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A Influência da Capacidade Absortiva no
Desempenho Inovativo dos Grupos de Pesquisa no Piauí
The Influence of Absorptive Capacity on t=
he
Innovative Performance of Research Groups in Piauí
<=
/o:p>
RESUMO
Este e=
studo
analisou a influência das dimensões da capacidade absortiva (potencial e
realizada) sobre o desempenho inovativo dos grupos de pesquisa das institui=
ções
de ensino superior públicas no Piauí. A pesquisa foi realizada entre julho e
setembro de 2023, abrangendo o universo de 670 grupos de pesquisa
nas seguintes instituições: Universidade Federal do Piauí (UFPI), Uni=
versidade
Estadual do Piauí (UESPI), Instituto Federal do Piauí (IFPI) e Universidade=
Federal
do Delta do Parnaíba (UFDPar ) .
A amostra foi composta por 90 líderes de grupos de pesquisa 13,4% do universo total pesquisado
Palavras-chave: Capacidade
Absortiva; Inovação; Instituição de Ensino Superior; Gestão Pública.
<=
/o:p>
ABSTRACT
This study analyzed the influence of absorptive
capacity dimensions (potential and realized) on the innovative performance =
of
research groups at public higher education institutions in Piauí, Brazil. T=
he
research was conducted between July and September 2023, covering 670 resear=
ch
groups from the Federal University of Piauí (UFPI), the State University of
Piauí (UESPI), the Federal Institute of Piauí (IFPI), and the Federal
University of Delta do Parnaíba (UFDPar ).
The sample consisted of 90 research group leaders, representing approximate=
ly
13.4% of the total population. The findings indicate that both dimensions of
absorptive capacity (potential and realized) have a positive effect on the
innovative performance of these groups, in terms of both behavioral and
technological innovation. Consequently, it is recommended that research gro=
up
leaders implement continuous routines and processes to renew their group’s
knowledge base in order to enhance innovation
outcomes. This study contributes to a deeper understanding of how absorptive
capacity influences the innovative performance of research groups.
Keywords : Absorptive Capacity; Innovation; Higher Education
Institution; Research Groups; Public Management.
Recebido
em 16/02/2024. Aprovado em 19/09/2024. Avaliado pelo sistema double blind peer review. Publicado conforme normas da ABNT.
https://doi.org/10.22279/navus.v14.18 72 =
1 INTRODUÇÃO
A teoria da =
visão
baseada no conhecimento da empresa aponta que o conhecimento é um recurso
estratégico para a competitividade e inovação, onde a base do desempenho
organizacional reside na capacidade de gerar, combinar, recombinar e explor=
ar o
conhecimento (Grant, 1996). Aliado a isso, a teoria das capacidades dinâmic=
as
surge com o propósito de explicar a habilidade da empresa em integrar,
desenvolver e reconfigurar suas competências, tanto internas quanto externa=
s, para
responder às mudanças do mercado. Em outras palavras, as capacidades dinâmi=
cas
são aquelas que levam a empresa a criar e modificar sua base de recursos
propositalmente para atender o ambiente de rápidas mudanças (Teece ; Pisano ; Shuen , 1997).
Nesse contex=
to, a
capacidade absortiva emerge como sendo a habilidade de uma empresa de
reconhecer o valor de uma nova informação externa, assimilá-la e aplicá-la =
para
fins comerciais (Cohen; Levinthal , 1990).
Posteriormente, os estudiosos de Zahra e George
(2002) dividiram a capacidade absortiva existente em capacidade absortiva
potencial e capacidade absortiva realizada. A capacidade absortiva potencial
refere-se à aquisição e assimilação do conhecimento, ou seja, compreende uma
combinação de rotinas e processos para identificar e adquirir novas formas =
de
conhecimento. Por sua vez, a capacidade absortiva realizada diz respeito à
transformação e exploração do conhecimento, ou seja, envolve a combinação d=
os
novos conhecimentos assimilados com os conhecimentos existentes, além da
aplicação desse novo conhecimento gerado às atividades da empresa (Zahra ; George, 2002).
Dado a relev=
ância do
tema, um número significativo de estudos buscou investigar o papel da
capacidade absortiva na melhoria do desempenho (Florêncio; Oliveira Júnior,
2022; Mikhailov; Reichert , 2019; Oliveira et al ., 2018; Xie; Zou ;
Qi, 2018). Esses estudos identificaram efeitos positivos da capacidade
absortiva sobre a inovação e o desempenho organizacional. No entanto, a
influência da capacidade absortiva sobre o desempenho de organizações públi=
cas tem
sido menos explorada na literatura. Sousa, Florêncio e Moraes (2022) defend=
em a
importância de se estudar a capacidade absortiva na inovação do setor públi=
co
como uma forma de entender como esse antecedente organizacional pode ajudar=
a
gerar inovações que respondam aos variados problemas da sociedade.
Autores como Harvey et al . (2010) e Murray et
al . (2011) também ressaltam a importância de investigar a capacidade
absortiva no setor público. Eles explicam que embora a capacidade absortiva
seja concebida originalmente para incentivar a inovação no setor privado, e=
ssa
capacidade organizacional pode ser benéfica para a administração pública ao
passo que pode ajudar no incremento da compreensão das demandas dos usuário=
s,
além de promover continuamente uma melhoria dos processos organizacionais e=
dos
serviços prestados à sociedade.
Nessa linha,=
Brandão
e Bruno-Faria (2013) chamam a atenção para a necessidade crescente de se
estudar os determinantes, indutores, facilitadores e barreiras na geração de
inovações no setor público, o que corrobora a necessidade de se investigar a
influência da capacidade absortiva sobre desempenho de organizações pública=
s.
Diante disso, Oliveira et al . (=
2018)
reforçam a lacuna de pesquisa apresentada, destacando a necessidade de novos
estudos que abordem a capacidade absortiva em sua forma potencial e realiza=
da e
seu impacto nas práticas inovadoras.
No Brasil, e=
ntre as
organizações públicas com grande potencial de geração de inovação, destacam=
-se
as Instituições de Ensino Superior (IES), que compreendem as Universidades =
e a
Rede de Educação Profissional, Científica e Tecnológica. Para tanto, divers=
as
políticas de inovação foram estabelecidas a fim de criar um ambiente favorá=
vel
para que essas instituições públicas produzissem inovações que pudessem ser
aproveitadas pela sociedade.
A Lei de Ino=
vação
(Lei nº 10.973/2004), por exemplo, criou a figura dos Núcleos de Inovação
Tecnológica (NITs ) com o objetivo de auxiliar na
gestão da política de inovação no âmbito das instituições públicas acadêmic=
as
(Brasil, 2004). Mais recentemente,=
foi
estabelecido o novo marco legal da inovação (Lei n. 13.243/2016) a fim de
eliminar as disfunções burocráticas para promoção da inovação e incentivar =
as
atividades de pesquisa científica e tecnológica (Brasil, 2016).
Na dinâmica =
de
produção do conhecimento, além das atividades de ensino e pesquisa, as
universidades possuem a terceira missão de criar valor na sociedade por mei=
o da
inovação (Castro, 2011). Com isso, espera-se que essas IES possuam um papel
mais ativo de engajamento regional, interagindo mais intensamente com atore=
s,
demandas, problemas e desafios específicos de cada região (Gimenez; Bonacelli , 2018).
Considerando=
a
importância das universidades para inovação, faz-se necessário estudar como=
os
grupos de pesquisa dessas instituições estão desenvolvendo pesquisas aplica=
das,
com o objetivo de gerar novas invenções e inovações úteis para a sociedade.=
É
relevante que os grupos de pesquisa das universidades atuem como fomentador=
es
de atividades de Pesquisa, Desenvolvimento e Inovação (PD&I), contribui=
ndo
com o desenvolvimento das diversas áreas do conhecimento (LEPES, 2020).
