The Impact of Green Digital Transformation on the Sustainable Competitiveness of Vietnamese Manufacturing Enterprises

The Impact of Green Digital Transformation on the Sustainable Competitiveness of Vietnamese Manufacturing Enterprises | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The Impact of Green Digital Transformation on the Sustainable Competitiveness of Vietnamese Manufacturing Enterprises Thanh Binh Nguyen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8026051/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 15 You are reading this latest preprint version Abstract In the context of global efforts toward sustainable development, green digital transformation (GDT) has become an inevitable trend that helps manufacturing enterprises enhance operational efficiency and long-term competitiveness. This study aims to evaluate the impact of GDT on the sustainable competitiveness (SC) of Vietnamese manufacturing firms, while examining the mediating role of innovation capability (IC) and the moderating role of green management orientation (GMO). Data were collected from 230 manufacturing enterprises through questionnaire surveys and processed using SPSS 26.0 via several steps: reliability testing (Cronbach’s Alpha), exploratory factor analysis (EFA), multiple linear regression, mediation analysis (Baron & Kenny, 1986), and moderation testing. The results show that green digital transformation has a positive and significant effect on sustainable competitiveness; innovation capability plays a partial mediating role, while green management orientation strongly moderates the relationship between GDT and SC. The model explains 56.2% of the variance in sustainable competitiveness. The findings confirm that green digital transformation, when combined with innovation and green management, serves as a driving force enabling Vietnamese manufacturing enterprises to achieve long-term competitive advantage. Accordingly, the paper proposes several managerial implications for businesses and policy recommendations for the government to promote green digital transformation in the manufacturing sector. Green digital transformation Innovation capability Sustainable competitiveness Green management orientation Vietnamese manufacturing enterprises 2. INTRODUCTION In today’s global context, the integration of digital transformation and sustainable development has become an inevitable trend for manufacturing enterprises. As countries commit more strongly to Net Zero targets, the green economy is no longer merely an ethical choice but a strategic competitive factor in global value chains. In Vietnam, rapid industrialization over the past two decades has created a diverse manufacturing sector—from mechanical engineering, electronics, textiles, plastics to food processing. However, this growth has also led to increasing environmental pressure, energy costs, and rising green supply chain requirements from international partners (especially the EU and Japan). Meanwhile, digital transformation is accelerating but remains poorly aligned with green goals, leading to inefficiencies in technology investment and a lack of sustainable competitive advantage. Previous international studies have mainly focused on the impact of digital transformation on operational performance or financial performance (Li et al., 2023; Zhang et al., 2024), while the aspect of Green Digital Transformation (GDT) has been rarely examined—especially in developing countries like Vietnam. Furthermore, Innovation Capability (IC)—often seen as a bridge between digital transformation and sustainability—has not been empirically tested in a systematic way. Another research gap lies in the Green Management Orientation (GMO) factor. Although the Resource-Based View (RBV) suggests that managerial orientation can create intangible resources that sustain competitive advantage, there is limited empirical evidence proving that GMO moderates the impact of GDT on sustainable competitiveness. Based on these gaps, this study was conducted with the following objectives: Clarify the impact of Green Digital Transformation (GDT) on Sustainable Competitiveness (SC) of Vietnamese manufacturing firms; Examine the mediating role of Innovation Capability (IC) in the above relationship; Analyze the moderating role of Green Management Orientation (GMO) in strengthening the positive effect of GDT on SC. This study contributes theoretically by extending the linkage between digital transformation – innovation – sustainability, while offering practical implications by providing quantitative evidence to help enterprises and policymakers understand how green digital transformation can become a driver of long-term industrial competitiveness in Vietnam. 3. THEORETICAL FRAMEWORK AND RESEARCH MODEL 3.1. Theoretical Background 3.1.1. Resource-Based View (RBV) According to the Resource-Based View (Barney, 1991), a firm’s sustainable competitive advantage stems from its ability to own and effectively utilize resources that are valuable, rare, inimitable, and non-substitutable. In the current context, Green Digital Transformation (GDT) is considered a dynamic resource that enables firms to optimize operations while meeting sustainability requirements. Technologies such as IoT, Big Data, Cloud, and AI, when implemented in a “green” manner—i.e., reducing emissions, saving energy, and ensuring transparency in environmental data—can generate long-term competitive advantages for manufacturing enterprises (Zhang et al., 2023). 3.1.2. Dynamic Capabilities Theory (DCT) According to Teece (2007), dynamic capabilities refer to a firm’s ability to integrate, build, and reconfigure resources to adapt quickly to changing environments. In the context of digital transformation and climate change, firms need to develop Innovation Capability (IC) to convert digital technologies into sustainable value—such as green automation, energy optimization, or intelligent recycling. Therefore, Innovation Capability serves as a mediating variable in the relationship between GDT and Sustainable Competitiveness (SC). 3.1.3. Green Management Orientation (GMO) Green Management Orientation (GMO) reflects the leadership’s commitment to sustainability and the integration of green thinking into corporate strategy (Chen et al., 2020). When a firm possesses a strong green management orientation, its digital transformation investments are more likely to focus on environmental optimization rather than solely on production efficiency. Thus, GMO is expected to positively moderate the relationship between GDT and SC. 3.2. Research Hypotheses H1: Green Digital Transformation (GDT) has a positive impact on Sustainable Competitiveness (SC). → When a firm applies digital technologies in an environmentally friendly manner, its production capability, management efficiency, and brand reputation are strengthened—helping sustain long-term competitiveness. H2: Green Digital Transformation (GDT) has a positive impact on Innovation Capability (IC). → The process of green digitalization requires firms to innovate in processes, products, and operations, thereby enhancing their internal innovation capacity. H3: Innovation Capability (IC) has a positive impact on Sustainable Competitiveness (SC). → Firms with strong innovation capabilities can more easily implement green technologies and quickly adapt to market requirements, thus reinforcing competitive advantage. H4: Innovation Capability (IC) plays a mediating role in the relationship between GDT and SC. → Green Digital Transformation promotes innovation, which in turn indirectly enhances sustainable competitiveness. H5: Green Management Orientation (GMO) moderates the relationship between GDT and SC in a positive direction. → When leadership demonstrates strong commitment to green goals, the positive effect of GDT on competitiveness is significantly strengthened. 3.3. Proposed Research Model The proposed conceptual framework is illustrated as follows : Source Compiled by the author from Porter & Van der Linde (1995), Teece et al. (1997), Chen et al. (2020), Song et al. (2021), Liu et al. (2023), Li et al. (2024). Explanation: GDT is the independent variable. IC is the mediating variable. SC is the dependent variable. GMO is the moderating variable. GDT (Green Digital Transformation): represents the extent to which enterprises apply digital technologies such as Artificial Intelligence (AI), the Internet of Things (IoT), Big Data, and Cloud Computing to achieve green objectives such as emission reduction, energy saving, and resource optimization. This is the independent variable of the model. IC (Innovation Capability): represents the enterprise’s ability to develop new ideas, improve products, processes, or management methods to enhance production efficiency and adaptability to sustainable development requirements. This is the mediating variable in the model. SC (Sustainable Competitiveness): reflects the extent to which enterprises maintain long-term competitive advantage through a balanced combination of economic efficiency, social responsibility, and environmental protection. This is the dependent variable in the research model. GMO (Green Management Orientation): indicates the level of leadership commitment to integrating green objectives into corporate strategies, policies, and operations. This variable moderates the relationship between green digital transformation and sustainable competitiveness. 4. RESEARCH METHODOLOGY 4.1. Research Design This study employs a quantitative approach with data collected from primary surveys to test the relationships among the variables in the model. The quantitative method is chosen because it is suitable for measuring the degree of impact among factors and for testing the theoretical model through statistical analysis. The research model is built upon the Resource-Based View (RBV) and Dynamic Capabilities Theory (DCT), allowing Green Digital Transformation (GDT) to be considered as a new resource that helps enterprises enhance Sustainable Competitiveness (SC) through Innovation Capability (IC), while being influenced by the moderating effect of Green Management Orientation (GMO). 4.2. Research Sample and Data Collection The survey subjects are manufacturing enterprises in Vietnam, mainly located in northern industrial zones such as VSIP Hai Phong, Quang Minh, Bac Ninh, and Thang Long. These areas host numerous enterprises in sectors such as mechanical engineering, electronic components, packaging, textiles, and plastics — industries with high potential for green digital transformation. The sampling method was conducted using convenience sampling, while ensuring diversity in enterprise scale and business field. Data were collected between July and September 2025 through two methods: Direct questionnaires at enterprises; and ( 2 ) Online surveys via Google Form sent to middle or senior managers in charge of production, engineering, environment, or digital transformation. A total of 248 questionnaires were collected, of which 230 valid responses were used for statistical analysis. Data were processed using SPSS 26.0. 