Problem statement: Recently, there has been growing interest in m-learning consequently emerging m-learning technology is promising technological and educational business. Nevertheless, m-learning is a new business and the different actors are still trialing by ways of a diversity of business models to overcome in able to achieve a sustainable and profitable place in this market. Numbers of mlearning products providers do not succeed in supplying m-learning products. In this situation not only a suitable business model is vital but also environmental and external factors have impact on mobile learning business model. Overall objective of this study was to survey the business model framework of m-learning with effect of external and environmental factors. Approach: Methodical approach was based on a classification of m-learning actors and its environmental factors. Based on this, we analyzed case studies description and developed main environmental factors that constitute the m-learning environment. Factors were summarized in morphological boxes and then through out its three steps result came up. Results: The results indicated technology, market and regulation are three major environmental factors which were forcing m-learning business model and business model should react to changes of these three factors to keep sustainable business. The research further argued the external factors of m-learning environment in order to understanding and developing the m-learning business and the ways these factors influence the business model of m-learning as well. Conclusion/Recommendations: M-learning business is based on many factors such as technology, changes in society, educational drivers, demand for flexible learning and the new learning paradigm but only three major drivers(technology, market and regulation) had considerable effect on m-learning business model and should be taken into account as a result if they change business model should be changed.
P2P lending, as a novel economic lending model, has imposed new challenges about how to make effective investment decisions. Indeed, a key challenge along this line is how to align the right information with the right people. For a long time, people have made tremendous efforts in establishing credit records for the borrowers. However, information from investors is still under-explored for improving investment decisions in P2P lending. To that end, we propose a data driven investment decision-making framework, which exploits the investor composition of each investment for enhancing decisions making in P2P lending. Specifically, we first build investor profiles based on quantitative analysis of past performances, risk preferences, and investment experiences of investors. Then, based on investor profiles, we develop an investor composition analysis model, which can be used to select valuable investments and improve the investment decisions. To validate the proposed model, we perform extensive experiments on the real-world data from the world's largest P2P lending marketplace. Experimental results reveal that investor composition can help us evaluate the profit potential of an investment and the decision model based on investor composition can help investors make better investment decisions.
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