Dottorato di Ricerca in Economia e Management
XXX CICLO
Frameworks,
Approaches and Tools to Prevent Startup Failures
Leonello Trivelli
A.A. 2016/17
Tutor:
Prof. Angela Tarabella, Università di Pisa
Coordinatore:
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Table of contents
Summary ... 3
1. Introduction ... 5
2. Measuring Entrepreneurship Education Performance in University-Based Entrepreneurship Ecosystems .13 3. Developing Innovative Entrepreneurship Education and Training Approaches ...54
4. Combining Lean Startup and Quality Fuction Deployment for New Product Development ...80
5. From Design to Implementation, Reinventing the Business Model Canvas. ...119
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Summary
The difficulties generated by the economic crisis have increased the interest of policy makers and research community on the contribution that entrepreneurship can provide in terms of economic growth and job creation. To this regard, a huge number of initiatives have been undertaken to provide aspiring entrepreneurs with the skills and knowledge that are necessary to deal with the creation of new companies and to allow them to succeed on the market. However, most new companies fail in the first years of activities and waste the possibility to create value for the whole society. On one side entrepreneurship education seems to fail in nurturing new entrepreneurs, on the other hand the most used tools for developing new business ideas struggle in passing from a business design phase to the implementation one. The present work aims at addressing the startup failures and at identifying innovative frameworks, approaches and tools that help new companies to succeed on the market.
To achieve such an objective a twofold approach has been adopted, on one hand the business environment has been investigated to understand how the whole entrepreneurship ecosystem can support new businesses to develop consistent business models and to deal with the market dynamism. On the other hand the development stages of new business were analyzed to identify the shortcomings of entrepreneurship education frameworks and to develop innovative and complementary approaches
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1. Introduction
When economic crisis reduces job opportunities, entrepreneurship may play a vital role in sustaining economic development and providing opportunities to young people. High unemployment rates, and reduced job opportunities from traditional business sectors such as manufacturing, textile and automotive industry, are forcing young people to face harder circumstances when entering the labour market and many people see starting a new business as a good career venue (Miles et al., 2017). EU Entrepreneurship 2020 Action Plan claims that to bring Europe back to growth and to increase the levels entrepreneurial skills and competences to launch and grow new businesses, and to start entrepreneurship path within existing companies. Such knowledge gap hinders the possibility for new companies to companies failing by the first 5 years (Bolinger & Brown, 2010; Zhang, 2015), thus exacerbating the shame of failure among young European entrepreneurs.
According to Khelil (2016) a complete understanding of the concept of failure requests the inclusion of both insolvent (Shepherd, 2003), and underperforming firms (DeTienne et al., 2008) whose effects in terms of resources destruction is much higher than the insolvent ones. This phenomenon is widely analysed by entrepreneurship literature from different perspectives. On one hand, large body of research has developed to explain the causes of business failure or survival (Artinger, 2016; Battistella et al., 2017; Giardino et al., 2014; Thornhill and Amit, 2003). On the other hand, an increasing amount of research has investigated the potential consequences of firm failure for entrepreneurs (Cope, 2011; Shepherd, 2003; Ucbasaran et al., 2013; Yamakawa et al., 2015).
An important contribution on understanding the reasons behind business failure has provided by Mellahi and Wilkinson (2004) who distinguish failures in two categories: new business failures due to the environments surrounding the company and new business failure because of entrepreneurs wrong decisions. Khelil (2012) complemented this approach by adding a third dimension that refers to entrepreneurs motivations and emotional aspects. Table 1 shows an overview of research works focusing on business failure causes belonging to this framework.
Several authors underlined how young companies fail more due to internal factors; on the contrary mature companies fail because of external causes (Lukason & Hoffmann, 2015; Thornill & Amit, 2003).
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Battistella et al. (2017) provided an extensive review by clustering startup failure causes according to the 6 main factors studied by researchers, scholars and practitioner all over the World. Among these factors, the most important causes refer to team (Ineffective team/low qualification/expertise/skills diversity), product/service (Low or no product/market fit - marketable product) and strategy (Unclear - or no -business plan or business model / narrow Planning breadth/little focus on long-term).
To overcome these issues, Governments and donors subsidize entrepreneurship training programs around the world to create future entrepreneurs (DeTienne & Chandler 2004, Fairlie et al., 2015).
Most of these initiatives focus on instilling the entrepreneurial mindset or supporting aspiring entrepreneurs to deal with early stage aspects of the startup creation (e.g. opportunity recognition) while addressing challenges and problems to be faced by more mature organizations acquire limited importance (Heinrichs, 2016; Mwasalwiba, 2010). Moreover, evidences demonstrating that entrepreneurship education can positively affect entrepreneurial outcomes are still to discuss, indeed if some scholars agree on that (Matlay, 2008; Secundo & Elia, 2014), some others refuse such assumption (Astebro, 2016; Oosterbeek et al., 2008). However, the great majority of new entrepreneurs participating in entrepreneurship education programs see their business failing by the first years of activities and for this reason entrepreneurship education seems to be at least not effective enough to help startups surviving at early stages difficulties and the Valley of Death.
Firstly, businesses often lack an appropriate ecosystem that enables them to grow (EC, 2012), thus effective entrepreneurial initiatives should be supported by holistic environments where entrepreneurship ecosystem actors facilita
Secondly, formal education is not enough and ideas are put into action when learning is supported by the creation of an environment, which encourages and facilitates practical experiential learning by all participants in entrepreneurship education (Hines & Richardson, 2007). Moreover, entrepreneurial
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learning occurs when relationship with other individuals are established and takes place in communities of practice (Wenger, 2000).
Thirdly, most of the entrepreneurship education programs rely on approaches like Lean Startup (Ries, 2011; Blank, 2012), and tools such as Business Model Canvas (Osterwalder & Pigneur, 2010) without providing the entrepreneurs with methodologies and skills to pass from the design phase to the implementation.
