Factors Influencing Customers’ Use Of Banks’ Facebook Pages For Communication: A Case Of Use By United Nations Staff In Nairobi

With Facebook pages boasting 65% of social interaction with brands, banks indicate that they have moved 70% of customer relations and formal communication globally to social platforms such as Facebook, but customer response has been poor. Guided by the unified theory of acceptancy of use of technology, the study examined factors that influence the choice to use Facebook pages for online communication by bank customers. A mixed methods research approach was used while adopting a concurrent triangulation strategy design to compare results, using surveys to obtain data from 377 United Nations staff in Nairobi in December 2018 and the Distil Radar Social Sentiment

Analytics tool to analyse users’ online sentiments for selected banks’ Facebook pages. A one-way ANOVA test showed that there was a statistically-significant relationship between the three independent variables—facilitating conditions, social influence, and behaviour intention—at p=0.000, and use of Facebook pages for communication. A Pearson correlation revealed facilitating conditions had the strongest influence at r=0.324 in comparison to social influence and behaviour intention, both at r=0.276. The sentiment analyses findings showed that there was noteworthy commentary about customer experiences (on average 125 mentions), indicating that bank customers are increasingly turning to social media to critique experiences with financial institutions, thus publicizing organizational inefficiencies. A t-test determined age, trust, and gender, and moderated the relationship between the variables—with trust having the highest level of moderation (t=105.661). One main limitation for this study was the focus on Facebook only, thus presenting use of other growing social media platforms for communication as possible areas for future research.

Keywords: social media use; Facebook; online communication; banks in Kenya; sentiment analyses; big data