Innovative Blockchain-Based Tracking Systems, A Technology Acceptance for Cross-Border Runners during and Post-Pandemic

Abstract

This study aims to design and implement an online blockchain-based and real-time parcel monitoring and tracking system for cross-border runners and the customer via an online platform, during and post the COVID-19 pandemic. A blockchain is a distributed ledger system that serves as a transparent, understandable, and trustworthy store of data and analysis on the platform for participants to engage with each other. The result of proposing a blockchain-based tracking system is promising. The result and UAT show positive feedback on the use and features of the blockchain-based tracking system. As the world reacted to the pandemic, many organizations provided monitoring with their deliveries, which is a terrific method for businesses to prevent losing valuable customers. According to the findings of the study, organizations prefer to have blockchain-based tracking systems.

1. Introduction

Blockchain technology, which is cutting-edge, has the potential to dramatically increase tracing performance by assuring complete transparency and security [1]. When using the blockchain for data authentication, the entire network can contribute and authenticate the data, and it is no longer vulnerable to hacking. Increased tracking information accuracy may have an impact on the security of the products being delivered by the runner.

Increased security, speed, transparency, and software-based processing are all advantages of a blockchain. Companies can build their platforms or leverage existing platforms such as Ethereum and Hyperledger to develop their apps [2]. Currently, seafood exports rely on a large number of boats; the most effective monitoring systems in Asia, such as Alibaba, Walmart, and JD.com, are now reliant on paperwork and reports; and full quality control is a challenging task in the current system [3].

The blockchain-based tracking system is a project that helps runners or businesses manage their customers’ parcel details. The system stores all the runner’s details or the details of the company that can be also used when setting a destination where a customer can pickup their packages or have their parcels delivered to their house. The system has a tracking feature that may be used to trace the progress of a customer’s package.

1.1. Background of the Study

A tracking system for packages is usually accompanied by a unique parcel identity that can be the only primary hub that has been detected until the item is delivered to the specified address [4]. Due to the development of online purchasing and the requirement for the runner to post the parcel, a product’s tracking system is becoming increasingly important. Online shopping is highly convenient because consumers only need to relax and press a few buttons to purchase the items they desire. Due to the rise of COVID-19, the trend of online buying and posting by runners has exploded, especially in the last several months.

In Brunei, courier services are provided by DHL, Poslaju Express, and others. Because the specific service provided by such companies is provided as a commodity, it is possible to say that the rate of a given carrier is appropriate in terms of space. As a result, a few online buying platform companies collaborate with courier carrier companies to provide a better courier service for both the seller and the clients. Following that, all courier companies can deliver any parcel that complies with government regulations [5]. Therefore, to manage the package, each and every one of the parties, along with the vendor and the parcel, can be tracked by the customer with the monitoring variety that is furnished through the courier carrier corporation. Using the monitoring variety, the advent time may be kept under control, and the parcel’s protection can be perfect for the customer.

Because of the growing number of online buyers, the delivery of goods in the form of packages, documents, and parcels continues to climb every day. It necessitates the provision of logistics services to deliver goods properly and efficiently. Furthermore, the logistics service for the distribution of online commodities always has more potential for innovation to provide good service. Significant changes in society because of the pandemic inspired a new habit of buying online, where things can be delivered to the location of customers’ orders. As a result, customers may be motivated to use this logistics service to deliver their orders without having to leave the house during the pandemic [6].

To begin the local delivery by the runner, the parcel must be handed over to the registered runner. When the runner receives the package, the entire link requires the physical package and tracking system handover to the runner, who then completes the specific run according to the scheduled route, and the delivery runner must return the data and real-time runner delivery information to the tracking system platform and the admin tracking system until the parcel is delivered to the recipient safely. The tracking platform’s goal is to determine the exact location of all parcels in real time and transfer that data to a controller system, which will utilize them to aggregate the goods into a simple statement at the end [7].

The need to monitor and trace items across a distribution network has long been acknowledged and logistic businesses have responded by providing data collection and tracking services to address this issue. Organizations that promote standardization are also actively participating in efforts to develop worldwide item identification techniques. As a result, the standards that have developed are largely concerned with item identification, with no clear definition of any links to product-tracking software [8]. Since the tightening of control measures following the recurrence of COVID-19 in Brunei, the demand for delivery services or runners for food and other products has risen.

Blockchain technology has a wide range of uses. For instance, in a cloud service based on blockchain technology, an environment for blockchain networks and peer-to-peer data access can be built [9].

After you register with the tracking system, you will get a unique tracking ID and your parcel will get updated, providing you with information about your parcel. The unique ID has also been provided as a useful reference for us. We chose a less ambitious goal to identify the object of interest, whereas the unique ID center creates a strategy for a broader scale identification system, an “Internet of things”. The system holds information about the item that can then be maintained/retrieved and enables the item to be tracked [10]. Every parcel has a unique ID. Before it arrives to your door, your parcel needs to register with our runner tracking system after it arrives at the post office.

1.2. Problem Statement

A parcel tracking number, often known as a tracking code, is a sequence of numbers that is provided with your packages after they are ordered. You can track and trace your parcel using a global package tracking number throughout its full delivery route. Depending on the delivery destination, tracking numbers can be used for both international and domestic packages. The customers and people involved in related local markets value this ability to track goods. The input data for the tracing inquiry blockchain network are tracking IDs or batch IDs. The basic phase in the tracing query technique is the validation of the authenticity of the searcher and the correction of the ID. The tracing users are divided between the common consumers and runners in this framework, and the tracing information for the two users is different [11]. Tracking a package with a tracking number works great, but sometimes problems occur, especially during the COVID-19 pandemic, which include not knowing where your package is located at a particular moment in time and not having a runner to deliver your item, which does not always imply that it is lost or harmed.

Parcel collection by a runner has become an issue, as some runners are not registered and they are not allowed to enter and exit the country, especially during the COVID-19 pandemic. Various variables, such as import customs rules, domestic and abroad transportation rules, and so on, restrict cross-border trade, resulting in longer logistics operation cycles and increased costs, which negatively impact foreign customers’ satisfaction and limit the cross-border growth of exports [12].

Delivery services are trying their best to create a better way of reaching out to customers. Customers would like to point out that if there are more delivery services or pickup outlets within the parameter of every city, the rate of undelivered products or services will be minimal [13]. There is frustration about delayed prescriptions and undelivered packages because of a lack runners, which can be due to runners being in quarantine.

1.3. Main Contribution the Study

• The implementation of a blockchain-based tracking system, which will secure the data and information within the systems.
• The advantages of a tracking system over the existing system, i.e., the manual way of registering and monitoring the movement of a freight or any parcel.
• The runner will be able to be tracked and known by the admin of the system and the customer.
• The proposed system will provide security to help track the movement of each delivery and parcel carrier on hold.
• The system will also eliminate some of the routine manual work that is prevalent in the manual system of tracking.

The remains of this paper are organized as follows: Section 2 describes the literature review, Section 3 discusses the methodology, Section 4 explains the system development, Section 5 describes the data analysis, Section 6 provides the discussion, and finally, Section 7 has the conclusions and future works.

2. Literature Review

The purpose of this section is to provide the theoretical background knowledge on the topic of Blockchain-Based Tracking Systems: Cross-Border Runners During and Post-COVID-19 Pandemic. This literature review begins with arguments and suggestions from the past literature on the cause and effects of the system, user, and runner. A focus is given to the literature on the subject of the blockchain-based tracking system for cross-border runners during the COVID-19 pandemic, especially the determinants and their effects.

2.1. Review of Prior Literature

According to [14], they provide information about the COVID-19 pandemic in this section, as well as discussing why blockchain technology is so important in addressing this pandemic. Because it enables efficient tracking and monitoring methods, the blockchain is currently showing significant potential to become a key part of the fight against COVID-19. This is possible because a blockchain is made up of a chronologically ordered list of encrypted signatures, as well as a secure distributed ledger with permanent transaction records shared by all the network users.

Ref. [15] states that it was important to be able to identify each product’s barcodes to uniquely track the distribution process. The duplication of the unique identifier is relatively rare.

Ref. [16] addresses the opportunity to create a fully and completely transformative cross-border tracking system, which aims to connect all the involved people in a connected, transparent, and data-rich environment with the correct strategies and procedures in place. A blockchain-enabled tracking system is expected to improve speed, visibility, security, and responsiveness for all the users, such as the runners, traders, customs agents, or government agencies.

Based on [17], the users, or the blockchain interface, integrate the existing systems and internal activities of a business. Blockchain networks, which are implemented as a unique code, allow you to communicate with structures and procedures beyond the blockchain. The users (the runner and customer) keep records of the implementation environment, and the status of the business operations that are in progress and run on a full blockchain tracking system.

Ref. [8] states that the dialog system proposes a uniform encoding strategy for the creation of a link between the physical things and the information and services that pertain to them, in order to present a simple distributed system that sends automatic notifications of a parcel’s movements to the “owner” of the parcel’s server. Many of its problems will be solved by communication and information collection and sharing, in which all the parties determine what information they wish to share and with whom. One of the system’s key advantages is that it employs existing coding standards and can thus be implemented quickly.

