A Blockchain-based Model with An Incentive Mechanism for Cross-border
Logistics Supervision and Data Sharing in Modular Construction
Liupengfei Wua, Xiao Lia*, Rui Zhaoa, Weisheng Lua, Jinying Xua, and Fan Xuea
a

Department of Real Estate and Construction, The University of Hong Kong, Hong Kong, China

*Corresponding author: Email: xl1991@hku.hk
This is the peer-reviewed post-print version of the paper:
Wu, L., Li, X., Zhao, R., Lu, W., Xu, J. & Xue, F. (2022). A blockchain- and IoT-based
smart product-service system for the sustainability of prefabricated housing
construction. Journal of Cleaner Production, 375, 133460. Doi:
10.1016/j.jclepro.2022.133460
The final version of this paper is available at: https://doi.org/10.1016/j.jclepro.2022.133460.
The use of this file must follow the Creative Commons Attribution Non-Commercial No
Derivatives License, as required by Elsevier’s policy.

Abstract
Sustainability in cross-border logistics requires issues such as fragmented management to be
addressed. Particular challenges arise in cross-border logistics in modular construction (CLMC)
because supervision is inefficient, primarily due to continued use of paper-based documentation.
5

Researchers have developed digital platforms that integrate accurate prefabricated module location
information but their centralized operation creates information security issues such as tampering.
Blockchain technology can overcome this limitation but relies on user participation. This study,
therefore, develops a blockchain-based supervision (BBS) model with incentives for application
in CLMC. The BBS model is developed using a design science research approach to enhance

10

supervision of CLMC and motivate users to share data promptly, and then a prototype system is
developed and evaluated in a CLMC case. The results show that the system brings a positive
change in product accountability (df=8, t=0.6601, p=0.528) compared with current paper-based
recording process (df=8, t=0.0035, p=0.997), and a positive change in data traceability (df=8,
t=1.468, p=0.180) compared with existing process (df=8, t=0.042, p=0.967). In addition, this study

15

obtains higher scores (552) than others in evaluating the incentive mechanisms. The security
analysis is also discussed through data immutability, non-repudiation, authentication, and

1

authorization. The findings of this study pave the way for a tamper-proof, incentive-enabled
supervision mechanism in modular construction.
Keywords: Blockchain; Modular construction; Cross-border logistics; Sustainability; Incentive
20

mechanism

1. Introduction
The construction industry is a major cause of sustainability challenges through its consumption of
energy and materials (Lu et al., 2018). Widely advocated as a more sustainable alternative to
25

traditional in-situ construction, modular construction involves fabrication of freestanding
integrated modules under controlled off-site conditions and transportation of the large units to
building sites for assembly (Wu et al., 2022a). It offers social, economic, and environmental
benefits, including enhanced safety and productivity and reduced waste (Lu et al., 2018). However,
successful modular construction project delivery involves complex logistics with multiple

30

processes and stakeholders (Lu et al., 2022). This is especially true in territories like Hong Kong
where high construction costs and labor shortages mean that modular housing production is
outsourced to factories in Guangdong Province, Mainland China, requiring cross-border logistics
(Li et al., 2021b). To ensure an efficient transition from production to on-site assembly, proper
technologies must be adopted to facilitate the cross-border logistics process.

35

The key to efficient cross-border logistics in modular construction (CLMC) is transparency of
information for involved entities (Chang et al., 2020a). With increasingly fierce competition in
the global construction market, advanced project management software such as enterprise resource
planning systems (Wang et al., 2020) and building information modeling (BIM) and geographic
40

information system (GIS) technologies have been adopted to facilitate information sharing and
decision support in CLMC (Li et al., 2022). However, current information systems or platforms
work in a centralized way, leading to information security issues (Xue & Lu, 2020). BIM, for
example, cannot ensure a single point of trust for any modification, creating a risk of tampering.
Where module quality becomes a focus of cross-border disputes in CLMC, logistics information

45

cannot be secured.
2

Blockchain technology has the potential to create a shift in information management practices
prevalent in construction. A blockchain is a distributed database of records linked with
cryptography and reliant on a decentralized consensus that effectively records and endorses
50

transactions between participants (Xue and Lu, 2020). Its benefits, therefore, include reinforced
security and traceability (Chang et al., 2020a). Blockchains also promote information sharing
through distributed networks and reduce costs by removing intermediaries (Li et al., 2021a). In
construction, blockchain technology has potential applications in internal administration (Wang et
al., 2020), self-executing transactions with smart contracts (Lu et al., 2021b), immutable records

55

of transactions (Li et al., 2021b), secure payment (Das et al., 2021), and in combination with BIM
and Internet of Things (IoT) (Lee et al., 2021). Blockchains have also been adopted for information
management in prefabricated construction (e.g., Zhang et al., 2020). However, in current
blockchain solutions, unwillingness of users to share data presents a challenge. Compared with
prefabricated components though, prefinished modules are larger and more valuable, requiring

60

better quality supervision and more efficient data sharing when using blockchain in CLMC.

Thus, this study aims to develop a blockchain-based supervision (BBS) model with incentives for
CLMC. The objectives of this study are: (1) to develop a BBS model to enhance the CLMC
supervision process; (2) to improve the willingness of BBS model users to share data; and (3) to
65

evaluate the proposed model through prototype system development. The rest of this paper is
organized as follows. Section 2 is a literature review of blockchain technology for information
management, logistics, and incentive mechanism. Section 3 reviews the current business process
of CLMC. Section 4 presents the research methodology. Section 5 gives the details of the BBS
model. Section 6 shows the prototype system and evaluates its performance. Section 7 offers our

70

discussion, and Section 8 concludes this research.

