pex, Capex and other variables) on ARPU and other KPIs. One of the most interesting models generated is presented in Fig. 2. In this model, ARPU 2006 was explained with a single independent variable, in terms of software spending of 2005. As can be seen, this single variable explains 51% of variance, with a positive coefficient of 0.71 (cf. Fig. 2). It should ne noted that the value of ARPU may be largely affected by the average level of wealth in the country, since a low average level of income in the country limits the possibilities for the growth in the customers' spending on communica- tions, and therefore limits ARPU. Indeed, according to the data, ARPU, besides being correlated to software spending, is also correlated with the Gross Domestic Product (GDP) at Purchasing Power Parity (PPP) per capita in the country (see the values of the Spearman's rho coefficients in Table 1). Effect of Software on CSP Performance 50.00 40.00 30.00 20.00 10.00 ARPU06 = 24.39 + 148.41*SwSp05 R2=0.51 SW spending percentage 2005 Fig. 2 ARPU 2006 explained in terms of software spending during 2005 (note: the estimation of regression coefficients is based on only 12 cases) Table 1 Correlation (Spearman's rho) between ARPU, software spending, and GDP PPP (the- triple in each cell includes the Spearman's rho coefficient, the significance, and the degree of freedom). Correlation coefficients significant at the 0.05 level are shown with * GDP PPP CSP's software CSP's software spending 2005 spending 2006 ARPU 2005 0,78* 0,70* 27,00 12,00 12,00 ARPU 2006 0,81* 0,69* 27,00 12,00 12,00 GDP PPP 20,00 24,00 The limited availability of KPI and software spending data for CSPs restricted the target set of CSPs in this analysis. However, reaching statistically significant correlations with a data set of this size encourages further empirical analysis on the impact of SW usage making use of industry- specific performance indicators. Insights from Operator Interviews Mirja Pulkkinen and Jari Veijalainen The previous section analysed use of softw