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ScienceDirect Procedia - Social and Behavioral Sciences 223 (2016) 305 – 312

2nd International Symposium "NEW METROPOLITAN PERSPECTIVES" - Strategic planning, spatial planning, economic programs and decision support tools, through the implementation of Horizon/Europe2020. ISTH2020, Reggio Calabria (Italy), 18-20 May 2016

Improve metropolitan competitiveness through innovation. The critical and moderating role of university spin-offs Christian Corsia, Antonio Prencipea,* a

Università degli Studi di Teramo, Teramo - 64100, Italy

Abstract The paper aims to explore if University spin-offs (USOs) can contribute to the innovative performance of metropolitan areas, as well as, positively, moderate the relationship between innovative performance and competitiveness in metropolitan areas. Based on a sample of 247 USOs, located in 11 Italian metropolitan areas, the results show that spin-out activities stimulate the innovative capabilities of metropolitan areas, which seem not to be influenced by their extent. Additionally, the innovative performance of metropolitan areas seems to increase their competitiveness. Nevertheless, the moderating role of USOs, in the relationship between innovation and competitiveness of the metropolitan area, is positive but slight. ©2016 2016The TheAuthors. Authors. Published Elsevier © Published by by Elsevier Ltd.Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of the organizing committee of ISTH2020. Peer-review under responsibility of the organizing committee of ISTH2020 Keywords:Competitiveness; Innovation; Metropolitan area; Urban area; University spin-offs.

1. Introduction Improving city competitiveness is a central issue. Indeed, it is essential to rise the welfare and prosperity of inhabitants and companies, generating employment. Therefore, it is imperative to increase the understanding of economic growth prospects in metropolises. In this regard, novel growth sectors such as biotechnology, ITC and environmental technology are becoming key elements in the academic context as well as for urban/metro managers (van den Berg et al., 2014). Nevertheless, there is a lack of a systematic knowledge about critical success factors that define the economic development of the cities and metropolitan area, with a few empirical evidence about the

* Corresponding author. Tel.: +39-340-8017130. E-mail address: [email protected]

1877-0428 © 2016 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of the organizing committee of ISTH2020 doi:10.1016/j.sbspro.2016.05.374

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Christian Corsi and Antonio Prencipe / Procedia - Social and Behavioral Sciences 223 (2016) 305 – 312

