This study investigates how research and development (R&D) moderates the relationship between digital transformation and financial performance in manufacturing enterprises within a transition economy.
Data from 365 manufacturing firms in Vietnam were extracted from the Vietnamese Stock Exchange. These firms received government support, as the manufacturing sector is a priority for digital transformation by 2030. Hausman’s test confirmed the random effect model as suitable for estimating R&D’s moderating effects.
The findings revealed that high-performing manufacturing firms that invest in R&D experience enhanced digital transformation, leading to improved financial performance. Additionally, firms in the same industry show varying results from digital transformation based on their operational contexts. Notably, Vietnamese manufacturing enterprises have effectively initiated digital transformation, largely due to supportive government policies. This transformation has enabled firms to boost revenue, enhance production efficiency and reduce costs through improved labour productivity, helping them remain stable amidst uncertainties like COVID-19.
The study highlights the importance of capital and tax incentives, investments in digital infrastructure and training programmes for digital transformation. These initiatives alleviate financial pressures and expedite the transformation process for Vietnamese manufacturers. The findings present valuable recommendations for foreign investors and serve as a reference for other countries facing similar transitions.
The article stands out for its in-depth examination of the digital transformation process in the R&D activities of manufacturing enterprises, which are considered vital for businesses. This study highlights the crucial role of the government in facilitating digital transformation for numerous small and medium-sized enterprises in emerging economies.
1. Introduction
In today’s rapidly changing business environment, manufacturing enterprises in Vietnam must continuously adapt and innovate to maintain their competitiveness (Feliciano-Cestero, Ameen, Kotabe, Paul, & Signoret, 2023). Digital transformation (DT) is emerging as a key driver of development and enhancement of production processes. DT is a process designed to improve an organization by implementing substantial changes to its attributes through the integration of information technology, computing, communications, and connectivity (Vial, 2019). Activities focused on implementing DT within enterprises – such as supply chain management; connecting customers and suppliers; and data analysis through process digitization, operational automation, and data integration – have allowed manufacturing companies to boost revenue, manage risks, and increase profitability. DT is an inevitable trend in the Fourth Industrial Revolution (Alcácer & Cruz-Machado, 2019; Weking, Stöcker, Kowalkiewicz, Böhm, & Krcmar, 2020) because the DT process has demonstrated its ability to help manufacturing enterprises optimize production processes, enhance productivity, reduce costs, innovate business operations more effectively, and improve the overall financial performance (FP) of these enterprises (Peng & Tao, 2022; Wang, Cao, & Wang, 2022; Zhai, Yang, & Chan, 2022; Heredia et al., 2023; Wang & Shao, 2024).
Integrating digital technology into every aspect of business operations, including production, marketing, and customer service, has significantly improved efficiency while maintaining value-creation processes (Li, 2020; Verhoef et al., 2021). Prior studies have demonstrated the link between DT and enterprises’ FP (Xie & Wang, 2023). Moreover, many studies have shown that DT can improve the FP of businesses (Wang et al., 2022; Zhai et al., 2022). However, some studies have indicated that DT can negatively impact FP due to the significant initial investment costs involved for enterprises (Nga et al., 2023; Sui & Yao, 2023; Vo, Vo, Dinh, & Tran, 2024). When DT is fully integrated across business operations, the profitability benefits will outweigh the initial investment costs, enhancing the enterprise’s FP. Jardak and Ben Hamad (2022) assert that investing in DT takes several years to show results, indicating a delay in its financial effectiveness. Furthermore, Wang et al. (2022) suggest that state-owned enterprises or those operating in highly marketized regions will show more potent effects. They also emphasize that regulatory factors affecting the relationship between DT and FP are important and should be considered (Guo & Xu, 2021).
The COVID-19 pandemic has severely impacted the running of manufacturing enterprises worldwide (Naseer et al., 2023). Like other sectors, the pandemic has significantly disrupted the production and business activities of most Vietnamese manufacturing enterprises (Chuong, 2020). In this context, initiatives such as digitizing automated production systems, developing management platforms, and using big data analytics have been identified as crucial solutions to help manufacturing enterprises mitigate the damages caused by the pandemic (Kien, Hung, Quan, & Hien, 2023). According to the Ministry of Planning and Investment (2022), manufacturing enterprises in Vietnam have been actively implementing DT initiatives. Specifically, 62% of manufacturing enterprises are implementing DT in human resource management, 54% in supply chain management, 48% in operations and quality control, and 40% in marketing and sales activities. However, DT activities are primarily concentrated in large manufacturing enterprises, while small and medium-sized enterprises (SMEs) face numerous limitations. The main reason for this difference is that large enterprises, with their substantial financial and technological resources, have effectively digitized their production processes, management, and customer connections. On the other hand, most SMEs face numerous challenges, such as financial difficulties (57%), a lack of skilled digital personnel (51%), and obstacles in integrating new technologies (VCCI, 2021).
