Redefining knowledge-generation-driven blockchain for healthcare use: Insights from medical institutions

Abstract

This study explores the factors influencing healthcare professionals’ willingness to adopt knowledge-generation-driven Blockchain technology (KGDBT) in government healthcare facilities, using the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) framework. It introduces transparency as an independent variable and examines the mediating role of knowledge generation in the relationship between transparency and healthcare professionals’ intention to adopt KGDBT. Data were collected from 322 healthcare professionals in government hospitals and analyzed using SPSS version 26 and SmartPLS version 3.9 for Partial Least Squares Structural Equation Modeling (PLS-SEM). The results strongly support the theoretical framework, demonstrating that performance expectancy, effort expectancy, social influence, facilitating conditions, and transparency significantly influence healthcare professionals’ adoption of Blockchain technology. Additionally, the study identifies knowledge generation as a critical mediating factor between transparency and behavioral intention to adopt KGDBT. This research addresses the challenges of implementing Blockchain technology in healthcare by proposing a knowledge management-oriented approach to enhance its effectiveness. It highlights the critical role of transparency in promoting technology adoption and fills a gap in the literature on Blockchain and knowledge management, particularly within the Iraqi healthcare context. This study offers new insights, contributing to a comprehensive understanding of the role of knowledge generation in Blockchain adoption.

Keywords

blockchain knowledge generation transparency technology adoption UTAUT2 healthcare SmartPLS

Introduction

The healthcare industry is undergoing rapid transformations driven by technological advancements, which have significantly altered how health services are delivered, managed, and consumed. The COVID-19 pandemic further accelerated this shift, forcing healthcare organizations to adopt new business models and digital solutions to ensure continuity and improve service efficiency.^1,2 As the sector adapts to these changes, it is crucial for healthcare institutions to explore innovative ways to manage knowledge effectively. Despite the growing literature on digital transformation in healthcare, the integration of Blockchain technology for knowledge management remains underexplored, particularly in regions with limited technological adoption, such as Iraq.³ This paper addresses this gap by investigating the factors influencing healthcare professionals’ behavioral intention to adopt Blockchain technology for knowledge generation within the Iraqi healthcare system.

While several studies have examined the impact of digital technologies on healthcare management, there is limited research on how Blockchain technology, with its unique attributes, can enhance knowledge creation, sharing, and utilization.⁴ Effective knowledge management plays a pivotal role in fostering collaboration among healthcare professionals, leading to innovative diagnostic tools and therapeutic practices.⁵ However, the pandemic introduced new challenges to knowledge sharing by limiting traditional in-person interactions. In response, healthcare organizations increasingly turned to digital platforms to manage information, with Blockchain emerging as a promising solution for secure and transparent data management.^6,7

Blockchain technology’s potential lies in its decentralized architecture, offering traceability, immutability, transparency, and secure knowledge sharing.^8,9 These characteristics make it well-suited for critical sectors such as finance, energy, governance, and healthcare, where data integrity and security are paramount.¹⁰ Despite its promise, Blockchain adoption remains in its early stages, with practical applications limited by concerns over privacy, governance, and operational challenges.¹¹ The World Economic Forum (2015) projects that by 2025, up to 10% of the global GDP will depend on Blockchain technologies, underscoring the importance of understanding its potential across industries.

In Iraq, Blockchain adoption has been slow due to infrastructural limitations, insufficient government investment, and a lack of awareness about its potential applications.¹² However, the growing demand for technological solutions in healthcare provides a unique opportunity to explore how Blockchain can address knowledge management challenges within the sector. This research aims to address a critical gap by investigating the factors shaping the willingness of healthcare professionals to adopt Blockchain technology, specifically for generating and managing knowledge within government healthcare facilities. The study employs the UTAUT2 framework as a theoretical model to explore how technological transparency influences the intention to adopt Blockchain and examines the role of knowledge generation as a mediating factor in this relationship.

The significance of this study lies in its focus on Iraq, a country facing complex healthcare challenges shaped by historical conflicts and displacement. Understanding how Blockchain technology can support knowledge management in this context offers valuable insights for improving healthcare delivery. Moreover, this research extends the literature by integrating the UTAUT2 framework with concepts of knowledge generation and Blockchain transparency, providing a theoretical foundation to explain healthcare professionals’ behavioral tendencies toward technology adoption.

This study’s contribution is twofold: First, it offers a novel framework for understanding the relationship between Blockchain technology and knowledge management in healthcare settings, with a specific focus on Iraq. Second, it provides practical insights for policymakers, administrators, and industry professionals seeking to implement Blockchain-based solutions to improve healthcare services. In doing so, the study moves beyond descriptive research by using advanced statistical techniques for model validation. Data were collected through physical and digital surveys, yielding 322 responses, and analyzed using SmartPLS v.3.9 and SPSS v.26 to test the proposed hypotheses.

This research holds particular relevance for healthcare institutions in Mosul, which are actively exploring advanced technologies to enhance patient care. By addressing the challenges of Blockchain implementation through a knowledge-centric approach, this study offers actionable solutions for integrating emerging technologies into healthcare. The findings will provide a roadmap for organizations looking to leverage Blockchain to improve service delivery, overcome geographical and operational barriers, and foster collaborative knowledge networks.

The remainder of this paper is organized as follows: Section 2 presents a comprehensive review of the literature and formulates the research hypotheses. Section 3 outlines the methodology employed to collect and analyze data. Section 4 evaluates the structural model, and Section 5 discusses the findings, practical implications, limitations, and recommendations for future research. Through these sections, the study aims to offer theoretical and practical contributions that advance the understanding of Blockchain’s role in healthcare knowledge management and provide insights for other contexts facing similar challenges.

