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ORIGINAL RESEARCH
Reconciling Product Flexibility with Cost, Delivery, and Quality:
The Importance of Bundling Mass Customization Practices
Alessio Trentin
1
•Enrico Sandrin
1
•Svetlana Suzic
1
•Chiara Grosso
2
•Cipriano Forza
1
Received: 4 September 2024 / Accepted: 6 December 2024
The Author(s) 2025
Abstract Reconciling product flexibility with cost, deliv-
ery, and quality is an ambidextrous organizational capa-
bility known as mass customization capability. This study
focuses on how this capability is affected by the joint
implementation of three organizational practices––knowl-
edge absorption from customers, product modularity, and
online sales configurator use––that directly correspond to
the three fundamental building blocks of mass customiza-
tion identified by prior, influential research. By drawing
upon a central tenet of resource orchestration theory, the
fit-as-covariation perspective, and prior mass customiza-
tion research, we conceptually develop the hypothesis that
the fit-as-covariation of these practices has a stronger
positive association with mass customization capability
than the same practices implemented in isolation. This
hypothesis was tested using covariance-based structural
equation modeling and survey data from 213 manufactur-
ing plants in three industries across 16 countries. Our
results support the hypothesis, showing that the joint effect
of these practices explains substantially more mass cus-
tomization capability variation (41.9%) than their isolated
effects (13.9%). This amount of variation indicates an
effect size that is greater than that reported by most pre-
vious survey-based studies on the antecedents of this
capability. Theoretically, this paper adds to the relatively
limited body of knowledge on the relationships among the
enablers of mass customization by highlighting the benefits
of a holistic approach in the implementation of the three
practices under investigation. Pragmatically, this study
helps companies create flexible systems that are able to
provide customized products without compromising cost,
delivery, or quality.
Keywords Flexibility
Knowledge absorption from customers
Mass customization Product configuration
Product modularity Resource orchestration
Abbreviations
AVE Average variance extracted
CB-SEM Covariance-based structural equation modeling
CFA Confirmatory factor analysis
CFI Comparative fit index
CR Composite reliability
df Degrees of freedom
HPM High-performance manufacturing
IFI Incremental fit index
IRAC Inter-rater agreement coefficient
IT Information technology
KAfC Knowledge absorption from customers
MCC Mass customization capability
OSCU Online sales configurator use
PM Product modularity
&Enrico Sandrin
enrico.sandrin@unipd.it
&Chiara Grosso
chiara.grosso@uniroma1.it
Alessio Trentin
alessio.trentin@unipd.it
Svetlana Suzic
suzicsvetlana@gmail.com
Cipriano Forza
cipriano.forza@unipd.it
1
University of Padua, Padua, Italy
2
Sapienza University of Rome, Rome, Italy
123
Global Journal of Flexible Systems Management
https://doi.org/10.1007/s40171-024-00429-5
RMSEA Root-mean-square error of approximation
ROT Resource orchestration theory
TLI Tucker–Lewis index
Introduction
Flexibility has long been recognized as a critical factor in
enabling companies to navigate rapidly changing socioe-
conomic landscapes (Singh et al., 2021). The need for
flexibility has intensified in today’s business environment,
as organizations must adapt to increasingly complex and
dynamic markets while transitioning toward sustainable
production and consumption paradigms (Basile et al.,
2024; D’Adamo et al., 2023; Sushil, 2018).
Research on flexibility is grounded in the manufactur-
ing/operations strategy literature, which has examined the
role of flexibility among other competitive priorities,
including cost, delivery, and quality (Pe
´rez-Pe
´rez et al.,
2019). Overcoming the traditional trade-offs between a key
aspect of a manufacturer’s flexibility––that is, product
customization––and cost, delivery, and quality is an
ambidextrous organizational capability known as mass
customization capability (MCC) (Trentin et al., 2020;
Zhang et al., 2024). This capability is gaining increasing
attention from companies, as product customization is
becoming the new norm in the manufacturing sector
(Genedge; PwC & Oracle Alliance, 2024). In this context,
MCC can foster innovation (Qi et al., 2020), improve
several dimensions of firm performance, including envi-
ronmental sustainability (Sheng et al., 2021), and become a
source of competitive advantage (Jain et al., 2022). The
growing importance of MCC in practice has spurred a
considerable body of research aimed at understanding how
to develop this capability (see Suzic
´et al., 2018 for a recent
review on the topic). Prior research has also observed that
improving MCC is a complex endeavor that requires put-
ting multiple enablers in place, and that uneven or inter-
rupted paths toward this goal are not unusual (Suzic
´et al.,
2018). To develop guidelines capable of supporting com-
panies in this challenging process, it is crucial to under-
stand the relationships among MCC enablers, as firms
implementing mass customization must decide not only
what enablers should be put in place but also the order in
which they should be implemented (Suzic
´et al., 2018).
However, most prior research on the enablers of MCC
has focused on their independent effects on this capability
(Sheng et al., 2023). The relationships among MCC
enablers and their combined effects on MCC require
additional research (Jain et al., 2023; Sheng et al., 2023). In
particular, the precedence relationships and the resulting
sequential path toward MCC that characterize most of the
mass customization implementation guidelines available in
the literature should be reconsidered (Suzic & Forza,
2023). A strong challenge to this sequential path is pre-
sented by resource orchestration theory (ROT), since ROT
posits that organizational capabilities, such as MCC, stem
from bundling (i.e., jointly deploying) multiple interde-
pendent and mutually reinforcing resources (Hitt et al.,
2016; Liu et al., 2016; Sirmon et al., 2007), defined as
tangible and intangible assets that a firm controls (Sirmon
et al., 2008), including organizational practices. However,
this theoretical lens has not been applied to the investiga-
tion of MCC antecedents (see Liu et al., 2023) and, par-
ticularly, the investigation of their combined effects on
MCC.
The present paper aims to narrow this gap by drawing on
ROT arguments to explain the effect on MCC of bundling
three organizational practices that directly correspond to
the fundamental enablers of mass customization identified
by prior influential research and whose joint effect on MCC
has remained unexplored (see ‘‘Appendix 1’’), namely
knowledge absorption from customers (KAfC), product
modularity (PM), and online sales configurator use
(OSCU). By integrating ROT arguments with the fit-as-
covariation perspective and prior MCC research, the pre-
sent study conceptually develops the hypothesis that the fit-
as-covariation (i.e., the bundling) of KAfC, PM, and OSCU
has a stronger positive association with MCC than the same
practices implemented in isolation. The present study tes-
ted this hypothesis using covariance-based structural
equation modeling (CB-SEM) and survey data from 213
manufacturing plants in three industries across 16 countries
in Asia, America, and Europe. By doing so, this study
improves the understanding of how to develop MCC,
which can help companies not only improve their prof-
itability but also foster innovation and transition toward
sustainable production.
The remainder of this paper is organized as follows. The
next section reviews the relevant literature and conceptu-
ally develops the research hypothesis. The following sec-
tions describe the data and the measures used to test the
hypothesis, show the quality of the measures, present the
results of the main analysis, and introduce the supple-
mentary analyses, whose details are reported in the online
supplement to this paper. The theoretical and managerial
implications of the findings, as well as the study’s limita-
tions and opportunities for future research, are then dis-
cussed. Finally, some concluding remarks are provided
concerning the main message of the paper.
Global Journal of Flexible Systems Management
123
Literature Review and Research Hypothesis
This section begins with a review of the literature linking
mass customization with flexibility. It then explains why
the three organizational practices of interest here map one-
to-one onto the fundamental building blocks of mass cus-
tomization identified by Salvador et al.’s (2009) influential
work. Finally, it presents the conceptual arguments that led
to the research hypothesis.
Mass Customization and the Role of Flexibility in its
Context
Mass customization seeks to combine the best of custom
manufacturing, in terms of individualized products, with
the best of mass production, in terms of cost, delivery, and
quality performance (Squire et al., 2006). Prior research
has distinguished different mass customization strategies
that reflect different customer utility functions (Trentin &
Salvador, 2023). For example, when customers value a
high degree of product customization more than they value
delivery speed, the mass customization strategy should
involve customers at the design or fabrication stage of the
value chain (Trentin & Salvador, 2023). On the other hand,
when customers are not willing to sacrifice delivery speed,
‘‘they must be involved at a later stage and customization
takes place within the constraints of pre-engineered pro-
duct options or variants’’ (Trentin & Salvador, 2023, p. 24).
In the former case, prior research has used the term ‘‘full
mass customization,’’ while in the latter case, it has spoken
of partial mass customization (e.g., Huang et al., 2010;
Sandrin et al., 2018).
The notion of mass customization (capability) has long
been related to the concept of flexibility in two different
ways. One perspective sees flexibility as an enabler of
MCC. This view dates back to Pine’s (1993) seminal work
on mass customization. Unsurprisingly, the earliest litera-
ture review on mass customization, conducted by da Sil-
veira et al. (2001), defined mass customization in a way
that emphasized the role played by flexible processes in the
development of this capability. Similarly, process flexibil-
ity was identified by Zipkin (2001) as one of the funda-
mental enablers of MCC. Two years later, Fogliatto et al.
(2003) considered not only process flexibility but also ‘‘the
flexibility of design and/or supply delivery’’ (pp.
1811–1812) in their flexibility-driven index, which aimed
to assess the feasibility of mass customization. Elements of
flexibility beyond the traditional boundaries of the firm
were also identified in studies by Brabazon et al. (2010),
Salvador et al. (2015), Trentin et al. (2015), and Ullah and
Narain (2022). Besides reiterating the message on the
importance of flexibility at the plant level, Brabazon et al.
(2010) showed the role that interdealer trading flexibility––
a form of routing flexibility involving the downstream
supply chain––plays in the pursuit of MCC in the auto-
motive industry. Regarding the upstream supply chain,
Trentin et al.’s (2015) longitudinal case study indicated
supplier flexibilization as one of the factors that had
improved MCC at the case company. In the same year,
Salvador et al. (2015) reported large-scale empirical evi-
dence of the MCC-enabling role of flexible manufacturing
resources, defined in that study as including flexible sup-
pliers. Seven years later, these findings were corroborated
and extended by Ullah and Narain’s (2022) survey-based
study, which found empirical support for the positive effect
on MCC of not only supplier flexibility but also sourcing
flexibility. At a higher level of abstraction, Kortmann et al.
