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Article

Leveraging Blockchain for Transparency: A Study on Organ Supply Chains and Transplant Processes

by
Rashmi Ranjan Panigrahi
1,
Subhodeep Mukherjee
1,*,
Zakir Hossen Shaikh
2,* and
Naji Mansour Nomran
2
1
Operations and Supply Chain Area, GITAM University, Visakhapatnam 530045, India
2
Department of Finance and Accounting, College of Business Administration, Kingdom University, Riffa 40434, Bahrain
*
Authors to whom correspondence should be addressed.
Logistics 2025, 9(1), 9; https://doi.org/10.3390/logistics9010009
Submission received: 4 October 2024 / Revised: 11 December 2024 / Accepted: 31 December 2024 / Published: 9 January 2025
(This article belongs to the Special Issue The Future of Inventory Management in Healthcare Supply Chains)
Browse Figure
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Abstract

:
Background: Organ transplants require proper monitoring and tracking. This research examines the adoption of blockchain in the organ supply chain to bring transparency and help patients avoid the fraud that may be faced in the organ transplant process. This study aims to develop a framework and measure for creating transparency in the organ supply chain. A rigorous literature review identified eight constructs for blockchain adoption in organ supply chains and proposed hypotheses. Methods: Using a structured questionnaire, 286 valid responses were collected from the hospitals. Structural equation modelling was used to test and validate the model. Results: The study’s findings indicate that social influence, trust, facilitating conditions, government support, performance expectancy, data security and privacy, and technology affinity positively impact blockchain adoption in organ transplants. The hypothesis that was rejected was related to effort expectancy and technology readiness. Most respondents agreed that blockchain technology is necessary for a tracking and tracing system in the organ supply chain. Conclusions: This research will support transparency in the organ supply chain, eliminate intermediaries from organ transplants, and ensure organ quality, ultimately benefiting the organ donor and receiver.

