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Journal of
Information and
Knowledge
Management
Journal of Information and Knowledge Management (2025) Vol. 15, No. 1
www.jeeir.com
___________________________________________________
* Corresponding author. E-mail address: * nkmnur@gmail.com
©Authors, 2025
Big Data Analytics Adoption in Malaysia Digital Status
Companies: The Moderating Role of Training
Nur Khairiah Muhammad*, Nor Hasni Osman, Nurul Azita Salleh
School of Technology Management and Logistics, Universiti Utara Malaysia, 06010 Sintok, Kedah, Malaysia
ARTICLE INFO
ABSTRACT
Article history:
Received 23 December 2024
Revised 21 January 2025
Accepted 10 Mar 2025
Online first
Published 1 April 2025
This study examines the factors influencing Big Data adoption and its
impact on organizational performance within Malaysia Digital Status
Companies, particularly in the Global Business Services (GBS) sector.
Grounded in the Technology-Organization-Environment (TOE)
framework and Resource-Based View (RBV) theory, the study explores
the roles of data quality management, data security, ease of use, and top
management support, with training acting as a moderating variable.
Based on 272 survey responses analyzed using Partial Least Squares
Structural Equation Modelling (PLS-SEM), the findings reveal that data
quality management, ease of use, and top management support
contribute significantly to organizational performance, whereas data
security does not exhibit a significant effect. Furthermore, training
enhances the influence of ease of use, highlighting the importance of
intuitive technology and skill development. This study supports the
Malaysia Digital Economy Blueprint by advancing data-driven
strategies, strengthening digital infrastructure, and boosting economic
competitiveness.
Keywords:
Big Data Adoption (BDA)
TOE Framework
Resource-Based View (RBV)
INTRODUCTION
In recent years, increasing adoption of digital technology has generated an unparalleled boom in data
generation, influencing the modern corporate landscape (Dubey et al., 2021). This exponential rise is
projected to continue as digital transformation speeds up across businesses (Paul et al., 2024). Big Data,
defined by its massive volume, velocity, variety, and veracity (Kamarulzaman & Hassan, 2019; Su et al.,
2022), has emerged as a critical enabler of data-driven decision-making, allowing enterprises to increase
productivity, optimize operations, and gain a competitive advantage. In Malaysia, the incorporation of big
data is consistent with the country's digitization ambition, with important projects led by the Malaysia
Digital Economy Corporation (MDEC) and policies such as the Malaysia Digital Economy Blueprint
(Economic Planning Unit, 2021). Despite these efforts, Big Data adoption is still low, with only 36% of
businesses employing sophisticated data solutions. One of the most pressing challenges is the shortage of
skilled data professionals, projected to reach 15,000 (Yusoff et al., 2021), combined with organizational
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resistance and data security concerns. These barriers hinder companies, particularly within the Global
Business Services (GBS) sector, from fully leveraging Big Data to improve operational performance and
long-term sustainability (Hashim et al., 2021). While previous studies have identified technological,
organizational, and environmental challenges affecting Big Data adoption (Maroufkhani, et al., 2020),
research remains limited in examining the role of training in addressing Malaysia’s skills gap. This study
addresses this gap by combining the Technology-Organization-Environment (TOE) framework and the
Resource-Based View (RBV) theory to investigate how training modifies the impact of key adoption factors
such as data quality management, data security, ease of use, and top management support on organizational
performance.
Understanding this relationship is crucial because good training initiatives can boost technical skills,
minimize adoption resistance, and improve data-driven decision-making. This study presents a strategic
roadmap for organizations looking to manage the complexity of digital transformation by providing new
insights into the relationship between training and Big Data adoption. The findings are particularly
important for policymakers, industry leaders, and stakeholders seeking to improve Malaysia's position in
the global digital economy.
LITERATURE REVIEW
Technology Factors
Data quality and ease of use are critical for the effective adoption of Big Data under the technology factors.
High-quality data ensures reliability and accuracy, forming the backbone of actionable insights, while ease
of use facilitates user accessibility and technology acceptance (Parulian et al., 2023; Shanmugam et al.,
2023). Data quality, a critical technological focus, is fundamentally challenged by issues of incompleteness,
inaccuracy, and inconsistency. Incomplete data, frequently resulting from the massive volume and
unstructured nature of Big Data, complicates extraction, transformation, and integration processes, leading
to inefficiencies and reduced compliance capabilities (Ali, 2023; Arunachalam & Kumar, 2018). Inaccuracy
further hampers organizational efficiency, as poor quality of data disrupts supply chains and blow up
operational costs (Onyeabor & Ta’a, 2018; Shanmugam et al., 2023). Similarly, inconsistent data weakens
decision-making and strategic alignment, obstructing businesses from fully leveraging data-driven insights
(Alfred, 2019; Dias et al., 2021). A study by Chuah & Thurusamry, (2021) highlighted that the primary
challenges for companies in Malaysia, often lead to issues with data quality, as these companies may lack
the necessary infrastructure and expertise to manage and maintain high-quality data, resulting in
incomplete, inaccurate, or inconsistent datasets.
Ease of use, another factor under technology, influences the adoption and effectiveness of Big Data
technologies. Complex tools, non-intuitive interfaces, and integration challenges represent significant
barriers (Ajah & Nweke, 2019; Asiri et al., 2024a). Complex tools often induce user resistance, reducing
adoption rates and limiting operational efficiencies (Smith, 2023). Non-intuitive interfaces frustrate users,
leading to errors and underutilization, emphasizing the need for user-friendly designs (Thanabalan et al.,
2024). Integration challenges disrupt workflows, creating data silos and hindering insights (Dias et al.,
2021). In Malaysia, many organizations encountered difficulties in adopting big data analytics due to
complexities in data management and a lack of user-friendly tools. A study by Zian et al. (2024) highlighted
that technological challenges, including the complexity of Big Data tools and the absence of intuitive
interfaces, are significant barriers to adoption among Malaysian organizations. These challenges often lead
to user resistance and underutilization of Big Data capabilities. Addressing these technological
impediments is essential for organizations to achieve the promised benefits of Big Data, including enhanced
decision-making and improved performance.
