| dc.description.abstract | This study delves into the complex relationship between financial innovation and the financial
performance of listed commercial banks in Nairobi County, Kenya. The specific objectives were to
establish the effect of lending innovation on the financial performance of listed commercial banks in
Kenya, to ascertain the effect of payment innovation system on the financial performance of listed
commercial banks in Kenya, to determine the effect of intelligence predictive system on the financial
performance of listed commercial banks in Kenya and to examine the moderating effect of firm size on
the relationship between financial innovations and the financial performance of listed commercial
banks in Kenya. The study design was a descriptive and correlational research design since it sought to
explore and examine the effect of financial innovation on the financial performance of listed
commercial banks in Kenya. Eleven staff members from each bank were purposively selected because
of their knowledge of the subject matter. These members of the team include member of the Finance
department, member of Strategy department, member of Business Development department, Sales and
Marketing department, member of Debt Recovery department, member of Operations department,
member of Credit department, member of Corporate Banking department, member of Business
Banking department, member of Retail Banking department, member of Risk and member of ICT
departments. A sample was taken from each of the eleven commercial banks traded on the NSE in
Nairobi County. The use of purposeful sampling was performed in order to get an appropriate unit that
was representative of the analysis. Primary data was collected during the investigation using a
questionnaire. The study instrument's concept and content validity were leveraged in the research
endeavor to assess the instrument's validity. The reliability was determined using Cronbach's alpha.
Analysis of quantitative data will be carried out using SPSS software version 22, as agreed upon. An
abundance of descriptive statistics was used to elucidate the data matrix. The mean and standard
deviation were among these statistical metrics, along with the maximum and lowest, which allowed us
to see how the data varied. Researchers in this study used inferential statistics like regression and
correlation to check whether the null hypothesis held. The research used a significance threshold of 5%
to conduct these statistical tests. Increasing lending innovation by one unit leads to a notable 0.138 unit
rise in financial performance (β1=0.138, P=0.002), increasing payment innovation by one unit leads to
a notable 0.457 unit increase in financial performance (β1=0.457, P=0.000), and increasing
intelligence predictive system by one unit leads to a notable 0.188 unit increase in financial
performance (β3=0.188, P=0.001). The research provided evidence for these claims by showing that,
after controlling for other study factors, the financial performance of the loan innovation would
improve by 0.138 units. An additional 13.0% change in financial performance is largely explained by
the company size interaction financial innovation components, with a coefficient of determination of
0.731 and a p-value of 0.000. Firm size now accounts for 73.1% of the overall percentage change in the
model. The study recommends ongoing investments in payment innovation and intelligence predictive
systems by Kenyan commercial banks, acknowledging the nuanced nature of these impacts. Overall,
the study enriches our understanding of the dynamics within Kenyan commercial banks and provides a
foundation for future investigations into the constituent components of payment innovation and
intelligence predictive systems. | en_US |
