The Impact of ERP Assimilation on Mass Customization Capability: A Dynamic Capabilities View

Abstract

Mass customization (MC) is a crucial strategy for manufacturers to gain competitive advantages. Enterprise resource planning (ERP) systems are widely adopted in manufacturing. This study applied the dynamic capabilities view (DCV) to explore how firms leverage ERP systems to facilitate the implementation of MC. This study examined the effect of ERP assimilation (ERPA) and organizational agility (OA) on mass customization capability (MCC), the mediating role of OA, and the moderating role of absorptive capacity (AC). Data were collected using a self-administered questionnaire survey that yielded 166 responses in Jiangsu province, China. The partial least square structural equation modelling (PLS-SEM) approach was employed to test the study hypotheses. Results revealed that ERPA affects OA, and OA also affects MCC. However, no significant relationship was found between ERPA and MCC. Again, data supported the mediating effect of OA between ERPA and MCC and the moderating effect of AC between ERPA and OA.

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Date of publication xxxx 00, 0000, date of current version xxxx 00, 0000.
Digital Object Identifier 10.1109/ACCESS.2017.Doi Number
The Impact of ERP Assimilation on Mass
Customization Capability: A Dynamic
Capabilities View
LIU ZONGYUAN1, HUO HAIYAN1
1Azman Hashim International Business School, Universiti Teknologi Malaysia, Kuala Lumpur 54100, Malaysia.
Corresponding author: Huo Haiyan (haiyan.huo@graduate.utm.my).
ABSTRACT Mass customization (MC) is a crucial strategy for manufacturers to gain competitive
advantages. Enterprise resource planning (ERP) systems are widely adopted in manufacturing. This study
applied the dynamic capabilities view (DCV) to explore how firms leverage ERP systems to facilitate the
implementation of MC. This study examined the effect of ERP assimilation (ERPA) and organizational
agility (OA) on mass customization capability (MCC), the mediating role of OA, and the moderating role of
absorptive capacity (AC). Data were collected using a self-administered questionnaire survey that yielded
166 responses in Jiangsu province, China. The partial least square structural equation modelling (PLS-SEM)
approach was employed to test the study hypotheses. Results revealed that ERPA affects OA, and OA also
affects MCC. However, no significant relationship was found between ERPA and MCC. Again, data
supported the mediating effect of OA between ERPA and MCC and the moderating effect of AC between
ERPA and OA.
INDEX TERMS Absorptive Capacity, ERP Assimilation, Mass Customization Capability, Organizational
Agility
I. INTRODUCTION
Given the rising trend of personalized consumer
preferences in the highly competitive market, many
businesses are contemplating mass customization (MC) as
a novel production strategy to bolster their competitive
edge [1]. Mass customization capability (MCC) refers to
“the capability to produce a wide range of customizable
product options in significant quantities for a large or niche
market while maintaining cost-effectiveness, prompt
delivery, and high quality [2].” MC combines the benefits
of precision in single-piece production with the speed and
cost-effectiveness of mass production. The adoption of this
strategy holds significant appeal for customers. Still, it
presents a substantial obstacle for a firm and poses a
potential risk of failure, mainly due to the rise in design and
manufacturing expenditures [3]. Task complexities are
heightened in the MC environment. Because of
differentiated customer requirements, an expanded range of
products, and greater inter-dependency across the supply
chain. Consequently, the level of uncertainty regarding
tasks increases, as does the volume of information that must
be processed [4]. To effectively and efficiently implement
MC under such circumstances, a firm must enhance its
information processing capabilities to accommodate the
increased demands [5].
Enterprise resource planning (ERP) systems have been
proposed to address the complexities associated with MC [6].
ERP refers to a set of systems a business employs to oversee
its financial operations and fundamental business functions
[7]. ERP systems, which can be quickly adjusted, allow the
development of flexible production with high productivity,
low cost, and large varieties. That meets the requirements of
MC [8]. However, Hong et al. [9] have tested the relationship
between various information technologies (IT) usage and
MCC. They found no significant relationship between the
usage of ERP systems and MCC. Thus, Peng et al. [4]
suggested that future studies should apply measurements that
effectively reflect the degree of IT usage and investigate
related theories and constructs in the field of MC.
In response to them, this study applies ERP assimilation
(ERPA) to measure the extent to which firms use ERP systems
in MC. Because ERPA provides a more accurate gauge of
post-adoption success than mere implementation [10], it is
defined as “the extent ERP technology is diffused in routine
business processes and the degree to which it supports
business decision-making at operational and strategic levels
[11]. ” ERPA is an ongoing and extended process, and the full
benefits of ERP applications can only be realized through
thorough integration into the organization [12], [13].
Notwithstanding the acknowledged necessity to facilitate
ERPA, many firms cannot fulfil this requirement [14]. In
China, despite firms successfully adopting ERP, many
struggle with assimilating the system, preventing them from
realizing its full range of advantages [10]. A previous study
This article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2024.3364390
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/

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ascertained that high MC performers employ ERP systems
more extensively than low MC performers in daily business
activities management [9]. Therefore, this study proposes that
a high level of ERPA can facilitate MCC for firms.
A prior study posited that firms must harness the full
potential of ERP systems to realize their value, necessitating
the cultivation of corresponding organizational capabilities
[15]. Therefore, this study postulates that ERP may exert its
influence on MCC through the mediation of other
organizational capabilities. MC strategies strive to provide
personalized products tailored to individual customers' needs
while minimizing the reduction in production efficiency.
