Market-Based Dynamic Capabilities for MSMEs: Evidence from Indonesia's Ornamental Fish Industry

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Journal of Open Innovation: Technology, Market, and Complexity 9 (2023) 100123
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Market-based dynamic capabilities for MSMEs: Evidence from Indonesia’s
ornamental sh industry
Andiga Dompak Baharaja Tarihoran
a
,
*
, Musa Hubeis
b
, Siti Jahroh
c
, Nimmi Zulbainarni
c
a
Doctoral Program of Management and Business, School of Business, IPB University, Indonesia
b
Industrial Management Science, IPB University, Indonesia
c
School of Business, IPB University, Indonesia
ARTICLE INFO
Keywords:
Dynamic capabilities
Ornamental sh
Export propensity
SmartPLS
Market-based capabilities
Agribusiness
ABSTRACT
In a dynamic and competitive market such as the ornamental sh industry, organizations need dynamic capa-
bilities (DC) beyond routine capabilities to thrive. Although many studies have been on DC, few still include
micro-enterprises as research subjects. This study aims to develop a conceptual model of Market-Based Dynamic
Capabilities (MBDC) for micro, small, and medium enterprises (MSMEs) in the Indonesian ornamental sh in-
dustry. The MBDC model integrates three different perspectives of management strategy to achieve a competitive
advantage: Resource-Based View (RBV), Market-Based View (MBV), and Knowledge-Based View (KBV) as an-
tecedents of MBDC and with the moderating variable of government support. The MBDC model was analyzed
based on the responses of 682 ornamental sh producers by using partial least squares structural equation
modeling (PLS-SEM) and applying the embedded two-stage approach for reective-reective and reective-
formative types of higher-order constructs. The results show that MBDC can be established in dynamic and
competitive market conditions for organizations with market-based resources, capabilities, and knowledge.
Furthermore, MBDC will inuence organizational performance and subsequently drive export propensity.
Government support also plays a role in strengthening or weakening the formation of MBDC.
1. Introduction
The ornamental sh industry is important to Indonesia’s economy
(Marlianingrum et al., 2022). Many freshwater sh species from this
country are exported for use in aquariums and ponds in other countries.
Based on the UN Comtrade database, in 2021, Indonesia exported
freshwater ornamental sh with a value of $27.9 million or 10% of total
freshwater ornamental sh exports worldwide or third behind Japan
and Singapore, which in the same year exported $40.3 million and $54.6
million respectively. Indonesia, one of the 17 megadiverse countries, has
a diverse ecosystem, including freshwater and marine ornamental sh of
economic value (von Rintelen et al., 2017). Industry clusters include the
province of West Kalimantan, West Java, Central Java, and East Java.
This sector is critical to the employment and nancial stability of local
communities. According to the agricultural census by BPS (Statistics
Indonesia), ornamental sh farmers in ponds and brackish water had
greater family earnings than food sh farmers (Ardi et al., 2017).
However, many ornamental sh breeders are not export-oriented. Based
on data from BPS, only four provinces, West Java, West Kalimantan, DKI
Jakarta, and Banten, out of thirty-three, have high export values and
represent 90% of Indonesia’s freshwater ornamental sh exports (Tar-
ihoran et al., 2023a). There were 251 exporting companies from 22
provinces exported throughout 2019 (KKP, 2019). This number is rela-
tively small compared to the 29 thousand ornamental sh producers
spread across 33 provinces based on BPS data in 2018. This is one of the
reasons while having immense potential to become the world’s largest
exporter, Indonesia has yet to realize it.
As part of agribusiness, the ornamental sh industry is challenged
with a changing market that requires sustainability (Mandal et al.,
2007). The agribusiness sector is characterized by its close links to
natural resources, geographical or commodity relationships, suscepti-
bility to political factors, production of perishable commodities, and
price volatility (Fleet et al., 2014; Huchet-Bourdon, 2011;
Vergara-Camus and Kay, 2017). In uncertain and challenging market
conditions, it is not enough for companies to have only ordinary capa-
bilities. They need dynamic capabilities (DC) to outperform competitors
and acquire a competitive edge (Eikelenboom and de Jong, 2019),
especially those related to the export market. Many scholars in the
* Correspondence to: School of Business, IPB University, Bogor, West Java, 16128, Indonesia.
E-mail address: andiga@yahoo.com (A.D.B. Tarihoran).
Contents lists available at ScienceDirect
Journal of Open Innovation: Technology, Market,
and Complexity
journal homepage: www.sciencedirect.com/journal/journal-of-open-innovation-technology-
market-and-complexity
https://doi.org/10.1016/j.joitmc.2023.100123
Received 31 March 2023; Received in revised form 13 August 2023; Accepted 23 August 2023

Journal of Open Innovation: Technology, Market, and Complexity 9 (2023) 100123
2
strategic management eld have used DC theory extensively as a source
of competitive advantage (Easterby-Smith and Prieto, 2008; Marsh,
2006). In addition, many studies have linked export or internationali-
zation and DC. Some of them include DC as a valuable foundation for
conceptualizing international growth processes (Knudsen and Madsen,
2002), DC impacts on international opportunity identication (Mostaz
et al., 2019), and DC effect on export performance (Efrat, 2018; A P
Monteiro et al., 2019a).
Micro, small, and medium-sized businesses (MSMEs) are different
from big businesses in a few ways, and those differences can vary from
country to country and culture to culture. Although DC research has
been widely used, few still include micro-enterprises as research sub-
jects. Most of the literature suggests ways to build capabilities for large
and medium-sized businesses but not for small businesses (Inan and
Bititci, 2015). This study aims to develop a market-based dynamic ca-
pabilities framework (MBDC) for MSMEs that can positively inuence
performance and export propensity. This study will contribute to the use
of DC theory in microenterprises and the ornamental sh industry,
which has been little researched in strategic management (Tarihoran
et al., 2023b). The MBDC model integrates three perspectives that are
often debated in strategic management, which are the Resource Based
View (RBV), Market Based View (MBV), and Knowledge Based View
(KBV) as antecedents of MBDC. In addition, the article aims to determine
whether the factor of government support can moderate the develop-
ment of MBDC.
A comprehensive literature review of the Indonesian ornamental sh
industry and the various theories that form the background of this
research have been conducted in Section 2. In addition, Section 3 out-
lines the study’s conceptual framework and hypothesis—furthermore,
Sections 4 and 5, respectively, present methods and results. Finally,
Section 6 describes the study’s conclusion, limitations, and implications.
