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BIG DATA ANALYTICS CAPABILITIES, INNOVATION AND
ORGANIZATIONAL CULTURE: SYSTEMATIC LITERATURE
REVIEW AND FUTURE RESEARCH AGENDA
Sabra Munir
Azman Hashim International Business School, University of Teknologi, (Malaysia).
E-mail: munir.sabra@graduate.utm.my
ORCID: https://orcid.org/0000-0003-3425-0262
Siti Zaleha Abdul Rasid
Azman Hashim International Business School, University of Teknologi Malaysia and
University of Business and Technology, Jeddah, (Saudi Arabia).
E-mail: : szaleha.kl@utm.my
ORCID: https://orcid.org/0000-0001-7200-05G72
Muhammad Aamir
Hailey College of Commerce, University of the Punjab, (Pakistan).
E-mail: aamir@hcc.edu.pk
ORCID: https://orcid.org/0000-0002-8956-1052
Ishfaq Ahmed
Hailey College of Commerce, University of the Punjab, (Pakistan).
E-mail: : ishfakahmed@gmail.com
ORCID: https://orcid.org/0000-0003-1980-5872
Recepción: 21/09/2021 Aceptación: 17/11/2021 Publicación: 14/02/2022
Citación sugerida:
Munir, S., Abdul, S. Z., Aamir, M., y Ahmed, I. (2022). Big data analytics capabilities, innovation
and organizational culture: systematic literature review and future research agenda. 3C Tecnología.
Glosas de innovación aplicadas a la pyme, Edición Especial, (febrero 2022), 209-235. https://doi.
org/10.17993/3ctecno.2022.specialissue9.209-235
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ABSTRACT
Big data analytics (BDA) have the power to modernize traditional ways of doing business.
Nevertheless, the impact of BDA capabilities on a rm’s innovation performance is still
not fully understood. The ‘Age of Data’ is thriving because new data is being produced
at an unprecedented rate and with an increasing volume, due to global usage of dierent
electronic devices and gadgets which are connected to each other through internet and
other networks. Such Big data has the potential to become a key source of competitive
advantage. However, proper analyses of both structured and unstructured data need to
be conducted to get deeper insights into customer behaviour. Innovation is a key part of
the obtaining business value. Since there is very little research on how organizations need
to change in order to leverage such innovations, and how business value can be obtained
from them, a growing number of studies has been investigating and theorizing about the
strategies and structures that might help rms acquire the capacity to continuously innovate
by introducing new products with the help of Process Oriented Dynamic Capabilities
(PODC). Most researchers explored the phenomenon of Big Data Analytics, from either a
theoretical point of view or neglected intermediate and moderate factors, such as PODC,
Organizational Culture. In this connection, the “dynamic” resource-based view of the rm
identies dynamic capabilities as the main source of sustainable competitive advantage in a
changing competitive landscape. However, to be able to innovate, there is a need to have an
organization wide culture that encourages such innovation in the rst place. As a result, the
current study aims to show the impact which Big Data Analytics (BDA) Capabilities have on
the organizational innovation performance with organizational culture as a moderator. The
current study will use data from surveys of CFOs, CEOs or CIOs of the pharmaceutical
companies of Pakistan and will test a proposed model, using bootstrapped moderated
mediation analysis. This research considers the resource-based view of the rm as well as
the socio-materiality theory. Practical implications for top executives are also discussed. To
this end, this research focuses on identifying the gaps in the existing literature as well as
proposing the course of action which can be undertaken for empirical study.
KEYWORDS
BDA Capabilities (BDA Cap), Big Data, Process Oriented Dynamic Capabilities (PODC),
Innovation, Organizational Culture (OC).
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1. INTRODUCTION
The contemporary time period is considered as the age of big data as newer data is being
produced at an unprecedented rate, from all organizations, industrial sectors as well as public
organizations and bodies (Mikalef, Boura, Lekakos, & Krogstie, 2019). The exponential
growth in the volume of data has resulted in big data being considered as the key source
of competitive advantage, business performance and innovation (Chaudhary, Pandey, &
Pandey, 2015; Grover, Chiang, Liang, & Zhang, 2018; Jelinek & Bergey, 2013; Mikalef et al.,
2019; Shahzad, Xiu, & Shahbaz, 2017). At present, over 3.2 billion people, of the world’s
population are connected onto the internet with 46% of them being connected through the
usage of smart phones (Clement, 2020). Furthermore, this massive shift of IP trac (web
trac, ow of data across the internet) from xed networks to wireless based networks is
likely to lead to a number of challenges for organizations. It is forecasted that global mobile
data trac from 2017-2022 (in exabytes per month) is from 11.51-77.49 (Clement, 2020).
