Abstract
This research examines the role of Entrepreneurship Education in cultivating Entrepreneurial Mindset and Entrepreneurial Intentions within Engineering Education. A bibliometric analysis of 381 Scopus publications (2015–2023) was performed using Bibliometrix (R), VOSviewer, and OriginPro. The field shows rapid growth, led by the United States, India, Indonesia, and Europe. Network visualisations reveal key thematic clusters—students, curricula, design thinking, innovation, e-learning, and self-efficacy—showing that Entrepreneurial Mindset develops at the intersection of pedagogical design and behavioural intention. Despite the conceptual differences, research highlights the significance of incorporating experiential and activity-based EE methods into engineering curricula. This study maps the intellectual structure of the field and offers guidance for strengthening EM and EI through curriculum design.
Keywords
Introduction
Widespread recognition exists that entrepreneurship serves as a catalyst for economic growth, with graduates contributing significantly to innovation-driven expansion. Consequently, developing an Entrepreneurial Mindset (EM) is now crucial for equipping students to handle uncertainty and pursue careers in entrepreneurship. In India, despite a developing entrepreneurial environment, fear of failure, limited hands-on experience, and cautious attitudes still hinder student involvement, highlighting the necessity for organised educational initiatives.
Entrepreneurship Education (EE) enhances students’ skills and mindsets by increasing experiential learning and decreasing uncertainty (Costa et al., 2022). EE in higher education supports EM by aiding in opportunity recognition, business idea development, and stakeholder engagement (Raju et al., 2023; Secundo et al., 2023). Project-based and open-ended learning also enhances engineering graduates’ curiosity, networking, and value creation skills (Whitaker, 2023).
Engineering programme integration with EE has increased substantially (Dillon et al., 2023; Sitaridis et al., 2023). Despite this, students often perceive EM development as taking place outside of formal curricula, showing the importance of carefully designed teaching methods that aim to develop their mindsets (Jackson et al., 2023). Consequently, due to their alignment with engineering problem-solving and opportunity recognition, activity-based learning and design-oriented courses are becoming more widely adopted (Bosman et al., 2023; Mozaffar et al., 2023).
Entrepreneurial Intention (EI) is a significant behavioural outcome of EE and EM, supported by technological enablement, creativity, design thinking, and prototyping (Keoy et al., 2023; Read-Daily et al., 2022), with personal characteristics and a family business background (Chilenga et al., 2022). Research in EE–EM–EI has experienced rapid growth, yet it remains conceptually fragmented, with little integration of the Theory of Planned Behaviour and Entrepreneurial Cognition Theory.
This study conducts a bibliometric analysis of EE, EM, and EI research in Engineering Education (2015–2023) to address this gap. • Examining the thematic and intellectual evolution of the field • Identifying the dominant and emerging research fronts • Contextualise findings through major theoretical frameworks
This study provides evidence-based insights to inform curriculum design, pedagogical development, and future research in higher education engineering.
Conceptual framework
EE, EM, and EI are interconnected constructs that collectively influence engineering students’ entrepreneurial actions. The framework in question is based on both the Theory of Planned Behaviour (TPB) (Ajzen, 1991) and the Entrepreneurial Cognition Theory to explain the impact of educational experiences on motivation and cognitive orientation.
Within TPB, EI is the most immediate predictor of entrepreneurial behaviour, formed by attitudes, subjective norms, and perceived behavioural control (Ahmed et al., 2025). EE influences these preconditions by increasing knowledge, skills, and self-efficacy through experiential learning, design-based projects, and applied problem-solving, thereby enhancing students perceived feasibility of taking entrepreneurial action.
The Entrepreneurial Cognition Theory views EM as a cognitive mindset that combines opportunity identification, risk assessment, and flexible problem-solving (Cacciolatti and Lee, 2015). EM evolves through systematic experiential processes such as reflective learning, design thinking, and iterative testing (Kolb, 1984; Sugarman, 1987), which closely correspond with the settings of engineering education.
