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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 lev-eraged 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.
Tenth Mediterranean Conference on Information Systems (MCIS), Paphos, Cyprus, September 2016 1
BIG DATA AND STRATEGY: A RESEARCH FRAMEWORK
Completed Research
Mikalef, Patrick, Norwegian University of Science and Technology, Trondheim, Norway, pat-
rick.mikalef@idi.ntnu.no
Pappas, O. Ilias, Norwegian University of Science and Technology, Trondheim, Norway, il-
pappas@idi.ntnu.no
Giannakos, N. Michail, Norwegian University of Science and Technology, Trondheim, Nor-
way, michailg@idi.ntnu.no
Krogstie, John, Norwegian University of Science and Technology, Trondheim, Norway,
john.krogstie@idi.ntnu.no
Lekakos, George, Athens University of Economics and Business, Athens, Greece,
glekakos@aueb.gr
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 lev-
eraged 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 theo-
retical 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 mecha-
nisms through which they should be leveraged, this paper attempts to add to literature on how big da-
ta should be examined as a source of a competitive advantage.
Keywords: Big Data, Dynamic Capabilities, Resource-Based View, Competitive Performance, IT
Strategy
Mikalef et al. /Big Data and Strategy
Tenth Mediterranean Conference on Information Systems (MCIS), Paphos, Cyprus, September 2016
2
1 Introduction
The notion of big data and its application in driving organizational decision making has attracted
enormous attention over the past couple of years. As the label itself indicates, big data refers to large
volumes of data generated and made available online and in digital media ecosystems (Constantiou &
Kallinikos, 2015). Associated with the notion of big data are aspects such as the diversity of data, var-
ying data quality, the frequency by which it is updated, and the speed at which it grows (Krogstie &
Gao, 2015). Many companies realize that the data they own or can get access to, combining own and
open data, and the way they use them can differentiate them from competition, and even provide them
with a competitive edge. Thus, todays companies try to collect and process as much data as possible.
The need to harness the potential of rapidly expanding data volume, velocity, and variety, has seen a
significant evolution of techniques and technologies for data storage, analysis, and visualization. Yet,
there is limited understanding of how organizations need to change to embrace these technological
innovations, and the business shifts they entail (McAfee et al., 2012). As big data tools and applica-
tions spread, they will inevitably change long-standing ideas about decision making, management
practices, and most importantly competitive strategy formulation (Kallinikos & Constantiou, 2015).
But as with any major change, the challenge of becoming a big data-driven enterprise can be enor-
mous. Nevertheless, it’s a transition that executives need to navigate through, with limited empirical
knowledge to guide their decisions.
Despite the hype surrounding big data, the aforementioned predicaments still remain largely unex-
plored (McAfee et al., 2012), severely hampering exploiting the business potential of big data. To
date, efforts have been primarily focused on infrastructure, intelligence, and analytics tools; substan-
tially disregarding how these technological developments should be incorporated into strategy and
operations thinking. Several research commentaries stress the potential value of big data as a strategic
tool (Constantiou & Kallinikos, 2015), but to date no empirical work has delved on this problem, leav-
ing practitioners in unchartered waters regarding their big data deployments. A recent highly-cited
framework sketches the emerging trends in big data tools and analytics techniques (Chen et al., 2012).
Another highly influential article underscores the importance of the data scientist in the big data-
driven enterprise, and emphasizes on the skills and knowledge that a successful data scientist should
have (Davenport & Patil, 2012). On a more strategic perspective, Constantiou and Kallinikos (2015)
outline how big data can be linked to strategic management theories, and propose a set of directions
for future research.
While the abovementioned studies attempt to explore the business value and transformations that big
data entail, they largely base their arguments on non-theoretical perspectives. The purpose of this pa-
per is to introduce a theoretical research framework for examining how big data lead to competitive
performance gains. By building on the theories employed in Management Information Systems (MIS)
and novel contributions in the strategic management domains, this paper aims to explain the different
levels and mechanisms by which big data can deliver business value.
