Entrepreneurial Competencies in the Era of Digital Transformation: A Systematic Literature Review

Abstract

Digital transformation (DT) is rapidly reshaping education at multiple levels, including curriculum, instructional practices, and institutional culture. Within this context, entrepreneurship education has become a key field for preparing individuals to navigate uncertainty and generate social and economic value in a digital society. Entrepreneurial competencies are increasingly conceptualized as a multidimensional construct that encompasses creativity, problem-solving, critical thinking, collaboration, and digital literacy. This study aims to identify core entrepreneurial competencies relevant to the digital era and examine how technology-integrated instructional strategies contribute to their development. A systematic literature review was conducted in accordance with PRISMA 2020 guidelines, analyzing 72 peer-reviewed journal articles published between January 2021 and June 2025. The findings indicate that DT drives structural changes in education beyond tool adoption, with technologies such as artificial intelligence (AI), data analytics, and digital collaboration platforms serving as catalysts for innovative thinking and entrepreneurial behavior. These technologies are not merely supportive tools but are embedded in competency-based learning processes. This review provides a comprehensive competency framework integrating three domains, AI-collaborative pedagogy validation, and implementation strategies, enabling educators, curriculum developers, and policymakers to redesign entrepreneurship education that aligns with the realities of digital learning environments and fosters future-ready entrepreneurial capabilities. This conceptual framework theoretically systematizes the integration of innovative thinking and ethical execution capabilities required in the digital era, contributing to defining the future direction of entrepreneurship education.

1. Introduction

Digital transformation (DT) has profoundly reshaped the educational landscape, particularly in higher, adult, and vocational learning contexts. It has altered teaching practices, curriculum frameworks, assessment strategies, and the overall learner experience. Beyond the mere adoption of digital tools, DT signifies a systemic reconfiguration of educational ecosystems and value creation mechanisms, propelled by the rapid advancement of smart technologies such as artificial intelligence (AI), big data, and learning analytics [1,2,3]. These developments introduce both novel opportunities—such as personalized learning pathways and real-time formative feedback—and multifaceted challenges to contemporary educational practice. As Al-Mamary et al. [3] observe, AI-driven technologies are fundamentally transforming educational paradigms by enabling personalization, predictive analytics, and learner-centered approaches to instructional design. These developments have significant implications for the future of entrepreneurship education.

Within this evolving digital society, entrepreneurship education has emerged as a pivotal domain for cultivating learners’ capacity to navigate uncertainty, address complexity, and transform challenges into opportunities for creating social and economic value through innovation. Empirical research supports this emphasis: Kang et al. [4] demonstrated that digital literacy significantly enhances university students’ entrepreneurial intentions, evidencing the strong interconnection between digital competency and entrepreneurial potential. The World Economic Forum [5] underscores the rising importance of entrepreneurial skill sets grounded in emergent technologies such as AI and big data, emphasizing their critical role in shaping future labor markets. Likewise, UNESCO [6] stresses the strategic function of entrepreneurship education in mitigating digital inequality and fostering social responsibility, advocating for integrative capabilities that merge technical proficiency with values-based action. The European Union conceptualizes entrepreneurial competency as a dynamic integration of knowledge, skills, and attitudes that empower individuals to act creatively and autonomously in response to complex problems [7]. This conceptualization aligns with the view of Bodescu et al. [8], who argue that entrepreneurship education in the digital age must encompass not only digital literacy but also AI literacy to effectively prepare learners for the knowledge-based economy.

Despite growing scholarly interest, existing research has yet to sufficiently explore the specific entrepreneurial competencies necessitated by DT or to examine how technology-integrated educational strategies contribute to their development. Prior studies have predominantly focused on traditional entrepreneurial traits—such as leadership, initiative, and creativity—or have treated digital tools largely in terms of instructional efficiency. There remains a notable gap in systematic analyses that investigate the structural relationship between emerging digital technologies and entrepreneurial competencies formation [9,10]. Given that entrepreneurial competency is shaped by a complex interplay of personal dispositions and demonstrable capabilities, further research grounded in both empirical evidence and educational theory is required to translate these competencies into actionable instructional frameworks.

Accordingly, this study aims to (1) identify the entrepreneurial competencies required in the DT era; (2) analyze instructional strategies that incorporate digital technologies to develop these competencies; and (3) derive implications for curriculum design and policy formulation.

To address these aims, a systematic literature review (SLR) was conducted in line with the PRISMA 2020 guidelines, focusing on peer-reviewed, English-language journal articles published between January 2021 and June 2025. This review offers a comprehensive synthesis of how DT influences entrepreneurship education from both conceptual and applied perspectives [11]. The study is guided by the following research questions:

• RQ1. How is DT reshaping educational environments, and what are its implications for entrepreneurship education?
• RQ2. What entrepreneurial competencies are essential in the DT era?
• RQ3. How are digital technologies being leveraged to foster entrepreneurial competencies?

This paper is organized into six sections. Section 1 introduces the context and aims of the study. Section 2 provides a literature review encompassing definitions of entrepreneurial competencies, their relationship with DT, and key research gaps. Section 3 outlines the methodology, detailing the systematic review process based on the PRISMA 2020 framework. Section 4 presents the findings, highlighting thematic patterns and geographic trends. Section 5 discusses the theoretical and practical implications. Finally, Section 6 concludes the paper and suggests directions for future research.

2. Literature Review

2.1. DT in Education

DT is defined as a comprehensive innovation process that goes beyond the mere adoption of digital technologies to fundamentally redesign an organization’s strategy, structure, value creation mechanisms, and overall learning environment [1,12]. Within the fields of technology management and educational technology, DT is conceptually distinguished from digitization, as it entails structural transformation driven by digital technologies across both institutional and educational systems. Vial [1] contends that digital technologies reconfigure organizational value creation pathways, cultures, and processes, thereby exerting structural influence on pedagogy, curricula, assessment practices, and learner experiences within educational institutions.

Bond et al. [13] emphasize that DT in higher education extends beyond infrastructure upgrades to encompass leadership, teaching and learning strategies, administrative systems, and institutional culture. This multilayered transformation necessitates a fundamental restructuring of governance models and learning ecosystems. Thus, DT is not a peripheral issue but a central force shaping contemporary educational environments.

International policy reports have also underscored the educational potential of DT. The OECD [2] outlines strategic priorities for building a digital education ecosystem, including the development of teachers’ digital competencies, the effective use of learner data, and the establishment of robust data governance frameworks. The report notes that learning analytics can enhance learner autonomy and strengthen problem-solving abilities. Similarly, UNESCO [6] highlights how digital technologies can improve the quality and inclusiveness of education, serving as essential tools for ensuring the continuity of learning during crises such as the COVID-19 pandemic.

However, critical perspectives caution against overly optimistic narratives of DT. Brown and Czerniewicz [14] argue that technological inequality, digital exclusion, and overreliance on digital systems may undermine educational equity and inclusion. Selwyn [15] likewise contends that educational technologies do not inherently produce progressive outcomes, emphasizing the need to critically examine the sociopolitical conditions surrounding their adoption. These critiques underscore the complex, non-neutral, and context-dependent nature of DT, positioning it as a process embedded within broader sociocultural and institutional dynamics.