2. Literature review
2.1. MSMEs in Indonesia
MSMEs are critical to GDP per capita development and create more
jobs than major corporations (Reeg, 2015), particularly in emerging
nations, and Indonesia is no exception (Tambunan, 2019). Individual
entrepreneurs and family or non-family enterprises are familiar sources
of MSME (Memili et al., 2015), and they demand unique operational
traits and resources to expand or sustain. The classication of MSMEs in
Indonesia can follow Law No. 20/2008, which divides MSMEs based on
the value of assets or sales, or follow the BPS classication, which di-
vides MSMEs based on the number of workers. In this study, the author
follows the BPS classication of small businesses comprising 5–19
workers and medium-sized businesses comprising 20–99 workers. From
this classication, we assumed that micro businesses, although not
mentioned by BPS, consist of 1–4 workers.
2.2. RBV, MBV, and KBV
In an organization’s endeavor to achieve a competitive advantage,
RBV, MBV, and KBV are three widely debated theories in strategic
management. The RBV is established on the argument that organizations
with a set of scarce, valued, imperfectly imitable, and irreplaceable re-
sources will have a competitive advantage (Barney, 1991; Mahoney and
Pandian, 1992). In contrast to this viewpoint, an MBV paradigm that
emphasizes the rm’s inherited market position is also established
(Makhija, 2003). On the other hand, KBV accentuates a company’s
competitive advantage that results from its role in creating, storing, and
using knowledge (Grant and Baden-Fuller, 1995). The KBV has evolved
from the RBV by emphasizing intangible resources over tangible assets
(Gassmann and Keupp, 2007).
The RBV has an intra-organizational focus and believes that success
is a product of rm-specic resources and skills (Barney, 1991;
Wernerfelt, 1984). If all rms had the same resources, there would be no
protability differences between them, as any rm in the same industry
could implement any strategy. Therefore, resources and capabilities can
be a factor of sustainable competitive advantage and superior rm
performance if they are valuable, rare, imitable, and non-substitutable
(VRIN) (Barney, 1991). RBV views the rm as a single, organized
group of heterogeneous assets that are generated, developed, updated,
evolved, and enhanced over time. As a unit of resources and capabilities,
the rm has generated interest in identifying these resources and eval-
uating their prot potential (Vivas-l´
opez, 2013). RBV has been widely
used in research in many industries, from manufacturing (Torugsa et al.,
2012), shipping (Progoulaki and Theotokas, 2010), pharmaceutical
(James, 2002), energy (Zhao et al., 2009), hotel and tourism (Alonso,
2016; Hossain et al., 2022), to agriculture (Campbell and Kubickova,
2020). Furthermore, RBV studies are not only for the subject of small
and medium enterprises (Salder et al., 2020) or large enterprises
(Beamish and Chakravarty, 2021) but also micro-enterprises (Duarte
Alonso and Bressan, 2016).
The MBV of strategic management can be traced back to the
Structure-Conduct-Performance paradigm in the 1950 s and 1960 s,
which was derived from the theory of Industrial Organization. In this
view, a company’s market success or failure depends on the industry
structure in which it operates and its management’s subsequent de-
cisions (Bechtel, 2007). Many factors inherent to a particular industry
can hinder the performance of an organization. These factors include,
among others, the bargaining power of suppliers and buyers, the pres-
ence of new entrants, the availability of substitute products, and
competition from other rms (Porter, 1989). The assumptions of MBV
are: rm strategies are inuenced by external factors; resources are
perfectly mobile, and all resources necessary for strategy implementa-
tion are readily available on the market; and business leaders make
logical decisions based on the need to maximize prots (Peters et al.,
2011). Similar to RBV, MBV study is also used not only in small and
medium enterprises (Purbasari et al., 2020; Will and Mertins, 2013) or
large enterprises (Choy et al., 2014) but also in micro-enterprises
(Hanggraeni et al., 2019).
The KBV emerged from the RBV, and knowledge is the key to
competitive advantage under the RBV framework (Spender, 2009). Ac-
cording to the KBV, knowledge is a rm’s most essential and strategic
asset, conceptualized as an institution for integrating knowledge (Grant,
1996). Although Grant (2015) argued that KBV could not be considered
a new theory of the rm, it is a rich source of insight and analysis into the
rm’s performance that conceptualizes a rm as an institution for
generating, preserving, processing, and application of knowledge. The
capacity to transfer knowledge from one unit to another has been shown
to improve rm organizational performance (Argote and Ingram, 2000).
KBV suggests that management abilities and competencies, implicit
knowledge, and tacit organizational routines are among the most crucial
determinants of a company’s success (Dess et al., 1995). A
knowledge-based organization is dened by four characteristics: pro-
cess, place, purpose, and perspective (Zack, 2003). KBV is also often
associated with knowledge-based resources, which in some studies,
affect internationalization and export performance (Martin and Javalgi,
2019; Stoian et al., 2018).
Several authors argued for RBV and MBV in their research, such as
Makhija (2003), who states that RBV factors will be more inuential
than MBV factors in such dynamic conditions. In another study, Peters
et al. (2011) summarized that MBV requires more nancial resources to
create a compelling brand image, whereas RBV necessitates strong
vision and network management abilities. In simple terms, MBV relates
to an outside-in perspective from the rm’s product side, while RBV
describes the company from an inside-out perspective (Sharma et al.,
2004). However, instead of comparing, some studies have combined the
two perspectives to achieve specic goals. For example, Bechtel (2007)
integrated MBV and RBV to achieve the market-based valuation of
human resources, Steininger et al. (2011) used both views to develop a
A.D.B. Tarihoran et al.

Journal of Open Innovation: Technology, Market, and Complexity 9 (2023) 100123
3
business model concept, and Ulrich et al. (2014) also used both per-
spectives in an effort to penetrate specic export markets. In summary,
MBV and KBV are complementary in achieving competitive advantage
(Wong et al., 2016). A rm’s sustainable competitive advantage can be
derived from its resource and capability position (RBV), but a temporary
competitive advantage can be derived from its market position (MBV)
(Huang et al., 2015).
Similarly, between RBV and KBV, there are those who contradict the
two views, yet there are also those who integrate the two in their study.
The KBV of the business is a relatively recent development of the RBV of
the rm and provides a solid theoretical foundation for organizational
learning and intellectual capital researchers (Curado and Bontis, 2006).