To support manufacturing enterprises' DT, the Vietnamese government has implemented several policies, such as covering 30% to 50% of the investment costs for digital technology and software, reducing taxes by 50% for the first 5 years for companies undertaking DT projects, investing in digital infrastructure in industrial zones, and enhancing the training of digital personnel (Decision No. 749/QĐ-TTg, 2020). These policies are expected to dismantle barriers and accelerate the DT process, thereby contributing to the development of Vietnam’s manufacturing enterprises in digital economic transformation (Tran, Le, Vo, & Vo, 2024).
To achieve effective DT, manufacturing enterprises must implement digitalization in their R&D activities (Liang & Li, 2022). R&D activities are considered fundamental to manufacturing enterprises in the digital age (Xu, Wang, & Liu, 2021). Investing in R&D enables manufacturing enterprises to create breakthrough products, thus enhancing their competitive advantage and delivering superior FP compared to their competitors (Belderbos, Gilsing, Lokshin, Carree, & Sastre, 2018). R&D activities also empower manufacturing enterprises to swiftly adapt to market changes and customer demands by utilizing digital technology (Li & Atuahene-Gima, 2001). Manufacturing enterprises that invest more in R&D tend to adopt digital technologies more effectively than their counterparts (Roper & Xia, 2014). Moreover, manufacturing enterprises with high levels of R&D often face significant competitive pressure, which compels them to enhance and innovate their products by applying digital technology (Huo, Motohashi, & Gong, 2019).
In summary, numerous studies have demonstrated the relationship between DT and FP. However, to the author’s knowledge, no research has yet examined the moderating effect of R&D activities on this relationship. Prior research has indicated that increasing investment in R&D activities enables firms to achieve higher FP than their competitors (Guo, Tang, Su, & Katz, 2017; Belderbos et al., 2018; Huo et al., 2019; Liang & Li, 2022; Yan, Cai, & Yang, 2023). Therefore, this study contributes to the literature by integrating upon Barney’s resource-based theory and Coase’s transaction cost theory. This study develops theoretical arguments that the role of differing DT practices across enterprises of varying sizes will result in divergent financial outcomes within a transition economy. The research expects that the government’s policy support for DT in Vietnam will help alleviate some of the financial challenges faced by manufacturing SMEs. Moreover, the study anticipates that the more manufacturing enterprises invest in R&D, the stronger their DT will become, resulting in higher FP compared to other enterprises. The experimental results clarify the extent to which R&D moderates the relationship between DT and FP in Vietnam’s emerging economy.
2. Theoretical framework and research model
2.1 Theoretical framework
In this study, the resource-based theory (Barney, 1991) and transaction cost theory (Coase, 1988) illustrate the moderating effect of R&D activities on the relationship between DT and the FP of manufacturing enterprises in Vietnam. According to Barney (1991), an organization’s business performance depends on the management and integration of its various resources. Numerous studies have applied the resource-based theory to explain the impact of DT on firms' FP (Elia, Giuffrida, Mariani, & Bresciani, 2021). Research findings indicate that DT greatly impacts innovation activities and value creation for firms (Van de Wetering, Versendaal, & Walraven, 2018).
Building on the resource-based theory (Barney, 1991; Lange, Drews, & Höft, 2021) argue that firms within the same industry may differ in outcomes not only because they utilize different resources but also due to their unique combinations of these resources in organizational operations. Technological resources play a critical role in the DT activities of organizations (Chwiłkowska-Kubala, Cyfert, Malewska, Mierzejewska, & Szumowski, 2023). In the context of the Fourth Industrial Revolution, a firm’s technological resources can be leveraged as an intrinsic strength to implement and generate competitive advantages (Fenech, Baguant, & Ivanov, 2019; Sony & Aithal, 2020). Technology integration enables organizations to allocate resources more effectively, aligning with the value chain of products and services while enhancing the firm’s reputation through safer and higher-quality offerings (Sony & Aithal, 2020).