In this study, EE, EM, and EI function as the enabling mechanism, cognitive mediator, and motivational outcome, respectively. This framework provides direction for interpreting bibliometric patterns and explains how the EE–EM–EI pathway in engineering education is viewed in global research.
Integrated theoretical framework linking EE, EM, and EI in engineering education.
Search string
TITLE-ABS-KEY (entrepreneurial AND mindset AND in AND students) AND PUBYEAR > 2014 AND PUBYEAR < 2024 AND (LIMIT-TO ( SUBJAREA, “ENGI”) OR LIMIT-TO (SUBJAREA, “SOCI”) OR LIMIT-TO (SUBJAREA, “BUSI”) OR LIMIT-TO (SUBJAREA, “ECON”) ) AND (LIMIT-TO ( DOCTYPE, “cp”) OR LIMIT-TO (DOCTYPE, “ar”) ) AND (LIMIT-TO ( PUBSTAGE, “final”) ) AND (LIMIT-TO ( EXACTKEYWORD, “Students”) OR LIMIT-TO (EXACTKEYWORD, “Entrepreneurial Mindset”) OR LIMIT-TO (EXACTKEYWORD, “Entrepreneurship Education”) OR LIMIT-TO (EXACTKEYWORD, “Entrepreneurship”) OR LIMIT-TO (EXACTKEYWORD, “Entrepreneurial Intention”) OR LIMIT-TO (EXACTKEYWORD, “Entrepreneurial Education”) OR LIMIT-TO (EXACTKEYWORD, “Entrepreneurial Skills”) OR LIMIT-TO (EXACTKEYWORD, “Entrepreneur”) OR LIMIT-TO (EXACTKEYWORD, “Design Thinking”) OR LIMIT-TO (EXACTKEYWORD, “Entrepreneurial Learning”) OR LIMIT-TO (EXACTKEYWORD, “Product Design”) OR LIMIT-TO (EXACTKEYWORD, “Entrepreneurial Activity”) OR LIMIT-TO (EXACTKEYWORD, “Entrepreneurial Intentions”) OR LIMIT-TO (EXACTKEYWORD, “Student Perceptions”) OR LIMIT-TO (EXACTKEYWORD, “Student Learning”) ) AND (LIMIT-TO ( SRCTYPE, “p”) OR LIMIT-TO (SRCTYPE, “j”) ).
Methodology
This study employs a bibliometric research methodology to investigate the development of EE, EM, and EI within engineering education. Figure 1 illustrates the refinement process used to construct the final dataset. Each block represents a filtering stage. The arrows indicate the reduction direction, and the counts show the remaining documents after each step. Document selection and screening process for the bibliometric analysis.
Scopus was chosen as the sole data source primarily due to its in-depth international coverage of engineering, education, and management research, as well as its compatibility with Bibliometrix (R Studio), VOSviewer, and OriginPro. The software’s structured metadata and citation links enable the analysis of thematic evolution and intellectual structures. Dependence on a solitary database may introduce indexing and language bias, a constraint that is recognised.
An initial search using the keyword “Entrepreneurial Mindset in Students” yielded 746 documents. The timeframe was limited to 2015–2023 to encompass the era when entrepreneurship education and entrepreneurial mindset frameworks achieved prominence within engineering education, concurrent with the global growth of experiential learning, innovation-driven curricula, and institutional entrepreneurship initiatives. The filter narrowed the dataset down to 648 documents.
Subject areas were restricted to fields such as Engineering, Social Sciences, Business, Management, Accounting, Economics, Econometrics, and Finance, which comprised 604 documents. Subsequently, the dataset was narrowed down to peer-reviewed journal articles and conference papers (526), and then further limited to publications at the final stage (514). A concept-driven keyword refinement, focusing on constructs related to students and entrepreneurship, reduced a dataset of 392 documents. Restricting the source types to journals and conference proceedings yielded a final dataset of 381 documents.
