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.
AbdulmuhsinAATarhiniAMasa’dehR, et al.Guest editorial: mass collaboration and knowledge management: reflections to achieve best practices. Int J Organ Anal2022; 30(5): 1041–1044.
2.
SrivastavaVMaharaTYadavP. An analysis of the ethical challenges of blockchain-enabled E-healthcare applications in 6G networks. Int J Cognitive Comput Eng2021; 2: 171–179.
3.
MassaroM. Digital transformation in the healthcare sector through blockchain technology. Insights from academic research and business developments. Technovation2023; 120: 102386.
4.
AyatollahiHZeraatkarK. Factors influencing the success of knowledge management process in health care organisations: a literature review. Health Inf Libr J2020; 37(2): 98–117.
5.
RadaelliGLettieriEMuraM, et al.Knowledge sharing and innovative work behaviour in healthcare: a micro‐level investigation of direct and indirect effects. Creativ Innovat Manag2014; 23(4): 400–414.
6.
AntwiMAdnaneAAhmadF, et al.The case of HyperLedger Fabric as a blockchain solution for healthcare applications. Blockchain: Res Appl2021; 2(1): 100012.
7.
ChengASKGuanQSuY, et al.Integration of machine learning and blockchain technology in the healthcare field: a literature review and implications for cancer care. Asia Pac J Oncol Nurs2021; 8(6): 720–724.
8.
SabuSRamalingamHVishakaM, et al.Implementation of a secure and privacy-aware E-Health record and IoT data sharing using blockchain. Global Transitions Proceedings2021; 2(2): 429–433.
9.
MiaoQLinHHuJ, et al.An intelligent and privacy-enhanced data sharing strategy for blockchain-empowered Internet of Things. Digital Commun Netw2022; 8(5): 636–643.
10.
HasselgrenAKralevskaKGligoroskiD, et al.Blockchain in healthcare and health sciences-A scoping review. Int J Med Inf2020; 134: 104040.
11.
McGhinTChooKKRLiuCZ, et al.Blockchain in healthcare applications: research challenges and opportunities. J Netw Comput Appl2019; 135: 62–75.
12.
AlShamsiMAl-EmranMShaalanK. A systematic review on blockchain adoption. Appl Sci2022; 12(9): 4245.
13.
NakamotoS. Bitcoin: a peer-to-peer electronic cash system. Decentralized Business Review2008; 1: 1–9.
14.
PaulSAdhikariABoseI. White knight in dark days? Supply chain finance firms, blockchain, and the COVID-19 pandemic. Inf Manag2022; 59(6): 103661.
15.
BajwaNK. Modelling and simulation of blockchain based education system. Concordia University, 2018.
16.
MingxiaoDXiaofengMZheZ, et al.“A review on consensus algorithm of blockchain,” 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC),Banff, AB, Canada, 2017, pp. 2567-2572, https://doi.org/10.1109/SMC.2017.8123011
17.
CiaianPKancsd.ARajcaniovaM. Interdependencies between mining costs, mining rewards and blockchain security. arXiv preprint arXiv:.081072021; 1: 1–34.
18.
AbdulmuhsinAATarhiniA. Impact of wise leadership, workplace friendships on open innovation in family firms: a developing country perspective. J Fam Bus Manag2020; 12(1): 1–23.
19.
SunYShahzadMRazzaqA. Sustainable organizational performance through blockchain technology adoption and knowledge management in China. Journal of Innovation & Knowledge2022; 7(4): 100247.
20.
PriyambodoDYBerawiMASariM. The Development of Blockchain Based Knowledge Management System Model at EPC Projects to Improve Project Time Performance. In: Kristiawan, S.A., Gan, B.S., Shahin, M., Sharma, A. (eds) Proceedings of the 5th International Conference on Rehabilitation and Maintenance in Civil Engineering. ICRMCE 2021. Lecture Notes in Civil Engineering, 2023, vol 225. Springer, Singapore, https://doi.org/10.1007/978-981-16-9348-9_75.
