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DOI: 10.4018/IRMJ.287907
Volume 35 • Issue 1
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*Corresponding Author
1
Malia Benedine Faasolo, University of Science and Technology, Beijing, China
Eli Sumarliah, University of Science and Technology, Beijing, China*
https://orcid.org/0000-0002-8680-8558
The research intends to examine the impacts of the technology, organization, and environmental
factors on the implementation of blockchain in the supply chains of SMEs in the Kingdom of Tonga.
These include regulatory support, competitive pressure, cost, upper management support, complexity,
and relative advantage. The research uses SEM-PLS to test the hypotheses and the artificial neural
network method to analyze and classify survey data from 201 SMEs. Findings show that relative
advantage, cost, complexity, and competitive pressure significantly affect implementing blockchain
in the supply chains. As SMEs frequently have limited capital to invest in technology but meets the
same obligations to streamline business operations to optimize profits, blockchain provides a feasible
choice for the firms’ sustainability with its characteristics of security, transparency, and immutability
that are prospective to develop SMEs’ performance. Thus, the paper provides novel insight regarding
the determinants of SMEs’ intention to implement blockchain in their supply chains.
Artificial Neural Network (ANN) Analysis, Blockchain, PLS-SEM, Supply Chain Management, Technology,
TOE Framework
Digital innovations are vital in processing and managing the interchange of indications in the supply
chain (SC) management and operations. Traditional SCs are dispersed in nature and need connection
preservation; consumer and supplier organization processes are no self-adequate (Buyukozkan &
Gocer, 2018). Earlier publications emphasize the significance of digital innovations in SC management
and operations such as blockchain (Queiroz & Wamba, 2019; Kshetri, 2018), artificial intelligence
(Baryannis et al., 2018), big data diagnostics (Govindan et al., 2018), and cloud computing (Maqueira,
& Ortiz-Bas, 2019; Gonul Kochan et al., 2018). Firms that provide investments in new technology
have identified their prospect to lessen cost and maintain competitiveness (Bar et al., 2018). Thus,
firms should move from operating non-integrated silos to incorporated improvement-operations all
over inner end-to-end activities and outer consumer interaction. Besides, Bollard et al. (2017) argue
that the adoption of technology and operative capacity should be used in mixture and in proper order
to attain an all-inclusive and multiple effects.
According to Sihn et al. (2016), SMEs (Small-Medium Enterprises) frequently have inadequate
resources for technological investments, but they have identical necessity to be effective and efficient in
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managing and allocating their capitals. A current SME’s chief executive-officer conference regarding
how the digital market can disturb industries in a developing country admitted that SMEs could not
depend on conventional activities anymore due to the world’s transformation into a digital economy
despite numerous issues regarding digitizing industries economy (Low, 2018). In a developing country
(including Tonga), SMEs should see technologies as investments instead of costs to support sustainable
development (Business Today, 2018). Adopting technologies will help SMEs improve products and
services, respond faster to consumers’ demands, and enhance “time-to-market”. However, although
SMEs achieve significant computerization, they find it complicated to tackle the digitalization
disparity for profit and productivity (Huawei, 2018). Thus, SMEs in Tonga rely on their capabilities
to implement technologies to realize higher advantages of digitization and knock market opportunities
through global and seamless platforms and supportive logistics and infrastructures.
SMEs play an essential role in Tonga’s lively industry environment as Tonga’s industrial sector
primarily comprises SMEs (Naidu & Chand, 2012). According to Faasolo and Sumarliah (in press),
Tonga is a country in the South Pacific with 106,000 people (70% of them live around Tongatapu,
the capital on the central island). Fifty-two islands are populated in Tonga, while 124 islands are
unpopulated. In 2018, Tonga had a 28,598 workforce. The primary sectors are manufacturing
(40.9%), which consists of SMEs (63%) and microbusiness (37%) (Naidu & Chand, 2012). Until
now, literature focusing on the intention to implement technology among SMEs in Tonga remains
scarce (Faasolo and Sumarliah, in press). The latest study conducted by Faasolo and Sumarliah (in
press) empirically investigated influential determinants of the intention to adopt mobile technology
by women’s micro and small businesses in Tonga. It found that the most influential determinant of
intent to use mobile technology is social support, which also influences perceived ease of use and
usefulness. Based on the authors’ search results on the Web of Science database, none of the existing
literature has focused on implementing blockchain technology to improve supply chain management
and operations among SMEs in Tonga.
The recognition and understanding of technology advantages on digitized SCs remain untouched
because of the absence or delay in transforming the organization (Buyukozkan & Gocer, 2018). SC,
reinforced for actual-time information collection, delivers end-to-end discernibility, allowing leaders to
retrieve much information for improved decision-making (Wamba et al., 2018). Kshetri (2018) argues
that blockchain has a noticeable technical advantage in the SC management area. The paper reckons
blockchain as technical development that can attain numerous goals of SC management, e.g., the
reductions of risk, dependability, cost, and the increases of speed and quality (Kshetri, 2018). Queiroz
& Wamba (2019) argue that most blockchain dealings are believed to be more traceable, transparent,
and secure. The traceability systems of blockchain can deliver enhanced legitimacy, authenticity, and
security attributes vital for SCs (Wang et al., 2019) and prevent counterfeits throughout SCs (Chen,
2018). However, studies concerning the users’ intent to implement blockchain remain scarce (Wang
et al., 2019). Current publications related to blockchain in the supply chain of SMEs are limited;
most of them are conceptual papers; while empirical works remain rare in this area (Moosavi et al.,
2021). By handling traceability and visibility issues, blockchain can solve challenges that the SCs of
SMEs face, but its implementation needs continued cooperation among external and internal actors
to increase digital change and profit firms. The gap in the literature of SC management-blockchain
incorporation exists, but the disrupting impacts are already noticeable, although blockchain remains
in its embryonic phase (Queiroz et al., 2019).
Thus, the main purpose f the paper is to answer the questions regarding whether the discovered
TOE (technical, organizational and environmental) aspects can affect the intention of Tongan SMEs
to implement blockchain in their SC management and operations. Derived from the innovation
adoption theory, the TOE model provides an all-inclusive, scope and business-friendly understanding
of implementation aspects and issues (Awa et al., 2016). This research increases the model to
comprehend how Tongan SMEs will direct the transformation of technologies in the Kingdom of
Tonga to survive in digital change to manage SC and operations. Furthermore, the research results
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suggest other developing markets in evaluating technological and organizational issues that SMEs
face in globalization and incorporating markets and industries. Based on those arguments, the paper
tries to respond to the research questions below:
Q1: What determinants are the intent to implement blockchain in SC management and operations
(BSCM) among Tongan SMEs?
Q2: Among the determinants, which has a more significant relationship with the intent to implement
blockchain?
The paper is organized in this way: this part provides the research gap and research questions.