Achieving this balance requires high organizational agility
(OA) [16]–[18]. OA signifies a firm's capability to swiftly and
effectively adapt and change in response to evolving
circumstances [19]. Empirical evidence has demonstrated a
positive correlation between ERPA and OA [20], [21].
Numerous scholars recognize OA as a vital factor that fosters
MCC [22], [23]. Thus, this study proposes that there is a high
possibility that a mediating effect of OA between ERPA and
MCC may exist.
Aburub [24] posited that establishing agility via ERP
systems may be contingent upon additional variables.
Therefore, future studies should investigate how IT-based
assimilation improves OA [25]. Assimilation of complex
technology is a process of mutual adaptation between
technology and mainstream business process, institutional
structure, and knowledge, which requires firms to improve
their absorptive capacity (AC) [26]. AC is “a firm's ability to
recognize the value of new information, assimilate it, and
apply it to commercial ends [27].” However, the IT-OA
relationship continues to face challenges regarding the
significance of AC [28]. As an organizational capability, AC
can potentially affect ERPA and its relationship with OA. AC
is positively related to ERPA, but differential investment in
resources results in varying levels of AC among firms. Those
with low AC often struggle with ERPA, leading to adverse
financial and operational consequences [29]. Firms will also
fail to obtain OA if AC is lacking. Thus, this study proposes
the possibility of a moderating effect of AC that strengthens
or weakens the relationship between ERPA and OA.
This study explores the connection between AC, ERPA,
OA, and MCC in manufacturing firms in Jiangsu province,
China. This study addresses four main research questions:
1. Does ERPA affect MCC?
2. Does ERPA affect OA?
3. Does OA affect MCC?
4. Does OA mediate the relationship between ERPA
and MCC?
5. Does AC affect the relationship between ERPA and
OA?
The rest of this study discusses the literature and hypothesis
development, followed by methodology, analysis, and
findings. Then, a discussion will further explain the findings.
Finally, the rest presents theoretical and managerial
implications, limitations, and future research directions.
II. LITERATURE REVIEW AND HYPOTHESES
DEVELOPMENT
A. THEORETICAL BACKGROUND
The dynamic capabilities view (DCV) provides a
theoretical lens to investigate the relationship between
enterprise systems and OA. Dynamic capabilities (DCs)
refer to “the firm's ability to integrate, build, and
reconfigure internal and external resources/competencies
to address and shape rapidly changing business
environments [30].” DCs have been conceptualized as a set
of capabilities, including sensing, seizing, and
reconfiguring [31]. Sensing capability refers to “identify,
develop, co-develop, and assess technological
opportunities based on customer needs.” Seizing capability
refers to “mobilize resources to tackle needs and
opportunities and to capture value from these actions.”
Transforming refers to “recombining and modifying
existing resources.” Previous studies highlight that OA is a
pivotal DC for organizations to secure lasting competitive
advantages and survival in dynamic environments [32]. OA
refers to a firm’s ability to sense environmental change and
respond readily [33]. Hence, OA comprises two elements:
sensing and responding. Sensing refers to an organizational
ability to quickly detect, interpret, and capture
organizational opportunities. Responding represents an
organizational ability to mobilize and transform resources
to react to the opportunities it senses [34]. Since agility
includes the capabilities to sense and respond, DCs provide
a suitable framework for characterizing OA.
Theoretically, many scholars consider DCs to be
hierarchical [35]. Organizations cultivate agility as a form of
high-level DCs by establishing suitable work routines and
harnessing lower-level DCs, such as IT utilization. Agility
enables them to improve, align, and adapt their core
operational capabilities [36]. ERPA is the extent to which
firms use ERP systems, representing lower-level DCs in this
study. According to the hierarchy of DCs, ERPA will facilitate
high-level DCs, namely OA. Meanwhile, the effective
implementation of MC also requires cultivating DCs [37]. OA
enabled by ERPA will promote MCC in the firm. Therefore,
OA could mediate the ERPA-MCC relationship.
Additionally, OA is constructed upon the foundation of AC.
AC refers to a set of organizational routines and processes by
which firms systematically acquire, assimilate, transform, and
exploit knowledge, producing a dynamic organizational
capability [38]. It assists firms in navigating environmental
uncertainties and shaping their approach to handling them.
Furthermore, it influences how organizations respond [28]. In
the ERP context, AC is conceptualized as a combination of
potential absorptive capacity (PAC) and realized absorptive
capacity (RAC) [39], [40]. PAC and RAC are both
independent and complementary. Suppose organizations have
no PAC to identify and acquire external technology and
knowledge. In that case, no resource can be converted and
utilized in the RAC. In addition, even if organizations have the
PAC, they may not have the RAC; that is, they cannot
transform and apply external resources [38].
This article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2024.3364390
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From the perspective of ERP, PAC denotes an
organization's ability to acquire and internalize external
knowledge specifically related to the ERP system. PAC
includes knowledge obtained from internal and external
sources, focusing on system-specific features. RAC pertains
to the organization's capability to effectively utilize ERP
systems, maximizing their advantages [40]. In this study, AC
is about how firms learn and apply ERP knowledge to gain the
ERP system’s value. In the field of ERP study, previous
studies found that AC can positively affect ERPA [40]–[43].