On one hand, the Lean Startup approach do not provide the entrepreneurs with a reference scheme to concretely translate customer needs into the features to be delivered through the Minimum Viable Product, so that product/market fit can be reached only at a theoretical level. On the other hand, Business Model Canvas do not help entrepreneurs in understanding how what designed within the tool would be actually feasible in the reality and in which way to manage all the business activities and the relationships between them. In particular, according to Brix and Jakobsen (2015) the BMC is not considered concerning entrepreneurship education and the tools used for new business model development have been studied through different research approaches.
In order to contribute to reduce the startup failures, the present research aimed at answering the following research questions:
How to prevent startup failures through innovating entrepreneurship education programs? How business design tools adopted in entrepreneurship education can be translated into approaches that ease business implementation and prevent startup failures?
This section is the first Chapter of the Thesis and (Chapter 1) allows delineating the boundaries of the research domain addressed by the present research. Then, the following chapters are based on the works published or submitted to international journals.
Chapter 2 provides an analysis of innovation and entrepreneurship ecosystems - i.e. the environments where the innovation grows and is transformed into sustainable business opportunities - have been performed. A qualitative analysis of existing approaches allowed the identification of the areas where to intervene. The importance of collaborative innovation has been deepened through the analysis of the characteristics of all the different actors involved in the innovation process. The research has been performed through an analysis of the literature as well as through the direct involvement in the University of Pisa system for supporting the transformation of innovative idea into businesses, adopted in order to boost technology transfer initiatives. Ethnographic and quantitative analyses have been adopted. This
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research provided an As- -Based Entrepreneurship
framework for the design of successful (research based) entrepreneurial initiatives.
Starting from the results of analyzing the University-based Ecosystem of the University of Pisa, Chapter 3 shows an innovative approach for entrepreneurship education, which aims to support new companies to be able to respond to market changes and to be able to overcome the
so-ecosystem in entrepreneurship education initiatives can generate good results for the startups participating in the education program itself. The evidences collected during this deep analysis of entrepreneurship education unveiled the limitations of most adopted tools and approaches for new business design which are addressed in the second part of the thesis.
Chapter 4 analyzes one of these limitations that refers to the possibility to address market needs with by providing the right product/service. In this chapter, different customer understanding approaches are taken into consideration in order to design a QFD-based approach that is able to translate
Chapter 5 instead focuses on the transformation of a business concept in a working company by providing a framework that relies on the Business Model Canvas to identify management tools that allow aspiring entrepreneurs to transform their business models into operational processes.
Final remarks from the analysis and the application of the proposed frameworks, approaches and tools are provided in Chapter 6.
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2. Measuring Entrepreneurship Education Performance in
University-Based Entrepreneurship Ecosystems
Introduction
High unemployment rates, particularly in Italy and in Southern Europe, and reduced job opportunities from traditional business sectors such as manufacturing, textile and automotive industry, are forcing young people to face harder circumstances when entering the labour market. In particular, due to the decreasing labour demand in these sectors, many talented people often find a job in the tertiary sector in a position that might not suit their aspirations. Furthermore, underemployment represents an increasingly significant issue and favours the inception of vicious conditions. On one hand starting salaries reduced because of the crisis (Almalaurea, 2016); on the other, high skilled people (in particular PhD owners) increasingly do less satisfying jobs because of their weak link with their research domain (Escardíbul & Afcha, 2016). For example, mathematicians are hired as analysts in banks and insurance write code for providing basic services. Southern Europe Countries and Italy, in particular, especially if compared with Scandinavian countries, do not offer many prospects to researchers and post-docs (European Commission, 2007), and the brain drain towards more academically stimulating and economically attractive countries is a phenomenon exacerbated by the economic crisis. While in the past being favoured also in Southern Europe Countries. This phenomenon depends by reduced opportunities to pursue a career within universities and research centers, by lower wages compared to wealthier Countries and by a higher global quality of life that can be found abroad (Bartolini et al. 2017).
Within this context, graduates, PhDs, and post-docs have been changing their attitudes and mindset, developing an entrepreneurial behaviour aimed at fulfilling professional and personal aspirations. If being an entrepreneur was not so attractive in the western world before the economic crisis, and people still preferred more traditional, and less risky, jobs, today creating a new company, is perceived as a career venue.
The above mentioned scenario has made the European Community understand the necessity of keeping qualified personnel in their nations, by promoting several actions
emigration and fostering entrepreneurship through policies (EC, 2012) focused on new venture creation and start-ups support. Even very influential private institutions, such as the Global Entrepreneurship
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Monitor, evidence the pivotal role of entrepreneurship in contributing to the economic development (Kelley et al., 2012). Academic institutions vary greatly in their responses to the demand for entrepreneurship education, and the necessity to develop university-based entrepreneurship ecosystems is widely recognized (Fetters et al., 2010).
Following the successful experiences of university-based entrepreneurship ecosystems put in place by the Polytechnics of Turin and Milan, the University of Pisa has asserted its crucial role in such societal challenge. Such process started in 2011 with the establishment of the PhD plus, an extracurricular programme aimed at supporting entrepreneurship at different levels so to stimulate the entrepreneurial spirit within the university itself or in existing companies, and to foster an entrepreneurial mindset by launching a start-up or making a company grow.
