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ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume IX Issue X October 2025
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The Impact of Digital Transformation on Labor Productivity and
Production Cost Reduction in Enterprises in Vietnam: Evidence from
SMEs in Hanoi and Neighboring Provinces
Nguyen Phuong Tu; Nguyen Duy Chuc
,
*
School of Economic – Hanoi University of Industry
*
Corresponding Author
DOI: https://dx.doi.org/10.47772/IJRISS.2025.910000780
Received: 07 November 2025; Accepted: 14 November 2025; Published: 24 November 2025
ABSTRACT
This study examines the impact of digital transformation (DT) on labor productivity (LP) and production cost
reduction (PCR) among small and medium-sized enterprises (SMEs) in Vietnam. Drawing on the resource-
based view and dynamic-capabilities theory, a mixed-methods design was applied to integrate qualitative
insights and quantitative validation. Semi-structured interviews with 15 SME managers were followed by a
survey of 351 SMEs in Hanoi and neighboring provinces, analyzed using SmartPLS 4. Results indicate that
DT significantly improves both LP (β = 0.46, p < 0.001) and PCR (β = 0.39, p < 0.001). Digital skills (DS)
partially mediate these effects, highlighting that technology yields performance benefits mainly when
supported by workforce competence. Moreover, firm characteristics—particularly size and sector—moderate
the DT–performance relationship, with medium-sized and manufacturing firms realizing greater efficiency
gains. The study contributes to the literature by providing empirical evidence from an emerging economy
where research on DT outcomes remains limited. Practically, it underscores the need for SMEs to align digital
investments with human-capital development and integrated process management. Policy recommendations
emphasize training support, infrastructure enhancement, and phased digital adoption strategies to accelerate
Vietnam’s SME digitalization.
Keywords: digital transformation, labor productivity, production cost reduction, digital skills, firm
characteristics, SMEs, Vietnam
INTRODUCTION
Background and Research Problem
Digital transformation (DT) has become a decisive factor reshaping the productivity dynamics and cost
structures of firms across the globe (Vial, 2019; Kane et al., 2015). The rapid diffusion of Industry 4.0
technologies—cloud computing, big data analytics, artificial intelligence (AI), Internet of Things (IoT), and
automation—has transformed production systems from labor-intensive to data-driven operations. Through
digitalization of processes, firms can enhance labor productivity, optimize resource allocation, and reduce
operational costs (Brynjolfsson & McAfee, 2017). For developing economies such as Vietnam, where small
and medium-sized enterprises (SMEs) account for over 97 percent of all enterprises and employ more than 50
percent of the workforce, the integration of digital technologies is crucial for competitiveness and sustainable
growth (Vietnam Ministry of Planning & Investment, 2023).
Nevertheless, despite governmental initiatives such as the National Digital Transformation Program 2025,
Orientation to 2030, Vietnamese SMEs remain constrained by limited financial resources, digital skills
shortages, and legacy production systems. Many SMEs in Hanoi and neighboring provinces—Bac Ninh, Hung
Yen, Phu Tho, and Ninh Binh—operate in traditional manufacturing and service sectors where manual labor
and outdated equipment still dominate. Consequently, productivity gains remain modest, and production costs
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remain high relative to regional competitors. This context raises an essential question: How does digital
transformation influence labor productivity and production cost reduction among Vietnamese SMEs?
Research Gap
Prior international studies have confirmed that digital transformation positively affects firm performance and
cost efficiency (Teece, 2018; Li et al., 2018). However, most evidence originates from developed economies
where digital infrastructure and institutional readiness are advanced (Bharadwaj et al., 2013). Limited
empirical work examines emerging markets, and even fewer focus on the micro-mechanisms linking DT with
productivity and cost reduction in SMEs. Vietnamese research often emphasizes adoption readiness or digital
maturity (Nguyen & Pham, 2022) rather than measurable outcomes such as productivity or cost efficiency.
Furthermore, existing studies predominantly employ quantitative designs without triangulating managerial
perceptions through qualitative inquiry.
