The global coffee supply chain continues to face financial exclusion, value asymmetry, and structural inefficiencies that disadvantage smallholder farmers and cooperatives. Blockchain-enabled Coffee Supply Chain Finance (CSCF) has been promoted as a mechanism to automate transactions, enhance transparency, and support more equitable value distribution. This study conducts a systematic literature review using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol, analyzing 60 peer-reviewed publications during 2020–2024 to identify how blockchain is being adopted in CSCF. The findings show that blockchain especially when integrated with Internet of Things (IoT) and Artificial Intelligence (AI) improves traceability, lowers transaction frictions, and expands access to finance through smart contracts, tokenized assets, and blockchain-based crowdfunding models. These mechanisms reduce intermediary dependence and strengthen risk monitoring across supply-chain actors. However, adoption remains constrained by high implementation costs, interoperability challenges, regulatory uncertainty, and limited digital readiness among farmers and cooperatives. Unlike previous blockchain reviews focused mainly on traceability, this study provides the first structured synthesis linking blockchain functions to financing models, adoption determinants, and policy requirements in the coffee sector. The review offers strategic implications for cooperatives, policymakers, and financial institutions seeking to develop scalable and inclusive CSCF ecosystems.

Despite the promising prospects of blockchain technology, research remains primarily confined to the analysis-design phase, highlighting a gap in support for actual implementation. This study aims to develop a framework for assessing the implementation readiness of a blockchain-based traceability system, using the Kintamani coffee agroindustry as a case study. The research commenced with a structured literature analysis to create an assessment model and lay the groundwork for survey questionnaire distribution among potential users. Utilizing Kansei Engineering, the data were analyzed using quantification theory type I (QTT1) to identify the critical elements influencing users’ adoption intentions. The findings led to the proposition of a new framework integrating the assessment model with the system development life cycle (SDLC). Analysis of 59 articles revealed that TAM, TOE, DOI, and TRI are the predominant theories used to assess blockchain technology adoption. The resulting assessment model comprises three contexts, two characteristics with 14 variables, and analyzed data showing individual characteristics—particularly elements OPT1, INS3, and DIS1—as pivotal, evidenced by their high partial correlation coefficients of 0.92 108, 0.89 168, and 0.89 050, respectively. An integrated participatory development (IPD) approach was suggested to enhance system development and improve user quality of life, interaction, and communication effectiveness in the SDLC. This novel method proposed bolstering blockchain systems’ real-world implementation success by adapting development iterations to user needs. It is anticipated to apply to other case studies, offering broader implications. Future research could explore different data analysis methods or case studies to enhance these findings through sensitivity comparisons and bias analysis.