Numerous studies have demonstrated that implementing DT enables firms to mitigate risks and enhance production processes (Bromiley & Rau, 2016). Based on this foundation, this research anticipates that manufacturing enterprises in Vietnam will achieve effectiveness in the early stages of DT, as a majority of these enterprises benefit from government support in terms of institutional frameworks and policies during the initial years of implementation.
Investing in technology has the potential to reduce production costs and enable customers to save on procurement expenses, all while ensuring consistent quality of goods. The transaction cost theory suggests that blockchain technology reduces transaction costs and improves the efficiency of business operations (Sun, Garimella, Han, Chang, & Shaw, 2020). When new technologies are implemented, costs can be reduced and efficiency improved, enabling businesses to bypass intermediaries and facilitate direct transactions with stakeholders such as partners and customers. As a result, transactions in the market become less expensive, allowing businesses to save on transaction expenses (Cuypers, Hennart, Silverman, & Ertug, 2021; Xie & Wang, 2023).
The transaction cost theory optimizes organizational efficiency by minimizing the total costs associated with transactional activities (Geyskens, Steenkamp, & Kumar, 2006). This theory is applied in manufacturing enterprises to manage the procurement and supply chain activities of both input and output goods (Grover & Malhotra, 2003; Ketchen & Hult, 2007). Ferreira, Fernandes, and Ferreira (2019) argue that DT assists manufacturing firms in reducing operational costs, including sales, financial, and overall management expenses. DT enables businesses to allocate financial resources toward R&D activities, enhancing organizational innovation efficiency (Meng & Gong, 2024).
2.2 Hypothesis and research model
The tools implemented for DT in manufacturing firms include big data, artificial intelligence, cloud platforms, and robotics, which optimise traditional production, sales, and service processes. Automating production processes, improving business workflows, and reducing costs are essential to DT (Gruzauskas & Ragavan, 2020; Sotnyk, Zavrazhnyi, Kasianenko, Roubík, & Sidorov, 2020). Moreover, cloud computing technology and big data analytics enable businesses to respond flexibly and reduce complexity in decision-making processes. By utilizing digital technology and platforms, businesses improve the efficiency of information feedback in both input and output activities. This enhancement is achieved not only through the application of manufacturing systems but also by integrating services such as supply chain information management and online sales systems (Negrini, Riedl, & Wibral, 2020), which allows businesses to generate value in today’s highly competitive environment. Through the comprehensive implementation of DT within enterprises, internal transaction costs will decrease, making DT a crucial driver for enhancing businesses’ financial efficiency.
However, recent studies suggest that DT may negatively impact the FP of enterprises in China (Sui & Yao, 2023) and the business outcomes of private companies in Vietnam (Nga et al., 2023; Vo et al., 2024). This discrepancy can be attributed to the differing levels of DT, which are influenced by the technological resources and digital capabilities of each organisation (Chwiłkowska-Kubala et al., 2023). The impact of DT is a complex issue influenced by an organisation’s capability to integrate and implement new technologies (Xie & Wang, 2023). Therefore, there are two opposing perspectives regarding the hypothesis, namely DT can have either positive or negative effects on FP. This study will examine the influence of DT on the FP of manufacturing enterprises, particularly in the context of support from the Vietnamese government. It is expected that government support policies will help these enterprises overcome initial barriers in the DT process, enabling them to achieve financial success even in the early stages of digitalisation. Based on this reasoning, the study posits that testing the following hypothesis is essential:
Digital transformation positively affects the financial performance of manufacturing companies in Vietnam.
Manufacturing enterprises regard investing in R&D as a key solution for improving production processes and creating groundbreaking products (Liu & Xia, 2018). To achieve this innovation, enterprises need to enhance their investment in R&D, as R&D activities have been proven to yield significant returns on investment for businesses (Gregory, Keil, Muntermann, & Mähring, 2015; Guo & Xu, 2021). In addition to the benefits of R&D, increasing investment in R&D is a crucial mechanism for driving DT in enterprises (Wang, Shao, Song, Shao, & Wang, 2023; Zhang, Ma, Pang, Xing, & Wang, 2023). Specifically, manufacturing enterprises utilise digital technology resources in R&D to extract information and identify market opportunities (Kleis, Chwelos, Ramirez, & Cockburn, 2012; Wang & Zhang, 2018), conduct new experiments, develop new products, and design innovative marketing models (Dai, Du, Byun, & Zhu, 2017). Although DT drives varying levels of R&D investment among enterprises, all of them ultimately achieve profitability from R&D activities (Liang & Li, 2022).
