2. caused an estimated 50 million deaths (Time Magazine Special Edition, 2020). The COVID-19
pandemic has caused and is causing major damage to human health, as well as to economic,
social, ethical and ecological dimensions of societies, globally (Ivanov and Dolgui, 2020a, b;
Hosseini et al., 2020). Contemporarily, the COVID-19 pandemic has highlighted
multidimensional societal challenges (Ben-Ammar et al., 2019; Dolgui et al., 2020a, b, c).
Systems disruptions or complete ruptures of many dimensions of society occurred with little or
no forewarning (Bala, 2012; Basole and Bellamy, 2014; Nunes et al., 2020). COVID-19 has caused
approximately 4.22 million human deaths, globally (https://covid19.who.int/; 2nd August,
2021). Pandemics are extraordinarily disruptive, in many ways, including all dimensions of
forward and reverse logistics due to their long duration, proliferation of problems that have
negative impacts upon multiple levels of societies and the ambiguity of when or how the
pandemic can be “controlled” (Ivanov and Dolgui, 2019; G€
olgeci and Kuivalainen, 2020).
The COVID-19 pandemic is the most recent in a series of pandemics in human history. Yes,
it has disrupted supply chains but far more importantly it has disrupted families, localities,
cities, nations and societies globally in numerous other ways than the supply chains (Kırılmaz
and Erol, 2017; Lai et al., 2018).
Some researchers and policymakers have even indicated that the COVID-19 pandemic is
similar to a World War where staying inside and closure of businesses result in severe
negative impacts upon the economy as often happens during wars (Wang et al., 2016; Wamba
et al., 2018). Although businesses are recovering slowly, the vital necessities cannot be
withheld for a longer time period (Wagner and Bode, 2006; Queiroz and Telles, 2018). The
COVID-19 pandemic caused extensive increases in risks in food insecurity, insufficient
medical equipment and supplies, disruption of transportation, disruption of education,
insufficient raw materials for many industrial sectors, financial vulnerabilities and risks
of spreading infection from contact deliveries (Bell and Griffis, 2010; Dhama et al., 2020; Yu
and Aviso, 2020).
Moreover, it is important to realize the expected challenges such as the style of
postpandemic supply chain management (SCM) operations, whether SCM activities will be
pursued between countries or within countries, and preparations for similar crises in future
(Dolgui et al., 2020b; Goldbeck et al., 2020). With the shutting down of manufacturing units
during the last several months and an indefinite continuation of COVID-19 restrictions across
the globe, the supply chain managers are trying to develop virtual possibilities for SCM
operations (Birkel and Hartmann, 2019; Calatayud et al., 2019).
Managers are intensively exploring technologically equipped autonomous SCM models
that can sustain disruptive impacts (Colicchia et al., 2019; Cousins et al., 2019; Shekarian et al.,
2020). As quoted from Fortune (2020), almost 94% of the companies in Fortune 1,000 are in
major trouble due to disturbances caused by COVID-19. The World Economic Forum (WEF)
(2020a, b) emphasized the urgency of significantly reengineering supply chain processes to
combat the COVID-19 pandemic and similar pandemics in the future.
The WEF underscored that enhanced activities related to transportation and production,
followed by efficient labor management, can extend temporary solutions, whereas the
widespread implementation of approaches such as digitization and data exchange can
provide more permanent solutions for pandemic disruptions, but digital disruptions can also
be risk-laden (G€
olgeci and Kuivalainen, 2020; Kamble et al., 2020). The synchronization of
technology and SCM is not new; however, in the context of the fatal impacts of COVID-19 and
the exchange of huge volumes of SCM data globally, the authors of this paper suggest that the
use of big data analytics (BDA) can help to build more sustainable strategies and procedures
for companies’ SCM processes. But the mere development of capabilities is not sufficient to
prevent or solve the undefined impacts of COVID-19 (Lawson et al., 2019; Søgaard et al., 2019;
Sohrabi et al., 2020). The robustness of BDA-enabled SCM is another big challenge for the
firms to address (Brinch et al., 2018; Wang et al., 2019).
IJLM
3. From a disruptive context, where the majority of manufacturers across the globe are
facing strict restrictions and deficiencies of products and services; another segment of
industries is struggling with the ripple effects (Liu et al., 2019; Scholten et al., 2019). Certainly,
purchasing and supply management capabilities (PSMC) constitute important functionalities
to enhance sustainable SCM outcomes. Firms are expected to handle uncertainties and risks
related to purchasing and supply management in pandemic situations, which can arise from
both internal and external sources and are capable of disrupting the competitive position of
the firms (Dash et al., 2013; Baryannis et al., 2019). Challenges faced by purchasing and supply
management professionals in pandemics include broken international supply links, steep
price increases from suppliers, nonavailability of the required quality grade of materials from
local or foreign sources and delays in delivery due to transportation issues during lockdowns
(Sarkis et al., 2020).
Organizations are facing new supply-related problems during this COVID-19 pandemic
and will have to address new problems afterward. These concerns challenge organizational
agility, resilience and sustainability. Among all, supply chain resilience (SCR) is the most
desirable aspect especially in the context of COVID-19 pandemic as it develops such
wonderful capacity in the supply chain, which enables traditional supply chains to either
accept or initiate self-transformations. Ivanov (2020) argued that a viable supply chain model
could guide an organization to rebuild their supply chain structure (organizational structure,
informational structure, technological structure, financial structure and process-functional
structure) after facing a long-term crisis.
Purchasing and supply management professionals have adopted various measures
during this pandemic to ensure continued viability of supply. A mix of behaviors has been
observed during this ongoing pandemic. For example, some companies chose to support
suppliers to prevent supplier collapse; others tried to change suppliers (Neirotti and Raguseo,
2017; Chiarini et al., 2020). These contrasting decisions and behaviors ultimately affect supply
chain performance. Organizations have changed their buying practices during this ongoing
pandemic. Due to lockdown rules such as maintaining of social distances, there have been
restrictions on physical meetings and visits. Buyers are using online meetings with suppliers
by using MS Teams or Zoom to monitor progress on contracts and to maintain supplier
relationships. The COVID-19 pandemic has adversely affected supplier management. These
are major risks to the firm that are endangering the sustainability dimensions of many of
them. The authors of this paper underscore that BDA tools (descriptive analytics, predictive
analytics and prescriptive analytics) can help SC managers to make timely decisions that are
beneficial for their organizations. However, there are few research studies that have studied
the roles of BDA in improving purchasing and supply capabilities in context to COVID-19
pandemic. The authors focused upon seeking answers to the research question (RQ):
RQ. How can BDA help companies transform their PSMC to be more resilient in the
context of the post COVID-19 pandemic?