Assim, Mikha=
ilov e Reichert (2019) verificaram que a capacidade absortiv=
a é um
importante antecedente organizacional para geração de inovações. Aliado a i=
sso,
Padilha (2020) identificou uma escassez de estudos que avalia a capacidade
absortiva em grupos de pesquisa para inovação.
Em função do
exposto, este estudo teve por objetivo analisar a influência das dimensões =
da
capacidade absortiva (potencial e realizada) no desempenho inovativo dos gr=
upos
de pesquisa das instituições de ensino superior públicas no Piauí. O estudo
adotou um recorte transversal, com dados coletados entre os meses de julho e
setembro de 2023, abrangendo 90 grupos de pesquisa de quatro instituições
públicas: Universidade Federal do Piauí (UFPI), Universidade Estadual do Pi=
auí
(UESPI), Instituto Federal do Piauí (IFPI) e Universidade Federal do Delta =
do
Parnaíba (UFDPar ). Esse recorte permitiu uma an=
álise
pontual da relação entre a capacidade absortiva e o desempenho inovativo de=
ntro
do contexto institucional e temporal delimitado.
<=
/o:p>
2 RELAÇÃO DA CA=
PACIDADE
ABSORTIVA E INOVAÇÃO
<=
/o:p>
Estudos seminais, a
exemplo de Cohen e Levinthal (1990) e Zahra e George (2002), propuseram mod=
elos
de capacidade absortiva em nível organizacional e evidenciaram seus efeitos
positivos sobre o desempenho da empresa e a inovação. Com isso, uma vasta
literatura, tanto nacional quanto internacional, buscou avaliar empiricamen=
te
tais efeitos, considerando diferentes contextos, metodologias e bases teóri=
cas
(Algarni et al ., 2023; Florênci=
o;
Oliveira Júnior, 2022; Cassol; Marietto; Martins, 2022; Fernandes et al ., 2020; Padilha, 2020; Mikha=
ilov;
Reichert, 2019; Xie; Zou; Qi, 2018; Oliveira et al ., 2018; Fosfuri; Tribó, 2008).
No tocante ao setor público, Oliveira et al . (2018) avaliaram as relações
entre a capacidade absortiva potencial, desempenho organizacional e a inova=
ção
a partir das percepções de 150 discentes de Administração de uma universida=
de
federal da região Nordeste. Eles descobriram que a capacidade absortiva
potencial afeta positivamente o desempenho organizacional e a inovação da
universidade pública analisada.
Fernandes et
al . (2020) analisaram as características de um órgão público de meio
ambiente em relação aos temas inerentes à inovação, como sistema de inovaçã=
o,
cultura inovativa, Capacidade Absortiva (CA) e regime de apropriabilidade. =
Eles
concluíram que a CA é o principal fator explorado na busca por melhorias
através da inovação. Somando-se a isso, Padilha (2020) estudou como 16
laboratórios de pesquisa de universidade pública adquirem, assimilam,
transformam e exploram a capacidade absortiva externa para gerar inovação. =
Para Algarni et
al . (2023), a capacidade absortiva potencial e a capacidade absortiva
realizada são capacidades complementares, mas distintas, envolvendo diferen=
tes
objetivos, estratégias e estruturas. A CA potencial é um processo e também =
uma
capacidade pela qual as empresas adquirem e assimilam conhecimento externo,=
o
que pode auxiliar no desenvolvimento da imitação e na inovação. Já a CA
realizada é um processo e uma capacidade através da qual as empresas
transformam e exploram o conhecimento absorvido que pode ajudar a desenvolv=
er a
inovação. Aliado a isso, Cassol, Marietto e Martins (2022) observaram que a=
CA
potencial e realizada possuem efeitos positivos, mas disintos sobre a
capacidade de inovação, o que reforça a necessidade de se investigar tais i=
nfluências
separadamente. Logo, assume-se nesse estudo que não há uma associação
igualmente forte da CA potencial e realizada sobre a inovação.
Portanto, as hipóteses da pesquisa são formu=
ladas
da seguinte forma:
H1: A Capacidade a=
bsortiva
potencial afeta direta e positivamente no desempenho inovativo dos laborató=
rios
públicos de pesquisa, ou seja, quanto maior a capacidade absortiva potencial
maior o desempenho inovativo.
H2: A Capacidade a=
bsortiva
realizada afeta direta e positivamente no desempenho inovativo dos laborató=
rios
públicos de pesquisa, ou seja, quanto maior a capacidade absortiva realizada
maior o desempenho inovativo.
Todavia, Mikhailov e Reichert (2019) alertam=
sobre
a necessidade de observar as forças externas (isto é, turbulência tecnológi=
ca,
dinamismo e competição, barreiras culturais e regimes de apropriabilidade) =
e as
forças internas (isto é, clima autônomo de P&D, aprendizado organizacio=
nal,
inteligência, cultura da organização, cultura de inovação e uso de sites de
redes sociais) que atuam sobre a relação da capacidade absortiva e a inovaç=
ão.
Nesse sentido, boa parte das forças internas=
e
externas apresentadas pelos autores foi analisada em estudos empíricos,
conforme mosta a revisão de literatura de Florêncio e Oliveira Júnior (2022=
).
Contudo, pouca pesquisa foi conduzida no sentindo de investigar o papel do
tempo de atuação do líder na relação das dimensões da capacidade absortiva
(potencial e realizada) sobre o desempenho inovativo. Porto (2006) explica =
que
existe um gap considerável entre o tempo em que o pesquisador inicia
suas atividades na universidade até o momento em que inicia sua trajetória =
na
coordenação de um grupo de pesquisa. Somando-se a isso, Verbree et al . (2011) argumentam que as
características do líder, a exemplo do tempo de atuação, têm maior
probabilidade de influenciar o desempenho do grupo de pesquisa indiretament=
e do
que na forma direta. Ademais, Freitas (2016) verificou que o tempo de víncu=
lo
do líder afeta positivamente nos resultados de pesquisa do grupo. Portanto,=
a
hipótese final é definida como:
H3: O=
tempo
de atuação do líder modera positivamente a relação entre as dimensões da
capacidade absortiva e o desempenho inovativo, isto é:
H3a: =
O tempo
de atuação do líder modera positivamente a relação entre a capacidade absor=
tiva
potencial e o desempenho inovativo.
H3b: =
O tempo
de atuação do líder modera positivamente a relação entre a capacidade absor=
tiva
realizada e o desempenho inovativo.
Com isso, pr=
opõe-se
o seguinte modelo conceitual da influência da capacidade absortiva sobre o
desempenho inovativo dos grupos públicos de pesquisa, conforme observa-se na
Figura 1.
<=
/o:p>
Figura
1 - Modelo conceitual da influência da capacidade absortiva no desempenho
inovativo
Fonte: Autores.
<=
/o:p>
Dessa forma,=
com
base nos estudos anteriores assume-se que a capacidade absortiva de
conhecimento por meio da aquisição, assimilação, transformação e exploração
afeta direta e positivamente o desempenho inovativo dos grupos públicos de
pesquisa, ou seja, o potencial de geração de inovação é fortemente determin=
ado
pela capacidade dos grupos de pesquisa em aproveitar o conhecimento externo
valioso em seus projetos de PD&I.
<=
/o:p>
3 METODOLOGIA
<=
/o:p>
Para alcança=
r o
objetivo da pesquisa, foi realizado um levantamento de dados (survey Hair Junior et al ., 2017).
