A Proposal for a Standard With Innovation Management System

MariaJesus Luengo and Maria Obeso
Table2: Descriptive statistics and validity and reliability checks
Asymmetry Kurtosis
Average Standard deviation Statist Standard error Statist Standard error
CNNEMPR 14,6038 5,96586 0,335 0,365 -0,463 0,717
CNNMERC 11,7431 8,21916 1,059 0,365 0,63 0,717
INFPROV 8,3986 4,30343 0,708 0,365 -0,287 0,717
INFCLIEN 8,1388 7,24574 1,215 0,365 0,383 0,717
INFCOOP 3,9429 3,5204 2,088 0,369 4,872 0,724
INFCTEC 3,275 2,68058 1,623 0,365 3,029 0,717
Cronbach alpha
Value All variables Value Econ. Impact variables Value Extern sources variables
0,82 6 0,711 2 0,869 4
4.2 Phase 1: Measurement model
In the model, we check if explanatory variables are appropriate for constructs. These models are
based in covariance structures, therefore they have indexes to evaluate the components of the factor
structure of the model and it requires factor loadings between 0,5 and 1 (Schumacker & Lomax,
2004). We check this condition (see Table 3), and all of the variables except INFCLIEN satisfy the
condition. As consequently, we delete the variable INFCLIEN, because it is not explain the construct.
Table 3: Standardized regression weights
CNNEMPR