Available via license: CC BY 4.0
Content may be subject to copyright.
1
BAR-Brazilian Administration Review, 20(3), e220170, 2023.
Research Article
Data Availability: José Carlos Oyadomari, Yen-Tsang Chen, Ronaldo Gomes Dultra-de-Lima, & Octávio Ribeiro de Mendonça Neto. (2023). Data for
"Exploring the influences of cybernetic and dynamic controls on flexibility and performance" published by BAR - Brazilian Administration Review [Data
set]. Zenodo. https://doi.org/10.5281/zenodo.8407956.
The authors claim that they do not have authorization from the ECF platform to make the survey data available to the public. BAR – Brazilian Administration
Review encourages data sharing but, in compliance with ethical principles, it does not demand the disclosure of any means of identifying research
subjects.
Plagiarism Check: BAR maintains the practice of submitting all documents received to the plagiarism check, using specific tools, e.g.: iThenticate.
Peer review: is responsible for acknowledging an article’s potential contribution to the frontiers of scholarly knowledge on business or public administration.
The authors are the ultimate responsible for the consistency of the theoretical references, the accurate report of empirical data, the personal perspectives,
and the use of copyrighted material. This content was evaluated using the double-blind peer review process. The disclosure of the reviewers’ information on
the first page is made only after concluding the evaluation process, and with the voluntary consent of the respective reviewers.
Copyright: The authors retain the copyright relating to their article and grant the journal BAR – Brazilian Administration Review, the right of first publication,
with the work simultaneously licensed under the Creative Commons Attribution 4.0 International license (CC BY 4.0) The authors also retain their moral rights
to the article, including the right to be identified as the authors whenever the article is used in any form.
Keywords:
cybernetic controls; dynamic controls;
flexibility; performance.
JEL Code:
M41
Received:
December 12, 2022.
This paper was with the authors for two revisions
Accepted:
September 01, 2023.
Publication date:
October 16, 2023.
Corresponding author:
José Carlos Oyadomari
Universidade Presbiteriana Mackenzie
Rua da Consolação, n. 930, Consolação,
CEP 01302-907, São Paulo, SP, Brazil
Editor-in-Chief:
Ivan Lapuente Garrido
(Universidade do Vale do Rio dos Sinos, Brazil).
Associate Editor:
Isabel Brusca Alijarde
(University of Zaragoza, Spain).
Reviewers:
Ilse Maria Beuren
(Universidade Federal de Santa Catarina, Brazil),
Carlos Eduardo Lavarda
(Universidade Federal de Santa Catarina, Brazil).
Editorial assistants:
Eduarda Anastacio, Kler Godoy, and Simone Rafael
(ANPAD, Maringá, Brazil).
ABSTRACT
This paper investigates the influence of cybernetic and dynamic controls on strategic
and operational flexibility and, consequently, on monetary and non-monetary perfor-
mance indicators. Even though business and operation strategies have been the object
of many studies, management accounting studies on how management control sys-
tems could contribute to flexibility are scarce. We conducted a survey with 89 profes-
sionals who work in Brazilian companies and employed structural equation modeling
(SEM) to test the relationships included in the theoretical model. Our findings indi-
cate that the relationship between monetary cybernetic control and strategic flexibil-
ity is not statistically significant. The results support the positive association between
non-monetary and dynamic controls on strategic flexibility. Finally, the findings also
support the mediating role of operational flexibility in the relationship between stra-
tegic flexibility and performance. This paper’s main contribution to the management
control literature is its explanation of the impact of management control systems on
strategic flexibility, operational flexibility, and organizational performance. For practi-
tioners, the results highlight the importance of role management control and business
strategy to leverage performance.
Exploring the Influences of Cybernetic and
Dynamic Controls on Flexibility and Performance
José Carlos Oyadomari1,3 , Yen-Tsang Chen2 , Ronaldo Gomes Dultra-de-Lima1,4 , Octávio Ribeiro de
Mendonça Neto1
1 Universidade Presbiteriana Mackenzie, São Paulo, SP, Brazil
2 NEOMA Business School, Reims, Champagne-Ardenne, France
3 Insper Instituto de Ensino e Pesquisa, São Paulo, SP, Brazil
4 Centro Universitário FEI, São Bernardo do Campo, SP, Brazil
How to cite: Oyadomari, J. C., Chen, Y-T., Dultra-de-Lima, R, G., & Mendonça Neto, O. R. (2023). Exploring the Influences of Cybernetic and Dynamic
Controls on Flexibility and Performance. BAR-Brazilian Administration Review, 20(4), e220170.
DOI: https://doi.org/10.1590/1807-7692bar2023220170
2
Exploring the influences of cybernetic and dynamic controls on flexibility and performance
BAR-Brazilian Administration Review, 20(3), e220170, 2023.
INTRODUCTION
The management accounting literature recognizes
the role of management controls as a set of artifacts,
tools, procedures, and systems used by companies
to encourage people to achieve organizational goals
(Dimes & Villiers, 2021; Ferreira & Otley, 2009; Jukka,
2023; Malmi & Brown, 2008; Merchant & Van der Stede,
2012). Cybernetic controls are part of a management
control package, which is fundamentally based on
the comparison of planned and actual performance
among dierent management controls. The benefits of
using these controls relate to identifying and correcting
deviations and implementing initiatives to approximate
the actual results to the targets (Biswas & Akroyd, 2022a;
Biswas & Akroyd, 2022b; Kaveski & Beuren, 2020; Malmi
& Brown, 2008; Merchant & Van der Stede, 2012).
However, when cyber control emphasizes mon-
etary indicators, it may induce managers to prioritize
short-term rather than long-term objectives, creating
the problem of managerial myopia (Merchant, 1990).
The use of non-monetary indicators is recommended
to avoid such decision-making bias, as they may bet-
ter reflect long-term performance (Bedford et al., 2016;
Bedford & Malmi, 2015; Dossi et al., 2010). The use of
both types of performance indicators is known as hy-
brid control targets (Malmi & Brown, 2008) or measure-
ment diversity (Bedford & Malmi, 2015). In this research,
we use those control mechanisms (cybernetic and dy-
namic controls) to analyze how they influence flexibil-
ity and performance.
In addition to issues concerning the performance
indicators used, the eectiveness of management con-
trols in monitoring managerial actions in uncertain,
complex, and particularly ambiguous environments
has been questioned because the scenarios are con-
stantly changing, making planning useless (Bennett &
Lemoine, 2014). Earlier studies observed these problems
and proposed alternative techniques and practices that
could be more eective in such environments. Those
techniques include rolling forecasting (Hansen, 2011),
continuous budgeting (Frow et al., 2010), and beyond
budgeting (Henttu-Aho & Järvinen, 2013; Østergren &
Stensaker, 2011).
Although researchers had suggested that these
techniques contribute to organizational flexibility,
Franco-Santos et al. (2012) questioned this. After an ex-
tensive literature review of the field of management ac-
counting, they found no clear answers to the question
of how current performance systems can encourage
flexibility and dynamism.
Some previous research investigated aspects of
organizational flexibility; for example, Abernethy and
Lillis (1995) found that a reduction in the use of per-
formance measures was related to increased organiza-
tional commitment to flexibility. Meanwhile, Chenhall
and Langfield-Smith (1998) linked dierentiation strate-
gy to operational flexibility through product flexibility. In
subsequent work, Chenhall, 2005 observed that an in-
tegrated performance measurement system could im-
prove flexibility, including a budget, balanced scorecard,
and strategic alignment. Moreover, Shuiabi et al. (2005)
and Patel (2011) suggested that fewer formal structures
should improve the system’s ability to handle change.
Endenich et al. (2022) recently emphasized the positive
role of management control systems (MCS) in innova-
tive and entrepreneurial processes. And Brüggemann
et al. (2022) suggest that organizational agility is an es-
sential factor (mediator) that reinforces the eects of
PMS on open innovation.
Few studies in management accounting have in-
vestigated the antecedents of flexibility and its impact
on performance, either by considering flexibility as an
organizational competency under the perspective of
resource-based theory (RBT) or as an outcome of a
set of management practices, according to the prac-
tice-based view (PBV). Therefore, the research question
is: What are the impacts of cybernetic and dynamic
controls on flexibility and performance (monetary and
non-monetary)?
To answer this research question, we conducted a
survey with professionals working in finance and con-
trollership in companies located in Brazil, from which
we obtained 89 valid responses. We used structural
equation modeling (SEM) to analyze the relationships
between the constructs.
Our study contributes to the management control
literature in three ways. First, we tested several man-
agerial controls as antecedents of strategic flexibility,
finding that non-monetary cybernetic and dynamic
controls contribute to flexibility. Second, our results
suggest that business strategy only improves perfor-
mance when mediated by functional strategy, repre-
sented by operational strategy in this study. And finally,
we defined and operationalized the concept of dynam-
ic controls.
Regarding management practice for companies, our
study also highlights to managers that control mecha-
nisms associated with business strategy are essential
to improve organizational performance. Moreover, our
findings also provide evidence that the mediation of
operational flexibility positively impacts the connection
between strategic flexibility and performance, which
means that more than strategic flexibility is needed to
leverage performance. Thus, managers must examine
the operational context when defining the business
strategy (Skinner, 1969; Wheelwright, 1984).
3
BAR-Brazilian Administration Review, 20(3), e220170, 2023.
J. C. Oyadomari, Y-T. Chen, R. G. Dultra-de-Lima, O. R. de Mendonça Neto
This manuscript is structured as follows: section
two discusses theoretical foundations and hypotheses;
section three presents the methodology; section four
presents and discusses the results; and section five pro-
vides final considerations.
THEORETICAL BACKGROUND
This section discusses the importance of management
control systems, their classifications, their relationship
with organizational strategic and operational
flexibility, and the relationship between flexibility and
firm performance. During the discussion, we will
demonstrate the reasoning that led us to our hypotheses.
Early studies have already observed the importance
of managerial practice. Langfield-Smith (1997) suggest-
ed that MCS practices are essential to business strategy
formulation and implementation. She suggested that
business strategy could be analyzed by its typology
(prospector or defender), business positioning (cost
leadership or dierentiation), and mission (hold, build,
or harvest). Combinations of dierent levels of these
three dimensions will require dierent types of MCS
to support business strategy implementation. Martins
et al. (2023) also found a positive association between
MCS practices and performance, which enhances
companies’ competitiveness; moreover, MCS provides
guidance for managers to deal with crises. This find-
ing is in line with Beuren and Vaz (2021). Rehman et al.
(2019) also found that MCS positively and significantly
impacts performance through organizational capabili-
ties. Furthermore, Bloom and Van Reenen (2010) and
Bloom et al. (2012) advocated for the importance of
managerial practice. They noted that simply adopting
publicly available managerial practices will not neces-
sarily improve organizational performance; the ben-
efits of these practices depend on how well they are
implemented and employed. These authors classified
managerial practices into three categories: (a) ‘moni-
toring’ what is happening inside the organization; (b)
‘targeting’ or setting goals, monitoring results, and act-
ing to keep the company oriented toward the goal;
and (c) an ‘incentive’ to promote and reward company
employees.
Using the same rationale as Bloom and Van Reenen
(2010) and Bloom et al. (2012), Bromiley and Rau (2014;
2016) posited that easily imitable managerial practices
could also explain firm performance. They explained
that, due to the information asymmetry, a firm might
not know the degree of adoption of the same man-
agerial practices in competing organizations; hence,
the degree of adoption of these imitable managerial
practices could explain variations in firm performance.
Furthermore, Bromiley and Rau (2014; 2016) discussed
the assumption of the resource-based theory (RBT) by
suggesting a theoretical approach alternative called the
practice-based view (PBV), whereby researchers would
use the adoption of publicly available managerial prac-
tices as an independent variable and firm performance
as a dependent variable. According to PBV, the purpose
of RBT is to explain sustained abnormal returns, while
that of PBV is to explain variations in firm performance
(Bromiley & Rau, 2014; 2016). Even though Brito and
Sauan (2016) recognized that a superior level of man-
agement practices as organizational capabilities is con-
nected to better performance (Dultra-de-Lima & Brito,
2023), they understand that RBT supports this eect;
therefore, no new theory would be required, such as
PBV. Yoshikuni et al. (2023) also highlight that strate-
gic enterprise management practices enable deci-
sion-making performance and gain strategic flexibility
in innovation.