The sections of this paper include: the theoretical underpinning (resource-based view (RBV)),
the research model and hypothesis development, research design, data gathering, analysis
and findings, discussion, conclusions and limitations.
2. Theoretical underpinning
2.1 Resource-based view (RBV) theory
The RBV theory is grounded on the optimum utilization of resources (Arag
on-Correa and
Sharma, 2003; Baryannis et al., 2019). The implementation of RBV in various disciplines along
with its theoretical extensions has significantly contributed to its widespread application
for management of supply chain operations (Vidal and Mitchell, 2018; Wong et al., 2020).
Big data
analytics and
SCM resilience
4. The RBV is one of the established and recognized theories to guide and monitor firms’
performances (Brandon-Jones et al., 2014; Chae et al., 2014) that are centered on sustainability
(Mahapatra et al., 2012; Bag, 2018a, b; Saberi et al., 2019). The essence of RBV is to preserve
the competitive edge while using limited and noninterchangeable resources responsibly
(Koberg and Longoni, 2019).
Supply chains connect suppliers and customers globally (Bostr€
om et al., 2015; Scuotto
et al., 2017). Eminent researchers have documented that COVID-19 has severely obstructed
and disrupted supply chain operations across the world (Jabbour et al., 2020; Antony et al.,
2020). Based upon the evidence, it is clear that existing management processes and policies
are not equipped to cope with COVID-19-like pandemic disruptions (Giannakis and
Papadopoulos, 2016; Ivanov, 2020); but they are riddled with systematic weaknesses. The
attributable reason for the ruptured supply chains was due to extended lockdown periods in
many regions of the world. Tangibly, everything reached a standstill, except the technology
through which many important things continued to be processed nationally and
internationally (Nkengasong and Mankoula, 2020).
In response to the above concern, the RBV can be used to facilitate the strengthening of the
linkages between the internal and external dimensions of the firm’s SCM (Vidal and Mitchell,
2018). If the managers of the firms seek to remain competitive, it is crucial for them to use the
power of data sharing to improve the overall functioning of their supply chain processes,
especially in disruptive situations such as pandemics (Tseng et al., 2019). Shan et al. (2019)
concluded that RBV provides a foundation upon which strategic, effective and resilient
decision-making toward SCM can be constructed. Ivanov and Dolgui (2020a, b) discussed the
importance of RBV while highlighting its effectiveness in helping companies to interlink their
purchase and supply management operations with digital systems globally. Cole et al. (2019)
highlighted the positive impacts (production of valuable strategic resources and firms can
work effectively and efficiently at minimized costs) of usage of the RBV in technology-
oriented SCM.
Sedera et al. (2016) and Neirotti and Raguseo (2017) stated that this theory helps in two
dimensions: heterogeneity and immobility. Heterogeneity implies that every firm’s resources,
tangible and intangible are dissimilar (Wang et al., 2006; Neirotti and Raguseo, 2017; Chiarini
et al., 2020). In other words, no two firms or more can work with the same types of resources
(Snacken et al., 1999; Schoenherr et al., 2008). The second dimension, “immobility,” implies
that each firm will use immobile resources with no possibility of exchanging them among
others (Trkman and McCormack, 2009; Neirotti and Raguseo, 2017; Baryannis et al., 2019).
However, these processes might not be fully correct currently, for example, 20 textile-
producing companies certainly have much that they can learn from each other and many
resources, supplies, equipment and human talents that can be shared and perhaps, be used
totally interchangeably.
The contingent side of this theory states that a firm can ensure good and sustainable
performance only when there is a proper alignment between their internal and external
components (Snacken et al., 1999; Mahapatra et al., 2012). It is frequently emphasized that
resources cannot provide sustainable outcome by themselves. Usage of the RBV can be used
to support the strengthening of the core competencies of the firm (Ivanov and Dolgui, 2019) by
extending sustainable competitive advantage. The BDA enables firms to investigate huge
amounts of data or data sets to unveil hidden information, different patterns and related
meaningful statistics (Collins, 2021). It helps organizations to explore new opportunities
irrespective of their domain of expertise, which enhances the possibilities of efficient work
output (Vidal and Mitchell, 2018). The BDA empowers organizations to produce improved
products with speedy delivery to the end users (Lawson et al., 2019). By using BDA,
organizations develop the capabilities to anticipate market requirements and provide
customized products and services to their ultimate users (Søgaard et al., 2019).
IJLM
5. Big data has been known to us since the early 1990s. Literature indicates that John Mashey
coined the term “big data.” However, the importance of BDA has increased multifold in this
era of fourth industrial revolution (Bag et al., 2020). Large data sets are being used in
manufacturing industries for performance improvement (Bag et al., 2021).
Big data are characterized by four V’s – volume, variety, velocity and veracity. Predictive
analytics is the use of data, statistical algorithms and machine learning methods to predict the
likelihood of future outcomes based on historical data (sas.com). Predictive analytics has
recently gained importance because of the availability of big data sets, user-friendly software,
faster computers and tougher economic conditions, which necessitate competitive
differentiation (sas.com). Many business analysts and managers are using BDA
technologies such as modeling machine learning, game theory and data mining. The main
reasons behind the use of predictive analytics are to help managers to find solutions for tough
problems and to explore new opportunities. This tool is commonly used for fraud detection,
optimizing marketing campaigns, enhancing operations and minimizing risks (sas.com).
Organizational resources must be configured to develop capabilities, to obtain a
competitive edge and sustainable existence (Chae et al., 2014; Pettit et al., 2019). Some authors
have used the RBV theory to help them improve their technological interventions to achieve
and maintain the competitive edge and market sustainability as highlighted by Hitt (2016a,
b). The tool, BDA, with the available big data, Internet connectivity and basic resources
(funds) can help companies to apply descriptive analytics, prescriptive analytics and
predictive analytics to strengthen their SCM and reduce their risks (Sivarajah et al., 2017).