Part 2 outlines the literature review and explains the hypotheses. Part 3 presents the method of the
research. Part 4 describes the data analysis and findings, and Part 5 presents the discussion of the
results. Part 6 provides the conclusion and implications of the study. Finally, Part 7 explains the
limitations and future studies.
Blockchain technology was initially announced in Bitcoin as a procedure regarding secure, transparent,
and open DLT (distributed ledger technology) that removes the necessity for reliable third parties
(Nakamoto, 2008). The unique characteristics of blockchain, according to Tapscott & Tapscott (2016),
include that: (1) it operates across cyberspace procedure, (2) it registers dealings in a trusted and
immutable manner via distributed consensus algorithm among a crowd of dispersed operators and
cryptographic methods.
Numerous studies have been focused on blockchain usage and the extent to which it enhances
values to SC. Nonetheless, studies on the intention to implement blockchain remain in their embryonic
phase. Scholarly publications, as mentioned in the previous part, have proposed a theoretical framework
on how blockchain can fulfill the goals of SCs; however, not many have concentrated, especially on
the perspectives of managers or SMEs. Blockchain-empowered dealings deliver transparency which is
vital to enhance supply-chain traceability. Abeyratne and Monfared (2016) propose that transparency
empowers managers and policymakers to comprehend the impacts of a decision, yet it is a complex
job to ensure data collection accuracy and establish a safe information stream among SC stakeholders.
Besides bringing other issues like hacking and technological abilities, trusting a sole inter-organization
intermediary leads companies to a sole point failure risk. Transparency enables SC members to access
and distribute the same data in the application; it is also crucial to increase customers’ trust in goods
and services by assuring product integrity, authenticity and origin (Montecchi et al., 2019).
From SC’s perspective, blockchain can increase the efficiency in SC operations and finally lessen
costs and wastes (Wang et al., 2019). Blockchain delivers information sources that will reduce supposed
risks and streamline SC by reducing third-party costs and safeguarding precious data (Montecchi et
al., 2019). Scholars have reinforced blockchain usage in tracking the sources of halal food (Hew et
al., 2020; Tan et al., 2020), halal fashion (Sumarliah et al., 2021a), SC security (Shanley 2017), and
product ownership (Toyoda et al., 2017). SC members can use blockchain to track raw materials and
final products and confirm information validity and identify counterfeits (Mackey and Nayyar, 2017).
To date, academic works related to blockchain in the SCs of SMEs are limited (Moosavi et al., 2021).
The review conducted by Moosavi et al. (2021) shows that most current publications are conceptual
papers, while empirical works remain rare in this area. The authors also searched related articles on
the Web of Science database in the last decade (2011-2021) with the keywords “blockchain” AND
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“supply chain” AND “SMEs”; the results were 14 articles. However, after the non-English articles
were excluded from the search result, only nine related articles were found. Out of these nine articles,
one is a literature review (Bai et al., 2021), three are conceptual papers focused on the supply chain
financing model (Chen et al., 2020; Yu et al., 2020; Wei & Qianqian, 2019), three are case studies
(Ali et al., 2021; Katsikouli et al., 2021; Li et al., 2020), and two are empirical studies (Bhardwaj et
al., 2021; An and Park, 2019). However, these two empirical works do not exclusively concentrate
on the TOE model, which this paper does.
A previous publication written by An and Park (2019) examines the influences of implementing
blockchain on SMEs, the policies and laws needed to implement it, and classifies the assignments. They
verify that blockchain can positively influence SMEs, e.g., enhancing transaction reliability, enabling
the flow of funds, enhancing product quality, easing export and import process forms, and maximizing
SC management. Another study conducted by Bhardwaj et al. (2021) investigates the influential
determinants of the intent to implement blockchain in the SCs of 216 SMEs in India. Bhardwaj et
al. (2021) use the integration of the Diffusion of Innovation (DOI) theory, Technology Acceptance
Model (TAM), and Technology-Organization-Environment (TOE). They find that vendor support,
perceived usefulness, top management support, technology readiness, technology compatibility, and
relative advantage positively affect the intent of Indian SMEs to implement blockchain in their SCs.
Besides, they find that cost and technology complexity play as inhibitors to the intention. Unlike the
previous works, this study focuses on technical, organizational, and environmental aspects that affect
users’ intent in implementing blockchain in the SCs of SMEs. Bhardwaj et al. (2021) use TOE as a
part of the integrated models, not a single model, to investigate the influential determinants of the
intention to adopt blockchain; they also do not include regulatory support and competitive pressure
environmental factors, which this study examines.
Numerous scholars have examined blockchain technology adoption in SCs. Typical frameworks
which have been used are UTAUT (United Theory of Acceptance and Use of Technology) (Queiroz &
Wamba, 2019), Institutional Theory and Innovation Diffusion Theory (Hew et al., 2020; Sumarliah et
al., 2021a), and TAM (Technology Acceptance Model) (Kamble et al., 2018). This paper uses another
method Tornatzky et al. (1990) developed, i.e., the TOE (Technology-Organization-Environment)
model because it focuses on technical, organizational, and environmental aspects that affect users’
intent in implementing technologies.
Mohtaramzadeh et al. (2018) argue that TOE delivers a more inclusive assessment regarding
adopting technologies because implementing a new system in a company relies on the organizational,
technical, and environmental aspects. The TOE model has combined non-human and human aspects in
a sole model; it makes higher robustness than other conventional frameworks like UTAUT, Innovation
Diffusion, and TAM (Awa et al., 2017). IT adoption research has employed TOE (Ooi et al., 2018; Yeh
& Chen, 2018). TOE model can be implemented in large settings based on the selected environmental,
organizational, and technical aspects as every innovation have unique adoption elements due to its
unique context and culture (Baker, 2012). TOE model can also explain the intention to implement
blockchain in organizations (Clohessy et al., 2018). A previous study uses the TOE model as a part
of an integrated technology implementation model to examine the intention to implement blockchain
among SMEs in India (Bhardwaj et al., 2021). This study examines the intention to implement
blockchain in SMEs in Tonga; thus, the TOE model is appropriate for this study.
Technical Factors
Technological infrastructure plays a crucial role in reckoning technology adoption, affecting ultimate
consumption, and enhancing competitiveness as integrated with current capitals (Yeh & Chen, 2018).
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Technical incorporation is also vital but complex for implementing blockchain, particularly in the SCs,
because it includes many stakeholders (Saberi et al., 2018). Complexity signifies the complication in
adopting technologies and the technologies themselves (Bhattacharya & Wamba, 2015). Usually, an
extreme level of complexity complicates operators and brings them problems in comprehending and
operating a new system, which in sequence negatively affects their intent to implement (Slade et al.,
2015). Previous research has also proven robust associations among components of practical benefits
to the intent to implement, implying how difficulty or simplicity in employing new technologies
influences its adoption (Dwivedi et al., 2017).