In the beginning, the PhD plus was conceived as a practical course aimed at strengthening new skills and attitudes not well developed among students. This was a time when the entrepreneurship culture was almost absent and the programme was meant to address an unsatisfied need. Over time, the PhD plus has evolved from a simple university course into a cultural experience, capable of fostering innovation concept of sustainable growth. This evolution has gone in parallel with a cultural change and the emersion of new needs in local and regional ecosystems and not only in the university environment. Locally, in four years many initiatives related to start- 1, to 2, to R.I.O.T.3 (an hackathon on the topic of Internet of Things solutions). Moreover, other
factors have contributed to the acceleration of an extremely dynamic system: in 2011 the University of Pisa joined the European Entrepreneurs Campus, a project funded by the European Union4; in 2012 the
Italian Business Angels association started attending the final pitches of the students (in 2012). Furthermore, Red Lions Ventures, the local new venture capital, was founded in 2013; a new business angels group (SAMBA) was launched early in 2014. Last, but not least, the project ENDuRE, coordinated by the University of Pisa has been financed by the Erasmus+ Knowledge Alliances5. All these elements
contributed to add complexities to the context in which entrepreneurship education and support activities were run. A more accurate monitoring system on behalf of organizing institutions was necessary
1A periodic event of networking then turned into an official Startup Grind chapter, powered by Google for Entrepreneurs. http://startupgrind.com/pisa/
2Promoted by the local coworking space “Talent Garden” http://spitchati.it/
3Revolutionizing The Internet of Things - an hackathon on the topic of Internet of Things technical and business solutions organized by Fablab Pisa http://www.fablabpisa.org/?p=1142
4EU LLP Programme – Leonardo Da Vinci (n° 2012-1-IT1-LEO05-02794): “EEC: European Enterpreuners Campus”. 5“European Network of Design for Resilient Entrepreneurship” (ENDuRE). 554337-EPP-1-2014-1-IT-EPPKA2-KA.
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to respond proactively to the new challenges rising from the local context and to allow new entrepreneurs being fully immersed in the entrepreneurship ecosystem.
Against this backdrop, the present research aims at providing a structured approach for improving the entrepreneurship education and supporting monitoring systems. In particular, it contributes to develop a continuous monitoring approach by mixing qualitative and quantitative methodologies and metrics. This can help universities in tuning entrepreneurship education and support initiatives with the continuous changes affecting entrepreneurship ecosystems dynamicity (in terms of actors involved and their needs).
Background
Fostering Entrepreneurship Education and Support
From the early 20th century, scholars contributed to deepen the impact of entrepreneurship on
economic development, and many of them confirmed the fundamental role that new venture creation plays on the economy at large (Audretsch & Acs, 2003; Audretsch, 2009; Kuratko, 2005; Mazzarol et al.,
However, about 50% of new businesses fail during their first five years (Aldrich, 1999) and the main reason of failure is that most of times startups do not develop a product or a service that satisfy customer needs (Taylor, 2008). According to Baum and Locke (2004), three factors could be considered as determinants for new venture success: personality traits, organizational factors and environmental factors. European businesses often lack an appropriate ecosystem that enables them to grow (EC, 2012) and the myriad of initiatives to support entrepreneurship (eg. training courses, coaching and mentoring activities, practical training and incubation) did not succeeded in developing personality traits and organizational factors (Bae et al., 2014; Chen et al., 1998; Gorman et al., 1997; Martin et al., 1997). As noted by Thornton (2003), . This statement clearly highlights the growing importance that local environment is gaining in its contribution to new ventures creation and economic development. Moreover, different approaches have been adopted to build entrepreneurship ecosystem and many actors contribute to such a process (Schmidt & Molketin, 2015). Manifestly, each stakeholder who plays a role within a certain ecosystem affects in several ways the entrepreneurial performance of firms in the ecosystem itself.
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Mason and Brown (2014) have described an entrepreneurial ecosystem as
entrepreneurial actors, entrepreneurial organizations, institutions and entrepreneurial processes which formally and informally coalesce to connect, mediate and govern the performance within the local
The growing interest on entrepreneurial ecosystem has led several scholars, practitioners and organizations to represent this concept effectively. The World Economic Forum (WEF 2013) has identified as follows the eight pillars constituting the entrepreneurial ecosystem:
A. Accessible Markets B. Funding and Finance
C. Regulatory Framework and Infrastructure D. Major Universities as Catalysts
E. Human Capital/Workforce F. Support System
G. Education and Training H. Cultural Support
These pillars were taken into account within the proposed framework and each of them affect in different manners the new venture creation process and the survival rate of start-ups. Recently, public and private organizations are paying increasing attention to entrepreneurship as a main way to foster economic growth and new jobs creation. Both the US Congress (2012) and the European Commission (EC, 2012) have outlined policies directed to achieve these objectives. In particular, some specific measures have been developed by several countries with the aim to ease both bureaucracy and financial access for new ventures. The Jobs Act in the U.S. and the Italian regulations about innovative start-ups are among those recently adopted. However, when States back the development of cutting-edge and high risky fields, they effectively ease entrepreneurship by enabling visionary entrepreneurs to create new products and services (Mazzucato, 2013).
These innovative regulations foster the rise of innovative funding options like crowdfunding (Belleflamme et al., 2013) to support the new venture creation process. During the different phases of start-up development, several ways of funding the business might be taken into consideration (Markova & Perkovska-Mircevska, 2009). According to Clarysse and Brunel (2007), personal savings, family and friends' loans, business angels and venture capitalists, contribute to the firm s growth in a peculiar
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start-ups funding process and to sustain their growth. Incubators, considered in the broad sense of the term (Dee et al., 2012), support entrepreneurial initiatives by providing business services and by entering mentoring programs are some of the activities promoted by incubators and other institutions focusing on startup founders and entrepreneurs. Specifically, mentors provide start-uppers with psychological and career-related support, which both affect business success (Waters et al., 2002). Mentors help startup founders in talent awareness and exploitation, providing also valuable business insight and practical support thanks to their experience within a particular industry (Cull, 2006). While mentoring is more focused on the personal development of startup founders, coaching activities support innovative firms by exploiting their technological expertise within the market (Clarysse & Brunel, 2007). Support systems also allow startup founders to create their own network and to attract customers within the local context. Consequently, the possibility to access most familiar markets fosters technology transfer and reduces the uncertainty typically related to unfamiliar markets (Gomes & Ramaswamy, 1999). Moreover, in order to successfully compete within a specific ecosystem, firms need to understand the features of local institutions, business environment and cultural issues (Johanson & Vahlne, 1977; Pedersen & Petersen, 2004). Accessing foreign markets, on the other hand, allows start-ups to strive business opportunities, and better exploit R&D investments (Schwens & Kabst, 2010). Obviously, in a global economy where most of the attention is paid to the role of knowledge, business success is increasingly connected to the acquisition and development of skilled human capital (Crook et al., 2011). The latter is strictly related with business opportunities exploitation (Unger et al., 2011); the knowledge and skills of start-up founders could effectively be exploited by firms in order to develop distinctive capabilities and grow more than other firms (Colombo & Grilli, 2010).