2. Literature Review
2.1 Blockchain Technology for Information Management

3

Blockchain technology has found information management applications in several spheres. Fan et
75

al. (2018) developed a blockchain-based system for medical information sharing, but privacy
issues compromised the willingness of users to share information. Recently, Nguyen et al. (2021)
used a deep belief network with a residual network model to secure blockchain-enabled systems
in healthcare. Cui et al. (2021) adopted 5G and blockchain to realize traceable vehicle-to-vehicle
information sharing, pointing out that vehicles may be reluctant to share data with others but failing

80

to explain how to solve this problem. Esposito et al. (2021) proposed a novel solution for
distributed management of identity in smart cities using blockchain but, again, encouraging
citizens to upload their identity information on the new platform would be a challenge. Mohan and
Gladston (2020) integrated Merkle Tree-based cloud audit and a blockchain-based recording
system to manage cloud data auditing results, but the system lacked incentives for users to upload

85

verification results to the system. Blockchain technology has also been used to manage Android
permission histories (Ouaguid et al., 2018) and donor profiles (Lamba et al., 2019). In all cases,
however, in the process of data sharing the fundamental problem of unwillingness to share is yet
to be overcome.

90

In construction, blockchain conceptual models and proof-of-concept work related to information
management have recently been investigated. For example, blockchain technology has been used
to share different types of construction information, including records related to design (Pradeep
et al., 2021), production (Li et al., 2021b), quality verification (Zhong et al., 2020), scheduling
(Zhang et al., 2020), on-site assembly (Wu et al., 2022a), equipment maintenance (Pan et al., 2022),

95

disputes (Saygili et al., 2022), carbon emission (Shu et al., 2022), and safety (Wu et al., 2022b).
While data sharing on the blockchain can reduce duplication of data collection and processing
efforts, again studies assume that users are willing to share their data. In addition, Das et al. (2021)
and Wu et al. (2022c) developed similar blockchain-based systems to manage payment
information for preventing late payment, but as payment verification is a manual process

100

participants have to be motivated to upload verification results on time. Hamledari and Fischer
(2021) attempted to improve construction payment automation using blockchain-enabled smart
contracts and robotic reality capture technologies. However, due to the complex and dynamic

4

nature of construction sites, the applicability of this innovation is limited to certain building
elements (e.g., columns).
105

Lee et al. (2021) and Li et al. (2022) integrated the IoT, BIM, and blockchain technology to support
automatic information sharing in construction projects. However, quality verifications at each
checkpoint during the construction process involve manual upload, so sharing verification results
on time is still an issue. Scott et al. (2021) reviewed 121 academic documents on blockchain in the
110

construction industry. The review chapter presented 33 application categories and was organized
into seven subject areas. An identified gap in research for further studies is the lack of an incentive
mechanism for blockchain data sharing.

2.2 Blockchain Technology for Logistics
115

In logistics, blockchains are mainly used to record product quality. For example, Singh et al. (2020)
used blockchain technology to record the transportation status of medicines in pharmacy logistics
but did not investigate the willingness of drivers to share information. Choi (2019) developed a
blockchain platform in luxury logistics and, Sreenu et al. (2022) developed a blockchain-based
ecosystem for vaccine logistics, and Saurabh and Dey (2020) have developed blockchain

120

architectures for tracing food quality. Blockchain-based supervision also covers e-commerce
logistics (Li et al., 2019a), air logistics (Choi et al., 2019), and port logistics (Ahmad et al., 2021).
Again, investigations of the willingness of users to share information are lacking in these studies.
In cross-border logistics, blockchain research is minimal. Li and Li (2020) proposed a blockchain
model for customs clearance of cross-border logistics without considering information-sharing

125

incentives, and Liu and Li (2020) proposed a blockchain-based framework for cross-border ecommerce logistics, pointing out the need to investigate incentive mechanisms in the future
research. Chang et al. (2020a) provided a holistic overview of the blockchain state-of-the-art,
challenges, gaps, and opportunities in cross-border logistics. They emphasized that government
agencies should study how to motivate private sectors to upload information to blockchains.

130

5

In construction logistics, blockchains have mainly been adopted to ensure the traceability of
products en route to construction sites. Shemov et al. (2020) and Tezel et al. (2021), however,
focused on testing blockchain security instead of evaluating the level of user participation. The
same is true in precast construction, where Wang et al. (2020) developed a blockchain-based
135

framework for improving logistics traceability, and in the development by Li et al. (2021a) of a
blockchain-based system to improve information transparency of prefabricated housing
construction. A literature search reveals few blockchain applications in cross-border logistics. Lu
et al. (2022) developed an e-Inspection 2.0 system for cross-border modular construction logistics.
However, their system application and testing are focused on the production phase rather than the

140

transportation phase. Given the larger physical volume and higher price of prefabricated modules
than components, stakeholders inevitably require better quality supervision and efficient data
sharing in CLMC, particularly as cross-border dispute resolution can be time-consuming and
expensive.

145

2.3 Incentive Mechanisms in Blockchains
Blockchain success depends on the contributions of users. Ren et al. (2018) used digital currency
as an incentive to encourage participants to share data in a blockchain for wireless sensor networks
but did not provide an evaluation. He et al. (2018) proposed a blockchain-based incentive
mechanism for distributed P2P applications, which applies cryptocurrencies to incentivize user

150

cooperation. However, cryptocurrencies are restricted by many governments. Xuan et al. (2020)
proposed an incentive mechanism for data sharing based on blockchain technology and Chang et
al. (2020) for edge-computing-based blockchain, neither of which were evaluated in real-life
practice. Huang et al. (2020) demonstrated a reputation-based incentive mechanism to encourage
users to endorse blockchains transactions, while Wang et al. (2018) developed native tokens as a

155

financial incentive for blockchain user participation in the consensus mechanism.

In construction, only a few studies involve incentive mechanisms in blockchains. Lu et al. (2021a)
demonstrated a blockchain-based supervision model with reputational and financial incentives for
recording project information, including logistics, but does not provide a detailed explanation
6

160

about the incentive mechanism nor test it. Lu et al. (2021b) proposed a smart construction objects
(SCOs)-enabled blockchain oracles framework and designed an incentive mechanism to reward
stakeholders who deploy SCOs during the process, but detailed information about the effectiveness
of the incentive mechanism is lacking.

165

Thus, the research gaps identified can be summarized as follows. Firstly, there is a lack of a model
to guide the establishment of an efficient supervision mechanism for CLMC and to help endorse
the information (e.g., loading conditions and status, customs clearance status, and arrival
conditions and status) at each checkpoint for clear product accountability and traceability in crossborder logistics. Secondly, a mechanism to increase user willingness to share data in blockchains

170

has not been investigated for CLMC. Thus, this study aims to develop a BBS model for CLMC.

