A Proposal for a Standard With Innovation Management System

MariaJesus Luengo and Maria Obeso
And as result, the final model is represented in Figure 1, where we can see the correlation value
between the constructs.In this sense, we identify a moderate correlation (around 0,48) between use
extern sources for innovation activities and the economic impact of innovations in turnover.
Figure 1: Equation structural relation between economic impact of innovations in turnover and Extern
sources for innovation activities
4.3 Phase 2: Structural model
In this section, we recognize the measures of global adjustment in order to identify how the model
predicts the initial data matrix. This adjustment will be perfect when there was a perfect
correspondence between the matrix reproduced by the model and the matrix of observations. For this,
we propose absolute measures of fit in order to evaluate the goodness of fit considering sample size
and number of indicators (Schumacker and Lomax, 2004). Meanwhile incremental adjustment
measures or descriptive are based on model comparison, and they compare proposed model with the
worst model. In this sense, this offers information about the degree of reality of the model. Finally,
parsimony adjustment measures is the degree to which the model fit achieve the adjustment for each
estimated coefficient. Table 6 contains the indicators and their recommend values.
Table 6: Global adjustment indexes
Index Measure Shorthand Acceptable fit
Absolute
Chi-square
≤ 5
adjustment Root Mean Square Error of Approximation RMSEA < 0,06
Standardized Root Mean Squared Residuals SRMR ≤ 0,08
Goodness of fit index GFI ≥ 0,95; ≤ 1
Incremental
Non-Normed Fit Index NNFI ≥ 0,97; ≤ 1
adjustment
Normed Fit Index NFI ≥ 0,95; ≤ 1
Relative Fit Index RFI ≥ 0,90
Comparative Fit Index CFI ≥ 0,97; ≤ 1
Incremental Fit Index IFI ≥ 0,90
Parsimony
Parsimony adjusted-NFI PNFI Next to 1
adjustment
Parsimony adjusted-CFI PCFI Next to 1
Minimum value of the discrepancy/Degrees of Freedom CMIN/DF ≥ 1; ≤ 5
Hoelter critical N in the output path for α = 0,01 Holter.01 ≥ 200
The model is recursive and it has five items for two endogenous constructs (dimensions). Degrees of
freedom are four and the number of estimate parameters is sixteen. In general, the model presents
good fits, with exception of two parsimony indicators (see Table 7).
4.4 Results
First model presents a loading factor more than 1 for INFCLIEN variable;therefore it has been
excluded as explanatory variable in INFEX construct. As consequently, we cannot verify or refute
hypothesis 1. Like customer´s information is very important for organizations (Chesbrough, 2003;
Laursen and Salter, 2006; Lichtenthaler, 2008), this variable and its impact in innovation results
should be analyze alone in future researches.
452