Research Objectives and Questions
This study aims to bridge the aforementioned gaps by integrating qualitative and quantitative evidence from
Vietnamese SMEs. The objectives are to:
1. Examine the impact of digital transformation on labor productivity and production cost reduction.
2. Identify mediating and contextual factors that influence the magnitude of this relationship.
3. Provide policy and managerial implications for SME digitalization strategies.
Accordingly, the study seeks to answer three guiding questions:
First, how does digital transformation influence labor productivity in Vietnamese SMEs?
Second, how does digital transformation contribute to production cost reduction?
Final, what organizational or contextual factors strengthen or weaken these effects?
Contributions
This research contributes to the literature in several ways. First, it provides contextualized evidence from an
emerging economy, extending the external validity of digital-productivity theories. Second, it employs a
mixed-method design, combining qualitative insights with quantitative validation through SmartPLS 4, thereby
enhancing robustness. Third, the study develops a conceptual model that integrates resource-based and
dynamic-capability perspectives, linking digital resources to tangible productivity and cost outcomes. Finally,
it offers practical recommendations for policymakers and managers seeking to accelerate SME digital
transformation in Vietnam.
Theoretical Background
Digital Transformation and Organizational Change
Digital transformation is defined as a comprehensive process of integrating digital technologies into all areas
of business, fundamentally altering how organizations create and deliver value (Vial, 2019). Unlike mere
digitization—the conversion of analog information to digital format—DT involves a strategic reconfiguration
of processes, culture, and business models (Warner & Wäger, 2019). From an organizational-change
perspective, DT requires leadership commitment, technological infrastructure, and human-capital readiness
(Westerman et al., 2014).
For SMEs, DT often manifests through incremental adoption of enterprise-resource-planning (ERP) systems,
automation tools, and digital marketing platforms. These technologies streamline information flows, reduce
coordination costs, and improve decision-making speed, all of which can enhance productivity (Li et al.,
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2018). Moreover, digitalization can substitute or complement labor input by automating repetitive tasks, thus
improving labor efficiency and enabling employees to focus on higher-value activities.
Labor Productivity and Production Cost Efficiency
Labor productivity measures the efficiency of labor input in generating output, typically expressed as output
per worker or per hour (Solow, 1957). In modern economies, productivity improvements derive not only from
capital accumulation but also from technological innovation and process optimization (OECD, 2022). Digital
transformation affects productivity through three channels:
1. Automation – reducing manual workload and errors;
2. Information integration – improving coordination and resource allocation;
3. Skill enhancement – enabling employees to leverage digital tools for higher output quality.
Production cost reduction refers to the ability of firms to minimize unit production costs through process
innovation, supply-chain optimization, and waste reduction (Porter, 1985). Digital technologies—such as
predictive analytics, IoT-based monitoring, and cloud-based resource management—enable real-time cost
control and preventive maintenance, reducing downtime and energy waste (Rojko, 2017).
The Resource-Based View (RBV)
The RBV posits that firms gain sustained competitive advantage through valuable, rare, inimitable, and non-
substitutable (VRIN) resources (Barney, 1991). In the digital era, data, platforms, and technological
competencies are strategic resources that enhance productivity and cost efficiency. However, simply
possessing digital assets is insufficient; firms must develop complementary capabilities—such as digital skills
and adaptive culture—to transform these assets into performance gains (Wade & Hulland, 2004).
Dynamic Capabilities Perspective
The dynamic capabilities framework extends the RBV by emphasizing the firm’s ability to integrate, build,
and reconfigure resources in response to environmental change (Teece, 2018). Digital transformation
exemplifies a dynamic capability, as it involves continual sensing of technological opportunities, seizing them
through investment, and transforming organizational routines accordingly. Firms with strong dynamic
capabilities can translate DT into higher productivity and lower costs by rapidly adapting processes to market
and technological shifts (Pavlou & El Sawy, 2011).