Besides developing knowledge and skills in a wider sense, universities and learning institutions are also increasingly committed in fostering entrepreneurial education focusing on the development of both knowledge, skills, attitudes, and personal qualities (Kusmintarti et al., 2016) among their students. The teaching of notions at any level (from primary schools to university) provides companies with personnel that could fit each business stage (from design to production and commercialization). However, the development of proper human capital requires specific training to allow people exploiting entrepreneurial opportunities (Solesvik et al., 2013), to foster technology transfer and the exploitation of research results (Markman et al., 2005). Moreover, education and training activities are crucial to strengthening innovation attitudes and business performance (Martin Cruz et al., 2009). To reach these goals, theoretical education proves insufficient and business schools need to develop new pedagogical approaches that mix theory and practice (Erikson & Laing, 2016). Ideas are put into action when learning is supported by experiencing the economic environment. Hence, a major involvement of enterprises
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becomes essential to ensure training in a real entrepreneurial environment and provide new entrepreneurs with the right technical and managerial support for accessing the market competitively (Chang & Rieple, 2013).
An increasing number of people is attending entrepreneurship courses and many of the latters include a business concept or a business plan competition that contribute to build the culture around such initiatives (Laud et al., 2015). The awards coming from these business competitions amplify start-up founders visibility (Vogel & Grichnik, 2014) and allow some of them to reach the notoriety thanks to newspapers and television broadcasts. Conversely, a deeper awareness of what being an entrepreneur means, can help to contribute reduce the gap between Europe and US in terms of acceptance of failure tolerance, which in Europe is lower than US (Burchell & Hughes, 2006).
Starting from this overview the following Table 2 summarizes the impact that the above-mentioned pillars have on the start-up activities. According to the EU (EC, 2012), this ecosystem needs to be nurtured by creating holistic programs that integrate key topics like management and R&D coaching, and networking with peers, potential suppliers, clients and investors. This can be achieved by bringing together all the above-mentioned actors as parts of a dynamic ecosystem capable of responding effectively to the mentioned challenges.
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The monitoring and evaluation process of university-based entrepreneurial ecosystems have to consider several factors that allow to understand how adapting them to the changes that occur over time. The following paragraphs detail the assessment methods of individual attitudes, the evaluation approaches of the ecosystem performance, the adaptation of entrepreneurial education, and the monitoring approaches for the latters.
Individuals: Motivations and Enablers
urial intentions and enablers, currently constitute a main research issue (Fayolle et al., 2006). Many studies have analysed the intrinsic the role of entrepreneurial education and training programmes, object of our study.
Ács (2011) has proposed to use qualitative and quantitative data for assessing three fundamental irations. Among the various theoretical approaches, the Theory of Planned Behaviour has been widely adopted to reflect the complex perception-based processes underlying intentional, planned behaviours such as new venture initiation (Krueger & Carsrud, 1993). From a practical perspective, the World Economic Forum (2011) has affirmed that entrepreneurs rely on four layers of support that allow them to succeed, including Personal Enablers such as mentors and education. According to Sánchez (2011), students, after an entrepreneurship programme, show a higher self-efficacy, and proactive risk taking and intention to become self-employed. An implication of this study, relevant for those academic actors, is the importance key to undertaking an entrepreneurial adventure, and not only the training in the knowledge and resources needed for starting a business, as traditionally -centric factors related venture creation and confidence in it; (ii) Knowledge and ability for venture creation; (iii) Intention of
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overseas venture creation with teamwork; (iv) Recognition of the importance of entrepreneurship education.
Ecosystems Performances
Several analyses, conducted by public and private research institutions, concentrate on the macro-level evaluation of the fundamental assets of the entrepreneurial environments, assessing demographic, socio-economic and cultural factors that influence the performance of an ecosystem. Most of these studies make use of quantitative data collection and structured surveys (Bosma, 2013) to provide a clear picture of the analyzed ecosystems. A well-known approach is the one proposed by the Global Entrepreneurship Monitor (Kelley et al., 2012), that combines analyses at global, national and local level. At national level, Isenberg (2011) has proposed to assess and evaluat
performance from a holistic point of view that includes Policy; Culture; Supports; Human Capital; and Markets.
From a local point of view, Roberts and Easley (2011) have analysed the entrepreneurial impact of universities and their effectiveness in creating a culture and programmes that make entrepreneurship feasible, while several works, such as the Startup Ecosystem Report (2012), combine statistical and correlational analyses in order to compare the performance of different entrepreneurship ecosystems around the world. An interesting evaluation conducted by Di Gregorio and Shane (2003) has measured four basic aspects (the availability of venture capital in the university area; the commercial orientation of university research and development; intellectual eminence; and university policies) so to explain why some universities generate more entrepreneurial initiatives than others do
have identified three separate, but inter-connected flows, all of which are important in the formulation, assessment and
Adapting entrepreneurship education to the changes occurring in the ecosystem.
Entrepreneurship education and support systems belong to a dynamic domain characterized by a continuous need of adaptation to the existing conditions that refer to markets, institutions and academia. Innovation affects the market and entrepreneurial behaviour of people; therefore, the choices that individuals and teams are likely to make in this domain are highly influenced by the context (Autio et al.,
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2014). Indeed institutions create dedicated policies and researchers develop innovative tools and frameworks to be integrated within the new venture creation process.