3. Analysis of the Business Process of Cross-border Logistics in Modular Construction
The CLMC business process usually involves logistics preparation, execution, and completion
(Figure 1). The scope of the CLMC process has been identified as follows: (1) the entry criterion
175

is that the manager of the prefabrication manufacturer signs the bills of transportation to start the
delivery; (2) the inputs are the loading lists and related details for the logistics tasks; (3) the exit
criterion is that the vehicle drivers return the signed delivery dockets to the manufacturer; and (4)
the outputs are the quality assured modules.

7

180

Figure 1. The business processes of cross-border logistics in modular construction: (a)
preparation; (b) execution and completion

8

Several existing information management issues have been found in the business process of CLMC:
185

•

Unclear product accountability: The product accountability in the module loading and
unloading stages is unclear, mainly due to the use of paper. Manual recording (e.g., paperbased inspection records and handwriting) often leads to input error.

•

Low real-time traceability of cross-border logistics records: The customs clearance status
is not notified to all stakeholders in a timely manner, resulting in information asymmetry
among participants.

190

•

Lack of incentives for data sharing: Due to the tight logistics schedule and repetitiveness of
verification works, operators and drivers are unwilling to share verification records promptly.
This problem worsened during the COVID-19 pandemic, as CLMC drivers must submit more
information (e.g., the receipt of nucleic acid tests) to local governmental agencies.

195

•

Logistics file loss and data manipulation: Current logistics file records may be lost or even
modified without strict supervision

As shown in Table 1, blockchain has several advantages over existing approaches that can address
these issues. It can securely provide user authentication and authorization to prevent impersonation
200

(Esposito et al., 2021). Blockchain provides a timestamp for each recorded transaction to ensure
that stakeholders can track logistics history and a decentralized network that can enhance recorded
timestamps to prevent loss (Chang et al., 2020a). Blockchain offers different options for
implementing incentive mechanisms, such as cryptocurrency-based incentives or combined with
external incentive models (Lu et al., 2021a). The distributed ledges of blockchain are scattered in

205

many places in a shared manner to log logistics data. Unlike traditional solutions that may suffer
from tampering risks or require the continued purchase of servers, blockchain can prevent data
loss because the same copy of the data record is replicated, encrypted, and stored in the peer
network (Pradeep et al., 2021). For these reasons, blockchain can be exploited to realize CLMC
supervision.

210

Table 1. Existing methods for addressing issues in CLMC and blockchain advantages
Issues

Existing tools

Blcokchain advantages

References

Unclear product
accountability

Paper-based documentation,
Barcodes, QR codes

Blocckahin can provide secure user
authentication and authorization

Esposito et al.
(2021)

9

Low real-time
traceability

Radio-frequency
identification (RFID) tags

Lack of
incentives for
data sharing

Incentive policies

Logistics file loss
and data
manipulation

Multi-server system, Cloud
system

Blockchain can provide the records
with a timestamp through a
decentralized network
Blcockhain can provide
cryptocurrency-based incentives or
be integrated with external
incentive model
Blockchain can offer distributed
ledgers with encryption

Chang et al.
(2020a)
Lu et al. (2021a)

Pradeep
(2021)

et

al.

4. Research Methodology
In this study, the design science research (DSR) approach was adopted to develop a BBS model
215

for CLMC. DSR is a scientific knowledge production philosophy that tries to produce creative
constructs that address real-world issues (Pradeep et al., 2021). Our study used three steps as shown
in Figure 2.

10

Figure 2. Research methodology

220

4.1 Designing Model
In the first step, to design a BBS model to meet the research objectives, the research team
brainstormed in four meetings in January 2021, analyzing and synthesizing knowledge obtained
225

from the literature. This process was non-linear and required multiple iterations to develop a
11

promising solution (because some solutions are feasible but not the most promising) for which the
model was developed.
4.2 Developing and Implementing the Prototype
230

The second step involved a two-phase development of the prototype system. In the first phase,
blockchain type, development platform, and consensus mechanism were selected to implement the
system. Among blockchain types (public, private, and consortium), the consortium blockchain was
selected because CLMC involves various organizations and only approved project members can
join the network. Then, Hyperledger Fabric was adopted as the development platform because it

235

provides developers with security-enhanced alternatives and resources (Li et al., 2021b). Then, the
crash fault tolerance (CFT) consensus was selected. CFT can avoid network crashes and partitions
(Li et al., 2021b) and is relatively fast compared to Byzantine fault tolerance (Lu et al., 2021a).
In the second phase, front- and back-end prototypes were develop in Linux version 5.4.0-58-

240

generic-lpae (5.4.0-58.64~18.04.1) (Ubuntu 18.04.1 LTS). The back-end prototype was
implemented using SpringBoot (version 2.4.0) allowing the research team to quickly develop
database management systems and web servers. AdminLTE (version 3.0) was used to develop
front-end prototypes to enable the user interfaces. Hyperledger Fabric Explorer was used to
provide users with browsing access to information on the blockchain.

245

4.3 Evaluating the Model Performance
In the third step, four evaluations were conducted in a modular construction case to demonstrate
that the model operates as intended. The case project involves two 17-story student residence tower
buildings on top of a 3-story podium structure, as shown in Figure 3(a). A total of 1008 modules
250

(excluding mock-up modules) of five types were delivered from Foshan, Mainland China, to Hong
Kong for assembly (see Figure 3(b)). The typical floor plan is shown in Figure 3(c).

12

Figure 3. The case project
255

Firstly, we aimed to evaluate whether the model can improve the CLMC supervision processes in
terms of product accountability by allowing users to record operations promptly. The evaluation
was done through comparative analysis of use of paper records and the BBS system over six weeks.
In previous studies (e.g., Farzan et al., 2008), comparative analysis has been used to test the
260

effectiveness of various information systems. Twenty-eight drivers confirm departure from Foshan,
posting the status of customs clearance at the border, and reporting on arrival at the site (see Figure
4). At each delivery, drivers should submit transactions for hold points. Every driver is assigned
one CLMC delivery per week. These 28 participants are called the control group and are instructed
13

to record on paper the six weeks of operations. Meanwhile, 28 drivers from the experimental group
265

use the same method to record the exact operations in the first three weeks and then switch to the
developed system in the last three weeks. Five evaluation indicators proposed by Li et al. (2019b)
were used to test the effectiveness of the system; that is, the time to record vehicle information,
driver information, module quality information, cross-border permit documents, and logistics
status.

