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improvements in the cost-to-income ratio (CIR) and reductions in non-performing loans
(NPLs) through big-data-driven credit scoring. International experience from DBS Bank
(Singapore) with its value-linked technology investment model and from BBVA with its
three-objective digital portfolio framework - system maintenance, scale-up, and
innovation - offers actionable benchmarks for Vietnamese banks.
Domestically, the evidence shows significant differentiation across bank clusters:
enterprise-oriented banks applying AI, Big Data, and cloud computing most intensively
record the highest performance; state-owned banks prioritise core infrastructure
modernisation and operational safety; retail-oriented banks focus on mobile banking
applications and super-apps; while small and medium-sized banks remain constrained by
resource limitations (SBV, 2024b; MIC, 2018–2024). Academic evidence for Vietnam
specifically is growing: Do et al. (2022) document a positive impact of digital
transformation on performance of Vietnamese commercial banks using DEA and Bayesian
GMM on 13 banks over 2011–2019. A recent empirical study (Le et al., 2025) using GLS
panel regression on 24 Vietnamese banks over 2018–2024 further corroborates a positive
association between digital transformation and both ROA and ROE, conditional on bank
size and governance quality, although this study is published in an emerging journal and
should be treated as indicative rather than definitive evidence. These studies, however,
measure digitalisation through single proxies (IT expenditure ratio, number of digital
products, or digital channel transaction share), reinforcing the measurement gap this
paper addresses. On the theoretical side, Bharadwaj et al. (2013) argue that digital
business strategy generates value through reconfiguration of firm-level resources rather
than technology adoption per se - a perspective that justifies the multi-dimensional,
cluster-based analytical approach adopted here (Barney, 1991; Teece, 2018).
2.2. Theoretical framework
The paper builds its analytical framework on three theoretical perspectives. First,
the Resource-Based View (Barney, 1991) holds that digital infrastructure, data capabilities,
and digital human capital constitute strategic resources capable of generating sustained
competitive advantage when allocated and exploited effectively. Second, the Technology
Acceptance Model (Davis, 1989) and its extensions explain digital banking adoption in
terms of perceived usefulness and ease of use. This framework underpins the
interpretation of rapid mobile banking uptake (88% CAGR) and Mobile Money diffusion
(10.2 million accounts, 72% in rural areas) documented in Section 3.2 - patterns
consistent with TAM's prediction that low-friction digital interfaces accelerate adoption
among previously underserved populations, thereby supporting financial inclusion. Third,
at the macro level, regulatory institutions, education systems, and technology markets
interact to shape the pace of digital economic transformation, as theorised in the
National Innovation System literature (Sahay et al., 2020; World Bank, 2022).
Integrating these three perspectives, this paper proposes that digital economic
transformation in banking is a function of three interacting dimensions: (i) digital
infrastructure and technology investment; (ii) data governance and analytical capabilities;
and (iii) institutional and regulatory enablers. Performance outcomes - measured through
ROA, ROE, CIR, NPL ratio, and digital transaction share - reflect the depth of
transformation and the degree of contribution to broader economic transformation.
2.3. Research methods
The study employs a mixed-methods design integrating three principal approaches:
(i) descriptive panel data analysis to quantify financial performance differentiation across

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