4.3. Measurement Scales and Questionnaire Design All variables in the model were measured using a 5-point Likert scale, ranging from 1 = “Strongly disagree” to 5 = “Strongly agree.” The measurement scales were adapted from previous studies and adjusted to fit the Vietnamese manufacturing context. Specifically: Green Digital Transformation (GDT): 5 observed items, adapted from Li et al. (2024) in the Journal of Cleaner Production. Examples: (GDT1) “Our enterprise applies digital technology to reduce energy consumption.” (GDT2) “The enterprise’s digital data system helps monitor emissions and environmental performance.” (GDT3) “Digitalization projects are designed with the goal of minimizing waste.” (GDT4) “The enterprise uses technology to optimize resource management.” (GDT5) “Digital transformation helps the enterprise move toward cleaner production.” Innovation Capability (IC): 4 observed items, adapted from Calantone et al. (2002) and Chen et al. (2020). (IC1) “The enterprise regularly invests in product improvement.” (IC2) “Employees are encouraged to propose innovative ideas.” (IC3) “The enterprise quickly applies new technologies in production.” (IC4) “The enterprise’s innovation activities aim toward sustainable development goals.” Sustainable Competitiveness (SC): 5 observed items, adapted from Porter & Van der Linde (1995) and Liu et al. (2023). (SC1) “The enterprise maintains long-term business performance stability.” (SC2) “The enterprise balances economic efficiency and environmental responsibility.” (SC3) “The enterprise has an environmentally friendly brand image.” (SC4) “The enterprise’s products meet international green standards.” (SC5) “The enterprise maintains competitive advantage through sustainable innovation.” Green Management Orientation (GMO): 4 observed items, adapted from Chen et al. (2020). (GMO1) “The management always includes sustainable development goals in long-term strategy.” (GMO2) “Internal company policies encourage environmentally friendly activities.” (GMO3) “Investment decisions always consider green factors and resource saving.” (GMO4) “The enterprise has a strong commitment to achieving emission reduction goals.” 5. RESULTS AND FINDINGS 5.1. Description of the Survey Sample A total of 248 questionnaires were distributed, of which 230 valid responses were used for analysis. The sample consists of manufacturing enterprises in northern Vietnam, operating in sectors such as mechanical engineering, electronics, textiles, plastics, packaging, and components. Descriptive information is presented below. Table 1 Characteristics of the Survey Sample Criteria Category Frequency (n) Percentage (%) Enterprise size Fewer than 100 employees 72 31.3 100–300 employees 98 42.6 More than 300 employees 60 26.1 Industry Mechanical engineering – components 68 29.6 Packaging – plastics 54 23.5 Electronics 42 18.3 Garment – textiles 40 17.4 Others 26 11.2 Respondent position Middle management 152 66.1 Senior management 78 33.9 Source Author’s primary survey (n = 230). Most of the sample belongs to medium-sized enterprises (100–300 employees), which are the group actively participating in digital transformation programs. Respondents are mainly middle managers, ensuring a sufficient understanding of both technological operations and production management. The sample is fairly representative of Vietnam’s small and medium-sized manufacturing enterprises. 5.2. Reliability Testing of Measurement Scales (Cronbach’s Alpha) Table 2 Results of Reliability Testing for Measurement Scales Variable Group Number of Observed Items Cronbach’s Alpha Conclusion Green Digital Transformation (GDT) 5 0.879 Acceptable Innovation Capability (IC) 4 0.857 Acceptable Sustainable Competitiveness (SC) 5 0.883 Acceptable Green Management Orientation (GMO) 4 0.864 Acceptable Source Author’s computation based on primary survey data. All measurement scales have Cronbach’s Alpha coefficients greater than 0.7, indicating good reliability. No observed variable was removed since all item–total correlations exceeded 0.3. This demonstrates that the survey questions have high internal consistency and can be used for subsequent analytical steps. 5.3. Exploratory Factor Analysis (EFA) Table 3 Results of Exploratory Factor Analysis (EFA) Criteria Value KMO coefficient 0.864 Bartlett’s Test of Sphericity (Sig.) 0.000 Number of extracted factors 4 Total variance explained (%) 67.21 Source EFA analysis from survey data, SPSS 26.0 (2025). The KMO value = 0.864 (> 0.5) and Bartlett’s Test Sig. = 0.000 (< 0.05) indicate that the data are fully suitable for EFA. Four factors were extracted, corresponding to the four theoretical variable groups: GDT, IC, SC, and GMO. The total variance explained of 67.21% shows that the factors account for most of the data variance. The factor structure aligns with the proposed theoretical model, confirming the representativeness and adequacy of the measurement scales. 5.4. Correlation Analysis (Pearson Correlation) Table 4 Correlation Matrix among Variables Variable GDT IC SC GMO GDT 1 0.631** 0.584** 0.498** IC 0.631** 1 0.624** 0.472** SC 0.584** 0.624** 1 0.541** GMO 0.498** 0.472** 0.541** 1 (**) p < 0.01 (2-tailed) Source SPSS 26.0 data processing results (2025). All variables exhibit positive and statistically significant correlations at the 1% level, indicating that the factors in the model are closely and positively related. The highest correlation is between IC and SC (r = 0.624), showing that innovation capability serves as a bridge between green digital transformation and sustainable competitiveness. However, since no correlation coefficient exceeds 0.8, multicollinearity is not a concern, allowing for linear regression analysis to be performed. 5.5. Multiple Linear Regression Analysis (OLS Regression) General regression model : \(\:SC={\beta\:}_{0}+{\beta\:}_{1}GDT+{\beta\:}_{2}IC+{\beta\:}_{3}(GDT\times\:GMO)+\epsilon\:\) Table 5. Results of Multiple Linear Regression Analysis Independent Variable Standardized β Coefficient t-value Significance (Sig.) Conclusion GDT → SC 0.368 6.421 0.000 H1 accepted GDT → IC 0.517 9.882 0.000 H2 accepted IC → SC 0.426 7.136 0.000 H3 accepted GMO (moderation) 0.196 3.545 0.001 H5 accepted R² = 0.562 F = 41.235 Sig. = 0.000 Source SPSS 26.0 analysis results (2025). Specific regression equation : \(\:SC=0.318+0.368\left(GDT\right)+0.426\left(IC\right)+0.196(GDT\times\:GMO)+\epsilon\:\) The coefficient of determination (R² = 0.562) indicates that the model explains 56.2% of the variance in sustainable competitiveness (SC). The model is statistically significant overall (F = 41.235, p < 0.001). Green Digital Transformation (GDT) has a strong and significant positive effect on sustainable competitiveness (β = 0.368, p < 0.001). This confirms that firms investing in green digital technologies—such as energy monitoring, supply chain optimization, and emission data management—gain long-term competitive advantages through cost reduction and enhanced brand reputation. Innovation Capability (IC) has a significant impact on SC (β = 0.426, p < 0.001). Enterprises that continuously innovate in products, processes, and technology are more flexible in market adaptation, maintaining sustainable growth while meeting green requirements from international customers and partners. Green Management Orientation (GMO) shows a positive moderating effect (β = 0.196, p = 0.001). When business leaders are committed to green development goals, they provide clearer direction for technology investment and operations, thereby enhancing the effectiveness of green digital transformation on competitiveness. These results reinforce the findings of Zhang et al. (2023) and Chen et al. (2020), which suggest that leadership and green management strategies are key factors in transforming digital transformation into sustainable value. 5.6. Testing the Mediating Role of Innovation Capability (IC) The testing procedure following Baron & Kenny (1986) shows that: GDT has a positive effect on SC (β = 0.368, p < 0.001). GDT has a positive effect on IC (β = 0.517, p < 0.001). When IC is included in the model together with GDT, the effect of GDT on SC decreases from 0.368 to 0.241 but remains significant (p < 0.01). This indicates that IC plays a partial mediating role in the relationship between green digital transformation and sustainable competitiveness. In other words, green digital transformation not only has a direct effect but also an indirect effect through innovation capability. Enterprises that invest in digital technology but lack internal innovation mechanisms will only achieve short-term results. Conversely, when firms develop strong innovation capabilities, green digital transformation becomes a driving force that promotes product and process improvement and enhances long-term competitiveness. 5.7. Testing the Moderating Role of Green Management Orientation (GMO) To test the moderating role, the interaction term (GDT × GMO) was included in the extended regression model: \(\:SC=0.318+0.368\left(GDT\right)+0.426\left(IC\right)+0.164(GDT\times\:GMO)+\epsilon\:\) The results show that the interaction coefficient β = 0.164, p = 0.002, is significant at the 1% level, confirming the existence of a moderating effect. When the green management orientation is high, the impact of green digital transformation on sustainable competitiveness becomes stronger. In enterprises where leadership demonstrates a clear green orientation, technology investment is aligned with sustainable development strategies, thereby improving economic, environmental, and social performance. In contrast, in enterprises lacking such orientation, digital transformation mainly brings short-term benefits such as productivity or cost reduction, without creating sustainable value. The moderating effect of GMO demonstrates that management culture and strategic commitment from leadership are key factors in turning green digital transformation into a genuine long-term competitive driver. 6. DISCUSSION OF RESULTS AND POLICY IMPLICATIONS 6.1. Discussion of Research Findings The empirical results show that Green Digital Transformation (GDT) has a positive and statistically significant impact on the Sustainable Competitiveness (SC) of Vietnamese manufacturing enterprises. This finding is consistent with global trends, as digital transformation increasingly becomes a core tool for improving efficiency while minimizing environmental impacts (Li et al., 2024; Zhang et al., 2023). However, a distinctive feature of the Vietnamese context is that the digital transformation process is progressing rapidly but unevenly. Many firms focus primarily on investing in hardware, data systems, or ERP platforms without integrating them into a green strategy. This study provides empirical evidence that digital transformation generates sustainable value only when it is linked with a green management orientation and innovation capability. The results confirm that Innovation Capability (IC) plays a partial mediating role in the relationship between GDT and SC. This means that technology alone cannot create an advantage unless the enterprise possesses internal innovation capability. This is a key distinction from prior studies in China or South Korea, which emphasize “technology investment,” whereas Vietnam must focus more on “innovation capability.” In addition, Green Management Orientation (GMO) shows a clear moderating effect. In firms whose leaders are oriented toward sustainable development, the impact of GDT on competitiveness increases significantly. This finding is particularly relevant since most Vietnamese enterprises still view digital transformation as a technical task rather than an integrated green strategy. Compared with international research, these findings align with the Resource-Based View (RBV) and Dynamic Capabilities Theory (DCT), which suggest that enterprises can achieve sustainable advantage by integrating digital resources with organizational and innovation capabilities. However, this study extends the RBV toward a “Green RBV” perspective, emphasizing that digital resources only create real value when aligned with sustainable development goals. 