21.
AsadMMRindAAAbdulmuhsinAA. The effect of knowledge management in educational settings: a study of education management organizations (EMOs) schools of Pakistan. Int J Organ Anal2021; 30(5): 1156–1171.
22.
AzeemMAhmedMHaiderS, et al.Expanding competitive advantage through organizational culture, knowledge sharing and organizational innovation. Technol Soc2021; 66: 101635.
23.
AlkhwaldiAFAl-AjaleenRT. Toward a conceptual model for citizens’ adoption of smart mobile government services during the COVID-19 pandemic in Jordan. Information Sciences Letters2022; 11(2): 573–579.
24.
BhatMAKhanSTAlkhwaldiAF, et al. Unlocking the potential: exploring the drivers behind blockchain and its influence on SMEs performance within TOE framework. TQM J2025; ahead-of-print: 1–28.
25.
YuDShengL. Knowledge diffusion paths of blockchain domain: the main path analysis. Scientometrics2020; 125(1): 471–497.
26.
FillH-GHaererF. Knowledge Blockchains: Applying Blockchain Technologies to Enterprise Modeling. Proceedings of the 51st Hawaii International Conference on System Sciences.AIS, 2018. pp. 4045-4054.
27.
AbdulmuhsinAAAL-TaieAAADbesanAH, et al. Impact of the organizational dimension of workplace friendship on knowledge management processes: an Islamic higher education perspective. J Islam Account Bus Res2025; ahead-of-print: 1–29.
28.
YumanYGoyalDSB. Knowledge management using IoT-blockchain technology: state of the art. Int J Electron Eng Res2022; 10(2): 183–189.
29.
DucréeJ. Research – a blockchain of knowledge?Blockchain: Research and Applications2020; 1(1–2): 1–4.
30.
AkhavanPPhilsoophianMRajabionL, et al. Developing a block-chained knowledge management model (BCKMM): beyond traditional knowledge management. The 19th European Conference on Knowledge Management (ECKM 2018). September, Italy. Academic Conferences Ltd, 2018.
31.
AndrewJIsravelDSagayamM, et al. Blockchain for healthcare systems: architecture, security challenges, trends and future directions. J Netw Comput Appl2023; 215: 1–36.
32.
SchinckusC. A Nuanced perspective on blockchain technology and healthcare. Technol Soc2022; 71: 102082.
33.
AloiniDBeneventoEStefaniniA, et al.Transforming healthcare ecosystems through blockchain: opportunities and capabilities for business process innovation. Technovation2023; 119: 102557.
34.
MuktaRPaikHLuQ, et al.A survey of data minimisation techniques in blockchain-based healthcare. Comput Netw2022; 205: 108766.
35.
HussienHMYasinSMUdzirNI, et al.Blockchain technology in the healthcare industry: trends and opportunities. Journal of Industrial Information Integration2021; 22: 100217.
36.
TandonADhirAIslamA, et al. Blockchain in healthcare: a systematic literature review, synthesizing framework and future research agenda. Comput Ind2020; 122: 103290.
37.
JamilFAhmadSWhangboT, et al. Improving blockchain performance in clinical trials using intelligent optimal transaction traffic control mechanism in smart healthcare applications. Comput Ind Eng2022; 170: 108327.
38.
SadeghiRJKPrybutokVRSauserB. Theoretical and practical applications of blockchain in healthcare information management. Inf Manag2022; 59(6): 103649.
39.
Abu-ElezzIHassanANazeemudeenA, et al.The benefits and threats of blockchain technology in healthcare: a scoping review. Int J Med Inf2020; 142: 104246.
40.
DhagarraDGoswamiMKumarG. Impact of trust and privacy concerns on technology acceptance in healthcare: an Indian perspective. Int J Med Inf2020; 141: 104164.
41.
NiuBDongJLiuY. Incentive alignment for blockchain adoption in medicine supply chains. Transport Res E Logist Transport Rev2021; 152: 102276.
