Effects of Data through social Media Tools and Forecasting on Supply

Chain Efficiency in Automotive Industry

Nabeila Arshad a, Dr.Urooj Pasha b,
a,b
Institute of Management Sciences,Bahuddin Zakariya University Multan, Pakistan

Abstract:
Scholars acknowledge the importance of big data and predictive analytics (BDPA) in achieving business value and firm performance.
However, the impact of BDPA on supply chain (SCP) under the mediation of supply chain agility and supply chain visibility while absorptive
capacity and organizational culture are the moderators has not been thoroughly investigated. To add ress this gap, this paper draws on
knowledge-based view and dynamic capability view. It conceptualizes BDPA effect on SCP and identifies the influence of SC agility and SC
visibility under the mediation effect of absorptive capacity and organizational cultu re on SCP. Survey questionnaire emailed to automotive
industries managerial level employees related to supply chain. Theoretical and managerial level implications are discussed.

industries.
Introduction
Among various technology innovations, big data characterized by
Interest in the concept of supply chain management has steadily volume, variety, velocity and value (Chen et al., 2013) plays a central role.
increased since the 1980s when companies saw the benefits of Wamba et al. (2015) further characterized it as 5 Vs: volume, velocity,
collaborative relationships within and beyond their own variety, veracity and value. Here volume refers to the large amount of data
organization. Supply chains were originally defined in the book of generated. From a statistical point of view, the results of data analyses are
Handfield and Nichols (1999) as “encompassing all activities statistically highly reliable with high sample size. With the recent advances
associated with the flow and transformation of goods from raw in the technology, the rate at which data is generated is fast. This
materials through to the end user, as well as the associated characteristic of the data is referred as velocity. Variety refers to the mix of
information flows”. Supply chain management was then further different data sources in different formats: unstructured data, semi-structured
defined as “the integration of supply chain activities through data and structured data. Veracity refers to the inherent unpredictability of
improved supply-chain relationships to achieve a competitive some data requires analysis of large data to gain reliable prediction and value
advantage”. Quinn (1997) defines the supply chain as "all of those refers to the extent to which one can derive economically worthy insights or
activities associated with moving goods from the raw -materials benefits through extraction or transformation. Wong (2012) and Manyika et
stage through to the end user. This includes sourcing and al. (2011) states that big data provides direction for firms to boost supply
procurement, production scheduling, order processing, inventory chain operations and innovations.
management, transportation, warehousing, and customer service. The power of big data is usually related to predictive analytics that uses
Importantly, it also embodies the information systems so necessary
to monitor all of those activities." statistical knowledge to forecast future events based on the assumption that
what has occurred in the past may have influence on future events (Oztekin,
In addition to defining the supply chain, several authors have 2017). After acquiring the raw data from the various sources, cleaning,
further defined the concept of supply chain management. As
integration, and other steps are followed to make it ready for further analyses
defined by Ellram and Cooper (1993), supply chain management
is "an integrating philosophy to manage the total flow of a using appropriate predictive techniques. Analyzing big data using predictive
distribution channel from supplier to ultimate customer". Monczka techniques may offer many advantages and benefits (Chen et al.,2014). Big
and Morgan (1997) state that "integrated supply chain management data & predictive analytics (BDPA) may be defined as an organizational
is about going from the external customer and then managing all capability which uses statistical knowledge to forecast future events based
the processes that are needed to provide the customer with value in on the assumption that what has occurred in the past may have influence on
a horizontal way". RR Lummus (1999) defined as supply chain future events (Gupta and George, 2016; Dubey et al. 2017).
management coordinates and integrates all of supply chain
Supply chains are configurations of firms that work together in the network
activities into a seamless process. However, there has been an
increasing attention placed on the performance of the Supply chain which constantly needs to improve their operations and capacity, either by
(BM. Beamon 1998). Data collected by the Supply Chain Council suppliers or customers. The concept of agility has experienced increasing
(1997) indicates that excellent supply chain performance can lower attention in production and supply chain management research due to its
cost by up to 7% and enhance cash flow by more than 30%. importance for managerial practice. Supply chain Agility (SCA) can be
Over the last years, supply chain management (SCM) has emerged considered a dynamic capability that enables firms to adapt to changes and
as a prime factor to increase organizational effectiveness and for provide quick responses throughout the entire supply chain (Lee, 2004; Lin
accomplishment of organizational goals. With the considerable et al., 2006; Swafford et al., 2006). SCA thus extends beyond a single firm
development in SCM, both researchers and practitioners are and involves alignment with major customers and suppliers. Recent
interested in measuring supply chain performance. Supply chain work on SCA includes the studies by Braunscheidel and Suresh (2009) and
performance (SCP) is defined as the performa nce of the various
Blome et al. (2013). While the former investigates the relationship between
processes included within a firm’s supply chain (Srinivasan et al.,
integration and SCA, the latter reviews empirical SCA studies and examines
2011). In recent years, the SCP measurement has been receiving
incessant attention from the scholars as well as practitioners. the link between SCA and operational performance.
