Abstract:
"
Being a part of social media is a current trend all over the world. Therefore, customers
are demanding the services of banks via social media platforms. But still, Sri Lankan
banks are new in adopting social media marketing and academic scholarship on
customer engagement, while banks using social media commercially remain limited.
Hence, their popularity is also comparatively limited to other banks worldwide. To
enhance the performance of Sri Lankan banks in social media, banks should know
what kind of content exactly need to posted and at what hour and day. Banks need to
be agile in their social media approach and avoid getting tied to a single platform.
Therefore the present study attempts to address this gap by constructing eight different
types of Regression models. It compares and arrives to address what kind of post
characteristics drive each consumer engagement on the Sri Lankan bank’s Facebook
and Instagram profile pages. Predictor variables are selected, specialised for the Sri
Lanka banking environment. The study results indicate that negative binomial
regression is the best model for all the engagement on Facebook except the model of
love and comment model on Instagram. Partial least squares regression is best for love
reaction on Facebook, and multilevel regression is best for the model of like on
Instagram. Further study indicates posting on Tuesday and during work increases the
Brand popularity of Sri Lankan banks.
It recommended as a model which is highly flexible and resistant to any future
modifications. It extends current knowledge on the performance of Facebook and
Instagram customer engagement by developing a framework that helps Sri Lankan
banks determine their customer engagement strategies to increase their brand
popularity."