Based on [18], using an IoT tracking technology and multi-objective decision making, this research develops an ideal management and coordination strategy for a cross-border e-commerce supply chain’s performance. Improving cross-border e-commerce logistics operations, it presents an overall supply chain optimization inventory strategy through inventory adjustment. It proposes a novel coordination model for optimizing the effectiveness of the cross-border e-commerce supply chain. It considers how to improve the efficiency of the cross-border e-commerce supply chain.

The total effective stock method, forced to focus by the supply chain through inventory collaboration, has become an important concern of cross-border e-commerce logistics. To focus on promoting third-party logistics organizations to enhance in terms of their logistics service quality, the support model of cross-border e-commerce supply chain contracts has been adopted, and cross-border e-commerce companies are interested in sharing a certain proportion of their total income with third-party delivery companies.

The intention of consumers to use a self-service parcel delivery service was the topic of this study. We created a three-component model of the consumer’s intention to use self-service parcel delivery by combining the situational and individual elements, and we propose a socializing aspect and present this three-component model of the consumer’s desire to use self-service parcel delivery. According to this study, the consumers’ intentions using the self-service parcel delivery service are highly recommended by the ease of location, confidence, creativity, and the desire for human interaction. It also shows that the consumer’s purpose to employ self-service parcel delivery services is positively influenced by the socialized element [19].

According to [1], in order to help practitioners and researchers apply blockchain technology-based traceability systems, it suggests a proposed design framework and a suitability software overview flowchart based on blockchain food supply chain structures. It also highlights the benefits and challenges of implementing these blockchain-based systems.

First, in order to obtain a better understanding of the properties of blockchain technology, the current development, applications, and solutions for food traceability issues are explored to fulfill the investigation goal and answer the major questions of the research. Second, and most crucially, an architecture design framework is presented in this study, and the appropriateness and food traceability solutions based on blockchains are being evaluated for their long-term viability. Finally, two proposed project examples from the literature are provided to show how blockchains are being developed in two sorts of food chains.

Ref. [3]. declares that the blockchain has the capacity to solve problems such as addressing the issues of transparency and traceability. Companies can use blockchain-based, secure, changeable, and irreversible records of every activity or trade within a supply chain on a blockchain network that is accessible by all users. This makes it feasible to respond to a problem in a targeted way once one has been identified, as well as to provide dispersed confidence. Product defects caused by faulty equipment or security vulnerabilities can be handled and dealt with more easily with blockchain.

Ref. [4]. discusses the development of the parcel tracking system, which is the subject of this study. Its suggested framework is for an overview of an online system that explains how it can be accessed by consumers using their mobile device. To create a parcel tracking system that is both efficient and secure, for both package management and tracking, the system used a web-based service. This system was created using the Adobe Dreamweaver platform, using the PHP and JavaScript programming languages, with MySQL as the system’s database.

To increase consumer confidence in food safety during the food supply chain, an integrated traceability of ideas architecture is offered, based on a blockchain. Using agricultural commerce, a conceptual framework is developed to fully utilize the current traceability systems in these two locations, and the logistical, data, and blockchain flow features are examined. This drives improvement in global agricultural commerce, and addresses whether bilateral or multilateral systems are vital for food safety and long-term trade development, as well as enhancing cooperation in the middle of a worldwide food crisis. For cross-border food, a very credible TS is required, and trade is still missing in the context of the construction and deployment of traceability systems (TSs) in many nations and areas [11].

According to [13], customers are becoming frustrated as these delivery problems increase, and businesses and delivery services are experiencing restless nights trying to figure out how to better reach out to their customers. One feature of electronic commerce that should not be overlooked is that it serves as a checkpoint for the completion of a business process. Adopting the new metallic bin system will be an excellent method for alleviating a few of the many issues associated with undelivered items.

The blockchain’s potential benefits extend beyond economics to include societal, humanitarian, and scientific considerations, and the blockchain’s capabilities are currently being used by certain parties to solve real-world problems. We should consider the blockchain as a type of information technology like the Internet, with multiple classes of applications for any type of investment registry, stock, and trade, which includes all aspects of finance, economics, and money, and tangible and intangible assets such as ideas, reputation, intention, health data, and information [9].

According to [7], as a refinement step after Lucas–Kanade–Tomasi corner tracking, an edge mapping phase is introduced to achieve the successful tracking of the parcel. It describes the development and operation of the tracking system in this research, demonstrating that the proposed algorithms are new and capable of tracking parcels in real time. The vision system’s purpose is to compute all the parcels’ corner coordinates, and they are then delivered into a controller, which combines them into a single statement at the output.

Based on [5], to achieve their goals, customers will be able to get the most up-to-date information using the GPS feature on their Android phones to find the host location. The applications for the user and the host were built with Android Studio, and the database for this system is the Firebase Realtime Database. Most online stores collaborate to provide a better service to their customers, and partner with courier providers. As a result, a package tracking system with pre-notifications has been developed so that clients can get the most up-to-date host location.

The design of a new parcel box can impact transit effectiveness, comfort, and processing time. The possibility of long- and short-term cage shortages is another difficulty in the process. As a result, a new cage tracking system will also be presented. The data suggest that a larger cage size can improve the transit efficiency and physical ergonomics, however the time required to handle the cages may increase. With a more automated system and fewer labor assignments during production, the new tracking system might minimize the handling time [20].

2.2. Gap in Literature Review

Tracking system phenomena have been documented throughout the literature and the factors that steer the use of the system’s tracking are similar. Most of the factors that are emphasized in the literature review the benefit of the tracking system. The purpose of this blockchain is to enable the capture and distribution of digital data without the ability to modify them. In this sense, a blockchain serves as the foundation for immutable ledgers, or transaction records that cannot be changed, erased, or destroyed.

A blockchain is constantly updated database that stores information. One of the major advantages of the blockchain is that the database is not maintained in a single location, which makes it extremely safe. As a result, hacking into the chain and corrupting the records will involve a massive amount of processing power.

There is also current research that has been contributing to the firm support of the runner issue during COVID-19, however, none of the literature has been performed with regard to Brunei, and the furthering of current research must be done in the context of Brunei, with unswerving and improved methodologies that will help to further justify the factors and effects of a tracking system in Brunei.

To overcome the gaps that have been identified, a system that tracks both the runner and the parcel itself is proposed and created. Then, for the blockchain issues, a study on developing the proposed system where it is applicable for the blockchain itself is required.

This proposed blockchain-based tracking system will be using the Notepad++ to edit the text editor and source code editor with Microsoft Windows, using Hypertext Preprocessor and Hypertext Markup Language as a server-side scripting language. MySQL is used for database management, as it offers data security and high performance, and XAMPP is used as the local web server for testing and deployment.

Last but not least, to tackle the gaps, a survey questionnaire will be distributed to participants in order to understand their points of views, suggestions, and opinions regarding the proposed concept system if this can be implemented to organizations during the COVID-19 pandemic.

3. Methodologies

3.1. The Waterfall Methodology

The methodology that will be used for the proposed project is the waterfall model, which has 6 phases, that are project planning, system analysis, system design, system implementation, and system support. The waterfall model offers the following advantages: it is simple to comprehend and apply, it is straightforward to manage, control, and schedule, and the phases may be processed and completed. A flowchart is required so that we can understand the flow of the proposed system. An entity relationship diagram (ERD) and data flow diagram (DFD) are also required to understand the relationship with the entity.

• System planning—this is where I plan the schedule, performing some research and a preliminary investigation about the tracking system for cross-border runners during the COVID-19 pandemic that I wish to implement for runners in Brunei. I will research more about tracking system and plan for the improvement of the problems. During this system planning, a flowchart is required so that we can understand the flow of the proposed system. An entity relationship diagram (ERD) and data flow diagram (DFD) are also needed to understand the relationship with each of the elements.
• System analysis—I need to understand the runner’s and customer’s requirements and needs. During the requirement determination stage, these were analyzed, and from there, an analysis on the possible end user requirements was performed to develop the system.
• System design—this is to figure out the possible solutions based on the requirements and analysis decisions. In this phase, I will determine how the system will be built by doing some design and create a rough plan regarding the requirements. This is to design the prototype system.
• System implementation—the system will be constructed, tested, and checked by the users. This could probably be the most time-consuming phase of all. It also includes activities such as overall testing, where the system will be tested to ensure that the functional requirements are satisfied and work.
• System support—this is to improve the tracking system, doing some maintenance if there is any problem with the system, and keep the system running.

This research strategy will provide the direction, as well as help to choose the correct methodology for collecting and analyzing the data. A quantitative approach is also used for this research, and, in conducting this study, primary data are utilized in the form of a questionnaire. The quantitative data allows us to look at the pattern in numeric data, which can be analyzed using statistics.

The methods that can be used include the questionnaire and polls, and secondary sources, such as journal articles, reviews, and academic publications, provide second-hand information and opinions from other scholars. The secondary sources describe, interpret, analyze, or synthesize the primary sources.

The Figure 1 shows the research framework for this study on blockchain-based tracking systems as the dependent variables. The independent variables constitute the four main factors, which are as follows:

i.

Perceived usefulness.

ii.

Innovativeness in new technology.

iii.

Intention to use.

iv.

Security and safety.

Figure 1. Research framework.

Figure 1. Research framework.

3.2. Research Method

There is one research method that will be used in this research study. This method is the use of questionnaires. These are used to collect both quantitative and also qualitative data. The purpose of this is to collect standardized, and thus, comparable information from a large number of people. The responses will later be analyzed using a statistical technique to generalize the whole population.