42.
LiXTaoBDaiHN, et al.Is blockchain for Internet of medical Things a panacea for COVID-19 pandemic?Pervasive Mob Comput2021; 75: 101434.
43.
RababJShikhaS, 4 - Blockchain applications for the healthcare sector: Uses beyond Bitcoin, Editor(s): Sudeep Tanwar, Blockchain Applications for Healthcare Informatics, Academic Press, 2022, Pages 71-92, https://doi.org/10.1016/B978-0-323-90615-9.00022-0
44.
ChondrogiannisEAndronikouVKaranastasisE, et al.Using blockchain and semantic web technologies for the implementation of smart contracts between individuals and health insurance organizations. Blockchain: Research and Applications2022; 3(2): 100049.
45.
VenkateshTThongXu. Consumer acceptance and use of information technology: extending the unified theory of acceptance and use of technology. MIS Q2012; 36(1): 157.
46.
AbdulmuhsinAAOwainHODbesanAH, et al. Knowledge management in metaverse: does knowledge storage matter as a factor affecting adoption and acceptance?Int J Organ Anal2025; ahead-of-print: 1–36.
47.
Abu AfifaMMVo VanHLe Hoang VanT. Blockchain adoption in accounting by an extended UTAUT model: empirical evidence from an emerging economy. J Financ Report Account2022; 21(1): 5–44.
48.
AlkhwaldiAFAbdulmuhsinAAMasa’dehR, et al. Business intelligence adoption in higher education: the role of data-driven decision-making culture and UTAUT. J Int Educ Bus2025; ahead-of-print: 1–21.
49.
FerreiraLOliveiraTNevesC. Consumer’s intention to use and recommend smart home technologies: the role of environmental awareness. Energy2023; 263: 125814.
50.
DbesanAHAbdulmuhsinAAAlkhwaldiAF. Adopting knowledge-sharing-driven blockchain technology in healthcare: a developing country’s perspective. VINE Journal of Information and Knowledge Management Systems2023; 55: 683.
51.
AlkhwaldiAFAbu-AlSondosIAbdulmuhsinA, et al. Toward an Understanding of Cutting Edge Technologies in Financial Industry: Cryptocurrency Adoption. In: Yaseen, S.G. (ed) Cutting-Edge Business Technologies in the Big Data Era. SICB, 2023. Studies in Big Data, vol 135 Springer, Cham, https://doi.org/10.1007/978-3-031-42463-2_8.
52.
ChangMWalimuniACKimM, et al.Acceptance of tourism blockchain based on UTAUT and connectivism theory. Technol Soc2022; 71: 102027.
53.
QueirozMMFosso WambaS. Blockchain adoption challenges in supply chain: an empirical investigation of the main drivers in India and the USA. Int J Inf Manag2019; 46: 70–82.
54.
TranLTTNguyenPT. Co-creating blockchain adoption: theory, practice and impact on usage behavior. Asia Pac J Mark Logist2021; 33(7): 1667–1684.
55.
ChongAY-LChanFTGohM, et al.Do interorganisational relationships and knowledge-management practices enhance collaborative commerce adoption?Int J Prod Res2013; 51(7): 2006–2018.
56.
LeeCPLeeGGLinHF. The role of organizational capabilities in successful e‐business implementation. Bus Process Manag J2007; 13(5): 677–693.
57.
MigdadiMMAbu ZaidMKSAl-HujranOS, et al.An empirical assessment of the antecedents of electronic-business implementation and the resulting organizational performance. Internet Res2016; 26(3): 661–688.
58.
KapnissisGVaggelasGKLeligouHC, et al.Blockchain adoption from the Shipping industry: an empirical study. Maritime Transport Research2022; 3: 100058.
59.
AbdulmuhsinAAOwainHDbesanA, et al. Acceptance of KM-driven metaverse technology in higher education institutions: are educators ready to be immersed?Information Discovery and Delivery, 2025.
