(PDF) University Management Practices and Academic Spin-offs

Building upon the recent literature on the empirical economics of management, we posit that different forms of management practices introduced by Universities to support academic entrepreneurship affect how well spin-offs get created, supported and evaluated and, eventually, account for academic spinoff performance. We draw on a longitudinal sample of 790 Italian University spin-offs founded by 42 different Italian Universities and observed over the period 2006-2014. Our results suggest that management practices contribute to explain the growth of academic spin-offs and that this effect varies across management practices (e.g. support operations management vs. target management) and spin-offs’ growth patterns (e.g. high-growth vs. low-growth spin-offs). Drawing on these results, the adoption of management practices by University managers should be carefully considered as they seem to serve different purposes. Their appropriateness can vary and, not surprisingly, some management prac...

UNIVERSITY MANAGEMENT PRACTICES AND ACADEMIC SPIN-OFFS MARIO BENASSI MATTEO LANDONI FRANCESCO RENTOCCHINI Working Paper 11/2017 SEPTEMBER 2017 DIPARTIMENTO DI ECONOMIA, MANAGEMENT E METODI QUANTITATIVI Via Conservatorio 7 20122 Milano tel. ++39 02 503 21501 (21522) - fax ++39 02 503 21450 (21505) http://www.economia.unimi.it E Mail: dipeco@unimi.it University Management Practices and Academic Spin-offs Mario Benassi Department of Economics,Management and Quantitative Methods, University of Milan, Italy Mario.Benassi@Unimi.it Matteo Landoni ICRIM, Università Cattolica del Sacro Cuore, Milan, Italy matteo.landoni@unicatt.it Francesco Rentocchini Department of Economics, Management and Quantitative Methods, University of Milan, Italy francesco.rentocchini@unimi.it & Southampton Business School, University of Southampton, UK f.rentocchini@soton.ac.uk Abstract Building upon the recent literature on the empirical economics of management, we posit that different forms of management practices introduced by Universities to support academic entrepreneurship affect how well spin-offs get created, supported and evaluated and, eventually, account for academic spinoff performance. We draw on a longitudinal sample of 790 Italian University spin-offs founded by 42 different Italian Universities and observed over the period 2006-2014. Our results suggest that management practices contribute to explain the growth of academic spin-offs and that this effect varies across management practices (e.g. support operations management vs target management) and spin-offs’ growth patterns (e.g. high-growth vs low-growth spin-offs). Drawing on these results, the adoption of management practices by University managers should be carefully considered as they seem to serve different purposes. Their appropriateness can vary and, not surprisingly, some management practices seem to be more effective than others in driving spin-offs’ growth. Key Words: Management practices; Academic spin-offs; Spin-offs Growth; Internal processes JEL Codes: M13; O30; M21; R11 2 1. Introduction Academic spin-offs – defined as companies that commercially valorise results of scientific and technological research of Universities (Shane, 2004) – have recently gained increasing attention from scholars (Rasmussen, Moosey and Wright, 2011). Academic spin-offs are intriguing, as they have a double facet. On the one hand, they originate from organisations whose primary goals are far from profit-oriented activities (Merton, 1973; Dasgupta and David, 1994). On the other hand, academic spin-offs seem an appropriate vehicle to extract value from scientific research in line with the recent interest on the third mission of Universities (Etzkowitz and Leydesdorff, 2000). Until now, research on academic spin-offs has overwhelmingly focused on two main areas. A first stream of the literature has focused on the antecedents of academic entrepreneurship, trying to address the main drivers leading to the creation of academic spin-offs at different levels of analysis (e.g. firm, TTO). Along this line of reasoning, scholars have investigated the role of Universities and local context in supporting the creation of academic spin-offs (Fini et al., 2011); the mechanisms supporting the creation of new companies at University level (Nosella and Grimaldi, 2009) and the specific features that make Universities and researchers more active in promoting spin-offs (Landry, Amara and Rherrad, 2006; Bigliardi, Galati and Verbano, 2013). A second stream of research has concentrated on the outcome of university entrepreneurship processes, evaluating the performance of academic spin-offs. Notably, scholars have analysed the effect of academic affiliation on Initial Public Offering (IPO) valuation (Bonardo, Paleari and Vismara, 2010); the conditions leading to the survival of academic patent-backed companies (Nerkar and Shane, 2003); the antecedents of the number of spin-offs founded by Universities (Di Gregorio and Shane, 2003) and the drivers of a ‘premium’ performance of spin-offs (Czarnitzki, Rammer and Toole, 2014). 3 Although these two streams of research have advanced greatly our understanding of the academic spin-off phenomenon, they both fall short in controlling for the internal processes by which Universities create, support and evaluate academic spin-offs since their inception. With the exception of some illustrative case-studies (Lechner and Delanoë-Gueguen, 2014), these internal processes have been mainly treated as a ‘black box’ by the current literature. A possible explanation for this inaccuracy refers to the lack of autonomy and competition of Universities, especially if publicly funded (Aghion et al., 2010). In several countries – especially in Europe - Universities must comply with a knitted web of laws and guidelines, regulating several aspects of academic life (for example on Italy, Muscio et al. 2015). However, when it comes to spin-offs, some degree of autonomy exists: each University is solely responsible for deciding whether or not to spin-off and which criteria to adopt (Lach and Schankerman, 2008; Rasmussen and Borch, 2010). Universities may exhibit marked differences in their attitude towards spin-offs (Benassi, 2014). Some Universities can have a credible commitment towards spin-offs’ creation, while others can have only a ceremonial engagement due to external pressure. Similarly, some Universities can set up specific structures to serve spin-offs, while others can re-use existing departments/offices to assist them. Finally, some Universities can internally adopt new rules that make the creation of spin-offs more efficient, whilst others can stick to existing routines and regulations. Following the entrepreneurial university mode (Etzkowitz and Leydesdorff, 2000), we observe that Universities’ spin-offs are functional to the extraction of value from research. However, to accomplish this task, academic spin-offs must face the liability of newness issue (Stinchcombe, 1965) and survive in a highly competitive environment. Universities, on the contrary, were set, designed and equipped to produce basic research and to foster education, two missions quite apart from the Third Mission. Accomplishing both tasks (research and teaching on the one side and third mission on the other) is not simple, as it requires adopting 4 internal procedures and practices, which call for different, and often conflicting, requirements. For example, decisions in education should follow a specific, often highly regulated path, whereas in business timely and fast decisions are often needed. We posit that internal procedures and practices can make a difference for academic spin-offs. Specifically, we explore the role of management practices for the growth of academic spinoffs. Management practices can be conceived as routines, rules and processes which relate to the deep-seated structure of organisations (Bloom et al., 2014). The nature and impact of management practices has been at the centre of the recent literature on the empirical economics of management (Bloom et al., 2014). For example, Bloom et al. (2012) show how management practices contribute to explain the variation of productivity across countries and firms. Management practices have also been shown to improve the performance for organisations as different as hospitals, schools, and Universities (Bloom et al., 2015a, 2015b; McCormack et al., 2014). Evidently, universities can vary in the adoption of management practices functional to the creation and growth of academic spin-offs. Not only, we also posit that different forms of management practices introduced by Universities foster academic entrepreneurship. Notably, we expect management practices to affect how well spin-offs get created, supported and evaluated and eventually account for academic spin-off performance. To investigate this, we rely on a longitudinal dataset comprising 790 Italian University spinoff companies observed over the period 2006-2014. The data come from a combination of three distinct data sources: a survey on a sample of 42 Italian Public Universities which gathers information on management practices relating to academic spin-offs, a list of Italian University spin-offs comprising information about more than 1200 academic spin-offs based 5 in Italy, and balance sheet data from the Bureau van Dijk AIDA database over the period 2006-2014. Our results show that management practices contribute to explain the performance of academic spin-offs and that this effect varies across management practices (e.g. support operations management vs target management) and spin-offs’ growth patterns (e.g. highgrowth vs low-growth spin-offs), even after controlling for a rich set of firm- and universitylevel characteristics. Notably, while ‘lean’ operations for the support of academic spin-offs and incentives management are positively related to spin-off performance, target and professional management are found to be negatively related to academic spin-off growth. Drawing on these results, the adoption of management practices should be carefully considered as they seem to serve different purposes. Their appropriateness can vary and, not surprisingly, some management practices seem to be more effective than others in driving spin-offs’ growth. Our study contributes to the recent debate in the academic entrepreneurship literature by focusing on University internal processes, which in turn contribute to the performance of academic spin-offs. In its current form, this paper provides a bridge between the two streams of the academic entrepreneurship literature focused on antecedents and outcome of spin-offs (Perkmann et al., 2013), by showing the relevance of internal processes, particularly management practices. We believe that greater understanding of management practices functional to the third mission of universities provides important insight on policy and managerial issues concerning university-industry interactions, the fine tuning of appropriate systems of incentives to spur the ‘third mission’ of universities and the role of education policy in responding to emergent industry needs. 6 The remaining of this article is organized as follows. Section 2 offers a review of most recent research on academic spin-offs and management practices and put forward our key research questions. Section 3 presents data and methodology, Section 4 introduces and discusses the results and Section 5 offers our conclusions. 2. Literature review and research questions 2.1 Academic entrepreneurship In the age of the entrepreneurial society (Audretsch, 2014), Universities – besides the primary goal of producing and disseminating new knowledge – are focusing on the so-called third mission. Third mission activities in Universities stimulate and direct the application and exploitation of knowledge to the benefit of the social, cultural and economic development of the society. Third mission aims at extracting value from knowledge Universities produce and requires them to adopt an entrepreneurial model (Etzkowitz and Leydesdorff, 1997, 2000; Gulbrandsen and Slipersaeter, 2007). Scholars have been debating in depth whether Universities can play an entrepreneurial role. Not surprisingly, opinions differ. Several scholars suggest Universities are a perfect setting for nurturing entrepreneurship (Lockett, Wright, Franklin, 2003). Nevertheless, rate of academic spin-offs occurrence varies significantly among universities (Di Gregorio and Shane, 2003). Universities produce innovative knowledge bases for new processes and new products. Universities’ scientific breakthroughs can spur new technological opportunities, to be leveraged from an economic perspective. According to this view, Universities should add business expertise to fully exploit their scientific breakthroughs. Academic spin-offs are a 7 possible vehicle to extract value from innovative knowledge Universities produce, alongside with patents’ licensing and reassignment (Di Gregorio and Shane, 2003). The academic support to spin-offs varies significantly among Universities, according to different policies, technology transfer practices, contractual arrangements and other issues (Clarysse et al., 2005). Besides offering the knowledge basis for a new company, Universities can support spin-offs in various ways. They can provide supporting institutions such as technologytransfer offices (TTO) (Grimaldi et al., 2011), offer favourable contractual agreements (Clarysse et al., 2005), and channel financial resources to the entrepreneurs, for example by the means of business plan competitions (Cannavacciuolo et al., 2006). Other scholars point out that institutional rules and cultural barriers can make entrepreneurship inside Universities a nearly impossible mission (Bercovitz and Feldman, 2008; Chiesa and Piccaluga, 2000; Kenney and Goe, 2004; Markman et al., 2004; Slaughter and Rhoades, 2004; Siegel et al., 2003). Universities are a source of constraint for spin-offs: their policies and their general institutional setting can seriously limit entrepreneurial autonomy and negatively affect spin-offs’ decision-making. Furthermore, norms and believes shared among faculties might significantly limit the entrepreneurial behaviour (Louis et al., 1989). These limits make some scholars sceptical of Universities’ contribution in fostering entrepreneurship (Cohen et al., 1998; Florida and Cohen, 1999). The debate between supporters and opponents of the entrepreneurial role of Universities parallels the emergence and the increased diffusion of academic spin-offs (Perkmann et al., 2013). For example, in US only 35 academic spin-offs started in 1980; in 2003 the number rose more than ten times, peaking 462 spin-offs. Altogether, between 1980 and 2003 4,543 new academic spin-offs started in US (AUTM, 2005; Siegel and Wright, 2015). Europe experienced a comparable increase: 1,650 spin-offs in the UK (1981-2003), 1,230 in France 8 (1984-2005), 320 in Belgium (1980-2003), and 300 in the Netherlands (1980-1999) (Wright, 2007). Scientific literature on academic spin-offs is widely extended and fragmented (Rothaermel et al., 2007; Djokovic and Souitaris, 2008), it has been growing in dispersed directions (Gilsing et al., 2010, 12), and has not produced a convergent conceptual definition so far (Bathelt et al., 2010). Most scholars agree that an academic spin-off is a new company founded to exploit new knowledge created in an academic institution - be it a new discovery, technology or application. This new knowledge might take the form of intellectual property - typically patents - thus requiring a specific contractual agreement for the spin-off (Shane, 2004; Wright et al., 2007). Transfer of knowledge from an academic institution is a recognized hallmark of an academic spin-off, but scholars disagree about a more precise and operational definition of spin-off. Transfer of knowledge might command licensing or assignment of an institution’s IP for initiation, but this is not always the case (Wright, 2007). Same disagreement exists for the equity stake directly owned by Universities. Depending on the institutional context, equity ownership is a sufficient but not necessary condition (Wright, 2007). In this paper we follow Nicolaou and Birley (2003) definition: an academic spin-off involves the transfer of knowledge from an academic institution, where either one of the founders is affiliated with the university or the University itself holds a share in the company. We adopt this last definition in accordance with the report of Netval, the Italian association for the valorisation of results from public research (Ramacciotti and Daniele, 2015). Despite several conceptual disagreements, supporters and opponents of the entrepreneurial role of Universities agree that assessing the effectiveness of the third mission is crucial. Curiously enough, so far the research on spin-offs is split between two separate streams. The first stream focuses on the antecedents. Antecedents are factors and conditions that affect 9 spin-offs’ creation and pertain to different levels of investigation (Di Gregorio and Shane, 2003). At micro level scholars have investigated individual attributes affecting spinoffs formation. For instance, Landry et al. (2006) found that the likelihood of launching university spin-offs increases as the researchers have knowledge assets in the fields of computer sciences and engineering rather than in the other natural sciences, have knowledge expertise in consulting, have higher social capital assets, have access to the resources of large research universities, have access to the resources of large laboratories, and have many years of experience in research. Grandi and Grimaldi (2003) explored the relational setting of the founding teams associated with success. They found that the articulation of roles of the spinoff positively affects the intention to set up relations with external agents. Relations and frequency of interaction with external agents positively affect spin-offs’ success. At macro level scholars explored the impact of policy changes on academic spin-offs creation (Mowery and Sampat, 2005; Damsgaard and Thursby, 2013). Fini et al. (2011) examined the role of universities and local context in supporting the creation of academic spin-offs. They found that universities’ support mechanisms have a significant impact on universities’ rates of spinoffs creation productivity. Conversely, their results show that the local context influences spin-off creation and productivity, but curiously, government R&D expenses do not have a positive impact on university spin-offs creation. The authors’ conclusion suggests a complementary of supporting mechanism at university and local level to foster the creation of academic spin-offs. A second stream of research focuses on outcome. The general idea here is assessing how well Universities are doing the entrepreneurial role as part of the third mission. Assessing the entrepreneurial role of universities is not an easy task for at least two reasons. First, Universities may contribute to entrepreneurship both directly – through academic spin-offs – and indirectly. Universities can indirectly support entrepreneurship in different ways. For 10 example, they can foster an entrepreneurial attitude among students and researchers; they can promote contacts and favour relationships with external companies; they can provide reputational support to the founding team, and the like. Second, assessing the performance of universities in promoting academic spin-offs would require a unitary and coherent framework. Such framework should set main cause-effect chains in order to factor universities’ different features affecting entrepreneurial attitude. Universities can be small or large; can have little or abundant resources; can be specialized or not specialized; can be located in entrepreneurial ecosystems or in depressed areas, and so forth. Scholars equalize the entrepreneurial role of Universities and the performance of academic spin-offs. Performance is measured before and after spin-offs went out of the universities’ door. Sheer number of spin-offs a University produces is the first, most popular measure (O’Shea et al., 2005). The underlying idea is that “the more spin-offs, the better”. The underlying assumption is that a high number of spin-offs offer more chances against environmental selection. However, according to the sceptics, “the more spin-offs the better“ approach paves the way to fragile, ultra-small, low value and not scalable new ventures. The number of spin-offs says nothing about their ‘quality’ and can produce controversial effects. Universities might support spinoffs that should not be, from a business perspective, supported. Survival rate of spin-offs is the second measure. Here the idea is that market selection is a satisfactory proxy of spin-offs’ performances. To a spin-off, staying in business or being selected out after some years is a positive sign. Staying in business might say little about performances, however. Scholars report that several academic spin-offs simply survive. According to Lerner (2005), the net number of spin-offs is small and their size, growth rates, revenues, and product generation are modest, at least in the first decade of their existence. On the other hand, Vincett (2010) shows that academic spin-offs have a positive economic impact. Estimating the lifetime impacts of companies spun-off to directly exploit university 11 created IP and comparing the impacts with all government funding, direct and indirect he found that spin-offs’ incremental contributions to GDP is much larger than the government funding. At a business level, other typical measures can be used to assess academic spin-offs’ performances. For instance, Zahra and colleagues (2007) used sales-per-employee as a measure of productivity, three years return on assets as a measure of profitability and the annual rate of revenue growth. To measure the performance of Oxford University, Oxford Brookes University and the government laboratories’ spin-off companies, Lawton Smith and Ho (2006) used employment, turnover, market capitalisation and patent and licencing activity as main variables. Also Wennberg et al. (2011) focussed on employment and sale growth to explore performance differences between university spin-offs and corporate spin-offs. Besides these common economic indicators, Czarnitzki et al. (2014) argued for a socially valuable outcome, or ‘performance premium’ indicator, i.e. the creation of new jobs. Measures and metrics examine academic spin-offs from different perspectives and offer useful insights about their performances. For example, they make a comparison of academic and corporate spin-offs possible (Wennberg, Wiklund and Wright, 2011). They also allow for a rigorous examination of spin-offs’ economic contribution in specific areas (Vincett, 2010). Measures and metrics of academic spin-offs have a dark side, however, for two reasons. First, using the performance of academic spin-offs after they went out of the door as a proxy measure of universities’ entrepreneurial ability might be misleading. Success and failure of academic spin-offs can depend about variables over which universities have a modest – if null -- control. Second, and more relevant for our argument, measures and metrics developed so far pay no attention to universities’ internal processes (Bozeman et al., 2015). 12 2.2 Management practices and Universities’ internal processes In this paper, we explore the relationship between management practices and the performance of academic spin-offs. We argue that managerial practices shed light on how internal processes at Universities affect the performance of academic spinoffs. The literature on management practices is gaining momentum in recent years (Bloom et al., 2012, 2014, 2017). This stream of research investigates and explains differences in management practices across organizations and countries in different sectors (Bloom et al., 2016). The authors show how practices put in place by managers contribute to explain a large proportion of the variability in the performance of organisations. Management practices can be conceived as routines, rules and processes which relate to the deep-seated structure of organisations and that become rooted into the organisational structure and culture (Bloom et al., 2014). 1 The former literature has grouped management practices into three broad areas: monitoring (how well organisations monitor what goes on inside and use this for continuous improvement), targets (whether organisations set the right targets, track the right outcomes, and take appropriate action if the two are inconsistent) and incentives (how well organisations promote and reward employees based on performance, and try to hire and keep their best employees) (Bloom and van Reenen, 2006). Management practices have been found to explain variation among organisations’ performance. Bloom and Van Reenen (2010) found that management practices are associated with firms’ performance in terms of productivity, sales and growth rate. Similarly, Bloom (2010) concluded that firms with better management practices tend to have a better performance on a wide array of dimensions. A well-managed organization is defined as one that continuously monitors and tries to improve 1 According to this definition, management practices should not be confused with managerial capital which refers mainly to the talent and skills of individuals (such as a manager) and it comes as the aggregation of individual contributions but lacks an organisational dimension (Bertrand and Schoar, 2003). 13 its processes, sets comprehensive and stretching targets, and promotes high-performing employees. Management practices are useful also for explaining variations among different kinds of organizations, be private or public such as hospitals and schools (Bloom et al., 2010 and 2014). Management practices affect Universities’ performances as well. Mc Cormack, Propper, and Smith (2014) explored management practices in British universities. They found that better management practices are associated with better performance for both research and teaching assessments. Building upon the former literature, we contend that actions to foster the internal process of spin-offs creation and support can be represented in terms of management practices adopted by Universities. Universities are a special kind of organizations. For a large majority, Universities are public and get financial support from the States. Although Universities must comply with rules and regulations, they enjoy some degrees of freedom. They can and do make decisions on several issues. For example, Universities can decide to launch new initiatives (e.g. new courses), allocate financial resources to internal units in different ways (e.g. give more to department A than B), hire specific human resources (e.g. secretaries or professors), and so on. Universities can set specific structures and organize their internal processes in different ways. They can set informal committees to discuss internal problems, assign specific responsibilities to internal units and new roles to their members and the like. Decisions about specialization of structures and coordination modes affect kind and quality of resources Universities can leverage. According to a number of scholars, Universities lack a proper set of competencies required to create and sustain spin-offs (Ambos et al., 2008; Rasmussen et al., 2011). To fill this gap Universities are setting up specific structures — like technology transfer offices and 14 incubators — and adopting new organizational practices to facilitate academic entrepreneurship (Hisrich et al., 1988; Mian, 1997; Siegel et al., 2003; Wright et al, 2007). Universities are clearly not all alike when it comes to internal processes. Our assumption is that internal processes, in the form of management practices, have a significant impact on the entrepreneurial role of Universities, in general, and on spin-offs, in particular. Existing studies on how different internal processes affect spin-offs suffer from two main limitations. First and foremost, the role and impact of internal processes is normally left in the background. For example, O’Shea et al. (2005) argued that knowledge accumulation inherent in the process of generating university spin-offs influences a university’s future ability to produce university spin-off companies. They hypothesized that Universities with a tradition of spinning out technology-based companies are more likely to be successful generators of spin-off activity. In a similar way, Pazos and colleagues (2012) show that the tradition of the university’s