An Investigative Study to Examine Impact of Digitalisation on
Manufacturing Supply Chains
SATYA SHAH, SIEW CHEN, ELMIRA NAGHI GANJI
Engineering Operations Management
Royal Holloway University of London
UNITED KINGDOM
Abstract: - This study delves into how the COVID-19 pandemic impacted the Malaysian glove industry's supply
chain and triggered the adoption of digitalization to enhance supply chain performance (SCP). Through surveys
and interviews, it was discovered that the pandemic negatively affected business performance but prompted
increased digitalization adoption. Commonly utilized digital solutions include Big Data Analytics (BDA),
Internet of Things (IoT), and Cloud Computing (CC), while less commonly adopted solutions include Augmented
Reality (AR) and Additive Manufacturing (AM). The research underscores a positive link between digitalization
and SCP, stressing the significance of digital capabilities in sustaining supply chain resilience and responsiveness.
However, challenges such as cultural shifts in work practices and investment apprehensions impede digitalization
endeavors. The study provides insights for industry practitioners on harnessing digitalization to alleviate supply
chain disruptions and offers recommendations for effective digitalization strategies.
Key-Words: - Digitalisation, Supply Chain, Manufacturing, Pandemic, Supply Chain Disruption
Received: August 22, 2023. Revised: February 21, 2024. Accepted: March 7, 2024. Published: May 8, 2024.
1 Introduction
The impact of Coronavirus was catastrophic to
human history with profound negative impact on
global economies and industries. The pandemic has
severely disrupted Supply Chain (SC) operations at a
global level that was unprecedented in the recent
history of Supply Chain Management (SCM)
literature. The drastic impact has deprived the
capabilities of global SC of healthcare system with
critical shortages of gloves and other personal
protective equipment (PPE) [1]. Disposable medical
rubber glove is one of the most important safety
products or PPE used by medical frontliners [2] to
protect their hands against any kind of harmful
substance or disease transmission such as Human
Immunodeficiency Virus (HIV) and Hepatitis B
Virus (HBV) [3]. The rubber glove industry is mainly
driven by the growth of global healthcare industry
resulted from factors such as increasing awareness of
hygiene, improving rigorous health regulations,
infections prevention, aging population and arising of
new diseases [4,5,6]. Malaysia glove industry has
started dominating the global supply during the
demand surge triggered by AIDS epidemic back in
1980s [5]. Currently, Malaysia supplies about 65% of
total global medical gloves [5]. Malaysia exported
about 182 billion gloves in 2019 with revenue of
USD4.31 billion [5]. It is forecasted that the global
demand for disposable surgical gloves will increase
at Compound Annual Growth Rate (CAGR) of
7.87% over the period of 2020-2026 [7]. Top Glove
Corporation, Hartalega, Kossan Rubber Industries,
and Supermax Corporation Berhad are among
Malaysia’s major glove producers [8,6,9]. As one of
the largest natural rubber producers worldwide,
Malaysia has plenty of key material resources for
rubber gloves production which has provided a
competitive advantage to local glove manufacturers
[4]. In addition, the support of Malaysia government
through tax incentives such as investment tax
allowance and pioneer status are the driving forces
for the growth of rubber glove industry [4]. Malaysia
rubber glove industry highly depends on foreign
labours due to low labour cost. In addition, the rubber
glove margins are also affected by other factors
including price fluctuation on key raw materials such
as latex and natural gas; packaging material,
weakening US currency and increase of minimum
wage [4]. The SC of glove industry has received less
research attention, let alone from the digitalisation
perspective. Considering the current unprecedented
SC interruption resulted from Covid-19 pandemic
and with active government encouragement to adopt
the disruptive advance technologies, the research
hopes to fill this gap by exploring how the adoption
of digitalisation would impact the Supply Chain
Performance (SCP) from the impact of Covid-19
pandemic in the Malaysia glove industry. To this
research, Covid-19 pandemic will be referred as
pandemic throughout the report.
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2 Background to Research Study
As a mean to curb the chain of Coronavirus, Malaysia
implemented various types of Movement Control
Order (MCO) at various stages of the pandemic from
18th March 2020 and it is still in place at the time of
writing the research. With this implementation, all
economy sectors are mandatory to comply with strict
standard operating procedure (SOP) such as
practicing physical distancing, avoid crowded and
confined places to stop virus transmission. Factories
were ordered to shut down temporary if employees
were infected with Coronavirus [5] at the workplace
and thus abrupted company operations. The
pandemic was a wakeup call to businesses to evaluate
the potential benefits of investing in advance
technologies to transform and step up their
manufacturing infrastructure and capabilities to meet
the current and future market demand. Numerous
articles claimed that the pandemic had motivated the
industries to tap into digitalisation and grasp the
opportunity to leverage technologies to improve SCP
[1,10-16]; farming [17]; education [18], and
insurance [19].
RQ1: To what extend does the pandemic have an
impact on digitalisation adoption in Malaysia glove
industry?
2.1 Digitalisation in Malaysian Manufacturing
In recent years, several major glove
manufacturers have started investing in R&D and
automated high-speed dipping technology in the
glove production lines in recent years [20] to boost
up production capacity and operation efficiency with
the intentions to better manage cost and minimise
reliance on foreign workforce [4]. One of the major
glove producers, Top Glove has significantly
succeeded in reducing the number of workers per
million gloves output to less than two workers from
five to ten workers a decade ago. This has proven that
automation in rubber glove production lines can
alleviate the requirement for foreign labour [21]. Top
Glove also has been aiming to intensify the adoption
of AI and autonomous robots as plant wide
digitalisation journey by penetrating all aspect of the
operations to boost production efficiency and quality
control, improve workplace safety and security [22].
Apart from Top Glove, other key players in the glove
industry namely Hartalega has also embarked on
digitalisation [20]. At the same time, studies to
escalate their digitalisation journey by adopting
robotics in its manufacturing processes in 2020 [20].
On the upstream of rubber glove industry, the rubber
agriculture sector has conducted ‘on-going efforts on
the trial of Automated Rubber Tapping System
(ARTS), which mechanises timed tapping, latex
collection and bulking to increase yield, with data
crunching of gram per tree per tapping (GTT)’ [20].
The Plantation Industries and Commodities Ministry
has suggested that Global Positioning System (GPS)
to be adopted in the farming section to ease the
execution of planting process of annual crops such as
rubber trees. From aforementioned information
extracted from industrial articles such as Rubber
Journal Asia, Channel News Asia (CNA) and Nikkei
Asia, clearly, various glove producers have embarked
on different pace of their automation adoptions. Top
Glove, Hartalega and Kossan have implemented
automation into their manufacturing processes over
the past few years and are now focusing on intelligent
technologies to reap the benefits of digitalisation.
While other glove producers are at the beginning
stage to automate their production lines. Nonetheless,
it is worth noted that major glove producers are
largely focusing on manufacturing processes as the
starting point on new technology adoption.
2.2 Supply Chain Disruption in Manufacturing
Studies define SC disruption as incidents that
interrupt the movement of goods from upstream to
downstream of the SC chain [23]. With the outbreak
of the pandemic, businesses in various industries
across the globe were hugely affected. To curb the
spread of the disease, countries adopted extreme
measures by restricting movement through closing
borders and lockdown. This drastic action with
immense geographical impact has negatively
influenced worldwide economic activities and global
SC operations [12, 24,25]. According to The
Organisation for Economic Co-operation and
Development (OECD), countries around the world
has shown negative year-on-year real GDP growth
[26] as shown in figure 1. Global SC has been
unprecedentedly broken with extremely limited
capabilities to function in which emergency supplies
of key materials or products from overseas fail to
meet and match the surging demand. Subsequently
global economy experienced fluctuation in stock
prices and declined in business earnings [12, 24]
resulted from factories shutdown and shipments
delayed. A 25% reduction in global air traffic as of
October 2020 compared to a year ago was reported
while 40% airplanes were not operating resulting
from travel ban [27]. The glove industry and its
supporting industries were experiencing the ripple
effects from the pandemic with sudden spike of
demand on key raw materials; i.e.: chemicals, latex
and pigment; machinery; packaging and logistics
services resulted from measures putting in to limit the
spread of Covid-19.
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Figure 1: Percentile Headline Inflation (2023,
2024) and Real GDP Growth [26]
Hence, paper explores what digital technologies
were adopted within the SC network to help the
partners to connect and response to external market
uncertainty with fast and accurate information to
maintain and strengthen SCP while mitigating any
possible supply risks.
RQ2: What digitisation solutions were adopted
within the Supply Chain structures of the Malaysia
glove industry?
RQ3: How will digitalisation impact Supply
Chain Performance of the Malaysia glove industry?
The research objectives cover three main aspects:
firstly, to identify to what extend the impact of
pandemic has on digitalisation adoption in Malaysia
glove industry. Second, to identify what digitisation
solutions were adopted within the SC structures of
Malaysian glove industry, and to identify how
digitalisation impacts SCP of Malaysian sector.
3 Literature Studies
The research explores the impact of digitalisation on
supply chain in the glove industry through the impact
of Covid-19 Pandemic in Malaysia. The literature
review shall cover areas concerning to pandemic and
digitalisation, SC and digital solutions as well as
digitalisation impact on SCP as set out by the
research questions below and area on digitalisation
challenges also be reviewed.
3.1 Pandemic and Digitalisation
The impact of Coronavirus was catastrophic to
human history with unexpected speedy
infectiousness rate that has spread across the globe
coupled with high fatality rate resulted a profound
negative impact on global economies and industries.
The severe disruption in SC operations triggered by
the impact of Coronavirus was unprecedented in the
recent history of SCM literature. Covid-19 has
unveiled the importance of digitalisation and many
countries made substantial progress in deploying new
technologies that are vital to manage a larger aspect
of daily activities such as education, healthcare,
distance connectivity and e-commerce [16]. Scholars
and industry experts had been intensively debating
that adopting innovative technologies could help the
industries to recover from the disruption and to
sustain business continuity [11,28,19,29,16]. The
rapid growth of digital transformation has amplified
the demand on advance technologies, for instance,
Cloud Computing, Artificial Intelligence, Internet of
Things and Big Data Analytics thus, has elevated
digital capabilities to enable better management on
daily issues such as physical interactions, business
operations and processes [16] which include working
from home to ensure business continuity. With
remote working, digital technologies enable data to
be stored in the cloud with customised secured access
for employees and employers [30]. These
technologies enable seamless virtual coordination in
flexible and mobile virtual office environment to
drive business activities. The state-of-the-art
technologies enable virtual exhibitions, business
conferences and trainings to be conducted virtually
over the internet platforms that allows global access
and interaction [30]. With the speedy expansion in
the digital space where industries steer to maximise
operations efficiency and competency to stay ahead
of market competition, technology innovations has
fast-tracked from five years to 18 months [31].
3.2 Digitalisation
The development of World Wide Web has
connected global population and greatly changed the
business environment [32,33]. With vigorous
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innovative technology development and increased
usage of digital technologies, new business models
have been dramatically created and redefined. This
evolvement has prompted the change in consumer
behaviours and social trends [34] that resulted a shift
of business competitive position in many industries
[32]. Furthermore, this has driven the path for
digitalisation whereby businesses and consumers are
creating and utilizing vast amount of digital
information online without restriction of time and
borders [32]. As of October 2020, 59% of the world
population were active internet users which
comprised of almost 4.66 billion people where 89%
were active social media users [35]. Digitalisation or
digital transformation is interpreted as adoption of
smart technologies BD, CC, AI, robotics, IoT and 3D
printing [36] to thoroughly boost business
performance with lower cost, increased precision and
speedy response that strengthen efficiency [37].
Digitalisation also refers to the process of
transforming organisation operations and processes
by using digital technologies in terms of digital
platforms, infrastructures, artifacts, business and
management applications [38,28,39] identify that
technologies that commonly adopted by leading
companies in the era of Industry 4.0 are autonomous
robots, simulation, system integration, Industrial IoT,
cyber security, CC, Additive Manufacturing (AM),
Augmented Reality (AR), BDA shown in Table 2.