Concerning the market, conditions and changes in the underlying technologies themselves lead to the necessity of constant shifts in business models and strategies to be adopted in order to set up the venture (Nambisan & Baron, 2013). For instance, the introduction of many disruptive technologies (Christensen, 2008) in the fields of ICT, has created a new environment where entrepreneurs have assumed new characteristics and own peculiarities, the so-called Internet Entrepreneurs (Price, 2000). The diffusion of these new entrepreneurial initiatives that make use of ICT solutions for accessing a worldwide market and scaling up rapidly, has implied a profound cultural change on how entrepreneurs are seen and on how future entrepreneurs should be educated and supported. New paradigms like Lean Startup (Ries, 2011) and Pretotypation (Savoia, 2011) are examples of innovative approaches for supporting the growth of new entrepreneurial initiatives proposed by experienced Internet Entrepreneurs.
Moreover, from an institutional point of view, the entrepreneurship ecosystem is characterized by high dynamicity. During the last decade, we faced a continuous growth and changes in the regulatory framework (Mayer-Schonberger, 2010) and in support systems, with the rapid expansion of local incubators, technology poles, etc. In addition, from a financial point of view, several innovations revolutionized the way new ventures can be supported. The rise of crowdfunding platforms (Belleflamme et al., 2013), in particular, has become a significant financing alternative in the wake of the global economic crisis (Kuti & Madarász 2014) and, with customers that turned into investors (Ordanini et al., 2011), it has implied important innovations in the process of formulation, development and realization of business ideas. The crowdfunding innovation has generated a new need of legislative adaptation to this revolutionary change. In the EU, Italy introduced in 2013 (Hollow, 2013) the first European regulation for the equity-based crowdfunding, whose effectiveness has been subjected to regulation by the authority responsible for the Italian securities market.
As already mentioned, increasing needs of adaptation can be highlighted also from an academic point of view. To this regard, the research activities in the entrepreneurship domain are continuously growing, reflecting on one side its relatively low maturity as a field of research (Bygrave, 1989; Brazeal & Herbert, 1999; Low, 2001; Carlsson et al., 2013) and on the other the high level of dynamicity, which characterizes this field. Along with the above-mentioned business driven paradigms, many scholars are continuously proposing new methodologies, such as those proposed by Osterwalder et al. (2005) and Adner (2012), that are markedly affecting the way practitioners and scholars approach the entrepreneurial path. Indeed, entrepreneurship is a phenomenon in a state of constant flux, shaped by the behaviour of entrepreneurs whose responses to perceived opportunities may be highly difficult to predict (Neergaard & Ulhøi, 2007). For this reason, there is an impelling need for all the involved actors to be able to acknowledge the
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external changes that could affect the effectiveness of the entrepreneurship education and support system, in order to establish a continuous and effective process of adaptation.
Continuous adaptation to dynamic contexts is possible if all the ecosystem variables are accurately monitored and evaluated. The evaluation needs moving beyond the specific aspects, and concentrate on wider social and economic benefits, such as the diffusion of knowledge, the creation of intangible assets behind the new venture process and the contribution to employment for social, cultural and economic development (Secundo & Elia, 2014). In particular, a system devoted to measuring the overall academic entrepreneurship, should contemplate the different views and expectations of every involved stakeholder (Agostino et al., 2012).
Monitoring entrepreneurship education
The correlation between firm start-up rates and the performance of entrepreneurship education systems, has been demonstrated (Curiel-Piñaet al., 2013; Martin et al., 2013). This is one of the reasons research (Donckels, 1991; Carter & Collinson, 1999; McMullan et al., 2002; Kirby 2006; Vesper & Gartner, 1997). Facing the multiplication of entrepreneurship education programmes and the increasing resources allocated, Fayolle et al. (2006) have developed an evaluation framework, based on the Theory of Planned Behaviour, which submits structured interviews before and after the programmes, so to evaluate variations of entrepreneurial intentions. A similar approach has been implemented by Stamboulis and Barlas (2014), who have based their evaluation on the analysis of ex-ante and ex-post questionnaires for the assessment of an entrepreneurship education programme in a Greek University. The questionnaires were based on four groups of questions (demography; personal future career expectations; perceptions on entrepreneurship barriers; perceptions on business success factors), and the results
studies have demonstrated the effectiveness of entrepreneurship programmes, little attention is given to an overall measurement of the performance of such programmes, and their capacity to face the ecosystem dynamicity. As for all the other domains of the entrepreneurship ecosystem, education and support systems must be guided by a strategic vision in order to anticipate contextual changes (Urbano et al., 2008).
A relevant attempt that goes in this direction is the performance measurement system proposed by Secundo and Elia (2014). The model, inspired to the knowledge triangle vision (education + research + innovation), represents a performance measurement tool based on an input-output logic, which
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process. The process performance is then opportunely measured through quantitative KPIs. The present research keeps the model proposed by Secundo and Elia (2014) as a starting point for the development of a wider framework that also takes into consideration the entrepreneurship ecosystem components, as well as both qualitative and quantitative methodologies for performance measurement.
The Case of the University of Pisa
Over the past five years, the University of Pisa clearly experienced the complexity and dynamicity of the local, regional and national entrepreneurship ecosystem. With the implementation of the entrepreneurship education and training programme, named PhD plus, in addition to other support and networking initiatives, in 2011 the University of Pisa aimed at fostering its technology transfer system and improving the entrepreneurial spirit of students and scholars. The obtained outputs of the implemented Technology Entrepreneurship Cycle are mainly:
University of Pisa Technological labs (as enterprises): university labs that have improved their capabilities in proposing and managing third party contracts with industries, and applying for public
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funds. These two paths of funding for an academic lab prove crucial, and this requires scholars to improve
University of Pisa Spin-offs: private entit
Born out of the academic environment, these companies still act as Technological Labs (as enterprises) long after their establishment (i), with most of the income coming from public research and development funds. The PhD plus initiative has attempted to solve this dynamics, helping the new spin-offs readiness to access and act in the market. At present, among the 37 University of Pisa s spinoffs, 10 have originated through the PhD plus programme.