This research will be obtained using an online questionnaire (a Google form), which is distributed through social media, such as WhatsApp and Email University Technology BruneI, to attract potential participants. The participants will be anonymous and no personal information will be obtained in the questionnaire, in order to maintain the confidentiality of the respondents. The researchers mentioned in the questionnaire that all the data will be directly received by the researchers and not distributed to other organizations.

3.3. Hypothesis Formulation

The study consists of the positive and negative relationship within the research hypothesis. These hypotheses will be based on the dependent and independent variables. In the last chapter, we discussed the study framework, which is made up of four independent variables: perceived usefulness, innovativeness in new technology, intention to use, and security and safety.

3.4. Definition of Variables

Variables are measurable aspects or characteristics of persons or objects that can take on various meanings. Characteristics that do not change, on the other hand, are constants. A hypothesis is a statement of an assumed relationship between two variables that may be investigated by using actual research. The values of both the variables increase or decrease together in a positive or negative relationship. That is, if one increases in value, the other does too, and if one decreases in value, the other does well.

H1: 

Perceived usefulness.

This measure shows a subjective opinion of a potential user possibility that the utilization of a particular software system will improve the work performance in a specific firm. The elements used in previous research were used to build the perceived usefulness scale, with appropriate modifications to make them microcomputer-specific [21]. The consumers’ perceived usefulness determines whether they utilize a new technology, based on how often they think that it will help them perform better. As a result of the employment of technology, it relates to the practical outcome, such as time savings, cost reductions, and increased efficiency [22].

H2: 

Innovativeness in New Technology.

The term “innovativeness” or “process innovation” is frequently used to describe the degree of uniqueness of a product or service. Highly inventive items are thought to have a high level of uniqueness, whereas low innovative solutions are on the other end of the scale. However, there is little consistency in the product literature as to who is looking at this degree of newness and defining what is new. Although most of the studies have taken a definite stance on newness, some have examined what is new to the world [23].

The technology acceptance model is a model which focuses on how new technologies are used. As a result, the technology acceptance model identifies two key attitudes that influence computer innovation acceptance: the perceived usefulness and perceived ease of use. The first is the user’s subjective probability that utilizing a given system will improve his or her performance in a specific task, whereas the perceived ease of use refers to the extent to which a user expects the target system to be effort-free [24].

H3: 

Intention to use.

The term “compatibility” refers to how well the crowdsourcing process fits in with customer views. The customers’ hectic lifestyles or sudden requests may profit from the advantages of crowd shipping. This service may also appeal to conscious shoppers [25]. The lower the rate of perceived unpredictability, the closer the product fulfills the needs of the buyer. This attribute raises the likelihood of the innovation being adopted [26].

Time consistency refers to the presence of a crossover between the time periods of the carriers and parcels. Based on the time consistency and capability accessibility of the parcel’s and path’s relevant drivers, an estimated time is a time range between the earliest possible time to arrive at a place from the originating site and the latest possible time to depart from this location, in order to be at the destination on time for a parcel or a driver [27].

Reliability is defined as a company’s decision to provide services in a timely and error-free manner. It is a measure of a firm’s performance stability and how a business may be reliable. The measures most related with this characteristic are accuracy and timeliness [28].

The relationship between time and service is explained by the reliability, which includes things like delivering on time to customers, offering on-time delivery, handling customer concerns, delivering damage-free goods, and doing things properly the first time [29].

Reliability is defined as the capacity to consistently and precisely deliver services as promised. Reliability, in its classic terms, relates to a company’s ability to meet promises, such as those regarding delivery, services, problem resolution, and pricing. Customers like to do business with companies who keep their promises, particularly when it comes to customer happiness and basic service attributes [30].

H4: 

Security and Safety.

The basic job of the carrier is to carry out transportation and preserve the safety of the delivery of products, which includes everything from the goods supplied to the goods delivered to the recipient. Consumers are usually concerned about the safety of delivery goods, since they purchase a delivery service, as stated in the previous sentence. The customer expects the goods delivered to the destination to be in good working order and free of damage [6].

3.5. Data Collection

This study’s aims are to design and implement an online blockchain-based and real-time parcel monitoring and tracking system for cross-border runners and customers, during and post-COVID-19 pandemic. A quantitative approach is used for this research and, in conducting this study, primary data are utilized in the form of a questionnaire. An interview for an open-ended question is a sort of qualitative survey in which the respondents are free to express themselves. Furthermore, they might elaborate on what they believe or feel about the questions. On the other hand, open-ended inquiries can take a long time to answer and require more data analysis.

This research will be performed using an online questionnaire through a Google form, which is distributed through social media such as WhatsApp and Email University Technology Brunei to attract potential participants. The participants will be anonymous and no personal information will be obtained in the questionnaire to maintain the confidentiality of the respondents. The researchers mentioned in the questionnaire that all the data will be directly received by the researchers and not distributed to other organizations.

User acceptance testing (UAT) is a technique for ensuring that the prototype is completely functional and that the user expectations are satisfied in line with this study [31]. The clients and/or end users are usually in charge of the UAT. A user acceptance test’s major goal is to figure out how a system will work and how it will serve the end user before it is put into production [32]. In total, 20 end users were given the chance to test and provide feedback on the prototype. While filling out the surveys, the respondents were asked to test the prototype system.

3.6. Data Analysis Method

For this study, an online survey will be conducted via a ‘Likert Scale ‘questionnaire, with scale from 1 to 5 (where 1 = strongly disagree and 5 = strongly agree). Before processing the data, it is necessary to conduct a reliability and validity test. The term “reliability” refers to the ability to assess the data’s internal consistency. Cronbach’s alpha score can be used to assess the reliability.

To obtain in-depth knowledge of the given topic, interviews will be used in this research. The interviewers’ observations will be based on the respective blockchain-based tracking systems. They will eventually complete the above-mentioned questionnaire, offering feedback on the four primary independent variables, which are perceived usefulness, innovativeness in new technology, intention to use, and security and safety. Statistical methods in the Statistical Package for Social Sciences will be used to analyze the data. The respondents’ responses are organized in total and will be organized into a table. The data presented, evaluated, and construed use weight mean, frequency counts, and percentage.

3.7. Measurement of Variables

Based on previous studies, the independent variables were identified to be the most important aspects in impacting the dependent variable, and in this case, are the factors that need to be considered in the user acceptance testing.

There are three sets of questions that were extracted from [19,32,33,34,35,36]. All the responses in the questionnaires were indicated with the extent of their agreement with each item on a 5-point Likert scale (1 = strongly disagree, 5 = strongly agree). Each of these dimensions were adopted from the various pieces of literature that were relevant to this study.

3.8. Data Management

To obtain information for the analysis in this study, I have distributed a questionnaire and conducted an interview, using questionnaires for a total of 130 respondents. A total of 40 respondents were used for the interview in my user acceptance testing for the Abell blockchain-based tracking system.

The interviewees will then be introduced to the Abell blockchain-based tracking system before answering the questionnaire, and will use the platform to focus on the variables, which include the content, multimedia, and system features.

The first survey, which is the main survey about the Abell blockchain-based tracking system, was calculated using a frequency analysis, which uses the SPSS Software. Then, for the user acceptance testing on the Abell blockchain-based tracking system, the average was determined, which is the total score divided by the forty respondents. The average was used to calculate the outcome, which was the total score divided by the forty respondents.

4. System Development

To understand the existing tracking system challenges, it is necessary to first comprehend the flow of the present runner and parcel process, in order to comprehend which work duties are connected to the system, how they relate to one another, and the human interactions that occur during the process. Therefore, in this case, I have achieved and developed all the requirements into following parts.

The following parts are:

• The flowchart of the blockchain-based tracking system.
• The entity relationship diagram (ERD)
• The prototype of the system design

The main goals of the new tracking system are related to the reasons for the runner shortages in the current procedure. One goal is to obtain better control over where the runner is, in order to minimize the runner shortage and reduce the parcel delivery lateness for clients. Knowing where the parcel and runner are located might be possible with a system that could provide more exact information [37,38].

4.1. System Planning & Analysis

Flowchart of Tracking System

In this phase, the entire system infrastructure is discussed. This can be to visualize the outline of the system and obtain its system workflow, in order to determine how the data will be managed once the database has integrated with the system. Below is a diagram of the workflow process of when the system has been implemented (Figure 2).

Before the system development process is implemented and designed, it needs to create the attributes of the database so that it can be implemented into the MySQL database. The database will be the first to be developed, and once the data entry processes begin and there is sufficient information to be used as testing materials, the user interfaces of the system will be developed, and thus, will experience a few testings to guarantee that the system features can work properly (Figure 3).

Before the system is developed, the understanding of its data relations to one another is crucial to avoid any data redundancies and data traffic that might cause some errors during its runtime. Thus, creating an ERD can help to visualize the overview of the data flow in the system.

Entity relationship diagrams are a graphical place to start for database structures and a tool for analyzing the different system requirements throughout an organization. After a relational database has been implemented, an ERD can still be utilized as a reference point if any troubleshooting or process improvement re-engineering is required. While an ERD is useful for organizing data with a relational structure, it is ineffectual for semi-structured or unstructured data. It is also unlikely to be useful in and of itself when it comes to integrating data into an existing information system [39,40,41].

