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
x market scope
x number of client countries
x competitive position
x innovation intensity
x relative innovation
x innovation effect on competitive advanatage
x innovation importance
x export to developed countries
x trigger factors group
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