The Influences of Strategic Information Systems on the Relationship between Innovation and Organizational Performance

Abstract

This study aims to identify the influences of strategic information systems (SIS) on the relationship of innovation (exploration/exploitation), ambidexterity and organizational performance (OP). We used the statistical technique of Partial least squares path modeling (PLS-PM) with a sample of 256 Brazilian companies from different sectors. The
data revealed that exploitative innovation was positively associated with OP. As a result of the study, it was confirmed that a strong SIS presence increases the influences of innovation (exploration and exploitation) and ambidexterity on OP.
Ambidexterity was positively associated with OP and presented higher path coefficients compared to the relationships between exploratory and exploitative innovation and OP. This relationship shows that ambidextrous organizations have higher OP. The study also confirmed that 96% of ambidextrous organizations have a strong SIS presence. This study may have implications for the management practices of organizations that use
SIS in their strategic planning stages by enabling innovation focused on improving OP.

Full text




Adilson Carlos Yoshikuni 1,†
1 FGV-EAESP, São Paulo, SP, Brazil
José Eduardo R. Favaretto 2,Ω
2 FGV-EAESP, São Paulo, SP, Brazil
Alberto Luiz Albertin 3,¥
3 FGV-EAESP, São Paulo, SP, Brazil
Fernando de Souza Meirelles 4,*
4 FGV-EAESP, São Paulo, SP, Brazil

The integration of information technology/information systems
(IT/IS) into business strategy has been studied extensively by aca-
demics and professionals for decades (MARABELLI; GALLIERS,
2017; MERALI; PAPADOPOULOS; NADKARNI, 2012; MIKALEF;
PATELI, 2017; PEPPARD; GALLIERS; THOROGOOD, 2014;
TEUBNER, 2013; WARD, 2012).
Seminal scientic research regarding strategic information sys-
tems (SIS) grounds studies in this theoretical context (CHAN, 2002;
CHAN; HUFF, 1992; KING, 1978), with a focus on clarifying the
contribution of SIS to business strategy process and content (CHEN,
D.Q. et al., 2010; NEWKIRK; LEDERER, 2006; PHILIP, 2007). The
academic literature reiterates that SIS and the appropriate and time-
ly use of IT/IS eectively support the phases of strategic planning in
order to maintain or gain competitive advantage and organizational
This work licensed under a Creative Commons Attribution 4.0 International License.
Corresponding author:
† FGV-EAESP, São Paulo, SP, Brazil
E-mail: ayoshikuni@terra.com.br
Ω FGV-EAESP, São Paulo, SP, Brazil
E-mail: jose@favaretto.net
¥ FGV-EAESP, São Paulo, SP, Brazil
E-mail: albertin@fgv.br
*FGV-EAESP, São Paulo, SP, Brazil
E-mail: fernando.meirelles@fgv.br
Received: 08/22/2017.
Revised: 09/22/2017.
Accepted: 11/30/2017.
Published Online: 06/26/2018.
DOI: http://dx.doi.org/10.15728/bbr.2018.15.5.3
ABSTRACT
is study aims to identify the influences of strategic information systems
(SIS) on the relationship of innovation (exploration/exploitation),
ambidexterity and organizational performance (OP). We used the
statistical technique of Partial least squares path modeling (PLS-PM)
with a sample of 256 Brazilian companies from different sectors. e
data revealed that exploitative innovation was positively associated with
OP. As a result of the study, it was confirmed that a strong SIS presence
increases the influences of innovation (exploration and exploitation) and
ambidexterity on OP.
Ambidexterity was positively associated with OP and presented higher
path coefficients compared to the relationships between exploratory and
exploitative innovation and OP. is relationship shows that ambidextrous
organizations have higher OP. e study also confirmed that 96% of
ambidextrous organizations have a strong SIS presence. is study may
have implications for the management practices of organizations that use
SIS in their strategic planning stages by enabling innovation focused on
improving OP.
Keywords: Strategic Information Systems, Exploration and exploitation
in innovation, Organizational performance, Ambidexterity.


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performance (JOHNSON; LEDERER, 2013; LEIDNER; LO; PRESTON, 2011; SEGARS,
A.H.; GROVER; TENG, 1998).
IT studies add that the creation of value and benets through the eective use of IS
(ALBERTIN; ALBERTIN, 2012; MELVILLE; KRAEMER; GURBAXANI, 2004) occurs
by developing skills (PAVLOU; EL SAWY, 2006; YOSHIKUNI; ALBERTIN, 2017) that
help organizations become agile and sensitive to changes and facilitate competitive strate-
gies involving explorative and exploitative innovation (LEIDNER; LO; PRESTON, 2011;
MARABELLI; GALLIERS, 2017).
Thus, in this article we nd that SIS facilitates business strategy process and content
and has the potential to increase positive eects in the relationships among exploratory and
exploitative innovation activities, ambidexterity and organizational performance.