These three analytics techniques can help companies to unlock values of big data. These
techniques provide different insights. For example, descriptive analytics can analyze the
trustable sources of supply and provide a list that can be used to guide where the supply
managers can do their purchasing during uncertain times. Companies obtain results from the
web server using Google analytics tools that help companies to learn what occurred in the
past and help them to make the right business decisions presently.
Whereas predictive analytics are predictive in nature and can predict what is likely to
happen in the future, for instance, the supply chain problems in the postpandemic era.
Companies generally use predictive modeling, root cause analysis, data mining, forecasting,
Monte Carlo simulation and pattern identification methods for performing predictive
analytics. The third technique known as prescriptive analytics can be used to help companies
learn how to get the best results. Natural language processing, machine learning and
operations research methods are used in prescriptive analytics (Sivarajah et al., 2017).
2.2 Theoretical framework
The theoretical framework for this study was developed based on the preceding discussion.
We have argued that clarity of BDA adoption objectives leads to BDA alignment with supply
chains, which further leads to development of PSMC. We have also argued that PSMC lead to
internal and external risk management (ERM), which finally leads to resilient supply chains.
Section 3 reviews the proposed theoretical model, which was designed to help companies
achieve SC resilience by using BDA and related tools (refer to Figure 1) for the study.
3. Research model and hypothesis development
3.1 Clarity of BDA adoption objectives and BDA alignment with SCM
Digital technologies have touched almost every sphere of the SCM processes. Since 2011,
supply chain managers have been updating their systems to Industry 4.0 and related AI,
capabilities for numerous applications. Recently, due to the COVID-19 pandemic, the
adoption of advanced technological techniques such as BDA (descriptive analytics,
Big data
analytics and
SCM resilience
6. predictive analytics and prescriptive analytics) to enhance the resilience of SCM operations
has increased in priority among many companies. New technologies are empowering firms
with BDA capabilities to effectively manage huge quantities of data, which otherwise are not
manageable. Secondly, technologies such as modeling machine learning, game theory and
data mining are bringing enhanced operational transparency. Also, the benefits such as
increased integration between supply chains, optimized stock and asset management,
successful relationships between manufacturers and suppliers and effective fulfillment of
demand-driven operations are expected from BDA. The BDA facilitates managers to obtain
quick and reliable answers in time, compared to traditional business process solutions
(Schauerte et al., 2021). The performance of suppliers can be traced in real time, which can
help to reduce risks. Managers can speedily trace the associated risks and thereby make
timely and effective decisions. The latest technologies are removing weaknesses of
traditional information systems to improve customer service quality. Today’s strong
computing power and information processing capabilities have made analyses much faster
than in the past. However, literature indicates that BDA adoption has not been successful in
small and medium-sized enterprises. Therefore, it is essential to understand the objectives of
BDA, which is to understand and predict the expected outcomes based on historic data. In
this paper it was applied to make improvements in the context of the COVID-19 pandemic.
The objective of the authors was to explore how sustainable SCM processes can be achieved
during pandemics (Kang et al., 2008; Sanders et al., 2019). Therefore, we hypothesize:
H1. Clarity of BDA’s concepts will have a positive influence on BDA association with
SCM to achieve sustainability.
3.2 BDA alignment with supply chain management
The prevalence of digital tools and innovative technologies can provide better business
capabilities. The BDA can help companies manage massive quantities of data and provide
competitive edge for them. Pettit et al. (2019) argue that BDA plays a fundamental role in
improving SCM activities. It extends acceptable solutions for various concerns arising
strategically operationally. It allows to manufacture products in less time, further reducing
gaps among manufacturers and end users. Adopting BDA is even more urgent for firms’ SCM
as manufacturing units are badly stricken due to the COVID-19 pandemic, globally. An
association between BDA and SCM is expected to deliver positive outcomes because it can
enable manufacturing firms to access transactional data from any part of world, whether
Source(s): Author
Clarity of BDA
adoption
Objectives
(BDAO)
H1 H2
BDA
Alignment with
SCM (BDASC)
Purchasing and
Supply
Management
Capabilities
(PSMC)
Internal Risk
Management
(IRM)
External Risk
Management
(ERM)
Supply Chain
Resilience
(SCR)
Control
Variables
H3
H4
H5
H6
H7
Figure 1.
Theoretical model to
achieve supply chain
resilience by using big
data analytics
IJLM
7. internal or external, structured or unstructured without leaving their home countries. The
BDA enables supply chains to be technologically sound, based upon extensive usage of
sensors and trackers. Even before the outbreak of COVID-19, Gartner forecasted the
implementation of 26bn technological devices (silicon chips, wearable gadgets, drones,
robots) by 2020 for managing SC operations. Aspects such as cloud computing, cluster
computing, digitization of warehousing facilities enable real-time SCM and analysis of data
with less time and space demands. Additionally, BDA tools (descriptive analytics, predictive
analytics and prescriptive analytics) can enhance purchasing and supply management
effectiveness (Min, 2010; Tumpa et al., 2019). Hence, we hypothesize:
H2. BDA alignment with SCM will have positive impacts upon PSMC in this new-
normal age.
3.3 Purchasing and supply management capabilities for internal risk management
Development of PSMC has always been an essential activity in SCM operations. With
growing uncertainties due to the COVID-19 pandemic, and vulnerability to procure the
minimum essentials (e.g. medicines), supply chain vulnerabilities and resilience to return to
the “new normal” are high priorities for manufacturers, suppliers, consumers and
governmental officials globally. The importance of developing and maintaining
purchasing and supply capabilities to minimize internal risks of supply chains has been
emphasized frequently. The effective management of internal purchasing and supply
capabilities that includes physical machines and intellectual capital enables firms to be more
resilient during the pandemic and other catastrophes such as severe storms, earthquakes and
so on. As witnessed, during the past 5–6 months, this pandemic has challenged the local,
regional and global supply chains. In order for firms to anticipate and/or to recover from the
current challenges, it is essential for them to adopt new BDA tools (descriptive analytics,
predictive analytics and prescriptive analytics) that are innovative and intelligent to help to
ensure the firm’s survival against internal and external risks. Furthermore, such efforts will
help the networks of firms to create agile, resilient, supply chains that will enable them to
function effectively during future pandemics (Ritvanen, 2008). Thus, we hypothesize:
H3. PSMC improved by BDA types of updates will have positive effects upon the
resilience of firms to anticipate and respond to internal risks caused by pandemics
and other catastrophes.