O questionár=
io é
composto de quatro partes principais: perfil dos respondentes, escala de
capacidade absortiva potencial, escala de capacidade absortiva realizada e a
escala do desempenho inovativo. As
dimensões da capacidade absortiva (potencial e realizada) foram medidas atr=
avés
das escalas de Flatten et al . (2011) e Padilha (2020). O desempenho inovativo foi medi=
do
por meio das escalas de Wang e Ahmed (2004) e Padilha (2020). Todos os iten=
s do
questionário foram avaliados em um intervalo de Lik=
ert
A técnica ad=
otada
para análise dos dados coletados foi a modelagem de equações estruturais us=
ando
mínimos quadrados parciais (PLS-SEM, do inglês Part=
ial
Least Squares and Structural Equation Modeling ) co=
m o uso
do software SmartPLS ®
v. 4.0.8.6 (Ringle ; Wende<=
/span>;
Becker, 2023).
<=
/o:p>
<=
/o:p>
4 RESULTADOS
4.1 Perfil dos
respondentes da pesquisa
<=
/o:p>
Os dados rev=
elaram
uma predominância de líderes entre os participantes do estudo, corresponden=
do a
87,8% 12,2%.
A escolha dos líderes para este estudo justifica-se pela sua posição es=
tratégica
na gestão dos grupos de pesquisa, sendo eles os principais responsáveis por
decisões que impactam diretamente o desempenho e a inovação no grupo. No que
diz respeito ao nível de escolaridade, 86,7%
Além disso, a
pesquisa mostrou que uma parte significativa desses líderes atua entre 3 e 7 anos=
(35,6%), seguida por aqueles com mais de 10 anos de experiência 5,6% 1 ano
de atuação, o que sugere que a maioria já possui uma experiência consolidad=
a na
condução de grupos de pesquisa.
Em termos de=
áreas
do conhecimento, os respondentes estão distribuídos em todas as grandes áre=
as,
com destaque para as Ciências Sociais Aplicadas (21,1=
%),
Ciências
Humanas (17,8%) e Ciências da Saúde
(15,6%). Por outro lado, as áreas com menor participação foram <=
b>"Outra"
(1,1%), Linguística, Letras e Artes e=
Ciências
Agrárias 4,4%
Após a
caracterização do perfil dos respondentes, foi realizada a análise das
dimensões da capacidade absortiva (potencial e realizada) e suas influências
sobre o desempenho inovativo dos grupos de pesquisa das IES públicas do Pia=
uí,
utilizando a modelagem de equações estruturais.
<=
/o:p>
4.2 Validação e
hipóteses do modelo
<=
/o:p>
A partir da =
técnica
de modelagem de equações estruturais, desenvolveu-se um modelo conceitual q=
ue
busca explicar a influência das dimensões da capacidade absortiva sobre o
desempenho inovativo dos grupos de pesquisa. Assim, buscou-se observar as
relações existentes entre os construtos de capacidade absortiva potencial,
capacidade absortiva realizada, e desempenho inovativo.
Para análise=
da
relação entre as dimensões de 2ª ordem e a possível interferência da variáv=
el
controle (tempo de atuação), foi criado um modelo estrutural parcial basead=
o em
variâncias, seguindo as etapas propostas por Lopes et al. (2020) adaptado de Hair Juni=
or et al . (2017), sendo elas: a)
especificação do modelo estrutural; b) avaliação do modelo de mensuração; c)
avaliação do modelo estrutural; e d) análise dos modelos pela análise multigrupo .
O modelo est=
rutural
estabilizou após 2 interações. Foram utilizados os seguintes critérios para
avaliar o ajuste do modelo PLS-SEM: raiz quadrada média residual padronizad=
a (Standardized Root Mean=
Square Residual , SRMR), distância euclidiana quadrada (square
Euclidean distance <=
/i> – dSED ), distância geodésica (G=
eodesic
distance - dG ) =
e índice
de ajuste normado (Norm=
ed
Fit Index - NFI). Os resultados confirmaram que o modelo estrutural
sugerido se ajustou aos dados com índices confiáveis como SRMR =3D 0,068, <=
span
class=3DSpellE>dSED =3D 3,087, dG =3D 0,=
664, NFI =3D
0,876 (Henseler ; Hubona;
Ray, 2016). Observou-se que o SRMR foi inferior a 0,08 (Henseler ;
Ringle ; Sarstedt , 2=
016) e o
NFI ficou acima do valor sugerido de 0,8 (Hu; Bentler<=
/span>,
1998), o que indica que o modelo se ajustou muito bem aos dados.
A análise do=
modelo
de mensuração é a primeira etapa no processo de avaliação do modelo estrutu=
ral
proposto. Fase esta, responsável por avaliar a confiabilidade das dimensões
latentes (variáveis não observáveis) com base em seus indicadores (variávei=
s observadas).
Em outras palavras, antes de proceder à análise do modelo estrutural
propriamente dito, é vital garantir que as dimensões estejam sendo medidas =
de
forma correta (Hair ; Howard; Nitzl ,
2020).
Para avaliar=
os
ajustes do modelo proposto, foram analisadas a consistência interna, utiliz=
ando
o Alfa de Cronbach (α) e a Confiabilidade
composta (ρc ), e a validade convergente,
analisando a Variância Média Extraída (VME) em função de suas pressuposiçõe=
s apresentadas
na Tabela 1, que apresenta os critérios de avaliação do modelo de mensuraçã=
o.
<=
/o:p>
Tabela 1 - Critérios de
avaliação do modelo de mensuração
Teste
Critérios
Consistência Interna =
Alfa de Cronbach<=
/span> ( a
0,7 < a
Confiabilidade Composta ( r
0,7 < r
Validade Convergente =
Variância Média Extraída – VME <=
/p>
VME > 0,5
Fonte: Lopes et al . (2020) adaptado de Ringle , S=
ilva e
Bido (2014).
<=
/o:p>
O Alfa de Cronbach é uma medida para avaliar a consistência int=
erna
entre os indicadores que compõem as dimensões da escala de mensuração, ou s=
eja,
avalia o quanto os erros aleatórios interferem na medição dos dados (Lopes =
et al ., 2020). Já a confiabilidade
composta é uma estimativa de consistência interna mais consistente no método
SEM (Structural =
Equation
Modeling ) por ser mais robusto em função de
analisar a variação das cargas fatoriais (=
l
Quanto à Var=
iância
Média Extraída (VME), esta é definida como sendo a média das cargas fatoria=
is
padronizadas ao quadrado e se refere ao grau em que as dimensões estão
correlacionadas. Em outras palavras, a VME mede a quantidade de variação de=
uma
dimensão captada em relação à quantidade de variação em função do erro de
medição (Santos; Cirillo , 2023).
A Tabela 2 a=
presenta
a avaliação das dimensões a partir das pressuposições da Tabela 1. <=
/p>
<=
/o:p>
Tabela 2 - Alfa de Cronbach , confiabilidade composta e variância média extraída
Dimensões
Alfa de Cronba=
ch
Confiabilidade composta
Variância Média Extraída
Aquisição
0,760
0,848
0,582
Assimilação
0,849
0,892
0,624
Capacidade Absortiva Potencial <=
/p>
0,850
0,883
0,558
Transformação
0,892
0,926
0,757
Exploração
0,868
0,911
0,720
Capacidade Absortiva Realizada <=
/p>
0,899
0,919
0,589
Comportamental
0,815
0,880
0,650
Tecnológica
0,694
0,815
0,530
Desempenho Inovativo
0,822
0,867
0,555
Fonte: Dados da pesquisa (2023).
Com isso,
observou-se que o modelo proposto apresenta excelente consistência interna,
cujos valores de α e ρc são superiore=
s a
0,6, e VME’s superiores a 0,5, ou seja, demonst=
ra que
os indicadores estão capturando a essência das dimensões e não estão tendo
intervenção por erros de medição ou outras dimensões não relacionadas no mo=
delo
(Shuai =
et al .,
2022). Lopes et al . (2020) escl=
arecem
que os valores de Alfa de Cronbach e da
confiabilidade composta entre 0,70 e 0,95 são considerados bons e eficiente=
s,
porém os valores abaixo de 0,60 sugerem uma falta de confiabilidade da
consistência interna do modelo.