Relevant studies have, thus, indicated that DT is the solution for enhancing financial efficiency (Gregory et al., 2015). Research has also individually examined the relationship between R&D and the operational efficiency of enterprises (Gregory et al., 2015; Wang et al., 2022). Evidence indicates that manufacturing enterprises in Vietnam that invest more in R&D significantly drive DT, especially in the technology and high-tech sectors. The study confirms the viewpoint expressed by Yan et al. (2023) that it is crucial to explore R&D as a moderating factor in the relationship between DT and business performance. Moreover, research has found that a significant number of Vietnamese manufacturing enterprises are eager to fully utilize R&D following their DT efforts. Therefore, the study predicts that as these enterprises invest more in R&D activities, their DT will become stronger, ultimately resulting in improved FP. Based on the above arguments, the following hypothesis was proposed:
Increased investment in R&D activities promotes digital transformation and improves the financial performance of enterprises.
The research model concerning the moderating roles of R&D on DT and the FP of Vietnam’s manufacturing enterprises is illustrated in Figure 1.
The model shows a first text box on the left labeled “Digital transformation.” Below the first text box, two dashed rectangles are present. The first dashed rectangle titled “Financial resources” contains three text boxes labeled “Fixed assets proportion,” “Revenue growth rate,” and “Financial leverage.” The second dashed rectangle titled “Proactive and Perspectives of Firm's Leader regarding: Implementing Digital transformation” contains three text boxes labeled “Gender of Firm's Leader,” “Labor size,” and “Firm age.” A rightward arrow from “Digital transformation” labeled H 1 (plus) leads to a second text box on the right labeled “Financial performance (Return on Assets).” A third text box on the top is labeled “R and D.” A dashed downward arrow from “R and D” labeled H 1 a (plus) leads to the arrow labeled H 1 (plus). A dashed arrow connecting the three text boxes in “Financial resources” leads to “Financial performance (Return on Assets).” A dashed arrow connecting the three text boxes in “Proactive and Perspectives of Firm's Leader regarding: Implementing Digital transformation” leads to “Financial performance (Return on Assets).”Hypothesis and theoretical research model. Source(s): The authors
The model shows a first text box on the left labeled “Digital transformation.” Below the first text box, two dashed rectangles are present. The first dashed rectangle titled “Financial resources” contains three text boxes labeled “Fixed assets proportion,” “Revenue growth rate,” and “Financial leverage.” The second dashed rectangle titled “Proactive and Perspectives of Firm's Leader regarding: Implementing Digital transformation” contains three text boxes labeled “Gender of Firm's Leader,” “Labor size,” and “Firm age.” A rightward arrow from “Digital transformation” labeled H 1 (plus) leads to a second text box on the right labeled “Financial performance (Return on Assets).” A third text box on the top is labeled “R and D.” A dashed downward arrow from “R and D” labeled H 1 a (plus) leads to the arrow labeled H 1 (plus). A dashed arrow connecting the three text boxes in “Financial resources” leads to “Financial performance (Return on Assets).” A dashed arrow connecting the three text boxes in “Proactive and Perspectives of Firm's Leader regarding: Implementing Digital transformation” leads to “Financial performance (Return on Assets).”Hypothesis and theoretical research model. Source(s): The authors
As Figure 1 shows, the factors related to DT and R&D activities within manufacturing enterprises are incorporated into the research model to examine their impact on the FP of the enterprises, as outlined in resource-based theory (Barney, 1991) and transaction cost theory (Coase, 1988). Accordingly, business leaders who make critical strategic decisions play a vital role in enhancing the success of enterprise activities, including sustainable aspects in an uncertain business environment. Decisions regarding DT within enterprises can be made proactively, even before responding to pressures from the board of directors (Caluwe, 2022). However, senior leaders who have a better understanding of the significance of DT play a crucial part in optimizing workforce scalability, as manufacturing enterprises often reduce their workforce during the DT process (Farooq, Vij, & Kaur, 2021). Furthermore, DT is expected to facilitate revenue growth, thereby helping enterprises stabilize their FP in a volatile business environment. Moreover, firm size and age are crucial determinants of FP in manufacturing enterprises, as larger firms with longer operational histories tend to have advantages in capital investment for technology and greater market credibility (Zhang et al., 2023).
Conversely, business leaders who demonstrate a proactive attitude and positive perspective on the critical role of DT practices tend to develop more effective and responsive strategies (Li, Li, & Ding, 2023). The decisions and strategic directions established by male leaders are also believed to result in higher FP for enterprises (Alegre & Parente, 2022). In contrast, the presence of female leaders has been shown to have a more positive impact on overall organizational effectiveness (Ngo, Van Pham, & Luu, 2019).