(2014) examined and reported large-scale empirical evi-
dence for the MCC-enabling role of strategic flexibility,
which depends not only on the flexibility of a firm’s
resources but also on the firm’s ability to reconfigure them.
Unsurprisingly, flexible manufacturing systems and
reconfigurable manufacturing systems have often been
mentioned as important enablers of mass customization
(Gao et al., 2021; He & Smith, 2024; Jain et al., 2023).
Other authors, however, have seen flexibility not as an
enabler of MCC but as one of the two poles of the tension
whose reconciliation is the very essence of MCC. For
example, Birkinshaw and Gupta (2013) observed that mass
customization makes it possible to reconcile efficiency and
flexibility in the manufacturing sector. In the same vein,
subsequent research has observed that mass customization
is expected to overcome the traditional trade-off between
flexibility and other dimensions of operational performance
(Trentin et al., 2020; Wiengarten et al., 2017). Similarly,
recent research in the construction industry has viewed
mass customization as the key to simultaneously increasing
flexibility and efficiency (Cao et al., 2021; Formoso et al.,
2022). Notably, this view implicitly equates flexibility with
the ability to provide customized products, that is, with
product flexibility as defined by Vickery et al. (1999). On
the other hand, the view of flexibility as an enabler of MCC
focuses on other dimensions of flexibility, such as process
flexibility or supplier flexibility, as explained above.
Focal Organizational Practices and Their Mapping
onto Mass Customization Building Blocks
Knowledge absorption from customers is the practice of
acquiring, assimilating, and exploiting knowledge gained
from customers (e.g., Yang & Yee, 2022; Zhang et al.,
2015b). This practice maps onto the first of the three fun-
damental building blocks of mass customization identified
by Salvador et al. (2009)—solution space development.
This building block points to the necessity for a mass
Global Journal of Flexible Systems Management
123
customizer to define its solution space, that is, the ‘‘pre-
defined space of possible outputs’’ (Salvador et al., 2020,
p. 4), based on a clear understanding of customers’
idiosyncratic needs and, specifically, of the product attri-
butes along which customers’ idiosyncratic needs diverge
the most (Salvador et al., 2009). As such, solution space
development clearly implies the acquisition, assimilation,
and exploitation of customer knowledge.
Knowledge absorption from customers adds to a com-
pany’s flexibility by enabling the organization to keep its
solution space aligned with its customers’ evolving needs
(Zhang et al., 2015b). For example, customers’ post-pur-
chase feedback on the value received from existing solu-
tions can be gathered, analyzed, and incorporated into
improved solutions. Keeping the solution space aligned
with changes in customers’ needs is a prerequisite to ful-
filling customer orders using predefined solutions and not
having to resort to engineered-to-order ones. In turn, this is
essential to infusing ‘‘flexibility into the product architec-
tures as well as into the sales and transformation pro-
cesses’’ without compromising other dimensions of
operational performance (Salvador et al., 2020, p. 4).
Product modularity is the practice of designing products
composed of parts that can be physically separated and
recombined to create a variety of product configurations
(Mikkola, 2007; Salvador, 2007; Wurzer & Reiner, 2018).
This practice maps onto the second of the three funda-
mental building blocks of mass customization identified by
Salvador et al. (2009)—robust process design. This build-
ing block points to the requirement that a mass customizer
fulfill differentiated customer needs by reusing or recom-
bining existing organizational and value-chain resources
(Salvador et al., 2009). Product modularity enables a
company to achieve this by creating product variety in a
combinatorial fashion from a relatively small number of
components (Ulrich, 1995). Consequently, product com-
ponents can be made in volume using mass-production
techniques, which allow manufacturers to achieve the low
cost and consistent quality typical of repetitive manufac-
turing (Duray et al., 2000).
Product modularity is a key enabler of strategy flexi-
bility (Worren et al., 2002). In particular, PM helps a
manufacturer keep its solution space aligned with changes
in customers’ functional requirements, as the number of
product components affected by these changes is minimal
(Gauss et al., 2022; Ulrich, 1995). In addition, PM allows
the postponement of the creation of product variety along
the manufacturing and distribution process (Verma &
Chatterjee, 2023), thus improving several dimensions of
operational performance (see Trentin & Salvador, 2023).
Finally, OSCU is the practice of enabling customers to
self-customize their product solutions online within the
constraints specified by the manufacturer (Sandrin et al.,
2017; Tseng & Piller, 2003). This practice maps onto the
last of the three fundamental building blocks of mass
customization identified by Salvador et al. (2009)—choice
navigation. This building block points to the necessity for a
mass customizer to help its customers identify their own
solutions within the manufacturer’s solution space ‘‘while
minimizing complexity and the burden of choice’’ (Sal-
vador et al., 2009, p. 74). An online sales configurator
enables a company to do this by giving customers the
freedom to explore the company’s solution space and make
their choices within the constraints specified by the com-
pany, ideally in a flexible and focused manner (Trentin
et al., 2013).
The use of an online sales configurator reconciles pro-
duct flexibility with cost, delivery, and quality in at least
two ways. First, it eliminates sales configuration errors,
which require time-consuming and costly interactions
between the customer and the manufacturer to fix, or if
these errors remain undetected, may impair product quality
(Heiskala et al., 2007). Second, OSCU increases the
number of customer orders that are fulfilled using prede-
fined solutions instead of engineered-to-order ones (Heis-
kala et al., 2007). Both mechanisms contribute to
improving cost, delivery, and quality performance when
customized products are provided (see Zhang, 2014 for a
review of the research on the impacts of configurators on
firm performance).
The practices outlined above can support a wide range
of mass customization strategies that are characterized by
different degrees of product customization (e.g., Duray
et al., 2000; Forza & Salvador, 2006). However, as the
degree of product customization approaches its maximum,
the implementation of OSCU becomes increasingly diffi-
cult (Forza & Salvador, 2006).
Hypothesis Development
A useful theoretical framework for this study is ROT.
According to ROT, organizational capabilities stem from
bundles of multiple, interdependent, and mutually rein-
forcing resources (Hitt et al., 2016; Liu et al., 2016; Sirmon
et al., 2007). It is the ‘‘fit or alignment’’ of these resources
‘‘rather than the independent effects of the individual
resources’’ (Liu et al., 2016, p. 14) that leads to organiza-
tional capabilities and, ultimately, to the creation of value
for customers and owners (Sirmon et al., 2007). This notion
of alignment among multiple interdependent and mutually
reinforcing resources is captured well by the fit-as-covari-
ation perspective identified by Venkatraman (1989). This
perspective, not unlike the perspectives termed ‘‘fit-as-
gestalts’’ and ‘‘fit-as-profile deviation’’ in the same paper,
conceptualizes fit as internal consistency among three or
more variables (Venkatraman, 1989). The fit-as-covariation
Global Journal of Flexible Systems Management
123
perspective, however, is more precise than the other two in
specifying the functional form that internal consistency
should take: ‘‘In this case, internal consistency requires that
a higher value of any one of the considered variables is
associated with higher values of all the other variables’’
(Sandrin et al., 2018, p. 338). Another reason why this
perspective on fit aligns well with ROT’s notion of
resource bundling is that the outcome of the multilateral
interactions among the three or more variables that com-
prise the bundle is captured well by the performance effect
of fit-as-covariation (Tanriverdi & Venkatraman, 2005).
In summary, ROT arguments and Venkatraman’s (1989)
work on the conceptualization of fit imply that organiza-
tional capabilities stem from the fit-as-covariation of
multiple, interdependent, and mutually reinforcing resour-
ces rather than from the independent effects of the same
resources considered in isolation. This major premise can
be combined with another premise, which rests on the
direct correspondence of KAfC, PM, and OSCU with the
fundamental building blocks of mass customization iden-
tified by Salvador et al.’s (2009) influential work. This
correspondence implies that KAfC, PM, and OSCU are key
resources for MCC. From these two premises, it follows
that the fit-as-covariation of these three specific resources
explains and predicts the organizational capability of mass
customization better than the independent effects of the
same resources considered in isolation.
The result of this deductive reasoning is corroborated by
a set of intertwined arguments that, for the sake of con-
ciseness, are reported in Appendices 2 and 3, where they
are also illustrated by company case examples based on the
MCC literature or the authors’ experience with manufac-
turers pursuing MCC. Appendix 2 provides a comprehen-
sive and systematic explanation of the mechanisms
whereby KAfC, PM, and OSCU mutually facilitate their
implementation in the pursuit of MCC (i.e., why PM
facilitates both OSCU and KAfC, why OSCU facilitates
both KAfC and PM, and why KAfC facilitates both PM
and OSCU). These mechanisms of mutual facilitation
contribute to explaining why MCC is better predicted by
the joint presence of these three practices than by the same
practices considered in isolation. Likewise, Appendix 3
provides a comprehensive and systematic explanation of
the mechanisms whereby the three practices mutually
reinforce their benefits in terms of MCC (i.e., why OSCU
reinforces the positive effect on MCC of both KAfC and
PM, why PM reinforces the positive effect on MCC of both
OSCU and KAfC, and how KAfC reinforces the positive
effect on MCC of both PM and OSCU). Since a manu-
facturer that understands these synergies is likely to
implement these practices together (see Aral et al., 2012,
for the same logical point, although applied to a different
research aim), these complementarity mechanisms
contribute to explaining why MCC is better predicted by
the joint presence of these practices than by their isolated
effects.
Besides corroborating the line of reasoning that com-
bines ROT arguments, Venkatraman’s (1989) notion of fit-
as-covariation, and Salvador et al.’s (2009) influential
work, Appendices 2 and 3 also overcome the concern that
simply advocating the bundling of KAfC, PM, and OSCU
based on this line of reasoning might be considered by
managers as overly generic or lacking in substance. Based
on the above, the following research hypothesis is posited:
Hypothesis: The fit-as-covariation of KAfC, PM, and
OSCU has a stronger positive association with a manu-
facturer’s MCC than KAfC, PM, and OSCU implemented
in isolation.
The effect of the fit-as-covariation of the three practices
on MCC is captured by the covariation model depicted in
Fig. 1a, while the effects of the same practices imple-
mented in isolation are captured by the independent-effects
model depicted in Fig. 1b. The research hypothesis
involves a comparison of the two models.