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Organization Factors
Top management support is crucial for the successful adoption of Big Data Analytics solutions, playing a
key role in directly influencing organizational performance and contributing to organizational factors.
Resistance to change, often related to fear of disruption, unfamiliar workflows, and perceived job insecurity,
remains a major barrier. Clear communication, inclusive planning, and targeted training driven by senior
management can foster a culture of collaboration, ensuring employees understand and embrace the benefits
of Big Data (Hafizal et al., 2023; Reyes-Veras et al., 2021). In Malaysia Digital Status companies,
leadership commitment is particularly critical for addressing these challenges and achieving seamlessness
(Al-Khasawneh, et al., 2022; Reza et al., 2021). In Malaysia, the significance of top management support
in Big Data adoption has been highlighted in various studies. For instance, research by Wahab et al. (2021)
identified that factors such as relative advantage, technological infrastructure, absorptive capability, and
government support significantly influence the adoption of Big Data analytics in the Malaysian
warehousing sector. Additionally, Baig et al. (2019) found that the complexity of Big Data analytics can
have a negative impact on top management support. The challenges associated with the complexity of Big
Data technology may cause reluctance among top management to invest in such initiatives, thereby
hindering adoption efforts.
Lack of awareness about Big Data’s strategic value further hinders adoption. Many organizations
struggle with limited knowledge and fail to recognize the potential of big data to enhance decision-making
and innovation. Proactive initiatives, such as conferences and external collaborations, led by top
management, are crucial for bridging this knowledge gap and fostering organizational learning (Alsyouf,
et al., 2022; Nasrollahi et al., 2021). In this context, Malaysia Digital Status companies benefit significantly
from creating a culture of continuous learning to maximize Big Data's potential (El-Haddadeh et al., 2021;
Falahat et al., 2023).
Shifting priorities due to market changes and internal realignments often deprioritize Big Data
initiatives. Embedding big data goals into core organizational strategies ensures consistent focus and
resource allocation, even amidst evolving priorities (Falahat et al., 2023; Zian et al., 2024b). In the Global
Business Services (GBS) sector, senior management must align outsourcing practices with internal
capability development to achieve sustainable digital transformation (Hanafizadeh & Zareravasan, 2020;
Iranmanesh et al., 2023). Ultimately, Big Data Analytics should be an integral part of an organization’s
strategic roadmap, embraced by outsourcing partners to drive innovation and align with the headquarters'
long-term objectives.
Environment Factors
The environmental factors influencing data security significantly impact organizational performance in the
context of Big Data adoption. Data privacy, security threats, and data breaches are critical sub-factors that
shape the security landscape. Data privacy ensures the protection of sensitive information from
unauthorized access, emphasizing compliance with ethical and legal standards. According to Anwar et al.
(2021) and Marr (2018), the importance of robust privacy measures in fostering trust and mitigating risks,
ultimately enhancing organizational performance. Without adequate privacy safeguards, organizations risk
losing stakeholder confidence, hindering their operational success.
Security threats, including cyber-attacks and malware, pose significant risks to data integrity and
availability. Studies by Kim and Cho (2018) and Mangla et al. (2020) stress the need for advanced
protocols, such as intrusion detection systems, to counter evolving threats. Proactive threat management
ensures operational resilience and continuity, vital for organizational sustainability in a data-driven
environment. For example, a study examining cybersecurity behavior among Malaysian government
employees found that enhancing threat awareness and promoting protective habits through targeted training
programs significantly improved employees' cybersecurity practices. The findings suggest that well-
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designed training initiatives can lead to better compliance with security protocols and a reduction in security
incidents, thereby positively impacting organizational performance (Sulaiman et al., 2022).
Data breaches, characterized by unauthorized access to sensitive information, can result in financial
losses and reputational harm. Insights from Tao et al. (2019) and Ibrahim Ahmed et al. (2023) highlight the
importance of comprehensive breach prevention strategies to minimize vulnerabilities. Organizations with
effective breach responses demonstrate resilience and maintain trust, safeguarding their long-term
performance.
In summary, data security that encompasses data privacy, security threats, and data breaches holds
essential roles in influencing big data adoption and an organization’s performance. Organizations that invest
in robust data security measures are better equipped to protect their data, ensure compliance, and maintain
operational resilience. As the digital landscape continues to evolve, ongoing research and adaptation of
security practices will be essential for sustaining organizational performance in the face of emerging data
security challenges.
Training
Training is critical to Big Data adoption because it provides employees with necessary skills and fosters an
environment conducive to creativity. Structured training programs help firms manage technology obstacles,
improve user competency, and optimize data-driven decision-making (Majnoor & Vinayagam, 2023; Ujang
et al., 2023). Training improves technical abilities, allowing personnel to manage difficult analytics jobs,
integrate new technologies with old systems, and eliminate security (Salleh & Janczewski, 2019; Ujang et
al., 2023). Targeted training programs also encourage ongoing learning and flexibility, which directly
contributes to improved organizational performance (Baharuden et al., 2019b). Furthermore, raising
awareness through training closes knowledge gaps, promotes accountability, and encourages strategic
engagement with Big Data technology. Employees that are aware of the strategic benefit of Big Data are
more aligned with company goals, increasing the likelihood of adoption and success. Training also serves
as a moderator, increasing the links between data quality management, data security, ease of use, and
leadership support, ultimately improving organizational performance.