A validade discriminante busca avaliar o grau em que uma dimensão
é distinta e diferente das demais dimensões do modelo , essa técnica
avalia até que ponto as
medidas de uma dimensão são diferenciadas
das medidas das outras dimensões (Alem et al ., 2016).
Quando se
trata de PLS-SEM algumas técnicas são comumente
utilizadas para avaliar a validade discriminante : Cargas Fatoriais Cruzadas ; Critério de Fornell-Larcker e Heterotrait-Monotrait Ratio (HTMT), avaliado pela técnica
de bootstrapping utilizando
5.000 subamostras . Ao empr=
egar
as referidas técnicas , os pesquisadores
podem ter maior confiabilidade que as dimensões
do seu modelo são distintas e medem diferentes especificidades do fenômeno pesquisado (Iwaya et al ., 2022). A Tabela
3 apresenta os critérios da validade discriminante do modelo <=
span
class=3DSpellE>proposto
Tabela 3 - Critérios p=
ara
análise da Validade Discriminante do modelo
Teste
Critérios
Validade Discriminante
Cargas Fatoriais Cruzadas (CFC) =
CForiginal > =
CFdemais
Critério Fornell-=
Larcker .
(CFL)
para i ¹
Critério Heterotrait-Monotrait Ratio (HTMT).
Confirmado pelo método bootstrapping
HTMT < 0,9
LS(HTMT) 97,5% <
1,0
Fonte:
Lopes et al . (2020), adaptado d=
e Ringle ; Silva; Bido (2014=
).
<=
/o:p>
Assim, a aná=
lise das
cargas fatoriais cruzadas (cross-loadings )
consiste em comparar as cargas de cada indicador na dimensão que ele preten=
de
medir (carga fatorial principal), bem como nas demais dimensões do modelo
(cargas fatoriais cruzadas) (Iwaya et al ., 2022). Neste estudo, observou-se que as cargas
fatoriais originais dos indicadores apresentaram excelentes correlações com
suas dimensões originais (<=
span
style=3D'font-size:12.0pt;line-height:115%;font-family:Symbol;mso-ascii-fon=
t-family:
"Myriad Pro";mso-hansi-font-family:"Myriad Pro";mso-char-type:symbol;
mso-symbol-font-family:Symbol'>l > 0,6). Quanto às cargas fatoriais cruzadas, ver=
ificou-se
que as correlações dos indicadores com as demais dimensões foram inferiores=
às
correlações com suas dimensões originais. Portanto, o modelo apresenta vali=
dade
discriminante.
O Critério d=
e Fornell-Larcker (FL), proposto por Fornell
e Larcher (1981), sugere que a raiz quadrada da VME das dimensões deve ser
maior do que os valores das correlações entre as dimensões do modelo. Já o
critério Heterotrait-Monotrait Ratio (HTMT) compara as correlações entre indicad=
ores
medindo diferentes construtos (heterotrait )
com as correlações entre indicadores medindo o mesmo construto (monotrait et al ., 2023). Conforme pode-se observar na Tabela 4, considera=
m-se
adequados os valores de HTMT abaixo de 0,90. Pela técnica de bootstrapping , os valores do limite superior da estim=
ativa
de HTMT devem estar abaixo de 1,0 para indicar uma boa validade discriminan=
te (Hair Junior e=
t al .,
2017). A Tabela 4 mostra os critérios Fornell-Larcker<=
/span>
e HTMT para o modelo da pesquisa.
<=
/o:p>
Tabela 4 - Critérios F=
L e
HTMT Dimensões =
Matriz
de Correlação de Pearson
CA
- Potencial
CA
- Realização
Desempenho
Inovativo
CA - Potencial
CA - Realização
Desempenho Inovativo
97,5% CA - Realização
Desempenho Inovativo
Fonte: Dados da pesquisa (2023).
Na Tabela 4,=
observa-se
que a menor raiz quadrada da VME (0,745) é superior à maior correlação entr=
e as
dimensões de 2ª ordem (DI vs CAP, r =3D 0,714),
portanto o critério FL foi confirmado. Já pelo critério HTMT, observou-se q=
ue
os valores do limite superior da estimativa de HTMT para 97,5% de confiabil=
idade
foram inferiores a 1,0. Portanto, ambos os critérios apresentaram validade
discriminante, e, juntamente com as cargas fatoriais cruzadas, apresentam b=
oas
condições para avaliação do comportamento estrutural do modelo.
Para avaliar=
o
modelo estrutural propriamente dito, recomenda-se, inicialmente, avaliar a =
multicolinearidade entre as dimensões.Essa multicolinea=
ridade ,
no contexto da PLS-SEM, pode ocorrer principalmente no modelo estrutural. A
técnica usada para avaliar a multicolinearidade =
é a
VIF (Variance In=
flation
Factor ), porém outros critérios (coeficiente de explicação – R²; Tamanho do efeito – f² e relevância preditiva – Q²) são
relevantes para avaliar o modelo, conforme se observa na Tabela 5.
<=
/o:p>
<=
/o:p>
<=
/o:p>
<=
/o:p>
Tabela 5 - Critérios p=
ara
avaliação do modelo estrutural
Teste
Critérios
Avaliação do Modelo Estrutu=
ral
Avaliação da Colinearidade =
Variance Inflation
Factor (VIF)
Coeficiente de Explicação (=
R²);
Confirmado pelo método Boostrapping
≤ R² ≤ 0,075 (efeito fraco);
Tamanho do efeito (f²)=
Confirmado pelo método Boostrapping
Relevância preditiva (Q²);<=
o:p>
Confirmado pelo método Blindfolding
Validade do coeficiente
estrutural ( b Confirmado pelo método Boostrapping
b ¹ tc . > 1=
,96 (p
< 0,05)
Fonte:
Lopes et al . (2020), adaptado d=
e Ringle ; Silva; Bido (2014=
).
<=
/o:p>
A VIF é uma =
regra
utilizada principalmente em regressão múltipla para diagnosticar a
colinearidade entre as variáveis exógenas e endógenas, e ocorre quando duas=
ou
mais variáveis estão altamente correlacionadas. A mult=
icolinearidade
pode ser um grande problema pois compromete a precisão e a interpretabilidade
dos coeficientes estruturais (Kyriazos ; Poga , 2023). Valores de VIF maiores que 1 indicam alg=
um
grau de multicolinearidade , porém como regra ge=
ral,
uma VIF acima de 5 sugere excesso de multicolinearidad=
e ,
porém esses limiares podem variar dependendo da literatura e do contexto (<=
span
class=3DSpellE>Alauddin ; Nghiem , 2010).<=
/span>
Para avaliar=
a
qualidade do modelo, utilizaram-se o coeficiente de explicação (R²) e o
coeficiente de relevância preditiva (Q²). O R² representa a parcela de vari=
ação
nas variáveis endógenas que é explicada pelas dimensões exógenas do modelo =
(Hidayat ; Wulandari , 2022)=
. O R²
varia entre 0 e 1, com valores mais próximos de 1 indicando que uma grande
parcela da variância é explicada pelo modelo, porém utiliza-se critérios de=
classificação
proposto por Hair Junior et al. (2017) e ajustado por Lopes et al . (2020) (Tabela 5). O tamanho do efeito f² serve para ava=
liar
se há impacto considerável na dimensão endógena quando uma dimensão exógena=
é
omitida do modelo (Lopes et al .,
2020). Já o Q² é uma regra usada para avaliar a capacidade preditiva do mod=
elo,
que no contexto da PLS-SEM o Q² é obtido usando a técnica de Stone-Geisser , também chamada de validação cruzada (Sharma =
et al ., 2021). Valores de Q2 maior=
es que
0 indicam relevância preditiva para o modelo, podendo ser classificados qua=
nto
ao seu grau de predição (Tabela 6).