By 2050 these gure are likely to be 95% of world population (Khan, Khan, Alam, & Ali,
2018). According to one estimate, the amount of global digital healthcare data will grow to
25,000 petabytes in 2020, from 500 petabytes in 2012 (Gardner, 2013).
Organizations are required to analyse, in a meaningful manner, structured as well as
unstructured data in order to obtain deeper insights into customer related behaviour, their
service usage as well as interests on a real-time basis (Mikalef et al., 2019; Riaz, Alam, & Ali,
2017) to enhance business performance, competitive advantage and innovation. Due to the
rapid increase of data volume, variety, velocity and veracity, considerable developments have
taken place and have also been documented, relating to such technologies and techniques
which involve the analysis, visualization as well as storage, of data (Mikalef et al., 2019).
Many organizations of dierent sizes are searching for ways with the aim of improving
their performance, innovation and business value, by extensive usage of big data analytics
(BDA) tools (Mikalef et al., 2019; Shinwari & Sharma, 2018; Yin & Kaynak, 2015). The
pharmaceutical industry is essentially dened by innovation (Petrova, 2014).
The prevalence of big data and the usage of the same can result in enhancement in
innovative performances, which then leads to further improvement in economic development
(Douglas, 2012; Shahzad et al., 2017). In other words, innovation, which can be termed
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as the implementation of creative ideas within the organization, in a very ecient and
eective manner, can and does lead to businesses achieving and sustaining competitive
advantages (Shahzad et al., 2017; Soares de Almeida, Del Corso, Rocha, da Silva, & da
Veiga, 2019; Tidd & Bessant, 2018).
Based upon the upcoming research on BDA Capabilities (Gupta, 2016; Mikalef, Pappas,
Krogstie, & Giannakos, 2018; Wamba et al., 2017), studies have shown that although big
data is an important resource, yet in itself is insucient to create any gains related to
business value. There are other complementary resources which are necessary and create
a synergy to drive an organization’s overall BDA Capabilities, in this regard big data is
supporting and giving guideline for decision making at strategic level for business value,
competitive advantage and innovation performance. BDA Capabilities, can be explained
as rm’s ability to capture and analyse data so as to be able to generate data insights by
eective orchestration and usage of the organizational data, its technology as well as skills
(Gupta, 2016; Mikalef et al., 2018).
Organizations which are users of big data proved to be the fundamental pillar in economic
development of any region in the world because they have the knowledge, skills and ability
to transform ideas to new products through innovation (Duval-Couetil, Shartrand, &
Reed, 2016).There is need for continuous improvement of their existing processes and
products, as well as the requirement to develop new products as per the requirements of
the market. As a result, an increasing number of studies have investigated and theorized
about the strategies and the structures which rms may need in order to build the capacity
for innovation on a continuous basis, by introducing new products with the help of Process
Oriented Dynamic Capabilities (PODC) (Kim, Shin, Kim, & Lee, 2011; Kohlbacher &
Reijers Hajo, 2013; Wamba et al., 2017). In this regard, the organisation’s dynamic resource-
based view indicates the dynamic capabilities as the main source of competitive advantage
which is sustainable for the rm, within a changing and competitive landscape (Mikalef et
al., 2019; Teece, Pisano, & Shuen, 1997; Wamba et al., 2017).
To be able to innovate, there is a need to have an organization wide culture that encourages
such innovation in the rst place (Shahzad et al., 2017). It relates to the collection of the
norms and values shared by individuals and groups within in the organization (Hill, Jones,
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& Schilling, 2014). These norms and values are likely to have an impact on the behavior
of the members of the organization when they interact with each other as well as with
stakeholders. According to (Shahzad et al., 2017) a signicant relationship exists between
organizational innovation performance and organizational culture. The exibility/support
to alter as well as the organizational climate is relatively signicant factors for the creativity
and the innovation performance (Shahzad et al., 2017).
