Industrial Marketing Management xxx (xxxx) xxx–xxx

Contents lists available at ScienceDirect

Industrial Marketing Management

journal homepage: www.elsevier.com/locate/indmarman

Achieving superior organizational performance via big data predictive

analytics: A dynamic capability view
Shivam Guptaa, Vinayak A. Draveb, Yogesh K. Dwivedic, , Abdullah M. Baabdullahd,
⁎

Elvira Ismagilovae
a
Montpellier Business School, Montpellier Research in Management, 2300 Avenue des Moulins, Montpellier 34185, Occitanie, France
b
Department and Graduate Institute of Business Administration, College of Management, Chaoyang University of Technology, No. 168, Jifeng East Road, Wufeng District,
Taichung City - 413, Taiwan
c
Emerging Markets Research Centre (EMaRC), School of Management, Swansea University Bay Campus, Fabian Way, Swansea SA1 8EN, UK
d
Department of Management Information Systems, Faculty of Economics and Administration, King Abdulaziz University, Jeddah, Saudi Arabia
e
School of Management, University of Bradford, UK

ARTICLE INFO ABSTRACT

Keywords: The art of unwinding voluminous data expects the expertise in analyzing meaningful decisions out of the ac-
Big data predictive analysis (BDPA) quired information. To encounter new age challenges, practitioners are trying hard to shatter the constraints and
Market performance work edge-to-edge to achieve higher performance (Market, Financial and Operational performance). It is evident
Operational performance that organizations desire to exploit maximum of their injected resources, but often fail to reap their actual
Financial performance
potential. Developing resource-based capabilities stands out to be the most concerned aspect for the firms in
Dynamic capability view theory
recent times, and the same is studied by the previous scholars. In the dearth of literature, it is challenging to find
out evidence which marks up the effect of strategic resources in the development of dynamic organizational
capability. This study is a two-fold attempt to examine the relationship between organizational capabilities, i.e.
big data predictive analytics while achieving superior organizational performance; also, examining the effect of
control variables on superior organizational of performance. We tested our research hypotheses using cross-
sectional data of 209 responses collected using pre-tested single-informant questionnaire. The results underpin
criticality human factor while developing analytical capabilities dynamic in nature in the process of achieving
superior performance.

1. Introduction et al., 2019) has presented evidence pointing out many inefficient
practices that create a barrier to sustainable growth. Often organiza-
Unlike in the past, when only a handful of organizations enjoyed the tions do not place enough importance on human interpretative skills
use of state-of-the-art technology, now almost all organizations have and rely primarily on machine output. This stands out as one of the
access to it, though they lack the proper skills to leverage it (Dubey significant factors for failure when deploying BDPA. Big data provides
et al., 2019). Companies focus on improving their operational and fi- both vertical and horizontal vertices of information that need to be
nancial performance with extensive strategies that aim to take ad- complemented with the capabilities of proper technical skills for pro-
vantage of information (data) that is available as big data (Duan, cessing and managerial skills for taking rational decisions. Dubey,
Edwards, & Dwivedi, 2019; Dwivedi et al., 2017; Dwivedi et al., 2019; Gunasekaran, Childe, Papadopoulos, et al. (2019) and Gunasekaran
Srinivasan & Swink, 2018). Gupta and George (2016) asserted based on et al. (2017) outlined a plethora of possibilities (healthcare supply
Gartner's study (Gartner, 2013) of 720 firms that 64% of organizations chain, transportation, etc.) for leveraging big data predictive analysis
invested a fixed amount in acquiring big data. Companies often mis- and achieving sustainable organizational growth. Firms aspire to an-
takenly make substantial investments to acquire data before investing chor their resources and operations with information and communica-
in technology and without acquiring and retaining the right human tion technologies (ICT) until they become a core capability in the or-
capital. A recent study (Dubey, Gunasekaran, Childe, Papadopoulos, ganization (Kache & Seuring, 2017; Schoenherr & Speier-Pero, 2015;

Corresponding author.
⁎

E-mail addresses: sh.gupta@montpellier-bs.com (S. Gupta), vinayakontop@gmail.com (V.A. Drave), y.k.dwivedi@swansea.ac.uk (Y.K. Dwivedi),
baabdullah@kau.edu.sa (A.M. Baabdullah), e.ismagilova@bradford.ac.uk (E. Ismagilova).

https://doi.org/10.1016/j.indmarman.2019.11.009
Received 12 November 2018; Received in revised form 7 October 2019; Accepted 7 November 2019
0019-8501/ © 2019 Elsevier Inc. All rights reserved.

Please cite this article as: Shivam Gupta, et al., Industrial Marketing Management, https://doi.org/10.1016/j.indmarman.2019.11.009
S. Gupta, et al. Industrial Marketing Management xxx (xxxx) xxx–xxx