Ensuring excellent data quality is essential for companies that want to draw accurate and useful
insights. Inconsistencies, errors, and inadequate data can lead to incorrect analysis and poor decision-
making. Training programs address these difficulties by providing staff with the necessary data
management abilities. The Malaysia Digital Economy Corporation (MDEC) has implemented specific big
data training programs that focus on data extraction, transformation, and integration to ensure data
reliability (MDEC, 2022). Empirical studies show that structured data governance training efforts reduce
data processing errors and improve the accuracy of analytics-driven decision-making (Ahmed et al., 2024;
Reddy Koilakonda, 2024). Similarly, training programs improve ease of use by shortening the learning
curve associated with sophisticated Big Data applications. According to research, training enhances
perceived ease of use, leading to increased acceptance and seamless integration of Big Data Analytics
(BDA) into corporate workflows (Rob et al., 2024; Vysotskaya & Prokofieva, 2024). For example, Telekom
Malaysia's workforce training project has considerably increased employees' ability to use data analytics
for strategic decision-making ((Telekom Malaysia Berhad, 2022).
Data security is a critical factor for businesses employing Big Data, particularly as the volume of
processed information grows. Training programs that focus on cybersecurity knowledge, encryption
techniques, and regulatory compliance assist employees in recognizing and mitigating security risks. A
study of cybersecurity awareness in Malaysian businesses revealed that structured training programs
significantly reduced data breaches and boosted compliance with data protection rules (Sulaiman et al.,
2022). Furthermore, Malaysian financial institutions have adopted cybersecurity awareness programs to
protect sensitive consumer data, emphasizing the need for structured training in combating cyber risks
(Krishnan et al., 2023).
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Meanwhile, top management support is required for developing a data-driven culture, as leadership is
key in securing resources for Big Data initiatives. The Malaysian Center of Applied Data Science (CADS),
in collaboration with Harvard Commercial School, provides executive training programs that help senior
leaders understand the commercial implications of data analytics (CADS, 2024). This ensures that leaders
continue to aggressively support big data strategies in their organizations.
Organizations can maximize the benefits of Big Data use by tackling important post-adoption
challenges with targeted training programs. Training not only enhances technical proficiency and security
awareness, but it also fosters a leadership culture that prioritizes data-driven decision-making. Structured
training programs improve data quality, ease of use, data security, and top management support; all of
which are crucial to Big Data success. As Malaysia continues its digital transformation journey,
comprehensive training activities will be critical to increasing organizational performance and ensuring
long-term competitiveness in a changing technological landscape.
Organizational Performance
Organizational performance is about how well a company achieves its goals, focusing on key areas like
operational efficiency, market value, and competitive advantage (Gutterman, 2023; Soebroto & Budiyanto,
2021). Operational efficiency is all about making processes smoother and reducing costs while getting the
best out of available resources. Using tools like big data and advanced analytics can make a huge difference
here, helping organizations forecast better and make quick decisions based on real-time information (Côrte-
Real et al., 2020; Davenport, 2019). This not only saves money but also ensures that quality stays high.
Market value reflects how much a company is worth in the eyes of its stakeholders, often influenced
by how innovative and customer-focused it is. Companies that use big data effectively often see an increase
in their values by understanding their customers better, responding to market trends faster, and aligning
their actions with strategic goals (Dias, 2021; Gutterman, 2023). This creates a positive value of higher
stakeholder satisfaction and sustainable growth (Rubio-Andrés et al., 2022).
Competitive advantage comes from standing out and doing things better than competitors. For Malaysia
Digital Status companies, embracing Big Data is a game-changer, helping them make smarter decisions,
scale their operations, and deliver an exceptional customer experience (Akbari, 2024). The Table 1 below
highlighted previous studies on Big Data adoption in Malaysia.
Table 1. Previous studies on Big Data adoption in Malaysia
Author(s)
Year
Industry
Title
Key Findings
(Aziz et al.,
2024)
2024
Hotel
The impact of big data
analytics on innovation
capability and
sustainability
performance of hotels:
evidence from an
emerging economy
Achieving benefits involves
technology infrastructure, data
management capabilities, and a
data-driven culture, stressing
BDA's importance in innovation
and competitive advantage.
(Thanabalan et
al., 2024)
2024
Manufacturing
Big Data Analytics
Adoption in
Manufacturing
Companies : The
Contingent Role of Data-
Driven Culture
This study examines factors
influencing Big Data Analytics
(BDA) adoption in Malaysian
manufacturing companies and
its impact on performance.
Findings reveal BDA adoption
enhances financial and market
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performance, with data-driven
culture moderating financial
performance, offering strategic
insights for businesses.
(Zian et al.,
2024)
2024
Education
Technological,
organizational and
environmental factors
influencing on user
intention towards big data
technology adoption in
Malaysian educational
organization
Propose suitable technologies,
intensive training programs, and
managerial support to
encourage data-driven decision-
making, and collaborate with
legislators on Big Data
adoption.
(Abdullah
Sani et al.,
2023)
2023
Public
Technology,
Organization and
Environment as Strategic
Factors of Big Data
Analytics Readiness and
Acquisition Intention to
adopt Big Data Analytics
in Malaysian Libraries
The study finds that legal,
architectural, social, and market
factors are significant
challenges for SMEs in
adopting big data analytics,
according to Lessig’s Four
Modalities.
(Ibrahim
Ahmed et al.,
2023)
2023
Manufacturing
Rationalising Factors
Influencing the Effective
Utilisation of Big Data in
Malaysian Fintech
Companies
The report emphasizes technical
preparedness, a competent
workforce, and a strong
infrastructure. Fintech requires
strategic investments and
regulatory assistance to harness
Big Data for service, efficiency,
and competitiveness.