Overall, the seamless flow of integrated information allows for Supply chain decision makers more often seek to increase their visibility of
improved SCP in terms of timely delivery, optimum inventory both demand and supply information, where visibility is defined by the
levels and cost effectiveness (Whitten et al., 2012) eventually availability (currency) and quality (accuracy, usefulness) of information
affects the overall organizational performance. Supply cha in (Zhou and Benton Jr. 2007, Barratt and Barratt 2011, Williams et al. 2013).
disruptions can impact supply chain performance adversely when
Visibility is the ability of the supply chain to enable access and sharing of
measured in terms of lost sales and damaged reputations.
information across the supply chain partners (Lamming et al., 2001).Supply
Information technologies are often viewed as enablers for supply chain visibility is a desired organizational capability to moderate risk
chain integration (Chae et al. 2005). According to Handfield and resulting from supply chain disruptions ( Juttner and Maklan, 2011).
Nichols (1999) information technology comprises information that
Consumer increasingly wants to know more that where and how the product
business creates and uses as well as broad-spectrum of increasingly
convergent and linked technologies that processes the information. they purchase are being made. To create clarity requires a company to gain
The rapid development that is taking place in the information visibility into its supply chain and disclose information to consumer.
technology has changed the competition territory in many Likewise, Absorptive capacity (AC) is related to a firm's ability to recognize
the value of new information, assimilate it, and apply it to commercial ends making processes. It is suggested, therefore, that firms that have the listed
(Cohen and Levinthal, 1990). Absorptive capacity, which enables firms to positive attributes of organizational culture (Adhocracy) are better suited to
determine, gather, analyze, comprehend, and creatively use the external handle the situation than those with the negative attributes (Hierarchy).
information (Lane et al., 2006), contributes to management in the creation of AC becomes a firm’s dynamic capability that it is valuable and difficult to
loyalty and satisfaction in customers (Tzokas et al.,2015). One of the imitate by competitors because it depends heavily on the trajectory and prior
important elements to harmonize all the resources and capabilities of the knowledge of each firm (Volberda et al., 2010). This capability becomes then
company with innovation is organizational culture (Aquilani, Abbate, & something scarce, difficult to imitate and replace that contributes to obtain
Codini, 2017). Dubey et al. 2017 suggest that firms which invest in right competitive advantages ahead of competitors. Thus, firms with a high AC
talent and build knowledge sharing culture are more successful in building may react much more effectively to customer’s needs with new or adapted
Big Data and Predictive Analytics capability which may help eliminate products, at the same time that they may improve their organizational
complexities resulting in supply chains due to information asymmetry routines and management practices which contributes positively to enhance
resulting from poor visibility. The authors argued that dynamic capabilities firm performance (Lane et al., 2006; Dobrzykowski et al., 2015).
are unique to every firm and may be built upon organizational culture or Wei and Wang (2010) define SCV as the capability to sense changes in the
history (Teece et al., 1997). market, gain knowledge from partners, manage SC relationships, and achieve
The BDPA has received wide recognition among scholars as an goal congruence in the SC. According to Lee et al., 1997, 2000;
organizational capability which may help to improve the supply chain Simatupang& Sridharan, 2002 visibility is a critical capability for improving
performance (Papadopoulos et al. 2017). Gunasekaran et al. (2017) argue that supply chain performance.
