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The purpose of this paper is to examine how the instructions of the lean startup methodology influence entrepreneurs. It explores what happens when such instructions are enacted by entrepreneurs in the context of a “prescriptive accelerator”. The goal is to shine a light onto the mechanisms by which these instructions are acquired and then utilised by entrepreneurs and to outline in some detail the implications of adhering to the lean startup methodology.

This paper employs an in-depth phenomenological semi-structured interview design. Two rounds of interviews were conducted, one at the beginning and one at the end of the programme with the CEOs and founders of 11 entrepreneurial ventures, totalling 22 interviews. The analysis of the interviews resulted in five second-order themes that are discussed in light of the processes of experiential and vicarious learning.

The findings suggest that through two distinct modes of vicarious and experiential learning, the instructions of the lean startup methodology are acquired, internalised and consequently put into practice by entrepreneurs. The paper further highlights the modifications to entrepreneurs’ governing variables and action strategies, as well as the resulting consequences of these modifications. This provides insights into possible outcomes of following the lean startup methodology in the context of a prescriptive accelerator where a strong focus on adhering to a systematic entrepreneurial methodology is a characteristic feature.

This paper contributes to our understanding of the under-studied and novel phenomena of the lean startup methodology and prescriptive accelerators. It complements the prevailing understanding of entrepreneurial learning as being largely experiential by accounting for the vicarious learning processes that occur in pedagogical settings such as prescriptive accelerators. It therefore shows that prescriptive accelerators provide unique learning situations where the combination of vicarious and experiential learning impacts the business development activities. Further, it provides a model of entrepreneurs’ theory of action as the outcome of the interactions between the lean startup methodology and the two modes of vicarious and experiential learning.

Uncertainty in the early development of digital business startups can benefit from data-driven testing of hypotheses. Startups face uncertainty not only in product development, but also over the structure of the business model and the nature of the customer or market to address. The authors conceptualize a new model, the Lean Discovery Process (LDP), which focuses on market-based testing from the early business idea through to fully realized product stages of an innovation. The purpose of this paper is to highlight a methodology to help digital business reduce uncertainty and apply lean principles as early as possible in the development of a business concept.

Examining literature in lean startups, lean user experience and lean software development, the authors highlight underlying assumptions of existing lean models. The authors then examine the LDP using the case of raiserve, a social entrepreneurship startup that focuses on the management of cause-based voluntary service.

Existing literature focuses on product development against an assumed customer base. Early hypothesis testing can be applied to business concept development to substantially reduce cost and time to market.

Further investigation of different forms of uncertainty in digital startups can open up opportunities to further apply lean methodologies. A more extensive empirical study to measure the potential impact is warranted.

The authors conceptualize the minimum viable customer and support early testing with concepts from market research and collective intelligence. The authors demonstrate early opportunities to apply lean principles and rigorous hypothesis testing in an LDP that results in significant reductions in time and expense of product development.

Startups have attracted increased attention over the past years. While entrepreneurs develop startups to capture new business opportunities, also large companies are turning to these fast-growing organizations in efforts to become more agile. However, managing business model innovation and validation is challenging. A number of methodologies, like the Lean Startup (LS), emerged to reduce uncertainties concerning innovation-based projects, and to contribute to business model validation. Despite its popularity, the literature on the LS and its key underpinnings (Agile Methodologies and Customer Development) is sparse, lacking an integrated and structured analysis of their impacts and potentialities. The paper aims to discuss this issue.

The authors conducted a comprehensive systematic literature review on the topic fully analyzing a final set of 71 papers.

There is a turning point in the research stream’s maturity with publications in conferences and major journals, with the predominance of empirical investigations in the European region. Articles on the topic are on the rise in several technology fields. However, the literature on the subject falls short on providing guidance to assist practitioners and scholars on the adoption and investigation of these methodologies.

The paper provides guidance for practice by presenting a staircase roadmap for the LS implementation drawing from the final set of papers reviewed.

The study categorizes the current literature through a concept map, and offers a structured research agenda beyond the categories from the thematic analysis.

The primary goal of a startup is to find a viable business model that can generate value for its customers while being effectively captured by the startup itself. This business model, however, is not easily defined, being a consequence of the application of tools involving trials, data analyses and testing. The Lean Startup (LS) methodology proposes a process for agile and iterative validation of business models. Given the popularity and importance of such methodology in professional circles, the purpose of this paper is to conduct a historical literature review of existing academic and professional literature, correlating LS concepts and activities to previous theory and alternative business model validation methods.

A historically oriented systematic literature review employing snowball sampling was conducted in order to identify academic and professional literature and references for iterative validation of business models. A total of 12 scholarly journals and professional magazines dealing with strategy, innovation, entrepreneurship, startups and management were used as data sources. The extensive literature review resulted in 963 exploratory readings and 118 papers fully analyzed.

The results position the LS as a practical-oriented and up-to-date implementation of strategies based on the Learning School of strategy making and the effectuation approach to entrepreneurship; the authors also identify a number of methods and tools that can complement the LS principles.

This paper identified and synthesized the scientific, academic and professional foundations that precede, support and complement the main concepts, processes and methods advocated by the LS methodology.

This paper aims to propose an integrated methodology for evaluating academic spin-offs (ASOs) for supporting both the development phase and performance evaluation. The ASOs have peculiar characteristics compared to other start-up companies and the debate on their evaluation is still open.

The proposed methodology, adopting a lean approach, faces the typical problems that characterize the growth of an ASO: the excessive attention to the technological aspects with respect to the commercial and managerial ones; and the need for evaluation systems that try to evaluate all risk areas and to highlight any misalignment. The methodology was built also starting from the results of an Erasmus + research project, co-funded by the European Commission, called spin-off lean acceleration.