Table 2: Enabling Technologies [40]
Through the advancement of technological
innovations, digitalisation solutions have becoming
increasingly intelligent and with smarter features and
options. Studies find that technology has changed the
way how information is communicated, i.e.: from
paper to digital [41-43]. Furthermore, digitalisation
has reshaped economy, society and industry with its
ubiquitous nature in influencing and changing the
ways how modern society communicates and
interacts in areas such as social, economic and culture
[44,10]. With the evolvement of different stages of
industrial revolution since 18th century till today as
depicted in Table 3, many developed nations have
benefited from deploying advance and innovative
technologies at various stages of the revolutions.
These efforts have boosted and changed the
competitiveness of the industries landscape and
contributed to economic growth.
Table 3: Stages of Industrial Revolution [45]
Table 4: Deployment of Advance Technology in
Different Countries [45]
Key technologies such as Cyber Physical System
(CPS), IoT, BDA, AM, CC and intelligent robots
were commonly adopted [45] in countries such as
Germany, China, USA and South Korea as tabulated
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in Table 4. As presented in Figure 1, countries like
USA, Germany, Japan, France, India and China are
developing cutting-edge technologies in different
sectors to attain Smart Manufacturing [45].
Figure 1: Countries in the Fourth Industrial
Revolution [45]
3.3 Digitalisation Outlook in Malaysia
Malaysia is still at the beginning stage of
digitalisation with the implementation of several
digital technologies namely, automation, IoT,
robotics, and BDA according to research [45].
Statista discloses the number of internet users in
Malaysia has been on the rise from 21.42 million in
2015 to 29.01 million in 2019 with 35% increase and
it was forecasted to increase another 15% from 2020
to 2025. Based on total population of 32.58 million
recorded in 2019, about 89% of Malaysia population
are accessing to internet [35]. Within Malaysia, the
level of low to high technology adoption was 76.3%
in which high technology consists of 43.2% [46].
According to Global Industrial Competitive
Performance Index 2020, a benchmark of countries’
ability to produce and export manufactured goods
competitively; Malaysia was ranked at 23rd among
152 countries [46]. The top three countries were
Germany, China and Republic of Korea. Among the
eleven Southeast Asia countries, Malaysia was
ranked 2nd with a massive gap behind Singapore.
Thailand came in as 3rd and followed by Vietnam
and Indonesia [46]. The Malaysia government has
acknowledged the benefits of intelligent technology
and has taken several strategic initiatives to promote
the adoption of advance technologies through
providing allowances, training and education [45].
First initiative, The Economic Transformation
Programme (ETP) was launched in 2010 with the
vision to transform Malaysia into a high-income
nation in 2020 with innovative technologies in the
economy and industry sectors [47,40,48]. Second
initiative, Transformasi Nasional 50 – or TN 50 was
launched in January 2017 with the purpose to develop
Malaysia through technologies [49]. Third initiative,
The Malaysia National Policy on I4.0 was launched
in October 2018 with the mission to steer toward
smart manufacturing by adopting I4.0 technology
[40] to strengthen and streamline the manufacturing
sector and other related sectors. Four main elements
were identified under this initiative, i.e.:
interoperability, digital twin, modularity and
flexibility [40]. One of the primaries focuses of the
third initiative is to adopt Information and
Communication Technology (ICT) associated with
innovation and automation related to the segment of
SCM, operations and management systems to
improve efficiency and business performance [45].
At present, Malaysia businesses in the manufacturing
sector have taken efforts to adopt smart technologies
to heighten their competency to be at par with other
developing nations [45]. The manufacturing is
crucial sector that contributed average 39% to the
nation’s GDP from 2009 to 2019 [35]. Forbes
reported that Malaysia businesses have begun the
deployment of digital technologies from sector such
as manufacturing, healthcare, electrical and
electronics to e-commerce to improve and support
the country’s recovery from the pandemic [50].
3.4 Supply Chain and Digital Technologies
Researchers argue that the primary objective of
SC is to maximise customer satisfaction [51] by
providing the right product to the right customer in
the right quantity at the right time at the right place
for the right price [52,44,53]. The actors within the
SC are interrelated and aligned their operations of
different natures to improve SC cost, lead time,
quality and flexibility in the process of transforming
raw materials until the finished product reaches end
customer [53,54]. This SC objective will be hindered
in the event of SC disruptions. The pandemic crisis
resulted loss of revenue, shortages of essential
materials, closure of factories, escalated
transportation cost, and alternatives of products [23].
Likewise, SC structures that are traditional, inflexible
and difficult to access to required data with arm’s
length business relationships [44] also contributed to
operations disruption and affected the capability to
achieve the objectives. Intelligent technological
approach aids to redesign, reconfigure and foster SC
systems integration to sense, communicate, react,
coordinate and manage the process flows along the
SC networks according to market dynamic via
orchestrating and synchronising digital devices,
business applications and networks [44]. The studies
further articulate that digitalised SC structure resulted
in automation, agility and virtual management that
minimised the impact of supply chain interruption
[44]. In addition, deployment of smart technologies
enables streamlining processes and operations,
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improvement in quality, time, cost and flexibility
[55]. Moreover, [56] evocate that embracing
innovative technologies will reap benefits in terms of
‘speed, flexibility, granularity, accuracy and
efficiency’. Also, [11] concluded that digitalisation
will bring business survival, shorten recovery process
and enhance business sustainability. According to
research, generally, CC, IoT and BDA have received
more attention and were widely studied especially
during the period of 2011 to 2017 while AM gained
more popularity in 2016 comparing to other
technologies such as blockchain, AI, drones,
autonomous vehicles, AR, or robotics. However, for
the purpose of current research, the nine technologies
discussed below shall be used to answer research
question RQ2 as most of them were listed under
Malaysia National Policy On Industry 4.0 and these
technologies are equally important to drive
improvement in SCP.
Big data analytics (BDA) - BDA is an innovative
tool that enables analysis to be performed speedily
and effectively on data that are massive, complicated
and high velocity [51,52]. Information generated
from analytical tool could be used to forecast events
such as market price, consumer behaviour, voting
patterns as well as to influence perceptions [57].
Internet of Things (IoT) - IoT is physical devices
embedded with sensors and software that are
interconnected physically and virtually to collect,
process and share information in the internet
platforms [52,55,58].
Cloud Computing (CC) - CC enables a series of
networks, servers, databases, storage devices, and
software applications via internet connectivity
[27,59] in which massive data could be universally
obtained in real time from shared pools of
customisable resources [51,60].
Autonomous robots - Autonomous robots are
intelligent products of latest robotic technology that
are designed and programmed to carry out tasks by
themselves with minimal to no human interaction or
interference [51,61].
Additive Manufacturing (AM) - It is a technology
that “adjoins materials together by knitting or
solidifying it using computer controls” [51] directly
and instantly from digital data to form three-
dimensional final products in variety of desired
shapes or structures [62,54,51,59].
Augmented Reality (AR) - AR is a technology that
enables human machine interface [63] in which
virtual images or information generated by computer
are overlaid on real-world environment in real-time
thus created a virtual world with intensified reality
and user experience [63] that aids timely decision
making [64].
Artificial Intelligence (AI) and Machine Learning
(ML) - AI is a technology that connects with several
innovative devices to perform a task through
thinking, sensing, recognizing and collaborating with
humans [33]. Machine Learning (ML) is part of AI.
It is a technology that enables machines access to
data, learn and identify pattern for the machine to
automatically make decision [33].
Cyber Physical System (CPS) - A new group of
system that integrates physical with digital system
with communication capabilities between them [65].
Table 5: Summary of Key Technologies from Recent
Research
Research on these technologies is summarised
briefly in table 5 as the key digital technologies that
received most attention from recent research. It also
reflects blockchain, virtual reality and AR,
autonomous robots and ML have beginning to
receive increasing emphasis. The evolution of
integrating information and communication
technology (ICT) in business operations and
processes [60] which not only focusing on machines
and production but also including activities within
the value chain of the entire SC [55]. As
demonstrated in Figure 2, apart from cloud
blockchain, autonomous robots and smart machines,
business applications such as Customer Relationship
Management, Supplier Performance Management
and Data Analytics as well as intelligence devices,
i.e.: sensors and hardware are enhancing the primary
and supporting activities of the value chain.
Researchers articulate that digital technologies
contribute to system and process-based cogitation in
which real time information generated at each value
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chain is readily made available to support the
dynamic information integration process within and
beyond the SC boundaries [39].
Figure 2: The Tools of Industry 4.0 in the Corporate
Value Chain [39]
Studies find that modernized SCs are moving toward
automated systems where information and data are
integrated in the cloud while operations are carried
out in virtual place [60,55,30]. This has transformed
the value chain processes and operations and
customer experience.
3.5 Digital Solutions and Supply Chain Structures
The process of SC focuses on five areas of the
supply chain which include plan, source, make,
deliver, and return [53,44]. Thus, within the scope of
current research, it adopts a more comprehensive
approach that involve five key supply chain
structures, i.e.: Procurement, Manufacturing,
Distribution, Supplier and Customer.
Digital solutions in the procurement area are
moving toward automation with broader intelligent
technologies where routine and repetitive
transactional processes are automated coupled with
AI, IoT and BDA [38,66]. Procurement software
enables digitally exchange of end-to-end information
in a transparent and real-time manner through
adopting cloud technology, BDA, and platforms with
intelligent execution capabilities [41,43] to respond
continuously and vigorously toward ever-changing
demand and supply restrictions of the external
environment [56]. Intelligent procurement analytics
enables business applications such as ERP and
contract management systems (CMS) to collect,
analyse and process information for prompt purchase
decisions [41,66] while managing supplier networks
in a simplify [67], transparent and traceable SC
networks [38]. With the combination of highly
advance technologies, machines, objects and human
allows communication and interaction (Brunetti et
al., 2020) electronically to control and organize
machines and production processes independently
and flexibly on their own in real-time to optimize
operation [38,60,58,55,68]. The intense integration
and interconnectivity help to minimise the
requirement of human intervention, reduces
monotonous tasks, improves accuracy and optimise
yield that drives operations efficiency and
responsiveness, ultimately this leads to improvement
in developing competitive strategies that creates
opportunities and value in manufacturing operations
[45] and subsequently benefit organisation bottom
line [39, 31]. Study finds that nine technologies, i.e.:
IoT, Simulation, Horizontal and Vertical Integration,
Cyber Security, CC, AM, AR, BDA and
Autonomous Robot [45] that steer toward smart
manufacturing are critical to develop intelligent
manufacturing processes to achieve the smart factory
environment [39].
AI, IoT, Drones, Unmanned Aerial Vehicles
(UAVs), Cloud Platforms and Blockchain play a
remarkable role in shaping the way physical goods
are handled and delivered from one end to another
along the SC partners [69] thus drastically uplifted
the distribution infrastructure [39, 70]. Leveraging
modern technologies significantly optimise
distribution route planning, optimise truck
utilization, reduce carbon footprint and costs [70,71]
thus increase efficiency, flexibility in the distribution
process [56]. Automatic guided vehicles (AGV),
mobile collaborative robots, mobile robotic storage
and retrieval systems, RFID, bar code scanners,
heads-up displays and other vision technologies
supervised by advanced control systems optimize
storage efficiency & material handling systems [69].
Supplier is a crucial SC partner that has direct
implication toward organisation competitiveness. It
is vital to establish effective relationship to mitigate
supply risk for sustainable performance. Creating an
eco-system which encompasses computer, BDA,
cloud technology, telecommunication networks,
software such ERP and EDI is essential for supplier
collaboration initiatives [42] and holistic view on
supplier performance [72]. Integrating supplier
management software into one digital platform could
seamlessly linked with related value chain activities
[42] and enables SC partners to achieve higher
integration and maximize information sharing,
transparency, visibility and consequently facilitate
communication effectiveness within the SC [42].
Transparency and traceability within the SC
ecosystem will strengthen buyer-supplier
relationships and level of trust [38]. Organisations are
increasingly putting efforts to create lasting and
effective customer relationship [73] with highly
competitive and uncertain market environment.
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Table 6: Digital Solutions Adopted within the Supply
Chain Structures
AI, AR, Blockchain, drones, IoT, ML and AM have
been identified as some of the digital technologies
that will reshape the business and customer
relationship [33]. Accurate demand forecast [56],
data-driven decision-making and responsiveness to
disruptions through employing advance technologies
improve SC efficiency and hence lead to improved
customer satisfaction [60]. Gartner forecasts that in
2022, 70% of customer interactions will associate
with innovative technologies such as chatbots and
ML application comparing to 15% in 2018 [74].