Method
This study follows the approach recommended for confir-
matory survey research, which is the type of survey
research that aims to test theory-driven hypotheses (Forza
& Sandrin, 2023). Accordingly, this section begins with a
description of the data collection procedures and continues
with a description of the measures used to test the
hypothesis and the results of the analyses performed to
assess measurement quality. Finally, this section describes
the testing procedure, which is based on Venkatraman’s
(1989) influential work on alternative notions of fit and the
associated testing procedures, as well on subsequent stud-
ies by the same author (Tanriverdi & Venkatraman, 2005;
Venkatraman, 1990).
Data Collection
The data for hypothesis testing were sourced from the
fourth and most recent edition of the High-Performance
Manufacturing (HPM) research initiative. This is an
international survey that has targeted, since its first edition,
medium-to-large manufacturing plants operating within
three industries: electronics, machinery, and transportation
equipment. The number of countries involved has pro-
gressively increased from one edition to the next and now
includes 16 countries across America, Asia, and Europe. In
exchange for its participation, each plant received a com-
prehensive benchmarking report that allowed it to compare
Global Journal of Flexible Systems Management
123
its levels of performance and practice adoption against the
values of descriptive statistical indicators, such as aver-
ages, maxima, and minima, derived from the subsample of
plants in the same industrial sector worldwide. This
incentive contributed to a response rate of around 65% per
country. Twelve distinct questionnaires, each covering
specific topics, were provided to each plant. Two plant
respondents for each questionnaire were selected based on
their expertise in the relevant subject matter, except for the
questionnaire on accounting, which was submitted to one
respondent.
1
The sample we used to test the hypothesis is
described in Table 1.
The fourth round of the HPM survey was specifically
designed to collect data on a set of manufacturing and
supply chain management practices that includes the three
practices of interest in the present study. In addition, the
HPM survey focuses on mid- to large-sized manufacturing
plants, whose relatively greater resources make it more
likely that the application of the surveyed practices can be
observed (Morita et al., 2018). Undeniably, practices such
as PM or OSCU may be more relevant in some industries
than in others. However, their adoption in the machinery,
transportation equipment, and electronics industries has
been discussed in business sources as well as in academic
papers (e.g., Bannasch et al., 2014; Blazek et al., 2021;
Mahlama
¨ki et al., 2020). Consequently, our sample is
appropriate for the purposes of the present study.
Measures and Their Quality
The measurement scales for the focal constructs are pre-
sented in Table 2. For MCC, we used Zhang et al.’s (2020)
validated four-item scale adapted from Tu et al. (2001). For
KAfC and PM, we employed, respectively, Phan et al.’s
(2020) validated five-item scale and Liu et al.’s (2010)
validated three-item scale. For all these scales, the
Fig. 1 Conceptual models to be
compared
1
More details on the data collection process can be found in Ortega-
Jimenez et al. (2020).
Global Journal of Flexible Systems Management
123
participants responded to each item on a five-point Likert-
type scale, where 1 = strongly disagree and 5 = strongly
agree. Finally, for OSCU, we used a single-item measure,
based on Bergkvist and Rossiter’s (2007) argument that
single-item measures are sufficient when a) ‘‘the object of
the construct [in our case, the online sales configurator] is
‘concrete singular,’ meaning that it consists of one object
that is easily and uniformly imagined [by the survey
respondents]’’ and b) ‘‘the attribute of the construct [in our
case, the extent to which the online sales configurator is
used] is ‘concrete,’ again meaning that it is easily and
uniformly imagined’’ (Bergkvist & Rossiter, 2007, p. 176).
The instructions for the OSCU item read: ‘‘For which of
the following marketing and sales activities does your plant
use the Internet or EDI [that is, electronic data inter-
change]? (1 = not at all, …, 5 = completely).’’
Since the focal constructs are plant-level characteristics
that were measured using individual-level data collected
from multiple respondents per plant, agreement among
informants is essential: ‘‘To the extent that responses
across multiple informants evaluating the same system […]
are consistent, the researcher infers this is due to a common
system-level trait driving the informant reports’’ (Ketokivi,
2019, pp. 383–384). To assess inter-rater agreement, we
used the ratio method developed by James et al. (1984). All
inter-rater agreement coefficients (IRACs) were well above
0.8 (Table 2), indicating good agreement among the
informants (Boyer & Verma, 2000). Consequently, the
informants’ scores were averaged to arrive at a plant-level
score for each focal construct.
Since the measures of the focal constructs are percep-
tual, we assessed their psychometric properties through
confirmatory factor analysis (CFA) performed using IBM’s
SPSS Amos version 22 software. To evaluate unidimen-
sionality and convergent validity, a model was specified in
which each item was constrained to load solely on its
intended construct while allowing the four latent constructs
to correlate freely. The model showed a good fit with the
data, as evidenced by standard fit indices: v
2
(df) = 142.98
(60), v
2
/df = 2.38; comparative fit index (CFI) = 0.92,
incremental fit index (IFI) = 0.92, Tucker–Lewis index
(TLI) = 0.90, root-mean-square error of approximation
(RMSEA) (90% confidence interval) = 0.081
(0.064–0.098). Additionally, standardized factor loadings
for all items exceeded 0.50 and were significant at
p\0.01. Finally, all average variance extracted (AVE)
scores exceeded 0.50. Together, these results suggest uni-
dimensionality and good convergent validity. All com-
posite reliability (CR) values were above 0.70, which
indicates satisfactory reliability. Finally, the application of
Fornell and Larcker’s (1981) procedure suggests good
discriminant validity (Table 3).
Finally, we assessed common method bias, which could
be a concern when using perceptual measures. However, it
is worth emphasizing that, in our case, this concern is
mitigated by the fact that the focal constructs were mea-
sured using different informants so that no informant was
shared by any pair of constructs (see Podsakoff et al.,
2003). Nonetheless, we conducted Harman’s single-factor
test using both exploratory and confirmatory factor analysis
(see Podsakoff et al., 2003). The results of these analyses
do not suggest that method bias is a concern in this study.
Testing Procedure
To test the hypothesis, we compared the effect on MCC of
a reflective, second-order latent factor representing the fit-
as-covariation of KAfC, PM, and OSCU with the inde-
pendent effects of the same three variables on the same
capability. In the former model, the second-order factor
accounts for the complementarity and covariance among
the considered practices in the pursuit of MCC (see Tan-
riverdi & Venkatraman, 2005, although in a different
context). On the contrary, the latter model does not posit
any incremental explanatory power for the complemen-
tarity and covariance of these practices (see Tanriverdi &
Venkatraman, 2005, although in a different context).
Table 1 Sample distribution by industry and country
Industry Frequency %
Electronics 70 32.9
Machinery 86 40.4
Transportation equipment 57 26.8
Total 213 100.0
Country Frequency %
Brazil 9 4.2
China 19 8.9
Finland 12 5.6
Germany 19 8.9
Israel 6 2.8
Italy 25 11.7
Japan 18 8.5
South Korea 21 9.9
Spain 16 7.5
Sweden 3 1.4
Switzerland/Austria 9 4.2
Taiwan 23 10.8
UK 13 6.1
USA 7 3.3
Vietnam 13 6.1
Total 213 100.0
Global Journal of Flexible Systems Management
123
In line with several previous studies on the determinants
of MCC (e.g., Liu et al., 2006; Zhang et al., 2014), we
controlled for the effects of the country and industry in
which a manufacturer operates and the size of the manu-
facturing company, measured with the natural logarithm of
the number of personnel employed (e.g., Huang et al.,
2008). Unlike plant size and the two industry dummies, the
numerous country dummies could not be included in the
two models due to our sample size. Thus, to rule out the
potential effects of country, we followed the approach of
Liu et al. (2006) and Sandrin et al. (2018) by employing in
all analyses the standardized residuals from the linear
ordinary least square regression analysis of each MCC item
on the country dummies.
Results
Using IBM’s SPSS Amos version 22 software, we esti-
mated and compared the two models through CB-SEM and
applied the criteria suggested by Venkatraman (1989) and/
or Venkatraman (1990). The first criterion involves
Table 2 Measurement scales
Construct
Composite reliability, Average variance extracted, Inter-rater
agreement coefficient
Measurement item (item code) Standardized factor
loading
a
Knowledge absorption from customers
i
(Phan et al., 2020, p. 395)
CR = 0.88
AVE = 0.59
IRAC = 0.91
‘‘We obtain a great amount of our product knowledge
from our customers.’’ (KAfC1)
0.76
‘‘Our customers provide us with valuable information on
product innovation.’’ (KAfC2)
0.74
‘‘We have learned a lot from our customers as part of our
product development process.’’ (KAfC3)
0.85
‘‘We quickly adopt new technologies by applying what
we learn from our customers.’’ (KAfC4)
0.78
‘‘We systematically check whether we have applied the
knowledge we acquire from our customers regarding
our products.’’ (KAfC5)
0.70
Product modularity
ii
(Liu et al., 2010, p. 1010)
CR = 0.76
AVE = 0.52
IRAC = 0.84
‘‘Our products are modularly designed, so they can be
rapidly built by assembling modules.’’ (PM1)
0.85
‘‘We have defined product platforms as a basis for future
product variety and options.’’ (PM2)
0.52
‘‘Our products are designed to use many common
modules.’’ (PM3)
0.77
Online sales configurator use
iii
(see Roth et al., 2008, p. 33)
CR = 1
AVE = 1
IRAC = 0.83
‘‘Providing online customized customer service, where
customers can configure the product within the
constraints stated by the plant.’’ (OSCU1)
1
Mass customization capability
iv
(Zhang et al., 2020, p. 492)
CR = 0.81
AVE = 0.52
IRAC = 0.86
‘‘We are highly capable of large-scale product
customization.’’ (MCC1)
0.72
‘‘We can easily add significant product variety without
increasing cost.’’ (MCC2)
0.64
‘‘We can customize products while maintaining high
volume.’’ (MCC3)
0.79
‘‘Our capability for responding quickly to customization
requirements is very high.’’ (MCC4)
0.73
a
All estimated factor loadings are statistically significant with p\0.01
i
Downstream supply chain management questionnaire
ii
New product development questionnaire
iii
Information system management questionnaire
iv
Process engineering questionnaire
Global Journal of Flexible Systems Management
123
evaluating the models’ goodness-of-fit statistics. Both
models exhibit similar and satisfactory fit indices. For the
covariation model, v
2
(df) = 162.53 (95), v
2
/df = 1.71;
CFI = 0.94, IFI = 0.94, TLI = 0.92, RMSEA (90% confi-
dence interval) = 0.058 (0.042–0.073). For the indepen-
dent-effects model, v
2
(df) = 158.00 (87), v
2
/df = 1.82;
CFI = 0.93, IFI = 0.94, TLI = 0.91, RMSEA (90% confi-
dence interval) = 0.062 (0.046–0.077). However, the
covariation model is preferred due to its greater parsimony
(Venkatraman, 1990).
