Does technological innovation undertaken alone have a real pivotal role? Product and marketing innovation in manufacturing firms

Abstract

This paper investigates the role of non-technological innovation on firms' innovation propensity and performance. We note that emphasis on technological innovation alone is misleading, as a firm's decision to undertake technological innovation brings about a more complex and general process which may involve new attitudes regarding organization and market orientation. We analyse the relationship between product and marketing innovation in manufacturing, focusing specifically on the food industry. We propose a bivariate probit model in which product and marketing innovation are estimated taking into account their reciprocal interactions. This enables us to provide more efficient and realistic estimates of a firm's probability of introducing either a new or improved product or a new marketing technique. In addition, the proposed model provides the determinants of such probabilities.

Keywords:

• product innovation
• market orientation
• marketing innovation
• european community innovation survey
• bivariate probit

JEL Classifications:

• 030
• 032
• 033

Acknowledgements

The authors would like to thank seminar participants at the EARIE 2013 Conference, Evora, Portugal, and two anonymous referees for their comments which have contributed to improve the paper. Needless to say, the usual disclaimer applies.

Disclosure statement

No potential conflict of interest was reported by the authors.

Funding

This paper is a result of collaboration with REM-Lab, Research Laboratory on Marketing and Retail at Università Cattolica del Sacro Cuore. Financial support is gratefully acknowledged.

Notes

1. The Community Innovation Survey will be briefly described in Section 3, as it represents the main source of our empirical study. We refer to the 2006–2008 period.

2. We refer to the Nace, Rev.2 classification of economic activities. The list of the manufacturing activities included is reported in Appendix 2.

3. The AIDA (Analisi Informatizzata delle Aziende) database is the Italian component of the European Amadeus database, distributed by Bureau van Dijk, and contains balance sheet information on about one million companies in Italy.

4. Summary statistics of the variables used in the empirical investigation are provided in Appendices A.1 and A.2.

5. The food industry refers to the food and beverage industries (NACE divisions 10 and 11) and includes: Processing and preserving of meat and production of meat products (10.1); Processing and preserving of fish, crustaceans and molluscs (10.2); Processing and preserving of fruit and vegetables (10.3); Manufacture of vegetable and animal oils and fats (10.4); Manufacture of dairy products (10.5); Manufacture of grain mill products, starches and starch products (10.6); Manufacture of bakery and farinaceous products (10.7); Manufacture of other food products (10.8 – this group includes the production of sugar and confectionery, prepared meals and dishes, coffee, tea and spices, as well as perishable and specialty food products); Manufacture of prepared animal feeds (10.9); Manufacture of non-alcoholic beverages and mineral water, manufacture of alcoholic beverages mainly through fermentation, beer and wine, and the manufacture of distilled alcoholic beverages (11.0).

6. See the AMA Dictionary at http://www.marketingpower.com/_layouts/Dictionary.aspx.

7. Recall that the observed correlation between variable SHARE and a variable reflecting firm size in term of employees is 0.64.

8. Indeed, the CIS does not permit gathering of information on R&D expenditure for non-innovative firms. For a discussion of this problem and the corresponding econometric issues, that is, censored variables and selectivity issues, see Mairesse and Mohnen (2010).

9. This variable includes all technological innovations – both product or process – as it is used to proxy the epidemic effect brought about by the number of contacts that a non innovating firm may have with innovating firms. The probability of such contacts increases of course with the sectoral proportion of innovators. We include all types of technological innovations as we want to use a broad indicator of sectoral technological opportunities, which in turn are summarized by this ratio.

10. We are aware of the fact that a negative sign on the coefficient of this variable could be plausible if the stock effect were operational (Karshenas and Stoneman 1993).

11. See ISTAT (2010) La Ricerca e Sviluppo in Italia, Anno 2008, Statistiche in breve.

12. Summary statistics of this variable are included in Appendix A.1.

13. Results are available on request.

14. We are aware of the fact that a firm's internal capability to pursue product innovation and knowledge acquisition (absorptive capacity, Cohen and Levinthal 1990), may be fostered by its internal labour quality. We have included the labour cost per employee in our regressions in order to proxy for labour quality. This variable was not significant and this may be explained on the grounds that on the one hand the inclusion of the INNOV variable reflects a similar mechanism (information acquisition) which is also related to the labour cost variable. On the other hand, the inclusion of the R&D dummy variable, which is closely related to labour quality and other variables derived from the firm's balance sheet, may interact with labour cost per employee, thus producing non-satisfactory results when this latter is included in the regression. For these reasons we decided to exclude this variable from the selected and preferred equation.