spin-offs activity and the existence of incubation services positively influence spin-offs. Vinig and Van Rijsbergen (2012) found that the stock of technology — in terms of scholarly publications — and the presence of an incubator have a positive impact of on the number of spin-offs. Second, when internal processes are taken into account, they are usually referred to as conditions favouring or discouraging spin-offs creation – that is to antecedents (see Lockett et al, 2003, on university spin-offs in the UK; and O’Shea et al., 2005 for the US). We argue that the universities’ internal processes are crucial to spin-off performance. Universities can introduce entrepreneurship courses, which are likely to stimulate students’ entrepreneurial attitudes (Åstebro, Bazzazian and Braguinsky, 2012), Universities can set up and manage TTO and incubators, which can offer specific services to prospect entrepreneurs. Universities can also adopt special compensation systems for staff and faculties’ member, to 15 make their involvement in spin-offs possible. Internal processes do also have an impact on spin-offs performance over time. Internal processes may select out fragile initiatives, and provide most promising initiatives services like coaching, proof of concept definition, support in the patenting process, and fund-raising. Internal processes can affect spin-offs even after they were launched. Universities can set challenging targets for spin-offs in terms of growth, value extraction, scientific and technological excellence. Universities can monitor spin-offs results over time, set incentives and take proper actions if needed. Accordingly, specific actions to foster the internal process of spin-offs can be represented in terms of management practices adopted by Universities. In this paper, we explore the relationship between management practices and the performance of academic spin-offs. On one hand, there are reasons to believe management practices do have a positive impact: a rigorous selection process, a correct monitoring and reward system should in principle produce better outcome. On the other hand, impact of management practices might be severely constrained by institutional barriers and organizational inertia. Either way, controlling for the impact of management practices on academic spin-offs is a way to open up the black box of universities’ internal processes. 3. Data and Methods 3.1 Data sources The empirical analysis is based on a longitudinal dataset comprising 790 Italian University spin-off companies observed over the period 2006-2014. It combines data from three main sources. 16 Our starting point is the list of Italian University spin-offs provided by Spinoff Italia (http://www.spinoffricerca.it/) as of June 2015, a database comprising information about 1226 academic spin-offs based in Italy. 2 Spinoff Italia reports the following information for these companies: spin-off name, University of affiliation; foundation year; year of exit. We match this information with balance sheet data from the Bureau van Dijk AIDA database over the period 2006-2014. 3 In this way, we are able to retrieve information on the companies on a yearly basis. Notably, we collect information on turnover, capital stock (tangible and intangible), industrial sector (2-digit NACE rev.2 industrial classification) and geographical location of companies (NUTS 2 level of geographical aggregation). 4 Lastly, we collect information about University management practices relating to spin-offs by administering a structured questionnaire to the key individuals in the academic spin-off’s process inside Italian Universities (mostly the head of the technology transfer office and/or their designate). The population of reference consists of 64 Public Universities who were invited to participate to a telephone interview. The survey was conducted between July 2015 and March 2016. We obtained 42 valid responses, totalling a 65.6% response rate.5 These responses were representative of the population of Italian public universities in relation to 2 Building upon the definition provided by NETVAL (Muscio et al., 2016) and adopted by the Italian National Agency for the Evaluation of the University (ANVUR), Spinoff Italia defines a company an academic spin-off if it satisfies one of the following conditions: (i) Italian University holds a share of the company; (ii) At least one member of the founding team is a tenured member of staff of a University. 3 We were not able to match information from Spinoff Italia and AIDA for 146 companies for two main reasons: (i) we lacked information on a unique company identifier from Spinoff Italia or (ii) the unique identifier was not found in AIDA. 4 We also retrieve information on foundation year and year of exit to cross-check and complement information from Spinoff Italia. As information on foundation year differs considerably between the two databases (Spinoff Italia and AIDA) for the period preceding 2000, we decided to keep only spin-offs created after 2000 where the concordance between the two databases is nearly perfect. This amounted to drop 35 firms from our sample. 5 We did not obtain responses for 22 Universities which generated 255 spinoffs over the period under consideration. For this reason, we were forced to drop information for these companies. 17 size (t=1.19, p-value=0.24), patenting activity (t=1.203, p-value=0.23), research funding (t=1.35, p-value=0.18) and contract research (t=1.114, p-value=0.268). 6 Since we combine three different data sources, potential problems of common method bias (CMV) are largely controlled for (Podsakoff et al., 2003). As a further check on the presence of a common method bias, we applied Harman’s one-factor test. We loaded all items that have been measured adopting a Likert scales into a factor analysis without rotation. Results show that the first and most important factor explains 14.48% of the total variance, lower than the threshold of 50% (Podsakoff et al., 2003), which points to the absence of any common method bias. To minimize the possibility of social desirability bias (SDB) (Moorman and Podsakoff, 1992), respondents were guaranteed anonymity. In addition, our respondents hold permanent positions and career advancement is mainly by seniority. We think it is unlikely therefore that we have biases in responses to the questionnaire, and particularly to the questions on the management practices relating to spin-off activity, which are central to build our explanatory variables. Our final sample comprises full information for the variables of interest for 790 companies. Our resulting dataset is thus an unbalanced panel of 790 academic spin-offs affiliated with 42 different Italian Universities and observed over the period 2006-2014. Table A1 in Appendix A shows the full list of 42 Universities with information about their spin-off generating activity, i.e. number of spin-offs generated, average turnover growth of spin-offs and average age. The table shows that over the period 2006-2014 Universities in our sample created, on average, 19 spin-offs. Spin-offs are on average four years old and have experienced a turnover growth of 19%. 6 Data on the number of patents, the amount of research funding and contract research comes from the Italian National Research Assessment (VQR 2004-2010) and refers to the period 2004-2010. Information on the size of Universities comes from the Ministry of Education, Universities and Research (MIUR) and refers to period 2006-2014. 18 [Table A1 ABOUT HERE] 3.2 Estimation method and dependent variable As discussed in the theoretical section, we are interested in examining the relationship between university-level management practices aimed at favouring academic spin-offs and firm growth. We measure company growth using data on the turnover retrieved from the Bureau van Dijk AIDA database. Specifically, our dependent variable is the turnover growth rate and has been calculated as the difference between the logarithm of real turnover in year t and the logarithm of real turnover in year t-1. 7 We are aware that firm growth can be investigated using a wide variety of measures (Delmar et al., 2003). Unfortunately, information about the number of employees is under-reported in our data, so we prefer to use turnover growth which has the advantage of maximising the number of non-missing information. Building upon the approach adopted in several empirical works, which focused on the determinants of firm growth, we employ a quantile regression approach (Coad and Rao, 2008; Goedhuys and Sleuwaegen, 2010; Kesidou and Demirel, 2012). When investigating firms’ growth quantile analysis is preferred over standard least squares for a number of reasons (Buchinsky, 1998). First, the quantile approach provides a more robust and efficient alternative to OLS when the error term is non-normal, as well as in the presence of outliers. Second, the distribution of growth rates is recognised to be highly non-linear and considerably heavy-tailed (Bottazzi and Secchi, 2003). The quantile approach allows for 7 To obtain real turnover, gross turnover has been deflated by adopting the ratio of current prices to chainedlinked prices (reference year 2010) at the higher level of disaggregation, as provided by the Italian National Institute of Statistics (ISTAT) at the NACE rev. 2 2-digit industrial level. 