A bibliometric analysis was performed using Bibliometrix for descriptive indicators and thematic evolution, VOSviewer for network visualisation, and OriginPro for publication trends. A threshold of five keyword occurrences and association-strength normalisation were applied. The cluster interpretation was validated by analysing high-frequency keywords and cross-referencing with the existing literature on EE–EM–EI.
The figure illustrates the step-by-step process of refining Scopus records using keyword search, limiting the timeframe to 2015–2023, filtering by subject area, document type, and publication stage, further refining by keyword, and selecting source types. The arrows indicate the filtering direction, and the document counts show the number of records kept at each stage, ultimately yielding a final dataset of 381 publications for analysis.
Abbreviations shown in Figure 1 SS - Social Sciences; BM - Business Management; AE - Accounting and Economics; EF - Econometrics and Finance, EM - Entrepreneurial Mindset; EE - Entrepreneurship Education; EI - Entrepreneurial Intention; EE - Entrepreneurial Education; ES - Entrepreneurial Skills; DT - Design Thinking; EL - Entrepreneurial Learning; PD - Product Design; EA - Entrepreneurial Activity; EI - Entrepreneurial Intentions; SP - Student Perceptions; SL - Student Learning are clearly defined in Note 1 and 2 below the figure to ensure transparency and interpretability of the section process.
Discussion of bibliometrics
The final dataset consisted of 381 Scopus-indexed publications from 2015 to 2023, illustrating a rapidly growing research area that intersects EE, EM, and EI within engineering and technical education. Evidence from several publications indicates that entrepreneurial learning has become a key strategic focus in engineering programmes worldwide, driven by growing institutional and policy support for innovation, employability, and value creation. This is consistent with the wider educational reforms that promote experiential learning, interdisciplinary collaboration, and design-led problem solving–key drivers of EM and EI development.
3 field plot
A three-field plot is used to link core references (CR), contributing authors (AU), and dominant keywords (DE) (Figure 2). The term EM is the most frequently occurring keyword, which is consistent with Entrepreneurial Cognition Theory, suggesting its importance in engineering education research. EE is the second most dominant term, supporting the TPB view that educational experiences affect attitudes and perceived behavioural control, ultimately influencing the EI. 3 field plot.
The reference cluster draws attention to influential scholars such as Bosman, Kuratko, Fayolle, and Liñán, showing that modern engineering-based research still uses traditional theories of intention formation and entrepreneurial cognition. Prominent authors, such as Secundo and Narmaditya, highlight the ongoing scholarly commitment to integrating EE and EM via experiential and design-based learning methods.
The three-field plot reveals a cohesive intellectual framework in which theories, authors, and keywords merge towards the EE → EM → EI pathway, underscoring the global shift towards EE that prioritises mindset development.
Annual scientific production
Research on EE, EM, and EI in engineering education showed a sustained expansion from 2015 to 2023 (Figure 3). The post-2019 acceleration indicates a shift in pedagogies towards experiential, digital, and innovation-oriented approaches, supporting theoretical claims from the Theory of Planned Behaviour and Entrepreneurial Cognition Theory that highlight the importance of mindset formation as a precursor to forming entrepreneurial intentions. Annual scientific production.
Short-term fluctuations, such as the decline observed in 2018, are consistent with changes associated with the number of publication cycles and do not affect the study’s overall developmental course. Growth continued through 2022, followed by stabilisation in 2023, implying that EE–EM–EI research has shifted from an emerging subject to a more established area of research. This trend supports the study’s goal of capturing the maturation and global institutionalisation of entrepreneurial competency frameworks within EE.
Most relevant sources
Figure 4 highlights the key publications that influence EM and EE research within the engineering education context. The significance of the ASEE Annual Conference highlights the crucial position of engineering education communities, especially in the United States, in implementing entrepreneurial skills through organised curriculum and teaching changes, such as Entrepreneurial Minded Learning (EML) projects. Most relevant sources.