Extending this discourse, Surjawan et al. [16] investigate the implementation of “smart campus” and “smart management” frameworks across teaching, administration, and organizational culture in higher education institutions. Their empirical findings illustrate how DT is evolving into a fully integrated process of digitalizing educational environments.

Building on these theoretical and empirical discussions, the present study explores the implications of DT for entrepreneurship education in higher education. Through an SLR, it seeks to identify structural patterns and recurring themes in the literature, offering theoretical and practical insights into the DT of entrepreneurship education.

2.2. Concept and Components of Entrepreneurial Competencies

Entrepreneurial competency is increasingly conceptualized as a multidimensional construct that extends beyond acquiring knowledge or skills solely for launching a business. It encompasses core competencies essential in the era of DT, such as creativity, problem-solving, critical thinking, and self-direction. This perspective underscores the importance of a competency-based educational approach that integrates knowledge (what to know), skills (how to perform the task), and attitudes (how to behave) [17]. Le Deist and Winterton [18] argue that entrepreneurial competency should not be regarded merely as a task-specific technical skill. Instead, it should be understood within an interdisciplinary framework encompassing behavioral, cognitive, and social dimensions. Their model emphasizes that competencies are cultivated through complex learning experiences and active engagement, integrating cognitive understanding with practical application and attitudinal development.

This integrative view is further elaborated in the EntreComp framework developed by the European Commission’s Joint Research Centre (JRC). EntreComp organizes entrepreneurial competencies into three interrelated domains: “Ideas and Opportunities”, “Resources”, and “Into Action”. Across these domains, the framework identifies 15 sub-competencies, including creativity, opportunity recognition, resource mobilization, teamwork, and perseverance [7]. Designed for application across diverse educational contexts, EntreComp is widely recognized for its flexibility and practical relevance—particularly in designing learner-centered strategies, aligning vocational and higher education curricula, and supporting lifelong learning. A synthesized overview of the 15 key entrepreneurial competencies identified in the EntreComp framework is presented in

Table 1. Fifteen key entrepreneurial competencies derived from the EntreComp framework (adapted from Bacigalupo et al. [7]).

Key Area	Sub-Competency	Description
Ideas and Opportunities	1. Spotting opportunities	Identifies and defines creative opportunities within social, cultural, and economic challenges.
	2. Creativity	Develops new ideas and innovative approaches to problem-solving.
	3. Vision	Establishes forward-looking goals and articulates a clear vision for the future.
	4. Valuing ideas	Assesses the economic and social value of ideas and judges their feasibility.
	5. Ethical and sustainable thinking	Considers responsible and long-term perspectives in entrepreneurial decision-making.
Resources	6. Self-awareness and self-efficacy	Recognizes personal strengths, weaknesses, and motivations, and builds confidence to take on challenges.
	7. Motivation and perseverance	Demonstrates sustained effort to achieve goals and overcome obstacles.
	8. Mobilizing resources	Identifies and effectively utilizes physical, digital, and social resources.
	9. Mobilizing others	Engages others through collaboration and mobilizes their support and resources.
	10. Financial and economic literacy	Understands and applies financial and economic concepts for sound decision-making.
Into Action	11. Taking the initiative	Proactively implements ideas and takes calculated risks.
	12. Planning and management	Organizes projects by managing time and resources efficiently.
	13. Coping with uncertainty, ambiguity and risk	Anticipates uncertain situations and responds with strategic approaches.
	14. Working with others	Communicates and collaborates effectively with individuals from diverse backgrounds.
	15. Learning through experience	Learns from both success and failure, and incorporates feedback for continuous growth.

.

The EntreComp framework extends beyond traditional start-up-focused competencies by incorporating socio-emotional dimensions such as self-awareness, ethical responsibility, and sustainability, thus reflecting a lifelong learning perspective on entrepreneurship. For example, competencies such as self-awareness, self-efficacy, and learning through experience align closely with self-directed learning and technology acceptance in the DT era, suggesting strong potential for educational integration. According to the OECD [18], entrepreneurship education in digital contexts must foster not only technical competencies—such as digital literacy, data analysis, and collaboration—but also attitudinal competencies, including creative thinking, autonomy, and ethical awareness. This highlights the multidisciplinary nature of entrepreneurial competency, which requires both theoretical synthesis and practical alignment across domains such as education, business, and psychology. Accordingly, this study reconstructs key entrepreneurial competencies based on the EntreComp framework by aligning them with Winterton’s knowledge–skill–attitude (KSA) model. Through this synthesis, the study proposes an expanded entrepreneurial competency model that incorporates digital-specific capacities such as AI collaboration, data utilization, and digital transformation. A classification of these core entrepreneurial competencies in the DT era is presented in

Table 2. Classification of core entrepreneurial competencies in the DT era (adapted from Le Deist and Winterton [17]).

Competency Domain	Key Components	Description	Examples
Knowledge (What to know)	Digital technologies, data literacy, AI and generative AI principles	Foundational knowledge that enables opportunity recognition and innovation in digital environments	- Understanding how Generative AI works - Understanding digital platform
Skills (How to perform the task)	Problem-solving, creative thinking, project-based learning, digital tool proficiency	Practice-oriented competencies for managing digital assets and collaborating effectively	- Designing entrepreneurial items through projects - Creating MVPs using low-code tools
Attitudes (How to behave)	Self-directedness, ethical digital behavior, technology acceptance, perseverance	Socio-emotional competencies emphasizing learning attitudes and ethical awareness	- Considering data ethics when using AI - Demonstrating resilience after failure

.

2.3. Impact of Digital Technology on Entrepreneurial Competencies

Digital technologies have become a pivotal force in shaping entrepreneurial competencies, influencing multiple dimensions including educational approaches, learner capabilities, and institutional infrastructures.

First, emerging technologies such as AI, generative AI (e.g., large language models), learning analytics, and digital collaboration platforms are transforming traditional educational practices by fostering experiential, problem-based, and participatory learning. Al-Mamary et al. [3] found that AI-powered personalized learning environments enhance learner engagement and promote innovative behavior. Similarly, Garcez et al. [19,20] emphasized the potential of digital tools to support the integrated development of both technical and interpersonal skills.

Second, digital technologies are increasingly aligned with core entrepreneurial competencies, including creativity, self-directed learning, and digital and AI literacy. Notably, AI literacy is now regarded as a foundational competency for the responsible use of emerging technologies and for recognizing new entrepreneurial opportunities [21,22]. Chang et al. [23] demonstrated that digital tools can empower a diverse array of learners, including older adults.

Third, the effectiveness and scalability of digital entrepreneurship education hinge on supportive institutional frameworks. Selwyn [15] warned that technology-driven learning could exacerbate inequalities depending on broader sociopolitical contexts. Winterton and Turnbow [24] further emphasized the need to align education policy and training systems with evolving labor market demands.

In sum, digital technologies should not be viewed solely as instructional tools but as structural enablers of entrepreneurial competency development. The integration of AI literacy, in particular, has emerged as a critical agenda in this transformation. Against this backdrop, the present study aims to propose a strategic framework and theoretical grounding for AI-integrated entrepreneurship education.

2.4. Critical Reflections on the DT

DT represents a paradigmatic shift that restructures not only educational systems but also broader labor and organizational domains. While DT offers considerable opportunities for innovation, personalization, and operational efficiency, its implementation must be critically examined beyond the lens of technological optimism to account for its ethical, social, and political–economic ramifications.