BDPA, as an organizational capability, may help to improve supply chain
visibility and unleash powerful insights to understand the current situation Knowledge Base View
and predict future possibilities. Although literature indicates that big data and The possession of knowledge resources gives the firm basic foundations to
predictive analytics (BDPA) convey a distinct organizational capability, little renew or reconfigure its resource base and to build dynamic capabilities (Wu,
is known about their performance effects in particular contextual conditions 2006), such as organizational agility. KBV theory can help to conceptualize
(inter alia, national context and culture, and firm size). the performance effects of IT investments (Pavlou et al., 2005). KBV states
The relationships between both dynamic capabilities (SCA and Absorptive that a firm's knowledge resources are unique and inimitable and that the
capacity) and their impact on firm performance are less studied. Although firm's primary function is to leverage them into productive outcomes (Grant,
several scholars have pointed out the importance of knowledge to manage 1996; Nonaka, 1995). Management studies use this theory (e.g, Nieves &
supply chains (Yusuf et al., 2004; Hult et al., 2004; Fugate et al., 2009; Marra Haller, 2014), as do studies in IT fields (e.g., Sher & Lee, 2004) to understand
et al., 2012), other scholars indicate the need to analyze the influence of AC the role of knowledge management in the creation of DC.
on several characteristics of a supply chain such as agility (Yang, 2014; The positive impact of information systems on supply chain performance is
Gligor et al., 2015). widely acknowledged by many researchers (Fawcett and Clinton 1996;
The purpose of this quantitative study is to investigate effect of SCA and Williams et al. 1997; Stank et al. 1999; Lambert and Cooper 2000; Lau and
SCV on BDPA and Supply chain performance and to understand the roles of Lee 2000; Brandon-Jones et al. 2014). Timely and accurate information on
AC and OC as moderators on the relationship between BDPA, SCA and inventories and stocks provided by logistics information systems help
SCV. This study aimed to produce a greater understanding of SCA and organizations minimize the inventory quantities and strategically allocate
SCV’s impact on BDPA and SC performance by manufacturing industries storage locations and logistics hubs in an optimum way (Chen et al. 2009).
perspective using Dynamic Capability view an extension of the resource- CôrteReal et al. (2017) empirically tested the KBV and dynamic capabilities
based view (RBV) (Hitt et al., 2015) and Knowledge base view. The DCV to understand the role of BDA in the creation of organizational agility. The
explains a firm's competitive advantage in changing environments (Teece et authors discovered that BDA could create agility through knowledge
al., 1997). Hence, the DCV may be defined as the firm's ability to integrate, management, which consequently affects organizational processes and
build and reconfigure internal and external competences to respond to rapid competitive advantage. The application of the KBV in the literature is vast.
changing environments (Teece et al., 1997). Considering big data studies, the theory is important in fomenting
discussions about the knowledge necessary to manipulate a massive quantity
of data filtering, analysis and other elaborate actions and how to create this
Theoretical Background knowledge, thus creating value and competitive advantage for the
organizations. So, based on these arguments the proposed framework of
research is:
Dynamic Capability View
In the past decade the Dynamic Capability (DC) perspective arose as one of
the most effective theoretical lenses for the strategic management field
(Schilke, 2014), attracting the interest of scholars not only in business, but
also in the IT management field (Helfat et al., 2009; Protogerou, Caloghirou,
& Lioukas, 2012). Although the literature has a broad range of definitions
for DC, one of the seminal papers defines DC as “the ability to integrate,
build, and reconfigure internal and external competencies to address rapidly-
changing environments” (Teece et al., 1997). Teece (2007) defines agility as
a higher-order dynamic capability that emerges over time, generally defining
agility as a capability with which firms can identify and respond to
environmental threats and opportunities and quickly adjust their behaviors
(Goldman et al.1995; Sambamurthy, Bharadwaj, & Grover, 2003).
Achieving agility demands processing a large and varied amount of
information (Goldman et al. 1995). This process is possible with BDA
applications.