Finally, following Zeitun, Tian, and Keen (2007) and Wang et al. (2022), this study incorporated the factors representing the financial resources of enterprises, including the proportion of fixed assets and financial leverage, into the research model as control variables to investigate their impact on the FP of manufacturing firms in Vietnam.
3. Methodology
3.1 Data collection
To evaluate the hypotheses of the proposed model, panel data were collected from 73 Vietnamese manufacturing enterprises. These data were gathered based on the completeness of information related to financial indicators and investment expenditures on software and technology for DT from 2018 to 2022. This dataset represents approximately 51.1% of the total 143 firms listed on the Vietnamese stock market, amounting to 365 observations. The study sample consists of 43 of 73 manufacturing enterprises (∼58.9%) from various industries and 30 of 73 manufacturing enterprises (∼41.1%) within the consumer goods sector. Data were collected from the firms’ annual business performance reports. Detailed definitions and measurement methods are presented in Table 1.
Variable definitions
| Variable | Definitions | Source |
|---|---|---|
| ROA | Net income to total assets ratio in percent | Guo and Xu (2021) |
| DT | Digital intangible assets/Total intangible assets | Wang et al. (2022) |
| Size | Logarit total assets | Zhang et al. (2023) |
| Age | Ln (Current year – Listing year + 1) | Wang et al. (2022) |
| Grow | Ln (Total operating revenue during the period (t)/Total operating revenue during the period (t – 1)) | Zeitun et al. (2007) |
| Asset | Fixed assets/Total assets | Zeitun et al. (2007) |
| Lev | Total debt/Equity capital | Wang and Shao (2024), Wang et al. (2022) |
| R&D | Intangible assets/Total fixed assets | Wang et al. (2022) |
| Gender | If the manager is male, encode as 1; if the manager is female, encode as 0 | Li et al. (2023) |
| Labour | Ln total number of labour | Farooq et al. (2021) |
| DT x R&D | The interaction between DT and R&D |
3.2 Data analysis
This study employs panel data, utilising the fixed effects model (FEM) and random effects model (REM) to examine the moderating effect of R&D activities regarding the connection between DT and FP. The Hausman test is conducted to identify the most appropriate model. This research proposes a moderating model of R&D activities on the relationship between DT and FP as follows:
This model measures ROA as profit divided by total assets for ith (i = 1, …, 365) in year tth (t = 2018, …, 2022). The independent variables include DTit (i = 1, n), the interaction term R&Dit (i = 1, n), and Controlit (i = 1, n) which act as exploratory variables to evaluate their effect on the company’s FP ith (i = 1, …, 365) in year tth (t = 2018, …, 2022);β0 is the intercept; β1, β2, β3 are coefficients corresponding to the independent variables, interaction terms, and control variables, respectively, that influence the FP of firm ith (i = 1, …, 365) in year tth (t = 2018, …, 2022; εit is the error term. The control variables consist of company size (Size) and company age (Age), fixed assets proportion (Asset), revenue growth rate (Grow), Financial leverage (Lev), Leadership gender (Gender), and labour size (Labour). The moderating variable is R&D activities. This model examines the moderating role of R&D activities on the relationship between DT and the FP of Vietnamese manufacturing enterprises. Table 1 displays the measurements for the variables.
4. Results and discussion
4.1 The moderating effect of R&D on the relationship between DT and the FP of Vietnamese manufacturing enterprises.
The findings displayed in Table 2 show that the correlation coefficients among the independent and dependent variables in the regression model are all under 0.5. This finding indicates the absence of multicollinearity, which helps ensure that the model’s results( remain unaffected (Hair, Black, Babin, & Anderson, 2010).