The existence of conferences such as the Frontiers in Education Conference, Sustainability, Education and Training, and the European Conference on Innovation and Entrepreneurship suggests that EM and EE research goes beyond discipline-specific instruction into more general issues concerning sustainability, digital transformation, and innovation systems. The distribution implies that developing an entrepreneurial mindset in engineering is viewed not only as a teaching issue but also as a strategic reaction to socio-economic and technological change.
Overall, the diversity of sources supports the notion that EM and EE research has become a mainstream area of study in engineering education scholarship.
Top 5 publishing sources and key areas.
Top 5 authors and research areas.
Table 2 illustrates that the ASEE Annual Conference serves as the principal knowledge centre for EM- and EE-focused engineering education research, highlighting a significant focus on experiential learning, innovation-driven teaching methods, and developing an entrepreneurial mindset. The significance of the Frontiers in Education (FIE) Conference and the European Conference on Innovation and Entrepreneurship (ECIE) Conference further suggests that EM research is closely connected to educational technology, curriculum innovation, and entrepreneurial strategy. Studies like Sustainability and Advances in Engineering Education show that research on EM and EE is increasingly linked with sustainability, digital change, and active learning, which supports the idea that entrepreneurial learning is now a key part of interdisciplinary engineering education discussions rather than just being limited to business-focused publications.
Table 3 showcases a concentrated group of scholars whose research has been pivotal in the advancement of EM as a fundamental educational goal in engineering design. Bodnar and Bosman highlighted the importance of experiential and student-centred teaching methods and curriculum reform, whereas Erdil and Secundo focused on assessment, design-based learning, and technology-enabled entrepreneurship. Carnasciali’s emphasis on educational technology and student engagement demonstrates the increasing significance of digital and blended learning environments in EM development. Together, these studies are in line with Entrepreneurial Cognition Theory, as they underscore opportunity recognition, design thinking, and self-efficacy as crucial cognitive processes underlying EM formation.
Combining the key sources and authors shows that the overall research environment in engineering education values the development of mindset, innovation capacity, and experiential learning. The identified patterns correspond directly with the bibliometric clusters found in the study’s later stages and further solidify EM and EI as crucial competencies for engineering graduates in innovation-driven economies.
Authors’ production overtime
Research in EE–EM–EI, as illustrated in Figure 5, is primarily driven by a small group of highly prolific scholars, including Bodnar, Secundo, and Erdil, whose continuous contributions have influenced experiential, design-based, and cognition-oriented approaches in engineering education. The productivity of authors around 2021 coincided with the COVID-19 pandemic, when digital, hybrid, and technology-driven pedagogies grew rapidly, drawing increased scholarly attention to the development of EM and adaptive learning models in engineering settings. Researchers like Liu, Huang-Saad, and Harichandran occasionally make significant contributions, often related to specific topics such as innovation ecosystems, design thinking, and teaching engineering in a leadership capacity. Together, these trends indicate a research field that is becoming more established, with a consistent framework from main contributors alongside a selective expansion into new areas due to changes in the context and teaching methods. Authors’ production overtime.
Most relevant authors
Figure 6 highlights a closely knit group of academics defining EE–EM–EI research in engineering education. Bodnar and Bosman appear to be the most frequent contributors, followed by Erdil and Secundo, highlighting a stable core of authors shaping the field’s conceptual and pedagogical growth. Their research consistently explores concepts such as experiential learning, design thinking, identifying opportunities, and curriculum innovation, aligning closely with the leading themes identified in this study. Their high output rate, particularly in top conferences like ASEE and FIE, implies that EM development in engineering education is led by a unified, specialised, and continually evolving research community rather than a fragmented research landscape. Most relevant authors.
Corresponding authors’ countries
Figure 7 illustrates the geographic distribution of corresponding authors in Single-Country Publications (SCP) and Multi-Country Publications (MCP), which indicates both domestic research capabilities and international collaboration trends in EE–EM–EI research. Corresponding authors’ countries.