4.2. Prototypes of System Design

This phase will decide the interface of the system for the end users’ viewing, as it was decided to develop it as a web-based platform to draft the system interface infrastructure before proceeding to the structure. Hence, we installed and used XAMPP as our localhost and Notepad++ as our code editor for this project. We also attempted to browse through the site and utilized a few of its features to analyze the websites for their general functionality, user-friendliness, and ease of web-browsing through all their web pages.

This chapter will discuss the layout feature of the Abell blockchain-based tracking system platform. This section will also show how the system will look if it is viewed using a monitor/laptop, iPad, and phone (Figure 4).

In this section, this is the final design of the tracking system based on the blockchain. Therefore, the layout of the systems is based on the user interfaces that are needed within the systems, such as:

• Home page
• Contact us page
• Feedback page
• Frequently asked questions page
• User page
• Runner page
• Director page
• Admin page

Figure 5 shows the home page design that consists of the navigation bar, which has the user login, user registration, the runner, director, and admin sign in buttons, and the FAQ navigation button.

5. Data Analysis

In this chapter, we will be discussing the results of the distribution questionnaire. I have two different types of questionnaires: the first survey is about the study Blockchain-Based Tracking Systems: Cross-Border Runners During and Post-COVID-19 Pandemic. The second survey is on the user acceptance testing for the study Blockchain-Based Tracking Systems: Cross-Border Runners During and Post-COVID-19 Pandemic. This part presents, analyzes, and interprets all the information acquired for this study. Tables will be used for its presentation. The data presentation is followed by a data analysis and interpretation. All the results were derived from the SPSS Software.

5.1. Frequency Analysis from First Survey

Descriptive statistics are used to quantitatively describe the properties of a set of data. These descriptive statistics include a frequency analysis. The frequency of an occurrence is defined in statistics as the number of times it occurs. A frequency analysis is a descriptive statistical method for displaying the number of times that each statement selected by the respondents has occurred.

Table 1. Frequency analysis for gender distribution.

	Frequency	Valid Percent	Cumulative Percent
Gender
Male	25	27.8	27.8
Female	65	72.2	100.0
Total	90	100.0

shows the gender data of the sample respondents for the research study. The total number of participants for this study was 90 people. Out of the 90 respondents, 25 (27.8%) of the respondents were male, and the remaining 65 (72.2%) respondents were female, which dominated the survey.

The respondents’ age data are shown in

Table 2. Frequency analysis for age distribution.

	Frequency	Valid Percent	Cumulative Percent
Age
18–30	60	66.7	66.7
31–45	27	30.0	96.7
46–54	0	0	96.7
55 Above	3	3.3	100.0
Total	90	100.0

. Out of the 90 respondents, 60 (66.7%) were between the ages of 18 and 30. Then, there were 27 (30.0%) people between the ages of 31 and 45. The age group over 55 ranked third, with three participants accounting for 3.3%. The lowest age range was 46–54, with 0% of the population in that age group.

The frequency analysis for education background is shown in

Table 3. Frequency analysis for education background distribution.

	Frequency	Valid Percent	Cumulative Percent
Education Background
PhD	2	2.2	2.2
Master’s Degree	8	8.9	11.1
Degree	38	42.2	53.3
Higher National Diploma and equivalent	22	24.4	77.8
Other	20	22.2	100.0
Total	90	100.0

. The largest percentage of the respondents were those with a degree, which was 38 (42.2%) out of 90. Then, came 22 (24.4%) of those with a Higher National Diploma or its equivalent. The next 20 (22.2%) had a variety of backgrounds, including O Levels, A Levels, PMBs, National Diplomas, Hntecs, Diplomas, Lcci, and Form 1. Those with Master’s Degrees came in fourth place, with a total of 8 participants and an 8.9% participation rate. The lowest level of education was 2 (2.2%) for those with a PhD.

Table 4. Frequency analysis for occupation distribution.

	Frequency	Valid Percent	Cumulative Percent
Occupation
Businessman/Businesswomen	2	2.2	2.2
Government Employee	20	22.2	24.4
Private Sector Employee	35	38.9	63.3
Student	23	25.6	88.9
Unemployed	9	10.0	98.9
Other	1	1.1	100.0
Total	90	100.0

shows the frequency analysis for occupation. The highest percentage of the participants (38.9%) was 35 (out of 90) who worked in the private sector. The occupation of students was followed by 23 (25.6%). Next, there was 20 (22.2%) respondents with the occupation of government employee. Then, this was followed by 9 (10.0%) with the occupation of being unemployed. Businessman/businesswomen fell at second last, with a total of 2 participants (2.2%). The lowest occupation was 1 (1.1%), which was banker from another occupation.

Table 5. Frequency analysis for education district distribution.

	Frequency	Valid Percent	Cumulative Percent
District
Brunei-Muara District	64	71.1	71.1
Tutong District	3	3.3	74.4
Temburong District	1	1.1	75.6
Belait District	22	24.4	100.0
Total	90	100.0

illustrates the respondents’ district data. Out of the 90 participants, the highest was 64 (71.1%) from the Brunei-Muara District. It was then followed by 22 (24.4%) from the Belait District. The Tutong District fell at third, with a total of 3 participants (3.3%). The lowest was 1, which was 1.1% from the Temburong District.

Table 6. Frequency analysis for the question: during the COVID-19 pandemic: would you recommend using runner delivery service?

	Frequency	Valid Percent	Cumulative Percent
Yes	89	98.9	98.9
No	1	1.1	100.0
Total	90	100.0

shows the frequency analysis for the question: during the COVID-19 pandemic, would you recommend using runner delivery service data? The sample respondents were asked this question for the research study. Out of the 90 respondents, 89 (98.9%) of the respondents answered yes, and the remaining 1 (1.1%) respondent answered no.

Table 7. Frequency analysis for the question: have you successfully completed a delivery request using the pickup service or parcel tracking system before?

	Frequency	Valid Percent	Cumulative Percent
Yes	69	76.7	76.7
No	21	23.3	100.0
Total	90	100.0

shows the frequency analysis for the question: have you successfully completed a delivery request using the pickup service or parcel tracking system before? The sample respondents were asked this question for the research study. Out of the 90 respondents, 69 (76.7%) of the respondents answered yes, and the remaining 21 (23.3%) respondents answered no.

Table 8. Frequency analysis for the statement: I will choose a blockchain-based tracking system in the future even though Brunei is already free from the COVID-19 pandemic.

	Frequency	Valid Percent	Cumulative Percent
Yes	85	94.4	94.4
No	5	5.6	100.0
Total	90	100.0

shows the frequency analysis for the statement: I will choose a blockchain-based tracking system in the future, even though Brunei is already free from the COVID-19 pandemic. The data of the sample respondents were collected for the research study. Out of the 90 respondents, 85 (94.4%) of the respondents answered yes, and the remaining 5 (5.6%) respondents answered no.

Table 9. Frequency analysis for the statement: I will recommend choosing a blockchain-based tracking system to my friends or others.

	Frequency	Valid Percent	Cumulative Percent
Yes	86	95.6	95.6
No	4	4.4	100.0
Total	90	100.0

shows the frequency analysis for the statement: I will recommend choosing a blockchain-based tracking system to my friends or others. The data of the sample respondents were collected for the research study. Out of the 90 respondents, 86 (95.6%) of the respondents answered yes and the remaining 4 (4.4%) respondents answered no.

Table 10. Frequency analysis for the statement: I will say positive things about blockchain-based tracking systems to others.

	Frequency	Valid Percent	Cumulative Percent
Yes	84	93.3	93.9
No	6	6.7	100.0
Total	90	100.0

shows the frequency analysis for the statement: I will say positive things about blockchain-based tracking systems to others. The data of the sample respondents were collected for the research study. Out of the 90 respondents, 84 (93.3%) of the respondents answered yes and the remaining 6 (6.7%) respondents answered no.

Table 11. Frequency analysis for the statement: you need your item/parcel in a hurry.

	Frequency	Valid Percent	Cumulative Percent
Yes	64	71.1	71.1
No	14	15.6	86.7
Other	12	13.3	100.0
Total	90	100.0

shows the frequency analysis for the statement: you need your item/parcel in a hurry. The data of the sample respondents were collected for the research study. Out of the 90 respondents, 64 (71.1%) of the respondents answered yes. Then, this was followed by 14 (15.6%) of the respondents answering no, and the remaining 12 (13.3%) respondents answering other, which was that, depending on the item, their answer might be yes and sometimes no.

Table 12. Frequency analysis for the statement: if I discovered a new information technology in Brunei, I would try to figure out how to try it out.

	Frequency	Valid Percent	Cumulative Percent
Yes	69	76.7	76.7
No	10	11.1	87.8
Other	11	12.2	100.0
Total	90	100.0

is shows the frequency analysis for the statement: if I discovered a new information technology in Brunei, I would try to figure out how to try it out. The data of the sample respondents were collected for the research study. Out of the 90 respondents, 69 (76.7%) of the respondents answered yes. Then, this was followed by 11 (12.2%) of the respondent answering other, with responses such as “it depends, because I am not an expert in IT”, “If I am really interested with the technology, maybe yes”, “Maybe, it depends on my interest”, and “it depends, I am the type that follows majority, and trying a new IT system is risky when money is involved, so I would need a bit of persuasion to reassure the public audience that it is safe and secure to use the app, along with it being user-friendly”. The remaining 10 (11.1%) respondents answered no.

