Human-in-the-Loop Blockchain Governance for Project Change Control: Balancing Smart-Contract Automation and Managerial Discretion
DOI:
https://doi.org/10.55737/trt/v-v.279Keywords:
Blockchain Governance, Smart Contracts, Project Change, Change Control, Managerial Discretion, Human-in-the-Loop, Process, Transparency, Project Performance, SPSSAbstract
Culture, Smart contracts provide a way to automate the change-control processes of your project, such as verifying the need for a change and how it is authorized and how many members need to be involved, implementation of the change, auditing the process and more. However, many if not most project alterations include dimensions where the information and requirements are ambiguous, where many different parties are conflicting, and where much of the information is not necessarily deterministically codable. This paper builds and evaluates a human-in-the-loop governance model merging smart-contract automation with formal managerial discretion. Analysis was implemented based on data provided by the researcher, consisting of 384 cases and 38 adapted items in the questionnaire. Cronbach's alpha values were in the range of 0.929 to 0.957. The measure of sample adequacy, Kaiser-Meyer-Olkin was 0.969 and 76.64% of variance was accounted for by the six factors. Using hierarchical regression, the overall results indicated that both two factors: smart-contract automation and process transparency, as well as structured managerial discretion, positively predicted the effectiveness of change control. Automation by discretion (beta = 0.105, p = 0.013) and discretion by project change complexity (beta = 0.108, p = 0.003) were both significant. Effectiveness of change control had a significant positive effect on project performance (Beta = .521; p < .001). Process transparency and change-control effectiveness were both indirectly important in bootstrap mediation analyses. The results suggest a complementarity principle: When the automation is backed up by bounded, auditable and context-sensitive human interaction, that is where it is most valuable. The workbook contains original field data collected from 384 respondents; therefore, the findings are presented as empirical evidence from the study sample.
References
Afzal, M., & Panagiotopoulos, P. (2025). How public officials perceive algorithmic discretion: A study of status quo bias in policing. Public Administration Review, 85(5), 1385-1397. https://doi.org/10.1111/puar.13957
Bag, S., Rahman, M. S., Routray, S., Shrivastav, S. K., & Agrawal, S. (2024). Exploring the potential of blockchain‐enabled smart contracts for achieving net‐zero emissions: An empirical study. Business Strategy and the Environment, 33(5), 3965-3985. https://doi.org/10.1002/bse.3691
Burton, J. W., Stein, M., & Jensen, T. B. (2020). A systematic review of algorithm aversion in augmented decision making. Journal of Behavioral Decision Making, 33(2), 220-239. https://doi.org/10.1002/bdm.2155
De‐Arteaga, M., Feuerriegel, S., & Saar‐Tsechansky, M. (2022). Algorithmic fairness in business analytics: Directions for research and practice. Production and Operations Management, 31(10), 3749-3770. https://doi.org/10.1111/poms.13839
Deng, B., Xie, W., Cheng, F., Deng, J., & Long, L. (2021). Complexity relationship between power and trust in hybrid Megaproject governance: The structural equation modelling approach. Complexity, 2021(1). https://doi.org/10.1155/2021/8814630
Hwang, B., & Low, L. K. (2012). Construction project change management in Singapore: Status, importance and impact. International Journal of Project Management, 30(7), 817-826. https://doi.org/10.1016/j.ijproman.2011.11.001
Kim, H., Glaeser, E. L., Hillis, A., Kominers, S. D., & Luca, M. (2024). Decision authority and the returns to algorithms. Strategic Management Journal, 45(4), 619-648. https://doi.org/10.1002/smj.3569
Kumar, A., Liu, R., & Shan, Z. (2020). Is blockchain a silver bullet for supply chain management? Technical challenges and research opportunities. Decision Sciences, 51(1), 8-37. https://doi.org/10.1111/deci.12396
Motawa, I., Anumba, C., Lee, S., & Peña-Mora, F. (2007). An integrated system for change management in construction. Automation in Construction, 16(3), 368-377. https://doi.org/10.1016/j.autcon.2006.07.005
Pfeil, A., Kifokeris, D., & Spyridis, P. (2025). Hyperledger Fabric for the (digitalized) lifecycle of construction products: Applied review on fastening technology. Civil Engineering Design, 7(1), 9-22. https://doi.org/10.1002/cend.202400027
Project Management Institute. (2021). A guide to the project management body of knowledge (PMBOK Guide) (7th ed.). Author.
Singh, S., Gaur, A., & Singh, D. (2023). Blockchain‐based governance: Implications for organizational boundaries and structures. British Journal of Management, 35(4), 1692-1699. https://doi.org/10.1111/1467-8551.12784
Trist, E. L., & Bamforth, K. W. (1951). Some social and psychological consequences of the Longwall method of coal-getting. Human Relations, 4(1), 3-38. https://doi.org/10.1177/001872675100400101
Wagner, B. (2019). Liable, but not in control? Ensuring meaningful human agency in automated decision‐making systems. Policy & Internet, 11(1), 104-122. https://doi.org/10.1002/poi3.198
Zhai, Z., Shan, M., Darko, A., & Le, Y. (2020). Visualizing the knowledge domain of project governance: A Scientometric review. Advances in Civil Engineering, 2020(1). https://doi.org/10.1155/2020/6813043
Zhang, R. S., & Ramesh, B. (2024). A configurational perspective on design elements and user governance engagement in blockchain platforms. Information Systems Journal, 34(4), 1264-1323. https://doi.org/10.1111/isj.12494
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