Therefore, to take full advantage of the functionality provided
by ERP systems to drive OA, it depends on the level of the
firm’s AC to acquire, assimilate, transform, and exploit ERP
knowledge. Fig. 1 presents the research framework.
<Insert Figure 1>
B. POPULARITY OF ERP SYSTEM IN CHINA
ERP is integrated cross-functional software that re-engineers
a firm’s manufacturing, distribution, finance, human
resources, and other basic business processes to improve its
efficiency, agility, and profitability [44]. The Chinese
government has implemented a sequence of policies to
stimulate the adoption of ERP and other industrial software
within the manufacturing sector to attain informatization [45].
Although large state-owned and private firms are the primary
users of ERP, more small and medium enterprises (SMEs) are
adopting ERP systems as they undergo digital transformation
[46]. According to a report from a Chinese consulting firm, the
utilization of ERP systems is relatively prevalent across
various sizes of firms. In 2019, firms with 100-499 employees
accounted for 37% of ERP applications, while those with 500-
999 employees accounted for 11%, and those with over 1000
employees accounted for 33%. The manufacturing sector
dominated the market, with a market share of over 40% [47].
C. FACILITATING ROLE OF ERP SYSTEM IN MCC
MC has risen as a significant manufacturing strategy, gaining
importance with a focus on shifts in demand and technology
advancements [48]. MC is a system that employs IT, flexible
processes, and organizational structures to provide diverse
products and services tailored to individual customer
requirements while maintaining costs close to those of mass-
produced items [49]. Firms have benefited greatly from
implementing MC concerning improvements in customer
value, customer satisfaction, operational performance, product
innovation, and firm performance [50]. The identification of
factors such as the development of the product platform,
product modularity, IT-based product configuration, parts
standardization, group technology, process modularity, and
the simultaneous product-process-supply chain integration has
been acknowledged as crucial determinants for the successful
implementation of MC [51].
The implementation of MC entails a complex process
involving transforming consumer-specific requirements into
corresponding products. This process requires coordination
across various aspects, such as product configuration,
manufacturing, and supply chain management. This intricate
procedure necessitates handling vast amounts of data [52].
However, in practical applications, customer orders frequently
exhibit high variety coupled with low volume, generating
substantial data. Firms are confronted with the imperative of
concurrently managing diverse orders, necessitating the
deployment of efficient enterprise systems for data
management [53].
A McKinsey study has highlighted that to attain profitable
MC, firms must enhance their IT infrastructure, which
includes investments in data warehousing and data analytics,
alongside upgrading ERP and legacy systems [54]. The
attributes of ERP, such as instantaneous data analysis and
dissemination, and the system's capacity for internal and
external integration collectively contribute to the advancement
of MCC [55]. The study from [56] experimentally simulated
the process of handling MC orders within an ERP system. As
depicted in Fig. 2, integrating sales, manufacturing, purchase,
and inventory modules in the ERP system allows for the
seamless conversion of sales orders into manufacturing orders
upon confirmation. Similarly, the automatic generation of
manufacturing orders or requests for quotations for missing
materials is evident. Customization details in sales orders are
reflected in the manufacturing order's bill of materials (BOM).
Throughout order execution, workstations receive precise
instructions from the manufacturing module and report the
completion of each manufacturing step. Integrating IT and
operational technology ensures the production of correct
variants, and individual order tracking facilitates the timely
delivery of the finished product to customers. As shown in
Table I, the findings of previous studies ([9], [55], [57]–[62])
indicated that adopting ERP systems can effectively support
the practical advancement of MC within firms from different
industries. Firms should extensively use ERP systems to
develop MCC. Thus, this study proposed the following
hypothesis:
H1: A high level of ERPA can positively affect MCC.
<Insert Figure 2>
<Insert Table I>
D. MEDIATING ROLE OF OA
ERP system facilitates the seamless dissemination of
information across the organization, resulting in a well-
informed, expeditious, efficacious, and streamlined decision-
making process. The system's unified database empowers the
processing, analysis, prediction, extraction, and
comprehension of pertinent task-related data, thereby assisting
organizations in attaining agility [63]. As shown in Table II,
several studies ([20], [21], [24], [63]–[66]) have established a
favorable correlation between ERP systems and agility, as
assessed by different measurements in various industries. A
prior study affirms that OA forms the bedrock of MCC [67].
Agility enhances the operational efficacy of the MC
production model. This enables firms to augment adaptability,
promptness, and proficiency in addressing the diverse
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content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2024.3364390
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VOLUME XX, 2017 2
requirements of their clients, thereby minimizing instances of
disruption [68]. A case from McKinsey showed the value of
agility in practice. During the COVID-19 pandemic, many
Chinese auto manufacturers adopted agile operations, which
enabled them to quickly adjust their production lines to
produce a large variety of protective equipment that greatly
satisfied medical demand. The entire process of transitioning
production only took a few days [69].