Table 13. Frequency analysis for the statement: I am normally the first among my colleagues to explore new information technologies, such as blockchain-based tracking systems.

	Frequency	Valid Percent	Cumulative Percent
Yes	30	33.3	33.3
No	60	66.7	100.0
Total	90	100.0

shows the frequency analysis for the statement: I am normally the first among my colleagues to explore new information technologies, such as blockchain-based tracking systems. The data of the sample respondents were collected for the research study. Out of the 90 respondents, 60 (66.7%) of the respondents answered no, and the remaining 30 (33.3%) respondents answered yes.

Table 14. Frequency analysis for the question: during this pandemic, do you think that this blockchain-based tracking system is the best option for everyone?

	Frequency	Valid Percent	Cumulative Percent
Yes	70	77.8	77.8
No	8	8.9	86.7
Other	12	13.3	100.0
Total	90	100.0

shows the frequency analysis for the question: during the pandemic, do you think that this blockchain-based tracking system is the best option for everyone? The data of the sample respondents were collected for the research study. Out of the 90 respondents, 70 (77.8%) of the respondents answered yes. Then, this was followed by 12 (13.3%) of the respondents answering other, with responses such as “some people may prefer traditional ways, everything being digitized is not easier for everyone”, “I do not know what a blockchain is”, “only for those who can afford it”, “cloud-based or blockchain-based, either is fine, as long as we are able to track”, and “yes, if they have internet access, otherwise, they will have to depend on alternative options that are available to them”. The remaining 8 (8.9%) respondents answered no.

Table 15. Frequency analysis for the question: would you still use runner tracking system if our country was free from the COVID-19 virus?

	Frequency	Valid Percent	Cumulative Percent
Yes	82	91.1	91.1
No	4	4.4	95.6
Other	4	4.4	100.0
Total	90	100.0

shows the frequency analysis for the question: would you still use runner tracking system if our country was free from the COVID-19 virus? The data of the sample respondents were collected for the research study. Out of the 90 respondents, 82 (91.1%) of the respondents answered yes. Then, this was followed by 4 (4.4%) of the respondents answering no, and the same number of 4 (4.4%) of the respondents answering other, with responses such as “I might do, it depends on the item”, “it depends on my availability”, “Sometimes”, and “Yes, if it does not cost more than the usual runner”.

Table 16. Frequency analysis for the question: when your item was delivered in front of your door, did you need to make a contact with the runner?

	Frequency	Valid Percent	Cumulative Percent
Yes	28	31.1	31.1
No	62	68.9	100.0
Total	90	100.0

shows the frequency analysis for the question: when your item was delivered in front of your door, did you need to make a contact with the runner? The data of the sample respondents were collected for the research study. Out of the 90 respondents, 62 (68.9%) of the respondents answered no and the remaining 28 (31.1%) respondents answered yes.

Table 17. Frequency analysis for the question: does your company assign authentication IDs or access cards for employees to track any unauthorized access?

	Frequency	Valid Percent	Cumulative Percent
Yes	46	51.1	51.1
No	44	48.9	100.0
Total	90	100.0

shows the frequency analysis for the question: does your company assign authentication IDs or access cards for employees to track any unauthorized access? The data of the sample respondents were collected for the research study. Out of the 90 respondents, 46 (51.1%) of the respondents answered yes and the remaining 44 (48.9%) respondents answered no.

As stated in the questionnaires, it was noted if the respondent answered “no” to the previous question. For this question, we asked the respondent to tick more than one answer if there were no authentication IDs or access cards but alternative methods, such as role-based access into systems, surveillance cameras, biometrics and others, were used for detecting unauthorized access.

Table 18. Frequency analysis for the question: if there are no authentication IDs or access cards, how is unauthorized access detected?

	N	Percent	Percent of Cases
E18_Role-based access into systems Yes = 1, No = 2	21	29.6%	47.7%
E18_Surveillance cameras Yes = 1, No = 2	29	40.8%	65.9%
E18_Biometrics Yes = 1, No = 2	16	22.5%	36.4%
E18_Other Yes = 1, No = 2	5	7.0%	11.4%
Total	71	100.0%	161.4%
Valid	44	48.9%
Missing	46	51.1%
Total	90	100.0%

is shows the frequency analysis for the question: if there are no authentication IDs or access cards, how is unauthorized access detected? The data of the sample respondents were collected for the research study. Out of the 44 respondents, the highest frequency was the 29 (40.8%) of the respondents that answered yes for surveillance camera, which was followed by 21 (29.6%) answering yes for role-based access into systems. Biometrics fell at third, with a total of 16 at 22.5%. The lowest was 5, with 7.0%, for other methods such as evidence of authenticity, i.e., a receipt or photo evidence of the delivery item.

Table 19. Frequency analysis for the question: does your company use any remote access network servers?

	Frequency	Valid Percent	Cumulative Percent
Yes	52	57.8	57.8
No	38	42.2	100.0
Total	90	100.0

shows the frequency analysis for the question: does your company use any remote access network servers? The data of the sample respondents were collected for the research study. Out of the 90 respondents, 52 (57.8%) of the respondents answered yes and the remaining 38 (42.2%) respondents answered no.

As stated in the questionnaires, it was noted if the respondent answered “yes” to the previous question. For this question, we asked the respondent to tick more than one answer if they answered yes to the previous question, with options such as virtual private network, dial-up, Wi-Fi, and other, for the remote servers that their organization used.

Table 20. Frequency analysis for the question: what type of remote servers does your organization implement?

	N	Percent	Percent of Cases
E20_Virtual Private Network Yes = 1, No = 2	25	34.2%	48.1%
E20_Dial-Up Yes = 1, No = 2	7	9.6%	13.5%
E20_Wi-Fi Yes = 1, No = 2	40	54.8%	76.9%
E20_Other Yes = 1, No = 2	1	1.4%	1.9%
Total	73	100.0%	140.4%
Valid	52	57.8%
Missing	38	42.2%
Total	90	100.0%

shows the frequency analysis for the question: what type of remote servers does your organization implement? The data of the sample respondents were collected for the research study. Out of the 52 respondents, the highest frequency was the 40 (54.8%) of the respondents that answered yes for Wi-Fi, then followed by the 25 (34.2%) that answered yes for virtual private network. Dial-up fell at third, with a total of 7 at 9.6%. The lowest was 1 at 1.4% for other options, such as using wide-area network (WAN).

Table 21. Frequency analysis for the question: which global security standards does your organization adhere to?

	N	Percent	Percent of Cases
E21_ISO Yes = 1, No = 2	68	68.0%	75.6%
E21_COBIT Yes = 1, No = 2	10	10.0%	11.1%
E21_Sarbanes–Oxley Yes = 1, No = 2	4	4.0%	4.4%
E21_Other Yes = 1, No = 2	18	18.0%	20.0%
Total	100	100.0%	111.1%
Valid	90	100.0%
Missing	0	0.0%
Total	90	100.0%

shows the frequency analysis for the question: which global security standards does your organization adhere to? The data of the sample respondents were collected for the research study. Out of the 90 respondents, the highest frequency was the 68 (68.0%) of the respondents that answered yes for ISO (International Organization for Standardization), then followed by the 18 (18.0%) that answered other, with answers such as Microsoft Power Bi, and some of the respondents were unsure. COBIT (Control Objectives for Information and Related Technologies) fell at third, with a total of 10 at 10.0%. The lowest was 4 at 4.0% with Sarbanes–Oxley.

Table 22. Frequency analysis for the question: what database technologies does your company apply?

	N	Percent	Percent of Cases
E22_Oracle Yes = 1, No = 2	34	24.8%	37.8%
E22_PeopleSoft Yes = 1, No = 2	22	16.1%	24.4%
E22_Microsoft SQL Yes = 1, No = 2	80	58.4%	88.9%
E22_Other Yes = 1, No = 2	1	0.7%	1.1%
Total	137	100.0%	152.2%
Valid	90	100.0%
Missing	0	0.0%
Total	90	100.0%

shows the frequency analysis for the question: what database technologies does your company apply? The data of the sample respondents were collected for the research study. Out of the 90 respondents, the highest frequency was the 80 (58.4%) of the respondents answering yes for Microsoft SQL, then followed by the 34 (24.8%) answering yes for Oracle. PeopleSoft was ranked third, with a total score of 22 and a 16.1% customer base. The lowest score was 1, with 0.7% response from other sources, such as Microsoft Power Bi.

Table 23. Frequency analysis for the question: please state a brief reason for the choice(s) of database technology.

	N	Percent	Percent of Cases
E23_User-friendly configuration and architecture Yes = 1, No = 2	75	44.4%	83.3%
E23_Cost Saving Yes = 1, No = 2	57	33.7%	63.3%
E23_Global Recognition and awards Yes = 1, No = 2	30	17.8%	33.3%
E23_Other Yes = 1, No = 2	7	4.1%	7.8%
Total	169	100.0%	187.8%
Valid	90	100.0%
Missing	0	0.0%
Total	90	100.0%

is shows the frequency analysis for the question: please state a brief reason for the choice(s) of database technology. The data of the sample respondents were collected for the research study. Out of the 90 respondents, the highest frequency was the 75 (44.4%) of the respondents answering yes for user-friendly configuration and architecture, then followed by the 57 (33.7%) for answering yes for cost saving. With a total of 30 prizes, global recognition and awards came in third place, accounting for 17.8% of the total. The lowest score was 7, with 4.1% of reasons coming from other sources such as security.