<=
/o:p>
<=
/o:p>
<=
/o:p>
<=
/o:p>
<=
/o:p>
<=
/o:p>
<=
/o:p>
<=
/o:p>
Tabela 6 -
Dimensões Exógenas
Dimensões Endógenas
Desempenho Inovativo
VIF
2,844
3,175
Efeito f² (p – valor)
0,189
(0,014)
0,152
(0,013)
R² (p=
–
valor)
0,580
(0,000)
Q²
0,504
Fonte: Dados da pesquisa (2023).
Analisando os
valores da Tabela 6, observou-se que o modelo não apresenta multicolinearidade
(VIF < 5). Quanto ao coeficiente de explicação, apresenta efeito forte (=
R²
> 0,19) e a relevância preditiva da dimensão endógena apresenta grau for=
te
(Q² > 0,25). Em relação ao tamanho do efeito (f²), percebe-se que, para o
desempenho inovativo, todos os efeitos das dimensões exógenas foram
considerados médios e significativos.
A seguir, são
apresentadas e avaliadas as hipóteses propostas no modelo inicial, bem como=
a
interferência da variável controle (moderadora) que é o tempo de atuação. A
Tabela 7 apresenta os valores de beta, desvio padrão, da estatística t e de=
p.
<=
/o:p>
=
Relações Es=
truturais
β=
DP*
Estat. t
p-valor
Situação
H1
CA – Potenc=
ial ® DI
0,475<=
/o:p>
0,149<=
/o:p>
3,187<=
/o:p>
0,001<=
/o:p>
Aceita=
H2
CA – Realiz=
ada ® DI
0,451<=
/o:p>
0,159<=
/o:p>
2,841<=
/o:p>
0,005<=
/o:p>
Aceita=
H3a
Tempo* CA –
Potencial <=
span
style=3D'mso-bookmark:_Hlk158988258'>® DI
0,163<=
/o:p>
0,254<=
/o:p>
0,640<=
/o:p>
0,522<=
/o:p>
Rejeita
H3b
Tempo* CA –
Realizada <=
span
style=3D'mso-bookmark:_Hlk158988258'>® DI
-0,461=
0,222<=
/o:p>
2,078<=
/o:p>
0,038<=
/o:p>
Rejeita
Nota:
DP =3D Desvio Padrão
Fonte: Dados da pesquisa (2023).
Os resultados
obtidos na Tabela 7 mostram que a capacidade absortiva potencial afeta posi=
tiva
e significativamente no desempenho inovativo dos grupos de pesquisa (β=
=3D
0,475, p < 0,05), o que corrobora a hipótese H1 . O estudo encontr=
ou
suporte para a H2 , pois foi verificado um efeito positivo e
significativo da capacidade absortiva realizada sobre o desempenho inovativo
(β =3D 0,451, p < 0,05). Por outro lado, o efeito indireto do tempo=
de
atuação do líder na relação entre a capacidade absortiva potencial e o dese=
mpenho
inovativo não foi substancial (β =3D 0,163, p > 0,05), o que refuta=
a
hipótese H3a .
Com relação a
hipótese H3b , observou-se que o tempo de atuação do líder modera
negativamente a relação entre capacidade absortiva realizada com o desempen=
ho inovativo
(β =3D 0,-461, p < 0,05). Portanto, essa relação foi avaliada em
separado, ou seja, através de uma análise comparativa entre os tempos de
atuação (isto é, Análise Multigrupo – MGA) (Henseler ; Ringle ; Sarstedt , 2016), conforme pode ser observado na Tabel=
a 8.
<=
/o:p>
Tabela 8 - Comparação entre tempos de
atuação (H3b) =
Tempo de Atuação
Relações Diretas
β
DP*
Estat. t
p-valor**
Até 10 anos (n =3D 61)
CA – Realizada ® DI
0,454
0,145
3,141
0,002
Acima 10 anos (n =3D 29)
CA – Realizada ® DI
0,704
0,187
3,759
0,000
Diferença
Até 10 – Acima 10
-0,250
---
---
0,256
* DP =3D Desvio Padrão
** Teste de Henseler
Fonte: Dados da pesquisa (2023).
Na Tabela 8,
utilizou-se o Teste de Henseler para comparar os
betas entre os tempos (H3), porém observou-se não existir diferença signifi=
cativa
entre os mesmos (p > 0,05). Isso significa que
tanto para os pesquisadores que atuam há menos de 10 anos quanto aqueles que
atuam há mais de 10 anos apresentaram relações positivas e significativas e=
ntre
a capacidade absortiva realizada com o desempenho inovativo (p < 0,05). =
Portanto,
a moderação significativa não inverteu ou desfavoreceu a relação.
A Figura 2 a=
presenta
o modelo estrutural final da influência das dimensões da capacidade absorti=
va
sobre o desempenho inovativo, com base na realidade dos grupos de pesquisa =
das
IES públicas no Piauí.
<=
/o:p>
Figura 2 - Modelo estrutural final da influência
das dimensões da capacidade absortiva sobre o desempenho inovativo
Fonte:
Dados da pesquisa (2023).
<=
/o:p>
5 DISCUSSÃO DOS
RESULTADOS
Estudos semi=
nais,
como os de Cohen e Levinthal (1990) e Zahra e George (2002), enfatizavam os efeitos positiv=
os da
capacidade absortiva sobre a inovação e o desempenho da empresa. Todavia, f=
oi
necessário observar em que contextos essa influência se mantinha positiva ou
não. Em razão disso, boa parte da literatura nacional e internacional dedic=
ou atenção
especial à avaliação de tais efeitos, considerando diferentes áreas de atua=
ção
das empresas privadas. No entanto, as pesquisas dessa natureza envolvendo
órgãos e organizações do setor público foram consideravelmente menos freque=
ntes.
Com essa lac=
una,
buscou-se investigar a influência das capacidades absortivas (potencial e
realizada) sobre a inovação dos grupos de pesquisa, considerando a realidade
das IES públicas do Piauí. Aliado a isso, foi examinado também como as forç=
as
internas, isto é, o tempo de atuação do líder no grupo agia sobre essa rela=
ção.
Assim, a hipótese inicial demonstra um impacto positivo e significativo da
capacidade absortiva potencial sobre o desempenho inovativo dos grupos de
pesquisa das IES públicas do Piauí. Esse resultado é consistente com as
conclusões de Oliveira et al . (=
2018),
que verificaram que a capacidade absortiva potencial percebida atua de mane=
ira
positiva nas práticas inovadoras de uma IES pública. Ou seja, indicaram que=
a
inovação é resultado do acolhimento de novos conhecimentos externos. Nesse
sentido, a capacidade absortiva potencial é importante porque pode levar a =
um
melhor desempenho por meio de produtos novos ou aprimorados (Fosfuri ; Tribó , 2008). Al=
ém
disso, Ali, Kan e Sarstedt (2016) mostraram que=
as
dimensões (aquisição e assimilação) da capacidade absortiva potencial se
relacionam positivamente com a inovação de produtos, a inovação de processo=
s e
a inovação de gestão. Esse achado hipotetizado =
do
estudo corrobora diversas outras descobertas que destacam o importante pape=
l da
capacidade absortiva potencial na inovação (Algarni et al ., 2023; Cassol; Marietto ; Martins, 2022; Florêncio; Oliveira Júnior, =
2022;
Mikhailov; Reichert , 2019).
