Innovative Behavior of IT Services Firms in Portugal and Denmark

1. Innovative Behavior of IT Services Firms in Portugal and Denmark Luísa Ferreira Lopes DIMETIC Session, Maastricht 8-19 October 2007

2. Research Questions • What different innovation profiles (patterns of innovative behavior) can be identified in IT services firms ? • Can we find alternative ways of measuring innovation intensity in services that may be more reliable than existing measures ?

3. Motivation • Why Services ? • Why IT ? • Why innovation assessment ?

4. Litterature Review • Effects of IT services as a source of innovation, elsewhere in the economy • Innovation activity within IT services Torrisi (1998) Howells (2000) Mamede (2002) Weterings (2006)

5. Methodology • Process of collecting data: semi-structured face-to-face interviews • Questions format: mostly closed and some opened

6. Methodology • Questionaire: 11 sections • General information • Markets • Supply • Innovation process • Innovation output • Innovation input • Innovation impact/effects • Conditioning factors of innovation • Management characteristics • Human resources • Networking

7. Methodology • Data Collection • No reference was made to innovation before the interview • 31 interviews in Denmark • 31 interviews in Portugal • With CEO (except 4 firms) • Most frequent duration 1h30m

8. Results – Cluster Analysis • 258 vars  72 vars  12 vars  6 vars • Trigger factors • Export to developed countries • Market scope • Innovation importance • Innovation intensity • Competitive position

9. Results – Cluster Analysis • 2 clusters: • “Active firms”  N=45 (72,6%) • Internal innovation trigger factors • Export to developed countries • Larger market scope • Innovation more important • Innovate more intensively • Consider they have a better competitive position • “Passive firms”  N=17 (27,4%) • The symetrical

10. Results – Discriminant Analysis • Examine whether firms in the two clusters can be distinguished from each other based on a linear combination of variables • Similar process for selecting the variabels  10 variables

11. Results – Discriminant Analysis • All statistical tests indicate a high quality of the discriminant model • Classification results: • 91.9 % original firms correctly classified • 90.3 % cross-validated firms correctly classified

12. Results – Discriminant Analysis • Discriminant score = innovation propensity index 0.446 x market scope + 0.003 x number of client countries - 0.099 x competitive position + 0.084 x innovation intensity - 0.072 x relative innovation + 0.272 x innovation effect on competitive advanatage + 0.002 x innovation importance + 0.901 x export to developed countries + 0.362 x trigger factors group + 0.169 x innovation effect on increase differentiation

13. Conclusions • Active/Passive firms – behavior profiles • higher/lower propensity to innovate • related to market and innovation variabels • possible reinforcement mechanism • Suggest alternative way of assessing innovation in services • indirect - measures innovation as a latent variable • combines several indicators - more robust • Sistematic bias – more innovative firms are more conservative in their evaluation of their innovation activities

14. Future Developments • Apply the discriminant score to a set of known firms • Larger data sets • Other sectors in services and manufacturing

15. Thank you for your attention