related policy actions (Nijkamp, 1999; van den Berg et al., 2014). However, several signals indicate competitiveness as emerging from successful collaboration among economic actors who form innovative facilities of companies and other organizations. Innovation has constantly been at the core of competitiveness. Investigation, exploration and an effort to exploit resources are as crucial for companies as they are for urban/metro regions (Denton, 1999). In this view, the pursuit of competitiveness through innovation is a praiseworthy objective of local and national policy, since innovation is a key function in the current modern knowledge-driven economy, mainly for urban/metro areas that start behind and wish to catch up (Cantwell, 2005). In this context, the university spin-offs (USOs) are included among the best entrepreneurial initiatives which offer effective and gainful ways for the dissemination of new technologies and innovation (Berbegal-Mirabent et al., 2015; Etzkowitz et al., 2000; Rodríguez-Gulías et al., 2015). Additionally, they are considered among the more proactive instruments to foster the birth and growth of knowledge-based economies, contributing to competitive development of certain urban/metro area (Benneworth and Charles, 2005; Sternberg, 2014).In line with the above configuration, several studies (Kennedy and Patton, 2011; Micozzi and Iacobucci, 2015) have emphasized that the creation and dissemination of knowledge by universities should be incorporated among the key driving forces for social and economic development, both at urban, metropolitan and national level (Müller et al., 2004). Further, it was also noted that the model of open innovation, of which are widely pervaded the current organizational and entrepreneurial paradigm (Tödtling et al., 2011; Dahlander and Gann, 2010), provides a suitable model to drive innovation internally generated for the revitalization and improvement of the external environment (Villasalero, 2014). Open innovation proposes that companies can pursue new knowledge from anywhere, inside the city region and outside, within the nation and abroad, but generally, the external environment is more central and the city region is a significant part of it (Van Geenhuizen and Soetanto, 2013). Regarding the spin-off phenomenon in Europe, it has been explored (Smith and Ho 2006); though, the same, in most European urban/metro regions, is expected to differ from that in more established high-tech context (as Silicon Valley and the Boston area) (Nicolaou and Birley 2003). This requires assessing the efficiency of USOs in European urban/metro regions, being open to the idea that their impact deviates considerably if changes, in the urban/metro contexts, are considered. Hence, this paper aims to fill this knowledge gap, by exploring the extent that the USOs can successfully contribute to the innovative performance of metropolitan areas, as well as their central role in moderating the metro competitiveness through innovative performance, continuing in part the studies about the economic impact of USOs starting by Benneworth and Charles (2005) and Iacobucci and Micozzi (2014). To this purpose, the study analyses a longitudinal sample of 247 USOs located in 11 Italian metropolitan area. Italy is one of the major European countries reporting a rapid development of the USOs phenomenon (Fini et al., 2011; Iacobucci et al., 2015). Actually, according to the latest report Netval (Netval, 2015), at 31.12.2014 the spinoffs, from public research surveyed in Italy, are 1102 and that about 87.4% of the 1102 spin-off companies, today recognized and active on the national territory, has been formed over the past ten years. Additionally, Italian urban system faced several changes in the last 10-20 years, especially in term of the urban governance, which require the introduction of inter-institutional forms of cooperation among various levels of government and the involvement of private sector institution, with the aim to improve the Italian cities competitiveness (Governa, 2010). The paper aims to provide a contribution to the knowledge – both in term of theoretical and practical perspective - about the role of innovation in fostering urban/metro competitiveness, with particular reference to the proactive and moderating role of university start-ups. Similarly, the paper wants to stimulate suitable policy actions to rise the urban/metro evolution, with the purpose of contributing to the economic diffusion of innovation and driving the competitiveness growth of the cities. 2. Literature Background The progressive enrichment of the university identity as generating opportunities for innovative forms of entrepreneurship - a phenomenon called entrepreneurial university - has altered the socio-economic role of universities in many urban/metro area (Etzkowitz, 2004). The creation of spin-off firms constitutes a central tool for the commercialization of the knowledge/technologies therein generated and, therefore, sustaining innovative activities (Shane, 2004; Wright et al., 2007). Indeed, these forms of business provide direct support to the development of the socio-economic urban/metro area, especially in terms of innovative impact (Martinelli et al., 2008). The claim is laudable considering that a greater entrepreneurial university orientation could significantly