270

Figure 4. The evaluation scenario for product accountability
14

Secondly, we aimed to evaluate whether the developed model can improve the CLMC supervision
275

processes in terms of data traceability by allowing users to query operations promptly. Similar to
the evaluation of the product accountability, five indicators were used to test the effectiveness of
the system; that is, the time to query vehicle information, driver information, module quality
information, cross-border permit documents, and logistics status.

280

Thirdly, a multi-factor scoring (MFS) method was used to evaluate whether the designed incentive
mechanism was more efficient than existing mechanisms encouraging users to publish transactions
promptly in blockchains. Based on the study conducted by Zuo et al. (2012), the first-level
indicators (Ii) for measuring the performance of an incentive mechanism were proposed (see Figure
5). Then, a list of detailed performance indicators was formulated built upon Li et al. (2019b).

285

After that, a workshop was conducted in late July 2021 with the logistics company involved in the
surveyed project to assess the suitability of the second-level indicators (Ii-j) and establish the values
of the relative weighting parameters Wi of Ii and wij of Ii-j. Next, a 3-part questionnaire was sent
to the logistics company in early August 2021. In Part I of the questionnaire, five incentive
mechanisms (including the incentive mechanism designed in this study) were explained to

290

respondents. Part II of the questionnaire gathered the demographic characteristics of the
respondents. In Part III, respondents were asked to score Ii-j based on the scaling statements (0,
Inferior; 3, Poor; 6, Fair; 9, Good; 12, Superior) for each mechanism. The score 𝑆𝑆𝑖𝑖 (the weighted

score from assessing Ii-j) was determined using Equation 1:
295

𝑆𝑆𝑖𝑖 = ∑𝑥𝑥𝑗𝑗=1 𝑤𝑤𝑖𝑖𝑖𝑖 𝑆𝑆𝑖𝑖𝑖𝑖

(1)

where x denotes the number of second-level indicators under Ii. The final value of each incentive
mechanism is defined as the total score collected from the assessment results on Ii-j, which are
subdivided from Ii. The final values were calculated by using Equation 2:

300

Final value = ∑𝑛𝑛𝑖𝑖=1 𝑊𝑊𝑖𝑖 𝑆𝑆𝑖𝑖

(2)

where n denotes the number of first-level indicators. Of the questionnaires distributed, we received
78 responses, but some were abandoned due to incompleteness. The remaining 56 were output and
analyzed in Excel spreadsheets. Regarding demographic characteristics, 85% of respondents held
15

a bachelor’s degree or above. About 50% had 1–5 years’ experience in the logistics industry, 17.9%
305

had 6–10 years’ experience, and 32.1% had more than 10 years’ experience.

Figure 5. Indicators of the multi-factor scoring (MFS) system

310

Finally, we aimed to discuss the security of the proposed blockchain-based model. According to
Lu et al. (2021b) and Li et al. (2021b), the security analysis of the proposed model can be discussed
through data immutability, non-repudiation, authentication and authorization. The analysis results
were disseminated to audiences in modular construction.

315

5. The BBS Model for CLMC
5.1 The BBS Model
The proposed BBS model has two parts: (a) an incentive mechanism (see Figure 6(a)); and a
blockchain-based supervision mechanism (see Figure 6(b)). The proposed model includes four
organizations: owner, manufacturer, transporter, and contractor, and every involved organization

320

has many operators to record their operation transactions. According to the delivery schedule, the
manufacturer’s operators first load the prefinished modules onto the delivery vehicles and record
16

the condition and status of modules. Then, the operators (e.g., drivers) of the transporter confirm
the condition of the loaded modules and deliver them to the border. After customs clearance, the
transporter’s operators should update the status of delivery and deliver modules to the site. The
325

contractor’s operators (e.g., site engineers) will verify the delivered modules (e.g., quality, quantity)
and sign the delivery dockets.

Figure 6. The blockchain-based supervision model for cross-border logistics in modular
330

construction: (a) incentive mechanism; (b) supervision mechanism
17

In the BBS model, the project owner can supervise the entire CLMC process by including an
information record-track supervision pattern and a consensus mechanism. Thus, transaction
information such as logistics preparation, execution, and completion can be recorded, published,
335

endorsed, and tracked. Logistics preparation information contains data about module ID,
production date, type, conditions, status, signature, and loading finishing time. Logistics execution
information includes data about transportation operations, while logistics completion information
includes the data about the receiving verification operations.

340

5.1.1 Model Structure
The first part of the model is a points-based incentive mechanism. The points system can motivate
users to make contributions in enterprise social networking as points can turn their participation
into a value for themselves (Farzan et al., 2020). Table 2 shows the calculation principles of the
system. In BBS, users will receive a point for each recorded transaction. Each recorded transaction

345

published within one hour of finishing the operation will help users earn an extra 30 points. The
total points are the sum of points received from recording transactions and publishing on time.
Five reputation statuses (fail, pass, credit, distinction, and high distinction) are also defined. The
reputation incentive is a long-lasting incentive mechanism, benefits of which include more
business opportunities, cost-free advertising, and higher company value (Huang et al., 2020). The

350

mechanism is also integrated with financial incentives to improve participant attitudes to sharing
data promptly and drive the progress of projects (Lu et al., 2021a). In the BBS model, each point
obtained by the user can be traded for three dollars from the owner. All transaction records are
stored in the blockchain to achieve a transparent and immutable incentive mechanism. The
incentive reward will be canceled if a participant cheats. Further, if modules are damaged, and the

355

difference between the actual cost and compensation payment received from the insurance
company exceeds 1,500 dollars, the corresponding participant will not obtain the incentives.

