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ISSN 2389-8186
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Vol. 8, No. 1
Enero-junio de 2021
doi: https://10.16967/23898186.683
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rpe.ceipa.edu.co
* PhD in Industrial Engineering. Universidad ESAN, Lima, Perú. E-mail: jdelcarpio@esan.edu.pe. ORCID: 0000-0001-6050-5754.
Google Scholar: https://scholar.google.com.pe/citations?user=5ZYcVIkAAAAJ&hl=es. Scopus Author ID: https://www.scopus.com/authid/
detail.uri?authorId=57204722072.
** PhD in Technology Management. La Salle-Universitat Ramon Llull, Barcelona, España. E-mail: francesc.miralles@salle.url.edu.
ORCID: 0000-0002-5251-5423. Google Scholar: https://scholar.google.com.pe/citations?hl=es&user=qhfDe3YAAAAJ.
*** Bachelor of Computer Statistics. Universidad ESAN, Lima, Perú. E-mail: esoria@esan.edu.pe. ORCID: 0000-0001-9377-7043.
Google Scholar: https://scholar.google.com/citations?hl=es&authuser=3&user=yeu0iRAAAAAJ.
Analyzing the Medium-Low and Low-
Technology Firms’ Innovative Behavior
in an Emerging Economy
JAVIER FERNANDO DEL CARPIO GALLEGOS*
FRANCESC MIRALLES**
EDUARDO JAVIER SORIA GÓMEZ***
ISSN 2389-8186
E-ISSN 2389-8194
Vol. 8, No. 1
Enero-junio de 2021
doi: https://doi.org/10.16967/23898186.683
COMO CITAR ESTE ARTÍCULO
How to cite this article:
Del Carpio, J.F., Miralles, F. and Soria,
E.J. (2021). Analyzing the Medium-
Low and Low-Technology Firms’
Innovative Behavior in an Emerging
Economy. Revista Perspectiva
Empresarial, 8(1), 36-54.
Recibido: 14 de noviembre de 2020
Aceptado: 02 de marzo de 2021
ABSTRACT
Objective. Design a model that shows what factors favor the development of
technological innovation in manufacturing companies of medium-low and low technological
intensity. Methodology. A sample of 1106 manufacturing companies that participated in the
innovation surveys in 2012 and 2015 was used, applying the partial structural equations
approach and estimating the invariance between the two groups. Results. The results of
this study from the structural model, which allow obtaining the positive and statistically
signicant coecients, which allow empirically validating the hypotheses. Conclusions. It
was evidenced that non-technological innovation, absorption capacity and technological
acquisition favor technological innovation in companies with low technological intensity.
This article conrms that manufacturing companies should guide eorts to improve their
capacity for innovation.
KEY WORDS
Innovation behavior, industry, technological change, Peru.
Análisis del comportamiento innovador de las empresas de tecnología media-
baja y baja en una economía emergente
RESUMEN
Objetivo. Diseñar un modelo que muestre qué factores favorecen el desarrollo de
la innovación tecnológica en las empresas manufactureras de media-baja y baja intensidad
tecnológica. Metodología. Se utilizó una muestra de 1106 empresas manufactureras
que participaron en las encuestas de innovación en 2012 y 2015, aplicando el enfoque
de ecuaciones estructurales parciales y estimando la invariancia entre los dos grupos.
Resultados. Con los resultados del modelo estructural del estudio se obtienen los coecientes
positivos y estadísticamente signicativos, lo que permite validar empíricamente las
hipótesis. Conclusiones. Se evidenció que la innovación no tecnológica, la capacidad de
absorción y la adquisición tecnológica favorecen la innovación tecnológica en las empresas
con baja intensidad tecnológica. Este artículo conrma que las empresas manufactureras
deben orientar sus esfuerzos a mejorar su capacidad de innovación.
PALABRAS CLAVE
comportamiento de la innovación, industria, cambio tecnológico, Perú.