19 richer characterisation of the data, as it estimates the effects of the different explanatory variables at the different quantiles of the growth distribution rather than at the conditional mean only. Since different types of management practices might have different effects on companies located at different points of conditional growth distributions (e.g. high-growth firms vs low-growth firms), the quantile approach can serve the purpose to uncover these effects. As our data have a hierarchical structure – our key explanatory variables are measured at the university level while the dependent variable is measured at the firm level – standard errors are likely to be clustered and this would lead to a loss of efficiency in the estimates. In an attempt to control for the presence of intra-cluster correlation in quantile regressions, we compute robust clustered standard errors at the University level following a recent development in the applied econometrics literature (Parente and Santos Silva, 2016). 3.3 Explanatory variables As for our key explanatory variables, we are interested in testing the relationship between management practices which support academic entrepreneurship and the growth of academic spin-offs. We capture the quality of management practices drawing upon an existing methodology that has been used in manufacturing (Bloom and Van Reenen, 2007; Bloom et al., 2012), health care (Bloom et al., 2015a), schools (Bloom et al., 2015b), and higher education (McCormack et al., 2014). Notably, we adapted the survey developed by McCormack et al. (2014) in their analysis of the effect of management practices on teaching and publication performance of UK Universities. The focus is on management practices which belong to five main categories. 20 Our first category relates to operations aimed at supporting the creation and development of spin-offs. The respondents to our questionnaire were asked to rate the importance of different practices supporting the creation and development of academic spin-offs in the period 20062014. The respondents were asked to rank the importance of the items on a five-point Likert scale, ranging from ‘not important’ to ‘highly important’. The different practices were: (1) coaching; (2) mentoring; (3) awards and internal competition; (4) training support; (5) support in the development of a proof-of-concept; (5) support for patenting activity and (6) help with fund raising activity. We run factor analysis on the six different items to synthesize the information in common factors underlying ‘lean’ management practices. The three resulting predicted factors are used as our first set of explanatory variables in the econometric model. Previous literature assists in the interpretation of these three constructs (Bloom et al., 2014). The first factor contains a range of items that involve support operations relating to training, such as counselling activity for the academic spin-off founding team, but also specific on-the-job training activities. Accordingly, this factor is labelled Support Operations Training. The second group, Support Operations Patent, includes two items that relate to the patenting activities which are conducive to the creation and development of the spin-offs: support in the proof of concept and assistance in the process of filing. The third group comprises a single item which refers to fund raising activity. The corresponding variable is labelled Support Operations Funding. Our second category of management practices captures the relevance of different targets/objectives for the spin-off firms. We use information about the management of targets/objectives as defined by the University TTO/incubator. We built this set of variables from responses to the following question contained in the survey: “How would you rate the level of importance for the following goals for the spinoffs your organization has contributed to create?” Respondents were asked to provide a score between one and five, with a higher 21 score indicating a better performance. Four items were present: (i) growth; (ii) scientific excellence; (iii) employment creation, and (iv) technological excellence. Similarly to our first set of explanatory variables, we run factor analysis to reduce the information in common factors underlying target management practices. The two resulting predicted factors are again used as explanatory variables in the econometric model. Quite straightforwardly, we obtain two factors. The first one is mainly related to growth targets - item (i) and (iii) above - and is labelled Target Growth. The second group includes items (ii) and (iv) above and relates to scientific/technological objectives (Target Scientific Excellence).8 Our third key variable relates to the monitoring activities in place to track the performance of the academic spin-offs. Respondents were asked whether the performance of the spin-offs was regularly tracked and whether this was done using specific measures (e.g. balance sheet information, reports from the spin-off management team, etc.). Monitoring Management is a dummy variable which takes value one if the respondents answered positively to both questions and zero otherwise. Fourth, we capture the existence of incentives management drawing on responses to the following question contained in the survey: “Do you have a reward system (e.g. rewarding or promoting high performers) for your employees linked to the achievement of targets/objectives set out for the spin-offs?”. Incentives Management takes the value one if the organisation has a reward system for personnel, and zero otherwise. 8 We run a number of robustness checks to evaluate the robustness of results from factor analyses for the first two sets of explanatory variables (support operations and target management). First, we adopted different methods of factor extraction – principal components, iterated principal factors and maximum likelihood – which yield consistent results. We further test the robustness of the factor analyses by running them with a polychoric correlation matrix, which has been shown to be more appropriate with ordinal variables (Flora and Curran, 2004). Finally, we included in the regressions the average value of the items entering each factor instead of the predicted factor. The results are robust to all these specifications and are available from the authors upon request. 22 Finally, we measure talent management with the ability of the TTO/Incubator to attract human capital from outside. As the creation and development of academic spin-offs is likely to entail the combination of skills and competences from an array of organisational dimensions, we expect the ability of the TTO to attract employees with business experience to be a good proxy for professional management within the academic spin-off generating process. Professional Management is thus computed as the share of TTO employees with at least two years of experience in the private sector. 3.4 Controls To account for other firm- and university-level attributes that might be associated with the growth of academic spin-offs, we considered some additional control variables. First, we control for a set of variables that are often included in growth rate regression models: the stock of investment in tangible (Tangible Capital Stock) and intangible (Intangible Capital Stock) assets. Investments are recognised as important explanatory factors when explaining firms’ growth (Hall, 1986). Tangible Capital Stock (Intangible Capital Stock) is calculated as the yearly net acquisition of tangible (intangible) assets plus the amortisation (Grazzi et al., 2015) 9. Moreover, based on Gibrat’s law and other works on firms’ growth (e.g., Audretsch et al., 2012), we control for initial firm size measured as the turnover of the firm (Turnover). All these variables have been lagged by one year to minimise problems of reverse causality and log transformed (plus one). 9 Investments are measured in millions of euros and deflated by adopting the ratio of current prices to chainedlinked prices (reference year 2010) at the higher level of disaggregation, as provided by the Italian National Institute of Statistics (ISTAT) at the NACE rev. 2 2-digit industry level. The investments have later been transformed into stocks. We adopt the standard approach found in the relevant literature and calculate it using the following formula: Kt = Kt−1(1 − δ) + Pt where Kt-1 is the stock of capital at year t-1, δ is the depreciation rate assumed at 5%, and Pt is the investment in year t. 23 Second, we included structural characteristics for the firms in our sample, such as firm age (Age), Herfindahl-Hirschman index as a popular measure of industry concentration which has been found to play a relevant role with respect to firms’ performance (Kaniovski and Peneder 2008) and University Size (the number of tenured professors per University) 10 . This information was obtained from the data provided by Bureau van Dijk AIDA and the Italian Ministry of Education, Universities and Research (MIUR). Finally, we include three different sets of dummy variables to control for industrial (NACE rev.2 2 digit level), geographical (NUTS 2 level) and time effects (period 2006-2014). Table 1 presents the descriptive statistics for the variables used in this study; Table 2 reports the correlation matrix of our variables. In general, correlation among the independent variables is low, and variance inflation factor range between 1.2 and 7.2 (well below the threshold value of 10) suggesting the absence of multi-collinearity problems. [Table 2 ABOUT HERE] 4. Results and Discussion 4.1 Core findings In Table 3, we examine how the probability to engage in different types of management practices changes across the spin-offs’ growth distribution (for the three terciles). We also include the values for the overall sample. Overall, Universities extensively rely on 10 HH index is the sum of the square of the turnover shares of firms operating in (NACE Rev.2) industries. University size is measured by the number of tenured professors per university because, owing to the data sources used, only they held relevance for spin-off establishment in our study (please refer to the definition of academic spinoff reported in footnote 2). 