Based on PBV and managerial practice, we propose
a model where MCS is related to business and opera-
tions strategy performance. Our model diers from that
of Santos-Vijande et al. (2012) in organizational capabil-
ities, in which they investigated cost leadership and dif-
ferentiation while we explored organizational flexibility.
In terms of firm performance, we measure monetary
and non-monetary performance.
MCS, performance, and strategic
and operational flexibility
MCS supports the conception of the business strategy
(Ho et al., 2014; Langfield-Smith, 1997) and monitors
strategy deployment (Chenhall, 2003; Yanine et al.,
2016). For example, Harlez and Malagueño (2016)
observed that performance measurement systems
correlate positively with firm performance when
aligned with business priorities and top managers’
backgrounds. Earlier, Bedford (2015) noted that
exploitative innovation is positively related to diagnostic
and boundary controls, while exploratory innovation
is associated negatively with boundary control and
positively with interactive control systems. More
operationally, Tenhiälä and Helkiö (2015) demonstrated
that capacity, material planning, and controls
are positively related to manufacturing flexibility.
Previous research has found that MCS is related to
strategic flexibility, but this is only the case for certain
forms of MCS. Considering that MCS comprises several
dimensions (Bedford et al., 2016; Simons, 1994), we de-
veloped our argument about cybernetic and dynamic
controls in the following hypotheses.
Figure 1 shows our proposed model relating MCS to
firm performance. Below, we discuss the rationale for
designing this model, the variables, and the hypotheses.
4
Exploring the influences of cybernetic and dynamic controls on flexibility and performance
BAR-Brazilian Administration Review, 20(3), e220170, 2023.
responsive to the business environment. Consequently,
it will reduce strategic flexibility. Thus, we expect that:
H1.1. Monetary cybernetic control is negatively as-
sociated with strategic flexibility.
Most non-monetary performance indicators are
related to learning, processes, and clients (Kaplan &
Norton, 1992), which are proxies of long-term perfor-
mance. A more comprehensive range of non-mone-
tary measurements predicts future performance better,
being less susceptible to manipulation than monetary
indicators and easier to update (Bedford et al., 2016;
Bedford & Malmi, 2015; Dossi & Patelli, 2010). Adopting
dierent measurement perspectives tends to balance
competing strategic priorities, and non-monetary in-
dicators can translate strategy into operational terms.
Given these characteristics of non-monetary cybernet-
ic controls, we expect that:
H1.2. Non-monetary cybernetic control is positively
associated with strategic flexibility.
Dynamic controls
One of the components that measure the eective-
ness of a management control system is adaptability,
which is the organization’s ability to respond to varia-
tions in the external environment (Bedford et al., 2016).
To do this, we need what we are defining as dynamic
controls.
Dynamic controls allow the organization to prompt-
ly identify changes in the scenario to adapt to new en-
vironmental conditions. Two characteristics that mark
these controls are the one that allows managers to
view the external environment, such as competitors’
The signal (positive or negative) next to each hy-
pothesis (H) is the expected result, given the potential
causal relationships based on the previous literature.
Cybernetic controls
The fundamental characteristic of cybernetic controls
is to compare the results achieved with targets, of-
ten derived from the company’s budget or system of
goals. These goals can be measured using monetary
and non-monetary indicators (Malmi & Brown, 2008).
Monetary controls involve comparing actual results
with budgeted (forecast) results, generally focusing on
indicators of profitability, sales, or market-share tar-
gets. Studies have suggested that defining goals based
on the classic budgetary reserve generates sub-opti-
mal performance, leading to dysfunctional behaviors
(Merchant, 1990). For instance, financial performance
indicators, such as profit, take time to provide infor-
mation about the strategy’s ecacy based on budget
compliance (Sliwka, 2002). Thus, the metrics are not re-
viewed dynamically, preventing managers from coping
with constant business changes. Consequently, man-
agers tend to miss business opportunities if budgets
have not been considered during the budget process.
The limitations of monetary cybernetic con-
trol might prevent organizations from responding to
changes in the business scenario since uncertainty
could invalidate pre-established budget goals. In short,
Libby and Lindsay (2010) observed that monetary con-
trol could be valuable if used with adequate human re-
sources performance assessment and long-term, per-
formance-oriented culture.
Due to the limitations of monetary cybernetic con-
trol, we expect it to make companies more rigid and less
Figure 1. Theoretical model.
Note. (*) Complementary hypotheses (5.1 and 5.2) for testing the mediation eect.
Figure 1. Theoretical model
Note: (*) Complementary hypotheses (5.1 and 5.2) for testing the mediation effect.
5
BAR-Brazilian Administration Review, 20(3), e220170, 2023.
J. C. Oyadomari, Y-T. Chen, R. G. Dultra-de-Lima, O. R. de Mendonça Neto
performance (Hansen, 2011; Henttu-Aho & Järvinen,
2013; Hope & Fraser, 2003), and another that expands
the time horizon, incorporating new periods in plan-
ning, as is the case with rolling forecasting (Hansen,
2011), or continuous budgeting (Frow et al., 2010).
One example of dynamic controls is beyond bud-
geting (Hope & Fraser, 2003). This artifact aims to solve
significant budgeting problems such as dysfunctional
behavior, high expenditure of time and resources, and
a lack of updating and customer orientation (Henttu-
Aho & Järvinen, 2013). It is widely reported in the busi-
ness literature as a growing artifact in large European
corporations and also appears in the international em-
pirical literature (Hansen, 2011; Henttu-Aho & Järvinen,
2013; Østergren & Stensaker, 2011).
Beyond budgeting is not a set of managerial prac-
tices; it is a managerial philosophy to introduce agility
into the managerial process (Beyond Budgeting Round
Table [BBRT], 2016). Beyond budgeting comprises seven
basic principles: (a) set relative goals focusing on con-
tinuous improvement; (b) share rewards; (c) base the
concept of success on relative performance; (d) plan
continuously; (e) base participatory process controls
on relative indicators and trends; (f) make resources
available when needed; and (g) coordinate interactions
dynamically (Østergren & Stensaker, 2011). In Østergren
and Stensaker’s (2011) view, the pillars of beyond bud-
geting are goals based on ambitions, the big picture,
and the identification of flexible possibilities.
Another control that favors this dynamism is roll-
ing forecasting, which consists of defining the plan at
month zero for a defined time horizon, such as the
next 12 months. This 12-month plan is regularly up-
dated after a specific time, such as every four months.
Thus, the company re-plans the next 12 months every
four months and will be more flexible and dynamic.
Pitkänen and Lukka (2011) pointed out that rolling fore-
casting operates in two temporal dimensions: ex-ante,
in planning and forecasting, and ex-post, in monitor-
ing the results achieved. This technique is not new;
it was observed as early as 1919. It is also known as
continuous budgeting (Bornia & Lunkes, 2007). Haka
and Krishnan (2005) revealed that rolling forecasting
could improve decision-makers’ learning about the
environment. Hence, managers who use rolling fore-
casting make better decisions than those who use tra-
ditional budgets, especially when the environment is
particularly uncertain. Järvenpää (2007) observed the
same relationship between rolling forecasting and per-
formance when companies deal with turbulent envi-
ronments. Similarly, Bhimani et al. (2018) observed that
monthly rolling budgets contributed to a more sensi-
tive uncertainty strategy.
Because rolling forecasting is not fixed in a static
calendar year, it can enable the organization to antic-
ipate events constantly without being limited to bud-
geting at a fixed date. This characteristic is also known
as planning flexibility. The company can conduct eec-
tive strategic planning by diverting from formal strate-
gic plans and identifying, recognizing, and responding
to opportunities in a changing business environment
(Dibrell et al., 2014). Furthermore, Østergren and
Stensaker (2011) view rolling forecasting as the heart
of the beyond budgeting process, replacing traditional
budget planning functions and constituting a process
apart from goal setting.
The focus of dynamic controls is to show dierent
views about performance, extending the time horizon
of planning or comparing the firm’s performance to
that of its competitors. Based on the characteristics of
dynamic controls, we expect the following:
H1.3. Dynamic control is positively associated with
strategic flexibility.
Strategic flexibility, operational
flexibility, and performance
Research on business strategy is vast and wide-ranging
(Langfield-Smith, 1997; Yuan et al., 2020), and there is
a consensus that business strategy is how companies
obtain competitive advantages and superior perfor-
mance (Shavarini et al., 2013). Porter (1996) suggest-
ed that superior performance could be obtained by:
(a) positioning the company correctly in the industry
and taking advantage of the industry’s structure; (b) an
inimitable interconnected operational process; or (c)
dierentiation or cost leadership. Porter’s three recom-
mendations can be implemented independently; how-
ever, to obtain a lasting advantage, a company should
combine the concepts of positioning, unique activities,
and operational eciency. Complementing Porter’s ap-
proach, the resource-based theory (Barney & Wright,
1998; Kraaijenbrink et al., 2010; Peteraf & Barney, 2003)
suggests that superior firms’ performance could be
achieved by exploring valuable, scarce, imperfectly imi-
table, and imperfectly organizational resources.
There are several ways companies compete against
each other, and they have also been classified into ty-
pologies such as prospector, defender, analyzer, and
reactor (Lin et al., 2014; Miles et al., 1978). Alternatively,
first, second, or timed mover (Gal-Or, 1985; Lieberman
& Montgomery, 1988; Xie et al., 2020). Scholars have
demonstrated that the first mover company can obtain
above-average performance by benefiting from tech-
nological leadership, obliging users that adopt the tech-
nology or product introduced by the first mover to pay
6
Exploring the influences of cybernetic and dynamic controls on flexibility and performance
BAR-Brazilian Administration Review, 20(3), e220170, 2023.
higher switching costs. Moreover, the first mover will
also profit from access to scarce resources (Lieberman
& Montgomery, 1988; Murthi et al., 1996; Robinson et
al., 1992; Szymanski et al., 1995).
Like first movers, strategic agility has also attracted
the attention of both practitioners and scholars, es-
pecially its facilitation of above-average performance
in turbulent business environments (Vecchiato, 2015).
According to Davis et al. (2009) and Eisenhardt et al.
(2010), companies must adapt their business process-
es to respond eectively to dynamic environments.
Therefore, strategic agility refers to how companies
adjust and adapt to the constantly changing business
environment by recombining their resource base,
reconfiguring the business model, and introducing
new products (Doz & Kosonen, 2008; Teece, 2007;
Vecchiato, 2015). In addition to strategic agility, strategic
flexibility is another approach to cope with fast-chang-
ing business environments. Strategic flexibility can be
understood as the means companies use to change
their scope, capabilities, and technologies in the face of
uncertainty and rapid environmental change (Aaker &
Mascarenhas, 1984; Sanchez, 1995). Through strategic
flexibility, a company could reactively or proactively re-
spond to demand volatility and modify its strategic plan
by flexibly coordinating its resources, thus maintaining
high performance after each change in the business
environment (Miroshnychenko et al., 2021). Eisenhardt
and Martin (2000) also see strategic flexibility as a dy-
namic capability that can influence operational excel-
lence. Therefore, even if it is not among the first mov-
ers, an agile and flexible company adapts to changes in
the business environment quicker than its competitors,
obtaining advantages similar to those of the first mover
(Kortmann et al., 2014).
However, a well-conceived business strategy is not
enough to obtain above-average performance since it
must be aligned with functional strategy and capabili-
ties before being implemented (Cui et al., 2015; Sardana
et al., 2016). For instance, Porter’s (1996) cost leadership
and dierentiation strategies should be executed us-
ing operational strategies that promote low costs and
quality, respectively. In the same line, the above-aver-
age performance of the first mover strategy is associat-
ed to agility (Hsiao et al., 2017; Vecchiato, 2015), which
requires the simultaneous development of operational
adaptability, consistency, and speed (Battistella et al.,
2017; Mathiassen and Pries-Heje, 2006).