Table 24. Frequency analysis for the question: what are the kinds of security issues you have faced in recent years?

	N	Percent	Percent of Cases
E24_System failure or data corruption Yes = 1, No = 2	76	28.5%	84.4%
E24_Infection by viruses and malicious software Yes = 1, No = 2	57	21.3%	63.3%
E24_Theft or fraud of computers Yes = 1, No = 2	42	15.7%	46.7%
E24_Theft or stolen information Yes = 1, No = 2	46	17.2%	51.1%
E24_Attacks by an unauthorized outsider Yes = 1, No = 2	43	16.1%	47.8%
E24_Other Yes = 1, No = 2	3	1.1%	3.3%
Total	267	100.0%	296.7%
Valid	90	100.0%
Missing	0	0.0%
Total	90	100.0%

shows the frequency analysis for the question: what are the kinds of security issues you have faced in recent years? The data of the sample respondents were collected for the research study. Out of the 90 respondents, the highest frequency was the 76 (28.5%) of the respondents that answered yes for system failure or data corruption, then followed by the 57 (21.3%) that answered yes for infection by viruses and malicious software. Theft or stolen information fell at third, with a total of 46 at 17.2%. Next, 43 (16.1%) answered yes for attacks by an unauthorized outsider. Then, this was followed by 42 at 15.7% answering yes for the theft or fraud of computers. The lowest was 3 at 1.1% for other, with answers such as exposed data, less security protections, or data encryption.

Table 25. Frequency analysis for the question: what are the types of staff-related incidents that have occurred in recent years?

	N	Percent	Percent of Cases
E25_Misuse of web access Yes = 1, No = 2	50	23.4%	55.6%
E25_Misuse of email access Yes = 1, No = 2	29	13.6%	32.2%
E25_Unauthorized access to systems or data Yes = 1, No = 2	48	22.4%	53.3%
E25_Misuse of confidential information Yes = 1, No = 2	38	17.8%	42.2%
E25_Loss or leakage of confidential information Yes = 1, No = 2	41	19.2%	45.6%
E25_Other Yes = 1, No = 2	8	3.7%	8.9%
Total	214	100.0%	237.8%
Valid	90	100.0%
Missing	0	0.0%
Total	90	100.0%

shows the frequency analysis for the question: what are the types of staff-related incidents that have occurred in recent years? The data of the sample respondents were collected for the research study. Out of the 90 respondents, the highest frequency was the 50 (23.4%) of the respondents that answered yes for the misuse of web access, then followed by the 48 (22.4%) that answered yes for unauthorized access to systems or data. The loss or leakage of confidential information fell at third, with a total of 41 at 19.2%. Next, 38 (17.8%) answered yes for the misuse of confidential information. Then, this was followed by 29 at 13.6% for the misuse of email access. The lowest was 8 at 3.7% for other, with answers such as not reported to or transparent with staff, not honest, spam, and phishing scams.

Table 26. Frequency analysis for the question: how often does your organization update its anti-virus software?

	Frequency	Valid Percent	Cumulative Percent
Once a year	46	51.1	51.1
Once every six months	27	30.0	86.7
Once a month	17	18.9	100.0
Total	90	100.0

shows the frequency analysis for the question: how often does your organization update its anti-virus software? The data of the sample respondents were collected for the research study. Out of the 90 respondents, 46 (51.1%) of the respondents answered once a year. Then, this was followed by 27 (30.0%) of the respondents answering once every six months, and the remaining 17 (18.9%) respondents answered once a month.

5.2. Beta Testing

Beta testing is a type of user acceptability testing in which a product development team distributes a nearly finished product to a set of target users, in order to assess its performance in the real world. In comparison to alpha testing, beta testing ensures that the prototype is not only of high quality, but also that it is ready for real-world use. To try the user acceptance testing, a beta test was conducted with an adequate sample size of 40 respondents.

5.3. Pre-Test (Pilot Test)

A pilot test was conducted with an acceptable sample size of 40 respondents for this research project, and the data were analyzed using a Cronbach’s alpha analysis from the SPSS software to measure and analyze the reliability scale of the items under the different types of variables that were used in the user acceptance testing research questionnaire.

The table below shows the outcome of the measure of the user acceptance testing, and the Cronbach’s alpha reliability statistics on the Abell blockchain-based tracking system’s platform, content, multimedia, and features.

The data in

Table 27. Cronbach’s alpha reliability statistics.

Reliability Statistics
Variables	Cronbach’s Alpha	N
Abell blockchain-based tracking system	0.820	4
Content of the system	0.918	4
Multimedia of the system	0.913	4
ABBTS Features	0.971	5

demonstrate that user acceptance testing on the Abell blockchain-based tracking system has reliability figure of 0.820. The content has a figure of 0.918, the multimedia has a figure of 0.913, and the Abell blockchain-based tracking system features have a figure of 0.971, which are all the variables of reliability. Because all of the Cronbach alpha figures are more than 0.7, it is safe to undertake additional testing.

5.4. Frequency Analysis from Second Survey (User Acceptance Testing)

The number of occurrences of each response chosen by the respondents is displayed using frequency analysis, which is a descriptive statistical method. In this frequency analysis, the mean, mode, median, maximum, minimum, skewness, kurtosis, and standard deviation will be calculated to analyze the results from the user acceptance testing (

Table 28. Statistics of Section A: Abell blockchain-based tracking system.

	A1 Strongly Disagree = 1, Disagree = 2, Neutral = 3, Agree = 4, Strongly Agree = 5	A2 Strongly Disagree = 1, Disagree = 2, Neutral = 3, Agree = 4, Strongly Agree = 5	A3 Strongly Disagree = 1, Disagree = 2, Neutral = 3, Agree = 4, Strongly Agree = 5	A4 Strongly Disagree = 1, Disagree = 2, Neutral = 3, Agree = 4, Strongly Agree = 5
Valid	40	40	40	40
Missing	0	0	0	0
Mean	4.4000	4.3750	3.9250	4.3750
Median	4.5000	4.0000	4.0000	4.5000
Mode	5.00	5.00	4.00	5.00
Minimum	3.00	3.00	3.00	3.00
Maximum	5.00	5.00	5.00	5.00
Sum	176.00	175.00	157.00	175.00

,

Table 29. Frequency analysis of system with the necessary security features (has encrypted password, can change password).

	Frequency	Percent	Valid Percent	Cumulative Percent
3.00	4	10.0	10.0	10.0
4.00	16	40.0	40.0	50.0
5.00	20	50.0	50.0	100.0
Total	40	100.0	100.0

,

Table 30. Frequency analysis of system with a user-friendly interface.

	Frequency	Percent	Valid Percent	Cumulative Percent
3.00	4	10.0	10.0	10.0
4.00	17	42.5	42.5	52.5
5.00	19	47.5	47.5	100.0
Total	40	100.0	100.0

,

Table 31. Frequency analysis of system working according to the specifications.

	Frequency	Percent	Valid Percent	Cumulative Percent
3.00	10	25.0	25.0	25.0
4.00	23	57.5	57.5	82.5
5.00	7	17.5	17.5	100.0
Total	40	100.0	100.0

and

Table 32. Frequency analysis of system suitable to be used in the organization.

	Frequency	Percent	Valid Percent	Cumulative Percent
3.00	5	12.5	12.5	12.5
4.00	15	37.5	37.5	50.0
5.00	20	50.0	50.0	100.0
Total	40	100.0	100.0

).

The tables (Table 32,

Table 33. Descriptives—Abell blockchain-based tracking System.

			Statistic	Std. Error
AVG_ABBTS	Mean		4.2688	0.08604
	Lower Bound	Lower Bound	4.0947
	Upper Bound	Upper Bound	4.4428
	5% Trimmed Mean		4.2986
	Median		4.3750
	Variance		0.296
	Std. Deviation		0.54416
	Minimum		3.00
	Maximum		5.00
	Range		2.00
	Interquartile Range		0.69
	Skewness		−0.682	0.374
	Kurtosis		0.087	0.733

and

Table 34. Tests of normality of Section A: Abell blockchain-based tracking system.

Tests of Normality
	Kolmogorov–Smirnova			Shapiro–Wilk
	Statistic	df	Sig.	Statistic	df	Sig.
AVG_ABBTS	0.165	40	0.008	0.917	40	0.006

Lilliefors significance correction.

) show the frequency analyses of the results of the questionnaire from the user acceptance testing in Section A: Abell blockchain-based tracking system, in which the respondents mostly answered “agree” to this statement. The average mean of the descriptive analyses of the Abell blockchain-based tracking system in this Section A is 4.2688.

Table 35. Statistics of Section B: content of the system.