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ARTÍCULOS ORIGINALES
JAVIER FERNANDO DEL CARPIO GALLEGOS, FRANCESC MIRALLES, EDUARDO JAVIER SORIA GÓMEZ
Revista Perspectiva Empresarial, Vol. 8, No. 1, enero-junio de 2021, 36-54
ISSN 2389-8186, E-ISSN 2389-8194
Análise do comportamento inovador de empresas de média-baixa e baixa
tecnologia em uma economia emergente
RESUMO
Objetivo. Desenhar um modelo que mostre quais são os fatores que
favorecem o desenvolvimento da inovação tecnológica em empresas manufatureiras de
média-baixa e baixa intensidade tecnológica. Metodologia. Foi utilizada uma amostra
de 1106 empresas de manufatura que participaram das pesquisas sobre inovação em
2012 e 2015, aplicando a abordagem de equações estruturais parciais e estimando a
invariância entre os dois grupos. Resultados. Com os resultados do modelo estrutural
do estudo, obtêm-se os coecientes positivos e estatisticamente signicativos, o que
permite validar empiricamente as hipóteses. Conclusões. Constatou-se que a inovação
não tecnológica, a capacidade de absorção e a aquisição tecnológica favorecem a
inovação tecnológica em empresas com baixa intensidade tecnológica. Este artigo
conrma que as empresas de manufatura devem focar seus esforços na melhoria de
sua capacidade de inovação.
PALAVRAS CHAVE
comportamento da inovação, indústria, mudança tecnológica,
Peru.
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ARTÍCULOS
JAVIER FERNANDO DEL CARPIO GALLEGOS, FRANCESC MIRALLES, EDUARDO JAVIER SORIA GÓMEZ
Revista Perspectiva Empresarial, Vol. 8, No. 1, enero-junio de 2021, 36-54
ISSN 2389-8186, E-ISSN 2389-8194
Introduction
Innovation has caught the attention of
academia, governments and business managers
alike. Academics would like to know what motivates
companies to innovate. Governments seek to foment
innovation because, according to Ahlstrom (2010),
employment. Business managers are interested
in innovation because it allows them to generate
competitive advantages (Urbancova, 2013) and
improve the performance of their companies
(Jansen, Van Den Bosch and Volberda, 2006).
In the literature on innovation, most of the
studies draw their data from developed countries
(Hervas-Oliver, Garrigos and Gil-Pechuan, 2011).
The relationships between absorptive capacity and
both technological innovation (Ali and Park, 2016)
and organizational innovation (Chen and Chang,
2012) have also been analyzed. How organizational
innovation mediates the relationship between
absorptive capacity and technological innovation
(Camisón and Villar-López, 2014) and how the
acquisition of machinery, hardware and software
improves innovation capability (Santamaría, Nieto
and Barge-Gil, 2009) have been studied as well.
analyzes the innovative behavior of manufacturing
As Latin American economies face the double
challenge of needing to keep growing while at the
same time reducing levels of poverty, understanding
develop innovation capacities is critical (Olavarrieta
and Villena, 2014). A second contribution is that
this study focuses its attention on the relationship
between non-technological and technological
innovation. Most studies have analyzed how
organizational innovation is related to technological
innovation (Camisón and Villar-López, 2014), but
these tend not to take into account marketing
innovation, which is one of the key elements of
non-technological innovation.
The third contribution is methodological: since
most of the aforementioned studies are cross-
sectional studies; for this research, a repeated
cross-sectional design was applied using the
database of two national surveys of innovation in
Peruvian industry corresponding to the years 2012
and 2015. Therefore, it was possible to measure
invariance, thus providing an opportunity to verify
that the averages and compound variances were
equal in the two groups. The groups were then
compared to identify the change in the innovative
behavior of medium-low- and low-technology
a response to the research question: How did
Peruvian manufacturing companies change their
innovative behavior between 2012 and 2015?
Based on this question, the approach aims to
capacity and technological innovation; (ii) absorptive
capacity and non-technological innovation; (iii)
non-technological innovation and technological
innovation; (iv) the acquisition of machinery,
hardware and software and technological innovation;
and (v) how non-technological innovation mediates
the relationship between absorptive capacity and
technological innovation.
It is worth noting that the context of this
research is the Peruvian economy, which has
shown sustained growth (Scott and Chaston, 2012)
making it one of the fastest-growing economies in
the region before the commodities crisis in 2014
(Brenes et al., 2016), which forced companies to
face a reality with the following characteristics:
(i) a government that promotes open innovation
(Ramírez and García-Peñalvo, 2018) and exports
very little in research and development and prefer
to innovate by buying machinery, hardware and
promote innovation (Pérez et al., 2018).
The unit of analysis is Peruvian manufacturing
companies that participated in the national
innovation surveys of the manufacturing industry
in the years 2012 and 2015 and that presented a
medium-low and low-technological intensity.
