| dc.description |
Fisheries and its value added products contributes substantially in the socio-economic of
developing countries including Tanzania. Researches shows that fisheries sector contributes
4.7% and 2.4% of the Gross Domestic Product (GDP) of Kenya and Tanzania respectively.
Despite its huge contribution to socio-economic of the country, the Tanzania fisheries
stakeholders remain challenged with limited access of fisheries information, knowledge, skills
and new technologies. This challenges hinders the fisheries sector development and reduces
income to stakeholders as well as the Government. This study investigated the fisheries
information collecting and distribution among fisheries stakeholders in Mara and Mwanza
regions of Tanzania. The study examined the channels owned and used by fisheries
stakeholders to gather and disseminate fisheries information. Data were collected by
administering a survey in four (4) districts purposively selected from the two regions and 400
respondents randomly selected was involved. The data were analyzed using python panda
library and presented using bar and pie charts. Using the collected data, channel dissemination
effectiveness probability of the six channels (short Message services, Cellular phone call,
Television, Radio, mobile application, and Website) were calculated and comprehensive
analysis performed using python plotly library. Furthermore, the study developed a multi channel fisheries information management system architectural framework and a participation reputation game based incentive mechanism namely EPRIGM to encourage the fisheries
stakeholders donate truthful information and feedback. We modeled and simulated the
dynamics of stakeholder’s strategy selection using replicator dynamic concept and derive the
evolutionary stable strategies for the stakeholders. Results revealed that there is no single
channel application that fits all stakeholders and that EPRIGM ensures truthful and honest
stakeholders participation in gathering and disseminating fisheries information. In this study,
we considered only seven parameters, namely channel coverage, listening ratio, watching ratio,
channel access, average access time, information usefulness, and information sharing, in
calculating channel effectiveness probability. Lastly, the empirical results of EPRIGM
simulation revealed that all information users and information providers will choose honest
strategy to capitalize on their earnings. We do recommend further studies to consider more
factors like channel carrying capacity and channel costs in calculating channel effectiveness
probability and consider application of EPRIGM in other domain of activities. |
|