Lex localis - Journal of Local Self-Government_11(1)_January

L EX LOCALIS - J OURNAL OF L OCAL S ELF -G OVERNMENT H. Wanivenhaus, J. Kovač, A. Žnidaršič & I. Vrečko: Vienna Construction Projects: Redirection of Project Management Critical Success Factors—More Focus on Stakeholders and Soft Skills Development 347 guidelines for VIF is that it should be below 10, while the tolerance should be above 0.2 (Field, 2013). As the highest VIF for the predictors in our model was 1.805 and all tolerances were above 0.55, the multicollinearity is not problematic (Table 2). Another assumption is that errors should be independent. As the Durbin-Watson statistic is close to 2 (2.147), we can conclude that errors (differences between the model and the observed data) are random and normally distributed (Table 3). Our four predictors were able to explain 46% of the variability of relevance of project management for the effective execution of construction projects. The results of the ANOVA (F = 37.60, p = 0.000) indicated that the obtained model fit the data well. Table 3 reports the regression coefficients for the model depicted below. All the regression coefficients showed a positive effect on the relevance of project management for the effective evolution of construct projects (EECP), and they were all statistically significantly greater than zero (p-values above 0.05). A larger beta coefficient indicates a greater contribution of that predictor to the model. Therefore, we can conclude that the highest contribution to the prediction of the relevance of project management for EECP was the planning of project controlling (Beta = 0.252, t = 3.387, p = 0.001). The obtained regression model (written with unstandardized coefficients): As presented above, in order to further investigate which factors from Figure 3 require more attention in order to achieve even greater success in the implementation of construction projects, respondents nominated up to five project management methods and measures. The largest percentage of respondents selected the motivation of project members (35.6%) and two factors from the project governance field: analysis of stakeholders (27.2%) and policy project support (26.7%) (Figure 4). In order to get deeper insights into patterns of nominations of project management methods and measures, we constructed and analyzed the network as follows. From five nominated project management methods and measures important for achieving even greater success in the implementation of construction projects, we constructed a two-mode network and then transformed it to a valued one-mode network. In this way, we gained clearer insights into connection patterns among the studied project management methods and measures. A detailed procedure with a basic definition of a (social) network analysis follows. Social network data consist of a set of units and at least one relation among them (Wasserman & Faust,