Assim, os re=
sultados
obtidos sugerem que, para que os grupos de pesquisa consigam um maior
desempenho inovativo, é importante que os líderes estejam dedicados a estim=
ular
a aquisição de conhecimento externo valioso para seus projetos e pesquisas,=
bem
como consigam assimilar esse conhecimento científico e tecnológico externo
adquirido à base de conhecimento do grupo. Em outras palavras, é relevante =
que
o grupo de pesquisa desenvolva rotinas e processos para adquirir conhecimen=
to
externo valioso para suas atividades, bem como estabeleça meios para analis=
ar e
interpretar esse novo conhecimento entre os seus membros. Todo esse conjunt=
o de
rotinas e processos é crucial para obter melhor desempenho em relação à
inovação, quer seja no aspecto comportamental quanto no tecnológico. =
O resultado =
da
segunda hipótese mostra um impacto positivo e significativo da capacidade
absortiva realizada sobre o desempenho inovativo. Esse achado corrobora est=
udos
anteriores que verificaram uma associação positiva da capacidade absortiva
realizada com a inovação (Algarni et al ., 2023; Cassol; Marietto ; Martins, 2022; Khan; L=
ew ;
Marinova , 2019; Florêncio; Oliveira Júnior, 202=
2;
Mikhailov; Reichert , 2019). Para Algarni et al . (2023), a capacidade absort=
iva
realizada é um processo e também uma capacidade
organizacional que facilita a integração do conhecimento existente com o
conhecimento recém-adquirido e assimilado para a inovação. Isso confirma a
posição adotada por Khan, Lew e Marinova
(2019), de que a inovação seja na forma radical ou incremental (isto é, exploration e exploita=
tion Marietto e Martins (2022) identificaram uma maior
influência da capacidade absortiva realizada na capacidade de inovação, o q=
ue
reforça a ideia de que a forma como o conhecimento é transformado e explora=
do
tem maior influência na inovação do que a aquisição e assimilação dele.
Esses result=
ados
apontam para a importância de os líderes dos grupos de pesquisa se envolver=
em
com rotinas e processos que melhorem a transformação e exploração do
conhecimento, a fim de gerar inovação para a sociedade. Nesse sentido, a
capacidade de explorar com sucesso os novos conhecimentos adquiridos nas
atividades do grupo de pesquisa é crucial na busca de soluções inovadoras e
incomuns e no aproveitamento de novas tecnologias dentro do grupo de pesqui=
sa.
O papel mode=
rador do
tempo de atuação do líder do grupo de pesquisa na relação entre as dimensõe=
s da
capacidade absortiva e o desempenho inovativo não foi identificado nesse
estudo, muito embora estudos anteriores como os de Ver=
bree
et al . (2011) e Freitas (2016),
apontem para os efeitos direto e indireto do tempo de vínculo do líder no
desempenho do seu grupo de pesquisa. Assim, o tempo de atuação do líder jun=
to
ao seu grupo parece não ser importante no que diz respeito ao estabelecimen=
to
de rotinas e processos de absorção de conhecimentos externos valiosos visan=
do a
inovação. Por outro lado, isso não significa dizer que a experiência do líd=
er
seja inútil. A liderança tem um papel importante na condução de um melhor
desempenho do grupo de pesquisa em termos de resultados de pesquisa, ou sej=
a, a
habilidade de liderança ajuda a concentrar os esforços dos membros na produ=
ção
de conhecimento de alta qualidade (Freitas, 2016; Verb=
ree
et al ., 2011).
<=
/o:p>
6 CONCLUSÃO
<=
/o:p>
A partir da liter=
atura
e das teorias existentes sobre a capacidade absortiva e a inovação no setor
público, desenvolveu-se um modelo que visa explicar como as dimensões da
capacidade absortiva (potencial e realizada) influenciam o desempenho inova=
tivo
de grupos de pesquisa no Piauí. Buscou-se também compreender se, e em que
medida, o tempo de atuação do líder do grupo de pesquisa favorece a relação=
da
capacidade absortiva com a inovação. Essa abordagem permite mostrar quais as
práticas específicas da capacidade absortiva de um grupo de pesquisa ajudam=
a
influenciar positivamente na inovação comportamental e na inovação tecnológ=
ica.
Os resultados do =
estudo
empírico indicam que a capacidade absortiva potencial tem um impacto
significativo no desempenho inovativo, ou seja, para que os grupos de pesqu=
isa
promovam uma inovação comportamental e tecnológica é importante que eles
estabeleçam rotinas e procedimentos para adquirir e assimilar os conhecimen=
tos
externos valiosos. Aliado a isso, os achados da pesquisa mostram também que=
a
capacidade absortiva realizada possui um importante papel no desempenho
inovativo, ou seja, o conhecimento transformado e explorado permite que os
grupos de pesquisa possam inovar. Em suma, observa-se que as dimensões da
capacidade absortiva (potencial e realizada) apresentam um efeito positivo e
significativo em relação ao desempenho inovativo. Isso mostra que os líderes
dos grupos de pesquisa precisam se concentrar em estabelecer rotinas e
processos para renovar continuamente o estoque de conhecimento do laboratór=
io,
visando obter melhores resultados em termos de inovação. <=
/span>
Mesmo com os
importantes resultados obtidos, é preciso avançar na compreensão do papel da
capacidade absortiva na inovação do setor público. Levando em conta a
administração gerencial advinda com o novo modelo de administração pública,=
é
urgente a adoção de práticas e rotinas organizacionais que permitam que as
organizações públicas possam produzir soluções novas e efetivas para respon=
der
às demandas da sociedade. Nessa linha, entende-se que o presente estudo deu=
o
primeiro passo na compreensão do importante papel da capacidade absortiva p=
ara
inovação no setor público, considerando a realidade dos grupos de pesquisa =
no
Piauí. No entanto, é preciso que novos estudos de cunho longitudinal avalie=
m se
tais efeitos se mantêm ao longo do tempo. Além disso, os novos estudos sobr=
e o
tema devem considerar diferentes contextos geográficos e outros tipos de
organizações do setor público. Também é importante levar em conta medidas m=
ais
objetivas de mensuração do desempenho inovativo como, por exemplo, o número=
de
patentes concedidas.
REFERÊNCI=
AS
ALAUDDIN, Mohammad; NGHIEMB,=
Hong
Son. Do instructional attributes pose multicollinearity problems? An empiri=
cal
exploration. Economic Analysis and Policy , v. 40, n. 3, p. 351-361,
2010. https://doi.org/10.1016/S0313-5926(10)50034-1.
ALEM, Farid; PLAISENT, Michel; ZUCCARO, Cataldo; BERNARD, Prosper.
Measuring e-learning readiness concept: scale development and validation us=
ing
structural equation modeling. International Journal of e-Education,
e-Business, e-Management and e-Learning , v. 6, n. 4, p. 193, 2016. http=
s://doi.org/10.17706/ijeeee.2016.6.4.193-207.
ALI, Murad; KAN, Konan Anderson Seny; SARSTEDT, Marka. Direct and
configurational paths of absorptive capacity and organizational innovation =
to
successful organizational performance. Journal of Business Research ,=
v.
69, n. 11, p. 5317-5323, 2016. https://doi.org/10.1016/j.jbusres.2016.04.13=
1.
ALGARNI, Mohammad; ALI, Murad; LEAL-RODRÍGUEZ, Antonio; ALBORT-MORAN=
T, Gema.
The differential effects of potential and realized absorptive capacity on
imitation and innovation strategies, and its impact on sustained competitive
advantage. Journal of Business Research , v. 158, p. 113674, 2023.
https://doi.org/10.1016/j.jbusres.2023.113674.
BRANDÃO, Soraya Monteiro; BRUNO-FARIA, M=
aria
de Fátima. Inovação no setor público: análise da produção científica em
periódicos nacionais e internacionais da área de administração. Revista =
de
Administração Pública , v. 47, n. 1, p. 227-248, 2013.
https://doi.org/10.1590/S0034-76122013000100010.
BRASIL. Lei nº 10.973, de 2 de dezembro =
de
2004. Dispõe sobre incentivos à inovação e à pesquisa científica e tecnológ=
ica
no ambiente produtivo e dá outras providências. Diário Oficial da União<=
/b>,
Poder Legislativo, Brasília, DF, 2 dez. 2004.