Due to the emergence of big data within the pharmaceutical industry, it has played
a very important role in streamlining dierent complicated business procedures as well
as improving eciency across the board (Joshi, 2019). Data-driven approach taken by
pharmaceutical companies gives leverage, related to the usage of big data to identify several
business procedures (Ibid). Based upon real-time information, it is possible to take relevant
actions without waiting for the extraction of data or manual data mining. Consequently,
investments worth $4.7 billion have been made in big data within the healthcare and
pharmaceutical industries(Joshi, 2019). The aim of such investments and further similar
investments within the pharmaceutical businesses is the development of several innovative
applications (Joshi, 2019). The theoretical framework in the current study provides the
guidance related to the systematic literature review and identies some ndings, related
to the value of BDAC. At the same time, it provides a path for several promising research
areas for the future.
2. METHODOLOGY
According to review Kitchenham et al. (2009) dierent stages followed for the establishment
of systematic literature review for the current study. Review protocol developed at rst
stage. On second stage current study had identied the main criteria for the inclusion and
exclusion of the latest and relevant publications. Thirdly, study carried out in the detailed
assessment for the current study, with the step followed by critical appraisal, extracting data
and synthesizing previous literature. All previously mentioned stages are described in the
next sub-sections. Fourthly, a detailed search for studies was conducted, followed by critical
appraisal, data extraction and a synthesis of past ndings. The next sub-sections describe
in detail the previously mentioned stages.
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Figure 1. Review Protocol.
Source: own elaboration.
2.1. DEVELOPMENT OF PROTOCOL
For the development of systematic literature review as per Cochrance Handbook for
Systematic Reviews of Intervention (Barclay, Higgins, & Thompson, 1995; O’Connor,
Green, & Higgins, 2008). According to the mentioned guidelines, the procedures and the
policies of the protocol helped in establishing the main research question which played
an important role in the selection of papers, the strategy used to conduct the search, the
criteria used for inclusion and the quality of material, as well as the method of synthesis.
The following research question guided the review process: What are the main aspects of
denitions, distinctive characteristics, problems, transformational changes in organizations,
innovation performance and business value associated with BDA and BDAC? Critically
focusing on above mentioned research questions, the relevant subject areas and relevant
publications and materials were searched.
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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. Under the
selection phase, studies were identied for including in the research, if they emphasised on
how big data could provide business value through the use of innovation. Publications from
2016 onwards, were selected as that is when the term gained momentum in the business
environment as well as the academic eld. The systematic review included the research
papers which had been published in the academic outlets, for example the conference
proceedings and journal articles, as well as reports focusing on business executives and a
larger audience, like scientic magazines. In progress research and thesis were not included
in this review. In this research our main aim was to identify quantitative, qualitative, survey
reports and business report in which business transformation that big data plays a role in.
2.3. SOURCES AND STRATEGY OF DATA AND QUALITY ASSESSMENT
Big data, Big data analytics capability, innovation performance, rm performance,
organizational performance, dynamic capabilities, process oriented dynamic capabilities,
socio-materiality, resource-based view, data scientist, competitive advantage and
organizational culture were the key words used. Keywords were searched within the
title, abstract, and keyword sections of the manuscripts. The search strategy included
electronic databases such as Sage, Scopus, Wiley, Emerald, Taylor & Francis, Springer,
Web of Knowledge, ABI/inform Complete and the Association of Information Systems
(AIS) library. To further complement our search, we applied the search terms in the search
engine Google Scholar. The search was started on the 25th of September, 2019 and was
concluded on the 30th of June, 2020. At that stage, 245 identied papers were entered into
the EndNote. In the second stage, all authors went through the titles of the dierent studies
compiled in the rst stage to determine the relevance of these studies to the systematic
review. At this stage, studies which were not related to the topic of business value of big data
were excluded from the research, regardless of whether they were empirical. Additionally,
articles which focused on big data for public administration were also not included in the
next stage of the research. The number of articles which were retained after the process
abovementioned were 170. In the third stage, all of the remaining articles were assessed in
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terms of their abstracts as well as their focus, related to the research question which had
been identied. However, there were few abstracts which were of varying quality. Some
lacked information about the contents of the article, whereas there were others which
apparently were not connected with their title and therefore did not t in our review. At this
stage, just like the previous stage, each papers’ abstracts were reviewed independently by
author. From the remaining 170 abstracts assessed, a further 57 were excluded. At the nal
stage of this process only 40 quality papers were identied for review of this study.