1. Introduction

The healthcare supply chain (HSC) is concerned with gathering resources, managing supplies, delivering goods, and serving patients [1,2]. To carry out these activities, the information related to tangible goods, medical products, and services typically passes through several independent stakeholders, including producers, hospitals, group purchasing organizations, and various government agencies. A crucial area of study is healthcare operations’ management in the supply chain (SC), as it enhances patient care and adds value to existing processes [3]. The success of a healthcare system can be significantly aided by the wise, vital decisions an operations manager makes. One of the most advanced healthcare operations is the organ transplant, which has become a very successful treatment when an organ fails to respond [4]. The removal of organs for transplantation was made more accessible by establishing this category for deceased people [5].
The organ transplant network consists of donor hospitals, organ procurement units, logistics supply, and transplant hospitals [6,7]. Organ donors are generally kept in hospitals before being transferred to remove the organ, and in the hospital, if necessary, analyses are performed, and organs or tissues are harvested [8]. Organ procurement procedures are included in the donation stage [9,10]. This takes place in hospitals where admitted patients are responsible for organ arrangement, procurement, and transplants [11]. However, some patients become caught up in fraud or pay more for the organ [12]. Sometimes, the organ donor donates their organs forcefully or through human trafficking.
Transactions made using blockchain technology (BLCT) in the HSC are crucial for tracking the complete drug and medical goods transfer process [13]. All transactions are logged on the ledger. Each node in the BLCT maintains a transaction record, making it easy to instantaneously validate the drug’s source, the vendor, and the distributor [14]. The BLCT’s distributed ledger also enables medical professionals and healthcare officials to verify and validate vendors’ credentials. Through appropriate and timely authentication procedures, pharmacies and healthcare practitioners can ensure that natural pharmaceuticals continue to reach the people who need them the most [15]. In this sense, developing a reliable vendor network enabling healthcare administrators to protect patients from shady vendors shows excellent promise for BLCT [16,17].
Additionally, BLCT promises significant improvements in demand forecasting, data provenance, fraud prevention, and transactions. The SC for organ transplants is one of the essential applications of BLCT in healthcare that has not been fully explored [18]. Every person interacting with the organ will be listed along with their location on the distributed BLCT ledger. An entire block of transactions cannot be changed after being added. This system for keeping track of transportation data can assist in tracking organs and distributing them to the right places in the allotted amount of time. Every user accessing a cryptographic key can follow their journey at every stage.
Blockchain technology for increasing transparency within organ supply chains and even organ transplantation procedures holds promise against vast and important problems related to fraudulence, mismanagement, and an unbalanced organ distribution chain. Blockchain technology may radically alter the organ supply chain scenario due to improved transparency, streamlined efficiency, and proper ethics that characterize the fair and balanced provision and management of organs at transplantation centres. More effectively managing the aforementioned problems can easily be facilitated in line with the overall legal perspective so that it does not seem to work in vain or as something less valid compared to systems without it.
Blockchain technology is increasingly becoming recognized as a revolutionary tool that profoundly influences the organ supply chain. This comes from its outstanding ability to overcome some of the most significant challenges that have dogged this area while improving transparency and efficiency. Blockchain enables the end-to-end tracking of organs from donors to recipients through their documentation and verification, which is near every step of the way. Blockchain can also eradicate illegal organ trading by authenticating and recording all the transactions and movements within the organ supply chain. With such transparent and updated information, stakeholders will react quickly and efficiently, thus significantly improving the prospects of successful organ transplants. The attributes of transparency and immutability associated with blockchain technology can be highly instrumental in boosting public confidence and trust in the organ donation process. Blockchain must be introduced into the organ supply chain for a system that prioritizes transparency, operates effectively, meets security requirements, and maintains the highest ethical calibre. The ability of blockchain technology to tackle many critical challenges in organ donation and transplantation—such as fraud, inefficiency in processes, and concerns regarding data security—is the hallmark of such groundbreaking change. This change can save many more lives through enhanced systems and practices.
Ref. [19] presented a BLCT and its impact on HSC, which also focuses on identifying non-functional requirements for BLCT-enabled systems. Ref. [20] created a scoring model to assess healthcare organizations’ readiness to adopt BLCT in electronic health records management. Ref. [14] outlined an approach based on the Ethereum BLCT that uses smart contracts and decentralized off-chain storage for effective product traceability in the HSC. Ref. [15] combined BLCT and decentralized storage to increase streamlined communication with stakeholders and transparency and shorten the procurement timeline while avoiding pricing discrepancies and misrepresentations. Ref. [21] tried to introduce the idea of BLCT, organ donation, medical SCs, and crowdsourcing medical research in healthcare use cases. Ref. [22] discussed how BLCT could meet pharmaceutical cold chain requirements such as waste management, data integrity, transparency, serialization and traceability, and pharmaceutical digital identity. Ref. [23] suggested using a system using the BLCT-based decentralized app for organ donation. Patients would register their information on a web form, including state, medical ID, blood type, and organ type. Unless a patient is in critical condition, the system will operate on a first-in, first-out basis. Ref. [24] showed the usage of BLCT in medicine drug procurement and the organ supply chain. No studies can be found in the literature discussing the adoption of BLCT in the organ supply chain.
Thus, to address this research gap in the academic literature, we formulated the research questions given below:
RQ1: What are the antecedents of BLCT adoption in the organ supply chain?
RQ2: Are healthcare professionals ready to adopt BLCT in the organ supply chain?
The relevant literature was reviewed to answer the research questions mentioned above. From the review, it is clear that there is negligible research on organ supply chains, so this study will contribute to the literature. BLCT adoption will help create transparency throughout the whole process. The benefits of BLCT in this sector are denoted in this study. With the adoption of BLCT, many frauds related to organ transplants can be eradicated. This will help in improving the efficiency of the organ supply chain. This research seeks to assist the healthcare sector in establishing efficient tracking and tracing systems for organ supply chains. This study proposes eight hypotheses to meet the research questions. Nevertheless, the two hypotheses—facilitating conditions and technology readiness—lack empirical support. This indicates the existing deficiency of technological infrastructure in the healthcare systems that require blockchain technology (BLCT). This research aims to illuminate the challenges associated with adopting BLCT in the healthcare industry.
Section 2 discusses the Literature Review, followed by the Research Methodology in Section 3, the Results in Section 4, and finally, the Conclusions.

