Blockchain technology is poised to significantly transform the corporate world, heralding a new era of innovation and efficiency. Over the past few years, its impact has been noted by leaders, academics, and government representatives around the globe this growing interest underscores businesses’ need to evolve and reconsider traditional operational models. To remain competitive, organizations must embrace this change. Before introducing such ground-breaking technology, it is crucial to assess the motivations of primary stakeholders concerning its implementation. This study looks into what influences the use of Blockchain technology in the oil and gas sector, primarily using a quantitative survey of Iraqi oil and gas companies. A questionnaire was distributed among 250 top-level managers, senior executives, project managers, and IT managers for analyzing the data, the study employs the Structural Equation Modelling-Partial Least Squares (SEM-PLS) technique, with Smart PLS for data processing. The findings suggest that the intention to utilise blockchain technology is influenced by one’s attitude towards it. Competitive pressure (environmental factors), functional benefit, and privacy/security (technological factors) significantly affect blockchain adoption intention. Nevertheless, there was no discernible correlation between regulatory backing and the desire to use Blockchain. Additionally, cost concern and perceived risk (organizational factors) two factors contribute negatively to the perception of blockchain technology. Besides the direct relationship, the findings revealed that attitude toward blockchain technology mediate the relationship between cost concern, perceived risk, and intention to adopt Blockchain. Built upon the Technology-Organization-Environment (TOE) model and the Theory of Reasoned Action, this research offers a comprehensive framework for investigating the intention to adopt blockchain technology. The results enhance both theoretical understanding and practical implementation by providing valuable insights into the emerging area of blockchain adoption intentions.

1. Abeyratne, S. A., & Monfared, R. P. (2016). Blockchain ready manufacturing supply chain using distributed ledger. International Journal of Research in Engineering and Technology, 05(09), 1–10. https://doi.org/10.15623/ijret.2016.0509001
2. Aburbeian, A. M., Owda, A. Y., & Owda, M. (2022). A Technology Acceptance Model Survey of the Metaverse Prospects. AI, 3(2), 285–302. https://doi.org/10.3390/ai3020018
3. Agha, A. M. Q., Massoudi, A. H., & Zaidan, M. N. (2023). The Influence of Individual, Environmental, Technology, and Manufacturing Factors on Iraqi Gas and Oil Companies. Cihan University-Erbil Journal of Humanities and Social Sciences, 7(1), 136-147. https://doi.org/10.24086/cuejhss.v7n1y2023.pp136-147
4. Ahmad, R. W., Salah, K., Jayaraman, R., et al. (2022). Blockchain in oil and gas industry: Applications, challenges, and future trends. Technology in society, 68(c), 101941. https://doi.org/10.1016/j.techsoc.2022.101941
5. Ajzen, I., & Fishbein, M. (1975). A Bayesian analysis of attribution processes. Psychological Bulletin, 82(2), 261–277. https://doi.org/10.1037/h0076477
6. Alaklabi, S., & Kang, K. (2018). Factors influencing behavioural intention to adopt blockchain technology. In: Proceedings of the 32nd International Business Information Management Association Conference.
7. Albarracín, D., Johnson, B. T., Fishbein, M., et al. (2001). Theories of reasoned action and planned behavior as models of condom use: A meta-analysis. Psychological Bulletin, 127(1), 142–161. https://doi.org/10.1037/0033-2909.127.1.142
8. Almajali, D. A., Masa’Deh, R., & Dahalin, Z. M. d. (2022). Factors influencing the adoption of Cryptocurrency in Jordan: An application of the extended TRA model. Cogent Social Sciences, 8(1). https://doi.org/10.1080/23311886.2022.2103901
9. Altamimi, A., Al-Bashayreh, M., AL-Oudat, M., et al. (2022). Blockchain technology adoption for sustainable learning. International Journal of Data and Network Science, 6(3), 983–994. https://doi.org/10.5267/j.ijdns.2022.1.013
10. Aneslagon, D. M. C., B. Lim, L. B., Tomongha, M. A. S., et al. (2024). Assessing the Nexus between Social Responsibility, Environmental Initiatives, and Profitability: A Sustainable Finance Perspective of the Universal Banks in the Philippines. International Journal of Management Thinking, 2(1), 38–51. https://doi.org/10.56868/ijmt.v2i1.40
11. Awa, H. O., & Ojiabo, O. U. (2016). A model of adoption determinants of ERP within T-O-E framework. Information Technology & People, 29(4), 901–930. https://doi.org/10.1108/itp-03-2015-0068
12. Beddu, S., Ahmad, M., Mohd Kamal, N. L., et al. (2024). A State-of-the-Art Review of Hydronic Asphalt Solar Collector Technology for Solar Energy Harvesting on Road Pavement. MATEC Web of Conferences. https://doi.org/10.1051/matecconf/202440003007
13. Blue, C. L. (1995). The predictive capacity of the theory of reasoned action and the theory of planned behavior in exercise research: An integrated literature review. Research in Nursing & Health, 18(2), 105–121. Portico. https://doi.org/10.1002/nur.4770180205
14. Chiarelli, A. (2023). Securing the Bridges Between Two Worlds: a Systematic Literature Review of Blockchain Oracles Security [Master’s thesis]. Aalto University.