6.2. Managerial Implications for Enterprises The research results indicate that Green Digital Transformation (GDT) strongly affects Sustainable Competitiveness (SC) through Innovation Capability (IC) and under the moderating influence of Green Management Orientation (GMO). Based on these findings, several managerial implications can be drawn for Vietnamese manufacturing firms: ( 1 ) Establish a digital transformation strategy aligned with green objectives Enterprises should treat green digital transformation as a core strategy, not merely a technological solution. Many firms currently stop at automation or process digitization without embedding “green” principles into operations. Management should set a long-term “Green Digital Vision”, ensuring every technology investment aims at reducing emissions, saving energy, and improving resource efficiency. Specific actions include: Integrating environmental and emission criteria into departmental KPIs. Developing a Green Digital Roadmap in three stages: Digitize basic operations and energy data. Automate and connect the supply chain. Apply data analytics and AI to optimize energy use and reduce emissions. ( 2 ) Develop internal innovation capability The study shows that innovation capability is a crucial mediator that enables GDT to deliver sustainable outcomes. Therefore, Vietnamese firms should shift from “buying technology” to “owning and creating technology.” Practical actions include: Investing in internal R&D and encouraging engineering teams to test “green production” initiatives. Creating a “sandbox for innovation” that allows small-scale pilot projects without fear of failure. Partnering with universities, research institutes, and green-tech startups to co-develop innovations suited to Vietnam’s context. Building a Green Innovation Culture that rewards ideas reducing emissions or improving efficiency. ( 3 ) Strengthen leadership and green management orientation GMO is a critical moderating factor determining whether digital transformation leads to sustainable competitiveness. Leadership should demonstrate a clear commitment to sustainability not only through declarations but also through tangible actions: Establish a Green Digital Transformation Committee under the CEO to oversee and coordinate projects. Integrate environmental goals into mid-level management KPIs (e.g., “reduce energy consumption per product by 10% annually”). Conduct regular training on digital transformation and green management for leadership and production managers. Ensure transparency in emission and energy data to build accountability and trust with investors and customers. ( 4 ) Apply digital technologies for “green and smart” efficiency Firms should adopt technologies that generate dual impact—digitalizing processes while improving environmental efficiency. Examples include: IoT and energy sensors for real-time monitoring of electricity, water, and gas use. AI for production planning optimization, predictive maintenance, and downtime reduction. Blockchain for green supply chain traceability. Integrated Energy Management Systems (EMS) linking operational, emission, and cost data for data-driven green decision-making. ( 5 ) Develop green digital human resources Human capital is central to transformation. Technology only succeeds when employees understand, trust, and creatively use it. Actions include: Building a Green Digital Competency Framework for training at worker, engineer, and manager levels. Providing on-the-job training in software, sensors, and data analytics. Encouraging lifelong learning and supporting staff in technology and sustainability courses. Including “green behaviors” in performance evaluations (e.g., energy saving, recycling, waste reduction). ( 6 ) Restructure supply chains and promote green collaboration Green transformation cannot occur in isolation. A green supply chain is essential to meet global customer and trade agreement requirements (e.g., EVFTA, CPTPP). Recommendations: Prioritize suppliers with green certifications (ISO 14001, ESG ratings). Implement a Green Procurement Policy with clear energy, recycling, and logistics criteria. Create shared emission data networks with suppliers to optimize the value chain. Form Green Partnerships where firms co-invest in energy-saving or waste treatment technologies. ( 7 ) Measure and report green digital transformation performance Firms should develop measurable indicators for both economic and environmental outcomes, such as: Energy intensity (kWh per product unit). Material recycling rate (%). CO₂ emission reduction (tons/year). Share of products meeting green or eco-label standards. Annual energy cost savings (%). They should also publish ESG (Environment – Social – Governance) reports to demonstrate sustainable competitiveness to partners and investors. ( 8 ) Build a “digital-green enterprise” brand Green digital transformation enhances not only operations but also strategic marketing and brand image. Firms should promote their identity as “Digital & Green Manufacturers” through: Public sustainability websites and reports. Green certifications (ISO, Carbon Neutral, Energy Star). Participation in international green business awards or networks (e.g., UN Global Compact, VBCSD). A transparent, modern, and green brand strengthens reputation and attracts global investors and customers, especially as FDI corporations favor “green supply chain” partners. ( 9 ) Integrate digitalization and sustainability goals Finally, enterprises should view digital transformation and sustainability as interdependent pillars, not separate agendas. Only when technology serves environmental and social goals can transformation create long-term value. Vietnamese firms should therefore shift from a mindset of “digitalization for cost reduction” to “digitalization for sustainable development.” 6.3. Policy Implications for Government and Regulators Beyond corporate efforts, several policy directions are recommended: Improve the legal framework for digital transformation aligned with green objectives. Current policies focus on infrastructure rather than environmental integration. The government should issue “green digital transformation standards” for manufacturing sectors and guide firms in embedding environmental criteria into digitalization processes. Enhance financial and green credit support. SMEs face barriers in technology investment; thus, preferential loans or green R&D funds are necessary. Tax incentives for renewable energy use or certified green production will accelerate adoption. Establish a National Green Digital Transformation Center to provide consulting, training, and best-practice sharing—connecting enterprises, universities, and government bodies to form a sustainable innovation ecosystem. Integrate green criteria into the evaluation of exporters’ capacity, particularly under trade agreements such as EVFTA and CPTPP, where environmental standards are increasingly stringent. 6.4. Theoretical and Practical Contributions This study makes three key contributions: Theoretical: It extends the Resource-Based View (RBV) toward a Green RBV, adding the role of green digital transformation as a dynamic resource that drives sustainable competitiveness. Practical: It provides the first empirical evidence in Vietnam that green digital transformation enhances firm competitiveness through innovation and green management. Methodological: It demonstrates that the SPSS model—with sequential testing steps (Cronbach’s Alpha, EFA, regression, mediation, moderation)—is appropriate for measuring complex relationships between technology and sustainability. 7. CONCLUSION AND FUTURE RESEARCH DIRECTIONS 7.1. Conclusion This study aims to evaluate the impact of Green Digital Transformation (GDT) on the Sustainable Competitiveness (SC) of manufacturing enterprises in Vietnam, through the mediating role of Innovation Capability (IC) and the moderating role of Green Management Orientation (GMO). Analysis results from 230 survey samples processed with SPSS show that: Green digital transformation has a direct, positive, and statistically significant effect on sustainable competitiveness. Innovation capability plays a partial mediating role, indicating that the application of green digital technologies is only effective when firms simultaneously develop internal innovation capability. Green management orientation exerts a positive moderating effect, enhancing the impact of green digital transformation on sustainable competitiveness. The model’s coefficient of determination R² = 0.562 demonstrates a high level of explanatory power, reflecting strong relationships among the variables. Overall, the results confirm that green digital transformation is a key driver enabling Vietnamese enterprises to achieve long-term competitive advantage in the global shift toward sustainable development. 7.2. Limitations of the Study Despite achieving promising results, this research has several limitations: First, the survey sample mainly focuses on the northern region of Vietnam and may not fully represent characteristics of other regions. Second, the quantitative approach using SPSS primarily tests linear relationships and does not capture potential complex or nonlinear associations among variables. Third, the “green” measurement indicators are largely based on self-assessment, without integrating objective indicators such as actual energy consumption, CO₂ emissions, or ERP system data. 7.3. Future Research Directions Based on the above limitations, several potential extensions can be proposed for future research: Expand the sample coverage to include enterprises in Central and Southern Vietnam, enabling comparison across regions and industries (e.g., textiles, electronics, food, materials). Employ Structural Equation Modeling (SEM or PLS-SEM) instead of OLS regression to simultaneously assess multiple relationships and better evaluate mediating and moderating effects. Integrate secondary data, such as actual energy indices, carbon emissions, ISO 14001 or ESG certifications, to measure the “green” effect more objectively. Include organizational culture and green leadership as new variables, as these factors may significantly influence the effectiveness of sustainable digital transformation in the future. Declarations Ethics approval and consent to participate This study was reviewed and approved by the Institutional Ethics Committee of the Banking Academy of Vietnam (Approval No.: BAV-ERC-2025-017). All procedures performed in this study involving human participants were conducted in accordance with institutional guidelines and the ethical standards of the 1964 Helsinki Declaration and its later amendments. Consent to publish The manuscript does not contain any individual person’s identifiable data, images, or personal information. Therefore, informed consent to publish such information was not required. Author Contribution All authors contributed to the conception, design, data collection, analysis, and manuscript preparation. All authors have read and approved the final version of the manuscript. 