<Insert Table II>
The information-processing capabilities inherent in ERP
systems contribute to an organization's sensing capability,
particularly in dynamic market demands. Through the
integration process of ERP systems, updates regarding
changes in market conditions are swiftly disseminated
throughout the organization. The automated messaging
function conveys change implications as “exception
messages” to relevant decision-makers, following system-
configured rules. The central database furnishes rich data for
optimal response analyses to the identified changes [70]. ERP
systems' powerful information processing capability can
promote the sensing capabilities required by developing an
MC strategy. Adopting ERP systems enables connectivity on
the shop floor that supports MC. Machines and workstations
can establish real-time data exchange by interfacing with the
ERP system [71]. Similarly, firms leverage ERP expansion to
integrate with subsystems (e.g., manufacturing execution
system (MES), customer relationship management (CRM)) to
enhance resource allocation, ensure swift, accurate, and secure
information flow, and foster innovation in production,
management, and business models, thereby aligning with MC
requirements [72]. Specifically, MES facilitates the realization
of MC, primarily achieved through functions such as
production planning, scheduling and order release, job
control-order control, and the collection of machine and
operation data [73]. Furthermore, ERP systems simplify MC
by effectively converting information related to consumer
needs and preferences into detailed product specifications,
which then can be shared with relevant supply chain members
and further improve inventory management [74]. In addition,
the demand forecasting module provided by the ERP system
can quickly and effectively confirm the quantity of
intermediate products required by the MC according to the
past sales data in the system, thus promoting the
implementation of an MC strategy [61].
Responding capabilities represent an organization’s
capability to utilize its enterprise system resources and
integrate them into its production development, systems
development, supply chain and production, and flexible
resource utilization [75]. Adopting ERP systems enables the
integration of internal functional units and fosters connectivity
with external customers and suppliers, bolstering
responsiveness to market opportunities [76]. It is imperative to
diligently coordinate and integrate activities across their
supply chains, as such integration significantly influences
their MCC [5]. When formulating MCC, managers should
consider various aspects of supply chain integration, including
information integration, operational integration, and relational
integration [77]. Utilizing ERP systems’ databases, firms
facilitate data sharing with suppliers, thereby achieving
information integration and enabling closer collaboration
between firms and suppliers in production planning, capacity
management, order delivery, and inventory levels [78]. MC
also necessitates rapidly creating new products or modifying
existing ones to address individual customer requirements as
an integral aspect of solution space management [79]. This
function can be accomplished by utilizing the product
configuration module offered by the ERP system, which aims
to translate customer needs, including functions and technical
attributes, into a clear and distinct product representation,
specifying a particular product variant. Subsequently, this
information can be employed for pricing and order completion
[80]. Manufacturing systems should exhibit swift adaptability,
flexibility, and reconfigurability in response to short-term
volume or product variations shifts, facilitating a profitable
MC approach [71]. The operation management of MC takes
an ERP system as the core and other management systems
(e.g., supply chain management, product lifecycle
management, supplier relationship management) as the
auxiliary to effectively and intuitively manage enterprise
resource status, product customization information, and
customer and supplier information. When connected to the
manufacturing execution layer, it can swiftly adapt to
fluctuations in factory production processes, optimize
customization product plans to address bottleneck issues, and
establish the most efficient production arrangement for
products [72].
Bouchard et al. [16] indicated that automation and digital
technologies can enhance agility, efficiency, and performance.
These improvements enable heightened responsiveness to MC
demands. When firms use ERP to achieve internal and
external integration, it can contribute to OA in sensing and
responding, which are required to develop MCC. Thus, this
study proposed the following hypothesis:
H2: A high level of ERPA can positively affect OA.
H3: OA can positively affect MCC.
H4: OA can mediate the relationship between ERPA and
MCC.
E. MODERATING ROLE OF AC
Earlier studies indicated that AC significantly mediates the
relationship between IT and OA [28], [81], [82]. Nevertheless,
it should be noted that the practical variability in firms' AC
towards ERP systems exists. As DCs, AC includes PAC and
RAC, which influence the ERPA from knowledge acquisition
to routinization and play a significant role in each stage of ERP
[41]. AC inevitably influences the achievement of OA through
utilizing ERP systems in firms. Therefore, AC can influence
ERPA and its connection to OA.
More excellent AC in a firm results in a greater willingness
to adopt new technology, adapt to new ERP-based business
processes and assimilate ERP technical features into
established routines. Which indicates a positive correlation
between AC and ERPA [12]. PAC guarantees that firms
This article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2024.3364390
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/

VOLUME XX, 2017 2
possess an adequate reservoir of ERP knowledge, thereby
enabling the maintenance of the system and subsequently
resulting in an elevated degree of ERPA [40]. PAC is also
related to the internalization of ERP knowledge. In many
instances, the process of internalizing knowledge occurs
following the implementation of a new ERP system and the
subsequent practice by business users to acquire the necessary
skills for its proficient use [83]. In comparison, the essence of
RAC lies in its capability to leverage the advantages offered
by ERP systems through the facilitation of enhanced
utilization [40]. To enhance ERPA, firms must bolster their
AC. Primarily, this necessitates a robust PAC, ensuring that
the firm possesses sufficient IT knowledge to maintain the
proper functioning of ERP. Based on the PAC, the RAC
facilitates the firm in utilizing the system with greater
efficiency, thereby attaining anticipated advantages, such as
an elevated level of OA.