15. Chowdhury, S., Rodriguez-Espindola, O., Dey, P., et al. (2022). Blockchain technology adoption for managing risks in operations and supply chain management: evidence from the UK. Annals of Operations Research, 327(1), 539–574. https://doi.org/10.1007/s10479-021-04487-1
16. Czachorowski, K. V. (2022). Digital Transformation in the Offshore Oil and Gas Exploration and Production Supply Chain Operations [PhD thesis]. University of South-Eastern Norway.
17. Esfahbodi, A., Pang, G., & Peng, L. (2022). Determinants of consumers’ adoption intention for blockchain technology in E-commerce. Journal of Digital Economy, 1(2), 89–101. https://doi.org/10.1016/j.jdec.2022.11.001
18. Faasolo, M. B., & Sumarliah, E. (2021). An Artificial Neural Network Examination of the Intention to Implement Blockchain in the Supply Chains of SMEs in Tonga. Information Resources Management Journal, 35(1), 1–27. https://doi.org/10.4018/irmj.287907
19. Gangwar, H., Date, H., & Raoot, A. D. (2014). Review on IT adoption: insights from recent technologies. Journal of Enterprise Information Management, 27(4), 488–502. https://doi.org/10.1108/jeim-08-2012-0047
20. Gefen, D., Straub, D., & Boudreau, M.-C. (2000). Structural Equation Modeling and Regression: Guidelines for Research Practice. Communications of the Association for Information Systems. https://doi.org/10.17705/1cais.00407
21. F. Hair Jr, J., Sarstedt, M., Hopkins, L., et al. (2014). Partial least squares structural equation modeling (PLS-SEM). European Business Review, 26(2), 106–121. https://doi.org/10.1108/ebr-10-2013-0128
22. Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: Indeed a Silver Bullet. Journal of Marketing Theory and Practice, 19(2), 139–152. https://doi.org/10.2753/mtp1069-6679190202
23. Haloul, M. I. K., Bilema, M., & Ahmad, M. (2024). A Systematic Review of the Project Management Information Systems in Different Types of Construction Projects. UCJC Business and Society Review (formerly known as Universia Business Review), 21(80).
24. Huijts, N. M. A., Molin, E. J. E., & Steg, L. (2012). Psychological factors influencing sustainable energy technology acceptance: A review-based comprehensive framework. Renewable and Sustainable Energy Reviews, 16(1), 525–531. https://doi.org/10.1016/j.rser.2011.08.018
25. Kapnissis, G., Vaggelas, G. K., Leligou, H. C., et al. (2022). Blockchain adoption from the Shipping industry: An empirical study. Maritime Transport Research, 3, 100058. https://doi.org/10.1016/j.martra.2022.100058
26. Khazaei, H. (2020). Integrating Cognitive Antecedents to UTAUT Model to Explain Adoption of Blockchain Technology Among Malaysian SMEs. JOIV: International Journal on Informatics Visualization, 4(2), 85–90. https://doi.org/10.30630/joiv.4.2.362
27. Khudhair, F. S., Rahman, R. A., Adnan, A. A. B. Z., & Khudhair, A. A. (2022). Study of the Transformational Leadership and Organizational Culture as Predictors of Employee Creativity and Innovation in the Iraq Oil and Gas Service Industry. Zien Journal of Social Sciences and Humanities, 15, 34-50.
28. Kumar Bhardwaj, A., Garg, A., & Gajpal, Y. (2021). Determinants of Blockchain Technology Adoption in Supply Chains by Small and Medium Enterprises (SMEs) in India. Mathematical Problems in Engineering, 2021, 1–14. https://doi.org/10.1155/2021/5537395
29. Lallmahamood, M. (2007). An Examination of Individual’s Perceived Security and Privacy of the Internet in Malaysia and the Influence of this on their Intention to Use E-commerce: Using an Extension of the Technology Acceptance Model. Journal of internet banking and commerce, 12(3), 1-26.
30. Lanzini, F., Ubacht, J., & De Greeff, J. (2021). Blockchain adoption factors for SMEs in supply chain management. Journal of Supply Chain Management Science. https://doi.org/10.18757/jscms.2021.5624
31. Lee, M.-C. (2009). Factors influencing the adoption of internet banking: An integration of TAM and TPB with perceived risk and perceived benefit. Electronic Commerce Research and Applications, 8(3), 130–141. https://doi.org/10.1016/j.elerap.2008.11.006
32. Lu, H., Huang, K., Azimi, M., et al. (2019). Blockchain Technology in the Oil and Gas Industry: A Review of Applications, Opportunities, Challenges, and Risks. IEEE Access, 7, 41426–41444. https://doi.org/10.1109/access.2019.2907695
33. Lule, I., Omwansa, T. K., & Waema, T. M. (2012). Application of technology acceptance model (TAM) in m-banking adoption in Kenya. International journal of computing & ICT research, 6(1), 31-43.