Literature review, hypothesis development, and research framework

Knowledge-generation-driven blockchain technology (KGDBT)

Satoshi Nakamoto’s seminal paper, Bitcoin: A Peer-to-Peer Electronic Cash System, published in 2008, introduced the concept of Blockchain as a solution to the double-spending problem within decentralized networks.¹³ Blockchain technology facilitates the direct exchange of information and assets among participants, eliminating the need for a trusted intermediary.¹⁴ This system consists of interconnected blocks, which serve as the foundational elements of the Blockchain structure.¹⁵ As noted by,¹⁶ each block contains a series of timestamped transactions, recording the precise date and time each transaction was initiated. New blocks are added to the Blockchain by miners through a consensus algorithm, ensuring agreement across the network.¹⁷

Contemporary enterprises are increasingly taking responsibility for managing the vast knowledge generated by advanced business models, such as open innovation.¹⁸ As discussed by,¹⁹ the rise of competitive economies has rendered traditional knowledge management practices inadequate for effectively organizing and managing knowledge within organizations. To comply with knowledge management standards, firms must adopt transparent, strategic approaches across multiple domains, including knowledge accessibility, generation, storage, dissemination, and application.^20,21 Consequently, successful businesses are increasingly embracing innovative, knowledge-based technologies to enhance their competitive advantage.²²

Among these emerging technologies, Blockchain has gained prominence for its potential to transform how information and communication technologies are utilized to improve operational efficiency.^23,24 Blockchain technology facilitates seamless information integration and promotes transparency, resulting in enhanced knowledge development. Moreover, it strengthens data privacy and reduces operational costs.²⁵

The concept of Knowledge-Generation-Driven Blockchain Technology (KGDBT) rests on the fundamental assumption that knowledge can be made explicit, represented through conceptual models that are accessible for processing by both machines and humans.²⁶ By leveraging distributed and decentralized databases protected by Proof-of-Work algorithms and supported by trust management systems, organizations can develop Blockchain-centric platforms focused on knowledge development.^27,28 As highlighted by,²⁹ the integration of Blockchain technology and smart contracts fosters desirable behavior in knowledge management. This is achieved by coordinating all knowledge management processes, authenticating their outcomes, and facilitating intellectual contributions across crucial business functions, including finance, marketing, human resources, production, and operations.

In the context of knowledge generation through Blockchain technology, each user functions as a node within the network, with knowledge conceptualized as a transaction that is stored and exchanged between at least two participants. When individuals seek to acquire knowledge, they gain access to all preceding transactions related to that specific knowledge. Furthermore, users can register and disseminate their expertise within the isolated network, facilitating the generation of new knowledge via Blockchain technology. The secure and transparent management of knowledge—particularly concerning ownership and intellectual property—is meticulously ensured.³⁰

KGDBT in healthcare

The advancement of emerging technologies has driven significant growth and transformation within healthcare organizations, reshaping their operational frameworks. These innovations have catalyzed a shift from traditional paper-based systems to electronic health records (EHRs), with the primary goal of enhancing the management and organization of sensitive patient data while facilitating healthcare research.³¹ As one of the largest global sectors, healthcare is characterized by a complex network of interconnected stakeholders navigating numerous regulations and challenges related to fragmented patient information.³²

The ongoing digital transformation in healthcare has created numerous opportunities to enhance the effectiveness of diagnostic and therapeutic interventions. Additionally, these advancements have opened avenues for reconfiguring administrative and organizational processes to deliver cost-efficient services.³³ However, the healthcare sector continues to face challenges in preventing the unauthorized disclosure of personally identifiable information, despite regulations that restrict the collection and use of personal data for specific purposes.³⁴

In this context, Blockchain technology has emerged as a promising solution to address these challenges effectively. The strength of this technology lies in its ability to restructure healthcare networks into decentralized frameworks for data storage. As noted by,³⁵ this decentralization enhances patient data security, confidentiality, and interoperability, fostering a more secure healthcare environment.

The implementation of Blockchain technology has emerged as a promising solution to address critical healthcare challenges, including the secure exchange of medical records and compliance with data protection regulations.^3,36 This technology shifts processing responsibilities from centralized servers to a decentralized platform, offering enhanced security and transparency.³⁷ The primary goal of this approach is to resolve security and efficiency issues related to the generation, storage, exchange, and application of knowledge systems within the electronic healthcare ecosystem.³⁸

The healthcare sector offers numerous applications for Blockchain technology, providing various benefits to healthcare organizations. These benefits include decentralization, enhanced security for data, information, and knowledge, privacy protection, ownership rights over health-related data, improved accessibility, resilience, transparency, trustworthiness, and content authentication.³⁹ Notable applications include electronic health records, which enable the efficient management of patient data,⁴⁰ and the management of medical supply chains.⁴¹ Additional applications involve tracking infectious diseases,⁴² monitoring medical equipment,⁴³ and safeguarding healthcare data within the Internet of Things (IoT) environment. Furthermore, Blockchain technology is increasingly utilized in various fields, including clinical trials, drug research, and health insurance.⁴⁴

Undoubtedly, Blockchain technology holds substantial potential to enhance healthcare services. However, these potential benefits require further exploration. Previous research has often adopted a fragmented, and at times exclusively technical, approach to investigating Blockchain applications.³ Such methodologies have failed to comprehensively analyze the impact of this technology on organizations, business models, and value-generation processes. To address this gap, the present study proposes a comprehensive research framework to evaluate the potential value generation of Blockchain technology within healthcare entities. The validity of this framework will be tested using appropriate statistical methods.

The UTAUT2 framework and hypothesis development

This study’s conceptual framework is designed to explore the factors influencing the adoption of Blockchain technology within the healthcare sector, drawing on the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2). UTAUT2, as introduced by,^45,46 integrates insights from eight foundational models and theories: Innovation Diffusion Theory (IDT), Theory of Reasoned Action (TRA), Theory of Planned Behavior (TPB), Social Cognitive Theory (SCT), Technology Acceptance Model (TAM), Motivational Model (MM), Model of Personal Computer Use (MPCU), and the combined TAM-TPB model. The primary purpose of UTAUT2 is to provide a comprehensive understanding of the factors that shape consumers’ willingness to adopt and use emerging technologies.^47,48 Recent research by,⁴⁹ supports the relevance of this model, demonstrating that UTAUT2 accounts for a significantly larger proportion of variability in behavioral intent—70% more—compared to the original UTAUT framework.

The conceptual model presented in this study highlights five critical elements of the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2): performance expectations, effort expectations, social influence, facilitating conditions, and price value. However, the current model excludes hedonic motivation, experience, and habit, as their influence evolves over time.⁴⁵ Moreover, given the non-recreational context of Blockchain technology adoption in healthcare, the relevance of hedonic motivation is limited within the scope of this research.

