e brings relatively little competitive advantage to the CSPs (since competing CSPs can be assumed to deploy similar software); and therefore allocating extensive resources to in-house OSS/BSS software is unlikely to result in a high return on investment, as compared with alternative investing opportunities. O. Mazhelis (X) Information Technology Research Institute, University of Jyväskylä, P.O. Box 35, 40014 Jyväskylä, Finland e-mail: Oleksiy.Mazhelis@jyu.fr P. Tyrväinen and O. Mazhelis (eds.), Vertical Software Industry Evolution, Contributions to Management Science, DOI 10.1007/978-3-7908-2352-3_7, C Springer-Verlag Berlin Heidelberg 2009 O. Mazhelis et al. Internal SW sp. Internal SW sp. External SW sp. External SW sp. Net income Net income Total SW sp. Total SW sp. R&D sp. EBIDTA R&D sp. EBIDTA Revenue Capex growth Capex Positive correlation, coefficient of determination (explained variance) 0.1<R2<0.5 Positive correlation, coefficient of determination (explained variance) 0.05<R2<0.1 Negative correlation, coefficient of determination (explained variance) R2>0.5 Negative correlation, coefficient of determination (explained variance) 0.1<R2<0.5 Negative correlation, coefficient of determination (explained variance) 0.05<R2<0.1 Fig. 1 Dependency between KPIs of a CSP (ARPU, revenue, net income, EBIDTA, and change in revenue) and software spending, Opex/Capex, and R&D spending One such alternative investing opportunity, which is apparently more cost- efficient, is acquiring software from independent software vendors: as suggested by the analysis, spending on external software acquisitions is positively correlated with the CSP's revenue. As expected, the data indicates the negative effect of Opex and the positive effect of Capex on the net income, EBIDTA, and the revenue growth. The R&D spending, according to the data, has a slight positive effect on the revenue. Stepwise linear regression models were constructed for analyzing the effect of software spending (total, internal and external software spending, R&D, O