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Artificial intelligence (AI) has started to receive attention in the field of digital entrepreneurship. However, few studies propose AI-based models aimed at assisting entrepreneurs in their day-to-day operations. In addition, extant models from the product design literature, while technically promising, fail to propose methods suitable for opportunity development with high level of uncertainty. This study develops and tests a predictive model that provides entrepreneurs with a digital infrastructure for automated testing. Such an approach aims at harnessing AI-based predictive technologies while keeping the ability to respond to the unexpected.

Based on effectuation theory, this study identifies an AI-based, predictive phase in the “build-measure-learn” loop of Lean startup. The predictive component, based on recommendation algorithm techniques, is integrated into a framework that considers both prediction (causal) and controlled (effectual) logics of action. The performance of the so-called active learning build-measure-predict-learn algorithm is evaluated on a data set collected from a case study.

The results show that the algorithm can predict the desirability level of newly implemented product design decisions (PDDs) in the context of a digital product. The main advantages, in addition to the prediction performance, are the ability to detect cases where predictions are likely to be less precise and an easy-to-assess indicator for product design desirability. The model is found to deal with uncertainty in a threefold way: epistemological expansion through accelerated data gathering, ontological reduction of uncertainty by revealing prior “unknown unknowns” and methodological scaffolding, as the framework accommodates both predictive (causal) and controlled (effectual) practices.

Research about using AI in entrepreneurship is still in a nascent stage. This paper can serve as a starting point for new research on predictive techniques and AI-based infrastructures aiming to support digital entrepreneurs in their day-to-day operations. This work can also encourage theoretical developments, building on effectuation and causation, to better understand Lean startup practices, especially when supported by digital infrastructures accelerating the entrepreneurial process.

The purpose of this study is to answer the following questions. What kind of entrepreneurial identities do students have that motivate them to choose either of the entrepreneurship course and university-based incubator? How do students involve in the entrepreneurship ecosystem at university based on their entrepreneurial identity?

For this study, the author began to gather information using previous knowledge and any aspect of a work, namely, from the literature review to represent interpretive syntheses of the meaning-making literature review addressing the research question.

This study suggests what happens to entrepreneur students from academia and the reason that they end up in one of the two aforementioned paradigms. This paper aims to underpin the issue of how various entrepreneurial identities of students cause substantial contributing factors in forming such entrepreneurial activities at university and throughout the entire innovation ecosystem.

Almost all of the content of the entrepreneurship education (EE) courses and incubator training is oriented towards consensual entrepreneurship methods, in accordance with entrepreneurship education. Although the core contents of the EE courses and university-based incubators’ training are the same, the outcomes are quite different.

This study considers the students’ entrepreneurial identities with a focus on their point of view that led them to end up in one of the two common entrepreneurship resources at universities: the EE course and entrepreneurial activities related to university-based incubators.

Lean Startup (LS) has gained considerable traction in the startup scene, especially within digital firms where the concept finds the concept's genesis. However, there are more and more calls in the entrepreneurship literature to study LS's application beyond the digital context. The purpose of this study is to explore the applicability of LS within the largely under-researched context of non-digital entrepreneurs. To structure the authors' understanding of the participating entrepreneurs' cognitive and behavioural logic, effectuation theory is applied.

To explore how LS is applied in non-digital settings, this study analyses the LS approach of 15 Dutch brick-and-mortar and click-and-mortar Small to Medium-sized Enterprises (SMEs) and startups.

This study provides empirical evidence that non-digital entrepreneurs pursue an effectual logic in their LS approach. However, the entrepreneurs' LS approach appeared to be contextualised to the non-digital contexts' inherent constraints and trade-offs. Such contextualisation calls for a particular set of skills and competencies, including applying mixed-methods cross-validation, affinity and passion for craftsmanship and aesthetics, inferring from limited, qualitative, and often skewed data, establishing an empathetic collaborative relationship with customers and suppliers, and leveraging prior market knowledge and experience.

This study advances the current understanding of the LS applicability and gives a more nuanced account of how LS is practised in the context of non-digital firms, the challenges entrepreneurs involved in non-digital firms need to overcome, and the skills and competencies they need to possess. In practical terms, the findings help non-digital entrepreneurs and coaches to be more heedful of the contextual peculiarities when employing LS.

Entrepreneurs prioritise and act on purposeful endeavours instigated to actions by the visions of profits and benefits in the perceived opportunities. In the state of maximum entropy, with disorderliness and disequilibrium, entrepreneurs select the preferred pathway, through the profit-sensing mechanism, with the best probability of success to bet on. Therefore, this paper unpacks the forces at work in the mechanism to explain how entrepreneurs respond to opportunity and interpret the signals to coalesce into organised actions.

This research is primarily a conceptual paper on entrepreneurial action and the mechanism leading to that action. It refers to thermodynamic principles and biological cases to explain the forces at work using mostly analogical comparisons and similarities.