facilitate the absorption of knowledge and technologies by companies operating in the urban/metro area, with a consequential creation of value for all the actors involved (Di Gregorio and Shane, 2003; Cohen et al., 1998). Emphasizing the impact generated by the university research capabilities and innovation developed, some theoretical and empirical evidences show that university can actually generate a spill-over knowledge into the socioeconomic environment (Audretsch et al., 2012; Leten et al., 2011). Nevertheless, this kind of experience tends to be much localized and seems strongly linked to the requirement of geographical proximity between the university and business environments. The urban/metro area is just this kind of proximity. The Italian urban context is characterized by constraints that undermine the effectiveness of the transfer of knowledge, skills and technologies from research to entrepreneurship (Cardamone et al., 2015). These constraints are mainly due to a cultural averse to cooperation between academicians and urban key actors, as entrepreneurial context (Muscio, 2008; On and Sohn, 2015). Thus, it becomes critical the role of liaison fulfilled by the USOs that, by acting as intermediaries in the dissemination of knowledge/technologies developed by the university to potential users such as businesses and institutions (Cardamone et al., 2015; Wright et al., 2008), constitute a pool of innovation both for the actual economic and competitive development of the urban/metro area, as for the renovation of the industry from traditional or low tech sectors to high-tech sectors (Iacobucci and Micozzi, 2015). The function of promoting innovation by USOs is remarked also by the Knowledge Spillover Theory of Entrepreneurship, an approach that widely recognizes the need of creating new businesses to improve competitiveness and innovation through knowledge/technologies developed by the university research (Carree et al., 2014; Audretsch and Lehmann, 2005). From an urban/metro area point of view, the spatial innovation theory suggests that the capacities of the cities to improve competitiveness and innovation among companies differ. Indeed, the possibilities of better innovative performance are higher in big metropolitan areas compared to small ones (Van Geenhuizen and Soetanto, 2013). According to these arguments, the urbanization economies – characterized by heterogeneous clusters of companies - are frequently related with big cities, although the localization economies - characterized by specialized clusters of companies in the same sector - are frequently related with small cities (Duranton and Puga, 2000). The cities may have a potential influence on the magnitude of innovation generated, especially in terms of open innovation. In this sense, the stronger agglomeration benefits in big city areas may facilitate the access to venture capital, skilled workforce, as well as expert suppliers and specialized customer, leading to be like innovative for the small cities. These last, in fact, are constrained in the access of key knowledge and financial resources due to the lack of agglomeration and network (Van Geenhuizen and Soetanto, 2013). Although, not all the empirical findings validate the big urbanized city as a powerful condition to generate innovation, putting the smaller urban area as a better solution for it (see for example Teirlinck and Spithoven (2008)); but the most recent evidences demonstrate the superior role of large urbanized city in facilitating innovation (Isaksen and Onsager, 2010; Van Geenhuizen and Soetanto, 2013). These considerations may help in understanding the potential impact of USOs on the innovative performance of the metropolitan area. Thus, it can be stated the following: H1: USOs have a positive impact on innovative performance of metropolitan areas; H2: Large metropolitan areas are more likely able to perform better in term of innovation. Additionally, taking into account the beneficial effect of innovative performance of urban/metro area on the city competitiveness –Cantwell (2005) argued that “competitiveness must be thought of as entailing a relative comparison of growth rates or benchmarking of performance to assess how well each participant has done in developing the capabilities for innovation and growth” (Cantwell, 2005, p. 2) – it could be stressed that spin-off activities with its innovative externalities may have a central and moderating role in the relation between innovation and competitiveness of the metropolitan area. Hence, according with the above considerations, it can be stated the following: H3a: The innovative performance of the metropolitan area has a positive impact on its competitiveness; H3b: USOs positively moderate the relation between innovative performance and competitiveness of metropolitan area.

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3. Method 3.1. Sample In order to test the research hypothesis above, it was analyzed a panel sample of 247 Italian USOs extracted from Netval database, which collects the population of spin-off research firms present in Italy, while data cover a period from 2004 to 2008. The collection of secondary data about USOs was performed by the analysis of financial statements and other corporate files extracted from Infocamere database and Aida BdV database (containing financial, biographical and merchandise data of about 700,000 Italian active companies). Information regarding the population, innovation and competitiveness of about 11 Italian metropolitan areas (namely Rome, Milan, Naples, Turin, Palermo, Genova, Firenze, Bari, Bologna, Catania and Venice) were collected by extracting data from the records stored by the Organization for Economic Co-operation and Development (OECD). 3.2. Variable definition 3.2.1. Dependent variables The first dependent variable applied in this study, the innovative performance of metropolitan area, was measured by the number of patent applications from the sampled metropolitan area (Metro Innovation). Scholars suggest that patents provide a fairly reliable measure of innovative activity, representing a key and efficiently measure of regional production of new knowledge, also at the lowest possible levels of geographical aggregation such as metropolitan city (Acs et al., 2002, Bottazzi and Peri, 2003). The second dependent variable aims to measure the metropolitan competitiveness and it was generated by the economic output or gross domestic product (GDP) per capita in US $ (Metro Competitiveness). Indeed, GDP per capita is considered one of the best measure of the economic performance of a region (Bailey et al., 2002). More precisely, GDP assesses the capability of the local economy to generate wealth and GDP per capita has a positive association with average ranks of income earned by inhabitants. GDP per capita is used by the Department of Trade and Industry (DTI) as one of its main indicators of regional competitiveness (DTI, 2000). 3.2.1. Independent variables With the aim to predict the potential effects of USOs on innovative performance of the metropolitan area, it was used the number of university spin-off firms located in the selected Italian metropolitan areas (Number USO). Indeed, the number of spin-off is a most useful measure to assess the impact of spill-over externalities on local context in term of economic effect. In this regard, Iacobuzzi and Micozzi (2014) used the number of spin-offs established in the Italian provinces in order to evaluate the impact of academic spin-offs on local development. Secondly, with the aim to assess the impact of the city size on the effectiveness of the USOs activities and their innovative externalities on the metropolitan area, it was used the total population (in person) for each metropolitan area sampled (Metro Size). Finally, the innovative performance of the metropolitan area is also used as independent variable in order to measure its effect on competitiveness of metropolitan areas (Metro Innovation). 3.3. Analytical approach In order to test the developed research hypothesis, a two linear mixed-effect model was used, which is a very useful statistical model for the analysis of longitudinal data, and which also offers a high flexibility in modeling the within-subject correlation, frequently existing in longitudinal data, while handling with both the balanced and as well as the unbalanced data (Verbeke and Molenberghs, 2009). In addition, linear mixed-effect models are more powerful than classical techniques (e.g., ANOVA, MANOVA, multiple regression analysis) in exploring the effects related with repeated measures, since they model the covariance matrix instead of imposing a definite form of structure as employed in standard univariate and multivariate methods (Shek and Ma, 2011). In line with these considerations, the following linear mixed-effect model was developed with the aim to predict the effect of USO