The rest of the paper is structured as follows. In section 2 the latest literature on big data analytics and
management is discussed. Section 3 provides the theoretical foundation upon which the conceptual
research model is built, presented in section 4. In closing, section 5 provides a discussion on research
directions and areas of future interest.
2 Literature Review
Big data have very quickly demonstrated that they are a great example of the impact of information
technology on business and decision making at the enterprise level (Constantiou & Kallinikos, 2015).
They transform the way that companies relate to both their customers and employees, and the way
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Tenth Mediterranean Conference on Information Systems (MCIS), Paphos, Cyprus, September 2016
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they enact and perform business operations (Wamba et al., 2015). Through appropriate implementa-
tion of big data initiatives, companies have the potential of renewed business value creation along with
increased productivity and innovativeness (Maglio & Lim). The importance of big data and business
analytics is evident throughout the literature, and as they evolve they have various applications creat-
ing multiple emerging research areas (Chen et al., 2012). A heated discussion over the past years has
been on the opportunities and challenges that big data bring to organizations, communities, and indi-
viduals (Constantiou & Kallinikos, 2015).
Past studies have highlighted the importance of big data and business analytics is various areas such as
customer relationship management, new business models, and short-term economic predictions
(Mcafee et al., 2012; Varian, 2014). The value of big data however has also been noted in terms of
combining information from various sources, creating innovative services (Varian, 2014). This sug-
gestion, of combining multiple sources of data in order to derive value, has been advocated by multi-
ple scholars following recent paradigms presented in the business world (Davenport, 2014). Although
these articles demonstrate the value of big data in improving several areas within a business, they do
not address the issue of how to incorporate big data in the wider change that transcends business strat-
egy. Therefore, it is important to develop a theoretically-driven understanding of how the overarching
concept of big data can be incorporated in a firm’s business strategy, and under what conditions in-
vestments in this direction can lead to a competitive advantage.
3 Theoretical Background
The growing interest in big data requires a focused discussion on how it can be examined empirically
and what theories can be used to understand the critical success factors as well as the business value
such technologies add. Within the information systems literature, several theoretical perspectives have
been used to explore these issues, with recent attempts building on the convergence of theories from
strategic management and operations research. When examining the impact of IT investments at the
firm level of analysis, the Resource Based View (RBV) of the firm has been one of the most employed
theoretical perspectives over the past two decades (Wade & Hulland, 2004). The main argument of the
RBV is that resources that are valuable, rare, in-imitable, and non-substitutable are the building blocks
of a competitive advantage (Bharadwaj, 2000). In terms of IT resources, they have been distinguished
into tangible (IT infrastructure), human (IT human skills & knowledge), and intangible (culture and
relationships) (Bharadwaj, 2000). This has enabled researchers and practitioners to identify the differ-
ent types of IT resources their firms should aim to acquire and strengthen. Nevertheless, despite the
RBV providing a basis for identifying the raw materials that are necessary to build a competitive ad-
vantage, it fails to explain how these resources are leveraged (Kraaijenbrink et al., 2010). In addition,
the RBV provides little explanation as to how companies react in the face of external changes and how
the dynamic of resources evolves (Kraaijenbrink et al., 2010). These shortcomings of the RBV have
also been documented in the IT literature, and have caused academics to rethink the theoretical per-
spectives that could complement this gap.