Additionally, the innovative use of Blockchain for knowledge development in Iraqi government hospitals presents a challenge, as healthcare professionals are not yet fully familiar with its implementation. As a result, it is necessary to reassess the contextual factors influencing adoption. The primary goal of excluding mediating variables from the proposed model is to create a simplified framework that can be applied universally in scenarios involving Blockchain-based knowledge generation. This approach minimizes dependence on situational factors such as age, gender, or experience, aligning with the design principles of established models like UTAUT and UTAUT2.^50,51

Furthermore, additional elements, such as transparency in Blockchain technology and knowledge generation, have been incorporated into the framework to enhance its predictive effectiveness. The inclusion of transparency within the UTAUT framework—commonly used to understand technology adoption—has been explored in several academic studies.^52–54 These studies highlight the significance of transparency as a critical factor influencing users’ willingness to adopt Blockchain technology. Additionally, prior research^55–57 emphasizes the essential role of knowledge generation in shaping individuals’ inclination to adopt and utilize emerging technologies. Guided by the conceptual framework illustrated in Figure 1 and the insights outlined above, the following hypotheses will be proposed:

Figure 1.

The proposed conceptual model. Source: Figure created by authors.

Performance expectancy

Performance expectancy refers to an individual’s belief in a technology’s ability to enhance job effectiveness.^58,59 The expectation of improved performance is a key determinant of behavioral intention, as⁶⁰ emphasized. Research in the healthcare sector,^61,62 underscores the significance of performance expectancy in influencing the adoption of health-related technologies. In the context of this study, performance expectancy refers to the extent to which healthcare professionals in Iraqi government hospitals believe that implementing Knowledge-Generation-Driven Blockchain Technology (KGDBT) will enhance their professional performance. Effective knowledge management and related processes can boost individual performance by fostering innovation, improving efficiency, and ensuring timely completion of tasks that rely on acquired and applied knowledge. 6⁶³ Based on this premise, we propose the following hypothesis:

Performance expectancy has a positive and significant impact healthcare professionals’ behavioral intention to adopt KGDBT.

Effort expectancy

Effort expectancy refers to a user’s perception of the physical or mental effort required to complete a task.⁶⁴ highlights a positive correlation between the ease of using technology and users’ willingness to adopt it. This factor plays a crucial role during the initial stages of technology implementation but becomes less significant with continued use.⁶⁰ Research in healthcare, such as,^62,65 has demonstrated that the level of effort required directly influences individuals’ willingness to adopt health-related technologies. This study explores healthcare professionals’ perceptions in Iraqi government hospitals regarding the user-friendliness and effort required to use the KGDBT application. Since the ease of technology use has a direct impact on adoption readiness (Davis et al., 1989), healthcare professionals are more likely to embrace a technology that requires minimal effort for deployment and operation. Based on this premise, we propose the following hypothesis:

Effort expectancy has a positive and significant impact on healthcare professionals’ behavioral intention to adopt KGDBT.

Social influence

Social influence refers to the extent to which individuals consider the opinions of significant people in their lives when deciding whether to adopt new technology.⁶⁶ According to social identity theory, individuals classify themselves into social groups, developing a sense of belonging in which their position within the group becomes central to social integration.⁶⁷ Social influence is most impactful during the initial stages of a person’s interaction with new technology but diminishes in importance as familiarity increases. With greater familiarity, the intention to use the technology is shaped more by practical considerations than social motivations. Previous studies, such as,^68,69 highlight the importance of social influence as a key factor in predicting the intention to adopt Blockchain technology. In the context of this study, social influence refers to the extent to which healthcare professionals in Iraqi government hospitals perceive influential individuals as drivers of KGDBT adoption. Based on this premise, we propose the following hypothesis:

Social influence has a positive and significant impact on healthcare professionals’ behavioral intention to adopt KGDBT.

Facilitating conditions

Facilitating conditions refer to an individual’s perception of the organizational and technological infrastructure that supports the effective use of technology by reducing barriers to its adoption.⁶⁰ This concept suggests that the availability of supportive conditions within a user’s environment can vary significantly, influencing their willingness to engage with the technology. Users who experience favorable facilitating conditions are more likely to develop a strong inclination toward adopting the technology.⁴⁵ In the healthcare sector, facilitating conditions play a critical role in motivating individuals to change their behavior and gradually embrace digital health technologies.^65,70 Previous studies, like,^71,72 highlight the significant impact of facilitating conditions on users’ intentions to adopt Blockchain technology. Based on these insights, we propose the following hypothesis:

Facilitating conditions have a positive and significant impact on healthcare professionals’ behavioral intention to adopt KGDBT.

Price value

Price value refers to the benefit that technology offers to an organization, typically assessed by weighing the financial investment against the quality and performance of the technology.⁷³ A positive price value occurs when the perceived benefits of the technology outweigh its monetary cost, significantly influencing individuals’ willingness to adopt it (Venkatesh et al., 2012). Previous research, such as,^74,75 has shown that lowering the perceived expenses associated with Blockchain technology enhances the likelihood of its practical adoption. In this study, price value refers to the extent to which healthcare professionals in Iraqi government hospitals perceive that KGDBT will reduce costs associated with their current information systems. When professionals believe that the benefits of the technology exceed its implementation costs, their willingness to adopt and utilize it increases. Based on these insights, we propose the following hypothesis:

Price value has a positive and significant impact on healthcare professionals’ behavioral intention to adopt KGDBT.

Blockchain transparency and the mediating role of knowledge generation

Transparency in Blockchain technology plays a crucial role in influencing users’ willingness to adopt this technology.⁵⁴ incorporated Blockchain transparency into the UTAUT model to explore the adoption of Blockchain technology within the Vietnamese banking sector. Their findings suggest that the integration of UTAUT with transparency significantly enhances users’ intention to adopt Blockchain technology. From a technical perspective, the ease of use and perceived utility of Blockchain also promote the development, application, and protection of knowledge, ultimately influencing users’ inclination to adopt the technology.⁷⁶ Previous research, like,^55–57 highlights the pivotal role of knowledge generation in the acceptance and utilization of emerging technologies. Furthermore, Blockchain transparency enables healthcare organizations to establish long-term value and gain a competitive edge.⁷⁷ By consolidating and validating knowledge, organizations can generate new insights and improve their efficiency and effectiveness.³⁰ When users understand the potential of Blockchain technology to extract knowledge from diverse sources and facilitate new idea generation through collaborative discussions, their inclination to adopt it increases. Based on these insights, we propose the following hypotheses:

Transparency has a positive and significant impact on knowledge generation among healthcare professionals.

Knowledge generation has a positive and significant impact on healthcare professionals’ behavioral intention to adopt KGDBT.

Knowledge generation has a mediating role in the relationship between transparency and behavioral intention to adopt KGDBT.

The mediation hypothesis (H8) suggests that the relationship between Transparency and Behavioral Intention is not solely direct but is partially or fully mediated by Knowledge Generation. Transparency in the Blockchain framework enhances the capacity for knowledge creation by ensuring the secure and seamless exchange of information.⁵⁰ This increased capacity for knowledge generation, in turn, positively influences healthcare professionals’ behavioral intention to adopt KGDBT.