3.4 Purchasing and supply management capabilities for external risks
The SCM is always exposed to daily challenges, but the current COVID-19 pandemic has
dramatically increased their severity. The difficulties for firms globally are looking for short-
and longer-term solutions. This pandemic raised an alarm, or it has warned the firms to explore
options to recover from this pandemic and to be ready for similar future challenges. The
susceptibility among people, firms and countries is inevitable; however, it increases external
risks for everyone. Being more adaptive will help firms to manage their SC and manufacturing
systems with more resilience. Management of external risks should be related to forecasting
problems, product design issues, confidentiality of unique selling processes of firms and many
other dimensions. Considering the COVID-19 pandemic time as a “Blessing-in-Disguise,” this is
an opportunity for industrial leaders to implement concepts and tools associated with BDA and
other evolving approaches. This research team proposes involvement of BDA in PSMC, which
can help firms recover quickly during pandemics and other catastrophes (Zsidisin, 2003;
Hallikas and Lintukangas, 2016). Therefore, we hypothesize:
H4. Updated, resilient PSMC will have positive impacts upon the firms’ capacities to
manage their external risks during and after pandemics and other catastrophes.
Big data
analytics and
SCM resilience
8. 3.5 Purchasing and supply internal risk management will lead to better management of
external risks
SCM is dependent on purchasing abilities of the firms, followed by the supply capabilities. In
the last few years, the field of SCM has gained momentum due to enhanced technology and
heightened entrepreneurial initiatives. The complex structure of SCM is not risk-free. Instead,
SCM is affected by both external and internal risks. However, there is a high probability that
if the firm manages its internal risks, it can reduce external risk factors as well. The
complexities in SC operations have reached their peak in the current situation of COVID-19
pandemic. Even the simplest purchase and supply activities have been dramatically affected.
Managers of firms are exploring how interventions with appropriate technologies can
contribute to streamlining their SCM processes. However, it is important for them to
understand that adopting the BDA tools can provide more accuracy and security. It can
certainly reduce internal purchase and supply risks related to quality of raw products and
help to ensure the timely availability of materials, which will allow firms to manufacture
products on time and continue to generate revenue even during the COVID-19 pandemic’s
challenges (Zsidisin, 2003; Hallikas and Lintukangas, 2016). Hence, we hypothesize:
H5. Purchasing and supply, internal risk management (IRM) will have a positive
influence on purchasing and supply, ERM in a pandemic situation.
3.6 Improved internal risk management to achieve enhanced supply chain resilience
SCR denotes flexibility or elasticity in SCM. The resilience characteristic of a SCM enables it
to reach its original status after undergoing disruptions. This feature would have been
extremely useful for the firms during this COVID-19 pandemic. The involvement of BDA can
improve SCR to a remarkable extent (Papadopoulos et al., 2017). It is especially important for
firm managers to understand how IRM of purchasing and supply risks can enhance the
resilience of supply chains. The effective management to avoid internal risks of delayed
product delivery and inefficient utilization of resources can help to increase supply chain
resiliency. Also, the RBV theory states that cordial management between internal and
external components of SCM can result in sustainable SCM. The use of BDA tools can be the
key to achieving resilient SCM during this pandemic. Adaptation to changes is the key to
survival and excelling in competitive environments. Proper changes of SCM operations are
expected to deliver more sustainable results. Furthermore, improvements of reliability of
purchasing and supply capabilities will have direct impacts upon SCR (Whitten et al., 2012).
Thus, we hypothesize:
H6. Improved IRM of the purchasing and supply chain will have a positive influence
on SCR.
3.7 Purchasing and supply external risk management on supply chain resilience
Purchasing and SCM external risks are another important aspect for improving supply chain
resiliency. This implies that resilience of SCM can also be understood from the functionality
perspective.
This means that a supply chain is resilient if it continues to function without being
affected by any disruptive situation such as the COVID-19 pandemic. If firms cannot fulfill
their SCM activities, BDA can provide some relief because they have the capacity to help a
company to virtually manage their information related to purchasing and supply of their
supply chain(s), which can help to remove operational barriers among countries. Notably,
managing the internal risks is a big challenge for the firms as everything has come to a
standstill; however, handling external risks is even more complex for the SCM firms.
Understanding BDA and its implementation in SCM can reduce or prevent certain risk
IJLM
9. management practices during uncertain times. Researchers have confirmed that the
appropriate technologies can help to provide acceptable solutions for various types of
purchasing and SCM of external risks. Hence, this article’s research team proposes to use
BDA to achieve SC resiliency in this pandemic (Whitten et al., 2012). Therefore, we
hypothesize:
H7. Improved ERM of purchasing and supply will have a positive influence on SCR.
4. The research methodology
This section outlines the research design used to conduct the survey, collect data and perform
hypotheses testing.
4.1 Questionnaire development
The questionnaire for the survey was established based upon on a five-point Likert scale
design. The key constructs/variables in the study were: Clarity of BDA Adoption Objectives
(BDAO), BDA Alignment with SCM (BDASC), PSMC, IRM, ERM and SCR. The measurement
items of the instrument were adapted from various research studies that included BDAO with
three items adapted from the publications of Kang et al. (2008) and Sanders et al. (2019);
BDASC with four items were adapted from Min (2010), Baryannis et al. (2019), Fahimnia et al.
(2019) and Sanders et al. (2019); PSMC with four items that were adapted from Ritvanen
(2008); IRM with five items and ERM with six items that were adapted from Zsidisin (2003)
and Hallikas and Lintukangas (2016); SCR with seven items that were adapted from Whitten
et al. (2012). The details are presented in Table 1.
4.2 Data collection
The initial request for filling answering the survey questionnaire was made in the first week
of June 2020. The structured questionnaire was prepared using a Google Form, and the
Google Form link was emailed to 375 potential respondents from the automotive industry.