2.4. EXTRACTION OF DATA AND SYNTHESIZE OUTCOMES
The rst step was taken to synthesize the research ndings and to categorize the studies
based upon the scope of our research. This step involved the researcher identifying the
main concepts from each of the studies, by using the authors’ original terms. Then the key
concepts were organized in a spreadsheet so as to enable comparing them across dierent
studies and translating the ndings into higher-order interpretations.
An analysis was the carried out based upon the following areas of focus: big data, rms’
performance results of big data, human skills and knowledge, innovation, tangible and
intangible resources, culture as well as organizational culture, the adoption as well as
diusion of big data initiatives within the context of the business environment. For
empirical studies, the researcher also recorded the kind of the study that was conducted (e.g.
quantitative, qualitative, case study etc), the size of the sample, the dierent instruments
used (e.g. surveys, observations, interviews), as well as factors surrounding the study in a
contextual manner (e.g. industry, country, rm size). Constant consensus meetings of all
the researchers established the data extraction stage and the categorization of publications.
The remaining 40 papers were thoroughly reviewed as per the coding scheme, and relevant
data were the extracted, analysed, and synthesized.
3. LITERATURE REVIEW
3.1. BIG DATA ANALYTICS (BDA)
There are some denitions of big data which focus exclusively on the data and the dening
characteristics of data (Abbasi, Sarker, & Chiang, 2016; Akter, Wamba, Gunasekaran,
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Dubey, & Childe, 2016; Davis, 2014) while other denitions cover the tools and techniques
as well as analytical procedures being used (Bharadwaj, El Sawy, Pavlou, & Venkatraman,
2013; Russom, 2011). There are others who have explained the eect of analyzing and
presenting of big data on the business’s value (Beyer & Laney, 2012; De Mauro, Greco,
& Grimaldi, 2016; Schroeck, Shockley, Smart, Romero-Morales, & Tufano, 2012; White,
2011). Whereas the BDA’s denition includes a broad range of dierent elements which
are critically important for the success of big data, the organizational resources needed to
convert big data into actionable insight are not included in these denitions. It is indeed a
complex and multifaceted task to become a data-driven organization and requires attention
from managers at dierent levels. To focus on the change towards a data-driven time period
and hence provide guidelines to the practitioners on deploying their big data initiatives,
the term ‘BDA Capabilities’ is being used. This is with reference to an organization’s skill
in utilizing big data so as to obtain insight of both strategic and operational nature. BDA
Denitions showed in (Table 1).
Table 1. Denitions BDA.
YEARS & AUTHORS DEFINITIONS
Loebbecke and Picot (2015)
BDA: an approach for the analysis and interpretation
of any type of information which is digital in nature.
Advances in BDA which are of technical and analytical
nature and which mainly identify the functional scope
of current services and products which are digital in
nature, are essential for developing improved articial
intelligence, business intelligence, and computing
capabilities which are cognitive in nature
Ghasemaghaei, Hassanein, and Turel (2015)
BDA, dened as different processes and tools
usually applied to large and varied datasets with
the aim of attaining insights which are meaningful,
has received considerable attention in Information
Systems research due to its ability to enhance an
organization’s performance
Müller, Junglas, Brocke, and Debortoli (2016)
BDA: the statistics-based modelling of large size, and
varied datasets of content as well as digital traces
which has been user-generated.
Source: own elaboration.
3.2. BDA CAPABILITIES
It is broadly dened as an organizational capability to provide insights into the use of data
management, infrastructure, and human capabilities to convert business into a competitive
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force (Akter et al., 2016; Kiron, Prentice, & Ferguson, 2014). The research conducted so
far in this domain has focused on strategic BDA Capabilities as well as the approaches
by which competitive advantages and the associated gains are obtained (LaValle, Lesser,
Shockley, Hopkins, & Kruschwitz, 2011). As per dierent scholars, BDA Capabilities which
are focusing on the processes that need to be put in place so that the advantages of using
big data can be obtained (Cao & Duan, 2014; Olszak, 2014). The crux of the matter is that
the concept of BDA Capabilities focuses on inclusion of all related organizational resources
which are essential in utilizing big data to their full strategic potential. Important denitions
explained in (Table 2).