24 management practices which refer to ‘lean’ operations, monitoring and target setting (more than 50% of the spin-off benefited from these practices). Much less diffused appear to be incentives and professional management practices (less than 50% of the spin-offs benefited from these practices). This descriptive result is coherent with the structural features of TTOs which focus on administrative processes rather than being designed to support fully business activities. As for differences across the growth rate distribution, a variegated pattern emerges. With the exception of Support Operations - Training which has a rather flat pattern across the different terciles, all other management practices are characterised by changes across different levels of the growth rate. Notably, monitoring and incentives management (Target Growth) have an increasing (decreasing) rate of adoption across the growth rate distribution. The remaining typologies (Support Operations – Patent, Support Operations – Funding, Target – Scientific Excellence and Professional Management) display curvilinear patterns across the different terciles of the growth rate distribution. We believe that this rich pattern, where different management practices differ across the growth rate distribution, supports the appropriateness of a quantile approach. [Table 3 ABOUT HERE] The main results are reported in Table 4. Model 1 presents the OLS estimates while Models 2 to 6 show results for different percentiles of the conditional growth rate distribution (10th, 25th, 50th, 75th and 90th percentiles). As for the controls we employ in our estimates, Table 4 shows that tangible capital stock has a positive and significant effect on spin-off’s growth. Interestingly, intangible capital stock 25 has a positive and significant effect only for high-growth spin-offs (Table 4 Model 6), pointing to the central role for growth of investment in intangible assets (Kaplan and Norton, 2004). A similar relationship is found for the size of University. In line with the literature which studies the relationship between age and performance (Huergo and Jaumandreu, 2004; Coad et al., 2013), we find that firm age is negatively related to spin-off’s growth. We now move to the core of our analysis, which is the relationship between University management practices to support academic spin-offs’ growth. Concerning `lean’ operations management which support the creation and development of spin-offs, we observe a positive and significant effect of Support Operations Training (β = 0.094, p < 0.1) and Support Operations Patent (β = 0.142, p < 0.01) on spin-off’s growth. Interestingly, when we take into consideration the effects at different points of the conditional growth rate distribution by adopting our quantile regression approach (Columns 2 to 6 of Table 4), we still observe a positive and significant effect of Support Operations Training (β = 0.153, p < 0.01; β = 0.023, p < 0.1 ) and Support Operations Patent (β = 0.116, p < 0.01; β = 0.074, p < 0.01), but only for low-growth companies which belong to the 10th and 25th percentiles. Regarding the role of target management, Table 4 reports negative and significant coefficients of Target Growth for OLS estimates as well as all the percentiles in the conditional growth rate distribution. Our results seem to point to a controversial outcome: setting a growth goal for academic spin-offs leads to a negative effect on the actual ability of these companies to realise turnover growth. As for incentives management, Table 4 provides good evidence of a positive relationship between reward systems for the achievement of specific objectives of the spin-offs and turnover growth across all the percentiles. Model 6 indicates that the highest effect of Incentives Management is to be found for high-growth companies (β = 0.665, p < 0.1). 26 Finally, as long as professional management is concerned, we find evidence of a negative relationship between the share of TTOs employees with at least two years of business experience and the conditional growth rate distribution. Models 1 to 6 always report a negative and significant coefficient of professional Management. [Table 4 ABOUT HERE] 4.2 Discussion Our results bear a number of implications in regards to the role of University management practices apt at influencing academic spin-offs. First and foremost, in line with the recent developments in the literature on the empirical economics of management (Bloom et al, 2014), we find evidence that University management practices contribute to explain the variation in the performance of academic spin-offs. At the same time, our results suggest that the overall picture is far from being unambiguous: while some types of management practices (support operations and incentives) show a positive correlation with the growth of academic spin-offs, other management practices (target and professional management) are negatively correlated. In what follows, we argue that the specific organisational context, namely a Public University system, can help to shed light on these contrasting results. We find that, out of the five possible different typologies of management practices, the most relevant ones are support operations and incentives management. As for the former, we show that the extent of the adoption of management practices to support patent and training is positively related to the growth of the spin-offs, although this positive correlation is significant for low-growth spin-offs only. This result resonates well with the historical reason 27 that brought Public Universities to establish TTOs in the first place. TTO offices were designed to economise on a number of functional services within Universities, particularly by pooling innovations and services across research units that were subject to economies of scale and learning, such as patenting (Macho-Stadler et al., 2007). This has been a key support for spin-offs which bet their destiny on a patent and which invest in patenting as their main strategic avenue (i.e. possibly to realise a profitable exit through acquisition). As for incentives management, it shows a positive and significant correlation with growth, particularly for high-growth spin-offs. This result relates well to the findings of the recent literature on management practices, which show that incentives are an effective way, even for public organisations (e.g. hospitals, schools, and universities), to react to external competition or institutional pressure (Bloom et al., 2015a, 2015b; McCormack et al., 2014). Similarly, incentives have been shown to be an important determinant of technology transfer in the technology transfer literature (Friedman and Silberman, 2003). We contribute to this literature by showing that incentives management contribute to explain the performance of academic spin-offs as well. Our result that professional management has a negative correlation with growth, with no remarkable differences across the conditional growth distribution, points to a double faceted selection process. On the one side, we interpret this result as evidence on the existence of an adverse selection process where Universities are often unable to attract external talent. Notably, the presence of a standardized contract, the lack of flexibility in offering adequate benefits or a compensation package comparable to the private sector as well as the sometimes opaque hiring procedure can limit the attractiveness of public job posts for candidates from the private sector (Karl and Sutton, 1998; Buelens and Van den Broeck, 2007). On the other side, even when Universities manage to attract excellent employees from the private sector, 28 problems can still arise. Notably, there could be a misalignment between the previous experience of the employee hired from the private sector and the real knowledge required to efficiently perform the job. Similarly, Universities may be unable to create the conditions whereby the newly hired employee is able to perform, due to the limited degrees of freedom or a lack of adequate flexibility. Finally, our most controversial result pertains to the negative association between target setting and the performance of academic spin-offs. A number of reasons can help explain the above result. First, the selection at entrance for academic spin-offs has been historically weak with the absence of appropriate support after the start-up phase (Siegel et al., 2007). Second, and more importantly, there is often a lack of credibility coming from setting growth objectives in Universities, as TTOs are often unable to enforce the achievement of these goals with credible actions (e.g. credible threats or rewards). On top of that, growth targets usually refer to the short-term but setting stringent goals in the short-term can actually be detrimental to medium or long-term growth, which is probably the main interest of academic spin-offs. This is particularly relevant in the valorisation of patents, when they form a central intellectual asset for the spin-off (Djokovic and Souitaris, 2008). These last two points together suggests that the existence of growth targets by Universities can be mere ‘ceremonial’ commitments. Being aware of that, academic spin-offs are likely to systematically miss these short term targets and aim for medium or long-term growth targets instead. 