-

Developing the ability to integrate the vision, the product/service portfolio, the business
processes and the implementation of strategies that meet the constant needs of the market
is a challenge perpetually faced by organizations. In this regard, an organization develops
the ability to create and absorb key technologies in order to promote competitive strategies
through the driving action of innovation (MINTZBERG; AHLSTRAND; LAMPEL, 2009).
Innovation can be classied as exploratory and exploitative (GUPTA; SMITH;
SHALLEY, 2006; JANSEN et al., 2006; SCANDELARI; CUNHA, 2013). Seminal resear-
ch regarding innovation with this approach emerged with March (1991) and later in studies
concerning organizational learning, strategy and entrepreneurship (JANSEN et al., 2006).
The term exploration in the context of the strategic role relates to the investigation of
new ideas and solutions, encompassing the organizational actions of search, discovery,
experimentation and risk-taking (HO; LU, 2015; MARCH, 1991). With this focus, it in-
volves experimenting with new ideas, paradigms, technologies, strategies and knowledge,
with the aim of discovering alternatives that will surpass or at least meet the needs of
the market (BENNER; TUSHMAN, 2003; LEWIN; VOLBERDA, 1999; SCANDELARI;
CUNHA, 2013). According to Jansen et al. (2006), exploratory innovation is based on
developing strategies that will meet new demands for products and services, in a frequent
cycle of reinventing the portfolio, accepting challenges to serve new markets, and deve-
loping new distribution channels and new units and production lines in order to achieve
competitive advantage. Companies that position themselves with exploratory innovation
practices develop the ability to frequently map the overall external environment with the
aim of identifying factors that enable them to launch new products and services, in order to
dierentiate themselves from competitors and establish themselves as a leading company
(MINTZBERG; AHLSTRAND; LAMPEL, 2009; PORTER, 1986). Companies that prac-
tice exploratory innovation thus require human, technological, and organizational capital
resources (KAPLAN; NORTON, 2008) with the ability to operate in competitive environ-
ments. Exploratory innovation strategies are associated with uncertainty and greater risks of
failure in implementing the strategy, but they oer superior performance gains (BENNER;
TUSHMAN, 2003; KAPLAN; NORTON, 2008; MINTZBERG; AHLSTRAND; LAMPEL,
2009; PORTER, 1986; SCANDELARI; CUNHA, 2013).
The term exploitation, in the strategic context, is related to using resources, processes,
and strategies to make incremental innovations, which are designed to meet the needs of
current customers and markets (BENNER; TUSHMAN, 2003; POPADIUK et al., 2010). In
this respect, the essence of exploitation in innovation is associated with continuous impro-
vement of existing competencies, technologies and paradigms (MARCH, 1991). According

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to Jansen et al. (2006), exploitative innovation is based on improving existing products and
services, with frequent and minor adjustments to the portfolio, in order to maintain and/
or expand their current customer and market share. Organizations that position themselves
with exploitative innovation practices develop the ability to frequently promote actions
that increase productive eciency and eectiveness by rationalizing the use of resour-
ces and making incremental innovations to existing products and services (MINTZBERG;
AHLSTRAND; LAMPEL, 2009; PORTER, 1986). Exploitative innovation strategies are
associated with risk aversion and a focus on the continuous improvement of existing abi-
lities, competencies and technologies, in order to streamline business processes (LEWIN;
VOLBERDA, 1999), legitimizing the standardization and automation of the routine, with a
strong appeal to the productive strategy in order to generate gains from economies of scale
(GUPTA; SMITH; SHALLEY, 2006).
The term “ambidextrous organization” is described by the seminal academic literatu-
re (DUNCAN, 1976; TUSHMAN; O’REILLY, 1996) as an organization that is seeking
a ‘balance’ between exploratory and exploitative innovation. Ambidexterity is the orga-
nizational ability to implement both incremental (exploitative) and radical (explorative)
changes to enable the organization to be successful over long periods of time. Other studies,
in addition to performing empirical tests on the inuence of organizational performance
and organizational ambidexterity in the context of technological innovation (HE; WONG,
2004; LEIDNER; LO; PRESTON, 2011; POPADIUK; BIDO, 2016), have examined am-
bidexterity from dierent conceptual perspectives, which indicated that ambidextrous or-
ganizations are capable of simultaneously exploiting competencies that already exist (ex-
ploitation) and exploring new opportunities (exploration) (BENNER; TUSHMAN, 2003;
LAVIE; STETTNER; TUSHMAN, 2010; RAISCH et al., 2009).