First, DT has the potential to subordinate human autonomy and judgment to algorithmic systems. Andrejevic [25] argues that automated media environments erode human agency, while Cohen [26] highlights the threats posed by surveillance capitalism to individual freedom and democratic governance. In the educational sphere, algorithmically driven learning environments may enhance efficiency and predictability, but they risk constraining creativity and diminishing learner agency.

Second, the increasing reliance on proprietary digital platforms may compromise the public character and institutional autonomy of education. UNESCO [6] warns that the growing influence of private actors in shaping education policy risks subjecting public education to market-oriented logic. Similarly, the OECD [18] notes that the automation of decision-making processes may undermine teachers’ professional discretion and curricular independence.

Third, DT may exacerbate educational inequalities across countries and demographic groups. UNESCO [27] underscores that disparities in digital infrastructure, educator readiness, and content accessibility contribute to unequal learning outcomes. Complementarily, the World Bank [28] points to limitations stemming from institutional capacity, policy frameworks, and national digital readiness, all of which constrain the efficacy of digital education reforms.

Fourth, dominant narratives surrounding DT often adopt a technologically deterministic stance, prioritizing operational efficiency over human-centered and ethical values. Vial [1] critiques this instrumentalist logic, cautioning that technology may become an end in itself. In education, this focus risks marginalizing the affective, critical, and sociocultural dimensions of learning.

Fifth, under the logic of surveillance capitalism, digital technologies increasingly mediate and govern societal functions—including education—through data extraction and predictive control. Hongladarom [29] warns that such practices may erode fundamental human values such as dignity, privacy, and autonomy.

In conclusion, while DT presents substantial opportunities for educational innovation, it also introduces critical challenges related to autonomy, equity, and the public accountability of education. Therefore, the integration of digital technologies—particularly within entrepreneurship education—must be guided by a critical educational framework that foregrounds ethics, human agency, and sociocultural context.

3. Methodology

This study presents a comprehensive analysis of peer-reviewed literature on entrepreneurial competencies in the context of DT. The analysis was conducted using the PRISMA 2020 systematic review methodology (see Supplementary Materials, Table S1, PRISMA Checklist). Specifically, the study examined the development of DT technologies and their educational implications, with a focus on their application in entrepreneurship education. The review protocol was pre-registered and made publicly available via the Open Science Framework (OSF) on 7 July 2025 (DOI: 10.17605/OSF.IO/H4GN2). The review followed a standardized four-stage process: identification, screening, eligibility, and inclusion of relevant studies (Figure 1). Data extraction was guided by a structured codebook that defined key categories such as study purpose, methodology, constructs, and findings (see Supplementary Materials, Table S3).

3.1. Identification

The literature search was conducted across four major databases—Scopus, the Web of Science, ProQuest, and Google Scholar—for the period from January 2021 to June 2025. Search strings combined core terms such as “digital transformation”, “entrepreneurship education”, “entrepreneurial competency”, “competency”, “skill”, and “capability”, along with their synonyms to maximize retrieval. Specifically, “entrepreneurship education” included variations such as “entrepreneurial learning” and “entrepreneurial training”; “entrepreneurial competency” was paired with “entrepreneurial capability” and “entrepreneurial skill set”; and “digital transformation” was supplemented with “digitalization” and “digital innovation.” Keyword combinations were applied using Boolean operators, and synonym sets were adapted to database-specific search functionalities.

To ensure comprehensiveness, backward citation tracking was performed, and Supplementary Databases (e.g., including conference proceedings) were consulted. The peer-reviewed status of Google Scholar sources was manually verified based on the presence of a CrossRef DOI, listing on the official publisher’s website, and inclusion in SCOPUS or SCIEI-indexed journals. The time frame beginning in January 2021 was selected to capture the acceleration of DT in education following the COVID-19 pandemic, as well as the rapid advancement of AI—particularly large language models (LLMs) and generative AI (GenAI)—that have transformed educational approaches in entrepreneurship education. This period reflects the convergence of two major drivers of change: the widespread adoption of remote and hybrid learning models during the pandemic, and the integration of cutting-edge AI technologies into educational practice. Differences in search result counts across databases stem from variations in scope, indexing policies, and metadata structures. In particular, Scopus applies stricter keyword-matching criteria and more limited metadata coverage compared to the relatively inclusive indexing approach of the Web of Science, resulting in fewer search hits.

3.2. Screening

An initial search identified a total of 281,710 records. After removing duplicates both automatically and manually using EndNote 21, 36,304 records remained. Two researchers independently screened the titles and abstracts for eligibility. During this process, 36,182 records were excluded due to the lack of relevance to the study topic (e.g., unrelated to education or not addressing transformation and entrepreneurial competency). As a result, 119 records were selected for full-text review; however, 18 of these could not be retrieved. The remaining 101 records underwent full-text review, leading to the exclusion of 9 articles deemed irrelevant to education and 20 articles unrelated to transformation and entrepreneurial competency. Consequently, 72 studies were finally included in this systematic review. Inter-rater reliability was calculated using Cohen’s κ coefficient, yielding a value of 0.88, which indicates an “excellent level of agreement” (κ ≥ 0.75) [30]. Disagreements regarding 31 records were resolved through consensus discussion, and when necessary, a third researcher made the final decision (search terms and applied filters are summarized in

Table 3. Search terms and filters applied.

Database	Search Terms	Records Identified	Filters Applied	Records Screened
Scopus	“digital transformation” AND “entrepreneurship education” AND “competency” OR “skill” OR “capability”	98	Year: 2021–2025 Language: English Document Type: Article	38
Web of Science	“digital transformation” AND “entrepreneurship education” AND “competency” OR “skill” OR “capability”	129,545	Year: 2021–2025 Language: English Document Type: Article	31,715
ProQuest	“digital transformation” AND “entrepreneurship education” AND “competency” OR “skill” OR “capability”	146,557	Year: 2021–2025 Language: English Limit: Full Text and Peer-Reviewed Source Type: Scholarly Journals Document Type: Article Subject: Education, Learning, Training	101
Google Scholar	“digital transformation” AND “entrepreneurship education” AND “competency” OR “skill” OR “capability”	5510	Year: 2021–2025 Language: English	4450
		281,710		36,304

).

3.3. Eligibility

For the 119 studies that passed the initial screening stage, full-text articles were retrieved and screened for eligibility based on four criteria: (1) relevance to the research topic, (2) methodological rigor, (3) implication to educational practice or theory, and (4) specificity in the application of digital technologies. Two researchers independently evaluated each article, with any disagreements (n = 7) resolved through discussion and consensus. Quality assessment was conducted using the Newcastle-Ottawa Scale (NOS), resulting in the following distribution: 17 studies (23.6%) were classified as moderate quality (score ≤ 6), 41 studies (56.9%) as good quality (score = 7), and 14 studies (19.4%) as high quality (score ≥ 8). A total of 72 studies that met the minimum quality threshold were included in the final analysis. During the eligibility assessment, studies were excluded if they fell into any of the following three categories:

• The lack of educational relevance: Studies focusing solely on the technical analysis of DT, general business research without an educational context, or research in fields such as healthcare and business consulting unrelated to education.
• Unverified peer-reviewed status: Non-academic sources, gray literature, or documents lacking confirmation of publication in an official journal or conference proceedings.
• Methodological flaws: Insufficient data, sampling bias, or substantial design flaws that undermined the reliability of findings.