	A1 Strongly Disagree = 1, Disagree = 2, Neutral = 3, Agree = 4, Strongly Agree = 5	A2 Strongly Disagree = 1, Disagree = 2, Neutral = 3, Agree = 4, Strongly Agree = 5	A3 Strongly Disagree = 1, Disagree = 2, Neutral = 3, Agree = 4, Strongly Agree = 5	A4 Strongly Disagree = 1, Disagree = 2, Neutral = 3, Agree = 4, Strongly Agree = 5
Valid	40	40	40	40
Missing	0	0	0	0
Mean	4.1500	4.2500	4.0500	4.2500
Median	4.0000	4.0000	4.0000	4.0000
Mode	4.00	4.00	4.00	4.00
Minimum	3.00	3.00	3.00	3.00
Maximum	5.00	5.00	5.00	5.00
Sum	166.00	170.00	162.00	170.00

,

Table 36. Frequency analysis of the content of the system is clear.

	Frequency	Percent	Valid Percent	Cumulative Percent
3.00	3.00	4	10.0	10.0
4.00	4.00	26	65.0	65.0
5.00	5.00	10	25.0	25.0
Total	40	100.0	100.0

,

Table 37. Frequency analysis of the content is easy to understand and easy to use.

	Frequency	Percent	Valid Percent	Cumulative Percent
3.00	5	12.5	12.5	12.5
4.00	20	50.0	50.0	62.5
5.00	15	37.5	37.5	100.0
Total	40	100.0	100.0

,

Table 38. Frequency analysis of the content is related to blockchain-based tracking system topic.

	Frequency	Percent	Valid Percent	Cumulative Percent
3.00	8	20.0	20.0	20.0
4.00	22	55.0	55.0	75.0
5.00	10	25.0	25.0	100.0
Total	40	100.0	100.0

and

Table 39. Frequency analysis of the content of the system is interesting.

	Frequency	Percent	Valid Percent	Cumulative Percent
3.00	6	15.0	15.0	15.0
4.00	18	45.0	45.0	60.0
5.00	16	40.0	40.0	100.0
Total	40	100.0	100.0

illustrates the results of the normality test for the sample size of 40 respondents. According to the results, and the sig. value for the Kolmogorov–Smirnov (K–S) test and Shapiro–Wilk (S–W) test, all the significant results are less than 0.05; hence, the data are not normally distributed.

The tables (

Table 40. Descriptives—content of the system.

			Statistic	Std. Error
AVG_CONTENTS	Mean		4.1750	0.09354
	Lower Bound	Lower Bound	3.9858
	Upper Bound	Upper Bound	4.3642
	5% Trimmed Mean		4.1944
	Median		4.0000
	Variance		0.350
	Std. Deviation		0.59161
	Minimum		3.00
	Maximum		5.00
	Range		2.00
	Interquartile Range		0.75
	Skewness		−0.184	0.374
	Kurtosis		−0.549	0.733

and

Table 41. Tests of normality of Section B: content of the system.

Tests of Normality
	Kolmogorov–Smirnova			Shapiro–Wilk
	Statistic	df	Sig.	Statistic	df	Sig.
AVG_CONTENTS	0.191	40	0.001	0.910	40	0.004

Lilliefors test for significance correction.

) show the frequency analyses of the results of the questionnaire from the user acceptance testing in Section B: content of the system, in which respondents mostly answered “agree” to this statement. The average mean of the descriptive analyses of Section B: content of the system in this section is 4.1750.

Table 42. Statistics of Section C: the multimedia element of the system.

	A1 Strongly Disagree = 1, Disagree = 2, Neutral = 3, Agree = 4, Strongly Agree = 5	A2 Strongly Disagree = 1, Disagree = 2, Neutral = 3, Agree = 4, Strongly Agree = 5	A3 Strongly Disagree = 1, Disagree = 2, Neutral = 3, Agree = 4, Strongly Agree = 5	A4 Strongly Disagree = 1, Disagree = 2, Neutral = 3, Agree = 4, Strongly Agree = 5
Valid	40	40	40	40
Missing	0	0	0	0
Mean	3.7750	3.5250	3.3750	3.9000
Median	4.0000	3.0000	3.0000	4.0000
Mode	4.00	3.00	3.00	4.00
Minimum	2.00	2.00	2.00	3.00
Maximum	5.00	5.00	5.00	5.00
Sum	151.00	141.00	135.00	156.00

illustrates the results of the normality test for the sample size of 40 respondents. According to the results, and the sig. value for the Kolmogorov–Smirnov (K–S) test and Shapiro–Wilk (S–W) test, all the significant results are less than 0.05; hence, the data are not normally distributed.

The tables (Table 42,

Table 43. Frequency analysis of the system has appropriate graphics.

	Frequency	Percent	Valid Percent	Cumulative Percent
2.00	1	2.5	2.5	2.5
3.00	15	37.5	37.5	40.0
4.00	16	40.0	40.0	80.0
5.00	8	20.0	20.0	100.0
Total	40	100.0	100.0

,

Table 44. Frequency analysis of the system has appropriate font type.

	Frequency	Percent	Valid Percent	Cumulative Percent
2.00	3	7.5	7.5	7.5
3.00	18	45.0	45.0	52.5
4.00	14	35.0	35.0	87.5
5.00	5	12.5	12.5	100.0
Total	40	100.0	100.0

,

Table 45. Frequency analysis of the system has appropriate font size.

	Frequency	Percent	Valid Percent	Cumulative Percent
2.00	7	17.5	17.5	17.5
3.00	16	40.0	40.0	57.5
4.00	12	30.0	30.0	87.5
5.00	5	12.5	12.5	100.0
Total	40	100.0	100.0

,

Table 46. Frequency analysis of the system has appropriate and suitable color.

	Frequency	Percent	Valid Percent	Cumulative Percent
3.00	13	32.5	32.5	32.5
4.00	18	45.0	45.0	77.5
5.00	9	22.5	22.5	100.0
Total	40	100.0	100.0

,

Table 47. Descriptives—the multimedia element of the system.

			Statistic	Std. Error
AVG_MULTIMEDIA	Mean		3.6438	0.11599
	Lower Bound	Lower Bound	3.4091
	Upper Bound	Upper Bound	3.8784
	5% Trimmed Mean		3.6389
	Median		3.5000
	Variance		0.538
	Std. Deviation		0.73355
	Minimum		2.25
	Maximum		5.00
	Range		2.75
	Interquartile Range		1.00
	Skewness		0.321	0.374
	Kurtosis		−0.497	0.733

and

Table 48. Tests of normality of Section B: content of the system.

Tests of Normality
	Kolmogorov–Smirnova			Shapiro–Wilk
	Statistic	df	Sig.	Statistic	df	Sig.
AVG_MULTIMEDIA	0.114	40	0.200 *	0.944	40	0.047

*. This is a lower bound of the true significance. Lilliefors significance correction.

) show the frequency analyses of the results of the questionnaire from the user acceptance testing in Section C: the multimedia element of the system, in which the respondents mostly answered “neutral” to this statement. The average mean of the descriptive analyses of Section C: the multimedia element of the system in this section is 3.6438.

Table 49. Statistics of Section D: the features of the system.

	A1 Strongly Disagree = 1, Disagree = 2, Neutral = 3, Agree = 4, Strongly Agree = 5	A2 Strongly Disagree = 1, Disagree = 2, Neutral = 3, Agree = 4, Strongly Agree = 5	A3 Strongly Disagree = 1, Disagree = 2, Neutral = 3, Agree = 4, Strongly Agree = 5	A4 Strongly Disagree = 1, Disagree = 2, Neutral = 3, Agree = 4, Strongly Agree = 5
Valid	40	40	40	40
Missing	0	0	0	0
Mean	4.3500	4.4750	4.3500	4.3750
Median	4.0000	5.0000	4.0000	4.0000
Mode	4.00	5.00	4.00	4.00
Minimum	3.00	3.00	3.00	3.00
Maximum	5.00	5.00	5.00	5.00
Sum	174.00	179.00	174.00	175.00

illustrates the results of the normality test for the sample size of 40 respondents. According to the results, and the sig. value for the Kolmogorov–Smirnov (K–S) test and Shapiro–Wilk (S–W) test, all the significant results are less than 0.05; hence, the data are not normally distributed.

The tables above (Table 49,

Table 50. Frequency analysis of successful user registration and user login.

	Frequency	Percent	Valid Percent	Cumulative Percent
3.00	2	5.0	5.0	5.0
4.00	22	55.0	55.0	60.0
5.00	16	40.0	40.0	100.0
Total	40	100.0	100.0

,

Table 51. Frequency analysis of successfully received unique ID once registered the parcel/package.

	Frequency	Percent	Valid Percent	Cumulative Percent
3.00	2	5.0	5.0	5.0
4.00	17	42.5	42.5	47.5
5.00	21	52.5	52.5	100.0
Total	40	100.0	100.0

,

Table 52. Frequency analysis of successful change of password and edited profile.

	Frequency	Percent	Valid Percent	Cumulative Percent
3.00	2	5.0	5.0	5.0
4.00	22	55.0	55.0	60.0
5.00	16	40.0	40.0	100.0
Total	40	100.0	100.0

,

Table 53. Frequency analysis of successfully viewed parcel/package status.

	Frequency	Percent	Valid Percent	Cumulative Percent
3.00	3	7.5	7.5	7.5
4.00	19	47.5	47.5	55.0
5.00	18	45.0	45.0	100.0
Total	40	100.0	100.0

,

Table 54. Frequency analysis of successfully viewed parcel/package remark.