Moreover, a user’s attitude toward a new system is mainly affected by the view to which the
system is complex (Dwivedi et al., 2017). The blockchain complexity is stimulating for users to
comprehend and be confident in involvement; if blockchain cannot be promptly incorporated into
the current system, few benefits will be obtained. As mentioned above, transaction systems of
blockchain possess the main speed issue. Saberi et al. (2018) argue that implementing blockchain
will also partially be inhibited by its irresponsibility security issues. Ultimately, end-operators will
be nervous as they possess an inadequate influence on the result from the technological application
(Rana et al., 2016). Companies will be less likely to implement the novel system if it is complicated
and incompatible with current operations (Wu et al., 2013; Shi & Yan, 2016).
Relative advantage signifies the optimistic disparity concerning organizational advantages and
the attempts to implement blockchain that primarily focuses on immaterial advantages like enhanced
responsiveness, increased consumer satisfaction, and improved reputation (Wu et al., 2013). It has
been a significant determinant of adopting novel technologies (Kapoor et al., 2014), e.g., commercial
intelligence systems (Puklavec et al., 2018) and SCs (Bhattacharya & Wamba, 2015). As successfully
integrated, SMEs implementing blockchain in their SC management and operations can experience
numerous benefits caused by better security and higher transparency for enhanced traceability in the
SCs. Besides, SMEs can experience higher productivity and quicker operation via efficient industry
activities.-
The research also expects that blockchain’s complexity and relative advantage have positive
associations with the costs of implementing blockchain. Beneficial technologies such as blockchain
are typically seen as expensive systems to adopt (Tashkandi and Al-Jabri, 2015). A lot of novel goods
are reckoned risky (Slade et al., 2015). Regardless of the benefits, risks in adopting blockchain exist
because of its inadequate knowledge, security issue, privacy concern, uncertainty, and complexity.
Thus, adopting complex technologies involves high costs, such as many training programs offered
to the final users to familiarize them with the novel yet complex blockchain system (Gallardo et al.,
2018). Thus, it is hypothesized that:
H1: Complexity negatively affects the intent to implement BSCM.
H2: Complexity positively affects cost.
H3: Relative advantage positively affects the intent to implement BSCM.
H4: Relative advantage positively affects cost.
Organizational Factors
Organizational aspects are employed to suggest whether or not companies possess the capitals and
finance to invest in technologies (Sealy, 2012) and signify the settings like willingness to offer
supports or barriers from the leaders’ perspective (Yeh & Chen, 2018). Upper management support
means “how upper management comprehends the significance of and participates” in adopting
blockchain (Ooi et al., 2018b). Organizational issues hugely affect the decision to adopt technology
and are frequently associated with the companies’ tactical objectives, particularly when implementing
technology (Yeh & Chen, 2018). Contrariwise, the commitment of upper management will drive the
technological dissemination but should stay proactively kept to attain anticipated outcomes (Dubey
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et al., 2018). Mougayar (2016) considers blockchain technology a scheme that needs novel software
and hardware, which requires high costs for organizations and stakeholders. The cost signifies the
payment on expenses for procurement and implementation of blockchain. The perceived values of
paid fees significantly affect the intention to implement, and high costs are typically obstacles to
adopt novel technologies amongst firms (Shi & Yan, 2016; Dwivedi et al., 2016). Thus, it can be
hypothesized that:
H5: Upper management support positively affects the intent to implement BSCM.
H6: Cost negatively affects the intent to implement BSCM.
Environmental Factors
The aspects of the environment reckoned in the research involve regulatory support and competitive
pressure. The aspects of the environment explain how blockchain schemes can solve several issues
like high time and financial costs (Schuetz and Venkatesh, 2019). Competitive pressure signifies the
inner desire and stress to obtain a competitive benefit that motivates firms to implement technology,
dealing with pressures from downstream and upstream actors in the SCs, and pressure from novel
improvements in industry standards and business frameworks (Shi & Yan, 2016). Besides, Lindman
et al. (2017) argue that the regulatory environment influences adopting blockchain technology.
However, issues associated with regulations and the implementation of decentralized schemes are
still unresolved and needed crucial business criteria (Guo and Liang, 2016). Mangla et al. (2018)
examine incentive and pressure by government and regulatory organizations in the sustainable SC and
propose that sustainability must be supported by handling challenges regarding risk, coordination,
and infrastructure management. This research signifies regulatory support as laws and policies that
significantly affect the intention to implement blockchain, and sufficient support can fasten the
adoption process (Shi & Yan, 2016). Hence, it can be hypothesized that:
H7: Regulatory support positively affects the intent to adopt BSCM.
H8: Competitive pressure positively affects the intent to implement BSCM.
Those hypotheses and their associations with the intent to implement BSCM are presented in
Figure 1. The figure shapes the fundamental framework of the research.
The data for this study is collected from 201 SMEs in the Kingdom of Tonga. The list of SMEs was
obtained from the Tonga Chamber of Commerce. Ooi et al. (2018b) advised that this study uses random
sampling to maintain the participants’ anonymity. Tonga Chamber of Commerce was chosen as the
reference for the sampling structure because it is a legal organization that manages firms in Tonga.
This study uses a certified data collector to disseminate questionnaires to participants from SMEs in
the Kingdom of Tonga. Using certified data gathering services provides reliability in accessing high-
quality data (Baumann et al., 2017). In addition, a comprehensive information page explaining recent
blockchain technology is delivered at the beginning of the questionnaires (after filling in respondents’
profiles) to provide respondents a strong comprehension of the blockchain system.
The questionnaires were distributed to 209 respondents on behalf of 209 SMEs (1 respondent from
each SME). Out of 209 questionnaires gathered, 201 (96.2%) were usable for statistical examination
because eight were cast off owing to unfinished answers. Generally, finished answers were collected
from 201 participants on behalf of 201 SMEs. Each company’s survey responses get one answer
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from each company (Venkatraman and Grant, 1986). Hence, all data investigation was directed with
a suitable sample size of 201 SMEs. The study’s sample size fulfills the least prerequisite advised by
Hair et al. (2014) to perform the SEM-PLS examinations in the ten-times law. SMEs have numerous
problems related to more prominent companies, but these issues are complicated to be tackled by
small firms primarily because of weak investment ability and scarce talent and resources (Dawson
Consulting, 2018). Moreover, due to low finances, SMEs frequently face a big issue to attain the
degree of capability and visibility needed to correspond to big companies (Dawson Consulting, 2018).
Moreover, small firms are frequently deficient in supplier associations and do not possess the
extent to control; thus, SMEs must recognize the control formations concerning supplier and buyer
(Vaaland & Heide, 2007). Challenges like novel technologies are typically not considered priorities
and are associated with management behavior (Vaaland & Heide, 2007). Besides, other challenges for
SMEs are responsiveness and coordination with other stakeholders in SCs due to their weak inventive
abilities (Kumar & Singh, 2017). Blockchain can be regarded as a reasonable answer, and SMEs can
have higher responsiveness to adopt this technology. In a less complex environment, SMEs possess
a briefer response period.