The United States leads the field with high SCP output and significant MCP activity, reflecting its robust institutional infrastructure for engineering entrepreneurship and the systematic integration of EM–oriented pedagogy. Indonesia and India follow with primarily SCP-driven output, signifying rapidly expanding domestic research environments that are still developing broader international partnerships.
In contrast, countries like the United Kingdom, China, Finland, and Italy exhibit relatively higher engagement levels in MCP, indicating active participation in cross-border research networks that are aligned with global engineering education reforms, digital transformation, and sustainability-oriented entrepreneurship. The global research landscape is increasingly reflected in the distribution, with collaborative-driven countries driving internationalisation, whereas SCP-dominant regions signify emerging yet solidifying hubs of EE scholarship.
Countries scientific production
Figure 8 showcases the global scientific output of EE–EM–EI research, highlighting significant regional variations in research activity. Countries scientific production.
The United States takes the lead with its well-established engineering entrepreneurship ecosystem and longstanding EM-oriented curricula. Other nations, including India, Australia, the United Kingdom, China, Indonesia, Finland, and Canada, are also showing considerable and increasing engagement, which aligns with their respective national agendas for innovation, skills development, and higher education reform.
Countries such as Germany, Italy, Japan, South Korea, and Brazil, which are considered moderate contributors, tend to concentrate on specific areas such as sustainability, digital transformation, and industry-academia partnerships, which aligns with the patterns found in the co-occurrence clusters. Regions with limited or no indexed results indicate disparities in global participation and emphasise potential opportunities for enhancing international collaboration in engineering entrepreneurship research.
Tree map
Figure 9 provides an overview of the dominant author keywords that underpin EE–EM–EI research. The emphasis on terms such as students, engineering education, curricula, and teaching suggests that EM is primarily viewed as an educational outcome focused on the learner, shaped by curriculum and teaching methodology rather than as a standalone psychological concept. Tree map.
The tree map supports this study’s conceptual approach, highlighting EE as the key factor in developing EM and subsequently EI, consistent with the thematic patterns found in the co-occurrence clusters.
Overall, the tree map reinforces this study’s conceptual framing of EE as the primary mechanism through which EM—and subsequently EI—is cultivated, closely aligning with the thematic patterns identified in the co-occurrence clusters.
Network visualization
Figure 10 illustrates a keyword co-occurrence network produced by VOS viewer, which consists of seven interconnected thematic clusters that collectively represent the intellectual structure of research in EM, EE, and Engineering Education. The proximity of nodes and the density of linkages indicate the frequency with which related concepts occur in the literature, highlighting key themes and their relative importance. Network visualization.
Cluster 1 (Green): Students and curriculum—the core of pedagogy
The cluster focuses on students, curricula, engineering education, and associated pedagogical terms, suggesting that EM research is fundamentally rooted in curriculum design and learner-centered interventions. The strong ties between students, curricula, and engineering networks resonate with work on entrepreneurial-minded learning initiatives, such as KEEN, which emphasise embedding EM into engineering curricula and co-curricular experiences (Bosman et al., 2023). Studies engaging first-year engineering cohorts and interdisciplinary collaboration with business students further support the view that early, structured EE experiences can shape EM and future EI (Brooks, 2023; Keoy et al., 2023).
Cluster 2 (Light Blue): Machine design & student outcomes: Practice-based learning
Machine design, engineering research, and student outcomes are found together, suggesting a focus on design-intensive, hands-on learning environments. Studies in this field have investigated how engineering assignments can be used to evaluate EM and how design projects simultaneously cultivate technical expertise and the ability to identify opportunities (Bosman and Soto, 2022; Zhu, 2021). The cluster indicates that practice-based learning is a crucial pathway through which EM is translated into quantifiable learning outcomes.
Cluster 3 (Yellow): Engineering networks and design thinking: Collaborative ecosystems
Engineering networks, first-year engineering, design, and product design nodes illustrate the significance of collaborative and PBL environments. Design-led projects often grant students the freedom to create or enhance products, thereby enhancing their emotional maturity through creative and innovative approaches (Fraley et al., 2018). Research on integrating entrepreneurial learning with PBL demonstrates how team-based engineering environments foster curiosity, connections, and value creation (Elsaadany et al., 2022; Scroccaro and Rossi, 2021).