The structure of the present study is as
hypotheses are presented; second, the methodology
40
ARTÍCULOS ORIGINALES
JAVIER FERNANDO DEL CARPIO GALLEGOS, FRANCESC MIRALLES, EDUARDO JAVIER SORIA GÓMEZ
Revista Perspectiva Empresarial, Vol. 8, No. 1, enero-junio de 2021, 36-54
ISSN 2389-8186, E-ISSN 2389-8194
discussion, conclusions, limitations and suggestions
for future research are presented.
Theoretical Background
This theoretical background begins by
acquisition by medium-low and low-technology
As shown by the fourth European Community
Innovation Survey (CIS4), which analyzes medium-
types tend to be characterized by innovations
in processes, organization or marketing and
have a high dependence on an external supply of
technologies in the form of machinery, hardware
the role of formal and informal knowledge is
important, as it has been discovered that they
innovate beyond activities directly related to
research and development (Sciascia et al., 2014;
Santamaría, Nieto and Barge-Gil, 2009).
recognize the value of new, external information in
order to assimilate it and apply it for commercial
purposes (Cohen and Levinthal, 1990). AC has
had a major impact on organizational research
and has attracted the attention of a large number
of researchers, as it is the capability that most
and Lyles, 2010).
the acquisition of machinery, equipment and
to improve their innovation capability. Moreover,
and pointed out that the purchase of machinery and
equipment had a positive effect on the innovation
Absorptive capacity and technological
innovation
the subject of several studies. Cohen and Levinthal
innovation process, since it increases in speed and
frequency as more innovations occur.
(Kim and Kogut, 1996), and Caloghirou, Kastelli
and Tsakanikas (2004) investigated the extent
their interaction with external information sources
affect their level of innovation. In addition, Wang
and Han (2011) conducted a study of small and
medium-sized enterprises in China that validated
the hypothesis that knowledge properties and AC
are two inseparable determinants of innovation
performance; they also indicated that AC moderates
the relationship between knowledge properties
and performance.
In this sense, the following hypothesis is
proposed:
Hypothesis 1: Absorptive capacity is related to
technological innovation in medium-low and low-
Absorptive capacity and non-
technological innovation
should consider knowledge to be one of their most
valuable resources (Liao and Wu, 2010). The
consolidation of acquired knowledge is determined
by AC development (Sun and Anderson, 2010).
to carry out product, process, organizational and
marketing innovations (Schmidt and Rammer,
2006). Along the same line, Calero-Medina and
Noyons (2008) found that the relationship between
AC and organizational innovation has not been given
much attention. In addition, Chen and Chang (2012)
degree of organizational innovation.
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ARTÍCULOS
JAVIER FERNANDO DEL CARPIO GALLEGOS, FRANCESC MIRALLES, EDUARDO JAVIER SORIA GÓMEZ
Revista Perspectiva Empresarial, Vol. 8, No. 1, enero-junio de 2021, 36-54
ISSN 2389-8186, E-ISSN 2389-8194
On the basis of the above, the following
hypothesis is proposed:
Hypothesis 2: Absorptive capacity is related to
non-technological innovation in medium-low and
Technological and non-technological
innovation
The relationship between technological and
non-technological innovation has caught the
attention of academics. Schmidt and Rammer
focused on product and process innovations, i.e.,
in other activities that lead them to develop
organizational and marketing innovations. When
al. (2011) found that higher levels of organizational
innovations favor the development of product
and process innovations and that higher levels of
marketing innovation favor the development of
product innovations.
Likewise, Mothe and Uyen (2012) point out
that marketing and organizational innovations
technological innovations. Along these lines,
Camisón and Villar-López (2014) have shown that
organizational innovation favors the development
the knowledge of the interactions between non-
technological and technological innovation
performance.
On the basis of the above, the following
hypothesis is proposed:
Hypothesis 3: Technological innovation is
related to non-technological innovation in medium-
Technological acquisition and
technological innovation
Ahuja and Katila (2001) have argued that it
is important to clarify that, in order to increase
innovation, it is not enough only to acquire
technology but also to evaluate whether its impact
will be favorable or not for the development of
will depend on the type of knowledge that will be
Calantone, Cavusgil and Zhao (2002)
determined that “innovative capacity is one of
performance” (p. 516). The acquisition of
machinery, hardware and software enhances a
(2009) stated that the purchase of machinery and
equipment favors the implementation of new or
improved products or processes.
In addition, Santamaría, Nieto and Barge-Gil
(2009) pointed out that not only are research
and development (R&D) activities sources of
as the knowledge and experience acquired through
the use of advanced machinery and tools constitute
a source of innovation in medium-low and low-
In addition, Zuniga and Crespi (2013) indicated
that innovation strategies consist of investment in
R&D, the acquisition of technology already on the
market through R&D contracting, technology and
licensing knowledge, contracting technical and
engineering services and acquiring machinery
and equipment.