This study uses a survey method using certified data gathering service; thus, it has removed the
problem of incomplete questionnaires and missing values because the ultimate submission of the
questionnaires was permitted only after filling answers to every query. Nevertheless, based on the
suggestion from Oppenheim (1966), the study observes any effect of time lag, causing initial and
the final submission of forms to be assessed, considering them as non-response bias. Armstrong and
Overton (1977) suggest that non-response bias is evaluated by contrasting early and late participants.
From the 209 questionnaires gathered, 117 were obtained in the first six weeks, 56 were obtained
between the sixth and tenth week, and the rest (36) after the tenth week. The total length of the
research was fourteen weeks, starting from January 5, 2021.
The study uses ANOVA to examine the existence of non-response bias. The disparity in the
mean values of three clusters formed on time lags in the questionnaire submission is employed as
the foundation for contrast. The ANOVA examination shows no substantial disparities in the means
on the chosen variables within the three clusters formed on the timeline of submitting completed
questionnaires signifying the nonappearance of non-response bias.
This study also examines the existence of common method bias. Based on the suggestion from
Podsakoff and Organ (1986), common method bias is examined employing Harmon’s single factor
score. Harmon’s single factor test shows that an enormous volume of inconsistency described by a
single factor was 46.1%, which signifies that the common method bias is not a crucial problem in
the research.
Figure 1. Research framework
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This study employs previous literature to develop variables and survey items to validate the
construct’s reliability and validity (see Appendix, Table 9). The measurement items for the intent
to implement BSCM are adopted from Yang et al. (2017). The items of regulatory support, cost,
and upper management support are adopted from Shi and Yan (2016), while competitive pressure,
complexity, and relative advantage are adapted from Wu et al. (2013). As the measurement scale of
all variables, this study uses a seven-point Likert scale which varied from “Totally Disagree” (1) to
“Totally Agree” (7).
This study uses SEM-PLS to test the hypotheses and ANN (Artificial Neural Network) method to
analyze and classify survey data from 201 SMEs. An ANN signifies a selection of extensively linked
basic mainframes named neurons. The MLP (multilayer perceptron) possesses identical construction
as a neural network, with no less than one middle tier between the output and input. Although the
MLP possesses a construction like a solitary-tier perceptron, it enhances the system capability
via nonlinearizing the output and input features of the middle tier and every component, hence
overwhelming the many drawbacks of the solitary-tier perceptron. It implies that the possessions of
the MLP are more improved as the number of tiers upsurges (Chen et al. in Lee, 2020).
A single neuron can be described as the activation function, i.e., the function f, which is nonlinear.
The output Y of the neuron is computed where the inputs X1, X2, and X3 possess weights W1, W2,
andW3. The f aims to present nonlinearity to the Y, which is crucial as reality data are generally
nonlinear.
Mathematically, neuron k can be explained by equation one and equation two as follows:
µk
j
m
kj j
w x=
=
∑
1
(1)
y b
k k k
= +
( )
ϕ µ (2)
with φ is the activation function, yk is the neuron’s output signal, bk is the bias, and µk is the linear
mixer output of the input signals (Chen et al. in Lee, 2020).
Table 1 displays the profile of the participants in this study.
This paper examines the excellence of the measurement framework. The construct reliability was
confirmed using composite reliability and Cronbach’s alpha values above .7 (Teo et al., 2015), as
presented in Table 2. The convergent validity was verified using the AVE (average variance extracted)
value which is above .5. This study used HTMT principle to validate discriminant validity, and Table
3 revealed that all HTMT ratio is below the limit of .90 (Henseler et al., 2014). Therefore, the research
framework has an excellent fit with the statistics.
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The measurement framework describes 15.13% variance in upper management support
(UMSP), 75.97% variance in cost, and 79.52% of the variance in the intent to implement BSCM
(see the R2 adjusted values in Table 4). According to Eom et al. (2006), the measurement framework
has satisfactory and substantive predictive influence as those values exceed ten percent. The big,
mediocre, and small effect sizes are shown by the f2 values, which exceed .35, .15, and.02, respectively
(Wassertheil and Cohen, 2006). Table 4 reveals the mediocre effect sizes of the relative advantage
(RLAD) on UMSP (.1645) and COST (.2905), whereas the effect size of complexity (CPXT) on
COST is big (1.235). The effect size of competitive pressure (CMPR) on the intent to implement
BSCM is mediocre (.3382). The remaining effect sizes of exogenous variables are minor. Table 5
shows the Q2 values (predictive relevance) according to Stone (1974) and Geisser (1975), which
are substantially high and positive; thus, the exogenous variables are exceptionally significant for
endogenous constructs.
Model fit indices such as the Root Mean Square Error of Approximation (RMSEA), Tucker-
Lewis index (TLI), Comparative Fit Index (CFI), degree of freedom (df), and Chi-square value (χ2)
are employed to assess the hypothesized framework (Hu and Bentler, 1999). The Confirmatory
Factor Analysis (CFA) of the measurement framework shows an acceptable fit with the values of
RMSEA=0.062, TLI=0.943, CFI=0.950, and χ2/df =1.636 (2048.22/1252) (Hu and Bentler, 1999).
Subsequently, the structural model test was conducted to estimate the hypothesized associations.
The structural framework reveals that from the total of eight paths, six (75%) have significance.
As presented in Table 6, factors that significantly affect the intent to implement BSCM are relative
advantage (RLAD) with p =< .001 and β=.4103; cost with p < .005 and β=.1747; complexity (CPXT)
with p < 0.001 and β = -.235; and competitive pressure (CMPR) with p < 0.001 and β=0.4905.
Nonetheless, factors that insignificantly affect the intent to implement BSCM are upper management
support (UMSP) with p=.748, β=.0244, and regulatory support (RGSP) with p= .061, β=.129. Cost is
significantly affected by complexity (p < .001, β=.6632) and relative advantage (p=<.001, β = .3219).
According to Liebana-Cabanillas et al. (2018), typical linear frameworks like structural equation
modeling (SEM) are insufficient for describing the complicated characteristics of the human
decision-making process because they can only identify linear associations. Besides, SEM is an
interchangeable model which assumes that a reduction in one element can be counteracted by a
surge in other elements derived from a linear equation that relates the endogenous variables to the
exogenous variables. Nonetheless, the exogenous variables in this research are non-compensatory.