A growing body of literature emphasizes on the role of dynamic capabilities as a source of sustained
competitive advantage, especially in turbulent and uncertain environments (Teece, 2007). The dynam-
ic capabilities view (DCV) of the firm posits that the ability to purposefully adapt an organizations
resource and capabilities in the face of external pressures is the ultimate source of sustained competi-
tive advantage (Eisenhardt & Martin, 2000). The DCV has only recently begun to attract the interest of
IS scholars in terms of helping determine how an IT-based competitive advantage can be achieved in
dynamic and rapidly changing environments. Nevertheless, there have been several papers that employ
the theory empirically (Pavlou & El Sawy, 2010) with results indicating that thinking of IT through
this lens has good explanatory power (Drnevich & Kriauciunas, 2011; Mikalef et al., 2016). The ra-
tionale developed in these studies is that IT that is embedded in specific capabilities can provide a
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competitive edge. As such, the value of IT does not lie in IT resources per se, although their availabil-
ity is a prerequisite, but rather, on the process of integrating them into the organizational fabric. The
DCV has seen a maturing in terms of its theoretical grounding over the past few years, and has been
tested empirically in a range of contexts. In this way, empirical studies have demonstrated how to con-
ceptualize and measure the firms’ capacity to do so by virtue of its IT assets. The general consensus in
these empirical studies is to identify between the routines or capabilities of which dynamic capabilities
comprise (Eisenhardt & Martin, 2000). While there are some differences in terms of the routines used,
the underlying philosophy remains the same in most of these studies.
While the RBV and DCV build on different ideas to support how a company can achieve competitive
advantage, there is a growing body in literature which identifies their complementarities (Helfat &
Peteraf, 2003). Despite the DCV being more appropriate in explaining competitive advantage in turbu-
lent and unpredictable business environments, it is noted in literature that the types of resources that a
firm possesses will ultimately have an effect on the responses they can initiate. The types of resources
and their influence on responsive actions have also been noted in IT literature, thus reinforcing the
theoretical linkages (Wang et al., 2012).
4 Conceptual Research Framework
A vast amount of data is available through the internet and as part of daily operations, and most com-
panies are already trying to process it in order to extract specific information that will increase their
value. In order to make sense out of this vast amount of data, various tools, methods and analytical
concepts are applied such as text mining and sentiment analysis. However, the amount of data that can
potentially be processed is vast, leading to information overload for company managers, decision
makers and executives. Decision makers also have limited understanding on how to adopt and imple-
ment big data initiatives that can help drive their business strategies. To this end, a theoretical frame-
work is put forth that aims to delineate the core areas that should be considered when adopting big
data initiatives with a strategic perspective.
The theoretical discussion of the previous section provides a basis for understanding how to approach
big data projects for business purposes and develop a research framework for future reference. The
proposed research framework can therefore be used to avoid common pitfalls that have been docu-
mented in past IT research. This issue is particularly prevalent when novel solutions are promoted, in
which case it is important to understand the boundaries of their business value and under what circum-
stances they can result in competitive performance gains. To do so, we demonstrate how the theoreti-
cal perspectives are associated, what core notions and aspects are relevant, as well as what contingen-
cy factors shape these relationships in the context of big data. Already there have been some studies
that attempt to describe the theoretical basis on which big data can be examined (Constantiou & Kal-
linikos, 2015), yet, there is no coherent theoretical framework, or an underlying unifying framework to
provide a clear view of the overall business potential and strategic value.
We start this discussion by isolating the different levels and dimensions pertinent to each theoretical
perspective, identify how they are relevant to the context of big data, and finally attempt to describe
how they are inter-related. The starting point is to analyse the RBV in the IT context. Over the past
decade there have been numerous studies that define the levels at which the RBV can be decomposed
as aforementioned in the previous section. Building on the distinction between IT infrastructure, IT
human skills and knowledge, and relational IT resources, we overview some of the work that could be
used to guide researchers in the big data and business analytics area. In terms of IT infrastructure and
the different types that exist several papers have proposed aspects that should be considered (Bha-
radwaj, 2000). In the context of big data there have been some attempts to describe the necessary in-
frastructure which span hardware, software, and mathematical and analytical tools (Chen et al., 2012).