Cluster 4 (Orange): Engineers and design: Professional identity and mindset formation
This cluster links engineers with designers, highlighting the growing importance of entrepreneurial thinking in engineers’ professional roles. Research in this field investigates how entrepreneurship-focused course curricula and design classes enable students to move beyond narrow technical positions and adopt a mindset focused on seeking opportunities and innovation (Erdil, 2020; Moore et al., 2022). The cluster supports the notion that EM is becoming a fundamental characteristic of modern engineering practice.
Cluster 5 (Red): Entrepreneurship and innovation: Conceptual foundations
The red cluster encompasses entrepreneurship, EE, innovation, the entrepreneur, and self-efficacy. These terms serve as the fundamental conceptual framework of EM research and closely correlate with the TPB and Entrepreneurial Cognition Theory. Research on engineering students has found that EE interventions can boost EM and EI by developing innovative skills and increasing entrepreneurial self-confidence (Kakouris and Liargovas, 2022; Vempala et al., 2021; Wang et al., 2022).
Cluster 6 (Purple): Engineering entrepreneurship and student learning: Outcomes-driven approaches
This cluster links engineering entrepreneurship with student learning outcomes, indicating a focus on the influence of entrepreneurship-oriented programmes on employability and learning outcomes. Typically, research in this area assesses shortcomings in design, problem-solving, and adaptability and investigates how EE initiatives and self-regulated learning strategies address these weaknesses (Sababha et al., 2021; Wang et al., 2022).
Cluster 7 (Dark Blue): E-learning, EE, and digital delivery: A technology-mediated mindset
In the last cluster, e-learning is linked to EE and engineering entrepreneurship. This reflects an increasing body of research on technology-facilitated experiential education, where online and blended learning environments are utilised to offer adaptable, expandable opportunities for the development of EM (Rodrigues, 2023). This study draws on previous research that views innovative digitally supported learning approaches as crucial to contemporary EE (Neck et al., 2014).
Bibliometric clustering reveals that EM is the central concept connecting curriculum design, experiential learning, and innovation. The pathway illustrated in Figure 10 shows that EE acts as the enabling mechanism and EM functions as the cognitive mediator in the context of engineering education.
An integrated analysis of the three-field plot, publication trends, prominent sources, authors, geographic distributions, keyword structures, and network visualisations reveals a consistent and theory-based intellectual path rather than individual descriptive patterns. Overall, these bibliometric factors support a comprehensive transition towards EE focused on EM, founded on the TPB and Entrepreneurial Cognition Theory, and signify the field’s growth from descriptive mapping to conceptual integration.
Key contributions of the study
EM through innovation, design thinking, and engineering education
The bibliometric clusters pinpoint students, curricula, innovation, design thinking, and machine design as the core conceptual underpinnings of EM development in engineering education. Engineering programmes promote EM through iterative design, prototyping, and problem-solving activities that enhance self-efficacy, creative confidence, and opportunity recognition.
Integrating engineering entrepreneurship into curricula strengthens students’ ties with collaborative design communities and professional engineering networks, enhancing EM development. Research findings support the notion that entrepreneurship contributes to economic expansion, as indicated in more extensive studies (Anish Kumar and Mihir Kumar, 2023; Chang et al., 2022). Identifying the determinants of EI is crucial because it is the strongest predictor of entrepreneurial behaviour. Figure 11 illustrates eight significant EI-related factors—financial literacy, entrepreneurial knowledge, creativity, handling ambiguity, emotional maturity, attitude, teaching practices, and core self-evaluation—which closely align with the co-occurrence and network patterns identified in this study. Factors effecting entrepreneurial intensions (EI).