BRASIL. Lei nº 13.243, de 11 de janeiro =
de
2016. Dispõe sobre estímulos ao desenvolvimento científico, à pesquisa, à
capacitação científica e tecnológica e à inovação e altera a Lei nº 10.973,=
de
2 de dezembro de 2004, [...]. Diário Oficial da União , Poder
Legislativo, Brasília, DF, 11 jan. 2016.
CASSOL, Alessandra; MARIETTO, Marcio; MA=
RTINS,
Cibele. Inovação em Pequenas e Médias Empresas: a influência da capacidade =
de
inovação. Revista Ciências Administrativas , v. 24, n. 62, p. 102-121,
2022. https://doi.org/10.5007/2175-8077.2022.e73575.
CASTRO, Maria Helena de Magalhães.
Universidades e inovação: configurações institucionais & terceira missã=
o. Cadernos CRH , v. 24, n. 63, p. 555-573, =
2011. https://doi.org/10.1590/S0103-49792011000300007.
CHEN, Yu-Shan.; LIN, Ming-Ji James; CHANG, Ching-Hsun. The positive
effects of relationship learning and absorptive capacity on innovation
performance and competitive advantage in industrial markets. Industrial
Marketing Management , v. 38, n. 2, p. 152-158, 2009. https://doi.org/10=
.1016/j.indmarman.2008.12.003.
CHEUNG, Gordon; COOPER-THOMAS, Helena; LAU, Rebecca; WANG, Linda.
Reporting reliability, convergent and discriminant validity with structural
equation modeling: A review and best-practice recommendations. Asia Paci=
fic
Journal of Management , v. 41, p. 1-39, 2023.
https://doi.org/10.1007/s10490-023-09871-y.
COHEN, Wesley; LEVINTHAL, Daniel. Absorptive capacity: A new perspec=
tive
on learning and innovation. Administrative Science Quarterly , v. 35,=
n.
1, p. 128-152, 1990. https://doi.org/10.2307/2393553.
FERNANDES, Eduardo da Sila; VECCHIA, Fábio Antonio Dalla.; VOLQUIND,=
Rafael;
GAYER, Bruno Dones. Inovação no Setor Público: análise de um orgão estadual de meio ambiente e estudo de casos. The Journal of Engineering and Exact Sciences - jCEC , v.6, n. 1, p.1-6, 2020. https://doi.org/10.18540/jcecvl6iss1pp0092=
-0097.
FLATTEN, Tessa; ENGELEN, Andreas; ZAHRA, Shaker; BRETTEL, Malte. A
measure of absorptive capacity: Scale development and validation. Europe=
an
Management Journal , v. 29, n. 2, p. 98-116, 2011.
https://doi.org/10.1016/j.emj.2010.11.002
FLORÊNCIO, Marcio Nannini; OLIVEIRA JÚNI=
OR, Antonio Martins. The Importance of Absorptive
Capacity in Technology Transfer and Organisational
Performance: A Systematic Review. International Journal of Innovation
Management , v. 26, n. 2, p. 2230001, 2022. https://doi.org/10.1142/S136=
391962230001X.
FORNELL, Claes; LARCKER, David. Evaluating structural equation models
with unobservable variables and measurement error. Journal of marketing
research , v. 18, n. 1, p. 39-50, 1981. https://doi.org/10.2307/3151312.=
FOSFURI, Andrea; TRIBÓ, Josep. A. Exploring the antecedents of poten=
tial
absorptive capacity and its impact on innovation performance. Omega
FREITAS, Pablo Fernando Pessoa. Relaç=
ões
entre Competências Gerenciais de Líderes de Grupos de Pesquisa e Resultados
Alcançados. 2016. 209 f. Dissertação (Mestrado em Administração).
Universidade de Brasília- UnB, Brasília, 2016. Disponível em: http://icts.u=
nb.br/jspui/bitstream/10482/19827/1/2016_PabloFernandoPessoadeFreitas.pdf.
Acesso em: 10 jan. 2023.
GIMENEZ, Ana Maria Nunes; BONACELLI, Mar=
ia Beatriz.
Universidades e região: a terceira missão como instrumento de inserção e de
engajamento local/regional. In: II SEMINÁRIO DE CULTURA E EXTENSÃO DA ESCOL=
A DE
ARTES CIÊNCIAS E HUMANIDADES DA UNIVERSIDADE DE SÃO PAULO, 2018, São Paulo,=
Anais...
São Paulo: USP, 2018, p. 1-5. Disponível em:
https://www.researchgate.net/publication/332910147_Universidades_e_regiao_a=
_terceira_missao_como_instrumento_de_insercao_e_de_engajamento_localregiona=
l.
Acesso em : 10 jan. =
2023.
GRANT, Robert. Toward a Knowledge-Based Theory of the Firm. Strat=
egic
Management Journal , v. 17, Ed. Esp., 109–122, 1996. https://doi.org/10.=
1002/smj.4250171110.
HAIR JUNIOR; Joseph; HULT, Tomas; RINGLE, Christian; SARSTEDT, Marko=
. A
primer on partial least squares structural equation modeling (PLS-SEM) .=
Los
Angeles: Sage publications=
,
2017. Disponível em:
https://eli.johogo.com/Class/CCU/SEM/_A%20Primer%20on%20Partial%20Least%20S=
quares%20Structural%20Equation%20Modeling_Hair.pdf.
Acesso em: 15 mai. 2023.
HAIR, Joe; HOWARD, Matt; NITZL, Christian. Assessing measurement mod=
el
quality in PLS-SEM using confirmatory composite analysis. Journal of
Business Research , v. 109, p. 101-110, 2020.
https://doi.org/10.1016/j.jbusres.2019.11.069
HARVEY, Gill; SKELCHER, Chris; SPENCER, Eillen; JAS, Pauline; WALSHE=
, Kieran.
Absorptive capacity in a non-market environment: a knowledge-based approach=
to analysing the performance of sector organizations. Public
Management Review, v.12, n.1, p. 77-97, 2010. https://doi.org/10.1080/1=
4719030902817923.
HENSELER, Jorg; HUBONA, Geoffrey; RAY, Pauline. Using PLS path model=
ing
in new technology research: updated guidelines. Industrial Management &a=
mp;
Data Systems , v. 116, p. 2-22, 2016.
https://doi.org/10.1108/IMDS-09-2015-0382.
HENSELER, Jorg; RINGLE, Christian; SARSTEDT, Marko. Testing measurem=
ent
invariance of composites using partial least squares. International
Marketing Review , v. 33, n. 3, p. 405–431, 2016. https://doi.org/10.110=
8/IMR-09-2014-0304.
HIDAYAT, Rachmat ; WULANDARI, Patricia. D=
ata
Analysis Procedures with Structural Equation Modelling (SEM): Narrative
Literature Review. Open Access Indonesia Journal of Social Sciences ,=
v.
5, n. 6, p. 859-865, 2022. https://doi.org/10.37275/oaijss.v5i6.142.
HU, Li-tze ; BENTLER, Peter. Fit Indices =
in
Covariance Structure Modeling: Sensitivity to Underpar=
ameterized
Model Misspecification. Psychological
Methods , v. 3, n. 4, p. 424-453, 1998.
https://doi.org/10.1037/1082-989X.3.4.424.
IWAYA, Gabriel Horn; STEIL, Andrea Valér=
ia;
LOPES, Luis Felipe Dias; CRUZ, Roberto Moraes. =
Predictors of the intention to purchase organic food in the Brazilian
context. Revista Psicologia: Organizações e Trabalho, v. 22, =
n.
2, p. 1943-1952, 2022. https://doi.org/10.5935/rpot/2022.2.22269.
KHAN, Zaheer =
; LEW, Yong
Kyu; MARINOVA, Svetla . Exploitat=
ive and
exploratory innovations in emerging economies: The role of realized
absorptive capacity and learning intent. International Business Review,
v. 28, n. 3, p. 499-512, 2019. https://doi.org/10.1016/j.ibusrev.2018.11.00=
7.