4.3 Robustness checks We check the stability of our results to two problems. First, there might be problems related to the misalignment of the time-frames for firm- and university-level information. While information referring to University management practices, which come from our survey, 29 refers to the period 2010-2014, we are able to control for firm-level characteristics (included turnover growth) for a longer period (2006-2014). Although management practices tend to be persistent and to take much effort and time to change, we checked the robustness of our results to this problem by estimating our models for the reduced time period 2010-2014 which represents a perfect overlap between firm- and university-level information. 11 We also consider the likelihood that our results are driven by a low number of high or low performing spin-offs in some universities. This is apparent from Table A1 where universities with a reduced number of spin-offs are characterised by an extremely high (or low) average turnover growth over the period 2006-2010. In order to control for this, we consider the sample of universities which have ten spin-offs or more. This amounts to drop twelve universities and 68 spin-offs from our initial sample. Table 5 and 6 provide evidence of the robustness of our main results with minor variations from our core findings. [Tables 5 and 6 ABOUT HERE] 5. Conclusion Academic spin-offs are a possible backbone of Universities’ third mission and can play a key role in transferring knowledge to local contexts. Existing literature has focussed on reasons that can facilitate spin-offs, on one hand, and how to interpret spin-offs outcome, on the other (Bigliardi, Galati and Verbano, 2013; Czarnitzki, Rammer and Toole, 2014). With some exceptions, the impact of internal processes has been so far overlooked. 11 The persistency of management practices for academic spin-offs has also been confirmed by introductory semi-structured interviews we had with 6 heads of TTOs and Incubators before starting with the large scale survey. Several interviewees stressed how the Italian University system has been historically characterised by long and painful adaptations to University systems of other European countries and that the support and practices for academic spin-offs do not represent an exception to this general trend. 30 Our study can be instrumental in filling the gap between these two views. The analysis of internal processes might help explain under which conditions spin-offs originate and how they evolve over time. From this perspective, our study contributes to the research stream of academic entrepreneurship by using the management practices framework. We interpret management practices as an outcome of universities’ decisions. We assume that, despite rules and regulations do constrain their autonomy, universities have degrees of freedom in structuring internal processes about spin-offs. Universities share the same institutional context, but their approaches can differ. We observed significant differences in how universities structure their processes. Management practices are not all alike and their adoption is uneven. In this study, we explored the impact of management practices on spin-offs performances. Our results do not allow us to assess if management practices are ‘good’ or ‘bad’ for universities per se. Notably, we do not have a solid conceptual framework about the impact management practices might have for universities. If we take a company perspective, we could argue that management practices are a good thing (for instance, incentive do stimulate members of the organization). If we take a university (public administration) perspective we might infer that some practices might induce internal misalignment and produce conflict (for instance, only members of some units might enjoy incentives, but not the large majority). This suggests that different management practices can be best suited to different environments (Bertrand and Schoar, 2003). We found that not all management practices have the same effect. Some management practices turned out to have a counterintuitive impact. This is specifically the case of the target ‘growth’ practice, which has a negative effect on spin-offs’ growth. This suggests that importing practices that were ‘invented’ in other institutional settings might be difficult, if not 31 impossible. It is reasonable to assume that there is a fit issue, as Universities are special organizations facing new challenges. Management practices might be in theory all adequate, but some of them may be difficult to adopt, others useless or in practice harmful. We believe that future research on different management practices and their impact on spin-offs performance might help in designing more appropriate governance structures and coordination mechanisms for universities. Our study has several limitations. First and foremost, it refers to Italy, whose institutional context is for several reasons distinguishable from other countries. We focussed on public universities, by far the large majority of the Italian population. Private universities might leverage management practices for academic spin-offs with more freedom. Clearly, there is more than the public versus private dichotomy. Academic spin-offs might differ –others things being equal – because their parent universities have different specialization –research versus teaching universities – and their local contexts also diverge (Fini et al., 2011). Second, our study covers a significant time interval as far as spin-offs performances is concerned, but does not offer comparable data and information on management practices’ adoption. We did not observe management practices from a longitudinal viewpoint. This is a clear limitation, for at least two reasons. First, we have very limited clues about the diffusion model of management practices. Management practices could just be a by-product of mimetic behaviour, and therefore get adopted for ceremonial reasons. However, management practices could emerge locally as the outcome of key players, who patiently act as evangelists (Lechner and Delanoë-Gueguen, 2014). The way management practices get adopted should have a direct impact on spin-offs performances. Second, we cannot rule out the possibility of reversed causality between observed results and specific management practices. This is the 32 case of the negative effect the variable ‘Target-growth’ has on spin-offs. This effect might well come from the reaction of TTO’s members to a below average growth of their spin-offs. Third, we assumed that management practices at universities level do affect spin-offs performances, but two caveats are relevant for our purposes. First, once spin-offs get out of the door, universities have limited say, for they hold a minority share. Second, as hierarchy is not the usual coordination mode universities use, the enforcement of management practices might differ widely. Some universities might simply suggest which practices spin-offs should adopt; others might have a say and directly influence spin-offs. More evidence on the real adoption of management practices from a spin-offs point of view is required. Despite these limitations, we hope our study could help universities to deal with spin-offs more effectively. Producing good, valuable spin-offs is a difficult task in any environment and nobody has the secret formula to do the job right. This is particularly true for universities, whose classic mission has always been generating and disseminating new knowledge. However, spin-offs are companies facing competitive selection under market conditions. A good management of spin-offs in their infancy is therefore a necessary, tough not sufficient condition. Academic spin-offs are a pillar of a quite different mission, requiring universities to be cross-eyed to accommodate different needs. Our study has some relevant policy implications for universities and policy makers. First, universities should be more aware of the management practices they adopt and how these practices fit into their internal organization. Second, universities should focus on practices they can directly enforce: for example, support training completely depends on universities, whereas target management practices can be more effectively enforced by the entrepreneurial team of spin-offs. Third, launching spin-offs requires time and is not a one shot activity. Universities can be equipped for providing services and assistance in the first stages, but too 33 much constrained in the following stages. 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Industrial and Corporate Change, 16(4), 569-608. 44 Table 1: Descriptive statistics (n=3695) Variable Turnover Growth Turnover Support operations - training Support operations - patent Support operations - funding Monitoring management Target - growth Target – scientific excellence Incentives management Professional management Tangible capital stock Intangible capital stock Industrial concentration index Age University size Mean 0.08 0.31 2.13 2.19 1.72 0.70 2.31 2.13 0.08 31.34 0.08 0.12 0.05 5.28 1165.77 Std. Dev. 2.57 2.13 0.82 0.75 0.97 0.46 1.06 1.01 0.27 33.12 0.36 0.45 0.06 2.73 783.52 Min -13.81 0 0 0.5 0 0 0 0 0 0 0 0 0.001 2 58 Max 13.81 97.53 3.75 3.5 4 1 3.5 3.5 1 100 6.21 7.44 0.95 14 4161 Descriptive statistics for tangible capital stock, intangible capital stock and turnover refer to the variables before natural log-transformation and are measured in million euros. 