Anggrian, Fengly (2024) Exploring Conventional Banks and E-commerce Synergies in Jakarta, Indonesian Financial Landscape. Masters thesis, Dublin, National College of Ireland.
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Abstract
In the evolving landscape of the banking industry, traditional banks are facing challenges in adapting to rapid technological changes and shifting consumer preferences. The collaboration between conventional banking and e-commerce has emerged as a crucial strategy to enhance customer experience by improving operational efficiency, expanding market reach, and increasing customer satisfaction. While e-commerce offers benefits such as global market access and lower operating costs, challenges such as cybersecurity and data protection must be addressed to maximize its potential. The aim of this research is to examine the variables that influence the customer experience in Indonesian traditional banking and e-commerce partnerships by incorporating the (TAM) Technology Acceptance Model to elucidate the aspects that substantially effect the customer experience. The Confirmatory Factor Analysis (CFA) approach was used in this study's factor analysis to create a structural model for analysis and to ascertain the relationships between the factors, then finalise with regression and random forest to predict the significancy of each factor. The study examines the factors influencing customer experience in Indonesia through the lens of the (TAM) Technology Acceptance Model. Confirmatory Component Analysis (CFA) confirms strong correlations between the variables, indicating a well-fitting model. The analysis reveals that perceived usefulness (E-commerce Effect), ease of adoption (Readiness to Adopt), and trust in the system (Security of Banking System) are key drivers of customer experience. Regression and Random Forest models show that these factors significantly impact customer satisfaction, highlighting the importance of system security alongside the benefits and ease of using digital financial innovations collaboration in enhancing customer experience.
Item Type: | Thesis (Masters) |
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Supervisors: | Name Email Byrne, Brian UNSPECIFIED |
Subjects: | H Social Sciences > HG Finance > Banking H Social Sciences > HF Commerce > Electronic Commerce H Social Sciences > HG Finance > Fintech T Technology > T Technology (General) > Information Technology > Fintech Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning |
Divisions: | School of Computing > Master of Science in FinTech |
Depositing User: | Ciara O'Brien |
Date Deposited: | 02 Aug 2025 13:15 |
Last Modified: | 02 Aug 2025 13:15 |
URI: | https://norma.ncirl.ie/id/eprint/8412 |
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