciences and a Researcher of NECE - Research Centre for Business Sciences. Her research interests are entrepreneurship education, sustainability, and environmental marketing.
Ricardo Gouveia Rodrigues (Ph.D. in Management) is currently Associate Professor of the University of Beira Interior and Principal Investigator of NECE - Research Centre for Business Sciences. His research interests are entrepreneurship and marketing.
Author contribution statement
Sara Morgado Marcelino: Conceptualization, Methodology, Formal Analysis, Writing – Original Draft, Writing – Review and Editing. Nathalia Suchek: Formal Analysis, Software, Writing – Original Draft, Writing – Review and Editing. Arminda do Paço: Conceptualization, Methodology, Writing – Original Draft, Supervision, Project Administration, Writing – Review and Editing. Ricardo Gouveia Rodrigues: Conceptualization, Methodology, Supervision, Writing – Review and Editing.
Conflicts of interest
The authors declare no competing interests.
Citation (APA style)
Marcelino, S. M., Suchek, N., Paço, A., & Rodrigues, R. G. (2026). A pedagogical framework to boost entrepreneurship skills in the healthcare sector. Journal of Entrepreneurship, Management and Innovation, 22(4), 115-138. https://doi.org/10.7341/20262246
Received 16 February 2026; Revised 8 May 2026, 21 June 2026; Accepted 26 June 2026.
This is an open-access paper under the CC BY license (https://creativecommons.org/licenses/by/4.0/legalcode).