The list of companies was randomly selected from the South African “automotive parts and
allied manufacturing association” database. We received 78 completed responses in late July
2020. Thereafter, the research team followed up in the first week of August 2020 and after
that received additional 146 responses.
Incomplete questionnaires were not included in the evaluations because the electronic
system did not evaluate incomplete submissions. The response rate was 38.93%. A summary
of sample responses is provided in Table 2.
The research team is confident that the quality of the data obtained is reliable and valuable
for achieving the objectives of this research (refer to Table 2).
4.3 Nonresponse bias test
Nonresponse bias/participation bias is a potential problem in survey-based research. It
happens when nonrespondents who failed to participate in the survey differ significantly
compared to the respondents. All surveys suffer from nonresponse bias issues; some occur at
a very small percentage level and can be ignored. However, the level of bias is dependent on
extent of the variation between respondents and nonrespondents and also on the proportion
of nonrespondents from the selected sampled (Lavrakas, 2008). The research team used
Leven’s variance test of homogeneity. We found that the values were not significant, and thus
we concluded that nonresponse bias was not present in the data due to the time differential of
being obtained (Armstrong and Overton, 1977).
Big data
analytics and
SCM resilience
10. Constructs Item No Items Source
Clarity of BDA adoption
objectives (BDAO)
BDAO1 Our employees and key stakeholders
are aware of the basic goals and
objectives of BDA
Kang et al. (2008),
Sanders et al. (2019)
BDAO2 BDA has assisted our systems to make
decisions and solve problems
BDAO3 The BDA objective is specific and
measurable, and meets time, budget,
and quality constraints
BDA alignment with SCM
(BDASC)
BDASC1 We have aligned BDA objectives with
our supply chain management (SCM)
objectives
Min (2010), Baryannis
et al. (2019), Sanders
et al. (2019)
BDASC2 Our BDA objective is to link the entire
SCM to a real-time network
BDASC3 We were able to adapt the (descriptive
analytics, predictive analytics and
prescriptive analytics) tools to meet
our particular supply chain
management needs
BDASC4 We have resources to apply advanced
analytics for SC risk management
Purchasing and supply
management capabilities
during the pandemic (PSMC)
PSMC1 We have developed integrative
capabilities
Ritvanen (2008)
PSMC2 We have developed relational
capabilities
PSMC3 We have developed innovative
capabilities
PSMC4 We have developed intelligence
capabilities
Internal risk management
(IPSR)
IRM1 Lowered inventory related risks such
as obsolescence, deterioration,
oversize buffering
Zsidisin (2003),
Hallikas and
Lintukangas (2016)
IRM2 Reduced new product development
problems
IRM3 Reduced relationship issues
IRM4 Improved forecasts
IRM5 Improved budgeting and funds
External risk management
(EPSR)
ERM1 Actively managed single source of
supply through rate contracts, and
vendor managed inventories
Zsidisin (2003),
Hallikas and
Lintukangas (2016)
ERM2 Developed mechanism to reduce price
fluctuations
ERM3 Developed strategies to minimize
inaccuracy of deliveries and lead times
ERM4 Developed digital quality control
system to avoid supply of poor-quality
materials
ERM5 Developed channels for improvement
in communication and information
delivery
ERM6 Focused upon technological
advancements
(continued)
Table 1.
Operationalization of
constructs
IJLM
11. 5. Data analyses and findings
The authors of this paper used the PLS-SEM method in the analyses work. We followed the
guidelines of Hair et al. (2011) before selecting the PLS-SEM method. The WarpPLS software,
Version 6.0 was used to check the model fit and to perform the hypothesis testing. The use of
Constructs Item No Items Source
Supply chain resilience (SCR) SCR1 We have a clear overview of our SC
and have the ability to quickly restore
it to its original functionality
Whitten et al. (2012)
SCR2 We have the ability to manage SC
risks effectively
SCR3 We have the ability to deliver goods to
our customers on time
SCR4 We have the capacity to deliver zero-
defect goods to our customers
SCR5 We have the ability to minimize
wastes in the supply chain
SCR6 We have the ability to deliver correct
batch sizes to our customers
SCR7 We have proper system to avoid
sending damaged and incorrect orders
to our customers
Source(s): Author Table 1.
Details
Respondent
Categories
Respondents
(In number)
Respondents
(In percentage)
Position in the company General manager 8 3.57
Senior manager 147 65.63
Manager 39 17.41
Junior manager 30 13.39
Experience (Years) Above 20 186 83.04
10–19 38 16.96
Below 10 0 0.00
Type of business OEM 75 33.48
OEM and dealers of parts and
accessories
37 16.52
Manufacturers of accessories
and replacement items
26 11.61
Manufacturers of associated
items
86 38.39
Dealers of associated items to
the automotive industry
0 0.00
Organization’s age (Years) 20 158 70.54
15–20 27 12.05
10–14 39 17.41
5–9 0 0.00
Below 5 0 0.00
Organization’s annual
turnover (South African
Rands)
R10 million 12 5.36
R50 million 78 34.82
R50 million 134 59.82
Source(s): Authors’ own compilation
Table 2.
Summary of survey
participants
Big data
analytics and
SCM resilience
12. this software is fairly easy and consists of five main steps. The first step was opening or
creation of a project; the second step consisted of reading raw data; the third step involved
data preprocessing; the fourth step involves drawing the SEM model and defining the links;
and the fifth step was running the SEM analysis and assessing the results. The research team
followed these steps; the findings are presented in the next subsections.
5.1 Measurement model
The measurement model was evaluated prior to examination of hypotheses testing output.
Model fit and quality indices parameters are given in Table 3. The explanation of these
indices depends upon the purpose of the research (Kock, 2020). These indices were used to
assess the model fit with the data collected during the survey.
The results of the analyses for the APC, ARS and AARS were statistically significant (p
value lower than 0.001) and confirmed that the model was robust. The AVIF and AFVIF
values were within acceptable range, that is, below 5.
The threshold goodness-of-fit value shows a good fit (above 0.36) and suggests that the
explanatory power of the model is high (refer to Table 3). Endogeneity is a big problem in
survey-based empirical research especially when researchers use cross-sectional data.
Endogeneity occurs when an independent variable correlates with the error term of the
regression equation, and researchers must account for it to avoid biased parameter estimates
that can result in minimizing the validity of the findings (Sande and Ghosh, 2018).