Correlation coefficients
| Variable | ROA | Size | Grow | Asset | Age | Lev | Gender | Labour | DT | R&D |
|---|---|---|---|---|---|---|---|---|---|---|
| ROA | 1.0000 | |||||||||
| Size | 0.1175 | 1.0000 | ||||||||
| Grow | 0.1571 | 0.0836 | 1.0000 | |||||||
| Asset | −0.0233 | 0.1229 | −0.1055 | 1.0000 | ||||||
| Age | 0.1261 | 0.1125 | 0.0285 | 0.0501 | 1.0000 | |||||
| Lev | −0.1473 | 0.0271 | 0.0456 | −0.1609 | 0.0023 | 1.0000 | ||||
| Gender | −0.0776 | −0.1860 | 0.0503 | 0.1720 | 0.0094 | 0.1071 | 1.0000 | |||
| Labour | 0.2574 | 0.7990 | 0.0172 | 0.1132 | 0.2280 | 0.0833 | −0.2569 | 1.0000 | ||
| DT | 0.0471 | −0.0863 | −0.0582 | −0.1004 | −0.1230 | 0.1927 | 0.1430 | −0.0785 | 1.0000 | |
| R&D | 0.0206 | 0.0640 | 0.0270 | −0.2093 | −0.0193 | −0.0911 | −0.1270 | 0.0771 | −0.3755 | 1.0000 |
The estimated outcomes of the regression model utilising the fixed effects model (FEM) and random effects model (REM) are presented in Table 3. The results of the Hausman test show that the P-value (Prob > χ2) is less than 0.05, suggesting that the FEM is more suitable than the REM. Consequently, this study selects the results from the FEM for further discussion. When assessing the model’s deficiencies, multicollinearity tests indicated VIF coefficients below 10, and the Wooldridge test for autocorrelation showed no statistically significant results, suggesting that the model is not affected by these two issues. However, the modified Wald test results were statistically significant at the 1% level, rejecting the null hypothesis and indicating that the model suffers from heteroscedasticity. This deficiency implies that the regression coefficients may not be reliable in terms of statistical significance, leading to potential bias in the conclusions drawn from models FEM1 and FEM2. Specifically, the relationship between DT and ROA has changed in sign compared to initial expectations. As a result, a GLS model was implemented to adjust for and address the issue of heteroscedasticity. The adjusted results revealed statistically significant changes in conclusions compared to the previous models, and the magnitude and direction of the regression coefficients align with the initial expectations of the research model.
The estimate results of the FEM and REM regression models
| Variable | Model 1 | Model 2 | VIF | ||
|---|---|---|---|---|---|
| FEM1 | REM1 | FEM2 | REM2 | ||
| DT*R&D | 0.281 (0.79) | 0.741** (2.31) | |||
| DT | −0.0383** (−2.32) | −0.0132 (−0.91) | −0.0419** (−2.44) | −0.0189 (−1.27) | 1.29 |
| R&D | 0.0420 (0.92) | 0.0698** (2.00) | 0.0370 (0.80) | 0.0556 (1.59) | 1.28 |
| Size | −0.0446*** (−3.72) | −0.0273*** (−3.70) | −0.0434*** (−3.59) | −0.0228*** (−3.12) | 2.89 |
| Grow | 0.00370*** (4.05) | 0.0356*** (3.96) | 0.0369*** (4.05) | 0.0348*** (3.82) | 1.04 |
| Labour | 0.0545** (2.41) | 0.0462*** (4.56) | 0.0480** (2.00) | 0.0384*** (3.77) | 3.15 |
| Asset | 0.0180 (0.42) | 0.0196 (0.59) | 0.0236 (0.54) | 0.0273 (0.82) | 1.22 |
| Lev | 0.00154 (0.57) | −0.00129 (−0.51) | 0.0017 (0.63) | −0.0011 (−0.44) | 1.11 |
| Age | 0.0390 (0.96) | 0.0079 (0.52) | 0.0408** (1.01) | 0.0090 (0.63) | 1.10 |
| Gender | −0.00254 (−0.19) | −0.0009 (−0.07) | −0.0031 (−0.23) | −0.0037 (−0.29) | 1.19 |
| Constant | 0.509* (1.73) | 0.296** (2.3) | 0.518* (1.76) | 0.248** (1.99) | |
| Number of Observation | 365 | 365 | 365 | 365 | 365 |
| R2 | 11.9% | 12.1% | |||
| Hausman Test | 0.0043 | 0.0000 | |||
| Lagrange | 0.0000 | 0.0000 | |||
| Wooldridge | 7.254 | 6.661 | |||
| Modifidge Wald | 0.0000 | 0.0000 | |||
Note(s): *; **; and *** represent p-values < 0.1, < 0.05, and < 0.01, respectively
Table 4 presents the moderating effect of R&D activities on the relationship between DT and the FP of Vietnamese manufacturing enterprises. After addressing the identified deficiencies, the estimated results from regression models 1 and 2 indicate significance levels denoted by ***, **, and *, corresponding to 1%, 5%, and 10%, respectively.