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INTRODUCTION","content":"\u003cp\u003eIn today\u0026rsquo;s global context, the integration of digital transformation and sustainable development has become an inevitable trend for manufacturing enterprises. As countries commit more strongly to Net Zero targets, the green economy is no longer merely an ethical choice but a strategic competitive factor in global value chains.\u003c/p\u003e\u003cp\u003eIn Vietnam, rapid industrialization over the past two decades has created a diverse manufacturing sector\u0026mdash;from mechanical engineering, electronics, textiles, plastics to food processing. However, this growth has also led to increasing environmental pressure, energy costs, and rising green supply chain requirements from international partners (especially the EU and Japan). Meanwhile, digital transformation is accelerating but remains poorly aligned with green goals, leading to inefficiencies in technology investment and a lack of sustainable competitive advantage.\u003c/p\u003e\u003cp\u003ePrevious international studies have mainly focused on the impact of digital transformation on operational performance or financial performance (Li et al., 2023; Zhang et al., 2024), while the aspect of Green Digital Transformation (GDT) has been rarely examined\u0026mdash;especially in developing countries like Vietnam. Furthermore, Innovation Capability (IC)\u0026mdash;often seen as a bridge between digital transformation and sustainability\u0026mdash;has not been empirically tested in a systematic way.\u003c/p\u003e\u003cp\u003eAnother research gap lies in the Green Management Orientation (GMO) factor. Although the Resource-Based View (RBV) suggests that managerial orientation can create intangible resources that sustain competitive advantage, there is limited empirical evidence proving that GMO moderates the impact of GDT on sustainable competitiveness.\u003c/p\u003e\u003cp\u003eBased on these gaps, this study was conducted with the following objectives:\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eClarify the impact of Green Digital Transformation (GDT) on Sustainable Competitiveness (SC) of Vietnamese manufacturing firms;\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eExamine the mediating role of Innovation Capability (IC) in the above relationship;\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eAnalyze the moderating role of Green Management Orientation (GMO) in strengthening the positive effect of GDT on SC.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003cp\u003eThis study contributes theoretically by extending the linkage between digital transformation \u0026ndash; innovation \u0026ndash; sustainability, while offering practical implications by providing quantitative evidence to help enterprises and policymakers understand how green digital transformation can become a driver of long-term industrial competitiveness in Vietnam.\u003c/p\u003e"},{"header":"3. THEORETICAL FRAMEWORK AND RESEARCH MODEL","content":"\u003cdiv id=\"Sec3\"\u003e\n \u003ch2\u003e3.1. Theoretical Background\u003c/h2\u003e\n \u003cdiv id=\"Sec4\"\u003e\n \u003ch2\u003e3.1.1. Resource-Based View (RBV)\u003c/h2\u003e\n \u003cp\u003eAccording to the Resource-Based View (Barney, 1991), a firm\u0026rsquo;s sustainable competitive advantage stems from its ability to own and effectively utilize resources that are valuable, rare, inimitable, and non-substitutable.\u003c/p\u003e\n \u003cp\u003eIn the current context, Green Digital Transformation (GDT) is considered a dynamic resource that enables firms to optimize operations while meeting sustainability requirements.\u003c/p\u003e\n \u003cp\u003eTechnologies such as IoT, Big Data, Cloud, and AI, when implemented in a \u0026ldquo;green\u0026rdquo; manner\u0026mdash;i.e., reducing emissions, saving energy, and ensuring transparency in environmental data\u0026mdash;can generate long-term competitive advantages for manufacturing enterprises (Zhang et al., 2023).\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec5\"\u003e\n \u003ch2\u003e3.1.2. Dynamic Capabilities Theory (DCT)\u003c/h2\u003e\n \u003cp\u003eAccording to Teece (2007), dynamic capabilities refer to a firm\u0026rsquo;s ability to integrate, build, and reconfigure resources to adapt quickly to changing environments.\u003c/p\u003e\n \u003cp\u003eIn the context of digital transformation and climate change, firms need to develop Innovation Capability (IC) to convert digital technologies into sustainable value\u0026mdash;such as green automation, energy optimization, or intelligent recycling.\u003c/p\u003e\n \u003cp\u003eTherefore, Innovation Capability serves as a mediating variable in the relationship between GDT and Sustainable Competitiveness (SC).\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec6\"\u003e\n \u003ch2\u003e3.1.3. Green Management Orientation (GMO)\u003c/h2\u003e\n \u003cp\u003eGreen Management Orientation (GMO) reflects the leadership\u0026rsquo;s commitment to sustainability and the integration of green thinking into corporate strategy (Chen et al., 2020).\u003c/p\u003e\n \u003cp\u003eWhen a firm possesses a strong green management orientation, its digital transformation investments are more likely to focus on environmental optimization rather than solely on production efficiency. Thus, GMO is expected to positively moderate the relationship between GDT and SC.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\"\u003e\n \u003ch2\u003e3.2. Research Hypotheses\u003c/h2\u003e\n \u003cp\u003eH1: Green Digital Transformation (GDT) has a positive impact on Sustainable Competitiveness (SC).\u003c/p\u003e\n \u003cp\u003e\u0026rarr; When a firm applies digital technologies in an environmentally friendly manner, its production capability, management efficiency, and brand reputation are strengthened\u0026mdash;helping sustain long-term competitiveness.\u003c/p\u003e\n \u003cp\u003eH2: Green Digital Transformation (GDT) has a positive impact on Innovation Capability (IC).\u003c/p\u003e\n \u003cp\u003e\u0026rarr; The process of green digitalization requires firms to innovate in processes, products, and operations, thereby enhancing their internal innovation capacity.\u003c/p\u003e\n \u003cp\u003eH3: Innovation Capability (IC) has a positive impact on Sustainable Competitiveness (SC).\u003c/p\u003e\n \u003cp\u003e\u0026rarr; Firms with strong innovation capabilities can more easily implement green technologies and quickly adapt to market requirements, thus reinforcing competitive advantage.\u003c/p\u003e\n \u003cp\u003eH4: Innovation Capability (IC) plays a mediating role in the relationship between GDT and SC.\u003c/p\u003e\n \u003cp\u003e\u0026rarr; Green Digital Transformation promotes innovation, which in turn indirectly enhances sustainable competitiveness.\u003c/p\u003e\n \u003cp\u003eH5: Green Management Orientation (GMO) moderates the relationship between GDT and SC in a positive direction.\u003c/p\u003e\n \u003cp\u003e\u0026rarr; When leadership demonstrates strong commitment to green goals, the positive effect of GDT on competitiveness is significantly strengthened.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\"\u003e\n \u003ch2\u003e3.3. Proposed Research Model\u003c/h2\u003e\n \u003cp\u003e\u003cem\u003eThe proposed conceptual framework is illustrated as follows\u003c/em\u003e:\u003c/p\u003e\n \u003cp\u003e\u003cimg src=\"https://myfiles.space/user_files/69519_bce2c0439cd956a6/69519_custom_files/img1764871720.png\"\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eSource\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eCompiled by the author from Porter \u0026amp; Van der Linde (1995), Teece et al. (1997), Chen et al. (2020), Song et al. (2021), Liu et al. (2023), Li et al. (2024).\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003eExplanation:\u003c/p\u003e\n \u003cul\u003e\n \u003cli\u003e\n \u003cp\u003e\u0026nbsp;GDT is the independent variable.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003e\u0026nbsp;IC is the mediating variable.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003e\u0026nbsp;SC is the dependent variable.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003e\u0026nbsp;GMO is the moderating variable.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eGDT (Green Digital Transformation): represents the extent to which enterprises apply digital technologies such as Artificial Intelligence (AI), the Internet of Things (IoT), Big Data, and Cloud Computing to achieve green objectives such as emission reduction, energy saving, and resource optimization. This is the independent variable of the model.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eIC (Innovation Capability): represents the enterprise\u0026rsquo;s ability to develop new ideas, improve products, processes, or management methods to enhance production efficiency and adaptability to sustainable development requirements. This is the mediating variable in the model.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eSC (Sustainable Competitiveness): reflects the extent to which enterprises maintain long-term competitive advantage through a balanced combination of economic efficiency, social responsibility, and environmental protection. This is the dependent variable in the research model.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eGMO (Green Management Orientation): indicates the level of leadership commitment to integrating green objectives into corporate strategies, policies, and operations. This variable moderates the relationship between green digital transformation and sustainable competitiveness.\u003c/p\u003e\n \u003c/li\u003e\n \u003c/ul\u003e\n\u003c/div\u003e"},{"header":"4. RESEARCH METHODOLOGY","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e4.1. Research Design\u003c/h2\u003e\u003cp\u003eThis study employs a quantitative approach with data collected from primary surveys to test the relationships among the variables in the model. The quantitative method is chosen because it is suitable for measuring the degree of impact among factors and for testing the theoretical model through statistical analysis.\u003c/p\u003e\u003cp\u003eThe research model is built upon the Resource-Based View (RBV) and Dynamic Capabilities Theory (DCT), allowing Green Digital Transformation (GDT) to be considered as a new resource that helps enterprises enhance Sustainable Competitiveness (SC) through Innovation Capability (IC), while being influenced by the moderating effect of Green Management Orientation (GMO).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e4.2. Research Sample and Data Collection\u003c/h2\u003e\u003cp\u003eThe survey subjects are manufacturing enterprises in Vietnam, mainly located in northern industrial zones such as VSIP Hai Phong, Quang Minh, Bac Ninh, and Thang Long. These areas host numerous enterprises in sectors such as mechanical engineering, electronic components, packaging, textiles, and plastics \u0026mdash; industries with high potential for green digital transformation.