To check for the presence of any endogeneity problems, the research team checked SPR,
RSCR, SSR and NLBCDR as per guidelines of Kock and Lynn (2012) and found these within
acceptable limits. The Simpson’s paradox ratio (SPR) is 1.00, which indicates that there was
no Simpson’s paradox, in the model. An instance of Simpson’s paradox occurs when a path
coefficient and a correlation associated with a pair of linked variables have different signs.
The R-squared contribution (RSCR) was found to be 1.00. R-squared contributions in the
model should be equal to or greater than 0.7, and the results indicated that 100% of the paths
in the model were free from statistical suppression. The RSCR index is a measure of the extent
to which a model is free from negative R squared contributions, which occur together with
Simpson’s paradox instances (refer to Table 4).
Model fit and quality indices Values
Average path coefficient (APC) 0.583
Average R-squared (ARS) 0.498
Average adjusted R-squared (AARS) 0.495
Average block VIF (AVIF) 1.224
Average full collinearity VIF (AFVIF) 2.653
Tenenhaus GoF (GoF) 0.480
Source(s): WarpPLS software output
Causality assessment indices Values
Simpson’s paradox ratio (SPR) 1.00
R-squared contribution ratio (RSCR) 1.00
Statistical suppression ratio (SSR) 1.00
Nonlinear bivariate causality direction ratio (NLBCDR) 1.00
Source(s): WarpPLS software output
Table 3.
Model fit and quality
indices
Table 4.
Causality assessment
indices
IJLM
13. 5.2 Validity assessment
Campbell and Fiske (1959) proposed two methods to calculate the construct validity of a test:
convergent validity and discriminant validity.
Convergent validity is the degree of confidence the researchers have that a trait is well
measured by the indicators they used. The criterion of Fornell–Larcker (1981) is generally
used to gauge the degree of shared variance between the latent variables of the model.
According to this criterion, the convergent validity of the measurement model can be
assessed by the average variance extracted (AVE) and composite reliability (CR). Therefore,
both SCR and AVE are related to the quality of a measure. The AVE is a measure of the
amount of variance that is captured by a construct in relation to the amount of variance due to
measurement error.
Scale composite reliability (SCR) was found to be above 0.7 and acceptable. The AVE was
found to be above 0.5 and satisfied the convergent validity criteria.
Discriminant validity is the degree to which measures of different traits are unrelated.
Table 5 indicates that discriminant validity is maintained in this work (Fornell and Larcker,
1981) (refer to Table 5).
5.3 Structural model
The results of hypothesis testing are presented in Figure 2.
The path coefficients value (beta coefficient) and their corresponding p values are
presented in Figure 2. The path coefficients denote the response of the dependent variable to a
unit change in an explanatory variable when other variables in the model were held constant.
L.v. s BDAO BDASC PSMC IRM ERM SCR
BDAO (0.746) 0.562 0.622 0.518 0.632 0.173
BDASC 0.562 (0.792) 0.743 0.655 0.557 0.063
PSMC 0.622 0.743 (0.722) 0.604 0.619 0.111
IRM 0.518 0.655 0.604 (0.597) 0.729 0.190
ERM 0.732 0.557 0.619 0.729 (0.629) 0.073
SCR 0.173 0.063 0.111 0.190 0.073 (0.559)
Source(s): WarpPLS software output
Clarity of BDA
adoption
Objectives
(BDAO)
BDA
Alignment with
SCM
(BDPASC)
Purchasing and
Supply
Management
Capabilities
(PSMC)
Internal
Purchasing and
Supply Risk
Management
(IRM)
External
Purchasing and
Supply Risk
Management
(ERM)
Supply Chain
Resilience
(SCR)
R2
= 0.57
R
2
= 0.60
R
2
= 0.65
R2
= 0.27
R
2
= 0.40
β = 0.76
p 0.01
β = 0.77
p 0.01
β = 0.63
p 0.01
β = 0.20
p 0.01
β = –0.50
p 0.1
β = 0.58
p 0.01
β = 0.32
p 0.01
Table 5.
Correlations among
l.vs. with sq. rts.
of AVEs
Figure 2.
Model obtained after
hypotheses testing
Big data
analytics and
SCM resilience
14. The p value was used to determine whether the hypotheses test results are statistically
significant. We considered that 95% confidence limit and p value less than 0.05 are desirable.
The results indicate positive association between BDAO→BDASC (β 5 0.76*);
BDASC→PSMC (β 5 0.77*); PSMC→IRM (β 5 0.63*); PSMC→ERM (β 5 0.32*);
IRM→ERM (β 5 0.58*); ERM→SCR (β 5 0.20*). All hypotheses except hypothesis H6
were supported. The effect of control variables such as age of firm (β 5 0.08) and size of firm
(β 5 0.06) was found to be nonsignificant.
6. Discussion
The present paper has tried to handle the important associations between BDA and SCM by
considering various factors that include clarity on BDA adoption, BDA alignment with SCM,
SCR, external and internal risks (see Figure 1). The results (see Figure 2) indicated that clarity
of BDA objectives plays a positive role in BDA alignment with SCM goals, which will further
allow firms to meet out the challenges related to BDA-facilitated SCM (Brinch et al., 2018;
Wang et al., 2019).
Further, the findings revealed that BDA alignment with SCM goals positively influences
the PSMC in the COVID-19 pandemic situation. Also, PSMC were found to positively
influence purchasing and supply management of internal and external risks. IRM of
purchasing and supply was found to positively influence management of ERM of purchasing
and supply.
Finally, ERM of purchasing and supply positively influenced SCR.
Wu et al. (2006) suggested that Information Technology (IT) is a valuable resource and can
contribute to a firm’s competitive advantage. Hair et al. (2011) suggested that IT has
contributed to competitive advantage in supply chains. The authors of this paper agree with
the findings of Wu et al. (2006), and we suggest that BDA, which is a set of advanced
information technology tools, is a valuable resource that can be used as a catalyst in
improving purchasing and supply management resources of firms by managing huge
number of data sets that reveal meaningful information (Collins, 2021). Improved purchasing
strategies can impact cost and innovativeness of the firm and thus, increase the probability of
achieving positive, sustainable outcomes (Brandon-Jones and Knoppen, 2018).