Regression results of the moderating role of R&D on the association between DT and FP
| Variable | Model 1 | Model 2 |
|---|---|---|
| GLS1 | GLS2 | |
| DT*R&D | 0.896*** (3.36) | |
| DT | 0.0181*** (3.72) | 0.0180*** (3.51) |
| R&D | 0.0613*** (5.05) | 0.0449*** (3.67) |
| Size | −0.0191*** (−6.33) | −0.0121*** (−4.77) |
| Grow | 0.0186*** (4.16) | 0.0183** (2.51) |
| Labour | 0.0389*** (8.95) | 0.0314*** (8.90) |
| Asset | −0.0376** (−2.49) | −0.0474*** (−3.88) |
| Lev | −0.0083*** (−4.89) | −0.0099*** (−9.39) |
| Age | 0.0033 (0.70) | 0.0058 (1.60) |
| Gender | −0.0001 (−0.01) | −0.00151 (−0.28) |
| Constant | 0.201*** (3.98) | 0.097** (2.33) |
| Number of observation | 365 | 365 |
4.2 Discussion
Drawing on the resource-based theory, this study demonstrates that manufacturing enterprises within the same industry implementing DT in their operations yield varying outcomes. These findings align with the observations of (Lange et al., 2021) and have been validated through empirical research conducted in Vietnam. This suggestion is reflected in the differences in company size and the varying levels of investment in DT between large enterprises and SMEs, resulting in distinct performance outcomes.
Moreover, by integrating the resource-based theory with the transaction cost theory, this research indicates that manufacturing enterprises that invest more in R&D not only drive DT more effectively but also improve their overall efficiency. The competitive pressures within the industry are substantial, compelling enterprises to innovate and enhance their products to align with market demands (Huo et al., 2019). Furthermore, larger manufacturing enterprises face intensified competitive pressures, which improves the effectiveness of their R&D activities (Greenhalgh & Rogers, 2006). Consequently, this study reveals that as a manufacturing enterprise’s scale increases, investment in R&D yields better FP than in smaller enterprises.
The majority of previous studies confirm that increased investment in R&D activities helps enterprises gain greater competitive advantages (Guo et al., 2017; Huo et al., 2019; Liang & Li, 2022; Yan et al., 2023). This study demonstrates that focusing on investment in R&D within Vietnamese manufacturing enterprises accelerates DT and improves competitive advantages over other firms in the industry. Moreover, this study emphasizes that R&D drives DT across various industrial and consumer goods manufacturing sectors, ultimately leading to strong competitive advantages. Vietnamese manufacturing enterprises have recognized the importance of R&D in improving competitiveness amidst competition and promoting sustainable development in the international market. They have also increased their investment in DT in this area. DT in R&D activities serves as a driving force for fostering innovation and enhancing the competitiveness of Vietnamese enterprises in the future. The findings of this study provide a basis for future research on other groups of enterprises in Vietnam or countries undergoing DT with contexts similar to Vietnam’s.
Furthermore, the research findings suggest that the level of investment in DT can lead to effective outcomes for Vietnamese manufacturing enterprises within the same business cycle. This result contradicts research by Jardak and Ben Hamad (2022). Moreover, DT activities have impacted enterprises’ competitive advantages, albeit with a certain lag. This finding means that investment in DT cannot yield effective results in the short term or, at least, within a single business cycle. However, in Vietnam, while DT activities for manufacturing enterprises are still in the early stages, they have received substantial support from government policies, including incentives related to capital and taxation, investments in digital infrastructure, and training for DT (Tran et al., 2024). These policies have contributed to alleviating financial burdens and accelerating the DT process of manufacturing enterprises in Vietnam (Nhi, Phuong, Quynh, Thanh, & Cong, 2022).
This study demonstrates that DT activities have enabled enterprises to increase revenue, improve production processes through R&D, and reduce costs by maximizing labour productivity, aligning with previous studies (Bromiley & Rau, 2016; Björkdahl, 2020; Farooq et al., 2021; Guo & Xu, 2021; Wang et al., 2022). Furthermore, the study indicates that DT aids manufacturing enterprises in mitigating risks, as evidenced by the standard error of Grow variables, which provides a degree of stability in FP, thereby enabling manufacturing enterprises to remain resilient in the face of uncertainties such as COVID-19.