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activities and metro size on innovative performance of metropolitan areas, and it allows fixed effects for time and firms: ‫݊݋݅ݐܽݒ݋݊݊ܫ݋ݎݐ݁ܯ‬௜௧ = f (Ⱦ଴ + Ⱦଵ ܰ‫ܱܷܵݎܾ݁݉ݑ‬௜ + Ⱦଶ ‫݁ݖ݅ܵ݋ݎݐ݁ܯ‬௜௧ + Ɂ௧ + Ԫ௜௧ )

(1)

where i indexes the universities and t indexes the years. In addition, ≈t is the time effect and Ԫ௜௧ is the error term. Additionally, in order to predict the impact of innovative performance of metropolitan areas on the competitiveness of the same, as well as the moderating role of USOs activities in improving the relationship between innovation and competitiveness of metropolitan areas, the following linear mixed-effect models were developed, allowing fixed effects for time and firms: ‫ݏݏ݁݊݁ݒ݅ݐ݅ݐ݁݌݉݋ܥ݋ݎݐ݁ܯ‬௜௧ = f (Ⱦ଴ + Ⱦଵ ‫݊݋݅ݐܽݒ݋݊݊ܫ݋ݎݐ݁ܯ‬௜௧ + Ⱦଶ ‫݁ݖ݅ܵ݋ݎݐ݁ܯ‬௜௧ + Ɂ௧ + Ԫ௜௧ )

(2)

‫ݏݏ݁݊݁ݒ݅ݐ݅ݐ݁݌݉݋ܥ݋ݎݐ݁ܯ‬௜௧ = f (Ⱦ଴ + Ⱦଵ ‫݊݋݅ݐܽݒ݋݊݊ܫ݋ݎݐ݁ܯ‬௜௧ *ܰ‫ܱܷܵݎܾ݁݉ݑ‬௜ + Ⱦଶ ‫݁ݖ݅ܵ݋ݎݐ݁ܯ‬௜௧ + Ɂ௧ + Ԫ௜௧ )

(3)

where i indexes the universities and t indexes the years. In addition, ≈t is the time effect and Ԫ௜௧ is the error term. 4. Results 4.1. Descriptive statistics Table 1 shows the descriptive statistics of variables used in the study. The results indicate that sample shows an average of innovative performance in the selected metropolitan areas, measured by the number of patent, of 197.09, with a moderate dispersion in the sample (S.D. = 159.71), and highlights as the sampled metropolitan areas differ in a remarkable manner in term of innovation generated. Also in terms of competitiveness, measured by GDP per capita, the sampled metropolitan areas show a high heterogeneity (S.D. = 22436.08), following the pattern characterizing the innovative performance. Regarding the size of the selected metropolitan areas, measured in terms of the total population, the sample shows an average of 2403883.87, and also in this case the heterogeneity is pretty high (S.D. = 1467709.26). Finally, the sampled metropolitan areas show an average of 38.86 USOs located therein, while the dispersion in the sample is high (S.D. = 19.03). The descriptive results remark as it exists a strong heterogeneity in the sampled Italian metropolitan areas in several structural performance features, suggesting as the urban/metro context is quite mixed in Italy. Table 1. Descriptive statistics. N

Min.