The second criterion is the target coefficient––that is, the
ratio of the v
2
value of the independent-effects model over
the v
2
value of the covariation model—whose maximum
value is 1. Here, the target coefficient is 0.97, which is
close to 1. This indicates that the covariation model, which
is more parsimonious, explains the relationships among the
first-order factors almost as well as the independent-effects
model.
The third criterion considers the statistical significance
of the loadings of the first-order factors on the second-order
factor, while the fourth criterion evaluates the statistical
significance of the structural path linking the second-order
factor to the outcome variable, that is, to MCC. As illus-
trated in Fig. 2, all the loadings of the first-order factors on
the second-order factor are positive and highly significant,
and the second-order factor has a highly significant positive
impact on MCC.
The last criterion compares the squared multiple corre-
lation of MCC in the two models, which is the equivalent
of R
2
in the regression analysis. The value of the MCC
squared multiple correlation in the covariation model
(41.9%) is much greater than the same value in the inde-
pendent-effects model (13.9%).
Collectively, these results corroborate our hypothesis by
favoring the covariation model (Fig. 2) over the indepen-
dent-effects model (Fig. 3). Specifically, criteria 1, 2, and 3
support the existence of the fit-as-covariation of KAfC,
PM, and OSCU in the pursuit of MCC. Criterion 4 shows
that the fit-as-covariation of these three practices has a
highly significant positive association with MCC. Finally,
criterion 5 shows that the fit-as-covariation of these
practices explains much more of the MCC variation in our
sample than do the independent effects of the same prac-
tices considered in isolation. Notably, in the independent-
effects model, the positive effects of KAfC and PM on
MCC are highly significant, but the positive effect of
OSCU on MCC is not significant, even with a generous
0.10 pvalue. This result provides further corroboration of
our conceptual argument that MCC is better explained and
predicted by a model in which the three practices are
bundled.
Finally, we performed a range of supplementary analy-
ses to assuage possible concerns about the measures used
for OSCU and MCC. These analyses, which are presented
in detail in the online supplement to this paper, supported
our hypothesis, thus showing the robustness of our
findings.
Discussion
This section begins with a discussion of the implications of
our findings for theory and practice. Subsequently, it
explains the limitations of our work and discusses the
corresponding opportunities for future research.
Theoretical Implications
Our findings have theoretical implications for three dif-
ferent but related streams of research: flexibility, MCC, and
resource orchestration. The first contribution lies in our
positioning of MCC research, including the results of the
present study, in the context of flexibility research. Our
systematic review of the literature linking flexibility and
mass customization shows that flexibility plays a key role
in both the conceptualization of MCC and the implemen-
tation of this capability. On the one hand, flexibility is one
of the poles of the tension that the ambidextrous organi-
zational capability of mass customization is expected to
reconcile (e.g., Wiengarten et al., 2017). On the other hand,
flexibility is one of the enablers of this capability (e.g.,
Fogliatto et al., 2003). Interestingly, these two roles have
coexisted in the literature, with some recent works focusing
on new solutions to increase manufacturing flexibility to
improve MCC (e.g., Hashemi-Petroodi et al., 2022; Vjes
ˇ-
tica et al., 2023), while other recent studies have viewed
mass customization as the key to simultaneously increasing
flexibility and efficiency (Cao et al., 2021; Formoso et al.,
2022). This dual role played by flexibility in the context of
MCC implies that MCC research is inherently relevant to
flexibility research.
Notably, these two roles do not constitute a contradic-
tion but are fully consistent with the results of prior
Table 3 Discriminant validity of MCC, KAfC, PM, and OSCU
Construct AVE square root Correlations
MCC KAfC PM OSCU
MCC 0.72 1.00
KAfC 0.77 0.22 1.00
PM 0.72 0.26 0.14 1.00
OSCU 1.00 0.15 0.09 0.13 1.00
Global Journal of Flexible Systems Management
123
research on flexibility types or dimensions.
2
How different
types of flexibility relate to each other is one of the issues
addressed by the stream of research on the conceptualiza-
tion and operationalization of manufacturing and supply
chain flexibility (Pe
´rez-Pe
´rez et al., 2019). Our systematic
literature review shows that when flexibility is involved in
the conceptualization of MCC, it is understood as product
flexibility; on the other hand, when it is mentioned as an
enabler of MCC, it is understood as the flexibility of the
manufacturing process or of the upstream or downstream
supply chain, with a focus that––reflecting the main
research trajectory identified by Varma et al. (2024) in the
domain of flexibility––has progressively broadened from
in-house flexibility to include inbound and outbound flex-
ibilities (see Dey et al., 2019 for this categorization of
flexibility types). Notably, one of the accepted relation-
ships between the various dimensions of flexibility is the
positive association between process flexibility and product
flexibility (Jain et al., 2013; Parker & Wirth, 1999). This
accepted relationship is entirely consistent with the two
roles that flexibility plays in the implementation and con-
ceptualization of MCC; that is, flexibility (referring to
process flexibility) enables MCC, which is defined as the
ability to reconcile flexibility (referring to product flexi-
bility) with low cost, fast delivery, and high quality. This
dual role can also be interpreted through the lens of the
input-throughput-output model recently used by Ho
¨se et al.
(2023) to classify the different types of flexibility, which
states that product-related (or output) flexibility stems from
process-related (or throughput) and resource-related (or
input) flexibilities.
Interestingly, the results of the present study can also be
discussed in relation to Pe
´rez-Pe
´rez et al.’s (2019) inte-
grative framework on the antecedents, building blocks, and
consequences of manufacturing and supply chain flexibil-
ity. Two of these building blocks can be related to the
organizational practices of interest here: KAfC and OSCU
map onto the ‘‘information sharing and communication’’
building block, while PM maps onto the ‘‘processes’’
Fig. 3 Independent-effects
model: structural model
estimates (***p\0.01;
**p\0.05;
NS
p[0.10)
Fig. 2 Covariation model:
structural model estimates
(***p\0.01; **p\0.05;
NS
p[0.10)
2
We use the terms ‘‘flexibility type’’ and ‘‘flexibility dimension’’ as
synonyms, in accordance with the vast majority of the flexibility
literature (e.g., Pe
´rez Pe
´rez et al., 2016).
Global Journal of Flexible Systems Management
123
building block (Pe
´rez-Pe
´rez et al., 2019, p. 12). Therefore,
our results regarding the added value of bundling KAfC,
OSCU, and PM can be seen as echoing Pe
´rez-Pe
´rez et al.’s
(2019) observation that the building blocks of flexibility
are ‘‘interconnected and reinforcing approaches’’ (p. 12). In
addition, Pe
´rez-Pe
´rez et al. (2019) observe that organiza-
tional capabilities, such as agility, are among the outcomes
of the interdependent and mutually reinforcing building
blocks of flexibility. This observation is echoed by our
findings, since we showed that bundling KAfC, OSCU, and
PM––three practices that map onto two of the flexibility
building blocks identified by Pe
´rez-Pe
´rez et al. (2019)––is
positively associated with the organizational capability of
mass customization.
The second contribution of the present study concerns
the relationships between MCC enablers and how these
relationships affect MCC. Even though prior research has
investigated the effect of one or another of our focal
practices––considered in isolation––on MCC (e.g., Zhang
et al., 2015b,2019), our conceptual and empirical results
are the first concerning their joint effect on MCC. Given
the lack of prior studies on this issue, we can only compare
our findings with prior research that explicitly or implicitly
suggests relationships between MCC enablers whose defi-
nitions conceptually overlap––to various extents––with
two or more of the practices of interest here (see Appendix
1).
On the one hand, our results are consistent with the
message that PM and OSCU are positively associated with
one another, which can be inferred from Peng et al. (2011)
and Zhang et al. (2015a). Likewise, our results are in line
with the positive association that was found by Tu et al.
(2004) between PM and KAfC. Finally, our results are
consistent with the pairwise complementarities found by
Salvador et al. (2015) among three different ‘‘resource
types’’ (Salvador et al., 2015, p. 618): customer involve-
ment, which overlaps with KAfC; the endowment of flex-
ible manufacturing resources, which embraces five
different resources, including PM; and product manage-
ment tools, which encompass three distinct resources,
including OSCU.
On the other hand, our results challenge the mainly
sequential logic that dominates the literature reviewed by
Suzic
´et al. (2018), that is, the literature that contains
guidelines for the implementation of mass customization.
Their review found an overall agreement that PM––as part
of product platform development––should precede infor-
mation technology–based product configuration, which
includes OSCU. This relationship, in which PM precedes
OSCU, might also be inferred from Purohit et al.’s (2016)
findings. However, our results showed that the joint effect
of PM, OSCU, and KAfC explains substantially more
MCC variation (41.9%) than the effects of the same
practices when considered independently (13.9%). The
amount of MCC variation explained by our covariation
model indicates a large effect size according to Cohen et al.
(2003). In addition, this effect size is greater than that
reported in most previous survey-based studies on the
antecedents of MCC. Of 22 studies that tested models with
MCC as a dependent variable, only three (i.e., Migdadi,
2022; Ullah & Narain, 2021; Wang et al., 2015; all of
which use partial least squares structural equation model-
ing) explain more MCC variation than our covariation
model. Conversely, the two highest amounts of MCC
variation explained by the studies that used regression
analysis or CB-SEM are smaller than the amount explained
by our covariation model: 36% in Salvador et al. (2015)
and 29% in Huang et al. (2010). Collectively, our con-
ceptual and empirical findings suggest that with a strictly
sequential implementation process, the adoption of the first
practice in the sequence (e.g., OSCU) could possibly fail
due to lack of support from the two other practices; in
addition, supposing this were not the case, the synergies
among the three practices would be fully captured only at
the end of the process, when all the practices have been
implemented. This means that a strictly sequential imple-
mentation, even if it did not lead to an interrupted path,
would certainly delay the full realization of the benefits of
KAfC, PM, and OSCU in the pursuit of MCC.