The study indicates that larger manufacturing enterprises have bigger capital advantages when investing in technology. Although their FP is currently declining, future improvements are expected, as suggested by the relationship between the size of enterprises and their FP. The reason manufacturing enterprises face a significant burden of costs associated with DT is substantial, as evidenced by the asset and leverage variables (Nga et al., 2023). This result is consistent with the conclusions of Nga et al. (2023), Nhi et al. (2022), Chechet and Olayiwola (2014), and Bunyaminu, Yakubu, and Bashiru (2021). Furthermore, due to the limitations of using publicly available company data, the variable indicating the proportion of fixed assets in this study does not accurately reflect the percentage of fixed assets dedicated specifically to DT activities. Consequently, this study does not identify the individual impact of fixed asset investment for DT on FP. Future research will clarify the effects of this relationship.
5. Conclusion and implications
5.1 Conclusion
This study was conducted to investigate the regulatory role of R&D on the relationship between DT and the FP of manufacturing firms in Vietnam based on evidence from the country. The findings indicated that the regulatory role of R&D exhibits a linear relationship with the connection between DT and the FP of Vietnamese manufacturing companies. The analysis showed a positive relationship between DT and the FP of these enterprises. Aligning with the resource-based view theory (Barney, 1991) and previous studies (Zhai et al., 2022; Wang et al., 2022), the study found that R&D activities may not have an indirect effect on FP, especially for manufacturing firms. Furthermore, the research may not have fully addressed the financial effectiveness of DT practices and R&D activities within manufacturing enterprises. Vietnamese manufacturing firms are key players in implementing DT within R&D activities, but the integration of these practices highlights the impact of R&D regulation on DT and FP in manufacturing. Accordingly, enterprises that invest more significantly in R&D are more likely to accelerate their DT, resulting in enhanced FP for manufacturing enterprises. This study showed that R&D activities significantly influence the FP of manufacturing enterprises in the emerging Vietnamese economy, both directly and indirectly. Furthermore, the study revealed that labour size and revenue growth positively impact FP.
5.2 Implications
5.2.1 Theoretical implications
The research findings demonstrate that DT positively impacts the FP of manufacturing enterprises in Vietnam. Investing more in DT enhances enterprises’ FP. The findings also provide substantial evidence for Vietnamese managers and foreign investors seeking opportunities in similarly emerging economies as Vietnam. It is crucial for enterprises to leverage government support through institutional frameworks and policies during the early stages of DT. Managers must understand the importance of developing and upgrading their resources to effectively engage with technology and local policies related to DT. This study shows that R&D activities significantly enhance the FP of enterprises. Furthermore, R&D activities have been recognized as vital for driving DT in enterprises, and investment levels in R&D differ among enterprises, which can enhance FP. Consequently, managers in manufacturing must grasp the significance of investing in DT and R&D activities to enhance profitability in a changing economy.
Moreover, the research findings indicate the impact of labour on the FP of enterprises. Firms with larger labour forces exhibit better FP compared to smaller enterprises, implying that enterprises should not only invest in technology but also focus on training their digital workforce to utilize resources efficiently and improve overall performance.
The study also found that factors representing financial resources, such as investment in total assets and financial leverage, negatively correlate with the FP of Vietnamese manufacturing firms. This result suggests that Vietnamese manufacturing companies need to consider management strategies that effectively utilize internal resources while leveraging government and external support to optimize performance. Furthermore, improving efficiency could provide a better foundation for more effective DT practices, including R&D activities.
5.2.2 Practical implications
The study suggests that to effectively support Vietnamese manufacturing enterprises in their DT, it is essential to leverage the advantages of these firms along with government support policies. This combination has helped firms achieve positive financial results in the early stages of DT. However, previous research indicates that there is often a time lag between the initiation of DT and its impact on FP (Jardak & Ben Hamad, 2022). In this context, the support from the Vietnamese government has reduced barriers, enabling significant DT in Vietnam’s transitioning economy by offering capital and tax incentives during the first 5 years of firms’ DT journey. This support will allow SME manufacturing enterprises to gain valuable technological insights into the market by leveraging government capital and tax support, as well as various local training programs aimed at improving the awareness of DT. Moreover, the result underscores the resource theory by asserting that DT is a crucial resource that allows firms to distinguish themselves from competitors. The effectiveness of this differentiation depends on the level of technology integration tailored to each entity’s specific needs. Thus, DT plays a vital role in improving FP in today’s digital economy.
This article is the result of the 2024 Ministry-level Scientific Research Project sponsored by the Ministry of Education and Training with the topic “Impact of Digital Transformation on the Cost Efficiency of Logistics Enterprises in the Mekong Delta”, Code: B2024-14 (Director: Nguyễn Thị Phương Dung)
