Assessing Influence of Digital Data Collection Tools on the Performance of the Small-Scale Irrigation Technology Project in Ngoma District

The study examined the influence of digital data collection tools on the performance of the Small-Scale Irrigation Technology (SSIT) Project in Ngoma District, Rwanda. Anchored in Systems Theory and the Technology Acceptance Model, the study assessed how digital data collection tools, real-time data transmission, and interactive dashboards and reporting influenced project performance. A mixed-methods cross-sectional design was employed. From a target population of 145 stakeholders, a sample of 106 respondents was selected using stratified random and convenience sampling techniques. Quantitative data were analyzed using SPSS through descriptive statistics, correlation analysis, ANOVA, and multiple regression, while qualitative data were analyzed thematically. The findings indicated that digital monitoring technologies positively influenced project performance, particularly by improving timeliness, accountability, resource utilization, and decision-making. High mean scores were recorded for digital data collection tools (M = 4.20), real-time data transmission (M = 4.18), and interactive dashboards (M = 4.14), indicating strong agreement among respondents regarding their contribution to monitoring efficiency. Multiple regression results showed that digital data collection tools (β = .440, p = .013), real-time data transmission (β = .264, p = .002), and interactive dashboards (β = .370, p = .004) significantly predicted project performance. Collectively, the three components explained 65.7% of the variance in project performance (R² = .657, F = 197.070, p < .001). The Durbin–Watson statistic of 1.967 indicated no problematic autocorrelation in the model. The qualitative findings further highlighted improved reporting timeliness, reduced data errors, enhanced responsiveness, greater transparency, and more informed decision-making. The study concluded that digital data collection tools were the strongest predictor of SSIT project performance, followed by interactive dashboards and real-time data transmission. These findings underscore the importance of integrating digital technologies into agricultural project monitoring and evaluation to improve data quality, operational efficiency, accountability, and the sustainability of irrigation interventions. The study recommends strengthening the use of digital data collection tools, improving rural internet connectivity, integrating interactive dashboards into district-level monitoring and evaluation systems, and providing continuous training in digital data management and analysis. It further recommends stronger stakeholder collaboration and appropriate data governance and cybersecurity measures. Future studies should examine the long-term sustainability of digital monitoring interventions, farmers’ digital readiness, the cost–benefit implications of digital technologies, and the application of predictive analytics and artificial intelligence in irrigation project management.