The following hypothesis is therefore proposed:
Hypothesis 4: The acquisition of machinery,
hardware and software is related to technological
innovation in medium-low and low-technology
The mediation of non-technological
innovation in the relationship between
absorptive capacity and technological
innovation
The extant literature indicates that non-
technological innovations and technological
innovations have been studied both independently
and in the way they relate to each other. Schmidt
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ARTÍCULOS ORIGINALES
JAVIER FERNANDO DEL CARPIO GALLEGOS, FRANCESC MIRALLES, EDUARDO JAVIER SORIA GÓMEZ
Revista Perspectiva Empresarial, Vol. 8, No. 1, enero-junio de 2021, 36-54
ISSN 2389-8186, E-ISSN 2389-8194
technological innovations (organizational and
marketing innovations) and compared them
with the effects of technological innovations.
Their results show that technological and non-
technological innovations are closely related;
thus, it can be said that marketing innovations
can coincide with product innovations or that
organizational innovations often introduce new
technological innovations into processes.
In addition, Battisti and Stoneman (2010)
noted that innovations can be placed into two
broad, complementary categories: organizational
and technological, which cannot act as substitutes
one for the other. Also, Camisón and Villar-López
(2014) conducted research on innovation and
development of technological innovations and that
Moreover, it should also be noted that Min,
Ling and Piew (2015) analyzed how organizational
innovation mediated the relationship between AC
and technological innovation. Recently, Del Carpio
and Miralles (2018) found that non-technological
innovation mediated the relationship between AC
and technological innovation.
In view of the above, the following hypothesis
is proposed:
Hypothesis 5: Non-technological innovation
mediates in the relationship between absorptive
capacity and technological innovation in medium-
Methodology
The present study is based on data obtained from
two different waves of a national innovation survey
of the Peruvian manufacturing industry carried out
in 2012 and again in 2015. The Instituto Nacional
de Estadísticas e Informática —INEI— collected the
using a questionnaire developed according to the
Bogotá Manual, which is based on the Oslo Manual.
survey in 2012, collecting information for the period
2009-2011 from a representative sample of 1220
the present research. Meanwhile, from the 2015
innovation survey, the information gathered
belongs to the period 2012-2014 and consisted
of a representative sample of 1452 large, medium
database, 1106 medium-low and low-technology
the relationship between the four constructs:
technological innovation, non-technological
innovation, absorptive capacity and technological
acquisition.
Non-technological
Innovation
Technological Acquisition
Absorptive Capacity
Technological Innovation
H3
H2
H1
H4
Figure 1. Proposed model. Source: author’s own elaboration.
In this research, the dependent variable is
technological innovation, which is composed of
two dimensions: product innovation and process
innovation (Gronum, Verreynne and Kastelle,
2012). Product innovation is the result of the
sum of the dichotomous answers to the question
to the market to: a new product, a new service, a
improved service. Process innovation is the result of
the sum of the dichotomous answers to the question
of whether or not the following were introduced:
Verreynne and Kastelle (2012) approach, non-
technological innovation has two dimensions:
organizational innovation and marketing
innovation. Organizational innovation is measured
as the sum of the dichotomous answers to three
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ARTÍCULOS
JAVIER FERNANDO DEL CARPIO GALLEGOS, FRANCESC MIRALLES, EDUARDO JAVIER SORIA GÓMEZ
Revista Perspectiva Empresarial, Vol. 8, No. 1, enero-junio de 2021, 36-54
ISSN 2389-8186, E-ISSN 2389-8194
questions related to the activities carried out by
organizing work and new methods of organizing
institutions. Marketing innovation is measured
as the sum of the dichotomous answers to four
questions that are related to the following items:
of the good or service, new means or techniques
of product promotion, new methods for product
positioning in the market or sales channels and new
methods of pricing goods or services.
Secondly, AC is calculated on the basis of three
variables: expenditure on research, training for
variables were transformed by applying logarithm
base 10, and the last one is a dichotomous variable.