Theoretical Integration
Integrating RBV and dynamic-capability theories suggests a multi-layered mechanism:
Digital assets (hardware, software, data systems) provide the foundation;
Digital capabilities (skills, leadership, innovation culture) mediate transformation;
Operational outcomes (productivity, cost reduction) manifest as performance gains.
This integrated view informs the conceptual model developed in the next section.
LITERATURE REVIEW AND HYPOTHESIS DEVELOPMENT
Digital Transformation and Labor Productivity
Empirical studies consistently demonstrate that digital technologies can boost productivity by enhancing
information flows and process efficiency. For instance, Brynjolfsson et al. (2002) found that IT investments
significantly increased labor productivity in U.S. firms when combined with organizational redesign. In
European contexts, Arvanitis & Loukis (2016) reported that digital integration in manufacturing improved both
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labor efficiency and innovation outcomes. Similarly, in Asian economies, Li et al. (2018) showed that SMEs
adopting cloud-based systems achieved 10–20 percent productivity gains.
In the Vietnamese SME context, empirical evidence on the productivity effects of digital transformation
remains limited and uneven. Recent studies indicate that firms adopting digital and e-commerce platforms
often achieve moderate improvements in labor productivity, but these gains are constrained by workforce skill
gaps and insufficient digital integration (Tran & Do, 2022; Kraus et al., 2021). Building on these insights, the
following hypothesis is proposed:
H1: Digital transformation has a positive and significant effect on labor productivity in SMEs.
Digital Transformation and Production Cost Reduction
The relationship between DT and cost efficiency is grounded in transaction-cost and process-innovation
theories. Digitalization reduces information asymmetry, improves coordination, and lowers transaction costs
across the value chain (Williamson, 1985). Studies by Mithas et al. (2012) and Kraus et al. (2021) confirm that
firms leveraging data analytics and automation report lower unit production costs and higher profit margins.
Digital tools enable predictive maintenance and supply-chain visibility, mitigating waste and idle time.
However, cost-saving effects are contingent on technology integration depth. Partial or fragmented
digitalization may increase overhead costs without delivering proportional savings (Bloom et al., 2019). For
resource-constrained SMEs, careful sequencing of digital investments is therefore critical. Hence:
H2: Digital transformation has a positive and significant effect on production cost reduction in SMEs.
Mediating Role of Digital Skills
Human capital plays a vital role in converting technological potential into performance outcomes. According
to the skill-biased technological-change theory (Autor et al., 2003), digitalization enhances productivity only
when employees possess adequate digital literacy. Vietnamese SMEs often lag in workforce training, leading
to underutilization of digital tools (Tran & Do, 2022). Accordingly:
H3: Digital transformation has a positive and significant effect on Digital skills and mediate the relationship
between digital transformation with labor productivity and production cost reduction.
H3a: Digital skills has a positive and significant effect on labor productivity.
H3b: Digital skills has a positive and significant effect on production cost reduction.
H4a:Digital skills mediate the relationship between digital transformation and labor productivity.
H4b: Digital skills mediate the relationship between digital transformation and production cost reduction.
Moderating Effects of Firm Characteristics
Firm size, age, and sectoral context can moderate DT outcomes. Larger firms typically benefit from economies
of scale and greater absorptive capacity, while younger firms may exhibit greater agility (Cohen & Levinthal,
1990). In manufacturing-intensive sectors, DT’s effect on cost reduction may be stronger than in service
sectors due to automation potential. Hence:
H5: Firm characteristics (size, age, sector) moderate the effects of digital transformation on productivity and
cost outcomes.
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CONCEPTUAL FRAMEWORK
Figure 1. The conceptual model
Source: Author's synthesis and research proposal
This model posits both direct and indirect effects of digital transformation on operational outcomes, moderated
by firm attributes.
RESEARCH METHODOLOGY AND FINDINGS
Research Design
This study adopts a mixed-methods approach combining qualitative and quantitative techniques to capture
both managerial perceptions and empirical relationships. Following Creswell (2018), a sequential exploratory
design was employed: an initial qualitative phase to refine constructs and measurement items, followed by a
quantitative phase using a structured survey and structural equation modeling (SEM) in SmartPLS 4.