Max.

Mean

S. D.

Variance

Metro Innovation

1235

7.530

527.380

197.089

159.719

25510.280

Metro Competitiveness

1235

22436.080

59423.790

44738.767

10389.784

107947611.877

Metro Size

1235

537466.000

4014266.000

2403883.868

1467709.262

2154170478242.070

Number USO

1235

3.000

58.000

38.862

19.032

362.234

It was checked for multicollinearity, formally using VIF statistics. It was found that the VIF scores did not exceed 2.82 for the Model [1], 2.51 for the Model [2] and 3.80 for the Model [3] - which are not close to the rule of thumb “threshold” value of 10 (Hair et al., 1998) – and an average of 2.82 for the Model [1], 2.51 for the Model [2] and 3.11 for the Model [3]; while the “tolerance” level shows an acceptable value higher than 0.10 in all the models, suggesting that multicollinearity is not a serious concern, therefore multiple regression analysis can be used to test the hypothesis. It must be specified that our estimation methods, linear mixed-model, do not allow the estimation of VIF scores. Therefore, we report the VIF scores obtained from estimating the models through ordinary least squares (OLS).

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4.2. Linear mixed-models estimation Table 2 shows the results of the Model [1] predicting the impact of USOs activity and city size on innovative performance of Italian metropolitan areas. H1 states a positive relationship between USOs activity and innovative performance of metropolitan area. The estimated coefficient for Number USO is positive and statistically significant (coeff. = 2.650, p < 0.001). Thus, the findings provide support to the H1. H2 states a positive relationship between USOs activity and innovative performance of a large metropolitan area. The estimated coefficient for Metro Size is statistically significant but its positivity practically is very small, almost inexistent (coeff. = 1.35, p < 0.001). Thus, the findings do not provide support to the H2. Table 2. Estimation of linear mixed-effect model regression with innovative performance of metropolitan area as the dependent variable. Variables

Model 1 B

S. E.

Number USO

2.651***

(0.045)

Metro Size

0.000***

(0.000)

Hypothesized effects

-2 Log likelihood restricted

9784.439

Akaike's information criterion (AIC)

9814.440

Hurvich and Tsai’s criterion (AICC)

9814.835

Bozdogan’s criterion (CAIC)

9906.186

Bayesian information criterion (BIC)

9891.186

*** p < 0.01; ** p < 0.05; * p < 0.10 (all two-tailed tests).

Table 3 shows the results of the Model [2] and Model [3] predicting the impact of the innovative performance of the metropolitan area on the competitiveness of the same, as well as the moderating role of USOs in improving the relationship between innovation and competitiveness of metropolitan areas. H 3a states a positive relationship between innovation and competitiveness of the metropolitan area. The estimated coefficient for Metro Innovation is positive and statistically significant (coeff. 1.394, p < 0.001). Thus, the findings provide support to the H 3a. H3b states a positive moderating effect of USOs activities in the relationship between innovation and competitiveness of metropolitan areas. The estimated coefficient for Metro Innovation*Number USO is positive and statistically significant but its practical impact is small (coeff. 0.025, p< 0.001). Hence, the findings provide a partially support to the H3b. Table 3. Estimation of linear mixed-effect models regression with competitiveness of metropolitan area as the dependent variable. Variables

Model 2

Model 3

B

S. E.

Metro Innovation

1.394***

(0.080)

Metro Size

0.000***

(0.000)

B

S. E.

0.000***

(0.000)

0.025***

(0.001)

Hypothesized effects

Metro Innovation*Number USO

-2 Log likelihood restricted

20043.190

20069.076

Akaike's information criterion (AIC)

20073.190

20099.076

Hurvich and Tsai’s criterion (AICC)