3.4. Inclusion

Following the eligibility assessment, 29 studies were excluded due to the lack of educational relevance, unverified peer-reviewed status, or methodological flaws. Consequently, 72 studies were included as the final dataset for this systematic review. Details of included and excluded studies are summarized in Supplementary Table S2.

3.5. Data Extraction and Synthesis

For the final set of included studies, two researchers independently extracted data using a pre-designed codebook. The extracted items included (1) author(s) and year of publication, (2) research design and analytical methods, (3) sample characteristics, (4) types of digital technologies applied, and (5) key educational outcomes and entrepreneurial competency variables. Reliability in data organization was ensured through iterative comparison and consensus-building.

Thematic analysis followed the six-phase procedure proposed by Braun and Clarke [31]: (1) familiarization with the data, (2) the generation of initial codes, (3) searching for themes, (4) reviewing themes, (5) defining and naming themes, and (6) producing the report. MaxQDA 24 was used to visualize code frequency and relationships between categories. To enhance inter-coder interpretive agreement, repeated discussions and code refinement processes were conducted. Thematic saturation was reached when no new themes emerged, at which point a total of six overarching themes and fourteen sub-themes were finalized. A detailed thematic structure and illustrative examples are presented in Supplementary Table S3.

3.6. Risk of Bias Assessment

The quality of the included studies was assessed using a modified version of the Newcastle–Ottawa Scale (NOS). The evaluation criteria were adapted as follows: (1) the clarity and representativeness of sampling procedures, including the absence of exposure in the comparison group; (2) the control of major confounding variables; and (3) the reliability and validity of measurement tools, procedures to prevent observer bias, and the completeness of outcome reporting. Each item was rated on a scale from 0 to 3 points. Based on the total score, studies were categorized into high quality (≥7 points), moderate quality (4–6 points), and low quality (<4 points). The overall average score across studies was 7.2 (±1.1).

3.7. Data Analysis

Data analysis was conducted in alignment with the study’s three research questions (RQs), with analytical categories structured accordingly.

First, in response to RQ1, the analysis explored structural transformations in the educational landscape induced by DT, particularly their contextual and environmental implications for entrepreneurship education.

Second, addressing RQ2, this review investigated how entrepreneurial competencies are conceptualized, categorized, and interrelated within the framework of DT.

Third, in relation to RQ3, the analysis identified empirical cases involving the application of digital technologies—such as AI, large language models (LLMs), and online collaborative platforms—to the development of entrepreneurial competencies and assessed their educational effectiveness.

A non-exclusive classification approach was employed to allow for the thematic allocation of studies across multiple analytical dimensions when appropriate. The analysis combined keyword extraction, thematic coding, frequency analysis, and narrative synthesis to derive patterns and insights. This analytical process was closely aligned with the overarching objective of the study: to develop a comprehensive understanding of the educational implications of DT, the reconfiguration of entrepreneurial competencies, and the integrative potential of technology-enhanced instructional strategies.

4. Results

4.1. DT and Education

DT is increasingly conceptualized not simply as the integration of technology, but as a systemic reconfiguration of the educational ecosystem, with far-reaching implications for entrepreneurship education.

First, in the domain of instructional design and curriculum development, DT facilitates the creation of personalized and problem-based learning environments through the deployment of digital tools and learning analytics [32,33]. Budiyanto et al. [34] contend that DT enhances curricular flexibility and implementability, enabling the construction of autonomy- and creativity-centered learning experiences aligned with the objectives of entrepreneurship education.

Second, at the organizational and institutional levels, DT demands strategic restructuring. Antonopoulou et al. [35] and Gkrimpizi et al. [36] underscore the necessity of cultivating a digitally adaptive organizational culture, supported by flexible leadership and institutional commitment, as prerequisites for effective transformation. Similarly, González-López et al. [37] emphasize the importance of fostering innovation and experimentation, facilitated by digital maturity assessment frameworks, to ensure institutional responsiveness [38].

Third, the impact of DT is highly context-dependent. Kayanja et al. [39] identify disparities in digital infrastructure and limited policy support as critical barriers for higher education institutions in developing regions. These findings highlight the need for localized, context-sensitive approaches to entrepreneurship education.

Fourth, emerging technologies—including AI, augmented and virtual reality (AR/VR), and cloud computing—are transforming educational practices and enabling immersive, interactive learning experiences [38]. These tools support learner engagement through personalized content delivery, gamified learning environments, and collaborative platforms while directly contributing to the development of core entrepreneurial competencies such as digital literacy, teamwork, and creativity.

In sum, DT is not a peripheral innovation but a foundational condition for transforming the structure and function of education. In the context of entrepreneurship education, it enables the reconfiguration of core competencies, the integration of digital tools into pedagogy, and the implementation of practice-oriented, learner-centered strategies.

In this regard, the key trends, thematic areas, and methodological approaches identified in the reviewed studies are summarized in

Table 4. SLR: DT and education.

Author (Year)	Title	Location	Methodology	Pertinent Findings
Aleksieva [32]	Preparing Pre-Service Teachers for DT	Europe	Qualitative	Emphasizes teacher digital literacy and curriculum redesign for DT.
Antonopoulou et al. [35]	DT in Higher Education under Uncertainty	Greece	Qualitative	Highlights flexibility and innovation as key DT strategies in uncertain contexts.
Budiyanto et al. [34]	Barriers and Readiness for DT	Indonesia	Mixed	Identifies readiness factors and barriers to DT adoption in HEIs.
D’Ambra et al. [33]	DT of Higher Education in Australia	Australia	Mixed	Explores curriculum, pedagogy, and cultural changes in DT adoption.
Eccott et al. [40]	DT of Physiotherapy Education	UK	Qualitative	Examines DT-driven shifts in teaching and assessment post-pandemic.
Fernández et al. [41]	DT Initiatives in Higher Education	Global	Qualitative	Integrates technology, human resources, and culture in DT processes.
Gkrimpizi et al. [36]	Defining the Meaning and Scope of Digital Transformation in HEIs	Greece	Qualitative	Tech-driven, coordinated change in higher education needing lifelong learning, collaboration, and quality focus.
González-López [37]	Measuring DT in Education 4.0	Spain	Quantitative	Proposes a digital maturity model for higher education.
Irwin et al. [42]	DT and Sustainability in Nursing Education	Australia	Qualitative	Links DT in nursing education with sustainability practices.
Kaimara et al. [38]	DT and Inclusive Education	Greece	Qualitative	Utilizes gamification, AR/VR, and transmedia for inclusive learning.
Kayanja et al. [39]	Fully Automated and Paperless DT Systems	Uganda	Mixed	Implements paperless DT systems in developing country contexts.
Kharchenko et al. [43]	Digital Technologies and Learning Transformation	Ukraine	Mixed	Connects DT with improved education quality and administration.
Lee and Yi [44]	Corporate Education for DT	South Korea	Qualitative	Focuses on leadership and pedagogy in corporate DT training.
Mourtajji and Arys-Chiss [45]	ChatGPT (GPT-3.5/early GPT-4) and Technology Acceptance in DT	France	Qualitative	Introduces an AI adoption model (AIA2M) for educational DT.
Trinh et al. [46]	Bibliometric Analysis of DT in Education	Vietnam	Quantitative	Maps global DT research trends in education.
Sheikh et al. [47]	DT of Nephrology POCUS Education	USA	Qualitative	Demonstrates AI–human collaboration in medical training.
Torres et al. [48]	Models for DT Implementation	Colombia	Qualitative	Identifies a lack of empirical DT implementation frameworks.
Wang et al. [49]	Drivers for DT in Higher Education	China	Quantitative	Analyzes institutional drivers for DT adoption.

