A comparison of two models to measure business success in microinsurance
-
DOIhttp://dx.doi.org/10.21511/imfi.14(3).2017.11
-
Article InfoVolume 14 2017, Issue #3, pp. 113-125
- 1030 Views
-
283 Downloads
This work is licensed under a
Creative Commons Attribution-NonCommercial 4.0 International License
Microinsurance is an insurance product offered to low-income earners charactrized by low profitability resulting from low premiums and high transaction costs. Insurance companies are socially challenged to also include this market segment in their portfolio of insurance products to contribute to economic development and servicing the low-income market. Business success in the microinsurance segment is, therefore, more than calculating profits. This article offers guidance to measure business success in this market. Two models were constructed to measure business success: one generalized and the other an industry specific model. These models are compared to determine which one would be the more suitable to employ as a tool to measure business success in the microinsurance industry. The analysis indicated that the generalized model is better model to use. However, the industry specific model also proves to be valuable and is more suitable for specific company applications than general industry analysis.
- Keywords
-
JEL Classification (Paper profile tab)A13, G20, G23, Q17
-
References31
-
Tables6
-
Figures3
-
- Figure 1. The general model 1
- Figure 2. The applied model
- Figure 3. Point of inflection
-
- Table 1. Business success influences in microinsurance
- Table 2. Factors of the models
- Table 3. Pearson correlation coefficients between common factors
- Table 4. Reliability of the factors in the two models
- Table 5. Comparison of KMO and Bartlett tests
- Table 6. Summary of comparative results
-
- Angove, J., & Dalal A. (2014). A business case for microinsurance: Follow-up study on the profitability of microinsurance. Microinsurance research paper, Geneva ILO, 32, 2-5.
- Angove, J., & Tande, N. (2011). A Business Case for Microinsurance. An analysis for the profitability of microinsurance for five insurance companies. Geneva ILO, 11, 1-4.
- Bama, A. (2014).
- Biese, K., McCord, M., & Sarpong, M. (2016). Landscape of Microinsurance in Africa 2015.
- Chummun, B. Z., & Bisschoff, C. A. (2015a). A theoretical model to measure business success of microinsurance in South Africa. International Business Conference, Livingstone, Zambia, 5-19.
- Chummun, B. Z., & Bisschoff, C. A. (2015b). A model to measure the business success component of microinsurance in South Africa. International Business Conference, Livingstone, Zambia, 101-109.
- Chummun, Z. (2013). Measuring business success in the microinsurance industry of South Africa. (Thesis – Ph.D.). Potchefstroom: North-West University.
- Cortina, J. (1993). What is coefficient alpha: an examination of theory and applications. Journal of Applied Psychology, 78, 98-104.
- Demirguc-Kunt, A., Beck, T., & Honohan, P. (2008). Finance for All? Policies and Pitfalls in Expanding Access. World Bank Policy Research Report. Washington, DC: World Bank.
- Demirguc-Kunt, A., & Klapper, L. F. (2012). Measuring finan¬cial inclusion: The global findex database.
- Du Plessis, J. L. (2010). Statistical consultation services. Department of Statistics. Potchefstroom: North-West University.
- Field, A. (2009). Discovering statistics using SPSS (3rd ed.) London: Sage.
- Fields, Z., & Bisschoff, C. A. (2014). Comparative analysis of two conceptual frameworks to measure creativity at a university. Problems and Perspectives in Management, 12(3), 46-58.
- Financial Sector Deepening Africa. (2017). Enterprise Finance in Africa: Learning from New Trends Opportunities and Challenges in Europe and the United States. Retrieved from https://www.fsdafrica.org/news/ (accessed on January 17, 2017).
- Hafiz, B., & Shaari, J. A. N. (2013). Confirmatory factor analysis (CFA) of first order factor measurement model-ICT empowerment in Nigeria. International Journal of Business Management and Administration, 2(5), 81-88.
- Hooper, D. (2012). Exploratory factor analysis. In Chen, H. (Ed.), Approaches to Quantitative Research - Theory and its Practical Application: A Guide for Dissertation Students. Cork: Oak Tree.
- International Monetary Fund (2014). Regional Economic Outlook: Sub-Saharan Africa. Washington, DC: IMF.
- Kerlinger, F. N. (1973). Foundations of behavioral research (2nded.) New York, NY: Holt, Rinehart and Winston.
- Kumbla, S. (2016). The Big Data Effect on the Insurance Industry.
- Luebke, S. (2017). The Growth of Micro-Insurance: Expanding Financial Inclusion: FSD Africa.
- MacCallum, R. C., Widaman, K. F., Preacher, K. J., & Hong, S. (2001). Sample size in factor analysis: The role of model error. Multivariate Behavioral Research, 36, 611-637.
- McCord, M., Steinmann, R., Tatin-Jaleran, C., Ingram, M., Mateo, M. (2012). The landscape of microinsurance in Africa 2012. Research paper Appleton, WI, Microinsurance Centre, 4, 4-8.
- National Treasury of South Africa (2011). The South African Microinsurance Regulatory Framework.
- Nordin, K., & Bowman, N. (2016). Big data for small policies.
- Rasool, F. (2011). The role of skills immigration in addressing skills shortages in South Africa. (Thesis – Ph.D.). Potchefstroom: North West University.
- Roser, M., & Ortiz-Ospina, F. (2017). World Poverty.
- Schwarz, J. (2011). Research methodology: tools. Lucerne University of Applied Sciences and Arts.
- Tavakol, M., & Dennick, R. (2011). Making sense of Cronbach Alpha. International Journal of Medical Education, 2, 53-55.
- Thom, M., Gray, J., Muller, Z., & Leach, J. (2014). Sale: Thinking big. Microinsurance Innovation Facility, Research Paper, 30.
- World Bank. (2014). Global Economic Prospects: Coping with Policy Normalization in High- Income Countries Washington, DC.
- World Bank (2017). Financial Inclusion Overview.