Similarly, if a company focuses on strategic flexibili-
ty, this organization should not only be able to identify
significant changes in the business environment, but
also be able to develop operational capabilities to ab-
sorb changes in the scope, variety, and volume of prod-
ucts required by the market (Kortmann et al., 2014; Wei
et al., 2014). According to Brozovic (2018), strategic flex-
ibility is built on the processes’ flexibility and their inter-
action with other managerial practices. Therefore, the
company should integrate several departments, levels,
and organizational processes to achieve strategic flexi-
bility, such as leadership, learning, planning, quality, and
supply chain management. Hence, strategic flexibility
should be linked to operational flexibility.
From the above rationale, our hypothesis two
suggests:
H2. Strategic flexibility is positively associated with
operation flexibility.
The purpose of a well-conceived and well-imple-
mented strategy is to obtain superior performance
(Barney, 1986; Shavarini et al., 2013). Porter and his
colleagues (McGahan & Porter, 1997; Porter, 1979) ad-
vocated that good company positioning within the in-
dustry structure is positively associated with financial
performance. Other studies have observed that inter-
nationalization and innovation improve financial per-
formance (Chittoor et al., 2009; Jia et al., 2014; Webb
& Pettigrew, 1999; Zahra et al., 2000). Similarly, an agile
business strategy can generate superior financial and
non-financial performance (Doz & Kosonen, 2008;
Mathiassen and Pries-Heje, 2006; Olson et al., 2005).
Companies that focus on strategic flexibility com-
pete by developing resource and coordination flexi-
bility. This combination creates technology, product
development, production, and distribution flexibility
(Sanchez, 1995). In addition, through modularization,
companies can develop changeover, material, and se-
quencing flexibility. However, Eisenhardt et al. (2010)
pointed out that companies must balance eciency
and flexibility in dynamic environments. This com-
bination enables manufacturing and product design
to adapt to customer requirements and changes, im-
proving the organization’s operational performance
(Schmenner & Tatikonda, 2005).
Strategically flexible companies can use operation-
al flexibility to adapt their internal resources to scope
and volume changes required by the market, making
them more competitive than less flexible firms and giv-
ing them higher overall performance. For instance, in
dynamic environments, strategic and operational flexi-
bility could improve the performance of firms that rely
on fast reactions to compete in the market (Nadkarni
& Narayanan, 2007). Therefore, Xiu et al. (2017) agree
that strategic flexibility positively impacts performance,
mainly in rapidly changing environments. In line with
this vision, Yousuf et al. (2019) also highlight the same
7
BAR-Brazilian Administration Review, 20(3), e220170, 2023.
J. C. Oyadomari, Y-T. Chen, R. G. Dultra-de-Lima, O. R. de Mendonça Neto
for operational flexibility. Moreover, Alolayyan and
Alyahya (2023) also point out that operational flexibili-
ty and management capability enhance organizational
responsiveness and influence performance. We make
the following hypotheses based on these arguments:
H3.1. Strategic flexibility is positively associated with
monetary performance.
H3.2. Strategic flexibility is positively associated with
non-monetary performance.
H4.1. Operational flexibility is positively associated
with monetary performance.
H4.2. Operational flexibility is positively associated
with non-monetary performance.
Mediation eect of operational flexibility
The strategic and operations literature has pointed
out that strategic flexibility is a dynamic capability that
can respond quickly to unpredictable environments
through processes and resource reconfiguration, such
as operational routines (Zhou & Wu, 2010), impacting
firm performance (Eisenhardt et al., 2010; Eisenhardt
& Martin, 2000; Helfat & Peteraf, 2009; Teece, 2007).
Kortmann et al. (2014) highlight that strategic flexibili-
ty influences operational capability, which, in turn, hits
operational eciency. Similarly, Eisenhardt et al. (2010)
also advocated that superior performance comes from
eectively balancing eciency and flexibility.
When we focus on operational flexibility, the op-
erations literature has found a positive relationship
between operational flexibility and performance out-
comes (El-Khalil, 2018; Yu et al., 2015) and its media-
tion characteristics between organizational source and
firm-oriented and supply chain-oriented performance
outcomes (Yu et al., 2015). As a dynamic capability, Ojha
et al. (2021) also tested the mediation eect between
innovation speed and competitive advantage. They
found that innovation speed is relevant and positive to
competitive advantage through operational flexibility.
Therefore, from the rationale above, strategic flexi-
bility is relevant to operational flexibility by which orga-
nizational performance is raised. From this perspective,
we state the following hypotheses:
H5.1. Operational flexibility positively mediates the
relationship between strategic flexibility and mone-
tary performance.
H5.2. Operational flexibility positively mediates
the relationship between strategic flexibility and
non-monetary performance.
RESEARCH METHOD
We started our study by conducting qualitative re-
search through a single exploratory case study. Even
though this methodology is not the main instrument
for the research problem focus and was designed only
to support the second quantitative phase, we followed
the procedures defined by Miles et al. (2013), Yin (2014),
and Krippendor (2013) in this initial phase. We also
used content analysis for assessing the interviews.
In this first phase, we interviewed nine subjects
(supervisors, managers, and directors) from a Brazilian
multinational subsidiary. The qualitative phase’s re-
sults suggested the relationships proposed in Figure
1, in which monetary cybernetic controls (CBMO),
non-monetary cybernetic controls (CBNM), dynamic
controls (CDIN), strategic flexibility (FLES), operational
flexibility (FLEO), monetary performance (PEMO), and
non-monetary performance (PELP) are integrated.
The literature has emphasized that combining mul-
tiple research methods provides both testability and
context and increases the robustness of the results
(Kaplan & Duchon, 1988). Consistent with this view,
Eisenhardt (1989) understands that combining mul-
tiple data collection represents a synergistic process.
Moreover, Brown (1997) also points out that a single
strategy (survey questionnaire) does not permit the
understanding of all the peculiarities of the results. In
other words, it provides a rich comprehension of the
phenomenon of interest (Mingers, 2001) using several
perspectives. Thus, it is essential to combine both qual-
itative and quantitative methods.
Understanding the company’s mechanism and pro-
cess through the first phase (case study) provides es-
sential information to test the relationship between the
constructs. Therefore, to test our model (Figure 1), we
designed a quantitative study based on a survey ques-
tionnaire due to the fact that this instrument is suit-
able for collecting opinions, perceptions, and actions
about a sample of the population to understand what
happens or how and why it happens regarding the re-
search problem in the determined segment (Freitas et
al., 2000).
The survey instrument comprised multi-item mea-
surement scales whose items we defined and adapted
from the literature (see Appendix). Each item was mea-
sured on a seven-point scale.
Before collecting the data, we asked two practi-
tioners to validate the survey, and they suggested mi-
nor corrections for the final questionnaire. We defined
8
Exploring the influences of cybernetic and dynamic controls on flexibility and performance
BAR-Brazilian Administration Review, 20(3), e220170, 2023.
our respondent profile as professionals working in fi-
nancial, accounting, and control departments, with at
least three years of experience. In both qualitative and
quantitative phases, we maintained the confidentiality
of interviewees, survey respondents, and companies.
Respondents were asked to agree with a free and clar-
ified consent term before answering the questionnaire.
The Ethical Committee does not require another proce-
dure when the subject concerns organizational aspects
and just perceptions captured by a survey.
We collected data through an electronic ques-
tionnaire from August 1 to October 25, 2016. We se-
lected respondents with the required profile from the
ANEFAC database (Brazilian Association of Finance,
Administration, and Accounting Executives) and
LinkedIn. We have chosen ANEFAC because it is one
of the leading entities in Brazil that bring together ex-
ecutives of several areas, including finance, accounting,
and corporate governance. As we needed executive
respondents from those departments, we understood
that collecting data from this association was adequate
for our paper, and their executives have the expertise
and knowledge regarding our research problem that we
needed to capture through the constructs.
The sample size definition was based on Hair et al.
(2009, p. 564), who suggest that the minimum recom-
mended size is between 100 and 150 respondents to
ensure stable solutions when using specific statistical
techniques. For instance, maximum likelihood estima-
tion (MLE) is the standard method employed by SEM.
Although we estimated around 3,500 potential respon-
dents from the two sites (ANEFAC and LinkedIn), only
242 respondents accessed the questionnaire. Most of
them did not complete the survey, or some respondent
profile was outside the research requirement; therefore,
they were eliminated from the base. As a result, we ob-
tained 89 valid respondents, representing around 2.5%
of the response rate.
Data analysis
According to Kaplan and Duchon (1988) and Eisenhardt
(1989), several statistical procedures for assessing col-
lected data improve the robustness of the results, but
each statistical procedure has a specific purpose. For
example, we use SEM to analyze the impact of the
exogenous construct on the endogenous construct.
Moreover, according to Farooq (2016), SEM is the prac-
tical way to deal with unidimensionality and multidi-
mensionality, as well as helpful in assessing the psy-
chometric properties of a scale/construct. We also used
some remedies proposed by Podsako et al. (2003) and
Podsako et al. (2012) to assess common method vari-
ance (CMV) (Bagozzi et al., 1991; Johnson et al., 2011;
Malhotra et al., 2006; Podsako et al., 2003, 2012). Since
our sample comprises professionals invited from the
ANEFAC database and LinkedIn, we performed a one-
way ANOVA. The results indicated that answers from
these two subject pools come from the same popula-
tion. Finally, we performed structural equation model-
ing to test the measurement model and the relationship
between the constructs using Smart-PLS 4.
Missing values are not a major problem in our data
collection. Eleven values were missing in only one key
variable and were randomly distributed. Therefore, we
replaced them with the average of their original vari-
ables (Corrar et al., 2009; Hair et al., 2009). We detect-
ed no potential outliers in our sample according to
Chebyshev’s theorem, which suggests potential outli-
ers as values above or below three standard deviations
(Clark-Carter, 2004; Corrar et al., 2009; Kline, 2016;
Sweeney et al., 2013).
The statistics tests proposed by Kline (2016) point
out that if the skew and kurtosis are below the cuto
value of 3.0 and 10.0, respectively, the data are normally
distributed. In our data, the skew and kurtosis are 1.495
and 2.334, respectively; therefore, they are below the
cuto value proposed by the author. However, the data
not being normally distributed is not a major problem
because the Smart-PLS is a nonparametric statistical
method and does not require this assumption (Hair et
al., 2014).
To safeguard against CMV, we followed the rem-
edies suggested by Podsako et al. (2003; 2012). First,
we used validated scales from the literature as much as
possible by adapting them to our purpose. This adapta-
tion process avoids item ambiguity because this could
aect the respondent’s comprehension. Second, during
data collection through the survey questionnaire, we
randomize the scales to prevent the respondent from
giving the same answer for all construct items. Third,
to guarantee the anonymity of the respondents, we in-
formed them that all the information was confidential
and would be analyzed privately. It was essential to re-
duce their predisposition to change the focus of their
responses. Fourth, we use statistical control to assess
CMV through Harman’s single factor test to examine
the observed variables. Our results suggested that the
only factor extracted was responsible for 28.18% of the
total variance, which is lower than the tolerated value
(50%); therefore, CMV is not a significant problem, and
our model needs no correction.
Additionally, we ran two more tests for analyzing
de CMV. The first test comprises the analysis of CMV
through the measured latent marker variable (MLMV)
approach (Chin et al., 2013; Podsako et al., 2012;
Rönkkö & Ylitalo, 2011) by including the budget rigid-
9
BAR-Brazilian Administration Review, 20(3), e220170, 2023.
J. C. Oyadomari, Y-T. Chen, R. G. Dultra-de-Lima, O. R. de Mendonça Neto
ity construct as our marker (see Appendix I, Table
8). This construct does not relate to the base mod-
el’s constructs. In Table 1, all the R-square varia-
tions of the constructs are below 10%, favoring CMV
non-existence in our data. As a result, no remedies
are necessary for data correction. The second test
comprehends the analysis of CMV through the un-
measured latent marker construct (ULMC) approach
(Chin et al., 2013) created by extracting the single in-
dicator constructs from all items of the base model.
Consistent with the MLMV results, the R-square vari-
ations of the constructs are below 10% (Table 1) for
ULMC in comparison with the base model; there-
fore, there is no problem with CMV.
Table 1. Comparison of the R-square of measured latent marker variable (MLMV) and unmeasured latent marker
construct (ULMC).
