A Proposal for a Standard With Innovation Management System

MariaJesus Luengo and Maria Obeso
On the other hand, we identify the construct Extern sources for innovation activities. It tries to explain
what were the sources of information used by companies to make decisions in innovation activities
and it includes four variables: (1) Information provided by suppliers of equipment, components or
software, (2) Information provided by customers, (3) Information provided by competitors or another
enterprises in the same industry and (4) Information provided by R+D consultants, commercial
laboratories or private institutes. We have not considered control variables because the number of
cases is near of the minimum, so their application would not contribute with significant information.
Table 1 includes a summary table with the relationships and the codes in the analysis.
3.3 Research method
We use Structural Equation Modeling (SEM) technique for this research. SEM technique allows
studying the causal relationships between directly observable data across the proposing the type and
direction of the expected relationships, and subsequent parameter estimation specifies by the
relationships in the theory (Barret, 2006). As consequently, SEM is called confirmatory analysis,
because its aim is confirm across the analysis of the sample the relationships (Jöreskog, 1979).
We use SPSS (Statistical Package for the Social Sciences) and more specifically the AMOS
application (version 19) in order to test our hypothesis. First, we check the validity of the model
analyzing the normality (uni and multivariate) and reliability scale. After these checks, the analysis per
se includes two phases: (1) evaluation of measure model and (2) evaluation of structural model
(Barclay et al., 1995)
Table 1: Summary about the relationships and the codes
Construct Explanatory variable
Code Description Code Description
CNIN Economic impact of innovations in CNNEMPR Percentage in the total turnover due to
turnover
goods and services that have been
introduced in the time 2008-2010 new in
the enterprise
CNNMERC Percentage in the total turnover due to
goods and services that have been
introduced in the time 2008-2010
INFEX Extern sources for innovation activities INFPROV Information provided by suppliers of
equipment, components or software
INFCLIEN Information provided by customers
INFCOOP Information provided by competitors or
another enterprises in the same industry
INFCTEC Information provided by R+D
consultants, commercial laboratories or
private institutes
4. Results and interpretation
4.1 Sensitivity analysis
The table of descriptive statistics informs about the univariate normality of the data (see Table 2).
Results show univariate normality of the data, with less than I3,00Iasymmetry and also less than
I8,00I kurtosis in observed variables (Bollen and Long,1993). In relation to the multivariate normality,
we calculate Mardia coefficient. Such as Mardia coefficient is less than p(p+2), where p is the number
of observed variables in the model, we identify multivariate normality (Bollen, 1989) because the
value is 23,527.
We use the Cronbach alpha in order to check validity and reliability of observed variables. The results
show 0,711 for variables in construct economic impact of innovations in turnover and 0,869 for
variables in construct extern sources for innovation activities. The minimum of the value for Cronbach
alpha is 0,7, so model data is reliable and valid (Nunnally, 1978). As consequently, results show the
suitability of the method of maximum likelihood estimation (ML) analysis.
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