This finding corroborates our findings that implementation of improved purchasing
capability development and deployment can reduce risks and reduce costs in the context of
the COVID-19 pandemic. Hult et al. (2010) argued that evolvement of SC disruptions due to
pandemics and managing in such situations is extremely different from managing on “day-to-
day,” supply chain operations. Craighead et al. (2020) stated that COVID-19 is one of deadly
pandemics that have affected nearly 200 countries across the globe since the 1918 flu
Epidemic.
The authors of this article underscore the urgency for corporate and governmental leaders
to revisit, revise and realign existing SCM theories, practices and policies to enhance their
resiliency in the context of similar crises in the future. Bode et al. (2011) shared similar
recommendations about the needs for improving the chronological and longitudinal facets
of SCM.
New concepts, processes, policies and training are needed to anticipate and to seek to
reduce the negative impacts of future disasters, be they caused by floods, fires or viruses!
The COVID-19 pandemic has caused dramatic changes in numerous ways in terms of
material and immaterial possessions across the world; many of the losses are irreparable.
However, some have gone so far as to state that the resultant shocks and lessons that should
be learned can be taken as “Blessings in Disguise!”
It has challenged us to realize how incredibly interdependent we are upon each other and
upon our ecosystems. We must realize how vulnerable numerous societal links are to
IJLM
15. challenges from extreme storms, civil strife, droughts, floods and pandemics. We have to be
holistically, proactive in preparing for such challenges. Therefore, we must identify and
strengthen or replace the weak links in our systems, including in our production and
consumption systems.
The COVID-19 pandemic has forced people, in all countries, to assess their weaknesses,
strengths, threats and opportunities. There are many untapped resources in IT and allied
spheres to deal with COVID-19 and with similar challenges with improved understanding,
knowledge and preparations. In agreement with this, Paulraj (2011) stated that strategic
purchasing is a key resource for a firm that is elementary as they seek to develop and
implement sustainable SCM practices. Therefore, focusing on BDA enablement and other
creative tools can help firms to build resilient SCs that anticipate and respond to internal and
external risks in “normal times” and in the context of totally nonnormal situations such as
those caused by COVID-19. We must improve our systems in many ways as we seek to
transition to equitable, sustainable, livable, post-COVID-19 societies.
6.1 Theoretical implications
The authors of this paper documented that BDA is playing important roles in developing and
strengthening, resilient purchasing and supply management systems in the normal
operations of firms as well as in helping them reduce risks in challenging situations such
as the COVID-19 pandemic.
Therefore, BDA-based resources, if properly integrated within the firm’s business
plans and procedures, can contribute to their sustained competitive advantage. Clarity of
BDA adoption goals can help managers of firms to focus management attention upon
internal and external resources, capabilities and competencies for BDA alignment with
SCM goals for competitive and sustainable advantage. The key criteria of RBV are that
resources must be valuable, rare, imperfectly imitable and nonsubstitutable. The
conceptual framework and tools of BDA are valuable resources because they can be
used to enable firms to adopt innovative purchasing and supply management strategies
that enhance their efficiency in normal times as well as for being proactively prepared to
respond to BIG challenges such as a pandemic. It is important to emphasize that BDA is
rare and not available to all firms.
This research extends the knowledge base in the area of BDA applications in pandemic
situations and opens up new research avenues for future researchers toward preparedness
(anticipated allocation of resources and planning for emergency situations), adaptation
(digital environment and allied technologies), sustainability (humanitarian SCM and digital
supply chain ecosystems) and recovery (forecasting techniques and integral manufacturing
capabilities). Future research studies must also consider the concept of artificial intelligence
in connection with BDA and SCM initiatives because of its strong connection with
automation.
6.2 Practical implications
Research is useful only if its implications have far-reaching effects. In similar vein, the
present study extends certain practical implications for the managers and policymakers
in industry and academia both. Clarity of BDA objectives is possible when managers
develop awareness programs to inform and update the goals of BDA with their employees
and key stakeholders. This will help to improve the alignment with SC. Secondly, effective
diffusion of BDA knowledge across all dimensions of the organization enables quick
acceptance among employees and stakeholders. The BDA adoption objectives will differ
from organization to organization depending upon the nature of business and their
business goals. Thirdly, the BDA has enabled systems to perform tasks such as:
Big data
analytics and
SCM resilience
16. problem-solving, decision-making, improved and open perception and improved
communication. Fourthly, BDA adoption objectives must be specific and measurable
and meet the time, budget and quality constraints. Focus on such BDA adoption
objectives can be helpful for sustainably running the operations and in achieving the
business goals during this new normal time. Fifthly, the BDA alignment with SC is
helpful when managers need to align their BDA-based strategies with SC strategies so
that BDA can be used to connect the entire SC management to a real-time network starting
from sources to the end customers and lastly, managers must select the necessary
resources and build capabilities to use BDA for SC risk management. Once BDA
alignment is perfectly done with supply network, then the organization will be in a
position to demonstrate its PSMC. This will enable the organization to improve abilities to
effectively manage internal purchasing and supply related risks, which in turn will
aid in managing external purchasing and supply management risks. Finally, the
organization will gain the ability to develop SCR. For any manufacturing companies in
the automotive industry, the suppliers are the source of competitive edge. During this
long-lasting pandemic, it is very important that the manufacturers leverage on BDA to
increase visibility and make timely decisions to eliminate supply risks and further
improve SCR.
7. Conclusions
Developing PSMC in this pandemic is important when managers need to strengthen their
PSMC to continue to function in normal and in nonnormal situations such as pandemics.
Manufacturing firms are confronted with diverse risks and uncertainties during such
pandemics. These risks and uncertainties are heightened when integrated with
remanufacturing, recycling and reverse logistics operations, which can be easily mitigated
by developing purchasing and supply capabilities including integrative, relational,
innovative and intelligence capabilities.
Internal purchasing and supply risk management of the supply chain manufacturing
businesses are dependent on the procurement and supply management capabilities to
mitigate the risks that arise from internal sources such as inventory related risks, problems
related to sourcing of components of the right specifications for new product developments,
relationship-related issues, lack of sales forecasts and poor budgeting. These risks can impact
the performance of the SCM to a great extent.