Student-Enterprises: startups and established companies founded by university students and graduates. Although there is no direct involvement of the University in these companies, most of them benefit from the link with the University environment and the whole local entrepreneurship ecosystem. Successful examples are companies like: Net 7; CrowdEngineering; Clowd4Wi; 3Logic; List Group; Alitec.
As for the Inputs of the adapted technology entrepreneurship cycle (Figure 1), the target audience (Input) of the PhD plus programme is at present: master students; graduates; PhD students; scholars (including post-docs; researchers; professors). This input audience has however changed over the years. Conceived as a programme for scholars (mainly post docs), PhD plus initial aim was to improve the technology transfer process and boost the transformation towards an effective Entrepreneurial University (Etzkowitz, 2004), through the exploitation of innovative ideas emerged from the academic environment.
Recently, the change in the ecosystem conditions entailed an adaptation which led to extend participation to graduates and master students. This, in turn, implied a change in the educational model, and an extension of the Entrepreneurial University concept: not only a university that acts in its third mission (technology transfer and exploitation of research results), but also in its first mission (education). The education and training of students and graduates allows indeed the growth of a local entrepreneurial ecosystem, where the universities challenge is to keep and monitor the linkage not only with its spinoffs (ii), but also with the start-ups founded within a common environment (i.e. (iii) Students Enterprises).
Current monitoring approaches are easily applicable to evaluate the ecosystem performance with respect to the output categories (ii) and (i), but become less effective when Student Enterprises (iii) are also included. Within the described context, the present study aims at improving the entrepreneurship education and support monitoring systems, in order to:
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Provide an approach capable of identifying the causes of such changes. This will prove innovative from the approaches that privilege the use of quantitative measures, but make scarce use of qualitative analyses that allow to better understand the reasons behind the change in the characteristics and needs of the considered stakeholders.
Next section describes the proposed methodology for monitoring University-based Entrepreneurship Ecosystem performance and changes. Then, findings from the implementation at the University of Pisa will be presented and discussed.
A Framework for Measuring the University-Based
Entrepreneurship Ecosystem Performance
The proposed framework (Figure 2) takes into account the already mentioned World Economic Forum (2013) entrepreneurship ecosystem pillars, with a particular focus on Education and Training and Support systems, which are the areas where a University (as catalyst) has a direct involvement. A broaden analysis of the other pillars can be conducted through the methodology proposed by the World Economic Forum (2013), which has been opportunely refined by the Aspen Network of Development Entrepreneurs (ANDE, 2014). With regard to Education and Training and Support systems, the methodology starts from the Technology Entrepreneurship Cycle and the quantitative KPIs proposed by Secundo and Elia (2014), in order to provide a comprehensive monitoring system which a university can adopt to perform a quantitative and qualitative analysis of the outputs obtained from the implementation of an entrepreneurship education and support initiative, according to the five steps of the Technology Entrepreneurship Cycle. The analysis is conducted on the Input-Output entities shown in Figure 2.
In comparison to the most common monitoring approaches, the proposed methodology aims at merging quantitative and qualitative measures in order to obtain:
An updated version of the framework proposed by Secundo and Elia (2014) that provides a comprehensive description of the analysed ecosystem;
A clear connection between the overall performance (mainly described in high level quantitative measures) and the reasons behind particular outcomes (obtained with qualitative research);
The necessary information to continuously adapt the ecosystem to the dynamicity of the entrepreneurship ecosystem context.
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The methodology shown in Figure 2 entails all the entrepreneurship ecosystem pillars, with a particular focus on the ones highlighted in dark grey, that directly involve the University education and support. The approach is based on quantitative and qualitative analyses performed throughout the 5 steps of the Technology Entrepreneurship Cycle proposed by Secundo and Elia (2014).
The next two paragraphs will show in detail the approaches proposed for the quantitative and qualitative performance measure, chiefly described as: Goal; Method; Data analysis.
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Qualitative Analysis
The proposed methods of qualitative analysis are based on the ethnographic approach, which many studies have opportunely framed to the entrepreneurship research field (see Johnstone, 2007). The main features of the ethnographic research have been suggested by Hammersley (1990):
Behaviour is studied in everyday context;
Observation is the primary means of data collection, although other techniques are also used; Data collection is flexible and unstructured to avoid pre-fixed arrangements;
The focus is normally on a single setting or group, and on a small scale.
The qualitative research proposed for the study is schematized in Figure 2, and ethnographic approaches mixed with more classical ones, like semi-structured interviews and analysis of documents and materials, are described in next sections.
Structured Interviews
Goal: To assess synthetic information about audience satisfaction statistics related to the short and long-term performance of the ecosystem.
Method: Analytic questionnaires. Different measurement scales are possible:
Reaction scales: couples of opposite adjectives correlated to a value scale (such as 1 to 5 linear scales, where 1 is easy and 5 is difficult);
Evaluation scales: linear scales evaluating the fulfilment of a particular requisite;
Opinion scales: where the level of agreement or disagreement related to a parameter is evaluated through a linear scale;
Synthetic questionnaires: overall evaluation of the provided tool. A few factors are investigated with a low level of detail.
Data analysis: Tools for extracting statistics from questionnaires. Semi-structured interviews
Goal: Self-reflection and self-evaluation interviews to highlight the input audience characteristics and assess their progresses on the implemented entrepreneurship education and support initiatives.
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Method: Written interviews delivered to all participants during the experimentation of the tools. Depending on the duration of the test, the analysis can be performed:
One-shot: one ex-post anonymous interview;
Multiple sessions: one or two interviews in progress plus a final follow-up interview. In this case, each participant responds anonymously but identifies himself through a nickname. The nickname allows and beliefs.
According to Johnstone (2007), questions can be Descriptive; Structural; Contrast dyadic; Contrast triadic; Contrast rating.
The questions should be conceived to answer the macro-criteria reported below (Lee et al., 2005; Timmons, 1999), prevailing in current entrepreneurship education literature.