By either echoing or challenging prior mass cus-
tomization research results, our findings add to the dis-
cussion on the relationships among MCC enablers and how
these relationships affect MCC. Although crucial to the
development of mass customization implementation
guidelines (Suzic & Forza, 2023; Suzic
´et al., 2018), this
issue has attracted relatively little research, especially on
the interplay among larger sets of enablers (Jain et al.,
2023). By advancing knowledge of this issue, our study
complements Salvador et al.’s (2009) influential work,
which identified three fundamental building blocks of mass
customization but left it up to managers to decide whether
to implement them simultaneously or in a particular
sequence. Since KAfC, PM, and OSCU map one-to-one
onto those building blocks, our results argue in favor of the
simultaneous, rather than sequential, implementation of the
fundamental building blocks of mass customization iden-
tified by Salvador et al. (2009).
A final contribution of the present paper to the MCC
literature lies in our borrowing of ROT arguments to
generate novel insights into the process of developing
MCC. The existing literature has adopted various theoret-
ical lenses to investigate the antecedents of MCC, includ-
ing the theory of sociotechnical systems and the resource-
based view (see Liu et al., 2023). However, to the best of
Global Journal of Flexible Systems Management
123
our knowledge, prior research on the antecedents of MCC
has not yet exploited ROT,
3
whose aim is to elucidate the
process through which executives manage firm resources to
create and maintain value for customers and owners (Sir-
mon et al., 2007,2011). A key step in this process consists
of bundling multiple interdependent and mutually rein-
forcing resources to develop organizational capabilities
(Chirico et al., 2025; Hitt et al., 2016; Liu et al., 2016). By
drawing upon this central tenet of ROT, the present paper
also makes an incremental contribution to the stream of
research on resource orchestration.
Prior research in this stream has conceptualized the
notion of resource bundling using the fit-as-profile devia-
tion perspective (e.g. Liu et al., 2016) or the fit-as-mod-
eration perspective (e.g. Deligianni et al., 2019). To the
best of our knowledge, the present study is the first to
establish a logical connection between ROT’s notion of
resource bundling and the fit-as-covariation perspective. In
a context in which ROT is attracting considerable attention
from researchers beyond the confines of strategy research
(e.g., Chirico et al., 2025; Jiang et al., 2024), this con-
nection may help future research take full advantage of
Venkatraman’s (1989) work on alternative notions of fit
and on the associated testing procedures. When the bund-
ling of three or more resources mutually reinforces their
effects on a given criterion variable and mutually facilitates
their creation, the other five perspectives identified by
Venkatraman (1989) appear less appropriate for concep-
tualizing the resource bundle. First, the fit-as-matching
perspective cannot capture the fit between more than two
variables (Venkatraman, 1989). Second, the fit-as-gestalts
perspective and the fit-as-profile deviation perspective
view fit as internal consistency between three or more
variables but do not require that the pattern of internal
consistency be a pattern of covariation (see Venkatraman,
1989). Finally, the fit-as-moderation perspective and the
fit-as-mediation perspective capture only part of the com-
plex set of intertwined relationships that explain the exis-
tence of such a resource bundle: The fit-as-mediation
perspective does not capture the fact that the bundled
resources mutually reinforce their effects on a given cri-
terion variable, while the fit-as-moderation perspective
does not capture the fact that the bundled resources
mutually facilitate their creation, thus affecting the same
criterion variable not only directly but also indirectly
through the other resources of the bundle (see Venkatra-
man, 1989).
Finally, the present study provides empirical corrobo-
ration for one of the central tenets of ROT—that is, that
organizational capabilities stem from bundles of multiple,
interdependent, and mutually reinforcing resources (Hitt
et al., 2016; Liu et al., 2016; Sirmon et al., 2007). By
focusing on a specific bundle that prior research has not
examined, our conceptual results apply this highly abstract
tenet of ROT to the organizational capability of mass
customization, and our empirical results corroborate this
tenet in the specific context of MCC. Overall, this middle-
range theorizing effort (see Bourgeois, 1979) adds to the
resource orchestration literature, as the latter has rarely
identified the specific bundles needed to develop specific
capabilities (Hughes et al., 2018).
Managerial Implications
From a practical standpoint, this study can help companies
create flexible systems that are able to provide customized
products without compromising cost, delivery, or quality. It
does so in two ways. First, it makes managers aware that
they should strive to simultaneously increase PM, KAfC,
and OSCU to reconcile product flexibility with low cost,
fast delivery, and high quality. Our conceptual arguments,
corroborated by our empirical findings, imply that any
partial approach in which only a subset of these practices is
implemented will make their implementation more difficult
and hinder the full realization of their potential benefits in
the pursuit of MCC. In particular, our data show that
adopting an online sales configurator without adequately
investing in the ability to absorb customer knowledge and
modularize the product architecture does not have a sta-
tistically significant positive effect on MCC. This finding
points once more to the risk of adopting technological
solutions for mass customization without what, three dec-
ades ago, Hart (1995) termed ‘‘organizational readiness’’
(p. 44). Likewise, any approach in which OSCU, KAfC,
and PM are implemented sequentially will certainly delay
the achievement of the same benefits and might possibly
lead to failure in the implementation of the early practices
in the sequence and, therefore, to an interrupted path. Of
course, resource constraints could prevent a simultaneous,
steep increase in the level of all these practices. In this
case, a feasible approach to avoid the disadvantages of
working on only one practice at a time might be to make
simultaneous but gradual improvements, for example, by
focusing on one product family at a time.
Second, this paper helps managers build support for the
possibly gradual but simultaneous increase of KAfC, PM,
and OSCU. This coordinated move requires information
exchange and collaboration across multiple functional
departments within a manufacturing organization (at the
very least, product design and engineering, manufacturing,
marketing and sales, and information systems) as well as
beyond the organizational boundaries (at the very least,
3
Jafari et al. (2022) drew upon ROT to address the question of
whether MCC mediates the relationship between supply integration
and firm performance, not to explain and predict MCC.
Global Journal of Flexible Systems Management
123
with customers). On the one hand, our comprehensive,
systematic, and detailed presentation of the mechanisms
whereby KAfC, PM, and OSCU mutually facilitate their
implementation and reciprocally reinforce their benefits in
the pursuit of MCC, along with the illustration of these
mechanisms by means of company case examples (see
Appendices 2 and 3), offers several selling points to
managers advocating a holistic approach to the imple-
mentation of these practices. On the other hand, possible
resistance from customers to sharing information on their
product preferences/experiences could be reduced by the
promise––corroborated by our empirical findings––of
greater product flexibility without compromising on cost,
quality, or delivery.
Limitations and Related Research Opportunities
The present study has some limitations that can be over-
come in future research. First, this paper focuses on prac-
tices that map one-to-one onto the fundamental building
blocks of mass customization identified by Salvador et al.
(2009) but does not fully cover them because of data
unavailability. Future research could develop comprehen-
sive measures of these building blocks and test the research
proposition––suggested by our results––that these building
blocks should be created simultaneously rather than
sequentially. Similarly, richer data would allow future
studies to empirically examine the effectiveness of the
proposed bundle of practices for different degrees of pro-
duct customization, corresponding to different mass cus-
tomization strategies, or the role that sales configurator
characteristics, such as user-friendliness, are likely to play
in strengthening the synergies among these practices in the
pursuit of MCC. Another focus for future research could be
the effect of complementing this bundle of practices with
cybersecurity systems aimed at mitigating data protection
concerns about capturing and storing rich customer infor-
mation as part of an organization’s KAfC.
Second, the present study, while presenting compelling
arguments and robust evidence supporting the simultane-
ous implementation of KAfC, PM, and OSCU to improve
MCC, does not address the question of how managers can
implement these practices simultaneously. While answer-
ing this ‘‘how’’ question will most likely require qualitative
research, we can safely conjecture that human resources
will play an important role in this process. This is because
the resource orchestration literature has explicitly recog-
nized that ‘‘multiple levels of management, with different
perspectives and pressures, must cooperate’’ (Chadwick
et al., 2015, p. 361) to orchestrate resources. However,
research on the micro-foundations of resource orchestration
is still in its infancy (Symeonidou & Nicolaou, 2018) and,
likewise, there is little prior research on the individual-
level enablers of MCC (Trentin et al., 2019). Future studies
could not only address these gaps but also complement the
results of previous studies on the crucial role played by
human resources in linking competitiveness and environ-
mental sustainability (e.g., Almanza Floyd et al., 2024) and
on the ways in which several dimensions of firm perfor-
mance are affected by human resource-related factors, such
as leadership style (e.g., Dimple & Tripathi, 2024; Prabhu
& Srivastava, 2023), top management team characteristics
(e.g., De la Gala-Vela
´squez et al., 2023), and human
resource flexibility (e.g., Dimple & Tripathi, 2024).
Future research could also apply a higher level of
analysis than the organization level adopted in the current
research. This is because the simultaneous increase in
KAfC, PM, and OSCU requires information exchange and
collaboration not only within but also beyond organiza-
tional boundaries. Thus, research on digital platform–based
ecosystems (see Helfat & Raubitschek, 2018; Suzic &
Chatzimichailidou, 2023), Industry 4.0 technologies (e.g.,
Pandey et al., 2024; Srivastava & Bag, 2023), and stake-
holder engagement (see D’Adamo, 2023; D’Adamo et al.,
2024; Kujala et al., 2022) might generate additional
insights into how to carry out this coordinated move.
We also recognize the need for additional research on
the extent to which our findings can be extended to
industries not covered by our sample. Finally, we under-
score that the cross-sectional nature of our data has led us
to formulate our hypothesis in correlational terms and,
certainly, a causal interpretation of our empirical findings
is not recommended. However, our conceptual arguments
support the causal effect of the bundling of KAfC, PM, and
OSCU on MCC, and this causal effect could be tested by
future longitudinal studies.
Conclusions
The present study joins the stream of research on how to
reconcile product flexibility with other dimensions of
operational performance, that is, how to develop MCC.
Prior research has shown that this ambidextrous organiza-
tional capability can help companies improve their prof-
itability, foster innovation, and transition toward
sustainable production. However, prior research has also
shown that developing MCC is a complex endeavor that
requires putting multiple enablers in place, and that uneven
or interrupted paths toward this goal are not unusual. To
develop guidelines capable of supporting companies
engaged in this challenging process, it is crucial to
understand the relationships between MCC enablers and
how these relationships affect MCC, as firms implementing
mass customization must decide not only what enablers to
implement but also in what sequence.