Methodology

Questionnaire development

A structured questionnaire was carefully designed to collect data from healthcare professionals working in state-run medical institutions in Mosul. The primary objective of this questionnaire was to identify the factors influencing healthcare professionals’ willingness to adopt knowledge-generation-driven Blockchain technology (KGDBT) within these institutions. The study incorporated six independent variables, one mediating variable, and one dependent variable. The research framework was guided by the Unified Theory of Acceptance and Use of Technology II (UTAUT2), which served as the theoretical foundation for the investigation. A total of 20 indicators were employed, with four items allocated to each variable. The questionnaire covered critical areas including performance expectancy (PE), effort expectancy (EE), social influence (SI), facilitating conditions (FC), and price value (PV). These constructs and measures were adapted from prior studies.^78–81

Transparency was assessed as the sixth independent factor using four criteria, following the framework outlined by 82. Additionally, four criteria were used to evaluate knowledge generation as a mediating factor, based on the studies by 83,84. The dependent variable, “behavioral intention (BI),” was examined using four additional indicators derived from the work of 45. The questionnaire was divided into five sections. Section A gathered demographic information about the participants, while Sections B, C, D, and E focused on UTAUT2 constructs, transparency, knowledge generation, and behavioral intention, respectively. In total, the questionnaire contained 32 measurement indicators used to evaluate the variables under investigation.

The indicators were modified, refined, and validated in collaboration with expert professors to ensure alignment with the specific objectives of this study. A five-point Likert scale was used to assess participants’ level of agreement with the questionnaire statements. To minimize the potential for response bias, the statements were standardized. The questionnaire was made available in both English and Arabic to accommodate participants. A reverse translation process was conducted by a proficient bilingual translator whose native language is Arabic. Any discrepancies between the original text and the back translation were carefully reviewed by academic experts and the translator, with inconsistencies resolved through mutual agreement. The use of the back translation method, a widely recognized approach in international research, served as an additional measure to ensure the translated content accurately reflected the original text.⁸⁵

This study adopted a cross-sectional approach to investigate the research domain within a defined timeframe. Data collection began in December 2022, with surveys distributed through both digital platforms and physical copies. A total of 330 questionnaires were disseminated, resulting in 325 responses. After a careful review, three invalid responses were excluded, leaving a final analytical sample of 322 participants. The statistical analysis was conducted using two software tools: SmartPLS v.3.9 and SPSS v.26.

Population and sampling

The hypotheses of the proposed model were tested empirically using a quantitative analytical approach. This study aims to identify the key factors influencing healthcare professionals’ behavioral intentions to adopt KGDBT in government medical facilities across Iraq, applying the UTAUT2 framework. The Ministry of Health in Iraq, established in 1921, oversees 21 health divisions, each aligned with a specific governorate. Notably, the capital city, Baghdad, is managed by three health departments. In northern Iraq, the Mosul Health Department plays a pivotal role, overseeing numerous government and private hospitals as well as primary care institutions within the Nineveh Governorate. Mosul is home to nine specialized government hospitals, including¹: Al-Jumhuri Teaching Hospital, Ibn Sina Teaching Hospital, Al-Salam Teaching Hospital, Al-Khansa Women’s and Children’s Hospital, Mosul General Hospital, Ibn Al-Atheer Children’s Hospital, Al-Batoul Teaching Hospital for Obstetrics and Gynecology, Al-Humiyat Hospital for Chest Diseases, Oncology, and Nuclear Medicine. This diverse range of facilities highlights Mosul’s importance in providing healthcare services to the region.

In 2016, Mosul’s healthcare infrastructure suffered extensive damage due to ISIS, necessitating ongoing repair efforts. The city, with a population of nearly 2 million, endured significant displacement and trauma caused by violence, resulting in widespread health challenges. The municipal authorities in Mosul are actively working to restore essential healthcare facilities, incorporating advanced technologies such as Blockchain 50. Additionally, the Mosul health departments are focused on enhancing the skills of healthcare professionals, particularly in the use of advanced technologies to improve patient care. Mosul’s strategic location makes it a vital hub for healthcare research, offering valuable insights into the unique health needs and challenges faced by the local population.

Additionally, this study surveyed healthcare professionals from the specified hospitals to identify the key factors influencing their willingness to adopt KGDBT. The study sample comprised 1636 healthcare professionals from various medical institutions, representing both male and female practitioners. A convenient sampling strategy, a non-probability sampling method, was employed based on ease of access to the target population. According to Stephen Thompson’s formula, the minimum required sample size was 312 responses. To ensure a representative sample reflecting the population and to facilitate communication via email, participants were selected from the Mosul Health Department’s healthcare database. Table 1 provides an overview of the demographic characteristics of the participants.

Table 1.

Demographic profile of respondents.

Demographics	Frequency	Percentage
Gender
Male	188	58.39
Female	134	41.61
Age (#years)
Less than 31	158	49.07
31–40	70	21.74
41–50	45	13.98
51–60	25	7.76
More than 60	24	7.45
Education level
BSc	197	61.18
MSc	34	10.56
PhD	91	28.26
Experience (#years)
1–5	158	49.07
6–10	41	12.73
11–15	41	12.73
16–20	19	5.9
More than 20	63	19.57

N = 322.

Source: Table created by authors.

Screening and cleaning the data

To ensure the credibility and reliability of the results, it is essential that the data used in this study be accurate and free from errors before conducting any inferential statistical analysis. This study employed four methodologies to assess the integrity of the data: (a) Detecting anomalies and handling missing values, (b) Verifying the absence of common method bias (CMB), (c) Analyzing the normal distribution of the data, and (d) Ensuring the absence of multicollinearity.^86,87

Missing values occur when certain information is unavailable or when participants fail to respond. Outliers, on the other hand, refer to extreme deviations from the normal distribution of a variable.⁸⁸ In this study, no missing values were found, and the data exhibited no significant deviations from the expected patterns. All 322 responses collected were retained for analysis. Common method variance (CMV) arises when the measurement method introduces systematic variance into the data, rather than capturing the true variance of the measured variables.⁸⁹ The data were thoroughly assessed to ensure that CMV did not compromise the validity of the measurements.

Two tests were conducted to verify the absence of common method variance (CMV) issues in the study data. The first test employed Harman’s single-factor analysis. The results revealed that the total variance explained by the first factor was 40.696%, which is below the established threshold of 50%, as presented in Table 2.