Table 6 illustrates examples of digital solutions such
as AI, DAB, IoT, 5G, GPS and ML that have been
adopted within SC structures to improve SC
operations.
3.6 Digitalisation and Supply Chain Performance
The ultimate target of digitalisation is to improve
Supply Chain Performance (SCP) [44]. Digitalisation
reinforces SCP by maximising connection,
integration and creation of knowledge [44],
accelerate SC innovations, drive down production
cost and optimise business revenues especially in
uncertain global condition [63]. 93% of corporate
leaders agreed that leveraging digitalisation is vital in
achieving corporate objectives and maintaining
competitive edge [15] by realign management
strategies and critical resources with real time,
reliable and quality information to drive organisation
toward performance improvement [75,38,60]. The
current research shall examine the SCP through the
lens of transparency, communication, collaboration,
flexibility and responsiveness which were adopted
from ‘Improved activities and business operations in
an integrated supply chain ecosystem through the
deployment of Digitalisation in Supply Chain (DSC)’
[56]. These five improvements are improvised into
three main perspectives which are integration,
visibility and responsiveness.
An integrated SC ecosystem includes external
stakeholders such as suppliers, customers and
internal value chain such as manufacturing,
procurement, logistics, distribution and marketing
[75,44,60]. Study finds that digitalisation simplifies
SC ecosystems and lead to improved productivity
and amplified integration between internal and
external SC operations [38] through information
integration, cooperation and sharing of resources and
organizational relationship connection [44].
Meanwhile, [28] identifies SCI as one of the
measurements for production recovery strategies in
their research. Studies find that integrated value chain
gather, analyse and share information from various
sources within the network and resulted complete
awareness and coordination of all levels of the value
chain with real-time feedback [75,68]. This leads to
increase visibility which ultimately increase
flexibility and resilient in SC [76]. Subsequently, it
drives greater trust and fosters deeper relationships
between the SC partners [60]. It is evident that
heighten integration of value chain is crucial in SCM
to maintain competitiveness in the dynamic business
world [51]. In essence, SCI is considered as an
important aspect in SCP with the positive impact
through digitalisation [77].
In general, SCV is defined as “traceability and
transparency of SC process” [78]. Research finds
that 79% of large organisations acknowledged their
top concern on SCV [79]. Absence of visibility will
result in inadequacy of knowledge, diminished
capability to access or provide relevant and timely
information for better and accurate decisions [80],
inability to manage, disruption and doubts which lead
to greater SC risk [78]. Maximising the utilization of
intelligent technologies will benefit SCM in accurate
forecasting and planning from the visibility of
materials and products flows [60], ability to obtain
updated information related to order fulfilment,
inventory location, cost and visibility of end to end
operations thus, greatly increase traceability and
visibility [80] and reduces bullwhip effects [58],
augment company’s flexibility to respond and adapt
in dynamic market situation [81], influence the
effectiveness and efficiency of the SC overall
performance [78,79,44]. With escalated SCV, the
capability to assess potential risk also could be
expanded and timely deploy appropriate response
strategies to suppress SC disruptions [80].
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With globalisation, cost driven SCs have become
long, complex and dynamic multi-national supply
networks have increase SC risks [82] and
increasingly challenging and costlier to control and
manage effectively in complex and uncertain [52]
global conditions with mismatch of supply and
demand [52]. Lacking responsiveness is associated
with SC risks. Organisations with higher SC risk has
lesser capability to react to disruption results in
negative consequences on the businesses [82,83].
Global SCs are under huge pressure to overcome
challenges such as longer response time, conflicting
priorities, lack of visibility and agility with the
outbreak of pandemic. Studies [44] articulate that
advance analytics with quality information enables
speedier responses such as adjusting competitive
strategies to stay ahead of competitors, detecting
changing pattern in demand and supply,
technological changes as well as increasing elasticity
in scaling operation capacities. This results in
magnifying SC robustness [84,60] which helps to
mitigate SC risks and bottlenecks [84] for long term
competitiveness and sustainability in current
extremely uncertain and high-risk market condition
[60]. Researchers [55] views that the advance
technologies result in optimise capabilities, increase
analytical usages and simplify SC activities hence,
digitalisation in SCM is mandatory to ensure SCR.
4. Research Framework
Studies have vigorous debated benefits of
digitalisation toward SCP; therefore, this research
intends to identify how will digitalisation impact SCP
in Malaysia glove industry as per conceptual
framework illustrated in Figure 3.
Figure 3: Conceptual Framework [85]
Under this framework, the researcher has
incorporated the elements extracted from
conventional SC [51] and four levers of SC [68] by
tabulating five key SC structures, i.e.: supplier,
procurement, manufacturing, distribution and
customer. The framework explains that with the
adoption of various digital technologies under the
influence of pandemic, the entire SCP will be
elevated through improved visibility, integration and
responsiveness in which the connections among
visibility, integration and responsiveness are
interrelated within an integrated SC ecosystem.
Researchers find that businesses will become
complacent and eventually will be replaced by those
who embraces innovative technologies to enhance
their business performance [33]. Nonetheless,
challenges in implementing digital transformation
should be well noted and addressed to ensure greater
success. The research is unique as it throws lights to
the current states of digitalisation in the SC of
Malaysia glove industry during the pandemic. The
significance of the research is twofold: firstly, it
contributes to the developing knowledge of
digitalisation in the SC of Malaysia glove industry.
Secondly, it advances the understanding and
knowledge on the impact of digitalisation on SCP.
4.1 Research Philosophy
This research adopted positivism philosophy
based on ontological assumptions as it was orientated
on the theory of the existence of “nature of social
world” [86] and “nature of reality or being” [87] of a
phenomenon that existed independently from the
researcher [86-88]. This research anchored in
ontological assumptions has associated with
quantitative method with deductive approach to test
the theory. The research employed both online survey
and interview as primary data collection methods as
it was cross-sectional research. The data gathered
from this strategy explained the research questions.
This research strategy enabled standardized data to
be obtained for the ease of comparison and analysis
from administering survey questionnaire [87]. A set
of self–administered online questionnaire was
prepared for primary data collection through survey
and interview. Online survey from cluster sampling
of target respondents from Malaysian Rubber Glove
Manufacturers Association (Margma) were
conducted. The main research questionnaire was
categorized into two sections that make up of closed-
ended and open-ended questions. Apart from
structured questionnaire, face to face structured
interviews with the management of a manufacturing
company who is pioneer in the glove industry were
conducted.
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4.2 Constructs Development
Under the deductive approach, the researchers
adopted hypotheses to be tested which enables
explanation of the findings between theory and social
research that are subjected to detailed scrutiny [86].
Five main constructs with respective measurements
focusing on major aspects of digitalisation in the
condition of current pandemic and its impact on SCP
were developed and illustrated in in construct tables
for each of the five constructs.
The first construct consisted of five questions
that examined the relation between pandemic and
business performance. There were five
measurements for this construct, i.e.: sales revenue,
production output, and customer satisfaction,
delivery commitment to customer and supplier
delivery performance.
No
Constructs
Measurements
Multiple Choice Answer
1
Covid-19
pandemic
and business
performance
Sales revenue
Range of Decrease 50% to
Increased
Production output
Range of Decrease 50% to
Increased
Customer
satisfaction
Range of Decrease 50% to
Increased
Customer delivery
commitment
Range of Delay 6 month to
Improved
Supplier delivery
performance
Range of Delay 6 month to
Improved
Respondents were able to select predefined multiple-
choice answers ranging from the category of
‘decrease more than 50%’ to ‘increase’ for questions
related to sales revenue, production output and
customer satisfaction. Questions related delivery
performance were given the options of ‘delay more
than 6 months’ to ‘improved’. This construct
measured the glove industry’s business performance
during the pandemic.
The second construct consisted of four
statements that explored the relation between
pandemic and adoption of digitalisation. The first
three statements were associated to three close-ended
questions that measured company investment,
implementation pace and company performance.
Respondents were able to select options of ‘strongly
disagree’, ‘disagree’, ‘neutral’, ‘agree’, and ‘strongly
agree’ with the use of five-point Likert-style rating
scale. The Likert scale was preferred as it measured
the strength and magnitude of the respondents’
replies that could be statistically evaluated [86]. The
fourth statement was created with multiple choice
answers that enabled respondents to select more than
one answer and to include addition comments despite
the suggested answers. The purpose of this statement
was to examine impact of digitalisation on company
performance in terms of sales, production output,
customer satisfaction, decision making, and real-time
information and exploit new market. The participants
answered the questions based on their general
perspectives. With the responses obtained from this
section, the researcher was able to analyse the
influence of pandemic on digital transformation.
No
Constructs
Measurements
Multiple Choice Answer
2
Covid-19
pandemic
and
digitalisation
adoption
Investment
Strongly disagree to Strongly
Agree (5 Points Likert Scale)
Implementation
pace
Strongly disagree to Strongly
Agree (5 Points Likert Scale)
Company
performance
Strongly disagree to Strongly
Agree (5 Points Likert Scale)
Area of
improvement
Sales, Production output,
Customer satisfaction,
Decision-making, Real-time
information, Exploit new
market, Others
The third construct explored digital solutions that
have been adopted and the intensity of the adoption
level within the SC structures. In research conducted
by [28], the researchers measured company’s level of
digitalisation through the adoption of technologies,
i.e.: Big Data, AI, Mobile, CC, IoT, Social and
Platform development with five-point Likert scale.
Current research adopted nine measures, i.e.: BDA,
IoT, CC, AM, Autonomous Robots, AI, AR, ML and
CPS to examine this construct as most of them were
listed under Malaysia National Policy On Industry
4.0 and these technologies are equally important to
drive improvement in SCP. Mobile and social were
not examined individually as these technologies were
embedded into the nine digital technologies
identified above while platform development was not
included as the research focused on the aspect of SC
operation of the manufacturing industry. This study
adopted the question and expanded it into two
questions that measured digital technologies or
related digital management platforms currently
adopted within SC structures and the adoption level
at each SC structures which were associated to
research question RQ2.
No
Constructs
Measurements
Multiple Choice Answer
3 (i)
Digital solutions
within supply
chain structure
Digital solutions
Big Data Analytics, IoT,
Clouds, Autonomous
Robots, Additive
Manufacturing, AI, AR,
ML, CPS, Not implemented
3
(ii)
Level of
digitalisation
adoption
Procurement
Very low to very high (5
Points Likert Scale)
Manufacturing
Very low to very high (5
Points Likert Scale)
Distribution
Very low to very high (5
Points Likert Scale)
Supplier
Coordination
and Relationship
Management
Very low to very high (5
Points Likert Scale)
Customer
Coordination
and Relationship
Management
Very low to very high (5
Points Likert Scale)
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The fourth construct comprised of four sub-
sections with fourteen questions to examine the
relationships between digitalisation and SCP. In this
respect, SCP constituted of elements i.e.: SCI, SCV
and SCR. The responses to this section assisted the
researcher to identify the impact of digitalisation on
SCP. A fourth element, relationship with customer
and supplier which was termed as CSR was included
to examine the performance under the impact on
digitalisation as it has direct implication toward SC
operations and SCP. The measurements of this
section were ranked with five-point Likert scale from
‘Strongly disagree’ to ‘Strongly Agree’.