60.
VenkateshVMorrisMGDavisGB, et al.User acceptance of information technology: toward a unified view. MIS Q2003; 27(3): 425.
61.
SchmitzADíaz-MartínAMYagüe GuillénMJ. Modifying UTAUT2 for a cross-country comparison of telemedicine adoption. Comput Hum Behav2022; 130: 107183.
62.
TavaresJGoulãoAOliveiraT. Electronic health record portals adoption: empirical model based on UTAUT2. Inf Health Soc Care2018; 43(2): 109–125.
63.
SahibzadaUFJianfengCLatifKF, et al.Interpreting the impact of knowledge management processes on organizational performance in Chinese higher education: mediating role of knowledge worker productivity. Stud High Educ2020; 47(4): 713–730.
64.
KhazaeiH. Integrating cognitive antecedents to UTAUT model to explain adoption of blockchain technology among Malaysian SMEs. JOIV: Int J Inform Visualization2020; 4(2): 85–90.
65.
JewerJ. Patients’ intention to use online postings of ED wait times: a modified UTAUT model. Int J Med Inf2018; 112: 34–39.
66.
van der WaalNEde WitJBolN, et al.Predictors of contact tracing app adoption: integrating the UTAUT, HBM and contextual factors. Technol Soc2022; 71: 102101.
67.
HoggMA.Social identity theory. In McKeownS.HajiR.FergusonN. (Eds.), Understanding peace and conflict through social identity theory: Contemporary global perspectives, 2016, pp. 3–17. Springer International Publishing/SpringerNature. https://doi.org/10.1007/978-3-319-29869-6_1
68.
MustafaMAlshareMBhargavaD, et al.Perceived security risk based on moderating factors for blockchain technology applications in cloud storage to achieve secure healthcare systems. Comput Math Methods Med2022; 2022: 6112815.
69.
PietersJJKokkinouAvan KollenburgT. Understanding blockchain technology adoption by non-experts: an application of the Unified Theory of Acceptance and Use of Technology (UTAUT). Oper Res Forum2021; 3(1): 1.
70.
DuartePPinhoJC. A mixed methods UTAUT2-based approach to assess mobile health adoption. J Bus Res2019; 102: 140–150.
71.
ClaudiaNAngela SiewHoong L. Factors Influencing the Acceptance of Blockchain Technology: Real Estate Industry. In Proceedings of the 2021 4th International Conference on Software Engineering and Information Management (ICSIM ‘21). Association for Computing Machinery, 2021,New York, NY, USA, 63–67. https://doi.org/10.1145/3451471.3451482
72.
UllahN.“Blockchain Technology in Smart Agriculture Environment: A PLS-SEM,” 2021 International Conference on Electronic Information Technology and Smart Agriculture (ICEITSA), Huaihua, China, 2021, pp. 514-519, https://doi.org/10.1109/ICEITSA54226.2021.00103
73.
WahidiSIPribadiTWPribadiSRW, et al.Implementation study of activity based costing system to define indirect costs on ship repair industries. IOP Conf Ser Mater Sci Eng2021; 1052(1): 012049.
74.
ChoSLeeKKCheongA, et al.Chain of values: examining the economic impacts of blockchain on the value-added tax system. J Manag Inf Syst2021; 38(2): 288–313.
75.
FalwadiyaHDhingraS. Blockchain technology adoption in government organizations: a systematic literature review. Journal of Global Operations and Strategic Sourcing2022; 15(3): 473–501.
76.
Al-EmranMMezhuyevVKamaludinA. Towards a conceptual model for examining the impact of knowledge management factors on mobile learning acceptance. Technol Soc2020; 61: 101247.
77.
SpanòRMassaroMIacuzziS. Blockchain for value creation in the healthcare sector. Technovation2023; 120: 102440.
78.
AbbasiGATiewLYTangJ, et al.The adoption of cryptocurrency as a disruptive force: deep learning-based dual stage structural equation modelling and artificial neural network analysis. PLoS One2021; 16(3): e0247582.