Waller & Fawcett, 2013). Gupta and George (2016) classified BDPA (Fig. 1) and the impact of BDPA capabilities, firm size and industry type
capabilities into tangible, intangible and human skills that can lead to on superior organizational performance. Section 5 discusses the theo-
higher organizational performance (market performance and opera- retical contribution and managerial implications of this research. Sec-
tional performance). Building organizational capability requires the tion 6 opens up possibilities for future research bearing in mind the
bundling of strategic resources that rely on a few distinctive skills limitations of the current research. Our paper finally reaches a con-
(Brandon-Jones, Squire, Autry, & Petersen, 2014). BDPA needs to be clusion after establishing a positive relationship between BDPA cap-
developed to a much greater extent; it is still in the embryonic stage in abilities and overall organizational performance. We also discuss the
most industries. However, many companies blindly race towards the effect of control variables such as firm type and size (in terms of op-
most in vogue technologies without assessing their actual utility, put- erations and human strength) on superior organizational performance.
ting an unnecessary financial burden on both companies and their To further strengthen the study, the operationalization of constructs is
vendors. Organizations whose primary focus is on sustainable busi- given in Appendix A, combined loadings and cross-loadings are shown
nesses operations (Teece, Pisano, & Shuen, 1997) should consider a in Appendix B and indicator weights are presented in Appendix C.
dynamic capability view (Hitt, Xu, & Carnes, 2016) so that these or-
ganizations become capable of developing not only distinct but adap- 2. Theoretical background
table capabilities. These dynamic resource capabilities assist organiza-
tions in leveraging these capabilities by providing their operations with 2.1. Big data predictive analysis (BDPA)
adaptability and becoming able to adopt, built and reconfigure their
internal and external processes. BDPA can provide competitive ad- Information stands out as the most potent fuel from which an or-
vantages (Akter, Wamba, Gunasekaran, Dubey, & Childe, 2016) in ganization can derive success. Data can be understood as information in
highly dynamic market conditions with proper guidance if nurtured a specified format following a set of closed patterns and used according
over time. Gupta and George (2016) showed the positive relationship to requirements. Data can be sorted, classified, arranged and analyzed
between BDPA capabilities and organizational efficiency and rational using proper tools and technology. George, Haas, and Pentland (2014)
decision making. A strong assertion by Hitt, Bierman, Shimizu, and have given five major sources of data, namely, “public data, private
Kochhar (2001) states that merely adopting technology will make no data, data exhaust, community data and self-quantification data”. Data
difference if it is not supported by the right human skill sets to trans- is the next most crucial factor after labor and land. According to Gupta
form BDPA from a resource to a capability (Akter et al., 2016; Gupta & and George (2016) data is the prerequisite for taking decisions, as data-
George, 2016; Wamba et al., 2017). Dubey, Gunasekaran, Childe, driven decisions are considered inherently more rational and wise.
Papadopoulos, et al. (2019) highlight the importance of human skills Exponential growth in data leads to the generation of voluminous and
when developing BDPA capabilities. Often researchers interchangeably complicated information, but if analyzed skillfully, it can be used as a
use the terminologies of market performance and organizational per- weapon to acquire a competitive advantage leading to sustainable
formance, providing evidence of a win-win situation for organizations growth (Galbraith, 1973). The basic understanding of BDPA is that it
and the market, but market-oriented firms ironically have the least provides a technique to process data that is inherently voluminous and
amount of per unit profit, leading to a unit price that is much lower that possesses high “velocity and variety” (Duan & Xiong, 2015; Wang,
than that of their competitors. Acting as a facilitator, firm size can Gunasekaran, Ngai, & Papadopoulos, 2016; Zhou, Chawla, Jin, &
complement business operations when scaling up technologies via Williams, 2014). To clarify, big data predictive analysis is nothing but
economies of scale. Every organization runs with a set of objectives and the technique of unwinding voluminous data in a format that will assist
an orientation with different drivers and components. It is sometimes in the taking of critical decisions for business operations. The complex
difficult to compare two firms, even when they operate in the same nature of big data makes it hard to handle and decode. The human skills
industry. Both firm size and industry type work as enablers and at the of a specific organization are necessary for handling the criticality as-
same time act as disablers, which might affect organizational perfor- sociated with predictive analytics i.e. “data capture, storage, transfer &
mance in all three vertices, i.e. operational, financial and market. In the sharing (system architecture), search, analysis and visualization (data
wealth of literature, many scholars have clearly explained the im- analytics)” (Chen, Chiang, & Storey, 2012; Duan & Xiong, 2015;
portance of a firm's big data analytical capability and organizational Erevelles, Fukawa, & Swayne, 2016). According to Azeroual, Saake, and
performance in the broader sense. There is a narrow understanding of Schallehn (2018), data profiling could be used as a tool to maintain
the importance of human skills (managerial and technical skills) when high standards of data.
developing dynamic capabilities like BDPA and the impact of these High-tech organizations whose operations are highly dependent on
capabilities on achieving higher grades of overall performance in the technology can distinguish themselves by mastering the process of data
firm. There is a dearth of empirical support that demonstrates the direct analytics. Examples like Big Open Linked Data (BOLD) (Dwivedi et al.,
relationship between firm size, industry type and a firm's overall per- 2017) can bring functional innovation to organizations. According to
formance. This study is a bi-fold attempt to address the objectives for Wamba et al. (2017), BDPA can work on a high level of organizational
examining the direct relationship between organizational capability capability by bringing together all the strategic resources. Akter et al.
and superior organizational performance in the sense of dynamic cap- (2016) studied direct outcome of resources and BDPA on superior or-
abilities, and to test whether industry type and industry size have any ganizational performance.
impact on the dependent variables above.
This paper is laid out in six sections. The next section (Section 2) 2.2. Dynamic capability view
discusses the theoretical background for the research in order to un-
derstand the fundamental concept and related literature. It provides The past decade has witnessed the emergence of DCV as one of the
reference literature for big data predictive analytics capabilities (man- most influential views in management (Schilke, 2014). Although it is an
agerial skills, technical skills) and superior organizational performance extension of the resource-based view, which explains that organizations
(operational, financial and market performance). In Section 3, we de- can obtain advantages over competitors based on their resources and
velop the research framework that shows the interconnection between capabilities, DCV explains the ability of a firm to sustain competitive
BDPA capabilities and overall organizational performance. Based on advantages in dynamic environments (Priem & Butler, 2001). Con-
this research framework, the study proposes six research propositions. ceived from “Schumpeterian's gale of creative destruction”, dynamic cap-
In Section 4, we discuss the research methodology that was followed, abilities work to enable organizations to integrate, create and re-
i.e. the survey, analysis of the collected data and the results of the configure their resources in constantly changing marketplace settings
empirical research that serve as the basis of our conceptual model (Teece et al., 1997). Regardless of variations in definitions, there is a

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S. Gupta, et al. Industrial Marketing Management xxx (xxxx) xxx–xxx

Operational
Big Data Predictive Contol Variables
Performance β=0.08
Analytics (BDPA): β=0.46 (OP) p=0.11
Resources & Capabilities p<.01
R2=0.52 β=0.03
H1 β=0.28 p=0.31 Firm Size (FS)
p<.01
β=0.35 β=0.09
Managerial H2 p<.01 p=0.09
Skills (MS) Market
H3 Performance β=0.02
β=0.35 (MP) p=0.38
p<.01
H4
R2=0.48 β=0.03
β=0.31 p=0.31
H5 Industry Type
p<.01
(IndTyp)
Technical H6 Financial
Skills (TS) β=0.06
β=0.27 Performance
p=0.21
p<.01 (FP)

R2=0.33

Fig. 1. Theoretical Model with PLS-SEM Analysis.

consensus in the literature that dynamic capabilities are a bundle of through proper talent hunting. However, its growth and sustainability
“identifiable and specific routines” (Eisenhardt & Martin, 2000). Past depend on the knowledge culture in an organization. The literature
studies have examined the use of information technology in developing defines it as a unique resource that can be acquired but not imitated.
organizational capabilities that could help organizations to further Companies invest in human capital expecting to leverage their skill sets
improve their current mode of operations (Mikalef & Pateli, 2017; and intellect and to develop it as a competency. Regardless of the or-
Mikalef, Pateli, & Wetering, 2016; Pavlou & El Sawy, 2006; Wang, ganizational hierarchy, companies introduce and incubate next-level
Liang, Zhong, Xue, & Xiao, 2012). technological knowledge to elevate the learning environment.
According to Gupta and George (2016), managerial skills develop over
2.3. Developing BDPA into dynamic capabilities time and can be nurtured by providing a learning environment inside
the organization. Organizations in which personnel are competitive in
Capabilities are mostly defined as a collection of strong, repetitive taking managerial decisions will find more significant methods of
abilities that are found in part in tacit knowledge (Winter, 2003). growth and demonstrate outstanding performance in both standard and
Though easy to adopt, BDPA is difficult to develop into a capability, as dynamic conditions. Technology is of no use if the manager is not able
it requires competitive skills to supplement it. Teece et al. (1997) ar- to extract insight and take strategic decisions by using his/her in-
gued that firms cannot simply acquire it; they have to build it. It is the tellectual skills. Soft skills such as interpersonal skills and acquiring
process of converting raw resources into high-end capabilities (Sirmon, trust are non-imitable and also non-substitutable (Mata, Fuerst, &
Hitt, Ireland, & Gilbert, 2011). Past research (Hitt et al., 2001) showed Barney, 1995). According to Galbraith (1973), companies should seek
that the adoption of technology alone will not make any difference to obtain personal skill sets, which could be utilized for information
unless until it is supplemented with the right human skill sets to processing at the time of technology failure or technology deficiency.
transform BDPA from resource into capability (Akter et al., 2016; Gupta
& George, 2016; Wamba et al., 2017). Furthermore, these capabilities 2.3.2. Technical skills
can be transformed into dynamic capabilities by introducing new pro- Technical skills are often categorized as the expertise/overall un-
cesses into the system and nurturing the culture of knowledge in the derstanding of specific technology along with familiarity with its
organization. In order to achieve dynamic capabilities, the overall or- functions and outputs when forming a set of data. Firms harm them-
ganizational performance requires a steady upgrading of technology selves by not acquiring technical skills. This leads to poor coordination
along with people who can leverage that particular technology. This of operations and continuously hampers the economic performance of
brings us to the role of the human quotient, which not only helps to the organization (Gupta, Väätänen, & Khaneja, 2016). Some of these
establish technology, but also plays a vital role in achieving the max- skills include mastery of machine learning, artificial intelligence, sta-
imum potential of that technology, and at a certain level it even over- tistical analytics, data extraction and cleaning, and last but not the least
performs as a competitive advantage for the organization. Taking this the understanding of programming languages (Davenport, 2014;
uniqueness into account, an absence of competitive human factors Russom, 2011). Another factor contributing to the failure of IT systems
(technical and managerial skills) will negatively affect the organization in organizations is that organizations often become biased while
when leveraging the true value of resources (technology and organi- choosing investment options and end up investing considerable re-
zational knowledge), but the presence of these skills not only helps the sources in technological infrastructure and giving less importance to
organization to build a culture of knowledge, but also helps when uti- acquiring technical skills (Dwivedi et al., 2015). Organizations' tech-
lizing the resources (technology) to an extent that cannot be compared nical skill sets should be able to adjust to the advancement of tech-
to others, making it a non-imitable capability for an organization. It is nology. Lean practices lead to the outsourcing of technical skills ac-
therefore evident that developing capabilities is highly depending on cording to requirements, thus creating a considerable crisis of technical
the resources that an organization plans to acquire (de Camargo Fiorini, capabilities in an organization. According to Gupta and George (2016),
Seles, Jabbour, Mariano, & de Sousa Jabbour, 2018). it is not only the technology, but also the skill that ensures the desired
outcome of the business. Analytical skills are not limited to responsi-
2.3.1. Managerial skills bilities or job roles; organizations should provide sufficient opportu-
Human capital is one of the core resources that can be acquired nities for human resources to develop analytical thinking among all