Construct
R-square Variation of R-square with base model
Base Model With marker variable
MLMV ULMC MLMV ULMC
Operational flexibility 0.715 0.726 0.716 1.54% 0.14%
Strategic flexibility 0.485 0.520 0.518 7.22% 6.80%
Monetary performance 0.177 0.177 0.183 0.00% 3.39%
Non-monetary performance 0.448 0.477 0.449 6.47% 0.22%
Note. (1) Budget rigidity as measured latent marker variable (MLMV) (2) The ULMC was extracted as a single indicator construct of all items from the base model.
(3) Note 3. If the variation in R-square is below 10%, there is no CMV problem. Source: Research data.
Demographic profile of sample
Table 2 below shows that 77.5% of the respondents
work for financial and accounting departments (tax,
accounting, controllership, and finance). Approximately
64% are in coordination positions or above (manager,
director, shareholder), and 52.8% have more than three
years of experience.
Most respondents, 66.3%, work for medium- and
large-sized companies; 37.1% are multinational compa-
nies, and 39.3% are family-controlled businesses.
Table 2. Demographic profile of sample.
Frequency Percentage Cumulative percentage
Department in which the respondent works
Tax 4 4.5 4.5
Accounting 22 24.7 29.2
Control 32 36.0 65.2
Finance 11 12.4 77.5
Administration 4 4.5 82.0
Auditing 7 7.9 8 9.9
Other 9 10.1 100.0
89 100.0
Respondent’s experience
Less than 3 years 42 47.2 47.2
3-5 years 8 9.0 56.2
5-10 years 13 14.6 70.8
10-15 years 10 11.2 82.0
15-20 years 7 7.9 8 9.9
20-25 years 3 3 .4 93.3
25-30 years 1 1.1 94.4
30-35 years 3 3.4 97. 8
Above 40 years 2 2.2 100.0
89 100.0
Respondent’s position
Analyst/Assistant 23 25.8 25.8
Coordinator/Manager 41 46.1 71.9
Consultant 7 7.9 79. 8
Director/Shareholder 16 18.0 97.8
Other 2 2.2 100.0
89 100.0
Number of employees
1-50 13 14.6 14.6
51-100 17 19.1 33.7
101-1,000 27 30.3 64.0
1,001-10,000 24 27.0 91.0
More than 10,000 8 9.0 100.0
89 100.0
Continues
10
Exploring the influences of cybernetic and dynamic controls on flexibility and performance
BAR-Brazilian Administration Review, 20(3), e220170, 2023.
RESULTS AND DISCUSSION
Appendix II presents a descriptive analysis of our data.
We discuss the measurement assessment and contin-
ue our structural equation analysis results using Smart-
PLS 4.
Measurement assessment
We first ran the model in Figure 1 through Smart-PLS
to check for convergent validity, using all the items (see
Appendix II). Next, for reflexive constructs, we removed
items with factor loadings below 0.50, and for forma-
tive constructs, we retained the items with itive load-
ings. Then, we refined the measurement model. Figure
2 shows the final model. We calculated Cronbach’s al-
pha and average variance extracted (AVE) to assess the
construct validity and reliability. In Table 3, all the AVE
exhibited values above the minimum requirement of
0.5 Fornell & Larcker, 198; Hair et al., 2009).
To check for discriminant validity, we calculated the
square root of AVE on the diagonal matrix (Table 3).
These values are above the intercorrelations of the con-
structs, ensuring discriminant validity (Fornell & Larcker,
1981). Another way to examine discriminant validity,
suggested by Fornell and Larcker (1981), is through fac-
tor loadings (Figure 2). Hair et al. (2009) suggest that
the appropriateness of factor loadings can be validat-
ed based on the sample size. For a sample above 85,
the factor loadings should be at least 0.60 for reflexive
constructs. Figure 2 shows that all the construct coe-
cients exceed the cuto value of 0.60.
We assessed the reliability of our constructs using
Cronbach’s alpha and composite reliability (Table 3).
Our constructs present acceptable Cronbach’s alpha
values since the coecients are above 0.70 (Tenenhaus
et al., 2005), except for strategic flexibility (0.699) and
operational flexibility (0.671). However, their indicators
are near the cuto value for the study (Hair et al., 2011;
Hair et al., 2009; Henseler et al., 2009). In addition, the
composite reliability coecient, which evaluates the
internal consistency of the constructs, is all above 0.70
(Hair et al., 2009, 2011; Henseler et al., 2009). Hence, we
considered all our constructs acceptable.
Structural equation analysis
As mentioned earlier, we used Smart-PLS 4 software
to assess our proposed model. We adopted this tech-
nique and software because they are suitable for small-
er samples (Smith & Langfield-Smith, 2004) and con-
structs with few indicators (Chin & Newsted, 1999).
Unlike covariance structural equation modeling (LISREL
and AMOS), PLS maximizes the explained variance of
the dependent variables (Chin & Newsted, 1999; Hair et
al., 2011; Oyadomari et al., 2014). Moreover, it is recom-
mended to test theories and models at their explorato-
ry and development stages (Hair et al., 2011).
Table 3. Latent variable correlations (square root of AVE on the diagonal).
1 2 3 4 6
Dynamic control 0.750
Monetary performance 0.270 0.882
Non-monetary performance 0.481 0.526 0.787
Operational flexibility 0.566 0.420 0.669 0.791
Strategic flexibility 0.644 0.356 0.566 0.845 0.778
Cronbach’s alpha 0.842 0.905 0.846 0.699 0.671
Composite reliability 0.884 0.934 0.890 0.833 0.820
Average variance extracted (AVE) 0.563 0.779 0.619 0.626 0.605
Note. Non-monetary cybernetic control and monetary cybernetic control are formative constructs. For this reason, we did not compute their reliability and AVE.
Source: Research data.
Frequency Percentage Cumulative percentage
Type of firm
State controlled 3 3.4 3 .4
Family business 35 39.3 42.7
Brazilian company not family controlled 13 14.6 57.3
Listed on the Brazilian Stock Exchange 4 4.5 61.8
Unlisted multinational 14 15.7 77.5
Multinational listed on the Stock Exchange abroad 19 21.3 98.9
Other 1 1.1 100.0
89 100.0
Note. Research data.
Table 2. Demographic profile of sample (continued).
11
BAR-Brazilian Administration Review, 20(3), e220170, 2023.
J. C. Oyadomari, Y-T. Chen, R. G. Dultra-de-Lima, O. R. de Mendonça Neto
PLS assesses the model’s goodness-of-fit (GoF)
by calculating the geometric mean of the average
communality and average R2 (Tenenhaus et al., 2005;
Wetzels et al., 2009). This calculation gave a coe-
cient of 0.532, which exceeds the cuto value of 0.25.
Hence, we conclude that our model is adequate.
According to Tenenhaus et al. (2005), redundan-
cy indicators also help assess the quality of the struc-
tural model. We also performed a bootstrap of 5,000
samples from 89 cases (4,999 degrees of freedom and
two-tailed test) to assess the stability coecients with
a confidence interval of 95% (Chin & Newsted, 1999;
Hair et al., 2011). Figure 3 considers model estimates
as significant for t-statistics values greater than 1.96,
which means p-values less than 5%. Table 4 presents
the bootstrapping path coecients and the respective
t-tests.
Table 4 shows that monetary cybernetic control is
positively related to strategic flexibility, but that the re-
lationship is not statistically significant, consequently
not supporting our hypothesis H1.1. The misalignment
between management control and business strategy
could explain this result. Jukka (2023) also advocates
that for a business strategy to be consistent, it must be
aligned with a particular type of MCS, whereas this mis-
match explains the dierence in performance.
However, non-monetary control has a sig-
nificant, positive impact on strategic flexibility
(α = 0.274; t-test = 3. 219). This result supports our hy-
pothesis H1.2.
Figure 2. Measurement and structural model.
12
Exploring the influences of cybernetic and dynamic controls on flexibility and performance
BAR-Brazilian Administration Review, 20(3), e220170, 2023.
Figure 3. Theoretical model of research in Smart-PLS.
Table 4. Bootstrapping — path coecients.
Hypotheses Original sample Standard deviation t-statistics p-values
Direct eects
Monetary cybernetic controls -> Strategic flexibility H1.1 0.046 0.089 0.512 0.609
Non-monetary cybernetic controls -> Strategic
flexibility H1.2 0.274 0.085 3.219 0.001
Dynamic controls -> Strategic flexibility H1.3 0.511 0.089 5.730 0.000
Strategic flexibility -> Operational flexibility H2 0.846 0.039 21.503 0.000
Strategic flexibility -> Monetary performance H3.1 0.02 8 0.164 0.168 0.866
Strategic flexibility -> Non-monetary performance H3.2 0.013 0.171 0.076 0.939
Operational flexibility -> Monetary performance H4.1 0.402 0.163 2.472 0.013
Operational flexibility -> Non-monetary performance H4.2 0.661 0.159 4.154 0.000
Indirect eects
Strategic flexibility -> Operational flexibility ->
Monetary performance H5.1 0.340 0.140 2.425 0.015
Strategic flexibility -> Operational flexibility ->
Non-monetary performance H5.2 0.559 0.140 3.998 0.000
Note. Source: Research data.
13
BAR-Brazilian Administration Review, 20(3), e220170, 2023.
J. C. Oyadomari, Y-T. Chen, R. G. Dultra-de-Lima, O. R. de Mendonça Neto
From the managerial point of view, when an orga-
nization is not meeting a specific non-monetary per-
formance indicator, it develops initiatives to modify
its structure or procedures, and this could be seen as
demonstrating strategic flexibility. Furthermore, from
Figure 3, we note that the two main items with the
highest loading of non-monetary cybernetic control
are CBNM3 and CBNM4. The first item refers to at-
tracting, retaining, and satisfying employees, while the
second refers to process productivity, security, and du-
ration. Given that, non-monetary cybernetic control is
positively related to strategic flexibility. Thus, these two
main items of non-monetary control could also be the
main items related to strategic flexibility. Even though
Widener (2006) did not explicitly test the relationship
between non-monetary control and strategic flexibility,
our results are consistent with her research and confirm
that non-monetary controls as antecedents of strategic
flexibility.
The path from dynamic control to strategic flexibility
was also positive and significant (α = 0.511; t-test = 5.73),
supporting our hypothesis 1.3. This result indicates that
adopting a combination of rolling forecasting and be-
yond budgeting principles (for instance, by comparing
the firm’s performance to its competitors, using lead-
ing indicators, and so on) enhances strategic flexibili-
ty. This finding partially answers the question raised by
Franco-Santos et al. (2012), who also wondered how
management control systems could encourage flexibil-
ity and dynamism. Moreover, this result addresses the
contributions of dynamic controls (beyond budgeting
and rolling forecasting) to improving strategic flexibility.
It corroborates the findings of former qualitative studies,
including those by Henttu-Aho and Järvinen (2013) and
Østergren and Stensaker (2011)
Moving forward to the relationship between flexi-
bility strategies and performance, as studies have sug-
gested, a well-conceived and well-implemented strat-
egy might improve performance (Spencer et al., 2009;
Swink et al., 2005). Based on our theoretical rationale,
we expected that strategic and operational flexibility
would improve performance; however, our results only
partly confirmed our expectations. The positive impacts
of strategic flexibility on performance are non-signifi-
cant, which did not support hypotheses 3.1 and 3.2. In
contrast, operational flexibility significantly improves
monetary (α = 0.402; t-test = 2.472) and non-monetary
performance (α = 0.661 t-test = 4.154), thus supporting
our hypotheses 4.1 and 4.2. At first glance, the lack of
support for hypotheses 3.1 and 3.2 seems illogical; how-
ever, this result is consistent with the operations litera-
ture. Swink et al. (2005) and JJoiner et al. (2009) suggest
that well-conceived and well-implemented strategies
improve performance; nevertheless, business strategy
implementation occurs via operations (Rehman et al.,
2019; Skinner, 1969; Wheelwright, 1984). Consequently,
we theorized in hypothesis 2 that strategic flexibility
should improve operational flexibility, and our results
support this hypothesis (α = 0.846; t-test = 21.503).