No
Constructs
Measurements
Multiple Choice Answer
4 (i)
Digitalisation
and customer
and supplier
relationships
Sales revenues
Strongly disagree to
Strongly Agree (5 Points
Likert Scale)
Customer
relationships,
forecast and
demand planning
Strongly disagree to
Strongly Agree (5 Points
Likert Scale)
Customer
satisfaction
Strongly disagree to
Strongly Agree (5 Points
Likert Scale)
Supplier
relationship with
higher degree of
trust and
partnership
Strongly disagree to
Strongly Agree (5 Points
Likert Scale)
Product quality and
delivery
commitment
Strongly disagree to
Strongly Agree (5 Points
Likert Scale)
4
(ii)
Digitalisation
and supply
chain
integration
Communication
and information
sharing
Strongly disagree to
Strongly Agree (5 Points
Likert Scale)
Cooperation and
collaboration
Strongly disagree to
Strongly Agree (5 Points
Likert Scale)
Planning and
forecast accuracy
Strongly disagree to
Strongly Agree (5 Points
Likert Scale)
4
(iii)
Digitalisation
and supply
chain visibility
Quality and
usefulness of
information
Strongly disagree to
Strongly Agree (5 Points
Likert Scale)
Availability and
accessibility to
real-time
information
Strongly disagree to
Strongly Agree (5 Points
Likert Scale)
Clarity and
visibility of
upstream and
downstream
operations
Strongly disagree to
Strongly Agree (5 Points
Likert Scale)
4
(iv)
Digitalisation
and supply
chain
responsiveness
Speedy and quality
decision making
Strongly disagree to
Strongly Agree (5 Points
Likert Scale)
Core competency
Strongly disagree to
Strongly Agree (5 Points
Likert Scale)
Deploy appropriate
strategies and react
timely
Strongly disagree to
Strongly Agree (5 Points
Likert Scale)
This study examined SCI with three measures,
i.e.: communication and information sharing [44,
89]; cooperation and collaboration [44,60] as well as
planning and forecast accuracy. The responses shed
lights on the state of SCI with the adoption of digital
solutions. Attributes such as quality and usefulness of
information, availability and accessibility to real-
time information [81,38,90], quality and usefulness
of information [90] as well as clarity and visibility of
upstream and downstream operations [38] were used
to examine SCV of the SC operations because of
digitalisation. The study adopted speedy and quality
decision making [38], core competency, deploy
appropriate strategies and react timely [91] to
measure SCR through the impact of digitalisation.
Measurements such as raw materials quality
standard, reject rate and service level [68] were
adopted and were re-categorized as product quality
and delivery commitment. A total of five statements
were used to measure CSR which included sales
revenues, customer relationships with forecast and
demand planning [92], customer satisfaction, trust
and partnership [38], supplier product quality and
delivery commitment [92,68] because of higher
degree of trust and partnership [92].
In the fifth construct, the researcher took the
opportunity to seek the perspectives of different
respondents concerning challenges in implementing
digitalisation during the pandemic and potential for
digitalisation after the pandemic. Multiple choice
answers were developed in the effort to examine the
challenges encountered by the respondents which
included capital allocation, ROI, awareness, job
opportunity, work culture and threats. The question
allowed respondents to express their general
viewpoints about digitalisation challenges to enrich
the dimension of data collected. The researcher also
interested to find out the industry perception on
digitalisation adoption after the pandemic with a
question as such as ‘yes’, ‘no’ or ‘maybe’.
No
Constructs
Measureme
nts
Multiple Choice Answer
5
(i)
Barriers to
digitalisation
adoption
during
Covid-19
pandemic
Challenges
Minimise capital investment
allocation due to market uncertainty
Unknown Return on Investment
(ROI) in volatile economy
Lacking awareness to embrace
advance technologies
Advance technologies reduce job
opportunities
Change of work culture
Cybersecurity threats
Others
5
(ii)
Digitalisation
adoption in
post Covid-
19 pandemic
Adoption of
digitalisation
Yes, No, Maybe
Adoption
timeframe
Open-ended Questions
Expectation
of ROI
Open-ended Questions
Most critical
area for
digitalisation
Open-ended Questions
Lastly, the researcher opted for open-ended questions
on three remaining questions to explore the
respondents’ expectation toward digitalisation after
the pandemic. Aspects such as adoption timeframe,
ROI expectation and critical area for digital
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Volume 6, 2024
technologies deployment were examined. The
additional information collected at this section have
not influenced the research objectives but were useful
information paving the path for potential research in
the future.
4.3 Data Collection
The scope of the research population covered the
supporting industries within the SC of glove industry
in Malaysia including raw materials, machinery,
equipment, latex, chemicals and others. With the
diverse nature of most businesses and their markets
coverage that spread across various industries, both
local and abroad, cluster sampling method was
identified to be more appropriate to narrow down the
population of the targeted industry for this research.
The Malaysian Rubber Glove Manufacturers
Association (Margma) is an official representative
and official voice of glove industry in Malaysia since
1989 with members comprising Malaysian rubber
glove manufacturers, associated suppliers and
supporting organizations [93]. Companies registered
under Margma were deemed to be most appropriate
as the targeted respondents for this research. As the
research focused on companies operating in
Malaysia, the total number of companies as published
at Margma website provided a sample size of 185.
Out of the total number, glove manufacturers
comprised of 31% with 58 members while the
supporting industries consist of 127 members. The
next largest groups were chemicals and industry
equipment and machinery sectors which represented
28% and 23% of the targeted participants.
Table 7: Profile of Targeted Participants
Balance 18% constituted from other sectors, i.e.:
latex, service and maintenance, packaging and
lubricant. With this cluster sampling approach, the
researcher managed to eliminate sampling bias in
which [86] articulates that sampling bias represents a
distortion in the sampling size selection method that
resulted some members of the population have no
opportunity to be selected. Primary or raw data were
gathered through internet-mediated survey and
structure interview. Interview was targeted at the
management of a pioneer company in the industrial
equipment and machinery sector who has been the
key supporter to glove industry for over 30 years.
Secondary data were collected from association
publications, newsletters, academic journals,
whitepapers as well as data available from related
organisations’ website such as Margma and other
websites pertaining to the aspect of digitisation.
4.4 Data Analysis Techniques
Responses collected from online survey and
interviews were recorded while responses to open-
ended questions were transcribed and coded to enable
these responses to be analysed similarly to close-
ended questions [86]. Pattern of similarities were
examined for all responses from open-ended
questions. The data were measured using computer
software, i.e.: IBM SPSS. The calculated results were
analysed and further interpreted to explain the
relationship of the examined variables of the
research. Variables were attributes [86] such as
events, time periods, objects, process that the
research tried to measure. In this research, the
variables were impact of pandemic, digitalisation,
and SCP; i.e.: SCI, SCV, SCR and CSR, barriers to
digitalisation. To test the reliability and validity of
the study, Cronbach’s alpha values was used to
examine internal reliability and consistency of the
scale for the data collected using five-point Likert
scale. Reliability test provides an indication of the
uniformity of the scale in measuring the variables of
respective constructs that reflects how well the
survey result relates to the true population [38]. In
other words, it examines the consistency and
reliability of data collection and analysis procedures
which leads to consistent findings [87]. Further to
that, descriptive analyses were conducted to examine
the mean and standard deviation of the constructs.
Mean was the average score of the responses while
standard deviation measured how far the scores of
responses were located on left and right side of mean
to support the consistency of the responses [38].
More analysis techniques such as correlation and
regression analyses were conducted based on the
necessity of the survey outcome.
5 Data Analysis and Findings
The table 8 exhibits the profile of 65 respondents
which constituted of 61 online survey and 4 face to
face structured interviews. Three follow up emails
were sent. The total final response rate was 35% from
the 185-sample size. According to studies, a sample
size of 30 is the minimum number accepted for
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statistical analyses within the overall sample [87,94].
Thus, the response rate for the current research was
considered acceptable. The largest respondents came
from industrial machinery and equipment sector with
60% while 16.9% from service and maintenance and
the third largest respondents came from chemical
sector with 7.7%. There was only 1 respondent from
the healthcare industry (glove producer). The low
response rate was probably linked to high infected
cases in the glove industry that has limited their time
and attention to participate the survey. More than
80% of the respondents came from locally owned
companies in which their business operations have
been more than 5 years. 61.5% of the respondents
were from small enterprise with less than 50
employees and annual sales revenue for 49.2% of the
respondents were less than RM50 million.
Table 8: Respondents Profile
Respondents
Profile
Count
Column
Valid N %
General
Information -
Industry
Industrial Machinery and
Equipment
39
60.0%
Service and Maintenance
11
16.9%
Chemicals
5
7.7%
Electronic and Electrical
4
6.2%
Latex
2
3.1%
Digital Solutions Providers
2
3.1%
Lubricants
1
1.5%
Healthcare
1
1.5%
Others
0
0.0%
Packaging
0
0.0%
Total
65
100.0%
General
Information -
Company
Ownership
Local
53
81.5%
Foreign
10
15.4%
Others
2
3.1%
Total
65
100.0%
General
Information -
Years in
Operation
> 5 years
56
86.2%
2 to 5 years
5
7.7%
> 2 years
4
6.2%
Total
65
100.0%
General
Information -
Annual Sales
< RM50,000,000
32
49.2%
RM50,000,001 to
RM200,000,000
18
27.7%
> RM500,000,000
9
13.8%
RM200,000,001 to
RM500,000,000
6
9.2%
Total
65
100.0%
General
Information -
Number of
Employees
< 50
40
61.5%
51 to 200
14
21.5%
> 200
11
16.9%
Total
65
100.0%
5.1 Reliability Test: Cronbach’s alpha
Table 9 presents Cronbach’s alpha for the six sub-
constructs ranged from 0.725 to 0.894. The threshold
for the acceptable level of internal reliability is
usually 0.80 [86] while other researchers suggest
Cronbach’s values 0.50 is also acceptable [38]. As
such, Cronbach’s alpha value 0.725 for the first sub-
construct impact of Pandemic on digitalisation
adoption is considered acceptable.
Table 9: Cronbach’s Alpha Reliability Statistics
While Cronbach’s value for the rest of the sub-
constructs were higher than the threshold of 0.80.
Thus, all these six sub-constructs were considered as
research tools that have strong internal reliability and
consistency.
5.2 Exploratory Data Analysis (EDA)
Tukey’s exploratory data analysis (EDA) is an
approach deployed to understand the relationships
between the variables using diagrams and non-
diagrams and acts a guide for the selection of analysis
techniques [87]. This approach provides the
flexibility to use any analysis methods that are
needed to evaluate new outcomes that enriches the
research findings in which the research did not
planned to investigate initially [87]. The research
adopted a simple and understandable approach to
analyse and present the survey result in a coherent
manner.
Figure 5: Impact of Pandemic on Company
Performance
The first construct examined the pandemic impact on
business performance of the glove industry in terms
of sales revenue, production output and customer
satisfaction as well as customer and supplier delivery.
Figure 5 depicts that 65% and 61% of the respondents
indicated that company performance on sales revenue
and production output have decreased while only
44% responded that customer satisfaction has also
decreased. Majority of the decreases were within the
range of 10% to 50% and followed by less than 10%.
On the contrary, some respondents informed that
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Volume 6, 2024
there were increase in sales revenue, production
output and customer satisfaction during the pandemic
with 23%, 17% and 12% respectively. Meanwhile
45% of the result affirmed that there was no change
on customer satisfaction. On the delivery aspects,
Figure 6 demonstrates that 68% responses disclosed
that customer delivery has suffered delayed from less
than one month to more than six months in which the
category for delayed from 1 month to 3 months and
more than six months have the most responses with
25% and 23% respectively. Supplier delivery
performance has not suffered any delay for more than
six months, however, 69% responded the delay from
supplier delivery was slightly higher than delivery to
customer.
Figure 6: Impact of Pandemic on Customer and
Supplier Delivery
Out of the 69% responses, 40% of them responded
the delay were within 1 month to 3 months, while
17% opted for delayed less than one month.
Improvement in customer and supplier delivery
during the pandemic were observed with 5%
responses each while about 26% to 27% respondents
opted for no change. As a result, more than 60%
responses indicated deterioration in the performance
of sales revenue, production output, and customer
and supplier delivery while 44% responded
deterioration in customer satisfaction during the
pandemic. The findings for this construct reveal that
there were different results pertaining to business
performance in terms of sales revenue, production
output and customer satisfaction as well as customer
and supplier delivery. Overall, more than 60% of the
responses expressed that the pandemic has negatively
impacted their business performance. A small
percentage (10%) of the respondents voted for
improved performance while the rest (27%) indicated
the pandemic has not impacted their performance.
5.3 RQ1: Impact of Pandemic on Digitalisation
Adoption
This section examines the impact of pandemic on
digitalisation adoption from two perspectives.
