The Strategic Kenyan Business Selection Tool for Msmes

Topics: Statistical hypothesis testing, Spearman's rank correlation coefficient, Covariance and correlation Pages: 20 (5476 words) Published: April 25, 2013
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

International Journal of Business and Management Tomorrow b. c. d. e.

Vol. 2 No. 12

Analyze the criteria used by strategic investors for aiding investment decisions. Establishing a process of assigning weights to the different criteria identified in (b). Weighting the preferences (answers) selected by the strategic investors based on the criteria identified in (b). Establishing a process of identifying the business types in a region (i.e. the investment opportunities available in the region).

The above objectives would be fulfilled by testing the following four null hypotheses: H1 : Business-selection models for MSMEs exist in Kenya. H2 : The criteria for investing in business types differ by investor’s profile (no correlation). H3 : The different MCDA techniques (models) yield different business ranking (no correlation). H4 : The strategic Kenyan business selection tool is not reliable. The rest of the paper is organized as follows: we start with a revisit of the conceptual framework where the MCDA family of models and other silent features are briefly discussed. This is followed by an outline of the research methodology pointing out how the objectives have been tackled. Next, we present results for the hypothesis tests. The paper ends up with recommendations and conclusion of the study.

2. Conceptual Framework

In the conceptual framework, operationalization of the MCDA is presented through a discussion of the model’s silent features shown in Figure 1. Specifically, we first present details of three types of deterministic - single decision-maker models and illustrate how they can be applied in real life. For the model to work, the parameters need to be obtained through a logical sound reason. Under model salient features, initially we discuss how to obtain the model’s criteria and justify the resultant model’s matrix through an intra-class correlation study. Next, we propose that the investor’s most probable response to the questions can be...

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
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