When measuring organizational performance, the indicators tend to measure success
along one of its two tracks: nancial or non-nancial results (ALBERTIN; ALBERTIN, 2012;
JÄÄSKELÄINEN; LUUKKANEN, 2017; MITHAS; RAMASUBBU; SAMBAMURTHY,
2011; MOSTAGHEL et al., 2015; REEFKE; TROCCHI, 2013; SEN; BINGOL; VAYWAY,
2017). Financial measurements represent the long-term value of the organization’s per-
formance (ATKINSON et al., 2011; KIM et al., 2011) and are the result of organizational
eectiveness in strategy implementation, productivity, and revenue growth (KAPLAN;
NORTON, 2008; OUAKOUAK; OUEDRAOGO, 2013; YOSHIKUNI; ALBERTIN,
2017).
According to Kaplan and Norton (2008), in order to achieve long-term value for sha-
reholders, we need to understand customer performance and environmental conditions
(MITHAS; RAMASUBBU; SAMBAMURTHY, 2011; YOSHIKUNI; ALBERTIN, 2014).
Customer performance is measured by customer satisfaction with product and service qua-
lity, customer relationships and retention, and brand image (KAPLAN; NORTON, 2008;
LEÓNSORIANO; MUÑOZTORRES; CHALMETAROSALEÑ, 2010; MOSTAGHEL
et al., 2015). The organization therefore, develops competencies to perform the activities
in the business value chain (PARK; LEE; CHAE, 2017; PERKINS; GREY; REMMERS,
2014; REEFKE; TROCCHI, 2013) in order to deliver the attributes requested by customers
and promote satisfaction (KAPLAN; NORTON, 2008), in addition to retain those custo-
mers (SILA, 2007). Measuring the dierent perspectives of organizational performance is
thus essential to understanding the causes of a company’s nancial result and their perfor-
mance in terms of non-nancial indicators (ALBERTIN; ALBERTIN, 2012; KAPLAN;
NORTON, 2008; PARK; LEE; CHAE, 2017; YOSHIKUNI; ALBERTIN, 2017).

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
Over the last several decades, research regarding IT/IS value creation for businesses
has been intensifying (MARABELLI; GALLIERS, 2017; MELVILLE; KRAEMER;
GURBAXANI, 2004; MERALI; PAPADOPOULOS; NADKARNI, 2012). An eective
use of IT/IS in the business strategy was highlighted as one of the most important factors
for CIOs and CEOs (JOHNSON; LEDERER, 2013; PHILIP, 2007). Studies have shown
that SIS supports business strategy process and content, improving competitive advantage
and organizational performance, even in highly competitive environments (CHEN, Y. et
al., 2014; MERALI; PAPADOPOULOS; NADKARNI, 2012; TEUBNER, 2013). SIS is
dened as a portfolio of IT/IS applications that collect, process, analyze and make data/
information available for decision making (O’BRIEN; MARAKAS, 2007; SABHERWAL;
CHAN, 2001), and it exists within business strategy process and content in order to achieve
business objectives.
Several studies have found that SIS supports the strategic planning process (NEWKIRK;
LEDERER, 2006; SINGH; WATSON; WATSON, 2002; YOSHIKUNI; JERONIMO, 2013)
and strategy content by facilitating strategic awareness through the dissemination of strategic
objectives/goals to the entire local organization (CHEN, D.Q. et al., 2010; SEGARS, A.H.;
GROVER; TENG, 1998); in the analysis of the company’s overall environment, by making
it possible to map opportunities and threats in the external environment (DAMERON; LÊ;
LEBARON, 2015; NEWKIRK; LEDERER, 2006; XUE, L.; RAY; SAMBAMURTHY,
2012); in the strategy design, by aligning internal—technological, human, and organiza-
tional resources and opportunities and mitigating threats (ARVIDSSON; HOLMSTRÖM;
LYYTINEN, 2014; LEIDNER; LO; PRESTON, 2011; SINGH; WATSON; WATSON,
2002); in the formulation, by selecting strategies to develop new business processes ena-
bled by the IT/IS architecture (JOHNSON; LEDERER, 2013; LEIDNER; LO; PRESTON,
2011; MARABELLI; GALLIERS, 2017; MERALI; PAPADOPOULOS; NADKARNI,
2012; SHOLLO; GALLIERS, 2016); and in the implementation and monitoring of the bu-
siness strategy, by supporting the change process, and the execution and control of action
plans (KAPLAN; NORTON, 2008; ROUHANI et al., 2016; SHOLLO; GALLIERS, 2016;
SINGH; WATSON; WATSON, 2002).
In short, SIS incorporates the strategic planning process and facilitates the cooperation,
analysis and participation of employees, enabling them to think about, analyze, deploy and
follow strategic planning through the IT/IS portfolios.
3.     

Based on the existing literature, we are able to identify relationships among SIS, explo-
ratory and exploitative innovation, ambidexterity and organizational performance, which
served as the foundation for developing the study’s conceptual model (Figure 1) and also
raised the respective hypotheses to be tested.