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employees throughout the organization (Prescott, 2014). This should be the setup of the firm is in a state of operational efficiency. Developing a
in addition to the technology and updated to adjust to technological substantial predictive capability helps the organization reap the
advancement; only then can it produce a stable output and provide a greatest possible advantage through big data analytics, which elevates
competitive advantage that can be leveraged for long-term operations the supply chain performance by shedding light on the structure
(Dubey, Gunasekaran, Childe, Papadopoulos, et al., 2019). (Barratt & Oke, 2007) and leading to higher organizational perfor-
mance (Gunasekaran et al., 2017; Schoenherr & Speier-Pero, 2015;
2.4. Organizational performance Waller & Fawcett, 2013). Big data complements information technology
along with the utilization capability, which helps enhance organiza-
Organizations try to build sustainable performance via proper tional performance (Kung, Kung, Jones-Farmer, & Wang, 2015). This
blending of organizational capabilities and resources to maintain the high degree of operational performance results in cost-effective opera-
equilibrium between operational and economic performance; this in- tions that can make it through difficult times and support overall sus-
volves sustaining and expanding economic growth (Székely & Knirsch, tainability. BDPA action capability can form the basis for this opera-
2005). Gupta and George (2016) show the positive relationship be- tional elevation. It also can enable organizations to make appropriate
tween big data analytics and superior organizational performance. This decisions (Keeso, 2015; Roman Pais Seles et al., 2018).
relationship impacts performance in both negative and positive ways.
The combination of quality management practices such as just-in-time, 2.5. Firm size
lean manufacturing and higher accuracy in data predication will help
serve the market, ensuring continuous growth and sustainable market Large organizations inherently have better options to mitigate risks
performance. The long-term sustainability of an organization depends and make their operations more scalable. They can also easily weather
on three vertices of performance, i.e. market, financial and operational drastic changes in technology compared to smaller firms. On the other
performance. The linear relationship between organizational cap- hand, they are less agile in their operations and suffer more frequently
abilities and dimensions of organizational performance should urge from underutilization of acquired resources. Organizations that are
business owners to develop dynamic capabilities after utilizing strategic racing towards achieving excellence in their operations and gaining a
resources. larger market share and high profits are continuously expanding not
only horizontally but vertically. Under certain market conditions, it is
2.4.1. Financial performance also true that smaller firms can surpass the profit levels of larger
The highest priority for organizations while achieving superior or- companies (Lotti & Santarelli, 2004). On the whole, it is debatable
ganizational performance is to have higher growth performance with whether the size of the firm actually affects organizational performance
regard to their financials, which can be achieved by inter-organiza- while impacting its scale of operations.
tional information systems leading to increased supply chain cap-
abilities (Rajaguru & Matanda, 2013). A higher degree of green prac- 2.6. Industry type
tices produces excellent results in economic performance (Zhu & Sarkis,
2004). Firms reduce by-products and emissions from the production In many organizations, firms are classified into different categories
process while leveraging lean systems to improve the process, which of industry based on the nature of their work and capabilities regardless
results in better economic performance (Pil & Rothenberg, 2003). of size. Even a small company can make considerable profits. Past
Previous research has tested improved methods and efforts and proven academics (Fatorachian & Kazemi, 2018) have discussed enablers of the
their positive effect on a firm's economic performance (King & Lenox, fourth industrial revolution, which allow industries to become tech-
2001). One of the primary goals of any organization is to capture a large nologically advanced in order to facilitate swift flows of information as
share of the market, which is only possible when organizations manage well as business orientation. Industries may sometimes be subject to
to have respectable margins and profits not only through their sales but constraints while deploying or upgrading technological advancements
also from monetary savings, because their optimized operations due to their internal structure. It is often seen that industries with short
strengthen the company's financial aspirations. By maintaining eco- product life cycles have more advantages over other industries, as they
nomic gains, organizations can also strategically develop capabilities are calibrated for continuous structural and fundamental changes to
that can complement superior organizational performance and help accommodate new product development.
capture market share. Economic performance helps to build an eco-
system for business process operations and makes it possible to meet 3. Theoretical model and hypothesis development
shareholders' demands.
This research follows the Dynamic Capability View (DCV) discussed
2.4.2. Market performance earlier in previous studies (Barney, 1991) as an extension of the re-
Businesses tend to focus on their market performance and opera- source-based view (RBV). The dynamic capability view explains a firm's
tional efficiencies to increase profitability. Gupta, Rudd, and Lee (2014) distinct and sustainable advantage over the other players in most
emphasize that market performance can be achieved through innova- competitive environments. DCV can be better understood as a firm's
tion, which has a parleying effect between market orientations and ability to respond to continually changing environments by developing
organizational learning. Obtaining superior and sustainable market internal and external competencies (Teece et al., 1997). This study is
performance depends on the alliance between quality management rooted in the DCV concept and asserts that organizations should nurture
practices like just-in-time and lean manufacturing, which facilitate a capabilities that can be non-imitable and not only provide competitive
strong brand image. Capable big data predictive analysis techniques advantages but also make the organization agile enough to leverage
equip organizations to efficiently manage the inflow of data so that it technological opportunities. Henderson and Venkatraman (1993) ar-
can accurately predict market requirements. This means that organi- gued that DCV is an on-going process of change rather than a temporary
zations can align their business processes and business strategies to event. Akter et al. (2016) argued that BDPA may be a dynamic cap-
cater to the current market needs at their highest potential and help ability through which firms can achieve a higher level of organizational
gain a better understanding of market aspirations to deal with future sustainability and edge out others, provided the operations are trans-
demands. parent and adaptive. Despite having vast strategic resources, some or-
ganizations fail or are unable to reach their desired outcome. The
2.4.3. Operational performance viewpoint provided by DCV therefore enables firms to crucially un-
When the actual value of performance exceeds expectations, then derstand how capability can improve organizational sustainability in