20073.585

20099.470

Christian Corsi and Antonio Prencipe / Procedia - Social and Behavioral Sciences 223 (2016) 305 – 312 Bozdogan’s criterion (CAIC)

20164.936

20190.822

Bayesian information criterion (BIC)

20149.936

20175.822

*** p < 0.01; ** p < 0.05; * p < 0.10 (all two-tailed tests).

5. Result discussion and Conclusion The paper aimed to study the impact of USOs on the metropolitan innovation and competitiveness. The results from a sample of 247 Italian USOs, Italian USOs located in 11 Italian metropolitan areas, show the positive impact of USOs on the innovative performance of the metropolitan area. These findings remark how the knowledge and technology output, of the university spin-out process, can have a beneficial role in the metropolitan area, stimulating its innovative capabilities by acting as key intermediary in the open innovation development of the local context (Van Geenhuizen and Soetanto, 2013). Additionally, in contrast with the studies of Duranton and Puga (2000) and Van Geenhuizen and Soetanto (2013), the large metropolitan area does not seem to achieve better innovative performance thanks to its resilient clustering benefits compared to small metropolitan area. The evidence supports the Teirlinck and Spithoven’s (2008) arguments, highlighting as also the small cities simplify the access to the key resource and expertise for increasing the innovation capabilities. In this context the role of the USOs may be also potentially effective, since that, as high-tech start-ups, external pivotal innovative resources and organizational/personal linkages are required for their entrepreneurial development and growth (Rodríguez-Gulías et al., 2015; Berbegal-Mirabent et al., 2015). Regarding the impact of innovative performance of metropolitan area on the competitiveness of the same, the results show a positive effect. These findings are in line with the study of Cantwell (2005), pointing out as the competitiveness of an urban/metro can be effectively improved through its innovation efforts, thanks to a stimulus of the basic elements of economic development and future growth. Nevertheless, taking into account the moderating role of USOs in improving the positive relationship between innovation and competitiveness of the metropolitan area, the results suggest as the effectiveness of USOs is not so prominent but still positive. These findings suggest as the USO activities have only an indirect role in stimulating competitiveness of a defined urban/metro area. However, spin-off firm remains one of the key actors in driving the urban evolution towards technological changes and new knowledge perspectives, increasing its performance of socio-economic growth. The study rises some remarkable practical and policy implications. First, the pivotal role of USOs in stimulating the innovative performance of the metropolitan area should call the attention of the policy makers in bringing solid and shared partnership among all the urban/metro players involved in the spillover and innovative processes. This is true especially for the small cities that require more solid fostering programs to rise up their innovative performance. Second, in order to improve the competitiveness of the cities, it is essential the function of the local governments and the local industry system, which should act more as facilitators in the exchange of knowledge and technology, by means the planning of jointly strategic actions to build a common and unique innovative network with the purpose of increasing the urban/metro competitiveness. References Acs, Z. J., Anselin, L., & Varga, A. (2002). Patents and innovation counts as measures of regional production of new knowledge. Research policy, 31(7), 1069-1085. Audretsch, D. B., & Lehmann, E. E. (2005). Does the knowledge spillover theory of entrepreneurship hold for regions?. Research Policy, 34(8), 1191-1202. Audretsch, D. B., Hülsbeck, M., & Lehmann, E. E. (2012). Regional competitiveness, university spillovers, and entrepreneurial activity. Small Business Economics, 39(3), 587-601. Bailey, N., Docherty, I., and Turok, I. (2002). Dimensions of city competitiveness: Edinburgh and Glasgow in a UK context, in Begg, I. (ed) Urban competitiveness: policies for dynamic cities, 135-160. Bristol: Policy Press. BarrioǦCastro, T. D., & GarcíaǦQuevedo, J. (2005). Effects of university research on the geography of innovation. Regional Studies, 39(9), 12171229. Benneworth, P., & Charles, D. (2005). University spin-off policies and economic development in less successful regions: learning from two decades of policy practice. European Planning Studies, 13(4), 537-557. Berbegal-Mirabent, J., Ribeiro-Soriano, D. E., & García, J. L. S. (2015). Can a magic recipe foster university spin-off creation?. Journal of Business Research, 68(11), 2272-2278.

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