This paper aims to present an alternative theoretical scaffolding for entrepreneurship researchers to explore non-rational entrepreneurial behaviours and actions in uncertain, unstable and non-equilibrium environments, thereby creating new and competing hypotheses under the backdrop of adaptive evolution and thermodynamics phenomena.

The discussion featuring instinctively and naturally forming responses cannot fully explain the real entrepreneurial action as there is an element of free will and choices that are not discussed. While strategic choice and free-will shape decisions, they are preceded first by the attraction of the gradients and the biased motion in the direction of profit-attractant.

There remain essential links and issues not addressed in this “natural science”, constituting life science and physical science, oriented entrepreneurship research and exploration. Conceptualising opportunity-as-artefact and entrepreneurship as design, significant incidences of entrepreneurial actions can be explained by the presence of gradients stimulating entrepreneurial actions.

This viewpoint of information causality in opportunity-as-artefact casts a new look at the venerable question of what causes entrepreneurial actions. Shane and Venkataraman brought into focus this conversation, initiating the conceptual definition of opportunity. To have entrepreneurship, entrepreneurial opportunities must come first. Figuring the signals arising from these opportunities and cueing entrepreneurs to action is the main focus of this study.

Considering the “mechanism” at work and the thermodynamical forces at play, the entrepreneurial design process appears to hold considerable promise for future research development.

It is the responsibility of the national governments to deliver healthcare services that are both effective and affordable to everyone. There are still gaps in this supply, which is extremely demanding. In this sense, companies are attempting to reach neglected markets and disrupt the marketplace with novel solutions. Although there are still anecdotal examples, a thorough literature evaluation is lacking. This study aims to provide a synthesis of the future of healthcare start-ups.

Papers that included the term “healthcare start-ups,” “health-tech start-ups,” “start-up,” “Artificial intelligence in healthcare,” and “Health tech start-ups in India” were considered for the analysis. The Biblioshiny package under the R programming tool was considered for a detailed analysis of the papers.

A total of 854 documents were related to healthcare start-ups, from which only 14 papers are related to health-tech start-ups and four papers are related to artificial intelligence in healthcare start-ups. It has been found from the past works of literature that the effectiveness of technology for information and communication in healthcare has significantly increased in recent years. Technology has already begun to permeate the healthcare market from other fields and industries. One way that the internet will help the industry evolve is by integrating digital health into daily life.

The study is not using other databases but is limited to Google Scholar and Scopus. A significant constraint of this study is the paucity of relevant literature in reputable publications on health and information systems. Another restriction was that gray literature, such as any journal or newspaper written by members of the health community about health-tech start-ups, was not taken into account.

Healthcare players should exhibit a fundamental openness to novel solutions to facilitate the digitalization of the healthcare system. Developing technology is widely used, and from an innovation perspective, a start-up should focus on innovation by employing technology and offering revolutionary healthcare solutions.

The novelty of this research is based on its presentation of an organized and thorough literature evaluation, which defines the current state of the art concerning green start-ups. To create a sustainable start-up, a thorough study of the information gained in respect of its healthcare start-up is presented.

Design thinking has become an omnipresent process to foster innovativeness in various fields. Due to its popularity in both practice and theory, the number of publications has been growing rapidly. The authors aim to develop a research framework that reflects the current state of research and allows for the identification of research gaps.

The authors conduct a systematic literature review based on 164 scholarly articles on design thinking.

This study proposes a framework, which identifies individual and organizational context factors, the stages of a typical design thinking process with its underlying principles and tools, and the individual as well as organizational outcomes of a design thinking project.

Whereas previous reviews focused on particular aspects of design thinking, such as its characteristics, the organizational culture as a context factor or its role on new product development, the authors provide a holistic overview of the current state of research.

Mastering innovation in highly regulated markets might require companies to overcome significant barriers. Rules, laws and limitations on social, economic and institutional dimensions can hinder the ability of a company to transfer knowledge within and across organizational boundaries. However, as recent research in innovation management increasingly advocates user involvement and early understanding of user needs as best practices, the inability to freely interact with customers due to highly regulated market restrictions can hinder the company’s capability to innovate. Hence, this paper aims to shed light on how an emerging managerial approach, such as Design Sprint, can support companies operating in highly regulated markets to overcome user involvement limitations and boost human-centered innovation.

This paper sheds light on how to boost innovation in a highly regulated market by leveraging an in-depth case study. The study investigates the use of the Design Sprint approach adopted by the pharmaceutical multinational Johnson & Johnson to revise the way its R&D department orchestrates the new product development process, overcoming the user involvement challenges of highly regulated markets.

In analyzing six different projects undertaken in the past two years, the findings illustrate three microfoundational dimensions of the Design Sprint approach in highly regulated markets, the so-called 3T model: team, time and tools. Indeed, deploying the Design Sprint in a highly regulated market has proven that being able to experiment in the early stages, building rough prototypes in real-time and openly collaborating with partners is crucial to boost innovation and anticipate constraints.