KYRIAZOS, Theodoros; POGA, Mary. Dealing with Multicollinearity in
Factor Analysis: The Problem, Detections, and Solutions. Open Journal of
Statistics , v. 13, n. 3, p. 404-424, 2023. https://doi.org/10.4236/ojs.=
2023.133020.
LABORATÓRIO DE ESTUDOS E PESQUISAS EM EC=
ONOMIA
SOCIAL. LEPES. Universidade de São Paulo. A importância dos Laboratórios=
de
Pesquisa. 2020. Disponível: lepes.fearp.usp.br. Acesso em: 10 jan. 2023=
.
LOPES, Luis =
Felipe
Dias; CHAVES, Bianca Michels; FABRÍCIO, Adriane; PORTO, Adriana; ALMEIDA, D=
amiana
Machado; OBREGON, Sandra Leonara; LIMA, Mauren
Pimentel, SILVA, Wesley Vieira; CARMARGO, Maria Emilia=
;
VEIGA, Claudimar Pereira; MOURA, Gilnei Luiz; S=
ILVA, Luciana
Santos Costa Vieira, COSTA, Vânia Medianeira Flores. Analysis of well-being and anxiety among university students. Int=
ernational
Journal Environmental Research and Public Health , v. 17, n. 11, p. 3874,
2020. https://doi.org/10.3390/ijerph17113874.
MIKHAILOV, Andrei; REICHERT, Fernanda.
Influência da capacidade absortiva sobre inovação: uma revisão sistemática =
de
literatura. Revista de Administração Mackenzie , v. 20, n. 6, p.
eRAMD190033, 2019. https://doi.org/10.1590/1678-6971/eRAMD190033.
MURRAY, K.; ROUX, Dirk; NEL, Jeanne; DRIVER, Amanda; FREIMUND, Wayne.
Absorptive capacity as a guiding concept for effective public sector manage=
ment
and conservation of freshwater ecosystems. Environmental manageme=
nt ,
v. 47, n. 5, p. 917-925, 2011. https://doi.org/10.1007/s00267-011-9659-7.
OLIVEIRA, Ronniel;
RABELO NETO, Alexandre.; NASCIMENTO, João Carlos; MELO, Rodrigo Santos. O
desempenho organizacional de uma instituição de ensino superior: uma anális=
e da
capacidade absortiva potencial e da inovação. Revista Base (Administraçã=
o e
Contabilidade) da UNISINOS , v. 15, n. 4, p. 291-316, 2018. https://doi.=
org/10.4013/base.2018.154.04.
PADILHA, Rafaela Oliveira. Análise da
capacidade absortiva para inovação: uma pesquisa empírica em laboratórios de
pesquisa universitários. 2020. 148f. Dissertação (Mestrado em Tecnologi=
as
da Informação e Comunicação). Universidade Federal de Santa Catarina – UFSC,
Araranguá, 2020. Disponível em:
https://repositorio.ufsc.br/handle/123456789/216114. Acesso em: 10 jan. 202=
3.
PEREIRA, Bruno Alencar; FARIAS, Josivania Silva. Absorptive capacity and the
diffusion of innovation in NTBFs and startups: A study in the Brazilian Fed=
eral
District. REGEPE Entrepreneurship and Small Business Journal , v. 12,=
n.
1, 2023. https://doi.org/10.14211/regepe.esbj.e2380.
PETERSON, Robert; KIM, Yeolib . On the
relationship between coefficient alpha and composite reliability. Journa=
l of
Applied Psychology , v. 98, n. 1, p. 194, 2013. https://doi.org/10.1037/=
a0030767.
PORTO, Geciâne Silveira.
A Decisão de Cooperação Universidade-Empresa sob a Ótica dos Líderes de
Grupos de Pesquisa da USP Cadastrados no Diretório de Pesquisa do CNPq.
2006. 187f. Tese (Doutorado em Administração). Universidade de São Paulo-US=
P,
São Paulo, 2006. https://doi.org/10.11606/T.96.2012.tde-23042012-113635.
RINGLE, Christian; WENDE, Sven; BECKER, Jan-Michael. SmartPLS 4. Bönnin=
gstedt :
SmartPLS , 2023.Disponível em:
https://www.smartpls.com/. =
Acesso<=
/span> em : 15 set. 2023.
TEECE, David; PISANO, Gary; SHUEN, Amy. Dynamic capabilities and
strategic management. Strategic Management Journal , v. 18, n. 7, p.
509-533, 1997. https://doi.org/10.1002/(SICI)1097-0266(199708)18:7<509:: AID-SMJ882>3.0.CO;2-Z
SHARMA, Pratyush Nidhi; SHMUELI, Galit; SARSTEDT, Marko; DANKS, Nich=
olas;
RAY, Soumya. Prediction ‐ oriented model selection in =
partial
least squares path modeling. Decision Sciences , v. 52, n. 3, p. 567-=
607,
2021. https://doi.org/10.1111/deci.12329.
SHUAI, Chunyan ; SHAN, Jun; BAI, Jincheng=
; LEE,
Jaeyoung ; HE, Min; OUYANG, Xin. Relationship an=
alysis
of short-term origin–destination prediction performance and spatiotemporal
characteristics in urban rail transit. Transportation Research Part A:
Policy and Practice, v. 164, p. 206-223, 2022. https://doi.org/10.1016/=
j.tra.2022.08.006.
SANTOS, Patricia M.; CIRILLO, Marcelo Ângelo. Construction of the
average variance extracted index for construct validation in structural
equation models with adaptive regressions. Communications in
Statistics-Simulation and Computation , v. 52, n. 4, p. 1639-1650, 2023.=
https://doi.org/10.1080/03610918.2021.1888122.
SOUSA, Romario Martins; FLORÊNCIO, Marci=
o Nannini;
MOARES, Thiago Assunção. Importância da Capacidade Absortiva na Inovação do
Setor Público. Revista FSA , Teresina, v. 19, n. 9, p. 49-62, 2022.
http://dx.doi.org/10.12819/2022.19.9.3.
VERBREE, Maaike; HORLING, Edwin; GROENEWEGEN, Peter; VAN DER WEIJDEN=
, Imge ; BESSELAAR, Peter. Addressing leadership and
management of research groups. A multivariate study. Working paper.
Rathenau Instituut , Den Haag, 2011. Disponível em :
https://hms.harvard.edu/sites/default/files/assets/Sites/Academy/images/Ins=
ights/Strong%20Research%20Groups%20Verbree.pdf.
Acesso em : 10 jan. 2023.
WANG, Catherine; AHMED, Pervaiz. The development and validation of <=
span
class=3DGramE>the organisational innovat=
iveness
construct using confirmatory factor analysis. European Journal of Innova=
tion
Management , v. 7, n. 4, p. 303 - 313, 2004. https://doi.org/10.1108/146=
01060410565056.
&nbs=
p;
XIE, Xuemei; ZOU, Hailiang ; QI, Guoyou . Knowledge absorptive capacity and innovation
performance in high-tech companies: A multi-mediating analysis. Journal =
of
Business Research, v. 88, p. 289-297, 2018.
https://doi.org/10.1016/j.jbusres.2018.01.019.
ZAHRA, Shaker; GEORGE, Gerard. Absorptive capacity: A
review , reconceptualization, and extension. Academy of Management
Review, v. 27, n. 2, p. 185-203, 2002. https://doi.org/10.2307/4134351.=
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A Influência da Capacidade
Absortiva no Desempenho Inovativo dos Grupos de Pesquisa no Piauí
IS=
SN
2237-4558 •<=
/span> Navus • <=
/span>Florianópolis
• SC • <=
/span>v.
14 • p. 01- 20 • jan. /dez . 2023
15
ISSN 2237-4558 • Navus • &n=
bsp;Florianópolis •
SC • v.9
• n.2 • <=
/span>p.
XX-XX • abr./jun. 2019
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