45 Table 2: Correlation Table [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [1] Turnover 1 Support operations - training 0.047 Support operations - patent -0.026 Support operations - funding 0.017 Monitoring management 0.031 Target – growth -0.021 Target – scientific excellence 0.033 Incentives management -0.018 Professional management -0.001 Tangible capital stock 0.541 Intangible capital stock 0.062 Industrial concentration index -0.014 Age 0.056 [2] [3] [4] [5] [6] [7] [8] [9] [10] 1 0.085 0.171 -0.204 0.144 0.161 0.130 -0.161 0.091 0.041 -0.048 0.034 1 0.296 -0.004 0.173 -0.258 -0.073 -0.167 0.026 0.099 0.037 0.018 1 0.005 -0.090 -0.035 -0.215 0.106 0.029 0.058 0.042 0.003 1 -0.101 0.007 -0.121 -0.265 -0.062 -0.015 0.013 -0.047 1 0.169 -0.015 -0.364 -0.001 0.073 0.028 0.079 1 0.176 0.250 0.036 -0.006 0.016 -0.006 1 0.201 -0.040 -0.047 -0.011 0.063 1 -0.011 -0.064 0.002 -0.112 1 0.454 1 -0.014 0.004 1 0.155 0.148 -0.014 46 [11] [12] Table 3: Engagement in University management practices for the terciles of the growth distribution of academic spinoffs 1st 2nd 3rd Overall Tercile Tercile Tercile Support operations - training 61.74 62.36 61.98 62.03 Support operations - patent 71.59 80.23 71.1 74.3 Support operations - funding 60.98 59.7 64.26 61.65 Monitoring management 69.7 71.1 74.14 71.65 Target – growth 65.53 57.03 68.09 63.54 Target – scientific excellence 80.3 72.62 75.29 76.08 Incentives management 6.06 6.46 8.75 7.09 Professional management 39.77 30.8 37.26 35.95 Obs 264 263 263 790 All variables are dichotomous variables and refer to whether the University has introduced a particular type of management practice in the period 2010-2014 47 Table 4: The effect of management practices on the growth of academic spinoffs Support operations - training Support operations - patent Support operations - funding Monitoring management Target – scientific excellence Target - growth Incentives management Professional management Log tangible capital stock -1 Log intangible capital stock -1 Ind concentration index -1 Log age -1 Log Univ size N (1) OLS 0.094* [0.052] 0.142*** [0.048] -0.014 [0.037] -0.046 [0.159] 0.064 [0.043] -0.095** [0.042] 0.291 [0.353] -0.004*** [0.001] 1.389*** [0.372] -0.269 [0.316] 0.642 [0.556] -0.182*** [0.059] 0.033 [0.089] 3695.000 (2) 10th Percentile 0.153*** [0.049] 0.116*** [0.039] 0.019 [0.031] 0.048 [0.148] 0.072 [0.049] -0.126*** [0.039] 0.528* [0.320] -0.005*** [0.002] 0.678*** [0.159] -0.151 [0.168] -0.172 [0.777] -0.039 [0.069] -0.034 [0.072] 3695.000 (3) 25th Percentile 0.023* [0.014] 0.074*** [0.017] 0.031 [0.023] -0.047 [0.072] 0.016 [0.017] -0.074*** [0.018] 0.271** [0.105] -0.003*** [0.001] 0.269** [0.113] -0.089 [0.142] 0.159 [0.216] -0.087*** [0.026] 0.034 [0.023] 3695.000 (4) 50th Percentile -0.007 [0.013] 0.013 [0.018] 0.019* [0.011] -0.029 [0.034] 0.006 [0.012] -0.035** [0.014] 0.133* [0.080] -0.002*** [0.000] 0.078 [0.074] -0.057* [0.032] 0.220 [0.137] -0.132*** [0.018] 0.012 [0.021] 3695.000 (5) 75th Percentile -0.013 [0.032] 0.033 [0.022] 0.031 [0.026] -0.035 [0.070] 0.023 [0.026] -0.045* [0.025] 0.371*** [0.137] -0.003*** [0.001] 1.310*** [0.352] 0.210 [0.169] 0.178 [0.232] -0.087** [0.043] 0.096*** [0.036] 3695.000 (6) 90th Percentile 0.036 [0.067] 0.074* [0.042] 0.006 [0.039] -0.030 [0.159] 0.044 [0.034] -0.105** [0.051] 0.665* [0.383] -0.005** [0.002] 2.241*** [0.363] 0.366* [0.193] 0.490* [0.281] 0.037 [0.079] 0.141** [0.068] 3695.000 Robust standard errors clustered at university level are reported in parenthesis. Initial turnover, industry, geographical and time dummies have been included in the estimates but not reported for space reasons. * p<0.10, ** p<0.05, *** p<0.01 48 Table 5: The effect of management practices on the growth of academic spinoffs - period 2010-2014 (1) 10th Percentile Support operations - training 0.048 [0.062] Support operations - patent 0.061 [0.062] Support operations - funding 0.022 [0.037] Monitoring management -0.068 [0.182] Target – scientific excellence -0.018 [0.048] Target - growth -0.065* [0.039] Incentives management 0.198 [0.483] Professional management -0.005** [0.002] Log tangible capital stock -1 0.644*** [0.155] Log intangible capital stock -1 0.122 [0.304] Ind concentration index -1 -0.074 [1.097] Log age -1 -0.029 [0.071] Log Univ size 0.048 [0.086] N 2855.000 (2) 25th Percentile 0.013 [0.013] 0.051*** [0.017] 0.004 [0.014] -0.047 [0.051] -0.003 [0.014] -0.046** [0.019] 0.198 [0.124] -0.002*** [0.001] 0.306*** [0.100] -0.050 [0.102] -0.007 [0.314] -0.078*** [0.029] 0.021 [0.036] 2855.000 (3) 50th Percentile -0.015 [0.012] 0.005 [0.016] 0.003 [0.012] -0.036 [0.031] 0.017 [0.013] -0.029** [0.013] 0.082 [0.078] -0.001*** [0.000] 0.060 [0.059] -0.016 [0.037] 0.247* [0.127] -0.105*** [0.017] 0.002 [0.020] 2855.000 (4) 75th Percentile -0.024 [0.034] 0.031 [0.029] 0.014 [0.026] -0.108 [0.080] 0.029 [0.025] -0.056** [0.029] 0.407** [0.199] -0.004*** [0.001] 1.304*** [0.389] 0.259* [0.155] 0.271 [0.260] -0.056 [0.041] 0.084* [0.048] 2855.000 (5) 90th Percentile 0.084 [0.054] 0.073 [0.055] -0.026 [0.041] -0.058 [0.122] 0.027 [0.043] -0.138** [0.057] 0.725*** [0.237] -0.005*** [0.002] 2.236*** [0.274] 0.439** [0.196] 0.537* [0.279] 0.062 [0.088] 0.119** [0.057] 2855.000 Robust standard errors clustered at university level are reported in parenthesis. Initial turnover, industry, geographical and time dummies have been included in the estimates but not reported for space reasons. * p<0.10, ** p<0.05, *** p<0.01 49 Table 6: The effect of management practices on the growth of academic spinoffs - Universities with 10 spinoffs or more (1) 10th Percentile Support operations - training 0.068 [0.070] Support operations - patent 0.151*** [0.047] Support operations - funding 0.062 [0.069] Monitoring management -0.741** [0.325] Target – scientific excellence 0.142*** [0.035] Target - growth -0.284*** [0.048] Incentives management -1.115 [0.732] Professional management -0.010*** [0.002] Log tangible capital stock -1 0.638*** [0.129] Log intangible capital stock -1 -0.128 [0.193] Ind concentration index -1 -0.002 [0.956] Log age -1 0.019 [0.100] Log Univ size -0.417** [0.167] N 3394.000 (2) 25th Percentile 0.024* [0.013] 0.052*** [0.017] 0.036* [0.019] -0.107** [0.054] 0.025 [0.016] -0.072*** [0.018] 0.154 [0.104] -0.003*** [0.001] 0.234** [0.103] -0.041 [0.135] -0.150 [0.512] -0.093*** [0.024] 0.018 [0.030] 3394.000 (3) 50th Percentile 0.003 [0.013] 0.002 [0.019] 0.020 [0.013] -0.018 [0.058] 0.035* [0.018] -0.028 [0.020] 0.197* [0.105] -0.002** [0.001] 0.065 [0.066] -0.043 [0.037] 0.078 [0.209] -0.135*** [0.019] 0.028 [0.028] 3394.000 (4) 75th Percentile 0.002 [0.032] 0.037 [0.024] 0.028 [0.038] 0.010 [0.120] 0.051* [0.029] -0.038 [0.030] 0.511** [0.211] -0.003** [0.001] 1.248*** [0.439] 0.211 [0.164] -0.272 [0.537] -0.096** [0.044] 0.118* [0.071] 3394.000 (5) 90th Percentile 0.055 [0.069] 0.073 [0.046] 0.010 [0.040] 0.079 [0.245] 0.038 [0.032] -0.108* [0.055] 0.922* [0.553] -0.005** [0.002] 2.236*** [0.301] 0.348* [0.190] 0.136 [0.915] 0.040 [0.091] 0.185* [0.096] 3394.000 Robust standard errors clustered at university level are reported in parenthesis. Initial turnover, industry, geographical and time dummies have been included in the estimates but not reported for space reasons. * p<0.10, ** p<0.05, *** p<0.01 50 Appendix Table A1: Characteristics of academic spinoffs by University University name Università degli Studi dell’Aquila Università degli Studi di Bergamo Università di Bologna Università degli Studi di Cagliari Università della Calabria Università degli Studi di Camerino Università degli Studi di Catania Università degli Studi ‘Gabriele D’Annunzio’ Chieti e Pescara Università degli Studi di Ferrara Università degli Studi di Firenze Università degli Studi di Genova Università degli Studi dell’Insubria Università degli Studi di Messina Università degli Studi di Milano Bicocca Università degli Studi di Milano Università degli Studi di Modena e Reggio Emilia Università degli Studi del Molise Seconda Università degli Studi di Napoli Università degli Studi di Padova Università degli Studi di Palermo Università degli Studi di Parma Università degli Studi di Perugia Università degli Studi del Piemonte Orientale ‘Amedeo Avogadro’ Università degli Studi di Pisa Politecnico di Bari Università Politecnica delle Marche Politecnico di Milano Politecnico di Torino Università degli Studi di Roma ‘La Sapienza’ Università degli Studi di Roma ‘Tor Vergata’ Università degli Studi del Salento Università degli Studi del Sannio Università degli Studi di Sassari Università degli Studi di Siena SISSA di Trieste Sant’Anna di Pisa Università degli Studi di Torino Università degli Studi di Trento 51 Number of spinoffs 9 5 30 22 23 9 7 Age 4.89 3.80 6.83 4.25 4.98 4.78 3.36 Turnover growth (2006-2014) 14.86 -13.25 3.35 33.98 7.91 -54.69 124.70 3 3.83 -20.86 20 21 33 2 2 14 26 5.95 4.12 4.32 3.25 2.50 3.32 4.50 16.91 25.31 38.48 0.65 -26.41 14.89 0.31 23 4.13 21.93 9 5 45 9 16 28 3.50 3.80 4.06 3.44 4.69 4.55 47.52 187.96 12.18 -5.43 -1.49 67.43 14 3.86 -28.58 36 21 29 29 63 4.24 4.21 4.02 5.43 4.84 45.29 8.43 29.35 24.85 -4.99 19 2.84 -22.73 27 3.37 23.85 24 10 10 20 3 28 23 10 3.69 4.00 3.40 4.60 7.00 5.30 3.57 2.75 41.29 37.06 -15.04 13.72 6.08 54.03 69.61 -10.47 Università degli Studi di Trieste Università degli Studi della Tuscia Università degli Studi di Udine Università degli Studi di Verona Average 15 5 33 10 18.8 52 3.80 3.30 4.42 3.20 4.16 5.53 -53.05 16.92 53.23 18.82

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