Table 2. A points-based incentive mechanism

18

Participants

Reward point

Total number

No. of transactions

Reward points

Total

for each

of transactions

recorded on time*

per transaction

points**

published

published

(no. of transactions not

(on-time)*

(T)

transaction

(variable)

recorded on time)

Manufacturer A

1

O

X (O−X), where X≤O

30

1×O+X×30

Transporter B

1

P

Y (P−Y) , where Y≤P

30

1×P+Y×30

Contractor C

1

Q

Z (Q−Z) , where Z≤Q

30

1×Q+Z×30

*Transactions published within 1 h after corresponding operations are completed.

360

**Total points < 500: fail; 500 ≤ total points < 650: pass; 650 ≤ total points < 750: credit; 750 ≤ total points < 850:
distinction; and 850 ≤ total points: high distinction.

The second part of the model is a blockchain-based supervision mechanism. The blockchain
consists of two layers. The lower layer includes three sidechains to manage information related to
365

logistics preparation, logistics execution, and logistics completion. These three sidechains are held
by the manufacturer, the transporter, and the contractor, respectively. The data frame structure in
the sidechain can be seen in Figure 7(a). Each transaction consists of a timestamp, a hash pointer
of the current transaction, a hash pointer of the previous transaction, and data. The data contains a
key-value pair, where the key gives the operation category, including “Logistics Preparation”,

370

“Logistics Execution”, and “Logistics Completion”. The value shows the data content, such as
module ID and custom clearance status. Under this data frame structure, operation transactions can
be converted to the same format. The upper layer is the main blockchain. Each block in the
mainchain contains a header and a set of transactions. The block header includes an index (the
sequence of blocks in the chain), a timestamp, a signature validator, and hash pointers of current

375

and previous blocks. Figure 7(b) shows that three categories of transactions from the sidechains
can be published to the mainchain, and the owner can also track records from the mainchain.

19

Figure 7. Transaction and blockchain: (a) data frame structure in sidechains; and (b) block
structure in the mainchain
380

5.1.2 Incentive and Supervision Processes in the BBS Model
The BBS model has three main processes to implement incentive and supervision mechanisms:
registration, supervision, and incentive, as shown in Figure 8.

20

385

Figure 8. Incentive and supervision processes in the blockchain-based supervision model

(1) Member Registration and Incentive Principle Notificaiton
Member registration is essential for access control in the BBS network. Participants first need to
390

verify their identities by sending membership registration requests to the owner (see Figure 8(a)).
The owner can then issue certificates to eligible participants and register them as network members.
21

Member registration ensures that participants are reviewed and given permissions, as well as
detailed information on the incentive mechanism for those who need to record and publish
transactions.
395

(2) Supervision Processes
A publish-track supervision pattern is adopted in the second process to allow members to publish
CLMC transactions to the main blockchain (see Figure 8(b)). The owner can also supervise the
CLMC processes by tracking the transactions. For instance, when the transporter’s operators begin
400

to deliver the modules to the site, the owner can track the transaction records of logistics execution.
The operators of the transporter then record and hash each operation in the logistics execution
information sidechain. Next, these operators publish the transaction hashes to the owner for
supervision. Similarly, the owner can establish this publish-track supervision pattern with the
manufacturer and the contractor.

405

The BBS model employs an ordering service to form orderly blockchains. The owner acts as an
ordering node in the network, ordering transaction hashes published by operators and packaging
them into new blocks. The ordering service cannot access the data in transactions, nor can it update
unapproved blocks. Instead, it can only deliver the packed blocks to participants for endorsements.
410

The BBS model adopts a CFT consensus algorithm to help participants to endorse logistics
transactions. Both valid and invalid transactions are recorded in blocks. Afterward, members will
be notified of the endorsement results.

(3) Incentive Implementation
415

The third process is to implement the incentive mechanisms (see Figure 8(c)). After the operator
completes the specified operation and publishes the transaction, the owner will calculate his/her
points, reputation status, and financial incentives based on the records in the BBS. For instance,
the transporter’s operator needs to record 40 transactions, of which 35 are recorded and published
22

within one hour of finishing the operations. According to Table 1, this operator will earn 1,090
420

points (1 × 40 + 35 × 30), a high distinction reputational reward, and a financial reward of $3,270.

6. Prototype System and Evaluation
6.1 Prototype System
A prototype system was developed to implement the BBS model for CLMC, containing four
425

organizations: (i) owner; (ii) manufacturer; (iii) transporter; (iv) contractor. The configuration
information of these four organizations is shown in Figure 9(a). Participants were registered as
members by using the cryptogen in Hyperledger Fabric to issue certificates (see Figure 9(b)). To
initialize the ordering service, the genesis block was configured (see Figure 9(c)). Subsequently,
Hyperledger Explorer was established to allow users to browse the blockchain network internally

430

(see Figure 9(d)).

23

Figure 9. System configuration: (a) organizations; (b) certificates; (c) genesis block; and (d)
explorer
435

The development of the front-end prototype makes the interfaces available to users. For example,
when a driver delivers a batch of modules to the border and passes through customs, the driver can
record and publish logistics execution information such as the driver ID, vehicle number, quantity,
module ID, customs clearance status, completion time, and the driver’s COVID-19 test report, as
440

shown in Figure 10(a). The JavaScript Object Notation (JSON) form plug-in will convert the
recorded information into a JSON file and store it in the sidechain. Then, the file will be hashed
and published to the owner in the mainchain. After reaching a consensus, the file will be appended
as the latest block to the participants’ blockchain.
24

445

(a) Transporter’s interface for publishing transactions

(b) Owner’s interface for tracking operations
Figure 10. User interfaces
450

25

The project owner can supervise the CLMC process by tracking logistics transaction information.
For example, the owner can use the interface shown in Figure 10(b) for tracking historical
operations. Corresponding information related to modules such as transaction ID, transaction
category, operation name, publisher ID, and publish time can be viewed through the interface. The
455

owner can also check the block details by clicking on any recorded transaction. These details
include a block index, timestamp, operator signature, and the hash pointers of the current and
previous blocks.
6.2 System Performance Evaluation

460

6.2.1 Evaluation Results of the Product Accountability
The time spent by the control and the experimental groups to record the five types of information
during the supervision processes is shown in Figure 11. For the control group, the recording time
for each kind of information remained constant for six weeks. In contrast, after introducing the
system in the 4th week, the experimental group spent less time recording each type of information

465

(see Figure 11). The t-test was used to compare the recording time spent by each group between
week 3 and week 4. The results showed almost no difference in the recording time spent by the
control group between weeks 3 and 4 (df=8, t=0.0035, p=0.997), whereas there was a change in
recording time spent by the experimental group after the system was introduced (df=8, t=0.6601,
p=0.528). Thus, the developed system implements effective product accountability for CLMC.