Enhancing financial literacy, creativity, and development of competence
The bibliometric patterns indicate a significant relationship between EE, financial literacy, entrepreneurial knowledge, and creativity. EE-related theoretical and practical courses improve students’ ability to decipher financial data, produce innovative ideas, and navigate uncertainty, which are skills essential for EM. Creative development enhances core self-evaluation and tolerance of ambiguity (Chaker and Dellagi, 2023).
Frameworks such as EntreComp outline the knowledge, skills, and attitudes that support EM, and EI strengthens both EM and EI (Abdelwahed and Alshaikhmubarak, 2023). Entrepreneurial mindset development is expedited through experiential learning, which encompasses prototyping, business modelling, and proof-of-concept activities (López-Núñez et al., 2022; Tunstall and Neergaard, 2022; Vuorio et al., 2023). Inconsistent EM definitions complicate the design of educational materials and the assessment of student learning outcomes (Larsen, 2022).
It is crucial that EM acts as a transformational mechanism: EI is only affected when EM is present (Chang et al., 2022). Employee motivation is enhanced by shaping attitudes and autonomy through experiential learning, leadership training, and mentoring (Alaraje, 2022; Bernardus et al., 2023; Jebsen et al., 2023).
Start-ups as learning ecosystems for creativity and entrepreneurial skill formation
Start-ups in the technology sector offer students dynamic learning environments that enable them to engage in benchmarking, conduct experimentation, develop products through iterative processes, and innovate business models (Beke et al., 2023). Consistently, creativity emerges as a strong predictor of EI and can be intentionally developed through structured teaching strategies (Chaker and Dellagi, 2023). Financial literacy improves entrepreneurial knowledge, which in turn reinforces entrepreneurial motivation, core self-evaluation, and entrepreneurial attitudes—emphasising the interconnectedness among EI-related variables.
Igniting EM through passion, self-efficacy, and ecosystem engagement
EE, along with psychological mediators, has a significant impact on shaping entrepreneurial behaviour. Exposure to sustained EE strengthens EM and directly affects EI, whereas entrepreneurial passion acts as a moderator between self-efficacy and entrepreneurial attitudes (Liao et al., 2022).
Higher education institutions are increasingly incorporating experiential learning into their curricula, cocurricular activities, and extracurricular programmes (Erdil et al., 2024). Engagement with peers, faculty, industry partners, and innovation ecosystems promotes opportunity identification, creativity, and cautious decision-making processes (Bodnar et al., 2020; Secundo et al., 2023). The entrepreneurial mindset develops through informed decision-making, deliberate reflection, and awareness of emerging technological and behavioural trends (Bosman and Phillips, 2022), enabling students to assess and utilise opportunities—key aspects of entrepreneurial thinking.
Practical, research, and policy implications
Implications for educational practices
The research outcomes underscore the necessity of integrating EE into engineering degree programmes in order to foster EM, identify opportunities, and develop innovative problem-solving skills. Core engineering coursework should incorporate activity- and problem-based learning, design thinking, business model development, and financial literacy. Additionally, interdisciplinary projects combining engineering, business, and social sciences can enhance entrepreneurial skills. To measure EM and EI and facilitate ongoing pedagogical improvement, systematic assessment tools must be used.
Implications for future research
Future studies should investigate the effectiveness of particular instructional methods—like experiential learning and design-based strategies—through empirical testing in enhancing the EE–EM–EI pathway across various engineering settings.
Implications for the policy
Embedding EE within engineering accreditation frameworks at the policy level can facilitate national innovation objectives, boost graduate employability, and contribute to sustainable economic growth.
Limitations
The research in this study utilised bibliometric methods to visualise research patterns on EM in connection with EE, EI, and Engineering Education from 2015 to September 2023. The analysis relied on Scopus publications and visualisation tools, such as R Studio, VOSviewer and Lucidchart.
Although the bibliometric approach provides a systematic overview, it has several limitations. First, restricting the dataset to a single database, such as Scopus, may exclude relevant studies listed elsewhere, thus reducing the completeness of the knowledge map. Bibliometric outputs are heavily reliant on the choice of keywords, with different keyword combinations potentially producing slightly varying thematic patterns. Third, citation-based metrics have inherent biases because highly cited works are not always as conceptually influential as they appear.