The methodology proposes to monitor the main risk areas (market, technological, implementation, governance and financial). For each of these areas, at first, a framework and a checklist are proposed for supporting the qualitative assessment of the potential of each areas. In the second part, a set of metrics for monitoring the performances and to understand if the spinoff is developing in the right direction is proposed. Moreover, the methodology was applied to the spin-offs at the University of Calabria (Italy), and the paper reports the first results obtained.

A new canvas model (lean acceleration canvas), more specific and suited to the context of ASOs, was developed and tested. A lean approach has been adopted also for understanding the weakness of traditional methods. The proposed methodology could be used by the technology transfer offices in their institutional activity of supporting ASOs.

Most studies investigating the adoption of lean startup (LS) practices by technology new ventures focus on software startups in mature entrepreneurial ecosystems and disregard their applicability for opportunity exploitation in other technological backgrounds. This study contributes to this research stream by exploring how Brazilian technology new ventures (in different technological fields) tentatively adopt LS to exploit opportunities and whether LS is suitable to their emerging economy context.

The authors adopt an exploratory multiple-case study based on qualitative data collection and analysis of nine Brazilian biotechnology, engineering and software startups.

The study shows how technology new ventures tackle the activities of opportunity exploitation – namely, developing a product or service, acquiring human resources, gathering financial resources and setting up the organization – by leveraging LS tools and practices for business model validation; also, it identifies six contextual constraints hindering the systematic adoption of LS and reveals how technology new ventures cope with such constraints in their early stages by integrating LS with complementary strategies and practices. Furthermore, the study reveals that the systematic and comprehensive adoption of LS nurtures the development of an entrepreneurial experimental capability to explore opportunities in a quasi-scientific and hypothesis-driven fashion.

The study investigates how Brazilian engineering, biotechnology and software startups exploit opportunities and overcome constraints to business model validation through the combined adoption of LS and complementary strategies and practices and provides a set of propositions to guide future research.

The purpose of this paper is to satisfy a clear gap in the main field of open innovation research whereabouts a very little scholarship try to analyze the mechanisms of innovative milieu down smart cities environments by applying through innovative projects that seem to support efficiently the entry of private firms and citizens in public collaborations.

The research performed an exploratory and qualitative evaluation based on the case study method built on the evaluation of organizational behavior and urban boosting innovation through smart city initiatives. In doing so, after a literature review in smart city as well in lean methodology fields, the case of Turin Smart City follows.

As acknowledged by international literature, the paper shows how a lean approach enables local government to define and realize smart projects and initiatives in a faster and more effective way. Particularly, the government in one of the main cities in Italy, id est Turin, combines a lean methodology with the job-to-be done approach, according a new concept of smart initiatives involving a startup mentality for the lead users which enables interesting predictions relating the human aspects of open collaborations.

The specificity of this inquiry highlights valuable insights from double-gate smart cities’ innovation, social and urban as well. The research is largely interpretative and exploratory and while this provides a solid scientific foundation for further research, it does not, itself, subject any hypothesis to statistical testing and validation.

Since the city approached the smart city subject in a lean way, it was able to realize some projects in a faster way. Through specific initiatives, the city acquires the ability to involve more and better all its stakeholders such as citizens, companies, and public employees, among others. In this regard, the paper invigorates managerial debates concerning the urban and social aspects of open innovation ecosystems which represent in our minds a superior level of open innovation, testbeds of positive knowledge, and stimulus of knowledge dissemination process around the city.

This paper aims to explore an adaptation of the National Science Foundation Innovation Corps (I-Corps^TM) program, which uses the Lean Startup methodology to help STEM scientists commercialize their research. The adaptation, known as I-Corps for Social Impact (I-Corps SI), extends the for-profit canonical model to include mixed revenue and non-profit business models, to help researchers generate social impact.

A research team of policy and non-profit experts observed and adapted a canonical I-Corps process, then interviewed academics who are scaling and sustaining socially impactful solutions from their research, including past I-Corps participants, to validate research team learning.

The paper describes limitations of the I-Corps model and modifications required to enhance social impact.

While the field of social entrepreneurship has grown rapidly over the past few decades, social scientists have lagged behind in translating evidence-based research into solutions that can be scaled and sustained to achieve social impact. The paper presents an evidence-based case for a pedagogical tool to close this gap.

A focus on validated learning and business model development supports a paradigm shift within the social sciences, which can help spur greater social innovation from evidence-based research.

The term Lean Startup (LS) was coined by Eric Ries, and his 2011 book has popularized the concept with organizations, both startups and established organizations, implementing LS. However the empirical grounding is thin and for a long time this subject has been neglected by academia. The purpose of this paper is to present a systematic literature review (SLR) on LS, while highlighting core knowledge and identify gaps.

A SLR was carried out based on the Preferred Reporting for Systematic Reviews and Meta-Analyses (PRISMA) protocol of Scopus and Web of Science databases. In total, 45 articles published in journals and conferences over 10 years were collected which revealed a number of LS research gaps.

The SLR revealed the tools and methods associated with LS, most cited pros and cons, reasons that cause LS failure, the challenges that companies face in the implementation of LS, and critical success factors (CSFs) that can support these challenges and minimize the reasons for failure.

The findings of the study can be beneficial to practitioners and senior managers in organizations who wish to delve into the journey of LS. The study also discloses challenges and barriers that can hinder the implementation of LS.

Academic publications regarding LS are sparse and this SLR is one of the first SLRs to explore both the critical failure factors (CFFs) and the CSFs based on peer-reviewed journal and conference proceedings.