For example, a surge in regulatory support does not compensate a reduction in upper management
support as these two variables are different in conceptualization and definitions; thus, they are not
substitutable. This problem can be tackled by using an artificial neural network (ANN). According
to Hew & Kadir (2016), ANN outperforms the SEM-PLS approach regarding its capability in
explaining nonlinear and linear associations in a non-compensable framework. Leong et al. (2015)
argue that ANN frameworks outperform the traditional statistical methods such as SEM because of
their excellent levels of estimation precision. Hew et al. (2018) state that, unlike the SEM model,
ANN frameworks are vigorous counter to multicollinearity, nonlinearity, homoscedasticity, non-
normality of distribution, and noises.
However, according to Hew et al. (2016), the ANN’s “black box” feature is unfitting to determine
the significance levels of underlying associations. Thus, following the recommendation from Chong
(2013), this study integrates ANN with SEM through employing the influential determinants found in
the SEM-PLS examination as the input neurons in the ANN frameworks. As Hew et al. (2018) advise,
this study uses multilayer perceptrons to calculate the normalized significance of the input neurons
and the RMSE (root mean square errors) derived from the feed-forward back-propagation algorithms.
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Table 1. Sample Profile
Description Frequency Percentage
Age (years old)
25 - 34 72 35.8
35 - 44 76 37.8
45 - 54 35 17.4
55 and older 14 7.0
Prefer not to say 4 2.0
Gender Female 112 55.7
Male 89 44.3
Job position
Senior management or Director 45 22.4
Middle management or Division Head 57 28.4
Junior management such as system analysis engineer or
assistant manager 71 35.3
Other 28 13.9
Main job area
Procurement 20 10.0
Information Technology 15 7.5
Human Resource 14 7.0
Administration 26 12.9
Marketing 29 14.4
Production 50 24.9
R & D 33 16.4
Other 14 7.0
Work experience
(years)
Less than 1 30 14.9
1 and less than 6 68 33.8
6 and less than 10 57 28.4
10 and above 46 22.9
Level of
comprehension on
blockchain
None 76 37.8
Learning the technology 69 34.3
Testing the technology 25 12.4
Adopting the technology 31 15.4
Firm sectors
Manufacturing 77 38.3
Retailing 45 22.4
Hospitality 21 10.4
Social service 19 9.5
Other 39 19.4
Firm size (workers
number)
Below 50 39 19.4
50 to 100 101 50.2
Above100 61 30.3
Firm age (years)
5 or below 36 17.9
between 5 and 10 74 36.8
10 or above 91 45.3
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As Leong et al. (2019) recommend, this study uses a ten-fold cross-confirmation method, employing
90% of data in training and 10% in testing the neural network to lessen the over-fitting issue. Leong
et al. (2013) propose that an ANN framework comprises output, hidden, and input layers. This study
selects sigmoid as the initiation function for output and hidden layers.
Table 7 shows the fitness of the research model and reveals that in the ANN framework 1 (in
which the intent is to implement BSCM as an endogenous variable), the mean values of RMSE
for testing and training are relatively low .073 and .072 correspondingly. Similarly, in the ANN
Table 2. Convergent validity
Variables Cronbach’s Alpha Composite Reliability AVE
Intent to implement (BSCM) .9729 .9882 .9374
Competitive pressure (CMPR) .9699 .9811 .8622
Complexity (CPXT) .9963 1.0004 .9425
Cost (COST) .9861 .9922 .9100
Relative advantage (RLAD) .9861 .9933 .9130
Regulatory support (RGSP) .9740 .9841 .9019
Upper management support (UMSP) .9851 .9912 .9059
Table 3. Heterotrait-Monotrait Ratio (HTMT)
Variables BSCM CMPR CPXT COST RLAD RGSP UMSP
Intent to implement (BSCM)
Competitive pressure (CMPR) .8917
Complexity (CPXT) .3788 .5007
Cost (COST) .6144 .7130 .8582
Relative advantage (RLAD) .8490 .8419 .5616 .6997
Regulatory support (RGSP) .3504 .2519 .0823 .0752 .2133
Upper management support (UMSP) .3423 .2580 .1574 .1391 .4042 .3687
Table 4. Predictive influence (R2) and Effect size (f2)
R2R2
Adjusted
f2
BSCM CMPR CPXT COST RLAD RGSP UMSP
BSCM .8033 .7952
CMPR .3382
CPXT .0782 1.235 .0071
COST .7627 .7597 .0295
RLAD .1361 .2905 .1645
RGSP .0650
UMSP .1605 .1513 .0020
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Table 5. Stone-Geisser’s predictive relevance (Q2) values
SSE SSO Q2(=1-SSE/SSO)
BSCM 166.1613 591 .7189
CMPR 281.6726 985 .7141
CPXT 184.6097 985 .8126
COST 224.4202 985 .7722
RLAD 220.8331 985 .7758
RGSP 216.2466 788 .7256
UMSP 230.4102 985 .7661
Note: SSE=Sum of square prediction errors, SSO=Sum of square observations.
Table 6. Path examination results
Hypotheses (effect) Path β p-value T-statistic Result
H1 (-) CPXT → BSCM -.235 .000** 3.579 Supported
H2 (+) CPXT →COST .6632 .000** 13.302 Supported
H3 (+) RLAD → BSCM .4103 .000** 5.684 Supported
H4 (+) RLAD → COST .3219 .000** 5.792 Supported
H5 (+) UMSP → BSCM .0244 .748ns .336 Not supported
H6 (-) COST → BSCM .1747 .017* 2.417 Supported
H7 (+) RGSP → BSCM .129 .061ns 1.879 Not supported
H8 (+) CMPR → BSCM .4905 .000** 5.778 Supported
Note: nsnot significant, **p < .001, *p < .05
Table 7. RMSEs of neural network framework 1* and framework 2**
Training Testing
SumFramework 1* Framework 2** Framework 1* Framework 2**
N RMSE SSE N RMSE SSE N RMSE SSE N RMSE SSE
179 .070 .843 179 .072 .890 22 .055 .063 22 .058 .069 201
179 .070 .826 179 .057 .556 22 .059 .072 22 .053 .058 201
181 .074 .936 179 .061 .644 20 .048 .044 22 .047 .045 201
182 .076 .998 181 .069 .815 19 .045 .036 20 .067 .084 201
179 .068 .787 182 .075 .980 22 .144 .431 19 .139 .343 201
182 .073 .921 178 .071 .850 19 .030 .016 23 .054 .062 201
181 .074 .939 177 .067 .759 20 .117 .257 24 .053 .063 201
179 .072 .885 179 .059 .594 22 .141 .408 22 .072 .107 201
180 .070 .849 182 .072 .889 21 .042 .036 19 .064 .073 201
182 .071 .872 183 .062 .661 19 .044 .036 18 .055 .050 201
Mean .072 .888 .067 .764 .073 .140 .066 .095
Error .002 .067 .006 .144 .044 .163 .027 .089
Note: RMSE=Root mean square error, SSE=Sum square error, N=sample size, *Endogenous variable= The intention to implemention (BSCM), **Endog-
enous variable=COST.