Table 2. BDA Capabilities Denitions.
YEARS & AUTHORS DEFINITIONS
Davenport Thomas and Harris (2007)
BDA Capabilities is dened as an organisation’s
specic capability in establishing a price which
is optimal, in the detection of important issues,
deciding the minimum inventory level which can be
possibly held, or trying to identify protable and loyal
customers, all within the environment of big data.
Kung, Kung, Jones-Farmer, and Wang (2015)
Competencies of Big data: an organizational capability
of acquiring, storing, processing, and analysing large
quantities of data in different forms, and delivering
required information to the related users thereby
allowing organizations in extracting value from big
data in a timely manner.
The resources of big data are considered to be
a combination different resources of Information
Technology, complementary in nature, which
are relevant in utilizing big data to improve the
performance of the concerned organizations.
Shuradze and Wagner (2016)
A data analytics capability is treated as the
organizational capability in mobilizing and deploying
resources which are related to data analytics, together
with resources and capabilities for marketing, which
comprise an innovation focused IT capability leading
to enhancement in organizational performance
Source: own elaboration.
Till today, there is very little empirical research related to the concept of BDA Capabilities.
A lot of the studies constitute of evidence which can best be considered as unreliable and
anecdotal and, specically related to the eect of an organizational BDA Capabilities on
organizational performance (Agarwal & Dhar, 2014; Akter et al., 2016). At the same time,
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there are dierent views about what comprises BDA Capabilities. This is because dierent
theoretical perspectives are often considered.
3.3. BDA CAPABILITIES RESOURCES
There is limited published research on BDA Capabilities. However, there are some studies
which relate to the resources required for developing such capability. These resources are
the fundamental building blocks upon which the organization’s overall BDA Capabilities is
developed. Most of studies till now have focused on both the resources and the processes
which are required for the strategic usage of the big data. However not much insight is
oered into the ways with which organizations tend to form a strong BDA Capabilities
(Gupta, 2016).
3.3.1. BDA TANGIBLE CAPABILITY
In an economy which is considerably data-oriented, the resources of data which possess the
characteristics specied previously are considered to be important for an organization in
order to achieve the competitive advantage (Kiron et al., 2014). Wamba, Akter, Edwards,
Chopin, and Gnanzou (2015) has mentioned that having data available and integrated
from various sources is very important. Traditionally this could be the result of extant
architectures related to Information Technology. The concerns relating to the availability
of data is also specied by Mikalef and Pateli (2017), who have found that commonly
companies purchase data to complement their analytical results and obtain better results
related to their operations and customers.
In addition to data itself, an infrastructure which is capable of storing, sharing, and analyzing
data is also important for the organizations. One of the main characteristics of Big data is
that it is unstructured and requires investments in sophisticated infrastructure in order to
derive meaningful and valuable information (Ren, Fosso Wamba, Akter, Dubey, & Childe,
2017). Some scholars consider organizational big data infrastructure in relation to the
amounts of investments made in specic kinds of technologies (Kamioka & Tapanainen,
2014), while other scholars emphasize on the technological aspects themselves (Akter et al.,
2016; Garmaki, Boughzala, & Wamba, 2016; Gupta, 2016; Wamba et al., 2015).
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3.3.2. BDA INTANGIBLE CAPABILITY
Keeping knowledge, skills, eective coordination of activities, resources, and tasks up to
date depends a lot on the capability to form and maintain networks, within the organization
as well as outside the organization (Ravichandran, Lertwongsatien, & Lertwongsatien,
2005). Hence the role of intangible resources is essential as it reects structures, ties and
roles which are developed for managing the dierent types of the available resources. One
of the most commonly used terms for including all the activities and decision-appropriation
mechanisms related to IT based resources is governance. Sambamurthy and Zmud (1999);
Tallon, Ramirez, and Short (2013) put forward a proposed framework particularly for
understanding the practices and structures which are meant for governing information
artifacts.
3.3.3. BDA HUMAN SKILLS AND KNOWLEDGE CAPABILITY
Human resources-based skills and knowledge level is one of the most important factors
related to the organizational capability to use big data tools and technologies (like the ones
specied above) and then be able to make strategic level decisions based on such outcomes.
