Information Systems and e-Business Management
Big data analytics capabilities: A systematic literature review and research agenda
--Manuscript Draft--
Manuscript Number: ISEB-D-16-00215R2
Full Title: Big data analytics capabilities: A systematic literature review and research agenda
Article Type: S.I. : Big Data and Business Analytics Ecosystems
Keywords: Big Data; Dynamic Capabilities; Resource-Based View; Competitive Performance; IT
Strategy
Corresponding Author: Patrick Mikalef
Norwegian University of Science and Technology
Trondheim, NORWAY
Corresponding Author Secondary
Information:
Corresponding Author's Institution: Norwegian University of Science and Technology
Corresponding Author's Secondary
Institution:
First Author: Patrick Mikalef
First Author Secondary Information:
Order of Authors: Patrick Mikalef
Ilias O. Pappas
John Krogstie
Michail Giannakos
Order of Authors Secondary Information:
Funding Information: Horizon 2020
(704110)
Dr. Patrick Mikalef
Abstract: With big data growing rapidly in importance over the past few years', academics and
practitioners have been considering the means through which they can incorporate the
shifts these technologies bring into their competitive strategies. To date, there has
been an emphasis on the technical aspects of big data with limited attention on the
organizational changes they entail and how they should be leveraged strategically. As
with any novel technology, it is important to understand the mechanisms and
processes through which big data can add business value to companies and have a
clear picture of the different elements and their interdependencies. To this end, the
present paper aims to provide a theoretical discussion leading up to a research
framework that can help explain the mechanisms through which big data lead to
competitive performance gains. The research framework is grounded on past empirical
work on IT-business, and builds on the resource-based view (RBV) and dynamic
capabilities view (DCV) of the firm. By identifying the main areas of focus for big data
and explaining the mechanisms through which they should be leveraged, this paper
attempts to add to literature on how big data should be examined as a source of a
competitive advantage.
Response to Reviewers: The authors would like to thank the two anonymous reviewers for their constructive
comments and feedback. In the new version of the manuscript we have incorporated
the best we could the suggestion put forward. Specifically, these include:
# The manuscript has been professionally copy-edited so the clarity and meaning are
more clearly conveyed
# The second and third paragraph of the introduction have been revised
# We have more clearly defined the significance for IT/management research
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Big data analytics capabilities: A systematic
literature review and research agenda
Patrick Mikalef, Norwegian University of Science and Technology, Trondheim, Norway
Ilias O. Pappas, Norwegian University of Science and Technology, Trondheim, Norway
John Krogstie, Norwegian University of Science and Technology, Trondheim, Norway
Michail Giannakos, Norwegian University of Science and Technology, Trondheim, Norway
Title Page
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Big data analytics capabilities: A
systematic literature review and research
agenda
Abstract
With big data growing rapidly in importance over the past few years, academics and practitioners have
been considering the means through which they can incorporate the shifts these technologies bring into
their competitive strategies. To date, emphasis has been on the technical aspects of big data, with limited
attention paid to the organizational changes they entail and how they should be leveraged strategically.
As with any novel technology, it is important to understand the mechanisms and processes through
which big data can add business value to companies, and to have a clear picture of the different elements
and their interdependencies. To this end, the present paper aims to provide a systematic literature review
that can help to explain the mechanisms through which big data analytics (BDA) lead to competitive
performance gains. The research framework is grounded on past empirical work on IT business value
research, and builds on the resource-based view and dynamic capabilities view of the firm. By
identifying the main areas of focus for BDA and explaining the mechanisms through which they should
be leveraged, this paper attempts to add to literature on how big data should be examined as a source of
competitive advantage. To this end, we identify gaps in the extant literature and propose six future
research themes.
Keywords: Big Data, Dynamic Capabilities, Resource-Based View, Competitive Performance, IT
Strategy
1. Introduction
The application of big data in driving organizational decision making has attracted much attention over
the past few years. A growing number of firms are focusing their investments on big data analytics
(BDA) with the aim of deriving important insights that can ultimately provide them with a competitive
edge (Constantiou & Kallinikos, 2015). The need to leverage the full potential of the rapidly expanding
data volume, velocity, and variety has seen a significant evolution of techniques and technologies for
data storage, analysis, and visualization. However, there has been considerably less research attention
on how organizations need to change in order to embrace these technological innovations, as well as on
the business shifts they entail (McAfee et al., 2012). Despite the hype surrounding big data, the issue
of examining whether, and under what conditions, big data investments produce business value, remains
underexplored, severely hampering their business and strategic potential (McAfee et al., 2012). Most
studies to date have primarily focused on infrastructure, intelligence, and analytics tools, while other
related resources, such as human skills and knowledge, have been largely disregarded. Furthermore,
orchestration of these resources, the socio-technological developments that they precipitate, as well as
how they should be incorporated into strategy and operations thinking, remains an underdeveloped area
of research (Gupta & George, 2016).