Thirdly, technological acquisition is made up of
the following variables: machinery investment,
hardware investment and software investment,
all transformed by applying logarithm base 10.
as a logarithm) is measured by the number of
employees (Schoenmakers and Duysters, 2006;
measured by the number of years (expressed as a
logarithm) from its foundation to the year in which
SMARTPLS 3 software, which applies the partial
structural equation estimation model in two steps,
according to Chin, Marcolin and Newsted (2003),
when determining the relationship between
the indicators and the latent construct. Second,
the structural model, in which the relationships
between the constructs are obtained through
estimated. Hair et al. (2019) stated that SMARTPLS
should be applied when the data is secondary and
when the data demonstrate a lack of normality;
the data for this study met both of these criteria.
Results
The results that were obtained using descriptive
statistics, the measurement model, the structural
model, mediation analysis, control variables,
invariance measurement and multi-group analysis
are shown below.
2012 and 2015, respectively, according to their size
(the number of employees), their age as measured
onwards, young) and their technological intensity.
Table 1. Description of manufacturing enterprises 2012 and
2015
2012 2015
Firm size
Small (≤50 employees) 478 390
Medium (>50 and ≤250 employees) 190 452
Large (>250 employees) 188 264
Total 856 1106
Firm Age
Old (over 36 years old) 209 173
Moderate (19 and 36 years old) 203 324
Young (under 19 years old) 444 609
Total 856 1106
Technological intensity
Low 505 706
Medium-low 351 400
Total 856 1106
Source: author’s own elaboration.
Table 2 shows the outer loadings of the
constructs for the years 2012 and 2015, respectively.
As can be seen, all loads are greater than 0.5, so the
constructs must remain in the model.
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ARTÍCULOS ORIGINALES
JAVIER FERNANDO DEL CARPIO GALLEGOS, FRANCESC MIRALLES, EDUARDO JAVIER SORIA GÓMEZ
Revista Perspectiva Empresarial, Vol. 8, No. 1, enero-junio de 2021, 36-54
ISSN 2389-8186, E-ISSN 2389-8194
Table 2. Outer loading on the constructs (models 2012 and 2015)
Variables
Technological
acquisition
Absorptive capacity
Technological
innovation
Non-technological
innovation
2012
ACAP1 0.839
ACAP2 0.807
ACAP3 0.693
INNO_COM 0.899
INNO_ORG 0.915
INNO_PROC 0.884
INNO_PROD 0.878
TECH1 0.790
TECH2 0.812
TECH3 0.761
2015
ACAP1 0.876
ACAP2 0.779
ACAP3 0.622
INNO_COM 0.870
INNO_ORG 0.873
INNO_PROC 0.847
INNO_PROD 0.876
TECH1 0.825
TECH2 0.772
TECH3 0.708
Source: author’s own elaboration.
Table 3 shows the reliability and validity
indicators for both years (2012 and 2015). It can be
seen that for the Cronbach alpha (CA), the constructs
have a value above 0.5. With respect to composite
reliability (CR), all constructs have values greater
above 0.5. In addition, it can be seen that, with regard
2
) for the relationship
between absorptive capacity and non-technological
innovation is 0.556 (for 2012) and 0.409 (for 2015),
and for the relationship between the following
independent variables: AC, non-technological
innovation and technological acquisition; and the
dependent variable: technological innovation, the
are considered moderate and weak, respectively.
Based on the results of the indicators, it is possible
to carry out the structural model.
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ARTÍCULOS
JAVIER FERNANDO DEL CARPIO GALLEGOS, FRANCESC MIRALLES, EDUARDO JAVIER SORIA GÓMEZ
Revista Perspectiva Empresarial, Vol. 8, No. 1, enero-junio de 2021, 36-54
ISSN 2389-8186, E-ISSN 2389-8194
Table 3. Reliability and validity indicators for 2012 and 2015
2012 2015
Latent variable CA CR AVE CA CR AVE
Technological innovation 0.713 0.874 0.777 0.654 0.852 0.742
Non-technological innovation 0.785 0.903 0.823 0.683 0.863 0.759
Absorptive capacity 0.688 0.825 0.612 0.651 0.807 0.588
Technological acquisition 0.7 0.831 0.621 0.671 0.813 0.593
Reference values >0.7 >0.7 >0.5 >0.7 >0.7 >0.5
Source: author’s own elaboration.
Larcker (1981).
Table 4. Discriminant validity of 2012 and 2015
Variables Absorptive capacity
Non-technological
innovation
Technological
innovation
Technological
acquisition
2012
Absorptive capacity 0.783
Non-technological innovation 0.501 0.907
Technological innovation 0.584 0.638 0.881
Technological acquisition 0.564 0.542 0.617 0.789
2015
Absorptive capacity 0.767
Non-technological innovation 0.432 0.871
Technological innovation 0.497 0.558 0.861
Technological acquisition 0.48 0.361 0.403 0.771
Note: Fornell-Larcker criterion: Diagonal elements (in bold) are the square root of the variance shared between constructs and
their measures (AVE). For discriminant validity, the square root AVE (in bold) is greater than the correlations between the other
latent variables.