The first perspective explores the changes in
digitalisation adoption during the pandemic with two
statements focused on company's investment on
digital technology in 2020 compared to 2019 and
pandemic accelerated the implementation of digital
transformation in various industries. Five-point
Likert scale ranging from ‘1=strongly disagree’ to
‘5=strongly agree’ were used to measure the
construct.
▪ The result in Table 10 indicates that 60% of the
respondents from the combination of both
categories, agree and strongly agree revealed that
company has invested more in digital technology
in 2020 as compared with 2019 (mean=3.74).
▪ The result also declared that 78.5% responded that
pandemic has accelerated the implementation of
digital transformation in various industries
(mean=4.06).
▪ The standard deviation for these two statements
were ranged from 0.788 to 0.923 which reflected
that responses were normally distributed around
the mean to support the consistency of the
responses [38].
Therefore, the results of this construct have answered
research question RQ1 as it is evident that pandemic
has impacted the adoption of digitalisation in the
glove industry as well as other industries. This
finding is consistent with previous studies [1,10-
13,16,29] that argue pandemic has expanded the
development of digitalisation. The second
perspective consists of two statements that explore
how digital transformation will impact company
performance.
▪ In the first statement, 76.9% of the respondents
agreed that digital transformation will improve
company performance from current pandemic
(mean=4.06) with standard deviation 0.726.
▪ The result for second statement was summarised
in Figure 7 where 20.5% of the respondents found
that digitalisation will improve company
performance in terms of availability of real-time
information while 19.5% found that adoption of
advance digital technology improves the
opportunity to exploit new market, 17.3% and
16.8% of the respondents found that digitalisation
will improve sales and customer satisfaction.
▪ This is followed by 13.5% found that
digitalisation will improve decision making and
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Volume 6, 2024
only 12.4% responded on production output
improvement.
▪ There was no additional opinion received from the
respondents to enrich the knowledge.
Table 10: Impact of Pandemic on Digitalisation
Adoption
Statistics
Digitalisatio
n - Do you
agree that
your
company's
investment
on digital
technology
in 2020 was
more than
2019?
Digitalisation -
Covid-19
pandemic has
accelerated the
implementation
of digital
transformation in
various
industries.
Digitalisation - Do
you agree that
digital
transformation will
improve company
performance from
current pandemic?
N
Valid
65
65
65
Missing
0
0
0
Mean
3.74
4.06
4.06
Std. Deviation
0.923
0.788
0.726
Frequency Table:
Digitalisation - Do you agree that your company's investment on
digital technology in 2020 was more than 2019?
Frequenc
y
Percent
Valid
Percent
Cumulative
Percent
Valid
Disagree
6
9.2
9.2
9.2
Neutral
20
30.8
30.8
40.0
Agree
24
36.9
36.9
76.9
Strongly
Agree
15
23.1
23.1
100.0
Total
65
100.0
100.0
Digitalisation - Covid-19 pandemic has accelerated the
implementation of digital transformation in various industries.
Frequency
Percent
Valid
Percent
Cumulative
Percent
Valid
Disagree
2
3.1
3.1
3.1
Neutral
12
18.5
18.5
21.5
Agree
31
47.7
47.7
69.2
Strongly
Agree
20
30.8
30.8
100.0
Total
65
100.0
100.0
Digitalisation - Do you agree that digital transformation will
improve company performance from current pandemic?
Frequency
Percent
Valid
Percent
Cumulative
Percent
Valid
Neutral
15
23.1
23.1
23.1
Agree
31
47.7
47.7
70.8
Strongly
Agree
19
29.2
29.2
100.0
Total
65
100.0
100.0
The second perspective unveils the perception of
the respondents in recognising that digitalisation has
the potential to improve company performance with
regards to availability of real-time information to
drive strategic functions, exploit new market
potential, improve sales, enhance decision making
and production output. The outcome of this construct
is like previous research [28] in which the studies
articulate that companies have becoming
increasingly aware of the potential that digitalisation
could bring during pandemic. Overall, the findings of
this construct indicates that pandemic has positively
impacted the adoption of digitalisation. This has
supported the answer to research question RQ1.
Majority of the respondents agree that their
companies invested more in digital technology in
2020 as compared with 2019 and that pandemic has
accelerated the implementation of digital
transformation in various industries. The respondents
also agree that through digitalisation, company
performance could improve with accessibility to real-
time information, potential to exploit new market,
increase sales and customer satisfaction, improve
decision making and production output.
Figure 7: How Digitalisation Will Improve
Company Performance
5.4 RQ2: Digital Solutions Adopted within Supply
Chain
This construct examines digital solutions that have
been adopted within the SC structures specifically in
Procurement, Manufacturing, Distribution, Supplier
and Customer. One statement was developed with
multiple choice answers that allowed participants to
select more than one answer in respective of each SC
structure. The survey outcome of digitalisation
adoption rate across the five SC structures was
displayed in Table 11.
Table 11: Digital Solutions Adopted within Supply
Chain Structures
BDA was the most adopted digital solution in each
individual SC structure where it was highly adopted
in Procurement and CCRM with 30% and 29.5%
responses respectively. On the contrary, BDA was
least adopted in Manufacturing with 20.1%
responses. IoT was most adopted in CCRM with
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Volume 6, 2024
23.8% responses, followed by SCRM and
Procurement with 21.5% and 20% responses
respectively. While IoT was least adopted in
Distribution with adoption rate of 16.4%. CC was
highly adopted in Procurement with 19.1%
responses. However, CC was least adopted in
Manufacturing with only 10.4% response which was
the lowest adoption rate among the top three
solutions, i.e.: BDA, IoT and CC. The adoption rate
for CC in Distribution, CCRM and SCRM were quite
balance within the range of 14.7% to 16.8%.
Autonomous Robots was highly adopted in
Manufacturing with 7.5% and followed by
Distribution with 6.9%. It was not commonly
adopted in rest of the SC structures with the low
response rate of 4.8% to 4.5%. AI was highly adopted
in Distribution and Manufacturing within the range
of 10.3% to 11.2% while it was least adopted in
CCRM with 2.9% responses. The result showed that
AM and AR were among the two least adopted
solutions with adoption rate ranged from 1.9% to
5.2% for AM while 1.7% to 4.8% for AR across all
individual SC structures. Meanwhile, ML has slightly
higher adoption rate compared to AM and AR with
the range of 5.2% to 8.6% across all individual SC
structure. Lastly, CPS has the highest adoption rate at
Manufacturing with 9% responses. However, CPS
was not being adopted in Procurement. CPS was also
one the least adopted solutions among the SC
structures.
The overall adoption rate indicated in Table 11
explains that BDA was the most adopted digital
solution with 26% adoption rate. This was followed
by IoT and CC with 20% and 15% responses
respectively. The adoption rate for AI, ML and
Autonomous Robots were relatively low within the
range of 7% to 6%. AR, AM and CPS were the three
least adopted solutions with 4% responses
individually. CCRM has the highest adoption on
digital solutions of 96.2% while Distribution was
reported to have the lowest adoption rate of 90.5%.
SCRM and Manufacturing were having similar
adoption rates of 93.5% and 93.3% respectively.
Procurement was the second SC with least digital
adoption of 91.8%. There was an average of 7%
responses indicated no digital solution was adopted
within their SC structures.
The construct of digital solutions adopted within
SC structures has been expanded to measure the
adoption level. Five statements were created to
measure the SC constructs with five-point Likert
scale ranging from ‘1=very low’ to ‘5=very high’.
Table 12 demonstrates that CCRM has the highest
level of digital solutions adoption among the five SC
structures with 81.6% responded from medium to
very high adoption level (mean=3.14). The data was
distributed relatively near the mean with standard
deviation 0.899.
Similarly, SCRM also has a significant response
rate of 78.5% combining medium to very high
adoption level (mean=3.05). 50.8% responded on
medium digital adoption level with standard
deviation 0.856. On the contrary, Procurement has
the lowest adoption level (mean=2.78) among the
five SC structures. The responses ranged from very
low to very high where 52.3% responded to medium
and 29.2% opted from low to very low. Thus, only
18.4% responded to high and very high with standard
deviation 0.927. Likewise, Distribution demonstrated
second lowest adoption level (mean=2.86). The
result detects that 30.7% responded to either low or
very low while 44.6% responded to medium. The
responses were spread slightly outward from the
mean with standard deviation 1.074. Similar trend
was observed at Manufacturing with third lowest
adoption level (mean=2.94) and 1.088 standard
deviation. 43.1% responded to medium while only
27.7% responded to either high or very high. From
the overall perspective, Table 12 expresses the
adoption level was ranging between low to medium
(mean=2.9538) and standard deviation 0.76548
without any outliers. In short, the findings of this
construct denote that even though respondents have
acknowledged the great potential of digitalisation
toward company performance, the average adoption
level was still considerably low.
Table 12: Adoption Level within the Supply Chain
Structure
Statistics
Digital
Solutions
Adoption
Level -
Procureme
nt
Digital
Solutions
Adoption
Level -
Manufac
turing
Digital
Solutions
Adoption
Level -
Distributi
on
Digital
Solutions
Adoption
Level -
SCRM
Digital
Solutions
Adoption
Level -
CCRM
N
Valid
65
65
65
65
65
Missing
0
0
0
0
0
Mean
2.78
2.94
2.86
3.05
3.14
Std. Deviation
0.927
1.088
1.074
0.856
0.899
Digital Solutions Adoption Level - Procurement
Frequency
Percent
Valid
Percent
Cumulative
Percent
Valid
Very Low
8
12.3
12.3
12.3
Low
11
16.9
16.9
29.2
Medium
34
52.3
52.3
81.5
High
11
16.9
16.9
98.5
Very
High
1
1.5
1.5
100.0
Total
65
100.0
100.0
Digital Solutions Adoption Level - Manufacturing
Frequency
Percent
Valid
Percent
Cumulative
Percent
Valid
Very Low
8
12.3
12.3
12.3
Low
11
16.9
16.9
29.2
Medium
28
43.1
43.1
72.3
High
13
20.0
20.0
92.3
Very
High
5
7.7
7.7
100.0
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Volume 6, 2024
Total
65
100.0
100.0
Digital Solutions Adoption Level - Distribution
Frequency
Percent
Valid
Percent
Cumulative
Percent
Valid
Very Low
9
13.8
13.8
13.8
Low
11
16.9
16.9
30.8
Medium
29
44.6
44.6
75.4
High
12
18.5
18.5
93.8
Very
High
4
6.2
6.2
100.0
Total
65
100.0
100.0
Digital Solutions Adoption Level - SCRM
Frequency
Percent
Valid
Percent
Cumulative
Percent
Valid
Very Low
3
4.6
4.6
4.6
Low
11
16.9
16.9
21.5
Medium
33
50.8
50.8
72.3
High
16
24.6
24.6
96.9
Very
High
2
3.1
3.1
100.0
Total
65
100.0
100.0
Digital Solutions Adoption Level - CCRM
Frequency
Percent
Valid
Percent
Cumulative
Percent
Valid
Very Low
3
4.6
4.6
4.6
Low
9
13.8
13.8
18.5
Medium
33
50.8
50.8
69.2
High
16
24.6
24.6
93.8
Very
High
4
6.2
6.2
100.0
Total
65
100.0
100.0
Statistics
Mean. DSAL
N
Valid
65
Missing
0
Mean
2.9538
Std. Deviation
0.76548
This finding is consistent with previous research
[55]. Research cautions that digitalisation has been a
crucial aspect of SC performance to compete and
survive in highly dynamic business environment
[44,60]. The type of digital solutions and adoption
level are greatly depending on digitalisation
objectives and nature of the supply chain functions of
a particular industry. For example, although previous
research identified that AI, BDA and IoT were the
three core solutions adopted in Procurement to
automate routine processes and improve strategic
role [55], apart from BDA and IoT, current study
finds that AI was not commonly used in Procurement
as well as throughout the SC structures where AI
adoption rate was found to be lower than BDA, IoT
and CC. This finding is consistent with research
conducted by [28] who comment lower AI adoption
rate compared to BDA and IoT in their studies on
digital adoption among the Chinese SMEs. The
finding from current research also found to be
consistent with previous research [95]. The
researchers find that BDA, IoT and CC have been
more developed and matured [95] whereby have been
largely successfully integrated into fundamental
business operations, i.e.: search engine, social media,
and web analytics [33]. Whilst AR, AM, CPS, ML
and Autonomous Robots have begun to evolve in
recent years with emerging industrial applications.