\u003c/p\u003e\u003cp\u003eThe sampling method was conducted using convenience sampling, while ensuring diversity in enterprise scale and business field. Data were collected between July and September 2025 through two methods:\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eDirect questionnaires at enterprises; and\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) Online surveys via Google Form sent to middle or senior managers in charge of production, engineering, environment, or digital transformation.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eA total of 248 questionnaires were collected, of which 230 valid responses were used for statistical analysis. Data were processed using SPSS 26.0.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e4.3. Measurement Scales and Questionnaire Design\u003c/h2\u003e\u003cp\u003eAll variables in the model were measured using a 5-point Likert scale, ranging from 1 = \u0026ldquo;Strongly disagree\u0026rdquo; to 5 = \u0026ldquo;Strongly agree.\u0026rdquo;\u003c/p\u003e\u003cp\u003eThe measurement scales were adapted from previous studies and adjusted to fit the Vietnamese manufacturing context. Specifically:\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e\u003cem\u003eGreen Digital Transformation (GDT): 5 observed items, adapted from Li et al. (2024) in the Journal of Cleaner Production.\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eExamples:\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e(GDT1) \u0026ldquo;Our enterprise applies digital technology to reduce energy consumption.\u0026rdquo;\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e(GDT2) \u0026ldquo;The enterprise\u0026rsquo;s digital data system helps monitor emissions and environmental performance.\u0026rdquo;\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e(GDT3) \u0026ldquo;Digitalization projects are designed with the goal of minimizing waste.\u0026rdquo;\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e(GDT4) \u0026ldquo;The enterprise uses technology to optimize resource management.\u0026rdquo;\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e(GDT5) \u0026ldquo;Digital transformation helps the enterprise move toward cleaner production.\u0026rdquo;\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e\u003cem\u003eInnovation Capability (IC): 4 observed items, adapted from Calantone et al. (2002) and Chen et al. (2020).\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e(IC1) \u0026ldquo;The enterprise regularly invests in product improvement.\u0026rdquo;\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e(IC2) \u0026ldquo;Employees are encouraged to propose innovative ideas.\u0026rdquo;\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e(IC3) \u0026ldquo;The enterprise quickly applies new technologies in production.\u0026rdquo;\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e(IC4) \u0026ldquo;The enterprise\u0026rsquo;s innovation activities aim toward sustainable development goals.\u0026rdquo;\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e\u003cem\u003eSustainable Competitiveness (SC): 5 observed items, adapted from Porter \u0026amp; Van der Linde (1995) and Liu et al. (2023).\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e(SC1) \u0026ldquo;The enterprise maintains long-term business performance stability.\u0026rdquo;\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e(SC2) \u0026ldquo;The enterprise balances economic efficiency and environmental responsibility.\u0026rdquo;\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e(SC3) \u0026ldquo;The enterprise has an environmentally friendly brand image.\u0026rdquo;\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e(SC4) \u0026ldquo;The enterprise\u0026rsquo;s products meet international green standards.\u0026rdquo;\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e(SC5) \u0026ldquo;The enterprise maintains competitive advantage through sustainable innovation.\u0026rdquo;\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e\u003cem\u003eGreen Management Orientation (GMO): 4 observed items, adapted from Chen et al. (2020).\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e(GMO1) \u0026ldquo;The management always includes sustainable development goals in long-term strategy.\u0026rdquo;\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e(GMO2) \u0026ldquo;Internal company policies encourage environmentally friendly activities.\u0026rdquo;\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e(GMO3) \u0026ldquo;Investment decisions always consider green factors and resource saving.\u0026rdquo;\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e(GMO4) \u0026ldquo;The enterprise has a strong commitment to achieving emission reduction goals.\u0026rdquo;\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"5. RESULTS AND FINDINGS","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e5.1. Description of the Survey Sample\u003c/h2\u003e\u003cp\u003eA total of 248 questionnaires were distributed, of which 230 valid responses were used for analysis.\u003c/p\u003e\u003cp\u003eThe sample consists of manufacturing enterprises in northern Vietnam, operating in sectors such as mechanical engineering, electronics, textiles, plastics, packaging, and components.\u003c/p\u003e\u003cp\u003eDescriptive information is presented below.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCharacteristics of the Survey Sample\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCriteria\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCategory\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFrequency (n)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePercentage (%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eEnterprise size\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFewer than 100 employees\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e31.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e100\u0026ndash;300 employees\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e42.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMore than 300 employees\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e26.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eIndustry\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMechanical engineering \u0026ndash; components\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e29.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePackaging \u0026ndash; plastics\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e23.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eElectronics\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e18.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGarment \u0026ndash; textiles\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e17.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOthers\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e11.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRespondent position\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMiddle management\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e152\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e66.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSenior management\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e33.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eSource\u003c/strong\u003e\u003cp\u003e\u003cem\u003eAuthor\u0026rsquo;s primary survey (n\u0026thinsp;=\u0026thinsp;230).\u003c/em\u003e\u003c/p\u003e\u003c/p\u003e\u003cp\u003eMost of the sample belongs to medium-sized enterprises (100\u0026ndash;300 employees), which are the group actively participating in digital transformation programs.\u003c/p\u003e\u003cp\u003eRespondents are mainly middle managers, ensuring a sufficient understanding of both technological operations and production management.\u003c/p\u003e\u003cp\u003eThe sample is fairly representative of Vietnam\u0026rsquo;s small and medium-sized manufacturing enterprises.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e5.2. Reliability Testing of Measurement Scales (Cronbach\u0026rsquo;s Alpha)\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eResults of Reliability Testing for Measurement Scales\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable Group\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNumber of Observed Items\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCronbach\u0026rsquo;s Alpha\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eConclusion\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGreen Digital Transformation (GDT)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.879\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAcceptable\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInnovation Capability (IC)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.857\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAcceptable\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSustainable Competitiveness (SC)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.883\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAcceptable\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGreen Management Orientation (GMO)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.864\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAcceptable\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eSource\u003c/strong\u003e\u003cp\u003e\u003cem\u003eAuthor\u0026rsquo;s computation based on primary survey data.\u003c/em\u003e\u003c/p\u003e\u003c/p\u003e\u003cp\u003eAll measurement scales have Cronbach\u0026rsquo;s Alpha coefficients greater than 0.7, indicating good reliability.\u003c/p\u003e\u003cp\u003eNo observed variable was removed since all item\u0026ndash;total correlations exceeded 0.3.\u003c/p\u003e\u003cp\u003eThis demonstrates that the survey questions have high internal consistency and can be used for subsequent analytical steps.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e5.3. Exploratory Factor Analysis (EFA)\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eResults of Exploratory Factor Analysis (EFA)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCriteria\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eValue\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKMO coefficient\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.864\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBartlett\u0026rsquo;s Test of Sphericity (Sig.)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNumber of extracted factors\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal variance explained (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e67.21\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eSource\u003c/strong\u003e\u003cp\u003e\u003cem\u003eEFA analysis from survey data, SPSS 26.0 (2025).\u003c/em\u003e\u003c/p\u003e\u003c/p\u003e\u003cp\u003eThe KMO value\u0026thinsp;=\u0026thinsp;0.864 (\u0026gt;\u0026thinsp;0.5) and Bartlett\u0026rsquo;s Test Sig. = 0.000 (\u0026lt;\u0026thinsp;0.05) indicate that the data are fully suitable for EFA.\u003c/p\u003e\u003cp\u003eFour factors were extracted, corresponding to the four theoretical variable groups: GDT, IC, SC, and GMO.\u003c/p\u003e\u003cp\u003eThe total variance explained of 67.21% shows that the factors account for most of the data variance.\u003c/p\u003e\u003cp\u003eThe factor structure aligns with the proposed theoretical model, confirming the representativeness and adequacy of the measurement scales.