Search results

Digital entrepreneurship is a highly topical, sub-specialist and contemporary branch of entrepreneurship which is providing ever-increasing range of platforms for pursuing entrepreneurship-related career options. While the academic discourses in this area are increasingly growing, there is an equal level of scant attention paid to inquire how proclivity toward this career choice is developed and influenced. In the present study, this gap has been addressed by identifying major antecedents of digital entrepreneurship intentions under the aegis of capital theory.

Using the survey data from 287 respondents, structural equation modeling was utilized to explore the association of four antecedents: digital entrepreneurial competence, innovative cognition, social media adroitness and digital entrepreneurship role models on the goal intentions. Subsequently the association between goal intention and implementation intention to start a digital venture was also examined.

The analysis confirms the significant impact of the identified antecedents on the digital entrepreneurial volition of potential entrepreneurs. Our results demonstrate that innovativeness has the greatest impact on the goal intentions to create new digital ventures followed by the presence of role models. Digital competence is also a significant contributor in enhancing the propensity to start digital ventures while being digitally adroit has the least impact on such inclination. Lastly, our study provides empirical evidence to the linkage between goal and implementation intentions.

This paper informs practice on entrepreneurship education especially the role of skill-based education programs to enhance the information technology–related knowledge of students and incubation support for hands-on-training on the various dimensions of digital ventures. At policy-level institutions providing entrepreneurial education can design special tasks and learning activities that are focused on acquainting students with design thinking perspectives and lean start-up approaches.

The impacts of institutional quality on entrepreneurship are well established. However, the effects of an external factor, such as the shadow economy, that moderates this relationship have largely been neglected in existing literature. As such, this paper investigates how the shadow economy moderates the effects of institutional quality on entrepreneurship in a global sample of 79 economies from 2006 to 2018, when the latest required data are available.

This paper utilizes the fixed-effect and generalized method of moments (GMM) estimation techniques. Various scenarios have been considered for the robustness of the analysis, including different estimation techniques, different estimates of the shadow economy and various subsamples of countries with different income levels.

Empirical findings indicate that improved institutional quality boosts entrepreneurship activities, while the extended shadow economy is associated with reduced entrepreneurship activities. Interestingly, the positive impacts of institutional quality on entrepreneurship will be lessened with a larger shadow economy. These findings have remained largely unchanged across samples of countries and different proxies and estimation techniques.

Findings from this paper offer policymakers the relationships between institutional quality, shadow economy and entrepreneurship and the moderating effects of shadow economy on the institutional quality–entrepreneurship nexus. The implication is that institutional quality should be strengthened while the shadow economy should be controlled to promote entrepreneurship initiatives.

To the best of the author's knowledge, this is the first empirical study to explore the moderating effects of the shadow economy on the institutional quality–entrepreneurship nexus.

Feature extraction from 3D datasets is a current problem. Machine learning is an important tool for classification of complex 3D datasets. Machine learning classification techniques are widely used in various fields, such as text classification, pattern recognition, medical disease analysis, etc. The aim of this study is to apply the most popular classification and regression methods to determine the best classification and regression method based on the geodesics.

The feature vector is determined by the unit normal vector and the unit principal vector at each point of the 3D surface along with the point coordinates themselves. Moreover, different examples are compared according to the classification methods in terms of accuracy and the regression algorithms in terms of R-squared value.

Several surface examples are analyzed for the feature vector using classification (31 methods) and regression (23 methods) machine learning algorithms. In addition, two ensemble methods XGBoost and LightGBM are used for classification and regression. Also, the scores for each surface example are compared.

To the best of the author’s knowledge, this is the first study to analyze datasets based on geodesics using machine learning algorithms for classification and regression.

Continuous advances in digital technology and business models digitalization have decisively altered the entrepreneurial landscape redefining the aims and the requirements of entrepreneurial education to suit the new digital reality in entrepreneurship. The purpose of this study is to shed light in a neglected niche in the intersection between digital entrepreneurship (DE) and entrepreneurship education and outline DE education as field of research. Given that the interdisciplinary growth of DE research outpaces instructional designs in terms of required knowledge and skills, it is important to document how entrepreneurship education responds to the ongoing integration of emerging digital technologies with the entrepreneurial process. Moreover, the introduction of a DE education conceptual framework would facilitate the discussion on theoretical and practical implications and promote new conceptualizations in future research, new educational approaches and new curriculum designs.

A concept-driven, semi-structured developmental literature review methodology, based on grounded theory and reinforced with increased systemization, was used for the identification and analysis of peer-reviewed articles. Previous literature reviews were used to define the search keywords. The articles from three databases were carefully selected, based on protocol and strict eligibility criteria. The papers in the final set were classified in four primary dimensions, synthesized from several sub-streams of research. Each sub-stream highlighting a different view of DE education emerged through open, axial and selective coding of articles. The combined perspectives of these dimensions resulted in a new DE conceptual framework.

Thematic interrelation between the studies examined, revealed an ample view of the various schools of thought in the research field, offering also a better understanding on how entrepreneurial education addresses the practical requirements of digital entrepreneurship. A four dimensional conceptual framework produced highlights pedagogy and learning, success factors and barriers, behavioral approach and ecosystems as the current trends of research. Also, directions for future research are proposed.

The classification framework proposed can serve as a roadmap for entrepreneurship educators seeking efficient pedagogical practices focused on the transfer of knowledge and training on the new skills required by DE and motivate future researchers aiming to propose novel educational interventions.

Although digital entrepreneurship research has gained significant momentum in recent years, little attention is paid to its increased educational requirements. The body of knowledge develops in an uncontrolled and fragmented manner, and the systematic study of the field from an educational perspective was missing. This study offers a representative picture of the topic, highlights current trends of research, synthesizes literature from different disciplinary origins, provides linkages between unconnected streams of research and points out research gaps. Finally, it proposes a conceptual framework to circumscribe DE education as a field of study and serve as a basis to help future research move forward.