Thus, lower adoption rate as shown in current
research. To determine any relationships between the
adoption levels among all SC structures, Spearman’s
Correlation Analysis was conducted. This analytical
method is preferred as the data set consists of ordinal
variables. The result in Table 13 explains that
Procurement and Manufacturing has a weak
correlation (rs=0.363, ρ<0.01 at 99% confidence
level). While Manufacturing and Distribution was
moderately correlated (rs=0.627, ρ<0.01 at 99%
confidence level). Strong correlation was observed
between SCRM and CCRM (rs=0.765, ρ<0.01 at
99% confidence level). Thus, the correlation analysis
suggests that respective SC structures were positively
correlated in terms of adoption level in a
multidimensional manner. This insight leads to
knowledge expansion in the respect of how adoption
level among SC structures will affect each other.
Table 13: Relationships on Digital Adoption Level
Correlations
Correlations
Digital
Solutions
Adoption
Level -
Procurem
ent
Digital
Solutions
Adoption
Level -
Manufact
uring
Digital
Solutions
Adoption
Level -
Distributi
on
Digital
Solutio
ns
Adoptio
n Level
- SCRM
Digital
Solutio
ns
Adoptio
n Level
- CCRM
Spearm
an's rho
Digital
Solutions
Adoption
Level -
Procurem
ent
Correlation
Coefficient
1.000
.363**
.648**
.664**
.488**
Sig. (2-tailed)
.
.003
.000
.000
.000
N
65
65
65
65
65
Digital
Solutions
Adoption
Level -
Manufact
uring
Correlation
Coefficient
.363**
1.000
.627**
.570**
.454**
Sig. (2-tailed)
.003
.
.000
.000
.000
N
65
65
65
65
65
Digital
Solutions
Adoption
Level -
Distributi
on
Correlation
Coefficient
.648**
.627**
1.000
.574**
.472**
Sig. (2-tailed)
.000
.000
.
.000
.000
N
65
65
65
65
65
Digital
Solutions
Adoption
Level -
SCRM
Correlation
Coefficient
.664**
.570**
.574**
1.000
.765**
Sig. (2-tailed)
.000
.000
.000
.
.000
N
65
65
65
65
65
Digital
Solutions
Adoption
Level -
CCRM
Correlation
Coefficient
.488**
.454**
.472**
.765**
1.000
Sig. (2-tailed)
.000
.000
.000
.000
.
N
65
65
65
65
65
**. Correlation is significant at the 0.01 level (2-tailed).
Different solutions were adopted across the SC
structures at various adoption level. The most
adopted solutions are BDA, IoT and CC while AR,
AM and CPS are least adopted. 7% of the result
indicated some organisations do not adopt any digital
solutions in their SC structures. Overall, the adoption
level is considerably low in which CCRM has the
highest adoption level while Procurement has the
lowest adoption among the five SC structures. The
analysis also reveals that SC structures influence
each other positively at varying level of adoption.
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5.5 RQ3: Impact of Digitalisation on Supply
Chain Performance (SCP)
This main construct has expanded into four sub-
constructs which examine the impact of digitalisation
on SCP from the perspective of CSR, SCI, SCV and
SCR. The participants may select the answer based
on five-point Likert scale ranging from ‘1=strongly
disagree’ to ‘5=strongly agree’. The survey result in
Table 14 reveals the standard deviation for all these
fourteen statements were ranged from 0.673 to 0.842
which indicates that the responses were closely
gathered near the mean without any outliers.
Table 14: Digitalisation Impact on Supply Chain
Performance
Descriptive Statistics
N
Mean
Std.
Deviation
CSR - Digital transformation has improved
sale revenues.
65
3.69
0.683
CSR - Digital transformation has improved
customer relationships and improved
forecast and demand planning.
65
3.92
0.692
CSR - Digital transformation has improved
customer satisfaction.
65
3.80
0.754
CSR - Digital transformation has improved
buyer-supplier relationship with higher
degree of trust and partnership.
65
3.62
0.842
CSR - High degree of trust and partnership
between buyer-supplier lead to
improvement in product quality and
delivery commitment.
65
3.89
0.773
SCI - Digital transformation has improved
communication and information sharing
within my company, suppliers and
customers.
65
3.97
0.809
SCI - Digital transformation has improved
cooperation and collaboration within my
company, suppliers and customers.
65
3.98
0.696
SCI - Digital transformation has improved
the efficiency on planning and forecast
accuracy.
65
3.98
0.673
SCV - Digital transformation has enabled
quality and usefulness of information.
65
3.97
0.684
SCV - Digital transformation has enabled
availability and accessibility to real-time
information.
65
4.08
0.735
SCV - Digital transformation has enabled
clarity and visibility of upstream and
downstream operations.
65
3.97
0.706
SCR - Digital transformation has enabled
speedy and quality decision making.
65
4.00
0.685
SCR - Digital transformation has improved
company's core competency
65
3.92
0.835
SCR - Digital transformation has improved
company's ability to deploy appropriate
strategies and react timely to counter
challenges in difficult situations.
65
3.85
0.755
Valid N (list wise)
65
Two statements with the highest means were
found to be suggesting positive responses to the
construct, i.e.: SCV-Digital transformation has
enabled availability and accessibility to real-time
information (mean=4.08) and SCR-Digital
transformation has enabled speedy and quality
decision making (mean=4.00). This was closely
followed with three statements under SCI with the
mean ranged from 3.98 to 3.97. Compared to the
above statements, two statements that least
suggesting positive responses to the construct both
fell under CSR which were digital transformation has
improved sale revenues (mean=3.69) and digital
transformation has improved buyer-supplier
relationship with higher degree of trust and
partnership (mean=3.62).
5.5.1 Supply Chain Integration (SCI)
SCI was examined with three measurements, i.e.:
communication and information sharing, cooperation
and collaboration as well as planning and forecasting.
The standard deviation for these three statements
were ranged from 0.673 to 0.809 where data were
close to the mean as tabulated in Table 14. 76.9% of
the respondents agreed that digital transformation has
improved the efficiency on planning and forecast
accuracy (mean=3.98) as illustrated in Table 15.
Table 15: Digitalisation Impact on Supply Chain
Integration
SCI - Digital transformation has improved communication
and information sharing within my company, suppliers and
customers.
Frequency
Percent
Valid
Percent
Cumulative
Percent
Valid
Disagree
2
3.1
3.1
3.1
Neutral
16
24.6
24.6
27.7
Agree
29
44.6
44.6
72.3
Strongly
Agree
18
27.7
27.7
100.0
Total
65
100.0
100.0
SCI - Digital transformation has improved cooperation and
collaboration within my company, suppliers and
customers.
Frequency
Percent
Valid
Percent
Cumulative
Percent
Valid
Neutral
16
24.6
24.6
24.6
Agree
34
52.3
52.3
76.9
Strongly
Agree
15
23.1
23.1
100.0
Total
65
100.0
100.0
SCI - Digital transformation has improved the efficiency on
planning and forecast accuracy.
Frequency
Percent
Valid
Percent
Cumulative
Percent
Valid
Neutral
15
23.1
23.1
23.1
Agree
36
55.4
55.4
78.5
Strongly
Agree
14
21.5
21.5
100.0
Total
65
100.0
100.0
While 75.4% of the respondents found that
digitalisation has improved cooperation and
collaboration within the company, suppliers and
customers (mean=3.98). Lastly, 72.3% of the
respondents found that digitalisation would improve
communication and information sharing within the
company, suppliers and customers (mean=3.97). The
result implies that significant numbers of the
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respondents acknowledged that digitalisation would
improve SCI. Studies find that SCI is crucial to SCP
with the integration between internal and external SC
operations [38] and heighten integration is crucial to
maintain competitiveness in the dynamic business
world [51] through the impact of innovative
technologies.
5.5.2 Supply Chain Visibility (SCV)
SCV was examined with three measurements, i.e.:
quality and usefulness Information, availability and
accessibility as well as clarity and visibility. The
standard deviations ranged from 0.684 to 0.735
claimed there was no outliers as data were clustered
around the mean as tabulated in Table 14. As
explained in Table 16, the statement digital
transformation has enabled quality and usefulness of
information received 80% positive responses
(mean=3.97). Similarly, digital transformation has
enabled availability and accessibility to real-time
information received 80% positive responses
(mean=4.08). While 73.9% positive responses for
digital transformation have enabled clarity and
visibility of upstream and downstream operations
(mean=3.97).
Table 16: Digitalisation Impact on Supply Chain
Visibility
SCV - Digital transformation has enabled quality and
usefulness of information.
Frequency
Percent
Valid
Percent
Cumulative
Percent
Valid
Disagree
1
1.5
1.5
1.5
Neutral
13
20.0
20.0
21.5
Agree
38
58.5
58.5
80.0
Strongly Agree
13
20.0
20.0
100.0
Total
65
100.0
100.0
SCV - Digital transformation has enabled availability and
accessibility to real-time information.
Frequency
Percent
Valid
Percent
Cumulative
Percent
Valid
Disagree
1
1.5
1.5
1.5
Neutral
12
18.5
18.5
20.0
Agree
33
50.8
50.8
70.8
Strongly
Agree
19
29.2
29.2
100.0
Total
65
100.0
100.0
SCV - Digital transformation has enabled clarity and visibility of
upstream and downstream operations.
Frequency
Percent
Valid
Percent
Cumulative
Percent
Valid
Neutral
17
26.2
26.2
26.2
Agree
33
50.8
50.8
76.9
Strongly
Agree
15
23.1
23.1
100.0
Total
65
100.0
100.0
Overall, the result signifies that majority of the
respondents agreed that digitalisation will impact
positively toward SCV as literatures find that SCV
enhances SCP [80,78, 44].
5.5.3 Supply Chain Responsiveness (SCR)
SCR was examined through the measurements of
speedy and quality decision making, core
competency as well as ability to strategies and react
timely. The responses of this construct were
distributed closely to the mean with standard
deviation ranged from 0.685-0.835 as per table 14.
Results in table 17 shows 80% of the respondents
agreed that digital transformation has enabled speedy
and quality decision making (mean=4.00).
While 72.3% of the respondents agreed that
digital transformation has improved company's core
competency (m=3.92). 69.3% of the respondents
agreed that digital transformation has improved
company's ability to deploy appropriate strategies
and react timely to counter challenges in difficult
situations (mean=3.85). Hence, a substantial number
of respondents agreed that digitalisation has positive
impact on SCR.
Table 17: Digitalisation Impact on Supply Chain
Responsiveness
SCR - Digital transformation has enabled speedy and quality
decision making.
Frequency
Percent
Valid
Percent
Cumulative
Percent
Valid
Disagree
1
1.5
1.5
1.5
Neutral
12
18.5
18.5
20.0
Agree
38
58.5
58.5
78.5
Strongly Agree
14
21.5
21.5
100.0
Total
65
100.0
100.0
SCR - Digital transformation has improved company's core
competency
Frequency
Percent
Valid
Percent
Cumulative
Percent
Valid
Strongly
disagree
1
1.5
1.5
1.5
Disagree
1
1.5
1.5
3.1
Neutral
16
24.6
24.6
27.7
Agree
31
47.7
47.7
75.4
Strongly Agree
16
24.6
24.6
100.0
Total
65
100.0
100.0
SCR - Digital transformation has improved company's ability to
deploy appropriate strategies and react timely to counter
challenges in difficult situations.
Frequency
Percent
Valid
Percent
Cumulative
Percent
Valid
Disagree
2
3.1
3.1
3.1
Neutral
18
27.7
27.7
30.8
Agree
33
50.8
50.8
81.5
Strongly Agree
12
18.5
18.5
100.0
Total
65
100.0
100.0
This finding is consistent with previous studies in
which enhance SCR with innovative technologies is
crucial for long term competitiveness and
sustainability in extremely uncertain and high-risk
market condition [44,60].