ERP systems' powerful data management capabilities
enable swift and efficient acquisition, sharing, and analysis of
information within firms, enhancing sensing capabilities. ERP
systems also facilitate the internal integration of different
departments and the external integration of suppliers and
customers, which promotes responding capabilities. Strong
AC guarantees firms acquire adequate knowledge for ERP
system maintenance and optimal utilization to maximize
system benefits, thereby facilitating the development of OA,
particularly in sensing and responding. Therefore, a high level
of AC will strengthen the relationship between ERPA and OA.
Thus, this study proposed the following hypotheses:
H5: A high level of AC can strengthen the relationship
between ERPA and OA.
III. METHODOLOGY
A. SAMPLE AND DATA COLLECTION
The study’s targeted populations were manufacturing firms
that have adopted ERP systems in Jiangsu province, China.
Jiangsu Province is one of China's most advanced
manufacturing provinces [84]. According to an official
government report [85], Jiangsu's manufacturing industry
achieved a digitalization level of 66.4% in 2022, thereby
attaining the highest position in China. Various types of
industrial software are extensively employed within firms,
facilitating the identification of users of ERP systems with
great ease.
This study employed convenience sampling, which refers
to acquiring data from individuals within the population who
are readily accessible and available to provide [86]. While
ERP systems are prevalent among manufacturing firms in
Jiangsu Province, there is a notable absence of business
directories or open databases delineating specific ERP users.
Consequently, we identified Jiangsu X Human Resources Co.,
Ltd (hereafter referred to as X Company) through personal
connections, which maintains collaborative partnerships with
over 1000 manufacturing firms within the province. This
significantly enhances the likelihood and convenience of
obtaining ERP users.
Data were collected through an electronic questionnaire on
Wenjuanxing (a popular survey tool like Google Forms in
China) between January and April 2023. We employed AI-
powered translation tools (Youdao Translation and DeepL)
and referenced corresponding Chinese scales to translate the
original questionnaire items into Chinese. Subsequently, we
invited three industry insiders with ERP usage experience to
complete the questionnaire and requested their feedback for
refinement. Given that this study included a moderated
mediation model involving second-order variables and
considered the established correlations among variables from
prior studies, we adhered to the recommendation of [87] and
opted to set an appropriate sample size within the range of 100
to 200.
Initially, we obtained an internal customer list from X
Company, including 110 manufacturing firms. Then, we sent
research invitations with the questionnaire link to contacts of
these firms via WeChat (a popular messaging and calling app
in China) through X Company's business relationship with
them. Suppose his firm uses the ERP system and expresses his
willingness to participate in this study. In that case, we kindly
requested them to forward the questionnaire link to other
employees/colleagues from different departments within his
firm. To optimize the response rate, the manager of X
Company assisted us in reminding participants to complete the
questionnaire promptly and ensured the distribution of the
questionnaire within their respective firms. Finally, 43 ERP
users in the manufacturing industry participated in the survey,
and we received 166 questionnaires from them. This aligned
with the anticipated sample size determination.
B. MEASUREMENT
This study adopted or partially modified the questionnaires
based on an extensive review of previous studies. This study
adopted electronic questionnaires. This study applied a 7-point
Likert scale ranging from 1 (strongly disagree) to 7 (strongly
agree). The Chinese version, which had been meticulously
translated, was employed during questionnaire distribution.
The measurement items employed in our study are displayed
in “Appendix.”
MCC is operationalized as a manufacturer’s capability to
customize products while maintaining high volume, without
significantly increasing costs, and with consistent quality, as
well as reorganizing production processes quickly in response
to customization requests [88]. MCC assessed with six items
in this study was adapted from [88].
ERPA is operationalized as “the extent to which the ERP
technology is used in facilitating business processes and the
degree to which it supports business decision-making at
operational and strategic levels [11]”. ERPA assessed in this
study is a second-order construct adapted from [13]. It consists
of three subconstructs that evaluate the level of ERP usage in
process, decision-making, and business strategy, containing
eight items.
OA is operationalized as sensing capabilities reflect the
firm’s ability to quickly detect, interpret, and capture
organizational opportunities. Moreover, responding
capabilities reflected a firm’s ability to mobilize and transform
This article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2024.3364390
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/

VOLUME XX, 2017 2
resources to react to the opportunities it sensed [33]. OA
assessed with six items in this study was adapted from [89].
AC is operationalized as the extent to which an organization
possesses the requisite know-how for assimilating the ERP
artefact [90]. AC is a second-order construct adapted from
[40]. It consists of two subconstructs, PAC and RAC,
containing eight items.
IV. ANALYSIS AND FINDINGS
This study employed self-reported questionnaires to gather
data from the identical participants across all variables. In
order to guarantee the reliability of the results, it was
imperative to evaluate the existence of common method
variance (CMV) as described by [91]. CMV refers to the
extent to which the measurement methods contribute to the
observed variances rather than the constructs under
investigation. To examine CMV, the current study followed
the recommendation of [91] by employing Harman's single-
factor analysis. Factor analysis was conducted on all measures
without rotation using SPSS, resulting in six factors that
accounted for 76.298% of the variance. The primary factor
explained 48.372% of the total variance, which is lower than
the threshold of 50%, indicating that method bias did not pose
a significant concern in this study.
To evaluate this study's hypotheses, a partial least squares
(PLS) approach was employed for structural equation
modelling. PLS was selected for its strong and adaptable
performance with varying data distributions and sample sizes,
as emphasized by [92]. Additionally, PLS can handle both
reflective and formative models, as highlighted by [93]. The
data analysis was conducted using SmartPLS 3 software.