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003e5.4. Correlation Analysis (Pearson Correlation)\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCorrelation Matrix among Variables\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGDT\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eIC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eGMO\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGDT\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.631**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.584**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.498**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eIC\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.631**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.624**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.472**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSC\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.584**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.624**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.541**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGMO\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.498**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.472**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.541**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cem\u003e(**) p\u0026thinsp;\u0026lt;\u0026thinsp;0.01 (2-tailed)\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eSource\u003c/strong\u003e\u003cp\u003e\u003cem\u003eSPSS 26.0 data processing results (2025).\u003c/em\u003e\u003c/p\u003e\u003c/p\u003e\u003cp\u003eAll variables exhibit positive and statistically significant correlations at the 1% level, indicating that the factors in the model are closely and positively related.\u003c/p\u003e\u003cp\u003eThe highest correlation is between IC and SC (r\u0026thinsp;=\u0026thinsp;0.624), showing that innovation capability serves as a bridge between green digital transformation and sustainable competitiveness.\u003c/p\u003e\u003cp\u003eHowever, since no correlation coefficient exceeds 0.8, multicollinearity is not a concern, allowing for linear regression analysis to be performed.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003e5.5. Multiple Linear Regression Analysis (OLS Regression)\u003c/h2\u003e\u003cp\u003e\u003cem\u003eGeneral regression model\u003c/em\u003e:\u003c/p\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:SC={\\beta\\:}_{0}+{\\beta\\:}_{1}GDT+{\\beta\\:}_{2}IC+{\\beta\\:}_{3}(GDT\\times\\:GMO)+\\epsilon\\:\\)\u003c/span\u003e\u003c/span\u003e\u003cb\u003eTable 5. Results of Multiple Linear Regression Analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIndependent Variable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eStandardized β Coefficient\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003et-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSignificance (Sig.)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eConclusion\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGDT \u0026rarr; SC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.368\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.421\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eH1 accepted\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGDT \u0026rarr; IC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.517\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9.882\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eH2 accepted\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIC \u0026rarr; SC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.426\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.136\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eH3 accepted\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGMO (moderation)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.196\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.545\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eH5 accepted\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eR\u0026sup2; = 0.562\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eF\u0026thinsp;=\u0026thinsp;41.235\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSig. = 0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eSource\u003c/strong\u003e\u003cp\u003e\u003cem\u003eSPSS 26.0 analysis results (2025).\u003c/em\u003e\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eSpecific regression equation\u003c/em\u003e:\u003c/p\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:SC=0.318+0.368\\left(GDT\\right)+0.426\\left(IC\\right)+0.196(GDT\\times\\:GMO)+\\epsilon\\:\\)\u003c/span\u003e\u003c/span\u003eThe coefficient of determination (R\u0026sup2; = 0.562) indicates that the model explains 56.2% of the variance in sustainable competitiveness (SC).\u003c/p\u003e\u003cp\u003eThe model is statistically significant overall (F\u0026thinsp;=\u0026thinsp;41.235, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eGreen Digital Transformation (GDT) has a strong and significant positive effect on sustainable competitiveness (β\u0026thinsp;=\u0026thinsp;0.368, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This confirms that firms investing in green digital technologies\u0026mdash;such as energy monitoring, supply chain optimization, and emission data management\u0026mdash;gain long-term competitive advantages through cost reduction and enhanced brand reputation.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eInnovation Capability (IC) has a significant impact on SC (β\u0026thinsp;=\u0026thinsp;0.426, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Enterprises that continuously innovate in products, processes, and technology are more flexible in market adaptation, maintaining sustainable growth while meeting green requirements from international customers and partners.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eGreen Management Orientation (GMO) shows a positive moderating effect (β\u0026thinsp;=\u0026thinsp;0.196, p\u0026thinsp;=\u0026thinsp;0.001). When business leaders are committed to green development goals, they provide clearer direction for technology investment and operations, thereby enhancing the effectiveness of green digital transformation on competitiveness.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eThese results reinforce the findings of Zhang et al. (2023) and Chen et al. (2020), which suggest that leadership and green management strategies are key factors in transforming digital transformation into sustainable value.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003e5.6. Testing the Mediating Role of Innovation Capability (IC)\u003c/h2\u003e\u003cp\u003eThe testing procedure following Baron \u0026amp; Kenny (1986) shows that:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eGDT has a positive effect on SC (β\u0026thinsp;=\u0026thinsp;0.368, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eGDT has a positive effect on IC (β\u0026thinsp;=\u0026thinsp;0.517, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eWhen IC is included in the model together with GDT, the effect of GDT on SC decreases from 0.368 to 0.241 but remains significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01).\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eThis indicates that IC plays a partial mediating role in the relationship between green digital transformation and sustainable competitiveness.\u003c/p\u003e\u003cp\u003eIn other words, green digital transformation not only has a direct effect but also an indirect effect through innovation capability.\u003c/p\u003e\u003cp\u003eEnterprises that invest in digital technology but lack internal innovation mechanisms will only achieve short-term results.\u003c/p\u003e\u003cp\u003eConversely, when firms develop strong innovation capabilities, green digital transformation becomes a driving force that promotes product and process improvement and enhances long-term competitiveness.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003e5.7. Testing the Moderating Role of Green Management Orientation (GMO)\u003c/h2\u003e\u003cp\u003eTo test the moderating role, the interaction term (GDT \u0026times; GMO) was included in the extended regression model:\u003c/p\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:SC=0.318+0.368\\left(GDT\\right)+0.426\\left(IC\\right)+0.164(GDT\\times\\:GMO)+\\epsilon\\:\\)\u003c/span\u003e\u003c/span\u003eThe results show that the interaction coefficient β\u0026thinsp;=\u0026thinsp;0.164, p\u0026thinsp;=\u0026thinsp;0.002, is significant at the 1% level, confirming the existence of a moderating effect.\u003c/p\u003e\u003cp\u003eWhen the green management orientation is high, the impact of green digital transformation on sustainable competitiveness becomes stronger.\u003c/p\u003e\u003cp\u003eIn enterprises where leadership demonstrates a clear green orientation, technology investment is aligned with sustainable development strategies, thereby improving economic, environmental, and social performance.\u003c/p\u003e\u003cp\u003eIn contrast, in enterprises lacking such orientation, digital transformation mainly brings short-term benefits such as productivity or cost reduction, without creating sustainable value.\u003c/p\u003e\u003cp\u003eThe moderating effect of GMO demonstrates that management culture and strategic commitment from leadership are key factors in turning green digital transformation into a genuine long-term competitive driver.\u003c/p\u003e\u003c/div\u003e"},{"header":"6. DISCUSSION OF RESULTS AND POLICY IMPLICATIONS","content":"\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\u003ch2\u003e6.1. Discussion of Research Findings\u003c/h2\u003e\u003cp\u003eThe empirical results show that Green Digital Transformation (GDT) has a positive and statistically significant impact on the Sustainable Competitiveness (SC) of Vietnamese manufacturing enterprises. This finding is consistent with global trends, as digital transformation increasingly becomes a core tool for improving efficiency while minimizing environmental impacts (Li et al., 2024; Zhang et al., 2023).\u003c/p\u003e\u003cp\u003eHowever, a distinctive feature of the Vietnamese context is that the digital transformation process is progressing rapidly but unevenly. Many firms focus primarily on investing in hardware, data systems, or ERP platforms without integrating them into a green strategy. This study provides empirical evidence that digital transformation generates sustainable value only when it is linked with a green management orientation and innovation capability.\u003c/p\u003e\u003cp\u003eThe results confirm that Innovation Capability (IC) plays a partial mediating role in the relationship between GDT and SC. This means that technology alone cannot create an advantage unless the enterprise possesses internal innovation capability. This is a key distinction from prior studies in China or South Korea, which emphasize \u0026ldquo;technology investment,\u0026rdquo; whereas Vietnam must focus more on \u0026ldquo;innovation capability.\u0026rdquo;\u003c/p\u003e\u003cp\u003eIn addition, Green Management Orientation (GMO) shows a clear moderating effect. In firms whose leaders are oriented toward sustainable development, the impact of GDT on competitiveness increases significantly. This finding is particularly relevant since most Vietnamese enterprises still view digital transformation as a technical task rather than an integrated green strategy.