Source: author’s own elaboration.
After evaluating the measurement models, the
structural model was estimated.
hypotheses, according to Hair Jr et al. (2014), the
bootstrapping technique was used, with 4000
samples.
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ARTÍCULOS ORIGINALES
JAVIER FERNANDO DEL CARPIO GALLEGOS, FRANCESC MIRALLES, EDUARDO JAVIER SORIA GÓMEZ
Revista Perspectiva Empresarial, Vol. 8, No. 1, enero-junio de 2021, 36-54
ISSN 2389-8186, E-ISSN 2389-8194
Table 5. Results of the structural model 2012 and 2015
2012 2015
Paths β t-value Β t-value
ACAP->IT 0.257** 7.502 0.277** 8.652
ACAP->INT 0.501** 18.09 0.433** 14.833
INT->IT 0.356** 10.919 0.393** 12.453
TECH->IT 0.312** 8.837 0.148** 5.155
Note: n=856; Bootstrapping 4000 samples; β= Standardized Coecients; **p<0.05.
Source: author’s own elaboration.
Although there is no minimum threshold, a value
greater than 0.31 is recommended (Camisón and
index shows a value of 0.53 and, for 2015 model,
0.45. In both cases the indices are higher than the
models for the years 2012 and 2015, shown in
following hypotheses: for hypothesis 1 (“There is
between AC and technological innovation”), it can be
stated that the results coincide with those obtained
in the studies carried out by Rangus and Slavec
time show higher levels of product and process
innovation capability.
With regard to hypothesis 2 (“There is a positive
AC and non-technological innovation”), it can be
by Chen and Chang (2012).
Regarding hypothesis 3 (“There is a positive
non-technological innovation and technological
innovation”), it should be pointed out that, unlike
the study by Camisón and Villar-López (2014),
in which it was concluded that organizational
capability, this study considers non-technological
innovation, which includes not only organizational
but also marketing innovation.
With respect to hypothesis 4 (“There is a
between the acquisition of machinery, hardware
and software and technological innovation”), it can
be concluded that it corroborates what was pointed
machinery, hardware and software.
When analyzing non-technological innovation,
not it is a mediating variable and, if so, whether total
or partial mediation is present. According to Hair Jr
et al. (2014), mediation refers to a situation in which
a mediating variable in some form absorbs the
effect of an exogenous construct (with independent
variables) in an endogenous construct (with a
dependent variable) in the PLS path model.
Table 6 shows the explained variance
the mediation process explains the variance of the
less than 20 %, one must conclude that there is no
20 % and less than 80 % could be characterized as a
typical partial mediation (Hair Jr et al., 2016), while
As noted, Table 6 shows that non-technological
innovation mediates the relationship between AC
and technological innovation. The analysis of the
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JAVIER FERNANDO DEL CARPIO GALLEGOS, FRANCESC MIRALLES, EDUARDO JAVIER SORIA GÓMEZ
Revista Perspectiva Empresarial, Vol. 8, No. 1, enero-junio de 2021, 36-54
ISSN 2389-8186, E-ISSN 2389-8194
and, for 2015, the indicator is 38 %. Therefore,
in both cases, non-technological innovation
partially mediates the relationship between AC
and technological innovation.
Ling and Tan (2016), who found that organizational
innovation partially mediates the relationship
between AC and technological innovation. The
present model shows that not only to organizational
innovation but also to marketing innovation as
a component of non-technological innovation
partially mediates this relationship.
Table 6. Mediation outcome for 2012 and 2015
2012 2015
Relation
Indirect
eect
Direct
eect
Total eect VAF (%)
Indirect
eect
Direct
eect
Total eect VAF (%)
ACAP>INT>IT
0.178 0.257 0.435
40.97 %
0.170 0.277 0.447
38 %
(<0.001) (<0.001) (<0.001) (<0.001) (<0.001) (<0.001)
Source: author’s own elaboration.
2012 and 2015, respectively.
Table 7. Mediation outcome for 2012 and 2015
2012 2015
Control Variables Coecient Standard dev. P-value Coecient Standard dev. P-value
Firm size -0.07 0.029 0.014 -0.065 0.024 0.007
Firm age 0.022 0.026 0.389 -0.001 0.023 0.98
Source: author’s own elaboration.