1. Intention of venture creation a
is the main effect of entrepreneurship education;
2. Knowledge and ability for venture creation. Increased knowledge of venture creation results in increased ability for venture creation;
3. Intention of overseas venture creation with teamwork.
to be achieved by an individual entrepreneur but requires teamwork and an open paradigm; 4. Recognition of the importance of entrepreneurship education. Entrepreneurship education
increases knowledge about venture creation. In addition, the following macro-criteria is proposes:
5. Recognition of the importance of team-working and knowledge sharing. Social interactions play a crucial role in venture creation. The ability of working in dynamic and changeable teams is very important. Moreover, the capacity of sharing knowledge and ideas is a key aspect for effectively transforming ideas into businesses.
Data Analysis: Answers can be analyzed and translated into numbers by means of a linear scale. Assigned numbers, and relative meanings, are the following:
0 = Null fulfilment of the criteria; 1 = Low fulfilment of the criteria;
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3 = Average fulfilment of the criteria; 5 = High fulfilment of the criteria.
Yes/No answers are of course easier to be classified with 0/5 numbers. More complex answers necessitate more attention in order to translate them in the best way.
The correlation among the answers (translated to numbers) and the criteria (opportunely weighed) can be then reported in a histogram. For each of the five macro-criteria the interviewees can be divided into 3 categories of fulfilment based on the scores assigned to the answers, respectively:
low fulfilment (x <50%);
medium fulfilment (50% <x <80%); high fulfilment (x> 80% ).
Experts Interviews
Goal: Track and analyze external contributions and suggestions for improving the implemented entrepreneurship education and support initiatives. Experts can be people from academic or industrial field who joined the programme as teachers or lecturers.
Method: A set of structured written or oral interviews conducted through questions belonging to the following clusters:
Goal: questions about expert personal opinion on the actual goal of the analysed initiative; Strengths: questions about the strengths of the analysed initiative;
Weaknesses: questions about the weaknesses of the analysed initiative;
Suggestions: requests for particular suggestions concerning the structure and implementation of the analyzed initiative.
Data Analysis: Data can be collected and analyzed through a SWOT (i.e. Strengths-Weaknesses- Opportunities-Threats; Zahra & Dess, 2001) matrix so to organize the information in a way that facilitates the process of improvement and adaptation.
Focus Groups
Goal: Collect feedbacks from participants and assess their progresses from previous semi-structured interviews results.
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Method: Focus Groups and real time interviews to be conducted after the implementation of the analysed tool in order to assess progresses in personal skills and to collect feedbacks about the implementation of the tool. The focus group can be highly or poorly structured and can be conducted through visual methods, brainstorming, checklists, etc. The focus group should be composed of 4-6 people including teachers, students and analyst.
Data Analysis: Data should be collected in a way that facilitates the interpretation and extrapolation of relevant information. As an example, in the case of a focus group concerning the assessment of student skills improvement after the learning programme, information can be clustered as follows:
Grey if positive progresses can be identified within the answer; Yellow if some issues are present within the answer;
Red in case of evident worsening or highly critical answers.
This clustering allows the analyst to easily synthetize the information needed to fill the indicators. Participant and Non Participant Observations
Goal: Track and analyse single lessons in order to understand the fulfilment of the planned learning and training goals, as well as feedbacks from students and teachers, via a direct participation or a non-intrusive observation.
instruments and their behaviour becomes a vital element of the research design. Researchers must balance their role as an outsider with their role as a participant. As a participant they must be able to interact with the subject group, share lives and activities, and understand their language. At the same time they must maintain their position and integrity as researchers and their ability to reflect critically
Method: Every activity conducted before, during and after the implementation of the tool must be well defined, observed and analysed. The key points of the analysis are the following: Goal; Strengths; Weaknesses; Feedback from participants; Tips.
Data Analysis: The interpretation of the collected data can be reported in the form of a SWOT matrix that works as a key reference for future actions and strategies.
Analysis of Documents and Materials
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Method: The analyst performs a detailed analysis of the output produced during and after the implementation of the tools, such as business ideas, business models/plans, innovative projects for companies, etc. The investigation can be supported by several tools such as Keywords research, Checklists, etc.
Data Analysis: The collected data can be analysed through an evaluation sheet in order to come up with aggregated judgments of the output that helps filling up the relative indicators.
Quantitative Analysis
Goal: To provide practical and easily interpretable statistics related to the short and long-term performance of the ecosystem.
Method: Data collection methods depend on the information sought:
Course participation forms: characteristics of the audience (age; gender; background; skills); Presence sheets: ongoing analysis of the attending audience;
Follow-up and outputs analysis: number of ventures created; prizes won; etc. Official documents: financial statements.
Data analysis: Analyses can be manual or supported by several tools, from Microsoft Excel to more sophisticated statistics software. Charts can help in organizing and visualizing the information sought such as clusters of participants, trends after several implementations of the tools, etc.
A renewed version of the KPIs provided by Secundo and Elia (2014) is shown in Table 3. The table highlights the KPIs taken into consideration within the analysis as well as the pillars of the entrepreneurial ecosystem to which these indicators refer. If in the first stages most of the ecosystem pillars are involved in the study, in the later stages only a few of them acquire greater importance. The measurement conducted by using these KPIs allows to collect punctual information concerning the performance of the university entrepreneurship ecosystem at a certain moment. The comparison of data collected in a time series according to Secundo and Elia (2014) would allow to understand the trends and correlations between the KPIs and the ecosystem pillars. This will provide more interesting insights on long term results and outcomes.
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Findings
The presented framework has been tested within the University-based Entrepreneurship Ecosystem of the University of Pisa, with a particular focus on the Education and Training and Support system that has in the PhD plus programme its cornerstone (Figure 3).