These two perspectives can be integrated and complement each other to
create value (Yazdanparast et al., 2010), boost customer satisfaction
(Lee, 2022), and assist an organization in accessing international mar-
kets (Kraus et al., 2022) by increasing its internationalization speed
(Mohr and Batsakis, 2014). Furthermore, the RBV-KBV combination will
bring the organization to achieve a sustainable competitive advantage in
the marketplace (Mahdi et al., 2018; Theriou, 2009).
Few studies have specically compared or integrated MBV and KBV
into their research, and even fewer studies have compared the three
perspectives of RBV, MBV, and KBV. However, three keywords: re-
sources, markets, and knowledge, are three essential things that should
not be separated in achieving sustainable organizational performance.
2.3. Dynamic capabilities
Dynamic capabilities (DC) are the rm’s ability to integrate, build,
and recongure internal and external expertise to face environmental
changes (Teece et al., 2003) or the ability to create, expand, or alter its
resource base deliberately (Helfat et al., 2007) following the CEO’s
vision and judgment (Zahra and Sapienza, 2006). The DC concept ex-
pands the RBV to accommodate a more dynamic understanding (Helfat
and Peteraf, 2009). DC framework comprises several factors, including
production factors, resources, organizational competencies, core com-
petencies, and the product itself. DC is a higher level of capability that
goes beyond ordinary or "zero-level" capabilities that only allow com-
panies to survive in the short term (Winter, 2003). When organizational
capabilities are associated with exploiting existing resources efciently,
an organization is said to have DC; DC refers to the efcient exploration
and implementation of new opportunities (March, 1991).
In a seminal contribution to the development of this theory, Teece
(2007) broke down DC into its three main micro-foundations: (1)
sensing, the ability to collect and analyze market data to understand
competitor and client demands; (2) seizing, the motivation to develop
new products or services to capitalize on opportunities that have been
identied; and (3) reconguring, the ability to maintain competitiveness
through the enhancement, combination, and reorganization of existing
resources. In another study, Cyfert et al. (2021) concluded that search-
ing for opportunities is the key to the precursor of DC. Finally, Pavlou
and Sawy (2006) developed a DC framework for how businesses can
recongure in four categories: market orientation, the capacity to
absorb new knowledge, coordination ability, and the ability to assemble
ideas. In conclusion, DC is about organizational transformation, which
involves overcoming obstacles and problems (Bojesson and Fundin,
2021), and routines and adaptability are essential to DC (Wollersheim
et al., 2013).
There have been many studies that use the DC perspective in small
and medium-sized enterprises, for example in terms of digital trans-
formation and customer value creation (Matarazzo et al., 2021), insti-
tutional environment and network competence (Torkkeli et al., 2019),
and business model innovation (Heider et al., 2021). Many researchers
link DC with SMEs and export activities (Miocevic, 2021; Oura et al.,
2016; Sternad et al., 2013). Furthermore, DC is used to integrate RBV
and MBV of the rm in order to improve understanding of the rm’s
strength in international business relationships (Grifth and Harvey,
2013), and it can be developed deliberately through learning in line with
the strategic objectives of the organization (Tallott and Rachel, 2016). In
summary, DC is a valuable framework for conceptualizing international
growth processes (Knudsen and Madsen, 2002) or as a fundamental to
global expansion and operations (Luo, 2000), and as a tool for interna-
tional marketing (Morgan et al., 2012).
2.4. Market-based capabilities
To achieve the highest level of competitive advantage, it is crucial for
a rm to pursue optimum proximity to its market potential actively
(Curado et al., 2021). Organizations that carry out market-driven ac-
tivities require exceptional capabilities to be able to win the competi-
tion, such as mastery in market sensing, customer linking capabilities
(Day, 1994), having a learning organization that is able to connect
values, knowledge, and attitudes (Morgan et al., 1998; Ranjan and
Nayak, 2023; Sinkula et al., 1997) and marketing capabilities in the
form of market research, pricing, product development, channels, pro-
motion, and market management (Vorhies et al., 1999). The goal of a
market-based organization is the creation of superior value for cus-
tomers, which can be obtained if the organization has a distinctive
capability that is difcult to own by competitors (Day, 1994). As a result,
market-driven business units developed higher levels of capability than
less market-driven rivals and outperformed them signicantly in orga-
nizational performance (Vorhies et al., 1999). In summary,
market-based capabilities create lasting customer value and inuence a
company’s nancial performance (Ramaswami et al., 2009).
3. Conceptual framework and hypothesis
3.1. Market-based dynamic capabilities
In this study, we dene market-based dynamic capabilities (MBDC)
as the organizational capabilities equipped with market-based resources
and knowledge that are continuously learning and adaptive to a dy-
namic and competitive market. The MBDC concept is formed by the
MBV, RBV, and KBV perspectives. Competition intensity and market
dynamism represent MBV, while market-based knowledge and market-
based resources and capabilities represent KBV and RBV, respectively.
In addition, we also investigate the inuence of government support on
the formation of MBDC, and we expect the formation of MBDC will
positively affect company performance. Furthermore, rm performance
will positively affect the rm’s propensity to export.
We adopted the micro-foundation of MBDC from several previous
studies by considering that the subjects of this study are many micro-
enterprises. From the analysis of existing literature, we formed three
micro-foundations of MBDC, namely market-change sensing capability
(Kump et al., 2019; Mikalef, 2017), learning to change capability (Kump
et al., 2019; Tseng and Lee, 2014) and change implementation capa-
bility (Li and Liu, 2014).
3.1.1. Competitive intensity, market dynamism, and MBDC
The ornamental sh business is a part of the agribusiness industry
characterized by a considerable level of competitive intensity and
market dynamism (Eakin et al., 2016; García-Zamora et al., 2013; Lin
et al., 2020). Competitive intensity is the level of competition experi-
enced by companies in their industry which can be recognized by price
wars and diverse product alternatives (Zhou et al., 2005). In the world of
ornamental sh, new sh variants continue to appear with more
attractive colors or patterns that make hobbies often change their
preferences (Rasal et al., 2016).
Market dynamism refers to a condition marked by uctuations in the
environment, market, or industry, including shifts in market demand,
which can give rise to a sense of uneasiness (Chen et al., 2017; Sirmon
et al., 2007). The escalating volatility of market dynamics and the swift
pace of changes have caused market turbulence (Acikdilli et al., 2020),
A.D.B. Tarihoran et al.