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turmoil conditions. therefore proposes its first hypothesis as follows:

Out of the three strategic resources of tangible, non-tangible and
H1. Managerial skills required in big data predictive analytics have a
human skills (technical and managerial skills), human skills stand out
positive impact on the operational performance of an organization.
as the most crucial to executing and developing BDPA as a capability
(Barney, 1991; Gupta & George, 2016) and directly contributing to According to Amess and Girma (2009), managerial skills are in-
market operational performance. Bilateral information flows in the dicators of distinctive organizational resources and excellent assets that
system require a higher degree of transparency and trust, which can be transforms stock resources into a value-loaded capability. The analogy
achieved through big data predictive analysis. Past research has shown between infrastructure, corporate skills and a firm's specific resources
that big data analytics bring more transparency and flexibility into works as an in-house incubator; it helps an indigenously developing
operations, leading to better performance (Gupta & George, 2016). firm's core capabilities to be dynamic in nature. Xu and Kim (2014)
Despite increasing interest in BDPA, only a small amount of litera- asserted the importance of managerial skills when creating business
ture is available (Akter et al., 2016; Dubey, Gunasekaran, Childe, intelligence capabilities. Existing literature emphasizes the importance
Papadopoulos, et al., 2019; Gupta & George, 2016; Hitt et al., 2016; of managerial skills, but at the same time creates confusion about their
Teece et al., 1997; Wamba et al., 2017). Garmaki, Boughzala, and direct effects on the market performance of an organization, which is an
Wamba (2016) argued that big data is not only about data and tools, equally crucially co-efficient. As this study is rooted in a dynamic re-
but incorporates a wider array of components. Firms must face a series source-based view, managerial skills can facilitate technology while
of challenges before successfully executing BDPA (Mikalef & Pateli, efficiently synthesizing and deciphering information (input and output)
2017; Vidgen, Shaw, & Grant, 2017). Nevertheless, it is still difficult to and helping to develop the technique into a capability. Technology acts
consider BDPA as a skill (Cao & Duan, 2014; Grover, Chiang, Liang, & as a core capability when it starts functioning as a mechanism of change
Zhang, 2018; Kung et al., 2015) or a capability (Olszak, 2014; and produces the factors that directly affect market performance. Dy-
Papadopoulos et al., 2017). Traditionally, it is a culmination of targeted namic managerial skills turn it into a capable resource when deploying
IT resources that are intended to provide decision support information. BDPA, making it a capability whose output successfully represents
Firms periodically combine their resources (technical skills and man- market information. As final market performance is positively affected
agerial skills) with the hope of forming a distinct capability that helps by results produced by BDPA, it starts to become a core capability
organizations to project themselves as data-driven decision makers and within the organization while catering to the market demand for in-
optimal operational capacity firms. formation. Managerial skills required for decision making play a crucial
Though BDPA have a considerable impact on business operations, role in transforming BDPA outputs, yet there is no consideration for this
which is evident in the literature (Singh & El-kassar, 2019; in the available literature. We have tried to connect the dots to address
Swaminathan, 2018; Zhao, Liu, Zhang, & Huang, 2017), they are mostly the gap in the existing literature. This study thus proposes its next
characterized as the core resource of the firm and not the capability. conjecture:
However, some scholars (Garmaki et al., 2016; Papadopoulos et al.,
H2. Managerial skills required in big data predictive analytics have a
2017) have presented a strong argument characterizing BDPA as a
positive impact on the market performance of an organization.
capability that firms can build by integrating an information culture
and strong intellect in their organizational structure. Utilization of Human capital is treated as a unique resource in the sense that it can
strategic resources (Gupta & George, 2016) like human skills not only be acquired but not imitated. Companies invest in their human capital,
complements technology, but also catalyzes the process of transforming aspiring to leverage skills from the pool of intellect. Regardless of in-
techniques into core capabilities within an organization. A highly cul- house hierarchy, in order to create a distinctive talent, companies in-
tivated knowledge culture provides an ecosystem suitable for out-of- cubate high-level technological learning and synchronize it with an
the-box thinking and can be seen in managerial decisions. Continuing elevated learning environment. The right blend of personal skills can
education also helps strengthen niche technical skills. Dubey et al. transform resources into value and become a symbol of excellence for
(2019a) defined the constructs of operational performance while dis- an organization. Polished managerial skills play an essential role at the
cussing the crucial need for managerial skills to leverage the best out of time of execution and can build business intelligence capabilities (Xu &
the available technology. A pool of intellectuals creates infinite possi- Kim, 2014). Smart managerial skills benefit organizations in multiple
bilities if channeled in a targeted direction. However, this is not the first ways when they produce impressive financial results. They save a hefty
piece of research where the human quotient has been identified as a amount of a firm's assets by utilizing them in the best possible manner.
crucial component; previous research has provided in-depth knowledge Managerial intellectual capability helps a firm to acquire unique skills
of the linear relationships between technology like BDPA and perfor- that can switch between over and under-utilization of resources. Fi-
mance at the organizational level (Dubey et al., 2019; Wamba, nancial performance is the bottom line of any business. Every organi-
Gunasekaran, Dubey, & Ngai, 2018). However, no studies have revealed zation invests a considerable amount while acquiring resources and
the contribution of human factors (managerial and technical skills) seeks to obtain maximum output. Managerial skills in the analysis of
while converting a standard technology into an organizational core information after the deployment of BDPA are crucial to the perfor-
competitive capability and further transforming it into a more adaptive mance of this task. The possible number of solutions can be infinite as
dynamic capability. Both managerial and technical skills affect the organizations are expected to acquire their resources and polish them
ongoing process of this development to a certain extent. Productive strategically. Managerial skills polished over time help BDPA while
technical skills compliment BDPA technique during functioning and accelerating the credibility of decision making and directly affecting the
polished managerial skills facilitate rational decision making. Both financial health of an organization while spreading the effects over
skills strengthen overall business processes and guide them to the extent other resources and assets. Organizations should hire competitive dy-
that they produce unexpected and incomparable results on critical namic BDPA managers who possess competitive financial decision-
business processes. Transparency and organizational support making ability and strengthen the decision-making process while
strengthen resources to mold the adaptive capability enough to con- leveraging the BDPA. In a sense, managerial skills can improve BDPA as
front the dynamic requirements or conditions. According to our first a capability, which can in turn positively affect the financial perfor-
proposition, polished managerial skills of BDPA managers nurtured mance of an organization. The existing literature has asserted the im-
over time by an organization help them to be more capable of taking pact of BDPA on superior organizational performance. However, the
real-time decisions. This, in turn, serves to optimize the BDPA process role of the dynamic human factor in developing BDPA into a capability
and strengthen it to a level that facilitates the organization as a cap- for superior financial performance is still unclear. With this, we propose
ability while achieving superior operational performance. The study our next conjecture:

5
S. Gupta, et al. Industrial Marketing Management xxx (xxxx) xxx–xxx

H3. Managerial skills required in big data predictive analytics have a implementing BDPA so that all the processes are under financial control
positive impact on the financial performance of an organization. during deployment. We realize that the existing literature has not
captured the actual effect of technical skills while developing BDPA into
A great deal of literature available on BDPA supports the relation-
a capability and leading to higher financial performance. Not only do
ship between customer analytics and a firm's operational performance
the right technical skills result in optimum performance, but they also
(Germann, Lilien, Fiedler, & Kraus, 2014). For instance, big data pre-
positively affect the firm's balance sheet. Considering the gap, we
dictive analysis enables firms to design management strategies by
therefore proposed our final conjecture, which links the effect of inertia
analyzing data sets (Brands, 2014).
due to specific technical skills on financial performance while devel-
Prominent scholars (Dubey, Gunasekaran, Childe, Papadopoulos,
oping BDPA as a core capability in an organization:
et al., 2019; Gupta & George, 2016) have provided evidence of a direct
relationship between BDPA and superior organizational performance H6. Technical skills required in big data predictive analytics have a
(financial and operational). Previous studies (Swaminathan, 2018) have positive impact on the financial performance of an organization.
put forward the influential role of BDPA in driving sustainable and
efficient operations. Recent studies (Mikalef & Pateli, 2017) have
4. Research design
treated BDPA as a component within an organization that adds value by
elevating performance. Similarly, it is quite clear that operational
4.1. Survey
performance directly or indirectly impacts a firm's market growth and
helps in capturing significant market share. As superior organizational
The survey was given to employees working in Indian organizations
performance is not the goal in and of itself, organizations always race
that use high technology in their operations. The organizations use big
towards opportunities to capture more significant market share. There
data analytics as part of their decision-making procedure and these
is thus a need to explore a different dimension of the literature to es-
capabilities are developed in-house. The data was collected for the year
tablish the relationship between organizational capabilities, i.e. tech-
2018 using an online questionnaire for respondents from diverse fields.
nical skills, and the firm's operational performance. Bearing in mind the
We used the 5-point Likert Scale for our questionnaire (1- strongly
importance of establishing this relationship, the following research
disagree, 2- disagree, 3- neutral, 4- agree, and 5- strongly agree)
proposition has been framed:
(Dwivedi, Kapoor, Williams, & Williams, 2013; Hair, Hult, Ringle, &
H4. Technical skills required in big data predictive analytics have a Sarstedt, 2013; Kim, Oh, Shin, & Chae, 2009). To confirm the validity,
positive impact on the operational performance of an organization. reliability and appropriateness of the questionnaire, it was pre-tested by
20 respondents. The weblink of the questionnaire was distributed to
Falkenreck and Wagner (2017) studied the scope of the Internet of
approximately 1100 respondents, of which we received data from 290
Things (IoT) in optimizing business operations based on the thinking
respondents. After evaluating and examining all 290 responses, a total
that it would inject a new level of visibility and flexibility into the
of 209 (19% response rate) completely filled-out and usable responses
system. Supply chain sustainability gets a boost from the big data
were considered for the study. The data was standardized and there
predictive analytics capability of a firm (Hazen, Skipper, Ezell, &
were no cases of missing data, zero variance or rank-related problems.
Boone, 2016), which is reflected in its superior organizational perfor-
Respondents from a broad age-range from 20 to 60 years and with di-
mance. Past scholars (Lu & Ramamurthy, 2011; Wamba et al., 2017)
verse academic qualifications were considered for this study. Each
have identified the link between information technology capability and
segment of respondents from different age groups represents a different
a firm's outcome; but the way these technical skills affect the market at
decision-making position, which creates a possibility of flexibility for
large still needs to be explored. Prescott (2014) advocated the im-
the system; details are given in Table 1 below. Fifty percent (104) of the
portance of analytical thinking at all hierarchy levels in an organiza-
total 209 respondents belonged to the 20–30-year-old group and the
tion. The current literature points towards the crucial presence of
lowest number of respondents were from the 41–60 age range. With
technical skills while executing BDPA. The extent to which it accom-
regard to educational qualification, 61% (128) of the 209 respondents
modates and affects market horizons needs to be studied. We feel that it
were post-graduates, whereas only 2% (5) of respondents held a PhD.
is essential for firms to treat technical skills as a strategic tool that not
Table 2 shows the field of work of the respondents and their corre-
only executes BDPA but also captures market sentiment and later helps
sponding work experience. Respondents from nine different work fields
in taking decisions that affect the market positioning of a firm and its
were considered. The firm size differs according to the scale of the op-
performance. Therefore, we propose the next conjecture while trying to
erations. It is clear from Table 2 that the respondents belong to diverse
establish a direct relationship:
fields. Thirty-one percent (65) out of a total 209 respondents were from
H5. Technical skills required in big data predictive analytics have a IT services/Software field, 16% (34) of respondents belonged to
positive impact on the market performance of an organization. Banking/Insurance/Financial services, and around 12% (26) of re-
spondents were from the Consulting and Manufacturing fields in each
A positive relationship can be seen between IT capabilities and a
case. Around 31% (64) of respondents had > 10 years of work experi-
firm's business process and financial performance in the existing lit-
ence followed by 27% (56) of respondents with 5–10 years of experience.
erature (Gibb, Thornley, Ferguson, & Weckert, 2011). According to
Table 3 showcases the role of the respondents in their respective
Dubey, Gunasekaran, Childe, Papadopoulos, et al. (2019), organiza-
company/institution and the employee job level. Of the 209 re-
tions that possess excellent BDPA capabilities will obtain a higher or-
spondents, 37% (78) of respondents were Managers/Sr. Managers and
ganizational performance, which directly effects its financial status in
53% (110) of respondents worked in a company/institute having >
the business lexicon. Financial performance and economic performance
have greater weight in overall performance and are directly linked
Table 1
(Mackey, Mackey, & Barney, 2007; Smith & Tushman, 2005) to orga-
Age group of employees and educational qualifications.
nizational capabilities. The inherent needs of managing a given profit
level always affect the firm's expenditure decisions over resources or Age-group (in years) Graduate Post-graduate PhD Total
skills, and also limit the firm while building these capacities. Zhu,
20–30 41 63 – 104
Sarkis, and Lai (2008, 2012) explored the platonic relationship between 31–40 25 54 3 82
economic and operational performance. The framework conceptualized 41–50 10 10 2 22
by Grover et al. (2018) strongly defined BDPA as a unique blend of 51–60 – 1 – 1
resources to create true business value. Technical skills help while Total 76 128 5 209

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S. Gupta, et al. Industrial Marketing Management xxx (xxxx) xxx–xxx

Table 2
Field of work of the employees and their work experience.
Domain of work Years of work-experience Total

< 1 year 1–3 years 3–5 years 5–10 years > 10 years

Banking/insurance/financial services 5 5 9 9 6 34
Construction/real estate/infrastructure – 2 1 3 5 11
Consulting 2 8 3 4 9 26
Education/ research 1 6 6 4 2 19
Food & beverage – 1 2 1 1 5
Government – – 3 2 3 8
IT services/ software 1 7 15 20 22 65
Manufacturing – 2 2 6 15 25
Retail 1 4 3 7 1 16
Total 10 35 44 56 64 209

Table 3
Role of employees in the company/institution and the number of employees.
Role in company/institution Number of employees Total

< 10 10–50 50–300 300–500 500–1000 > 1000

After-sales support executive – 1 – 1 – 2 4

AVP/ VP/ EVP – 1 4 1 2 7 15
Consultant 7 4 3 4 4 14 36
Corporate finance executive/ analyst – 2 1 2 1 11 17
Director/ CEO/ founder 2 6 – 1 – 1 10
Engineer 1 2 3 2 7 26 41
Manager/ Sr. manager 2 4 7 7 11 47 78
Sales/ marketing executive 1 – 1 2 2 2 8
Total 13 20 19 20 27 110 209