79.
ChenXMirazMHGaziMAI, et al.Factors affecting cryptocurrency adoption in digital business transactions: the mediating role of customer satisfaction. Technol Soc2022; 70: 102059.
80.
ParkKO. A study on sustainable usage intention of blockchain in the big data era: logistics and supply chain management companies. Sustainability2020; 12(24): 10670.
81.
SomonK. Understanding the variables influencing the adoption of the blockchain technology. University of Twente, 2020.
82.
Al-JabriIMRoztockiN. Adoption of ERP systems: does information transparency matter?Telematics Inf2015; 32(2): 300–310.
83.
AkhavanPGhojavandSAbdaliR. Knowledge sharing and its impact on knowledge creation. J Info Know Mgmt2012; 11(02): 1250012.
84.
Devi RamachandranSChoy ChongSIsmailH. The practice of knowledge management processes. Vine2009; 39(3): 203–222.
85.
AlkhwaldiAFAbdulmuhsinAA. Understanding user acceptance of IoT based Healthcare in Jordan: Integration of the TTF and TAM. Studies in Computational Intelligence2022; 1010: 191–213.
86.
AbdulmuhsinAAHusseinHDAL-AbrrowH, et al.Impact of artificial intelligence and knowledge management on proactive green innovation: the moderating role of trust and sustainability. Asia-Pac J Bus Adm2024; 17(3): 765–795.
87.
AbdulmuhsinAAOwainHOAlkhwaldiAF. Understanding the academic use of KM-driven Metaverse technology: insights from medical colleges. J Sci Technol Policy Manag2024; ahead-of-print: 1–31.
88.
KwakSKKimJH. Statistical data preparation: management of missing values and outliers. Korean J Anesthesiol2017; 70(4): 407–411.
89.
TehseenSRamayahTSajilanS. Testing and controlling for common method variance: a review of available methods. Jms 20142017; 4(2): 142–168.
90.
BonoRArnauJAlarcónR, et al.Bias, precision, and accuracy of skewness and kurtosis estimators for frequently used continuous distributions. Symmetry2019; 12(1): 19.
91.
ShresthaN. Detecting multicollinearity in regression analysis. Am J Appl Math Stat2020; 8(2): 39–42.
92.
AlkhwaldiAFAl-QudahAAl-HattamiH, et al. Uncertainty avoidance and acceptance of the digital payment systems: a partial least squares-structural equation modeling (PLS-SEM) approach. Global Knowledge, Memory and Communication2023; 73: 1119–1139.
93.
KimJH. Multicollinearity and misleading statistical results. Korean J Anesthesiol2019; 72(6): 558–569.
94.
PallantJ, SPSS survival manual. 2020.
95.
AbdulmuhsinAAAlkhwaldiAFDbesanAH, et al. Empirical insights on organisational sustainability: exploring the influence of communities of practice and associated technologies. Sustain Account Manag Policy J2025; ahead-of-print: 1–29.
96.
JrJFHMatthewsLMMatthewsRL, et al.PLS-SEM or CB-SEM: updated guidelines on which method to use. International Journal of Multivariate Data Analysis2017; 1(2): 107.
97.
Al-QudahAAAl-OkailyMShiyyabFS, et al.Determinants of digital payment adoption among generation Z: an empirical study. J Risk Financ Manag2024; 17(11): 521.
98.
RönkköMChoE. An updated guideline for assessing discriminant validity. Organ Res Methods2020; 25(1): 6–14.
99.
HenselerJRingleCMSarstedtM. A new criterion for assessing discriminant validity in variance-based structural equation modeling. J Acad Market Sci2014; 43(1): 115–135.
100.
JabbarA. Relationship between information technology-enabled dynamic capabilities and competitive performance: mediating role of sensing and responsiveness agility. Tanmiyat Al-Rafidain2025; 44: 82–109.