Akter et al. (2016) argue using DCV logic that BDPA can provide
competitive advantage to an organization in highly dynamic situation when Hypothesis Development
due to lack of transparency the organization, despite of having stock of
strategic resources, often fails to translate into desired competitive Relationship between Big data and Predictive Analytics and SC
advantage. Liu et al. (2010) argued that OC can impact managers' ability to performance
process information, rationalize, and exercise discretion in their decision- LaValle et al. (2011) noted that top-performing organizations use analytics
five times more than low performers. Raffoni et al. (2018) argue that big data to improve information currency, firms need supporting organizational
analytics, if used cautiously, can help the organization to achieve better infrastructure and processes that enable them to quickly acquire,
performance. Gunasekaran et al. (2017) noted that the big data and predictive process and analyze data. The insights gained through increased
analytics capability have positive impact on supply chain and organizational information processing capacity can reduce uncertainty, especially when
performance. The BDPA has received wide recognition among scholars as markets are volatile and operational tasks are complex (i.e., highly
an organizational capability which may help to improve the supply chain interdependent). These basic principles have found renewed relevance,
performance (Papadopoulos et al. 2017). Minelli et al. (2012) define big data considering the emergence of vastly improved data availability (e.g., “big
as the next generation of data warehousing and business analytics for data”) and computing power. Srinivasan and Swink (2017) noted that
delivering a higher level of performance. supply chain visibility is a prerequisite for building data analytics capability
and vice versa. Supply chain visibility and BDACs are complementary, in
H1- Big data has positive impact on Customer satisfaction. the sense that each supports the other (Gunasekaran et al. 2017). Srinivasan
H1a- Big data has positive impact on Value added services. and Swink (2017) argue that organizations that invest in building analytics
H1b- Predictive Analytics has positive impact on Customer satisfaction. capabilities are likely to invest in visibility, because visibility provides the
H1c- Predictive Analytics has positive impact on Value added services. raw data upon which analytics systems and process operate.

Relationship between BDPA and SCA H4- Big data is positively associated with Supply Chain Visibility.
Swafford et al. (2008) found that IT capability has positive effect on SCA. H4a- Predictive analytics is positively associated with Supply Chain
Gunasekaran et al. 2017 argue that supply chain disruptions have negative Visibility.
effects and agile supply chain enablers were progressively used with the aid
of big data and business analytics to achieve better competitive results. The Relationship between SCV and SC performance
need for Big data analytical capability is heightened by volatile and complex The missing link in many research studies examining the role of information
task environments, where high levels of uncertainty make effective planning sharing and improved performance is visibility (Barratt & Oke, 2007).
and decision making difficult. Choi et al. (2017) argue that big data has Bartlett et al. (2007), the levels of visibility have ranged from opaque (where
significant effects on operations management practices. Adoption of BDA no information is shared) to translucent (sharing partial information) to
technologies could improve organization capabilities in today’s rapidly transparent (sharing information that leads to knowledge and collaborative
changing dynamic market environment (Meredith et al., 2012). BDPA abilities). Visibility is assessed in relation to the key performance criteria of
Organizations are not only harnessing and analyzing big data for improved each business function in three dimensions (cost, quality, and delivery). Wei
transparency and decision-making, but also for improving collaboration and Wang (2010) propose to measure the effectiveness of SCV based on
(Waller and Fawcett, 2013; Schoenherr and Cheri, 2015; Hazen et al., 2014; improved market learning and trust-building capabilities, while Caridi et al.
Wang et al., 2016a; Kache and Seuring, 2017). Gold et al. (2010) observe (2014), McIntire (2014) and Lee and Rim (2016) measure the effectiveness
that collaboration among partners in supply chains is used to meet of SCV by the extent to which visibility is used to automate decision making,
sustainability goals, and address environmental (Vachon and Klassen, 2008), or reduce the performance gap in business processes.
social, and governance issues (Pagell and Wu, 2009). According to McIntire (2014) SCV is assumed to have a direct impact on
business performance and the effectiveness of SCV is measured by changes
H2- Big data is positively associated with Supply Chain Agility. in overall business outcome. MasonJones and Towill (1998; 1999)
H2a- Predictive analytics is positively associated with Supply Chain Agility. demonstrated that “information enriched” supply chains perform
significantly better than those that do not have access to information beyond
Relationship between SCA and SC performance their corporate boundaries. Further, exchanging ideas in supply chain
Eckstein et al. (2015) view SCA as a dynamic capability that not only helps meetings can clarify many causal ambiguities for producing supply chain
to meet customers’ demand but also helps to enhance the firm’s profitability. performance, and therefore result in adaptive adjustments to the existing
Whitten et al. (2012) tested empirically, using a survey of 132 respondents configuration or more radical reconfiguration for more fundamental changes
that SCA along with other capabilities (i.e supply chain adaptability and (Zollo & Winter, 2002).
supply chain alignment) has a positive impact on supply chain performance
and supply chain performance further positively affects organizational H5- Supply Chain Visibility is positively associated with Customer
performance. Blome, Schoenherr and Rexhausen (2013) put forward the idea satisfaction.
that supply chain agility is a dynamic capability able to positively influence H5a- Supply Chain Visibility is positively associated with Value added
the operational performance of the firm. services.