2. Literature Review

2.1. Theoretical Underpinning

The unified theory of acceptance and use of the technology (UTAUT) model included exogenous constructs for predicting behavioural intention and use, including performance expectancy, social influence, effort expectancy, and facilitating conditions. The UTAUT is a crucial model in several research areas, and the original model has been used in numerous studies and has influenced academics to suggest some modifications [25,26]. Previous studies have used the UTAUT theory in various areas, such as RFID [27], E-government services [28], big data [29], AI [30,31], and mobile payment [32,33].
Individual adoption and organizational adoption were the two categories into which these theories were divided. Personal adoption research focuses primarily on a person’s intention to adopt an innovation or actual adoption behaviour. On the other hand, studies on business adoption investigate how large companies’ aggregates, like departments, agencies, or businesses, adopt new technologies. According to Ref. [34], innovation adoption has three dimensions: the technological, organizational, and environmental context. These situations interact with others to sway their opinions on technology adoption [35,36]. The technological context has complexity, integration, and perceived usefulness [37].
A company’s size and capabilities, managerial structure centralization, formalization and complexity, human capital quality, and internal slack resources are part of the organizational context [38]. The environmental context concerns defining factors that describe the organization’s market or community, such as industry and market structure, competitors, government relations, and other external activities [39]. Many previous studies have used the TOE framework for various technology adoption studies like ERP [40,41,42,43], cloud computing [44], big data analytics [45,46,47], RFID [48,49], and AI [50,51]. This study uses construct performance expectancy, social influence, effort expectancy, and facilitating conditions from the UTAUT model. Trust, technology readiness, government support, technology affinity, data security, and privacy have been adopted [52,53].

2.2. Development of Hypothesis and Research Model

2.2.1. Performance Expectancy (PE)

PE is defined as the extent to which a person believes that using the system will enable him to improve his job performance [54]. According to multiple works of earlier research, the adoption of technology in various technological contexts is influenced by PE. Individual (employee) willingness to adopt and utilize new technology is correlated with their perception of some benefits, including the practical level of the technology in their daily work activities [55,56]. Furthermore, BLCT can use its decentralized nature to reduce process complexity and uncertainty, particularly in operational processes based on smart contracts. The use of BLCT by employees in organizations is related to PE, or the belief that it will improve SC results. To increase the organ performance of organ supply chain employees, we need to adopt BLCT technologies. Ref. [57] found performance expectancy to be an essential factor in predicting dental students’ intentions for using technology. Thus, by adopting BLCT, productivity, professional performance, and efficiency can increase.
H1: 
PE will positively impact the adoption of BLCT in the organ supply chain.

2.2.2. Effort Expectancy (EE)

When technology is seen as simple to understand and operate, it demands less effort than when working with complicated systems. This is because effort expectancy is defined as the degree of ease connected with the use of technology. Users are more inclined to accept technology when it is easy to use and requires little learning effort. EE measures the effort a business must put into acquiring and utilizing cutting-edge technology. Ref. [58] found that effort expectancy positively relates to adopting the IoT in e-health. If users find integrating new technology into organizational tasks simple, the likelihood of them using the technology will be higher. Ref. [59] states that effort expectancy is essential for adopting mobile health. Thus, the following hypothesis is proposed, considering that the employees will find it easier to use BLCT in the organ supply chain.
H2: 
EE will positively impact the adoption of BLCT in the organ supply chain.

2.2.3. Facilitating Conditions (FCs)

The facilitating condition refers to the degree to which an individual perceives that an administrative and technical framework is in place to facilitate the system’s use. It refers to employees’ understanding of the organizational resources available to support using BLCT. The BLCT infrastructure stores a copy of the transactions, allowing for queries at any time and providing the easy traceability of products and services to SC members [53]. Although BLCT offers numerous advantages, its deployment and implementation necessitate a meticulous evaluation of how it may satisfy requirements and how stakeholders’ involvement can increase the probability of success. Like all other forms of technology, BLCT is fundamentally an enabler. When implemented correctly, it seamlessly integrates with the existing infrastructure. Ref. [58] examined the adoption of IoT-based e-health among patients in France and found facilitating conditions to be an essential factor. Ref. [60] showed that facilitating conditions impact consumer acceptance of wearable healthcare devices. The following hypothesis is proposed.
H3: 
FC will positively impact the adoption of BLCT in the organ supply chain.

2.2.4. Trust (TRU)

Trust is a belief held by the innocent party that the other will fulfil their obligations as expected [61]. It also shows people’s willingness to take risks to meet their needs. Numerous studies have examined the relationship between trust and various antecedents, including factors influencing the adoption of new technology [62,63,64]. Studies on perceived risk and its effects, such as loyalty and the intention to make repeat purchases, as well as significant long-term obstacles to user adoption and technical and organizational factors that affect trust, such as data ownership and transparency, are among these [65,66,67]. BLCT has the potential to improve current procedures, which is why businesses are more likely to invest in the infrastructure and resources necessary for its adoption. Adopting BLCT in the organ supply chain is contingent upon establishing trust, as it communicates critical information. The hypothesis is consequently given as follows:
H4: 
Trust will positively impact the adoption of BLCT in the organ supply chain.