, providing a synthesized overview of how DT influences educational structures, educational strategies, and competency development within entrepreneurship education.

4.2. The Concept and Components of Entrepreneurial Competencies

In the era of DT, entrepreneurial competency is increasingly conceptualized as a multidimensional and integrative capacity that transcends technical proficiency to encompass creativity, problem-solving, and socio-emotional skills. The literature reviewed in this study underscores that these competencies are formed through the convergence of cognitive, social, and digital domains, with an emphasis on their application in complex and uncertain digital environments.

First, within the cognitive domain, higher-order thinking skills—such as creativity, critical thinking, problem-solving, and opportunity recognition—are consistently identified as foundational. Bentz et al. [50] emphasize that the capacity to creatively reframe and resolve problems is central to entrepreneurship education in digitally mediated contexts. Similarly, Camara and De Boni [51] highlight the growing importance of innovative thinking and agile decision-making in digital learning settings.

Second, socio-emotional competencies emerge as critical for reinforcing the collaborative and participatory nature of entrepreneurial learning. Cruz-Sandoval et al. [10] demonstrate that empathy, community engagement, and collaboration significantly enhance learner involvement in project-based and experiential entrepreneurship education. In parallel, Juezo-Ponce et al. [52] and López Sánchez et al. [53] reveal that emotional regulation and self-efficacy are integral to fostering entrepreneurial mindsets.

Third, digital competency and techno-creativity constitute essential dimensions of entrepreneurship education in the DT era. Atmojo et al. [54] and Comesana-Conesana et al. [55] identify digital literacy, social media fluency, and AR/VR-based problem-solving as key operational skills for navigating contemporary entrepreneurial landscapes. Buzady et al. [56] further argue that the integration of such technologies facilitates creative practice through experiential, learning-by-doing approaches.

Fourth, the specific configuration of entrepreneurial competencies varies across industrial sectors and regional contexts. Lee and Yi [44] highlight digital leadership, organizational culture, and collaborative learning as crucial in corporate education within the semiconductor industry. Ramadani et al. [57] underscore sustainability and ethical awareness as essential to social entrepreneurship.

Taken together, entrepreneurial competency in the DT era can be synthesized into a tripartite framework comprising (1) cognitive competency, (2) socio-emotional competency, and (3) digital–technical competency. These domains are interdependent and should be systematically integrated into entrepreneurship education as explicit learning outcomes to promote holistic development and creative capability. The search terms and filters used in this study are summarized in

Table 5. SLR: entrepreneurial competencies.

Author (Year)	Title	Location	Methodology	Pertinent Findings
Alkaabi and Senghore [58]	Education, role models, gender in entrepreneurship	UAE	Quantitative	Education, role models, and gender shape student competency and mindset.
Arshad et al. [59]	Female entrepreneurship & coaching	Malaysia	Quantitative	Self-efficacy and sales experience impact performance; coaching boosts skills.
Baltador et al. [60]	Design thinking for entrepreneurship	Romania	Mixed	Design thinking program enhances student entrepreneurial competencies.
Bawn et al. [61]	Entrepreneurship for research careers	United Kingdom and USA	Qualitative	Entrepreneurial thinking and networks improve research career development.
Comesaña-Conesaña et al. [55]	Technocreativity & social networks	Spain	Quantitative	Technocreativity and networking support entrepreneurship.
Ertem [62]	Goal orientation & 21st-century skills	Turkey	Quantitative	Both predict pre-service teachers’ entrepreneurship.
Etemad [63]	International entrepreneurship framework	Canada	Qualitative	Dynamic capabilities and networks aid internationalization.
Farransahat et al. [64]	University incubators & social entrepreneurship	Indonesia	Qualitative	Incubators build social entrepreneurship skills in digital contexts.
García and Olaz Capitán [65]	Disabilities & entrepreneurship	Spain	Quantitative	Entrepreneurship builds autonomy and social value for disabled people.
Huang et al. [66]	Policies & regional innovation	China	Mixed	Policy combinations activate regional innovation capabilities.
Iwu et al. [67]	Lecturer competency & pedagogy in HE	South Africa	Qualitative	Lecturer skills, curriculum, and pedagogy are key to education quality.
Jaimes-Acero et al. [68]	Soft skills in engineering entrepreneurship	Colombia	Quantitative	Soft skill gap exists; training in admin areas needed.
Karimi and Ataei [69]	Ecosystem & agriculture student skills	Iran	Quantitative	Ecosystem boosts skills; emotional intelligence mediates effects.
Lechuga-Jimenez et al. [70]	Transversal skills & sustainability	Spain	Mixed methods	Communication and teamwork key for sustainable entrepreneurship.
Malinda et al. [71]	Experiential learning for entrepreneurship	Indonesia	Quantitative	Project-based learning enhances skills and entrepreneurial spirit.
Nam et al. [72]	Corporate entrepreneurship & social capital	Korea	Quantitative	Corporate entrepreneurship shapes attitudes; social capital moderates.
Nofrida et al. [73]	Measuring student entrepreneurship skills	Indonesia	Quantitative	Developed and validated skills measurement tool.
Olutuase et al. [74]	Entrepreneurship education in Africa	South Africa	Quantitative	Education boosts skills; local context alignment is vital.
Otiniano León et al. [75]	Key competencies in Peruvian students	Peru	Quantitative	Creativity, risk-taking, initiative affect entrepreneurial intention.
Pazos et al. [76]	Teamwork competencies & performance	Spain	Mixed	Teamwork skills improve performance; cognitive conflict helps.
Planck et al. [77]	Sustainability in entrepreneurship education	Germany	Qualitative	Integrating sustainability enhances outcomes in HE.
Ramadani et al. [57]	Social entrepreneurial competencies	India	Mixed	Eight key competencies for sustainable social entrepreneurship.
Rosas et al. [78]	Cash + training for youth entrepreneurship	Sierra Leone	Quantitative	Intervention boosts employment, entrepreneurship, and resilience.
Schweickart et al. [79]	Biomedical entrepreneurship program	USA	Mixed	Course increases knowledge, confidence, and commercialization likelihood.
Simba et al. [80]	Soft skills in African entrepreneurship	South Africa	Quantitative	Soft skills drive readiness; process mediation observed.
Şirin and Tarkın Çelikkıran [81]	STEM & entrepreneurship skills	Turkey	Mixed	STEM activities improve risk-taking, achievement, communication.
Slišāne et al. [82]	Doctoral research & entrepreneurship	Latvia	Mixed	High research but low entrepreneurship skills; need balance.
Somià et al. [83]	Gender & entrepreneurial competencies	USA/Italy	Mixed	Gender differences suggest need for sensitive curricula.
Sousa and Costa [84]	Problem-based learning & competencies	Portugal	Quantitative	PBL develops collaboration and self-directed learning.
Bardales-Cárdenas et al. [85]	Skills & local economic development	Peru	Quantitative	Skills strengthen local development; support needed.
Vázquez-Parra et al. [86]	Social entrepreneurship & complex thinking	Mexico	Mixed	SEL4C method builds both social entrepreneurship and thinking.
Ventín-Sánchez et al. [87]	Media entrepreneurship skills in HE	Colombia	Qualitative	Need interventions; projects often stall at prototype.
Ver Steeg Jr. [88]	Social capital in MBA entrepreneurship	The Netherlands/Taiwan	Qualitative	Social capital acquisition supports social entrepreneurship.
Zhu et al. [89]	Institutional management & teacher competency	China	Quantitative	Management boosts competency via entrepreneurial behavior.