(Zuniga and Crespi, 2013). However, Benavente
(2006) argues that, in some cases, factors other
than size, such as demand pressure, encourage
literature shows mixed results. Nieto, Santamaría
should be more prone to innovate because of the
experience they have acquired, but Cucculelli (2018)
states that a negative relationship is questionable,
and assume greater risks.
The invariance of the composite models should
be measured before comparing the groups used in
the 2012 and 2015 models. As SMARTPLS software
was used, Henseler, Ringle and Sarstedt (2015)
recommend using the MICOM (“measurement
invariance of composite models”) procedure.
The MICOM procedure requires three steps to
be carried out. The three steps are as follows: (i)
and equality of mean values and (iii) composite
variances. Step (i) does not require statistical
treated identically for both groups. Step (ii) involves
48
ARTÍCULOS ORIGINALES
JAVIER FERNANDO DEL CARPIO GALLEGOS, FRANCESC MIRALLES, EDUARDO JAVIER SORIA GÓMEZ
Revista Perspectiva Empresarial, Vol. 8, No. 1, enero-junio de 2021, 36-54
ISSN 2389-8186, E-ISSN 2389-8194
performing the permutation test. If the permutation
test reveals that the correlation c (the average of
the correlation obtained by permutation) is not
invariance is established. In this study, 5000
permutations were carried out. Step (iii) assesses
the equality of the mean values and composite
variances. If the statistical test determines that
the mean values and composite variances are not
values and composite variances is established.
As shown in Table 8, in both cases, the null
hypothesis is rejected, so that the averages and
Table 8. MICOM model results
Constructo (Step 2) c-value (=1) 95 % condence interval Compositional invariance?
ACAP 0.999 [0.997; 1.000] Yes
TECH 0.999 [0.995; 1.000] Yes
INT 1.000 [0.999; 1.000] Yes
IT 1.000 [0.999; 1.000] Yes
Constructo (Step 3a)
Difference of the mean value of the
construct (=0)
95 % condence interval Equal average value?
ACAP 0 [-0.092; 0.091] Yes
TECH -0.001 [-0.089; 0.090] Yes
INT -0.001 [-0.090; 0.089] Yes
IT -0.001 [-0.092; 0.089] Yes
Constructo (Step 3b)
Logarithm of the variance ratio of the
construct (=0)
95 % condence interval Equal variance?
ACAP -0.001 [-0.176; 0.170] Yes
TECH -0.002 [-0.174; 0.171] Yes
INT -0.002 [-0.128; 0.124] Yes
IT -0.001 [-0.131; 0.140] Yes
Source: author’s own elaboration.
In conclusion, the results obtained, after
applying the procedure for measuring invariance,
conclude that the invariance is complete and,
therefore, it is possible to proceed with the analysis
of the two groups.
Multi-group analysis was conducted to
behavior in 2012 and 2015. A total of 2000
permutations were used for greater robustness of
the results. As shown in Table 9 and according to
Chin and Dibbern (2010), two t-tests are carried out.
and the t-parametric (EV) indicator is obtained.
The second t-test assumes that the variances are
different and the t-parametric indicator (NEV) is
obtained. After applying the tests, it can be seen
that the results are similar.
49
ARTÍCULOS
JAVIER FERNANDO DEL CARPIO GALLEGOS, FRANCESC MIRALLES, EDUARDO JAVIER SORIA GÓMEZ
Revista Perspectiva Empresarial, Vol. 8, No. 1, enero-junio de 2021, 36-54
ISSN 2389-8186, E-ISSN 2389-8194
Table 9. Multi-group comparison test results
Relation
Path
(2012)
Path
(2015)
Di
(2012-2015)
t-parametric
(EV)
t-parametric
(NEV)
Permutation
P-val
Signicance
ACAP->IT 0.257 0.277 0.020 0.422 0.424 0.040 No
ACAP->INT 0.501 0.433 0.068 1.713* 1.754* 0.664 Yes
INT->IT 0.356 0.393 0.037 0.793 0.803 0.789 No
TECH->IT 0.312 0.148 0.164 3.719*** 3.694*** 0.001 Yes
Note: *signicant at 0.1 (t distribution of 2 tails); **signicant at 0.05 (t distribution of 2 tails); ***signicant at 0.01 (t distribution
of 2 tails).
Source: author’s own elaboration.