2.2.5. Technology Readiness (TRE)

The ability to accept and use innovative technologies to achieve goals in a person’s personal or professional life is known as technology readiness [68]. Two prerequisites for technology readiness are the possession of an appropriate infrastructure and the confidence in one’s ability to complete the task in SC. A person’s technological readiness is determined by how much they perceive that a particular piece of technology will enhance their performance. A positive perception enhances business intelligence due to the user’s increased technological proficiency. There is a generally positive correlation between technology readiness and acceptability, as evidenced by various research studies that have coupled readiness with different acceptance models [68,69,70]. For sustainable SC, technical knowledge and accessibility are considered crucial.
H5: 
TRE will positively impact the adoption of BLCT in the organ supply chain.

2.2.6. Government Support (RS)

Regulatory uncertainty and related issues like compliance and intellectual property concerns are significant barriers to BLCT adoption. Businesses that want to use technology in current processes must first comprehend the challenges of integration and whether they will benefit from it [71]. The most valuable quality of BLCT is its immutable traits, which produce a transparent and trustworthy environment ideal for traceability. In addition to technical configuration, the successful implementation of infrastructure can be influenced by regulatory actions when integrating BLCT into the organ supply chain. Regulatory issues are minor when used for organ supply chain shipment and tracking because these BLCTs are administered. However, they are still in permissionless BLCT because anyone can participate and transact. The biggest obstacle to adoption is regulatory complexity, followed by legacy integration.
H6: 
RS will positively impact the adoption of BLCT in the organ supply chain.

2.2.7. Data Security and Privacy (DSP)

Security refers to the precautions a BLCT provider takes to safeguard the system or a customer company’s data resources from unauthorized assaults [72]. When a business or a government organization misplaces sensitive data, such as personal information, it constitutes a security breach [73,74,75]. The shared environment provided by the BLCT, which combines computing and storage, raises security concerns [76]. The client companies will be hesitant to accept BLCT services if the BLCT provider does not create sophisticated security protocols and identity management. Concerns about security and privacy significantly deter businesses from adopting BLCT [77]. To control the SC of organ procurement/placement and provide an audit control approach to analyze data in any pre- or post-operation event, BLCT seems to be a good fit. BLCT can assist in ensuring that all parties that sign up for it comply with the norms and regulations and demand visibility for individuals with access rights when used in conjunction with the proper procedures, such as a cyber security framework or maturity model for the healthcare industry.
H7: 
DSP will positively impact the adoption of BLCT in the organ supply chain.

2.2.8. Technology Affinity (TA)

A person’s inclination for actively utilizing or avoiding technology to deal with technology is referred to as having a technology affinity. It is recognized as a valuable tool for navigating technology personally. Early research demonstrated that attitudes toward technology are critical in determining the adoption of a wide range of technologies; people with a higher affinity are more hopeful for more efforts and motivations [78,79]. Users who adopt an upbeat attitude toward technology will naturally be driven to learn about and use it. The less perceived effort required and the higher the performance, the more skilled a person is with a given technology.
H8: 
Technology affinity will positively impact the adoption of BLCT in the organ supply chain.

3. Research Methodology

3.1. Questionnaire Design

Four academic researchers and three industry experts consented to provide their perspectives on the current questionnaire. The experts altered the questionnaire and recommended that the language be enhanced and three queries be eliminated. Consequently, the relevant modifications were implemented following the experts’ recommendations. Thus, a questionnaire was created, and the items were then sent to experts (seven researchers and four active professionals in the healthcare industry) for review to ensure that they were adequately covered. The objective of the entire exercise was to ensure the questionnaire’s content validity, which was achieved. Scaling involved creating a continuum where measured objects were located [80].