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framework 2 (COST as an endogenous variable), the RMSE mean values for testing and training
are meager at .066 and .067 correspondingly. It verifies that the ANN frameworks are appropriate
for the data set. This study computes the R2 and finds that the ANN frameworks describe 48.82%
of the discrepancy in the intention to implement BSCM and 31.24% of the discrepancy in COST,
as advised by Leong et al. (2018). This study also computes the normalized significance to contrast
the significance of the determinants: the percentage value of the comparative significance divided
with the tremendous comparative significance. Table 8 displays that for the intention to implement
BSCM as the endogenous variable (Framework 1), competitive pressure-CMPR is of the highest
normalized significance (100%); the next is relative advantage-RLAD (63.1%), followed by COST
(37.5%) and complexity-CPXT (29.9%). Table 8 also reveals that when COST is the endogenous
variable (Framework 2), CPXT is the most significant determinant (100% normalized significance);
the next is RLAD (51.7%).
The findings of the ANN method provide more detailed information than those of the SEM-
PLS method. Based on the above analysis, the SEM-PLS provides three findings: (1) factors that
significantly affect the intent to implement BSCM, and (2) factors that insignificantly affect the intent
to implement BSCM, and (3) factors that significantly affect cost. As mentioned earlier, the significant
factors based on SEM-PLS examination are RLAD, COST, CPXT, and CMPR; the insignificant
factors are UMSP and RGSP, and COST is significantly affected by CPXT and RLAD. Meanwhile,
the ANN method provides more detailed findings: (1) when the intention to implement BSCM is
the endogenous variable, the exogenous factors with the highest significance is CMPR, followed
by RLAD, COST, and CPXT; and (2) when COST is the endogenous variable, the most significant
determinant is CPXT, followed by RLAD. These findings are in line with Leong et al. (2015), who
argue that ANN frameworks outperform the traditional statistical methods such as SEM because of
their excellent levels of estimation precision.
Table 8. Sensitivity test results of neural network framework 1* and framework 2**
Comparative significance
Framework 1* Framework 2**
CPXT RLAD COST CMPR CPXT RLAD
Network 1 .157 .243 .187 .428 .729 .286
Network 2 .145 .223 .169 .478 .799 .216
Network 3 .160 .264 .205 .387 .854 .161
Network 4 .165 .262 .179 .409 .682 .333
Network 5 .133 .347 .018 .517 .574 .442
Network 6 .141 .330 .150 .394 .691 .325
Network 7 .094 .331 .123 .467 .652 .364
Network 8 .082 .234 .149 .550 .790 .225
Network 9 .154 .288 .159 .413 .753 .263
Network 10 .152 .236 .206 .421 .790 .225
Average significance .138 .276 .154 .447 .731 .284
Normalized significance (%) 29,9 63,1 37,5 100 100 51,7
Note: * Endogenous variable=the intention to implement of BSCM; ** Endogenous variable=COST
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The research displays influential determinants of the intention to implement BSCM in technical,
organizational, and environmental aspects that can be employed as an underpinning source to develop
the intent to implement blockchain technology among SMEs for SC management and operations.
The summary of the factors influencing the intention to implement BSCM among SMEs in the
Kingdom of Tonga is as follows: the most influential determinants as found in this research include
relative advantage (RLAD), followed by cost (COST), complexity (CPXT), and competitive pressure
(CMPR). Meanwhile, the other variables, i.e., upper management support (UMSP) and regulatory
support (RGSP), are insignificant.
The findings show that RLAD is a vital exogenous variable that influences the intent to implement
BSCM. It is in line with previous publications regarding the intention to implement (Bhardwaj et
al. 2021; Hong & Hales, 2021; Ramayah et al., 2016; Bhattacharya & Wamba, 2015). Results show
that based on the measurement items listed in Table A1, SMEs in the Kingdom of Tonga consider
BSCM: is suitable to manage SC and operations of (RLAD1), is helpful for their SCs and operations
(RLAD2), can increase their profits (RLAD3), can cause efficient SC management and operations
(RLAD4), and can quickly finish their operations (RLAD5). Galvin (2017) has proposed that
blockchain can be the game-changer in the SCs. The prospect is understood regarding delivering a
schmoozed information ledger that is reachable and distributed in actual time by all individuals in the
networks. It supports transparency and progressively will cause the making of a sole form of fact for
the entire stakeholders in the networks. The most vital advantage of blockchain usage can be removing
expensive inter-organizational intermediaries that will postpone the networks. Thus, blockchain can
reduce operational expenses and achieve significant efficiencies (Hong & Hales, 2021). Linking SC-
associated stakeholders stimulates the incorporation of information flows, logistics, and commodities,
saving operational expenditures followed by profit-making (Hong & Hales, 2021). A better awareness
regarding those benefits over the current system increases opportunities and associations with many
SC participants. Implementation will, hence, rely on the evident proposal of benefits. It is in line
with a prior study that suggests perceived advantages or usefulness as the most significant factor
influencing the intention to implement blockchain in SC (Wang et al., 2019; Bhardwaj et al., 2021).
This study also finds that CPXT significantly affects the intent to implement BSCM. The CPXT
of blockchain is conveyed as regards system functionality, usage, and process efficiency. For SMEs
in the Kingdom of Tonga, their intentions to implement BSCM are discouraged by their perceptions
that: BSCM tools are not simple to operate (CPTX1), their companies do not comprehend how to
operate BSCM (CPTX2), using BSCM needs sufficient experience (CPTX3), learning how to use
BSCM needs many attempts (CPTX4), and learning how to use BSCM is not easy (CPTX5). From
a technological viewpoint, blockchain is a technology that develops novel methods of scalability and
information control, but despite the numerous suggested advantages, blockchain also carries operation
problems (Lu, 2019). On the other hand, technology awareness lessens the supposed CPXT (Vasseur
and Kemp, 2015). Awareness of blockchain is interpreted as needing a shorter time to complete tasks,
and the less CPXT, the higher the improvement it possesses on work performances. Consequently,
the nervousness in operating blockchain can cause lessen the willingness to implement, which is also
verified by a prior study (Bhardwaj et al. 2021; Tsai et al., 2013).
The findings of this study show that CPXT and RLAD affect cost. The reason can be that SMEs
in Tonga observe that an advantageous yet complicated system such as blockchain is expensive to
adopt (Tashkandi & Al-Jabri, 2015; Gallardo et al., 2018).