Such knowledge and skills can be further divided into technical knowledge, business
knowledge, relational knowledge and business analytics knowledge. Technical knowledge
includes aspects related to management of databases, retrieval of data, programming
knowledge, and management of cloud services. Business knowledge relates to organizational
decision making, utilization of strategic foresight for deployments of big data, and using
the insights obtained. Related know how involves the communicating and collaborating of
employees’ skills from backgrounds of dierent types. Business analytics knowledge involves
mathematical and statistical modeling, simulation and developing dierent scenarios as well
as visualization of interactive data. Although an important things about data science is
having the capability to analytically think about the data, this skill set is critical for the data
scientist as well as for organization wide employees (Prescott, 2014).
3.4. ORGANIZATION INNOVATION PERFORMANCE
Henderson and Clark (1990)conducted a research on architectural innovation in order to
identify what exists in between the above-mentioned extremes. The researchers discovered
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that very small changes can also, sometimes, have a signicant eect on the competitive
position. As a result, they included the levels of component and architectural innovation.
From the economic point of view the focus on innovation is related to the implication
which innovation has on the relevant markets(Abernathy & Clark, 1985). It is important
to understand that innovations which are incremental, lead to small improvements in the
existing products, and in this manner, they are not new to the market. On the other hand,
innovations which are of a radical nature, result in a product which is totally new for the
market.
3.4.1. ORGANIZATIONAL CULTURE AND INNOVATION PERFORMANCE
Innovation is dened by Amabile, Conti, Coon, Lazenby, and Herron (1996) as being when
creative thoughts are executed eciently within an organization. A very important point for
innovation is to be able to implement creativity practically. This encourages creative ideas
to continue and hence be able to play their role in the innovation and its implementation.
Market based innovation relates to either using a new marketing related program for
existing products or trying to develop new markets for existing or new products. A number
of studies show that there exists a signicant relationship between culture and innovation
(De Clercq, Thongpapanl, & Dimov, 2010; Hislop, Bosua, & Helms, 2018; Laforet & Tann,
2006; Mavondo & Farrell, 2003; Miron, Erez, & Naveh, 2004). Wang and Ahmed (2004)
considered innovations as the introduction of methods which are modern and current and
are related to management and production, adopting technologies which are innovative in
nature, and improving management related systems which relate to products. Organizations
further develop such cultures which encourage their employees to focus on innovation
in terms of ideas and also participate in management-based decisions and innovation
related strategies. The study by Hislop et al. (2018) showed that organizational values and
beliefs, knowledge sharing, work environment and all the cultural happenings within an
organization have a substantial impact on organizational innovation and learning.
In accordance with the KBV theory of organization related culture, ideas generated by
individuals are treated as intangible asset, thereby playing an important role within the
development of the organization. An organization’s culture is considered as the employees’
beliefs and values, which are shared within the organization at all levels and showing
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the organization related characteristics (Schein, 1984). Although creativity is related to
individuals and/or a team, changes happen within the organization. An organization’s
culture is essential to enhance the sharing of knowledge amongst the creative minds within
the organisation, which are considered essential for the success of an organization (De Long
& Fahey, 2000).
3.5. ORGANIZATIONAL DYNAMIC CAPABILITIES RESOURCES
Organizational dynamic capabilities manage to alter its resources including physical,
human, and organizational assets. As a result, organizations should constantly adapt to
such changes by consistently renewing, reconguring and recreating their own resources
and capabilities within the competitive environment. The organization must be able to
respond to external changes via developing their core capabilities, although the process
by which dynamic capabilities are embedded within each organization is likely to be
specic to the organization and the industry(Wang & Ahmed, 2007). Helfat et al. (2007)
had described dynamic capabilities as an organization’s purposefully developed capacity
to create, extend, and improve its resources. These resources include organization’s
tangible, intangible, and human resources as well as those capabilities which are owned and
controlled by the organization so that the organization can achieve higher economic value
than its competitors. Mathiassen and Vainio (2007)had claimed that dynamic capabilities
are intended to capture the organizational capability to adapt to unpredictable and rapidly
changing environments by allowing the organization to alter its resources and respond to
market changes eectively.