The paper sheds light on the Design Sprint approach by initially grounding an emerging managerial approach on organizational and management theory, leveraging the lens of microfoundations. In doing so, this study suggests how Design Sprint is based on the pillars of experimentation, knowledge transfer and co-creation usually neglected in highly regulated markets where user involvement is challenging. Finally, this study discloses the importance of using a design-based methodology in fostering innovation in highly regulated markets.

The study aims to identify the status quo of artificial intelligence in entrepreneurship education with a view to identifying potential research gaps, especially in the adoption of certain intelligent technologies and pedagogical designs applied in this domain.

A scoping review was conducted using six inclusive and exclusive criteria agreed upon by the author team. The collected studies, which focused on the adoption of AI in entrepreneurship education, were analysed by the team with regards to various aspects including the definition of intelligent technology, research question, educational purpose, research method, sample size, research quality and publication. The results of this analysis were presented in tables and figures.

Educators introduced big data and algorithms of machine learning in entrepreneurship education. Big data analytics use multimodal data to improve the effectiveness of entrepreneurship education and spot entrepreneurial opportunities. Entrepreneurial analytics analysis entrepreneurial projects with low costs and high effectiveness. Machine learning releases educators’ burdens and improves the accuracy of the assessment. However, AI in entrepreneurship education needs more sophisticated pedagogical designs in diagnosis, prediction, intervention, prevention and recommendation, combined with specific entrepreneurial learning content and entrepreneurial procedure, obeying entrepreneurial pedagogy.

This study holds significant implications as it can shift the focus of entrepreneurs and educators towards the educational potential of artificial intelligence, prompting them to consider the ways in which it can be used effectively. By providing valuable insights, the study can stimulate further research and exploration, potentially opening up new avenues for the application of artificial intelligence in entrepreneurship education.

This study aims to emphasize the integration of lean start-up and design thinking approaches and investigate how they may be used together.

The report uses a systematic literature review methodology to analyze and summarize previous research on combining lean start-up and design thinking. Inferences were discovered and analyzed after relevant publications were chosen based on predetermined inclusion criteria.

The research shows that combining lean start-up and design thinking significantly impacts entrepreneurship. Start-ups can efficiently uncover consumer needs, reduce risks and improve their product or service offerings by combining the client-centricity of design thinking with the iterative and data-driven concepts of lean start-up. This integration promotes an innovative culture, gives teams the freedom to try new things and learn from mistakes and raises the possibility of start-up success.

The dependence on pre-existing literature, which might cover only some potential uses and circumstances, is a weakness of this research. It is advised that more empirical research be conducted to determine the precise circumstances in which the integrated strategy performs best. Future studies should also explore the difficulties and drawbacks of using these approaches to offer suggestions for overcoming them and maximizing their advantages.

The findings have significant ramifications for business owners and other professionals working in the start-up environment. The combination of lean start-up and design thinking emphasizes the relevance of early customer interaction and empathy-driven design. To foster creativity and hasten the expansion of start-ups, practitioners are urged to create a comprehensive strategy that integrates the advantages of both techniques. Through this integration, business owners may develop solutions that appeal to their target market, increasing adoption rates and market competitiveness.

This study is interesting in comparing lean start-up and design thinking, emphasizing the overlaps and benefits of their application to entrepreneurship. This study discusses successful start-up methods by offering suggestions for future research and practice. It also provides a basis for further developing and adopting the integrated approach.

The paper introduces a new conceptualisation of entrepreneurship that promotes a broader perspective of the phenomenon. The purpose of the paper is to re-conceptualise the act of entrepreneurship so as to reduce it to the fundamental behaviours and processes.

The paper sets out the motivations for and challenges in establishing a broader definition of entrepreneurship. Following this, current approaches to defining entrepreneurship are reviewed. In light of these, a definition of entrepreneurship is offered that captures a new perspective in understanding entrepreneurship. A critique of the offered definition is offered with regards to promoting theory development, empirical research, quality predictions and a distinctive research domain.

The authors argue that a definition of entrepreneurship that is focussed on the development and validation of ideas provides a thought-provoking re-conceptualisation of entrepreneurship. Extant perspectives on entrepreneurship as business/organisation creation, uncertainty, innovation, value creation and opportunity recognition/creation are drawn on to demonstrate the applicability of the definition.

The pursuit for an encompassing definition of entrepreneurship has been both extensive and earnest, which has inadvertently resulted in a sizable pool of definitions. The authors offer a re-conceptualisation of entrepreneurship with the intent to provide a broad yet coherent definition that encompasses all acts of entrepreneurship. A benefit of this conceptualisation is the establishment of the endpoint of the entrepreneurship process that delineates it from the domain of management.