(Vachkova et
al., 2023)
2023
SME
Big data and predictive
analytics and Malaysian
micro-, small and medium
businesses
The study emphasizes the
challenges that SMEs face, such
as limited funds, qualified labor,
and technological
infrastructure, but also finds
them more adaptive to big data
adoption. Government
incentives and training can help
boost their competitiveness and
growth.
(Anawar et al.,
2022)
2022
Telecommunica
tion
Security and Privacy
Challenges of Big Data
Adoption: A Qualitative
Study in
Telecommunication
Industry
Highlights security and privacy
issues related to telecom Big
Data adoption, emphasizing
threats from data breaches,
complex regulatory
frameworks, and obsolete IT
infrastructure, all of which
necessitate robust and up-to-
date security solutions.
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Underpinning Theories
The integration of the Technology-Organization-Environment (TOE) framework and the Resource-Based
View (RBV) theory provides a robust foundation for understanding the factors influencing Big Data
adoption and its subsequent impact on organizational performance. The TOE framework, initially
developed by Tornatzky and Fleischer (1990), categorizes key drivers into three dimensions: technology
(data quality management, ease of use), organizational (top management support), and environmental (data
security). This approach highlights how external pressures, such as regulatory requirements and
competition, interact with internal organizational readiness to shape adoption outcomes (Hashim et al.,
2022; Salleh & Janczewski, 2019; Zian et al., 2024). The RBV theory complements this by emphasizing
the strategic importance of internal resources, such as human capital and training, which are critical for
leveraging Big Data technologies effectively (Barney, 1991; Garavan, 2020). Training, in particular, serves
as a key moderating variable, transforming technical complexity into strategic opportunities by enhancing
workforce competencies and ensuring seamless technology integration (Al-Khasawneh et al., 2022; Wahab
et al., 2021). Recent studies validate this integrated approach, demonstrating how organizations can
strategically align external and internal factors to overcome barriers and achieve competitive advantages
(Ibrahim Ahmed et al., 2023; Maroufkhani, Tseng, et al., 2020). This dual-framework approach is
particularly relevant for Malaysian Global Business Services (GBS) companies, addressing their unique
challenges in navigating global operational complexities and local regulatory demands.
Hypothesis Development
The development of hypotheses in this study is grounded in a robust theoretical foundation through the
integration of Technology-Organization-Environment (TOE) framework and the Resource-Based View
(RBV) theory to explore the determinants influencing Big Data adoption and its impact on organizational
performance. The combination of these two theories is in accordance with current research. For example,
(Al-Khasawneh, et al., 2022) underline the importance of taking a holistic approach to understanding both
internal (RBV) and external (TOE) factors that influence Big Data adoption and performance. These studies
emphasize the need for a comprehensive approach that encompasses both external technological aspects
and internal resources in boosting organizational performance during digital transformation.
Data Quality Management and Organizational Performance
High-quality data is essential for reliable analysis and decision-making, encompassing key attributes such
as accuracy, relevance, completeness, timeliness, and accessibility. Without these characteristics,
organizations risk making erroneous decisions that lead to operational inefficiencies and poor strategic
outcomes (Ghasemaghaei & Calic, 2019; Nilashi et al., 2023; Wook et al., 2021). Studies have highlighted
the detrimental effects of poor data quality on organizational performance. Incomplete data can create
information gaps, increasing risks in decision-making and reducing transparency (Solana-González et al.,
2021). Inaccurate data disrupts efficiency, raising operational costs as companies work to correct errors
(Onyeabor & Ta’a, 2018; Shanmugam et al., 2023). Inconsistent data further complicates decision-making
by hindering integration efforts, leading to misaligned strategies (Dias et al., 2021; Nilashi et al., 2023).
From the Technology-Organization-Environment (TOE) framework perspective, data quality is a key
technological factor that significantly influences organizational performance. High-quality data ensures
more accurate, relevant, and timely decision-making, thereby enhancing operational efficiency and strategic
effectiveness (Ghasemaghaei & Calic, 2019; Nilashi et al., 2023). The TOE framework positions
technological readiness, including data quality, as a crucial driver of performance improvements through
Big Data adoption (Tornatzky and Fletscher, (1990). Effective data quality management strengthens data
integrity, completeness, and accuracy, which are essential for high-performing organizations Dias, (2021).
Based on the above discussion, the study proposes the below hypothesis:
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H1: There is a significant positive relationship between data quality management and organizational
performance.
Data Security and Organizational Performance
The increasing reliance on data-driven decision-making has raised concerns about data security, particularly
regarding the protection of sensitive information (Amalina et al., 2019; Anwar et al., 2021; Asif & Hassan,
2023; Falahat et al., 2023). Ensuring data integrity, confidentiality, and availability is crucial for
maintaining corporate trust, regulatory compliance, and operational reliability. Sweeney (1997) and other
early discussions on data security highlighted its significance in public health, emphasizing the need to
balance technological innovation with regulatory frameworks. This perspective is further supported by Dias
et al. (2021) and Fatt & Ramadas, (2018).
Data privacy plays a vital role in building stakeholder confidence and ensuring adherence to legal and
ethical standards (Anwar et al., 2021; Marr, 2018; Salleh & Janczewski, 2019). Cyber threats, including
malware and unauthorized access, pose significant risks to data integrity and operational continuity (H. Y.
Kim & Cho, 2018; Salleh & Janczewski, 2019). Effective security threat management not only mitigates
these risks but also enhances organizational performance, as evidenced by studies from (Asiri et al., 2024a;
Ibrahim Ahmed et al., 2023; Tao et al., 2019).