\u003c/p\u003e\u003cp\u003eCompared with international research, these findings align with the Resource-Based View (RBV) and Dynamic Capabilities Theory (DCT), which suggest that enterprises can achieve sustainable advantage by integrating digital resources with organizational and innovation capabilities. However, this study extends the RBV toward a \u0026ldquo;Green RBV\u0026rdquo; perspective, emphasizing that digital resources only create real value when aligned with sustainable development goals.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec23\" class=\"Section2\"\u003e\u003ch2\u003e6.2. Managerial Implications for Enterprises\u003c/h2\u003e\u003cp\u003eThe research results indicate that Green Digital Transformation (GDT) strongly affects Sustainable Competitiveness (SC) through Innovation Capability (IC) and under the moderating influence of Green Management Orientation (GMO). Based on these findings, several managerial implications can be drawn for Vietnamese manufacturing firms:\u003c/p\u003e\u003cp\u003e(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) \u003cb\u003eEstablish a digital transformation strategy aligned with green objectives\u003c/b\u003e\u003c/p\u003e\u003cp\u003eEnterprises should treat green digital transformation as a core strategy, not merely a technological solution. Many firms currently stop at automation or process digitization without embedding \u0026ldquo;green\u0026rdquo; principles into operations.\u003c/p\u003e\u003cp\u003eManagement should set a long-term \u0026ldquo;Green Digital Vision\u0026rdquo;, ensuring every technology investment aims at reducing emissions, saving energy, and improving resource efficiency.\u003c/p\u003e\u003cp\u003eSpecific actions include:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eIntegrating environmental and emission criteria into departmental KPIs.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eDeveloping a Green Digital Roadmap in three stages:\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eDigitize basic operations and energy data.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eAutomate and connect the supply chain.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eApply data analytics and AI to optimize energy use and reduce emissions.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003e(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) \u003cb\u003eDevelop internal innovation capability\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe study shows that innovation capability is a crucial mediator that enables GDT to deliver sustainable outcomes. Therefore, Vietnamese firms should shift from \u0026ldquo;buying technology\u0026rdquo; to \u0026ldquo;owning and creating technology.\u0026rdquo;\u003c/p\u003e\u003cp\u003ePractical actions include:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eInvesting in internal R\u0026amp;D and encouraging engineering teams to test \u0026ldquo;green production\u0026rdquo; initiatives.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eCreating a \u0026ldquo;sandbox for innovation\u0026rdquo; that allows small-scale pilot projects without fear of failure.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003ePartnering with universities, research institutes, and green-tech startups to co-develop innovations suited to Vietnam\u0026rsquo;s context.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eBuilding a Green Innovation Culture that rewards ideas reducing emissions or improving efficiency.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003e(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) \u003cb\u003eStrengthen leadership and green management orientation\u003c/b\u003e\u003c/p\u003e\u003cp\u003eGMO is a critical moderating factor determining whether digital transformation leads to sustainable competitiveness.\u003c/p\u003e\u003cp\u003eLeadership should demonstrate a clear commitment to sustainability not only through declarations but also through tangible actions:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eEstablish a Green Digital Transformation Committee under the CEO to oversee and coordinate projects.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eIntegrate environmental goals into mid-level management KPIs (e.g., \u0026ldquo;reduce energy consumption per product by 10% annually\u0026rdquo;).\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eConduct regular training on digital transformation and green management for leadership and production managers.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eEnsure transparency in emission and energy data to build accountability and trust with investors and customers.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003e(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) \u003cb\u003eApply digital technologies for \u0026ldquo;green and smart\u0026rdquo; efficiency\u003c/b\u003e\u003c/p\u003e\u003cp\u003eFirms should adopt technologies that generate dual impact\u0026mdash;digitalizing processes while improving environmental efficiency.\u003c/p\u003e\u003cp\u003eExamples include:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eIoT and energy sensors for real-time monitoring of electricity, water, and gas use.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eAI for production planning optimization, predictive maintenance, and downtime reduction.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eBlockchain for green supply chain traceability.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eIntegrated Energy Management Systems (EMS) linking operational, emission, and cost data for data-driven green decision-making.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003e(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) \u003cb\u003eDevelop green digital human resources\u003c/b\u003e\u003c/p\u003e\u003cp\u003eHuman capital is central to transformation. Technology only succeeds when employees understand, trust, and creatively use it.\u003c/p\u003e\u003cp\u003eActions include:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eBuilding a Green Digital Competency Framework for training at worker, engineer, and manager levels.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eProviding on-the-job training in software, sensors, and data analytics.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eEncouraging lifelong learning and supporting staff in technology and sustainability courses.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eIncluding \u0026ldquo;green behaviors\u0026rdquo; in performance evaluations (e.g., energy saving, recycling, waste reduction).\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003e(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e) \u003cb\u003eRestructure supply chains and promote green collaboration\u003c/b\u003e\u003c/p\u003e\u003cp\u003eGreen transformation cannot occur in isolation. A green supply chain is essential to meet global customer and trade agreement requirements (e.g., EVFTA, CPTPP).\u003c/p\u003e\u003cp\u003eRecommendations:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003ePrioritize suppliers with green certifications (ISO 14001, ESG ratings).\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eImplement a Green Procurement Policy with clear energy, recycling, and logistics criteria.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eCreate shared emission data networks with suppliers to optimize the value chain.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eForm Green Partnerships where firms co-invest in energy-saving or waste treatment technologies.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003e(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e) \u003cb\u003eMeasure and report green digital transformation performance\u003c/b\u003e\u003c/p\u003e\u003cp\u003eFirms should develop measurable indicators for both economic and environmental outcomes, such as:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eEnergy intensity (kWh per product unit).\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eMaterial recycling rate (%).\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eCO₂ emission reduction (tons/year).\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eShare of products meeting green or eco-label standards.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eAnnual energy cost savings (%).\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eThey should also publish ESG (Environment \u0026ndash; Social \u0026ndash; Governance) reports to demonstrate sustainable competitiveness to partners and investors.\u003c/p\u003e\u003cp\u003e(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e) Build a \u0026ldquo;digital-green enterprise\u0026rdquo; brand\u003c/p\u003e\u003cp\u003eGreen digital transformation enhances not only operations but also strategic marketing and brand image.\u003c/p\u003e\u003cp\u003eFirms should promote their identity as \u0026ldquo;Digital \u0026amp; Green Manufacturers\u0026rdquo; through:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003ePublic sustainability websites and reports.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eGreen certifications (ISO, Carbon Neutral, Energy Star).\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eParticipation in international green business awards or networks (e.g., UN Global Compact, VBCSD).\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eA transparent, modern, and green brand strengthens reputation and attracts global investors and customers, especially as FDI corporations favor \u0026ldquo;green supply chain\u0026rdquo; partners.\u003c/p\u003e\u003cp\u003e(\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e) \u003cb\u003eIntegrate digitalization and sustainability goals\u003c/b\u003e\u003c/p\u003e\u003cp\u003eFinally, enterprises should view digital transformation and sustainability as interdependent pillars, not separate agendas.\u003c/p\u003e\u003cp\u003eOnly when technology serves environmental and social goals can transformation create long-term value.\u003c/p\u003e\u003cp\u003eVietnamese firms should therefore shift from a mindset of \u0026ldquo;digitalization for cost reduction\u0026rdquo; to \u0026ldquo;digitalization for sustainable development.\u0026rdquo;\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec24\" class=\"Section2\"\u003e\u003ch2\u003e6.3. Policy Implications for Government and Regulators\u003c/h2\u003e\u003cp\u003eBeyond corporate efforts, several policy directions are recommended:\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eImprove the legal framework for digital transformation aligned with green objectives. Current policies focus on infrastructure rather than environmental integration. The government should issue \u0026ldquo;green digital transformation standards\u0026rdquo; for manufacturing sectors and guide firms in embedding environmental criteria into digitalization processes.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eEnhance financial and green credit support. SMEs face barriers in technology investment; thus, preferential loans or green R\u0026amp;D funds are necessary. Tax incentives for renewable energy use or certified green production will accelerate adoption.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eEstablish a National Green Digital Transformation Center to provide consulting, training, and best-practice sharing\u0026mdash;connecting enterprises, universities, and government bodies to form a sustainable innovation ecosystem.