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Employees working in this area should have analytical thinking, ability to handle large amounts of
data, knowledge of analytics techniques and statistical modelling, as well as the capacity to tackle
problems with a data-driven approach (Davenport & Patil, 2012). In terms of relational IT resources,
in the context of big data and business analytics, it is important that companies establish a virtual pres-
ence on mediums where they can interact with involved parties, and clearly articulate communications
and interactions strategies with their existing or potential customers (Grégoire et al., 2015). An addi-
tional aspect that has been noted as being a core resource when dealing with big data, pertains to the 4
V`s, volume, velocity, variety, and veracity (Erl, 2016). Volume concerns not being restricted to sam-
ples but observing all available information. Velocity has to do with getting data on real-time. Variety
in big data is achieved when having access to various informational format resources (text, images,
audio, log-file data etc). Finally, veracity has to do with the quality of the data and its accuracy.
Although the four afore-mentioned aspects of big data are critical in order to gain a competitive edge,
they are of limited value if not leveraged appropriately. This means that the infrastructure, human
skills and knowledge, relational resources, and data must be put into action and into specifically di-
rected initiatives. To do so, a firm must have the IT competencies, i.e. the collective capacity to coor-
dinate activities and transform and bring together individual IT resources into IT-enabled dynamic
capabilities. While IT competencies have been described in previous studies (Cragg et al., 2011), they
are not the main area of focus of the present paper. This does not mean that they are of lesser im-
portance; in fact, they are particularly relevant and important in the process of transforming big data
IT resources into a potentially competitive asset. Yet, there is considerable qualitative research that
needs to be conducted to define them and they are highly likely to be context-specific.
While the ability to effectively orchestrate IT resources may also result in operational capabilities, the
focus of our research framework will be on dynamic capabilities due to their importance in contempo-
rary businesses. Therefore, we seek to explain the routines of which IT-enabled dynamic capabilities
comprise, and how big data initiatives can be infused into them with the purpose of augmenting them.
Researchers have sought to quantify the notion of dynamic capabilities by identifying distinct and
measurable dimensions, or else, capabilities (Teece, 2007; Pavlou & El Sawy, 2010; Mikalef et al.,
2016; Mikalef & Pateli, 2016). These capabilities include sensing, learning, coordinating, integrating,
and reconfiguring (Mikalef et al., 2016). A sensing capability concerns the capacity of a firm to spot,
interpret, and make sense of opportunities and threats in the business environment (Teece, 2007). Big
data and business analytics can be leveraged to enhance a firms sensing capability by helping identify
customer requirements, gaining feedback on existing products or services, or even monitoring compet-
itor moves and their customers’ responses (He et al., 2013; Risius & Beck, 2015). A learning capabil-
ity is defined as the capacity to acquire, assimilate, transform, and exploit new knowledge that enables
informed decision making (Zahra & George, 2002). While this capacity closely resembles a sensing
capability, it differs in that it doesn’t solely rely on spotting trends, but creates distilled information
that can be used in competitive actions. A coordinating capability is defined as the ability to orches-
trate and deploy tasks and resources, and synchronize activities with involved stakeholders (Pavlou &
El Sawy, 2011). Through feedback iterations with customers over social media and developing mean-
ingful analytics, firms can coordinate their efforts with various departments and at different stages of
development, and come up with products or services that are tailored to their likings. In addition, by
analysing in real-time log files of business processes, coordination can be enhanced and improved
(Vera-Basquero et al., 2013). An integrating capability includes the capacity to evaluate external re-
sources and competences, and embed and exploit them in new or revamped ways (Woldesenbet et al.,
2012). Big data and business analytics can be employed towards strengthening this capability by gath-
ering information from multiple sources and utilize them in combination, or else, through the process
of bisociation. Finally, a reconfiguring capability is defined as the ability of a firm to effectuate strate-
gic moves and demonstrate agility when there is a need to change existing modes of operation (Lin &
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Wu, 2014). For this particular capability, big data and business analytics are particularly relevant since
by generating information at a constant flow, or else nowcasting, decision makers are equipped with
knowledge that allows them to respond instantaneously, thus increasing operational agility.
