Purpose: Warehouse receipt system (WRS) schemes aim to revitalize the market of cash crops, particularly those of the smallholder farmers. However, the existing government schemes for the smallholder farmers have not been much successful on account of issues related with transparency and trust deficit, thereby limiting farmers' access to credit for their farming activities as channeled via the formal financial institutions. Conceding that blockchain is an emerging technology, this study aims to theoretically posit the potential to optimize WRS in real time to bolster farmers' trust and privacy.Design/methodology/approach: The research bases itself on the design science research (DSR) approach. Using the DSR approach backed by the literature review and evolutionary prototyping, a blockchain-based WRS has been advanced in the study.Findings: This study provides a design of a digital WRS, which serves as a proof of concept of blockchain technology (BCT) applications in improving transparency in the WRS in the agricultural sector. The findings from this study present the possibility of leveraging blockchain smart contracts to improve the linkages between the WRS stakeholders to promote trust and transparency that would eventually unlock the potentials of WRS schemes. Furthermore, the findings inform decision-makers and practitioners to prioritize the emerging technologies in revamping the classical WRS.Research limitations/implications: The study leaves research pointers for implementing BCT in agricultural supply chain and furthering the findings from the present study with an empirical agenda.Originality/value: Whilst research on the BCT applications across myriad sectors abounds, research on the BCT applications to address societal needs is conspicuously limited. Furthermore, research on the practical implementation of the BCT in real-world scenarios is few and far between. The present study seeks to plug these gaps by underlining the application and utility of blockchain in WRS. The study makes a significant contribution to the theory and practice of the agrarian supply chain management, specifically the WRS, by invoking design knowledge obtained through an iterative design process besides serving as an edifice for the integration of emerging technologies, notably BCT in the agricultural sector.

Healthcare resource allocation remains a persistent challenge in Botswana, primarily due to inefficiencies in data management that obstruct equitable distribution and evidence-based decision-making. Traditional allocation approaches in Botswana exhibit severe fragmentation, low interoperability, and an absence of real-time data analytics factors that contribute to service delivery disparities, especially in rural and underserved areas. In contrast, developed countries have leveraged data warehousing to optimize healthcare resource planning, offering Botswana a proven yet untapped strategic opportunity. This study designs and validates a context-sensitive data warehouse methodology, applying the Kimball Lifecycle model as the guiding framework. A mixed-methods design was adopted, incorporating qualitative interviews with 24 healthcare practitioners and administrators across public and private health facilities, along with quantitative surveys assessing the state of 12 existing health data systems. Results reveal systemic shortcomings in data accuracy (average error rates of 22%), timeliness (with a median data update lag of 14 days), and accessibility (only 38% of facilities had centralized access). Post-implementation of the prototype data warehouse, significant improvements were noted: data accuracy increased by 47%, data accessibility across departments rose to 85%, and decision turnaround time was reduced by 33%. The warehousing also demonstrated cost-effectiveness, reducing redundant data handling expenses by an estimated 18% over six months. In conclusion, this study presents a robust, scalable, and locally adaptable data warehousing framework that effectively addresses Botswana’s systemic challenges in healthcare resource allocation.