3.2. Data Collection Procedure

This study focuses on hospitals that provide organ transplant services. A list of hospitals was prepared after searching the hospital’s names online. The contact information of senior-level professionals, lab technicians, social workers, counsellors and psychologists, and physicians, such as phone numbers and e-mail addresses, was found by visiting the hospitals’ websites, participating in chat rooms, or calling the numbers listed. Responses were taken from senior professionals and medical practitioners with relevant organ supply chain experience. Other potential hospitals and respondents were asked from the respondents. The respondents also helped in obtaining the contact details of twenty-seven other respondents. The medical practitioners and senior employees were the professionals managing the organ supply chain and transplant; only these professionals were contacted for this study.
When conducting the study offline, the respondents were informed regarding the study objectives and given advance notice. A soft copy of the questionnaire and a brief explanation of our research and its significance were sent via e-mail to the respondent to increase their awareness and understanding of the study. Responses to the hard copy of the questionnaire were gathered on the day of the in-person interview. In the case of online data collection, respondents were contacted via e-mail and phone, and the purpose of the study was discussed either over the phone or via e-mail. Around 981 hospital-working professionals were contacted from August 2023 to March 2024. Out of 981 questionnaires sent, only 294 questionnaires came back to us. Eight questionnaires were not filled and had more than 10% missing values. Therefore, only 286 questionnaires were considered for the study as they were adequately supplied. We did not find any significant differences between offline and online modes of data collection. To ascertain the feasibility of the study at the outset, a pilot survey was implemented with 46 participants. The objective of the pilot study was to assess the questionnaire’s validity. The questionnaire’s reliability was evaluated using Cronbach’s alpha. The pilot study yielded results that exceeded the recommended threshold value of >0.7 for each of the questionnaire’s constructs.

3.3. Common Method Bias

The EFA was conducted, and the results showed that the first component could only account for 15.230% of the variance, much less than the required figure of 50% [81]. The data suggest that it is free from common method bias.

4. Data Analysis

Data reliability was assessed using Cronbach’s alpha [82]. The measured values should be higher than 0.70, which is the suggested level, and in this study, we achieved the threshold level. Table 1 displays Cronbach’s alpha scores for each item.

4.1. Exploratory Factor Analysis (EFA)

Table 1 shows the factor loadings for every item of the constructs. Principal axis factoring (PAF) was used to find significant predisposition and related explicit traits. The varimax rotation was made possible by the assumption that its factors were correlated and supported by the relevant literature [83]. For EFA to be regarded as an appropriate procedure, the measurement obtained from this test had to be critical (p < 0.05). All factor loading values were discovered to be higher than or equal to 0.5, which served as the acceptance threshold. Construct validity was estimated to test the hypothesis and the structural components. Different construct validity tests like composite reliability, convergent validity, and divergent or discriminant validity were used to measure the construct validity. The composite reliability (CR) values were >0.7, which shows the reliability of the CR measures [84].
Table 1. Factor loadings and Cronbach’s alpha.
Table 1. Factor loadings and Cronbach’s alpha.
IndicatorsReferencesCronbach’s AlphaFactor LoadingsCRAVE
PE 1: I believe blockchain technologies would benefit the procurement of organs. [52]0.7870.7700.7960.513
PE 2: I can complete tasks more rapidly by utilizing blockchain technologies.0.853
PE 3: My productivity is enhanced by the utilization of blockchain technologies.0.864
PE 4: The utilization of blockchain technologies will enhance my likelihood of achieving superior performance in the procurement of organs. 0.565
EE 1: I find acquiring the knowledge necessary to operate blockchain technology effortless.[53]0.8290.8060.8330.625
EE 2: My engagement with blockchain technology is transparent and comprehensible.0.864
EE 3: I find blockchain easy to use.0.850
FC 1: The appropriate resources for BC are available at my facility.[52]0.8460.7970.8370.633
FC 2: If technical assistance is necessary, my hospitals possess the expertise. 0.832
FC 3: My firm knows that it is necessary to operate BC0.832
FC 4: My hospital lacks sufficient expertise regarding the implementation of BC. 0.757
TRU 1: I have faith in the efficacy of BC.[84]0.8550.8650.8560.598
TRU 2: I am confident that BC can maintain the security of the data and reduce the likelihood of fraud.0.807
TRU 3: I am confident in operating BC consistently and without error.0.798
TRU 4: I think BC will consistently deliver satisfactory and effective outcomes in the workplace.0.770
TRE 1: The introduction and potential of BC are well-received by my hospitals.[84]0.8420.8600.8450.646
TRE 2: The appropriate infrastructure for BC integration is in place at my organisation.0.897
TRE 3: My hospital has the necessary security measures for BC.0.802
GS 1: My firm’s decision to implement BCSCM would depend on industry standards in place[84]0.880.8510.8820.714
GS 2: Market volatility and policy normalisation0.904
GS 3: Effective government environment for data protection and consumer protection0.891
DSP 1: There are security concerns regarding the adoption of AI. [45]0.9060.9200.910.771
DSP 2: SE 2: Traditional technologies are more secure than the current technologies. 0.904
DSP 3: There is a belief that AI will make the firm’s data safe and secure. 0.856
TA 1: I possess a high level of proficiency in utilizing a diverse array of computer technologies.[84]0.8510.8650.8510.656
TA 2: I enjoy evaluating the capabilities of emerging technological systems.0.831
TA 3: I enjoy dedicating time to acquiring a novel technological system.0.854
BAOSC 1: In the future, I plan to implement blockchain technology. Added by the Authors0.8980.8540.8980.688
BAOSC 2: In the future, I anticipate that I will implement blockchain technology.0.849
BAOSC 3: In the future, I intend to implement blockchain technology in my hospital.0.861
BAOSC 4: I intend to transform SC at my hospital digitally.0.871

