Assessing blockchain technology's technical utility in construction supply chains : A multi-KPI decision support approach via use cases
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A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
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en
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19
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Computers in Industry, Volume 175
Abstract
Blockchain technology (BCT) holds significant potential to transform construction supply chains (CSCs) by addressing longstanding challenges related to transparency, efficiency, and traceability. This study investigates and develops a rigorous, KPI-centric framework that systematically maps blockchain’s enabling capabilities (ECs) to key performance indicators (KPIs) critical to CSC performance. Through a hybrid methodology combining content analysis and design science research (DSR), the paper introduces a web-based Decision Support Tool (DST) to guide stakeholders in evaluating the technical suitability of blockchain for construction projects. The DST operates in two phases: first, assessing blockchain applicability through a structured diagnostic; second, recommending ‘best-fit’ blockchain stacks by aligning selected KPIs with relevant use cases and ECs. Validation via simulated case scenarios demonstrates the DST’s robustness in supporting early-stage, technically grounded decision-making and recommends blockchain solutions tailored to user-defined KPIs and use cases. The findings reveal that BCT, through automation, immutable data sharing, decentralized governance, and the like, can significantly improve CSCs' performance. By bridging the gap between conceptual promise and practical application, this research provides both theoretical advancements and actionable insights for digital transformation in the construction industry. It contributes a replicable decision-support architecture for technology adoption and performance optimization in complex, multi-stakeholder supply chain environments.Description
Publisher Copyright: © 2025 The Authors.
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Olawumi, T O, Ojo, S, Muftaudeen, S T, Odeh, A O & Amoo, T 2026, 'Assessing blockchain technology's technical utility in construction supply chains : A multi-KPI decision support approach via use cases', Computers in Industry, vol. 175, 104429. https://doi.org/10.1016/j.compind.2025.104429