1000 employees. This study has considered diverse employee roles, threshold value of 0.7 as shown in Table 6. The average variance extracted
especially those working at middle or senior level management, since (AVE) is > 0.5 (accepted value) (Hair, Anderson, Tatham, & Black, 2005),
employees at such levels have authoritative control in organizations, also shown in Table 6 below. Multicollinearity among the variables is
which in turn is crucial for implementing the strategies laid out by the measured by Variance Inflation Factor (VIF), which for this study is in the
top management of the organization. acceptable value of < 5 for each variable (Kock & Lynn, 2012).
The discriminant validity test is used to identify the relationship of
4.2. Data analysis indicators with the constructs given in Table 7. The square root of the
average variance is more than the construct correlations (Fornell &
Various disciplines of management science like marketing, strategic Larcker, 1981).
management, psychology, etc., have deployed Structural Equation The result and the supported/unsupported hypotheses are given in
Modeling (SEM) for data analysis (Astrachan, Patel, & Wanzenried,
2014). There are two types of SEM techniques: one is Covariance-Based Table 4
(CB) SEM and the other is Partial Least Squares (PLS)-based SEM (Hair Model fit and quality indices.
et al., 2013). When the relationship between dependent and in-
Average path coefficient (APC) 0.196, P < .001
dependent variables is exploratory in nature, PLS-SEM is more suitable
Average R-squared (ARS) 0.441, P < .001
as opposed to CB-SEM for confirmatory studies (Hair et al., 2013; Average block VIF (AVIF) 2.240, acceptable if ≤ 5, ideally ≤ 3.3
Henseler et al., 2014). Furthermore, normally distributed data is not
required for the PLS-SEM (Hair, Ringle, & Sarstedt, 2011; Kock, 2015).
Since the objective of this study is exploratory in nature, PLS-SEM is the Table 5
more suitable technique for data analysis. WarpPLS 6.0 has been em- Causality assessment indices.
ployed to perform the PLS-SEM. The efficiency of the parameter esti-
Simpson's paradox ratio (SPR) 0.917, acceptable if ≥ 0.7, ideally = 1
mation becomes higher when PLS-SEM is used (Hair et al., 2013). R-squared contribution ratio (RSCR) 0.999, acceptable if ≥ 0.9, ideally = 1
Table 4 below, shows the model-fit and quality indices (Kock, 2015). Statistical suppression ratio (SSR) 1.000, acceptable if ≥ 0.7
Average path coefficient (APC), Average R-squared (ARS) and Average
block VIF (AVIF) are all significant. The P-value is < 0.05 and the AVIF
is 2.240, which are in the ideal range. Table 6
The correctness of the research model is given by the causality as- Latent variable coefficients.
sessment indices in Table 5. All the causality assessment indices
MS TS OP MP FP
(Simpson's paradox ratio (SPR), R-squared contribution ratio (RSCR),
and Statistical suppression ratio (SSR)) are within acceptable range. R-squared coefficients – – 0.518 0.479 0.327
The internal validity of the scale is measured using Cronbach's alpha Adjusted R-squared coefficients – – 0.509 0.468 0.313
and composite reliability, with an accepted value of 0.7 or higher Composite reliability coefficients 0.966 0.949 0.937 0.925 0.964
Cronbach's alpha coefficients 0.966 0.949 0.939 0.925 0.965
(Nunnally & Bernstein, 1994; Tellis, Yin, & Bell, 2009). Cronbach's alpha
Average variances extracted (AVE) 0.85 0.788 0.79 0.754 0.871
and composite reliability coefficients calculated for this study reflect the Variance inflation factors (VIF) 4.025 3.865 3.746 3.015 1.818
strong reliability of the instrument, and coefficients are all beyond the

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S. Gupta, et al. Industrial Marketing Management xxx (xxxx) xxx–xxx

Table 7 technical skills with operational performance. The above relationship

Correlations among latent variables with square root of AVEs. has been proven positive in light of recent results.
MS TS OP MP FP Similarly, H5 and H6 must be accepted following an empirical in-
vestigation of the data, and successfully state the positive impact of
MS 0.922 technical skills on both market and financial performance when devel-
TS 0.847 0.888
oping BDPA. Fig. 1 and Table 8 show the acceptance of all six hy-
OP 0.694 0.66 0.889
MP 0.657 0.651 0.8 0.868
potheses, demonstrating that all the p values are < 0.05, which means
FP 0.531 0.534 0.657 0.55 0.933 95% of the time, the dependent variable, i.e. operational performance,
market performance and financial performance was positively affected
Note: Square roots of average variances extracted (AVEs) shown on diagonal. by the enablers (managerial skills and technical skills) of BDPA. This
study also investigated the effect of control variables, i.e. firm size and
Table 8 below. industry type, on all three performance indicators (operational, market
Fig. 1 below depicts the dependency of organizational performance and financial). This relationship stands out as negative after empirical
on BDPA capabilities. Based on the results, the study proposes a re- investigation, as the p-values are 0.11, 0.31, 0.09, 0.38, 0.31 and 0.21,
search framework that shows the direct relationship between manage- respectively, which are not considered significant. A fuller understanding
rial and technical skills and the variables of organizational performance of the results shows that firm size and industry type does not affect su-
(operational, market and financial). perior organizational performance, and this remains valid almost 95% of
the time. Our contribution to the literature and industry further provides
5. Discussion two explanations concerning the theoretical contribution, which is a
significant addition to the existing academic literature and helps extend
This section sheds light on the findings of this study by validating the understanding of the concept by filling a gap in the existing litera-
the research propositions. Gupta and George (2016) and Wamba et al. ture. Also, it gives scholars a new direction to take up future research.
(2017) have advocated the use of BDPA as an organizational capability The second part, managerial contribution, explains the way managers
and Papadopoulos et al. (2017) has showcased that these BDPA cap- follow the path of tested knowledge and create a whole new arena of
abilities can be employed in niche areas to better understand disaster possibilities. This study places significant and noticeable emphasis on
resilience in supply chains so as to promote sustainability. In an era of managerial implication, which helps organizations to understand the
data deluge, when organizations treat themselves to new technological importance of big data predictive analysis (BDPA) capabilities (man-
advancements and skills, market share is also worth mentioning. A agerial skills and technical skills) in the firm's overall performance.
firm's overall credibility is a dependent function of its resources and
capabilities, which is reflected in the market performance. Managing a 5.1. Theoretical contributions
volatile market is a challenge for a firm that allows no room for error.
From the beginning, this whole line of thought is setup on the backdrop This study attempts to link organizational capability to overall per-
of DCV (Barney, 1991), which motivates organizations to acquire formance. Considering BDPA as one of the capabilities with the highest
strategic resources. These resources can help to build up flexible and potential, especially for technology-intensive firms, the specific im-
dynamic capabilities to confront abrupt changes in demand and supply. portance of skills/resources required by the organization has been stu-
This study seeks to demonstrate a positive relationship between orga- died. This research helps to build robust and distinct dynamic BDPA
nizational capability (BDPA in this case) and organizational perfor- capabilities that make the system responsive enough to face abrupt
mance. The existing literature also follows the weight of operational changes and market volatility. Despite growing interest in big data and
and economic performance. Our first hypothesis H1 is based on the predictive analysis, limited literature is available in which scholars have
establishment of a relationship between managerial skills, which is one shown some of the relevant findings. Furthermore, dynamic capabilities
of two crucial components of BDPA capability, and the operational (Wamba et al., 2017) have shown the potential to elevate performance to
performance of the firm, which is further verified and supported em- a desired level. A whole new array of possibilities for big data predictive
pirically. H2 further strengthens the observation that managerial skills analysis have opened up and need to be investigated. The classification of
are not only an inherent part of the organizational structure, but they BDPA capabilities given by Gupta and George (2016) helps users focus on
help in expanding firms' location and value in the market, thus helping the weak functional capabilities in an organization. Giannoccaro (2018)
them acquire a larger market share. Following this, H3 shows the im- took it a step further and talked about the importance of cognitive
portance of managerial skills in developing BDPA into a distinct cap- analytical skills, which is a crucial aspect of managerial skills required by
ability for performing well not only in term of sales but also in financial decision makers. Martin and Bachrach (2018) argued that dynamic
terms (financial performance). This study also considered other crucial managerial capability helps a firm to network, which affects superior
skills (technical skill) for achieving superior performance. H4 connects organizational performance overall. Most of the previous findings shed

