Toward a digital model for tracking human population dynamics in Tanzania: A multi-actor interaction mechanism

Accurate, real-time tracking of human population dynamics is essential for evidence-based policymaking, effective service delivery, and sustainable development. In Tanzania, systems such as the National Identity System and birth, death, and migration systems operate largely in isolation, resulting in fragmented human population dynamics data and limited interoperability. This study develops a multi-actor interaction mechanism to address these limitations. The study is guided by the Model of Population Dynamics Theory (MPDT), Actor-Network Theory (ANT), the Unified Theory of Acceptance and Use of Technology (UTAUT), and the DeLone and McLean IS Success Model. A cross-sectional mixed-methods design was implemented in Dodoma and Mpwapwa Local Government Authorities (LGAs). Data were collected from 187 participants representing government agencies dealing with human population dynamics tracking, LGA employees, community leaders, health workers, ICT specialists, and accommodation providers through surveys, interviews, focus groups, and document reviews. In this study, data were analysed using multiple regressions, primarily to evaluate the hypotheses developed by use of SPSS Version 21. Findings show moderate to strong positive correlations (r = 0.60 to 0.74), indicating that there is stronger collaboration in tracking human population dynamics between Village Executive Officers (VEOs), accommodation providers, religious leaders, and health officers. These findings highlight that improved cooperation among actors enhances data integration, reduces inconsistencies, and strengthens service delivery. From this study the proposed mechanism assigns clear roles, establishes standardised data-sharing protocols, and incorporates both formal and informal communication channels to strengthen system interoperability. Adoption of this multi-actor mechanism offers substantial potential to improve human population dynamics tracking in Tanzania, enabling inclusive, integrated, and responsive population governance.