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How do new entrepreneurs innovate?

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Abstract

This paper builds upon Pellegrino et al. (Struct Chang Econ Dyn 23:329–340, 2012) further analysing the determinants of product innovation in Italian young innovative companies (YICs) by looking at in-house and external R&D and at the acquisition of external technology in its embodied and disembodied components. A Tobit approach is applied to study jointly the occurrence of product innovation and the intensity of such innovation. Results provide evidence that in-house R&D is linked to product innovation both in mature firms and YICs; however, YICs turn out to be less in-house R&D-based and, unlike their mature counterparts, more dependent on external sources of knowledge. While this outcome corroborates and further reinforce what found—using a different methodology—in Pellegrino et al. (Struct Chang Econ Dyn 23:329–340, 2012), in this study, other entrepreneurial attitudes such as the ability to cooperate with other firms in producing innovation or the capacity to develop significant organizational changes are also investigated. The results of the econometric estimations show that these attitudes turn out to be key innovative strategies in the incumbent firms but, in some specific cases (such as for the ability to cooperate), appear to be far less important in the YICs. These results are somehow worrying, since they show that Italian innovative entrepreneurs are mostly driven by routinized rather than creative strategies.

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Notes

  1. For example, several EU member states have introduced new measures to support the creation and growth of YICs, especially by improving their access to funding (see BEPA 2008; Schneider and Veugelers 2010).

  2. For complementary interpretations of the transatlantic productivity gap, see Ortega-Argilés et al. (2011, 2014).

  3. Methodologically, this is well represented by the shift from the R&D-focused Frascati Manual (‘Guidelines for the collection of R&D data’, first published in 1963) to the Oslo Manual, published in the 1990 s (OECD 1997).

  4. The embodied nature of technological progress was originally discussed by Salter (1960) and Solow (1960); in particular, vintage capital models describe an endogenous process of innovation in which the replacement of old equipment is the main way through which firms update their own technologies (see also Jorgenson 1966; Hulten 1992; Greenwood et al. 1997; Hercowitz 1998).

  5. See Nelson and Winter (1982) and Dosi (1988) for an extended and more articulated view of the innovative process across firms.

  6. A related stream of literature instead focuses the attention on the so-called ‘New Technology Based Firms’ (NTBFs, see Storey and Tether 1998; Colombo and Grilli 2005), where only YICs in the high-tech sectors are analysed.

  7. In this case, in contrast with risk aversion, organizational change is positively correlated with the entrepreneurial ability.

  8. Given the aims and scope of this paper, attention has been limited to the manufacturing sectors.

  9. Firm selection was carried out through a ‘one step stratified sample design’. The sample in each stratum was selected with equal probability and without reimmission. The stratification of the sample was based on the following three variables: firm size, sector, regional location. Technically, in the generic stratum h, the random selection of n_{h} sample observations among the N_{h} belonging to the entire population was realized through the following procedure:

    - A random number in the 0–1 interval was attributed to each Nh population unit;

    - Nh population units were sorted by increasing values of the random number;

    - Units in the first nh positions in the order previously mentioned were selected.

    Estimates obtained from the selected sample are very close to the actual values in the national population. The weighting procedure follows Eurostat and Oslo Manual (OECD 1997) recommendations: weights indicate the inverse of the probability that the observation is sampled. Therefore, sampling weights ensure that each group of firms is properly represented and correct for sample selection. Moreover, sampling weights help to reduce heteroscedasticity commonly arising when the analysis focuses on survey data.

  10. In fact, mergers and acquisitions may break the link between innovative inputs and outputs (a link that must be studied within the context of the same economic entity over time).

  11. Given that our aim is to analyze the nature of the relationships within the innovative process (and not, for example, the effect of different inputs in determining the probability of innovating), this data limitation does not raise a problem of selection bias in our context. Since we are interested in the internal mechanisms of the innovative process, we have to focus on a randomly-selected sample of innovative firms (that is, randomness must hold within the innovative sub-sample, not in comparison with the non-innovative one where such mechanisms are obviously absent). For a study based on a comparison between innovative and non-innovative Italian firms, see Parisi et al. 2006. Moreover, as Mairesse and Mohnen (2010) pointed out, since CIS data provides little information about non-innovating firms, in the absence of additional information about these firms obtained by merging the innovation survey data with other firm data, not much room is left to distinguish between innovators and non-innovators and to correct appropriately for potential selectivity biases.

  12. As far as the age of the firms in the ‘young firms’ sub-sample is concerned, the threshold of 8 years was chosen to take into account the trade-off between a lower age and the representativeness of the sub-sample of YICs (here more than 10 % of the entire sample). However, the estimates discussed in Sect. 4 were replicated using a larger sample of young firms no more than 10 years old. The results, available from the authors upon request, do not change substantially.

  13. The estimates will include three groups: science-based, specialised supplier and scale intensive firms, where the default category will be the low-technology group of the supplier dominated firms.

  14. We are aware of the limits deriving by the use of this variable as a proxy of risk aversion. However, taking into account the information provided by the CIS questionnaire, this variable represents the most accurate proxy at our disposal of the degree of firm’s risk aversion.

  15. In the "Appendix", Table 7 reports the correlation matrix; as can be seen, all the correlation coefficients are less than 0.245, showing that data are not affected by serious collinearity problems. Finally, Table 8 reports the CIS questions on the basis of which the variables were constructed.

  16. As discussed at in Sect. 3, the CIS3 data adopted are collected from a representative sample of Italian manufacturing firms with more than 10 employees; this means that micro firms (which however are very rarely innovative) are excluded from the dataset, while SMEs are fully included.

  17. This also explains the negative and significant coefficient of MAC in the estimate referring to the incumbents (second column of Table 4).

  18. However, this may simply be due to possible inaccuracy in the adopted proxy, the only one available in our dataset.

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Acknowledgments

The authors would like to thank Andrea Conte, Giovanni Seri and the ADELE Laboratory at ISTAT in Rome for the provision of CIS 3 data. Comments by the discussant Simon Parker and the other participants at the ‘1st Joint DIW Berlin/IZA Workshop on Entrepreneurship Research’ (Bonn, February, 25–26, 2010) led to significant improvements to the paper.

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Authors and Affiliations

  1. SPRU (Science and Policy Research Unit), University of Sussex, Brighton, UK

    Gabriele Pellegrino

  2. Dipartimento di Scienze Economiche e Sociali, Università Cattolica del Sacro Cuore, Via Emilia Parmense 84, I-29122, Piacenza, Italy

    Mariacristina Piva

  3. Università Cattolica del Sacro Cuore, Milan, Italy

    Marco Vivarelli

  4. IZA, Bonn, Germany

    Marco Vivarelli

Authors
  1. Gabriele Pellegrino
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  2. Mariacristina Piva
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  3. Marco Vivarelli
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Correspondence to Mariacristina Piva.

Appendix

Appendix

See Tables 7 and 8.

Table 7 Correlation matrix
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Table 8 The questionnaire
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Pellegrino, G., Piva, M. & Vivarelli, M. How do new entrepreneurs innovate?. Econ Polit Ind 42, 323–341 (2015). https://doi.org/10.1007/s40812-015-0015-4

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