This study does not include empirical validation. The insights derived stem from aggregate patterns found in published literature, rather than from primary data collected from learners or educators. These limitations should be considered when interpreting the study findings.
Future research directions
Research on EM development should progress beyond basic descriptions and use rigorous empirical methods—like experiments, quasi-experiments, and long-term studies—to illustrate how EE, EI, design thinking, digital pedagogy, and models focused on innovation influence EM development over time. Comparative studies are required to determine which curricular arrangements—compulsory, elective, or embedded within co-curricular structures—are most effective in fostering EM outcomes among engineering students. Investigating the extension of EE to secondary or senior school levels is also warranted, as early exposure may enhance the entrepreneurial engineers’ developmental trajectory.
National policy initiatives, such as Start-up India, Digital India, and eBiz Portal, offer promising avenues for investigating how institutional ecosystems and policy frameworks impact EI and EM development. Ultimately, multi-country bibliometric and empirical studies are crucial for understanding how cultural, economic, and institutional contexts influence EM development worldwide, which in turn helps inform more nuanced and adaptable educational interventions.
Conclusion
This study presents a bibliometric synthesis of research on EE, EI, and EI in engineering education from 2015 to 2023. The analysis highlights a unified intellectual framework in which EE acts as the primary pedagogical tool, EM is the central cognitive link, and EI is the resulting behavioural outcome, as predicted by both the TPB and Entrepreneurial Cognition Theory.
The research indicates a noticeable shift towards EE that focuses on EM, which is closely linked to design thinking, experiential learning, and engineering curricula that emphasise innovation. Strengthening engineering programmes with EE integration enhances the recognition of opportunities, creativity, and self-efficacy—key abilities that facilitate innovation-driven economic growth (Anish Kumar and Mihir Kumar, 2023; Chang et al., 2022). The synthesis also underscores the need for systematic pedagogies and evaluation methods to facilitate the development of EM, as noted by (Dillon et al., 2023). In general, this study advances the field by going beyond descriptive mapping and instead elucidates how entrepreneurial competencies are structured and combined within engineering education, thereby encouraging the conduct of more empirical and cross-contextual research.
Footnotes
Acknowledgements
The authors gratefully acknowledge the academic support and encouragement provided by Dr M. Rajya Laxmi, Associate Professor and Head, School of Business, SR University, which contributed to a supportive research environment for this study. The authors also acknowledge Dr Gurunadham Goli, Associate Professor, School of Business, SR University, for his valuable suggestions and institutional support that helped strengthen the conceptual and methodological clarity of the work. The authors further acknowledge Prof. Deepak Garg, Vice Chancellor, SR University, for fostering a research-oriented academic ecosystem that promotes scholarly inquiry and innovation. Finally, the first author sincerely acknowledges the guidance and mentorship of his supervisor, Dr N. Suman Kumar, whose constructive feedback and academic supervision were instrumental throughout the development of this study.
Ethical considerations
This study is based exclusively on secondary bibliometric data retrieved from the Scopus database and did not involve human participants or primary data collection; therefore, informed consent and ethical approval were not required.
Consent for publication
All authors have reviewed and approved the final manuscript for submission.
Funding
The authors received no financial support for the research, authorship, and/or publication of this article.
Declaration of conflicting interests
The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Data Availability Statement
The data supporting the findings of this study were obtained from the Scopus database and analyzed using established bibliometric techniques. The processed data and analytical outputs are available from the corresponding author upon reasonable request.
AI use declaration
Artificial intelligence–based tools were used in a limited and supportive capacity during manuscript preparation. Trinka AI was employed for grammatical correction and language refinement, while ChatGPT was used to assist in improving the organization and clarity of the authors’ own ideas. AI tools were not used for data collection, data analysis, interpretation of results, or generation of references. The authors retain full responsibility for the intellectual content and conclusions of the manuscript.