Figure 1 Conceptual Research Framework
Building on the main theoretical arguments presented and their underlying concepts and notions, the
research framework presented above and the main associations can help guide future studies in deter-
mining the business value of big data and business analytics. By separating IT resources from IT-
enabled dynamic capabilities, it is possible to understand the nexus of relationships through which
competitive performance gains can be realized. While IT resources related to social media analytics
are necessary, they are of very limited value if they are not transformed into IT-enabled dynamic ca-
pabilities through effective orchestration and management (i.e. to develop the necessary IT competen-
cies at the group or business unit level). IT-enabled dynamic capabilities have been shown to be an
important part of gaining a competitive edge, especially in turbulent and highly dynamic markets.
Nevertheless, their effect on competitive performance has empirically been proven to be and indirect
one, mediated by other organizational capabilities and contingent upon business strategy (Barreto,
2010). It is therefore important to examine how IT-enabled dynamic capabilities relevant to big data
and business nalytics operate, in terms of changing the existing modes of operation and decision mak-
ing.
5 Discussion
An ever increasing numberof companies are attempting to use big data and business analytics in order
to analyse available data and aid decision making. For these companies, it is important to leverage the
full potential that big data and business analytics can offer in the aim of gaining a competitive ad-
vantage. Nevertheless, since big data and business analytics are a relatively new technological and
business paradigm, there is little research support on how to effectively manage them and use them in
the most effective manner. Early studies have shown the benefits of using big data in different con-
texts, yet, there is a lack of theoretically driven research on how to utilize these solutions in order to
gain a competitive advantage. This work identifies the need for a paradigm shift on firms’ view of big
data, and focuses at the same time on the yet underserved but highly needed and requested area, that of
big data and business strategy.
To this end, this study proposes a conceptual framework that is based on concepts from strategic man-
agement and management information systems literature, building on the core areas of big data as
identified by early studies. The framework therefore provides a reference for the broader implementa-
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7
tion of big data in the business context. While the elements present on the research framework are on a
high-level and can be interpreted as quite abstract, they are purposefully described in such a manner so
that they can be adapted depending on the company at hand. This poses a novel perspective on big
data literature, since the vast majority focuses on tools, technical methods (e.g., data mining, text anal-
ysis, and sentiment analysis), network analytics, and infrastructure. Thus, the proposed framework
contributes to big data and business strategy literature by covering the aforementioned gap. It is more
important for managers and decision makers to learn how to implement big data and business analytics
in their competitive strategies, than to simply perform raw data analysis on large data sets without a
clear direction of where it contributed to the overall business strategy.
Furthermore, this study argues that the main source of a competitive edge, especially in highly dynam-
ic and turbulent environments will stem from companies being able to reinforce their dynamic capabil-
ities through targeted use of big data and business analytics. This of course does not lessen the im-
portance of IT resources, since their availability and VRIN (Valuable, rare, in-imitable, and non-
substitutable) characteristics can determine the strength of the associated IT-enabled dynamic capabili-
ties developed (Bowman & Ambrosini, 2003). The concepts used in the proposed framework may help
managers better understand, plan and organize the process of implementing big data analytics within a
business strategy.
This paper offers a theoretical framework on how to increase business value and competitive perfor-
mance through targeted application of big data. Future studies should empirically test and evaluate this
framework by using surveys, interviews, observation, focus groups with experts (e.g., managers, deci-
sion makers) and with customers’, as well as case studies from the industry. Also, both qualitative and
quantitative methods of data collection should be employed. For each different type of data, more than
one ways of analysis should be used (e.g., structural equation modelling, qualitative comparative anal-
ysis). The main argument made in this paper is that the value of big data does not solely rely on the
technologies used to enable them, but are apparent through a large nexus of associations that are even-
tually infused with organizational capabilities. Strengthening these core dynamic capabilities by virtue
of big data is what will lead to competitive performance gains.
Acknowledgments
This project has received funding from the European Union’s Horizon 2020 research
and innovation programme under the Marie Sklodowska-Curie grant agreement No
704110
This work was carried out during the tenure of an ERCIM ’Alain Bensoussan‘ Fellowship Programme.