3.5.1. COMPETING VALUES MODEL (CVM)
Organizational culture relates to a system of beliefs, values and assumptions shared
throughout the organizations and which helps both individuals and groups to function
eectively within the organizations(Lee & Kim, 2017). By way of managerial values and
rituals, an organization’s culture can mould the behaviour of the employees and inuence
the organizational investment as well as resource allocation decisions (Chan, Shaer, &
Snape, 2004).
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Several alternative ways have been proposed by dierent scholars for categorizing
organizational culture (like relationship- and transaction-oriented culture(McAfee,
Glassman, & Honeycutt Jr, 2002) and focus- and control-oriented culture (Khazanchi,
Lewis, & Boyer, 2007)), so that the role of organizational culture in improving innovation
performance can be explored. In one particular research the framework for CVM
(Competing Values Model) which had been put forward by both Cameron and Quinn
(Cameron & Quinn, 2011) had been used for investigating an organization’s culture. The
main reasons for choosing the Competing Values Model for studying an organization’s
culture are mentioned below.
Firstly the organizational culture’s measures, which assess the CVM, both directly and
indirectly had been managed in over 10,000 organizations worldwide, within such academics
related disciplines as accounting, marketing, management, supply-chain management,
social services, health care as well as hospitality (Hartnell, Ou, & Kinicki, 2011). Secondly,
the CVM focuses on those problems associated with organizational change which are of
great relevance to understanding innovation (Naranjo‐Valencia, Jiménez‐Jiménez, & Sanz‐
Valle, 2011). Thirdly, the CVM revealed the complexity involved in value orientations and
allowed comparing organizations’ value orientations. It is therefore considered a suitable
model in “Fig 2.” for such studies which are related to organization based culture and which
are conducted with reference to developing economies which have considerable potential
for evolutionary dynamics (Liu, Ke, Wei, Gu, & Chen, 2010) (p. 375).
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4. THEORETICAL AND PRACTICAL CONTRIBUTION BY THE
STUDY
This study contributes to the knowledge within the domain of BDA Capabilities and
innovative performance theoretically, practically as well as methodologically. This study
will investigate the impact which BDA Capabilities have on the organizational innovative
performance. It will also include the impact of the organizational culture. The study will
focus on top management among pharmaceutical companies in Pakistan. On the behalf of
organization, the CEOs, CIOs or CFOs who represents top management, will be contacted
through a questionnaire-based survey to collect data. Provided a conceptual framework
and empirical support by determining the relationships among BDA Capabilities and the
impact upon OIP in Pakistan. Current study will be answered the call for more research of
BDA Capabilities in contextualization of industry under developing countries
5. CONCLUSIONS
From the above-mentioned systematic literature review, some of the important aspects in
the eld of BDA Capabilities and its impact on the Organizational innovation performance
via the process oriented dynamic capabilities and the role of organizational culture have
been discussed. The important thing is to consider how the BDA Capabilities can be utilized
into developing the organization’s innovation performance. This is because it is innovation;
through the development of process oriented dynamic capabilities which can help the
organization develop its business value in changing environment and hence changing
business situations. All this is possible in an organizational culture which encourages such
innovations and risk taking to take place. If such a culture is not present in the organization,
the potential benet which BDA Capabilities can deliver will likely not be obtained and the
optimum business value which could be generated will be left untapped. The current study
anticipates examining the eect of BDA Capabilities on the Organizational Innovative
Performance (OIP) through its impact on the process oriented dynamic capabilities
(PODC). It also examines this relationship considering the organizational culture (OC) as
moderator. More specically, the study aims to examine the following research questions: To
what extent BDA Capabilities aect OIP? How PODC mediates the relationship between
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BDA Capabilities and OIP? How organizational culture (OC) moderates the relationship
between BDA Capabilities and OIP?
Based on the above-mentioned research questions, the following are the research objectives
of this research. To determine the impact of BDA Capabilities on OIP. To determine the
extent to which PODC mediates the relationship between BDA capabilities and OIP. To
determine the extent to which Organizational Culture moderates the inuence of BDA
capabilities on OIP.
Literature has been extensively reviewed on the relationships in this study so as to develop a
sound foundation of the framework. The framework of this research study and relationship
among the selected variables lays its foundation on the integration of two famed and largely
recognized theories that is resource-based theory and socio-materiality theory. Thereafter
the model of this study will be tested empirically.
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