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5.5.4 Impact of Digitalisation on Customer and
Supplier Relationship
There were five measurements under this construct,
sales revenues, customer relationships with forecast
and demand planning, customer satisfaction, supplier
relationships with trust and partnership as well as
product quality and delivery commitment that
measure the impact of digitalisation on CSR. Among
the five statements, three statements examined
digitalisation impact on customer relationships. As
explained in Table 18, 72.3% of the respondents
found that digital transformation will improve
customer relationships, forecast and demand
planning (mean=3.92) while 63.1% agreed that
digitalisation will also improve customer satisfaction
(mean=3.820). However, only 56.9% of the
respondents agreed that digitalisation will improve
sales revenues (mean=3.69). Table 14 shows the
standard deviations for these three statements were
0.692, 0.754 and 0.683 respectively. The data implies
that the responses were spread closely to the mean.
Table 18: Digitalisation Impact on Customer and
Supplier Relationship
Customer and Supplier Relationship - Digital transformation has
improved sale revenues.
Frequency
Percent
Valid
Percent
Cumulative
Percent
Valid
Neutral
28
43.1
43.1
43.1
Agree
29
44.6
44.6
87.7
Strongly
Agree
8
12.3
12.3
100.0
Total
65
100.0
100.0
Customer and Supplier Relationship - Digital transformation has
improved customer relationships and improved forecast and
demand planning.
Frequency
Percent
Valid
Percent
Cumulative
Percent
Valid
Neutral
18
27.7
27.7
27.7
Agree
34
52.3
52.3
80.0
Strongly
Agree
13
20.0
20.0
100.0
Total
65
100.0
100.0
Customer and Supplier Relationship - Digital transformation has
improved customer satisfaction.
Frequency
Percent
Valid
Percent
Cumulative
Percent
Valid
Disagree
1
1.5
1.5
1.5
Neutral
23
35.4
35.4
36.9
Agree
29
44.6
44.6
81.5
Strongly
Agree
12
18.5
18.5
100.0
Total
65
100.0
100.0
Customer and Supplier Relationship - Digital transformation has
improved buyer-supplier relationship with higher degree of trust
and partnership.
Frequency
Percent
Valid
Percent
Cumulative
Percent
Valid
Disagree
4
6.2
6.2
6.2
Neutral
28
43.1
43.1
49.2
Agree
22
33.8
33.8
83.1
Strongly
Agree
11
16.9
16.9
100.0
Total
65
100.0
100.0
Customer and Supplier Relationship - High degree of trust and
partnership between buyer-supplier lead to improvement in
product quality and delivery commitment.
Frequency
Percent
Valid
Percent
Cumulative
Percent
Valid
Disagree
1
1.5
1.5
1.5
Neutral
20
30.8
30.8
32.3
Agree
29
44.6
44.6
76.9
Strongly
Agree
15
23.1
23.1
100.0
Total
65
100.0
100.0
The results from two statements created to
measure the impact of digitalisation on supplier
relationships received 50.7% responses agreed that
digital transformation will improve buyer-supplier
relationship with higher degree of trust and
partnership (mean=3.62). While 67.71% of the
respondents agreed that higher degree of trust and
partnership will lead to improvement in product
quality and delivery commitment (mean=3.89). The
responses were close to the mean without outliers
with respective standard deviation 0.842 and 0.773.
In addition, the participants agree that an increase in
transparency and traceability will strengthen buyer-
supplier relationships and the level of trust. [38].
Overall, the result declares that respondents
agreed that digitalisation will improve customer
relationships under the studied measurements.
However, the survey also reflects that half of the
respondents agreed that digitalisation will improve
buyer-supplier relationship with high degree of trust
and partnership even though larger number of
respondents agreed that higher degree of trust and
partnership will lead to improvement in product
quality and delivery commitment.
A Spearman’s Correlation Analysis was
performed to determine any significant relationships
between digitalisation and SCP. The results in Table
19 disclose that there are significant positive
correlations between digitalisation (D) and SCP, i.e.:
SCI (rs=0.569), SCV (rs=0.592), SCR (rs=0.603) and
CSR (rs=0.544), ρ<0.01 with 99% confidence level.
As such we could validate the conclusion that
digitalisation will positively impact and elevate SCP
as research question RQ3 sets out to examine. It is
worth noting the survey result also unveils that
variables among the SCP are also found to be
positively correlated. For instance, SCI is strongly
correlated to SCV, SCR, CSR with Spearman’s
Correlation Coefficient (rs) 0.701, 0.714 and 0.824
respectively with ρ<0.01. Thus, the results enrich
knowledge development in another dimension with
regards to the intensity of interrelationships among
the variables toward SCP under the impact of
digitalisation.
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Table 19: Digitalisation and Supply Chain
Performance Relationships
Correlations
Mean.
Digitalisation
Mean.
SCI
Mean.
SCV
Mean.
SCR
Mean.
CSR
Spearma
n's rho
Mean.
Digitalisation
Correlation
Coefficient
1.000
.569**
.592**
.603**
.544**
Sig. (2-
tailed)
.
.000
.000
.000
.000
N
65
65
65
65
65
Mean. SCI
Correlation
Coefficient
.569**
1.000
.701**
.714**
.824**
Sig. (2-
tailed)
.000
.
.000
.000
.000
N
65
65
65
65
65
Mean.
SCV
Correlation
Coefficient
.592**
.701**
1.000
.739**
.758**
Sig. (2-
tailed)
.000
.000
.
.000
.000
N
65
65
65
65
65
Mean.
SCR
Correlation
Coefficient
.603**
.714**
.739**
1.000
.646**
Sig. (2-
tailed)
.000
.000
.000
.
.000
N
65
65
65
65
65
Mean.
CSR
Correlation
Coefficient
.544**
.824**
.758**
.646**
1.000
Sig. (2-
tailed)
.000
.000
.000
.000
.
N
65
65
65
65
65
**. Correlation is significant at the 0.01 level (2-tailed).
Overall, the finding for this construct supports
the assumptions of the conceptual framework
developed for current research that digitalisation will
enhance SCP. It is evident that by leveraging digital
technologies and elevating digital capabilities will
positively impact SCP through improvement in SCI,
SCV and SCR. Hence, this finding has answered
research question RQ3 on how digitalisation will
impact SCP. At the same time, the result is also
consistent with previous studies [44,60,63] on the
positive impact of digitalisation on SCP. Moreover,
it also aligned with the research outcome conducted
by [14,15] in which majority of the corporate leaders
agreed that digitalisation is crucial to meet corporate
objectives and sustain business core competency.
In addition, digitalisation also contribute to
improvement in CSR in terms of increasing sales
revenues and customer satisfaction, accurate forecast
and demand planning, enhance trust and partnership,
heighten product quality and delivery commitment. It
is also found that the variables such as SCI, SCV,
SCR and CRS are strongly correlate and the
interrelationships among these variables will have
implications toward SCP under the impact of
digitalisation.
5.6 Barriers to Digitalisation
As summarised in Figure 8, change of work culture
has the highest responses (30%). The second highest
responses were minimising capital investment
allocation due to market uncertainty (17%). While,
advance technologies reduce job opportunities,
cybersecurity threats, unknown return on investment
(ROI) in volatile economy and lacking awareness to
embrace advance technologies were 11% to 15%.
Figure 8: Barriers to Digitalisation during Pandemic
Additional comments were received from two
responses where both found that increased in
production capacity as barriers to digitalisation. The
result reflects that the respondents were particularly
concerned with change of work culture as key barrier
for digital transformation. This finding is consistent
with previous research [60]. Apparently, workforce’s
acceptance and adaptability have impactful
consequences due to their direct involvement in
digitalisation [38]. Study evocates that it is important
that digital culture within the organisation can foster
transparency and optimistic behaviours among the
workforce toward digitalisation [10] and address the
challenge on cybersecurity threats.
Moderate response rates of 14% ~ 17% on
minimise capital investment allocation and unknown
ROI which did not respond highly to researcher’s
assumption as the two key barriers to digitalisation
during pandemic. Nonetheless, these factors should
not be neglected as they were ranked second and forth
respectively. Lacking the awareness to embrace
advance digital solutions was obviously a critical
barrier as witnessed during the interview sessions.
Two interviewees demonstrated limited
understanding on AM, ML and CPS and sought for
explanation on ‘What is CPS, ML, AM?’ and ‘What
are their functions?’
The result also suggests that respondents were
less concerned on reduced job opportunities in
relation to digitalisation. This was inconsistent with
previous studies as the studies alarmed that
digitalisation have negatively impacted employment
at some occupations [95-97]. In this regard, the
researcher seeks to determine if company’s plan on
digitalisation after pandemic would moderate the
response toward reduce job opportunities. A linear
regression test was conducted, and the result showed
in Table 20 reflects that Model Summary, R2 = 0.033
which shows company’s plan to digitalisation after
pandemic accounts for 3.3% of the variance in reduce
job opportunities; while ANOVA, F (1, 63)=2.155,
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Volume 6, 2024
ρ=0.147 which was more than 0.05 significant level.
This reveals that company’s plan to digitalisation
after pandemic was not a significant predictor of
reducing job opportunities. This finding could imply
the perspective of the respondents that Malaysia is
still at the beginning stage of digitalisation [45] and
therefore the threat of reducing job opportunities to
advance technologies is less impactful. Nonetheless,
future studies could be carried out when there is more
digitalisation in Malaysia glove industry.
Table 21: Digitalisation Plans after Pandemic
Will your company plan to adopt or accelerate its digital
transformation after the pandemic?
Frequency
Percent
Valid
Percent
Cumulative
Percent
Valid
Yes
31
47.7
47.7
47.7
No
2
3.1
3.1
50.8
Maybe
32
49.2
49.2
100.0
Total
65
100.0
100.0
The next stage constitutes three open-ended
questions where the replies were categorised and
coded. Table 21 reflects the responses on company’s
digitalisation plan after the pandemic were divided
into two categories which were maybe and yes with
49.2% and 47.7% respectively.
Table 22: Correlation of Digitalisation during and
after Pandemic
Correlations
Digitalisation
- Do you
agree that
your
company's
investment
on digital
technology
in 2020 was
more than
2019?
Will your
company plan to
adopt or
accelerate its
digital
transformation
after the
pandemic?
Spearman's
rho
Digitalisation - Do
you agree that your
company's
investment on digital
technology in 2020
was more than
2019?
Correlation
Coefficient
1.000
-.481**
Sig. (2-tailed)
.
.000
N
65
65
Will your company
plan to adopt or
accelerate its digital
transformation after
the pandemic?
Correlation
Coefficient
-.481**
1.000
Sig. (2-tailed)
.000
.
N
65
65
**. Correlation is significant at the 0.01 level (2-tailed).
A minority of 3.1% responded to no implementation.
As the research was being conducted between third
quarters of 2020 till first quarter of 2021, the
researcher is keen to find out any change of
digitalisation initiation in view of the progress and
development of the coronavirus and the vaccine
development during this period. A Spearman’s
correlation is conducted to examine the relationships
between these two variables.
Table 22 verified that digital technology investment
in 2020 was more than 2019 was negatively
correlated to digitalisation after pandemic (rs = -
0.481, ρ<0.01 at 99 percent confidence level) with
moderate influences. This finding suggests that there
was a change in the digitalisation initiatives with the
progress of the pandemic. The digitalisation
initiatives will accelerate during the pandemic and
will decrease after the pandemic. Therefore, the
researcher concluded that digitalisation is impacted
by Covid-19 pandemic. This outcome has provided
further evidence to support the assumption behind
research question RQ1.
Table 23: Adoption Duration and ROI Expectation
Adopt or accelerate.
Adopt or accelerate Duration
Frequency
Percent
Valid
Percent
Cumulative
Percent
Valid
Less than 1
year
31
47.7
47.7
47.7
1 to 3 years
16
24.6
24.6
72.3
3 to 5 years
4
6.2
6.2
78.5
Unsure
12
18.5
18.5
96.9
No
Intention
2
3.1
3.1
100.0
Total
65
100.0
100.0
Expectation ROI
Frequency
Percent
Valid
Percent
Cumulative
Percent
Valid
Less than 1
year
1
1.5
1.5
1.5
1 to 3 years
40
61.5
61.5
63.1
3 to 5 years
8
12.3
12.3
75.4
Unsure
14
21.5
21.5
96.9
No
Intention
2
3.1
3.1
100.0
Total
65
100.0
100.0
Survey result in Table 23 also revealed that 47.7%
respondents felt that digitalisation should be carried
out in less than 1 year. While 24.6% opted 1 to 3
years. The rest were either unsure or opted for longer
period whilst 3% responded no intention to digitize.