Table 2.

Common method variance.

Component	Initial eigenvalues			Extraction sums of squared loadings
Component	Total	% of Variance	Cumulative %	Total	% of Variance	Cumulative %
1	13.023	40.696	40.696	13.023	40.696	40.696
2	2.395	7.484	48.18	2.395	7.484	48.180
3	2.012	6.288	54.468	2.012	6.288	54.468
4	1.660	5.187	59.654	1.660	5.187	59.654
5	1.317	4.117	63.771	1.317	4.117	63.771
6	1.005	3.140	66.912	1.005	3.140	66.912
7	0.773	2.416	69.327	0.773	2.416	69.327
8	0.714	2.230	71.557	0.714	2.230	71.557

Note. KMO = 0.942; Chi- square = 6334.300; df = 496; sig. = 0.000.

Source: Table created by authors.

Additionally, the results of the Kaiser-Meyer-Olkin (KMO) test, with a value of 0.942, along with a Chi-square of 6334.300 (df = 496, p < .001), indicate no concerns regarding CMV. The second assessment employed Levene’s test for equality of variances and a t test for equality of means to compare early and late responses. A total of 30 early responses and 30 late responses were identified based on the sequence of submissions. Assessing significant statistical differences between early and follow-up responses is essential to ensure the absence of response bias. The results showed no significant statistical differences among the variables between these two groups of responses. Therefore, there is no evidence of bias due to delayed responses.

This study evaluated the data distribution characteristics by analyzing skewness and kurtosis. Skewness measures the asymmetry of the distribution, indicating whether deviations are biased toward the left or right side of the mean. Similarly, kurtosis assesses the heaviness and length of the distribution’s tails.⁹⁰ When skewness and kurtosis values are close to zero, the data distribution aligns closely with a normal distribution. The results presented in Table 3 indicate that the data follows a normal distribution, ensuring that potential inaccuracies in the standard deviation do not impact the statistical significance of correlation and effect relationships.

Table 3.

The description of study constructs.

Construct	Mean	Standard deviation	Kurtosis	Skewness	VIF
PE	3.488	0.633	0.309	−0.074	1.782–2.308
EE	3.470	0.736	−0.579	0.024	1.775–2.129
SI	3.355	0.631	0.147	0.143	1.734–2.017
FC	3.428	0.628	0.133	0.190	1.757–2.107
PV	3.429	0.654	0.032	0.268	1.793–2.133
BI	3.502	0.662	−0.051	−0.190	1.832–2.026
Tr	3.520	0.657	0.173	0.045	1.708–1.857
Kg	3.493	0.616	0.183	0.002	1.545–1.902

Source: Table created by authors.

Multicollinearity arises when multiple linear regression models include predictors that are highly correlated with each other or the dependent variable.⁹¹ Diagnostic techniques such as the variance inflation factor (VIF) and correlation matrix are applied to address multicollinearity.^92,93 This study utilized the correlation matrix to confirm the absence of strong collinearity (with correlations exceeding 0.85) and evaluated the VIF, which should ideally remain below 3.⁹⁴ As shown in Table 3, all VIF values and correlation coefficients fell within acceptable ranges, indicating no issues with multicollinearity.

Model estimation

The SmartPLS software was employed to evaluate the validity of the measurement model used in this study. This evaluation involved several tests, including assessments of convergent validity, discriminant validity, and reliability.⁹⁵ Convergent validity was assessed through four key criteria: individual item loadings exceeding 0.70, Composite Reliability (CR) above 0.70, Average Variance Extracted (AVE) greater than 0.50, and Cronbach’s Alpha surpassing 0.70.⁹⁶ The results of the convergent validity assessment are presented in Table 4, which outlines the study variables, including performance expectancy (PE), effort expectancy (EE), social influence (SI), facilitating conditions (FC), price value (PV), transparency (Tr), knowledge generation (Kg), and behavioral intention (BI). The findings confirm that all item values meet the required global thresholds, indicating that each construct—PE, EE, SI, FC, PV, Tr, Kg, and BI—accurately reflects a consistent concept. Furthermore, these values demonstrate statistical robustness, confirming the reliability of the measurement model. As a result, none of the 32 research items associated with these variables required exclusion from the analysis.

Table 4.

Constructs reliability and validity.

Constructs	Factor loadings (min-max)	Cronbach’s alpha	Composite reliability	Average variance extracted (Ave)
PE	0.806–0.873	0.859	0.904	0.703
EE	0.796–0.861	0.839	0.892	0.674
SI	0.801–0.852	0.843	0.895	0.680
FC	0.783–0.861	0.852	0.899	0.691
PV	0.803–0.853	0.860	0.905	0.704
Tr	0.783–0.848	0.830	0.885	0.659
Kg	0.721–0.847	0.814	0.877	0.642
BI	0.815–0.843	0.847	0.897	0.686

Source: Table created by authors.

Discriminant validity assesses the extent to which a variable in the measurement model is distinct from other variables.^97,98 In this study’s context of variance-based structural equation modeling, such as the Partial Least Squares Structural Equation Modeling (PLS-SEM) approach, discriminant validity is evaluated using several techniques. These include cross-loading analysis, the Fornell-Larcker criterion, and the heterotrait-monotrait ratio of correlations (HTMT) matrix.^99,100 Cross-loading analysis examines the relationships between data attributes across dimensions. Discriminant validity is confirmed when the loading factors of a variable’s items are substantially higher than the loading factors of items belonging to other variables.¹⁰¹ This ensures that each construct captures a unique concept, thereby enhancing the reliability of the measurement model.

The Fornell-Larcker criterion reflects the average variance shared among a variable’s individual items, also known as shared variance. According to this criterion, discriminant validity is established when the square root of the Average Variance Extracted (AVE) for a variable exceeds its correlations with other variables.¹⁰² In addition, the heterotrait-monotrait ratio (HTMT) is used to assess the similarity between variables. It serves as a benchmark for comparing correlations, with acceptable thresholds commonly set below 0.85 or 0.90. If the HTMT value exceeds these thresholds, it indicates a lack of discriminant validity.^99,103 As demonstrated by the data presented in Tables 4–6, the loading factors of each variable’s items are higher for their respective constructs than for other variables, confirming the discriminant validity of the measurement model.

Table 5.

Fornell-larcker criterion.