Global Journal of Flexible Systems Management
123
The present study adds to the relatively limited body of
knowledge on these issues. It does so by conceptually and
empirically investigating, for the first time, the added value
of bundling three organizational practices (i.e., KAfC, PM,
and OSCU) that map one-to-one onto the fundamental
building blocks of mass customization identified by prior,
influential research. Our conceptual and empirical results
consistently support a holistic approach to the implemen-
tation of these practices, as advancements in the imple-
mentation of each of them are facilitated and made more
effective by advancements in the implementation of the
other two. Theoretically, these results have a bearing on
how the effects of these practices on MCC should be
modeled to better explain and predict a manufacturer’s
MCC, at least in the industries covered by our sample.
Pragmatically, our results suggest a specific path to
improving MCC––a path that challenges the mainly
sequential logic that dominates the literature on
the guidelines for the implementation of mass
customization––and offer several selling points to man-
agers striving to overcome resistance to this specific path.
Overall, the present study supports flexible systems man-
agement by enriching the body of knowledge on how to
create a system capable of reconciling product flexibility
with low cost, fast delivery, and high quality.
Appendix 1: Prior Research Results
on the Relationships between MCC Enablers
Whose Definitions Conceptually Overlap with Two
or More of the Focal Organizational Practices
Several studies on the enablers of MCC have explicitly or
implicitly suggested relationships among constructs whose
definitions overlap to various extents with KAfC, PM, and
OSCU. However, none of these studies has focused on the
effect on MCC of bundling exactly KAfC, PM, and OSCU.
Source MCC enabler overlapping with PM or
KAfC or OSCU (formal conceptual
definition)
Conceptual overlap with…(justification) Results relevant to the present study
Zipkin
(2001)
Elicitation (‘‘Any elicitation process is an
artful means of leading customers
through the process of identifying
exactly what they want. And it reduces
the costs associated with customers’
laborious searching.’’ (Zipkin, 2001,
p. 82))
OSCU (‘‘mass customization usually
employs the computer and the Internet.
Advances in user interfaces now make it
possible, fairly cheaply and quickly, to
construct a program to guide customers
or salespeople through an array of
choices.’’ (Zipkin, 2001, p. 83))
No explicit hypothesis concerning the
interplay among MCC enablers
For a mass customization system to work,
elicitation and process flexibility, besides
logistics, ‘‘must be linked tightly to form
a coherent, integrated whole’’ (Zipkin,
2001, p. 84)
Process flexibility (‘‘a high-volume but
flexible process [that] translates
[customer-specific] information into the
physical product’’ (Zipkin, 2001, p. 83))
PM (‘‘Flexibility-enhancing innovations
range from modular design and lean
operations to the increasing use of
digital-information technology for
controlling manufacturing equipment’’
(Zipkin, 2001, p. 83))
Tu et al.
(2004)
Customer closeness (‘‘the practice of
keeping close contact with customers, to
communicate with customers
effectively, and to understand
customers’ individual needs’’ (Tu et al.,
2004, p. 150))
KAfC (understanding customers’ needs
entails the acquisition and assimilation
of customer knowledge)
Large-scale empirical support for the
hypothesis that: ‘‘Firms that are closer to
customers will have higher levels of
modularity-based manufacturing
practices’’ (Tu et al., 2004, p. 153)
Modularity-based manufacturing practices
(second-order construct reflected by
product modularity, process modularity
and dynamic teaming. ‘‘Product
Modularity is the practice of using
standardized product modules so they
can be easily reassembled/rearranged
into different functional forms, or shared
across different product lines’’ (Tu et al.,
2004, p. 151))
PM (by its definition, the second-order
construct termed modularity-based
manufacturing practices includes PM)
Global Journal of Flexible Systems Management
123
continued
Source MCC enabler overlapping with PM or
KAfC or OSCU (formal conceptual
definition)
Conceptual overlap with…(justification) Results relevant to the present study
Salvador
et al.
(2009)
Solution space development (the ability to
‘‘identify the idiosyncratic needs of its
customers, specifically, the product
attributes along which customer needs
diverge the most […] Once that
information is known and understood, a
business can define its ‘solution space,’
clearly delineating what it will offer —
and what it will not’’ (Salvador et al.,
2009, p. 72))
KAfC (by its definition, solution space
development implies the acquisition,
assimilation and exploitation of
customer knowledge)
‘‘A company might decide to improve all
three capabilities simultaneously or,
rather, to prioritize one or two of them’’
(Salvador et al., 2009, p. 76)
Robust process design (the ability to
‘‘reuse or recombine existing
organizational and value-chain resources
to fulfill a stream of differentiated
customer needs’’ (Salvador et al., 2009,
p. 73))
PM (enables a company to create product
variety in a combinatorial fashion from a
relatively small number of components
(Ulrich, 1995))
Choice navigation (the ability to ‘‘support
customers in identifying their problems
and solutions while minimizing
complexity and the burden of choice’’
(Salvador et al., 2009, p. 74))
OSCU (enables a company to give
customers the freedom to explore its
solution space, ideally in a flexible and
focused manner, and to make their
choices within the constraints specified
by the company (Trentin et al., 2013))
Peng
et al.
(2011)
Modular product design (‘‘Modular
product design allows firms to de-
construct a product and its underlying
production processes based on a formal
product architecture, which
subsequently allows one to
reconfigure those underlying
components into new products and
associated processes’’ (Peng et al., 2011,
p. 1027))
PM (by its definition, modular product
design includes PM)
Large-scale empirical support for the
hypothesis that: ‘‘Modular product
design is positively associated with
product configurator IT’’ (Peng et al.,
2011, p. 1029)
Product configurator IT (‘‘[information
technology] tools made available to
customers and salespeople to let them
participate in activities related to the co-
design of a product, and to guide
customers through the process of
designing a product’’ (Peng et al., 2011,
p. 1027))
OSCU (by its definition, product
configurator IT includes an online sales
configurator)
Zhang
et al.
(2015a)
Elicitation (‘‘Elicitation refers to ‘a
mechanism for interacting with the
customer and obtaining specific
information’ (Zipkin, 2001, 82)’’ (Zhang
et al., 2015a, p. 461))
OSCU (see the observations made above
concerning Zipkin, 2001)
Large-scale empirical support for the
hypotheses that elicitation positively
affect process-flexible technology and
vice versa
Process-flexible technology (‘‘process-
flexible technology, refers to a
‘production technology that fabricates
the product according to the
information’ (Zipkin, 2001, 82)’’ (Zhang
et al., 2015a, p. 462))
PM (see the observations made above
concerning Zipkin, 2001)
Global Journal of Flexible Systems Management
123
continued
Source MCC enabler overlapping with PM or
KAfC or OSCU (formal conceptual
definition)
Conceptual overlap with…(justification) Results relevant to the present study
Salvador
et al.
(2015)
Customer involvement (‘‘the extent to
which a manufacturer engages in
interactions with its customers to
understand and respond to their needs
and to receive feedback on quality and
delivery’’ (Salvador et al., 2015, p. 619))
KAfC (by its definition, customer
involvement entails the acquisition,
assimilation and exploitation of
customer knowledge)
Empirical support for the pairwise
complementarity effects of customer
involvement, flexible manufacturing
resources, and product management
tools on MCC. Lack of empirical
support for their three-way
complementary effect on MCC
a
Flexible manufacturing resources (‘‘the
aggregate availability of machines,
production workers, product
architectures, process architectures, and
suppliers that are flexible’’ (Salvador
et al., 2015, p. 622))
PM (is one of the five first-order constructs
that form the second-order construct
termed flexible manufacturing
resources)
Product management tools (‘‘IT
[information technology] and software
applications that the plant personnel
utilize to support decision making and
monitor information related to the
configuration, the manufacturing, and
the delivery performance of products’’
(Salvador et al., 2015, p. 619))
OSCU (one of the three first-order
constructs that form the second-order
construct termed product management
tools is product configurator, which
includes a sales configurator to ‘‘acquire
complete and nonconflicting customer
specifications’’ (Salvador et al., 2015,
p. 619))
Purohit
et al.
(2016)
Modularity-based practices (‘‘an attribute
of the product system that characterises
the ability to mix and match independent
and interchangeable product building
blocks with standardised interfaces in
order to create product variants’’
(Purohit et al., 2016, p. 776))
PM (by its definition, the notion of
modularity-based practices coincides
with PM)
Modularity-based practices strongly drive
the use of web-based interactive systems
but weakly depend on the latter
Web-based interactive systems (‘‘online
product configurator, interface among
SC [supply chain] partners’’ (Purohit
et al., 2016, p. 776))
OSCU (by their definition, web-based
interactive systems include online sales
configurators)
Suzic
´
et al.
(2018)
Product platform development (‘‘defining a
set of design parameters (features,
components, etc.) that form a common
structure from which a stream of
derivative products (product
family/families) can be efficiently
developed and produced’’ (Suzic
´et al.,
2018, p. 871))
PM (‘‘PP [product platform development]
embeds M [product modularization]’’
(Suzic
´et al., 2018, p. 860))
‘‘Figure 2. Resulting model of enabler
relationships derived from the analysis
of articles.’’ (Suzic
´et al., 2018, p. 871)
The model depicted in this figure suggests
that product platform development be
implemented before IT-based product
configuration
Information technology (IT)-based product
configuration (‘‘a set of IT-backed
activities that support order acquisition
and fulfilment by translating each
customer’s specific needs into correct
and complete product information’’
(Suzic
´et al., 2018, p. 871))
OSCU (IT-based product configuration
includes OSCU, as it ‘‘guides users in
defining feasible product variants which
satisfy his/her needs, supplies users with
real-time information on the overall
characteristics of the product
configuration’’ (Suzic
´et al., 2018,
p. 871))
a
Flexible manufacturing resources and product management tools are second-order constructs of formative nature and, therefore, the first-order
constructs underlying each of these two higher-order constructs are not expected to co-vary (see Jarvis et al., 2003). This implies that (Salvador
et al. 2015)’s empirical results on the three-way complementarity of customer involvement, flexible manufacturing resources and product
management tools cannot be extended to the constructs underlying the last two variables. Doing this would be an instance of ecological fallacy,
according to (Certo et al. 2017) definition: to ‘‘mistakenly assume that a relationship at one level mirrors the relationship at another level’’ (p.