Finally, by combining our results and assessments of
hypotheses 2 and 4, we infer that the eect of the busi-
ness strategy on performance is not direct but mediat-
ed by operational strategy. In our study, the impact of
operational flexibility is greater on non-monetary than
monetary performance. This eect is not surprising be-
cause variations in monetary performance, which is a
proxy for overall company performance, may depend
on other organizational aspects. Meanwhile, we mea-
sured non-monetary performance using operational in-
dicators, and hence, its variations should depend more
on operational flexibility.
Drawing insight from the above, Table 4 presents the
indirect eect of strategic flexibility on performance by
operational flexibility mediation. The results provide evi-
dence that strategic flexibility impacts positively and sig-
nificantly (H5.1: α = 0.340; t-test = 2.425; H5.2: α = 0.559;
t-test = 3.998) organizational performance through
operational flexibility, which supports the hypotheses.
These results align with Kortmann et al. (2014), who
found a significant influence of strategic flexibility on
operational eciency by operational capability (mass
customization). Yu et al. (2015) also highlight that oper-
ational flexibility is a mediator between intra-organiza-
tional sources or inter-organizational sources and firm
or supply chain-oriented performance. Therefore, our
findings reinforce the literature by evidencing strategic
flexibility’s indirect and relevant impact on performance.
We also analyzed the structural model by using a
contingency control variable, such as number of em-
ployees, which defines the size of the company on
monetary performance, non-monetary performance,
strategic flexibility, and operational flexibility. Our results
show there is no eect on dependent variables.
DISCUSSIONS
Regarding theoretical contributions, RBT postulates that
when companies run resources (tangible or intangible),
they accordingly improve their capabilities and maxi-
mize profitability (Barney, 1991; Henri, 2006). Therefore,
to obtain a competitive advantage, companies must
domain and control their valuable and rare resourc-
es aligned with their organizational competencies
(Barney, 2011; Barney & Clark, 2007; Peteraf & Barney,
2003). Developing their competencies, such as man-
agement control, and aligned with business strategy
and operational capabilities, contribute to performance
14
Exploring the influences of cybernetic and dynamic controls on flexibility and performance
BAR-Brazilian Administration Review, 20(3), e220170, 2023.
(Bedford et al., 2016; Jukka, 2023; Malmi & Brown,
2008), leading to competitive advantage. Our findings
corroborate management control and strategic litera-
tures when they demonstrate that the impact of this
alignment is positive and significant. In short, enhanc-
ing performance passes by recognizing the importance
of management control, business strategy, and oper-
ational capabilities. These findings are also consistent
with Brito and Sauan (2016), Dultra-de-Lima and Brito
(2023), and Martins et al. (2023), who found a positive
association between management practices and supe-
rior performance, and management capabilities.
For practitioners, our findings guide managers to
focus on the strategic alignment of operational ca-
pabilities for leveraging productivity, performance,
and competitiveness. The existing literature has ad-
vocated that strategic alignment benefits companies
(Chenhall, 2005) and influence business performance
(Schniederjans & Cao, 2009). We also show that MCS
as antecedents of operational capabilities causes a
positive variance in performance. However, managers
focusing on strategic flexibility must pay careful atten-
tion because its influence on performance is not direct,
but happens through operational capabilities. Thus, our
results align with operations literature that postulates
leveraging operational capabilities creates value for
companies (Martins et al., 2023; Rehman et al., 2019;
Skinner, 1969; Wheelwright, 1984).
CONCLUSION
This study aimed to investigate management control
systems as antecedents of strategic flexibility and their
eects on business performance.
Regarding our results, they provide evidence that
MSC is relevant for strategic flexibility as well as ca-
pabilities for performance. The study indicates that
non-monetary cybernetic controls positively influence
strategic flexibility, which supported H1.2. The result re-
inforces the benefits of this type of control to achieve
business performance, confirming the results of Sundin
et al. (2010), who observed that organizations with
many stakeholders and multiple objectives often adopt
non-monetary cybernetic controls. By the same to-
ken, the finding also shows that when MCS is eective
with strategy, it produces positive performance, which
aligns with Jukka (2023).
We also analyzed the eect of dynamic controls on
strategic flexibility (H1.3). The study also proves that dy-
namic control positively influences strategic flexibility,
supporting the hypothesis. This finding is consistent
with the proposal of Malmi and Brown (2008) that
management controls are an overall package and with
the configurational approach proposed by Bedford et
al. (2016) and is in line with the empirical findings by
Fainshmidt et al. (2016).
Furthermore, the next step was to analyze the re-
lationship between strategic and operational flexibility
(H2). Our results revealed that strategic flexibility pos-
itively impacts operational flexibility, reinforcing that
business strategy should be implemented through
operational strategy (Krause et al., 2014; Wheelwright,
1984), supporting the hypothesis.
After that, we draw attention to the relationship be-
tween operational flexibility and performance (H4.1 and
H4.2). Our findings provide evidence that the construct
significantly enhances monetary and non-monetary
performance, thus supporting our hypotheses. These
results are also well grounded in the academic litera-
ture that postulates the importance of operational flex-
ibility in dealing with environmental uncertainty and
heterogeneity to impact performance (Yousuf et al.,
2019; Yu et al., 2015).
Finally, our results support the concept that the im-
pact of business strategy on monetary and non-mon-
etary performance is mediated by operational strategy
(H5.1 and H5.2). Therefore, we noted that strategic flex-
ibility did not influence performance directly but indi-
rectly through operational flexibility, supporting hypoth-
eses H5.1 and H5.2. At first glance, this finding does not
make sense. However, carefully reading the operations
strategy literature shows it is reasonable. Seminar pa-
pers such as those by Skinner (1969) and Wheelwright
(1984) posited that business strategy needs to be trans-
lated into operations strategy to run an organization
successfully. Similarly, Ward and Duray (2000), regard-
ing strategy and manufacturing, stated that the relation-
ship between competitive strategy and performance is
mediated by manufacturing strategy. Consistent with
this idea, Yousuf et al. (2022), studying Hungarian food
industries, identified that strategic flexibility responds to
20% of the changes in performance. They highlighted
the importance of flexibility in resources and coordi-
nation to achieve performance. Hence, strategic flex-
ibility must be implemented through operational poli-
cies to provide the required flexibility. The congruence
between business strategy and operational practice is
understood as strategic alignment.
By contrast, our study has not supported some
hypotheses. For instance, the lack of support for our
hypothesis H1.1 raises the question of whether mon-
etary cybernetic control harms strategic flexibility as a
common belief. This reflection is necessary since or-
ganizations and practitioners use monetary cybernetic
control extensively. This first finding contradicts the ob-
servations of Libby and Lindsay (2010), who confirmed
the value of cybernetic controls, and Rehman et al.
15
BAR-Brazilian Administration Review, 20(3), e220170, 2023.
J. C. Oyadomari, Y-T. Chen, R. G. Dultra-de-Lima, O. R. de Mendonça Neto
(2019), who suggest a significant impact of cybernetic
controls, rewards and compensation, and administra-
tive controls on organizational performance. A possible
explanation for these findings (contradictions) could
be the misalignment between management control
and business strategy, which is consonant with Jukka
(2023). This author highlights that for a business strate-
gy to be viable, it must be aligned with a particular MCS.
By contrast, if the mismatch between business strate-
gy and MCS occurs, it would explain the dierence in
performance. The next step, we analyzed the hypoth-
eses H3.1 and H3.2. These hypotheses raise concerns
for not supporting the relationship between strategic
flexibility and performance. Therefore, our paper does
not provide statistical evidence of a positive relation-
ship between the constructs. This finding is backed by
the operations literature that suggests strategic flexibil-
ity as a responsive construct. It focuses on identifying
and evaluating specific traits from the environment
and setting their resources to deal with them (Brozovic,
2018). Eisenhardt and Martin (2000) understand stra-
tegic flexibility as a dynamic capability influencing op-
erational excellence; however, the construct does not
impact performance directly.
The implications for the accounting literature: Malmi
and Brown (2008) set MSC as a package, and the exist-
ing accounting literature has tested this contribution in
various fields, such as strategic management of innova-
tion (Biswas & Akroyd, 2022b), inter-organizational col-
laborative relationship (Biswas & Akroyd, 2022a), MCS
in a crisis context (Martins et al., 2023), as dynamic con-
trols (Bedford et al., 2016), rolling forecasting (Hansen,
2011), or continuous budgeting (Frow et al., 2010), and
so on. However, this paper proposes to extend this lit-
erature by testing a set of the MSC package and its in-
fluence on capabilities, which is decomposed into two
constructs (strategic flexibility and operational flexibility)
and their eect on organizational performance (mone-
tary and non-monetary). The main contribution of this
paper is to propose new insights into analyzing MSC and
its relevance to capabilities and which of them impact
monetary and non-monetary performance. The paper
proves that only some MSC packages and capabilities
are related by testing these streams. For instance, mon-
etary cybernetic controls, built from financial reports,
are irrelevant to strategic flexibility. Therefore, strategic
flexibility only captures the benefits of non-monetary
cybernetic and dynamic controls to deal with contin-
gency factors, giving orientation to operational flexibil-
ity. In short, these configurations have proved eective
in performance.
The implications for managers: from a practical per-
spective for managers, our findings call attention to the
critical role of setting the MSC because it leverages
organization capabilities, which are relevant for per-
formance (Rehman et al., 2019). As a result, managers
must establish MSCs that control strategic operations
eciently and, by so doing, increase productivity and
flexibility, reduce waste of time, setups, and materials,
and improve quality, among others. However, only
specific controls influence operations and managers
should focus on those that deal with better contingen-
cy factors.
The first limitation of our study is typical for surveys
based on non-probabilistic samples. In addition, due to
the small sample size and lack of industry control, our
coecients’ magnitudes require parsimonious inter-
pretation. Hence, our contribution lies in our testing for
the existence of relationships between the constructs.
The second limitation concerns the measurements
used. Despite our analyses indicating reliable indexes
of these constructs, as we adjusted them based on the
literature, our study may have ignored specific dimen-
sions of the constructs. The third limitation relies on or-
ganizational performance in which we work with four
monetary and five non-monetary performances.
We recommend that future studies investigate oth-
er dimensions of management control systems that
could be antecedents of strategic flexibility, such as cul-
ture control or planning control. In addition, the paper
analyzed the mediation eect of operational capabili-
ties on performance, but we suggest complementing
these analyses by introducing the moderation eects
of business turbulence. The contingency factors eval-
uation could give new insights into how MSC impacts
performance through capabilities. Finally, we did not
control for industry, and future studies should also fo-
cus on a particular sector to verify the dynamic of our
proposed model.
REFERENCES
Aaker, D. A., & Mascarenhas, B. (1984). The need for strategic flexibility. Journal of
Business Strategy, 5(2), 74–82. https://doi.org/10.1108/eb039060
Abernethy, M. A., & Lillis, A. M. (1995). The impact of manufacturing flexibility on
management control system design. Accounting, Organizations and Society,
20(4), 241–258. https://doi.org/10.1016/0361-3682(94)E0014-L
Alolayyan, M. N., & Alyahya, M. S. (2023). Operational flexibility impact
on hospital performance through the roles of employee engagement
and management capability. BMC Health Services Research, 23(1).
https://doi.org/10.1186/s12913-023-09029-y
Bagozzi, R. P., Yi, Y., & Phillips, L. W. (1991). Assessing Construct Validity
in Organizational Research. Administrative Science Quarterly, 36(3), 421.
https://doi.org/10.2307/2393203
Barney, J. (1991). Firm resources and sustained competitive advantage. Journal
of Management, 17(1), 99–120.
Barney, J. B. (1986). Strategic Factor Markets: Expecations, Luck And Business
Strategy. Management Science, 32(10), 12.
Barney, J. B. (2011). Gaining and sustaining competitive advantage (4th Edition).
Pearson.
16
Exploring the influences of cybernetic and dynamic controls on flexibility and performance
BAR-Brazilian Administration Review, 20(3), e220170, 2023.
Bagozzi, R. P., Yi, Y., Phillips, L. W. (1991). Assessing construct validity in
organizational research. administrative Science Quarterly, 36(3), 421.
https://doi.org/10.2307/2393203
Barney, J. B., & Clark, D. N. (2007). Resource-Based Theory: Creating and
Sustaining Competitive Advantage.