The organizational factors, i.e., COST and UMSP, affect the intention to implement BSCM
differently. Unexpectedly, the COST is experientially reinforced as an instigator that pushes the
intent to implement BSCM, not as an inhibitor. SMEs in Tonga show a willingness to implement
BSCM although they think that: the cost of approving transactions in BSCM is expensive (COST1),
the BSCM cost is high for their companies (COST2), the BSCM cost is uncertain and not easy to
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understand (COST3), BSCM adoption will enhance maintenance and operation costs (COST4), BSCM
implementation will enhance facility and hardware costs (COST5). Nevertheless, this stimulating
finding is inconsistent with previous works (Bhardwaj et al., 2021; Shi & Yan, 2016). It could be due
to the relative advantages delivered by blockchain; even if this technology is seen as an expensive
instrument, SMEs remain going to implement it.
The effect of UMSP is not significant in the research. SMEs in the Kingdom of Tonga are not
motivated to implement BSCM. However, they think that: upper management inspires innovations
(UMSP1), upper management encourages workers to adopt the newest blockchain technology in
everyday tasks (UMSP2), upper managers are eager to take risks in BSCM adoption (UMSP3),
upper managers show their supports through offering materials, finances, and labor resources for
BSCM (UMSP4), and upper managers pay attention and proactively respond to project initiation
(UMSP5). The possible reason is that top management does not have adequate knowledge or is not
persuaded of the blockchain’s advantages. This finding is not in line with previous work (Bhardwaj
et al., 2021). Frequently, SMEs’ decisions regarding investment and management are conducted by
upper management support (Maduku et al., 2016); if upper management is more well-informed about
technology, it will be more prospective to build an optimistic intention to implement the technology and
support its implementation. It is in line with this research’s results that UMSP is affected by RLAD.
The environmental factors, i.e., CMPR and RGSP, also have different effects on the intention to
implement BSCM. The effect of CMPR on the intent to implement blockchain is significant, implying
that SMEs are bound to remain competitive and relevant in their industrial ecosystem. It exposes
that the decisions and rivalry are motivated by the capability to remain at the front of technology
innovations. SMEs in Tonga intend to implement BSCM because they think that: competitive pressures
push their companies to explore BSCM (CMPR1), societal features like cultures and customs push
their companies to explore BSCM (CMPR2), their companies believe that other companies in the
same business have started to look into BSCM (CMPR3), their companies think that adopting BSCM
to attain competitiveness is vital in strategic decision-making (CMPR4), their companies believe that
consumers will decrease if BSCM is not used (CMPR5). Previous publications regarding CMPR have
similarly developed that technology innovations are vital to maintaining a firm’s competitiveness
(Wang et al., 2018; Iansiti & Lakhani, 2017).
Blockchain is the newest technology that interrupts and changes businesses. Blockchain is
complicated, and artificial intelligence can aid or enhance it (Xing & Marwala, 2018). In artificial
intelligence, the legitimate issue has been the main problem, and regulatory support is crucial,
especially regarding how the government can establish an adequate legal framework, regulations, and
policy to direct and avoid misapplication of technologies (Duan et al., 2019). An inclusive knowledge
regarding emergent technologies like blockchain is required to build relevant governing schemes
(Lu, 2019), including the rules for blockchain in the Kingdom of Tonga. However, this paper shows
that RGSP is not significant. SMEs in Tonga do not intend to implement BSCM. However, they
believe that: today’s regulations and laws are adequate to safeguard the usage of BSCM (RGSP1), the
government provides supports in using BSCM (RGSP2), the government introduces relevant policies
to increase BSCM development (RGSP3), government or related authorities give financial support
for BSCM development (RGSP4). The absence of regulations and standards to promote blockchain
systems among Tongan SMEs is involved. As mentioned above, blockchain remains in its embryonic
stage in Tonga; despite its advantages, the actual implementation is scarce. Big companies in Tonga
have sandboxed or piloted blockchain, but numerous are still at the theoretical phase; thus, it is not
unexpected that Tongan SMEs are presently not absorbed with technologies. Perhaps, SMEs are also
less motivated to try out blockchain due to the absence of trialability.
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The research provides a summary of influential determinants of thought from an all-inclusive viewpoint
through the TOE model. This study answers research question Q1 by revealing that relative advantage,
complexity, and competitive pressure significantly affect the adoption of BSCM in Tongan SMEs.
Interestingly, the cost was not experientially reinforced as an inhibitor but a driver pushing the intent
to implement, which is inconsistent with previous works (Bhardwaj et al., 2021; Shi & Yan, 2016).
It could be due to the relative advantages delivered by blockchain; even if this technology is seen as
an expensive instrument, SMEs remain going to implement it.
Conversely, upper management support and regulatory support are not significant. In this study,
it could be because (1) top management does not have adequate knowledge or is not persuaded on
the blockchain’s advantages, and (2) the absence of regulations and standards to promote blockchain
systems among Tongan SMEs is involved.
Regarding research question Q2, the ANN examination reveals competitive pressure as the most
influential determinant of adopting BSCM. Perhaps, the research is not inclusive, but it does involve
several typical predictors like cost, competitive pressure, regulatory support, and management support,
and the findings are astonishing. Thus, the paper provides a recommendation to practitioners and
researchers.
Theoretical Implications
This study provides several theoretical implications. First, the research has responded to a request of
Ying et al. (2018), who emphasized the existence of current crucial demand to augment the existing
condition regarding blockchain studies, which is highly investigative in feature, using empirical
verification. Until now, the publications related to blockchain in the supply chain of SMEs are limited
(Moosavi et al., 2021). The latest review conducted by Moosavi et al. (2021) shows that most current
publications are conceptual papers, while empirical works remain rare in this area. The authors also
searched related articles on the Web of Science database in the last decade (2011-2021) with the
keywords “blockchain” AND “supply chain” AND “SMEs”; the results were 14 articles. After the
non-English articles were excluded from the search result, there were only nine related articles. Out
of these nine articles, one is a literature review (Bai et al., 2021), three are conceptual papers focused
on the supply chain financing model (Chen et al., 2020; Yu et al., 2020; Wei & Qianqian, 2019), three
are case studies (Ali et al., 2021; Katsikouli et al., 2021; Li et al., 2020), and two are empirical studies
(Bhardwaj et al., 2021; An and Park, 2019). However, these two empirical works do not exclusively
concentrate on the TOE model, which this paper does. Thus, via the empirical evidence of SMEs in
Tonga and theoretic spectacles of the TOE model, the research is awaited to subsidize the ever-rising
publications regarding BSCM and add variety to the research on the behavioral intention of using
technologies with an empirical method.
Second, the findings of this study show that technical factors, i.e., RLAD and CPXT, significantly
affect the intent to implement BSCM among SMEs in Tonga, which supports previous literature such
as Hong & Hales (2021) and Bhardwaj et al. (2021). CPXT and RLAD also affect the cost, supporting
earlier studies such as Gallardo et al. (2018). Besides, the findings also reveal that organizational
factors including COST and UMSP have different effects on implementing BSCM. COST is found
to be an instigator that pushes the intent to implement BSCM, which is not consistent with previous
works (Bhardwaj et al., 2021; Shi & Yan, 2016). It could be due to the relative advantages delivered
by blockchain; even if this technology is seen as an expensive instrument, SMEs in Tonga remain
going to implement it.
