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Innovative Behavior of IT Services Firms in Portugal and Denmark

This study explores the innovative behavior of IT services firms in Portugal and Denmark, identifying different innovation profiles and alternative methods of measuring innovation. Through literature review, methodology, and results analysis, it highlights active and passive firms, discriminant analysis findings, and concludes with implications for assessing innovation in services. Future directions include broader applications across sectors.

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Innovative Behavior of IT Services Firms in Portugal and Denmark

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  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

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