Constructs	BI	PV	EE	FC	Kg	PE	SI	Tr
BI	0.828
PV	0.764	0.839
EE	0.513	0.458	0.821
FC	0.715	0.654	0.394	0.831
Kg	0.676	0.598	0.359	0.635	0.801
PE	0.701	0.616	0.374	0.577	0.527	0.838
SI	0.731	0.689	0.382	0.562	0.729	0.520	0.825
Tr	0.459	0.517	0.353	0.426	0.423	0.436	0.459	0.812

Notes: Bold numbers = Root square of AVE.

Source: Table created by authors.

Table 6.

Heterotrait-monotrait ratio of correlations (HTMT) criterion.

Constructs	BI	PV	EE	FC	Kg	PE	SI
BI
PV	0.840
EE	0.605	0.537
FC	0.830	0.760	0.459
Kg	0.801	0.701	0.422	0.746
PE	0.820	0.715	0.441	0.669	0.625
SI	0.821	0.806	0.449	0.656	0.866	0.609
Tr	0.527	0.607	0.411	0.486	0.487	0.502	0.539

Source: Table created by authors.

The results of the Fornell-Larcker criterion confirm that the square root of the AVE for each variable exceeded its correlation coefficients with other variables, indicating strong discriminant validity. Additionally, the HTMT values were found to be below the 0.85 threshold, satisfying the HTMT criterion.^104,105 Based on these findings, the measurements of all variables demonstrate sufficient discriminant validity, ensuring that there is no concern about overlap among the components of the variables within the measurement model.

The reliability of the measurement model was assessed using composite reliability (CR) scores and Cronbach’s alpha coefficients, both of which should exceed the threshold of 0.70. As shown in Table 4, all CR and Cronbach’s alpha values surpassed this benchmark, indicating the robustness and reliability of the measurement methodology employed in this study. Furthermore, the internal consistency of the items corresponding to the study’s variables was satisfactory, providing a solid foundation for subsequent statistical analyses.

Assessment of the structural model

Evaluating the structural model is essential for validating the study’s conceptual framework.¹⁰⁶ The primary objective is to determine whether the collected data supports the hypotheses proposed by the structural model. In the PLS-SEM methodology, the assessment involves several key metrics, including the coefficient of determination (R²), effect size (F²), predictive relevance (Q²), beta coefficient (β), T-value, and p-value.¹⁰⁷

Additionally, metrics such as the standardized root mean squared residual (SRMR) and the normed fit index (NFI) are used to evaluate the model’s fit. According to 108, NFI values range from 0 to 1, with values closer to one indicating a stronger alignment between the model and the data. The coefficient of determination (R²) measures the proportion of variability in the dependent variable that is explained by the independent variables, reflecting their ability to account for changes in the dependent variable. An R² value above 0.10 is considered sufficient to indicate an adequate level of explained variance.^109,110

This study incorporates eight hypotheses, consisting of seven direct hypotheses and one mediating hypothesis. The structural model was evaluated using the SmartPLS software. As shown in Table 7 and illustrated in Figure 2, the analysis results provide insights into the structural model and hypothesis testing. The findings reveal a positive and statistically significant impact between PE and BI (β = 0.231; T = 6.920; p = .000), supporting Hypothesis 1 (H1). These results are consistent with prior studies such as.^61,62

Table 7.

Test the structural model of the study.

Relationships	Path coefficient	T Statistics	F²	R²; Q²	p Values	Results
Performance expectancy → BI	0.231	6.920	0.126		0.000	Accept
Effort expectancy → BI	0.121	3.482	0.051		0.002	Accept
Social influence → BI	0.262	7.477	0.060	0.780; 0.745	0.000	Accept
Facilitating conditions → BI	0.163	4.868	0.097	0.780; 0.745	0.000	Accept
Price value → BI	0.177	4.145	0.066		0.000	Accept
Knowledge generation→ BI	0.171	9.153	0.057		0.000	Accept
Transparency→ Knowledge generation	0.428	5.299	0.135	0.119; 0.110	0.000	Accept
Transparency→ Knowledge generation→ BI	0.073	4.905	0.192	0.780; 0.745	0.000	Accept

Note. SRMR = 0.057; NFI = 0.808.

Source: Table created by authors.

Figure 2.

The study’s structural model. Source: Figure created by authors.

Similarly, a significant correlation was found between EE and BI, with a positive coefficient of 0.121, a t-value of 3.482, and a p-value of 0.002. These results support Hypothesis 2 (H2) and are consistent with findings from previous studies such as.^62,65 Additionally, a statistically significant positive relationship was observed between SI and BI (β = 0.262; T = 7.477; p = .000), confirming Hypothesis 3 (H3). These results align with prior research, such as those of.^111,112

Furthermore, a positive and statistically significant relationship was identified between FC and BI (β = 0.163; T = 4.868; p = .000), supporting Hypothesis 4 (H4) and aligning with previous research such as.^65,70 Similarly, the results reveal a significant positive relationship between PV and BI (β = 0.177; T = 4.145; p = .000), confirming Hypothesis 5 (H5) and corroborating the findings of earlier studies such as.^74,75

The statistical analysis reveals a significant positive impact between transparency and knowledge generation (β = 0.428; T = 9.153; p = .000), confirming Hypothesis 6 and supporting the findings of prior research by.³⁰ Additionally, the correlation between knowledge generation and BI is statistically significant, with a coefficient of 0.171, a t-value of 5.299, and a p-value of 0.000. These results align with the conclusions of earlier studies such as,^55–57 thereby confirming Hypothesis 7.

The study found that transparency has a positive and significant impact on BI, with this relationship being mediated by knowledge generation. The coefficients for this mediated relationship are as follows: β = 0.073, T = 4.905, p = .000. These findings confirm that knowledge generation mediates the effect of transparency on Iraqi healthcare professionals’ behavioral intention to adopt KGDBT in public hospitals, supporting Hypothesis 8.

The model fit metrics—R², Q², and F²—indicate high accuracy and strong predictive power for the structural model. Additionally, the SRMR value of 0.057 and the NFI value of 0.808 demonstrate strong model fit and predictive alignment in the current study.

Discussion

The primary objective of this study is to enhance understanding of the Unified Theory of Acceptance and Use of Technology II (UTAUT2) by examining the relationships between Performance Expectancy (PE), Effort Expectancy (EE), Social Influence (SI), Facilitating Conditions (FC), Price Value (PV), Transparency (Tr), and Behavioral Intention (BI) toward the adoption of Knowledge-Generation-Driven Blockchain Technology (KGDBT) in the healthcare sector. Additionally, the study investigates how knowledge generation mediates the relationship between transparency (Tr) and behavioral intention (BI). Data were collected from 322 healthcare professionals employed in public hospitals across Mosul city and analyzed using statistical methods.

