A Proposal for a Standard With Innovation Management System

Table 7: Adjustment of the model
MariaJesus Luengo and Maria Obeso
Absolute adjustment Incremental adjustment Parsimony adjustment
Measure Value Acceptability Measure Value Acceptability Measure Value Acceptability
Chi-square 4,58 Good NNFI 0,97 Very good PNFI 0,25 Bad
RMSEA 0,05 Very good NFI 0,95 Good PCFI 0,27 Bad
SRMR 0,01 Very good RFI 0,80 Marginal CMIN/DF 1,14 Good
GFI 0,95 Very good CFI 0,99 Very good Holter.01 122,00 Marginal
IFI 0,99 Very good
On the other hand, INFPROV, INFCT and INFCOOP show high loadings factor, thus the explain
quality of the construct is high too and we can confirm hypothesis 2, hypothesis 3 and hypothesis 4. In
this sense, we highlight the information remitted by suppliers, consultants and competitors in order to
improve the innovation activity in organizations.
The case of INFCOOP variable is very interesting. It is linked with competitors and, as consequently,
it is linked with coopetition theory (cooperation + competition). Coopetition theory identifies
cooperation with competitors like an essential factor in order to improve their competitiveness,
especially in the case of small and medium enterprises because across the coopetition they can
overcome the barriers that they have in some aspects such as innovation and internalization.
Therefore, cooperation with competitors is a key question in a model based in sustainable and
competitive innovation (Larrea, 2010). In future researches, we could explore more about this
interesting relationship.
The principal result is our finding about the moderate correlation between use extern sources for
innovation activities and the economic impact of innovations in turnover, because it highlights the
importance of the relational capital and the relationship between the agents of the value chain. This
discovers suppose a contribution in innovation area and a start point for future researches. In relation
to model adjustment, the accuracy of the model gives very good results, a fact which is repeated with
incremental adjustment measures. This means that proposed model is approximated to reality and, as
consequently, hypothesis 5 is confirmed (excluding customers in external agents).
However, parsimony adjustment is far from recommended values, so we cannot affirm that the
behaviour of the model will be the same with another sample. In this sense, in a future research we
can reply the analysis for other samples, in order to compare the situations and obtain more
conclusions about the topic.
5. Conclusions
In our global world, where innovation is a fundamental source in order to obtain competitive
advantages, relevant information sources are essential in decision making process for managers in
businesses.
In this sense, we present a measurement model with highlight loadings factors in constructs based in
the case of Spanish firms where the variables explain the content of the constructs. The study shows
a moderate correlation between use extern sources (exclude customers) for innovation activities and
the economic impact of innovations in turnover that highlights the importance of information by
external agents in innovation. This means that enterprises focused on the market (not only with
customers) and this finding is linked with relational capital and the relationships in value chain, both of
them are interesting topics for empirical analysis in future researches.
The research reveals that in Spain the best external information sources in business are competitors
and other enterprises in the same industry. As consequently the use of coopetition (cooperation +
competition) is an interesting factor because it could be a source of competitive advantages.
In addition, we identify a relationship between information provided by economic agents (not
customers) and turnover from innovation activities (positive effect) if managers incorporates this
information to decision making process. However, the results of parsimony analysis show that this
analysis is effective with this Spanish data, but we cannot to know what happen if we repeat this
analysis with another data. So in future researches we want to replicate this analysis with another
453