Digital Transformation Management

Almunawar, Mohammad Nabil; Islam, Md Zahidul; Pablos, Patricia Ordóñez de

doi:10.4324/9781003224532

Cited by 13 publications

(5 citation statements)

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“…It offers a structured approach for identifying elements to be eliminated or reduced to create space for raising and creating new value propositions. This aligns with the industry insights regarding exponential growth potential and the surge in digital banking competitors, highlighting the need for strategic differentiation and innovation (Almunawar, Islam, & de Pablos, 2022)…”

Section: ) Growth Potential and Competitive Dynamicssupporting

confidence: 72%

Elevating Service Design and Customer Experience: Customer Value Proposition Validation and Design for New Digital Bank “Bank S” in Indonesia

Silalahi,

Windasari

2024

Journal of Multidisciplinary Academic and Practice Studies

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Purpose: This thesis aims to provide a comprehensive framework for digital banking in Indonesia, highlighting the strategic importance of integrating business strategy and marketing to achieve competitive advantage in the digital era. Digital banking has become essential to Indonesia's financial industry, as it offers convenient and efficient solutions to customers. Banking institutions in Indonesia must understand the strategic framework required to gain a competitive advantage in the digital banking landscape. Method: This thesis explores the Elevating Service Design and Customer Experience: Customer Value Proposition validation and Design for New Digital Bank “Bank S” in Indonesia’s digital banking context. Result: This study examines the impact of digital transformation on banks’ competitive advantage by examining how emerging technologies, such as Artificial Intelligence, blockchain, cloud computing, and data science, drive innovation in the sector. Additionally, this study investigates how banks leverage multi-channel delivery, particularly mobile banking, to offer cost-effective services and enhance customer experience. Finally, it analyzes the role of trust and security in promoting the customer adoption of digital banking services. Conclusion: The findings from this study contribute to understanding the strategic approaches that banks in Indonesia can adopt to gain a competitive edge in the digital banking landscape.

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Section: ) Growth Potential and Competitive Dynamicssupporting

confidence: 72%

Elevating Service Design and Customer Experience: Customer Value Proposition Validation and Design for New Digital Bank “Bank S” in Indonesia

Silalahi,

Windasari

2024

Journal of Multidisciplinary Academic and Practice Studies

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“…Asia, home to over 60% of the world's population, has seen tremendous growth in digital transformation [76]. Its expansion in technological and innovation capabilities, supported by a sizeable market, has given it a crucial role in the global digital revolution.…”

Section: Case Study: Crowdsourced Citizen Complaints In Tangerang Cit...mentioning

confidence: 99%

Classifying Crowdsourced Citizen Complaints through Data Mining: Accuracy Testing of k-Nearest Neighbors, Random Forest, Support Vector Machine, and AdaBoost

Madyatmadja,

Sianipar,

Wijaya

et al. 2023

Informatics

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Crowdsourcing has gradually become an effective e-government process to gather citizen complaints over the implementation of various public services. In practice, the collected complaints form a massive dataset, making it difficult for government officers to analyze the big data effectively. It is consequently vital to use data mining algorithms to classify the citizen complaint data for efficient follow-up actions. However, different classification algorithms produce varied classification accuracies. Thus, this study aimed to compare the accuracy of several classification algorithms on crowdsourced citizen complaint data. Taking the case of the LAKSA app in Tangerang City, Indonesia, this study included k-Nearest Neighbors, Random Forest, Support Vector Machine, and AdaBoost for the accuracy assessment. The data were taken from crowdsourced citizen complaints submitted to the LAKSA app, including those aggregated from official social media channels, from May 2021 to April 2022. The results showed SVM with a linear kernel as the most accurate among the assessed algorithms (89.2%). In contrast, AdaBoost (base learner: Decision Trees) produced the lowest accuracy. Still, the accuracy levels of all algorithms varied in parallel to the amount of training data available for the actual classification categories. Overall, the assessments on all algorithms indicated that their accuracies were insignificantly different, with an overall variation of 4.3%. The AdaBoost-based classification, in particular, showed its large dependence on the choice of base learners. Looking at the method and results, this study contributes to e-government, data mining, and big data discourses. This research recommends that governments continuously conduct supervised training of classification algorithms over their crowdsourced citizen complaints to seek the highest accuracy possible, paving the way for smart and sustainable governance.

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“…Furthermore, it is interesting to recognize the delivery platform's ecosystem in Brunei's context. Research focusing on the digital transformation, digital business ecosystem of the platform (Almunawar et al, 2022a(Almunawar et al, , 2022b is an intriguing prospect of study, as the digital platform extends through numerous associations.…”

Section: Limitation and Future Researchmentioning

confidence: 99%

Customer acceptance of online delivery platform during the COVID-19 pandemic: the case of Brunei Darussalam

Almunawar

Anshari²

2022

JSTPM

Self Cite

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Purpose As a result of the COVID-19 pandemic, numerous businesses have migrated to an online delivery platform (ODP) to survive and reconnect with their customers. This study aims to focus on how the public perceives ODP. It examined the acceptance of digital platforms for delivering daily necessities, especially food, in Brunei Darussalam during the COVID-19 pandemic. Design/methodology/approach The online survey collected 350 valid samples, and the online questions were distributed using a snowball sampling method, with the questionnaire’s softcopy prepared in Qualtrics and sent via email and social media as hyperlinks. In 2021, we sent out the questionnaire link via email, WhatsApp and Facebook to people and organizations for about six months. Findings According to the findings of the study, product quality is a critical factor that consumers consider while making online purchases of different products. The COVID-19 condition positively affects customer acceptance, performance, effort and product quality. This research indicates that service quality, online habits and trust do not influence customer acceptance of an ODP. Research limitations/implications The study contributes to the body of knowledge regarding factors influencing the acceptance of ODPs. The factors that influence people’s acceptance of ODPs, especially during the COVID-19 pandemic, are assessed as to whether the COVID-19 pandemic influences people’s acceptance and the identified factor that has the strongest influence on the acceptance of digital delivery platforms. Practical implications The study contributes to the growing body of knowledge about how customer behaviors triggered by the COVID-19 condition influence customer acceptance of ODP and how behavior influences customer acceptance of ODP. Originality/value This paper is useful to academics, practitioners and policymakers in public administration and policy management. The research provides some insights into massive adoption of ODPs that affects the disruption of conventional business practices.

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Digital Transformation Management

Cited by 13 publications

References 0 publications

Elevating Service Design and Customer Experience: Customer Value Proposition Validation and Design for New Digital Bank “Bank S” in Indonesia

Elevating Service Design and Customer Experience: Customer Value Proposition Validation and Design for New Digital Bank “Bank S” in Indonesia

Classifying Crowdsourced Citizen Complaints through Data Mining: Accuracy Testing of k-Nearest Neighbors, Random Forest, Support Vector Machine, and AdaBoost

Customer acceptance of online delivery platform during the COVID-19 pandemic: the case of Brunei Darussalam

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