Collaborative Networks as a Mechanism for Strengthening Competitiveness: Small and Medium Enterprises and Non-state Actors in Tanzania as Cases

Industrial organizations are increasingly facing more challenges in the market and society. These challenges include the scarcity of resources, short delivery time requirement, frequent emergence of new technologies, demand for wide variety of competencies, and limited availability of up-to-date experts. Coping with these challenges requires continuous restructuring and managing changes in organizations. However, only large organizations can afford to institute these changes. It also requires continuous innovation in deployment of emerging technologies and management concepts. Thus, due to their small size, lack of competitive capital and inability to acquire complex opportunities, majority of SMEs and non state actors (NSA) find it difficult to cope with the required speed of change. However, both research and practice have shown that dynamic time/cost-effective and fluid creation of temporary collaborative networks wrought by ICTs is an enabler for the small and medium enterprises (SMEs) and NSAs in quest of enhancing competitiveness in the marketplace. This article contributes to the understanding of the challenges related to the establishment of collaborative networks of organizations in developing economies and proposes a customizable model for establishing those networks.   Key Terms: Collaborative networks, developing economies, ICTs, SMEs, non state actors, collaborative capital &#160

A Stochastic Modelling Approach to Student Performance Prediction on an Internet-Mediated Environment

Student performance prediction presents institutions and learners with results that assist them to gauge their academic abilities within their context of learning. Performance prediction has been done using different approaches over the years. In this case, stochastic modelling is used and it takes into consideration the use of random variables in the prediction process. The random variables are generated from different scenarios in order to generate a possible output. As a result, the generated output is used to indicate the likelihood of very rare occurrence scenarios which may or may not take place at a future date. With the vast availability of educational data that is available within the learning sector, this data forms the basis of input data that is required for the prediction of student performance within internet-worked environments. This paper develops the prediction model using Stochastic Differential Equations (SDEs). This then gives way to the analysis of data collected from varied respondents within universities leading to the generation of a student performance trajectory

E-learning uptake among academicians and students in Tanzanian universities

The purpose of this study was to ascertain the extent of current e-learning uptake in Tanzanian universities. The quantitative approach involving survey design was adopted in the collection of data. Data were collected through a questionnaire survey of 400 respondents, with a rate of return 85.5%. The average reliability of variables 0.949 was determined using Cronbach's Alpha. Fuzzy Logic model and t-test were adopted for data analysis. The findings revealed that the average extent of current e-learning uptake among students and academicians were less than half of threshold amounting to 50% (i.e. level of awareness was 16%, availability was 20.6%, accessibility was 17%, attitude was 15% as variables used). There was no statistically significant difference in e-learning uptake among students and academicians as the value of p > 0.05. The findings of this study established a base ground and guidelines to inform the e-learning stakeholders and policymakers to find and establish suitable policy as well as mechanism to adopt and encourage sustainable use of e-learning systems for life-long teaching and learning. The originality of this study is based on the addition of new variables and methodologies employed as empirical evidence based on the extent of e-learning uptake in Tanzanian universities.               Keyword:  ICTs, e-learning uptake, fuzzy logic model, students, academician