Qualitative Phase
The qualitative stage explored how SME managers perceive the influence of digital transformation (DT) on
productivity and cost reduction. Semi-structured interviews were conducted with 15 executives (CEOs,
production managers, IT heads) from SMEs in Hanoi, Bac Ninh, Hung Yen, Phu Tho, and Ninh Binh.
Participants were selected through purposive sampling, ensuring variation across industry and firm size. Each
interview lasted 45–60 minutes and was audio-recorded with consent.
Quantitative Phase
Insights from the interviews informed the development of the quantitative survey. The cross-sectional survey
targeted SME managers and supervisors directly involved in digital transformation initiatives. Respondents
were asked to assess DT, labor productivity (LP), production cost reduction (PCR), and digital skills (DS)
using a 5-point Likert scale (1 = Strongly Disagree; 5 = Strongly Agree).
Ethical and Methodological Considerations
A more explicit discussion of ethical and methodological considerations is warranted to strengthen the
transparency and credibility of the study. From a methodological perspective, the use of mean imputation to
address missing data, while operationally convenient, may artificially reduce score variability and introduce
bias into parameter estimates. More rigorous approaches—such as multiple imputation, expectation–
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maximization, or full information maximum likelihood—would provide a stronger foundation for handling
incomplete responses. Additionally, the exclusive reliance on self-reported survey data raises concerns about
common-method variance. Although statistical checks were performed, the absence of procedural remedies
(e.g., temporal separation of measures, multi-source data, or integration of objective productivity indicators)
remains a methodological limitation that future studies should address.
Ethically, the manuscript would benefit from clearer documentation of the procedures used to safeguard
participant rights. This includes specifying how informed consent was obtained, whether participation was
voluntary, and what measures were implemented to ensure confidentiality and anonymity of respondents—
particularly given the sensitivity of firm-level operational data. Explicit mention of institutional ethical
approval should also be included to ensure compliance with standard research governance procedures.
Strengthening these ethical and methodological disclosures will enhance the study’s rigor, replicability, and
overall trustworthiness.
All participants were informed about the purpose of the research, their right to withdraw at any time, and the
voluntary nature of their participation. Informed consent was obtained prior to data collection, and respondents
were assured that all information would be treated with strict confidentiality. No personally identifiable data
were collected, and all survey responses were anonymized to protect participant privacy.
Sampling and Data Collection
Population and Sampling
The research population comprised small and medium enterprises registered under Vietnam’s SME Law 2017,
operating within Hanoi and surrounding provinces. A multi-stage sampling strategy was used:
1. Stage 1: Selection of five provinces with high industrial concentration.
2. Stage 2: Random sampling from provincial SME directories.
3. Stage 3: Snowball referrals to ensure adequate representation of manufacturing and service sectors.
Data Screening
Out of 420 questionnaires distributed between March and May 2025, 351 valid responses were retained
(response rate = 83.6 %). Missing data were handled using mean imputation, and outliers were detected using
Mahalanobis D². The final dataset satisfied the minimum sample size requirements for PLS-SEM (Hair et al.,
2021).
Demographic Profile
Table 1. Respondent Profile (n = 351)
Variable
Category
Frequency
%
Gender
Male
211
60
Female
140
40
Age (yrs)
< 30
62
18
30–39
133
38
40–49
106
30
Position
≥ 50
50
14
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Dept. Head
97
28
Manager
130
37
Director
82
23
Owner
42
12
Firm Size
Small (< 100)
123
35
Medium (100–499)
173
49
Large (≥ 500)
55
16
Industry
Manufacturing
138
39
Services
145
41
ICT
68
20
Firm Age (yrs)
< 5
66
19
5–10
107
30
11–20
116
33
> 20
62
18
Source: Author's synthesis
Measurement of Constructs
All constructs were adapted from validated scales and contextualized for Vietnamese SMEs.