In the aspect of ROI, 61.5% expected ROI within 1
to 3 years while 21.5% replied unsure and 1.5%
expected less than 1 year. Likewise, 3% responded
no intention to embark on digitalisation and the rest
responded 3 to 5 years. The last statement for this
construct was to find out the most critical department
to adopt digitalisation to improve company
performance. As it was an open-ended question, eight
respondents provided more than one answers and two
responded all the departments. To prevent any
intentions of wrong interpretation which leads to
false conclusion [87], the researcher has summarised
and coded the responses [86] in two methods to
determine if the results were consistent.
The first method was to consider only the first
answer based on the assumption that most important
department would usually be first listed out. The two
‘all’ answers were disregarded as the answers did not
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Volume 6, 2024
inform which department and it would not impact the
outcome. The second method was to consider all
answers received including the two answers on all
which implied every department. The results of both
methods were exhibited in Table 24 and Table 25.
Table 24: Most Critical Department to Adopt
Digitalisation (1st Method)
Most Critical Department to Adopt Digitalisation
(Consider 1st Answer)
Frequency
Percent
Valid
Percent
Cumulative
Percent
Valid
Marketing
21
32.3
32.3
32.3
Distribution
1
1.5
1.5
33.8
Procurement
1
1.5
1.5
35.4
Production
23
35.4
35.4
70.8
Others
6
9.2
9.2
80.0
No Comment
9
13.8
13.8
93.8
Not Relevant
4
6.2
6.2
100.0
Total
65
100.0
100.0
Table 24 depicted three most critical department
were Production (35.4%), Marketing (32.3%) and
others (9.2%). Meanwhile, Table 25 confirmed that
Production (33%), Marketing (29.8%) and
Distribution (12.8%) were the three most critical
department to adopt digitalisation.
Table 25: Critical Department to Adopt
Digitalisation (2nd Method)
Most Critical Department to Adopt Digitalisation
(Consider All Answers)
Responses
Percent of
Cases
N
Percent
Critical Dept.
to Adopt
Digitalisation
a
Most Critical Department -
Production
31
33.0%
47.7%
Most Critical Department -
Marketing
28
29.8%
43.1%
Most Critical Department -
Procurement
9
9.6%
13.8%
Most Critical Department -
Distribution
12
12.8%
18.5%
Most Critical Department - Other
Section
3
3.2%
4.6%
Most Critical Department - No
Comment
8
8.5%
12.3%
Most Critical Department - No
Intention
2
2.1%
3.1%
Most Critical Department - No
Reply
1
1.1%
1.5%
Total
94
100.0%
144.6%
a. Dichotomy group tabulated at value 1.
Both methods reflected consistent results for top two
answers in which Production and Marketing were
ranked first and second in the same order. Overall,
the major barrier to digitalisation was change of work
culture and followed by mindset in terms of minimise
investment and unknown ROI during pandemic. The
initiatives for digitalisation were higher during the
pandemic and lesser after the pandemic. Averagely,
respondents expected to embark digitalisation within
1 year and expected ROI within 1 to 3 years. The
most critical department to implement digitalisation
is Production that matched the general perspective in
the glove industry.
6 Conclusions and Recommendations
This research contributes to SCM literature by
developing an integrated conceptual framework to
promote better awareness on how digitalisation could
contribute to a heighten SCP under the influence of
pandemic and broaden the understanding of the
underlying instruments that link and correlate to
drive SCP improvement. Additionally, the research
framework serves as a useful mechanism for in-depth
SCM research for other industries. The research
hypothesizes digitalisation will impact SCP in which
SCP is conceptualized in a perspective that embraces
SCI, SCV and SCR. Moreover, the research has
extended the approach by integrating CSR with other
key aspect of SCP, i.e.: SCI, SCV and SCR thus
create a holistic overview to achieve a
comprehensive dimension of SCP unlike other study
[38] that focused on specific aspect of SC. This
conception was motivated from the aspect of an
integrated SC ecosystem in which suppliers and
customers are critical SC partners who are crucial to
SCP. This approach is consistent with previous
research [89] who examined the SCP through the
aspects of supplier, customer and internal integration
under the effect of SC innovativeness.
Through this study and by adopting this
comprehensive approach of integrating SCI, SCV,
SCR and CSR, the research enriches the
understanding on how each construct significantly
links and contributes toward magnifying SCP in a
holistic perspective. Furthermore, the fundamental
element of each construct correlates the constructs
and overarches the impacts from within internal
dimension toward external dimension of the SCP and
subsequently resulting a complete and well-
integrated supply chain ecosystem. The results also
enrich knowledge development in another dimension
with regards to the intensity of interrelationships
among the variables toward SCP under the impact of
digitalisation. These findings support the assumption
made by the researcher through the development of
the conceptual framework.
The research constructs were critically developed
and were closely associated to research objectives in
which the constructs were critically articulated with
sufficient analysis to examine the key variables
where the impacts were adequately explained to
answer the research questions. Although the findings
are not exhaustive, the researcher contemplated that
they are inclusive as they answered all the research
questions set out in the research. Furthermore, the
research has led to expanding the knowledge on the
subject matter. As the research is an ‘initial’ or
‘pioneer’ in examining digitalisation in gloves
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industry in Malaysia under the impact of pandemic.
This study enlightens the practitioners on the
understanding of innovative technologies which
could help company to minimise negative impact
triggered from unexpected events that disrupted SC
activities. The research presents several key digital
technologies such as BDA and IoT that have been
developed in recent decades and their usage within
the SC operations have enhanced various businesses
performance in different dimensions. Thus, the
importance of digitalisation in current era could not
be underestimated as it touches every aspect of
organisation [44].
The research advances the knowledge of the
managers on the positive impacts of digitalisation in
fostering better SCP among the identified SC
partners. The research measures SCP in terms of
integration, visibility and responsiveness that are
closely related to digitalisation as shown in the
survey findings. This finding presents critical
implications of adopting advance digital technologies
within an organisation improves its core competency
for business survival during volatile economy as
study stresses that competition in current business
environment is centred on digitalisation [45]. At the
same time, the research alerts the importance of
addressing challenges to digitalisation. With this
important understanding, managers would be able to
scrutinise the company’s objective, readiness and
develop suitable strategies for the implementation of
digitalisation.
6.1 Research Limitations
Even though the research produces important
findings and implications for the SCM literature and
practitioners, there are several limitations. The
limitations have restricted the generalisability of the
findings across other industries and other countries.
First, the sample size is small with 35% of
response rate. Within this 35% respondents, 61.5%
were from small enterprise with less than 50
employees while half of the respondents reported
their annual sales revenue were less than RM50
million. Thus, there is limited diversity in terms of
business scale and operations hence, data collected
were unable to represent the whole industry and this
has restricted the generalisability of the findings.
Second, the study adopts a general overview and
assumption that pandemic has accelerated the
adoption of digitalisation and digitalisation drives
positive outcome toward SCP during pandemic. The
researcher acknowledged the gaps existed in
addressing the literature of this research topic and the
development of the theoretical framework. Critical
discussions on the aspects of business processes and
operational changes in relation to the types of digital
technologies and how these would impact the
company performance could be further explored.
Third, the research presents the SCP from the
perspective of five SC components, i.e.:
Procurement, Manufacturing, Distribution, Supplier
and Customer. Other components such as Marketing
and Sales, Warehouse, Technology Development and
Infrastructure were not included in data evaluation.
Fourth, the research has limited the scope of digital
solutions and did not comprehensive review all
available technologies such as Blockchain,
Simulation and 5G. Moreover, several technologies
such as ML and CPS have been more intensively
researched and developed in recent years, thus the
application of these technologies have yet to realise
its fullest potential and received lesser attention
comparing to BDA, IoT and Cloud [95]. Fifth, in-
depth discussion on interrelationships between
adoption level and influence on SCP among the SC
partners were limited in respect of the correlation of
dependent and independent variables to help explain
their influences through the effect of moderation or
mediation. Sixth, the structured research strategy has
limited the ability to collect more comprehensive
information to enable a more detailed analysis as
compared to mixed method approach [94]. Therefore,
the current research is unable to cover extensively the
pandemic impact on digitalisation in the field of SCM
across the vast industrial sectors.
6.2 Recommendations for Future Research
The current research has led to new knowledge
development. For instance, does pandemic impact the
level of digitalisation at different SC partners? Does
the level of digitalisation adopted at respective SC
partners affect the adoption level of other SC partners
and the influence on the performance of respective
SC partners? Will the performance of SC partner
impact its digital adoption level? Therefore, future
studies with multidimensional approach to address
these aspects are recommended. Detailed
examination could be augmented to explore the
digitalisation impact in the coordination and
relationship management with supplier and customer.
This will extend the boundary of internal operations
by including external operations ranging from
upstream to downstream of a well-integrated SC
network. The scope and location of the research
could be expanded to other industries such as
automotive, agriculture, construction, electrical and
electronics within Southeast Asia with
comprehensive research on similarities and
differences on adoption of innovative technologies
and industry performances, digitalisation strategies
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Volume 6, 2024
and challenges encountered among the industries and
countries. The process of SC focuses on five areas of
the supply chain which include plan, source, make,
deliver, and return [53,44]. Future research could
explore how to leverage digitalisation to manage the
aspect of return in a closed loop SC ecosystem.
Finally, further exploration on digitalisation and
job security could be conducted as current study find
that it is not a critical barrier to digitalisation which
is not inconsistent with previous research [96,97].
6.3 Final Conclusions
Scholars and industry experts have debated recent
pandemic has driven rapid digital transformation
[1,10,11]. Thus, the research is set out to identify to
what extend the impact of pandemic has on
digitalisation adoption, what digitisation solutions
were adopted within the SC structures and how will
digitalisation impact SCP by focusing on Malaysia
glove industry.
The current research adopts quantitative method
with structured online survey and interview. The
research questionnaire is categorized into two
sections that make up of closed-ended and open-
ended questions. Five main constructs with
respective measurements focusing on major aspects
of pandemic and business performance, pandemic
and digitalisation, digital solutions and level of
adoption, digitalisation and SCP and barriers to
digitalisation are developed. The calculated results
are analysed and interpreted to explain the
relationships of the examined variables. The research
findings indicate that majority of the respondents
expressed that pandemic has negatively impacted
their business performance and pandemic has
positively impacted the adoption of digitalisation.
The finding has answered research question RQ1.
BDA, IoT and CC are identified to be commonly
adopted within the SC structures while AR, AM and
[95] are least adopted. Survey also revealed not all
SC structures adopt digital solutions. Different
solutions were adopted across individual SC
structures with different adoption level. The finding
has answered research question RQ2. Overall, the
adoption level is considerably low in which CCRM
has the highest adoption level while Procurement has
the lowest adoption. The analysis also revealed that
SC structures would influence each other positively
at varying level of adoption. Digitalisation has
positively impacted SCP with improved digital
capabilities. The interrelationships among SCI, SCV,
SCR and CRS are found to be correlated and they
have implications toward SCP. This finding supports
the assumption of the conceptual framework and
answered research question RQ3. Change of work
culture has been identified as the key barrier to
digitalisation comparing to mindset, i.e.: minimise
investment and unknown ROI. The initiative for
digitalisation is negatively correlated to pandemic.
This finding has reinforced evidence for research
question RQ1.
The research contributes to the knowledge
development in SCP under the influence of pandemic
and broaden the understanding of the underlying
instruments that link and correlate to drive SCP
improvement. This study also enlightens the
practitioners on the understanding of innovative
technologies which could help company to minimise
negative impact triggered from unexpected events
that disrupted SC activities and the challenges in
implementing digitalisation. The research highlights
the findings restriction and ends with several
recommendations related to digitalisation and
coordination with supplier and customer,
digitalisation strategies and challenges, leverage
digitalisation in a closed loop SC ecosystem and
interrelationships among digitalisation, level of
adoption and performance of SC partners in a
multidimensional approach.
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