| dc.description.abstract | Against the backdrop of heightened competition for the unbanked population, retention of
existing members, changing customer preferences and the need for revenue growth at low
operating costs for profitability, commercial banks in the country are increasingly adopting
product innovation. It however remains scantily explored in the Kenyan body of knowledge, how
these product innovations have influenced the intended financial performance of commercial
banks in the country. While several related empirical studies have been conducted in the Kenyan
literature, notable contextual, methodological, and conceptual gaps still remain. Occasioned by
these gaps, the present study sought out to assess the effect of product innovation on financial
performance of commercial banks in Kenya. More specifically, the study sought to examine the
effect of issuance of credit cards on the financial performance of banks in Kenya; to examine the
effect of the use of internet banking on the financial performance of banks in Kenya; to
determine the effect of the use of mobile banking on the financial performance of banks in
Kenya; and to determine the effect of use of agency banking on the financial performance of
banks in Kenya. The study was grounded on three theories, including the Dynamic Capability
theory, Diffusion of Innovation Theory and Blue Ocean Theory. This research used the
descriptive research design method, with the target population comprising all 39 Commercial
Banks as at December 31, 2022. Owing to the relatively manageable population size, the present
study adopted a census survey of all 37 commercial banks. The study utilized secondary
quantitative data that was collected from the commercial banks’ annual integrated financial
reports. The study adopted multiple linear regression, whereby five-year average data points for
each variable were used. A combination of both descriptive and inferential statistics was used in
data analysis. The regression model used in this study was Y = β0 + β1X1 + β2X2 + β3X3 + β4X4 +
ε where, Y=Financial Performance (ROA), X1 = Issuance of Credit Cards, X2 = Internet banking
X3 = Mobile banking, X4 = Agency banking, β0 = Constant, and β1, β2, β3 and β4 = Regression
Coefficients and ε = Error Term. The study found that this model could was statistically
significant and that it could be used to explain 24.3% of the bank’s performance measured in
return on assets for banks in Kenya. The study found that Agency Banking had the most
influence on return on assets and that Mobile banking had the least influence | en_US |