External purchasing and supply risk management pertains to single sources of supply,
fluctuations of prices, inaccuracies of deliveries, supply of poor-quality materials, poor
communications from suppliers and technologically backward suppliers are the major
sources of external purchasing and supply risks. These risks can cause serious damages to
the SCM and need to be minimized by expanding the company’s capabilities to
mitigate them.
Enhancement of SCR can be achieved when managers are committed to reconfiguring
their resources and building their capabilities. SCR mainly revolves around parameters
such as: working transparently through all phases of the supply chain, effectively
managing SCM risks, eliminating or reducing wastages in the supply chain, building
agility and consistently meeting the customer’s expectations and needs. Managers must
understand that clarity of BDA adoption objectives can directly and indirectly impact
their supply chain’s resilience.
The main contribution of this study that it was designed to help managers reduce their
internal and external risks by strengthening their SCM processes, so they can continue to
reliably, produce products and provide services in normal times as well as in the context of
nonnormal times such as in the COVID-19 pandemic.
IJLM
17. This study was conducted in South Africa, based upon inputs from managers of companies
in the automotive industry using cross-sectional data, which may cause difficulty in
generalizability of results. However, this study was performed in a scientific manner and opens
new opportunities for researchers interested to work in this area. The research team cautions
future researchers to interpret the results carefully by keeping these limitations in mind.
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23. About the authors
Surajit Bag is Associate Professor of Practice in Supply Chain Management at the
Department of Transport and Supply Chain Management, University of Johannesburg,
South Africa. He earned his second PhD in Engineering Management from the
Postgraduate School of Engineering Management, University of Johannesburg, South
Africa. He earned his first PhD in Logistics and Supply Chain Management from the
School of Business, University of Petroleum and Energy Studies, India. Prior to getting a
PhD, he obtained an MBA from MAKAUT (formerly West Bengal University of
Technology), India. His substantive areas of interest include Industry 4.0, big data, AI,
green supply chain management, circular economy and supply chain sustainability. Surajit’s research has
been published in top journals (e.g. International Journal of Production Economics, Technological
Forecasting and Social Change, Journal of Cleaner Production, Annals of Operations Research,
International Journal of Logistics Management, Information Systems Frontiers, Production Planning and
Control, Journal of Enterprise Information Management,International Journal of Manpower andComputers
and Industrial Engineering). Surajit worked in the industry for 11 years prior to joining academia.
Dr. Pavitra Dhamija is an Assistant Professor at Fortune Institute of International
Business, India and a Senior Research Associate at the University of Johannesburg,
South Africa, where she has previously worked as a Postdoctoral Fellow (College of
Business and Economics). Holding a PhD in Management from PEC University of
Technology, India, she has a rich experience of nearly ten years in academia, research
and industry. Her research interests include human behaviour, emotional labour,
sustainability, quality of work-life, supply chain management, job satisfaction and
Industry 4.0. Dr. Pavitra has in her repertoire several articles that are published in the
journals of repute, namely, Resources, Conservation Recycling, Journal of Cleaner Production,
Production, Planning and Control, Resources Policy, the TQM Journal, the Benchmarking: An
International Journal, South African Journal of Economics, the International Journal of Productivity and
Performance Management and the International Journal of Manpower and Technology in Society.
Additionally, she has served as a resource person for various research-oriented workshops/seminars
and was associated with the institutes of repute in India on various teaching and research assignments
at the Indian Institute of Management Rohtak (IIM-R), Indian Institute of Technology Delhi (IIT-D) and
Maharaja Agrasen University.
Dr. Sunil Luthra is working at Ch. Ranbir Singh State Institute of Engineering and
Technology (CRSSIET), Jhajjar, India. He is Honorary Visiting Professor (Research and
Training) at Centre for Supply Chain Improvement (CSCI), University of Derby, United
Kingdom (UK). He has contributed over 180 research papers in international referred and
nationaljournalsandconferencesatinternationalandnationallevel.Hisnameappearedin
the top 2% global list of researchers prepared by Stanford University. He has an excellent
research track record (over 650 cumulative research impact points; received more than
7,100 citations on Google Scholar; H-index – 46 on Google Scholar). He has received many
awards and honors for the research and teaching. He is working as a guest editor of many reputed journals
such Journal of Cleaner Production, Technology Forecasting and Social Change, Production Planning and
Control, Resources Policy, Resources, Conservation and Recycling, International Journal of Logistics
Research and Applications, Annals of Operations Research and so on. He is on editorial board of many
reputed journals. He has published books with reputed publishers such as CRC Press, Taylor and Francis
Group, LLC and New Age International Publisher (P) Ltd. and so on. His research interests are: Sustainable
Production and Consumption; Green/Sustainable/Circular Supply Chain Management (GSCM/SSCM/
CSCM); Industry 4.0; Circular Economy and Industrial Engineering and so on. AUTHOR ID: 43361407000.
Sunil Luthra is the corresponding author and can be contacted at: sunilluthra1977@gmail.com
Big data
analytics and
SCM resilience
24. Dr Donald Huisingh was born in Spokane, Washington, on March 13, 1937. He grew up
in Washington, North Dakota and Minnesota. He was awarded his BS from the
University of Minnesota in 1961 in Science Specialization, Economics and Horticulture
and his PhD from the University of Wisconsin in 1965 in Biochemistry and Plant
Pathology. He taught and did research at North Carolina State University in Raleigh
North Carolina for 23 years and then moved to Europe where he has taught at Erasmus
University in The Netherlands and in Lund University, in Lund, Sweden, since 1987. He
has been guest lecturer in more than 60 other universities and has worked on helping to
implement Cleaner Production in more than 300 companies and organizations. Presently he teaches part-
time at the following institutions: the University of TN in Knoxville, TN, Lund University in Sweden,
Erasmus University in The Netherlands and at the Central European University in Budapest, Hungary.
He has published more than 350 articles, books, videos and simulations. He is the Founder and Editor-in-
Chief of Elsevier’s Journal of Cleaner Production that is now being published for the 20th year. He is
skilled in interdisciplinary education and holistic approaches to defining and solving society’s problems
so that effective and equitable approaches can be made toward sustainable societies.
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