1538)
Global Journal of Flexible Systems Management
123
Appendix 2: Mechanisms Whereby PM, KAfC,
and OSCU Mutually Facilitate Their
Implementation in the Pursuit of MCC
There are several mechanisms whereby PM, KAfC, and
OSCU mutually facilitate their implementation. These
mechanisms contribute to explaining why MCC is better
predicted by the joint presence of these three practices than
by the same practices considered in isolation.
Product modularity facilitates both KAfC and OSCU
There are two reasons why PM facilitates KAfC: First, it
minimizes the number of product components affected by
changes in customers’ functional requirements (see the
section titled ‘‘Focal Organizational Practices and Their
Mapping onto Mass Customization Building Blocks’’). As
a result, knowledge about these changes can be incorpo-
rated into new product solutions more quickly and at a
lower cost (Ulrich, 1995). Second, PM increases compo-
nent commonality across product solutions (see the section
titled ‘‘Focal Organizational Practices and Their Mapping
onto Mass Customization Building Blocks’’). As a conse-
quence, knowledge acquired from one customer with
regard to a specific solution can be incorporated into other
solutions more easily (Wang et al., 2014). For instance,
following requests from a few customers, a carwash
equipment manufacturer developed a new module that
mixes detergent with air, which not only produces foam, as
requested by those customers, but also reduces detergent
consumption. This product innovation was immediately
shared among other solutions of the same manufacturer
thanks to their modular architecture.
Likewise, there are two mechanisms whereby PM
facilitates OSCU: One is by simplifying the ‘‘dynamic
pricing’’ of the product (Tseng & Piller, 2003, p. 11),
meant as the dynamic generation of the product price based
on the answers provided by a customer during the sales
dialogue.
4
This is a consequence of the fact that in a fully
modular product, the different product attributes that a
customer can customize are mapped onto distinct physical
components (Ulrich, 1995). This allows the association of
each possible value of each customizable attribute with a
specific price. In turn, this enables the configurator to
compute the price of the configured product by simply
adding up the prices associated with the answers provided
by the customer during the sales dialogue (Forza & Sal-
vador, 2006), as illustrated by Dell workstations (Dell.-
com). This is a quick approach, which does not require
implementing the so-called generic bill of materials to
determine the cost of the configured solution and then
applying a markup (Forza & Salvador, 2006). Furthermore,
this approach simplifies price updating, as prices are
directly linked to the possible answers in the sales dialogue
and can be modified without having to update the costs of
materials and the costs of production activities in the
respective master files (Forza & Salvador, 2006). In sum-
mary, PM facilitates OSCU by reducing the effort needed
to define and maintain the pricing model of an online sales
configurator. Another mechanism whereby PM facilitates
OSCU is observed whenever the product under consider-
ation needs to be specified by at least some of its target
customers in terms of functions rather than of product
components, as often happens especially for complex
products (see Felfernig et al., 2001). If this is the case, a
functional view of the product must be codified in the sales
configuration model underlying the sales dialogue. Nota-
bly, the creation of this ‘‘function structure,’’ which rep-
resents the product’s functions and their interconnections,
is also a fundamental step in the definition of a modular
product architecture (Ulrich, 1995, p. 421). Thus, PM
facilitates OSCU also by sharing with the latter the costs of
defining and maintaining a functional view of the product.
Online-sales-configurator use facilitates both KAfC and
PM There are two mechanisms whereby OSCU facilitates
KAfC: One is by enabling a systematic analysis of past
product configurations and customers’ clickstream data.
For example, a systematic analysis of clickstream data can
provide information about product solutions that have been
evaluated but not ordered, thus ultimately leading to a
refined solution space (Salvador et al., 2009). The other
mechanism is by freeing resources in the technical office
(Hvam et al., 2013) thanks to both the elimination of sales
configuration errors and the reduction of the risk of
unnecessarily developing ad hoc solutions (see the section
titled ‘‘Focal Organizational Practices and Their Mapping
onto Mass Customization Building Blocks’’). The resour-
ces thus freed can be invested in the assimilation and
exploitation of knowledge from customers, thereby
reducing the necessity of procuring additional resources to
increase KAfC. At a manufacturer of mold bases for plastic
injection molding, for example, OSCU eliminated config-
uration errors in the tendering phase and, consequently,
freed the time that was previously spent in the technical
office to correct bills of materials and production sequen-
ces. The resources thus freed helped improve the com-
pany’s KAfC, as witnessed by the subsequent development
of new product solutions that better satisfied the needs of
certain target markets.
As for the mechanism whereby OSCU facilitates PM,
this revolves around the creation of a product’s function
structure. As mentioned above, a functional view of the
product must be codified in the sales configuration model
4
This dialogue guides customers to progressively specify the values
of all the product attributes that can be customized and prevents
customers from defining inconsistent or invalid values thanks to the
constraints that are included in the logic structure––the sales or
commercial model––behind that dialogue (Forza & Salvador, 2006).
Global Journal of Flexible Systems Management
123
whenever the product needs to be specified by at least some
of its target customers in terms of functions. At the same
time, as mentioned above, the creation of a product’s
function structure is a fundamental step in the definition of
a modular product architecture. Thus, OSCU and PM
facilitate one another by sharing the costs of defining and
maintaining a functional view of the product.
Knowledge absorption from customers facilitates both
PM and OSCU Product modularity is facilitated by KAfC
because the latter helps a manufacturer keep its solution
space aligned with its customers’ evolving needs. This
reduces technical personnel’s workload for ad hoc engi-
neering (see the section titled ‘‘Focal Organizational Prac-
tices and Their Mapping onto Mass Customization Building
Blocks’’), and the resources thus freed can be employed to
improve PM. For instance, at a manufacturer of electric
linear actuators, the workload for ad hoc engineering in the
technical office had become excessive due to the acquisition
of new customers who demanded the same operating ranges
provided by their previous suppliers. The company devel-
oped a routine––revolving around customers’ compiling of a
detailed technical form––aimed at understanding for which
applications customers were demanding a certain operating
range and, therefore, whether they really needed an ad hoc
solution or would be equally satisfied by a predefined solu-
tion with a different operating range. This improvement in
the company’s KAfC reduced the number of customer orders
requiring ad hoc engineering. The resources thus freed in the
technical office were employed to increase the combinability
of the product’s functional modules, thereby achieving a
higher degree of PM.
Finally, KAfC facilitates OSCU because the ability to
elicit adequate information from customers, to assimilate it
and to apply it in the implementation of an online sales
configurator leads to fewer trial-and-error loops in the
development of a configurator that suits its target users
(Hvam et al., 2008). For example, it is easier to implement
a sales configurator capable of explaining the benefits of
the various choice options in a way that reduces both
cognitive complexity and anticipated regret in target cus-
tomers’ decision processes (Trentin et al., 2013). Clearly,
such a configurator will be more easily accepted by its
target users (Haug et al., 2019), and this will lead to a
higher level of OSCU.
Appendix 3: Mechanisms Whereby PM, KAfC,
and OSCU Mutually Reinforce Their Benefits
in the Pursuit of MCC
There are several mechanisms whereby PM, KAfC, and
OSCU mutually reinforce their benefits in the pursuit of
MCC. These complementarity mechanisms contribute to
explaining why MCC is better predicted by the joint
presence of these three practices than by the same practices
considered in isolation. While there is a common rationale
behind all these mechanisms (that is, each practice con-
tributes to realizing the potential MCC benefits of the other
two to a larger extent), the individual mechanisms are
idiosyncratic in their details and, therefore, deserve to be
presented separately.
OSCU reinforces the positive effect on MCC of both
KAfC and PM Knowledge absorption from customers helps
manufacturers define and maintain a solution space that
adequately reflects their target customers’ idiosyncratic
needs over time. Such a solution space is a prerequisite to
fulfilling those needs by means of predefined solutions
instead of ad hoc solutions, thus achieving higher MCC
(see the section titled ‘‘Focal Organizational Practices and
Their Mapping onto Mass Customization Building
Blocks’’). However, the improvement of MCC does not
fully materialize if customers are offered, instead of the
predefined solutions that fully meet their needs, either
predefined solutions that fit their needs worse or solutions
that require ad hoc engineering. This could happen because
the salesperson serving a customer has not been informed
of, or has forgotten about, the existence of the predefined
solution that best fits the customer’s needs (Salvador &
Forza, 2004). Since OSCU reduces the risk of this occur-
ring (see the section titled ‘‘Focal Organizational Practices
and Their Mapping onto Mass Customization Building
Blocks’’), OSCU helps manufacturers get the most out of
KAfC in terms of MCC improvement. At a manufacturer of
carwash equipment, for instance, OSCU ensured that cus-
tomers were offered a few innovative solutions (e.g., for-
ward arrow signal lamps equipped with high-luminosity
light-emitting diodes) that best fitted their specific needs
and were available in the company’s solution space but
were often overlooked by salespeople because of the little
attention these people usually paid to little product
improvements.
Likewise, OSCU helps manufacturers fully exploit the
potential benefits of PM in enhancing MCC. These
potential benefits get lost, at least in part, whenever a
customer’s order is fulfilled with an ad hoc solution despite
the availability of a suitable solution in the predefined,
modular solution space. Again, the risk of this occurring is
lowered by OSCU (see the section titled ‘‘Focal Organi-
zational Practices and Their Mapping onto Mass Cus-
tomization Building Blocks’’). At the carwash equipment
manufacturer mentioned above, for example, OSCU con-
tributed to decreasing the number of customer orders ful-
filled with engineered-to-order products by 90% in 3 years.
This helped the company take full advantage of the mod-
ular product architecture of its predefined solutions.
Global Journal of Flexible Systems Management
123
PM reinforces the positive effect on MCC of both OSCU
and KAfC Online sales configurator use has the potential
for improving MCC through the reduction in the number of
customer orders fulfilled with engineered-to-order products
(see the section titled ‘‘Focal Organizational Practices and
Their Mapping onto Mass Customization Building
Blocks’’). This potential benefit, however, does not mate-
rialize if a customer using the online sales configurator fails
to create an acceptable product configuration within the
solution space and for this reason demands ad hoc engi-
neering. The risk of this occurring is reduced by PM, as the
latter makes it easier to develop an online sales configu-
rator that enables its users to change the choice made at any
previous step of the configuration process without having
to start it over again.