Journal of Open Innovation: Technology, Market, and Complexity 9 (2023) 100123
4
posing challenges for stakeholders in discerning the appropriate courses
of action and strategies to adopt presently and in the future. According
to Freel (2005), there are three degrees of environmental uncertainty: 1)
the level of the economic environment, which includes regulatory un-
certainty, standardization, and requirements; (2) the level of the
industry/market, which includes the uncertainty of consumers, sup-
pliers, and competitors; and (3) the level of the company/resource,
which includes the uncertainty of technology, expertise, and nance.
Several studies have related competitive intensity, market dyna-
mism, and DC. For example, Wilden et al. (2013) summarized that the
performance effects of DC depend on the intensity of competition the
rm faces, while Venkatesh et al. (2021) attribute competition intensity
to moderating the DC effect. In another study, Markovich et al. (2021)
studied how each DC’s distinct has a different impact on rm perfor-
mance at low and high levels of competitive intensity. Related to market
dynamism, current research indicates that the type of capabilities an
organization develops depends on its external environment’s degree of
market dynamism (Eisenhardt and Martin, 2000). Furthermore, DC and
market dynamism signicantly impact innovation (Spanos and Prasta-
cos, 2004). Thus, we set the following hypothesis:
Hypothesis 1 (H1). . Organizations in more dynamic and competitive
markets (DCM) have a higher opportunity to form MBDC.
3.1.2. Market-based knowledge and MBDC
The term market-based knowledge (MK) is not extensively employed
in current scholarly investigations. However, several studies issue this
term, such as Hutton et al. (2021), which states the need for MK sources
in the open innovation process, and Taneo et al.(2020), which states that
MK in strengthening competitiveness or Kumar (2020), which states MK
transfer is crucial for effective and moderate supply chain collaboration.
MK is similar to the market-based organizational learning framework
that affects company performance (Ranjan and Nayak, 2023; Sinkula
et al., 1997). The transmission and facilitation of MK can be achieved
through the rm’s social network or external engagements that occur
after the scanning operations (Hutton et al., 2021). In this study, MK is
one of the antecedents of MBDC.
DC cannot be separated from knowledge. Some studies state that
knowledge is an antecedent of DC (Hidalgo-Pe˜
nate et al., 2019; Kaur,
2019). Knowledge acquisition can occur through two primary means:
knowledge production, which involves the development of information
from personal experiences, and knowledge acquisition from external
sources or individuals outside the organization. The effective manage-
ment of knowledge entails its preservation, sharing among people
within the organization, and subsequent use to optimize organizational
performance (Kaur and Mehta, 2016; Nielsen, 2006). Zollo and Winter
(2002) emphasize knowledge’s importance by pointing out that DCs can
be developed through experience accumulation, knowledge articula-
tion, and knowledge codication. Based on the ndings of previous
research, we set the following hypothesis:
Hypothesis 2 (H2). . Organizations with better market-based knowl-
edge (MK) have a higher opportunity to form MBDC.
3.1.3. Market-based resource and capabilities and MBDC
Market-based resources are dened as a rm’s assets and capabilities
related to marketing activities, such as brand-building, relationship-
building, innovation, and knowledge acquisition (Srivastava et al.,
2001). In addition, market-based resources can be in the form of tech-
nology and R&D capabilities, and innovation capabilities (Kozlenkova
et al., 2014). Several studies use market-based resources to inuence
power in international relationships (Grifth and Harvey, 2013) and
gain a competitive advantage (Srivastava et al., 2001). An organiza-
tion’s primary market-based resources include its relational resources,
encompassing brand equity, customer equity, and channel equity (Var-
adarajan, 2020).
DCs are higher-order capabilities that can be established by
companies that have VRIN resources (Teece, 2014), assets, and market
knowledge (Grifth and Harvey, 2013). The preceding discussion leads
to the following hypothesis:
Hypothesis 3 (H3). . Organizations with better market-based re-
sources and capabilities (RC) have a higher opportunity to form MBDC.
3.2. MBDC and rm performance
Numerous studies have demonstrated the impact of DC on rm
performance and the signicance of identifying opportunities and
threats and capitalizing on them for a company’s prosperity and success
(Pervan et al., 2017). More effective DC, such as better product inno-
vation and alliance procedures, will likely give one business a compet-
itive advantage over another (Eisenhardt and Martin, 2000). The impact
of DC on SME performance manifests through its capacities to facilitate
exploration and exploitation, foster innovation, and enhance branding
capabilities (Ferreira et al., 2020). Even amidst challenging circum-
stances like the COVID-19 pandemic, empirical evidence supports the
notion that DC ties positively impact the performance of MSME (Clampit
et al., 2021; Dejardin et al., 2023). Learning capability, one of the di-
mensions of DC, has the highest impact on SME performance (Hern´
an-
dez-Linares et al., 2021). In addition, DC is also evidently a positive
inuence on export performance (Albertina Paula Monteiro et al.,
2019b). Thus, this discussion leads to the following hypothesis:
Hypothesis 4 (H4). . MBDC has a positive effect on rm performance.
3.3. Firm performance and export propensity
Previous research has examined the relationship between business
performance and exports (Younas and Rehman, 2021). For instance,
Ganotakis and Love (2012) have found evidence suggesting that rm
performance, as measured by worker productivity, has a favorable
impact on the likelihood of engaging in export activities. By conducting
a systematic review, Haddoud et al. (2021) created an integrative
framework that mentions rm resources in the form of productivity as
one of the determinants of exporting. In addition, organizations that
have a competitive advantage also have a higher export propensity
(Serra et al., 2012). However, in contrast to those studies, export pro-
pensity or intensity positively affects performance (Gao et al., 2010;
Kuivalainen and Sundqvist, 2018). Some of these studies’ ndings
conclude a correlation between performance and export propensity,
leading to the following hypothesis:
Hypothesis 5 (H5). . Firm performance has a positive effect on export
propensity.
3.4. Moderation of government support
In relation to MSMEs, government support (GS) plays a signicant
role in the success of organizational performance. The government can
support MSMEs by providing a variety of nancial assistance, including
tax credits, grants, loan guarantees, and subsidized loans (Medase and
Barasa, 2019), formulating supportive policies and regulations (Jiao
et al., 2020) and assistance for digital transformation (Chen et al., 2021).
GS is also a moderator between innovation capability and SME perfor-
mance (Otache and Usang, 2022); GS moderates the impact of rms’
prior international experience in foreign direct investment entries (Lu
et al., 2014) and moderates rms in acquiring foreign market knowledge
(Holtbrügge and Berning, 2018).