Initially, the reliability and validity of the measurement model
were assessed, and subsequently, the proposed relationships in
the structural model were examined. To ascertain the
significance levels of loadings, weights, and path coefficients,
the bootstrapping technique with 5,000 resamples was
utilized, following the recommendation of [92].
A. ASSESSMENT OF MEASUREMENT MODEL (FIRST
ORDER)
The measurement model establishes the connection between
latent constructs and their associated items. In this study, the
measurement model comprised both first-order and second-
order components. In the first order, all latent constructs were
reflective; thus, their validity and reliability were assessed.
Reliability was determined using factor loading and composite
reliability (CR) measures. In this study, all variables' factor
loadings and CR exceeded 0.7, indicating satisfactory
reliability at both the item and construct levels (Table III).
Convergent and discriminant validity were assessed to
determine the validity of the measurement model [94]. All
variables' average variance extracted (AVE) exceeded 0.5,
meeting the required criterion.
To examine discriminant validity, this study adopted the
Fornell and Larcker criteria [95] and the HTMT method.
Based on Table IV, the square root of the average variance
extracted (AVE) exhibited higher values on the diagonal
compared to the corresponding values in columns and rows.
In addition, according to Table V, all values are less than 0.85.
Therefore, the discriminant of this study was achieved.
<Insert Table III>
<Insert Table IV>
<Insert Table V>
B. ASSESSMENT OF MEASUREMENT MODEL (SECOND
ORDER)
The utilization of second-order constructs necessitates the
inclusion of multiple latent constructs and can be theoretically
distinguished from first-order constructs. In the present
investigation, the second-order variables demonstrate
reflective-reflexive characteristics. As a result, the criteria for
assessing reliability and validity remain consistent. The
indicators for ERPA and AC all satisfy the predetermined
thresholds, as indicated in Table VI and Table VII.
Consequently, the measurement model is valid for the second-
order construct. Subsequently, the measurement model
demonstrates satisfactory validity and reliability at both the
first and second orders, enabling progression to the subsequent
analysis stage.
<Insert Table VI>
<Insert Table VII>
C. STRUCTURAL MODEL ASSESSMENT
In this study, the structural model was evaluated using path
coefficients, coefficient of determination (R2), effect size (f2),
and predictive relevance (Q2) measures [94], [96]–[98]. The
significance level of the path coefficient was determined using
a bootstrapping procedure with 5,000 resamples, as
recommended by previous studies [99].
The findings in Table VIII provide an overview of the path
coefficients, standard errors, associated t-values, and the
explanatory power of the estimated model, denoted by the R2
values. ERPA (β = 0.523, p < 0.01) was related positively to
OA. Thus, H2 was supported. Then, OA (β = 0.647, p < 0.01)
was related positively to MCC, supporting H3. However,
ERPA does not affect MCC; therefore, H1 was rejected.
The results showed that ERPA and OA accounted for
55.8% of the variance in MCC. Specifically, ERPA explained
52% of the variance in OA. To evaluate the magnitude of the
effects, as recommended by [94], the effect size (f2) was
calculated. The effect size, determined by Cohen's equation
[100], quantifies the significance of exogenous variables in
explaining the variance within the endogenous construct. He
suggested that effect size values of 0.35, 0.15, and 0.02
correspond to substantial, moderate, and weak effects.
This article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2024.3364390
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/

VOLUME XX, 2017 2
In this study, ERPA revealed a weak effect (f2 = 0.021) on
the variance explanation MCC. However, the results indicated
a strong effect (f2 = 0.329) of ERPA in explaining the variance
observed in OA. As stated by [101], a Q2 value exceeding 0
signifies the predictive relevance of the model. The Q2 values
for MCC (Q2 = 0.409) and OA (Q2 = 0.397) in this study were
both above 0, indicating that the model demonstrates
satisfactory predictive relevance (Table VIII).
<Insert Table VIII>
In order to examine H4, which posited that OA acts as a
mediator in the relationship between ERPA and MCC, the
author employed Preacher and Hayes' [102] bootstrapping
method to assess the indirect effect. By utilizing 5,000
resamples for the bootstrapping analysis, the results indicated
that the indirect effect, denoted as β = 0.362, was statistically
significant, as evidenced by a t-value of 6.528. Furthermore,
the 95% confidence interval, CI: [LL = 0.261, UL = 0.471],
did not include the value of 0, lending additional support to the
significance of the mediation effect of OA. Consequently, the
authors concluded that H4 was substantiated (Table IX).
To explore the moderating impact of AC on the relationship
between ERPA and OA, the product indicator approach, as
described by [94], was employed. The findings presented in
Table IX and Fig. 3 provided evidence in favor of the
interaction effect (β = 0.085, p < 0.01). These results imply
that AC has the potential to moderate the association between
ERPA and OA. Therefore, H5 was supported.
<Insert Table IX>
<Insert Figure 3>
V. DISCUSSION
This study mainly investigated whether ERPA can affect
MCC through OA in manufacturing firms. In addition, this
study also tested whether AC could moderate the relationship
between ERPA and OA. The analysis results partially
supported the hypotheses.