26

470

Figure 11. Evaluation results of the product accountability

6.2.2 Evaluation Results of the Data Traceability
The time spent by the control and the experimental groups to trace the five types of information
475

during the supervision processes is shown in Figure 12. The amount of time the control group
spent tracing each type of information remained constant. After introducing the system, the time
27

the experimental group spent on tracing information decreased significantly. The t-test showed
only a small difference in the tracking time spent by the control group between weeks 3 and 4
(df=8, t=0.042, p=0.967), whereas there was a statistically significant difference between weeks 3
480

and 4 in the experimental group (df=8, t=1.468, p=0.180). Thus, the developed system implements
effective data traceability for CLMC.

Figure 12. Evaluation results of the data traceability
28

485

6.2.3 Evaluation Results of the Incentive Mechanism
The evaluation results of the incentive mechanisms is presented in Table 3. Compared with Xuan
et al. (2020) and Huang et al. (2020), the first three studies meet physiological needs by introducing
financial rewards, encouraging users to share information promptly. Due to the use of blockchain,
all studies received close scores in meeting security needs. Chang et al. (2020b) received the lowest

490

score in terms of social needs. This may be because their incentive mechanism is designed to
encourage fierce competition, leading to discordant relationships. The first and fifth studies
obtained higher scores than others for the fourth dimension because they considered reputational
incentives. Also, the first study received the highest scores for meeting self-realization needs,
mainly because points can turn participation into value for participants. Overall, the incentive

495

mechanism proposed in this study received the highest value.

Table 3. Evaluation results of the incentive mechanism
No.

Selected studies

W1S1

W2S2

W3S3

W4S4

W5S5

Final value

1

This study

273.03

157.65

57.27

39.92

24.13

552

2

Wang et al. (2018)

179.70

144.51

56.70

18.03

16.06

415

3

Chang et al. (2020b)

181.06

159.14

22.81

11.30

15.78

390

4

Xuan et al. (2020)

84.55

157.45

56.77

18.27

15.96

333

5

Huang et al. (2020)

58.54

158.65

50.30

38.59

23.92

330

6.2.4 Analysis Results of the System Security
500

The security analysis of the proposed system is discussed through data immutability, nonrepudiation, authentication and authorization.
•

Data immutability and non-repudiation: For a transaction to have data immutability, it
cannot be modified during its transmission. All exchanged transactions in the system are
tamper-proof with timestamps. Furthermore, to prevent “man in the middle” attacks and
otherwise secure communications, Transport Layer Security (TSL) in Hyperledger Fabric

505

facilitates a transaction immutability check among participants.
•

Authentication: Authentication mechanisms rely on digital signatures requiring each
participants to hold a cryptographical key pair: a public key is made widely available and
29

acts as an authentication anchor, and a private key is used to produce digital signatures on
transactions.

510

•

Authorization: This study uses membership service provider (MSP) in Hyperledger
Fabric to prove authorized peers’ identity. Only authorized peers can record and trace
corresponding CLMC data.

515

7. Discussion
In CLMC, numerous operation records need to be recorded, endorsed, and tracked, and failure of
supervision may lead to costly and protracted cross-border litigation. The first contribution of this
research is to examine the business process of CLMC, identify its information management
problems, and define the scope of supervision on three types of logistics operation records, i.e.

520

preparation, execution, and completion.

Although construction stakeholders have begun to explore blockchain technology as a means of
enhancing traceability of construction supply chain management, an incentive mechanism to
increase users’ willingness to share data is lacking. The second novel contribution of this research
525

is to propose a BBS model that enhances supervision of CLMC while improving the willingness
of users to share data. The incentive mechanism uses points to turn participation into an activity
that is essential to users. Reputational and financial incentives are integrated with the points system
to further motivate users to share data within the predefined period. The third novel contribution
of this study is that the BBS model is an adaptive structure that can extend applications to off-site

530

production and on-site assembly in modular construction. It can also include other participants
such as governmental agencies and consultants.

The study has several limitations. First, the operation data recorded on the blockchain is uploaded
by humans, so there is a risk of opportunistic behavior. Second, the designed incentive mechanism
535

is not dynamic in nature. For example, the reputation status needs to be adjusted manually based
on the total number of uploaded transactions. Third, the proposed model lacks automation for
implementing incentives.
30

8. Conclusions
540

Sustainability issues have prompted the construction industry to seek ways to mitigate the impacts
of inefficient operations. In modular construction, supervision of cross-border logistics is
challenging because the wide adoption of paper-based documentation means that information
verification is inefficient. Although digital platforms (e.g., BIM and GIS) have been developed to
improve the supervision of CLMC, they work in a centralized way, leading to information security

545

issues such as tampering. If module quality becomes the focus of cross-border dispute, the logistics
information cannot be secured. Blockchain technology has the potential to improve information
security and support CLMC supervision, but requires investigation of incentive mechanisms to
increase user willingness to share data.

550

This study develops a blockchain-based supervision (BBS) model with incentives for CLMC. The
model is developed using a DSR approach to enhance supervision of CLMC and motivate users to
share data promptly. The proposed model achieves the research objectives through three stages:
member registration and incentive principle notification, supervision processes, and incentive
implementation. A prototype system is developed to evaluate the proposed model in a CLMC case.

555

The results show a positive change in recording time spent by the experimental group in terms of
product accountability after the system was introduced (df=8, t=0.6601, p=0.528). Also, the
evaluation results show a statistically significant difference between weeks 3 and 4 in the tracing
time spent by the experimental group in terms of data traceability (df=8, t=1.468, p=0.180). In
addition, this study obtains higher scores (552) than others in evaluating the incentive mechanisms.