Some studies have found a relationship between GS and DC, such as
GS in input-supporting policies becoming a moderator between DC
development mechanisms and company performance (Malik and
Kotabe, 2009). In other studies, GS can also create an innovation
capability enhancing effect on new product development (Mukhtar
et al., 2021). Hence, the following hypotheses are proposed:
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Journal of Open Innovation: Technology, Market, and Complexity 9 (2023) 100123
5
Hypothesis 6a (H6a).. The greater the GS, the stronger the rela-
tionship between DCM and the formation of MBDC.
Hypothesis 6b (H6b).. The greater the GS, the stronger the rela-
tionship between MK and the formation of MBDC.
Hypothesis 6c (H6c).. The greater the GS, the stronger the rela-
tionship between RC and the formation of MBDC.
4. Methods
4.1. Data collection
This quantitative research design focuses on ornamental sh actors
in Indonesia, including farmers, traders, collectors and exporters. The
sampling method was using purposive sampling, particularly for those in
four regions with a high concentration of freshwater ornamental sh
businesses: West Java, Central Java, East Java and West Kalimantan.
According to BPS data from 2018, there were 28,770 ornamental sh
businesses in Indonesia, with 23,118 in the four provinces. As a result,
these four provinces are home to 80% of Indonesian ornamental sh
producers. We collected 743 samples from this population, which is
more than the 379 samples using the formula suggested by Krejcie and
Morgan (1970). The survey was cross-sectional and lasted three months,
from September 2022 to November 2022. We were using online and
traditional surveys using a survey form with the assistance of sheries
extension workers from the Ministry of Marine Affairs and Fisheries
(KKP) throughout the four main provinces. Although online surveys are
often conducted due to speed and cost-effectiveness (Wright, 2005), for
this questionnaire, many respondents found it difcult to complete the
online survey due to their limited ability to use internet technology and
their unfamiliarity with conducting online surveys. The online survey
was conducted using a Google form, and respondents were invited to ll
in using Whatsapp, which was distributed with the assistance of the
extension workers from KKP. As for the traditional survey, extension
workers from KKP consisting of 15 people in 13 districts/cities distrib-
uted survey forms to be lled in by ornamental sh business actors in
their area. There were a total of 425 respondents who completed the
survey using the traditional survey.
4.2. Measurement
The online survey used in this study consists of 31 questions for ten
constructs. As shown in Table 1, the ten constructs represent competitive
intensity, government support, market-based knowledge, market-based
resources and capabilities, market dynamism, market-change sensing,
learning to change, change implementation, performance, and export
propensity. The measurement items used were modied from several
previous studies and adapted to the research subjects concerning micro-
enterprises. Prior to the survey, we conducted a pilot study using an
online survey of 35 respondents to identify any aws in the measuring
instrument (Srinivasan and Lohith, 2017) and ensure that all survey
questions were clear and understandable. They were not included in the
nal survey results because there was a slight change in the wording of
the survey questions to make them easier to understand. Each question
was evaluated using a ve-point Likert scale with ratings ranging from
"strongly disagree (1)" to "strongly agree (5)" and written in Bahasa
Indonesia with wording that is easily understood by the majority of
respondents who are in micro businesses.
4.3. Data analysis
This study uses partial least squares-structural equation modeling
(PLS-SEM) analysis because there are formative second-order constructs
that are preferred over using covariance-based structural equation
modeling (CB-SEM) (Hair Jr. et al., 2017). The ability to specify both
formative and reective measurement models is the most signicant
Table 1
Questionnaire items.
Construct Code Item Adopted From
Competitive
Intensity
CI1 The level of competition
in this business is high.
(Jayaram et al., 2014;
Wilden et al., 2013)
CI2 New entrants can easily
enter the business,
resulting in higher
competition.
CI3 Price competition is high.
Government
Support
GS1 Government policies and
programs support the
development of the
ornamental sh business.
(Jun et al., 2021;
Kaya, 2019)
GS2 The government supports
our business in terms of
assisting with nancing.
GS3 The government supports
our business in terms of
providing technical
assistance.
Market-Based
Knowledge
MK1 We are capable of learning
by ourselves from
experience to know the
market conditions and
demands in this business.
(Cui et al., 2005; Kaur
and Mehta, 2016)
MK2 We are capable of
acquiring information or
knowledge sourced from
others about market
conditions and demands
in this business.
MK3 We always share
knowledge with others in
our company about
market conditions and
demands in this business.
Market-Based
Resource and
Capabilities
RC1 We have good human
resources and assets to
meet market needs.
(Martin and Javalgi,
2019; Sachitra and
Padmini, 2020;
Zaridis et al., 2021)
RC2 We are capable of
collaborating with other
parties for mutually
benecial purposes, such
as getting new
broodstock, cheaper feed,
or selling products at
better prices.
RC3 We have sufcient
technological capabilities
to meet market needs.
RC4 We have a strong network
of suppliers, fellow
traders/farmers,
exporters, and distributors
for sales.
RC5 We are capable of
producing or selling new
sh variants that are
preferred by the market.
RC6 We are capable of doing
digital marketing through
internet media.
Market Dynamism MD1 New sh products/
variants often appear in
the market.
(Ghobadian et al.,
2008; Li and Liu,
2014)
MD2 Government policies in
the ornamental sh
industry change
frequently.
MD3 Consumer preferences or
market demands are
unpredictable and change
frequently.
MD4 Prices of sh products are
frequently uctuating.
(continued on next page)
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Journal of Open Innovation: Technology, Market, and Complexity 9 (2023) 100123
6
advantage of PLS-SEM, whereas CB-SEM is limited to reective models
only (Dash and Paul, 2021). In addition, there are several advantages of
PLS-SEM, including easily incorporating single-item measures, obtain-
ing solutions for much more complicated models (Akter et al., 2017;
Astrachan et al., 2014), primarily used for exploratory research (Hair Jr.
et al., 2017), and more appropriate for prediction and theory develop-
ment (Mohamad et al., 2019). The data were analyzed using the soft-
ware SmartPLS 3.2. Higher-order constructs must consider the
measurement models of the lower-order components and the
higher-order construct’s overall measurement model (Sarstedt et al.,
2019).