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On the other hand, the effect of UMSP is not significant in the research, possibly because top
management does not have adequate knowledge or is not persuaded on the blockchain’s advantages.
Finally, the effects of environmental factors such as RGSP and CMPR are also different. RGSP is
not significant, possibly because blockchain remains in its embryonic stage in Tonga; despite its
advantages, the actual implementation is scarce. Meanwhile, CMPR significantly affects the intent
to implement BSCM, supporting previous literature (e.g., Wang et al., 2018).
Managerial Implications
The paper exhibits the inter-connection among the essential variables as hypothesized. The intent
to implement blockchain and cost shows a robust discrepancy of 80.3% and 76.3% correspondingly.
Cost is also highly influenced by complexity, and relative advantage employs a reasonable effect on
the intention to implement blockchain. Besides, the study signifies that competitive pressure must be
put in top importance to improve the intention to implement blockchain; the next is cost and RLAD.
Thus, competitiveness is the driving power to secure the SMEs’ intent to implement blockchain if
enhanced. It is a significant exposure because SMEs have been limited by poor diffusion of innovative
supplies and can not safeguard competitive advantages (Rao and Kumar, 2018). Developing countries
have started to discover the implementation of blockchain technology for SCs (Manning, 2019),
including Tonga. Nonetheless, despite the enhanced transparency offered by blockchain technology,
companies should carefully analyze participants’ responses to completely transparent SCs where it
is possible to closely monitor competitors and consumers (Montecchi et al., 2019).
Regarding the development in costs, understanding the relative advantage of implementing
blockchain is vital. The SME segment in Tonga is a significant marketplace for blockchains. Managers
must understand the benefits of implementing blockchain to develop effective decisions regarding
supply chain management and operations. SMEs can remain competitive by taking a significant
move toward discovering technologies – they possess the benefit of fewer stakeholders in a smaller
business environment and the capability for quicker response.
A marvelous possibility exists for future studies in this field. First, the research is carried out in SMEs
in the Kingdom of Tonga. Upcoming research can reckon cross-nation or neighboring nations that
are more industrially sophisticated. Second, the paper reckons chosen aspects in the TOE model;
future studies can extend the TOE to enhance understandings of the results. Blockchain can remove
third-party organizations and provide network nodes to establish trusts (Ying et al., 2018). Future
research should be conducted to comprehend the role of blockchain in guarding confidential data and
the effects of privacy, data integrity, and confidentiality on adopting blockchain. If respondents were
not convinced to operate the technology apps, transparency could be problematic, and product-flow
traceability could not be maintained (Tonnissen & Teuteberg, 2019). Thus, there is no clarity regarding
the effect of blockchain on the disintermediation of SCs and their relevance. The accessibility issues
regarding blockchain also exist; thus, firms trying to implement the technology should have a more
concentrated and inclusive assessment from a measured viewpoint. Besides, the intrinsic features of
blockchain should be evaluated regarding its feasibility to adopt from many viewpoints like costs,
transaction speed, and interoperability.
Moreover, every company has a particular industry segment, infrastructure, and culture that can
cause distinctive decisions regarding implementing blockchain. So far, current publications have either
proposed blockchain-empowered models of industrial activities frameworks or technologies but not the
association among both. Hence, future research should be aware of the effects of resources and data
sharing to assist companies in adopting decisions (Pan et al., 2019). Thus, the research results cannot
be considered as a pair of shoes that fits all. Limited publications have comprehensively proposed
the costs related to the intention to implement blockchain besides case study and viability research
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(Hughes et al., 2019). The shortage obstructs this research from comparing to comparable studies
on the same technologies, and per se, companies trying to integrate blockchain into their current
systems will need additional thought regarding the need for this new system (Queiroz et al., 2019).
The extent of consideration that blockchain has created shows that companies can no longer uphold
conventional methods of operating businesses but should transform. Technologies fundamentally
change industrial processes, and companies should be ready.
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Table 9. Measurement Items of All Variables
Variables Measurement Items
Intent to implement
BSCM
BSCM1: My company aims to alter supply chain management and operations using BSCM
digitally.
BSCM2: I think I will use BSCM in the future.
BSCM3: I think my company will adopt BSCM.
Complexity CPTX1: I think BSCM tools are not simple to operate.
CPTX2: I think my company does not comprehend how to operate BSCM.
CPTX3: I think using BSCM needs sufficient experience.
CPTX4: Learning how to use BSCM needs much attempt.
CPTX5: Learning how to use BSCM is not easy.
Relative Advantage RLAD1: BSCM is suitable for me to manage supply chain and operations.
RLAD2: BSCM is helpful for supply chain and operations
RLAD3: BSCM can increase company’s profits
RLAD4: BSCM can cause efficient supply chain management and operations
RLAD5: BSCM can quickly finish the company’s operations
Upper Management
Support
UMSP1: Upper management inspires innovations
UMSP2: Upper management encourages workers to adopt the newest blockchain technology
in everyday tasks.
UMSP3: Upper managers are eager to take risks in BSCM adoption.
UMSP4: Upper managers show their supports through offering materials, finances, and
labor resources for BSCM.
UMSP5: Upper managers pay attention and proactively respond to project initiation.
Cost COST1: The cost of approving transactions in BSCM is expensive.
COST2: The BSCM cost is high for my company.
COST3: The BSCM cost is uncertain and not easy to understand.
COST4: BSCM implementation will enhance maintenance and operation costs.
COST5: BSCM implementation will enhance facility and hardware costs.
Regulatory Support RGSP1: Today’s regulations and laws are adequate to safeguard the usage of BSCM
RGSP2: Government provides supports in using BSCM
RGSP3: Government introduce relevant policies to increase BSCM development
RGSP4: Government or related authorities give financial support for BSCM development
Competitive Pressure CMPR1: Competitive pressures push my company to explore BSCM.
CMPR2: Societal features like cultures and customs push my company to explore BSCM
CMPR3: My company believes that other companies in the same business have started to
look into BSCM
CMPR4: My company thinks that adopting BSCM to attain competitiveness is vital in
strategic decision-making
CMPR5: My company believes that consumers will decrease if BSCM is not used.
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Malia Faasolo is a Ph.D. candidate in the School of Economics and Management, University of Science and
Technology Beijing. Her research interest includes Entrepreneurship, E-commerce, Supply Chain Management,
and Technology.
Eli Sumarliah is a Ph.D. candidate in the School of Economics and Management, University of Science and
Technology Beijing. Her research interest includes Supply Chain Management, Information Management,
Sustainable Supply Chain, E-commerce, and Consumer Behavior. (Research ID: AAS-2473-2020), ORCID ID:
https://orcid.org/0000-0002-8680-8558.
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