The Strategic Kenyan Business Selection Tool for Msmes
International Journal of Business and Management Tomorrow
Vol. 2 No. 12
The Strategic Kenyan Business Selection Tool for MSMEs
Anwar Hood Ahmed*, Assistant Lecturer, Mombasa Polytechnic University College Henry M. Bwisa, Professor of Entrepreneurship, Jomo Kenyatta University of Agriculture & Technology Romanus O. Otieno, Deputy Vice-Chancellor (Academic Affairs) and Professor of Statistics, Jomo Kenyatta University of Agriculture & Technology
Abstract
Strategic investors are faced with a dilemma of selecting a business type that suits their profile within the MSMEs sector in Kenya. The business selection problem can be modeled using the multi-criteria decision analysis (MCDA) technique with inherent families of models (WSM, WPM and AHP) that have similar data structure. In this paper, we present our online tool and discuss the underlying conceptual framework. Proof of concept is made through hypothesis test using frequency distribution (qualitative), intra-class correlation and Spearman rank correlation approximated to normal distribution. The results on the tests indicate a promising future for the tool. Keywords: Multi-Criteria Decision Analysis; Business Selection; MSMEs; Jua Kali Sector.
1. Introduction
We extend the idea advanced by Ahmed, Bwisa and Otieno (2012a) on the business selection tool using multicriteria decision analysis (MCDA). Specifically, the idea is applied to the Kenyan scenario covering the MSMEs sector of the economy. The online tool targets a single investor who needs to select an MSME business type. The research has the following objectives: (1) To document any existing business selection models in Kenya (2) To rate their efficiency and effectiveness (or to identify their shortcomings) (3) To design a suitable Kenyan business ranking model with the following specific objectives: a. Present a new model using MCDA techniques that will prioritize business opportunities. ISSN: 2249-9962 December|2012 www.ijbmt.com Page | 1
Vol. 2 No. 12
The Strategic Kenyan Business Selection Tool for MSMEs
Anwar Hood Ahmed*, Assistant Lecturer, Mombasa Polytechnic University College Henry M. Bwisa, Professor of Entrepreneurship, Jomo Kenyatta University of Agriculture & Technology Romanus O. Otieno, Deputy Vice-Chancellor (Academic Affairs) and Professor of Statistics, Jomo Kenyatta University of Agriculture & Technology
Abstract
Strategic investors are faced with a dilemma of selecting a business type that suits their profile within the MSMEs sector in Kenya. The business selection problem can be modeled using the multi-criteria decision analysis (MCDA) technique with inherent families of models (WSM, WPM and AHP) that have similar data structure. In this paper, we present our online tool and discuss the underlying conceptual framework. Proof of concept is made through hypothesis test using frequency distribution (qualitative), intra-class correlation and Spearman rank correlation approximated to normal distribution. The results on the tests indicate a promising future for the tool. Keywords: Multi-Criteria Decision Analysis; Business Selection; MSMEs; Jua Kali Sector.
1. Introduction
We extend the idea advanced by Ahmed, Bwisa and Otieno (2012a) on the business selection tool using multicriteria decision analysis (MCDA). Specifically, the idea is applied to the Kenyan scenario covering the MSMEs sector of the economy. The online tool targets a single investor who needs to select an MSME business type. The research has the following objectives: (1) To document any existing business selection models in Kenya (2) To rate their efficiency and effectiveness (or to identify their shortcomings) (3) To design a suitable Kenyan business ranking model with the following specific objectives: a. Present a new model using MCDA techniques that will prioritize business opportunities. ISSN: 2249-9962 December|2012 www.ijbmt.com Page | 1
References: Ahmed, A., Bwisa, H., and Otieno, R. (2012a). Business selection using multi-criteria decision analysis. International Journal of Business and Commerce. 5(1) 64-81 Ahmed, A., Bwisa, H., and Otieno, R. (2012b). A strategic Kenyan business selection tool for MSMEs. Retrieved November 12, 2012, from http://www.business-selection.com. Bwisa, H. (2012). Selecting the right business for yourself. Retrieved December 20, 2011, from http://www.professorbwisa.com. Central Bureau of Statistics, International Center for Economic Growth (ICEG) & K-Rep Holdings Ltd (1999). National Micro and Small Enterprise Baseline Survey 1999, Survey Results. Nairobi: Central Bureau of Statistics. Fishburn P.C., (1967), “Additive utilities with incomplete product set: applications to priorities and assignments”, Operatio ns Research Society of America, Baltimore. Harris, A. (1913). On the calculation of intra-class and inter-class coefficients of correlation from class moments when the number of possible combinations is large. Biometrika (Biometrika Trust) 9 (3/4): 446–472. International Monetary Fund (2011). World Economic Outlook (WEO): Slowing growth, rising risks. Chapter 2. Kenya National Bureau of Statistics (2012). Kenya 2009 Population and housing census highlights. Retrieved January 1, 2012, from http:// www.knbs.or.ke/docs/KNBSBrochure.pdf. Kenya National Federation of Jua Kali Associations (2012). Retrieved January 1, 2012, from http://www.industrialization.go.ke/. Miller D. W. and Starr M.K., (1969), “Executive decisions with operations research”, Prentice Hall, Englewood Cliffs, New Jer sey. Orwa, B. (2007). Jua Kali Associations in Kenya: A Force for Development and Reform. Center for International Private Enterprise. REFORM Case Study No. 0701. Republic of Kenya (2006). Private Sector Development Strategy 2006-2010. Ministry of trade and industry, Nairobi: Government Printer. Saaty, T. (1980). The Analytic Hierarchy Process: planning, priority setting, resource allocation. Decision Making Series. New York: McGraw-Hill. ISSN: 2249-9962 December|2012 www.ijbmt.com Page | 11