According to Porter (1986), companies develop specic abilities and competencies to
formulate technological strategies for incremental innovations (exploitation), through cost
leadership, which seek greater intensity in order to optimize processes and improve existing
products; or radical innovations (exploration), through dierentiation, with the ability to
identify, choose and explore knowledge and technologies—external and internal—in order
to oer products and services that provide a perception of value creation to the market by
means of market dierentiation, novelty and exclusivity.

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The dierent approaches to innovation strategy (MINTZBERG; AHLSTRAND;
LAMPEL, 2009) emphasize actions focused on accurately understanding the external en-
vironment (exploration) in order to develop new products and services, and developing
internal expertise in business processes in order to achieve gains in operational eciency
and eectiveness (exploitation).
Assuming that innovation strategies are developed in order to increase the company’s
economic and nancial sustainability through revenue and productivity growth (KAPLAN;
NORTON, 2008; MINTZBERG; AHLSTRAND; LAMPEL, 2009; PORTER, 1986;
SCANDELARI; CUNHA, 2013; YOSHIKUNI; JERONIMO, 2013) and that organizatio-
nal performance is related to the company’s ability to use internal resources in business
processes (YOSHIKUNI; ALBERTIN, 2017), as well as based on previous studies in stable
economies, which found that innovation inuences organizational performance (FANG;
LEVINTHAL, 2009; HE; WONG, 2004; JANSEN et al., 2006; SCANDELARI; CUNHA,
2013; UOTILA et al., 2009), we propose the following hypotheses:
H1: Exploratory innovation is positively associated with organizational performance
H2: Exploitative innovation is positively associated with organizational performance.
An implicit premise in the studies by March (1991) is that organizations with supe-
rior performance seek to implement both exploratory and exploitative innovation activi-
ties. Studies that analyzed organizational ambidexterity (DUNCAN, 1976; TUSHMAN;
O’REILLY, 1996) also conrmed that the best performing organizations are ambidextrous.
Figure 1. Conceptual research model

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We also identify evidence of a generally positive relationship between organizational
ambidexterity and organizational performance in several empirical studies (GIBSON;
BIRKINSHAW, 2004; HE; WONG, 2004; LUBATKIN et al., 2006).
Recent studies suggest that organizations should seek ambidexterity in order to in-
crease competitive advantage and performance (LEIDNER; LO; PRESTON, 2011;
SCANDELARI; CUNHA, 2013). Thus, we propose the following hypothesis:
H3: Ambidexterity is positively associated with organizational performance.


According to Chen et al. (2010) and Segars et al. (1998), SIS facilitates the ability to
successfully execute business strategy process and content. The authors argue that business
strategy success involves the company’s ability to develop eective cooperative work from
employees, enabling them to think about, analyze and execute the strategy supported by IT/
IS. When SIS is incorporated into the strategic planning process, it facilitates strategic aware-
ness, promoting top-down and bottom-up communication/integration/cooperation (CHEN,
D.Q. et al., 2010), without any local or global borders, in order for everyone to understand
the strategic priorities (KARPOVSKY; GALLIERS, 2015; O’BRIEN; MARAKAS, 2007),
thus achieving organizational commitment through teamwork (SEGARS, ALBERT H.;
GROVER, 1998).
Pavlou and El Sawy (2010) found that the eective use of SIS enables a real-time per-
ception of a company’s existing resources, enabling them to adapt to transformations in the
external environment. SIS enables an organization to map the external factors of the overall
environment (DAMERON; LÊ; LEBARON, 2015; DAVENPORT; HARRIS; MORISON,
2010; JARZABKOWSKI; KAPLAN, 2015; NEWKIRK; LEDERER, 2006) and develop
innovation strategies that capture opportunities (KAPLAN; NORTON, 2008; PORTER,
1986).
SIS supports the design stage of business strategy, allowing a company to recon-
gure its existing operational capabilities to better respond to environmental changes
(ARVIDSSON; HOLMSTRÖM; LYYTINEN, 2014; LEIDNER; LO; PRESTON, 2011;
SEGARS, ALBERT H.; GROVER, 1998; SINGH; WATSON; WATSON, 2002), and faci-
litates the ability to spontaneously recongure existing resources during the construction
of new operational capabilities and address urgent, unpredictable and new environmental
situations (PAVLOU; EL SAWY, 2006, 2010).
SIS facilitates exibility and agility during the formulation stage of strategic planning,
enabling decision-making on strategies related to aggressiveness, analysis, proactivity, risk
or risk aversion, defensiveness and innovation (CHAN; HUFF, 1992).
Thus, SIS facilitates competencies that are essential to an organization eectively de-
veloping creativity strategies and/or productivity (control) strategies as a product of the
strategic planning process (CHEN, D.Q. et al., 2010). By denition, it is reasonable to
conclude that when an exploratory innovation strategy is supported by SIS, it focuses on
the company’s creativity through the creation of new products and services and new ap-
proaches using IT/IS resources, whereas an exploitative innovation strategy focuses on
the abilities promoted by SIS for control, i.e., for organizational eciency and produc-
tivity (MARABELLI; GALLIERS, 2017; MARTINEZ-SIMARRO; DEVECE; LLOPIS-
ALBERT, 2015; PHILIP, 2007). Incorporating SIS into the strategic planning process thus
helps disseminate strategic awareness, analyze external factors, and promote cooperation for
designing, developing, implementing and monitoring competitive strategies (NEWKIRK;
LEDERER, 2006) for exploratory/exploitative innovation (MARTINEZ-SIMARRO;