Barney, J. B., & Wright, P. M. (1998). On becoming a strategic partner:
The role of human resources in gaining competitive advantage. Human
Resource Management, 37(1), 31–46. https://doi.org/10.1002/(SICI)1099-
050X(199821)37:1<31::AID-HRM4>3.0.CO;2-W
Battistella, C., De Toni, A. F., De Zan, G., & Pessot, E. (2017). Cultivating
business model agility through focused capabilities: A multiple case study.
Journal of Business Research, 73, 65–82.
Beyond Budgeting Round Table. (2016). The Beyond Budgeting principles.
https://bbrt.org/what-is-beyond-budgeting/
Bedford, D. S. (2015). Management control systems across different modes
of innovation: Implications for firm performance. Management Accounting
Research, 28(September), 12–30. https://doi.org/10.1016/j.mar.2015.04.003
Bedford, D. S., & Malmi, T. (2015). Configurations of control: An
exploratory analysis. Management Accounting Research, 27(June), 2–26.
https://doi.org/10.1016/j.mar.2015.04.002
Bedford, D. S., Malmi, T., & Sandelin, M. (2016). Management control
effectiveness and strategy: An empirical analysis of packages
and systems. Accounting, Organizations and Society, 51, 12–28.
https://doi.org/10.1016/j.aos.2016.04.002
Bennett, N., & Lemoine, G. J. (2014). What a difference a word makes:
Understanding threats to performance in a VUCA world. Business Horizons,
57(3), 311–317. https://doi.org/10.1016/J.BUSHOR.2014.01.001
Beuren, I. M., & Vaz, P. V. C. (2021). Effects of the Environmental Strategy
and Management Control System Package on Managerial Performance.
Journal of Environmental Accounting and Management, 9(4), 429–443.
https://doi.org/10.5890/JEAM.2021.12.007
Bhimani, A., Sivabalan, P., & Soonawalla, K. (2018). A study of the linkages
between rolling budget forms, uncertainty and strategy. The British
Accounting Review, 50(3), 306–323. https://doi.org/10.1016/J.BAR.2017.11.002
Biswas, S., & Akroyd, C. (2022a). Collaborative inter-organisational
relationships and management control change. Accounting and Finance,
62(4), 4569–4586. https://doi.org/10.1111/acfi.12955
Biswas, S., & Akroyd, C. (2022b). Management control systems and the
strategic management of innovation. Qualitative Research in Accounting and
Management, 19(5), 513–539. https://doi.org/10.1108/QRAM-04-2021-0083
Bloom, N., Genakos, C., Sadun, R., & Van Reenen, J. (2012). Management
practices across firms and countries. The Academy of Management
Perspectives, 26(1), 12–33. https://www.nber.org/papers/w17850
Bloom, N., & Van Reenen, J. (2010). Why do management practices differ
across firms and countries? The Journal of Economic Perspectives, 24(1),
203–224. https://doi.org/ 10.1257/jep.24.1.203
Bornia, A. C., & Lunkes, R. J. (2007). Uma Contribuição à Melhoria do
Processo Orçamentário. Contabilidade Vista & Revista, 18(4), 37–59. https://
revistas.face.ufmg.br/index.php/contabilidadevistaerevista/article/view/339
Brito, L. A. L., & Sauan, P. K. (2016). Management practices as capabilities
leading to superior performance. BAR - Brazilian Administration Review,
13(3), 1–21. https://doi.org/10.1590/1807-7692bar2016160004
Bromiley, P., & Rau, D. (2014). Towards a practice-based view
of strategy. Strategic Management Journal, 35(8), 1249–1256.
http://dx.doi.org/10.1002/smj.2238
Bromiley, P., & Rau, D. (2016). Operations management and the resource
based view: Another view. Journal of Operations Management, 41, 95–106.
https://doi.org/10.1016/j.jom.2015.11.003
Brown, S. A. (1997). Knowledge, communication, and progressive use of
information technology [dissertation]. University of Minnesota, USA.
Brozovic, D. (2018). Strategic flexibility: A review of the literature: Strategic
flexibility. International Journal of Management Reviews, 20(1), 3–31.
http://dx.doi.org/10.1111/ijmr.12111
Brüggemann, E. R., Monteiro, J. J., & Lunkes, R. J. (2022). The influence
of performance measurement systems on organizational agility
and open innovation. Revista de Contabilidade e Organizacoes, 16.
https://doi.org/10.11606/issn.1982-6486.rco.2022.193897
Chenhall, R. H. (2003). Management control systems design within its
organizational context: findings from contingency-based research and
directions for the future. Accounting, Organizations and Society, 28(2–3),
127–168. https://doi.org/10.1016/S0361-3682(01)00027-7
Chenhall, R. H. (2005). Integrative strategic performance measurement
systems, strategic alignment of manufacturing, learning and strategic
outcomes: An exploratory study. Accounting Organizations and Society,
30(5), 395–422. https://doi.org/10.1016/j.aos.2004.08.001
Chenhall, R. H., & Langfield-Smith, K. (1998). The relationship between strategic
priorities, management techniques and management Accounting: an empirical
investigation using a systems approach. Accounting, Organizations and Society,
23(3), 243–264. https://doi.org/10.1016/S0361-3682(97)00024-X
Chin, W. W., & Newsted, P. R. (1999). Structural Equation Modeling Analysis With
Small Sample Using Partial Least Squares. In R. H. Hoyle (Ed.), Statistical Strategies
for Small Sample Research (pp. 307–348). Thousand Oaks: Sage Publications.
Chin, W. W., Thatcher, J. B., Wright, R. T., & Steel, D. (2013). Controling for
common method variance in PLS analysis: the measured latent marker variable
approach. In New Perspectives in Partial Least Squares and Related Methods (pp.
231–239). Springer.
Chittoor, R., Sarkar, M. B., Ray, S., & Aulakh, P. S. (2009). Third-world copycats
to emerging multinationals: Institutional changes and organizational
transformation in the indian pharmaceutical industry. Organization Science,
20(1), 187–205. https://doi.org/DOI 10.1287/orsc.1080.0377
Clark-Carter, D. (2004). Quantitative psychological research: A Student’s
Handbook (2nd ed.). Psychology Press.
Corrar, L. J., Paulo, E., & Dias Filho, J. M. (2009). Análise Multivariada: para os
Cursos de Administração, Ciências Contábeis e Economia (1. ed., 2.). Atlas.
Cui, T., Ye, H. J., Teo, H. H., & Li, J. (2015). Information technology and open
innovation: A strategic alignment perspective. Information & Management,
52(3), 348–358. https://doi.org/10.1016/j.im.2014.12.005
Davis, J. P., Eisenhardt, K. M., & Bingham, C. B. (2009). Optimal structure, market
dynamism, and the strategy of simple rules. Administrative Science Quarterly,
54(3), 413–452. https://doi.org/10.2189/asqu.2009.54.3.413
Dibrell, C., Craig, J. B., & Neubaum, D. O. (2014). Linking the formal
strategic planning process, planning flexibility, and innovativeness to
firm performance. Journal of Business Research, 67(9), 2000–2007.
https://doi.org/10.1016/J.JBUSRES.2013.10.011
Dimes, R., & de Villiers, C. (2021). How management control systems enable
and constrain integrated thinking. Meditari Accountancy Research, 29(4), 851–
872. https://doi.org/10.1108/MEDAR-05-2020-0880
Dossi, A., & Patelli, L. (2010). You Learn From What You Measure: Financial and
Non-financial Performance Measures in Multinational Companies. Long Range
Planning, 43(4), 498–526. https://doi.org/10.1016/J.LRP.2010.01.002
Dossi, A., Patelli, L., & Zoni, L. (2010). The Missing Link between Corporate
Performance Measurement Systems and Chief Executive Ocer
Incentive Plans. Journal of Accounting, Auditing & Finance, 25(4), 531–558.
https://doi.org/10.1177/0148558X1002500404
Doz, Y. L., & Kosonen, M. (2008). Fast strategy: How strategic agility will help you
stay ahead of the game. Pearson Education.
Dultra-de-Lima, R. G., & Brito, L. A. L. (2023). How absorptive capacity influences
project performance. International Journal of Managing Projects in Business,
16(3), 610–637. https://doi.org/10.1108/IJMPB-02-2022-0025
Eisenhardt, K. M. (1989). Building theories from case study research. Academy of
Management Review, 14(4), 532–550.
Eisenhardt, K. M., Furr, N. R., & Bingham, C. B. (2010). CROSSROADS -
Microfoundations of performance: Balancing eciency and flexibility
in dynamic environments. Organization Science, 21(6), 1263–1273.
https://doi.org/10.1287/orsc.1100.0564
Eisenhardt, K. M., & Martin, J. A. (2000). Dynamic capabilities: what are
they? Strategic Management Journal, 21(10–11), 1105–1121. https://doi.
org/10.1002/1097-0266(200010/11)21:10/11<1105::AID-SMJ133>3.0.CO;2-E
El-Khalil, R. (2018). The mediating eect of lean management on the relationship
between flexibility implementation and operational metrics in US automotive
manufacturing plants. Journal of Manufacturing Technology Management,
29(8), 1376–1399. https://doi.org/10.1108/JMTM-04-2018-0108
Endenich, C., Lachmann, M., Schachel, H., & Zajkowska, J. (2022). The
relationship between management control systems and innovativeness
in start-ups: Evidence for product, business model, and ambidextrous
innovation. Journal of Accounting and Organizational Change.
https://doi.org/10.1108/JAOC-06-2022-0087
Fainshmidt, S., Pezeshkan, A., Lance Frazier, M., Nair, A., & Markowski, E. (2016).
Dynamic Capabilities and Organizational Performance: A Meta-Analytic
Evaluation and Extension. Journal of Management Studies, 53(8), 1348–1380.
https://doi.org/10.1111/joms.12213
Farooq, R. (2016). Role of structural equation modeling in scale
development. Journal of Advances in Management Research, 13(1), 75–91.
https://doi.org/10.1108/JAMR-05-2015-0037
Ferreira, A., & Otley, D. (2009). The design and use of performance management
systems: An extended framework for analysis. Management Accounting
Research, 20(4), 263–282. https://doi.org/10.1016/j.mar.2009.07.003
Fornell, C., & Larcker, D. F. (1981). Evaluating Structural Equation Models with
Unobservable Variables and Measurement Error. Journal of Marketing Research,
18(1), 39–50. https://www.jstor.org/stable/3151312
17
BAR-Brazilian Administration Review, 20(3), e220170, 2023.
J. C. Oyadomari, Y-T. Chen, R. G. Dultra-de-Lima, O. R. de Mendonça Neto
Franco-Santos, M., Lucianetti, L., & Bourne, M. (2012). Contemporary
performance measurement systems: A review of their consequences and a
framework for research. Management Accounting Research, 23(2), 79–119.
https://doi.org/10.1016/j.mar.2012.04.001
Freitas, H., Oliveira, M., Saccol, A. Z., & Moscarola, J. (2000). O método de
pesquisa survey. Revista de Administração, 35(3), 105–112. http://www.clam.org.
br/bibliotecadigital/uploads/publicacoes/1138_1861_freitashenriquerausp.pdf
Frow, N., Marginson, D., & Ogden, S. (2010). ‘‘Continuous” budgeting: Reconciling
budget flexibility with budgetary control. Accounting, Organizations and
Society, 35(4), 444–461. https://doi.org/10.1016/j.aos.2009.10.003
Gal-Or, E. (1985). First mover and second mover
advantages. International Economic Review, 649–653.
https://www.diva-portal.org/smash/get/diva2:229467/FULLTEXT01.pdf
Hair, J. F., Black, W. C., Babin, B., Anderson, R. E., & Tatham, R. L. (2009). Análise
Multivariada de Dados (6. ed.). Bookman.