Table 8
Results of hypothesis testing.
Hypothesis β and p-value Supported or Not-Supported

H1: Managerial skills required in big data predictive analytics has a positive impact on the operational performance of an β = 0.46 p < .01 Supported
organization
H2: Managerial skills required in big data predictive analytics has a positive impact on the market performance of an β = 0.35 p < .01 Supported
organization
H3: Managerial skills required in big data predictive analytics has a positive impact on the financial performance of an β = 0.31 p < .01 Supported
organization
H4: Technical skills required in big data predictive analytics has a positive impact on the organizational performance of an β = 0.28 p < .01 Supported
organization
H5: Technical skills required in big data predictive analytics has a positive impact on the market performance of an β = 0.35 p < .01 Supported
organization
H6: Technical skills required in big data predictive analytics has a positive impact on the financial performance of an β = 0.27 p < .01 Supported
organization

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S. Gupta, et al. Industrial Marketing Management xxx (xxxx) xxx–xxx

light on the direct relationship between BDPA, which can be a capability can give a firm an edge over the others and the potential to capture a
resource, and organizational performance. There is no evidence in the much larger market share via performance. Bolstering the work of
literature that defines the importance and contribution of selected stra- Mackey et al. (2007), the current study demonstrates the influence of
tegic resources (human factor) while developing a dynamic competency BDPA capability when building a profit structure that can actually reg-
for sustainable and superior organizational performance. Adding a new ulate the market in the long term. The findings also help organizations to
dimension to previous work on operational performance (Dubey et al., learn and think in an oriented way so that they can focus more on ac-
2019a) that promotes the direct link between BDPA capabilities and fi- quiring the right personnel and nurturing the technical as well as man-
nancial as well as operational performance of an organization under the agerial capabilities they utilize in their analytical culture. Data-driven
resource-based view, our study extends into the downstream relationship decisions often prove to be a winning tool in the market, but they are
while defining human resources as an important ally (technical and even stronger when driven by technical analysis and decisive manage-
managerial skills), thus completing the whole chain of BDPA as a cap- ment skills. The results of our study help managers confronted with non-
ability development process from strategic resources that are dynamic in productive operations even after investing huge amounts to acquire re-
nature overall to achieving all of the dimensions of performance. As this sources with the hope of building the capability in its various forms.
study is rooted in the dynamic capability view (DCV), Barney (1991) Firms can now invest in the right talent by deploying focused strategies
continuously encourages organizations to strengthen their technical and and nurturing the data culture, which can act as a BDPA catalyst and
managerial skill sets, which can add to their BDPA capabilities. Secondly, enhance its capability to respond in the most critical situations.
nothing in the study reveals how the fundamental functions of superior Our results further help managers not to be overly concerned with
organizational performance (operational and financial) (Gupta & George, size and type of industry when developing an individual firms' capability,
2016) actually affect the relationship between the organization's data as it will not positively affect the firm's overall performance. Managers
analytical capability (BDPA) and market performance (Schoenherr, need to think in a way that enables organizations to balance their fi-
2012; Zhu & Sarkis, 2004; Zhu, Sarkis, & Geng, 2005; Zsidisin & nancial performance, keeping control of financial expenditures as well as
Hendrick, 1998). In addition, there is no evidence in the literature about adopting green and flexible technologies to achieve higher levels of op-
the connection between superior organizational performance (financial, erational performance, which leads to overall gains in market share.
operational) and BDPA capabilities and market performance. This study
also includes control variables within its scope. It is worth mentioning 6. Limitations and future scope of the research
that no evidence can be found in the literature that addresses the effect of
control variables such as firm size and industry type on overall superior Though this study investigates new dimensions in BDPA, it also has
organizational performance. The study enriches the literature and shows some limitations. One of the first limitations is that the data has been
the negative effect of the overall size of the firm and specific field of gathered at a single point of time, and could go further by collecting
industry on superior organizational performance, demonstrating that the longitudinal data. A longitudinal study would enrich our understanding
study can be deployed throughout operational organizations regardless about the causal relationship between the constructs (Guide & Ketokivi,
of their size and type. The study strongly asserts that firms that are 2015).
willing to initiate operational change to better nurture their resources Secondly, the current study puts emphasis on organizations that are
will be rewarded in capability, which can help them to reap superior technologically advanced in their operations and can easily build BDPA
organizational performance in all three of its facets. Furthermore, it is not facilities and nurture their environment. This is a bigger challenge for
certain that size and industry type will act as constraints. The results after organizations that are not quite as technically competent and that are
analysis show that all of the hypotheses considered for the study are skeptical of building and outsourcing BDPA. Furthermore, their long-
accepted. This relationship is crucial considering the dynamic competi- term motivation differs when choosing to build or outsource the BDPA
tive environment. This unfolds into a crucial relationship between BDPA facility. It would be interesting to see that how human capital working
capabilities and overall organizational performance. Finally, the litera- in non-high-tech organizations would complement or create constraints
ture as well as organizations benefit from the findings of the research in BDPA capabilities.
finding and the concept that there is a convincing relationship between Finally, the demographic constraint of the sample does not make it
big data predictive analysis (BDPA) and sustainable market performance. possible to generalize the findings. The data collected for this study is from
a developing economy (India), and it would be worthwhile to compare the
5.2. Managerial implications results with data collected from organizations in a developed economy.
The same result can be applied to the organizations of developed econo-
The current study shows a clear awareness of the importance of mies, and it would be interesting to study the extent of market perfor-
different skill sets that organizations need to acquire over time in order to mance's effect on the relationship between BDPA capability and sustain-
leverage them during capacity building. The overwhelming amount of able performance. We therefore anticipate that future studies will be able
data often pressures firms to acquire the latest technology for tech-driven to include data from different industries and a wider range of locations.
operations, but they do not actually utilize it optimally due to a lack of
required skill sets/indigenous resources. Furthermore, underutilization 7. Conclusion
of resources adds to the financial liabilities of organizations and poor
market share can be the resulting collateral damage. From the industrial Despite the hype around the constant growth of big data, the me-
market viewpoint, both opportunity costs and sunk costs are wasted, chanisms and conditions through which innovation can be enhanced
limiting the growth of an organization. The purpose of this study is to remain an under-explored part of the research. To address this gap, this
explain the importance of crucial resources (data, technical skills, man- study is built on two core aspects of big data: information and big data
agerial skills) for big data predictive analytics discussed by previous re- analytics capability. Supported by the DCV and past studies in the field
searchers (Schoenherr, 2012; Zhu & Kraemer, 2005; Zhu & Sarkis, 2004; of information systems, we examine the dependencies that characterize
Zsidisin & Hendrick, 1998) so that this will be taken up by the man- the relationship between information governance and a firm's big data
agement for critical consideration. These resources help smooth out the predictive analytics capabilities. We examined primary survey data
challenges (Mikalef & Pateli, 2017; Vidgen et al., 2017) faced by orga- from 209 high-level executives and used PLS-SEM analysis to in-
nizations during implementation of BDPA in organizational operations. vestigate our hypothetical relationships. By doing so, we add to the
The findings of this study give a clear picture of the vital role of emerging literature on the importance of information culture in the big
human skills in building BDPA capability. With reference to the results of data era, and the criticality of establishing a robust scheme for maturing
this study, it has now been empirically tested that good BDPA capability a firm's big data analytics capability, as well as for harnessing insight