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DEVECE; LLOPIS-ALBERT, 2015); thereby, inuencing competitive advantage and orga-
nizational performance (CHEN, D.Q. et al., 2010). Therefore, we can formulate the hypo-
thesis that a strong or weak SIS presence inuences (MARTINEZ-SIMARRO; DEVECE;
LLOPIS-ALBERT, 2015) the relationship between innovation strategies and organizational
performance.
H4: A strong or weak SIS presence moderates the relationship between exploratory in-
novation and organizational performance.
H5: A strong or weak SIS presence moderates the relationship between exploitative
innovation and organizational performance.
An important study by Chen et al. (2010) contributed to the academic literature regarding
SIS by identifying typologies. This study was expanded by Leidner, Lo and Preston (2011),
who included an analysis of ambidexterity and provided empirical evidence of the positive
relationship between SIS and organizational performance; this same study also found that
ambidextrous organizations are considered to have the highest performance. Other studies
related to SIS involving ambidexterity identied the organizational challenge of simulta-
neously ‘balancing’ explorative and exploitative activities, with a focus on organizational
learning and innovation (MARABELLI; GALLIERS, 2017; MERALI; PAPADOPOULOS;
NADKARNI, 2012). Therefore, we can formulate the following hypothesis:
H6: A strong or weak SIS presence moderates the relationship between ambidexterity
and organizational performance.

Control variables (CV) are critical to management research because they simplify the in-
terpretation of the results of statistical analyses (CARLSON; WU, 2012). Given that orga-
nizations have signicant expenses and investments related to IT/IS use and management, a
study performed annually by Fundação Getulio Vargas found that the services sector spent
11% of its net income in 2017, whereas the industrial sector spent 4.5% (MEIRELLES,
2018). Thus, this study uses control variables in an attempt to investigate the inuences
of an organization’s characteristics (MELVILLE; KRAEMER; GURBAXANI, 2004)—its
sector and size, based on the number of employees—on the relationship between innova-
tion and organizational performance.


To evaluate innovation (exploration/exploitation), we decided to use measurements and
items at the level of the organizational unit, taken from Jansen, Van Den Bosch and Volberda
(2006). We measured SIS using the scale by Singh (2002) and Newkirk and Lederer (2006)
and specialists in the eld validated it through content analysis (MORGADO et al., 2018),
and the reliability, validity and parsimony of the items were conrmed, as recommended by
Wieland, Durach, Kembro and Treiblmaier (2017). We used the scale to measure organiza-
tional performance (KAPLAN; NORTON, 2008), proposed by Yoshikuni et al. (2014), for
the dimensions of nancial performance, market, internal process, and learning and growth.
For the innovation and SIS items, we need to perform translations and consult with subject
specialists, who made semantic modications in order to make it comprehensible without
compromising the validity of the content. All the latent variables had at least three items,

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which made it possible to measure them adequately according to the recommendations of
Hair, Hult, Ringle and Sarsdest (2013).
Specialists in the eld of strategy (researchers and professors) and IS, who had more
than 10 years of experience, evaluated the research questionnaire. The evaluation of the
instrument was positive and showed that the questionnaire represented the perception of
the variables used in the scales.
We evaluated all the items using a 7-point Likert scale, ranging from 1 (strongly disa-
gree) to 7 (strongly agree). The full scale with the constructs, assertions (variables/indica-
tors), and their factor loadings is available in Appendix I of this study as supplementary
material.