4.2. Confirmatory Factor Analysis (CFA)

AMOS 22.0 was used in this study due to its user-friendliness and powerful graphical representation. Composite reliability, convergent validity, and discriminant validity were measured for the measurement model in CFA. Table 2 shows discriminant validity and the HTMT test.
Table 2 (A) demonstrates discriminant validity using inter-item correlation. Constructs such as TRU, EE, PE, DSP, TA, FC, GS, TRE, and BAOSC met the Fornell–Larcker criterion, where the square root of AVEs on the diagonal line exceeded inter-item correlations [85,86]. Additionally, Table 2 (B) reports the heterotrait–monotrait (HTMT) ratio, below 0.85 for all constructs, satisfying the criteria suggested by [87,88,89]. Thus, the measurement model confirmed discriminant validity.

4.3. Structural Model

SEM shows the effect of latent variables on the dependent variable. SEM consolidates authenticating factor assessment. SEM was used to test the hypotheses [90]. The output of the model is provided in Table 3. Finally, all the latent variables and their indicators were fitted into the model to test the results. All the values were within the range. Figure 1 shows the validated model for adopting BLCT in the organ supply chain.
Ref. [91] identified challenges impacting the adoption of BLCT in the Indian healthcare sector. Ref. [92] studied the implementation of BLCT in healthcare sectors of emerging economies. Although traceability is crucial for SC, there are still numerous obstacles when deploying traceability solutions [93]. This research aims to study the adoption factors of BLCT in the organ supply chain. This capability results from every network user having a replicated copy of the data, enabling accountability and preventing information tampering [94]. Contrary to other industries, organ transportation is highly time-sensitive, so if one step of the SC process is delayed or stopped, a recipient may lose their chance to receive a transplant [83]. Implementing BLCT and adding time alerts might eliminate the possibility of errors. The lack of innovation and complexity makes SC management in organ transplants a workable and practical solution [95]. Due to its low novelty and complexity, tracking organ transport via BLCT is functional and has a high adoption rate. Using BLCT will make it easier to communicate in real-time, spot any problems during transport, and better use scarce resources. Using BLCT in HSC can make tracking each step in the journey more accessible, from the organ’s purchase to the recipient’s surgery.
Hypothesis (H1) stated that PE would positively impact the adoption of BLCT in the organ supply chain, which is supported. The respondents believe that their job performance will increase with adopting BLCT in the organ supply chain. Ref. [52] showed how BLCT adoption is positively related to the SC in India and the USA. Hypothesis (H2) stated that EE would positively impact the adoption of BLCT in the organ supply chain, which is not supported. Ref. [84] showed the relationship between EE and the adoption of BLCT in the context of Malaysia. [52] found a positive relationship between BLCT adoption in the SC for Brazil. EE [96,97] found a positive relationship with the adoption of various technologies. Ref. [98] performed a systematic literature review to understand the trustworthiness of using BLCT in healthcare SC.
Hypothesis (H3) stated that FC would positively impact the adoption of BLCT in the organ supply chain, which is supported. FC found a positive relationship for adopting BLCT in the SC [52,53,99]. FC was found to be positively related to adopting technology for the study of electronic government [100] and information systems and information technology [37]. Ref. [101] proposed a framework based on BLCT to address the lack of transparency in the drug SC. Hypothesis (H4) stated that trust can positively impact the adoption of BLCT in the organ supply chain. The respondents trust the BLCT in the organ supply chain as they think it will bring transparency to the process. The studies of [52,53] found that trust shows a positive relationship with adopting BLCT in SC. [102] survey is based on a developing country’s (Ghana) context for adopting cloud computing. It can be concluded that trust is an essential element. If people are comfortable trusting technology, they can quickly adapt cloud technology for healthcare.
Hypothesis (H5) stated that TRE positively impacts the adoption of BLCT in the organ supply chain, which is not supported. The possible reason for rejecting the hypothesis may be that healthcare lacks the technological infrastructure for adopting BLCT. Hypothesis (H8) stated that TA can positively impact the adoption of BLCT in the organ supply chain, which is supported. Ref. [71] found that Bangladesh’s SMEs are not technology-ready to adopt cloud computing. Ref. [84] showed a positive relationship between TRE and TA for adopting BLCT in the SC of Malaysia. Hypothesis (H6) stated that GS can positively impact the adoption of BLCT in the organ supply chain, supported by values β = 0.166 and p = 0.007. Hypothesis (H7) stated that DSP could positively impact the adoption of BLCT in the organ supply chain, which is supported. However, as per [102], GS is not a significant contributor. However, the present study has been identified as a significant contributor. Ref. [71] showed a positive relationship between data security and privacy but a negative relationship between government support and cloud computing adoption.