Based on our analysis, the findings reveal a positive correlation between Performance Expectancy (PE) and Behavioral Intention (BI), aligning with previous studies by.^61,62 This result supports the proposed hypothesis (H1). When healthcare professionals believe that technology can enhance their job performance, assist them in achieving their goals, develop their skills, and expedite task completion, they are more inclined to adopt and utilize it.

Furthermore, the study demonstrates that the perceived level of effort expectation (EE) in implementing KGDBT positively correlates with behavioral intention (BI) in adopting this technology. These findings support the second hypothesis (H2) and align with previous research.^62,65 Notably, reducing the effort required to implement the technology increases the likelihood of its adoption.¹¹³ Within the context of KGDBT, the integration of smart contracts can enhance processing capabilities and accelerate the distribution and utilization of knowledge by automating algorithm execution.¹¹⁴

The findings indicate that Social Influence (SI) plays a significant role in shaping healthcare professionals’ willingness to adopt KGDBT. This supports the third hypothesis (H3) and is consistent with prior research.^111,112 When considering new technologies, individuals often seek guidance from their social networks, and their decisions are influenced by how others perceive the value of the technology. Social networks play a crucial role in facilitating knowledge exchange among individuals. The use of Blockchain technology and smart contracts further promotes positive knowledge-sharing behavior, fostering the creation of new knowledge.²⁹

Furthermore, the results highlight the importance of Facilitating Conditions (FC) in influencing healthcare professionals’ intentions to adopt KGDBT. This finding supports the fourth hypothesis (H4) and aligns with the conclusions of previous studies.^65,70 Access to technical, organizational, and human support when using this technology contributes to a more positive user experience, thereby enhancing the likelihood of adoption. The availability of online courses, demonstrations, and live chat support further reduces uncertainties surrounding the technology, facilitating smoother implementation.

The findings of this study reveal a positive correlation between Price Value (PV) and Behavioral Intention (BI), consistent with previous research,^74,75 thereby supporting the fifth hypothesis (H5). Healthcare professionals in Iraqi government hospitals perceive that the benefits of adopting KGDBT outweigh the costs associated with its implementation. This favorable perception may be influenced by their prior experience with similar cloud-based technologies. Additionally, the study demonstrates a strong association between Transparency and Knowledge Generation. This alignment can be attributed to Blockchain technology’s ability to enhance knowledge sharing through the use of smart contracts, which safeguard intellectual property rights and restrict access to authorized individuals. These findings logically support the sixth hypothesis (H6).

The data also reveals a positive correlation between Knowledge Generation (Kg) and Behavioral Intention (BI), consistent with previous research findings,^55–57 thereby supporting the seventh hypothesis (H7). Healthcare professionals in Iraqi government healthcare institutions believe that Knowledge Generation through Blockchain Technology can streamline the creation of knowledge and the accumulation of experiences by providing clear and immutable intellectual property records. As a result, they perceive KGDBT as a reliable and user-friendly innovation. Additionally, the study found a positive relationship between the transparency factor and behavioral intention, mediated by knowledge generation, supporting the eighth hypothesis (H8). This suggests that healthcare professionals in Iraqi government hospitals are more inclined to adopt KGDBT when they recognize the openness and transparency of the technology, which facilitates and accelerates the process of knowledge creation.

Theoretical implications

This study makes important theoretical contributions by addressing significant gaps in the existing literature on Blockchain technology and knowledge management. Although Blockchain has been widely explored across various sectors, limited research has focused on its application in the public healthcare sector, especially within the context of government-operated hospitals.^115,116 By integrating Blockchain technology with knowledge generation, the study offers a new theoretical framework for understanding the adoption behavior of healthcare professionals. This perspective expands on the UTAUT2 model⁴⁵ by introducing transparency as an independent variable, alongside established constructs such as performance expectancy, effort expectancy, social influence, facilitating conditions, and price value.

One of the key theoretical contributions of this study lies in demonstrating the mediating role of knowledge generation (KG). The findings show that transparency significantly influences behavioral intention through knowledge generation, highlighting Blockchain’s ability to facilitate secure, traceable knowledge-sharing.³⁰ This interaction adds a novel dimension to UTAUT2 by emphasizing that technological transparency does not merely enhance trust but also boosts knowledge exchange and generation, leading to improved behavioral outcomes. The study aligns with prior research suggesting that smart contracts and Blockchain networks promote positive knowledge-sharing behaviors.²⁹

Moreover, by addressing the interplay between knowledge generation and technology adoption, this research fills a gap in the intersection of Blockchain and knowledge management. Previous studies^55,57 have explored technology adoption in the context of knowledge management, but few have focused on how Blockchain facilitates continuous knowledge creation and organizational learning. The findings offer valuable insights for expanding theoretical discussions on how decentralized technologies like Blockchain can foster collaborative learning and knowledge transformation in healthcare institutions.

Future research should build on these findings by exploring additional constructs, such as the role of trust in decentralized systems and the long-term impact of transparency on knowledge ecosystems in healthcare. Furthermore, studies could examine the different dimensions of knowledge generation facilitated by Blockchain, such as tacit versus explicit knowledge exchange, to provide a more comprehensive understanding of the impact of technology.

Practical implications

This study offers several practical implications for healthcare institutions, particularly those in the public sector, by demonstrating how Blockchain technology can address challenges related to knowledge management and operational inefficiencies. The findings suggest that government healthcare facilities can benefit from integrating Blockchain into their knowledge management processes to enhance transparency, security, and knowledge generation.³⁷ Blockchain technology shifts the burden of information management from centralized client-server systems to decentralized platforms, providing reliable solutions for tracking, sharing, and safeguarding medical knowledge.¹¹⁷

The positive relationship between transparency, knowledge generation, and technology adoption highlights the importance of establishing clear and open communication channels within healthcare institutions. By leveraging smart contracts, hospitals can create secure knowledge-sharing environments that protect intellectual property and limit access to authorized users.¹¹⁴ This not only improves collaborative learning but also accelerates the generation of new medical knowledge, which is crucial for addressing emerging healthcare challenges.