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S. Gupta, et al. Industrial Marketing Management xxx (xxxx) xxx–xxx

and transforming it into action. While there is significant anecdotal up for industrial management, which can lead users as well as aca-
evidence concerning the role of BDPA on accelerating a firm's in- demics to further deploy and explore BDPA and its capability in the
novative capability, there is very limited theoretically grounded re- future. In addition, this study helps the industrial and marketing
search to verify such a relationship. management audience to understand how a firm can help its smart
Based on the concept of the Dynamic Capability View, we have capabilities to manage their operations as well as to impact the market
conceptualized big data predictive analysis as a distinctive capability in the age of industry 4.0. This study is a useful contribution to research
that can positively affect market performance. Empirical testing in this in BDPA and its direct effect on financial, operational and market
study provides proof that human skills (managerial and technical skills) performance (Gupta & George, 2016). Finally, our study offers many
are game-changing and will add analytical traits to the organization. research opportunities based on our limitations, which can be explored
Also, our study shows that firm size and industry type possess zero or in the future with a sample of different non-high-tech industries.
minimal effect on a firm's overall performance. New options will open

Appendix A. Operationalization of constructs

Latent variable Indicator Measurement constructs

Big Data Predictive Analytics (BDPA) Managerial Skills (MS)

Gupta & George, 2016; Dubey, Gunasekaran, Childe, Papadopoulos, e- MS1 Big data analytics managers are able to work with functional managers, suppliers, and
t al., 2019 customers
MS2 Big data analytics managers are able to coordinate big data-related activities
MS3 Big data analytics managers are able to anticipate the future business needs
MS4 Big data analytics managers have a good sense of where to apply big data
MS5 Big data analytics managers are able to understand and evaluate the output extracted from
big data
Technical Skills (TS)
TS1 Big data analytics training to employees
TS2 Hire new employees that already have the big data analytics skills
TS3 Big data analytics staff has the right skills to accomplish their jobs successfully
TS4 Big data analytics staff has suitable education to fulfill their jobs
TS5 Big data analytics staff holds suitable work experience to accomplish their jobs successfully
Operational performance (OP) OP1 Productivity has exceeded compared to competitors
Gupta & George, 2016 OP2 Profit rate has exceeded compared to competitors
OP3 Return on investment (ROI) has exceeded compared to competitors
OP4 Sales revenue has exceeded compared to competitors
Financial performance (FP) FP1 Average Return on Investment
Gupta & George, 2016 FP2 Average Profit
FP3 Profit Growth
FP4 Average Return on Sales
Market Performance (MP) MP1 Exploring new markets more quickly than competitors
Gupta & George, 2016 MP2 Introducing new products or services into the market faster than competitors
MP3 Success rate of new products or services has been higher than competitors
MP4 Market share has exceeded that of competitors

Appendix B. Combined loadings and cross-loadings

MS TS OP MP FP Type SE P value

MS1 0.898 −0.096 0.084 −0.102 0.046 Reflective 0.058 < 0.001
MS2 0.924 −0.147 0.181 −0.17 −0.015 Reflective 0.058 < 0.001
MS3 0.93 −0.157 −0.059 −0.032 0.046 Reflective 0.058 < 0.001
MS4 0.938 −0.121 −0.064 0.078 0.009 Reflective 0.058 < 0.001
MS5 0.92 −0.007 −0.022 0.005 −0.026 Reflective 0.058 < 0.001
TS1 −0.099 0.841 0.146 −0.079 −0.138 Reflective 0.059 < 0.001
TS2 −0.177 0.858 −0.063 0.006 0.011 Reflective 0.059 < 0.001
TS3 −0.069 0.929 0.039 0.009 −0.061 Reflective 0.058 < 0.001
TS4 0.022 0.897 −0.122 −0.081 0.106 Reflective 0.058 < 0.001
TS5 −0.042 0.912 −0.085 0.016 −0.01 Reflective 0.058 < 0.001
OP1 0.106 −0.046 0.82 −0.215 −0.126 Reflective 0.059 < 0.001
OP2 −0.066 −0.016 0.935 −0.024 −0.029 Reflective 0.058 < 0.001
OP3 −0.211 0.178 0.902 −0.122 −0.051 Reflective 0.058 < 0.001
OP4 −0.056 −0.096 0.894 −0.062 0.072 Reflective 0.058 < 0.001
MP1 −0.053 0.014 −0.117 0.865 0.051 Reflective 0.059 < 0.001
MP2 0.106 −0.119 −0.167 0.882 −0.039 Reflective 0.059 < 0.001
MP3 −0.045 0.011 −0.113 0.912 −0.059 Reflective 0.058 < 0.001
MP4 0.01 −0.202 0.165 0.812 0.041 Reflective 0.059 < 0.001
FP1 0.029 −0.114 −0.046 0.059 0.899 Reflective 0.058 < 0.001
FP2 −0.074 0.094 −0.14 0.064 0.943 Reflective 0.058 < 0.001
FP3 −0.075 0.072 −0.041 −0.003 0.941 Reflective 0.058 < 0.001
FP4 −0.029 0.039 −0.123 0.044 0.95 Reflective 0.058 < 0.001

Note: Loadings are unrotated and cross-loadings are oblique-rotated. Standard errors (SEs) and p-values are for loadings. P-values < .05 are desirable for reflective
indicators. The data that is bold in Appendix B corresponds to the respective indicators with regards to the concerned latent variable.

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Appendix C. Indicator weights

MS TS OP MP FP SE p-value

MS1 0.143 0.067 0.017

MS2 0.231 0.066 < 0.001
MS3 0.243 0.066 < 0.001
MS4 0.253 0.066 < 0.001
MS5 0.175 0.067 0.005
TS1 0.124 0.068 0.034
TS2 0.15 0.067 0.013
TS3 0.33 0.065 < 0.001
TS4 0.197 0.067 0.002
TS5 0.257 0.066 < 0.001
OP1 0.148 0.067 0.015
OP2 0.34 0.065 < 0.001
OP3 0.278 0.066 < 0.001
OP4 0.28 0.066 < 0.001
MP1 0.21 0.066 < 0.001
MP2 0.309 0.065 < 0.001
MP3 0.398 0.064 < 0.001
MP4 0.135 0.067 0.023
FP1 0.151 0.067 0.013
FP2 0.285 0.066 < 0.001
FP3 0.26 0.066 < 0.001
FP4 0.332 0.065 < 0.001

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