This study found no significant relationship between ERPA
and MCC, not supporting H1. This finding did not come as a
surprise, given its alignment with prior empirical results [4],
[9]. Presently, the ubiquitous nature of information and
communication technology (ICT) renders it readily obtainable
for firms in the marketplace, posing a challenge for them to
attain a distinctive competitive edge solely by leveraging ICT
[103]. It explained that the utilization of ERP systems does not
exert a substantial influence on MCC. Because regardless of
whether the MC performance is favorable or unfavorable,
organizations universally rely on ERP systems [9]. Thus, firms
must use ERP systems with other organizational capabilities
to facilitate the development of MCC and then achieve
competitive advantages.
Concerning the impact of ERPA on OA, a substantial
correlation was observed between them, thus supporting H2.
This favorable result aligns with previous studies [20], [21],
[104]. Previous studies investigating the impact of ERP usage
on OA in non-manufacturing industries such as banking and
hospitality have reached the consensus that ERP usage does
not fully support the development of OA [24], [63], [66].
Conversely, this study found that ERPA can fully support OA.
A plausible rationale could be attributed to industry variances,
where users within the manufacturing sector perceive an
advantage offered by ERP systems in facilitating swift
adaptation, modification, and reconfiguration of production
systems instead of operating without enterprise systems.
In relation to the influence of OA on MCC, a notable
association was observed between them, thus providing
support for H3. This result aligns with a previous study that
demonstrated that OA exerts a positive impact on MCC [84].
This study confirms the value of OA that integrates agility into
MC, which helps firms to respond to fluctuating customer
demands and volatile market needs quickly [105].
OA fully mediated the relationship between ERPA and
MCC, supporting H4. This finding is partially consistent with
the existing study on the mediating role of supply chain agility
between big data analytics capability and MCC [106].
Therefore, a high level of ERPA is not fully guaranteed to
promote the improvement of MCC. In order to maximize the
advantages offered by digital technologies, firms need to
possess a certain degree of OA [107]. To achieve MCC, a firm
must translate the usage of an ERP system into OA.
Regarding the moderation effect of AC, H5 was supported.
This result revealed that the variation in agility among ERP
users can be attributed to the discrepancy in their AC towards
the system. If firms possess a considerable degree of
absorption capacity, ERPA will exert a robust impact on their
OA and vice versa. Undoubtedly, it is worth acknowledging
that firms allocate varying resources towards fostering AC,
resulting in disparate levels of association between ERPA and
OA.
This study presents a framework for leveraging ERP
systems to cultivate MCC. Prior research has recognized ERP
systems as comprehensive management systems that integrate
multiple subsystems, offering practical functionalities aligned
with the developmental needs of MCC [73]. Positioned as a
crucial IT resource for firms, ERP systems, when viewed
through the lens of DCs, necessitate the development of
advanced organizational capabilities, specifically OA, to
effectively contribute to enhancing MCC. However, the
utilization of ERP, being a complex system, for fostering OA
is contingent upon the AC of the firm itself. Only firms
possessing a high level of AC can attain the desired OA,
thereby facilitating the development of MCC and enabling
competitive advantages.
VI. IMPLICATIONS
A. THEORETICAL IMPLICATIONS
This study has two main theoretical implications. First, this
study successfully connected ERPA and MCC through OA as
mediator, which has been a lack of evidence establishing a
direct correlation in previous studies [9]. DCV provides
theoretical support that explains how ERPA affects the
development of MCC in manufacturing firms. In this study,
This article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2024.3364390
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VOLUME XX, 2017 2
OA represented DCs, which were conceptualized as sensing
and responding capabilities. The findings reaffirm that ERP
systems continue to serve as crucial facilitators of OA in
enterprise systems research. Most importantly, the findings
expand the significance of OA within the context of MC
research, emphasizing OA as a pivotal higher-order DCs for
cultivating MCC. Hence, to foster MCC through ERP
systems, firms must utilize the systems to attain the OA
essential for MCC.
Second, this study strengthens the understanding of the
relationship between ERPA and OA by introducing AC as a
moderator. This study answered speculation from [24] that
establishing agility via ERP systems may be contingent upon
additional variables. The findings offer a more extensive
clarification of the contextual limitations influencing the
relationship between ERPA and OA. The findings validate the
significance of AC in the establishment of OA within the
realm of enterprise systems research. Hence, if firms want to
obtain a high level of OA enabled by ERP systems, they must
possess strong AC.
B. MANAGERIAL IMPLICATIONS
Our study also presents practical recommendations for ERP-
using firms. In the contemporary business landscape,
manufacturers frequently encounter diverse customization
requests from customers, often characterized by small order
quantities and numerous variants, such as different colors and
sizes within a single order. Ignoring such orders could result
in missed profit opportunities. Consequently, to better meet
customer demands and survive in intense competition, firms
must cultivate MCC. Beyond employing necessary
customization practices like configuration technology,
product and process modularization, or postponement, firms
also need to leverage ERP systems to manage the entire
production and operational process. This study's findings
emphasize that firms must enhance OA, specifically sensing
and responding capabilities, by utilizing ERP systems for
efficient MC implementation. Therefore, the study
recommends elevating digitalization levels within the
organization, eliminating information silos to facilitate the
internal integration of ERP and its subsystems. This promotes
real-time information sharing among departments, enabling
visualized management. Simultaneously, the study suggests
interfacing with supplier ERP systems to share inventory
information and enhance collaboration. Indeed, this falls
significantly short; firms must additionally engage in the
continuous real-time assessment of customer demands.