The method was tested starting from the 2013 edition of the programme, which involved more than 100 participants (Table 7). The programme, which aims at promoting creativity, innovation and an entrepreneurial mind-set among students, consists of a series of lectures given by national and international speakers from academia, public and private companies. The main topics are related to patenting, business creation, enhancement of scientific ideas, innovation management, communication and self-promotion. Besides the seminars, students are involved in a coaching activity, carried out with the support of experts from the regional tech parks, who help them develop their business plan and promote their ideas. Moreover, a selected group of students is given the opportunity to participate in the Entrepreneurship module of the University of Pisa MBA so to further deepen their business ideas. The selection of participants is normally made by the MBA students during the idea pitches.
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The ecosystem performance measurement was run according to the methodologies described in Table 4. According to this, qualitative and quantitative analysis were used to properly monitor each stage of the technology entrepreneurship cycle (See Figure 1).
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Qualitative Analysis
Participation Statistics: Structured Interviews
Firstly, an analysis of the statistics emerged from the course Evaluation Forms has been performed. A first set of data referred to the purposes that each participant had in attending the programme.
2nd Seminar 3rd Seminar
Sample: 61 participants Sample: 52 participants
An additional analysis regarded the participation dynamics. Participants have been clustered on the basis of their academic background. The attendance trend throughout the course has been analysed as well. The Analysis indicates that seminars about patents and IPRs have been a critical subject for the whole programme. If students purposes indicate that a considerable percentage of them joined the PhD plus for their interest in these subjects, the participation evolution analysis confirm the participants number drop off during this session and a successive stabilization after the first three seminars. This aspect strongly contributed to the 30% participation drop off. On the other hand, the first three lessons about a very specific topic such as intellectual property may have negatively contributed to the motivation
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of some participants, as confirmed by a few interviews. Conversely, the considerable participation increase for lessons about business planning and entrepreneurship confirm the high expectations toward these subjects (Figure 4).
As for participants academic background, engineering and computer science had the highest participation. Nevertheless, while computer science led to a low participation decrease, the course lost almost 50% of participants from the engineering field. Bioengineering had a considerable participation with respect to past editions, and remained almost stable for the whole programme. Human sciences backgrounds still have a low participation in the programme.
Semi-Structured Interviews
All participants were provided with three written Semi-Structured Interviews during the PhD plus seminars. Each participant responded anonymously but identified himself/herself by a nickname.
The questions are thus a representation in natural language of a particular criterion, which is part of one of the five macro-criteria introduced in the previous sections. Questions are reported in Table 6. Each question (i.e. criterion) has been opportunely weighed on a linear scale (1-low; 3-medium; 5-high). The table also indicates if they were asked in the first, second or third interview. Participants could answer without any limits of space.
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According to paragraph 3.1.2, the correlation among the answers (translated to numbers) and criteria (opportunely weighed) provided the results reported in Figure 5. For each of the five macro-criteria the interviewees were divided into 3 categories of fulfilment based on the scores assigned to the answers, respectively:
low fulfilment (x <50%);
medium fulfilment (50% <x <80%); high fulfilment (x> 80% ).
This means, for instance, that 23% of participants demonstrated a high fulfilment of the first macro-criterion (Awareness of the importance of entrepreneurship education).
39 network of contact (fifth criterion) can be identified as the most critical aspects. While importance of entrepreneurship education and team-working seem to be positively acknowledged by participants. Basic aspects to be taken into consideration when starting a business (third criterion) were quiet understood
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since the beginning. This means that the audience also had a general perception of their training and education gaps in this field.
A general interpretation of the results indicates the strong difference among participants characteristics. A qualitative interpretation of the answers demonstrates that participants experience, objectives, and motivation are strongly divergent.
Experts interviews
The aim was to track and analyse external contributions and suggestions for improving the entrepreneurship programme and understanding the most relevant issues concerning interviews conduction.
In the first lesson of the PhD plus programme, two web conferences and two live presentations with testimonials from the business world had been scheduled. The four entrepreneurs invited, each with a different background and international approach, have contributed to motivate and inspire participants. The interpretation of collected observations has been performed through a SWOT matrix, which is not reported due to confidentiality reasons. Collected data have been classified into Strengths, Weaknesses Opportunities and Threats related to the adoption of interviews and entrepreneurship experience proofs for educational goals. This allowed the identification of several strategies reported in the central quarters of the SWOT.
Participant Observations
These activities involved observation and interpretation, via a direct participation, of the functioning and general dynamics of the two weeks of the PhD plus+MBA experience.
As for the MBA module in particular, the direct involvement of students and team working have allowed a clear understanding of the strengths and weaknesses of such initiative.
Strengths
A great occasion for team building and co-working; Team building has been generally positive and rapid;
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Weaknesses
The idea selection process for the MBA was affected by evident difference of purposes between the PhD plus students and the MBA students. This led to the exclusion of powerful ideas that MBA students perceived as too much complicated in order to positively conclude the Entrepreneurship Module;
The economic and managerial background of MBA students was expected to be higher;
The lack of previous managerial experience did not allowed PhD plus students to take complete advantage of this joint programme regarding the business planning aspects.
Focus Groups
The analysts have collected feedbacks from PhD plus participants who attended the MBA Entrepreneurship module and have assessed their progresses starting from the results of the semi-structured interviews. Given the results that PhD plus +MBA students obtained with the semi-semi-structured interviews presented above, most relevant questions have been posed again after their participation in the MBA intensive module.
The MBA Entrepreneurship module has been positively evaluated by all participants. A general group performed (see paragraph 3.1.4). All this in addition to the tangible improvement in the business ideas presented during the elevator pitches. Nevertheless, some critical issues have emerged during the discussion. The most critical is perhaps that MBA groups did not always meet PhD plus student expectations, especially in one case. Since this was the first experimentation, the results are generally satisfactory.
Non-participant Observations
Detailed collected data from non-participant observation analysis have been organized in a SWOT matrix, as a key reference for future actions and strategies. Various strategies that could be used for the next PhD plus editions have emerged. Among the most important the following should be surely mentioned:
Organize a practical session on how to write and manage a patent;
Availability of co-working spaces within the university to develop and share ideas; Promote and encourage internships within local companies;