, which also includes additional scholars identified during the analysis beyond those mentioned in the text above. A synthesized summary of these results is provided in Table 5.

4.3. The Application of Digital Technologies in the Development of Entrepreneurship

Digital technologies serve not merely as supplementary tools but as strategic enablers in the development of core entrepreneurial competencies. The literature reviewed in this study illustrates how different forms of digital technology contribute to cultivating competencies such as creativity, problem-solving, opportunity recognition, financial literacy, and networking.

First, digital tools significantly foster creative problem-solving and innovative thinking. Almeida et al. [90] reported that digital technologies enhance learners’ ability to identify problems and explore alternative solutions. Likewise, Garcez et al. [19,20] emphasized the importance of integrating both hard and soft skills within digitally mediated entrepreneurship education to equip learners with multidimensional problem-solving capabilities.

Second, digital literacy and financial technologies are pivotal in shaping entrepreneurial intention and venture execution. Kang et al. [4] demonstrated that digital and financial competencies substantially influence innovation-driven entrepreneurship, while Wang et al. [49] highlighted the role of digital financial tools in enhancing both strategic planning and operational efficiency in startup environments.

Third, the effects of digital technologies differ across industries and regional contexts. Chang et al. [23] showed that digital applications in the agricultural sector are particularly effective among experienced entrepreneurs.

Fourth, AI-driven educational approaches have been shown to enhance learner engagement and autonomy. Al-Mamary et al. [3] observed that AI-based feedback systems positively influence learner motivation, while Erdisna et al. [91] found that digital learning models based on the 4C framework—communication, collaboration, critical thinking, and creativity—effectively promote cognitive and affective development in entrepreneurship education.

Finally, digital entrepreneurship increasingly demands a global mindset and platform-based orientation. Etemad [63] argued that success in digital entrepreneurial ecosystems depends not only on technological proficiency but also on strong networking and dynamic capabilities. Collectively, these findings underscore that digital technologies constitute both essential infrastructure and educational mechanisms for entrepreneurial competency development, highlighting the critical importance of their strategic integration into educational design. A synthesized summary of these results is provided in

Table 6. SLR: digital formation and entrepreneurial competencies.

Author (Year)	Title	Location	Methodology	Pertinent Findings
Bodescu et al. [8]	Skills for Employment and Entrepreneurship in DT	Romania	Quantitative	Digital skills are essential in entrepreneurship.
Cruz-Sandoval et al. [10]	Student Views on Social Entrepreneurship Skills	Mexico	Quantitative	Digital and social competencies are key for social entrepreneurship.
Comesaña-Comesaña et al. [55]	Technocreativity and Entrepreneurship Skills	Spain	Mixed	Social media and technocreativity enhance creativity and problem-solving.
Erdisna et al. [91]	4-D Model for Digital entrepreneurial Competencies	Indonesia (Padang)	Mixed	4C competencies improve cognitive, affective, and psychomotor skills.
Citraningrum and Khusaini [92]	Digitalization, Knowledge & Social Media in Student Entrepreneurship	Indonesia	Quantitative	Digitalization boosts entrepreneurship interest; mixed effects for other factors.
Garcez et al. [19]	Hard Skills in Digital Academic Entrepreneurship	Portugal	Qualitative	Digitalization boosts entrepreneurship interest; mixed effects for other factors.
Garcez et al. [20]	Soft Skills in Digital Academic Entrepreneurship	Portugal	Qualitative	Individual traits, culture, and knowledge sharing are key soft skills.
Kang et al. [4]	Financial Literacy & Digital Skills Impact on Entrepreneurship	South Korea	Quantitative	Financial literacy and digital skills raise entrepreneurial intention.
Marzo-Navarro and Berné-Manero [93]	Cross-Cutting Competencies in Online Entrepreneurship	Spain	Mixed	Online learning develops transversal entrepreneurial competencies.

.

4.4. Summary of Findings

This study conducted an integrated analysis of 72 studies to examine the impact of DT on entrepreneurial competencies, revealing that different types of digital technologies enhance distinct sets of competencies. AI and large language models (LLMs) were found to be effective in fostering creativity, problem-solving, and self-directed learning. Data analytics technologies contributed to the development of strategic thinking and financial competency, while online collaboration platforms were particularly effective in strengthening collaboration skills and digital teamwork.

In addition, regional approaches to entrepreneurship education appear to be closely tied to underlying cultural value systems. European contexts tend to emphasize social values such as community and inclusion; North American approaches focus on economic outcomes such as profitability and venture competency; and Asian contexts are more oriented toward technological proficiency and the utilization of digital tools. These regional patterns align with Hofstede et al.’s [94] concept of culture as “the collective programming of the mind that distinguishes the members of one group or category of people from others”. Hofstede’s six cultural dimensions—such as power distance, individualism versus collectivism, and long-term orientation—serve as a useful analytical framework for interpreting regional variations in entrepreneurship education.

For example, countries in Asia, characterized by high power distance and long-term orientation, tend to prioritize digital proficiency and hierarchical instructional design. In contrast, North America, with low power distance and a strong emphasis on individualism, favors a practical, revenue-driven approach. While the majority of studies reported positive educational effects of digital technologies, some highlighted negative outcomes resulting from disparities in technological infrastructure, insufficient teacher competency, and varying levels of technology acceptance.

In particular, findings regarding the impact of digital technologies on socio-emotional competencies were inconsistent, indicating the limitations of technology-centered instructional models and underscoring the need for human-centered design principles. These findings suggest that digital technologies function not merely as instructional tools but as core mechanisms for entrepreneurial competency development. Accordingly, the strategic selection of technologies and the incorporation of regional and cultural contexts are essential considerations in instructional. A synthesized summary of the key findings by research question is presented in

Table 7. Summary of key findings by research question.

Research Question	Key Findings	Supporting Studies (n)	Representative References
RQ1	DT transforms educational design, strategy, and culture, with approaches varying by region and cultural context.	19	Aleksieva [32], D’Ambra et al. [33], Budiyanto et al. [34]
RQ2	Core competencies include creativity, problem-solving, digital literacy, financial competency, and collaboration.	42	Bentz et al. [50], Comesaña-Conesaña et al. [55], González-López et al. [37]
RQ3	AI and LLMs enhance creativity and problem-solving; data analytics strengthen strategic and financial competencies; online collaboration tools improve teamwork.	11	Almeida et al. [90], Garcez et al. [19,20], Kang et al. [4], Erdisna et al. [91]

.