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... However, although BD efforts were focused on infrastructure, tools and technologies, many researchers highlighted the need to tackle the strategic incorporation of BD technological developments and the link between BD and strategic management (SM) (Falsarella, Jannuzzi, & Sugahara, 2017;Mikalef, Pappas, Giannakos, Krogstie, Lekakos, 2016). ...
... While BD technologies have been developing rapidly, academic research on the integration of BD with SM is still in its infancy (Al-Qirim, Rouibah, Serhani, Tarhini, Khalil, Maqableh, & Gergely, 2019;Mikalef, Pappas, Giannakos, Krogstie, Lekakos, 2016;Lin & Kunnathur, 2019;Shams, & Solima, 2019;Wang, Kung, & Byrd, 2018). Polese et al. (2019) argue that the strategic use of BD in the organization strategy can be implemented through the integration of related processes and technological architectures. ...
... There is a strong relationship between BD and SM (Şen, Körük, Serper, & Çalış Uslu, 2019). However, BD efforts are focused on infrastructure, tools and technologies (Mikalef, Pappas, Giannakos, Krogstie, Lekakos, 2016). According to Braganza et al. (2017), BD is more than a technology, and to be fully effective it should be incorporated into corporate strategy. ...
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The purpose of this research is to study the impact of big data initiatives on strategic management process. While the majority of strategic management disciplines have had research dedicated to the use of strategic management theories to understand how big data affect organizational performance (Alnoukari, 2020-a). The body of research on big data lacks of an academic work capable of examining how to integrate big data into strategic management process (Mikalef, Pappas, Giannakos, Krogstie, Lekakos, 2016). The main contributions of this work are: (1) it highlights the strategic use of big data; (2) it analyses the main frameworks/models proposed by scholars that support the use of big data as a strategic management tool, and provides the research gap; and (3) it proposes a new framework that integrates big data within strategic management process based on balanced scorecard methodology.
... In addition, BDA is a relatively new sub-category within the broader fields of BA, Business Intelligence (BI), and Business Intelligence Systems (BIS). For example, as part of the development of a research framework for big data and strategy, Mikalef et al (2016) aggregated big data and BA in the context of its impact on business decision-making: ...
... For example, Mikalef et al. (2016) argued, ...
Thesis
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Business use of data analytics and its potential impact on firm performance have become topics of deep interest within both the business practitioner and academic communities. While previous research has demonstrated relationships between data analytics and firm performance in larger firms, there is limited research on whether and how data analytics is used within and impacts Small-to-Medium-sized Business (SMB) settings. Given the preponderance of SMBs within the US economy, and their contribution to employment and economic activity, it is important for SMB owners to understand what management practices lead to effective use of data analytics that in turn impacts SMB performance. Drawing upon the Resource-Based View (RBV) of the firm and prior empirical research on practices within large firms, this dissertation identifies the resources that are needed to form a Data Analytics Capability (DAC) and examines the relationship between the maturity of DACs and the extent of business value realized. The research model was tested using Partial Least Squares-Structural Equation Modelling (PLS-SEM) analysis of survey data gathered from a sample of 300 SMB firms in the US, complemented with qualitative interviews of SMB owners. The results provide evidence that a more developed DAC can lead to higher Data Analytics Business Value across business functions. Keywords: business analytics, small-to-medium-sized business, business analytics capabilities, analytics business value, PLS
... Moreover, Sadovskyi et al. (2014) argue that BDA adds additional characteristics to the conventional data analysis including; innovated technologies and skills that enable organizations to own deep analytical capabilities, and integration of wide range of data types from a large number of relatively unreliable data source in order to provide a meaningful and reliable source of business information. Aligned with this viewpoint, Fosso Wamba et al. (2017) and Holmlund et al. (2020) consider BDA as an innovative approach to deliver sustained value, and enable competitive advantage (Mikalef, Pappas, Giannakos, Krogstie, Lekakos, 2016). BDA allows firms to manage and analyze strategy through data lens (Fosso Wamba, Gunasekaran, Akter, Ren, Ji-fan, Dubey, & Childe, 2017;Shams, & Solima, 2019). ...