34. Marengo, A., & Pagano, A. (2023). Investigating the Factors Influencing the Adoption of Blockchain Technology across Different Countries and Industries: A Systematic Literature Review. Electronics, 12(14), 3006. https://doi.org/10.3390/electronics12143006
35. Masmoudi, H. M., & Gargouri, F. (2021). “Does blockchain technology matter?”: Understanding consumer purchase intention in a blockchain technology for olive oil context. International Journal of Education and Science, 4(3–4). https://doi.org/10.26697/ijes.2021.3.4
36. Mukherjee, S., Baral, M. M., Lavanya, B. L., et al. (2023). Intentions to adopt the blockchain: investigation of the retail supply chain. Management Decision, 61(5), 1320–1351. https://doi.org/10.1108/md-03-2022-0369
37. A. Nahi, H., M. Hashim, S., & J. Kreem, D. (2023). Blockchain for baccalaureate examination sheets protection in Iraq. Indonesian Journal of Electrical Engineering and Computer Science, 29(2), 1183. https://doi.org/10.11591/ijeecs.v29.i2.pp1183-1191
38. Namahoot, K. S., & Rattanawiboonsom, V. (2022). Integration of TAM Model of Consumers’ Intention to Adopt Cryptocurrency Platform in Thailand: The Mediating Role of Attitude and Perceived Risk. Human Behavior and Emerging Technologies, 2022, 1–12. https://doi.org/10.1155/2022/9642998
39. Nath, S. D., Khayer, A., Majumder, J., et al. (2022). Factors affecting blockchain adoption in apparel supply chains: does sustainability-oriented supplier development play a moderating role? Industrial Management & Data Systems, 122(5), 1183–1214. https://doi.org/10.1108/imds-07-2021-0466
40. Nazim, N. F., Razis, N. M., & Hatta, M. F. M. (2021). Behavioural intention to adopt blockchain technology among bankers in islamic financial system: perspectives in Malaysia. Revista Română de Informatică Și Automatică, 31(1), 11–28. https://doi.org/10.33436/v31i1y202101
41. Nguyen, T., Gosine, R. G., & Warrian, P. (2020). A Systematic Review of Big Data Analytics for Oil and Gas Industry 4.0. IEEE Access, 8, 61183–61201. https://doi.org/10.1109/access.2020.2979678
42. Pham, C. T., & Thi Nguyet, T. T. (2023). Determinants of blockchain adoption in news media platforms: A perspective from the Vietnamese press industry. Heliyon, 9(1), e12747. https://doi.org/10.1016/j.heliyon.2022.e12747
43. Plano Clark, V. L., & Creswell, J. W. (2015). Understanding research: A consumer’s guide 1st ed. Pearson.
44. Prewett, K. W., Prescott, G. L., & Phillips, K. (2019). Blockchain adoption is inevitable—Barriers and risks remain. Journal of Corporate Accounting & Finance, 31(2), 21–28. Portico. https://doi.org/10.1002/jcaf.22415
45. Ramachandran, T., & Stella, M. (2022). Behavioural intention towards cryptocurrency adoption among students: A fintech innovation. Journal of Positive School Psychology, 6(6), 5046-5053.
46. Ruangkanjanases, A., Qhal, E. M. A., Alfawaz, K. M., et al. (2023). Examining the Antecedents of Blockchain Usage Intention: An Integrated Research Framework. Sustainability, 15(4), 3500. https://doi.org/10.3390/su15043500
47. Salim, T. A., El Barachi, M., Mohamed, A. A. D., et al. (2022). The mediator and moderator roles of perceived cost on the relationship between organizational readiness and the intention to adopt blockchain technology. Technology in Society, 71, 102108. https://doi.org/10.1016/j.techsoc.2022.102108
48. Sciarelli, M., Prisco, A., Gheith, M. H., et al. (2021). Factors affecting the adoption of blockchain technology in innovative Italian companies: an extended TAM approach. Journal of Strategy and Management, 15(3), 495–507. https://doi.org/10.1108/jsma-02-2021-0054
49. Sohaib, O., Hussain, W., Asif, M., et al. (2020). A PLS-SEM Neural Network Approach for Understanding Cryptocurrency Adoption. IEEE Access, 8, 13138–13150. https://doi.org/10.1109/access.2019.2960083
50. Taouab, O., & Issor, Z. (2019). Firm Performance: Definition and Measurement Models. European Scientific Journal ESJ, 15(1). https://doi.org/10.19044/esj.2019.v15n1p93
51. Wang, X., Liu, L., Liu, J., et al. (2022). Understanding the Determinants of Blockchain Technology Adoption in the Construction Industry. Buildings, 12(10), 1709. https://doi.org/10.3390/buildings12101709
52. Wu, R., Ishfaq, K., Hussain, S., et al. (2022). Investigating e-Retailers’ Intentions to Adopt Cryptocurrency Considering the Mediation of Technostress and Technology Involvement. Sustainability, 14(2), 641. https://doi.org/10.3390/su14020641