5. Results and discussion
5.1. Respondents’ prole
From the initial 743 respondents, we removed 61 straight-line re-
sponses to eliminate careless respondents (Weathers and Bardakci,
2015) and got 682 nal respondents. Many of the respondents are
micro-entrepreneurs who have low education and may not be interested
in lling out the questionnaire seriously. Of the 682 respondents, the
majority are millennials, numbering 324 respondents (47.5%) and
generation X as many as 242 respondents (35.5%). Although not sig-
nicant, there are 53 female ornamental sh actors participated in the
survey. This result shows that women can also do the ornamental sh
business. For the education level, the majority are in high school, and
the age of the business is less than ve years. As mentioned earlier, we
have selected four major ornamental sh-producing regions in
Indonesia, with the majority of respondents located in Central Java
(29.9%), East Java (29.2%), West Java (24.0%), and West Kalimantan
(14.5%). Based on business scale, 96.2% are classied as micro
enterprises (having 1–4 workers), and the rest are small (having 5–19
workers) and medium enterprises (having 20–99 workers), as shown in
Table 2.
5.2. Measurement model evaluation
The PLS-SEM model and hypotheses are analyzed in two steps. PLS
analysis of item-construct relationships is the rst step in instrument
validation. The second step is to test the hypothesized relationships
using structural model testing and bootstrap resampling (Anderson and
Gerbing, 1988; Henseler et al., 2009). In addition, the requirements for
the measurement model evaluation must be met for convergent validity
(rst-order and second-order construct) and discriminant validity.
We used the embedded two-stage approach to analyze the higher-
order of MBDC model as an alternative to the repeated indicators
approach (Sarstedt et al., 2019). The model used in this study has two
types of higher-order constructs. The rst construct is a competitive and
dynamic market (DCM) that is type II (reective-formative) measured
with two formative indicators capturing the latent variable scores of
competitive intensity (CI) and market dynamism (MD) from stage one.
The second construct is MBDC which is type I (reective-reective)
reective of market-change sensing (MS), learning to change (LC), and
change implementation (CH), as shown in Fig. 2.
5.2.1. First-order construct
Lower-order and higher-order construct convergent and discrimi-
nant validity are prerequisites for successfully evaluating a measure-
ment model. According to Hair Jr. et al.(2017), convergent validity has
been attained in the model if the loading and average variance explained
(AVE) are greater than 0.5. In exploratory research, Cronbach’s alpha
and composite reliability (CR) values should also be close to 0.6 and 0.7
to ensure internal consistency reliability (Hair et al., 2017). Due to a
factor loading of less than 0.50, MD1 was disqualied. Table 3 displays
that the measurement model’s items have valid loadings between 0.696
and 0.913, with AVE between 0.51 and 0.795 and CR between 0.828 and
Table 1 (continued )
Construct Code Item Adopted From
Market-change
sensing
MS1 We stay up to date with
changing trends and
market preferences.
(Kump et al., 2019;
Mikalef and Pateli,
2017)
MS2 We look to the market for
new opportunities.
MS3 We keep an eye on what
other competitors are
doing.
Learning to Change LC1 We regularly absorb new
information into new
knowledge about the
business market.
(Kump et al., 2019;
Tseng and Lee, 2014)
LC2 We will learn to change
our practice if we gain
new knowledge.
Change
Implementation
CH1 We make changes in
business strategy
according to changes in
market conditions.
(Kump et al., 2019; Li
and Liu, 2014; Nieves
and Haller, 2014)
CH2 We make innovations
according to market
demand.
CH3 We improve resources or
assets according to market
conditions.
Performance PF1 Our business performance
is improving every year.
(Cui et al., 2005)
PF2 Compared to competitors,
our current prot or
business performance is
better.
PF3 Compared to competitors,
we are still able to get
good performance even
during difcult times.
Export Propensity EP We want to export our sh
products
(Haddoud et al.,
2018)
Table 2
Characteristics of the respondents (N =682).
Characteristics Items Frequency (%)
Age
≤25 years (Gen Z) 68 10.0%
26–41 years (Millennials/Gen Y) 324 47.5%
42–57 years (Gen X) 242 35.5%
>57 years (Boomers) 48 7.0%
Gender
Male 629 92.2%
Female 53 7.8%
Last education
Elementary School 51 7.5%
Middle school 66 9.7%
High school 382 56.0%
Diploma 46 6.7%
Bachelor 128 18.8%
Postgraduate 9 1.3%
Years in Business
0–5 years 349 51.2%
10–15 years 83 12.2%
5–10 years 179 26.2%
>15 years 71 10.4%
Total stafng
1–4 labor 656 96.2%
5–19 labor 24 3.5%
20–99 labor 2 0.3%
Province
Banten 13 1.9%
West Java 164 24.0%
Central Java 204 29.9%
East Java 200 29.3%
West Kalimantan 98 14.4%
Other 3 0.4%
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Journal of Open Innovation: Technology, Market, and Complexity 9 (2023) 100123
7
0.921. The rst-order construct’s measurement model is thus con-
vergently valid and internally consistent.
The discriminant validity of the variables was determined using the
Fornell–Larcker criterion by comparing the square roots of the AVEs and
the inter-construct correlations (Fornell and Larcker, 1981; Henseler
et al., 2015). As shown in Table 4, good discriminant validity is indi-
cated by all of the AVE square roots on the diagonal line being greater
than the inter-construct correlation. In addition, we also analyze the
discriminant validity using heterotrait–monotrait ratio (HTMT) method,
and the results show that all values are below the conservative threshold
of 0.85 (Henseler et al., 2015).
5.2.2. Second-order construct
The two-stage approach used latent variable scores from the rst-
order model to create the second-order measurement model. The
model has two types of higher-order constructs, each with different as-
sessments. First, the formative construct required the consideration of
weights and collinearity. In a different way of evaluation, the reective
construct requires the consideration of internal consistency, convergent
validity, and discriminant validity (Sarstedt et al., 2019).
Fig. 1. presents the hypotheses and ows of the theoretical model of MBDC.
Fig. 2. The research model of MBDC.
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Journal of Open Innovation: Technology, Market, and Complexity 9 (2023) 100123
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For the formative construct of DCM, outer weights were found to be
signicant as T-value is clearly above 1.96 (p <0.05). The multi-
collinearity evaluation of formative indicators using the VIF value was
acceptable because the result showed a value <3, as suggested by Hair
et al. (2019).
For the reective construct of MBDC, internal consistency was
evaluated by Cronbach’s alpha and composite reliability (CR) values,
and the results were satisfactory for exploratory research (Cronbach’s
alpha =0.792, CR =0.878) based on recommendations Hair et al.