We selected a sample from the directory provided by the Center for Applied Information
Technology (Centro de Tecnologia de Informação Aplicada – GVcia) at the São Paulo
School of Business Administration (Escola de Administração de Empresas de São Paulo
– EAESP), Fundação Getulio Vargas (FGV). We chose the respondents based on their posi-
tion, experience and professional knowledge (KIM et al., 2011), and they provided reliable
information about the characteristics of the group or organization; they included senior
business managers with appropriate knowledge of IT/IS and strategic business processes.
We administered the study via email through the distribution of 1353 invitations to or-
ganizations, of which 256 (19%) responded to the questionnaire using a form available on
the Internet. The sample size met the requirements for partial least squares path modeling
(PLS-PM) (HENSELER; RINGLE; SINKOVICS, 2009; URBACH; AHLEMANN, 2010).
Of those who responded to the questionnaire on behalf of their respective organizations,
39% were presidents, directors or superintendents, 36% were managers or coordinators,
and 25% were supervisors with decision-making power.
Table 1 describes the composition of the companies included in the sample in terms of
the sector in which they operate and the number of employees.
As observed from the data presented in Table 1, 93% of the sample was composed of
companies in the services and manufacturing sectors, and 40% of the sample was compo-
sed of organizations with more than 500 employees.

After evaluating the descriptive statistics of the demographic variables, the scale was
modied using conrmatory factor analysis (convergent validity, discriminant validity and
reliability).
We estimated the analytical structural model using PLS-PM by analyzing common is-
sues involving the simultaneous analysis of multiple variables, for example, with asymme-
tric variable distribution or limited data (RINGLE; SARSTEDT; STRAUB, 2012), using
the SmartPLS 2.0 M3 software package for all analyses (RINGLE; WENDE; WILL, 2005).
Sector Number of Employees
Agrobusiness 4% ≤ 9 9%
Government 3% 10 – 49 11%
Manufacturing 36% 50 – 99 16%
Services 56% 100 – 249 14%
250 – 499 9%
≥ 500 40%
Table 1. Sample demographic data – sectors and number of employees

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

We dened the dimensions a priori based on the theoretical reference and were maintai-
ned for conrmatory factor analysis.
After eliminating two items that had a factor loading less than or equal to 0.5 in addi-
tion to high cross-loadings (lack of discriminant validity), the convergent validity of all
the constructs was considered adequate, with items greater than 0.707, and all the cons-
tructs presented an average variance extracted greater than 0.5 (HENSELER; RINGLE;
SINKOVICS, 2009; RINGLE; BIDO; DA SILVA, 2014). We nd the values on the dia-
gonal (square root of average variance extracted) to be greater than the values outside the
diagonal (correlations), and thus, there is discriminant validity (HAIR et al., 2013). The
reliability is also adequate, with composite reliability values greater than 0.7, as can be
observed from Table 2 (HAIR et al., 2013; ROUHANI et al., 2016).
From Table 2, we can observe that the organizational performance (OP) constructs (1,
2, 3 and 4) are correlated (0.46 to 0.58), which conrms the possibility of using them as
indicators for a second-order construct. The second-order OP variable produced an average
variance extracted of 0.642 and a composite reliability estimate of 0.93. In a comparison of
the Fornell-Larcker criterion with the square root of average variance extracted values of
the OP variable (0.801), the criterion was revealed to be satisfactory.

The operationalization of the model was complex because it involved evaluating the
moderating eect of SIS and the control variables, in addition to including a latent variable
to eliminate common method variance (i.e., the measured latent marker variable, hereafter
the MLMV method). We apply the MLMV approach by Chinn W.W. et al., (2013) to con-
trol common method variance. Specically, four items were designed to have the lowest
possible logical correlation with the other constructs under investigation (see Chart 1).
Therefore, we analyze the model in more than one case (Table 3), and we discuss those
results in the following sections.
In case 1, the relationship between exploitative innovation and OP was 0.307 (p<0.001),
and in case 2, without the latent variable (MLMV), it was 0.291 (p-value < 0.001); we con-
clude that the common method variance was minimal (0.02), and the result of case 2 will
Construct 1 2 3 4 5 6 7
1 - Financial (IF) 0.89
2 - Market (MA) 0.46 0.78
3 - Business Process (IP) 0.53 0.57 0.78
4 - Learning & Growth (LG) 0.46 0.51 0.58 0.75
5 - Exploratory innovation (EXIN) 0.37 0.27 0.58 0.42 0.80
6 - Exploitative innovation (EXIP) 0.44 0.48 0.64 0.53 0.71 0.76
7 - SIS 0.56 0.50 0.65 0.63 0.61 0.67 0.87
Average variance extracted 0.79 0.62 0.61 0.56 0.64 0.58 0.76
Composite reliability 0.92 0.83 0.82 0.79 0.90 0.87 0.94
Means 4.58 5.40 4.97 5.16 4.23 5.01 4.81
Standard Deviation 1.35 0.96 1.14 1.10 1.31 1.18 1.14
Coefficient of Variation 30% 18% 23% 21% 31% 24% 24%
Table 2. Matrix of correlations between the first-order constructs