For successful implementation, senior management must provide both technical and organizational support, including resources such as training programs, software tools, and live support. Facilitating internal social networks can further encourage collaboration and information exchange among healthcare professionals, enhancing their familiarity with Blockchain technology and fostering adoption. This aligns with previous research that emphasizes the role of internal networks and social influence in promoting new technology adoption.^111,112

Additionally, the study suggests that continuous education and awareness programs are essential for motivating healthcare professionals to embrace advanced technologies. Workshops, seminars, and courses should highlight the practical benefits of Blockchain in enhancing task efficiency and improving patient care. Future advancements should also focus on building robust infrastructures that integrate Blockchain with existing hospital systems to ensure compatibility and smooth operations.

Lastly, future research should compare public and private healthcare sectors to identify key differences in the adoption of Blockchain technology. This study’s focus on public hospitals in Mosul, Iraq, presents valuable insights, but further comparative research could offer a broader understanding of how Blockchain can be tailored to different healthcare environments. Exploring cultural factors and sector-specific constraints will help institutions design context-sensitive solutions that maximize the benefits of Blockchain technology across various healthcare settings.

Healthcare policy implications

Healthcare policies should focus on incentivizing the adoption of KGDBT by providing both financial and technical support to public hospitals. The findings show that Performance Expectancy (PE), Effort Expectancy (EE), and Price Value (PV) are crucial factors influencing healthcare professionals’ behavioral intention to adopt Blockchain technology.⁵⁰ Policies could offer subsidies or grants to hospitals for implementing KGDBT, thus alleviating the financial burden of adopting new technology. The Iraqi healthcare institutions could establish incentive programs that offer tax benefits or grants to government hospitals that successfully integrate Blockchain solutions. This would align with the findings that price value influences the intention to adopt KGDBT and encourages broader adoption.

The study highlights the importance of transparency in driving the adoption of KGDBT. Policymakers should focus on developing healthcare policies that mandate transparent information-sharing protocols across healthcare institutions.¹¹⁸ A national Blockchain-based knowledge-sharing network can allow hospitals to share treatment protocols, patient records, and research findings securely while limiting access to authorized professionals through smart contracts. The Iraqi healthcare institutions could implement policies requiring all public hospitals to participate in a Blockchain-enabled electronic health record (EHR) network. This network would improve transparency and facilitate the exchange of medical knowledge across hospitals, enabling healthcare professionals to build on shared insights and enhance patient care.

The findings indicate that Facilitating Conditions (FC), such as technical infrastructure and organizational support, play a key role in fostering technology adoption. Policies should focus on building robust IT infrastructure in hospitals and offering technical training programs to healthcare professionals. Investments in secure digital platforms will ensure compatibility between Blockchain technology and existing hospital systems.¹¹⁹ The healthcare institutions could launch an infrastructure development initiative aimed at upgrading hospital IT systems and providing training programs on Blockchain technology. Policies could also encourage public-private partnerships to share resources and expertise in building advanced digital infrastructure.

Healthcare professionals’ familiarity with advanced technologies influences their willingness to adopt Blockchain-based systems. The study emphasizes the role of social influence and internal networks in promoting knowledge exchange. Therefore, healthcare policies should prioritize continuous education programs, including workshops, seminars, and certification courses focused on Blockchain and knowledge management.¹ Healthcare institutions could mandate annual training programs on Blockchain technology and knowledge management for healthcare staff, offering certifications to professionals who complete these programs. Policies could also support the development of online learning platforms that allow professionals to engage with peers, facilitating collaborative knowledge generation.

The findings reveal that knowledge generation (KG) acts as a critical mediator between transparency and behavioral intention. Policies should encourage the development of collaborative networks within and between hospitals to promote knowledge sharing. This can be achieved by creating incentives for collaborative research and by establishing knowledge-sharing platforms supported by Blockchain technology. Healthcare institutions could implement policies that reward hospitals for participating in collaborative research initiatives or contributing to a national medical knowledge repository. Such policies would encourage professionals to share insights through Blockchain-enabled platforms, facilitating continuous knowledge generation.

Since the study focused exclusively on public hospitals, the results highlight the need for policies that foster collaboration between public and private healthcare institutions. Blockchain-enabled knowledge-sharing platforms could bridge the gap between these sectors, promoting consistency in treatment protocols and enhancing patient care. The Iraqi government could introduce policies that require public and private hospitals to share anonymized patient data through Blockchain networks, ensuring both sectors benefit from shared insights and medical advances. This would address fragmentation and enhance the quality of care across the healthcare ecosystem.

Conclusion

This study investigated the factors influencing healthcare professionals’ willingness to adopt Knowledge-Generation-Driven Blockchain Technology (KGDBT) in government-operated hospitals in Iraq. The research was guided by the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) as its conceptual framework. Notable contributions included the integration of new factors, such as Transparency and Knowledge Generation. Before conducting hypothesis testing, rigorous steps were taken to ensure the reliability and validity of the research instruments. The findings demonstrate a strong positive relationship between healthcare professionals’ intention to adopt KGDBT and factors such as Performance Expectancy (PE), Effort Expectancy (EE), Social Influence (SI), Facilitating Conditions (FC), and Price Value (PV). Additionally, the results underscore the significant role of Transparency in shaping behavioral intention. The relationship between Transparency and the intention to adopt KGDBT is further mediated by the level of Knowledge Generation, emphasizing its importance in driving adoption.

Limitations and future research

This study has several limitations. One key limitation is its cross-sectional design, which may influence the relationships observed between the variables. Future studies should consider employing a longitudinal design to provide a more comprehensive assessment of the factors that influence behavioral intention to adopt KGDBT over time. Additionally, this research focused exclusively on the public sector by collecting data solely from government hospitals in Mosul, Iraq. As a result, the findings cannot be generalized to private hospitals, limiting the broader applicability of the results. Future studies would benefit from comparing public and private healthcare sectors to identify potential differences in adoption behaviors.

Another limitation lies in the limited body of literature linking knowledge management with Blockchain technology, which represents a theoretical constraint. Future research could address this gap by incorporating additional relevant factors, such as specific attributes of Blockchain technology and their influence on individuals’ intention to adopt it. Moreover, expanding the sample size could provide deeper insights into the topic. Future studies are encouraged to replicate this research in different cultural contexts and across various sectors to enhance the generalizability of the findings.

Footnotes

Acknowledgments

We extend our sincere gratitude to our universities—Mosul, Jordan, Shenzhen, A’Sharqiyah, and Mutah—as well as the Nineveh Health Directorate and the Iraqi Ministry of Health for their support and encouragement throughout the completion of this study. We also appreciate the valuable insights and expertise contributed by our colleges colleagues, which significantly enriched the research.

ORCID iD

Amir A. Abdulmuhsin

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Note

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