101.
LiYWenZHauK, et al. Effects of cross-loadings on determining the number of factors to retain. Struct Equ Model: A Multidiscip J2020; 27(6): 841–863.
102.
FornellCLarckerDF. Evaluating structural equation models with unobservable variables and measurement error. J Mark Res1981; 18(1): 39–50.
103.
AbuyabesMAlshekhhossinABatainehI. The impact of artificial intelligence on accounting information systems in Jordanian banks: an exploratory study. Tanmiyat Al-Rafidain2025; 44: 353–368.
104.
AlshaherAHasanMMHAbdulmuhsinAA. A multi-dimensional approach for accepting and using e-learning cloud: a developing country's perspective. Int J Knowl Manag Stud2022; 13(2): 172.
105.
AsadMMRindAAAbdulmuhsinAA. Impact of transformational leadership on educational institutes culture: a quantitative study in the context of Pakistan. Int J Organ Anal2022; 30(5): 1235–1250.
106.
NiazAAsadMMAbdulmuhsinAA, et al.Risk factors for the rice crop farming community in China: a documentary analysis of the challenges during post COVID-19. Asian Educ Dev Stud2021; 11(2): 298–310.
107.
YusofSUbaidullahNMohamedZ. An Evaluation of Structural Model for Independent Learning Through Connectivism Theory and Web 2.0 Towards Students’ Achievement. Proceedings of the International Conference on Applied Science and Engineering (ICASE 2018). Advances in Engineering Research, 2018, volume 175. Atlantis Press. pp. 1-5. https://doi.org/10.2991/icase-18.2018.1
108.
BentlerPMBonettDG. Significance tests and goodness of fit in the analysis of covariance structures. Psychol Bull1980; 88(3): 588–606.
109.
FalkFMillerN. A primer for soft modeling. University of Akron Press, 1992.
110.
AbdulmuhsinAAValeriMAl-TaieA, et al. Understanding the Academic Personality of Workplace Friendship and Knowledge Management: Insights from Higher Education. In: RicciardelliA.ValeriM. (eds) Organisational Behaviour and Design. Contributions to Management Science, 2025. Cham: Springer. https://doi.org/10.1007/978-3-031-80422-9_3
111.
JangSHKimRHLeeCW. Effect of u-healthcare service quality on usage intention in a healthcare service. Technol Forecast Soc Change2016; 113: 396–403.
112.
SladeEWilliamsMDwivediY. “An extension of the UTAUT 2 in a healthcare context” (2013). UK Academy forInformation Systems Conference Proceedings2013. 55. Association for InformationSystems. https://aisel.aisnet.org/ukais2013/55
113.
DavisFD. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q1989; 13(3): 319.
114.
WahabSNLooYMSayCS. Antecedents of blockchain technology application among Malaysian warehouse industry. Int J Logist Syst Manag2020; 37(3): 427.
115.
BatubaraFRUbachtJJanssenM. Challenges of blockchain technology adoption for e-government: a systematic literature review. Proceedings of the 19th Annual International Conference on Digital Government Research: Governance in the Data Age. Association for Computing Machinery. 2018; 19. https://doi.org/10.1145/3209281.3209317
116.
ShahaabAKhanIAMaudeR, et al.Public service operational efficiency and blockchain – a case study of Companies House, UK. Gov Inf Q2023; 40(1): 101759.
117.
NyameGQinZObour AgyekumKOB, et al.An ECDSA approach to access control in knowledge management systems using blockchain. Information2020; 11(2): 111.
118.
AbedSNAbdulmuhsinAAAlkhwaldiAF. The factors influencing the innovative performance of leaders in nurses’ professional: a developing country perspective. Leader Health Serv2021; 35: 228–245.
119.
ChuriPPawarAVAbdulmuhsinAA. Perception of privacy issues and awareness in health-care knowledge management systems: empirical study in Indian health-care context. Int J Organ Anal2021; 30(5): 1100–1119.