Murata Manufacturing [108] posits that the foundational
technologies for achieving MC are continually evolving. It
suggests that firms embrace digital marketing to digitize
customer demands, employing artificial intelligence to infer
and formulate product specifications that fulfill requirements
based on historical sales and marketing data. This can be
facilitated through the CRM module within ERP systems.
Furthermore, IoT One [109], a consultancy dedicated to digital
transformation, recommends that firms employ interactive
product configurators to swiftly gather customized
requirements from customers. This functionality can be
implemented through the product configurator module within
ERP systems.
AC determines the extent to which ERPA enhances OA. An
empirical study shows that a lack of knowledge management
systems and formal post-implementation training programs
are major issues affecting AC and will impede ERPA within
firms [42]. In reality, due to the inadequate allocation of
internal resources, numerous firms face challenges in ERPA
due to their limited AC to effectively absorb ERP innovation
into their business operations [29], further hindering the
leverage of ERP systems to achieve OA. Hence, firms must
allocate sufficient resources to train employees in ERP usage.
Developing employee skills for complex ERP systems
demands special training that differs from office suites.
Additionally, knowledge transfer for consultants may be
difficult when dealing with non-IT-literate individuals.
Training for ERP systems often incurs hidden costs for
organizations, and neglecting employee training can lead to
problems [110]. Training is essential to enhance individual
AC. Proper training can compensate for the deficiency in prior
related knowledge and educational background in related
fields. Users with different educational backgrounds can
become proficient ERP users and progress in the assimilation
hierarchy through adequate training. It is argued that
individual ERPA has a direct correlation with organizational
ERPA [111]. In practice, although firms delivering ERP
systems use training for their employees, there remains a
deficit in employees' understanding and motivation to
effectively engage with the ERP systems [112]. As an
illustration, organizations can improve employee satisfaction
by implementing reward and punishment systems to enhance
users' ongoing utilization of ERP systems. Furthermore, firms
should offer leadership development programs to their
management, fostering an environment wherein direct
supervisors actively monitor the regular usage of corporate
systems, oversee user advancement, and promptly recognize
and rectify emerging issues [113].
VII. LIMITATIONS AND FUTURE RESEARCH
This study is not devoid of limitations. Firstly, this study
utilizes a relatively small sample size of 166 questionnaires
collected from 43 ERP-adopting firms through a non-public
source. Although the sample size meets the requirements of
running statistical techniques, high-quality statistical results
necessitate a larger sample size. Hence, we recommend that
future studies seek collaboration with ERP vendors,
authoritative consultancy firms, or governmental entities to
obtain a larger sample size and enhance the validity of our
findings. Secondly, constrained by time and budget, this study
focused solely on the manufacturing sector within Jiangsu
Province, China. Given China's regional economic disparities
and substantial cultural variations, the findings may not be
generalized nationwide. Hence, future studies may explore
other economically developed provinces, such as Zhejiang and
Guangdong, and further engage in comparative analyses of
regional disparities, such as those between the Yangtze River
Delta and the Pearl River Delta. Thirdly, this study has
This article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2024.3364390
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/

VOLUME XX, 2017 2
exclusively examined the general manufacturing sector. In
practical terms, due to industry-specific variations, the
adoption of distinct ERP solutions and corresponding MC
strategies varies. Therefore, future studies could employ a
mixed-methods approach to investigate specific industries,
offering valuable insights.
The findings highlight various prospects for future study.
Firstly, in the context of Industry 4.0, the forthcoming study
may explore the influence of cutting-edge technologies, such
as big data, the Internet of Things, and cloud computing, on
MC. Moreover, how to integrate these emerging technologies
with existing ERP systems to improve the efficiency of MCC.
Secondly, future studies could investigate the impact of other
manufacturing practices, such as lean manufacturing, on
MCC. Moreover, it explores the how to leverage these
manufacturing practices to improve MCC. Thirdly, given the
technological maturity of cloud-based ERP systems, their
implementation costs are comparatively lower than on-
premise ERP solutions, and their configurations are more
flexible, allowing firms to select modules based on their
specific requirements. This presents opportunities for the
development of MCC within SMEs. Consequently, future
studies may explore how SMEs can strategically leverage
cloud-based ERP systems to foster the growth of MCC.
APPENDIX
Questionnaire.
ACKNOWLEDGMENT
We extend our heartfelt gratitude to Jiangsu X Human
Resources Co., Ltd in Suzhou for their valuable assistance in
facilitating data collection for this research. Their support
greatly expedited the acquisition of the necessary study
samples.
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content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2024.3364390
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LIU ZONGYUAN is a doctoral student at Azman
Hashim International Business School, Universiti
Teknologi Malaysia. He received the Bachelor of
Marketing from Guangling college of Yangzhou
university in 2019. And Master of Business
Administration from Azman Hashim International
Business School, Universiti Teknologi in 2021.
His research focus is SMEs management.
HUO HAIYAN is a doctoral student at Azman
Hashim International Business School, Universiti
Teknologi Malaysia. She received the Bachelor of
Management (in Engineering Management) from
Great Wall college of China University of
Geosciences in 2015. And Master of Management
(in Industrial Engineering) from China University
of Mining and Technology in 2019. Her research
focus is marketing and consumer behavior.
This article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2024.3364390
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/