	Frequency	Percent	Valid Percent	Cumulative Percent
3.00	4	10.0	10.0	10.0
4.00	18	45.0	45.0	55.0
5.00	18	45.0	45.0	100.0
Total	40	100.0	100.0

,

Table 55. Descriptives—the features of the system.

			Statistic	Std. Error
AVG_FEATURES	Mean		4.3800	0.09137
	Lower Bound	Lower Bound	4.1952
	Upper Bound	Upper Bound	4.5648
	5% Trimmed Mean		4.4222
	Median		4.2000
	Variance		0.334
	Std. Deviation		0.57788
	Minimum		3.00
	Maximum		5.00
	Range		2.00
	Interquartile Range		1.00
	Skewness		−0.392	0.374
	Kurtosis		−0.476	0.733

and

Table 56. Tests of normality of Section D: the features of the system.

Tests of Normality
	Kolmogorov–Smirnova			Shapiro–Wilk
	Statistic	df	Sig.	Statistic	df	Sig.
AVG_FEATURES	0.258	40	0.000	0.804	40	0.000

Lilliefors test for significance correction.

) show the frequency analyses of the results of the questionnaire from the user acceptance testing in Section D: the features of the system, in which the respondents mostly answered “agree” to this statement. The average mean of the descriptive analyses of Section D: the features of the system in this section is 4.3800. Table 56 illustrates the results of the normality test for the sample size of 40 respondents. According to the results, and the sig. value for the Kolmogorov–Smirnov (K–S) test and Shapiro–Wilk (S–W) test, all the significant results are less than 0.05; hence, the data are not normally distributed.

6. Discussion

The last chapter will discuss the summary and discussion of the findings, the implications and limitations of the study, suggestions for future research, and finally, the conclusion.

6.1. Summary of Findings

The main purpose of this study is to design and implement an online blockchain-based and real-time parcel monitoring and tracking system for cross-border runners and customers, during and post-COVID-19 pandemic. There are three objectives in this study. The first objective is to evaluate the most important tracking system in Brunei during the COVID-19 pandemic, the second is to propose a framework of a blockchain-based tracking system for cross-border runners in Brunei during the COVID-19 pandemic, and the last is to examine and implement this effective real-time parcel monitoring and tracking system.

Therefore, to achieve the objectives of the research, the several research questions mentioned in chapter 1, as follows, shall be answered.

(1)

What is important for implementing a tracking system for runners and customers during the Pandemic COVID-19 pandemic?

(2)

What is a possible framework of a blockchain-based tracking system?

(3)

How can a tracking system be effectively implemented to overcome the challenges during this COVID-19 pandemic?

6.2. Discussion of Findings

Research Question 1: What is important for implementing a tracking system for runners and customers during the COVID-19 pandemic?

COVID-19 restrictions have brought changes into our lives. In comparison to pre-COVID-19, we could go outside freely, but now it is a fear for most people. During the pandemic, runners were always fully booked and there is a limited amount of runners in Brunei. In Brunei, only a registered company can go across the border. According to the question in the survey, “have you successfully completed a delivery request using the pickup service or parcel tracking system before?”, 76.7% of the users answered yes to this statement, and 21 (23.3%) answered no. Therefore, from this, it is important to implement the tracking system for the registered runner with a blockchain.

Evaluating the most important tracking system in Brunei during the COVID-19 pandemic is the main objective of this Abell blockchain-based tracking system, as is ensuring that people can safeguard and manage to use the system without any disappointment. According to the survey statement, “I will recommend choosing a blockchain-based tracking system to my friends or others”, out of the 90 respondents, 86 (95.6%) of the respondents answered yes to this statement. This is because COVID-19 has influenced human behavior in ways that have never been seen before. Even though people are sociable creatures by nature, distancing is the new normal, and one cannot help it given the circumstances, at least for the time being. According to the survey statement, “I will choose a blockchain-based tracking system in the future, even though Brunei is already free from the COVID-19 pandemic”, out of the 90 respondents, 85 (94.4%) of the respondents answered yes to this statement.

Customer expectations of home delivery services have also increased dramatically because of the pandemic’s impact. It is important to use modern contactless delivery via a runner to ensure smooth deliveries that safely deliver to the doorstep. According to the survey question, “when your item was delivered in front of your door, did you need to make contact with the runner?”, out of 90 respondents, 62 (68.9%) of the respondents answered no to this statement, because it was for everyone’s safety. Due to the results of the study, we can come to the conclusion that the majority of Bruneians agree that an Abell blockchain-based tracking system’s performance will be successful if implemented in Brunei. According to the frequency analysis for the question, “during the COVID-19 pandemic, would you recommend using a runner delivery service?”, the data of the sample respondents were analyzed for the research study. Out of the 90 respondents, 89 (98.9%) of the respondents answered yes, and the remaining was only 1 (1.1%) respondent that answered no.

The credentials, or the user unique ID, are one of the primary functions and aspects of the purposed idea system. After a runner or user registers with the system, the system generates a unique ID for them. Its purpose is to safeguard the system’s security and safety. According to the survey question, “does your company assign authentication IDs or access cards for employees to track any unauthorized access?”, the data analysis shows the frequency analysis results, which demonstrate that 46 (51.1%) of the respondents answered yes, which meant that their organization had given authentication IDs.

Furthermore, if an approved breach into the system occurs, the typical system may be unable to detect it. As a result, we inquired as to how they would detect unauthorized access in the situations of those who had not been provided with an authorized ID. We provided a variety of options, including role-based access into systems, surveillance cameras, and biometrics. According to frequency analysis results, 29 (40.8%) of the respondents answered yes to surveillance cameras, and this was then followed by 21 (29.6%) answering yes for role-based access into systems. Biometrics fell at third, with a total of 16 at 22.5%.

Research Question 2: What is a possible framework of a blockchain-based tracking system.

To propose a framework of a blockchain-based tracking system for cross-border runners in Brunei during the COVID-19 pandemic, it needs to maintain the tracking, and the blockchain would need to store and secure important data at the system level. However, depending on the amount of accessibility, only a trustworthy member has access to a specific data collection.

The system is a user-friendly software interface for submitting requests to the blockchain-based tracking system. According to the user acceptance testing survey statement, “the system has a user-friendly interface”, out of the 40 users in the testing, 19 (47.5%) of the respondents answered strongly agree, and 17 (42.5%) answered agree.

Requests may be made to read, add, or validate the transactions for inclusion in the shared ledger. Each member can run the blockchain-based tracking system app on various devices with varying access rules. According to the survey question “during the pandemic, do you think this blockchain-based tracking system is the best option for everyone?”, out of the 90 respondents, 70 (77.8%) of the respondents answered yes. For example, a status of the tracking reader from the runner or the admin can be easily connected to a channel for posting and tagging. The customers can also access some data using a different app that connects to the same blockchain, but with limited data availability.

Research Question 3: How can a tracking system be effectively implemented to overcome the challenges during this COVID-19 pandemic?

The blockchain has the potential to solve problems such as visibility and traceability issues. Companies can use blockchain technology to record every activity or transaction in a supply chain on a public blockchain that all users can access, ensuring that the data is secure, immutable, and irrevocable. According to the survey user acceptance test statements, “the system has the necessary security features, the system has a user-friendly interface, the system works according to the specifications, and the system is suitable to be used within the organization”, most of the respondent answered strongly agree.

The COVID-19 pandemic has affected people’s buying habits across the world, including on e-commerce platforms. During the time of COVID-19, many people who were formerly opposed to online buying have switched to it. According to the survey statement, “if I discovered a new information technology in Brunei, I’d try to figure out how to try it out”, out of the 90 respondents, 69 (76.7%) of the respondents answered yes.

As a result, delivery services face numerous challenges in delivering large amounts of products to customers’ doorsteps while adhering the COVID-19 precautionary and contactless delivery. Because of these interconnected challenges, the process itself requires modern solutions.

Blockchain technology is so important in addressing this pandemic. Because it enables efficient tracking and monitoring methods, blockchains are currently showing significant potential to become a key part of the fight against COVID-19, as are secure distributed ledgers with permanent transaction records that are shared by all network users. According to the survey user acceptance test about the features of the system, “successful user registration and user login, successfully received unique ID once the parcel/package was registered, successfully changed password and edited profile, successfully viewed parcel/package status, and successfully viewed parcel/package remark”, most of the respondent were in agreement with the statements.

According to the survey question, “would you still use a runner tracking system if the country was free from the COVID-19 virus?”, out of the 90 respondents, 82 (91.1%) of the respondents answered yes. This is because the blockchain networks, which are implemented as unique code, can interact with systems and processes outside the blockchain using this client. The users (the runner and customer) keep track of the execution context and the state of running of the business processes which are run on a full blockchain tracking system.

7. Conclusions

This section concludes the findings of the study by carrying out a discussion of the summary of the findings, the implications of the study, and finally, its limitations and suggestions for future studies. The blockchain elements of the purposed blockchain-based tracking system design have been stated, and the system can be accessed outside of the company and by an iPad or smart phone. A blockchain-based tracking system, on the other hand, has excellent security features that prevent unauthorized access. According to the findings of the study, organizations prefer to have a blockchain-based tracking system.

The implementation of a spatial blockchain for a further tracking system is promising. These spatial approaches will provide the real-time mapping of the runner to avoid any malfunctions or misplacing of the runner in relation to their carried products and services.