Browsing by Author "Tadesse, Samson"

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    Marketing Challenges and Prospects of Innovative Multinational Pharmaceutical Companies: The Case of Ethiopia
    (Addis Ababa University, 2015-05) Tadesse, Samson; Ibrahim, Yassin (PhD)
    objectives: The aim of this study is to investigate the marketing challenges and prospects of innovative multinational pharmaceutical companies operating in Ethiopia. Method: The study design was descriptive study conducted from February 2015 to May 2015 in Addis Ababa. The research used primary data from all the managers of the existing Innovative multinational companies towards the Ethiopian pharmaceuticals market opportunities and prospect in ensuring their companies future existence through semi structured interview question and self administered questionnaires. In addition, the study covers challenges with regards to product registration, government import regulations, supply chain and distribution, counterfeit products and generic product competition as the main factors to investigate the research question. Findings: The available 12 country managers, 8 marketing managers and 4 Key account managers and 12 senior medical sales representative of the existing 12 Innovative Multinational pharmaceutical companies in Ethiopia were invited to participate in the study and all of them has responded giving the response rate of 100%. The result showed that the major driving force for the marketing activity of these companies is due to the acclaimed rapid economic growth of the country. These companies have disease areas of cardiology and metabolism as a future growth potential market unlike previously in which infectious disease was a major focus for their marketing activity. Product registration challenge and generic product competition are the major challenge for all the participants. All managers responded that counterfeit product in not a major challenge unlike other African countries. Conclusion: The empirical result showed that Innovative Multinational pharmaceutical companies operating in Ethiopia currently view the country as a ripe market potential to expand their marketing activity better than the neighboring countries. Basically rapid population growth, the acclaimed double digit economic growth with the increase in modern health care seeking behavior of the society and the increase in prevalence of cardiometabolic diseases are the major driving force for the marketing activity of these companies. On the other hand, in the process of trying to avail their product in Ethiopian market: Product registration, foreign currency allocation, import regulation, low cost generic products competition and distribution system failure are considered to be prominent system related challenges faced by the multinationals.
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    Possible Application of Data Mining Technology in Supporting Term Loan Risk Assessment: The Case if United Bank S.C.
    (Addis Ababa University, 2009-01) Tadesse, Samson; VNV, Manoj (Professor)
    A Commercial Bank is a financial intermediary that holds deposits for individuals and businesses in the form of checking and savings accounts and certificates of deposit of varying maturities while it issues loans in the form of personal and business as well as mortgages. It arises due to a debtor's non-payment of a loan or other line of credit. In order to control and manage the risk, banks normally have discipline called risk management. Hence it is very important to develop and implement an effective technology that can support risk management. This research focused on the application of data mining techniques in supporting loan risk assessment taking as case study United Bank Share Company. It used two data mining techniques namely, decision tree and neural network. Different decision tree models using j48 algorithm were constructed during the experiments and among them a tree with overall accuracy of 95.65% with conceivable rule was selected. The important attributes that were identified by the selected decision tree were: Networking capital, Current Ratio, Total Asset, TL/TA, Current Liability, Collateral Value, Years in; Business, Number of prior term loans settled, Performance of term PriorLoans, Collateral Type, Credit Relationship with other bank, Trade Sector, Performance in; other types of loan ;and Current Asset. Based on the above selected attributes different types of neural network models with multilayer perceptron algorithm were constructed and a model that maximizes the accuracy in predicting poor payment performance was selected with over all accuracy of 92.83%.When evaluation was done, the overall accuracy of decision tree found better than the neural network even if further research is needed. In addition the result of decision tree is more interpretable than neural network. In general the result showed the possible application of data mining in loan risk assessment term loan.
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    Possible Application of Data Mining Technology in Supporting Term Loan Risk Assessment: The Case of United Bank S.C.
    (Addis Ababa University, 2009-01) Tadesse, Samson; VNV , Manoj (Prof.)
    A Conunercial Bank is a financial intermediary that holds deposits for individuals and businesses in the form of checking and savings accounts and certificates of deposit of varying maturities while it issues loans in the form of personal and business as well as mortgages. It arises due to a debtor's non-payment of a loan or other line of credit. In order to control and manage the risk, banks normally have discipline called risk management. Hence it is very important to develop and implement an effective technology that can support risk management. This research focused on the application of data mining techniques in supporting loan risk assessment taking as case study United Bank Share Company. It used two data mining techniques namely, decision tree and neural network. Different decision tree models using j48 algorithm were constructed during the experiments and among them a tree with overall accuracy of 95 .65% with conceivable rule was selected. The important attributes that were identified by the selected decision tree were: Networking capital, Current Ratio, Total Asset, TLfA, Current Liability, Collateral Value, Years in Business, Number of prior term loans settled, Performance of term Preordains, Collateral Type, Credit Relationship with other bank, Trade Sector, Performance in other types of loan and Current Asset. Based on the above selected attributes different types of neural network models with multilayer perception algorithm were constructed and a model that maximizes the accuracy in predicting poor payment performance was selected with over all accuracy of 92.83%. When evaluation was done, the overall accuracy of decision tree found better than the neural network even if nether research is needed in addition the result of decision tree is more interpret able than neural network. In general the result showed the possible application of data mining in loan risk assessment term joan.