Table 2. Measurement of Constructs
Construct
Dimensions / Indicators
Sources
Digital Transformation (DT)
Strategy alignment, process automation, data
analytics, digital culture, infrastructure readiness
Vial (2019); Li et al.
(2018)
Labor Productivity (LP)
Output quality, efficiency, time utilization, process
flexibility
OECD (2022);
Brynjolfsson &
McAfee (2017)
Production Cost Reduction (PCR)
Unit cost reduction, waste minimization, resource
optimization, inventory control
Porter (1985); Rojko
(2017)
Digital Skills (DS)
Technical literacy, problem-solving, data analysis,
collaboration via digital tools
Tran & Do (2022)
Source: Author's synthesis
Each latent construct was measured reflectively with 3–5 items. Prior to data collection, the instrument was
translated into Vietnamese and back-translated to ensure semantic equivalence. A pilot test with 30
respondents confirmed reliability (Cronbach’s α > 0.75 for all constructs).
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Data Analysis Technique
SmartPLS 4 was employed due to its suitability for prediction-oriented models and small-to-medium samples
(Hair et al., 2021). The analysis followed a two-step procedure:
1. Measurement-model assessment – testing indicator reliability, internal consistency, convergent validity,
and discriminant validity.
2. Structural-model assessment – evaluating hypothesized relationships via bootstrapping (5,000 samples).
Qualitative Findings
Emerging Themes
Thematic analysis (Braun & Clarke, 2006) revealed three dominant themes:
Automation and Efficiency Gains: Managers emphasized automation as the first visible outcome of DT:
“We reduced manual data entry by 70 percent after integrating the ERP system; employees now focus on
analysis instead of paperwork.” (CEO – electronics SME)
Data-Driven Decision-Making: Firms adopting analytics tools reported improved forecasting and cost control:
“Digital dashboards let us track energy use daily, cutting waste and downtime.” (Operations Manager – textile
firm)
Skill Gaps and Cultural Resistance: A recurring challenge involved limited digital competence among older
workers:
“Machines are ready, but people are not. We need mindset training as much as technology.” (HR Director –
mechanical SME)
These insights confirmed the theoretical assumption that digital skills mediate the DT–performance
relationship.
Quantitative Results
Measurement Model
Table 3. Reliability and Convergent Validity
Construct
Cronbach’s α
CR
AVE
DT
0.912
0.939
0.721
LP
0.885
0.920
0.698
PCR
0.872
0.908
0.666
DS
0.901
0.931
0.731
Source: Author's synthesis
All indicators loaded > 0.70 on their respective constructs; AVE > 0.50 confirms convergent validity.
Discriminant validity assessed via HTMT ratios were all < 0.85, indicating clear construct separation.
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Structural Model
Collinearity checks (VIF < 3) indicated no multicollinearity. Model fit statistics: SRMR = 0.054, NFI = 0.911,
RMS_theta = 0.12, within recommended thresholds.
Table 4. Hypothesis Testing Results (Bootstrapping = 5,000 resamples)
Hypothesis
Path
β
t-value
p-value
Result
H1
DT → LP
0.46
8.21
< 0.001
Supported
H2
DT → PCR
0.39
7.02
< 0.001
Supported
H3
DT → DS
0.52
9.43
< 0.001
Supported
H3a
DS → LP
0.33
5.68
< 0.001
Supported
H3b
DS → PCR
0.28
4.96
< 0.001
Supported
H4a
DT → LP (Indirect via DS)
0.17
4.01
< 0.001
Supported
H4b
DT → PCR (Indirect via DS)
0.15
3.82
< 0.001
Supported
H5 Moderation
DT×Firm Size → LP
0.09
2.11
0.035
Supported
Source: Author's synthesis
Coefficient of Determination
R² values: LP = 0.54; PCR = 0.49; DS = 0.27.
These indicate that DT and DS explain 54 % of the variance in labor productivity and 49 % in cost reduction,
reflecting substantial explanatory power (Hair et al., 2021).
Effect Size and Predictive Relevance
f² values: DT→LP = 0.36 (large), DT→PCR = 0.29 (moderate).