... There is a strong relationship between BDA and strategic management (Şen, Körük, Serper, & Çalış Uslu, 2019;Mikalef, Pappas, Giannakos, Krogstie, Lekakos, 2016). BDA in the lens of strategic management are the capabilities required to gain organizational performance (Walls & Barnard, 2020). ...
Chapter
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Boundaries between business intelligence (BI), big data (BD), and big data analytics (BDA) are often unclear and ambiguous for companies. BD is a new research challenge; it is becoming a subject of growing importance. Notably, BD was one of the big buzzwords during the last decade. BDA can help executive managers to plan an organization's short-term and long-term goals. Furthermore, BI is considered as a kind of decision support system (DSS) that can help organizations achieving their goals, creating corporate value and improving organizational performance. This chapter provides a comprehensive view about the interrelationships between BI, BD, and BDA. Moreover, the chapter highlights the power of analytics that make them considered as one of the highly impact's organizational capability. Additionally, the chapter can help executive managers to decide the way to integrate BD initiatives as a tool, or as an industry, or as a corporate strategy transformation.
... Since its initial publication, the UTAUT has been used in a variety of research studies to explore technology adoption, such as in video-based learning. (Mikalef et al, 2016) CRM systems looked into the factors that influence CRM system acceptance and use. (Pai and Tu, 2011) in social media (Curtis et al., 2010), It investigated how non-profit public relations professionals used social media resources in online banking (Abu-Shanab and Pearson, 2009), this study was carried out in order to gain a deeper understanding of the adoption of Internet banking. ...
... These review articles from the strategic adoption point have focused highly on individual components of 'Data Science'. In the recent past 'Big Data' (Ciampi et al. 2020;Günther et al. 2017;Mikalef et al. 2016Mikalef et al. , 2020aNelson and Olovsson 2016;Olszak and Mach-Król 2018;Wamba et al. 2015;Zhao-hong et al. 2018), 'Big Data and dynamic capabilities' (Rialti et al. 2019), 'Big Data business models' (Huberty 2015;Wiener et al. 2020), and 'Big Data assessment models' (Adrian et al. 2016) have been explored. Researchers have also focused upon 'Big Data Analytics' (Adrian et al. 2017;Al-Sai et al. 2020;Bogdan and Lungescu 2018;Inamdar et al. 2020;Maroufkhani et al. 2019;Mikalef et al. 2019a;Singh et al. 2021;Sivarajah et al. 2017), Artificial Intelligence (Alsheiabni et al. 2020;Borges et al. 2021;Keding 2020;Kitsios and Kamariotou 2021;Markus 2017), Business Analytics (Cao and Duan 2017), and Business Intelligence and Analytics (BI&A) (Chen et al. 2012;Eggert and Alberts 2020;Lautenbach et al. 2017;Llave 2017;Moreno et al. 2019;Sang et al. 2020). ...
Article
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While embracing digitalization that is further accentuated by the Covid-19 pandemic, the real business outcome is achieved through a robust and well-crafted ‘Data Science Strategy’ (DSS), as significant constituent of Enterprise Digital Strategy. Extant literature has studied the challenges in adoption of components of ‘Data Science’ in discrete for various industry sectors and domains. There is dearth of studies on comprehensive ‘Data Science’ adoption as an umbrella constituting all of its components. The study conducts a “Systematic Literature Review (SLR)” on enablers and barriers affecting the implementation and success of DSS in enterprises. The SLR comprised of 113 published articles during the period 1998 and 2021. In this SLR, we address the gap by synthesizing and proposing a novel framework of ‘Enablers and Barriers’ influencing the success of DSS in enterprises. The proposed framework of ‘Data Science Strategy’ can help organizations taking the right steps towards successful implementation of ‘Data Science’ projects.
