Thesis:

Technological innovations on monitoring and evaluation in selected infrastructural projects: a study in Nairobi county, Kenya.

Effective M&E is critical for ensuring the successful implementation of infrastructural projects. However, traditional M&E practices face challenges such as inefficiencies which hinder project accountability and performance. This study explored the influence of technological innovations on M&E in selected infrastructural projects in Nairobi County, Kenya. Specifically, it examined the influence of mobile data collection tools, Big Data analytics, Artificial Intelligence technologies, and crowdsourcing platforms on monitoring and evaluation in infrastructural projects. The study used Diffusion of Innovations Theory and Technology Acceptance Model. The research adopted a convergent parallel mixed-method design. The study targeted 65 participants. Census sampling technique was used to sample participants. The sample size was 65 participants comprising of 20 project managers, 20 M&E professionals, 10 county officers, 10 accounting officers and 5 MEPAK officials. Questionnaire and interview guide were used to collect data. Reliability and validity of the research tools was tested. The data that were obtained from this study was analyzed using SPSS software program version 26. Quantitative data was analyzed using descriptive statistics mainly percentages and frequencies whereas multiple linear regression model statistical tool was used to analyze the relationship between variables. The qualitative data was analyzed thematically. Ethical consideration was observed. The study found that the adoption of mobile data collection tools in monitoring and evaluation practices for infrastructural projects in Nairobi County was moderate. While digital tools significantly enhance M&E effectiveness, their adoption remains inconsistent. Data revealed that only 16.7% of respondents often use mobile data collection tools, despite 68.4% rating them effective particularly for improving accuracy (70.0%), GPS-based tracking (80.0%), and community participation (80.0%). Big data analytics adoption was low at 21.7%, yet 75.0% of users acknowledged improved decision-making, 60.0% noted enhanced visualization, and 80.0% highlighted better transparency. Artificial Intelligence technologies show stronger uptake, with 76.7% of respondents reporting full or large-scale integration. About 65.0% noted a transformative or significant impact on M&E through faster data analysis, predictive insights, and improved accuracy. However, challenges remain: high costs (36.7%), limited expertise (25.0%), and weak infrastructure. Crowdsourcing platforms were used sometimes. While 68.4% found them effective in enhancing stakeholder engagement and feedback, barriers such as sustainability (43.3%), language (30.0%), and digital literacy (15.0%) were cited. The findings imply that while technological innovations hold strong potential to transform M&E practices in infrastructural projects across Nairobi County, their full impact is hindered by financial, technical, and infrastructural constraints. Addressing these barriers through targeted investments in digital capacity building, infrastructure development, and supportive policy frameworks is essential to unlock the transformative power of technology in M&E.