5
As a result, customers can conduct
more trial-and-error tests to evaluate the effects of their
initial choices and improve upon them during the time span
they are willing to devote to the configuration task (Trentin
et al., 2013).
The same logic explains why PM helps manufacturers
get the most out of KAfC to improve MCC. Defining and
maintaining a solution space that adequately reflects cus-
tomers’ idiosyncratic needs over time, thanks to KAfC,
might not be sufficient to reduce the number of customer
orders fulfilled with engineered-to-order products. If cus-
tomers fail to identify an acceptable product solution
within the solution space, despite the existence of such a
solution, they may end up requiring an ad hoc solution. The
risk of this occurring is reduced by PM, as the free com-
binability of choice options facilitates trial-and-error
experimentation within the solution space, as explained
above.
KAfC reinforces the positive effect on MCC of both
OSCU and PM The mechanisms whereby OSCU may
improve MCC (see the section titled ‘‘Focal Organizational
Practices and Their Mapping onto Mass Customization
Building Blocks’’) are inhibited whenever a customer is not
satisfied with the solution space modeled in the online sales
configurator and, hence, demands an ad hoc solution. Since
KAfC reduces the risk of this occurring (see the section
titled ‘‘Focal Organizational Practices and Their Mapping
onto Mass Customization Building Blocks’’), KAfC works
as a catalyst for these mechanisms. This is well illustrated
by the manufacturer of carwash equipment mentioned
above. This company embarked on the implementation of a
sales configurator without the resources needed to effec-
tively survey target customers’ needs. As a result, the
solution space initially modeled in the configurator was
inadequate, as witnessed by the fact that the first two
customer orders processed after the implementation of the
configurator required ad hoc engineering and did not ben-
efit at all from OSCU. This experience prompted the
company’s decision to improve its KAfC.
Similarly, the mechanisms whereby PM may enhance
MCC (see the section titled ‘‘Focal Organizational Prac-
tices and Their Mapping onto Mass Customization Build-
ing Blocks’’) are inhibited if customers demand ad hoc
solutions that cannot be easily derived from the extant
modular architecture (Salvador et al., 2020). Accordingly,
prior research has repeatedly recommended that PM be
based on a thorough understanding of customer needs (e.g.,
Tu et al., 2004). In other words, to fully exploit the
potential benefits of PM in enhancing MCC, KAfC is a
prerequisite.
Supplementary Information The online version contains
supplementary material available at https://doi.org/10.1007/s40171-
024-00429-5.
Acknowledgements We acknowledge the financial support from the
University of Padua (project IDs: BIRD217418, DOR2271783/22,
and DOR2334949/23).
Authors Contribution AT, ES, CG, and CF contributed to intro-
duction and conclusions; AT, ES, SS, CF, and CG were involved in
literature review and research hypothesis; ES, SS, CF, and AT con-
tributed to method and results; AT, CF, ES, and CG were involved in
discussion; and ES, AT, SS, and CF contributed to appendices and
supplementary material.
Funding Open access funding provided by Universita
`degli Studi di
Roma La Sapienza within the CRUI-CARE Agreement. This research
was supported by the following grants: BIRD217418, DOR2271783/
22, and DOR2334949/23.
Data Availability The data that has been used is confidential. The
detailed results are provided within the manuscript.
Declarations
Conflict of interest The authors declare no competing interests.
Open Access This article is licensed under a Creative Commons
Attribution 4.0 International License, which permits use, sharing,
adaptation, distribution and reproduction in any medium or format, as
long as you give appropriate credit to the original author(s) and the
source, provide a link to the Creative Commons licence, and indicate
if changes were made. The images or other third party material in this
article are included in the article’s Creative Commons licence, unless
indicated otherwise in a credit line to the material. If material is not
included in the article’s Creative Commons licence and your intended
use is not permitted by statutory regulation or exceeds the permitted
use, you will need to obtain permission directly from the copyright
holder. To view a copy of this licence, visit http://creativecommons.
org/licenses/by/4.0/.
5
This is because the essential design principle for a modular product
architecture is that every product component must specialize in
fulfilling a specific function and can be altered without affecting the
other components (Ulrich, 1995).
Global Journal of Flexible Systems Management
123
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Key Questions
1. How can manufacturers create flexible systems that are able
to provide customized products without compromising cost,
delivery, or quality?
2. For manufacturers pursuing this goal, what is the added value
of bundling three, long recommended, mass customization
practices? Conversely, what are the risks of implementing
only a subset of these practices or of implementing them in
some sequence?
3. How can managers overcome resistance to a holistic
approach in the implementation of these practices? How can
human resource management, stakeholder engagement, and
digitalization facilitate this coordinated move?
4. How is the research stream on mass customization related to
that on manufacturing and supply chain flexibility?
Publisher’s Note Springer Nature remains neutral with regard to
jurisdictional claims in published maps and institutional affiliations.
Alessio Trentin is an assistant professor of Man-
agement Engineering at the University of Padova
(Italy). His research interests focus on mass cus-
tomization, its antecedents and its consequences,
embracing topics such as form postponement,
product configuration, organization-level and
individual-level enablers of mass customization,
the interplay of mass customization with country-
of-origin strategies and with environmental sustainability, and mass
customization implementation guidelines. He has (co)authored more
than 60 publications, of which 22 published in international peer-
reviewed journals, including the International Journal of Operations
& Production Management (IJOPM), the International Journal of
Production Economics, the International Journal of Production
Research, Production Planning & Control, Computers in Industry,
and Industrial Management & Data Systems, and his current h-index
is 17 (15) in Scopus (Web of Science) database. At the 23
rd
inter-
national EurOMA conference, he was awarded the ‘‘2016 Harry Boer
Highly Commended Award’’, supported by Emerald and IJOPM. He
participated in mass customization-related research projects funded
by several organizations, including the European Union and the
Zaragoza Logistics Center (Spain), a joint research center of MIT
(US) and Aragona government (Spain). Since 2014, he has served on
the scientific committee of the International Conference on Mass
Customization and Personalization - Community of Europe.
Enrico Sandrin is an associate professor of
Management Engineering at the University of
Padova. Formerly, he worked as a knowledge
engineer for product configuration, a buyer, and a
controller at a company in the machinery industry,
where he contributed to several business trans-
formation projects, often collaborating with lead-
ing consulting firms. His research focuses on
innovative approaches to managing organizations and technology in
high product variety and customization contexts, including the digi-
tization of product configuration, organizational design and human
resource management for mass customization, and the relationship
between mass customization and environmental sustainability. His
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work has been published in international journals such as the Inter-
national Journal of Operations & Production Management, the
Journal of Manufacturing Systems, the International Journal of
Production Economics, Computers in Industry, Industrial Manage-
ment & Data Systems, the Journal of Systems & Software, and the
International Journal of Industrial Engineering and Management,
where he also serves on its editorial board. His research has earned
recognition, including the Harry Boer Highly Commended Award at
the 23
rd
EurOMA international conference (EurOMA 2016) and the
Best Paper Award at the 25
th
International Workshop on Configura-
tion (ConfWS 2023).
Svetlana Suzic earned a PhD in Management
Engineering from the University of Padova (Italy).
Subsequently, she worked as a post-doc researcher
in a European project titled ‘‘Mass Customization
4.0’’. Currently she runs a custom-based service
enterprise she set up in 2023. Her research has
been focused on mass customization strategies,
with particular attention to the impact of an online
sales configurator on an organization’s mass-customization capabil-
ity. She has investigated how to combine online sales configurator
use, product modularity, and an organization’s product knowledge
absorption from customers to improve mass customization capability.
She has also investigated the adoption of mass customization in small
and medium companies as well as the definition of customized paths
of improvements towards mass customization. Her research outputs
have led to publications in the International Journal of Industrial
Engineering and Management and various conference proceedings.
Chiara Grosso is an assistant professor in Man-
agement Engineering in the Department of Com-
puter, Control, and Management Engineering at
Sapienza University of Rome. She earned her PhD
in Management Engineering from the University
of Padova. Her research focuses on innovation
technology and management, with focus the twin
green and digital transition towards sustainable
digital technologies. Her work put emphasis on value engineering and
social science models applied to human-centred technologies, user
experience, platform-based configuration tools, e-learning, and
e-health. Currently, she is investigating innovative processes for
waste management, digital tools for collaborative eco-design, circular
ecosystem platforms, and industrial symbiosis. Her research are
published in international journals such as the Journal of Intelligent
Information Systems, the International Journal of Industrial Engi-
neering and Management, and the Journal of Industrial Management
& Data Systems. Since 2014, she has been an active member of the
international research community on Mass Customization and Per-
sonalization and serves on the scientific committee of the Interna-
tional Workshop on Configuration.
Cipriano Forza is a full professor of Management
and Operations Management at the University of
Padova where he teaches Product Variety Man-
agement, Digital Customization, Organization
Design, and Quality Management. His current
research focuses on product variety management,
including mass customization, concurrent product-
process supply chain design, and product modu-
larity and configuration. His work considers organizational, market-
ing, operational, internationalization, and IT issues. He has been
published in several journals, including the Journal of Operations
Management (JOM), the International Journal of Operations and
Production Management (IJOPM), Production Planning and Control
(PPC), the International Journal of Production Economics, the
International Journal of Production Research, Integrated Manufac-
turing Systems (IMS), the Journal of Knowledge Management,
Industrial Management and Data Systems, Computers in Industry,
and the Journal of Systems & Software. He has served as a reviewer
for various academic journals and as an associate editor for JOM and
Decision Sciences. He edited—as a guest editor—a special issue of
JOM in 2005 titled ‘‘Coordinating Product Design, Process Design,
and Supply Chain Design Decisions’’ and a special issue of Pro-
duction & Operations Management in 2010 titled ‘‘Mass Cus-
tomization’’. He is an active member of international scientific
associations and conferences, including EurOMA, the Configuration
Workshop, and MCP-CE. He has been recognized as a EurOMA
fellow for his scientific merits. He has received awards for journal
articles published in JOM (2005), PPC (2004), IMS (1996), and
IJOPM (1995), as well as for conference papers presented at the
Configuration Workshop (2023), EurOMA (2016), and the Academy
of Management (2008).
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