IMPORTANT FACTORS THAT AFFECT CUSTOMER SATISFACTION WITH DIGITAL BANKS IN INDONESIA

Customer satisfaction has been considered as the measure of information system investment success in many businesses. Customer satisfaction could be difficult to clearly define but is considered as crucial evaluation construct for business investments. Covid-19 has triggered many financial institutions to invest heavily in technology to improve customer satisfaction and also generating more interaction. Indonesia banking industry evolution and revolution happened in accelerated manner to address this need. Traditional banks are creating and launching their digital applications, new banks are launched as digital bank. These banks invested significantly in building these digital solutions. In order to be successful, important factors influencing customer satisfaction in using the application should be considered to continuously improved the digital application and sustaining the business of this digital banks. This research aimed to evaluate hypothesis related to customer satisfaction in using these digital banks application by factoring in Ecosystem, Company Image, Promotion, Perceived Usefulness and Actual System Use. The benefit for this research was aimed to provide insights for digital banks to improve their strategy for their digital application and in the end will benefit the business. The result from the research contributed that ecosystem definitely a key component that need to be further researched to increase customer satisfaction with the digital bank application while also there is a need to do a deep dive for the offers/capability in the digital bank apps addressing specific customer needs to ensure they stay and use the digital bank application and less switching to other provider/banks;and in the end increased satisfaction and business for the bank. © 2023 Little Lion Scientific. All rights reserved.

Perception of the digital transformation as a strategic advantage through the Covid 19 crisis? case of Tunisian banks

Today, digital transformation as a worldwide phenomenon has taken a great deal in corporate strategies. The implementation of strict confinement has resulted in a quite cancelation of transactions and movements. Digital transformation, synonym to accessibility, rapidity and reliability has been widely triggered during the COVID-19 pandemic. In essence, this research explores the effect of digital transformation on the pandemic outcome through identifying how digitization embraces opportunity and innovative strategy. A research model was proposed and empirically tested with partial least squares path-modeling approach, based on the methodological survey completed with Tunisian banks' CEO and operational service managing. The results have demonstrated the necessity of digitization as strategic planning to be deployed in both the short and long terms. It is considered a vector of innovation and sustainable development. It helps identify the essential aspects of business processes and how they should be employed to survive and thrive during crises. © 2021 Informa UK Limited, trading as Taylor & Francis Group.

Sentiment Analysis on Customer Satisfaction of Digital Banking in Indonesia

Southeast Asia, including Indonesia, is seeing an increase in digital banking adoption, owing to changing customer expectations and increasing digital penetration. The pandemic Covid-19 has hastened this tendency for digital transformation. However, customer satisfaction should not be left unmanaged during this transition. This research aims to obtain customer satisfaction of digital banking in Indonesia based on sentiment analysis from Twitter. Data collected were related to three digital banks in Indonesia, namely Jenius, Jago, and Blu. Total of 34,605 tweets were collected and analyzed within the period of August 1st 2021 to October 31st 2021. Sentiment analysis was conducted using nine standalone classifiers, Naïve Bayes, Logistic Regression, K-Nearest Neighbours, Support Vector Machines, Random Forest, Decision Tree, Adaptive Boosting, eXtreme Gradient Boosting and Light Gradient Boosting Machine. Two ensemble methods were also used for this research, hard voting and soft voting. The results of this study show that SVM among other stand-alone classifiers has the best performance when used to predict sentiments with value for F1-score 73.34%. Ensemble method performed better than using stand-alone classifier, and soft voting with 5-best classifiers performed best overall with value for F1-score 74.89%. The results also show that Jago sentiments were mainly positive, Jenius sentiments mostly were negative and for Blu, most sentiments were neutral.