(2017). Furthermore, convergent validity was analyzed using AVE value
and found acceptable with a threshold value >0.5 given by Hair et al.
(2019). Lastly, discriminant validity using Fornell–Larcker criterion and
HTMT ratio was also found satisfactory as shown in Table 6.
Table 3
Assessment of the rst-order measurements.
First Order Construct Item Factor Loading Cronbach’s Alpha Composite Reliability (CR) Average Variance Extracted (AVE)
Market Dynamism (MD) MD2 0.696 0.703 0.836 0.631
MD3 0.844
MD4 0.833
Competitive Intensity (CI) CI1 0.804 0.689 0.828 0.618
CI2 0.723
CI3 0.827
Market-Based Knowledge (MK) MK1 0.717 0.726 0.841 0.639
MK2 0.840
MK3 0.836
Market-Based Resource and Capability (RC) RC1 0.733 0.808 0.862 0.510
RC2 0.677
RC3 0.713
RC4 0.750
RC5 0.764
RC6 0.643
Government Support (GS) GS1 0.869 0.861 0.915 0.783
GS2 0.887
GS3 0.897
Market Sensing (MS) MS1 0.869 0.796 0.881 0.712
MS2 0.882
MS3 0.777
Learning to Change (LC) LC1 0.887 0.709 0.873 0.774
LC2 0.872
Change Implementation (CH) CH1 0.837 0.757 0.861 0.673
CH2 0.846
CH3 0.777
Performance (PF) PF1 0.879 0.871 0.921 0.795
PF2 0.913
PF3 0.881
Export propensity (EP) EP 1.000
Note: MD1 was deleted due to low loading.
Table 4
Fornell and Larcker criterion (HTMT ratio) for rst-order discriminant validity.
CH CI EP GS MK LC MD MS PF RC
CH 0.82
CI 0.19 (0.26) 0.79
EP 0.09 (0.1) 0.08 (0.1) 1
GS 0.3 (0.37) 0.03 (0.08) 0.16 (0.17) 0.88
MK 0.35 (0.45) 0.08 (0.11) 0.02 (0.03) 0.24 (0.28) 0.8
LC 0.59 (0.8) 0.1 (0.15) 0.16 (0.19) 0.34 (0.44) 0.39 (0.51) 0.88
MD 0.21 (0.29) 0.42 (0.61) 0.08 (0.1) -0.07 (0.13) 0.07 (0.12) 0.17 (0.25) 0.79
MS 0.58 (0.74) 0.18 (0.24) 0.13 (0.14) 0.31 (0.37) 0.41 (0.52) 0.63 (0.83) 0.21 (0.28) 0.84
PF 0.42 (0.52) -0.09 (0.11) 0.14 (0.15) 0.4 (0.46) 0.33 (0.4) 0.39 (0.49) -0.08 (0.21) 0.39 (0.47) 0.89
RC 0.5 (0.64) 0.01 (0.08) 0.1 (0.11) 0.4 (0.48) 0.48 (0.62) 0.49 (0.63) -0.01 (0.17) 0.5 (0.62) 0.57 (0.68) 0.71
Table 5
Assessment of the second-order measurements.
Construct Item Measure Weights Loading T value/ AVE VIF Cronbach’s alpha CR
DCM (Dynamic and Competitive Market) Market Dynamism Formative 0.681 0.886 4.278 1.197
Competitive Intensity 0.507 0.783 2.803 1.197
MBDC (Market-Based Dynamic Capabilities) Market Sensing Reective 0.408 0.857 0.707 1.767 0.792 0.878
Learning to Change 0.39 0.841 1.704
Change Implementation 0.391 0.824 1.583
Table 6
Fornell and Larcker criterion (HTMT ratio) for second-order discriminant
validity.
MBDC PF RC
MBDC 0.841
PF 0.457 (0.546) 0.891
RC 0.574 (0.709) 0.574 (0.681) 0.714
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5.3. Structural model evaluation
After the outer measurement model has been analyzed in PLS-SEM,
the structural model (inner model), shown in Fig. 3, is evaluated.
First, the PLS algorithm and bootstrapping procedure with 5000 sub-
samples were applied to produce path coefcients and their correlating
t-values in determining whether or not these paths are signicant. Then,
structural model analysis was used to test the hypotheses (Hair et al.,
2017).
The coefcient of determination (R
2
) values were examined to assess
the model’s quality, relevance, and t: MBDC =0.452, EP =0.019, and
PF =0.209. As a guideline from Henseler et al. (2009), R
2
values of 0.75,
0.50, and 0.25 can be considered signicant, moderate, and weak,
respectively. Furthermore, we discovered that an acceptable level of
predictive relevance (Q
2
) greater than 0 indicates that exogenous vari-
ables are predictive of the model’s endogenous variables (Sarstedt et al.,
2019). Hair et al. (2014) recommend that the effect size (f
2
) be assessed
using Cohen’s (1988) guidelines, with values of 0.02, 0.15, and 0.35 for
small, medium, and large effects, respectively. As shown in Table 6,
there are medium effects for RC ->MBDC, MBDC ->PF, and PF ->EP.
Lastly, collinearity is also not an issue as all VIF results <3, as Hair et al.
(2019). recommended.
The PLS structural model was used to examine the direct relation-
ships in this study’s model, and the results are shown in Table 5. The
table shows the outcomes for all hypotheses. All ve hypotheses, H1, H2,
H3, H4, and H5, are positively signicant for direct effect testing. In H1,
H2, and H3, dynamic and competitive market (DCM), market-based
knowledge (MK), and market-based resource and capabilities (RC) are
found to have a positive and signicant effect on the formation of MBDC.
This result demonstrates that the three views of MBV, RBV, and KBV can
be integrated into the antecedents of MBDC. These results also
strengthen previous research on the creation of DCs, which require
competitive intensity (Wilden et al., 2013), good knowledge (Zollo and
Winter, 2002), and better resources (Grifth and Harvey, 2013; Teece,
2014). In H4, MBDC is also shown to positively and signicantly affect
rm performance (PF). This result supports previous studies regarding
whether DC will positively affect company performance (Abbas et al.,
2019; Scheuer and Thaler, 2022). Finally, in H5, rm performance is
conrmed to positively and signicantly affect export propensity (EP).
This result is consistent with the previous study by Ganotakis and Love
(2012).
