Assessing Current Systems and Establishing Digital Requirements for Part-Time Lecturer Payment Tracking in Kenyan Public Universities - Roy Obiero Awiti,; Charles Ochieng' Oguk, (IJRDO - Journal of Applied Management Science, 2025)
This article examines the current systems utilized for tracking part-time lecturer payments in Kenyan public universities and establishes the necessary requirements for a digital tracking system to address identified deficiencies. The prevalence of delayed, partial, or non-payment for part-time lecturers in these institutions results in significant financial strain, potentially leading to debt, and insolvency for individuals. Simultaneously, universities face challenges including damage to reputation, demotivated staff impacting education quality, and financial losses through inefficiencies like double payments. Drawing on a descriptive research approach utilizing surveys and questionnaires among part-time lecturers and administrative staff in selected Kenyan public universities, the study's findings reveal that existing payment management systems are predominantly manual or spreadsheet-based, lack unique identifiers for claims, and critically, employ no effective tracking technology. This results in document loss, inability to track claims, and significant delays. Based on identified user and administrative needs, the study establishes comprehensive requirements for a digital tracking solution. Key requirements include web-based and mobile accessibility, automated claim processing and payment calculation, real-time claim status tracking and notifications, integration with existing university systems, enhanced security, and overall usability. The findings underscore the urgent need for a dedicated digital system to provide transparency, efficiency, and accountability in part-time lecturer payment processes, mitigating financial precariousness for lecturers and improving administrative functions for universities. Future research could explore the integration of advanced techniques such as artificial intelligence - AI for predictive budgeting or digital ledger technologies to enhance transparency and prevent financial irregularities, further addressing issues related to debt and financial stability.