5. Discussion

This study conducted an SLR to analyze the integrated significance of entrepreneurial competencies and digital technologies in the era of DT. It examined the educational, organizational, and technological dimensions of change introduced by DT in the field of entrepreneurship education. This section discusses the findings in relation to existing theories, highlighting the study’s theoretical implications, practical implications, and critical reflections, along with directions for future research.

5.1. Theoretical Implication

This study systematically examined the impact of DT on entrepreneurship education, addressing limitations in prior research and proposing possibilities for interdisciplinary integration. Grounded in three core research questions (RQ1–RQ3), it bridges the fields of education, management, and psychology, thereby contributing to the theoretical expansion of entrepreneurial competency frameworks.

First, regarding RQ1—concerning the educational changes brought about by DT and their educational implications—existing studies have often focused narrowly on technology adoption or infrastructure upgrades [1,2]. By contrast, this study conceptualizes DT as a structural transformation encompassing teaching strategies, curriculum frameworks, organizational dynamics, and policy ecosystems. It highlights how digital technologies enable interactive, learner-centered ecosystems that directly foster creativity, self-directed learning, and collaboration—core goals of entrepreneurship education.

Second, in relation to RQ2—which explores the entrepreneurial competencies required in the DT era—prior research has largely emphasized traditional competencies such as creativity and leadership or remained confined to specific industries, age groups, or national contexts. This study presents a multidimensional model of entrepreneurial competency integrating cognitive, socio-emotional, and technology-based domains. Specifically, it identifies emergent digital competencies—such as AI collaboration, digital ethics, data-driven decision-making, and sustainable value creation—as essential in the evolving landscape.

Third, addressing RQ3—regarding the ways in which digital technologies influence the development of entrepreneurial competencies—most existing studies have treated these technologies as supplementary tools. This study, by contrast, investigates how AI, LLMs, and digital financial technologies affect learners’ cognitive, emotional, and social development. It further theorizes the underlying learning mechanisms—such as feedback loops and participatory pathways—while integrating institutional and cultural moderators into a comprehensive analytical model.

Furthermore, this study’s conceptualization of entrepreneurial competency in the DT era aligns with and extends established competency models. Le Deist and Winterton’s tripartite model—integrating cognitive, functional, and social competencies—has been widely adopted as a foundational framework in human resource development [17]. Building on this, Winterton and Turnbow [24] emphasize the need for the contextual adaptation of competency frameworks to evolving socio-economic and technological conditions. The OECD/INFE Policy Handbook on National Strategies for Financial Education [18] further underscores that competency development in the 21st century requires a policy-level integration of digital and financial literacies. Complementarily, the World Economic Forum’s Future of Jobs Report 2025 [5] projects that the accelerated diffusion of AI and other emerging technologies will transform skill demands across industries, with entrepreneurial adaptability, digital ethics, and interdisciplinary collaboration emerging as critical competencies. By synthesizing these perspectives, our proposed EntreComp+ model positions entrepreneurial competency as a dynamic construct, responsive to both global policy trends and rapid technological transformation.

Moreover, this study aligns with the expanding landscape of AI-driven entrepreneurial ecosystems. As Tamayo et al. [95] project, approximately 32% of job roles are expected to be restructured by AI within the next 15–20 years, indicating increased opportunities for AI-enabled entrepreneurship. Accordingly, AI collaboration tools, personalized learning systems, and automated start-up support environments are emerging as core components of entrepreneurship education. These developments provide empirical relevance to the proposed framework of “AI-collaborative entrepreneurial competency.” The EU AI Act [21], which mandates AI literacy education, and Salesforce’s [96] definition of AI literacy as “the ability to understand and effectively use AI to improve work performance,” both emphasize the importance of integrating ethical sensitivity and entrepreneurial thinking into AI competency frameworks. This reinforces the validity of the proposed EntreComp+ model tailored for the AI era.

5.2. Practical Implications

Reflecting the structural paradigm shift brought about by DT, this study proposes practical strategies for advancing entrepreneurship education. The educational application of digital technologies—particularly AI—is gaining attention as a means to enhance core competencies such as creative problem-solving, self-directed learning, and collaboration. However, it is not the mere adoption of technology that matters, but rather how these competencies are concretely realized in educational settings. Based on this perspective, the following implementation strategies are proposed.

First, educational institutions should restructure their entrepreneurship curricula to foster learner-centered competency development within digitally enhanced environments. DT enables individualized learning pathways, real-time feedback, and problem-based learning—all of which are directly linked to the core competencies demanded in entrepreneurship education, such as creativity, autonomy, and collaboration. For instance, activities such as ideation using generative AI, team-based projects via online collaboration platforms, and training in data-driven decision-making serve as effective strategies to operationalize these competencies. These technologies, in particular, can promote learner engagement and improve the quality of experiential learning. This aligns with Le Deist and Winterton’s tripartite competency framework [17], which emphasizes the integration of cognitive, functional, and social dimensions into coherent learning designs, and with Winterton and Turnbow’s [24] call for adaptive curricula that respond to rapidly evolving technological and socio-economic contexts.

Second, entrepreneurship support organizations should strengthen practice-based programs that leverage digital technologies to increase ecosystem adaptability in the face of DT. Recent research underscores the role of digital tools in enhancing entrepreneurs’ opportunity recognition and problem-solving capabilities. Incubators and accelerators, for instance, can incorporate AI-driven market analysis training, workshops in digital customer analytics, and online collaboration-based minimum viable product (MVP) development as part of systematic skill cultivation. In doing so, such organizations should integrate the OECD/INFE’s [18] policy guidance on embedding financial literacy and digital capability into entrepreneurship support mechanisms, ensuring that participants acquire both technological fluency and sound decision-making skills in complex markets.

Third, at the policy level, performance evaluation frameworks for entrepreneurship education must be recalibrated to reflect the digital era’s core competencies. Emerging competencies—including digital ethics, effective data utilization, and collaborative problem-solving—are now foundational, transcending basic technical literacy. The WEF’s Future of Jobs Report 2025 [5] emphasizes that interdisciplinary collaboration, adaptability, and ethical governance will be among the most in-demand skills by 2030, underscoring the need for national-level strategies that integrate these competencies into lifelong learning systems. To this end, policy initiatives should prioritize (1) the establishment of digital entrepreneurship education certification systems, (2) the implementation of digital portfolio-based assessment models, and (3) the development of curriculum guidelines that reflect industry-specific skill demands.