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be used to discuss hypotheses H1 and H2. Therefore, hypothesis H1, which presents the
relationship between explorative innovation and OP, did not present statistical signicance,
with a p-value > 0.05. Hypothesis H2 was supported and presented the inuence of exploi-
tative innovation on OP (0.291; p-value < 0.001).
To verify hypothesis H3, we classify ambidextrous organizations as those with a va-
lue higher than the average for the explorative and exploitative innovation variables, in a
total of 103 cases; we create the ambidexterity (interactive term) variable by cross-multi-
plying all the standardized items of the explorative and exploitative innovation variables
(CHIN, WYNNE W; MARCOLIN; NEWSTED, 2003; LEIDNER; LO; PRESTON, 2011).
Hypothesis H3 is supported, as the inuence of the relationship between ambidextrous or-
ganizations and OP is statistically positive and signicant on performance (0.377; p-value
< 0,001).
SIS presented an inuence on the dependent variable (case 2; 0.524; p-value < 0.001),
indicating moderation of the variable in the relationship between innovation and OP
(CARLSON; WU, 2012). Thus, to test hypotheses H4 and H5, we create heterogeneous
databases to evaluate the dierences in structural coecients between groups (HAIR et
MLMV_1: It is easy to reach my goals.
MLMV_2: I have never given up on the dream of having my own business.
MLMV_3: I have a positive attitude towards others.
MLMV_4: I always imagine my future home.
Chart 1. Formative indicators used for the MLMV analysis
Case Structural Models Structural coefficient Standard error t-value p-value R²
1Exploratory
innovation -> OP -0.045 0.073 0.620 0.535 60.60%
Exploitative
innovation -> OP 0.307 0.090 3.235 0.001
SIS -> OP 0.504 0.083 6.322 0.000
SECTOR -> OP 0.077 0.103 0.767 0.443
SIZE -> OP 0.036 0.058 0.588 0.557
MLMV -> OP 0.137 0.055 2.083 0.037
2Exploratory
innovation -> OP -0.032 0.077 0.410 0.682 59.50%
Exploitative
innovation -> OP 0.291 0.078 3.753 0.000
SIS -> OP 0.524 0.081 6.542 0.000
MLMV -> OP 0.132 0.061 1.878 0.060
3Exploratory
innovation -> OP 0.030 0.074 0.268 0.789 40.00%
Exploitative
innovation -> OP 0.289 0.084 3.311 0.001
SIS -> OP 0.433 0.073 6.013 0.000
4Exploratory
innovation -> OP -0.112 0.081 1.615 0.106 39.50%
Exploitative
innovation -> OP 0.413 0.079 5.416 0.000
SIS -> OP 0.412 0.072 5.552 0.000
Table 3. Standardized regression coefficients of the structural models
Caption: We measure the sector by using two formative indicators (dummy) to represent the following categories: agribusi-
ness, government, manufacturing and services.
Note 1: We estimate the significance by using bootstrapping with N= 256 cases and 1000 repetitions in SmartPLS 2.0 M3.

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al., 2013) in the relationship between innovation and OP. The groups classied in case 3
(“weak SIS presence”) have averages less than or equal to 4 points (100 cases), and those
in case 4 (“strong SIS presence”) had averages greater than 4 points (156 cases).
The relationship between explorative innovation and OP in both cases 3 and 4 does not
present statistical signicance (p-value > 0.05), and thus hypothesis H4 is not supported.
However, the relationship between exploitative innovation and OP presented statistical sig-
nicance in both cases 3 and 4 (0.289; 0.413; p-values < 0.001), conrming hypothesis H5.
Comparing the path eects between the SIS groups, we observe a signicant variation of
0.125 (30%).
To evaluate hypothesis H6, we separate the database into two groups. The rst is the
group of ambidextrous companies with a strong SIS presence (99 cases), which presents
positive and signicant eects on performance (0.359; p-value < 0.001; R2 = 12.6%).
Given the insucient size of the second group, ambidextrous companies with weak SIS
(only 4 cases), we are not able to verify the relationship’s eects or statistical signicance,
and therefore, the hypothesis is partially supported.
Using the data, we are able to perform additional analyses regarding the inuence of SIS
on the relationship between innovation and OP. First, independent groups of companies are
classied as having a strong presence of exploratory (46 cases) and exploitative (210 cases)
innovation activities, moderated by a strong or weak SIS presence. Next, the relationship
between exploitative innovation and OP is analyzed for groups with strong (158 cases) and
weak (52 cases) SIS, and the path eects are positive and statically signicant (strong SIS;
0.557; p-value < 0.001; R2 = 31.1%; and weak SIS; 0.339; p-value < 0.05; R2 = 11.5%),
presenting a dierence of 0.22 (39%) between path coecients. Then, we perform the same
analysis for the relationship between explorative innovation and OP for groups with strong
(37 cases) and weak (9 cases) SIS. For the strong SIS group, the eect on the relationship
between explorative innovation and performance is positive and statistically signicant
(0.453; p-value < 0.001; R2 = 20.5%); however, as there are only 4 cases for the weak SIS
group, it is impossible to statistically test the relationship.
The sector and number of employee CV presented no statistically signicant eect (p-
-value > 0.05) on the OP construct, and they were extracted from the model in order to
remove their eect on the relationships of interest to this study (CARLSON; WU, 2012).
As indicated by the values of R2 listed in Table 3, the determination coecients indicate
that the relationship between innovation and OP has a large eect (HAIR et al., 2013).