Hair, J. F., Hunt, G. T. M., Ringle, C. M., & Sarstedt, M. (2014). A Primer on Partial
Least Squares Structural Equation Modeling (PLS-SEM). Sage Publications.
https://doi.org/10.1108/EBR-10-2013-0128
Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: Indeed a Silver
Bullet. The Journal of Marketing Theory and Practice, 19(2), 139–152.
https://doi.org/10.2753/MTP1069-6679190202
Haka, S., & Krishnan, R. (2005). Budget Type and Performance — The
Moderating Eect of Uncertainty. Australian Accounting Review, 15(35), 3–13.
https://doi.org/10.1111/j.1835-2561.2005.tb00247.x
Hansen, S. C. (2011). A theoretical analysis of the impact of adopting rolling
budgets, activity-based budgeting and beyond budgeting. European Accounting
Review, 20(2), 289–319. https://doi.org/10.1080/09638180.2010.496260
Harlez, Y. de, & Malagueño, R. (2016). Examining the joint eects of strategic
priorities, use of management control systems, and personal background
on hospital performance. Management Accounting Research, 30, 2–1 7.
https://doi.org/10.1016/J.MAR.2015.07.001
Helfat, C. E., & Peteraf, M. A. (2009). Understanding dynamic capabilities:
Progress along a developmental path. Strategic Organization, 7(1), 91–102.
https://doi.org/10.1177/1476127008100133
Henri, J.-F. (2006). Management control systems and strategy: A resource-
based perspective. Accounting, Organizations and Society, 31(6), 529–558.
https://doi.org/10.1016/j.aos.2005.07.001
Henseler, J., Ringle, C. M., & Sinkovics, R. R. (2009). The Use of Partial Least Squares
Path Modeling in International Marketing. Advances in International Marketing,
20(2009), 277–319. https://doi.org/10.1108/S1474-7979(2009)0000020014
Henttu-Aho, T., & Järvinen, J. (2013). Field study of emerging Beyond Budgeting
practice in industrial companies: an institutional perspective. European Accounting
Review, 22(4), 765–785. https://doi.org/10.1080/09638180.2012.758596
Ho, J. L. Y., Wu, A., & Wu, S. Y. C. C. (2014). Performance measures, consensus
on strategy implementation, and performance: Evidence from the operational-
level of organizations. Accounting, Organizations and Society, 39(1), 38–58.
https://doi.org/10.1016/j.aos.2013.11.003
Hope, J., & Fraser, R. (2003). Beyond budgeting: How managers can break free
from the annual performance trap. Harvard Business School Press.
Hsiao, Y., Chen, C., Guo, R., & Hu, K. (2017). First‐mover strategy, resource
capacity alignment, and new product performance: a framework for
mediation and moderation eects. R&D Management, 47(1), 75–87.
https://doi.org/10.1111/radm.12157
Järvenpää, M. (2007). Making business partners: A case study on how
management accounting culture was changed. European Accounting Review,
16(Apr.), 99–142. https://doi.org/10.1080/09638180701265903
Jia, F., Lamming, R., Sartor, M., Orzes, G., & Nassimbeni, G. (2014). International
purchasing oces in China: A dynamic evolution model. International Business
Review, 23(3), 580–593. https://doi.org/10.1016/j.ibusrev.2013.09.006
Johnson, R. E., Rosen, C. C., & Djurdjevic, E. (2011). Assessing the impact of
common method variance on higher order multidimensional constructs. The
Journal of Applied Psychology, 96(4), 744–761. https://doi.org/10.1037/a0021504
Joiner, T. A., Spencer, X. S. Y., & Salmon, S. (2009). The eectiveness of
flexible manufacturing strategies: The mediating role of performance
measurement systems. International Journal of Productivity and Performance
Management, 58(2), 119–135. https://www.emerald.com/insight/content/
doi/10.1108/17410400910928725/full/html
Jukka, T. (2023). Does business strategy and management control system fit
determine performance? International Journal of Productivity and Performance
Management, 72(3), 659–678. https://doi.org/10.1108/IJPPM-11-2020-0584
Kaplan, B., & Duchon, D. (1988). Combining qualitative and quantitative
methods in information systems a case study. MIS Quarterly, 12(4), 571–586.
https://doi.org/10.2307/249133
Kaplan, R. S., & Norton, D. P. (1992). The balanced scorecard--measures
that drive performance. Harvard Business Review, 70(1), 71–79. https://hbr.
org/1992/01/the-balanced-scorecard-measures-that-drive-performance-2
Kaveski, I. D. S., & Beuren, I. M. (2020). Influência dos sistemas de controle
gerencial e da criatividade sobre o desempenho no trabalho. Cadernos
EBAPE.BR, 18(3), 543–556. https://doi.org/10.1590/1679-395120190024
Kline, R. B. (2016). Principles and practices of structural equation modeling
(Fouth Edit). The Guilford Press.
Kortmann, S., Gelhard, C., Zimmermann, C., & Piller, F. T. (2014). Linking
strategic flexibility and operational efficiency: The mediating role of
ambidextrous operational capabilities. Journal of Operations Management,
32(7–8), 475–490. https://doi.org/10.1016/j.jom.2014.09.007
Kraaijenbrink, J., Spender, J.-C., & Groen, A. J. (2010). The resource-based
view: a review and assessment of its critiques. Journal of Management,
36(1), 349–372. https://doi.org/10.1177/0149206309350775
Krause, D., Youngdahl, W., & Ramaswamy, K. (2014). Manufacturing - Still
a missing link? Journal of Operations Management, 32(7-8), 399–402.
https://doi.org/10.1016/j.jom.2014.09.001
Krippendorff, K. (2013). Content analysis: An introduction to its methodology
(3rd ed.). Sage Publications.
Langfield-Smith, K. (1997). Management control systems and strategy:
A critical review. Accounting, Organizations and Society, 22(2), 207–232.
https://doi.org/10.1016/S0361-3682(95)00040-2
Libby, T., & Lindsay, R. M. (2010). Beyond budgeting or budgeting
reconsidered? A survey of North-American budgeting practice. Management
Accounting Research, 21(1), 56–75. https://doi.org/10.1016/j.mar.2009.10.003
Lieberman, M. B., & Montgomery, D. B. (1988). First-mover advantages.
Strategic Management Journal, 9(S1), 41–58.
Lin, C., Tsai, H.-L., & Wu, J.-C. (2014). Collaboration strategy decision-
making using the Miles and Snow typology. Journal of Business Research,
67(9), 1979–1990. https://doi.org/10.1016/j.jbusres.2013.10.013
Malhotra, N. K., Kim, S. S., & Patil, A. (2006). Common Method Variance
in Is Research: A Comparison of Alternative Approaches and a
Reanalysis of past Research. Management Science, 52(12), 1865–1883.
https://doi.org/10.1287/mnsc.l060.0597
Malmi, T., & Brown, D. A. (2008). Management control systems as a package-
Opportunities, challenges and research directions. Management Accounting
Research, 19(4), 287–300. https://doi.org/10.1016/j.mar.2008.09.003
Martins, A., Oliveira, C., Silva, R., & Castelo Branco, M. (2023). management
control practices as performance facilitators in a crisis context.
Administrative Sciences, 13(7), 163. https://doi.org/10.3390/admsci13070163
Mathiassen, L., & Pries-Heje, J. (2006). Business agility and diffusion of
information technology. European Journal of Information Systems, 15, 116–
119. http://dx.doi.org/10.1057/palgrave.ejis.3000610
McGahan, A. M., & Porter, M. E. (1997). How much does industry matter,
really? Strategic Management Journal, 18(Special Issue-Summer), 16.
Merchant, K. A. (1990). The effects of financial controls on data manipulation
and management Myopia. Accounting, Organizations and Society, 15(4),
297–313. https://doi.org/10.1016/0361-3682(90)90021-L
Merchant, K. A., & Van der Stede, W. A. (2012). Management control systems:
Performance measurement, evaluation and incentives (Third Edit). Prentice
Hall.
Miles, M. B., Huberman, A. M., & Saldaña, J. (2013). Qualitative data analysis:
A methods sourcebook (3rd ed.). Sage Publications.
Miles, R. E., Snow, C. C., Meyer, A. D., & Coleman, H. J. (1978). Organizational
strategy, structure, and process. Academy of Management Review, 3(3),
546–562. http://dx.doi.org/10.5465/AMR.1978.4305755
Mingers, J. (2001). Combining IS research methods: Towards a
pluralist methodology. Information Systems Research, 12(3), 240–259.
http://dx.doi.org/10.1287/isre.12.3.240.9709
Miroshnychenko, I., Strobl, A., Matzler, K., & De Massis, A. (2021). Absorptive
capacity, strategic flexibility, and business model innovation: Empirical
evidence from Italian SMEs. Journal of Business Research, 130, 670–682.
http://dx.doi.org/10.1016/j.jbusres.2020.02.015
Murthi, B. P. S., Srinivasan, K., & Kalyanaram, G. (1996). Controlling for
observed and unobserved managerial skills in determining first-mover
market share advantages. Journal of Marketing Research, 33(3), 329–336.
https://doi.org/10.1177/002224379603300307
Nadkarni, S., & Narayanan, V. K. (2007). Strategic schemas, strategic flexibility,
and firm performance: The moderating role of industry clockspeed. Strategic
Management Journal, 28(3), 243–270. https://doi.org/10.1002/smj.576
Ojha, D., Struckell, E., Acharya, C., & Patel, P. C. (2021). Managing environmental
turbulence through innovation speed and operational flexibility in B2B service
organizations. Journal of Business and Industrial Marketing, 36(9), 1627–1645.
https://doi.org/10.1108/JBIM-01-2020-0026
18
Exploring the influences of cybernetic and dynamic controls on flexibility and performance
BAR-Brazilian Administration Review, 20(3), e220170, 2023.
Olson, E. M., Slater, S. F., & Hult, G. T. M. (2005). The performance
implications of fit among business strategy, marketing organization
structure, and strategic behavior. Journal of Marketing, 69(3), 49–65.
https://doi.org/10.1509/jmkg.69.3.49.66362
Østergren, K., & Stensaker, I. (2011). Management Control without Budgets: A
Field Study of ‘Beyond Budgeting’ in Practice. European Accounting Review,
20(1). https://doi.org/10.1080/09638180903487842
Oyadomari, J. C. T., Pedrique, A. D. L., Bido, D. de S., & Rezende, A. J.
(2014). Uso do Controle Gerencial e Decisões em Organizações de Saúde
Brasileiras: um Estudo Exploratório. Brazilian Business Review, 11(2), 1–34.
https://www.redalyc.org/pdf/1230/123030600001.pdf
Patel, P. C. (2011). Role of manufacturing flexibility in managing
duality of formalization and environmental uncertainty in emerging
firms. Journal of Operations Management, 29(1–2), 143–162.
https://doi.org/10.1016/j.jom.2010.07.007
Peteraf, M. A., & Barney, J. B. (2003). Unraveling the resource-
based tangle. Managerial and Decision Economics, 24(4), 15.
https://doi.org/10.1177/002224379603300307
Pitkänen, H., & Lukka, K. (2011). Three dimensions of formal and informal
feedback in management accounting. Management Accounting Research,
22(2), 125–137. https://doi.org/10.1016/j.mar.2010.10.004
Podsako, P. M., MacKenzie, S. B., Lee, J.-Y., & Podsako, N. P. (2003). Common
method biases in behavioral research: a critical review of the literature and
recommended remedies. The Journal of Applied Psychology, 88(5), 879–903.
https://doi.org/10.1037/0021-9010.88.5.879
Podsako, P. M., MacKenzie, S. B., & Podsako, N. P. (2012). Sources of
method bias in social science research and recommendations on
how to control it. Annual Review of Psychology, 63(January), 539–569.
https://doi.org/10.1146/annurev-psych-120710-100452
Porter, M. E. (1979). How competitive forces shape strategy. Harvard Business Review,
57(2), 137–145. https://www.hbs.edu/faculty/Pages/item.aspx?num=10692
Porter, M. E. (1996). What is strategy? Harvard Business Review, 74(6).
https://hbr.org/1996/11/what-is-strategy
Rehman, S., Mohamed, R., & Ayoup, H. (2019). The mediating role of
organizational capabilities between organizational performance and
its determinants. Journal of Global Entrepreneurship Research, 9(1).
https://doi.org/10.1186/s40497-019-0155-5
Robinson, W. T., Fornell, C., & Sullivan, M. (1992). Are market pioneers intrinsically
stronger than later entrants? Strategic Management Journal, 13(8), 609–624.
