Big Data Analytics Capabilities and Innovation: The Mediating Role of Dynamic Capabilities and Moderating Effect of the Environment
| Author | George Lekakos,Patrick Mikalef,Maria Boura,John Krogstie |
| DOI | http://doi.org/10.1111/1467-8551.12343 |
| Published date | 01 April 2019 |
| Date | 01 April 2019 |
British Journal of Management, Vol. 30, 272–298 (2019)
DOI: 10.1111/1467-8551.12343
Big Data Analytics Capabilities and
Innovation: The Mediating Role of Dynamic
Capabilities and Moderating Eect of the
Environment
Patrick Mikalef, Maria Boura,1George Lekakos1and John Krogstie
Department of Computer Science, Norwegian University of Science and Technology, Sem Sælandsvei 9, 7491,
Trondheim,Norway, and1Department of Management of Information Technology, Athens University of
Economics and Business, Athens, Greece
Corresponding author email: patrick.mikalef@ntnu.no
With big data analytics growing rapidly in popularity, academics and practitioners have
been considering the means through which they can incorporatethe shifts these technolo-
gies bring into their competitive strategies. Drawing on the resource-based view, the dy-
namic capabilities view,and on recent literature on big data analytics, this study examines
the indirect relationship between a big data analytics capability (BDAC) and two types
of innovation capabilities: incremental and radical. The study extends existing research
by proposing that BDACs enable firms to generate insight that can help strengthen their
dynamic capabilities, which in turn positively impact incremental and radical innovation
capabilities. Totest their proposed research model, the authors used survey data from 175
chief information ocers and IT managers working in Greek firms. By means of partial
least squares structural equation modelling, the results confirm the authors’ assumptions
regardingthe indirect eect that BDACs haveon innovation capabilities. Specifically, they
find that dynamic capabilities fully mediate the eect on both incremental and radical in-
novation capabilities. In addition, under conditions of high environmental heterogeneity,
the impact of BDACs on dynamic capabilities and, in sequence, incremental innovation
capability is enhanced, while under conditions of high environmental dynamismthe eect
of dynamic capabilities on incremental innovation capabilities is amplified.
Introduction
The ‘Age of Data’ is currently thriving, with new
data being produced from all industries and pub-
lic bodies at an unprecedented rate. This phe-
nomenon has resulted in a massive hype, with orga-
nizations striving to leverage big data analytics in
order to create value (Constantiou and Kallinikos,
2015). As a result, there is much attention from
This project has received funding from the European
Union’s Horizon 2020 research and innovation pro-
gramme, under the Marie Sklodowska-Curiegrant agree-
ment No 704110.
both academics and practitioners on the value that
organizations can create through the use of big
data analytics (Manyika et al., 2011). Following
the rapid expansion of data volume, velocity and
variety, substantial developments have been doc-
umented in terms of techniques and technologies
for data storage, analysis and visualization. Nev-
ertheless, there is significantly less researchon how
organizations need to change to embrace these in-
novations, and what business value can be derived
by them (McAfee, Brynjolfsson and Davenport,
2012). Empirical research on the value of big data
analytics is still at a rudimentary stage, which is
C2019 British Academy of Management. Published by John Wiley & Sons Ltd, 9600 Garsington Road, Oxford OX4
2DQ, UK and 350 Main Street, Malden, MA, 02148, USA.
Big Data Analytics Capabilities and Innovation273
surprising, given the surge of companies making
investments in big data. Most reports on the busi-
ness value of big data to date have been from con-
sultancy firms, popular press and individual case
studies that lack theoretical insight. As a result,
there is limited understanding of how firms should
approach their big data initiatives, and scarce em-
pirical support to back-up the claim that these in-
vestments result in any measurable business value
(Mikalef et al., 2018).
Addressing these critical gaps in the literature is
important, as there is very little knowledge about
how big data analytics can be leveraged at the firm
level, and through what mechanisms value can be
created. In this study, we build on the notion of big
data analytics capability(BDAC), which is defined
as the ability of a firm to capture and analyse data
towards the generation of insights by eectively
orchestrating and deploying its data, technology
and talent (Gupta and George, 2016; Mikalef
et al., 2018). Grounded on the emerging research
on BDACs (Gupta and George, 2016; Mikalef
et al., 2018; Wamba et al., 2017), this study posits
that big data is a necessary resource, but not suf-
ficient condition to result in business value gains.
In order to be able to leverage big data to support
and guide strategic decision-making, a number
of complementary resources are necessary, which
synergistically drive a firm’s overall BDAC. As
such, firms must acquire and develop a combi-
nation of technological, human, financial and
intangible resources to create a dicult to imitate
and transfer BDAC. Despite some scarce studies
examining big data through such a holistic per-
spective (Gupta and George, 2016; Wamba et al.,
2017), there is still limited empirical understanding
on the mechanisms through which a BDAC can
generate business value. The scarcity of work in
this direction has resulted in a lack of understand-
ing about the potential value of big data analytics,
and leaves practitioners in uncharted waters
when faced with such implementations in their
firms. To obtain any meaningful theoretical and
practical implications and identify critical areas
for future research, it is important to understand
how the core constituents of big data analytics
are shaped and how they result in business value
(Constantiou and Kallinikos, 2015). Building on
the concept of BDAC, this study seeks to answer
two closely related research questions: (1) Does
a firm’s BDAC result in enhanced innovation
capabilities, if so, through what mechanisms? (2)
How do environmental factors influence the eect
of BDAC on a firm’s innovation capabilities?
To provide answers to these questions, we
ground our study theoretically on the resource-
based view (RBV) and the dynamic capabilities
view of the firm, which are presented in the next
section. In addition, we define the notion of a
BDAC and illustrate how it is developed concep-
tually. In the third section, we provide a discussion
on how a BDAC aects two types of innovation
capabilities: incremental and radical capabilities.
We posit that the eect is indirect, and is medi-
ated through a firm’s dynamic capabilities, which
help sustain evolutionary fitness. To explore these
questions,we develop a survey-based study and de-
scribe the data-collection proceduresand measures
for each concept used. In sequence, we present
the results of our empirical analysis, followed by
a discussion on the theoretical and practical im-
plications of the findings, as well as some core
limitations.
Theoretical background
Big data as a source of business value
Big data analytics has been regarded as the
next frontier for innovation, competition and
productivity (Manyika et al., 2011). As a result,
there is much attention from both academics and
practitioners on the value that organizations can
create through the use of big data analytics. A
commonly accepted definition in the literature
regards big data analytics as ‘a new generation of
technologies and architectures, designed to eco-
nomically extract value from very large volumes
of a wide variety of data, by enabling high velocity
capture, discovery and/or analysis’ (Mikalef et al.,
2018). Despite the vast majority of claims on the
value of big data analytics being anecdotal, the
few empirical research studies in the area have
documented a positive relationship between the
decision to invest in firm-wide deployment of
big data analytics and performance (Gupta and
George, 2016; Wamba et al., 2017). Through the
deployment of big data analytics, firms are able
to make sense of vast amounts of data, generate
critical insight and reconfigure their strategies
based on trends that are observed in their compet-
itive environment (H. Chen, Chiang and Storey,
2012). As such, the major contribution of big
data analytics lies in the fact that it enables better
C2019 British Academy of Management.
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