4.4. Theoretical Implications

In this study, we developed a model for adopting BLCT in the organ supply chain. Our study appears to be a valuable contribution to the growth of the IT adoption field and organ supply chain, considering the anticipated effects of BLCT. Our research model was derived from the earlier literature [25,26,52,53,99,103], but we also conducted additional research and made an effort to fill a real gap in the body of knowledge regarding the adoption of BLCT in the organ supply chain. Few empirical studies in the fields of operations, production, and SC, particularly those focusing on BLCT adoption, have been conducted on the rapidly developing and popular topic of BLCT [52,53,99,104,105].
This study suggested and validated the extended and revised version of the UTAUT to analyze an essential component of BLCT acceptance in the organ supply chain with convincing results. This model included the trust construct, technology affinity, technological preparedness, data security and privacy, and government support from the TOE framework. This study is the first to measure the adoption of BLCT in the organ supply chain.

4.5. Practical Implications

The results of this study have important implications for all parties involved in the organ transplantation process, including managers, practitioners, healthcare professionals, and decision-makers. Our findings demonstrated that while healthcare infrastructure is generally considered a challenge in emerging economies, enabling conditions do not positively impact BLCT adoption in the organ supply chain. This finding advises managers in the healthcare industry to make significant investments in their infrastructure and organizational capabilities. Managers and healthcare professionals must pay close attention to trust as it plays an important role. Thus, they should be curious about the reasons behind and the extent to which the belief in BLCT held by organ supply chain professionals can impact an individual’s adoption of BLCT.
Healthcare professionals should constantly monitor the organ supply chain to quickly spot behaviours that can affect the trust in BLCT, given our finding that trust positively impacts the intention to adopt BLCT. Furthermore, having confidence in and access to the information shared by SC participants is necessary for trusting BLCT. In reality, the SC members’ trust in one another is strengthened by validating transactions. Due to this, it becomes essential to rely on information sharing through the SC and trust the integrity of the process without appropriate and efficient technical aspects, etc. According to this study, most respondents had little experience with the technology and were unsure how BLCT would affect their healthcare.
This means that the BLCT primarily concerns whether the hospitals have the necessary infrastructure, resources, and critical personnel interested in exploring new technologies. The hurdles to adoption that have been investigated are the difficulties in altering culture, the reluctance to adopt new systems, the lack of tools for BLCT implementation, and the negative perceptions of BLCT. Hospitals considering BLCT use in the organ supply chain must address the lack of knowledge about the technology and develop the knowledge, enthusiasm, environment, and trust necessary for its successful deployment.

5. Conclusions, Limitations and Future Developments

BLCT can play an essential role in the organ supply chain. Many of the challenges it currently faces can be addressed and solved. There can be more transparency in the SC, and everything can be monitored. This research found the factors necessary for adopting BLCT in the organ supply chain. Organ transplant requires transparency throughout the process. This research adopted constructs from the UTAUT and TOE frameworks. Eight hypotheses were proposed, out of which only seven were supported, and two were not supported. This study was conducted in hospitals, and the respondents were professionals and medical practitioners involved in the organ supply chain. A questionnaire-based survey was conducted. Many frauds are associated with organ transplants; patients are often cheated when they pay money and do not receive the organs. The intermediaries in the organ supply chain can be removed with the help of BLCT. There can be proper tracking and tracing systems for an organ transplant.
The study had some limitations, like the sample size being significantly smaller, as the researchers found difficulty in finding respondents who were related to the organ supply chain in the healthcare industry. Given that this study was conducted in a single nation, the problem of generalizing the research methods, findings, and results is another drawback.
Future research directions should include a higher sample size and samples from developed countries’ healthcare domains that use advanced technologies.