Data security is classified as an environmental factor within the Technology-Organization-Environment
(TOE) framework, as it aligns with regulatory compliance, data protection laws, and cyber risk management
(Anawar et al., 2022). As organizations adopt Big Data technologies, preventing breaches remains critical
to sustaining operational resilience and stakeholder trust.
Based on the above discussion, this study proposes the following hypothesis:
H2: Positive relationship between data security and organizational performance.
Ease of Use and Organizational Performance
Davis (1989) proposed that perceived ease of use is essential in technology adoption as it influences user
acceptance and engagement with digital systems. Contemporary studies (Ghaleb et al., 2021; Haddad et al.,
2019; Loh & Teoh, 2021; Thanabalan et al., 2024) have refined this concept, emphasizing minimal
cognitive effort and efficient task execution as key determinants of user satisfaction. Ease of use directly
enhances organizational effectiveness by simplifying adoption processes and reducing training costs
(Mohamad et al., 2020). A user-friendly system promotes widespread adoption, maximizing its intended
benefits (Smith, 2023). Furthermore, it increases employee productivity by reducing cognitive burden and
facilitating faster decision-making (Asiri et al., 2024a).
Within the Technology-Organization-Environment (TOE) framework, ease of use is a critical
technological factor for effective Big Data implementation. Ensuring system accessibility and alignment
with user needs contributes to improved organizational performance and competitive advantage (Asiri et
al., 2024b; Ujang et al., 2023). In summary, ease of use serves as a key enabler of Big Data adoption. It not
only optimizes resource utilization but also enhances organizational adaptability in an increasingly data-
driven business landscape.
Given these findings, this study proposes the following hypothesis
H3: Positive relationship between ease of use and organizational performance.
Top Management Support and Organizational Performance
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Top management support is critical in promoting organizational performance, particularly the adoption of
cutting-edge technologies like Big Data. Onyekwere et al. (2023) emphasized that effective organizational
change requires strong leadership. In line with this perspective, successful leaders must establish a clear
vision, develop strategic objectives, communicate effectively, and foster commitment to change initiatives
to achieve sustainable performance improvements.
Leadership engagement entails establishing a clear goal, assigning appropriate resources, and building
a culture that promotes risk-taking and continual learning. Without this support, companies frequently
experience issues such as insufficient training, low employee engagement, strategic misalignment, and
reluctance to change (El-Haddadeh et al., 2021; Schroeck et al., 2012).
Furthermore, Mikalef & Gupta (2021) argued that senior management must address leadership, human
resource management, technology capabilities, and decision-making to fully realize Big Data's potential.
This is consistent with the Technology-Organization-Environment (TOE) framework, which identifies top
management support as a critical organizational element that supports resource allocation, strategic
alignment, and opposition mitigation, hence promoting Big Data adoption.
Based on this discussion, the study proposes the following hypothesis:
H4: Positive relationship between top management support and organizational performance.
Training as a Moderator in Data Quality Management and Organizational Performance
Training is critical to accelerate Big Data adoption and improving organizational performance. It provides
personnel with the skills and information required to effectively use Big Data technology, resulting in better
decision-making and operational efficiency (Christopher & Nelson, 2024). While training is frequently
viewed as a direct influencer of technology adoption, its role as a moderating factor between Big Data
adoption and organizational performance is underexplored, particularly in Malaysian organizations (Al-
Rahmi et al., 2019; Baharuden et al., 2019b). Chui et al. (2021) argued that companies that invest in training
can improve data literacy and analytical skills, resulting in better performance outcomes. Training enhances
technical abilities (Baharuden et al., 2019b), assists in navigating the difficulties of system integration
(Salleh & Janczewski, 2019), and raises awareness to promote responsibility and informed decision-making
(Maroufkhani et al., 2020). However, it should be highlighted that training may not always produce
beneficial results due to challenges such as outdated content and a lack of ongoing assistance (Thanabalan
et al., 2024). The study investigates how insufficient training in large corporations affects the outcome of
Big Data adoption.
From the discussion, the study proposes the following hypothesis:
H5(a): Training significantly moderates the relationship between data quality management and
organizational performance.
Training as a Moderator in Data Security and Organizational Performance
Ensuring robust data security is paramount for organizations adopting big data, as security breaches can
lead to financial losses, reputational damage, and regulatory non-compliance (Tao et al., 2019). Effective
data security management involves implementing encryption protocols, access controls, and intrusion
detection systems to safeguard sensitive information (Ntizikira et al., 2023). However, the effectiveness of
these security measures is heavily dependent on employees' ability to understand, implement, and adhere
to data security policies. Training plays a crucial moderating role by equipping employees with the
necessary skills to mitigate cybersecurity threats, identify vulnerabilities, and comply with security
frameworks (Tolossa, 2023). Without adequate training, employees may inadvertently expose the
organization to data breaches through human errors, phishing attacks, or weak password management,
thereby undermining the overall effectiveness of data security initiatives (Amoresano & Yankson, 2023).
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From the discussion, the study proposes the following hypothesis:
H5(b): Training significantly moderates the relationship between data security and organizational
performance.
Training as a Moderator in Ease of Use and Organizational Performance
The ease of use of Big Data technologies significantly influences their adoption and subsequent impact on
organizational performance. When employees find data systems intuitive and user-friendly, they are more
likely to engage with them effectively, leading to improved decision-making and operational efficiency
(Davis, 1989; Wamba et al., 2017). However, despite advancements in user-friendly interfaces, many
organizations still encounter challenges related to complex tools, non-intuitive designs, and integration
difficulties (Mlekus et al., 2020). Training serves as a crucial moderating factor in this relationship by
equipping employees with the necessary skills to navigate Big Data systems efficiently, reducing the
learning curve, and enhancing system usability (Maroufkhani et al., 2019). Without adequate training, even
highly sophisticated yet user-friendly platforms may fail to deliver optimal outcomes due to a lack of user
competence and confidence in handling data-driven processes (Thanabalan et al., 2024).
