OPTIMIZING BSI MOBILE BANKING SERVICE THROUGH A THREE-DIMENSIONAL ANALYSIS : SOCIAL MEDIA, EXTERNAL  AND INTERNAL

Wahyudi Wahyudi; Harimukti Wandebori

doi:10.31955/mea.v9i1.4986

Wahyudi Wahyudi School of Business & Management, Institut Teknologi Bandung, Indonesia
Harimukti Wandebori School of Business & Management, Institut Teknologi Bandung, Indonesia

DOI: https://doi.org/10.31955/mea.v9i1.4986

Keywords: BSI Mobile, Twitter, CRISP-DM Framework, VRIO Analysis, BYOND

Abstract

The rise of mobile banking in Indonesia underscores the strategic role of platforms like BSI Mobile in meeting customer expectations. This study examines user perceptions of BSI Mobile services using big data from X (formerly Twitter) as a real-time source of public opinion. Sentiment data from X was analyzed using the CRISP-DM framework, employing the Naive Bayes method for category and sentiment classification, which was then mapped onto the Importance-Performance Matrix (IPM). External analysis, using PESTEL, Porter's Five Forces, and competitor analysis, highlighting general, industry and competitor challenges. Internally, resource evaluations conducted through value chain analysis and the VRIO framework revealed that transitioning from BSI Mobile to the BYOND platform is a strategic step to strengthen technical and operational capabilities. The findings indicate an urgent need to enhance the app’s features and functionalities, followed by improvements in speed, reliability, security, and UI/UX. The study underscores the importance of strengthening both offline and online marketing strategies to promote BYOND’s features and functionalities, leveraging its Unique Selling Proposition (USP) as a sustained competitive advantage to drive digital adoption among both existing and new customers. Big data analytics helps identify improvements and track service performance.