In summary, this study outlines a comprehensive set of practical implications spanning institutional, organizational, and policy levels in response to the evolving demands of DT. Crucially, AI and other digital technologies should not be regarded as ends in themselves, but as strategic enablers that foster learner-centered ecosystems and support the internalization of core entrepreneurial competencies. Achieving this requires a clear articulation of the purpose behind technology integration and its alignment with instructional design, curriculum development, and assessment systems. In practical terms, our findings both confirm and expand upon existing implementation models in entrepreneurship education. Traditional experiential learning models (e.g., Kolb’s cycle) are enriched by DT-enabled feedback loops, real-time data visualization, and AI-mediated ideation processes. This moves beyond prior emphasis on physical incubation spaces by introducing scalable, virtual ecosystems. Moreover, while UNESCO and OECD guidelines stress digital literacy as a basic skill, our results suggest that entrepreneurship curricula should advance toward “digital fluency,” where learners can strategically deploy technology to create and capture value. Such an approach complements competency-based education models but shifts assessment from static knowledge checks to dynamic, portfolio-driven demonstrations of applied skills in digital environments

5.3. Research Synthesis and Future Directions

Synthesizing across the reviewed studies, this research proposes the EntreComp+ for the AI era model as a unified framework that integrates cognitive, socio-emotional, and technology-mediated competencies. It addresses a research gap where digital technologies were previously analyzed in isolation from competency theory, offering instead a systemic view of their co-evolution. Future research should empirically validate this model across diverse educational contexts and cultural settings, particularly examining longitudinal competency growth in AI-augmented learning environments. Comparative studies between technology-rich and resource-constrained ecosystems would illuminate contextual dependencies. Additionally, interdisciplinary collaborations—linking education, human–computer interaction, and innovation policy—are essential to refine ethical guidelines, scalability strategies, and equity considerations. This trajectory aligns with the need for entrepreneurship education to remain adaptive in the face of rapid technological, economic, and societal change.

6. Conclusions

6.1. Summary

This study identified the core competencies required for entrepreneurship education in the era of DT and systematically analyzed the structural impact of digital technologies on the development of these competencies. An SLR was conducted in accordance with the PRISMA 2020 guidelines, targeting English-language academic journal articles published between January 2021 and June 2025. The selection process followed four stages—identification, screening, eligibility, and inclusion—resulting in the final inclusion of 72 studies from an initial pool of 281,710. Two independent researchers carried out the screening procedure, and inter-rater reliability was confirmed with a Cohen’s κ coefficient of 0.88, indicating a high level of agreement.

The analysis revealed that DT goes beyond the mere adoption of new technologies; it constitutes a structural reconfiguration of the educational ecosystem, encompassing curricula, teaching and learning methods, assessment practices, and institutional culture (OECD, 2023; UNESCO, 2023). These systemic changes have fundamentally transformed both the delivery and content of entrepreneurship education. In particular, competencies such as creativity, self-directed learning, collaboration, and digital literacy were found to play a central role and were effectively cultivated within digitally enhanced environments.

Digital technologies function not merely as instructional tools but as strategic catalysts that foster active learner engagement and interaction. They enable co-constructive learning environments in which teachers and students collaboratively define and solve problems (OECD, 2023). Within such structures, learners engage in authentic problem-solving and value creation processes, internalizing entrepreneurial mindsets and behaviors through experiential learning. By providing a theoretical foundation for digitally integrated entrepreneurship education, this study contributes to the development of strategic approaches for instructional design, learning environment configuration, and policy development. Ultimately, it offers practical insights for navigating the educational and societal transitions brought about by DT.

In the DT era, entrepreneurial competencies are shaped by the unique demands of each industry. For example, entrepreneurs in the architecture sector require expertise in sustainable design, proficiency in Building Information Modeling (BIM)-based digital tools, and strong project management and interdisciplinary collaboration skills, alongside creative problem-solving capabilities. In contrast, entrepreneurs in the information technology (IT) sector need agile learning abilities to adapt to rapidly changing technological trends, advanced proficiency in AI, big data, and cloud computing, as well as skills in agile-based product development and digital marketing for global markets.

For entrepreneurs in the cultural, arts, and content sectors, core competencies include creative storytelling, digital platform management, intellectual property (IP) management, and the ability to design personalized audience experiences through data analytics. Finally, entrepreneurs in the manufacturing and engineering sectors require knowledge of smart manufacturing technologies, competency in digitalizing supply chains, and expertise in sustainable production systems.

Across all sectors, DT demands a shared foundation of creativity, self-directed learning, digital literacy, and collaboration skills. However, integrating sector-specific technological, market, and regulatory competencies is essential, underscoring the need for tailored educational and policy strategies that reflect the distinctive requirements of each industry.

The findings of this study hold significant implications for theory, practice, and policy. Theoretically, the integration of digital technologies into the EntreComp+ model provides a renewed lens for understanding entrepreneurial competency in the DT era (OECD, 2023; UNESCO, 2023). Practically, educators can design more adaptive, technology-mediated learning environments that foster creativity, collaboration, and digital fluency. For policymakers, the evidence supports the formulation of strategies that embed digital competency development into entrepreneurship education at multiple levels. These contributions align with the broader societal need to prepare learners for rapidly evolving technological, economic, and cultural landscapes, ensuring both adaptability and sustainability in future entrepreneurial ecosystems.

6.2. Research Integration, Limitations and Future Research Directions

This study systematically analyzed the interplay between DT and entrepreneurship education, producing meta-analytic insights across four thematic categories.

First, in terms of Convergent Findings, most studies consistently emphasized creativity, self-directed learning, collaboration, and digital literacy as key entrepreneurial competencies in DT contexts. Digital technologies were repeatedly found to function not merely as auxiliary tools but as strategic enablers for learner-driven problem solving and value creation. Second, Divergent Perspectives emerged, with some studies framing digital technology adoption primarily through technical–operational lenses, while others emphasized sociocultural and ethical implications. Competency priorities also varied by industry sector, reflecting strong context dependence. Third, Methodological Gaps were evident, as inconsistencies in conceptual definitions and classification criteria reduced analytical coherence. Many studies relied on conceptual frameworks without empirical validation, and the exclusion of gray literature potentially omitted practice-based and policy-oriented insights. Fourth, Emerging Priorities indicate that AI-driven entrepreneurial ecosystems and AI literacy education are becoming pivotal in shaping future entrepreneurship education. Key research themes include the effectiveness of AI-assisted ideation tools in fostering opportunity recognition, the influence of AI ethics education on social entrepreneurship orientation, and the development and validation of AI literacy assessment instruments.

Despite these contributions, several limitations must be acknowledged. First, the literature reviewed was predominantly concentrated in European, North American, and Chinese contexts, limiting the global generalizability of findings. Second, the proposed competency framework has not yet undergone empirical validation, leaving its educational impact untested. Third, the exclusion of gray literature may have omitted valuable practice-based and policy-relevant evidence. Fourth, the database scope was limited, as Naturegroup databases were not included; this may have resulted in the omission of some cutting-edge studies in the field. Fifth, due to the inherent limitations of SLR, rapidly evolving trends in AI-based entrepreneurial education were not fully captured, partly because of the temporal gap between data collection and publication and partly because technically focused studies without direct educational relevance were excluded. Future research should address these limitations by

• Conducting comparative studies across diverse regions and cultural contexts;
• Empirically validating the proposed competence model through Delphi studies, pilot interventions, and pre–post experimental designs;
• Adopting standardized analytical frameworks for defining and categorizing entrepreneurial competences and incorporating gray literature;
• Expanding database coverage to include Naturegroup and other major sources to capture the latest research;
• Applying learning analytics-based methods to evaluate the impact of instructional strategies;
• Conducting sector-specific analyses of competence priorities alongside the development of digital maturity assessment tools.

Such efforts will enhance the educational and policy utility of the proposed digital entrepreneurial competency model, ensuring its adaptability and practical relevance in an increasingly dynamic digital and AI-driven environment.