From the discussion, the study proposes the following hypothesis:
H5(c): Training significantly moderates the relationship between ease of use and organizational
performance.
Training as a Moderator in Top Management Support and Organizational Performance
Top management support plays a crucial role in facilitating Big Data adoption by providing strategic
direction, allocating necessary resources, and fostering a data-driven culture within organizations (Shafique
et al., 2024). When leadership actively champions Big Data initiatives, employees are more likely to
perceive the value of such technologies and align their efforts accordingly, leading to improved
organizational performance (Prakash, 2024). However, despite strong managerial commitment, challenges
related to skill gaps and technological complexity may hinder the effective implementation of Big Data
strategies (Maroufkhani et al., 2019). Training serves as a key moderating factor in this relationship by
ensuring that employees develop the necessary competencies to execute top management’s strategic vision
effectively (Baharuden et al., 2019). Without structured training programs, even well-supported Big Data
initiatives may face resistance, operational inefficiencies, and suboptimal performance outcomes
(Thanabalan et al., 2024).
From the discussion, the study proposes the following hypothesis:
H5(d): Training significantly moderates the relationship between top management support and
organizational performance.
METHODOLOGY
Framework of the Study
Based on Figure 1, the theoretical framework of the study integrates the Technology-Organization-
Environment (T-O-E) framework with the Resource-Based View (RBV) to investigate the relationship
between key determinants and organizational performance in the context of Big Data adoption. The T-O-E
framework is particularly suitable for exploring how technological, organizational, and environmental
factors influence the adoption of Big Data technologies, offering a structured approach to analyze external
and internal determinants (Tornatzky & Fletscher, 1990). The factors selected are Data Quality
Management, Data Security, Ease of Use, and Top Management Support.
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©Authors, 2025
In parallel, the RBV theory emphasizes the strategic role of internal resources, particularly training, in
enhancing organizational capabilities and supporting technology adoption (Barney, 1991). Training in big
data analytics, as introduced in this study, is conceptualized as a moderating variable that strengthens the
relationship between organizational determinants and performance. According to (Al-Khasawneh, et al.,
2022), recent research has validated the synergistic application of the T-O-E and RBV frameworks,
highlighting the importance of training interventions in addressing complex adoption scenarios and
enhancing organizational performance. This integrated framework provides a comprehensive lens for
understanding Big Data adoption and its organizational impact, particularly in the context of Malaysia’s
Digital Status companies.
Figure 1. Research Framework
Sample Size
The data was collected from 428 Global Business Services (GBS) companies registered with the Malaysia
Digital Economy Corporation (MDEC), targeting data professionals and management board members.
Krejcie and Morgan's (1970) table determined a minimum sample size of 201 respondents, while G*Power
(2013) confirmed 129 as sufficient for a 95% confidence level. The study began with simple random
sampling, a strategy generally renowned for its ability to reduce selection bias and improve generalizability.
However, due to poor response rates, a common issue in organizational survey the approach was changed
to convenience sampling during the COVID-19 pandemic. This change was required to maintain data
integrity, ensuring that the study included qualified responders with technical competence and strategic
supervision of Big Data technology.
The decision to employ dual sampling techniques was carefully made to balance methodological rigor
with practical constraints. Research on survey-based methodologies highlights the importance of flexibility
in survey methodologies to ensure valid outcomes, especially during unforeseen circumstances like the
COVID-19 pandemic (K.S. Kim, 2021).
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©Authors, 2025
Data Collection Procedure
For this study, a self-administered online questionnaire hosted on Google Forms was used for data
collection. The survey link was shared via email, WhatsApp, LinkedIn, and social media for cost-effective,
confidential participation (Noor, 2020). To address low response rates, follow-up reminders were sent
(Shiyab et al., 2023). The questionnaire employed a five-point Likert scale ("strongly disagree" to "strongly
agree") to effectively capture respondents' perceptions, enhancing data validity and reliability (Coombes et
al., 2021). This method ensured ease of administration and robust engagement despite challenges associated
with online surveys.
DATA ANALYSIS AND RESULT
Data Analysis
This study employed SmartPLS version 4.0 to perform Partial Least Squares Structural Equation Modeling
(PLS-SEM), evaluating measurement and structural models. As illustrated in Table 2, structural model
results revealed significant positive relationships between data quality management (t=2.19, p=0.029), ease
of use (t=6.391, p<0.001), and top management support (t=2.483, p=0.013) with organizational
performance, while data security showed no significant effect (t=1.066, p=0.286). Moderation analysis
indicated that training significantly enhanced the relationship between ease of use and organizational
performance (t=1.854, p=0.032) but not for other variables.
The findings suggest that to achieve significant improvements in organizational performance, the
implementation of big data solutions should prioritize the adoption of user-friendly and intuitive systems.
Furthermore, focused training programs are essential to maximize the skill capability of data professionals
in the organizations.