Hypotheses for moderating effect of government support on market-
based knowledge, dynamic and competitive markets, and market-based
resources and capabilities on the formation of MBDC, only H6c is sup-
ported. In H6c, government support for organizations with market-
based resources and capabilities is found to strengthen the formation
of MBDC signicantly. While in H6a, government support in dynamic
and competitive market conditions will actually weaken the formation
Table 7
Structural model hypothesis testing.
Hypothesis Relationship Path Coefcient Std Error t-value P-Values Decision f
2
VIF
H1 DCM ->MBDC 0.274 0.032 8.433 0.000 Supported 0.122 1.125
H2 MK ->MBDC 0.161 0.035 4.583 0.000 Supported 0.036 1.321
H3 RC ->MBDC 0.412 0.035 11.624 0.000 Supported 0.209 1.483
H4 MBDC ->PF 0.457 0.030 14.984 0.000 Supported 0.264 1
H5 PF ->EP 0.137 0.035 3.870 0.000 Supported 0.019 1
H6a Gov.Support*DCM ->MBDC -0.092 0.033 2.772 0.006 Not supported 0.016 1.109
H6b Gov.Support*MK ->MBDC -0.049 0.037 1.327 0.185 Not supported 0.004 1.316
H6c Gov.Support*RC ->MBDC 0.112 0.038 2.980 0.003 Supported 0.016 1.334
Fig. 3. Structural model evaluation.
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Journal of Open Innovation: Technology, Market, and Complexity 9 (2023) 100123
10
of MBDC. This result contradicts the initial hypothesis that government
support in dynamic and competitive markets will strengthen the for-
mation of MBDC. As for H6b, government support for market-based
knowledge is not signicant to the formation of MBDC. Although
many studies have stated that government support is necessary for the
advancement of organizational performance (Songling et al., 2018;
Szczygielski et al., 2017) and as a moderator of rm performance
(Nakku et al., 2020; Park et al., 2020), not many have addressed the
topic of government support as a moderator of DC formation. Therefore,
the results of this study are a new contribution to the GS moderating
effect concerning DC formation.
6. Conclusions and policy implications
6.1. Theoretical contributions
The results of this study provide a new contribution to DC in micro-
enterprises. By obtaining the result that 96% of the respondents are
micro businesses, we prove that DC applies not only to large or small and
medium enterprises. This research also provides new insight into DC
theory in agribusiness, particularly in the ornamental sh industry,
which has not been widely discussed in business strategy and strategic
management studies. We contribute to putting forward a new concept of
MBDC. The MBDC contemplated in this research can be established by
integrating three perspectives: MBV, KBV, and RBV. This nding evi-
denced that MBDC can be formed in dynamic and competitive condi-
tions in organizations with market-based resources, capabilities, and
knowledge. Finally, we also provide new perspectives about government
support that can encourage or discourage the formation of MBDCs.
6.2. Practical contributions and policy implications
This research was motivated by the fact that few ornamental sh
businesses in Indonesia are export-focused. The results of this study
suggest that a rm’s inclination to engage in exporting can be enhanced
when it demonstrates superior performance compared to its rivals,
particularly within a competitive and dynamic sector such as the orna-
mental sh industry. This study’s ndings provide a comprehensive
understanding of micro, small, and medium-sized business managers
operating in a competitive and dynamic market environment. Specif-
ically, we have identied several managerial contributions for orna-
mental sh businesses and the government as a policy maker.
Ornamental sh farmers and business owners must comprehensively
understand prevailing market conditions and demand dynamics per-
taining to exportation. To remain competitive, individuals must be able
to effectively analyze market trends and promptly adapt their strategies
to prevent falling behind. They must learn quickly and innovate or
adjust their business strategy according to market conditions. Late
decision-making will have an impact on the organization’s business
performance. For example, many ornamental sh businesses have suf-
fered losses during the COVID-19 pandemic (Nanayakkara et al., 2021),
but many made more prot than usual (Laily, 2021). Many failed
because they did not want to change their business model. Those who
usually sell directly in the local market must be willing to learn to sell
online and nd information on how to sell products to other countries
through transshipper services.
The results have shown that GS can strengthen or weaken the for-
mation of MBDC. GS, which aims to develop market-based resources and
capabilities of the company, can strengthen the formation of MBDC in
industries with competitive and dynamic markets. Several programs
that the government can carry out include providing easy access to
credit, export nancing facilities, technical knowledge assistance for
breeding methods, feeding knowledge, and water circulation technol-
ogy. In addition, the government can also facilitate licensing for busi-
nesses to export through cooperatives, provide superior breeds that can
improve product quality, provide equipment or facilities for breeding or
enlargement, and help promote the product domestically and abroad
through international exhibitions. However, we found also that the
MBDC formation process requires a dynamic and competitive market
environment. Therefore, government support to discourage market
dynamism and competition will weaken the development of MBDCs.
Export propensity may not necessarily translate into export realiza-
tion if the export process and procedures are complicated and an
impediment to ornamental sh businesses. Therefore, government pol-
icies to facilitate exports while considering sustainability will be
essential. Favorable policies and supporting programs will improve the
quantity and quality of production, thereby increasing the volume and
value of exports, making Indonesia the world’s largest ornamental sh
exporter.
6.3. Limitations and future research directions
This study makes a valuable contribution to understanding the for-
mation of MBDC for MSMEs by examining three key antecedents: dy-
namic and competitive market, market-based knowledge, and market-
based resources and capabilities. However, it is essential to acknowl-
edge that certain limitations are inherent in this study, which presents
opportunities for future research.
First, the study is considered exploratory, meaning that conrmatory
research to get better measurements of the constructs used in this study
will be required in the future. Second, this research needs to be con-
ducted in other industries with dynamic and competitive market char-
acteristics and other countries outside Asia to compare and generalize
ndings. Finally, there is a need for subject-specic DC analysis for
micro-enterprises but without the involvement of small, medium, and
large enterprises.
Funding
This research did not receive any specic grant from funding
agencies in the public, commercial, or not-for-prot sectors.
CRediT authorship contribution statement
Andiga Dompak Tarihoran: Conceptualization, Methodology,
Software, Formal analysis, Investigation, Writing −original draft,
Visualization. Musa Hubeis: Supervision, Project administration. Siti
Jahroh: Supervision. Nimmi Zulbainarni: Supervision.
Declaration of Competing Interest
The authors declare that they have no known competing nancial
interests or personal relationships that could have appeared to inuence
the work reported in this paper.
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