References

Barney, J. (1991). Firm Resources and Sustained Competitive Advantage.
BSI. (2023). BSI Annual Report.
Chen, H., Chiang, R. H. L., Storey, V. C., & Robinson, J. M. (2012). SPECIAL ISSUE: BUSINESS INTELLIGENCE RESEARCH BUSINESS INTELLIGENCE AND ANALYTICS: FROM BIG DATA TO BIG IMPACT. https://doi.org/https://doi.org/10.2307/41703503
Çitilci, T., & Akbalik, M. (2019). The importance of PESTEL analysis for environmental scanning process. In Handbook of Research on Decision-Making Techniques in Financial Marketing (pp. 336–357). IGI Global. https://doi.org/10.4018/978-1-7998-2559-3.ch016
Et al., N. K. (2023). Harnessing the Power of Big Data: Challenges and Opportunities in Analytics. Tuijin Jishu/Journal of Propulsion Technology, 44(2). https://doi.org/10.52783/tjjpt.v44.i2.193
Han, J., Kamber, M., & Pei, J. (2011). Data Mining. Concepts and Techniques, 3rd Edition (The Morgan Kaufmann Series in Data Management Systems).
Hermiyetti. (2024). TOWARDS THE FUTURE: DIGITAL TRANSFORMATION IN INDONESIAN BANKING AND ITS IMPLICATIONS FOR ECONOMIC GROWTH AND PUBLIC PROSPERITY. International Journal of Economic Literature (INJOLE), 2(2), 505–520.
Hitt, M. A. ., Ireland, R. Duane., & Hoskisson, R. E. . (2011). Strategic management : competitiveness & globalization : concepts. South-Western Cengage Learning.
Juventius, I., Gurning, T., Adikara, P. P., & Perdana, R. S. (2023). Analisis Sentimen Dokumen Twitter menggunakan Metode Naïve Bayes dengan Seleksi Fitur GU Metric (Vol. 7, Issue 5). http://j-ptiik.ub.ac.id
King, Brett. (2018). Bank 4.0 : banking everywhere, never at a bank. Marshall Cavendish Business.
Kuhn, M., & Johnson, K. (2013). Applied Predictive Modeling.
Kumar, N. (2023). Harnessing the Power of Big Data: Challenges and Opportunities in Analytics. Tuijin Jishu/Journal of Propulsion Technology, 44(2). https://doi.org/10.52783/tjjpt.v44.i2.193
Latinia. (2024). Banking evolution: Key statistics shaping the future of customer experience. Latinia.
Martilla, J. A., & James, J. C. (1977). Importance-Performance Analysis. Journal of Marketing, 41(1), 77–79. https://doi.org/10.1177/002224297704100112
May Shofiah, S., Ardly Kaisar Fakhriza, M., & Piksi Ganesha, P. (2022). PENGARUH KUALITAS MOBILE BANKING TERHADAP KEPUASAN NASABAH BANK BJB (STUDI PADA PENGGUNA BJB DIGI DI KOTA BANDUNG). 6(2), 2022.
Mazikana, A. T. (2023). AN EVALUATION OF STRATEGIC PLANNING ON ORGANIZATIONAL PERFORMANCE. A PROPOSED FRAMEWORK FOR TRADE UNIONS IN ZIMBABWE. https://www.researchgate.net/publication/376076598
Michal Maliarov. (2024). Mobile Banking in 2024: Trends, Features and What’s Missing. Tapix.
Paula Oliveira. (2024). 5 UX user-centric designs to enhance mobile banking apps. Ebankit.
Rashid, C. A. (2023). PESTEL Analysis and Porter’s Five Forces as marketing tools to evaluate Morrison’s performance and strategy. Journal of Global Social Sciences, 4(15), 75–83. https://doi.org/10.58934/jgss.v4i15.187
Sahu, A., & Deshmukh, G. K. (2020). Journal of Critical Reviews MOBILE BANKING ADOPTION: A REVIEW. https://doi.org/10.31838/jcr.07.14.435
Saleh, A. (2015). Implementasi Metode Klasifikasi Naïve Bayes Dalam Memprediksi Besarnya Penggunaan Listrik Rumah Tangga.
Sengendo, R. (2024). Title Advancing CRISP-DM:Tomorrow’s Approach for Big Data Analytics. https://doi.org/10.13140/RG.2.2.34933.59361
Shen, M., Cheng, A., & Bi, Y. (2024). An integrated framework for importance-performance analysis of product attributes and validation from online reviews and maintenance records. Design Science, 10. https://doi.org/10.1017/dsj.2024.15
Sijabat, R. (2024). MOBILE BANKING ADOPTION: THE ROLE OF PERFORMANCE AND TRUST. Riset, 6(2), 052–069. https://doi.org/10.37641/riset.v6i2.2115
Sururi, M., PTIQ Jakarta, U., Raya Batan No, J., & Jakarta Selatan, C. (2024). Sistem IT Bank BSI Sering Trouble: Analisis Sentimen Netizen di Aplikasi X.
Vishnuvardhan, B., Manjula, B., & Naik, R. L. (2020). A study of digital banking: Security issues and challenges. Advances in Intelligent Systems and Computing, 1090, 163–185. https://doi.org/10.1007/978-981-15-1480-7_14
Wandebori, H. (2019). Proposed Business Strategy Improvement Through Service Quality Gap Model to Increase Membership Coverage of BPJS Ketenagakerjaan. Jurnal Manajemen Teori Dan Terapan | Journal of Theory and Applied Management, 12(3), 189. https://doi.org/10.20473/jmtt.v12i3.14833
Witten, Frank, & Eibe. (2005). Data Mining: Practical Machine Learning Tools and Techniques, Second Edition.
Yudiono, N., & Iqbal, M. (2019). VRIO Analysis to Measure E-Business Readiness in the Automotive Industry in East Java (Study on Otobus Company Kalisari and Otobus Company Menggala). 22(4).
Zulkarnaen, W., Fitriani, I., & Yuningsih, N. (2020). Pengembangan Supply Chain Management Dalam Pengelolaan Distribusi Logistik Pemilu Yang Lebih Tepat Jenis, Tepat Jumlah Dan Tepat Waktu Berbasis Human Resources Competency Development Di KPU Jawa Barat. Jurnal Ilmiah MEA (Manajemen, Ekonomi, & Akuntansi), 4(2), 222-243. https://doi.org/10.31955/mea.vol4.iss2.pp222-243.