Effect of System on Post-Merger and Acquisition Performance of Commercial Banks in Kenya

Doreen Mushira, Dr Daniel Wanyoike, Dr. James Kahiri and Dr. Allan Mugambi
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Doreen Mushira: PhD Candidate, Jomo Kenyatta University of Agriculture and Technology
Dr Daniel Wanyoike: Lecturer, Jomo Kenyatta University of Agriculture and Technology, School of Business and Entrepreneurship, Nairobi, Kenya.
Dr. James Kahiri: Lecturer, Jomo Kenyatta University of Agriculture and Technology, School of Business and Entrepreneurship, Nairobi, Kenya.
Dr. Allan Mugambi: Lecturer, Jomo Kenyatta University of Agriculture and Technology, School of Business and Entrepreneurship, Nairobi, Kenya.

International Journal of Research and Innovation in Social Science, 2024, vol. 8, issue 9, 1158-1181

Abstract: This study examines the effect of System on post-merger and acquisition (M&A) performance of commercial banks in Kenya, using the McKinsey 7S model as a framework. System, serving as the independent variable, is operationalized through knowledge mapping and assessment, knowledge transfer and retention, and continuous improvement. Post-M&A integration, the moderating variable, is measured by the degree and rate of integration. The study is anchored on the Resource-Based View and Universalistic Best Practices theories. Out of the 38 commercial banks in Kenya, 29 have undergone M&As. A sample of 10 banks, involved in M&A activities for a period ranging from six months to five years, was selected. This timeframe recommended by Masoud et al., (2020) is critical for M&As as it allows the assessment of both short-term and medium-term integration outcomes, where initial challenges are addressed and operational synergies start manifesting. Data was collected using both primary and secondary sources. Primary data, analyzed through content and framework analyses provided qualitative insights into integration practices. Secondary data, primarily consisting of financial ratios of bank performance (Return on Assets, Return on Equity, Return on Investment, Operating Profit Margin, and Net Profit Margin) was analyzed using the Independent Sample T-test in SPSS version 28.0. Correlation analysis and inferential statistics were employed to measure the strength of relationships between study variables. The study employed a 95% confidence level with a 5% level of precision, ensuring that the results were robust and could be generalized with minimal error. These levels of precision and confidence enhance the reliability and quality of the research by reducing the likelihood of making incorrect inferences about the broader population. The findings demonstrate that knowledge mapping and assessment, knowledge transfer and retention, and continuous improvement are essential for smooth post-M&A integration, safeguarding critical knowledge, and maintaining the merged entity’s competitiveness. The results further confirmed that the conceptual framework accurately predicted post-M&A performance, with System having a positive, statistically significant effect. Additionally, post-M&A integration was found to moderate the relationship between System and post-M&A performance, amplifying the positive outcomes when the integration was well-executed. The study recommends the adoption of a comprehensive artificial intelligence (AI) strategy, focusing on augmenting human capabilities in M&A integration processes rather than replacing them. Future research should explore the impact of regulatory changes and market conditions on post-M&A performance, particularly how evolving regulations, competition, and macroeconomic factors influence outcomes in the banking sector.

Date: 2024
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