H3- Supply Chain Agility is positively associated with Customer Mediating role of SCA and SCV
satisfaction. SCA has been analyzed as a mediating effect in a few studies. For example,
H3a- Supply Chain Agility is positively associated with value added services. Vickery et al. (2010) tested the mediating role of agility in the relationship
between antecedents (supply chain information technology and supply chain
Relationship between BDPAC and SCV organizational initiatives) and firm performance. Agility was also posited by
From the RBV perspective, capabilities are performance enhancement Swafford et al. (2008) as a mediator linking the effect of information
constructs (see Newbert, 2007; Brandon-Jones et al., 2014). The technology integration to competitive business performance. Similarly,
information systems literature broadly conceptualizes analytics Blome et al. (2013) found a mediating effect in the relationship between
capability as a technologically enabled ability to process large volumes supply and demand side competence and performance, and Danese and
and varieties of data with the velocity required to gain relevant insights Romano (2013) found that a fast supply network structure influences the
(chen et al. 2012, McAfee and Brynjolfsson 2012), thereby enabling relationship between customer integration and efficiency performance. A
firms to gain competitive advantages (Kiron and Shockley 2011, Lavalle comprehensive perspective on supply chain agility needs to include both
et al. 2011). Barratt and Oke (2007) have conceptualized supply chain visibility and velocity dimensions and progress is needed in both domains to
visibility (SCV) as a capability that helps an organization to generate achieve a higher level of supply chain agility. This perspective is also
sustainable competitive advantage. Supply chain visibility is achieved supported by Christopher and Peck (2004). Visibility captures organizational
through developing external lateral relations with customers and vigilance to state of affairs and events taking place in the supply chain, which
suppliers. can range from simple monitoring of inventory levels to assessment of the
However, firms are finding advantages in making real-time decisions probability of plant shutdowns due to inclement weather. It captures the
through insights gained from disparate sources of data and analyzed scope of events across which an organization needs to be vigilant
using powerful, automated tools. To reduce information lead times and (Braunscheidel and Suresh, 2009; Chiang et al., 2012; Tallon and
Pinsonneault, 2011). Velocity captures the time dimension, and subsumes the Supply Chain Agility.
quickness in recognition of an event and the rapidity of response. Exchanging H13a- Organizational Culture moderates the relationship between big data
demand information from downstream to upstream SC echelons reduces and Supply Chain Visibility.
uncertainties in the inter-organizational relationship and, accordingly, H14- Organizational Culture moderates the relationship between predictive
enhances trust between the participants (Kim et al., 2011, p. 668). analytics and Supply Chain Agility.
H14a-Organizational Culture moderates the relationship between predictive
H6- Supply Chain Agility positively mediates the relation between big analytics and Supply Chain Visibility.
data and predictive analytics and Supply Chain performance. H15-Organizational Culture moderates the relationship between supply
H6a- Supply Chain Visibility positively mediates the relation between big chain agility and Supply chain performance.
data and predictive analytics and Supply Chain performance. H15a-Organizational Culture moderates the relationship between supply
chain visibility and Supply chain performance.
Moderating role of Absorptive capacity
Cohen and Levinthal (1990, p. 128) defined Absorptive capacity (AC) as
‘‘the ability of a firm to recognize the value of new, external information,
assimilate it, and apply it to commercial ends is critical to its innovative
capabilities”. Tsai (2014) proves the moderating influence of the ACAP in
the international expansion of companies from emerging economies in his
study of 200 Taiwanese companies. The contributions where the AC is used
as a moderating variable is verified in Kohlbacher et al. (2013), who explore
the impact of AC on the innovation in a business cluster in Central Europe
and Aljanabi et al. (2014) relate the organizational factors of support to a
group of IT companies from Kurdistan with technological innovation.
Recently, absorptive capacity receives extensive consideration in supply
chain research for its effects on supply chain collaboration (Zacharia et al.