Author Contributions

R.R.P.: Conceptualization, Methodology, Empirical Validation, Supervision, Project administration. S.M.: Conceptualization, Methodology, Discussion, Preparing of final draft. Z.H.S.: Data Collection, Data Cleaning, Discussion, Implications, Overall Review and Journal Communication. N.M.N.: Literature Review, Conceptual Model, Theory Building, Discussion, Conclusions, Limitations and Future Developments. All authors have read and agreed to the published version of the manuscript.

Funding

The authors would like to acknowledge that this research work was partially financed by Kingdom University, Bahrain, from the research grant number KU-SRU-2024-03.

Data Availability Statement

The raw data supporting the analysis, discussion and conclusions of this article will be made available by the authors upon request.

Acknowledgments

R.R.P. and S.M. sincerely thank all co-authors for their invaluable assistance in data collection, funding arrangements, and proofreading.

Conflicts of Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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Logistics 09 00009 g001
Figure 1. The final model for measuring blockchain adoption in the organ supply chain.
Figure 1. The final model for measuring blockchain adoption in the organ supply chain.
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Table 2. Discriminant validity (Fornell and Larcker approach and HTMT) test.
Table 2. Discriminant validity (Fornell and Larcker approach and HTMT) test.
(A) Discriminant Validity (Fornell and Larcker Approach)
TRUEEBAOSCPEDSPTAFCGSTRE
TRU0.773
EE0.213 **0.791
BAOSC−0.046−0.0760.83
PE0.197 **0.0760.0740.716
DSP−0.019−0.1040.215 **−0.258 ***0.878
TA0.134 †−0.0670.334 ***−0.0910.303 ***0.81
FC0.399 ***0.290 ***0.0960.106−0.0470.0990.796
GS−0.052−0.150 *0.250 ***−0.0110.199 **0.203 **0.0410.845
TRE0.0020.351 ***−0.122 †0.109−0.083−0.1090.170 *−0.0390.804
(B) Discriminant Validity (HTMT) Analysis
TRUEEBAOSCPEDSPTAFCGSTRE
TRU
EE0.205
BAOSC0.0390.079
PE0.2230.0590.068
DSP0.0070.1160.2110.254
TA0.1360.0840.3390.0870.311
FC0.4230.2880.0930.0920.0590.11
GS0.0450.1580.2480.0150.2150.2160.047
TRE0.0030.3640.140.0980.0910.1080.170.057
Significance of Correlations: † p < 0.100, * p < 0.050, ** p < 0.010, and *** p < 0.001.
Table 3. Model fit parameters.
Table 3. Model fit parameters.
ParametersEstimate SECRpHypotheses
BAOSC <--- PE0.1520.0482.4100.016Supported at less than LOS 5%
BAOSC <--- EE−0.0100.043−0.1570.876Not supported
BAOSC <--- FC0.1260.0502.0030.045Supported at less than LOS 5%
BAOSC <--- TRU−0.1350.054−2.1540.031Supported at less than LOS 5%
BAOSC <--- TRE−0.1090.038−1.7500.080Not supported
BAOSC <--- GS0.1660.0572.6860.007Supported at less than LOS 5%
BAOSC <--- DSP0.1420.0412.3670.018Supported at less than LOS 5%
BAOSC <--- TA0.2740.0604.251***Supported at less than LOS 1%
*** refers to 0.001.
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MDPI and ACS Style

Panigrahi, R.R.; Mukherjee, S.; Shaikh, Z.H.; Nomran, N.M. Leveraging Blockchain for Transparency: A Study on Organ Supply Chains and Transplant Processes. Logistics 2025, 9, 9. https://doi.org/10.3390/logistics9010009

AMA Style

Panigrahi RR, Mukherjee S, Shaikh ZH, Nomran NM. Leveraging Blockchain for Transparency: A Study on Organ Supply Chains and Transplant Processes. Logistics. 2025; 9(1):9. https://doi.org/10.3390/logistics9010009

Chicago/Turabian Style

Panigrahi, Rashmi Ranjan, Subhodeep Mukherjee, Zakir Hossen Shaikh, and Naji Mansour Nomran. 2025. "Leveraging Blockchain for Transparency: A Study on Organ Supply Chains and Transplant Processes" Logistics 9, no. 1: 9. https://doi.org/10.3390/logistics9010009

APA Style

Panigrahi, R. R., Mukherjee, S., Shaikh, Z. H., & Nomran, N. M. (2025). Leveraging Blockchain for Transparency: A Study on Organ Supply Chains and Transplant Processes. Logistics, 9(1), 9. https://doi.org/10.3390/logistics9010009

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