Table 2: Result Path Analysis (t- value, p-value, f2 )
Relationship
t-value
(β/σ)
p-value
(α=0.05)
f²
Effect Size
Supported
H1: DM → OP
2.19
0.029
0.035
SMALL
YES
H2: DS → OP
1.066
0.286
0.008
NO EFFECT
NO
H3: EOU → OP
6.391
0.000
0.217
MODERATE
YES
H4: TM → OP
2.483
0.013
0.028
SMALL
YES
TNG × DM → OP
1.379
0.084
0.035
SMALL
NO
TNG × DS → OP
1.221
0.111
0.012
NO EFFECT
NO
TNG × EOU →
OP
1.854
0.032
0.210
MODERATE
YES
TNG × TM → OP
0.587
0.279
0.032
SMALL
NO
Data Quality Mgt (DM), Data Security (DS), Ease of Use (EOU), Top Management (TM), Org Performance
(OP), Training (TNG)
Measurement Model Assessment
Convergent validity was established in this study by evaluating factor loadings, Composite Reliability (CR),
and Average Variance Extracted (AVE), as recommended by Hair (2009). Table 3 illustrates the factor
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©Authors, 2025
loadings were retained for all 39 items, ranging between 0.569 and 0.888, meeting the acceptable threshold
of 0.4 and exceeding 0.7 for most items. Cronbach’s alpha values ranged from 0.811 to 0.938, and CR
values ranged from 0.869 to 0.950, both surpassing the recommended minimum of 0.7 (Fornell & Larcker,
1981; F. J. Hair et al., 2014). The AVE values, ranging from 0.548 to 0.731, also exceeded the
recommended threshold of 0.5, further confirming convergent validity. Specifically, AVE values were
0.621 for data quality management, 0.646 for data security, 0.548 for ease of use, 0.731 for top management
support, 0.655 for organizational performance, and 0.570 for training. These results validate the outer
model, confirming its reliability and internal consistency for subsequent structural analysis.
Table 3: Convergent Validity Analysis
Factor
Item
Cronbach’s
alpha
Composite
reliability
Average Variance
Extracted (AVE)
Data Quality Mgt (DM)
7
0.896
0.919
0.621
Data Security (DS)
7
0.907
0.927
0.646
Ease of Use (EOU)
6
0.830
0.878
0.548
Top Management (TM)
7
0.938
0.950
0.731
Org Performance (OP)
7
0.912
0.930
0.655
Training (TNG)
5
0.811
0.869
0.570
Discussion of study findings
This study presents empirical information on the factors influencing Big Data adoption and its influence on
organizational performance in Malaysia Digital Status (MDS) organizations. The study uses the
Technology-Organization-Environment (TOE) framework and the Resource-Based View (RBV) paradigm
to investigate how data quality management, ease of use, data security, and top management support affect
performance, with training acting as a moderator. Four of the eight hypotheses evaluated were supported,
whereas the direct effect of data security on organizational performance and the moderating effects of
training on data quality management, data security, and top management support were not statistically
significant. This section explains the outcomes in light of the existing literature, emphasizing comparative
analyses and explaining why certain relationships persist while others do not.
Data Quality Management and Organizational Performance (H1 supported)
The study confirms a significant positive relationship between data quality management and organizational
performance (H1 supported), which is consistent with previous research (Al-madhrahi et al., 2022; Dias et
al., 2021; Shanmugam et al., 2023; Wook et al., 2021). High-quality data improves decision-making
capabilities, operational efficiency, and strategic insights, making it a key driver of performance. This study
confirms the arguments of Peltier et al. (2013) and Kalra (2020), who underlined that organizations’
willingness to invest in Big Data technology is heavily influenced by their data quality management
capabilities.
While this relationship is well-established, the study also emphasizes challenges associated with
incomplete, inaccurate, and inconsistent data, echoing concerns raised by Onyeabor and Ta’a (2018) about
the complexities of managing Big Data quality in real-world applications. Poor data quality can impair
analytical accuracy, weaken predictive capacities, and erode trust in data-driven decision-making (Khong
et al., 2023; Parker & Parker, 2023). These findings imply that ongoing expenditures in data governance
frameworks and standardized quality assurance processes are critical to maintaining the benefits of Big
Data adoption.
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Data Security and Organizational Performance (H2 not supported)
The study concludes that data security has no significant relationship with organizational performance
(H2, not supported), which is consistent with prior findings by Ghasemaghaei (2020), Yadegaridehkordi et
al. (2018) and Nilashi et al. (2023). While data security is an important issue in compliance and risk
management, it does not always convert into obvious performance gains. A possible explanation is that
companies operating under strict regulatory frameworks (e.g., financial services, healthcare) have baseline
security measures in place, making security a non-differentiating factor in generating performance gains
(Al-Khasawneh et al., 2022). Furthermore, Dias et al. (2021) and Ghaleb et al. (2023) propose that
companies prioritize benefits such as innovation and operational efficiency over security threats when
implementing Big Data technology. This study calls into question long-held beliefs that data security is a
critical facilitator of performance. Instead, it suggests that other elements, such as strategy alignment and a
data-driven culture, may have a stronger impact on Big Data adoption success.
Ease of Use and Organizational Performance (H3 supported)
The findings show that ease of use has a significant positive effect on organizational performance (H3),
which is consistent with studies by Asiri et al. (2024), El-Haddadeh et al. (2021), and Loh & Teoh (2021).
User-friendly Big Data technologies improve adoption rates by reducing technological complexity,
lowering training expenses, and improving employee engagement. Furthermore, Grover et al. (2018) and
Fosso Wamba et al. (2019) show that enterprises that deploy highly sophisticated big data solutions without
taking usability into account frequently face adoption resistance and inefficiencies. These data support the
claim that ease of use is a critical factor in converting technical investments into measurable performance
outcomes.
Top management support and Organizational Performance (H4 supported)
The study also finds a substantial positive effect between top management support and organizational
performance (H4 supported), which is similar to previous research by Al-Rahmi et al. (2019), Falahat et al.
(2023), and Asiri et al. (2024). Senior leadership is critical in aligning Big Data efforts with strategic goals,
providing resources, and cultivating a data-driven culture (Ghaleb et al., 2021; Haddad et al., 2019).
However, the efficacy of top management support is determined by execution. According to Tabesh et al.
(2019) and Huynh et al. (2023), leadership commitment is insufficient unless it is supported by operational
capabilities, staff engagement, and aligned incentives. The findings demonstrate the importance of active
managerial involvement beyond initial support in ensuring that big data methods lead to persistent
performance improvements.