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Over the past few years, several research commentaries have stressed the importance of delving into
the whole spectrum of aspects that surround BDA (Constantiou & Kallinikos, 2015; Markus, 2015).
Nevertheless, exploratory empirical literature on the topic is still quite scarce (Gupta & George, 2016;
Wamba et al., 2017). Past literature reviews on the broader information systems (IS) domain have
demonstrated that there are multiple aspects that should be considered when examining the business
potential of IT investments (Schryen, 2013). Furthermore, the particularities of each technological
development need to be thoroughly examined in order to fully capture the interdependencies that
develop between them, and how they produce value at a firm level. Past literature on IT business value
has predominantly used the notion of IT capabilities to refer to the broader context of technology within
firms, and the overall proficiency in leveraging and mobilizing the different resources and capabilities
(Bharadwaj, 2000). It is therefore important to identify and explore the domain-specific aspects that are
relevant to BDA within the business context (Kamioka & Tapanainen, 2014).
While there is a growing stream of literature on the business potential of BDA, there is still limited
work grounded on established theories used in the IT-business value domain (Gupta & George, 2016).
The lack of empirical work in this direction significantly hinders research concerning the value of BDA,
and leaves practitioners in unchartered territories when faced with implementing such initiatives in their
firms. Hence, in order to derive meaningful theoretical and practical implications, as well as to identify
important areas of future research, it is critical to understand how the core artifacts pertinent to BDA
are shaped, and how they lead to business value (Constantiou & Kallinikos, 2015). Therefore, we
employ a systematic literature review grounded in the established resource-based view (RBV) of the
firm, as well as the emerging dynamic capabilities view (DCV). We select these theoretical groundings
since the former provides a solid foundation upon which all relevant resources can be identified and
evaluated towards their importance, while the latter enables examination of the organizational
capabilities towards which these resources should be directed in order to achieve competitive
performance gains (Mikalef et al., 2016). As such, the DCV exerts complementarities in relation to the
RBV by providing an explanation of the rent-yielding properties of organizational capabilities that can
be leveraged by means of BDA (Makadok, 2001). Our theoretical framework that guides the systematic
literature review uncovers some initial findings on the value of BDA, while also providing a roadmap
on several promising research streams.
The rest of the paper is structured as follows. In section 2, we describe the research methodology used
to conduct the systematic literature review, and outline the main steps followed. Next, in section 3, we
distinguish between the concepts of big data, BDA, and BDA capability, and present some definitions
as described in literature for each. In section 4, we proceed to describe the main theoretical foundations
upon which we build on and develop the proposed research framework. We then summarize existing
work on the business value of BDA according to the identified themes. In section 5, we outline a series
of areas that are currently under-researched and propose appropriate theoretical stances that could be
utilized in their examination. In closing, section 6 presents some concluding remarks on the area of
BDA and their application to the strategic domain.
2. Research methodology
Following the established method of a systematic literature review (Kitchenham, 2004; Kitchenham,
2007; Kitchenham et al., 2009), we undertook the review in distinct stages. First, we developed the
review protocol. Second, we identified the inclusion and exclusion criteria for relevant publications.
Third, we performed an in-depth search for studies, followed by critical appraisal, data extraction and
a synthesis of past findings. The next sub-sections describe in detail the previously mentioned stages.
2.1 Protocol development
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The first step of the systematic literature review was to develop a protocol for the next steps. In
accordance with the guideline, procedures, and policies of the Cochrance Handbook for Systematic
Reviews of Intervention (Higgins & Green, 2008), the protocol established the main research question
that guided the selection of papers, the search strategy, inclusion and quality criteria, as well as the
method of synthesis. The review process was driven by the following research question: What are the
definitional aspects, unique characteristics, challenges, organizational transformations, and business
value associated with big data? By focusing on these elements of the research question, the subject
areas and relevant publications and materials were identified.
Figure 1 Stages of the study selection process
2.2 Inclusion and exclusion criteria
Due to the importance of the selection phase in determining the overall validity of the literature review,
a number of inclusion and exclusion criteria were applied. Studies were eligible for inclusion if they
were focused on the topic of how big data can provide business value. Publications were selected from
2010 onwards, since that is when the term gained momentum in the academic and business
communities. The systematic review included research papers published in academic outlets, such as
journal articles and conference proceedings, as well as reports targeted at business executives and a
broader audience, such as scientific magazines. In-progress research and dissertations were excluded
from this review, as were studies that were not written in English. Finally, given that our focus was on
the business transformation that big data entails, along with performance outcomes, we included
quantitative, qualitative, and case studies. Since the topic of interest is of an interdisciplinary nature, a
diversity of epistemological approaches was opted for.
2.3 Data sources and search strategy
The search strategy started by forming search strings that were then combined to form keywords. In
addition, during the search we employed wildcard symbols in order to reduce the number of search
strings. Combinations of two sets of keywords were used, with the first term being ‘big data,’ and the
second term being one of 12, which were reviewed by a panel of five experts. These search terms
included: analytics capability, competitive performance, firm performance, organizational
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