Q² values (Blindfolding): LP = 0.33, PCR = 0.28 > 0, confirming predictive relevance.
Moderation by Firm Characteristics
Multi-group analysis (MGA) revealed that DT’s effect on LP is stronger in medium-sized firms (β = 0.52) than
in small ones (β = 0.31), supporting economies-of-scale theory. Manufacturing firms exhibited a higher DT–
PCR path coefficient (β = 0.44) than service firms (β = 0.28), confirming sectoral heterogeneity.
Summary of Empirical Findings
1. Direct Effects: Digital transformation significantly enhances both labor productivity and cost reduction
among Vietnamese SMEs.
2. Indirect Effects: Digital skills mediate the DT–performance link, highlighting the necessity of human-
capital investment.
3. Contextual Differences: Firm size and industry moderate DT outcomes, with manufacturing and
medium-sized enterprises benefiting most.
4. Qualitative Validation: Interview evidence supports the quantitative findings, revealing automation, data
use, and cultural adaptation as central mechanisms.
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Figure 2. Structural Model Results (SmartPLS 4 Summary)
(Model Fit = SRMR 0.054; R² (LP)=0.54; R² (PCR)=0.49)
Source: Author's synthesis
Robustness and Validation
To ensure robustness, a bootstrapped confidence-interval approach (Bias-Corrected 95 %) confirmed all paths
significant. Harman’s single-factor test indicated no serious common-method bias (first factor = 36 % < 50 %).
Multicollinearity (VIF < 3) and normality diagnostics further supported model reliability.
Interpretation of Findings
The empirical results confirm that digital transformation acts as a productivity and cost-efficiency catalyst for
Vietnamese SMEs. Automation and data integration streamline production, while digital culture and leadership
promote continuous improvement. Importantly, digital skills emerge as the mechanism translating technology
into measurable performance outcomes—supporting the skill-biased technological-change perspective (Autor
et al., 2003).Moreover, the moderating role of firm characteristics aligns with dynamic-capability theory:
larger or manufacturing-oriented firms possess greater absorptive capacity to exploit digital assets (Cohen &
Levinthal, 1990). Conversely, small service firms face scale and resource constraints limiting DT returns.
DISCUSSION
Overview of Findings
The results of this mixed-method study demonstrate that digital transformation (DT) significantly improves
labor productivity (LP) and production cost reduction (PCR) among SMEs in Vietnam. The findings are
consistent with global literature emphasizing that digital technologies, when strategically integrated, enhance
operational efficiency and competitiveness (Kane et al., 2015; Kraus et al., 2021). However, this study extends
existing research by confirming that digital skills (DS) serve as a key mediating mechanism through which DT
exerts its impact, and that firm characteristics—particularly size and sector—moderate the strength of these
relationships.
Qualitative insights highlighted automation, data-driven management, and skill development as critical levers,
while quantitative analysis using SmartPLS 4 statistically validated these effects. Together, these findings
support a resource-based and dynamic-capability perspective, illustrating that DT functions as both a resource
and a capability that must be mobilized through skilled human capital to realize productivity and cost gains.
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Theoretical Contributions
Integration of RBV and Dynamic-Capability Perspectives
This study enriches the Resource-Based View (RBV) by empirically validating digital transformation as a
composite resource encompassing tangible assets (infrastructure, data systems) and intangible ones (digital
culture, skills). However, its contribution to productivity and cost efficiency depends on dynamic
capabilities—the firm’s ability to sense, seize, and reconfigure resources (Teece, 2018). SMEs that cultivate
agility and continuous learning exhibit stronger DT–performance links.
Mediating Role of Digital Skills
The results confirm that digital skills mediate the relationship between DT and performance outcomes,
reinforcing the skill-biased technological change theory (Autor et al., 2003). Merely adopting digital tools is
insufficient; productivity improvements occur when employees possess the competencies to exploit those tools
effectively. This aligns with recent findings by Tran and Do (2022), who noted that Vietnamese SMEs often
underperform in digital transformation due to human-capital constraints. By explicitly quantifying this
mediating effect, the present study advances understanding of the human factor in digital capability building.