560

The security analysis is discussed through data immutability, non-repudiation, authentication, and
authorization. Thus, the system implements effective supervision for CLMC through product
accountability and data traceability. It also achieves efficient data sharing and security for CLMC.

The limitations of this study provide opportunities for future research. More research is needed on
565

the blockchain oracles that connect the off-chain and cyber worlds. Lu et al. (2021b) recently
explored SCOs as blockchain oracles to provide a data authenticity mechanism. Future research
31

can integrate the proposed model with the SCO-enabled blockchain oracles and evaluate the
integrated solution in real-life projects. For example, researchers can explore decentralized SCOs
to protect data for secure upload to BIM and blockchain. Future research can also focus on dynamic
570

incentive mechanisms to further maintain user participation. Das et al. (2020) used smart contracts
to improve the efficiency of construction payments. Therefore, future studies can explore the
combined application of the BBS model and smart contracts to reward users. Researchers can also
integrate the BBS model with BIM, GIS, and 5G technologies. Finally, the model can be extended
to industries other than construction.

575

Data Statement
Some or all data, models, or code that support the findings of this study are available from the
corresponding author upon reasonable request. (Blockchain prototype code).

580

Acknowledgments
The work presented in this paper was financially supported by the Hong Kong Innovation and
Technology Commission (ITC) with the Innovation and Technology Fund (ITF) (No.
ITP/029/20LP).

585

References
Ahmad, R. W., Hasan, H., Jayaraman, R., Salah, K., & Omar, M. (2021). Blockchain applications
and architectures for port operations and logistics management. Research in Transportation
Business & Management, 100620.
Chang, Y., Iakovou, E., & Shi, W. (2020a). Blockchain in global supply chains and cross border

590

trade: a critical synthesis of the state-of-the-art, challenges and opportunities. International
Journal of Production Research, 58(7), 2082-2099.
Chang, Z., Guo, W., Guo, X., Zhou, Z., & Ristaniemi, T. (2020b). Incentive mechanism for edgecomputing-based blockchain. IEEE Transactions on Industrial Informatics, 16(11), 71057114.
32

595

Choi, T. M. (2019). Blockchain-technology-supported platforms for diamond authentication and
certification in luxury supply chains. Transportation Research Part E: Logistics and
Transportation Review, 128, 17-29.
Choi, T. M., Wen, X., Sun, X., & Chung, S. H. (2019). The mean-variance approach for global
supply chain risk analysis with air logistics in the blockchain technology era. Transportation

600

Research Part E: Logistics and Transportation Review, 127, 178-191.
Cui, J., Ouyang, F., Ying, Z., Wei, L., & Zhong, H. (2021). Secure and Efficient Data Sharing
Among Vehicles Based on Consortium Blockchain. IEEE Transactions on Intelligent
Transportation Systems.
Das, M., Luo, H., & Cheng, J. C. (2020). Securing interim payments in construction projects

605

through a blockchain-based framework. Automation in Construction, 118, 103284.
Esposito, C., Ficco, M., & Gupta, B. B. (2021). Blockchain-based authentication and authorization
for smart city applications. Information Processing & Management, 58(2), 102468.
Farzan, R., DiMicco, J. M., Millen, D. R., Dugan, C., Geyer, W., & Brownholtz, E. A. (2008).
Results from deploying a participation incentive mechanism within the enterprise. In

610

Proceedings of the SIGCHI conference on Human factors in Computing Systems (pp. 563572).
Fan, K., Wang, S., Ren, Y., Li, H., & Yang, Y. (2018). Medblock: Efficient and secure medical
data sharing via blockchain. Journal of Medical Systems, 42(8), 1-11.
He, Y., Li, H., Cheng, X., Liu, Y., Yang, C., & Sun, L. (2018). A blockchain based truthful

615

incentive mechanism for distributed P2P applications. IEEE Access, 6, 27324-27335.
Huang, C., Wang, Z., Chen, H., Hu, Q., Zhang, Q., Wang, W., & Guan, X. (2020). Repchain: A
reputation based secure, fast and high incentive blockchain system via sharding. IEEE Internet
of Things Journal, 8(6), 4291 – 4304.
Hamledari, H., & Fischer, M. (2021). Construction payment automation using blockchain-enabled

620

smart contracts and robotic reality capture technologies. Automation in Construction, 132,
103926.
Lamba, R., Gupta, Y., Kalra, S., & Sharma, M. (2019). Preventing waiting list manipulation and
black marketing of donated organs through Hyperledger Fabric. 2019 International
Conference on Computing, Communication, and Intelligent Systems (ICCCIS) (pp. 280-285).

625

IEEE.
33

Lee, D., Lee, S. H., Masoud, N., Krishnan, M. S., & Li, V. C. (2021). Integrated digital twin and
blockchain framework to support accountable information sharing in construction projects.
Automation in Construction, 127, 103688.
Li, M., Shen, L., & Huang, G. Q. (2019a). Blockchain-enabled workflow operating system for
630

logistics resources sharing in E-commerce logistics real estate service. Computers &
Industrial Engineering, 135, 950-969.
Li, X., Shen, G. Q., Wu, P., & Yue, T. (2019b). Integrating building information modeling and
prefabrication housing production. Automation in Construction, 100, 46-60.
Li, X., & Li, Q. (2020). The Model of Blockchain Application in the Customs Clearance of Cross-

635

Border Logistics from the Perspective of “The Belt and Road”. Design Engineering, 661-671.
Li, C. Z., Chen, Z., Xue, F., Kong, X. T., Xiao, B., Lai, X., & Zhao, Y. (2021a). A blockchain-and
IoT-based smart product-service system for the sustainability of prefabricated housing
construction. Journal of Cleaner Production, 286, 125391.
Li, X., Wu, L., Zhao, R., Lu, W., & Xue, F. (2021b). Two-layer Adaptive Blockchain-based

640

Supervision model for off-site modular housing production. Computers in Industry, 128,
103437.
Li, X., Lu, W., Xue, F., Wu, L., Zhao, R., Lou, J., & Xu, J. (2022). Blockchain-enabled IoT-BIM
platform for supply chain management in modular construction. Journal of construction
engineering and management, 148(2), 04021195.