2011) and capability development (Patel et al. 2012; Setia and Patel 2013). Research method
Meanwhile, absorptive capacity is considered a critical dynamic capability For the purpose of data collection, an online survey would be conducted from
that enhances operational efficiency and innovation in a buyer-supplier Pakistan related to automotive industries. Well structured questionnaire
relationship setting (Sáenz et al. 2014; Whitehead et al. 2016) would be used as a research instrument to collect information with respect to
the related variables given in the proposed framework. Population of the
H7- Absorptive capacity moderates the relationship between BDPA study would include all companies/organization from automotive sector.
capability and Supply chain performance. Sample size would be calculated from the final list of population by
H8- Absorptive capacity moderates the relationship between big data and employing random sampling.
supply chain agility. Dubey et al. 2018 used WarpPLS 5.0 software to test the model. The software
H8a- Absorptive capacity moderates the relationship between big data and employs the partial least squares (PLS) structural equation modeling method
supply chain visibility. or in short form PLS SEM (Kock, 2014, 2015). PLS is a prediction-oriented
H9- Absorptive capacity moderates the relationship between predictive tool which allows researchers to assess the predictive validity of the
analytics and supply chain agility. exogenous variables (Peng and Lai, 2012). Scholars argue that PLS is better
H9a- Absorptive capacity moderates the relationship between predictive suited for explaining complex relationships as it avoids two serious
analytics and supply chain visibility. problems: inadmissible solutions and factor indeterminacy (see, Peng and
H10- Absorptive capacity moderates the relationship between supply chain Lai, 2012; Henseler et al. , 2014; Moshtari, 2016; Pratono, 2016; Akter et al.,
agility and Supply chain performance. 2017; Martí-Ballester and Simon, 2017; Dubey et al., 2018). Martinez-
H11- Absorptive capacity moderates the relationship between supply chain Sanchez and Lahoz-Leo, 2018 to test the mediating effect of SCA in the
visibility and Supply chain performance. relationship between AC and firm performance, used the methodology
proposed by Baron and Kenny (1986) with a focus of structural equations
Moderating Role of Organizational culture modelling (SEM), in which a structural equation model is adjusted in four
The Competing Values Framework model proposed by Cameron and Quinn successive steps.
(1999) identifies four types of organizational culture that the company can Srinivasan and Swink (2017) used the correlation matrix for the constructs
apply: adhocracy, clan, market and hierarchy. These four cultures are in the study. They used confirmatory factor analysis (CFA) to validate the
configured in two dimensions related to flexibility, using two dimensions: measures used in this study. In addition to the above tests, they investigated
flexibility versus control and external versus internal focus. Flexibility bivariate correlations between the independent variables and corresponding
orientation allows organization to be creative and risk-taker and open for dependent variables to check for outliers. This study is tested by using partial
embracing changes in the environment. In contrast, control orientation least squares structural equation modelling (PLS-SEM). As described in
emphasizes uniformity, coordination, efficiency, and close adherence to rules many current literature (e.g., Chin et al. (2003); Hair et al. (2009); Hair et al.
and regulations. (2011); Tenenhaus et al. (2005)), PLS-SEM is considered a good alternative
The second dimension, the internal–external axis, concerns a focus on to and advantageous in comparison to the covariance-based structure
activities occurring within or outside the firm. An internal focus emphasizes equation modelling method. The results would be calculated with satisfied
smoothing activities and alliances, while an external focus stresses respondent rate. Data analysis would be conducted by employing SPSS
competition and environmental differentiation. Liu et al. 2010 check the software.
effect of organizational culture and institutional theory to investigate how
institutional pressures motivate the firm to adopt Internet-enabled Supply Theoretical and Practical implications
Chain Management systems (eSCM) and how such effects are moderated by This study will contribute to both theory and practice in several ways. This
organizational culture. In the current research, we adopt the framework of study will be conducted by using knowledge base view as it is less studied in
flexibility-control orientation in the Competing Values Model (CVM) the perspective of BDPA. This study will analyze the internal and external
proposed by Quinn and Rohrbaugh (1983). capabilities impact on SC performance. This study will examine the
application of BDPA can enhance the performance of the firm which is very
H12- Organizational Culture moderates the relationship between BDPA useful practical insight for business managers. Lastly this study will provide
capability and SC performance. actionable information to the practitioners and policy makers to make
H13- Organizational Culture moderates the relation between big data and sensible decisions in multiple areas, such as strategic management, supply
chain management and marketing about the connectivity and information (2017). Big data and predictive analytics for supply chain and organizational performance. Journal of
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