Logistic Regression Analysis of Effect of Perceived External Determinants on Memebership Churn in Professional Organizations in Kenya: A Case Study of the Kenya Institute of Management

Abstract: Membership churn brings about significant challenges to professional organizations, thus threatening their financial stability and long-term sustainability. This study investigates the perceived external determinants influence on membership churn at the Kenya Institute of Management using logistic regression. Specifically, the study evaluated the effect of economic conditions, availability of similar services and industry and professional changes on membership churn. A cross-sectional research design was employed and with data collected from 384 KIM members through structured online surveys based on quota stratified sampling. The logistic regression model identified industry changes as the most significant predictor of churn (OR = 1.51, p < 0.001) followed by economic conditions (OR = 1.28, p = 0.012). Availability of similar services was found not to be statistically significant (p = 0.071). Model evaluation by Hosmer-Lemeshow test (p = 0.8403) confirmed a good model fit supported by Nagelkerke’s R² showing that 72.2% of churn variance was explained by the perceived predictors. The findings suggest that professional organizations should strategically adapt to industry and professional shifts and economic fluctuations to reduce member churn. Implementation of flexible payment plans and differentiation of services help mitigate churn risks. This study enhances the understanding of membership dynamics in professional bodies and offering strategic insights to enhance member retention in similar organizations.

Membership churn, Similar Services, Industry changes, Economic conditions, Logistic regression

1. Ahn, J. H., Han, S. P., & Lee, Y. S. (2006). Customer churn analysis: Churn determinants and mediation effects of partial defection in the Korean mobile telecommunications service industry. Telecommunications Policy, 30(11), 552-568. doi:https://doi.org/10.1016/j.telpol.2006.09.006 [Google Scholar] [Crossref]

2. Box, G. E. P., & Tidwell, P. W. (1962). Transformation of the independent variables. Technometrics, 4(4), 531–550. https://doi.org/10.1080/00401706.1962.10490038 [Google Scholar] [Crossref]

3. Creswell, J. W. (2014). Research Design: Qualitative, Quantitative, and Mixed Methods Approaches (4th ed.). Thousand Oaks, CA: Sage Publications. [Google Scholar] [Crossref]

4. Cochran, W.G. (1977). Sampling Techniques. 3rd Edition, John Wiley & Sons, New York. [Google Scholar] [Crossref]

5. Giudicati, G., Riccaboni, M., & Romiti, A. (2013). Experience, socialization and customer retention: Lessons from the dance floor. Marketing Letters, 24, 409–422. doi:https://doi.org/10.1007/s11002-013-9233-6 [Google Scholar] [Crossref]

6. Hosmer Jr, D., Lemeshow, S., & Sturdivant, R. X. (2013). Applied logistic regression (3rd Edition). John Wiley & Sons. doi:DOI:10.1002/9781118548387 [Google Scholar] [Crossref]

7. Kim, S., Gupta, S., & Lee, C. (2021). Managing Members, Donors, and Member-Donors for Effective Nonprofit Fundraising. Sachin Gupta, Clarence, 85(3), 220-239. doi:https://doi.org/10.1177/0022242921994587 [Google Scholar] [Crossref]

8. Krejcie, R. V., & Morgan, D. W. (1970). Determining sample size for research activities. Educational and Psychological Measurement, 30(3), 607-610. doi:https://doi.org/10.1177/001316447003000308 [Google Scholar] [Crossref]

9. Moser, S., Schumann, J. H., Wangenheim, F. v., Uhrich, F., & Frank, F. (2018). The Effect of a Service Provider’s Competitive Market Position on Churn Among Flat-Rate Customers. Journal of Service Research, 21, 319-335. doi:https://doi.org/10.1177/1094670517752458 [Google Scholar] [Crossref]

10. Mugenda, O. M., & Mugenda, A. G. (2003). Research Methods: Quantitative and Qualitative Approaches. Nairobi: ACT. [Google Scholar] [Crossref]

11. Park, W., & Ahn, H. (2022). Not All Churn Customers Are the Same: Investigating the Effect of Customer Churn Heterogeneity on Customer Value in the Financial Sector. Sustainability, 14(19). doi:https://doi.org/10.3390/su141912328 [Google Scholar] [Crossref]

12. Routh, P., Roy, A., & Meyer, J. (2020). Estimating customer churn under competing risks. Journal of the Operational Research Society, 1138-1155. doi:https://doi.org/10.1080/01605682.2020.1776166 [Google Scholar] [Crossref]

13. Saunders, m., Lewis, P., & Thornhill, A. (2016). Research Methods for Business Students (7th ed.). Harlow: Pearson. [Google Scholar] [Crossref]

14. Tavakol, M., & Dennick, R. (2011). Making sense of Cronbach's alpha. International Journal of Medical Education, 53-55. doi:https://doi.org/10.5116/ijme.4dfb.8dfd [Google Scholar] [Crossref]

15. Wald, A., & Wolfowitz, J. (1940). On a test whether two samples are from the same population. The Annals of Mathematical Statistics, 11(2), 147-162. https://doi.org/10.1214/aoms/1177731912 [Google Scholar] [Crossref]

• Patriarchy and Gender Roles in Shobhaa De's Second Thoughts and Easterine Kire’s Life on Hold
• Comparative Effects of Zeolite and Fly Ash on Soil Properties and Agricultural Productivity
• Assessing Interrelationships among Banking Satisfaction, Cooperative Perspectives and Rural Service Beliefs in Cooperative Banking in Northern Nigeria
• High-Performance Alloys and Composites’ Applications in Production Engineering
• Integrating Tidal Energy into Sub-Saharan Africa’s Power Mix: A Strategic Framework for Renewable Energy Expansion