Contextualization to Emerging Economies
Most digital transformation research originates from developed countries. By focusing on Vietnamese SMEs,
this paper addresses a critical contextual gap. It demonstrates that while digital transformation principles are
universal, their practical outcomes depend on infrastructural readiness, institutional support, and workforce
adaptability. The results confirm that DT is a viable path to productivity enhancement even in resource-
constrained settings—provided that complementary investments in digital literacy and leadership are made.
Comparison with Prior Research
The positive relationship between DT and labor productivity observed here mirrors findings by Brynjolfsson
and McAfee (2017) and Li et al. (2018), who reported that digital technologies boost productivity through
automation and data integration. However, the Vietnamese context exhibits unique nuances:
First, technology adoption patterns: Vietnamese SMEs tend to prioritize low-cost, modular digital tools (ERP,
CRM, accounting software) rather than full-scale automation due to cost constraints.
Second, human-capital bottlenecks: Unlike in developed economies, skill gaps remain a dominant barrier,
limiting productivity potential.
Final, cultural adaptation: The qualitative phase revealed that leadership mindset and organizational openness
are as vital as technology itself—echoing Warner and Wäger (2019).
Similarly, the DT–cost reduction relationship aligns with Porter’s (1985) view that process innovations drive
competitive advantage. Digital monitoring and predictive maintenance help Vietnamese firms reduce energy
consumption and waste. Yet, the magnitude of cost reduction (β = 0.39) is moderate compared to productivity
gains, reflecting transition costs and partial digital integration stages.
Interpretation of Mediating and Moderating Effects
The mediation analysis reveals that digital skills explain approximately 30–35% of the total DT effect on
performance outcomes. This underscores the principle that technological transformation without human
transformation yields limited returns.
Moderation analysis confirmed that firm size and industry matter: medium-sized and manufacturing
enterprises derived greater benefits from DT. These findings corroborate Cohen and Levinthal’s (1990)
concept of absorptive capacity, which posits that organizational learning potential scales with firm resources
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and prior experience. Conversely, small firms, especially in service sectors, face limitations in both
infrastructure and workforce training, thereby reducing transformation efficiency.
Practical Implications and Enhanced Visual Clarity of the Findings
The study’s findings offer several practical implications for SMEs and policymakers in Vietnam. First, the
strong effects of digital transformation on labor productivity and production cost reduction indicate that digital
investment yields tangible operational benefits. SMEs can use these insights to prioritize automation, data
integration, and process digitalization as immediate pathways to improve efficiency. Second, the mediating
role of digital skills shows that technology alone is insufficient; firms must complement digital adoption with
targeted workforce training to fully realize productivity and cost advantages. This highlights the need for
continuous upskilling programs, digital competency frameworks, and cross-functional learning. Third, the
moderating influence of firm characteristics suggests that medium-sized and manufacturing enterprises benefit
most, guiding managers to tailor digital strategies according to firm size, sector, and resource availability. For
policymakers, the results reinforce the importance of supporting digital-skills development, improving
infrastructure, and offering phased adoption programs to increase SME readiness.
To enhance interpretability and impact, the paper incorporates clear visual models, including the conceptual
framework and the structural model with β-coefficients. These diagrams summarize complex relationships—
direct, indirect, and moderating effects—allowing readers to grasp key mechanisms at a glance. The use of
visual models also improves transparency of the analytical process and strengthens the practical relevance of
the findings.
Managerial Implications
Strengthening Digital Leadership
SME leaders must recognize DT as a strategic imperative rather than a technological upgrade. Top
management should articulate a clear digital vision, allocate dedicated budgets, and appoint digital champions
responsible for implementation and change management. Leadership commitment mitigates resistance and
aligns employees with transformation goals.