The testing of hypothesis H1 (exploratory innovation -> OP) did not present statistical
signicance; this result diers from those of other studies on innovation (MARTINEZ-
SIMARRO; DEVECE; LLOPIS-ALBERT, 2015; UOTILA et al., 2009). However, in a
specic analysis of the group of 37 companies with a strong presence of exploratory inno-
vation activities and a strong SIS presence, the relationship between exploratory innovation
and corporate performance had a positive and statistically signicant eect. This analysis
allows us identifying—in a group restricted to the 14% of companies with a strong SIS pre-
sence—the inuence of the relationship between exploratory innovation activities and OP,
in line with other studies regarding SIS (LEIDNER; LO; PRESTON, 2011; XUE, LING;
RAY; SAMBAMURTHY, 2012).
The testing of hypothesis H2 (exploitative innovation -> OP) provides support to con-
rm that innovation has a more substantial inuence on an organization’s performance
under conditions with a strong SIS presence. The study shows that companies that use SIS
intensively in business strategy processes have a 30% higher contribution to achieving
OP. The result was expanded, and we found that companies with a strong presence of

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exploitative activities and a strong SIS presence have a 39% higher inuence on OP than
companies with a weak SIS presence. We believe that incorporating SIS into the strategic
planning process facilitates the content of exploitative innovation strategies, thereby allo-
wing the company to regularly implement small adaptations in its portfolio of products and
services, making itself present in its local market, expanding its relationship with the custo-
mer and improving its eciency and eectiveness in business processes. The results are in
line with studies performed at Brazilian companies by Yoshikuni and Albertin (2017), who
found that IT/IS are intensied to make productivity gains through operational eciency
and eectiveness.
The testing of hypothesis H3 (Ambidexterity -> OP) is supported (0.377; p-value <
0.001) and shows that 40% of the companies in the sample develop ambidextrous activities,
conrming that not all companies develop this ability (DUNCAN, 1976; LEIDNER; LO;
PRESTON, 2011).
Hypothesis H4, which tested a strong or weak SIS presence in the relationship between
exploratory innovation and OP, is not supported in the complete sample. However, in a
group of companies (37 cases) with a strong SIS presence and a strong presence of explo-
ratory innovation, we nd a positive and statistically signicant association with OP. This
relationship identies that the stronger the SIS presence, the greater the contribution of
exploratory innovation to OP.
Hypothesis H5, which tested a strong or weak SIS presence in the relationship between
exploitative innovation and OP, is supported. A strong SIS presence shows a 30% higher
contribution to the path coecient for the relationship between exploitative innovation and
OP, when compared to a weak SIS presence. For a group of companies (158 cases) with
a strong SIS presence and a strong presence of exploitative innovation activities, there is
a 40% higher contribution to the path coecient for the relationship between exploitative
innovation and OP, compared to a weak SIS presence. This relationship signies that the
stronger the SIS presence, the greater the contribution of exploitative innovation to OP.
Hypothesis H6 is partially supported, as the group of companies with a weak SIS presen-
ce is insucient for the statistical test (4 cases). However, the study shows that a strong SIS
presence has a positive and statistically signicant contribution to the relationship between
ambidexterity and OP. This relationship means that a strong SIS presence is one of the de-
termining factors for organizational ambidexterity.

The study made it possible to identify dierent impacts of a strong or weak SIS presence
on the relationships among the variables of innovation (exploitation and exploration), am-
bidexterity and organizational performance. In the research method, we use the PLS-PM
statistical approach with the SmartPLS software, which appears to be an appropriate tool
for analysis in the study.
The study’s main theoretical contribution is that a strong SIS presence increases the in-
uences of innovation (exploitation and exploration) and ambidexterity on organizational
performance.
An additional contribution to management practices is that when SIS is incorporated
into the strategic planning process through a portfolio of IT/IS applications, it enables
organizations to develop radical innovation activities (exploration), with an emphasis on
strategies for launching new products/services, focused on reaching emerging customers,
markets or distribution channels; and incremental innovations (exploitation), by adapting
and enhancing existing products and services, and their productive capabilities, designed to
meet the needs of existing customers.

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This article also makes practical contributions, demonstrating that organizations should
carefully examine how SIS can benet their phases of strategic planning by enabling inno-
vation focused on improving organizational performance.
This study is limited by the method used to collect the data. The sample is not probabi-
listic, and the results obtained for a given population cannot be generalized.
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