As shown in Table 9, when comparing the
years 2012 and 2015, the relationships between
AC and technological innovation and between
non-technological innovation and technological
innovation remained constant. This situation was
not evident for the relationship between AC and
non-technological innovation or for the relationship
between the acquisition of machinery, hardware
and software and technological innovation. In the
2012 survey made greater efforts to develop higher
levels of AC and invested more resources acquiring
machinery, hardware and software, and, in this way,
improved their innovation capability.
Conclusions
This research work focuses on understanding
the changes between the differences in innovative
behavior of Peruvian manufacturing medium-
2012 innovation survey developed higher levels
of absorptive capacity and increased expenditure
of resources for the acquisition of machinery,
the sample of 2015. Although this initial perspective
could seem contradictory to the main assumptions
of the model, an overall study of the results exhibits
new perspectives on the evolution of innovative
The main point in this discussion starts from
the evidence that the mediation effect of non-
technological innovation in the relationship
between absorptive capacity and technological
innovation appears in the two samples. In both
samples, the effect is very similar (Table 6)
and shows that it is necessary to develop non-
technological innovation to favor technological
innovation. This work results show that this
effect has not changed in two different periods.
on digital transformation this mediation effect
has not reduced its importance. This behavior
could suggest that the effect of non-technological
innovation in technological innovation is something
permanent and that opportunities in technological
innovation either could come from or can be favored
by non-technological innovation efforts.
Deepening the analysis of the models for each
between the sample of 2012 and the sample of
capacity on technological innovation is lower
in the sample of 2015 (0.433) than in sample of
2012 (0.501). This result means that technological
capacity for medium-low and low-technology
this result can suggest that low-technological
capacity and proposes to explore for new factors
50
ARTÍCULOS ORIGINALES
JAVIER FERNANDO DEL CARPIO GALLEGOS, FRANCESC MIRALLES, EDUARDO JAVIER SORIA GÓMEZ
Revista Perspectiva Empresarial, Vol. 8, No. 1, enero-junio de 2021, 36-54
ISSN 2389-8186, E-ISSN 2389-8194
On the other hand, the path from technological
acquisition to technological innovation is lower in
the sample of 2015 (0.148) than in the sample of
2012 (0.312). Taking into account the increasing
reduction on the effect of technological acquisition
into technological innovation suggests a delay
on the effect of new equipment and hardware
on technological innovation. Also, taking into
consideration the persistence of the mediating
effect of non-technological innovation between
absorptive capacity and technological innovation,
this result suggests that acquisition of new
equipment and hardware has to be accompanied
of organizational and marketing changes that could
mediate in the effects on technological innovation;
which has been related to the commoditization of
information technologies (Carr, 2003).
This research work intended to contribute to
a better understanding of innovation efforts in
manufacturing medium-low and low-technology
In this vein, this research work aims to contribute
to this understanding by shedding some new
light to the relationship of absorptive capacity,
non-technological innovation and technological
acquisition on technological innovation. The
the persistence of the mediating effect of non-
technological innovation between absorptive
capacity and technological innovation, to be
aware of new factors that could complement
absorptive capacity, and the commoditization of
proposes new challenges regarding those factors
or variables that can help to understand how
technological or non-technological innovation can
be developed in medium-low and low-technology
organization learning has been used to understand
how ERP implementation affects organizational
performance in a context of digital transformation
and where the impact of technology is found to have
many different facets when it is adopted by small
The development of this study makes it
possible to identify some practical implications.
Thus, the managers of medium-low and low-
in absorptive capacity and other factors with
the intention of developing more technological
innovations, i.e. product or process innovation.
allocate resources for the acquisition of machinery,
hardware and software, and include those
organizational changes that can accompany the
implementation of new equipment to participate
in developing technological innovations. Overall,
decision-makers in low-technological intensity
technology as an organizational change challenge
and take into consideration all impacts that can
affect the overall organization.
The present study is not without limitations.
single source, namely the databases of the national
innovation surveys of the manufacturing industry
in Peru. It is suggested that future research be
carried out in other Latin American economies in
order to make comparisons and generalizations of
the relationships that can be established between
the constructs.
Second is the use of samples that include all
industrial sectors with lower technological intensity.
It would be very valuable to develop research in
garment industry or basic chemical products, to
technological innovation in each industry.
Third is how absorptive capacity was measured.
and Tribó (2009) were used and adapted to the
database of the INEI of Peru. Rather, it has been
suggested that questionnaires be developed to
better measure the absorptive capacity construct
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