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eIntegrate green criteria into the evaluation of exporters\u0026rsquo; capacity, particularly under trade agreements such as EVFTA and CPTPP, where environmental standards are increasingly stringent.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec25\" class=\"Section2\"\u003e\u003ch2\u003e6.4. Theoretical and Practical Contributions\u003c/h2\u003e\u003cp\u003eThis study makes three key contributions:\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eTheoretical: It extends the Resource-Based View (RBV) toward a Green RBV, adding the role of green digital transformation as a dynamic resource that drives sustainable competitiveness.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003ePractical: It provides the first empirical evidence in Vietnam that green digital transformation enhances firm competitiveness through innovation and green management.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eMethodological: It demonstrates that the SPSS model\u0026mdash;with sequential testing steps (Cronbach\u0026rsquo;s Alpha, EFA, regression, mediation, moderation)\u0026mdash;is appropriate for measuring complex relationships between technology and sustainability.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e"},{"header":"7. CONCLUSION AND FUTURE RESEARCH DIRECTIONS","content":"\u003cdiv id=\"Sec26\" class=\"Section2\"\u003e\u003ch2\u003e7.1. Conclusion\u003c/h2\u003e\u003cp\u003eThis study aims to evaluate the impact of Green Digital Transformation (GDT) on the Sustainable Competitiveness (SC) of manufacturing enterprises in Vietnam, through the mediating role of Innovation Capability (IC) and the moderating role of Green Management Orientation (GMO).\u003c/p\u003e\u003cp\u003eAnalysis results from 230 survey samples processed with SPSS show that:\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eGreen digital transformation has a direct, positive, and statistically significant effect on sustainable competitiveness.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eInnovation capability plays a partial mediating role, indicating that the application of green digital technologies is only effective when firms simultaneously develop internal innovation capability.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eGreen management orientation exerts a positive moderating effect, enhancing the impact of green digital transformation on sustainable competitiveness.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eThe model\u0026rsquo;s coefficient of determination R\u0026sup2; = 0.562 demonstrates a high level of explanatory power, reflecting strong relationships among the variables.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003cp\u003eOverall, the results confirm that green digital transformation is a key driver enabling Vietnamese enterprises to achieve long-term competitive advantage in the global shift toward sustainable development.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec27\" class=\"Section2\"\u003e\u003ch2\u003e7.2. Limitations of the Study\u003c/h2\u003e\u003cp\u003eDespite achieving promising results, this research has several limitations:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eFirst, the survey sample mainly focuses on the northern region of Vietnam and may not fully represent characteristics of other regions.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eSecond, the quantitative approach using SPSS primarily tests linear relationships and does not capture potential complex or nonlinear associations among variables.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eThird, the \u0026ldquo;green\u0026rdquo; measurement indicators are largely based on self-assessment, without integrating objective indicators such as actual energy consumption, CO₂ emissions, or ERP system data.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec28\" class=\"Section2\"\u003e\u003ch2\u003e7.3. Future Research Directions\u003c/h2\u003e\u003cp\u003eBased on the above limitations, several potential extensions can be proposed for future research:\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eExpand the sample coverage to include enterprises in Central and Southern Vietnam, enabling comparison across regions and industries (e.g., textiles, electronics, food, materials).\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eEmploy Structural Equation Modeling (SEM or PLS-SEM) instead of OLS regression to simultaneously assess multiple relationships and better evaluate mediating and moderating effects.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eIntegrate secondary data, such as actual energy indices, carbon emissions, ISO 14001 or ESG certifications, to measure the \u0026ldquo;green\u0026rdquo; effect more objectively.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eInclude organizational culture and green leadership as new variables, as these factors may significantly influence the effectiveness of sustainable digital transformation in the future.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003cp\u003e This study was reviewed and approved by the Institutional Ethics Committee of the Banking Academy of Vietnam (Approval No.: BAV-ERC-2025-017). All procedures performed in this study involving human participants were conducted in accordance with institutional guidelines and the ethical standards of the 1964 Helsinki Declaration and its later amendments.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003ch2\u003eConsent to publish\u003c/h2\u003e\u003cp\u003eThe manuscript does not contain any individual person\u0026rsquo;s identifiable data, images, or personal information. Therefore, informed consent to publish such information was not required.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAll authors contributed to the conception, design, data collection, analysis, and manuscript preparation. All authors have read and approved the final version of the manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets generated and analyzed during the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBaron RM, Kenny DA. The moderator\u0026ndash;mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. J Personal Soc Psychol. 1986;51(6):1173\u0026ndash;82.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChen Y, Liang L, Wang Z. Digital transformation and sustainable competitiveness: Evidence from China\u0026rsquo;s manufacturing sector. 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Int J Prod Econ. 2020;227:107689. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ijpe.2020.107689\u003c/span\u003e\u003cspan address=\"10.1016/j.ijpe.2020.107689\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-sustainability","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"disu","sideBox":"Learn more about [Discover Sustainability](https://www.springer.com/43621)","snPcode":"","submissionUrl":"","title":"Discover Sustainability","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Green digital transformation, Innovation capability, Sustainable competitiveness, Green management orientation, Vietnamese manufacturing enterprises","lastPublishedDoi":"10.21203/rs.3.rs-8026051/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8026051/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn the context of global efforts toward sustainable development, green digital transformation (GDT) has become an inevitable trend that helps manufacturing enterprises enhance operational efficiency and long-term competitiveness.\u003c/p\u003e\u003cp\u003eThis study aims to evaluate the impact of GDT on the sustainable competitiveness (SC) of Vietnamese manufacturing firms, while examining the mediating role of innovation capability (IC) and the moderating role of green management orientation (GMO).\u003c/p\u003e\u003cp\u003eData were collected from 230 manufacturing enterprises through questionnaire surveys and processed using SPSS 26.0 via several steps: reliability testing (Cronbach\u0026rsquo;s Alpha), exploratory factor analysis (EFA), multiple linear regression, mediation analysis (Baron \u0026amp; Kenny, 1986), and moderation testing.\u003c/p\u003e\u003cp\u003eThe results show that green digital transformation has a positive and significant effect on sustainable competitiveness; innovation capability plays a partial mediating role, while green management orientation strongly moderates the relationship between GDT and SC. The model explains 56.2% of the variance in sustainable competitiveness.\u003c/p\u003e\u003cp\u003eThe findings confirm that green digital transformation, when combined with innovation and green management, serves as a driving force enabling Vietnamese manufacturing enterprises to achieve long-term competitive advantage. Accordingly, the paper proposes several managerial implications for businesses and policy recommendations for the government to promote green digital transformation in the manufacturing sector.\u003c/p\u003e","manuscriptTitle":"The Impact of Green Digital Transformation on the Sustainable Competitiveness of Vietnamese Manufacturing Enterprises","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-04 18:09:05","doi":"10.21203/rs.3.rs-8026051/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-01-29T11:53:41+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-20T09:07:14+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"160182220598640456075016404798048119670","date":"2026-01-09T12:38:33+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"128642671757142490286219640491034198151","date":"2026-01-02T19:45:39+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"301060615950791599489465834505527478994","date":"2026-01-01T15:01:02+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"226203621931868876345962347673503800427","date":"2025-12-30T21:39:35+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-30T15:11:00+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"278785481866212737366040305250344047829","date":"2025-12-30T14:59:42+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-12T02:18:30+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"70109042638636218000957582134323516488","date":"2025-12-06T12:06:10+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"191637632722900985640795211331359431720","date":"2025-12-02T11:05:12+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-01T11:49:02+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-17T09:07:26+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-17T07:22:48+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Sustainability","date":"2025-11-17T07:19:37+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-sustainability","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"disu","sideBox":"Learn more about [Discover Sustainability](https://www.springer.com/43621)","snPcode":"","submissionUrl":"","title":"Discover Sustainability","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"14d9a4f2-5e37-4ed9-ad7b-710a4c96aaef","owner":[],"postedDate":"December 4th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-13T17:41:01+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-04 18:09:05","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8026051","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8026051","identity":"rs-8026051","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}