... To achieve a better performance in complex environments, big data must be leveraged appropriately by firms, which need to develop distinctive and difficult-to-imitate capabilities (Gupta & George, 2016;Mikalef, Framnes, Danielsen, Krogstie, & Olsen, 2017;Mikalef, Ilias, Giannakos, Krogstie, & Lekakos, 2016;Pavlou & El Sawy, 2006;Wang, Kung, Wang, & Cegielski, 2017). Data acquisition investments alone are not sufficient to generate business value: firms need to develop a digital business strategy (Bharadwaj, El Sawy, Pavlou, & Venkatraman, 2013) -i.e. an organizational strategy formulated and implemented by leveraging digital resources to create differential value. ...
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In order to face the complexity of business environments and detect priorities while triggering contingency strategies, we propose a new methodological approach that combines text mining, social network and big data analytics, with the assessment of stakeholders' attitudes towards company core values. This approach was applied in a case study where we considered the Twitter discourse about core values in Italy. We collected more than 94,000 tweets related to the core values of the firms listed in Fortune's ranking of the World's Most Admired Companies (2013-2017). For the Italian scenario, we found three predominant core values orientations (Customers, Employees and Excellence) - which should be at the basis of any business strategy - and three latent ones (Economic-Financial Growth, Citizenship and Social Responsibility), which need periodic attention. Our contribution is mostly methodological and extends the research on text mining and on online big data analytics applied in complex business contexts.
... While several definitions have been proposed so far, some focus on the necessary processes that must be put in place to leverage big data (Cao and Duan, 2014;Olszak, 2014), while others emphasize on the investment of necessary resources and their alignment with strategy (Xu and Kim, 2014). In essence, the notion of BDAC extends the view of big data to include all related organizational resources that are important in leveraging big data to their full strategic potential (Mikalef, Pappas, Giannakos, Krogstie, & Lekakos, 2016). ...
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The age of big data analytics is now here, with companies increasingly investing in big data initiatives to foster innovation and outperform competition. Nevertheless, while researchers and practitioners started to examine the shifts that these technologies entail and their overall business value, it is still unclear whether and under what conditions they drive innovation. To address this gap, this study draws on the resource-based view (RBV) of the firm and information governance theory to explore the interplay between a firm’s big data analytics capabilities (BDACs) and their information governance practices in shaping innovation capabilities. We argue that a firm’s BDAC helps enhance two distinct types of innovative capabilities, incremental and radical capabilities, and that information governance positively moderates this relationship. To examine our research model, we analyzed survey data collected from 175 IT and business managers. Results from partial least squares structural equation modelling analysis reveal that BDACs have a positive and significant effect on both incremental and radical innovative capabilities. Our analysis also highlights the important role of information governance, as it positively moderates the relationship between BDAC’s and a firm’s radical innovative capability, while there is a nonsignificant moderating effect for incremental innovation capabilities. Finally, we examine the effect of environmental uncertainty conditions in our model and find that information governance and BDACs have amplified effects under conditions of high environmental dynamism.
... Big Data implementation is covered by critical factors that organizations should identify to guarantee successful implementation [40]. The increasing investments in Big Data projects request a focused investigation on identifying the CSFs and understanding their effect on the implementation process [10], [45]. Although various studies focused on understanding the CSFs in the adoption of Information Systems (IS), limited academic research has been investigated on identifying the critical factors influencing the Big Data success [9]. ...
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The quality of data models has been investigated since the midnineties. In another strand of research, data and information quality has been investigated even longer. Data can also be looked upon as a type of model (on the instance level), as illustrated e.g. in the product models in CAD-systems. We have earlier presented a specialization of the general SEQUAL-framework to be able to evaluate the combined quality of data models and data. In this paper we look in particular on the identified issues of ‘Big Data’. We find on the one hand that the characteristics of quality of big data can be looked upon in the light of the quality levels of the SEQUAL-framework as it is specialized for data quality, and that there are aspects in this framework that are not covered by the existing work on big data. On the other hand, the exercise has resulted in a useful deepening of the generic framework for data quality, and has in this way improved the practical applicability of the SEQUAL-framework when applied to discussing and assessing quality of big data. © 2015 IFIP International Federation for Information Processing.