Rönkkö, M., & Ylitalo, J. (2011). PLS marker variable approach to diagnosing and
controlling for method variance. Thirty Second International Conference on
Information Systems, 2054–2069. https://aisel.aisnet.org/icis2011/proceedings/
researchmethods/8/
Sanchez, R. (1995). Strategic flexibility in product
competition. Strategic Management Journal, 16, 135–159.
https://aisel.aisnet.org/icis2011/proceedings/researchmethods/8/
Santos-Vijande, M. L., López-Sánchez, J. A., & Trespalacios, J. A. (2012).
How organizational learning aects a firm’s flexibility, competitive strategy,
and performance. Journal of Business Research, 65(8), 1079–1089.
http://dx.doi.org/10.1016/j.jbusres.2011.09.002
Sardana, D., Terziovski, M., & Gupta, N. (2016). The impact of strategic
alignment and responsiveness to market on manufacturing firm’s
performance. International Journal of Production Economics, 177, 131–138.
https://doi.org/10.1016/j.ijpe.2016.04.018
Schmenner, R. W., & Tatikonda, M. V. (2005). Manufacturing process flexibility
revisited. International Journal of Operations & Production Management, 25(12),
1183–1189. https://doi.org/Doi 10.1108/01443570510633585
Schniederjans, M., & Cao, Q. (2009). Alignment of operations strategy,
information strategic orientation, and performance: An empirical
study. International Journal of Production Research, 47(10), 2535–2563.
https://doi.org/10.1080/00207540701673465
Shavarini, S. K., Salimian, H., Nazemi, J., & Alborzi, M. (2013). Operations
strategy and business strategy alignment model (case of Iranian industries).
International Journal of Operations & Production Management, 33(9), 1108–
1130. https://doi.org/10.1108/IJOPM-12-2011-0467
Shuiabi, E., Thomson, V., & Bhuiyan, N. (2005). Entropy as a measure of
operational flexibility. European Journal of Operational Research, 165(3), 696–
70 7. https://doi.org/10.1016/j.ejor.2004.01.033
Simons, R. (1994). How new top managers use control systems as levers
of strategic renewal. Strategic Management Journal, 15(3), 169–189.
https://www.jstor.org/stable/2486965
Skinner, W. (1969). Manufacturing—Missing link in corporate strategy. Harvard
Business Review, 47(3), 136–145. https://doi.org/10.1016/S0267-3649(00)88914-1
Sliwka, D. (2002). On the use of nonfinancial performance measures in
management compensation. Journal of Economics & Management Strategy,
11(3), 487–511. https://doi.org/10.1111/j.1430-9134.2002.00487.x
Smith, D., & Langfield-Smith, K. (2004). Structural equation modeling in management
accounting research: critical analysis and opportunities. Journal of Accounting
Literature, 23, 49–86. https://www.proquest.com/openview/63e2d4a06c95e0f5c0
23bdbf1d0e96f6/1?pq-origsite=gscholar&cbl=31366
Spencer, X. S. Y., Joiner, T. A., & Salmon, S. (2009). Dierentiation strategy, performance
measurement systems and organizational performance: Evidence from Australia.
International Journal of Business, 14(1), 83–103. https://www.researchgate.net/
publication/267560915_Differentiation_Strategy_Performance_Measurement_
Systems_and_Organizational_Performance_Evidence_from_Australia
Sundin, H., Granlund, M., & Brown, D. A. (2010). Balancing multiple competing
objectives with a balanced scorecard. European Accounting Review, 19(2), 203-246.
https://doi.org/10.1080/09638180903118736
Sweeney, D. J., Williams, T. A., & Anderson, D. R. (2013). Estatística aplicada à
Administração e Economia (3 ed.). Cengage Learning.
Swink, M., Narasimhan, R., & Kim, S. W. (2005). manufacturing practices and strategy
integration: Eects on cost eciency, flexibility, and market-based performance.
Decision Sciences, 36(3), 427–457. https://doi.org/10.1111/j.1540-5414.2005.00079.x
Szymanski, D. M., Troy, L. C., & Bharadwaj, S. G. (1995). Order of entry and business
performance: An empirical synthesis and reexamination. The Journal of Marketing,
59(4), 17–33. https://www.jstor.org/stable/1252325
Teece, D. J. (2007). Explicating dynamic capabilities: The nature and
microfoundations of (sustainable) enterprise performance. Strategic Management
Journal, 28(13), 1319–1350. https://doi.org/10.1002/smj
Tenenhaus, M., Vinzi, V. E., Chatelin, Y.-M., & Lauro, C. (2005). PLS path
modeling. Computational Statistics & Data Analysis, 48(1), 159–205.
https://doi.org/10.1016/j.csda.2004.03.005
Tenhiälä, A., & Helkiö, P. (2015). Performance eects of using an ERP system for
manufacturing planning and control under dynamic market requirements. Journal
of Operations Management, 36, 147–164. https://doi.org/10.1016/J.JOM.2014.05.001
Vecchiato, R. (2015). Creating value through foresight: First mover advantages
and strategic agility. Technological Forecasting and Social Change, 101, 25–36.
https://doi.org/10.1016/j.techfore.2014.08.016
Ward, P. T., & Duray, R. (2000). Manufacturing strategy in context: environment,
competitive strategy and manufacturing strategy. Journal of Operations
Management, 18. www.elsevier.comrlocaterdsw
Webb, D., & Pettigrew, A. (1999). The temporal development of strategy:
Patterns in the UK insurance industry. Organization Science, 10(5), 601–621.
https://pubsonline.informs.org/doi/abs/10.1287/orsc.10.5.601
Wei, Z., Yi, Y., & Guo, H. (2014). Organizational learning ambidexterity, strategic
flexibility, and new product development. Journal of Product Innovation
Management, 31(4), 832–847. https://doi.org/10.1111/jpim.12126
Wetzels, M., Odekerken-Schröder, G., & van Oppen, C. (2009). Using PLS path
modeling for assessing hierarchical construct models: Guidelines and Empirical
Illustration. MIS Quarterly, 33(1), 177–195. https://misq.umn.edu/using-pls-path-
modeling-for-assessing-hierarchial-construct-models-guidelines-and-impirical-
illustration.html
Wheelwright, S. C. (1984). Manufacturing strategy: Defining the missing link. Strategic
Management Journal, 5(1), 77–91. https://doi.org/10.1002/smj.4250050106
Widener, S. K. (2006). Associations between strategic resource importance and
performance measure use: The impact on firm performance. Management
Accounting Research, 17(4), 433–457. https://doi.org/10.1016/J.MAR.2005.10.002
Xie, F. T., Donthu, N., & Johnston, W. J. (2020). Beyond first or late mover advantages:
timed mover advantage. Journal of Business & Industrial Marketing. https://www.
emerald.com/insight/content/doi/10.1108/JBIM-11-2018-0334/full/html
Xiu, L., Liang, X., Chen, Z., & Xu, W. (2017). Strategic flexibility, innovative
HR practices, and firm performance. Personnel Review, 46(7), 1335–1357.
https://doi.org/10.1108/PR-09-2016-0252
Yanine, F., Valenzuela, L., Tapia, J., & Cea, J. (2016). Rethinking enterprise flexibility: a new
approach based on management control theory. Journal of Enterprise Information
Management, 29(6), 860–886. https://doi.org/10.1108/JEIM-06-2015-0054
Yin, R. K. (2014). Case study research: Design and methods (Fifth Edit). SAGE.
Yoshikuni, A. C., Dwivedi, R., Dultra-de-Lima, R. G., Parisi, C., & Oyadomari, J. C. T. (2023).
Role of Emerging Technologies in Accounting Information Systems for Achieving
Strategic Flexibility through Decision-Making Performance: An Exploratory Study
Based on North American and South American Firms. Global Journal of Flexible
Systems Management, 24, 199-218. https://doi.org/10.1007/s40171-022-00334-9
Yousuf, A., Haddad, H., Pakurár, M., Kozlovskyi, S., Mohylova, A., Shlapak, O., & János,
F. (2019). The eect of operational flexibility on performance: A field study on
small and medium-sized industrial companies in Jordan. Montenegrin Journal of
Economics, 15(1). https://doi.org/10.14254/1800-5845/2019.15-1.4
Yousuf, A., Kozlovskyi, S., Leroux, J. M., Rauf, A., & Felfoldi, J. (2022). How does
strategic flexibility make a dierence for companies? An example of the Hungarian
food industry. Problems and Perspectives in Management, 20(3), 374–386.
https://doi.org/10.21511/ppm.20(3).2022.30
19
BAR-Brazilian Administration Review, 20(3), e220170, 2023.
J. C. Oyadomari, Y-T. Chen, R. G. Dultra-de-Lima, O. R. de Mendonça Neto
Yu, K., Cadeaux, J., & Luo, B. N. (2015). Operational flexibility: Review and
meta-analysis. International Journal of Production Economics, 169, 190–202.
https://doi.org/10.1016/j.ijpe.2015.07.035
Yuan, Y., Lu, L. Y., Tian, G., & Yu, Y. (2020). Business strategy and corporate
social responsibility. Journal of Business Ethics, 162(2), 359–377.
https://link.springer.com/article/10.1007/s10551-018-3952-9
Zahra, S. A., Ireland, R. D., & Hitt, M. A. (2000). International expansion by new
venture firms: International diversity, mode of market entry, technological
learning, and performance. Academy of Management Journal, 43(5), 925–950.
https://www.jstor.org/stable/1556420
Zhou, K. Z., & Wu, F. (2010). Technological capability, strategic flexibility,
and product innovation. Strategic Management Journal, 31, 547–561.
https://doi.org/10.1002/smj
Authors
José Carlos Oyadomari
Universidade Presbiteriana Mackenzie
Rua da Consolação, 930, Consolação, CEP 01302-907, São Paulo, SP, Brazil
josecarlos.oyadomari@mackenzie.br
Insper Instituto de Ensino e Pesquisa
Rua Quatá, 300, Vila Olímpia, CEP: 04546-042, São Paulo, SP, Brasil
Yen-Tsang Chen
NEOMA Business School
32 Rue Blondel, 51100, Reims, Champagne-Ardenne, France
yen-tsang.chen@neoma-bs.fr
Ronaldo Gomes Dultra-de-Lima
Universidade Presbiteriana Mackenzie
Rua da Consolação, 930, Consolação, CEP 01302-907, São Paulo, SP, Brazil
ronaldo.lima@mackenzie.br
Centro Universitário FEI
Av. Humberto de Alencar Castelo Branco, 3972, Bairro Assunção,
CEP 0 9850-90 1, São Bernardo do Campo, SP.
Octávio Ribeiro de Mendonça Neto
Universidade Presbiteriana Mackenzie
Rua da Consolação, 930, Consolação, CEP 01302-907, São Paulo, SP, Brazil
octavio.mendonca@mackenzie.br
Authors' contributions
1st author: conceptualization (lead), data curation (supporting), formal analysis
(supporting), funding acquisition (lead), investigation (equal), methodology
(equal), project administration (lead), resources (equal), software (supporting),
supervision (supporting), validation (equal),visualization (supporting), writing –
original draft (lead), writing – review & editing (supporting).
2nd author: conceptualization (lead), data curation (lead), formal analysis
(lead),funding acquisition (supporting), investigation (equal), methodology
(equal), project administration (supporting), resources (equal), software (lead),
supervision (supporting), validation (equal), visualization (lead), writing –
original draft (lead), writing – review & editing (supporting).
3rd author: conceptualization (supporting), data curation (lead), formal
analysis (supporting), funding acquisition (supporting), investigation (equal),
methodology (equal), project administration (supporting), resources (equal),
software (lead), supervision (supporting), validation (equal), visualization (lead),
writing – original draft (supporting), writing – review & editing (lead).
4th author: conceptualization (supporting), data curation (supporting),formal
analysis (supporting), funding acquisition (supporting), investigation (equal),
methodology (equal), project administration (supporting), resources (equal),
software (supporting), supervision (lead), validation (equal), visualization
(supporting), writing – original draft (supporting), writing – review & editing
(lead).