645

Liu, Z., & Li, Z. (2020). A blockchain-based framework of cross-border e-commerce supply chain.
International Journal of Information Management, 52, 102059.
Lu, W., Chen, K., Xue, F., & Pan, W. (2018). Searching for an optimal level of prefabrication in
construction: An analytical framework. Journal of Cleaner Production, 201, 236-245.
Lu, W., Wu, L., Zhao, R., Li, X., & Xue, F. (2021a). Blockchain Technology for Governmental

650

Supervision of Construction Work: Learning from Digital Currency Electronic Payment
Systems. Journal of Construction Engineering and Management, 147(10), 04021122.
Lu, W., Li, X., Xue, F., Zhao, R., Wu, L., & Yeh, A. G. (2021b). Exploring smart construction
objects as blockchain oracles in construction supply chain management. Automation in
Construction, 129, 103816.

34

655

Lu, W., Wu, L., Xu, J., & Lou, J. (2022). Construction E-Inspection 2.0 in the COVID-19
Pandemic Era: A Blockchain-Based Technical Solution. Journal of Management in
Engineering, 38(4), 04022032.
Mohan, A. P., & Gladston, A. (2020). Merkle tree and Blockchain-based cloud data auditing.
International Journal of Cloud Applications and Computing (IJCAC), 10(3), 54-66.

660

Nguyen, G. N., Le Viet, N. H., Elhoseny, M., Shankar, K., Gupta, B. B., & Abd El-Latif, A. A.
(2021). Secure blockchain enabled Cyber–physical systems in healthcare using deep belief
network with ResNet model. Journal of Parallel and Distributed Computing, 153, 150-160.
Ouaguid, A., Abghour, N., & Ouzzif, M. (2018). A novel security framework for managing
android permissions using blockchain technology. International Journal of Cloud

665

Applications and Computing (IJCAC), 8(1), 55-79.
Pradeep, A. S. E., Yiu, T. W., Zou, Y., & Amor, R. (2021). Blockchain-aided information
exchange records for design liability control and improved security. Automation in
Construction, 126, 103667.
Pan, X., Zhong, B., Sheng, D., Yuan, X., & Wang, Y. (2022). Blockchain and deep learning

670

technologies for construction equipment security information management. Automation in
Construction, 136, 104186.
Ren, Y., Liu, Y., Ji, S., Sangaiah, A. K., & Wang, J. (2018). Incentive mechanism of data storage
based on blockchain for wireless sensor networks. Mobile Information Systems, 2018,
6874158.

675

Singh, R., Dwivedi, A. D., & Srivastava, G. (2020). Internet of things based blockchain for
temperature monitoring and counterfeit pharmaceutical prevention. Sensors, 20(14), 3951.
Saurabh, S., & Dey, K. (2020). Blockchain technology adoption, architecture, and sustainable agrifood supply chains. Journal of Cleaner Production, 124731.
Shemov, G., de Soto, B. G., & Alkhzaimi, H. (2020). Blockchain applied to the construction supply

680

chain: A case study with threat model. Frontiers of Engineering Management, 7(4), 564-577.
Saygili, M., Mert, I. E., & Tokdemir, O. B. (2022). A decentralized structure to reduce and resolve
construction disputes in a hybrid blockchain network. Automation in Construction, 134,
104056.

35

Shu, Z., Liu, W., Fu, B., Li, Z., & He, M. (2022). Blockchain-enhanced trading systems for
685

construction industry to control carbon emissions. Clean Technologies and Environmental
Policy, 1-20.
Scott, D. J., Broyd, T., & Ma, L. (2021). Exploratory literature review of blockchain in the
construction industry. Automation in construction, 132, 103914.
Sreenu, M., Gupta, N., Jatoth, C., Saad, A., Alharbi, A., & Nkenyereye, L. (2022). Blockchain

690

based secure and reliable Cyber Physical ecosystem for vaccine supply chain. Computer
Communications, 191, 173-183.
Tezel, A., Febrero, P., Papadonikolaki, E., & Yitmen, I. (2021). Insights into Blockchain
Implementation in Construction: Models for Supply Chain Management. Journal of
Management in Engineering, 37(4), 04021038.

695

Wang, Z., Wang, T., Hu, H., Gong, J., Ren, X., & Xiao, Q. (2020). Blockchain-based framework
for improving supply chain traceability and information sharing in precast construction.
Automation in Construction, 111, 103063.
Wang, J., Li, M., He, Y., Li, H., Xiao, K., & Wang, C. (2018). A blockchain based privacypreserving incentive mechanism in crowdsensing applications. IEEE Access, 6, 17545-17556.

700

Wu, L., Lu, W., Zhao, R., Xu, J., Li, X., & Xue, F. (2022a). Using Blockchain to Improve
Information Sharing Accuracy in the Onsite Assembly of Modular Construction. Journal of
Management in Engineering, 38(3), 04022014.

705

Wu, H., Zhong, B., Li, H., Chi, H. L., & Wang, Y. (2022b). On-site safety inspection of tower
cranes: A blockchain-enabled conceptual framework. Safety Science, 153, 105815.
Wu, L., Lu, W., & Xu, J. (2022c). Blockchain-based smart contract for smart payment in
construction: A focus on the payment freezing and disbursement cycle. Frontiers of
Engineering Management, 1-19.
Xuan, S., Zheng, L., Chung, I., Wang, W., Man, D., Du, X., ... & Guizani, M. (2020). An incentive
mechanism for data sharing based on blockchain with smart contracts. Computers & Electrical

710

Engineering, 83, 106587.
Xue, F., & Lu, W. (2020). A semantic differential transaction approach to minimizing information
redundancy for BIM and blockchain integration. Automation in Construction, 118, 103270.
Zhang, Z., Yuan, Z., Ni, G., Lin, H., & Lu, Y. (2020). The quality traceability system for
prefabricated buildings using blockchain: An integrated framework. Frontiers of Engineering

715

Management, 7(4), 528-546.
36

Zuo, W., Wang, Q., & Yang, P. (2012). Research on the current situation of peasant-workers in
construction industry based on AHP. Systems Engineering Procedia, 5, 405-411.

37