Investing in Workforce Digital Skills
Given the mediating effect of digital skills, SMEs should design comprehensive training programs targeting
three competence domains: (1) basic digital literacy, (2) operational data use, and (3) problem-solving with
digital tools. Partnerships with universities, vocational institutes, and government agencies can expand access
to affordable training. Internal mentoring systems and cross-generational learning can also bridge gaps
between younger and older employees.
Prioritizing Process Integration
Many SMEs adopt isolated technologies that fail to interact effectively. Managers should prioritize end-to-end
digital integration—linking procurement, production, logistics, and finance. Adopting integrated platforms
(e.g., ERP + IoT + analytics) enables visibility across the value chain, which enhances both productivity and
cost control.
Phased Digital Investment Strategy
Given capital constraints, SMEs should adopt a phased transformation roadmap:
1. Digitize core processes (accounting, inventory, HR);
2. Automate repetitive tasks (quality inspection, reporting);
3. Leverage data analytics for predictive decision-making.
This incremental approach ensures quick wins and builds confidence for deeper transformation.
INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)
ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume IX Issue X October 2025
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Page 9542
Cultivating a Digital Culture
Beyond systems and skills, transformation success depends on fostering a culture of innovation and
adaptability. Managers should encourage experimentation, reward digital initiatives, and view failure as a
learning opportunity. Cultural alignment reinforces employee engagement and accelerates technology
acceptance.
Policy Implications
Government Support and Ecosystem Development
The Vietnamese government’s National Digital Transformation Program provides a valuable policy
framework but requires enhanced execution at the SME level. Policies should emphasize:
1. Tax incentives and low-interest loans for digital investments.
2. Public-private training partnerships to develop regional digital academies.
3. Shared service platforms for SMEs (e.g., cloud-based accounting and logistics tools).
Strengthening Digital Infrastructure
SMEs in industrial zones outside Hanoi often face limited broadband connectivity and unstable power supply,
hampering digital adoption. Policymakers should prioritize infrastructure upgrades in these regions, ensuring
equitable access to digital resources.
Digital Skills and Education Reform
Integrating digital competency frameworks into national education curricula will ensure a future-ready
workforce. The EntreComp framework (European Commission, 2018) offers a model for embedding
entrepreneurial and digital competencies into vocational and higher education programs.
Benchmarking and Data Transparency
Establishing national SME digital maturity benchmarks and data-sharing initiatives can guide firms in
assessing progress. A centralized monitoring system under the Ministry of Planning and Investment could
facilitate data-driven policy refinement.
Limitations and Future Research
Although this study offers robust insights, several limitations open avenues for future research:
1. Cross-sectional design: The data capture a single time point. Longitudinal studies could better capture the
temporal dynamics of DT and productivity growth.
2. Self-reported measures: Despite validity checks, common-method bias remains possible. Future research
should integrate objective performance indicators (e.g., output per employee, cost ratios).
3. Regional scope: The sample focuses on Hanoi and neighboring provinces. Extending the study to other
regions (Central and Southern Vietnam) would enhance generalizability.
4. Comparative analysis: Future studies could compare Vietnamese SMEs with counterparts in other
ASEAN countries to explore regional heterogeneity.
5. Advanced modeling: Employing fsQCA or PLS-MGA could uncover configurational effects and
complex causal relationships.
By addressing these limitations, future research can refine understanding of how digital transformation shapes
productivity trajectories in emerging economies.
INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)
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CONCLUSION
This study empirically confirms that digital transformation is a key driver of labor productivity and production
cost reduction in Vietnamese SMEs. Using a mixed-method design and SmartPLS 4, the research provides
strong evidence that:
1. Digital transformation directly enhances both productivity and cost efficiency.
2. Digital skills act as a mediating mechanism translating technology into performance.
3. Firm size and sector moderate transformation outcomes.
The findings underscore that digital transformation is not merely a technological challenge but a strategic,
organizational, and human one. SMEs that align digital investments with skill development and process
integration achieve greater efficiency and competitiveness. For Vietnam, accelerating SME digitalization
represents both an economic necessity and a policy priority to sustain growth in the digital age.
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