https://ir.zou.ac.zw/files/original/a6dec2e4b6f49c261bd3db46cb78fa94.pdf 7b385fca929a378d5d8ded453a7f485c Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Staff Publications Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource AFRICAN CONTEXT FOR TECHNOLOGICAL FUTURES FOR DIGITAL LEARNING AND THE ENDOGENOUS GROWTH OF A KNOWLEDGE ECONOMY Creator An entity primarily responsible for making the resource GABRIEL KABANDA Description An account of the resource The Southern African countries, embodied as the Southern African Development Community (SADC) region, face sustainable development problems and low global competitiveness. Innovation in the education sector presents a promissory note that can stimulate an endogenous growth of the knowledge economy and reduction of poverty. Technological futures in digital learning are largely influenced by complexity, simulation and modeling, and decision-making capabilities. The paper is purposed to develop an endogenous growth model for a knowledge economy for SADC countries where digital learning is the engine for sustainable growth with its associated technological futures and complexity. The learners in ubiquitous learning environments are able to access the various contents on the Web, search the electronic databases, interactively communicate with instructors and other learners and obtain knowledge anytime and anywhere through wireless technologies. The diffusion discourse and the social embedded innovation can achieve a desirable impact in development, mainly through ICTs for development (ICT4D). Social media is one area that has introduced complexity in the digital learning environment. Chaos Theory is used to seek understanding of the aperiodic behaviour in deterministic, non-linear dynamical systems in a digital learning environment and the design thereof. The Lorenz attractor for such a learning environment is innovation that brings solutions and relevancy to the developmental agenda, with Lyapunov exponents expressed by divergent trajectories of ICT4D. The Jacobian matrix grows exponentially with each technology that goes through diffusion and adoption. The Neo-classic theory of growth is about technical progress premised on exogenous factors and driven by labour, capital and technology. Technology diffusion in SADC is not exogenous. The endogenous growth theory is a model of long-run economic growth that emphasizes that technological change is influenced by economic incentives and a great diversity of resources in an African environment, which largely supports innovation, an embodiment of knowledge in capital and learning by doing. The mixed method methodology is used in this research, which is a research study of the SADC region countries. Mixed methods often combine nomothetic and idiographic approaches in an attempt to serve the dual purposes of generalisation and in-depth understanding—to gain an overview of social regularities from a larger sample while understanding the other through detailed study of a smaller sample. The methodology used was largely qualitative on human capital development and technology diffusion, and quantitative on GDP and Infodensity covering 18 countries in East and Southern Africa. The 18 countries covered by the qualitative study are South Africa, Angola, Bostwana, Burundi, D.R. Congo, Kenya, Lesotho, Madagascar, Malawi, Mauritius, Mozambique, Namibia, Rwanda, Swaziland, Tanzania, Uganda, Zambia and Zimbabwe. An endogenous model for sustainable economic growth is developed through panel data analysis. Panel Data is a data set that contains repeated observations over time, i.e., observations on multiple phenomena observed over multiple time periods for the same firms, individuals, households, enterprises, countries, or any set of entities that remain stable through time. An endogenous model for a knowledge economy for SADC countries is proposed. Publisher An entity responsible for making the resource available Basic Research Journal of Engineering Innovation Date A point or period of time associated with an event in the lifecycle of the resource 2013 Chaos Theory digital learning environment endogenous growth model ICT4D Knowledge economy sustainable development technological future