Training and Data Quality Management (H5a not supported)
The outcomes show that training has no significant effect on the relationship between data quality
management and organizational performance (H5a is not supported). While training helps individuals
improve their technical abilities, it does not directly improve the essential characteristics of data quality,
such as correctness, completeness, and consistency. This is consistent with the views of Mahmood et al.
(2023) and Akter et al. (2016), who argue that data quality improvements are mostly driven by systematic
governance, strong data management frameworks, and technical investments rather than single training
efforts. These findings indicate that businesses should prioritize automated data validation processes,
stringent data governance regulations and real-time data monitoring systems to assure high-quality data,
rather than relying exclusively on training programs to handle data quality issues.
Training and Data Security (H5b not supported)
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Training has no significant effect on the relationship between data security and organizational performance
(H5b is not supported). This finding is similar to previous studies by Maroufkhani et al. (2019) and
Nasrollahi et al. (2021), which found that regulatory compliance, organizational risk appetite, and
technology security measures had a greater influence on security performance than staff training alone. The
success of security training is heavily influenced by an organization's overall cybersecurity strategy, as well
as its capacity to incorporate security protocols into operational workflows (Anawar et al., 2022). This
suggests that, while security awareness campaigns are vital, they may not immediately improve
organizational performance unless combined with complete security policies, advanced encryption
technology, and proactive threat detection techniques.
Training and Ease of Use (H5c supported)
The study finds that training significantly enhances the relationship between ease of use and organizational
performance (H5c supported), corroborating findings by Fosso Wamba et al. (2019) and Hadidi & Power,
(2020). This suggests that training acts as a facilitator, helping employees leverage user-friendly tools more
effectively. According to Alzahrani and Seth (2021) and Grover et al. (2018), training bridges knowledge
gaps, enhances digital literacy, and reduces resistance to technology adoption, all of which contribute to
higher performance outcomes. These findings indicate that even the most intuitive big data tools require
structured training programs to maximize their potential.
Training and Top Management Support (H5d not supported)
Training has no substantial effect on the relationship between top management support and organizational
performance (H5d is not supported). While top management support is critical in creating a data-driven
culture and pushing Big Data adoption, training does not always increase its influence on performance
results. This supports the findings of Ijab et al. (2019) and Hashim et al. (2021), who contend that strategic
decision-making at the leadership level frequently occurs independently of employee training initiatives.
Effective top management assistance is primarily demonstrated by budget allocation, long-term strategic
vision, and the promotion of a data-driven culture, rather than through direct training interventions. These
findings underline the importance of companies aligning training programs with strategic leadership
initiatives to ensure that staff development efforts result in demonstrable performance benefits.
LIMITATIONS AND RECOMMENDATIONS
This study offers valuable insights into the relationship between Big Data adoption and organizational
performance within Malaysia Digital Status (MDS) companies. However, some limitations must be
acknowledged. First, the study employed a one-dimensional sampling method, focusing on managers and
data specialists. Future research should adopt a multidimensional sampling approach, incorporating
respondents across organizational levels and utilizing both quantitative and qualitative methods, such as
surveys and in-depth interviews, to gain a more comprehensive understanding of organizational dynamics.
Second, the study focused exclusively on MDS companies within the Global Business Services (GBS)
sector, limiting the generalizability of findings to other industries. Expanding future research to sectors like
manufacturing, agriculture, and healthcare would provide actionable insights into sector-specific big data
challenges and opportunities, contributing to Malaysia’s digital transformation.
Lastly, the use of a cross-sectional study design limits the ability to observe the evolving impacts of Big
Data adoption. A longitudinal design would allow researchers to track changes over time, particularly the
long-term effects of training on organizational performance. This approach would provide deeper insights
into how organizations adapt to technological advancements and workforce development.
For recommendations, expanding incentives for skill certification and fostering collaboration between
academia, industry, and government would significantly help bridge the talent gap and build a robust
pipeline of skilled professionals. Furthermore, organizations, particularly those in the GBS sector, should
61 Muhamad et al. / Journal of Information and Knowledge Management (2025) Vol. 15, No. 1
©Authors, 2025
prioritize investments in training programs tailored to their specific needs. Structured and continuous
training focusing on data governance, analytics, cybersecurity, and ethical data practices would strengthen
internal capabilities and reduce reliance on external providers.
CONCLUSION
This study explored the underexplored role of training as a moderating factor, emphasizing its critical
influence on improving Big Data adoption outcomes in Malaysia Digital Status Companies. Using the
Technology-Organization-Environment (TOE) framework integrated with the Resource-Based View
(RBV) theory, the study provides a comprehensive perspective on how organizational and environmental
factors interact to impact performance.
The findings address Malaysia’s challenges in digital transformation, including low adoption rates and
a shortage of skilled data professionals. This study offers practical contributions by presenting actionable
recommendations for policymakers, government agencies, and businesses to improve Big Data strategies
and develop training programs aligned with national initiatives like the Malaysia Digital Economy
Blueprint. These recommendations aim to enhance organizational competitiveness and economic growth
through effective data management practices.
The study further emphasizes the ethical dimension, advocating for data integrity based on Islamic
principles, such as the Quranic injunction to uphold truthfulness and transparency (Quran 2:42). This
perspective highlights the importance of maintaining data accuracy and trust, which are critical to strategic
decision-making and accountability in Big Data adoption. From a theoretical perspective, this study
advances existing frameworks by incorporating training, offering a novel lens to analyze Big Data adoption
dynamics. Its findings provide a roadmap for improving organizational performance, with broader
implications for replicability across sectors and regions, fostering innovation and sustainable practices
globally.
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